direraf - 2nd interim report...

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DIRERAF

Agreement number: No 2004122 Contracting period: 1/11/2004 – 30/10/2007

Year: 2004 Country: EL Project duration: 36 months

Title: Development of Public Health Indicators for Reporting Environmental/Occupational Risks related to Agriculture and Fishery

Contractor: NATIONAL AND KAPODISTRIAN UNIVERSITY OF ATHENS

Period covered by the report From: .01/11/2005 To: 31/10/ 2006

Contract amendments Yes No X

Name of Scientific Co-ordinator: ATHENA LINOS Signature: Place & Date: ATHENS

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Table of contents

A. Acknowledgments 3 B. Summary of the progress 4

C. Problems encountered / changes in the project 4 D. Progress per work package

a. WP1 5 b. WP2 9 c. WP3 15 d. WP4 17 e. WP5 21 f. WP6 23 g. WP7 25 h. WP11 27

E. Results/Products/Processes 30

F. Annex

a. Report on WP2&3 32 b. Working meeting for WP2 c. Working meeting for WP3 d. Report on WP4 e. Report on WP5 f. Panel of experts meeting g. Report on WP6 h. Report on WP7 i. 2nd partners’ meeting j. Publications & Announcements

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Acknowledgements

The following have contributed to the compilation of this report:

Dr Dimitris Kouimintzis (National Kapodistrian University of Athens)

Dr Christos Chatzis (National Kapodistrian University of Athens)

Ms Ioanna Kotsioni (Institute of Preventive Medicine, Environmental and

Occupational Health)

Professor Athena Linos (National Kapodistrian University of Athens)

Special thanks to:

Ms Evaggelia Chronopoulou (National Kapodistrian University of Athens)

Ms Eirini Papageorgiou (Institute of Preventive Medicine, Environmental and


Ms Panagiota Karnaki (Institute of Preventive Medicine, Environmental and


Ms Kelly Karavolou (Institute of Preventive Medicine, Environmental and


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This is a revised interim report aiming to clarify, complete and make more

comprehensive the interim report submitted on January 2007, covering the

period between 1/11/2005 and 31/10/2006. Given that several months have

already elapsed, the current report includes progress and achievements attained

in the more recent period.

In the second year of the DIRERAF project’s implementation we have been

successful in developing the methodology to be used for the different

components of the project and in progressing with the development of the main

products, such as the list of proposed indicators. Obstacles relating to delays of

completion of the assigned work due to unforeseen difficulties have been

overcome and lost time has been gained.

In more detail, the management of the project has run relatively smoothly and

the progress with the dissemination of the project (update of website, scientific

announcements and publications) has been satisfactory. In the same way,

quality assurance has been so far ensured for the main procedures and products

of the project.

With regards to the outcomes of the project, the following deliverables have

been concluded: a report on the findings of work package 2 and 3 titled

“Identification of policies and practices and identification of the minimal common

dataset” and a report on the findings of work package 4 “Identification and

categorization of production specific risks”. Moreover, significant progress has

been achieved with the products of work package 5, 6 and 7 which are currently

close to a final form. Therefore a draft report on the proposed indicators, a draft

A. Summary of progress

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report on the disaggregated indicators targeting vulnerable population groups,

and a draft report on the pilot testing of the indicators are included in the current

version of the interim report.

B. Problems encountered / Changes in the project

A major problem we encountered was the death of the distinguished Director of

International Centre for Pesticides and Health Risk Prevention (ICPS) and leader

of the Italian research team, Professor Maroni, who passed away shortly after

the 2nd partners’ meeting, on the 29th of June 2007. ICPS is the leading partner

in many of the work packages of the project and together with the NKUA team

are the primary parties involved in all its stages throughout its course. Although

the Italian team has continued to be active, there was some delay in progressing

with some of the tasks.

C. Progress per Work package

WP 1: Project coordination, management and strategic project

planning

The aim of this work package is to control and assure the efficient

implementation of the project, both in terms of planning and managing the

project’s activities and of facilitating the coordination among the project partners.

Coordination of the project has in general terms been smooth however the

management of the project has encountered some difficulties that have played a

role in delaying the completion of some outputs. The tasks were such that not

only managerial but also scientific coordination was necessary. Due to the fact

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that the partnership was quite large and in combination with the heavy

workload, the communication line was not equally strong among the partners.

While communication with some partners has been weaker than anticipated,

enhanced cooperation between the coordinator and partners leading work

packages 2 and 3 has been achieved nevertheless by organising additional

bilateral meetings.

In more detail, the Italian team (ICPS) who was responsible for work package 2

met with the Greek team (NKUA, Prolepsis) so as to ensure that the work would

be concluded in a co-ordinated manner between the two research teams. The

meeting took place in Athens on the 5th of May 2006. In the meeting the

methodology for addressing questions that was not possible to be answered with

the completed questionnaires was extensively discussed. More details for the

results of the meeting can be found in the description of work package 2. The

minutes of the meeting can also be found in the Annex.

A second working meeting took place on the 26th-27th of September 2006

between the Spanish (IMIM) and Greek (ICPS) research teams. The meeting

took place in the Medical School of the National and Kapodistrian University of

Athens and the aim was to discuss the progress of work package 3 and the way

forward to work package 5. More details for the results of the meeting can be

found in the description of work package 3. The minutes of this meeting can be

found in the Annex.

Moreover during the reported period the 2nd partners’ meeting took place in

Milan, Sacco Luigi Hospital, on the 17th of June 2006. In the meeting the results

of the project were discussed as well as the work plan for the future months.

Regarding dissemination, the partners decided to contribute papers for a special

issue of the Public Health Journal that will be especially devoted to the DIRERAF

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project. Finally, the idea to organise a panel of experts meeting that will convene

in Athens to discuss the outputs of work packages 2, 3, and 4 and will develop

an initial list of indicators, was discussed and agreed upon. The minutes of the

2nd partners’ meeting can be found in the Annex.

Finally the panel of experts meeting that took place in Athens in February 2007

was very constructive for the development of indicators.

Experts from Europe, USA and the WHO were invited in the panel of experts to

discuss the methodology and the criteria for the development of the indicators.

The results of the meeting are discussed in the section describing the progress

for work package 5.

Regarding delays in the finalisation of the deliverables of certain work packages

it should be stressed the interrelation of the work packages which is of critical

importance to the prompt presentation of the deliverables. In more detail the

work for work package 3 could only begin once the output of work package 2

was finalised. Moreover the work for work package 5 depended on the outcomes

of work packages 2, 3 and 4. Additionally, the work for work package 6 has to be

based on the results of work package 5. In order to keep up with the timetable

we proceeded with the concurrent progress of several work packages.

Especially in what concerns work package 2 and work package 3 it should be

stressed that these delays were partly attributed to the complete lack of

homogeneity in the available data across the various Member States and to the

fragmentation of the existing information.

To sum up while individual outputs have been delayed in relation to the initial

work plan, we managed to overcome the factual difficulties with the project flow

and are currently concluding work package 7. At the same time and while the

first edition of the reports (outputs) of work packages 2, 3, 4, 5, and 6 has been

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drafted - these reports are continuously updated. The results from other work

packages, including work package 7, will be taken into account in the revision of

these reports so as to produce a cohesive final report.

For reasons of clarity, any new developments in the work process will also be

explained along the description of each work package’s progress. It should be

noted that in some work packages, such as work package 4, the work carried out

was deliberately expanded by 75% than initially planned in order to include as

much information as possible. In addition, as an extension of the literature

review for work package 4 the partnership prepared a series of review articles

currently under publication by the Journal of Public Health which disseminate not

only the work carried out in the project but most importantly suggest areas

where policy action is needed. Therefore despite the delay in delivering certain

products in relation to the initial timetable, the quality of work is higher, more

extensive and substantiated than originally planned.

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WP 2: Identification and review of existing policies and practices

Introduction

This work package addresses the need for identifying and reviewing specific

policies and practices with regards to data collection and reporting at national

and European level in relation to environmental/occupational risks in agriculture

and fishery. While in the first year of the project, the main focus was the

development of the methodology for querying national and international data

sources and using the partnership network to extract the information needed,

during the second year the main objectives were to fill in the missing information

from the remaining European countries, to extend and deepen the search into

international databases and to elaborate the results, so that the outcome could

be easily processed for the next work package.

The questionnaire, which was compiled by the project coordinator and which was

discussed, improved and approved by the first meeting of partners, requested

information on the following topics:

Topic Details requested Number of persons employed in agriculture and fishery

For each national authority collecting data: • Organization name and contact

details Items requested:

• Age intervals • Gender aggregation • Data on retired labour force • General workforce data • Time period of data collection • Availability of data • Source of data • Accessibility of data

National data sources on Occupational Hygiene, Health

For each national source collecting data: • Institution name and contact details

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and Safety and Environmental Health regarding Agriculture and Fishery

• Legal framework Items requested:

• Type of data collected • Collection method • Level of aggregation • Accessibility of data

- Availability of data on the employment of persons under 14 years of age in agriculture and fishery - Availability of data on the employment of persons over 68 years of age in agriculture and fishery - Availability of data on the employment of immigrants in agriculture and fishery

For each national source collecting data: • Institution name and contact details • Legal framework

Items requested: • Type of data collected • Collection method • Level of aggregation • Accessibility of data

Availability of indicators for mortality and morbidity

For each national source collecting data: • Institution name and contact details • Legal framework


Existence of mortality and morbidity indicators

• All-cause specific • Cause specific • Age specific • Gender specific

Availability of data based on specific health indicators for persons employed in agriculture and fishery

For each national source collecting data: • Data source name and contact

details • Legal framework

Items requested: • Indicator name • Type of data collected • Collection method • Level of aggregation • Accessibility of data

Available data referring to occupational diseases regarding agriculture and


details

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fishery • Legal framework Items requested:

• Type of data collected • Collection method • Level of aggregation • Accessibility of data

Obligatory registered occupational diseases in agriculture and fishery

Name of each disease in each of the two sectors

National health programs on health education and health promotion targeting those employed in agriculture and fishery

• Program title • Responsible authority • Sector • Targeted population

Available data on annual use of pesticides by specific produce category


details • Legal framework


Following the collection of the requested information from the partnership

countries, ICPS, the lead partner for this work package together with the

University of Athens, the project coordinator, agreed on the methodology for

collecting the requested information for the “missing” EU countries. This was

discussed and agreed during the May 5th, 2006 (Athens, Greece) meeting

between the two partners. The methodology which was agreed upon included

the following steps:

• First, thorough search in the websites of international and national

institutions and data collection authorities was conducted to identify

relevant published data as well as expert contacts that could be addressed

to complete the questionnaire.

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• Second, electronic correspondence with the identified experts was

established requesting specific information to complete sections of the

questionnaire or to verify information in completed questionnaires.

Particular attention has been given to the communication with National

Statistics Services, Labour organizations, Ministries of Health, Agriculture

and Social Insurance Organizations, as well as Institutions dealing with

Health and Safety in Agriculture and Fishery.

• Third, the completed questionnaire was distributed to national authorities

and to field experts, for commenting and verification, by means of email

and written correspondence. For quality assurance purposes, the research

team persisted in receiving official replies from national authorities on

each of the questionnaire questions.

• Fourth, datasets routinely collected by international authorities or

organizations, like EUROSTAT, ILO, FAO, WHO and OECD, were

thoroughly queried.

The following table is a summary of the collection method used for data retrieval

in each country:

European Union 27 Member States (as of January 1st,

2007) Country Partners OSHA/ISPESL Coordinator

Austria

Belgium

Bulgaria1

Cyprus

Czech Republic

Denmark

Estonia

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Finland

France

Germany

Greece

Hungary

Ireland

Italy

Latvia

Lithuania

Luxembourg

Malta

Netherlands

Poland

Portugal

Romania1

Slovakia

Slovenia

Spain

Sweden

United Kingdom Non-EU countries included in the project Country Partners OSHA/ISPESL Coordinator Norway 2

1: Bulgaria and Romania are full Member States as of January 1st, 2007 2: Data collected only for fishery in Norway

The outcome of the 5th of May working meeting and the results of the above

mentioned methodology were presented during the second meeting of partners,

on June 17th, 2006 (Milan, Italy). ICPS, the lead partner, delivered a preliminary

report, based on the findings of the first months, which was later expanded by

the project coordinator (NKUA), when all requested material had been collected

and verified.

It should be noted that the collection of information was a complicated task. The

workload carried out in terms of the number of stakeholders contacted, the

fragmentation of data sources, the identified inconsistencies in data collection

methods and the time-consuming efforts to retrieve all information from each EU

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member state has led to a considerable extension of this work package

compared to the initial time schedule.

The outcome of the research of work package 2 as presented in the combined

report for work package 2 and 3 (attached here in the Annex with the

deliverables) includes:

• a presentation of employment in agriculture and fishery data collection

policies by country and a cross-country comparison (chapter 2 of the report)

• a presentation of health and safety policies (mortality and morbidity data,

health care data, social insurance coverage) by country and a cross-country

comparison (chapter 3 of the report)

• a presentation of the reporting mechanisms for occupational diseases in

agriculture by country and a cross-sectional comparison (chapter 4 of the

report)

• a presentation of the availability of national data on the use of pesticides and

a cross-country comparison

• a presentation of selected national health promotion and safety programs and

policies by country (chapter 4 and section 6.4. of the annex)

• a presentation of relevant international datasets by organization (EUROSTAT,

FAO, ILO, OECD, World Bank) and by topic

• a description of the methodology used in work package 2 (section 6.1. of the

annex)

• the questionnaire that was used to collect most of the information (section

6.2. of the annex)

• a comprehensive list and a short description of national authorities (section

6.3. of the annex)

• an additional list of national health promotion and safety programmes

(section 6.4. of the annex).

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WP 3: Identification of collected data

The aim of this work package is to assess the extent to which similar data are

collected and policies implemented in different European countries. It practically

addresses the issue of harmonization of relevant data collection across different

countries and at the European Union level.

The outcome of this work package is based on the review and analysis of the

data collected in work package 2. Collected data was processed using SPSS for

Windows v.13 and a descriptive analysis was carried out by the Athens research

team (NKUA and Prolepsis).

Moreover the project team used the material developed during WP2 to extract

the minimum set of available data. Although both availability and the level of

aggregation were to be compared, the focus of interest was primarily set on the

availability of data collected across the EU countries, because of the small

number of datasets commonly available.

The availability of the collected data by topic was recoded into a scale variable,

depicted in the form of 1 to 5 boxes (1 box: available data in 0-5 countries (red);

2 boxes: 6-10 countries (orange); 3 boxes: 11-15 countries (yellow); 4 boxes:

16-20 countries (light green); 5 boxes: 21-27 countries (dark green)). All topics

that have been addressed by the questionnaire: employment, morbidity-

mortality, health services, occupational diseases, and agroenvironmental

indicators have been recoded to derive minimal common datasets.

To facilitate the review of the compiled material of work package 2 and its

analysis, the coordinating team and the IMIM team (Spain) responsible for this

work package, worked together during a two-day meeting organized for this

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purpose in Athens (September 2006). During this session, the two teams

discussed the issue of data comparability for each topic, on which data was

available.

Deliverables of this work package are the descriptive statistics from the analysis

of the collected work package 2 data and the minimal common datasets which

are presented in the combined report for work package 2 and 3.

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WP 4: Identification and categorisation of production specific risks

The aim of this work package is to identify, describe and categorize production

specific risks in agriculture and fisheries. Population groups, methods and types

of production which are associated with an increased risk of adverse health

effects have been identified through an extensive review of the recent scientific

literature.

The preparation for this work package was already initiated during the first

partners’ meeting. The task of reviewing the literature was assigned mainly to

NKUA however partners worked complimentarily, offering local scientific output

and feedback based on the expertise of their members.

The following online electronic databases were used for the research of the

relevant published literature: AGRICOLA (AGRICultural OnLine Access),

EMBASE.com, PubMed, Science Citation Index (Web of Science) and TOXNET.

The databases used were queried for related literature until early 2006. In the

first stage of the research a group of keywords, for each system/organ or

disease were selected and used, in conjunction with a group of keywords

depicting the production type and the type of risk. Keywords used are the

following:

Specific risk type keywords “occupation” , “occupational”, “work” , “work-related” - “environmental” + “exposure” , “risk”, “hazard” Specific exposure group keywords “agriculture” , “farm” , “farming” , “farmer” , “worker” , “grower” , “applicator”, “migrant” , “immigrant” , “seasonal” , “children” , “elderly”

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Specific hazards keywords “pollen” , “tractor” , “animals” , “motor vehicle”, “machinery”, “tools”, “noise” , “radiation” , “ultraviolet” , “sunburn” , “thermal” , “cold” , “heat”, “vibration” “diesel”, “exhaust” , “benzene” , “dust” , “solvent” “pesticides”, “insecticides”, “herbicides” , “fungicides” , “biocides” , “fertilizers” Specific production type keywords eg “greenhouse” OR “vineyard”, “wine-maker”, “grape” OR “citrus”, “orange” , “lemon”, etc System/organ/disease specific keywords “trauma” , “injury” , “wound” , “emergency” , “accident” , “fall” , “disease”, “illness”, “disorder” “musculoskeletal” , “fracture”, “strain” , “sprain”, “spine” , “hip” “cancer” , “malignancy” , “malignant” “leukemia” , “lymphoma” , “myeloma” “sarcoma” , “mesothelioma” “skin”, “dermatitis” , “melanoma” “respiratory” , “asthma” , “COPD” , “lung” , “nasal” , “laryngeal” , “tuberculosis” “sensitization” , “allergy” “kidney” , “renal” , “bladder” “hepatic” , “liver” , “hepatitis” , “cirrhosis” “developmental”, “reproductive” , “semen” , “abortion” , “fertility”, “birthweight” “mental”, “nervous” , “neuropsychiatric”, “stress” “hearing loss” “poisoning” , “intoxication”

After running the first queries, this initial list of keywords was refined. Moreover

a second round of search was conducted based not only on online scientific

databases but also on reference articles cited in the papers retrieved during the

first search stage. After completing the summary of the literature by production

type, a general search on agriculture and farming activities was conducted using

the same research methods to secure that publications addressing the health

effects of all types of farming combined or the general occupational title of the

farmer were also included in our review.

The criteria that were used for including studies in the literature review are the

following:

• Study published in a peer-reviewed journal after 1995

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• Study written in any of the official EU languages.

However, the majority of the included studies were published in English

because: 1) most peer-reviewed and internationally renowned journals in the

field are published in English, 2) English abstracts of research studies

published in another language are available at online scientific databases. In

the cases where such studies were identified approximately 30 (mostly in

Italian, but also a few in German, Dutch, Danish and Polish), the full text was

retrieved and was forwarded to a native speaker. Moreover, all partners were

requested to perform a literature review at the national level and to forward a

short report of all relevant published studies in their native language to the

project coordinator.

• The focus of the study was on the farming population, including farm

workers, their spouses, children or other people in close proximity of

farms, and on the fishing population

• All possible health effects were examined in relation to the farming or

fishing occupation

Studies although reviewed are excluded from our report if :

• The outcome of the study did not include a risk estimate figure

(proportionate mortality ratio [PMR], standardized mortality ratio [SMR],

prevalence/incidence rate, Odds Ratio [OR], Relative Risk [RR],

Attributable Risk [AR]) of adverse health effects

• The studies were case reports or studies of ecological design or exposure

assessment only studies

With regards to fisheries, especially, the scientific literature on the health risks

involved is scarce. Most studies report on acute health effects, such as annual

mortality due to accidents. The most recent review on health risks associated

with the fishing occupation has estimated the annual mortality rate to be

between 1.3 to 5.7 cases per 1,000 fishermen, attributed to fatal accidents,

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diseases of the cardiovascular system, homicide and suicides. Less information

related to morbidity risk estimates is available.

The outcomes of the literature search have been compiled in a report that

includes an executive summary of all selected review studies related to

agriculture and an executive summary of all selected review studies related to

fisheries. Moreover all examined literature is presented in tables that include

summaries and an evaluation of the findings of each study. Therefore for each

study, the reported estimates of relative risk, odds ratio, standardized incidence

ratio, standardized mortality ratio or proportionate mortality ratios and

corresponding confidence intervals, are presented, along with information on the

population size, confidence intervals, country of origin, and citation of the

authors. On each topic, the number of studies included or excluded based on the

predefined criteria is also mentioned.

The deliverable of this work package is not just a list of categories of identified

risks (health and accidents) but also a large health risk database which covers

nearly all published literature of the last 12 years, if just original articles are

taken into account. The database is still being updated and so far it contains

over 750 records of studies related to health risks associated with agriculture and

fisheries. This database has also been used already to prepare a series of

articulate reviews describing health risks associated with specific types and

methods of production. These reviews will be published in the “Journal of Public

Health – Springer Verlag, Germany” in 2007.

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WP 5: Development of indicators

The aim of this work package is to identify and describe the most important

functional indicators which could measure the occupational and environmental

health risks associated with agriculture and fisheries.

This work package is based on the deliverables of work packages 2, 3, 4 and on

the combined efforts of the partnership and the panel of experts.

Methodologically, the development of the indicators is based on three pillars:

a) Scientific knowledge of the health risks pertaining to agriculture and fisheries.

Work package 4 summarised the existing scientific knowledge identifying

population groups, methods and types of production associated with increased

risk of health adverse effects.

b) mapping of the current monitoring systems and data collection authorities.

Work packages 2 and 3 have adequately explored current data collection

systems and identified minimal common data sets that could be used for the

calculation of potentially proposed indicators at the European level. Some of

these data are routinely collected for reasons other than monitoring health or

environmental risks (i.e. economy, demography). Yet, they can be easily utilised

for monitoring health and environmental risks related to agriculture and fisheries.

c) expert knowledge of hazards and health risks in the agriculture and fisheries

sectors.

A panel of experts was summoned to offer expert input for the design of the

methodology and the criteria that should be used for the selection of the

indicators.

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The involvement of the panel of experts was crucial in evaluating the collected

material and in recommending areas of interest that require special attention for

the proposal of indicators that could effectively monitor the identified risks for

humans and the environment.

It should be stressed that the DIRERAF methodology considers public health

indicators to be more than just an exact or approximate measure of health,

economic, social or other characteristics of the target populations. Public health

indicators therefore serve an additional role: the one of providing the link

between an underlying disease or condition and the political will to monitor these

in time and across different backgrounds. Ideally the public health indicators

produced by this project will subsequently be used to formulate public health

measures at the political level, such as E.U. directives and regulations or national

laws aiming at improving the health status of European citizens: farmers,

fishermen, rural communities and consumers at large.

The deliverable of this work package is a report containing a list of indicators,

accompanied with implementation guidelines, as well as a set of

recommendations for alterations or additions to the existing data collection

practices.

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WP 6: Development of socioeconomically and demographically specific

indicators relevant to public health policies

The aim of this work package is to disaggregate proposed occupational risk

indicators by gender, major age group and nationality or citizenship status. The

availability of data for different population groups, usually more vulnerable than

the average population, is crucial for informing policy makers on the necessity of

policies targeting particular populations based on their vulnerability to specific

risks.

Selecting for which specific populations disaggregated data would be useful has

been based both on the findings of the literature on hazards and relative health

risks and on the notion of social exclusion.

Social exclusion components that define the populations we should be targeting

are: type of employment (precarious, seasonal), age (both children and senior

citizens are affected more severely by social exclusion and might be more

vulnerable to health effects depending on the exposure factor), nationality

(minority groups are more severely affected by social exclusion).

Moreover using the literature evidence provided in the report for work package 4

we have identified high risk population groups:

- migrant farm and fisheries workers who receive less or no training related to

health and safety practices and work significantly more hours than the average

farm worker performing also heavier tasks

- elderly people occupied in agriculture and fisheries who run a serious risk of

accidents, as increased injury mortality statistics prove

- children living and working in farms have one of the highest mortality rates,

due to a variety of factors intrinsic to the farm work environment (tractors, silos,

tools, pesticides, etc).

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- women occupied in agriculture who have a significant contribution to the farm

output usually work intensively during harvesting periods and take up strenuous

and even dangerous tasks while at the same time are not monitored as farm

workers

- seasonal and temporary workers who are occupied on an irregular basis in

agriculture, and have been associated with an increased risk of accidents and

injuries, a higher frequency of stress and anxiety due to job insecurity and a

wide variety of work-related morbidity related with low adherence to health and

safety measures

The deliverable of this work package is a report containing a matrix with public

health indicators disaggregated by the population groups they should be

monitoring. The report attached in the annex is a draft version which is currently

finalised.

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WP 7: Pilot testing of indicators

This work package aims to test the feasibility and applicability of a sample of

indicators. Moreover feedback on the effectiveness and scientific validity of the

tested indicators is sought be means of an evaluation form that data collectors in

each country have completed.

The sample of indicators was selected after discussion among the project

partners on the rationale that indicators at different levels of difficulty should be

tested. The level of difficulty for each indicator has been estimated based mainly

on the knowledge from work package 2 of the availability, accessibility, collection

method and level of aggregation of the data required for its calculation.

The testing phase ran in five partner countries: Greece, Germany, Finland, Italy

and Poland. For each country, the same protocol was adopted and followed, so

that the results would be as comparable as possible. Involved parties had to

contact national authorities, enquiry data sources, collect relevant information,

complete the questionnaire forms and return them to the project co-ordinator.

The set of criteria for rating each pilot indicator on its feasibility, applicability and

usefulness are summarised below.

Criterion Description Rating scale

Availability Data sources are available for computing the indicator, as well as for each level of aggregation

1 (poor) to 5 (excellent)

Quality Refers to the quality of the collection method of the original data required for the computation of the indicator


Policy relevance

Refers to the capacity of the indicator to diagnose health threats which could be managed by a policy intervention


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Validity The indicator can measure what is supposed to measure and is easy to understand


Discernment The indicator is sensitive enough to monitor trends in time


Comparability The value of the indicator can be compared across the EU countries


The questionnaires that partners had to use to report the results of the pilot

testing and their evaluation of the indicators have been completed and returned

to NKUA. The research team of NKUA and Prolepsis will analyse the results of the

pilot testing to assess the comparability of the results between countries, the

availability and accessibility of the required data and the capacity of the

indicators to inform policy making. Initially, results on each indicator will be

presented descriptively (means and distribution) and evaluation of the indicators

will follow.

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WP 11: Dissemination

Dissemination activities have been planned throughout the project’s lifetime.

Extensive dissemination of the project results will be intensified towards the end

of the project. However, in the second year of the project, covered by this

interim report, we have succeeded in producing a special issue for the DIRERAF

project in the Journal of Public Health that is an important dissemination

deliverable in addition to our contractual obligations.

This issue, which will most likely be published in August 2007, consists of a

collection of papers based on the results of the extensive literature review

conducted for work package 4 of the project.

The topics addressed in the special issue, currently under publication, are the

following:

- specific health problems for farmers working on typical Northern European crops (article by Fuchs et al) - health effects of livestock farming in Europe (article by Kouimintzis et al ) - health problems of tobacco cultivators (article by Schmitt et al) - exposure of greenhouse workers to pesticides and other biological agents (article by Jurewicz et al ) - exposure of agriculture workers to arsenic (article by Bencko and Slámová) - exposure of people working in agriculture and their families to pesticides (article by Vida and Morreto) - review of the Italian system of reporting agricultural occupational health (article by Mammone et al) - review of national social protection provisions for the coverage of self- employed farmers (article by Kotsioni et al).

28

The DIRERAF team has is planning to present the project in major European and

international scientific forums.

In this context, Professor Linos presented the DIRERAF methodology at the 16th

International Congress of Agricultural Medicine and Rural Health (IAAMRH)

“Building New Tools for Health Promotion in Rural Areas” in Lodi, June 2006.

Prof. Linos made a presentation on “Occupational environmental risks related to

crop farming in Southern Europe: the DIRERAF methodology applied” and co-

chaired a session “Rural Health Profiles and Indicators as New Tools for

Prevention”.The abstract and the programme of the Conference can be found in

the Annex.

In the 14th annual EUPHA Conference: “Politics, policies and/or the public’s

health” which took place between the 16th and 18th November 2006 in Montreux,

Switzerland a poster on the health risks pertaining to North Europe agricultural

practices and production types was presented. The abstract as well as the

poster can be found in the Annex.

Moreover, Dr. Dimitrios Kouimintzis from NKUA participated at the 28th

International Congress on Occupational Health which took place in Milan

between the 11th and 16th of June 2006. At the Congress he had the chance to

disseminate the project to a worldwide network of experts and officials who are

involved in health and safety in agriculture and fishery.

NKUA researchers have also personally communicated with over 100 local and

national officials or stakeholders involved in the fields of health and safety in

agriculture and fishery, during the quality check phase of work package 2

questionnaire.

29

The NKUA DIRERAF research team also participates in the DG Public Health

“Working Party for Health and Environment” where the project’s methodology

and progress have been presented in the Working Party meetings.

Lastly, the website has been updated at regular intervals and has continued to

function as a main dissemination tool. Below some basic statistics are presented

regarding website use [2770 visits – 6340 pages viewed – 31695 files served in

the period between March 2006 – December 2006]

Visits Page Views Hits

Dec-06 312 432 1121Nov-06 201 391 2189Oct-06 231 474 2078Sep-06 252 648 3578Aug-06 255 572 2593Jul-06 300 692 3168

Jun-06 311 1650 10906May-

06 272 482 2082Apr-06 238 379 1603Mar-06 398 620 2377Totals 2770 6340 31695

30

D. RESULTS / PRODUCTS / PROCESSES

Results and / or products and / or processes Types of support N° Full title % of

realisation Languages

Web site CD ROM Printed Other (specify)

1 Report / deliverable of Work package 2&3

100% english X

2 Report / deliverable of Work package 4

100% X

3 Library with scientific articles used for the literature review

100% english X

4 Panel of experts meeting

100% X meeting


100% X


90% X


80% X

8 Website (updated regularly)

100% english X

9 Updated directory of stakeholders

100% english X

31

10 Abstract for 16th International Congress of Agricultural Medicine and Rural Health

100% english X

11 Abstract and poster for EUPHA conference

100% english X

12 Participation at 28th International Congress on Occupational Health

100% english X meeting

13 Special issue of Journal of Public Health (under publication)

100% english X

Public Health Programme 2004

Development Of Public Health Indicators For Reporting Environmental/Occupational Risks Related To Agriculture And Fisheries - DIRERAF

Work Package II “Identification of policies and practices”

Work Package III “Identification of data collected”

DIRERAF: work package 2 & 3 2


Table of contents

Executive summary....................................................................................................5 1. Introduction............................................................................................................6 2. Identification of polices and practices with regards to data collection..................8

2.1. Labour Force in Agriculture and Fisheries .....................................................8 2.1.1. Number of persons employed in agriculture and fisheries ......................8

Collected material by country........................................................................8 Data analysis ................................................................................................15

2.1.2. Number of retired in agriculture and fisheries.......................................20 Collected material by country......................................................................20 Data analysis ................................................................................................21

2.1.3. Number of children employed (<14 years)............................................23 Collected material by country......................................................................23 Data analysis ................................................................................................23

2.1.4. Number of elderly employed (>68 years)..............................................25 Collected material by country......................................................................25 Data analysis ................................................................................................27

2.1.5. Number of immigrants employed ..........................................................28 Collected material by country......................................................................28 Data analysis ................................................................................................30

3. Availability of segregated data focusing on the health and safety of farmers and fishermen.......................................................................................................33

3.1. Mortality and morbidity indicators by country.........................................33 Collected material by country......................................................................33 Data analysis ................................................................................................37

3.2. Health care among farmers and fisheries workers....................................39 Collected material by country......................................................................39 Data analysis ................................................................................................42

3.3. The Insurance system in Agriculture ........................................................44 4. Risk assessment of occupational and environmental risks in agriculture and fisheries ................................................................................................................48

4.1. Occupational diseases in agriculture.........................................................48 Collected material by country......................................................................48 Data analysis ................................................................................................52

4.2. Availability of national data on use of pesticides .....................................55 Collected material by country......................................................................55 Data analysis ................................................................................................57

4.3. National health promoting programmes in agriculture and fisheries........58 List of national programmes & policies.......................................................61 Legal framework..........................................................................................63

5. Datasets available from international organizations ............................................64 5.1. Presentation of relevant organizations/authorities ........................................64

5.1.1. EUROSTAT...........................................................................................64 Farm Structure Survey .................................................................................64 Labor Force Survey......................................................................................65 Fisheries Statistics........................................................................................66

5.1.2. FAO (Food And Agriculture Organization Of The United Nations).....67 FAO Country Profiles and Mapping Information System...........................67

5.1.3. ILO (International Labor Office) ...........................................................68



5.1.4. OECD (Organisation for Economic Co-operation and Development) ..68 5.1.5. World Bank............................................................................................68

5.2. Available information by topic .....................................................................69 5.2.1. Labour force...........................................................................................69

ILO...............................................................................................................69 EUROSTAT.................................................................................................69 OECD...........................................................................................................69 UNDP...........................................................................................................69

5.2.2. Labour force: Immigrants ......................................................................69 ILO...............................................................................................................69 EUROSTAT.................................................................................................70

5.2.3. Labour force: workers over 65 years old ...............................................70 EUROSTAT.................................................................................................70

5.2.4. Mortality and morbidity indicators ........................................................70 EUROSTAT.................................................................................................71 ILO...............................................................................................................71

5.2.5. Health care .............................................................................................71 WHO............................................................................................................71 OECD...........................................................................................................71 EUROSTAT.................................................................................................71

5.2.6. Occupational diseases ............................................................................72 ILO...............................................................................................................72

5.2.7. Pesticides................................................................................................72 EUROSTAT.................................................................................................72 FAO..............................................................................................................72

5.3. Available information by organization .........................................................72 ILO (www.laborsta.ilo.org) .............................................................................72 EUROSTAT.....................................................................................................79 AGROFARMA (Report 2001) ........................................................................80 OECD...............................................................................................................80 WHO................................................................................................................81 FAO – FISHERIES..........................................................................................81

FAO – Country Profiles ...............................................................................81 FAO – Faostat ..............................................................................................81

UNDP...............................................................................................................81 6. The minimal common dataset..............................................................................82

Employment.........................................................................................................82 Morbidity – Mortality ..........................................................................................84 Health Services Indicators ...................................................................................85 Occupational diseases ..........................................................................................86 Agroenvironmental indicators .............................................................................87 7. Annexes ............................................................................................................89 7.1. Methodology of the research ........................................................................90

The list of data collection policies and practices .............................................91 National data sources .......................................................................................92 International data sources ................................................................................94 Panel of experts................................................................................................94

7.2. The questionnaire..........................................................................................96 7.3. List of national authorities & data sources .................................................111



7.4. Presentation of programmes of special importance and a presentation of indicative authorites by country.........................................................................136



Executive summary This report presents the results of the research conducted in the framework of work package 2 “Identification and review of existing policies and practices” and workpackage 3 “Identification of collected data”.

The aim of work package 2 is to identify and review specific policies and practices with regards to data collection and reporting at national and European level in relation to environmental and occupational risks in agriculture and fisheries. Work package 3 discusses the issue of homogeneity between Member States based on the findings of work package 2. The work performed for the two work packages is so interrelated that the only functional way for presenting the results achieved is in a common report. Therefore in the current report we present by topic both the identified data sources and the results from the comparative analysis identifying common datasets currently applied at the EU level. In the framework of work package 2 and work package 3, the project team performed an extensive search among national institutions and organizations across the European Union, in order to identify sources of information and subsequently to collect and process information in the form of datasets related to agriculture, fisheries and the environment. During the first partners meeting, extensive lists of potential indicators were discussed. Yet, it was unclear whether the information needed by the partnership was available and whether this information could be obtained. For these lists of indicators, information has been collected on the availability of data, the quality of the collected material, the method of collection, the level of aggregation and other aspects of the data collection process. The outcome of this research has been used to conduct a cross-country comparison of existing data. The by-product of this search by country was a list of the responsible authorities who collect relevant data, as well as an indicative list of already implemented national programmes and policies, focusing on health promotion, occupational health and safety among farmers and fishermen. In addition, the websites of international organizations were queried on the same topics and the results of this search have been coded by stakeholder and topic and are presented in the prism of data availability across the EU Member States. The present deliverable is all inclusive, thus containing results produced within work package 2 and work package 3 during the first and second year of the project. What is included is a list of authorities collecting relevant data and a list of specific data collection practices and policies across the EU as well as results of the comparative cross-country analysis presented with the use of descriptive statistics and of codified common datasets by topic. Furthermore, in the annex section lies all relevant information regarding the first meeting of the project partners, the tool in form of a questionnaire used to acquire the necessary information from the Member States, the methodology used for data retrieval and a detailed list of national data sources and institutions contacted during the research for this work package.



1. Introduction Work packages 2 and 3 serve a key role in the project: to investigate sources of data related to agriculture and fisheries, which could be used as material to produce effective and feasible public health indicators that could be proposed for monitoring the health status of these populations; furthermore, to examine if there are existing policies and practices in the Member states in the fields of health and safety in agriculture and fisheries. The area under examination is the European Union of 25 Member States plus Bulgaria. Norway was also partly included. The methodology for collecting national data and analyzing collected information by country is presented in the annex of this report. To achieve the goals of the two work packages, we have worked intensively; utilizing partners meetings, a questionnaire, as well as detailed Internet search, but mostly we have utilized the experience and expertise of the partnership. Among the key elements of this work package was to create an extensive and exhaustive list of authorities (both at the national and international level), collecting data relevant to farmers and fishermen’s health, exposures and risk, as well as environmental risks stemming from farming activities. To further expand our understanding on practices and policies on data collection, we completed a detailed questionnaire for each member state. The content of the questionnaire was decided on the first partners meeting. The methodology included three steps: a) completion of the questionnaire by a partner or an expert, b) search through the Internet for data identification, c) verification of the data by the relevant authority. Collected data was then reviewed and processed using SPSS for Windows: v.13. The descriptive analysis was carried out primarily by the Greek research team (NKUA and Prolepsis). To facilitate the comparison of the compiled material of work package 2, the coordinating team and the IMIM team responsible for this work package, worked together during a two-day meeting organized for this purpose in Athens (September 2006). During this session, the two teams worked to improve the quality of the existing material and discussed the issue of data comparability for each topic on which data were available. A draft working document which included a set of indicators, to be proposed for monitoring the health of farmers and fishermen, was also produced based on already available data and is included in the annex of this report. In addition to the descriptive statistics, the main outcome of workpackage 3 was the development of common datasets based on the findings of work package 2. Although both availability and the level of aggregation were to be compared, the primary focus was on the availability of data collected across the EU countries, because of the small number of datasets commonly available.



The availability of the collected data by country was recoded into a scale variable, depicted in the form of 1 to 5 boxes (1 box: available data in 0-5 countries (red); 2 boxes: 6-10 countries (orange); 3 boxes: 11-15 countries (yellow); 4 boxes: 16-20 countries (light green); 5 boxes: 21-27 countries (dark green)). The result of this review of the available datasets is presented in chapter 6. The results of this extensive search by item for each member state of interest are presented in the next pages and include information on labour force (chapter 2.1), health and safety data, health care, morbidity and mortality, insurance systems and environmental and occupational risk data (chapter 3 and 4). The legal framework is also discussed briefly. Data derived from international organizations (Eurostat, FAO, ILO, OECD, UNPD, WHO is also presented both by item of interest (chapter 5.2) and by organization (chapter 5.3).



2. Identification of polices and practices with regards to data collection

2.1. Labour Force in Agriculture and Fisheries The data collection concerning the labour force in agriculture and fisheries provided a significant amount of information. Five major topics are the focus of the research:

1. Number of persons employed in agriculture and fisheries (presented by gender)

2. Number of people retired in agriculture and fisheries 3. Child labour in agriculture and fisheries 4. Elderly labour in agriculture and fisheries 5. Immigrant labour in agriculture and fisheries

The latter three categories (children, elderly and immigrants), as well as the categorization of all data by gender, aim at addressing the issue of protection and equality in rural areas. Three main sources of data have been identified for agriculture: 1) national censuses, which are usually conducted every 10 years and include the whole population. The National Statistical Service of each country is responsible for planning, collection, processing of data and dissemination of results. 2) the Labour Force Survey for each country, which covers a percentage of the population and is conducted by national authorities, following a methodology which is generally common for all EU countries. EUROSTAT provides the methodology, receives, processes and publishes the data for each country, 3) the Farm Structure Survey (FSS) for each country includes a part, titled “Farm labour force”, which incorporates detailed information on people involved in agricultural activities. For the fisheries sector, apart from national statistics and Labor Force Survey (LFS) data, fisheries statistics conducted by national authorities also provide data on employment, as well as other production-specific figures.

2.1.1. Number of persons employed in agriculture and fisheries The amount of information we secured varies greatly by country, depending greatly on the institution’s willingness to provide detailed information and the availability of information on the Internet. We opted to present the maximum of available relevant information for each country, in hope that differences could lead to overall improvement.

Collected material by country Austria The data which are available from Statistics Austria are divided by family, non-family labour force in agriculture as well as the workforce of owners and employees by



gender. Data are available every two or four years depending on Eurostat and accessibility is free of charge. Specific age range intervals were explicitly included in the 1999 survey, which was a full statistics research. In addition, data on the workforce population by gender can also be found. The Social Insurance for Farmers (Sozialversicherungsantalt der Bauern SVB) -,collects data on an annual basis concerning employment in agriculture of all self-employed who are obligatory insured, by gender and the following age clusters: 20-24yrs, 25-29 yrs,30-34yrs, 35-39yrs, 40-44 yrs, 45-49 yrs, 50-54 yrs, 55-59 yrs, 60-64 yrs, 65-69 yrs, 70-74 yrs, 75-79 yrs, 80 and above. Fishing is not an occupation of significance for Austria. Belgium The National Statistics Office of Belgium provided data on the number of persons employed in agriculture. The figures are available from the May 2005 agricultural census. The data are split by gender, volume of labour, family and non-family regular labour force, non family regular occupation and non family irregular occupation. In addition, information on the age of the manager is also available. Accessibility is free of charge. Bulgaria Employment data is available for agriculture only. The data is reported by ten-year cluster range (15-24, 25-34, 35-44, 45-54, 55-64 and over 65 yrs) and not by sex. (2004 data). In addition, data on workforce population is available but it is not divided by age clusters and gender. The collection period is not mentioned. The source of information is the National Statistical Institute. Accessibility of data is commercially charged. Cyprus The last available data are based on the Labour Force Survey (LFS) 2003, and refers to the full-time equivalent number of working persons. The LFS covers a sample of 3,600 households in all districts of Cyprus, which are allocated according to the number of households that reside in urban and rural areas. For purposes of comparability of the results in all Member States, the survey covers the population of private households only. It does not cover persons residing in collective households (i.e. institutions, homes for the aged, hospitals, monasteries etc.), conscripts on compulsory military service as well as students who study abroad and Cypriots who work abroad. In Cyprus collection of data, from the Statistical Service, is conducted through personal interviews and the use of portable computers as well as through telephone interviews. The source is Labour Statistics – Series II Report No 22 (this report is published annually since 1982) and its accessibility is free of charge. The report from 1994 is issued in Greek and English. It incorporates data on employment, unemployment, vacancies, placements, government labour force, foreign workers, social insurance statistics, labour disputes, occupational accidents, wages, salaries, consumer price index and cost-of-living allowance. The data for employment, wages, salaries and prices are collected by the Statistical Service through special surveys. The data on unemployment, vacancies, placements, labour disputes, occupational accidents and social insurance are collected by the Ministry of Labour and Social Insurance. The data published in this Report for the years up to



mid-1974 cover the whole country, including Turkish Cypriots, while thereafter they refer only to the Government controlled area. In addition, there are data on the economically active population by sex and age group 2000-2003. Czech Republic Men and women employed in the sectors of agriculture and fisheries are reported by age group: 15-24 yrs, 25-29 yrs, 30-44 yrs, 45-59 yrs, above 60 yrs (3rd quarter of 2005). For agricultural workers, there are also data deriving from the Agrocensus since 2000 and from the Structural Survey in Agriculture since 2003 (published on Statistical Yearbook of Czech Republic 2004), which reports employment by five-year age groups: < 24 yrs, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64 and above 65 yrs. In addition, data on workforce population is also available by gender and age. The source of information for both Statistics is the Czech Statistical Office and accessibility of the data is free of charge. Denmark The sector of agriculture is unified with the fisheries sector and data on self-employment in these sectors by age clusters (-15 years, 16-19 years, 20-24 years, 25-29 years, 30-34 years, 35-39 years, 40-44 years, 45-49 years, 50-54 years, 55-59 years, 60-66 years, 67+ years) and gender is available by the National Statistics Office. Moreover, information can be found on the employed population by industry, sex, socioeconomic status (self employed, assisting spouses, top managers, employees - upper level, employees - medium level, employees -basic level), age and time and this type of data has been collected since 1997. In addition, the labor force aged 16-66 by region, sex, age and time is available and this type of data has been collected since 1981. Accessibility is free of charge. Estonia Employment data are available by the National Statistics Office and are free of charge. For 2005 information can be found about persons employed in agriculture and fisheries as economic activity and occupation by gender. In the agriculture sector information can be found about the total labor force, family labor force, regular employees and non-regular employees covering the years 2001, 2003, 2005. Some of these data can be found disaggregated by:

• labour force in agricultural holdings of natural persons (by indicator, county and kind of labour force)

• labour force in agricultural holdings (by indicator, legal form of holder and kind of labour force)

• employed persons aged 15-74 by economic activity and year (1997-2005) • employed persons aged 15-74 by major group of occupations, sex and year.

As for the total employment, the employment rate by sex, age group and year have been collected since 1989.

Finland The Farm Register of the Ministry of Agriculture and Forestry provides employment data for agriculture only, which are updated annually (last available year 2003). Employed people are reported by gender and by five-year age groups: < 24 yrs, 25-29,



30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64 and above 65 yrs. Accessibility of this source is free of charge. The Farmer Social Insurance Institution collects annually the number of insured farmers and fishermen (but not by age range) as well as data coming from the Labour Force Survey of Statistics on an annual basis. Both sources are accessible free of charge. The Statistics Service of Finland provides data on the workforce population by gender. In the questionnaire, the time period of data collection and availability is not mentioned. France The INSEE-National Statistics Office provides data on age-specific male and female activity rates and labor force in employment, classified by employment status for the time period 2005. Accessibility is commercially charged. Germany Men and women employed in agriculture are reported by the following age groups: 15-29 yrs, 30-39 yrs, 40-49 yrs, 50-59 yrs and above 60. The Federal Statistical Office (Destatis) collects data about agriculture at 2-year intervals within the agricultural survey. The data mentioned are the latest from the survey of agriculture in 2003. The survey includes data from all constant workers in different age groups and in addition, the total number of seasonal workers without information about age composition. The employed population in fisheries are not available by age or by gender but as whole employment for fresh water fisheries and deep sea/coastal fisheries. Statistics of the workforce population in the field of fisheries are not exclusively carried out in responsibility of the Federal Statistical Office. Some of the data are accumulated by other institutions. Data on persons employed in the freshwater fisheries are collected by the Federal Statistical Office. These data are collected within the survey of freshwater fisheries that was carried out in 1994. The latest data about persons employed in freshwater fisheries will be published in June 2005 since the survey of freshwater fisheries is carried out in 10 years intervals. An age composition of the employed persons is not given. Data about number of persons employed in the deep sea fisheries and coastal fisheries (e.g. deep sea fishing) are not collected by “Destatis”. Instead, information could be found by means of the social security board for seamen which belongs to the system of statutory health and occupational insurances. These data are published in the “Annual report on German fisheries in 2004” by the Federal Ministry of Consumer Protection, Nutrition and Agriculture. The report provides data on all employed personnel of the vessel fleet (large deep sea vessels, small deep sea vessels), including coastal fisheries. Greece Men and women employed in the sectors of agriculture and fisheries are reported for the following age groups: 15-39 yrs, 40-64 yrs, 65-74 year intervals and by gender. According to the National Statistical Service, the employed workforce includes all persons aged 15 years old and over, who during the week that the census took place, have worked for at least one hour for remuneration in the form of wage or salary, for profit or family gain or had a job or an enterprise but were not in work. The source is the 2001 Census (which takes place every 10 years) from the National Statistical Service of Greece. In addition, quarterly data are available by surveys. Accessibility is free of charge.



Hungary The Hungarian Central Statistical Office (HCSO) provides relevant information divided for manual/non-manual workers in agriculture and fisheries. The number of persons employed is divided for part-time and full time. Ireland Men and women employed in the sector of agriculture and fisheries are reported for the following age groups: 15-19 yrs, 20-24 yrs, 25-34, 35-44, 45-54, 55-64, and above 65 years. The source is the 2002 Census available from the Central Statistics Office. In addition, the Quarterly National Household Survey that is authorised by the Central Statistics Office (CSO) collects data on employment of the general population. Accessibility is free of charge. Concerning agriculture and fisheries, data is reported for employed men and women (age intervals not available) from the Labour Force Survey (the same applies for agriculture). The data is also available from the Central Statistics Office. The data available covers the years 1995 to 2004 for the time period of March-May of each year and for persons aged 15 years and over. In addition, there are available data on the labour force population by gender and 5-year age clusters. Accessibility is free of charge. Italy Men and women employed in the sectors of agriculture and fisheries are reported by five-year age groups, starting from the age of 15 up to over 65 years old. The source is the decennial Census, which is organised by the Istituto Nacionale di Statistica (ISTAT) and the last available data is for year 2001. The data is available free of charge. Workforce population by gender is also available by ISTAT (without time period definition in the questionnaire). There is no mention about accessibility. Latvia Employment of men and women in the sectors of agriculture and fisheries is reported, but data is not available by age range. The source is the Statistical Yearbook of Latvia, 2003 from the Central Statistical Bureau. No mention is made concerning accessibility of these data. Lithuania The Ministry of Agriculture reports employment as the as the total number of persons employed in the sectors of agriculture, hunting, forestry and fisheries,. The Census (data collected from 1998 to 2005) from National Statistics reports the number of men and women employed in agriculture and fisheries as well as the number of skilled workers (in this case agriculture and fisheries workers are not separated). The Agricultural Census in Lithuania was conducted in June 2003 (2-30 June). All farmers and land users, who grew agricultural products have been enumerated. Land, crops, livestock, housing, agricultural equipment, the number of family members working in agricultural sector, the number of hired employees, their working hours, the farm group by income level and fisheries are key questions which were asked. Accessibility is free of charge.



Statistics Lithuania provides data on labour force by age, gender as well as urban/rural breakdown, employed and employees by kind of economic activity, economic sector, urban/rural breakdown, gender, period and statistical indicators, etc. This data is available via the website http://db.stat.gov.lt/sips/Database/sipsen/ databasetree.asp and is free of charge. Luxembourg The Central Service of Statistics and Economic Studies (STATEC) collects data on employment in agriculture, domestic employment in agriculture and number of farms since 1990. In addition, the annual average of national employment can be found since 1995. Accessibility is free of charge. http://www.statec.lu/ Malta Men and women employed in agriculture and fisheries are reported in the national statistics by economic activity and by occupation. Agriculture and fisheries skilled workers (men and women) are not divided into separate categories. The source is the National Statistical Office that has been collecting employment data quarterly from the year 2000, classified by economic activity and by occupation. The last available data are from the 3rd quarter of 2005. The National Statistics Office also provides data on full-time and part-time employed persons, classified by economic activity. Furthermore, the total employment classified by age group (10 years clusters) and gender and the last available data are from the third quarter of 2005. Total employment figures have been collected since 2000 by quarter and the data has been collected with census. Accessibility is free of charge. The Netherlands Men and women employed in the sectors of agriculture and fisheries (which are unified), are reported by ten-year age clusters: 15-24 yrs, 25-34 yrs, 35-44 yrs, 45-54 yrs, 55-64 yrs and over 65 years. The open-access source of data is the National Statistics (year 2000) and the organization is Statistics Netherlands. Data on the workforce population by age (10 year intervals) and gender are also available without specific time period on the questionnaire. An important note has been written down by our Dutch partners: “values are rounded off, hence, low numbers not very accurate”. Another source of data has been identified: the Agricultural Economics Research Institute LEI. Within the Netherlands, it is the leading institute for economic research in the field of agriculture, horticulture and fisheries, the management of rural areas, agribusiness and the production and consumption of foodstuffs. Data are collected through yearly surveys. The access is open for publications and general databases. Norway The National Statistics Office collects data on employed persons in agriculture and fishing by gender, age and volume of labour in working hours (short part-time. 1-19 working hours, long part-time. 20-36 working hours, full-time. 37 or more working hours) and have been collected since 1996. The age clusters are: 16-19ys, 20-24 ys, 25-39ys, 40-54 ys, 55-66ys, 67-74ys. Data on labour force by gender and age have been collected since 1972. Poland



Information concerning employment is available only for the agriculture sector. Data are reported by gender and by the following age groups: 15-29 yrs, 30-44 yrs, 45-59 yrs and over 60. The source is the Yearbook of Labour Statistics (data collected during the year 2002), from the Central Statistics Office. Moreover, there are data on the workforce population by gender and age clusters which are the same with the above (the time period is not reported). No mention is made about accessibility and data availability. Portugal Data on total employment as well as employment in agriculture and fisheries are available by gender and by age groups of ten years and over 65 years from the Instituto Nacional de Estatiditsica (INE) requiring approval. The time period covered is from 1998 till 2005. There are available statistics on employment in the sectors of agriculture and fisheries, as well as the Gross Volume Added (total and in Agriculture and Fisheries) and the Gross Domestic Product, in Portugal from 1999 to 2002 by the “Ministerio do Trabalho e da Solidariade Social”. The statistical sources are the Portuguese Annual National Accounts (base 2000) and the Labour Force Survey, both from the “Instituto Nacional de Estatística (INE)” (Lisboa). It is important to indicate that the estimates of employment provided in the national accounts take into consideration three different concepts of employment:

• Employment – Individuals – All the persons (employees as well as employers).

• Employment in full-time equivalents – Volume – This employment is defined as the total number of hours divided by the annual average of hours worked in full-time.

• Employment – Jobs – A Job is defined as an explicit or an implicit contract by which a person supplies his work in change of money. So, it includes the several employments of a person.

The Ministry of Agriculture (GPPAA) collects data on agriculture population by gender, by age (5 year clusters and over 74 years) number of farms, annual work units, annual work employment in total employment, etc that are provided by the Instituto Nacional de Estatiditsica (INE) and are free of charge. Slovakia The Statistics Office of Slovakia provides data on workers and employees in agriculture by 5-years age cluster and over 65 years. In addition data on all workers and employees in Slovakia by age groups (as above) are available. The data that can be found in the questionnaire are from 2003 to 2005 and are available by the Statistical Yearbook of Slovak Republic.Accessibility is free of charge. Slovenia Agriculture and fisheries are considered together, as a single category. Data reported are the sum of men and of the women employed. The source is the Labour Force Survey, conducted quarterly and the collection period is the 2nd quarter of year 2004. It is accessible free of charge from the Statistical Office of Republic of Slovenia which also provides information on holders of farms by age groups. Furthermore



information was provided about the labour force in agriculture on family farms (AWU) and the general labour force by gender. Spain Men and women employed in agriculture and fisheries sectors are reported by five-year age groups, apart from the first one that is 16-19 yrs, until the last group over 70 yrs. There is also information on workforce population by gender and age. Data is accessible by internet from the “Encuesta de Población Activa”(1st quarter 2006). To access the complete database, a petition form has to be sent to the National Institute of Statistics. Data have been collected since 1976. http://www.ine.es/inebase/cgi/um?M=%2Ft22%2Fe308_mnu&O=inebase&N=&L= Sweden Employment data are available for men and women for the age range 16-64 for the agriculture and fisheries sectors,. The source is the RAMS-Register based branch/region statistics (year 2003) from Statistics Sweden. Information on employees 16-64 years by occupation, industrial classification SNI 2002, age, gender and period with 4-year age cluster, age distribution of farmers 1961 – 2003 is also available. Furthermore, the 2003 Labour Force Survey collected data on workforce population by gender. The data are freely accessible from the following website: www.scb.se United Kingdom Men and women employed in the agriculture sector are reported for full –time, part-time and seasonal or casual workers by gender from the Department for Environment, Food and Rural Affairs (DEFRA)- Agricultural Statistics and Analysis Division. The data is collected from 1984 to 2005, which is the last available year. Concerning fisheries, the total number of fishermen is reported, including those holding regular and part-time jobs from the DEFRA-Fisheries Statistics Unit. The data is collected since 1938 through to 2004. This Department also provides data on the number of fishermen by district and in regular or part-time jobs from 2003 to 2004.

Data analysis Figure #1 presentes information on the availability and accessibility of employment data in agriculture does not seem to be a problem for the EU countries. 21 of the countries examined confirmed the availability of the data, while the rest did not collect enough material to prove otherwise.



Figure #1

Accessibility of employment data in agriculture in Europe

Data Accessible;

n=21

Insufficient data; n=6

n = number of countries

Figure #2 presents data collection periodicity for farmers’ employment in Europe. National data on employment in agriculture has been collected in 14 countries between 3 to 31 years ago. For the 27 countries examined, 5 conduct data collection surveys/censuses once every trimester/quarter, 6 annually, Germany every two years and Italy every 5 years. For the rest of the countries, information regarding the periodicity of the data collection was not obtained.

Figure #2

Data collection periodicity for farmers employment in Europe

Annualy; n=6

Once in a trimester/ quarter;

n=5

Insufficient data; n=6

Every 5y;n=1

N/A; n=14


Figure #3 presents information on the availability of data by age aggregation in Europe. Data were available for 16 countries. Employment data aggregation by age in the sector of agriculture is similar in over 50% of these countries, as the cluster age



range is per 5 years. 38% of the countries provide only 10-year age clusters and only Greece seems to provide a 20-year cluster range.

Figure #3

Cluster age range distribution for employment in agriculture in Europe

20-yr age intrerval

n=1

10-yr age interval

n=6 5-yr age interval

n=9


Figure #4 presents information on the accessibility of employment data in fisheries in Europe. The accessibility of employment data in fisheries is a common practice among EU countries, as 16 of the countries examined confirmed the available status of the data, while for 4 countries, access was restricted. For the rest, not enough material was obtained to confirm the accessibility status.



Figure #4

Accessibility of employment data in fisheries in Europe

data accessib le

n=16data not

accesssib len=4

insufficient datan=7


Figure #5 presents data on the periodicity of collecting data on employment in fisheries in Europe. National data on employment in fisheries has been confirmed to be collected since 3 to 66 years ago in 11 countries. For the 27 countries examined, 5 conduct data collection surveys/censuses once every trimester/quarter, 4 annually, Germany every 5 years and Italy every 10 years. For the rest of the countries, information regarding the periodicity of the data collection was not obtained.



Figure #5

Data collection periodicity for fisheries in Europe

Once every 10 years

n=1

Once a trimester

n=5

Once every 5 years

n=1

insufficient datan=13

Once a yearn=5


Figure #6 presents the availability of data by age range in fisheries in Europe. As it can be seen, data are available only for nine countries. Employment data aggregation by age in the sector of fisheries varies among the 27 countries, as the cluster age rangers from 5 to 20 year intervals.



Figure #6

Cluster age range distribution in fisheries in Europe

10-yr age interval

n=3

5-yr age interval

n=4

15-yr age interval

n=1

20-yr age interval

n=1


2.1.2. Number of retired in agriculture and fisheries Only a few completed questionnaires provided information on the topic of retired workers in agriculture and fisheries. The collected material is presented and discussed below.

Collected material by country Czech Republic The Czech Statistical Office collects data on retired agricultural workers. France The information on retired people can be found on Caisse Nationale d’Assurance Vieillesse ( Cnav ) http://www.cnav.fr/infos/frameset.htm Poland The data on the retired workers in Poland is collected by gender and is available only for agriculture. This kind of data can be found in the Central Statistics Office of Poland.



Hungary The annual report of the Hungarian Central Statistical Office, includes information on employed pensioners. Greece The Agricultural Insurance Organization collects data on retired agricultural workers and fishermen. The data are available both from quarterly and annually surveys. Accessibility is free of charge. Sweden Statistics Sweden (RAMS-register-based branch/region statistics 2003) provides data on retired workers in agriculture and fisheries, by gender.

Data analysis Figure #7 presents facts regarding the availability of employment data of retired persons in agriculture in Europe. National data for the number of retired workers in agriculture exist only for Greece, the Czech Republic, Sweden, France and Poland. Fourteen other countries confirmed lack of this kind of data, while for 8 countries Neither positive nor negative could be secured.

Figure #7

Data availability on retired persons occupied in agriculture in Europe

Not applicable

n=8

Yesn=5

Non=14




As seen on figure #8, among the 5 countries with existing nationa data on retired agricultural workers, aggregation by gender in available in Poland, Sweden, Czech Republic and Greece, but not for France.

Figure #8

Data availability on retired persons occupied in agriculture stratified by

gender in Europe

Yesn=4

Non=1


As presented in figure #9, available data on retired workers in fisheries exist only for 3 countries, e.g. in the Czech Republic, Sweden and Greece, and they are aggregated by gender. For 16 member states, it was stated that data do not exist whereas 8 countries (Estonia, Belgium, Cyprus, Lithuania, Ireland, France, Hungary and Slovenia) did not answer the relevant question.



Figure #9

Data availability on retired persons occupied in fisheries in Europe

Not applicable

n=8

Yesn=3

Non=16


2.1.3. Number of children employed (<14 years) Completed questionnaires received from most countries indicate that this data are mostly unavailable, arguing that such data are not collected in their country, because child work is illegal.

Collected material by country Greece The only country that has reported data about child labour in agriculture and fisheries sectors is Greece. The data are collected by gender, and production area (wheat, vegetables, vineyards etc, fisheries).The source is the 2001 Census available from the National Statistical Service. Accessibility is free of charge. Portugal The Ministry of Agriculture (GPPAA) collects information on total family farm population for the age-groups 0-4, 10-14, 20-24, 30-34, 40-44, 50-54, 60-64, 70-74, 60-84 years. The source of this type of data is the Instituto Nacional de Estatiditsica (INE).

Data analysis



As seen on figure #10, two countries (Greece and Portugal) seem to be the only countries that collect child labor in agriculture data. No answer was given for Belgium, France and Slovenia.

Figure #10

Data availability on employment of younger persons(<14 yrs old) in

agriculture in Europe

Not applicable

n=3

Non=23

Yesn=1


As seen in figure #11, Greece seems to be the only country that collects child labor in fisheries data. No answer was given for Belgium, France and Slovenia.



Figure #11

Data availability on employment of younger persons(<14 yrs old) in

fisheries in Europe

Yesn=1

Non=23

Not applicable

n=3


2.1.4. Number of elderly employed (>68 years) Information about data availability of this issue has been partly covered from the delivered questionnaire as there are many countries which do not have any information on elderly persons being employed in agriculture or fisheries. Among international organizations, EUROSTAT and ILO also provide some information (see relative chapter).

Collected material by country Austria The Sozialversicherungsantalt der Bauern (SVB) compiles data on elder (>68 years) persons employed in agriculture, particularly the type of data that is collected is the age range of the insured farmers by the database standard report and the data are free of charge. Bulgaria Employment of the elderly is reported for those over 68 years for both sectors of Agriculture and Fisheries. The data is available from the National Statistical Institute and from the National Social Security Institute Central Department.



Czech Republic Employment of elderly in agriculture is reported for those aged over 65 years. The data is available from the Czech Statistical Office free of charge. Denmark The Statistics Denmark collects data on elder persons (>68 years) employed in agriculture and fisheries. Estonia The Statistics Office provides free of charge data on employed persons 15-74 by economic activity and year, employed persons aged 15-69 by major group of occupations, sex and year. http://www.stat.ee/ Germany Employment of elderly is reported for the age groups between 65 until 69 as well as 70 an older. Data are available at the Federal Department of Statistics – DESTATIS. Greece Number of people over 68 employed in agriculture and fisheries sectors is reported. Data are collected by gender, and production area (wheat, vegetables, vineyards etc, fisheries). The source is the 2001 Census available from the National Statistical Service. Accessibility is free of charge. Ireland People over 65 years old who work in agriculture and fisheries are reported in the 2002,Census. Accessibility is free of charge from the Information Section of the Central Statistics Office. Italy Aggregate data are available for workers older than 55 years old. In addition, the National Institute for Statistics provides data on employment for age groups over 65 years. The source is the 2001 Census. Data refer only to “legal workers”. Accessibility is free of charge. Norway The National Statistics Office provides data on employed persons 16-74 years old, by age and industry sector. Data concern the 4th quarter of 2005. The age clusters are: 16-19ys, 20-24 ys, 25-39ys, 40-54 ys, 55-66ys, 67-74ys. Accessibility is free of charge. More information can be accessed from http://www.ssb.no/english/subjects/06/01/regsys_en/tab-2006-06-14-10-en.html Poland Data are reported for men and women employed in the agriculture sector who are older than 65 years. The source of this information is unclear. Portugal The Ministry of Agriculture (GPPAA) collects information about the total family farm population for age-groups 0-4, 10-14, 20-24, 30-34, 40-44, 50-54, 60-64, 70-74, 60-84 years. The source of this type of data is INE.



United Kingdom Even though employment data for the elderly are not reported, there are available data on fatal injuries of the employed population over 65 years which occurred in Great Britain between the 1st of April 2004 and the 31st of March 2005. Data are also available for the ten-year period (1994/95-2003-04). The information is accessible free of charge through the website of the National Agricultural Centre (Agriculture & Food Sector).

Data analysis Figure #12 presents information on data availability on employment of elderly persons (>68 years old) in agriculture in Europe. Less than half of the countries examined seem to collect labour statistics in agriculture for workers over 68 years of age. For Poland, Slovenia, Belgium and France, the specific question was not answered.

Figure #12

Data availability on employment of elderly persons(>68 yrs old) in

agriculture in Europe

Not applicable

n=4

Non=12

Yesn=11


Figure #13 presents information on data availability on employment of elderly persons (>68 years old) in fisheries in Europe. Denmark, Greece, Italy, Norway, Estonia, Bulgaria and Ireland seem to collect labor statistics in fisheries for workers over 68 years of age. For Belgium, France and Slovenia, the specific question was not answered.



Figure #13

Data availability on employment of elderly persons(>68 yrs old) in fisheries

in Europe

Not applicable

n=3

Non=17

Yesn=7


2.1.5. Number of immigrants employed

Collected material by country Austria Statistics Austria, Hauptverband and Arbeitsmarktservice (AMS) have information on immigrants who are employed in agriculture. www.ams.at Cyprus The Demographic Report, compiled by the Department of Social Insurance has data on migration movements from 1981 to 2004. Data is collected for foreign workers by economic activity. Moreover, the Department of Social Insurance collects data on foreign workers by economic activity from 1998 and accessibility is free of charge. According to this source, 9% of the immigrants work in Agriculture sector. More information is available from the following website, http://www.mlsi.gov.cy/mlsi/sid/sid.nsf/dmlcontactus_en?OpenForm Czech Republic The Ministry of Labour and Social Affairs provides data on this subject. Last update: December, 2005. http://www.mpsv.cz Germany



Data are available on employment of foreign workers. The data give aggregated information about the number of foreign persons who are insured in a German statutory social security and are employed in agriculture and fisheries at the same time.. The source is the Federal Department of Statistics. Greece Data with regards to immigrant employment provided by the Agricultural Insurance Organization are not separate for agriculture and fisheries workers. These two occupations are unified. The information includes the number of immigrants insured in OGA by nationality and is routinely collected. Accessibility of data is free of charge. The National Statistical Service collects data on employed immigrants by gender and production area according to the 2001 census and are free of charge. Ireland The Information Section of the Central Statistics Office provides data which are free of charge on:

- Population aged 15 years and over in the labour force, classified by broad occupational, group and nationality

- Males aged 15 years and over in the labour force, classified by broad occupational group and nationality

- Females aged 15 years and over in the labour force, classified by broad occupational group and nationality

More information is available from www.cso.ie Italy ISTAT provides data on legal immigrant workers; quinquennial age interval up to 35 years old, then ten-year intervals/ activity sector/ sex. More information can be found on the following website www.istat.it More data are collected by Italian Caritas (number of immigrant worker by activity sector - agriculture, industry, services and others- type of contract and region). More information is available on the following website www.db.caritas.glauco.it Latvia The data provided by the Agricultural Insurance Organization is not separate for agriculture and fisheries workers. More information is available from the following website, www.vdi.gov.lv Lithuania The data provided from National Statistics concern immigration and emigration by sex that takes place in Lithuania and is available from 2003 till 2005. Norway The National Statistics Office collects information on employed total and first generation immigrants, by industrial classification, region of birth, time and contents. Accessibility is free of charge. More information is available from the following website http://statbank.ssb.no/statistikkbanken/default_fr.asp?PLanguage=1



Poland Data available for agriculture only. Portugal INE after approval can provide data on immigrants employed in agriculture and fisheries from 1998 to 2005. Spain Work Permits to Foreigners The source of information is derived from the forms “Request of work and residency permit” ("Solicitud de permiso de trabajo y residencia"), which must be completed by the applicant (worker or company) in order to receive their work permit. Foreign workers The information offered concerns workers of non Spanish nationality affiliated with the Social Security System. It also provides information on labor discharges (firing) and corresponding pending claims of these workers. The source is the National Institute of statistics (Instituto Nacional de Estadística). Data are available in spanish language. http://www.ine.es/inebase/menu2_dem.htm#2 Sweden Data provided by the Agricultural Insurance Organization is not separate for agriculture and fisheries workers. More information is available from the following websites: www.scb.se; www.ams.se

Data analysis Figure #14 presents information on data availability of immigrants employment in agriculture in Europe. Although most countries collect some data on immigrant’s employment, only ten out of the 27 countries examined seem to collect data for immigrants employed in the sector of agriculture. For Slovenia, Lithuania, Belgium, France and Sweden, the specific question was not answered.



Figure #14

Data availability on employment of immigrants in agriculture in Europe

Not applicable

n=5

Non=12

Yesn=10


Figure #15 presents information on data availability of immigrants employment in fisheries in Europe. Eight out of the 27 countries examined seem to collect data for immigrants employed in the sector of fisheries. For Slovenia, Lithuania, Belgium, France and Sweden, that specific question was not answered.



Figure #15

Data availability on employment of immigrants in fisheries in Europe

Yesn=8

Non=14

Not applicable

n=5




3. Availability of segregated data focusing on the health and safety of farmers and fishermen Examining whether already routinely collected data are used to form strategies or are already implemented in the form of a national policy aiming at the equal protection of rural citizens is a cumbersome task. To do that, one has to examine whether such data actually first exist and to which degree this data truly reflects the magnitude of exposure, risk and outcome to the farming and fisheries population, as well as to the environment. Second, one has to gain an understanding on how equal protection of citizens in rural areas could be implemented or is currently a reality (eg the structure of health care services, insurance policy, etc). As it has been seen from the presented data on employment, relatively few member states collect data segregated even by the basic categories, that could potentially face social exclusion, such as age and immigrant status. To further understand the situation, the project team attempted to collect information on three topics: mortality and morbidity statistics in Europe, health care indicators for farmers and fishermen and the insurance system for farmers in the European Union. The results of the above are extremely poor, denoting a remarkable lack of policies for even monitoring these populations, let alone intervene.

3.1. Mortality and morbidity indicators by country A critical source of information regarding a population’s health is mortality and morbidity statistics. Although mortality statistics are routinely collected by all WHO member countries, morbidity statistics are extremely scarce. Furthermore, even mortality data are not reported routinely by occupation. Yet we were able to identify some data sources that are presented in the following pages.

Collected material by country Austria Soziale Unfallversicherung (AUVA) provides data on all cases of accidents at work and all kinds of occupational diseases by gender and by age, which are free of charge. Information is found on the following websites: www.hauptverband.at – www.statistik.at Belgium The FPS Employment, Labour and Social Dialogue provides data on accidents by sector and gravity and accessibility is free of charge. Information is found on the following website: http://meta.fgov.be/pa/ena_index.htm Czech Republic The Czech Statistical Office reports data about fatal industrial injuries, industrial injuries resulting in incapacity for work exceeding 3 days, industrial injuries without incapacity for work, and occupational diseases cause and gender specific for 2004. Accessibility is free of charge. (http://www.czso.cz/csu/edicniplan.nsf/t/F5004F1C0B/$File/33052166.xls)



Another source of information, which does not contain information relevant to our questionnaire and which has restricted accessibility is the Annual Report of Czech Occupational Safety Office (now State Labour Inspection Office) in 2004. More information is available from http://www.suip.cz/default/drvisapi.dll?MIval=/www/index.html Cyprus The Department of Labour Inspection of the Ministry of Labour provides data on incidence of injuries, mortality due to injuries, in the agriculture and fisheries sector. The method of collection is notification of occupational accidents and computerized information system. Data refer to mortality/morbidity that is caused by occupational accidents and fatal injuries by gender and age. Accessibility is free of charge. The Annual Report of the Department of Labour Inspection of the Ministry of Labour and Social Insurance provides data on work accidents by economic activity sector, gender and degree of injury, including the agriculture and fisheries sectors. Denmark The Division for Investigation of Maritime Accidents provides information on maritime accidents. In addition, Statistics Denmark is able to deliver commercially charged data on mortality due to injuries, mortality due to cancer and mortality due to respiratory diseases for people employed in agriculture and fisheries. If needed data can be specified by age, gender and other variables. Estonia The Social Statistics Department provides statistics on occupational and fatal accidents in Estonia in 2000-2004 by field of activity. Finland Indicators for morbidity and mortality are available for the entire work force including the agriculture sector. Data available for morbidity and mortality are cause specific, gender and age specific. The incidents are reported by doctors to insurance institutions and industrial safety authorities. The source is the Finnish Institute of Occupational Health (http://www.ttl.fi). The Farmers’ Social Insurance Institution (semi-public institution) is another source that provides available indicators for mortality and morbidity for insured farmers and fishermen. The data are cause, gender and age specific. The incidents are reported to the Institute by farmers who claim for compensation because of an accident or a disease. France The Mutualité Sociale Agricole (MSA) collects data on fatal and non-fatal accidents by gender, age, type of accident in agriculture (http://www.msa.fr/front/id/msafr/S1120156495483). Germany The Federal association of agriculture and Social security board of the seaman provide data about the number of notifiable, non notifiable injuries and accidents on the way to work.



Greece Data are split by accidents and only mortality data are available. The source is the available processed data by the Agriculture Insurance Organization (OGA), the processed statistical data and information provided by OGA departments, as well as the annual reports of the organization. The information is available only in Greek language and is free of charge (http://www.oga.gr). Ireland Reports of occupational fatality and non-fatal injuries by gender are collected and reported by the Health and Safety Authority. Reports of occupational injuries made to the health and safety authorities under the Safety, Health and Welfare Act 1989. Data are split by incident type, by economic activity (Agriculture and fisheries including), by age of victim, by accident trigger. The period covered runs from 2004 to 2005. The data are collected annually since 1998 and are split by gender since 2000 (http://www.hsa.ie/publisher/index.jsp?pID=93&nID=95). Moreover, the Central Statistics Office (CSO) estimates the number of persons who suffered a work-related injury or ill-health through the Quarterly National Household Survey (QNHS). The QNHS is a source for the number of workers who self-report occupational injury and ill-health. The CSO asks persons whether they have suffered an injury incurred at work or an illness that the respondent believes was caused or made worse by their work in the past 12 months. Italy Information concerning mortality and morbidity are available only for Agriculture. ISPESL (Istituto Superiore per la Prevenzione e la Sicurezza del Lavoro – Italian Institute for occupational prevention) provides the following information: fatal and non-fatal injuries by gender, age, inability to work, region and local health unit. Period from 1994 to 2004 (http://www.ispesl.it/stat_it.htm). Another data source is INAIL (Italian Institute for Insurance) collecting agricultural injuries by age, gender and body part injured severity of injury, accident trigger. All information is split by region and by province. The period covered is 2000 to 2005 (http://bancadati.inail.it/prevenzionale/indennizzati.htm). Latvia The Central Statistics bureau of Latvia provides information about number of injures persons with one or more days lost and number of deaths without determining specific sectors. Statistics on morbidity cover only those persons who had applied either to a health facility or to a private practitioner. The source is the State Labour Inspection. The period covered is from 2000 to 2005 (http://www.csb.lv/avidus.cfm). Lithuania The State Labour Inspectorate collects data on fatal accidents by gender and age and the information is free of charge. The statistics cover reported injuries of all types of occupational accidents. Commuting injuries are included and compiled in separate files. Statistics on occupational diseases are compiled in a separate register at the Centre of Occupational Medicine at the Institute of Hygiene. These data are collected and published separately through forms investigating minor accidents at work, which are sent to the State Labour Inspectorate by employers, and thorough forms investigating serious and fatal accidents. These forms are drawn up by the State



Labour Inspectorate, by labour inspectors in participating employers and employees’ delegates on safety and health at work. The periodicity is every six months (http://www.vdi.lt). Luxembourg The Association of Assurance against the accidents [L'Association d'Assurance contre les Accidents (AAA)] can provide information on accidents at work by sector, fatal accidents and accidents that are categorised according to the age and the nationality of the victim. Particularly in agriculture statistics can be found on fatal and non fatal accidents since 1960 which are collected on an annual base. The data are free of charge (http://www.aaa.lu/index.html). Malta The National Statistics Office collects data on mortality/morbidity by cause specific, age and gender. The National Statistics Office of Malta provides data on accidents at work by economic activity of enterprise and occupation. Furthermore, it gives information on accidents at work by age group of victim and by type of injury as well as other details that have to do with accidents. The data is extracted from the administrative records of the Department of Social Security. Revisions to the previous year's data are carried out annually in the first and subsequent quarters of the following year. Accessibility is free of charge. Netherlands The national register of occupational accidents provides data stratified by sector (including agriculture-fisheries), by age and gender. On request, data can be made available stratified by age and gender (an official request is needed – moderate cost fee). The responsible organization is Statistics Netherlands (http://www.cbs.nl/en-GB/default.htm?languageswitch=on). Norway The Occupational Health and Safety Service (Landbrukets HMS-tjeneste) collects data on fatal accidents in agriculture for the members of the organization. Moreover, SINTEF Fisheries and Aquaculture has some data about accidents in fisheries. Portugal Accidents in agriculture and fisheries by gender and age groups of ten years and above the age of 65 years old, type of location, etc are available from the DGEEP –Direccao-Geral de Estudos, Estatistica e Planeamento (document Acidentes de Trabalho 2001, released and accessible from the following site: www.dgeep.mtss.gov.pt/estatistica/acidentes/index.php. Data are available in Portuguese. Spain Information on occupational accidents comes from the monthly exploitation of data contained in the Delt@ system that is carried out by the General Subdivision of Social and Labour Statistics. These data concern occupational accidents which are reported by the provincial labor authorities in this system, except for the independent communities of the Country of Vascs and Catalonia, which send separate information.



Information is available in Spanish (http://www.mtas.es/Estadisticas/EAT/welcome.htm) Generic data about morbidity and mortality are available from the National Health Survey and are notified by the System of Diseases of Obligatory Declaration (EDO) that, through the continuous observation of the appearance and distribution of the cases of the considered diseases, it allows the creation of risk patterns and the adoption of pertinent control measures. Sweden The National Board of Health and Welfare provides cancers, cause of death and hospital discharge diagnoses. However data on occupation or industry is not included in these registers. Such data can be included through matching, on the level of the individual, with registers at Statistics Sweden. This would require legal assent (www.socialstyrelsen.se; http://www.ssd.scb.se/databaser/makro/start.asp?lang=2) In the 2000 report the Work Environment Authority (Central Supervision Department, Statistics Division) collected data on reported occupational accidents per 1,000 gainfully employed in 1999, by branch of economic activity and gender. It reported occupational accidents per 1,000 gainfully employed in 1999, by branch of economic activity, employees and self-employed persons by gender and fatal work accidents by branch of economic activity, 1989-1999 can be found. The employer is responsible for ensuring that all work injuries are reported to the Social Insurance Office. The Social Insurance Office is required to send a copy of the report to the Work Environment Inspectorate, where reports are examined and supplemented. If a report is incomplete, the employer is contacted for further information. Reports are then sorted, encoded and computer-recorded. In the case of injuries classed as work-related diseases, the administrative data are encoded and computer-recorded, after which the report is sent to the head office of the Swedish Work Environment Authority for further encoding and computer recording. Until 1978 the annual reports on Occupational Injuries were published within the series of Official Statistics of Sweden by the National Social Insurance Board. Afterwards, annual statistics on occupational accidents and work-related diseases has been compiled by the Swedish Work Environment Authority (formerly the National Board of Occupational Safety and Health), in collaboration with Statistics Sweden (1979-1993). Since 1994 the Swedish Work Environment Authority is responsible for the statistics. The statistics for each year includes a preliminary and a final compilation. The preliminary data until 2001 was published in the series Statistical Reports with the title Occupational injuries. In the preliminary statistics for 2002 and later on, the Swedish Work Environment Authority is responsible for the analysis as well as the technical production ([email protected]; http://www.av.se/inenglish/aboutus/contact/index.aspx) United Kingdom Statistics concerning injuries to workers by industry and severity of injury are compiled by the Office for National Statistics (from 1999 to 2002) http://www.statistics.gov.uk/STATBASE/Expodata/Spreadsheets/D3986.xls).

Data analysis Morbidity is expressed as illness or disability rate, usually expressed per 1000 population. Mortality is defined as death rate per defined population, usually



expressed per 1000. The most frequent indicators in the European countries are injury and accident related. Below the findings from the questionnaire search are presented and discussed. Table 1: Availability of morbidity and mortality indicators by country and by type of indicator

Mortality indicators Morbidity indicators

Agriculture Fisheries Agriculture Fisheries

AUSTRIA Injury mortality

Injury morbidity Data on occupational diseases

BELGIUM Injury mortality Injury mortality

Injury morbidity Data on occupational

diseases


diseases

BULGARIA n/a n/a n/a n/a

CYPRUS Injury mortality Injury mortality


diseases


diseases

CZECH REPUBLIC Injury mortality Injury mortality

Occupational disease morbidity

Injury morbidity

Occupational disease morbidity

Injury morbidity

Injury mortality Injury mortality DENMARK

Data on mortality due to injuries, cancer and respiratory diseases for people employed in agriculture and fisheries (commercial charged)

ESTONIA Injury mortality Injury

mortality Injury morbidity Injury morbidity

Injury morbidity Injury morbidity FINLAND

Injury mortality

Indicators of mortality and morbidity are available for the entire work force Data on occupational diseases (A+F)

FRANCE Injury mortality


GERMANY Injury mortality Injury mortality


Injury morbidity

GREECE Injury mortality Total deaths

Injury mortality Total deaths n/a

HUNGARY n/a n/a

IRELAND Fatalities Fatalities


Injury morbidity

ITALY unspecified Occupational diseases (fisheries and agriculture unified)

LATVIA cancer and mortality register unspecified Data on occupational diseases (A+F)

LITHUANIA Injury mortality n/a Morbidity and prevalence of active tuberculosis by

gender, case and period

LUXEMBOURG Injury mortality n/a

Data on occupational diseases

MALTA Injury mortality (agriculture and fisheries)

NETHERLANDS



Injury morbidity

injury morbidity(not s)

NORWAY Injury mortality

Injury mortality(not system)

Data on occupational diseases (A+F) Deaths by underlying cause of death in the whole country Disability by sex and region Chronic illness by sex and region

POLAND n/a n/a



PORTUGAL Injury morbidity Injury morbidity

SLOVAKIA n/a

Data on occupational diseases (A+F)

SLOVENIA n/a

SPAIN Information on occupational injuries and diseases n/a

SWEDEN

Cancer mortality unspecified, injury mortality injury mortality

Injury morbidity Reported occupational diseases

Injury morbidity Reported occupational diseases

UK Injury mortality n/a Data on occupational diseases

n/a: Data not available As it can be seen from the above table, injury data are generally available, especially when the injury is fatal. Data on other cause morbidity are scarce and fragmented, varying from tuberculosis to cancer and occupational diseases. Definitely utilization of data other than accident mortality is currently impossible and serious changes should take place before any progress could be made at the European level.

3.2. Health care among farmers and fisheries workers Health care data vary, ranging from health services consumption, medicine consumption to absenteeism and occupational diseases. In the following paragraphs we describe the data we could verify that exist for each country. Because of the variability and discrepancies of data collected, we present only one chart and a table that summarizes our findings.

Collected material by country Austria The “Sozialversicherungsantalt der Bauern” (SVB) compiles data on hospital services and consumption of medical services for all self-employed obligatory insured and this type of information is collected by the database report and is free of charge. Bulgaria The National Centre of Health Informatics which belongs to the Ministry of Health, is a health institution in the structure of the National Healthcare System. It deals with the problems of public health, creates and supports information on resources and activities of the health care system (health facilities, health personnel, economical indicators of the health facilities). Reports from 2000 to 2004 are available (http://www.nchi.government.bg/Eng/Engli6.html) and the data refer to the rural and urban population (ex. births, deaths, infant mortality, stillbirths and infant deaths under 1 year,, registered cases of diseases in health facilities by classes of diseases, registered cases of infectious diseases subject to obligatory reporting, registered cases of active tuberculosis etc). According to information from the completed questionnaire data is not available on the fisheries sector. Czech Republic The Institute of Health Information provides indicators about health status, health services and cancer registry by period of the survey and region (http://www.uzis.cz/cz/dps/english/index.html).



The National Institute of Public Health provides quantitative data on exposures of employees to risk factors. The collection of data is an integral part of inspection of health at work. Information access is restricted (http://www.szu.cz/cem/hpcem.htm). Denmark Statistics Denmark are able to deliver commercial charged data on health services consumption (primary health care and hospitalization) and medicine consumption for people employed in agriculture and fisheries. If needed data can be specified by age, gender and other variables. Estonia The National Statistic Office-Estonia has data on health status of persons aged 16 and older by year and place of residence and are free of charge. Finland The Farmers´ Social Insurance Institution provides insurance data in agriculture as incidents are reported to the Institute by farmers who claim for compensation because of an accident or a disease (http://www.mela.fi). Furthermore, the National Centre for Agricultural Health offers data on health services consumption of farmers who have joined the farmers occupational health services. France The Mutualité Sociale Agricole (MSA) collects insurance data of farmers. Germany Reporting systems about specific health indicators for employed persons regarding the mentioned sectors are not available. Data are routinely ascertained by statutory health insurances for reasons of deduction. Some of these aggregated data can be useful for reporting systems based on information of data holding institution like statutory health insurances. But a big part of these are not public. Data about number of persons legally insured can be given. The Federal Association of Agricultural Statutory Health Insurances and Social security board of seaman routinely collects insurance data about number of insured persons. Greece The data provided is unified and includes the agriculture and fisheries population. The health indicators, which are available are the following:

o Insurance expenditure o Medical examinations consumption o Hospitalization expenditure o Medicine consumption o Maternity expenditure.

Sources of this information are the available processed data of Agriculture Insurance Organisation (OGA), the processed statistical data and information provided by OGA departments, as well as the annual reports of the organization (http://www.oga.gr). Italy



INAIL (Istituto Nazionale per l’Assicurazione contro gli Infortuni sul Lavoro - Italian Workers Compensation Authority) collects insurance data (http://www.inail.it/multilingua/ingleseindex.htm). ISTAT, the Italian Institute for Statistic general health services statistics provides the Italian health for all database (HFA-DB) (http://www.istat.it/sanita/Health/). Ireland Employers are required to report all workplace injuries that cause more than 3 days absence to the Authority. Incidents can be reported by phone/fax/internet. Approximately 8000 incidents are reported annually. All workforce fatalities are legally required to be reported to the Health and Safety Authority. Lithuania Incidence of occupational diseases is available by the State Register of Occupational Diseases and the types of data, which are collected, are new cases of occupational diseases with cards posted by mail as the collection method. Luxembourg The Association of Assurance against accidents [L'Association d'Assurance contre les Accidents (AAA)] can provide information on absenteeism (>3 days), cause of accidents in agriculture and it is available by gender and age. Netherlands A private organisation responsible for occupational health care in agriculture provides data about sickness absence and permanent disability in agriculture-fisheries. There is a level of aggregation according to type of agriculture (15 categories). The collection method is company registries. The website is in Dutch language only (www.agroarbo.nl). Norway Information is available from the Statistics Office, free of charge about health services consumption, disability and need of help by gender and region (per cent), chronic illness by gender and region (per cent), deaths by underlying cause of death (http://www.ssb.no/vis/english/about_ssb/contacts.html). Poland Health indicators about insurance data, absenteeism data are available for the rural population. The Source is the Agricultural Social Insurance Fund. Sweden In the 2000 report from the Swedish Work Environment Authority data can be found on the number of days sickness absence per reported occupational accident in 1998, by principal event and gender for employees and self-employed persons. United Kingdom The Health and Safety Executive (Agriculture & Food Sector) provides estimated prevalence and rates (%) of self-reported illness caused or made worse by current or



most recent job. Information is provided by occupational major, sub-major and minor group, for people working in the last 12 months for 2003/04 and 2001/02 and estimated days (full-day equivalent) off work and associated average days lost per worker due to self-reported illness caused or made worse by current or most recent job, by occupational major and sub-major group for 2003/04 and 2001/02 (http://www.hse.gov.uk/statistics/occupation.htm).

Data analysis Based on data collected through our questionnaire, it became evident that health data referring to farmers and fishermen are very scarce throughout Europe. Although it seems that several countries may have some data, the type of data is different in each country. For example, Greece has data on medical consumption, but not on insurance, whereas Germany has insurance data but not expenditure data. All these data are presented in the following tables:

Figure #16

Health care related data availability for farmers in Europe

Not availablen=6

Non=6

Yesn=15




Figure #17

Health care related data availability for fishermen in Europe

Not availablen=10

Non=9

Yesn=8


Table 2: Availability of health care related data by country and type of data for farmers and fishermen in Europe

Aus

tria

Bel

gium

Bul

garia

Cyp

rus

Cze

ch R

epub

lic

Den

mar

k

Esto

nia

Finl

and

Fran

ce

Ger

man

y

Gre

ece

Hun

gary

Irel

and

Italy

Latv

ia

Lith

uani

a

Luxe

mbo

urg

Mal

ta

Net

herla

nds

Nor

way

Pola

nd

Portu

gal

Slov

akia

Slov

enia

Spai

n

Swed

en

Uni

ted

Kin

gdom

Medicine consumption A × × × × × × × × × × × × × × × × × × × × × × × ×Hospital treatment A × × × × × × × × × × × × × × × × × × × × × × × × × ×Health services consumption × × × × × × A × × × × × × × × × × × × × × × × ×Absenteeism (>3 days) × × × × × × × × × × × × × × × A × × × × × × × ×Insurance data × × × × × × × A A × × × × × × × × × × × × × × ×Medical examinations consumption

× × × × × × × × × × × × × × × × × × × × × × × × × ×Insurance expenditure × × × × × × × × × × × × × × × × × × × × × × × × × ×Hospitalization expenditure × × × × × × × × × × × × × × × × × × × × × × × × × ×Maternity expenditure × × × × × × × × × × × × × × × × × × × × × × × × × ×Incidence of occupational diseases

× × × × × × × × × × × × × × × × × × × × × × × × × ×Prevalence and rates of self-reported work-related illness

× × × × × × × × × × × × × × × × × × × × × × × × × ×

Prevalence and × × × × × × × × × × × × × × × × × × × × × × × × × ×



rates of self-reported musculoskeletal disorders caused or made worse A : available health indicators in agriculture

: available health indicators in agriculture and fisheries ×: not available health indicators in agriculture and fisheries

3.3. The Insurance system in Agriculture This section deals with the diversity of national social security provisions specific to self-employed farmers in the European Union (EU) and attempts an initial research and categorisation of social protection provisions applying to the European population of self-employed farmers. The social security provisions, which relate to the health and safety of farmers and fishermen have been also investigated mainly by the research team of Prolepsis. The websites of the European Union, the European Agency for Safety and Health At Work, Eurostat and the websites of the Ministries of Employment, Health, Social Protection and of relevant Insurance Funds in all 25 EU MS were visited. In total 88 websites were visited. Where information was available in English, French, or Greek this has been reviewed extensively to investigate the specificities of the social protection schemes that cover farmers in particular regarding the coverage for occupational injuries and diseases. The internet search has been guided by the information provided in the document “Organisation of social protection charts and descriptions – Situation on 1 January 2006” of the Mutual Information System on Social Protection of the European Commission. The websites in many countries had limited information in English while in some countries an English version was not available at all. Due to the limited accessibility to detailed data, we primarily depended on information provided in the Mutual Information System on Social Protection document entitled “Social Protection of the self-employed – Situation on 1 January 2006”. In the cases were language restrictions did not pose an obstacle a cross-checking of the information presented by MISSOC was performed. Due to the diversity of provisions foreseen in the EU Member States for the social protection of farmers and especially of self-employed farmers a crude categorization of the diversified social protection schemes for self-employed farmers would be useful. Such a categorization has been attempted here. In Table 3, countries are divided in three categories, depending on whether farmers are covered by a general social protection scheme (a basic social protection system that is the same for all professional groups or even for the whole population), on whether they are covered by a social protection scheme that covers all self-employed persons, or on whether they are covered by a special scheme applying only for farmers. Information presented in Table 3 is derived from the Mutual Information System on Social Protection – MISSOC document: Social Protection of the self-employed –



Situation on 1 January 2006. Directorate-General for Employment, Social Affairs and Equal Opportunities, European Commission, 2006 Table 3: Categorization of EU countries according to the social protection scheme they have in place for covering self-employed farmers Countries General scheme for

all (employed and self-employed persons)

Scheme for self-employed persons

Special scheme for farmers only

Family coverage

Austria √ √ Assisting family members Belgium √ √ Assisting spouses born

after 1st January 1956 are obligatorily covered

Cyprus √ Czech Republic

√

Denmark √ √ Assisting spouses Estonia √ (Except for the

unemployment benefits)

Finland √ (Except for employment accident

insurance and earnings-related unemployment

benefits)

France √ √ Assisting family members and farm associates

Germany √ √ Assisting family members Greece √ √ Family members Hungary √ (Except for the

unemployment benefits)

Ireland √ Italy √ Latvia √ (Except for the

unemployment benefits and the

insurance for employment injuries

and occupational diseases)

Lithuania √ √ Assisting family members Luxembourg

√

Malta √ (Except for the unemployment

benefits)

Poland √ (depending upon size of

farm insurance is

either compulsory or voluntary

√ Family members



Portugal √ Slovakia √ (Except for

employment injuries and occupational

diseases)

Slovenia √ Spain √ √ Family members Sweden √ The Netherlands

√ (Except for the unemployment

benefits)

United Kingdom

√ (Except for the unemployment benefits and the

insurance for employment injuries

and occupational diseases)

In Table 4, countries are placed in three categories depending on the type of coverage that is applicable for farmers regarding employment injuries and occupational diseases. The information presented in Table 2 is derived from the Mutual Information System on Social Protection – MISSOC document: Social Protection of the self-employed – Situation on 1 January 2006. Directorate-General for Employment, Social Affairs and Equal Opportunities, European Commission, 2006

Table 4: Categorization of EU countries according to whether they have in place obligatory, optional or no coverage for occupational injuries and diseases for self-employed farmers Countries Obligatory coverage

for occupational injuries and diseases

Optional coverage for occupational injuries and diseases

No coverage for occupational injuries and diseases

Austria √ Belgium √ Cyprus √ Czech Republic √ Denmark √ Estonia √ Finland √ France √ Germany √ Greece √ Hungary √ Ireland √ Italy √ Latvia √ Lithuania √ (Voluntary insurance

against occupational accidents)

Luxembourg √ Malta √ Poland √ (Depending on √ (Depending on



farmland size and other conditions)

farmland size and other conditions)

Portugal √ (For employment injuries they are required to be insured with private insurance companies while for occupational diseases benefits are granted according to the general system covering the employed)

Slovakia √ Slovenia √ Spain √ Sweden √ The Netherlands √ (No special

protection system: covered by sickness insurance, insurance against invalidity, and old age and survivor’s insurance)

United Kingdom √

As illustrated in Table 2, 8 out of the 25 EU MS have no provisions for the coverage of self-employed farmers in the case of occupational injuries and diseases and 6 out of those 8 countries are new EU MS. Moreover all 7 countries that implement a special social protection scheme for farmers (as illustrated in Table 1) have in place provisions for the coverage of farmers in the case of employment injuries and diseases.



4. Risk assessment of occupational and environmental risks in agriculture and fisheries Several difficulties occur when examining existing policies and practices that could lead to risk assessment of occupational and environmental risks in agriculture and fisheries. In this case, one has to have concise knowledge of the existing initiatives reflecting each country’s national policy on agriculture, fisheries and the environment, which represent a level of response to each country’s population needs and level of sensitivity. As this is not the primary goal of this project, this topic discusses the issue mainly by presenting a number of existing national programmes in the form of best practices, which lead to the assessment of occupational and environmental risks in agriculture and fisheries. Three areas that could be used for risk assessment are presented in the form of case studies: a) the availability of data on occupational diseases in agriculture and fisheries, b) the availability of data on the use of pesticides in agriculture, and c) the existence of national health programmes focusing on the agricultural population of Europe. A first view of the findings confirms that due to lack of collected health and risk-related data, old and new EU Member states fall into the same category, as far as health monitoring of the rural population is concerned. The results are presented below.

4.1. Occupational diseases in agriculture According to ILO/WHO, occupational diseases are considered disorders to which the work environment and performance of work contribute significantly as one of the several causative factors. We have used the questionnaire to identify which countries have legally recognized occupational diseases in agriculture and fisheries and which ones don’t.

Collected material by country Austria The Labor Inspectorate for Agriculture and Forestry (“Land – und Fortswirtschaftinspektion), the Sozialversicherungsantalt der Bauern (SVB) and the Soziale Unfallversicherung (AUVA) collect data on occupational diseases in agriculture. The occupational diseases which are reported are the following: ● Farmer’s lung ● Asthma ● Hearing problems cause of noise ● Encephalitis transmitted via paracides



Belgium The “Fonds des maladies professionnelles - Institution publique de sécurité sociale” provides information on occupational diseases in agriculture and fisheries from 2001 until 2005 (http://www.fmp-fbz.fgov.be/fr/fmp_fr01.htm). Cyprus The Department of Labour Inspection – Ministry of Labour and Social Insurance reports accidents at work and occupational diseases data by employer and or in the case of occupational diseases by doctors. Free of charge. www.mlsi.gov.cy/dli Czech Republic The National Institute of Public Health has been publishing the list of occupational diseases caused by chemical substances, physical factors and biological factor, but not sector specific, since 1996 (http://www.szu.cz/chpnp/index_en.php). The Register of occupational diseases regarding the specific sectors is published every year under the title of “Occupational Diseases in the Czech Republic” accessible though http://www.szu.cz/chpnp/pages_en/NZP/NZP_en.htm. Estonia The Labour Inspectorate also collects information on occupational accidents and cases of occupational diseases from employers and doctors. The data do not cover self-employed people but only those who work on the basis of employment contract and have had an accident at work or an occupational disease. The most recent statistical data on occupational accidents and diseases are available for 2004: 595.500 people were employed, 35.000 of these people worked in the sector of agriculture and 29% were female (Source: Social sector in figures, 2005). In 2004 there were 57 serious accidents and 2 fatal accidents at work. In 28 cases occupational diseases were diagnosed and registered. These data have been quite stable during the years although some fluctuations have been observed. Finland The Finnish Institute of Occupational Health provides information regarding Agriculture and Fisheries. The occupational diseases reported are the following:

- allergic respiratory diseases (asthma, farmer’s lung, allergic rhinitis) - repetitive strain injuries (fisheries) - skin diseases - hearing losses

France The “Mutualité Sociale Agricole” (MSA) collects data on accidents at work and occupational diseases in the sector of agriculture. The legal framework of the provided occupational health services is the following :

- Special principle of surveillance: article 32 of the decree of May 11,1982 relating to the organization and the operation of the health services to work in agriculture;

- List the work carried out in the agricultural companies and requiring special medical supervision: decree of the October 20,2004;

- Contents of the special medical supervision: texts specific to particular risks (asbestos, noise, wearing of loads, etc).



In addition, on the “Phyt attitude” survey: balance sheet 2002-2003 on toxicology vigilance ban data can be found on symptoms by pesticide category and number of cases. Germany The Federal Association of agriculture which has the liability for agricultural safety and insurance provides data about occupational diseases caused by chemical agents, physical agents, parasites or tropical pathogens. Hungary The occupational diseases reported in agriculture are the following:

- formaldeide poisoning - zoonoses - hearing losses - musculoskeletal disorders

Ireland The Central Statistic’s Office Quarterly Household Survey of 2002, shows the distribution of farmers with long term health problems by type for those engaged in farming, forestry and fishing. These health problems are not all associated with farming activities but nevertheless give some indication of the level of ill health on farms. Health issues associated with back problems, limbs, respiratory system and the cardiovascular system accounted for most of the disability reported. In addition there are available data in the Teagasc National Farm Survey regarding farming related illnesses (http://www.cso.ie/statistics/AgricultureandFishing.htm). Italy ISPESL and INAIL provide yearly a register for occupational diseases. Data on agriculture and fisheries are unified. Any disease occurring among workers has to be announced even if occupational aetiology is not proved yet. Asbestosis, ankylostomiasis, hypoacusia and deafness, allergies diseases, skin diseases, bronchial asthma, alveolitis, muscular and skeletal diseases are the main diseases reported in the Agricultural and fisheries sectors (http://www.ispesl.it/stat_it.htm). Latvia The occupational diseases reported are the following: Agriculture: Toxic and irritant effects; Neurological diseases; Musculoskeletal diseases; Cardiovascular diseases Fisheries: Musculoskeletal diseases; Neurological diseases; Coding of the toxic and irritant effects; Respiratory diseases Lithuania The State Register of Occupational Diseases was established in 1994, and all new cases of occupational diseases are registered according to obligatory legal regulations. Complicated cases of occupational diseases are registered and analysed in the Central Occupational Medicine Expert Commission under the Order of Ministry of Health since 1995. The calculated incidence of occupational diseases was 2,4 cases per 10 thousand of the employed population. In the period 1995-2001 the largest proportion of occupational diseases was registered among the workers in agriculture and forestry.



The main causes of occupational diseases in these sectors were related with exposure to physical hazards, mostly related to noise and vibration and factors of strain. Luxembourg STATEC provides data on reported cases of tuberculosis which is a main disease in agriculture. Moreover, the Association of Assurance against accidents [L'Association d'Assurance contre les Accidents (AAA)] provides data on occupational diseases in Agriculture for the time period from 1960 to 2005. Netherlands In the Netherlands notification is officially mandatory, but there is no incentive or penalty for reporting, thus, it all depends on the willingness of the worker (informed consent of worker is needed) and the occupational physician. The full list of diseases can be found on the website of Netherlands Centre for Occupational Diseases ( www.occupationaldiseases.nl). Norway The Norwegian Labour Inspection Authority (Department of Labour) collects data concerning occupational diseases and accidents. The data concerning occupational diseases is reported by physicians employed in different hospitals and clinics around the country. Accident data are reported by the employer (http://www.arbeidstilsynet.no/c26840/artikkel/vis.html?tid=29289). Poland Occupational diseases are reported only for agriculture.

- Infectious and parasitic disease (borreliosis - 89.4%) - Asthma - Vibration syndrome - Bilateral permanent

Slovakia The Institute of Health Information and Statistics in Bratislava provides information about occupational diseases and other harms of health at work in these sectors. Spain Information on occupational injuries and disease statistics is provided by Estadística de Accidentes deTrabajo y Enfermedades Profesionales (only in Spanish) and is free of charge (http://www.mtas.es/Estadisticas/EAT/eat05dic/index.htm). Sweden All incidents occurred, which were characterized as work-related are registered. United Kingdom Annual cases and incidence rates for work related ill health are reported by the Health and Occupation Reporting network (THOR) The Network records disease specialists, and cases assessed with compensable prescribed diseases under the Industrial Injuries Scheme (IIS), in the period 2002 to 2004, for all industries and for agriculture, forestry and fishing. The occupational diseases reported are the following:



- Musculoskeletal disorders - Upper Limb Disorders - Mesothelioma - Dermatitis - Asthma - Vibration White Finger - Infections (general) - Occupational Deafness - Spine/Back Disorders - Asbestosis - Diffuse Pleural Thickening - Stress - Zoonoses Data concerning occupational ill health and disease is available from the self reported work related (SWI) surveys. These can be viewed from the HSE website at www.hse.gov.uk/statistics/causdis/index.htm. The HSE website contains statistical information and links to other data sources. The SWI surveys provide an indication of the overall prevalence of work related illness and its distribution by major disease groups and other demographic factors. There have been several such surveys in recent years, carried out in conjunction with a general labour force survey. These surveys cover a wide area of industry, not just agriculture. HSE obtains morbidity data in agriculture from receiving notifications under the Reporting of Injuries, Diseases and Dangerous Occurrences Regulations (RIDDOR). These Regulations require employers and the self-employed to report certain diseases and ill health to HSE. The HSE website is a useful source of data from RIDDOR reports.

Data analysis



Figure #18

Occupational diseases data availability in agriculture in Europe

Not availablen=4

Non=4

Yesn=20


Figure #19

Occupational diseases data availability in fisheries in Europe

Yesn=13

Non=9

Not availablen=7




Relevant data seem to be generally available for the agricultural sector – and to a smaller degree for the fisheries sector - in the EU countries, although the categorization of diseases, the method of data collection, the reporting status and the periodicity of collected data varies to a great extend. Reporting of respiratory diseases, musculoskeletal disorders and hearing loss among farmers is the most common practice in agriculture, whereas respiratory, musculoskeletal and skin disorders are the most commonly registered occupational diseases in fisheries. Table 5: Availability of data on officially recognized occupational diseases for farmers and fishermen in Europe by country and by disease

Aus

tria

Bel

gium

Bul

garia

Cyp

rus

Cze

ch R

epub

lic

Den

mar

k

Esto

nia

Finl

and

Fran

ce

Ger

man

y

Gre

ece

Hun

gary

Irel

and

Italy

Latv

ia

Lith

uani

a

Luxe

mbo

urg

Mal

ta

Net

herla

nds

Nor

way

Pola

nd

Portu

gal

Slov

akia

Slov

enia

Spai

n

Swed

en

Uni

ted

Kin

gdom

occupational diseases in agriculture X D D V X V X V X D V

occupational diseases in fisheries X X D D X X X X V X X V X V

Farmer’s lung A Asthma A

AF AF A AF

Alveolitis AF Allergic respiratory diseases (asthma, farmers lung, allergic rhinitis)

AF A A AF A

Exogenic allergic alveolitis A

Diffuse Pleural Thickening AF

Asbestosis AFInfections (general) A AFErysipeloid

AF

Tularemia

AF

Leptospirosis

AF

Trichophytosis F Tick-borne meninggoencephalitis

F

Ankylostomiasis AF Encephalitis transmitted via paracides

A

Musculoskeletal disorders A A A AF AF AF

Back problem A Upper Limb Disorder AFSpine/Back Disorder AFRepetitive strain injuries AF

Mesothelioma AFStomach/Liver/Kidney Cancer A

Dermatitis AFSkin diseases

AF A AF

Vibration White Finger AF

Vibration syndrome / Bilateral permanent A



Formaldeide poisoning A A

Toxic and irritant effects A A

Neurological diseases A AF

Coding of the toxic and irritant effects F

Hearing loss A A A AFHypoacusia and deafness AF

Hearing problems because of noise A

Eyesight A Cardiovascular diseases F

Heart/Blood pressure A Mental problems A Stress AFDiabetes A

D: not available V: yes X: no A: agriculture F: fisheries

4.2. Availability of national data on use of pesticides Pesticides are considered one of the main risks for farmers and the environment. It often creates major reactions among European citizens both because they have been implicated in certain types of nosology among farmers, but also because they are considered an important environmental burden for the entire population, which could be contaminated through the food chain or the greater environment.

Collected material by country Austria The Labour inspectorate for Agriculture and Forestry collects data on annual use of pesticides by produce category and are free of charge. Czech Republic State Phytosanitary Administration provides data about the use of pesticides by specific produce category (http://www.srs.cz/srs/spo_vyk/spotreba04/ucinne_latky.htm) Estonia The National Statistic Office provides data on use of pesticides in agricultural holdings by counties. Data are from 2004. Finland The Finnish Food Safety Authority Evira provides the annual summary of pesticides sales (http://www.evira.fi/portal/en/). Germany



The number of sales of plant protection products are available. The legal framework of these data is the Act Concerning the protection of Crop Plants (Plant Protection Act). The source is the Federal Department of consumer protection and safety of foods; Branch office Braunschweig. Greece The Hellenic Crop Protection Association (HCPA) provides data on sales of pesticides. HCPA was established in 1970, and is based in Athens. It represents companies involved in the crop protection industry and other associated members, namely distributors of crop protection products. Data are not available by produce category (http://www.esyf.gr/newsite/e1_int.php). Italy Agrofarma, the association that represents companies producing crop protection products, provides data about the annual use of pesticides in Italy split by type and by crop ( http://85.18.34.105/agrofarma/Home.nsf/GlobalHome?OpenForm http://85.18.34.105/agrofarma/Home.nsf/0/A3D5CDAF4C3D2BB1C1256FBF0048E80D/$FILE/Italian%20market%20of%20crop%20protection%20products%20in%20quantity.pdf). More information about the use of pesticides is available from the Ministry of Agriculture (SIAN – Ministero delle Politiche agricole e Forestali) and are subdivided for uses and crops (http://www.sian.it/). Malta The Agriculture and Fisheries Statistics Unit of the National Statistics Office, offers information on plant protection products usage that was collected from a stratified sample of farmers and growers across Malta and Gozo with holdings larger than 0.1 ha, and the data collected was raised using information from the 2001 farm census, to give national estimates of use. Twenty crops were included in the survey: wheat grown for fodder, potatoes, broad beans, onions, vegetable marrows, cauliflowers, cabbages, carrots, lettuce, peas, tomatoes (both outdoor and under protection), sugar melons and water melons, grapes, strawberries, peaches, nectarines, olives and citrus crops. The survey period covered the crop year from October 2004 to September 2005. Enumerators were provided with a list of plant protection products, which were imported into Malta so as to serve as a guideline and facilitate the data collection. Information is free of charge (http://www.nso.gov.mt). Netherlands The Board for the authorisation of pesticides (CTB) has the duty of making decisions on the authorisation of pesticides and provides data on use and the sale of pesticides (http://www.ctb-wageningen.nl/). Poland The source is the Central Statistics Office (production of pesticides by monthly supply and consumption of pesticides) (http://www.stat.gov.pl/english/). Norway Data are accessible through the National Statistics Office concerning the the use of pesticides in agricultural holdings by year, pesticide and indicator, use of pesticide in areas of different crops, by type of pesticides, consumption of commercial fertilizers and sale of mixed feed, holdings with spraying on area of different crops and area



sprayed. Accessibility is free of charge. Statistics Norway carried out the survey on pesticide use for the first time in 2001 (http://www.ssb.no/en/). Slovakia The Ministry of Environment provides data on use of pesticides but accessibility is restricted. Collection of data is obligatory and regular. Moreover, data is available on the amount of pesticide residues in water from 2000 until 2003 as well as the concentration of DDT (1995-2001) in water, animals, soil and human tissues. Slovenia The Statistical Office of the Republic of Slovenia provided information about pesticides and the wholesale on Slovene market, kg active ingredients. This type of data is collected annually. Sweden These data are reported by the Swedish Chemicals Inspectorate (www.kemi.se)

Data analysis Data on the annual use of pesticides is available in 13 countries. In some of the countries, data are collected by private organizations, but for the majority data comes from official governmental agencies. Aggregation by production type is done in 11 of 13 countries. Sales figures by pesticide categories are more readily available in many European countries and is usually provided by the national association of plant protection products industry.

Figure #20

Annual data of pesticides availability in Europe

Not availablen=7

Non=7

Yesn=13




Figure #21

Annual use of pesticides disaggregated by production type in Europe (in countries where data are

available

Non=2

Yesn=11


4.3. National health promoting programmes in agriculture and fisheries In many of the 27 countries which the project examined there are official authorities or non-governmental institutions which aim at providing health and safety promoting services in the populations that are employed in the sectors of agriculture and fisheries (see pie charts). Some initiatives could be presented indicatively, such as complete health promotion programmes towards a specific goal (e.g. safety and health for use of pesticides) or risk assessment tools for specific agricultural activities.



Figure #22

National data sources on Occ. Hygiene, H&S, Env. Health in Agriculture and

Fisheries in Europe

Not availablen=7

Non=2Yes

n=11




Figure #23

National health promoting programmes availability in Agriculture in Europe

Not availablen=9

Non=8

Yesn=12


Figure #24

National health promoting programmes availability in Fisheries in Europe

Non=10

Yesn=6


National programmes for the Health and Safety of fishermen exist predominately in countries with a large fishing industry, like Norway, Poland and Denmark. In agriculture, such programmes are implemented in more countries, regardless of industry size.



List of national programmes & policies An indicative list of national programmes and policies on health and safety in agriculture and fisheries is presented below. A list of programmes of special importance and a presentation of indicative authorities by country is included in the annex of this report. AUSTRIA Program title Authority Sector Target *“Mit Sicherheit gut beraten” (2003) Schwerpunktaktion “Gefahrstoffe” (2005) “Sicherheit fuer Bauernkinder” “Sicherheit und Gesundheit fuer Senioren” “Bewusst bewegt am Bauernhof”

Social Insurance for Agriculture

Agriculture Population in Agriculture 496.556 Persons 183.000 owners

Safety and Health for use of pesticides (plant protection products)

Labour Inpectorates for Agriculture and Forestry

Agriculture 1.494 enterprises (about 3, 000 Persons)

Landwirtschaftliche Krananlagen (agricultural cranes)


Agriculture 362 enterprises (about 800 Persons)

Safety and Health at forest work Labour Inpectorates for Agriculture and Forestry

Agriculture 362 enterprises (about 800 Persons)

Safety and health, work time and wages in professional training of young people


Agriculture 595 P

Safety and Health, work time and wages of the employees, above all the women, mothers and young people in horticulture


Agriculture 541 enterprises about 2.500 Persons

* good advised on safety, action on dangerous materials, safety for children on agriculture, safety and health for retired, consciously moving on land enterprise,…

CZECH REPUBLIC Program title Authority Sector Target National agriculture information campaign carried out in 2004

Cesky urad bezpecnosti prace (Czech Occupational Safety Office); Statni urad inspekce prace (State Labour Inspection Office) since 2005

Agriculture Agriculture employees

Workplace Health Promotion National Institute of Public Health Prague. Rural Development and Multifunctional Agriculture” Operational Programme (Agriculture OP)

http://www.mze.cz/attachments/_Toc64279765


GREECE Program title Authority Sector Target Booklet: First Aid Guide For agriculture workers http://www.ypakp.gr/ downloads/texts/1712.pdf

Ministry of Employment and Social Protection


“Dimitra” database of published articles

National Research Foundation (N.AG.RE.F) http://argo.ekt.gr/opac2/zConnectELL.html Address: Xalepa and Egialias 19, 15125, Athens, Greece

Agriculture-fisheries

Researchers

Safe Use Initiative Hellenic Crop Protection Association Agriculture Greenhouse pesticide applicators

IRELAND



Program title Authority Sector Target Guides to farmers on the following topics: “Make Farming Good for your Health” “Safe use of chemicals in agriculture” “Safety with Farm Machinery and Equipment”

Health and Safety Authority

Agriculture People on farms

Reps Training Programme Theme: ‘Think Safety & Take Action’

Agriculture and Food Development Authority Agriculture Farmers

FRS (Far Relief Services) safe tractor driving skills training course for teenagers

Agriculture and Food Development Authority Agriculture Teenagers on farms

BMI Safety Training The training consists of three units: 1. Personal Survival Techniques, 2. Elementary First Aid, Fire Prevention and 3. Safety Awareness

BIM Bord Iascaigh Mhara Fisheries All fishing vessel crew members

SPAIN

Program title Authority Sector Target Monitoring of health for the prevention of labor risks in the agricultural sector (Vigilancia de la salud para la prevención de riesgos laborales en el sector agrario).

Ministry of Health and Consumption (Ministerio de Sanidad y Consumo)

Agriculture and Fisheries

Agrary sector in general Sector of Agriculture in general.

Protocols for specific sanitary monitoring of weedkillers. (Protocolos de vigilancia sanitaria específica de plaguicidas).

Ministry of Health and Consumption (Ministerio de Sanidad y Consumo)

Agriculture People exposed to weedkillers.

UNITED KINGDOM Program title Authority Sector Target Leaflet that were prepared by the Agriculture Industry Advisory Committee and have been agreed by the Health and Safety Commission. They contain notes on good practice which are not compulsory but which may be helpful for businesses in considering what employers need to do.

Health & Safety Executive (Agriculture & Food Sector) National Agricultural Centre Stoneleigh, Kenilworth Warwickshire CV8 2LZ Tel. 02476 698350 Fax. 02476 696542 Agriculture Industry Advisory Committee

Agriculture Rural population

CERTIFICATES IN HEALTH AND SAFETY (AGRICULTURE & HORTICULTURE) LEVELS 2 & 3 Level 2 Certificate in Working Safely -for anyone working in the sector, or about to join it Level 3 Certificate in Controlling Risks to Health and Safety - for supervisors, skilled employees, unit managers, union and worker safety representatives and self-employed farmers

Lantra Awards: The qualifications have been developed in partnership with the HSE, the Transport and General Workers Union and the National Farmers Union. They have further benefited from assistance and guidance from Lantra Sector Skills Council, the Employment National Training Organisation (ENTO), the Qualifications and Curriculum Authority and awarding bodies for qualifications in the land-based sector. www.lantra-awards.co.uk

Agriculture Farmers and growers



Legal framework The legal framework of health and safety in agriculture and fisheries has also been explored through the questionnaire. The project partners were requested to collect information from all Member States, including the, at the time, acceding countries. As for the implementation of relevant European law by acceding countries (eg registration of plant protection products, regulations on drinking water, etc), the ratification of the European law, was a perquisite for the 12 new Member States, in order to join the European Union. An exception to this rule relates to some provisions on the circulation of a few products currently available only on the markets of these countries. As for data collection policies and practices, it is worth mentioning that these countries report a great deal of statistics to EUROSTAT and take part in surveys such as the Farm Structure and Labour Force surveys.



5. Datasets available from international organizations To enhance the material collected via the questionnaires, we examined in detail all international organizations which collect, aggregate and provide related information on the health of the working population in agriculture and fishing. A lot of effort has been made so that the available information is harmonized for each country as far as original data permits. For all organizations sources of information are national authorities responsible for routinely collecting such information, or other surveys conducted by private institutions. In the following pages, a short introduction is provided for each organization. Then, the datasets available from each one are presented by topic, then by organization. Although most of the information provided is available from the organizations’ websites, we opted to present it in order to make it easier for users of this report to understand and utilize data and also to support our decision making in the indicator choices in the work packages 5 and 6.

5.1. Presentation of relevant organizations/authorities

5.1.1. EUROSTAT Eurostat is the Statistical Office of the European Community, situated in Luxemburg. Its task is to provide the European Union with European-level statistics that enable comparisons between countries and regions. Eurostat’s main role is to process and publish comparable statistical information at the European level. Eurostat attempts to utilize a common statistical ‘language’ that embraces concepts, methods, structures and technical standards. Data is available for the 25 European Union countries plus Japan, the United States, the central European countries and other main economic partners of the Union. The Eurostat database contains high quality macroeconomic and social statistics data organised into nine statistical themes; General Statistics, Economy and Finance, Population and Social Conditions, Industry, Trade and Services, Agriculture and Fisheries, External Trade, Transport, Environment and Energy, Science and Technology (http://epp.eurostat.ec.europa.eu/portal/page?_pageid=1090,30070682,1090_33076576&_dad=portal&_schema=PORTAL)

Farm Structure Survey The following information on the Farm Structure Survey is quoted from the EUROSTAT website: (http://europa.eu.int/estatref/info/sdds/en/eurofarm/eurofarm_sm.htm)



The main aim of the Community Farm Structure Surveys (FSS) is to provide a common list of characteristics observed using common rules and procedures, thus ensuring the possibility of comparison of holding all over the European Union. As a result complex statistical data sets are established. FSS data are available for the following years: 1989/90 (1990), 1993, 1995, 1997, 1999/2000 (2000), 2003 and 2005. The basic surveys are in line with the FAO recommendations and are carried out every 10 year. The intermediate surveys are organised 3 times between censuses. All surveys are relating crop years and the exact reference periods are determined in legislations. The surveys in 1990 and 2000 covered a period between 01.12.1988 and 01.03.1991, respectively 01.12.1998 and 01.03.2001, thus the actual survey, which took place varied from country to country. EU data are compiled on the basis of national figures. All surveys are undertaken by the Member States, and the transmission deadlines of the data (statistical tables and/or individual data files) are determined by the legislation. The Member States are authorised to use information already available from sources other than statistical surveys. The basic surveys are full scope censuses, but there is a possibility to carry out the census on a sample base for certain characteristics. The intermediate surveys are conducted on a random sample base. Aggregating is used to produce EU data, following the various enlargements. The aggregation can be simple summing up, but in case of sample survey, the extrapolation factors provided by the Member States to each holding are taken into account. A set of procedures to improve coverage and data quality is undertaken by the Member States. Most published data contain sampling errors. The estimated sampling error – especially for cells based on very fine classification – can be in the order of 20% or higher. In addition, a further error is introduced during the rounding treatment. The extent of this error for a given table cell depends mainly on the value of the cell or the number of holdings contributing to this cell; the smaller the value is or the number of the holding are, the higher the relative rounding error is. The sum of the individual cells does not systematically match with the value of the `total` cell. The errors can be eliminated and in most cases estimations can be obtained from Eurostat on request. The individual data are validated by Member States and Eurostat using strict rules, later the aggregated data are checked again. These data may nevertheless be subject to subsequent correction, if some errors are detected during a specific analysis. For more information: Farm Structure – Methodology of Community Surveys, Brussels, Luxembourg, 1996 and Methodological reports on FSS in the European Union Accession Countries, Statistics Sweden - Landsis, 2002.

Labor Force Survey



The following information on EU-Labor Force Survey is quoted from EUROSTAT’s website (http://europa.eu.int/estatref/info/sdds/en/employ/employ_lfsi_sm.htm): The European Union Labour Force Survey (EU LFS) provides population estimates for the main labour market characteristics, such as employment, unemployment, inactivity, hours of work, occupation, economic activity and much else as well as important socio-demographic characteristics, such as sex, age, education, households and regions of residence. The EU LFS results are produced in accordance with the international classification systems. The main classifications used are NACE Rev.1 (NACE Rev.1.1 from 2005) for economic activity, ISCO 88(Com) for occupation and ISCED 1997 for education level. The EU-LFS covers all the territories of the Member States of the European Union, the EFTA countries (excluding Lichtenstein), as well as Bulgaria, Croatia and Romania. In case of Cyprus, however, the data only refer to the territory under the control of the Government of the Republic of Cyprus. Data for France do not include the overseas departments (DOM). A specific survey is conducted for these territories; however the results are used in regional statistics only. The EU-LFS covers all industries and occupations. The EU-LFS results cover the total population usually residing in Member States, except persons living in collective or institutional households. While demographic data are gathered for all age groups, questions relating to labour market status are restricted to persons in the age group 15 years or older except for Spain, Sweden (before 2001), the United Kingdom, Iceland and Norway where this age limit is 16 years. In Denmark, Estonia, Latvia, Hungary, Finland, Sweden, Iceland and Norway questions on the labour market characteristics are also restricted to those younger than 75 years of age. In the EFTA countries, Iceland, Norway and Switzerland, population data are not provided for the age-groups outside the scope of labour market questions. The EU-LFS is a rotating random sample survey of persons in private households. The sample design and rotation patterns are not harmonised. The data is acquired by interviewing the sampled individuals directly. Proxy interviews are allowed through a responsible person in the household. In most countries at least the first wave interview is conducted in person while subsequent follow-up interviews can be conducted via telephone. Participation in the survey is compulsory in Belgium, Germany, Greece, Spain, France, Italy, Cyprus, Malta, Austria, Portugal and Norway. Part of the data can be supplied by equivalent information from alternative sources, including administrative registers, provided the data obtained are of equivalent quality. Typically, the Nordic countries supply the demographic information directly from the population registers. The LFS, like all surveys, is based upon a sample of the population. The results are therefore subject to the usual types of errors associated with random sampling. Based on the sample size and design in the various Member States, Eurostat implements basic guidelines intended to avoid publication of figures that are too small to be reliable or to give warning of the unreliability of the figures. From 2005, all EU countries conduct a quarterly survey and EU data are complied as follows: Levels are the simple average of the quarterly aggregates. Rates/ratios/averages are produced by summing up the quarterly aggregate nominators and denominators before producing the European rate/ratio/average.

Fisheries Statistics The following information on Fisheries Statistics is quoted from EUROSTAT’s website (http://europa.eu.int/estatref/info/sdds/en/fish/fish_base.htm): The “Fisheries statistics” domain contains the following data: 1. Landings of fisheries products in the ports of EEA member countries by fishing region and by quantity and value (in tonnes landed weight and in national currency) 2. Catches of fish, crustacean, molluscs and other aquatic organisms by fishing area and by country for all the countries in the World (in live weight equivalent of the landings)



3. Catches of tuna and tuna-like species by all countries fishing in the Atlantic and Indian Oceans (in tonnes live weight equivalent of the landings) 4. Aquaculture production for all the countries in the world by species and by environment and by volume and value (in tonnes live weight and in € respectively) 5. Global production from capture fisheries and aquaculture production by major fishing area for all countries in the World 6. Fishing fleet statistics. The data are on the number, total tonnage and total power of fishing vessels by tonnage classes, power classes and age classes and cover EEA countries. 7. Employment in the fisheries sector. Data on the number employed in fishing and in aquaculture production in European countries 8. Foreign trade in fisheries products. Summary on the trade on groups of fisheries products with data from Eurostat’s COMEXT database (for EU countries) and from FAO’s trade databases (for all countries in the world) 9. Supply balance sheets for total fisheries products for all the countries in the world. The balance sheets have been compiled by FAO Fisheries Department Periodicity: Annual except landing statistics (monthly) Data are disseminated simultaneously to all interested parties through a database update and on Eurostat's website. Data are submitted by national authorities either under the terms of EU legislation (catch, landings and aquaculture production statistics), by gentlemen’s agreements (employment data) or extracted from other databases (fishing fleet, foreign trade and supply balance sheets Methodological documentation is available for each set of data: catch by fishing region, tuna catch statistics, landing statistics, aquaculture production, fishing fleet statistics, employment statistics, foreign trade statistics, supply balance sheets. Information can be requested via e-mail to: [email protected] Basic methodology on fisheries statistics is available in the Handbook of fisheries statistics standards.

5.1.2. FAO (Food And Agriculture Organization Of The United Nations) FAO leads international efforts to defeat hunger serving both developed and developing countries. FAO helps developing countries and countries in transition to modernize and improve agriculture, forestry and fisheries practices and ensure good nutrition for all. FAOSTAT-FAO Statistical Database- contains data from over 210 countries and territories covering statistics on agriculture, nutrition, fisheries, forestry, food aid, land use and population. FAOSTAT-Agriculture, provides statistics on crops, livestock, irrigation, land use, fertilizer, pesticide consumption, and agricultural machines. FAOSTAT-Fisheries, provides statistics on fish production and primary products (http://www.fao.org/waicent/portal/statistics_en.asp).

FAO Country Profiles and Mapping Information System The FAO Country Profiles and Mapping Information System presents the Organization's vast archive of knowledge on agriculture and food security by country and thematic area (Sustainable Development, Economic situation, Agriculture sector, Forestry sector, Fisheries sector and Technical Cooperation). The system brings together documents, statistical data, project details and maps (http://www.fao.org/countryprofiles/default.asp?lang=en).



5.1.3. ILO (International Labor Office) The ILO's Key Indicators of the Labour Market has annual time series data for 20 key indicators of the Labour Market, for 250 countries, areas and territories. The key topics covered include: labour force participation, employment, unemployment, educational attainment, hours of work, wages and earnings, productivity, labour costs, poverty and income distribution (http://laborsta.ilo.org/; http://www.ilo.org/public/english/bureau/stat/portal/topics.htm#children).

5.1.4. OECD (Organisation for Economic Co-operation and Development) The OECD is an international alliance of national governments that acts as a forum for member countries to develop economic and social policies. It currently has 30 member countries which collectively account for two thirds of the world's goods and services. As part of its work, the OECD collects and disseminates economic data on a wide range of industrial and economic indicators. The OECD produces annual, quarterly and monthly data for around 3,800 economic indicators. The variables cover national accounts, industrial production, employment, prices, business trends and trade. The OECD data are considered to be accurate and reliable and provide an authoritative means to compare economic indicators across national boundaries. The “Labour Force Statistics” database contains detailed statistics on working-age population (15-64), labour force, employment and unemployment, broken down by age and sex as well as employment/population ratios, participation rates and unemployment rates by age and sex. Labour force statuses are reported by level of education attained based on the International Classification of Education Systems of 1997. Data are available on this basis only since 1997. The 30 member countries of the OECD are: Australia (AUS), Austria (AUT), Belgium (BEL), Canada (CAN), Czech Republic (CZE), Denmark (DNK), Finland (FIN), France (FRA), Germany (Unified DEU, Western Germany DEW), Greece (GRC), Hungary (HUN), Iceland (ISL), Ireland (IRL), Italy (ITA), Japan (JPN), Korea (KOR), Luxembourg (LUX), Netherlands (NLD), New Zealand (NZL), Norway (NOR), Mexico (MEX), Poland (POL), Portugal (PRT), Slovak Republic (SVK), Spain (ESP), Sweden (SWE), Switzerland (CHE), Turkey (TUR), United Kingdom (GBR), United States (USA) (http://www.oecd.org/home/0,2987,en_2649_201185_1_1_1_1_1,00.html).

5.1.5. World Bank



The World Development Indicators contains statistical data available for 630 development indicators. Data includes social, economic, financial, natural resources, and environmental indicators. Annual time series data from 1960 – 2004 (http://web.worldbank.org/WBSITE/EXTERNAL/DATASTATISTICS/0,,contentMDK:20535285~menuPK:232599~pagePK:64133150~piPK:64133175~theSitePK:239419,00.html).

5.2. Available information by topic

5.2.1. Labour force

ILO Employment by economic activity (Agriculture, Fisheries, A+F, Total employment) and by sex – last available year (Elaborated); Hours worked by economic activity and by sex.

EUROSTAT Agricultural holders above the age of 65. Agricultural holders under the age of 35. Agricultural holders being a natural person. Holding managers. Female managers. Full-time regular farm labour force. Part-time regular farm labour force. Family farm labour force. Female regular farm labour force. Total farm labour force.

OECD (for OECD members only) Total Labour Forces, Labour in Agriculture by professional status and by sex.

UNDP Human Development Report 2005 Employment by economic activity, sex and human development index

5.2.2. Labour force: Immigrants

ILO



The ILO International Labour Migration Database is based on responses of ILO member states to a questionnaire survey mailed in 1998 to obtain basic data on stocks and flows of migrant labour. The database complements efforts of other organizations, such as EUROSTAT, to collect information on various aspects and dimensions of migration, as well as on its impact on the labour markets of origin and destination countries. EUROSTAT Eurostat produces statistics on a range of issues related to international migration and asylum. Data are supplied on a monthly, quarterly and annual basis by national statistical institutes and by ministries of justice and the interior. Many of these statistics are sent to Eurostat as part of a joint migration data collection organised by Eurostat in cooperation with the United Nations Statistical Division, the United Nations Economic Commission for Europe, the Council of Europe and the International Labour Office. It can be difficult to measure accurately the scale and patterns of migration. Countries differ in the way they produce migration statistics and who they consider to be a migrant. In some countries, migration statistics are based on administrative data taken, for example, from systems for issuing residence permits or from a population register; other countries use survey-based data. These variations in data sources and definitions result in problems when comparing the migrant counts for different countries. (Source: Eurostat in figures. Eurostat Yearbook 2006-7 http://epp.eurostat.ec.europa.eu/cache/ITY_OFFPUB/KS-CD-06-001/EN/KS-CD-06-001-EN.PDF ) Available data are aggregated by:

• Citizenship • Age group

Non-national populations: http://epp.eurostat.ec.europa.eu/cache/ITY_OFFPUB/KS-NK-06-008/EN/KS-NK-06-008-EN.PDF

5.2.3. Labour force: workers over 65 years old

EUROSTAT Results from the Farm Structure Survey provide data on the number of agricultural holders over the age of 65.

5.2.4. Mortality and morbidity indicators



EUROSTAT Serious and fatal occupational accidents by sector, by age and by sex from 1994 to 2005

ILO Occupational accidents, occupational injuries, incapacity of work, cases of occupational injury with lost workdays and fatal cases by economic activity, by sex. Data are available from 1967 to 2004.

5.2.5. Health care

WHO Health for All Database. European health for all database (HFA-DB) Contains data on about 600 health indicators, including basic demographic and socioeconomic indicators; some lifestyle- and environment-related indicators; mortality, morbidity and disability; hospital discharges; and health care resources, utilization and expenditure. Allows easy and user-friendly analysis of trends and international comparisons for a wide range of health statistics to support the formulation and monitoring of health policy at national and international levels (http://www.euro.who.int/hfadb). The available data are not, however, disaggregated by occupation.

OECD OECD Health Data 2006 (Statistics and Indicators for 30 Countries), offers the most comprehensive source of comparable statistics on health and health systems across OECD countries. It is an essential tool for health researchers and policy advisors in governments, the private sector and the academic community, to carry out comparative analyses and draw lessons from international comparisons of diverse health care systems. The database includes key indicators covering health status, health resources and utilisation: life expectancy, maternal and infant mortality, congenital anomalies, health employment, in-patient beds, medical technology, immunisation, average length of stay, discharges, surgical procedures, and transplants and dialyses (http://www.oecd.org/document/9/0,2340,en_2825_495642_2085193_1_1_1_1,00.html). The available data are not, however, disaggregated by occupation.

EUROSTAT Health care indicators from the national Health Interview Surveys



5.2.6. Occupational diseases

ILO In 2002 ILO published a Recommendation concerning the list of occupational diseases and the recording and notification of occupational accidents and diseases. This list is under review now (http://www.ilo.org/public/english/protection/safework/health/quest05/list_e.pdf; http://www.ilo.org/public/english/standards/relm/gb/docs/gb295/pdf/meulod.pdf).

5.2.7. Pesticides Availability on national level on this data are scarce. However, some sources of information have been detected.

EUROSTAT Use of plant protection products in agriculture: amounts of plant protection products, expressed in tonnes of active ingredient, for each of the different main crops during the respective ‘harvest year’. Based on national surveys, conducted by the European Crop Protection Association (ECPA) [www.ecpa.be]; Available for the EU-15 member countries until recently. Sales of pesticides: amounts of plant protection products, expressed in tonnes of active ingredients, sold in each of the EU-25’s Member States and in a few EFTA countries for each of the main functional categories of products (herbicides, fungicides, insecticides, others). Totals for EU-15 are also presented and when the collection is complete, totals for EU-25 should also be presented.

FAO The Statistics Division of the Food and Agriculture Organization of the United Nations started collection of data on consumption of major individual pesticides products about three decades ago. The database refers to the quantity of pesticides used in or sold to the agricultural sector expressed in metric tons of active ingredients. Information on quantities applied to single crops is not available (http://www.fao.org/waicent/FAOINFO/economic/pesticid.htm).

5.3. Available information by organization

ILO (www.laborsta.ilo.org)

- employment by economic activity (Agriculture, Fisheries, A+F, Total employment) and by sex – last available year (Elaborated);

- Hours worked by economic activity and by sex.



- injuries by economic activity (fatalities, non-fatalities, permanent, temporary, total) and by sex – last available year (Elaborated);

- Injury rates by economic activity and sex (fatal, non-fatal); - Injury cases by economic activity and sex (fatal, non-fatal); - Days lost by cases of temporary incapacity by economic activity and by sex.

The following tables present the availability of data from ILO for each of the countries that the DIRERAF project has examined. Please note that farming and fishing are included as independent economic activities and therefore data are available. Data availability and aggregation are specified in each table cell: Austria Belgium Bulgaria Cyprus Czech

Republic Total and economically active population by age group

1996-2005 by gender

1996-2003 by gender

2000-2005 by gender

2000-2005 by gender

1996-2005 by gender

Employment, general level

1996-2005 by gender

1996-1999 official estimates 1996-2005 LFS

1996-2005 by gender except from 1997-2000

1999-2005 by gender

1996-2005 by gender

Total employment by economic activity

1996-2005 by gender

1996-2005 by gender

2003-2005LFS(by gender) 1996-2005 official; estimates by gender

1999-2005 by gender

1996-2005 by gender

Total employment by occupation

1996-2005 by gender

1996-2005 by gender

2003-2005 by gender

1999-2005 by gender

1996-2005 by gender

Cases of injury, with lost workdays, by economic activity

1996-2004 fatal cases

1996-2001,2004 fatal cases1996-2001,2004 non fatal cases1996-2001,2004 cases of temporary incapacity 1996-2001,2004 cases of permanent incapacity

1996-2004 fatal cases 2003-2004 fatal cases by gender1996-2004 non fatal cases2003-2004 non fatal cases by gender 2003-2004 cases of permanent incapacity by gender 2003-2004 cases of temporary incapacity by gender

1996-2005 fatal cases by gender 1996-2005 non fatal cases by gender




Rates of occupational injuries by economic activity

1996-2004 rates of fatal injuries

1996-2001 rates of fatal injuries 1996-2001 rates of non fatal injuries

1996-2004 rates of fatal injuries 2003-2004 rates of fatal injuries by gender 1996-2004 rates of non fatal injuries 2003-2004 rates of non fatal injuries by gender


1996-2005 rates offatal injuries by gender1996-2005 rates ofnon fatal injuries by gender

Hours of work by economic activity

1996-2005 hours actually worked employees by gender 1996-1997 hours paid for employees by gender

1996-1999 (no Agriculture and Fisheries)

2000-2004 1996-2005 hours paid for employees by gender 1996-2005 hours paid for wage earners by gender 1998-2005 hours paid for salaried employees by gender

NA for Agriculture and Fisheries



Denmark Estonia Finland Germany Greece Total and economically active population by age group

1996-2005 by gender

1997-2005 by gender

1996-2005 by gender

1996-2005 by gender

1996-2005 by gender


1996-2005 by gender

1996-2005 by gender Persons aged 15 to 74 years. Prior to 1997: persons aged 15 to 69 year

1996-2005 by gender

1996-2005 by gender

1996-2005 by gender


1996-2005 by gender

1996-2005 (by gender) Persons aged 15 to 74 years. 2Prior to 1997: persons aged 15 to 69 year

1996-2005 by gender Persons aged 15 to 74 years

1996-2005 by gender

1996-2005 by gender. Prior to 1998: persons aged 14 years and over.


1996-2005 by gender

1996-2005 by gender Persons aged 15 to 74 years. Prior to 1997: persons aged 15 to 69 year

1996-2005 by gender Persons aged 15 to 74 years

1996-2005 by gender

1996-2005 by gender Prior to 1998: persons aged 14 years and over.


1996-2001 fatal cases by gender 1996-2001 non fatal cases by gender 1996-2001 cases of temporary incapacity by gender

1996-2005 fatal cases 1999-2005 fatal cases by gender 1996-2005 non fatal cases 1999-2005 non fatal cases by gender

1996-2004 fatal cases 1998-2004 fatal cases by gender 1996-2004 non fatal cases 1998-2004 non fatal cases by gender


1996-2002 fatal cases +non fatal cases+ 1997-1998 fatal +non fatal cases by gender


1996-2001rates of fatal injuries 2000-2001 rates of fatal injuries by gender 1996-2001 rates of non fatal injuries 2000-2001 rates of non fatal injuries by gender



1996-2002 rates of fatal injuries(total) 1996-2002 rates of non fatal injuries (total)

1996-2002 rates of fatal injuries 1996-2002 rates of non- fatal injuries


NA 1996-2004 1996-2004 by gender

1996-2005 by gender (not for fisheries) Permanent workers

1996-2005 (by gender)



Hungary Ireland Italy Latvia Lithuania Total and economically active population by age group

1996-2005 by gender

1996-2005 by gender

1996-2005 by gender

1996-2005 by gender

1996-2005 by gender


1996-2005 by gender

1996-2005 by gender

1996-2005 by gender

1996-2005 by gender

1996-2005 by gender


1996-2005 by gender

1996-2005 by gender

1996-2005 by gender

1996-2005 by gender

1998-2005 LFS (by gender)1996-2001 official estimates (by gender)


1996-2005 by gender

1996-2005 (by gender

1996-2005 by gender

1996-2005 by gender

1997-2005 (by gender) Persons aged 14 years and over


1996-2005 fatal cases (by gender) 1996-2005 non fatal cases (by gender) 1996-2005 cases of permanent incapacity (by gender) 1996-2005 cases of temporary incapacity (by gender)

1996-2005 non fatal cases1998-2005 non fatal cases (by gender)1996-2003 fatal cases2000-2002 fatal cases by gender

1996-2005 fatal injuries (by gender)1996-2005 non fatal injuries (by gender) 1996-2005 cases of permanent incapacity(by gender) 1996-2005 cases of temporary incapacity (by gender)

1996-2005 fatal injuries 1996-2005 non fatal injuries

1996-2005 fatal injuries( by gender)1996-2005 non fatal injuries ( by gender)


1996-2005 rates of fatal cases 2001-2005 rates of fatal cases (by gender) 1996-2005 rates of non fatal cases 2001-2005 rates of non fatal cases (by gender)

1996-2002 rates of fatal injuries (1999 for A+F)

1996-2004 rates of fatal injuries1996-2004 rates of non fatal injuries

2002-2004 rates of fatal injuries 2002-2005 rates of non fatal injuries

1996-2005 rates offatal injuries2002-2005 rates of fatal injuries (by gender) 1996-2005 rates non fatal injuries 2002-2005 rates of non fatal injuries (by gender)


NA for Agriculture and Fisheries

1996-2005 hours actually worked (by gender) 2001hours paid for wage earners A+F Permanent workers

1996-2005 (by gender)

1996-2005 hours actually worked(by gender)

1996-2004 hours paid for employees



Luxembourg Malta Netherlands Norway Poland Total and economically active population by age group

1996-2005 by gender

1996-2004 by gender

1996-2005 by gender

1996-2005 by gender

1996-2005 by gender


1996-2003 1996-2005 by gender

1996-2005 by gender

1996-2005 by gender

1996-2005 by gender


1996-2005 1996-2005 by gender

1996-2005 by gender

1996-2005 by gender

1996-2005 by gender


NA 2001-2005 by gender

1996-2005 by gender

1996-2005 by gender

1996-2005 by gender


2002-2003 fatal injuries ( by gender) 2002-2003 non fatal injuries ( by gender)

2001-2004 fatal injuries(by gender)2001-2004 non fatal injuries(by gender)2001-2004 cases of tempor. Incap. (by gender)

1997-2000 fatal injuries

1996-2005 fatal injuries 1998-2004 fatal injuries (by gender) 1996-2005 non fatal injuries 1998-2004 non fatal injuries (by gender) Excl. sea fishing.

1996-2005 fatal injuries (by gender)1996-2005 non fatal injuries (by gender)


2002-2003 rates of fatal injuries (by gender) 2002-2003 rates non fatal injuries (by gender)

2001-2004 rates of fatal injuries(by gender)2001-2004 rates of non fatal injuries(by gender)

NA 1998-2005 rates of fatal injuries 1998-2005 rates of non fatal injuries

1996-2005 rates of fatal injuries Excl. private farms in agriculture


NA 2000-2004 hours actually work (by gender)

1996-2004 hours paid for employees (by gender)

1996-2005 by gender

1996-2005 by gender



Portugal Slovakia Slovenia Spain United Knigdom

Total and economically active population by age group

1996-2005 by gender

1997-2005 by gender

1996-2005 by gender

1996-2005 by gender

1996-2005 by gender


1996-2005 by gender

1996-2005 by gender

1996-2005 by gender

1996-2005 by gender

1996-2005 by gender


1996-2005 by gender

1996-2005 by gender

1996-2005 by gender

1996-2005 by gender

1996-2005 by gender


1996-2005 by gender

1996-2005 by gender

1996-2005 by gender

1996-2005 by gender

1996-2005 by gender


1996-2000 Non-fatal cases 1999-2000 Non- fatal cases by gender 1996-2000 Fatal injuries 1999-2000 (Fatal injuries by gender)

1996-2005 Fatal injuries (by gender) 1996-2005 Non-fatal cases (temporary + permanent incap, by gender)

1997-2005 Fatal cases 1997-2005 Non fatal cases

1996-2004 Non-fatal cases (temporary + permanent incap, by gender) 1996-2002 (cases of temporar incap by gender) 1996-2004 Fatal injuries (by gender)

1996-2004 Non-fatal cases (temporary + permanent incap, by gender) 1996-2004 Fatal injuries (by gender)


1996-2000 Non-fatal cases 1999-2000 Non- fatal cases by gender 1996-2000 Fatal injuries 1999-2000 (Fatal injuries by gender)

1996-2005 Fatal cases (by gender)1996-2005 Non fatal cases (by gender)

1997-2005 Fatal cases 1997-2005 Non fatal cases

1996-2005 (rates of fatal injuries by gender2003-2004) 1996-2005 (rates of non-fatal injuries by gender2003-2004)

1996-2004 (rates of fatal injuries by gender)1996-2004 (rates of non-fatal injuries by gender)


1996-2003 (Hours actually worked, by gender) 1999-2005 (Hours paid for Employees, by gender)

1996-2005

1996-2005 by gender

1996-2005 by gender

1996-2004 by gender



EUROSTAT (http://epp.eurostat.cec.eu.int/portal/page?_pageid=1090,30070682,1090_33076576&_dad=portal&_schema=PORTAL) Agricultural holders above the age of 65. Agricultural holders under the age of 35. Agricultural holders being a natural person. Holding managers. Female managers. Full-time regular farm labour force. Part-time regular farm labour force. Family farm labour force. Female regular farm labour force. Total farm labour force. Pesticides sale by type (fungicides, insecticides, herbicides, total) the EU 15 (1995-2002). Pesticides use by type – as active ingredient - (fungicides, insecticides, herbicides) and by crop (cereals, maize, potatoes, fruit etc) for the EU 15 (1992-1999). Fertilizers use for the EU 15. The table below depicts the availability of data by country from Eurostat for each of the topics of the project questionnaire:

Aus

tria

Bel

gium

Bul

garia

Cyp

rus

Cze

ch R

epub

lic

Den

mar

k

Esto

nia

Finl

and

Fran

ce

Ger

man

y

Gre

ece

Hun

gary

Irel

and

Italy

Latv

ia

Lith

uani

a

Luxe

mbo

urg

Mal

ta

Net

herla

nds

Nor

way

Pola

nd

Portu

gal

Slov

akia

Slov

enia

Spai

n

Swed

en

Uni

ted

Kin

gdom

Total farm labour force

Empl. in A by age and gender

x ⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧Total F empl. by gender

Empl. in F by age and gender

⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧

Workforce pop. by age ⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧Empl. rate by gender Empl. of immigrants in A and F

⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧

Accidents in A, Fo&H by fatal accidents and gender

⌧⌧⌧ ⌧ ⌧ ⌧⌧ ⌧ ⌧ ⌧⌧

Accidents in A, Fo and H by fatal accidents and age

⌧⌧⌧ ⌧ ⌧ ⌧⌧ ⌧ ⌧ ⌧⌧

Accidents ⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧



in F by severity and age Accidents in F by severity and gender

⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧

Absenteeism indicator cause of accident in A, Fo & H by gender

⌧⌧⌧ ⌧ ⌧ ⌧⌧ ⌧ ⌧ ⌧⌧

Absenteeism indicator cause of accident in A, Fo & H by age

⌧⌧⌧ ⌧ ⌧ ⌧⌧ ⌧ ⌧ ⌧⌧

Occupational diseases in A and F

⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧

Other mortality / morbidity indicators in A and F

⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧

Health indicators (except absenteeism cause of accident)

⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧⌧

Use of PPP by produce category

⌧⌧⌧ ⌧ ⌧ ⌧⌧⌧⌧ ⌧⌧ ⌧⌧

A: Agriculture, F: Fisheries, Fo: Forestry, H: Hunting, PPP: plant protection products, pop.:population, empl.: employment : Available

×: Not available

AGROFARMA (Report 2001) Use of pesticides in the EU 15, in the year 1999 (source: ECPA). Use of pesticides by type in the EU, in the year 1999. Agricultural surface used in the EU 15 in the year 1998 (source FAO).

OECD for OECD members only (http://www.oecd.org/statsportal/0,2639,en_2825_293564_1_1_1_1_1,00.html) Number of physicians per 1000 inhabitants; Total Labor Forces, Labor in Agriculture by professional status and by sex;

Aus

tria

Bel

gium

Cze

ch R

epub

lic

Den

mar

k

Finl

and

Fran

ce

Ger

man

y

Gre

ece

Hun

gary

Irel

and

Italy

Luxe

mbo

urg

Net

herla

nds

Nor

way

Pola

nd

Portu

gal

Slov

akia

Spai

n

Swed

en

Uni

ted

Kin

gdom



WHO Health for All Database (all files downloaded for EU 25) (http://data.euro.who.int/hfadb/) % of urban population Labour force % Density of population per square Km General Practitioners per 100,000 pop. Health expenditure PPP per capita Primary care health units per 100,000 pop UNDP development index

FAO – FISHERIES (http://www.fao.org/figis/servlet/static?dom=root&xml=tseries/index.xml) Fishing fleets

FAO – Country Profiles (http://www.fao.org/countryprofiles) Profiles concerning: Sustainable development, economy, agriculture, fisheries further

FAO – Faostat http://faostat.fao.org Agricultural surface used Pesticide trade by type Pesticide consumption and type

UNDP Human Development Report 2005 http://hdr.undp.org/statistics/ Employment by economic activity, sex and human development index

Employment rates by age and gender

Part-time employment rate

Self-employment rate by gender



6. The minimal common dataset In addition to the descriptive statistics, the main outcome of workpackage 3 was the development of common datasets based on the findings of work package 2. Although both availability and the level of aggregation were to be compared, the primary focus was on the availability of data collected across the EU countries, because of the small number of datasets commonly available. The availability of the collected data by country was recoded into a scale variable, depicted in the form of 1 to 5 boxes (1 box: available data in 0-5 countries (red); 2 boxes: 6-10 countries (orange); 3 boxes: 11-15 countries (yellow); 4 boxes: 16-20 countries (light green); 5 boxes: 21-27 countries (dark green)). The following topics were explored:

• Employment statistics • Health status (mortality-morbidity) • Health services • Occupational diseases • Environmental burden (agroenvironmental indicators)

Employment Number of persons employed in agriculture (A) and fisheries (F)

availability:

aggregation: by gender; 5-year band; [national data sources; Eurostat; ILO;

OECD]

Number of persons under 14 yrs of age employed in agriculture and

fisheries

availability:

aggregation: none specified [national data sources]

Number of retired persons in agriculture and fisheries

availability: [A] [F]

aggregation: by gender; [national data sources]

Number of persons greater than68 yrs of ageemployed in agriculture and

fisheries





Number of immigrants employed in agriculture and fisheries



Agricultural holders above the age of 65

availability: [EU-15 data]

aggregation: none specified [Eurostat]

Agricultural holders under the age of 35



Agricultural holders being a natural person



Holding managers



Female managers



Full-time regular farm labour force



Part-time regular farm labour force



Family farm labour force





Female regular farm labour force



Total farm labour force



Total and economically active populaton

availability:

aggregation: by gender; by age group; by economic activity [ILO]

Hours of work

availability:

aggregation: by gender; by economic activity [ILO]

Employment rate

availability: [EU OECD members]

aggregation: by gender; by economic activity; by age

Employment in the fisheries sector

availability:

aggregation: by gender; by environment; by age; by type of employment

[Fisheries Statistics Eurostat]

Morbidity – Mortality Injury mortality



Accidents in Agriculture, Forestry and Hunting


aggregation: by cause; by gender; by age group [Eurostat]



Absenteeism indicator cause of accident in Agriculture, Forestry and

Hunting


aggregation: by gender; by age group [Eurostat]

Cancer mortality



Injury morbidity



Cases of injury, with lost workdays

availability:

aggregation: by gender; by economic activity; by outcome (fatal/non-fatal)

[ILO]

Registered occupational diseases morbidity



Health Services Indicators Medical consumption



Health services consumption



Absenteeism >3 days





Insurance data


aggregation: none specified [national data sources; WHO]

Hospitalization expenditure



Insurance expenditure



Maternity expenditure



Population density per km2

availability: [WHO]

General practitioners per 100,000pop

availability: [WHO; OECD; Eurostat]

Health expenditure

availability: [WHO; Eurostat]

Occupational diseases Respiratory diseases morbidity



Musculoskeletal diseases morbidity



Skin diseases morbidity





Neurological diseases morbidity



Hearing disorders morbidity



Occupational cancer incidence



Toxic & irritant effects incidence



Rates of occupational injuries

availability:

aggregation: by gender; by economic activity; by outcome (fatal/non-fatal)

[ILO]

Agroenvironmental indicators Use of pesticides

availability:

aggregation: by produce category; annual data [national data sources;

ECPA EU15 data]

Sales of pesticides

availability:

aggregation: annual data [national data sources; ECPA EU15 data]

Use of fertilizers

availability: [EU15 data]

aggregation: none specified

Agricultural use of land



availability:

aggregation: by production type [Farm Structure Survey Eurostat]

It is evident that datasets of the populations at risks are commonly found across the EU countries. Moreover the level of aggregation is not satisfactory and would require amendments. Additionally, the question remains on whether some data, which are made available for most EU countries by international organizations, are reliable enough to reflect the true state of the situation.



7. Annexes 1. Methodology of the research 2. Questionnaire 3. List of national sources & authorities 4. Presentation of programmes of special importance and a presentation of indicative authorites by country



ANNEX 1

Research methodology



7.1. Methodology of the research

The list of data collection policies and practices During the first meeting of partners, the work within this work package was scheduled and tasks were allocated. Among other issues, the project team proposed a set of key points that should be explored in every EU Member state. Once these indicators were finalised, a questionnaire was drafted and revised after receiving feedback from the partners. The following table depicts the requested topics and the details for each point requested by this questionnaire. Topic Details requested Number of persons employed in agriculture and fisheries

For each national authority collecting data: Organization name and contact details Items requested: Age intervals Gender aggregation Data on retired labour force General workforce data Time period of data collection Availability of data Source of data Accessibility of data

National data sources on Occupational Hygiene, Health and Safety and Environmental Health regarding Agriculture and Fisheries

For each national source collecting data: Institution name and contact details Legal framework Items requested: Type of data collected Collection method Level of aggregation Accessibility of data

- Availability of data on the employment of persons under 14 years of age in agriculture and fisheries - Availability of data on the employment of persons over 68 years of age in agriculture and fisheries - Availability of data on the employment of immigrants in agriculture and fisheries

For each national source collecting data: Institution name and contact details Legal framework Items requested: Type of data collected Collection method Level of aggregation Accessibility of data

Availability of indicators for mortality and morbidity

For each national source collecting data: Institution name and contact details Legal framework Items requested: Type of data collected Collection method Level of aggregation



Accessibility of data Existence of mortality and morbidity indicators All-cause specific Cause specific Age specific Gender specific

Availability of data based on specific health indicators for persons employed in agriculture and fisheries

For each national source collecting data: Data source name and contact details Legal framework Items requested: Indicator name Type of data collected Collection method Level of aggregation Accessibility of data

Available data referring to occupational diseases regarding agriculture and fisheries

For each national source collecting data: Data source name and contact details Legal framework Items requested: Type of data collected Collection method Level of aggregation Accessibility of data

Obligatory registered occupational diseases in agriculture and fisheries

Name of each disease in each of the two sectors

National health programs on health education and health promotion targeting those employed in agriculture and fisheries

Program title Responsible authority Sector Targeted population

Available data on annual use of pesticides by specific produce category

For each national source collecting data: Data source name and contact details Legal framework Items requested: Type of data collected Collection method Level of aggregation Accessibility of data

Furthermore, the questionnaire contained a section where recommendations for health indicators that could be considered during the indicator development and selection phase could be placed.

National data sources For countries, where a project partner was available, their team was responsible for the completion of the questionnaire, based on national data sources and contacts. Additionally, other sources of contacts were the Italian National Institute of



Occupational Safety and Prevention (ISPESL) and the European Focal Points of Occupational Safety and Health Agency (OSHA). The methodology for obtaining the necessary material from the countries, where no project partner existed, was developed during a meeting, which was organized between the coordinating team and the ICPS team in Athens (May 2006). The late Professor M. Maroni, on behalf of the ICPS team, provided valuable new insights for dealing with the issue of data collection. The two teams agreed on using the following methodology: A thorough web search was conducted in order to identify international - national institutions - organization / data collecting authorities’ websites. Through these websites published data and expert contacts in the corresponding fields were sought. Electronic correspondence with contact persons in the above institutions, requesting information on specific points of the questionnaire, according to the role of the institution contacted. Particular attention was given to communication with the National Statistics Services, Labour organizations, Ministries of Health, Agriculture and Social Insurance, as well as Institutions dealing with Agriculture and Fisheries, Health and Safety and Insurance organizations. For quality assurance purposes, the research team sought to collect official replies from national authorities on each point in question. The completed questionnaire (with all available information) was subsequently distributed to national data sources and authorities, and also to field experts of each Member State, for commenting and verification, by means of email and written correspondence. The following table is a summary of the collection method used for data retrieval:

European Union 27 Member States (as of January 1st, 2007)

Country Partners OSHA/ISPESL Coordinator Austria

Belgium

Bulgaria1

Cyprus

Czech Republic

Denmark

Estonia

Finland

France

Germany

Greece

Hungary

Ireland

Italy

Latvia



Lithuania

Luxembourg

Malta

Netherlands

Poland

Portugal

Romania1

Slovakia

Slovenia

Spain

Sweden

United Kingdom Non-EU countries included in the project Country Partners OSHA/ISPESL Coordinator Norway 2

1: Bulgaria and Romania are full Member States as of January 1st, 2007 2: Data collected only for fisheries in Norway

The collection of information for the topics described above was a substantially complicated and extensive task. The collected material for each topic was also processed by means of a statistical software package (SPSS for Windows, v.13) and descriptive analysis was carried out. The whole material collected by each country was used to compile a short directory of available national data sources for each specific point of the questionnaire. In addition, the whole spectrum of authorities, which the project team contacted during its research, has been recorded in a detailed directory. For each authority, a small summary and contact information is provided.

International data sources Besides national data sources, querying international organizations and institutions was the second step. This step was very important as organizations, such as EUROSTAT, apart from collecting national data, also set the method collection and the level of aggregation and are in charge of the harmonization process, so that presented data are comparable across EU countries and in time. The above organizations were queried for the same points, which comprised the questionnaire sent to national sources and the result was a list of relevant indicators already available. The data retrieved from the international organizations are presented by topic and by organization.

Panel of experts Throughout this work package the partnership and the coordinating team in particular, contacted numerous national authorities, local and international institutions and organizations, in order to acquire the information requested for its successful



completion. During this process, frequent contact was established between the research team and a number of prominent members of the scientific community, as well as key representatives of national and international authorities. These contacts have been mobilized for running work package 5, where a selected team of the above experts was requested to assess, among others, the results of this WP2. The research team of DIRERAF has established a communication line with the panel of experts and a joint meeting is scheduled to take place in Athens in the beginning of 2007.



ANNEX 2

The Questionnaire



7.2. The questionnaire

“Development of Public Health Indicators for Reporting

Environmental/Occupational Risks related to Agriculture and

Fisheries”

Questionnaire for data sources and survey of available data

COUNTRY:

REGION:

NAME:

ADDRESS:

PHONE:

FAX:

E-MAIL:

BEST CONTACT SOURCE:



1. Number of persons employed in agriculture and fisheries. (Please specify age

intervals according to which data are readily available and no computation/data

manipulation is needed)

AGRICULTURE (1)

FISHERIES(1) WORKFORCE

POPULATION(1) Age (yrs) Men Women Men Women Men Women

1st interval

2nd interval

3rd interval

4th interval

….. (add more lines if needed)

Total

Retired

Organization (name, address, fax, e-mail, responsible person)

Time period of data collection

Availability of data

Source of data

Accessibility of data (free of charge, approval required, commercial-charged, restricted)

2. National data sources on Occupational Hygiene, Health and Safety and

Environmental Health regarding Agriculture and Fisheries.

AGRICULTURE

Responsible institution Address Web site Contact person e-mail address Legal framework (state, private, NGO, other-please specify)

Type of data collected Collection method



Level of aggregation (e.g. entire population, part of population...)

Accessibility of data (please select the appropriate) free of charge approval required commercial-charged restricted

ADD MORE TABLES IF NECESSARY

FISHERIES

Responsible institution Address Web site Contact person e-mail address Legal framework (state, private, NGO, other-please specify)

Type of data collected Collection method Level of aggregation (e.g. entire population, part of population...)



3.1. Is data available on the employment of younger persons (< 14 years)?

AGRICULTURE YES NO FISHERIES YES NO

AGRICULTURE

Data source(s) Type of data Address Web site Contact person e-mail address



Legal framework (state, private, NGO, other-please specify)


Accessibility of data free of charge approval required commercial-charged restricted


FISHERIES Data source(s) Type of data Address Web site Contact person e-mail address Legal framework (state, private, NGO, other-please specify)






3.2 Is data available on the employment of elder persons (> 68 years)? AGRICULTURE YES NO FISHERIES YES NO AGRICULTURE

Data source(s) Type of data Address Web site Contact person e-mail address Legal framework (state, private, NGO, other-please specify)




FISHERIES Data source(s) Type of data Address Web site Contact person e-mail address Legal framework (state, private, NGO, other-please specify)






3.3 Is data available on the employment of immigrants? AGRICULTURE YES NO FISHERIES YES NO AGRICULTURE





FISHERIES







4. Availability of indicators for mortality and morbidity (add lines if necessary) Potential cause specific indicators: incidence of injuries, mortality due to injuries, mortality due to cancer, respiratory diseases, brucellosis. Please indicate with an asterisk if data refers to the general rural population. I. AGRICULTURE

MORBIDITY SPECIFY All causes Cause specific Age specific Gender specific

MORTALITY SPECIFY All causes Cause specific Age specific Gender specific









II. FISHERIES




MORTALITY SPECIFY All causes Cause specific Age specific Gender specific

MORBIDITY SPECIFY All causes Cause specific Age specific Gender specific







5. Availability of data based on specific health indicators for persons employed in agriculture and fisheries. ADD MORE TABLES IF NECESSARY Potential indicators: health services consumption, insurance data, absenteeism data, medicine consumption. AGRICULTURE YES NO FISHERIES YES NO

AGRICULTURE

INDICATOR Data source Type of data Address Web site Contact person e-mail address Legal framework (state, private, NGO, other-please specify)



FISHERIES

INDICATOR

Data source

Type of data

Address

Web site

Contact person

e-mail address

Legal framework (state, private, NGO, other-please specify)

Type of data collected

Collection method

Level of aggregation (e.g. entire population, part of population...)

Accessibility of data (please select the appropriate)

free of charge



approval required commercial-charged restricted

6. Is there availability of data referring to occupational diseases regarding the

specific sectors?


AGRICULTURE Data source(s) Type of data Address Web site Contact person e-mail address Legal framework (state, private, NGO, other-please specify)




FISHERIES



Accessibility of data (please select the appropriate)



free of charge approval required commercial-charged restricted


6.1. If yes, please mention obligatory registered occupational diseases in the sectors of agriculture and fisheries. (e.g. Brucellosis)

AGRICULTURE Name of disease

FISHERIES Name of disease

7. Does your country implement national health programs on health education,

health promotion specifically targeting those employed in agriculture and

fisheries?


If YES, please specify by filling the following table.

Program Title Responsible Authority Area (Agriculture-Fisheries)

Population Targeted



8. Availability of data on the annual use of pesticides by specific produce category.

Yes No

Data source(s)

Type of data Address Web site Contact person e-mail address Legal framework (state, private, NGO, other-please specify)



9. Please suggest appropriate indicators that may be useful in measuring

environmental/occupational risks related to agriculture, animal farming and

fisheries. This question is to be used as a brainstorming activity that will be used

to compile the DIRERAF’s suggestions to the Commission. (add more lines if

needed)

1

2

3

4

5

6

7



Glossary (1) Agriculture: includes crop and livestock production, land and water, agricultural inputs and services. (Source: FAO) (2) Fisheries: an activity leading to harvesting of fish. It may involve capture of wild fish or raising of fish through aquaculture. (Source: FAO) (3) Workforce population: It comprises persons in employment and unemployed persons. (Source: Eurostat "Labour force survey: Methods and definitions, 1998", Office for Official Publications of the European Communities, Luxembourg, 1999, p.13) (4) Occupational Hygiene is the discipline of anticipating, recognising, evaluating and controlling health hazards in the working environment with the objective of protecting worker health and well-being and safeguarding the community at large. (Source: International Occupational Hygiene Association) (5) Occupational health and safety (OHS): a discipline with a broad scope involving many specialized fields. It aims at:

- the promotion and maintenance of the highest degree of physical, mental and social well-being of workers in all occupations

- the prevention among workers of adverse effects on health caused by their working conditions

- the protection of workers in their employment from risks resulting from factors adverse to health

- the placing and maintenance of workers in an occupational environment adapted to physical and mental needs

- the adaptation of work to humans. (Source: ILO, http://www.itcilo.it/english/actrav/telearn/osh/intro/introduc.htm) (6) Environmental Health: Environmental health comprises those aspects of human health and disease that are determined by factors in the environment. It also refers to the theory and practice of assessing and controlling factors in the environment that can potentially affect health. (Source: WHO) (7) Indicator: a variable, applicable to a health or health-related situation, with characteristics of quality, quantity and time used to measure, directly or indirectly, changes in a situation and to appreciate the progress made in addressing it. It also provides a basis for developing adequate plans for improvement. (Source: WHO 2002) (8) Morbidity: Illness or disability rate, usually expressed per 1000 population. (Source: WHO, European Parliament 1998) (9) Mortality: Death rate per defined population, usually expressed per 1,000. (Source: WHO, European Parliament 1998)



(10) Occupational diseases: disorders to which the work environment and performance of work contribute significantly as one of the several causative factors. (Source: Joint ILO/WHO Committee on Occupational Health 1989) (11) Health education: Education that increases the awareness and favorably influences the attitudes and knowledge relating to the improvement of health on a personal or community basis. (Source: WHO) (12) Health promotion: all measures that enable individuals, groups or organisations to have increased control over the determinants of health. The objective of all measures is the improvements of the health of individuals, groups, organisations and communities. (Source: WHO) (13) Environmental Risk: Likelihood, or probability, of injury, disease, or death resulting from exposure to a potential environmental hazard. (Source: European Environment Agency, http://www.eea.eu.int) (14) Occupational Risk: The occupational risk is the probability of a health disorder (loss) or death, connected with carrying out the duties according to labour contract (or in other cases, stated by law) (Source: Nikolay V. Matveev, MD, PhD, Nizhny Novgorod Research Institute for Hygiene and Occupational Pathology, Nizhny. Novgorod, Russia “Medical informatics in occupational and environmental health of Russia: Need for Reforms”)



ANNEX 3

List of national authorities & data sources



7.3. List of national authorities & data sources

This list includes al national authorities and data sources contacted by the project researchers. Austria

Austria

Sozialversicherungsantalt der Bauern (SVB) (Social Insurance for Farmers)

Ghepastrasse 1, 1031 Wien, Tel: + 43 (1) 79706, Fax: +43(1) 79706-1300 Contact: Dr. Herbert Rohn (chief-doctor Salzburg) [email protected] Mag. Rudolf Schulz [email protected] http://www.svb.at/esvapps/page/page.jsp?p_pageid=127&p_menuid=52344&p_id=5

Austria Soziale Unfallversicherung (AUVA)

Contact: Mag. Beate Mayer, [email protected] Department of statistics 0043-1-33-111/343 Fax: 00431-1-33-111/860 [email protected] http://www.auva.at/esvapps/page/page.jsp?p_pageid=120&p_menuid=15&p_id=1 contact: Karl Grillitsch, [email protected]

Austria STATISTIK AUSTRIA

Guglgasse 13, 1110 Wien, Austria Contact: Mag. Peter Bayer, [email protected] Mag. Monika Hackl, [email protected] http://www.statistik.at/neuerscheinungen/gesundheit2004.shtml

Austria Labour Inspectorate for Agriculture and Forestry (“Land – und Fortswirtschaftinspektion)

Contact: Dipl. Ing. Joesef Funovits email: [email protected] Monika Hackl, [email protected]

Belgium

Belgium FPS Economy - Directorate-general Statistics Belgium rue de Louvain,44

1000 Bruxelles http://statbel.fgov.be/info/contact_en.asp



contact: Viviane Massart collaborator administrator, [email protected]

Data on employed persons in agriculture

Belgium FPS Employment, Labour and Social Dialogue Communication Department, 1, Ernest Blerotstreet in 1070

Brussels, fax: 02 233 42 57, e-mail:. [email protected] http://meta.fgov.be/pa/ena_index.htm

Morbidity-mortality indicators

Belgium Fonds des maladies professionnelles - Institution publique de sécurité sociale

Avenue de l'Astronomie 1 B-1210 Bruxelles tél: +32 (0)2 226 62 11 fax: +32 (0)2 219 19 33 Contact: Hector De Waele, [email protected], FMP - Etudes Générales + 32 (0)2 226 64 55 http://www.fmp-fbz.fgov.be/fr/fmp_fr01.htm

Data on occupational diseases in agriculture and fisheries Bulgaria

Bulgaria National Statistical Institute 2, P. Volov Str.; 1038 Sofia, Bulgaria.

Contact: [email protected]; [email protected] Web: www.nsi.bg

Employment data. Commercial charged.

Bulgaria National Social Security Institute (NSSI) Central Department

Sofia 1303 Al. Stambolijsky Bul. 62-64 Tel: (+359 2) 926 10 10 fax: (+359 2) 926 16 66 Contact: [email protected]; Web: http://www.nsi.bg/Index_e.htm

Employment data. Commercial charged.

Bulgaria National Centre of Health Informatics 15 Akad. Ivan Geshov blvd; 1431 Sofia

Contact: [email protected] Web: www.nchi.government.bg

Data based on specific health indicators for persons employed in agriculture and fisheries.



Cyprus

Cyprus Statistical Service of Cyprus Central office: Mihalaki Karaoli , 1444 Leukosia, Cyprus

Website http://www.mof.gov.cy/cystat Email [email protected] Fax +357 22661313

Data are based on the Employment Survey which is a continuous quarterly survey carried out by telephone. Free of charge.

Cyprus Department of social insurance, Ministry of Labour and Social Insurance

7 Byron Avenue 1465 Nicosia Phone: 22401600 http://www.mlsi.gov.cy/mlsi/sid/sid.nsf/dmlcontactus_en?OpenForm

Foreign workers by economic activity. Annual Report of fatal injuries/Occupational accident. Free of charge.

Cyprus Department of Labour Inspection – Ministry of Labour and Social Insurance

Contact: Dr Athanasios Athanasiou, [email protected] Web: www.mlsi.gov.cy/dli

Accidents at work and occupational diseases data reported by employer and or by doctors in the case of occupational diseases. Free of charge.

Cyprus Agricultural Research Institute (ARI) http://www.ari.gov.cy/index.htm

Cyprus Department of Agriculture

http://www.moa.gov.cy/moa/da/da.nsf/dmlindex_gr/dmlindex_gr?OpenDocument

Cyprus Ministry of Agriculture, Natural Resources and Environment http://www.moa.gov.cy/moa/agriculture.nsf/index_gr/index_gr?OpenDocument Czech Republic

Czech Rep. Czech Statistical Office Na padesátém 81, 100 82 Prague 10

Contact: Ms. Ing. Dana Salusova



[email protected] , [email protected] Or ontact: Jana Bondyova, Director of Information Service Department. [email protected] Tel: +420 274 051 111 Contact: Ms. Vera Grandeova, [email protected] http://www.czso.cz/

http://www.czso.cz/csu/edicniplan.nsf/41fed56aacfa8529c1256f5100518b03/1e7fc5db4dd4ec94c1256f55004a4c3d/$FILE/1436.xls (for employment in Agriculture and Fisheries) http://www.czso.cz/csu/ (for pesticides data) Statistical Yearbook of Czech Republic 2004. Free of charge. Indicators of morbidity and mortality. Web: http://www.czso.cz/csu/edicniplan.nsf/t/F5004F1C0B/$File/33052166.xls

Czech Rep. State Labour Inspection Office Janska 785/2, 746 01 Opava

Contact: [email protected] Web : www.suip.cz

Annual Report of Czech Occupational Safety Office. Access restricted.

Czech Rep. Ministry of Labour and Social Affairs Na Poricnim pravu 1, 128 01 Prague 2

Contact: Ms. Ing. Olga Dusankova, [email protected] Web: http://www.mpsv.cz

Data concerning employment of immigrants. Access restricted.

Czech Rep. State Phytosanitary Administration Tesnov 17, 117 05 Prague 1

Contact: [email protected]

Data on the annual use of pesticides by specific produce category. Free of charge Web : http://www.srs.cz/srs/spo_vyk/spotreba04/ucinne_latky.htm

Czech Rep. National Institute of Public Health (NIPH) Srobarova 48, Prague 10

Contact: Mr. Jaroslav Samanek, M.D. [email protected] Web: http://www.szu.cz/english.htm

Database of Categorisation of works (KaPr). Quantitative data of exposures of employees to risk factors. Access restricted.

Czech Rep. National Register of Occupational Diseases Contact: Pavel Urban, M.D. [email protected]

Phone: 420 (0)2 67082658



Fax: 420 (0)2 67311236 Web: http://www.szu.cz/chpnp/index_en.php?page=contact

National Register of Occupational Diseases. Free of charge.

Czech Rep. Ministry of Agriculture Tesnov 17, 117 05 Praha 1

Contact: Stanislav Kozak Web: http://www.mze.cz/default.asp?lang=en

Regular Regional Reports. Access restricted.

Czech Rep. Ministry of Environment Vrsovicka 65

CZ 100 10 Prague 10 Contact: Ing. Karel Blaha, [email protected]

Web: http://www.env.cz/env.nsf

Periodical Reports and Reporting of Accidents. Access restricted. Denmark

Denmark Denmarks Statistik (Statistics Denmark) Sejrøgade 11, DK-2100 København Ø

Tel. +45 39173917, Fax +45 39173999 [email protected], www.dst.dk contact: Katja Stage, tef. +45 39 17 34 77 , [email protected] Lisbeth Laursen, Senior Adviser, Welfare , Phone: +45 39 17 31 03 , Email: [email protected] Barbara Spano, [email protected]

Denmark Danish Maritime Authority

• P O Box 2605, 38 C, Vermundsgade, DK – 2100 Copenhagen Ø

Tel: +45 39 17 44 00. email: [email protected] http://www.dma.dk/sw420.asp

Denmark Division for Investigation of Maritime Accidents http://www.sofartsstyrelsen.dk/sw416.asp

The Division for Investigation of Maritime Accidents has the following responsibilities: Investigation of accidents at sea, damages, accidents at work and pollution incidents



as well as diving accidents. The Division for Investigation of Maritime Accidents was founded in January 1990 and is an independent part of the Danish Maritime Authority. The Division is responsible for investigating maritime accidents to determine the cause(s) of those accidents. This allows for the Danish Maritime Authority and others to take measures to prevent similar future accidents. The Division for Investigation of Maritime Accidents has the following responsibilities: Investigation of accidents at sea, damages, accidents at work and pollution incidents as well as diving accidents. The Division for Investigation of Maritime Accidents was founded in January 1990 and is an independent part of the Danish Maritime Authority. The Division is responsible for investigating maritime accidents to determine the cause(s) of those accidents. This allows for the Danish Maritime Authority and others to take measures to prevent similar future accidents.

Denmark Research Unit of Maritime Medicine (RUMM) 81-83 Oestergade, DK-6700 Esbjerg

Tel.+45 79 18 35 61 - Fax +45 79 18 22 94 http://web.sdu.dk/fmm/UKSMIFORSIDE.HTM

The mission of the Research Unit to provide expertise for development and safeguard of the best possible working environment as well as maximum health and safety of seafarers, fishermen and employees in the offshore industry. The mission is accomplished through research, development, documentation and counseling, teaching and clinical studies.

Denmark Danish Maritime Occupational Health Søfartens Arbejdsmiljøråd, Amaliegade 33 B, 1256 København K.

tel: +45 3311 1833, Fax: +45 3311 1460, [email protected] http://www.maritimmedicin.dk/

Denmark Danish Environmental Protection Agency

http://www.mst.dk/homepage/

Denmark Danish Ministry of Environment http://www.mem.dk/ukindex.htm

Denmark Ministry of Food, Agriculture and Fisheries http://www.fvm.dk

Denmark Danish Agricultural Council http://www.landbrugsraadet.dk/view.asp?ID=624

Denmark Danish Institute for fisheries research http://www.dfu.min.dk/uk/default_uk.htm

Denmark Ministry of Employment http://www.bm.dk/english/default.asp



Denmark Statistics Denmark

http://www.dst.dk/HomeUK.aspx Statistics Year book 2005 http://www.dst.dk/HomeUK/Statistics/ofs/Publications/Yearbook/2005.aspx

Denmark Ministry of the Interior and Health http://www.im.dk/Index/mainstart.asp?o=2&n=3&s=5

Estonia

Estonia Statistical Office of Estonia Endla 15, 15174 Tallinn, Tel: +372 6259 300, Fax: +372 6259 370,

e-mail: [email protected] http://www.stat.ee/

Estonia Labour Inspectorate of Estonia

Gonsiori 29, 10147 Tallinn, Estonia, www.ti.ee. Contact: Mr Helle Nigul, coordinator of International Relations, [email protected]

Estonia Ministry of Social Affairs, Working Life Development Department

29 Gonsiori, 15027 Tallinn Contact: Mr Ivar Raik [email protected] http://www.hot.ee/tkburoo/home.htm

Estonia Ministry of Agriculture

http://www.agri.ee/eng/

Estonia Labour Market statistics http://www.tta.ee/eng/tta_inglise_tabelid.pdf Finland

Finland The Farmers´ Social Insurance Institution Revontulentie 6, 02100 Espoo, Finland.

Contact: Teuvo Siitonen. [email protected] Web: http://www.mela.fi

Data of insured farmers and fishermen. (Number of accidents and occupational diseases among farmers: the incidents are reported to the Institute by farmers who claim for compensation). Available annually. Free of charge

Finland Ministry of Agriculture and Forestry PO box 30

FI-00023 GOVERNMENT, Helsinki, Finland.



Fax: 09-16054202 Contact: Tarja Kortesmaa. [email protected] Web: www.mmm.fi/english

Data from Farm Register. Available annually. Free of charge.

Finland Statistics Finland 00022 Tilastokeskus, Helsinki, Finland

Contact: [email protected] Web: www.stat.fi

Data from labour force survey. Available annually. Free of charge. Contact: Marco Ylitalo. [email protected] Statistics Finland is not responsible for collecting data but receives an annual data set from both FAII and FSII. Free of charge. Some aggregated tables can be produced.

Finland Finnish Institute of Occupational Health PB 93, 70701 Kuopio, Finland

Contact: Paivi Rissanen. [email protected] Contact: Kirsti Taatola. [email protected] Web:http://www.ttl.fi

Number of work related diseases from Register of work related diseases. Group and cause specific morbidity of incidents reported by doctors to insurance institutions and industrial safety authorities. Free of charge

Finland Federation of Accident Insurance Institutions Contact: Juha Hemminki. [email protected]

Web:http://www.tvl.fi The Federation has a law-defined responsibility of collecting and maintaining databases on accidents at work and occupational diseases across all branches. All cases of accidents at work and occupational diseases compensated for agricultural wage-earners – basic information on cases and complete compensation records. Cases are reported to individual insurance institutions for compensations, they in turn send their data to FAII. Accessibility of data: Depending on type and amount of data required.

Finland The National Centre for Agricultural Health PB 93, 70701 Kuopio, Finland

Contact: Kirsti Taatola. [email protected] Web:http://www.ttl.fi/mytky, http://www.kttk.fi

Consumption of occupational health services of farmers (data collected by survey). Available for farmers who have joined the Farmers’ Occupational Health Service only. Free of charge.

Finland The Plant Production Inspection Centre (KTTK) PL 42,00501 Helsinki

Contact: Mervi Savela. [email protected] Web : http://www.kttk.fi

Total summary of the pesticides sales. Traders report the sales of pesticides annually. Free of charge.



France

France National Institute for Statistics and Economic Studies (INSEE) Insee Info Service, 195 rue de Bercy,

Tour Gamma A, 75582 Paris Cedex 12 http://www.insee.fr/en/home/home_page.asp

France Ministere de l’agriculture et de la Peche

http://www.agriculture.gouv.fr/spip/

France Institut Maritime de Prevention

3 Bd Cosmao-Dumanoir 56100 LORIENT Tel : 02.97.35.04.30 Fax : 02.97.35.04.31 [email protected] http://www.imp-lorient.com/pages/actu.php

France MSA (Mutualité Sociale Agricole)

Les Mercuriales, 40 rue Jean Jaurès, 93547 BAGNOLET CEDEX Tel: 01.41.63.77.77 Fax: 01.41.63.72.66 http://www.msa.fr/ Contact: Anne Roudot, [email protected]

MSA’s contribution concerning French data on occupational and environmental health risks in agriculture. The MSA, the regime of social protection of the agricultural and rural world in France manage the legal and complementary protection to all agricultural workers (agricultural owners and employees, also their families), amounting to more than 4 million people. The following are available:

- 2003 Statistics regarding risks of injuries of salaried workers in agriculture - Presentation regarding 2004 statistics on occupational risks for non salaried

workers in agriculture - “Phyt attitude” study: balance sheet 2002-2003 on toxicology vigilance - “Summer 2003” survey on medical supervision of professional risks of

salaried workers.

France Caisse Nationale d’Assurance Vieillesse ( Cnav ) 110 avenue de Flandre75951 PARIS cedex 19 ,

http://www.cnav.fr/infos/frameset.htm

France Ministere de l’ecologie http://www.ecologie.gouv.fr/sommaire.php3



Germany

Germany Federal Statistical Office – DESTATIS Branch office Bonn

PO Box 170 377 53029 Bonn, Germany Contact: Helga Zepuntke. [email protected] Web: http://www.destatis.de/themen/e/thm_land.htm

The Federal Statistical Office (Destatis) collects data about agriculture at 2 years interval within the agricultural survey. The data mentioned above are the latest from the agriculture survey of 2003. This survey gives data about agriculture related topics e.g. structure of production and capacity of agricultural holding as well as economical and social conditions of the manager of agriculture holdings. In additions, the outcomes of this agriculture survey inform about stocks of animals, use of farmland and grazing land, number of workforce population in agriculture. But, the areas of agriculture and animal farming are not separately acquired.

Germany Federal Association of Agricultural Health Insurance Fund Weißensteinstr. 70 - 72

34131 Kassel, Germany Contact: Uwe Kallweidt, [email protected] Web: http://www.lsv.de

Germany Federal association of agriculture having liability for agricultural

safety and insurance Weißensteinstraße 70-72

34131 Kassel, Germany Contact: Bernd Henning. [email protected]

Leading organization for occupational diseases and on a federal level.

Germany Social security board of the seaman Reimerstwiete 2

20457 Hamburg Web: http://www.see-bg.de/seeberufsgenossenschaft/ http://www.see-bg.de/seekasse/

Germany Federal Statistical Office (for data about foreign workers)

Branch office “Employment” III D Gustav-Stresemann-Ring 11 - 65189 Wiesbaden Contact: Inge Krzyzanowski Tel. +49 611 75-3187 Fax. +49 611 72-4000

Germany Federal Ministry of economic affairs and employment Villemombler Str. 76,

53123 Bonn Germany Fax: 01888-615-2745 Web: http://www.bmwi.de/English/Navigation/root.html

Germany Federal Department of consumer protection and safety of foods Branch office Braunschweig



Dr. Joermann Messeweg 11/12 38104 Braunschweig, Germany Email: [email protected]

Numbers of sales of plant protection products are available. Greece

Greece National Statistical Service of Greece 46, Piraeus Avenue, 8510, Piraeus

Contact: Kourakos Alexandros (library) [email protected] 210 4852313-15

Data form 2001 census. Employment by sex, and production area (wheat, vegetables, vineyards etc, fisheries). Free of charge.

Greece Agriculture Insurance Organization (OGA) 7, Averof str.,10433, Athens, Contact: Mrs. Tsiora, Web:

www.oga.gr Sources are the available processed data of OGA, the processed statistical data and information provided by OGA departments, as well as the annual reports of the organization. Number of immigrants insured by OGA by nationality. Free of charge.

Greece National Poisoning Centre Children’s Hospital “Aglaia Kiriakou”, Goudi, Athens

210 7793777, Contact: Dr. Fountas Web: http://www.cc.uoa.gr/health/poisonic/RIGHT.HTML

Number of incidents of poisonings reported to the centre, by type of pesticide. The incidents are reported to the centre by individuals, hospitals, doctors, pharmacists, health centres, usually by telephone. Free of charge.

Greece Labour Inspectorate 40, Piraeus Street – Athens

Web: http://www.ypakp.gr/ Annual data: number of accidents by occupational activity. Available until 2003. The employers have to report any accident that occurs to his/her employees. These reported accidents are collected, written down to the accident book of the organization and are statistically processed. The data refer to farmers, who are employees but does not include self-employed agriculture workers. Accesssibility is free of charge.

Greece Social Insurance Institute 48, Agisilaou Street -104 36 –Athens

Contact: [email protected] Web: http://www.ika.gr/gr/infopages/stats/stat_report.cfm

Annual data: Number of accidents by age, gender and part of body injured as well as fatal accidents. Data collected by reported by the employer accidents are collected and processed annually. It refers to farmers who are employees (thus insured in Social Insurance Institute instead of Agriculture Insurance Organization) but not to self-employed agriculture workers. The data are free of charge.



Greece Hellenic Crop Protection Association

53 Patission Str, Athens +30 2105229786, fax. +30 2105221542 Contact: [email protected] Web: [email protected]

Annual data: Sales of pesticides by category – collected from member industries Hungary

Hungary Hungarian Central Statistical Office (HCSO) 1024 Budapest Keleti K. Str. 5-7

Contact: Istuan Szabo Head of Dissemination Department. Web: http://portal.ksh.hu/

Statistical Yearbook of Hungary 2003. Annual Report of employed in Agriculture and Fisheries. Employed pensioners included.

Hungary Fodor Jozsef National Center for Public Health 1096 Budapest Nagyvarad Ter 2. Hungary

Contact: Galgoczy Gabor Director. [email protected] Web: www.fjokk.hu

National Institute of Occupational Health

Hungary Ministry of agriculture and regional development

http://www.gak.hu/fm/

Hungary Ministry of employment and labour

http://www.fmm.gov.hu/main.php?folderID=3318 Ireland

Ireland Central Statistics of Ireland Tel: 353-21-4535000

Fax: 353-21-4535555 LoCall: 1890 313 414 Contact: Amelia Murray Information Section [email protected] Tel:+353-21-4535028 Web. http://www.cso.ie/

Labour Force Survey. Census of Agriculture. Free of charge.

Ireland Health and Safety Authority 10 Hogan Place

Dublin 2 Tel: 1890 289 389



Fax: 01-614 7020 From overseas: 00353 1 6147000 Contact: Marie Dalton, Research and Statistics Officer, [email protected] Alice Healy, HSE - Employers Agency [email protected] Web: http://www.hsa.ie/publisher/index.jsp?pID=93&nID=95 http://www.hsa.ie/publisher/index.jsp?pID=93&nID=95

Fatalities by economic activity, injuries and illnesses statistics. Free of charge

Ireland BIM Bord Iascaigh Mhara BIM, P.O. Box 12, Crofton Road,

Dun Laoghaire, Co. Dublin People in Fisheries. Personal Flotation Devices. BIM Sea Survival CD Rom

Ireland The department of agriculture and food http://www.agriculture.gov.ie/ Italy

Italy ISTAT, Istituto Nazionale di Statistica www.istat.it

http://dawinci.istat.it/daWinci/jsp/MD/dawinciMD.jsp

Italy Ministry of Public Health

www.ministerosalute.it

Italy Ministry of Agriculture www.politicheagricole.it

Italy INAIL Istituto Nazionale per l’Assicurazione contro gli Infortuni sul Lavoro

www.inail.it

Italy Epicentro, Epidemiology National Institute Istituto Superiore di Sanità

www.iss.it

Italy ISPESL – National Institute of Occupational Safety and Prevention www.ispesl.it

Italy CARITAS Italiana www.caritasitaliana.it

(Statistical dossier on immigration - number of immigrant worker by activity sector)



Italy Agrofarma www.agrofarma.federchimica.it (annual use of pesticides)

Italy SIAN – Sistema Informativo agricolo nazionale – Ministry of Agriculture

http://www.sian.it Latvia

Latvia Ministry of Agriculture http://www.zm.gov.lv/?setl=2

Latvia Ministry of Environment of the Republic of Latvia Contact: [email protected]

Web http://www.vidm.gov.lv/Esakums.htm Annual use of pesticides by type

Latvia Agriculture and Rural Area of Latvia http://www.zm.gov.lv/doc_upl/Angliskais_24.01.2005.doc

Latvia Rural Support Service (RSS) of Latvia http://www.lad.gov.lv/index.php?l=2

Latvia Central statistics bureau of Latvia Lāčplēša iela 1, Rīga, LV-1301, Latria

Contact: [email protected] Web: http://www.csb.lv/avidus.cfm

Statistical Yearbook of Latvia (2003). Employment in the sector Agriculture and Fisheries.

Latvia State Labour Inspectorate of the Republic of Latvia 38, Kr.Valdemara street

Riga, LV – 1010, Latria Contact: Valentina Turovska. [email protected] Web: http://www.vdi.gov.lv

Data about health risks collected through inspection visits. All workers except self-employed. Free of charge. Approval required.

Latvia Riga Stradins University Contact: Maija Elite. [email protected]

Web: http://www.rsu.lv/en/index.html Specific health indicators for agricultural and fisheries workers Lithuania



Lithuania Lietuvov statistikos departamentas www.std.lt/

Lithuania National Statistics Gedimino ave 29, LT-01500 Vilnius, Lithuania Company code:

188600177 Phone +370 5 236 48 00 , fax +370 5 236 48 45 2006 05 31 [email protected] Ms Jurate Jokubauskaite , Chief Specialist, Statistical Information Dissemination Division E-mail [email protected]

Lithuania Institute of Hygiene

Contact: Dr Remigijus Jankauskas, [email protected]

Lithuania State Register of Occupational Diseases Etmonu str.3/6, Vilnius, Lithuania, LT-01128

Mrs Danute Krisiuleviciene, Head of the State Registre of Occupational Diseases, [email protected] Mr Audrius Sceponavicius Director of Public Health Department [email protected]

Occupational injuries and diseases. Free of charge

Lithuania Ministry of Agriculture Address: Gedimino av. 19 (Lelevelio 6), LT-01103 Vilnius

Phone: +370 5 2391001, Fax +370 5 2391212 E-Mail: [email protected] http://www.zum.lt/min/index.cfm?langparam=EN

Work force. Free of charge Luxembourg

Luxembourg

Central Service of Statistics and Economic Studies (STATEC)

13, rue Érasme, L-1468 Luxembourg, tel. 478-4219, fax 26 20 19 02, [email protected], http://www.statec.lu/ contact : Tilly Mathieu

Luxembourg Association of Assurance against the accidents [L'Association d'Assurance contre les Accidents (AAA)].

125, route d’Esch, L-2976 LUXEMBOURG Tél: +352-261915 – 1, Fax: +352-495335 [email protected] http://www.aaa.lu/index.html

Luxembourg Ministère de l'Agriculture, de la Viticulture et du Développement rural



1, rue de la Congrégation L-1352 Luxembourg Tél: +352 478 25 00 Fax: +352 46 40 27

Information in French

Luxembourg Agrarstatistik

http://www.ser.public.lu/statistik/index.html

Information in German

Luxembourg Ministère de la Santé

Allée Marconi - Villa Louvigny L - 2120 Luxembourg www.etat.lu/MS

Information in French Malta

Malta Agricultural Services & Rural Development – Ministry for Rural Affairs and the Environment

http://www.agric.gov.mt/

Malta Ministry of Health, the Elderly and Community Care - Department of Public Health

Manager Health Inspectors’ Office, 37/39, Rue D’Argens, Msida MSD 05 http://www.sahha.gov.mt/pages.aspx?page=30

Malta Ministry of Rural Affairs and the Environment

http://www.maltafisheries.gov.mt/index.htm

Malta Fisheries Conservation & Control Division (FCCD)

http://www.maltafisheries.gov.mt/aboutus.htm

Malta Fisheries Country Profiles (FCP) http://www.maltafisheries.gov.mt/mfcprofile.htm http://www.health.gov.mt/dph/public%20health%20services%20in%20malta_files/index.htm

Malta Malta national Statistics http://www.nso.gov.mt/



The Netherlands

Netherlands

Statistics Netherlands

Web: www.cbs.nl/statweb National register of occupational accidents, stratified by sector of industry etc. Free of charge. Branch of industry, agriculture, fisheries & forestry. On request, data can be made available stratified on age and gender. Need to submit an official request, costs a modest fee.

Netherlands

Agricultural Economics Research Institute (LEI)

Web: www.lei.nl One of the research institutes at the Wageningen University and Research Centre. Its head offices are situated in The Hague. Within the Netherlands, it is the leading institute for economic research in the field of agriculture, horticulture and fisheries, the management of rural areas, agribusiness and the production and consumption of foodstuffs. Yearly surveys. Availability: open access of publications and general databases.

Netherlands

STIGAS - Private organisation responsible for occupational health care in agriculture

(in Dutch only) Postbus 115, 3454 ZJ De Meern Tel 030-6693712 Web: www.agroarbo.nl

Sickness absence and permanent disability. Registries within companies. Free of charge.

Netherlands

CTB Board for the Authorization of Pesticides

Postbus 217, 6700 AE Wageningen. Phone: 0317 – 471810 Web: www.ctb-wageningen.nl

Type and quantity of pesticides. Overview of type of pesticides allowed. Availabilty: open access. Information on estimated quantity of use in different applications (restricted).

Netherlands Platform Arbeid

(in Dutch) Contact: Huub Oude Vrielink, [email protected] Tel: 0317-476460 Web: www.groenkennisnet.nl/platformarbeid

-overview of all relevant research reports on working conditions in agriculture -detailed research project with overview on working conditions in sectors of agriculture



-in the same report data on sickness absence from private insurance company with > 50% coverage in agriculture is reported

Netherlands European Agency for Safety and Health, Dutch agency at Work

Web: www.arbo.nl/statistics Report on state of OHS in the Netherlands, underlying raw data may available for additional analysis (permission needed)

Netherlands

NCVB: national office for occupational diseases

Web: www.occupationaldiseases.nl Overview on request, but severe underreporting, certainly in agriculture. In 2000, 99 records in agriculture with 50 about wart/swelling as part of occupational skin diseases. Very unreliable data source. This information is submitted to EUROSTAT. Norway

Norway SINTEF Fisheries and Aquaculture Contact: Halvard L. Aasjord, PhD - Senior Scientist,

[email protected] SINTEF Fisheries and Aquaculture Fisheries Technology Adress: 7465 Trondheim, Norway Dir. tlf. +47 917 23 365 Tlf. sentr. 40 00 53 50 Telefaks 93 27 07 01 http://www.sintef.no/content/page2____693.aspx

Accident data, analysis and statistics about human accidents in the Norwegian Fishingh fleet for different periods etc. Their activities are related to different projects, and also are funded by different institutions or research funds, but for the time being they have low activity in this field.

Norway The Norwegian Occupational Health and Safety Service -Landbrukets HMS-tjeneste

Contact: Anne Marie Heiberg, HES-manager Phone: +47 32 29 90 35, Mobile: + 47 909 63 813, [email protected] Postboks 120, 3602 Kongsberg - Tlf: 32 29 90 30 - [email protected] http://www.lhms.no/default.asp Registration on work environment of farmers: http://www.lhms.no/download.asp?dafid=664&daaid=57 Norwegian only

Occupational fatalities which are reported. The health information which is collected is only from the members, who amount to be about 10 % of all farmers in Norway.

Norway Norwegian Institute of work environment http://www.stami.no/Forsiden/



Norway Fishermen organization-Norgacs Fiscarlag

http://www.fiskarlaget.no/

Norway The Norwegian Labour Inspection Authority http://www.arbeidstilsynet.no

[email protected]

Occupational diseases and injuries

Norway Centre for Rural Research www.bygdeforskning.no

[email protected] Contact person: Reidar Almås

Social agricultural research

Norway Statistics Norway www.ssb.no

[email protected] Bibliotheca: [email protected] http://www.ssb.no/english/library/

Norway Norwegain Maritime Directorate

http://www.sjofartsdir.no/no/

Norway Norwegian Institute of Public Health PO Box 4404 Nydalen

N-0403 Oslo http://www.fhi.no/eway/default0.asp?e=0&pid=225 Contact: Hanna Hånes, [email protected]

Poland

Poland Central Statistic Office Warsaw, Poland

Web: http://www.stat.gov.pl/english/ Yearbook of labour statistics. Data concerning the annual use of pesticides. Free of charge.

Poland The Agricultural Social Insurance Found 190 Niepodległości St. Data on health and safety about rural population (insurance and absenteeism data). Free of charge. Approval required.

Poland National Health Register



Health Service data. Availability: approval required

Poland Nofer Institute of Occupational Medicine Contact: Joanna Jurewicz. [email protected];

Web: www.imp.lodz.pl

Workforce population. Free of charge. Portugal

Portugal Istituto Nacional de Estatistica INE http://www.ine.pt/index_eng.htm

Portugal Ministry of Agriculture, Rural Development and Fisheries (only in Portuguese)

http://www.min-agricultura.pt/servlet/page?_pageid=417,419&_dad=extcnt&_schema=PORTAL30

Portugal General Directorate of Fisheries (Direcção Geral das Pescas e Aquicultura (DGPA)

Portugal DGEEP –Direccao-Geral de Estudos, Estatistica e Planeamento (only in Portuguese)

contact: Lucilia Gomes, [email protected]

www.dgeep.mtss.gov.pt/estatistica/acidentes/index.php Slovakia

Slovakia Slovak Statistical Office

Slovakia Ministry of Agriculture

Slovakia Ministry of Environment

Slovakia National Inspectorate of Work

Slovakia National Institute of Public Health of Slovak Republic

Slovakia Institute of Health Information and Statistics of Slovak



Republic

Identification and characterization system for occupational diseases. Slovenia

Slovenia Statistical Office of the Republic of Slovenia Vožarski pot 12,

SI-1000 Ljubljana Tel:+386 1 241 51 04 Fax:+386 1 241 53 44 Email:[email protected]

Labour Force Survey. Free of charge. Spain

Spain National Institute of Statistics Paseo de la Castellana, 183 - 28071 Madrid

President: Carmen Alcaide Guindo Tel: +34 91 583 91 00. Fax: +34 91 583 9158

Work force data. Farm Census, Survey on Farm Exploitation Structure, Farm surface and production, Livestock Census and Production, Farm economical indicators, Farm machinery and fertilizers, Silviculture, hunting and fisheries Free of charge Contact: Rafaela Otero Tel: +34 91 583 48 94 Fax: +34 91 583 91 58 Web: http://www.ine.pt/index_eng.htm http://www.ine.es/inebase/cgi/um?M=%2Ft22%2Fe308_mnu&O=inebase&N=&L= Census of active population (quarterly 1976-2006). Survey on Labour Cost (quarterly 2000-2005). Survey on the Working time (annual 1996 y 2000).Registered unemployment (monthly 1996-2006). Registered Labour Movement (monthly 1996-2006). Labour conditions and Work Relations (monthly 1990-2006).Labour associations (annually 1993-2004). Survey of Wage Structure (annual 1995 and 2002). CDs are provided through fax contact: +34 91.583.91.58 Web:http://www.ine.es/inebase/menu3_soc.htm Demography and population. Population natural movement. Statistics on residential variations. Migrations survey http://www.ine.es/inebase/menu2_dem.htm#2 Survey on hospital morbidity (no distinguished in economic activity) http://www.ine.es/inebase/cgi/um?M=%2Ft15%2Fp414&O=inebase&N=&L Deaths by cause of death (nodistinguished in economic activity)



http://www.ine.es/inebase/cgi/um?M=%2Ft15%2Fp417&O=inebase&N=&L Survey on disabilities, deficiencies and state of health (no distinguished in economic activity ) http://www.ine.es/inebase/cgi/um?M=%2Ft15%2Fp418&O=inebase&N=&L National Health survey (no distinguished in economic activity ) http://www.ine.es/inebase/cgi/um?M=%2Ft15%2Fp419&O=inebase&N=&L= Diseases that are notified (no distinguished in economic activity ) http://www.ine.es/inebase/cgi/um?M=%2Ft15%2Fp063&O=inebase&N=&L

Spain National Institute of Health and Safety at Work - Ministry of Labour and Social Affairs

Work conditions and occupational injuries: Survey on labour situation (trimestral 1999-2005). Survey on quality of life and at work (yearly 2001-2004). Survey on Continuous Professional Training (yearly 1999). Occupational injuries and diseases statistics (1999-2005).Collective Contracts statistics (yearly 1999-2004).Strikes and Lock Outs statistics (yearly 2000-2004).Mediacion, Arbitration and Reconciliation statistics (yearly 2000-2004).Employment Regulation statistics (yearly 2000-2005). Permits of work to foreigners statistics (yearly 1999-2001). Free of charge. Contact: Angel Rubio Ruiz [email protected] Web: http://www.mtas.es/insht/index.htm (main page) http://www.mtas.es/insht/men_esta.htm (statistical data) Yearly Labour and Social Affaire statitics (Anuario de Estadísticas Laborales y de Asuntos Sociales). Labour market: Work licenses for foreigners, Foreign workers. Free of charge Web: http://www.mtas.es/estadisticas/ANUARIO2004/index.htm Occupational injuries and diseases statistics http://www.mtas.es/Estadisticas/EAT/welcome.htm Sweden

Sweden Statistics Sweden http://www.ssd.scb.se/databaser/makro/start.asp?lang=2

Sweden Ministry of Agriculture, Food and Consumer Affairs http://isi.phoneticom.com/cgi-bin/regeringenrsnav?url=www.regeringen.se/jordbruk

Sweden Fisheries Administration http://www.fiskeriverket.se/



Sweden The Swedish migration board - Statistics http://www.migrationsverket.se/english.jsp

Sweden Work Environment Authority (Central Supervision Department, Statistics Division)

Ekelundsvägen 16 SE-171 84 Solna [email protected]

http://www.av.se/inenglish/aboutus/contact/index.aspx United Kingdom

UK Department of Health http://www.dh.gov.uk/Home/fs/en

UK DEFRA – Department for Environment, Food and Rural Affairs Agricultural Statistics and Analysis Division

Contact: Keith Seabridge. [email protected]

Labour Force – Agriculture. Free of charge

UK DEFRA - Fisheries Statistics Unit Area 6E, 3-8 Whitehall Place,

London SW1A 2HH Tel: 020 7270 8096 - Fax: 020 7270 8072 Email: [email protected] Web:http://www.defra.gov.uk/ Contact: Emma James, [email protected] http://statistics.defra.gov.uk/esg/publications/fishstat/default.asp

Labour Force Fisheries. Free of charge

UK Agriculture Industry Advisory Committee (AIAC) Health and Safety Executive, Rose Court, 2 Southwark

Bridge, London SE1 9HS HSE Infoline – 0845 345 0055 Secretariat – John Holland, [email protected] & Claire Lyons,[email protected]

Draft Work Plan 2005/2006. Free of charge.

UK National Agricultural Centre Health & Safety Executive



Stoneleigh, Kenilworth Warwickshire CV8 2LZ Tel. 02476 696518; Fax. 02476 696542 Head of Agriculture Sector: Roger Nourish [email protected] Safety Section Manager Alan Plom [email protected]

UK Health and Safety Commission

National Statistics Fax +44 (0)1633 652747 Telephone +44 (0) 845 601 3034 Email:[email protected] Web: http://www.statistics.gov.uk/CCI/nugget.asp?ID=12

Number of fatal injuries to workers (Employees , Self-employed and Workers) in Agriculture and Fisheries 1992/93 to 2004/05p as reported to all enforcing authorities. Free of charge.

UK Health and Safety Executive

Contact: Victoria Canham from HSE Infoline Alastair Mitchell, Health, Education and Chemicals Section, Agriculture and Food Sector, HSE VPN: 536 3608, Tel: 01905 743600 Fax: 01905 723045

Data concerning occupational ill health and disease is available from self-reported work related (SWI) surveys. website: http://www.hse.gov.uk/statistics/causdis/index.htm



ANNEX 4

Presentation of programmes of special importance and a presentation of indicative

authorites by country



7.4. Presentation of programmes of special importance and a presentation of indicative authorites by country

CYPRUS Organicmed http://www.organicmed.org/index1.html OrganicMed is a 30month pilot project under the European Programme Leonardo da Vinci 2000-2006.The main objective of OrganicMed project is to provide support and guidance to organic crop farmers of the Mediterranean, through a work-linked vocational training programme focused directly on their needs and the Mediterranean particularities. CZECH REPUBLIC Current National programmes aiming at health promotion in the Czech Republic are focused mostly on industrial enterprises. In agriculture and fisheries fields until now health promotion related activities are absent according to available information. Actions aiming at ecological food production are much more popular. DENMARK Reducing pesticide consumption in agriculture is still sound business http://www.mst.dk/homepage/ The Pesticide Research Programme - Pesticides Action Plan II http://www.mst.dk/homepage/ The project analysed developments in prices of crops, and in prices of pesticides, including operational costs. The project also looked at possibilities and costs in connection with reduced use of pesticides. Selection of methods of analysis and models for the project were based on the farm economic analyses of the Bichel Committee, however, new technical knowledge and an improved data and model basis also played a part. Identification of target figures and index calculations for treatment with pesticides has created greater knowledge about current pesticide consumption at farm and crop levels. The Integrated Product Policy (IPP) http://www.mst.dk/homepage/ The Integrated Product Policy (IPP) aims at minimizing environmental impact of a product throughout its life cycle while taking into consideration the product’s ability to perform on a competitive market. This means supporting the development and marketing of products that are more environmentally sustainable than competing products of equal functionality, in order to reduce the environmental impact from production, use and disposal of the products. National Action Plan for Employment (NAP) http://www.bm.dk/english/publications/napuk2004/nap2004-final.pdf Danish Reform Programme 2005 http://www.bm.dk/english/publications/nrp2005/default.asp



http://www.bm.dk/english/publications/nrp2005/The%20Danish%20Reform%20Strategy.pdf The Danish government therefore wishes to reduce the burden on the environment and health and to control over-consumption of pesticides. In connection with the publication of the report from the Bichel Committee, the government decided on action to achieve these objectives on the three-pronged strategy recommended by the Bichel Committee. This strategy implies a general reduction of pesticide usage, reduction of the exposure of biotopes and increased restructuring for organic production. The Danish Reform Programme is this year for the first time prepared as a complete overview of the Danish reform strategies and considerations. It replaces and brings together a number of previous reports, including the national action plan for employment (NAP) and the so-called “Cardiff-report” concerning the product and service markets. The Reform Programme presents in chapter 5 the Danish employment policy. The position of Danish Agriculture on the future agricultural policy of the EU http://www.landbrugsraadet.dk/view.asp?ID=760 Centre of Maritime Health Service This centre is a knowledge and competence centre for the maritime health service and has a consultancy function answering questions from the users i.e. the total Danish merchant fleet and fishing fleet. The centre draws up and updates the technical regulation for the maritime health service; rules and guidances on medicaments and equipment in ships; rules on disease treatment and disease prophylaxis on board ships.; contract and instructions according to which Radio Medical doctors work; guidelines for the content and completion of education and training programmes. The centre supervises and educates treaters of disease on ships, doctors at Radio Medical and teachers teaching on behalf of the Danish Maritime Authority at AMU centres – centres offering adult vocational training. The centre evaluates the maritime health service and organizes development tasks to improve the maritime health service. More information through contacting: [email protected] Medical examinations of seafarers and fishermen General information about medical examinations of seafarers and fishermen. Mandatory medical examination of seafarers and fishermen. Vision and hearing test for yachtsmen. General information about medical examinations of seafarers and fishermen Everybody working on board a Danish merchant ship of 20 GT or above or on fishing vessels regardless of size must be medically examined periodically. Seafarers below 18 years of age are examined once a year while seafarers over 18 years of age must normally be examined every second year. In Denmark, the medical examination must be carried out by a marine doctor appointed by the Danish Maritime Authority. For further information, you can send an e-mail to the Office for Seafarers and Fishermen. E-mail: [email protected] Mandatory medical examination of seafarers and fishermen Nobody is allowed to work as a seafarer or professional fisherman without a valid health certificate. The health certificate is the official proof that someone is fit health wise, possibly with limitations. The health certificate is issued/endorsed after a prescribed medical examination, which is valid for not more than two years. In



Denmark only doctors who have special authorization from the Danish Maritime Authority are allowed to carry out medical examinations of seafarers and fishermen. The medical examination is free of charge for all seafarers and professional fishermen. Vision and hearing tests for yachtsmen Seafarers including masters who serve or are to serve on a Danish merchant ship must go through a separate vision and hearing test in connection with issuing maritime trading certificates. The minimum age for working on a Danish ship is 16 years of age and therefore, the age condition must also be met. The following categories must be medically examined: Persons signing on for the first time as well as persons who have not been employed on a ship within the last five years must only be medically examined if they have an agreement about serving on a Danish merchant ship; Professional fishermen who have a registration card for professional fishermen, category E or EB; Students who in connection with admission to a maritime vocational school, training school for commercial fishermen or a training ship will be asked to present a valid health certificate before admission can take place. The student must have turned 16 before the medical examination may be carried out. Persons who are to acquire or renew a maritime trading certificate valid for merchant or fishing vessels and whose health certificate has expired. If other than the above-mentioned persons are examined, the medical certificate for seafarers and fishermen must not be used. The person examined cannot get a health certificate issued or endorsed either. The Danish Maritime Authority does not pay for the above examination. FRANCE Institut Maritime de Prevention (IMP) The Institut Maritime de Prevention (IMP) is a non-profit-making organisation established around ENIM, the national welfare institution for seafarers. The Maritime Prevention Institute, kept acting for more than 12 years in favour of health protection and safety of seafarers, contributing in fleet modernisation, in personnel training, in development of new ways for organisation and equipment, close to the specific features of the daily activities in each specialised profession. IMP carries out programmes for ergonomic analysis of work situations, and to identify and rank the different potential factors (technical, organisationnal, environmental, behavioural) that could put seafarers' safety or health at risk. IMP also carries out clinical and statistical analysis of accidents at work, in order to categorize the most significant and identify their causes. IMP uses its experience to produce and disseminate a set of documents and training tools, including project reports, methods for analysis, technical reference documents and guides, video DVDs. This material is available to all, trainers and workers (http://www.imp-lorient.com/pages/actu.php). Mutualité Sociale Agricole (MSA The Mutualité Sociale Agricole (MSA) offers the only social welfare protection system that manages occupational medicine and the prevention of occupational risks internally. Since the establishment of ATEXA: Accident du Travail des Exploitants Agricole (Occupational Injuries of Non Salaried Workers) in 2002, it has provided coverage for workplace health and safety both for agricultural employees and for



farmers. The teams of ST (Health-Safety to Work) are staffed by occupational physicians and prevention advisers who work in collaboration to improve working conditions of agricultural workers and to decrease accidents or diseases related to work. In addition MSA carry out the medical surveillance of salaried agricultural workers concerning occupational risks and provide leaflets with data on exposure to chemical, biological and physical agents, and on accidents and occupational diseases in agriculture (http://www.msa.fr). FINLAND Farmers´ Occupational Health Services (Municipal Health Centres and the National Centre for Agricultural Health) http://www.aaem.pl/pdf/10045.pdf This study attempted to develop farmers' health and farmers occupational health services (FOHS) by examining the effects and feasibility of empowered farmers' teams on walk-through surveys of Finnish dairy farms. Work safety promotion (The Farmers Insurance Institution) Support person network for farmers (Agricultural organizations) GERMANY NATIONAL HEALTH PROGRAMME (Occupational and Environmental Risk Assessment Policies) Federal Ministry of economic affairs and employment Villemombler Str. 76, 53123 Bonn, Germany Fax: 01888-615-2745, Web: http://www.bmwi.de/English/Navigation/root.html The Agricultural Accident Insurance Society (AAIS) is responsible for dealing with health and safety at work in agriculture. To prevent accidents they issue accident prevention regulations (APR). These regulations are enforced by regular farm inspections carried out by the technical service of the AAI. If the regulations are not met a warning and a deadline for repair or replacement is issued. The AAIS has the ultimate option (which is seldom used) of making the farmer liable if accidents happen because of non-adherence to the APR. The AAIS insures all persons who carry out farm labour irrespective of their status and whether they are paid or not (i.e. farm manager, workers, family members, friends etc.) It does not, however, cover accidents caused by activities not related to farm work carried our by anybody including children and the public. To cover non farm-work related accidents by visitors and the public on the farm, farmers will often have private liability insurance. Certificates of competence are being introduced more and more into agriculture for various reasons. While mandatory certificates of competence for spraying pesticides were mainly introduced for environmental reasons and the certificate of competence for livestock transport for animal welfare reasons, training against health and safety hazards form an important part in the preceding training. Also most full time farmers



in Germany have gone through professional training where training against accident prevention is always involved. The principle task of the AAIS is to prevent work related accidents and illnesses and other health hazards. They are also responsible for helping those which have suffered an accident at or travelling to work and occupational illness to recover, and compensate in case of work related death. HUNGARY Agricultural and rural development - The Government National Agricultural Program http://www.gak.hu/fm/eng/agrirural_dev.htm National action plan for employment – 2004 http://www.fmm.gov.hu/upload/doc/200412/nap_final_en_041208.pdf ITALY “La prevenzione nell’impiego di antiparassitari in agricoltura” – Region of Lombardy “Piano di sviluppo rurale 2002-2006” http://www.politicheagricole.it/SVILUPPO/primopiano/Orientamenti%20CSR%203-02-05.pdf http://www.agricoltura.regione.lombardia.it/sito/tmpl_action.asp?SezioneId=2305010000&action=Sezione “Piano di Sicurezza nel comparto pesca: Profili di rischio nei comparti produttivi dell’artigianato, delle piccole e medie industrie e pubblici esercizi” (ISPESL, Fisheries) http://www.ispesl.it/profili_di_rischio/_pesca/index.htm "Safety Check sicurezza e salute a bordo delle imbarcazioni da pesca” (risk assessment related fisheries) (ISPESL, Fisheries) http://www.ispesl.it/profili_di_rischio/sitopesca/fisheries.htm “Linee guida per un sistema di gestione della salute e sicurezza sul lavoro(S.G.S.L.)” – ISPESL http://www.ispesl.it/linee_guida/sgsl.htm SFOP- Strumento Finanziario di Orientamento della Pesca 2000 – 2006 (Ministry of Agricolture related fisheries) http://www.politicheagricole.it/PESCA/SFOP/HOME.ASP LATVIA Strategy of the Ministry of Agriculture for 2003-2005 http://www.zm.gov.lv/index.php?sadala=38&id=897 Rural development programme 2003-2005 http://www.zm.gov.lv/index.php?sadala=38&id=897 MALTA Rural Development Plan for Malta 2004-2006



http://www.agric.gov.mt/ http://www.agric.gov.mt/Documents/RDD%20Documents/Measures/Amended%20Malta%20RDP.pdf Malta’s Rural Development Plan (RDP) is intended to co-ordinate in an integrated manner the natural, human and financial resources of the agricultural and rural communities of Malta with a view to ensuring sustainable growth of the rural economy and improvement of the rural way of life in a fair and balanced manner. As such the RDP is primarily a domestic tool aimed at maximising the national development effort. NORWAY The Norwegian Health and Safety Service (LHMS) started an Agricultural Health, Environment and Safety School. (Agricultural HES-school). They offer all farmers courses on different subjects on different levels. One course is about practical and basic knowledge and skills in HES at work, built on the governmental demand of systematic HES-work in any company. This course is a combination of e-learning and traditional groups with a teacher. This course is now sold to some agricultural schools. PORTUGAL AGRO - Programa Operacional Agricultura e Desenvolvimento Rural http://www.programa-agro.net/ SLOVAKIA Current National programes aiming at health promotion in the Slovak Republic are focused mostly on industrial enterprises. In agriculture and fisheries fields until now health promotion related activities are not available according to current information. Activities aiming at ecological food production are much more popular (the same holds for the the Czech Republic). 1. National Health Promotion Program 2. National Environment and Health Action Plan 3. Workplace Health Promotion (National Institute of Public Health Bratislava) SWEDEN The Environmental and Rural Development Plan for Sweden 2000-2006 http://isi.phoneticom.com/cgi-bin/regeringenrsnav?cmd10.x=0&cmd10.y=0&lang=en&speed=none&url=http%3A%2F%2Fwww%2Esweden%2Egov%2Ese%2Fsb%2Fd%2F574%2Fa%2F26093 UNITED KINGDOM



The Health Safety Executive (HSE) has developed an electronic self-assessment tool for farmers to use to help them assess risks to health and safety arising from their work activities. This covers a wide range of health and safety issues. It encourages farmers to look critically at their own business in order to benchmark standards against which they can measure their own performance. The self assessment tool also directs farmers to HSE information and advice, and is very much an educational and promotional tool. For farmers in Great Britain, it can be down loaded from the HSE website, or alternatively it is available on CD rom. Data concerning occupational ill health and disease is available from the self reported work related (SWI) surveys. website from: http://www.hse.gov.uk/statistics/causdis/index.htm Sustainable development action plan - DEFRA http://www.defra.gov.uk/environment/sustainable/action-plan.htm Strategy for Sustainable Farming and Food - Facing the Future http://www.defra.gov.uk/farm/sustain/newstrategy/index.htm National Pesticides Strategy http://www.pesticides.gov.uk/environment.asp?id=1539

DIRERAF: Working meeting on WP2 1


DIRERAF Working Meeting for Work Package 2 Date: Athens 5th May 2006 Participants UoA: Athena Linos, Christos Chatzis, Dimitris Kouimintzis Prolepsis:Ioanna Kotsioni, Eirini Papageorgiou ICPS: Marco Maroni, Teresa Mammone, Patrizia Vida. The working meeting focused on the remaining work for work package 2 and the way forward. The pending issues identified were the following: - Collect information for remaining countries - Quality assurance of the data collected The questions of the questionnaire that needed to be revisited were the following: 1) Number of employed in Agriculture and Fishery 2) List of national data sources 3) Employed <14 yrs 4) Employed > 68 yrs 5) Employed: immigrants 6) Indicators of Morbidity and Mortality 7) Occupational diseases 8) National Health Programmes 9) Pesticides The methodology decided upon was the following: - Web search: international databases and contacts; - Contact (by email) institutions (National Statistics Service, Labour Organisations, Ministries of Health) to request information on specific questions; - Request from personally established contacts to perform a quality check on completed questionnaires. For that reason Dimitris Kouimintzis from the NKUA team should attend the ICOH meeting where he will be able to meet experts and request their assistance with the quality assurance of completed questionnaires. The countries for which the questionnaires need to be completed/revisited were the following: Austria



Belgium Bulgaria Denmark Estonia France Hungary Latvia Lithuania Portugal Spain Sweden Additional issues addressed in the meeting were the following: - International sources to be searched. ILO was identified to have data about the number of employed and their demographic composition, for most of the countries, for Agriculture and Fisheries separately. - Regarding child labour it was stressed that it would be highly unlikely to be able to collect data for people below the legal age. However there are some studies that can offer an estimate but no official data. Italian ISPESL has carried out a study-estimate of children below the age of 16 working in the sector of Agriculture. - Regarding retired people there is a European Association that has data concerning the number of retired people. Data can also be collected from national insurance funds. - Regarding migrants who work illegally it was agreed that we can only have an estimation of the number employed. The same applies for seasonal workers. - 2 projects from the Region of Lombardy were also mentioned that could be presented in the 2nd partners’ meeting in Milan in June. The first is a survey in more than 100 randomly selected farms to assess how Occupational Health and Safety measures are implemented. The second is the development of Risk Profiles in Agriculture where the main determinants for pesticides risk in each crop have been identified. - In the meeting the agenda for the 2nd partners’ meeting to be organised in Milan in June was discussed and decided upon.

DIRERAF: Working meeting for WP3 1


DIRERAF Working Meeting for Work Package 3 Date: 26-27/9/2006 Place: Athens, Greece Participants: A. Linos, C. Chatzis, D. Kouimintzis, E. Chronopoulou (University of Athens) M. Kogevinas, M. Mirabelli (IMIM)

1. Presentation of the DIRERAF project – progress

D. Kouimintzis presented the progress of the project and the way forward.

2. Plans for panel of experts participants – methodology

Dr. Linos presented the concept of the panel of experts and a list with the invited experts.

The date of the event has been set for the 25-27 February, 2007.

3. Comparative analysis of WP2 results and identification of the minimal

common dataset applied at the EU level

E. Chronopoulou presented a list of all indicators and data sources on which information

had been collected; i.e. Injury mortality indicators, Farm Structure Survey, Labour Force

Survey.

Participants studied the results of the questionnaires’ data processing which was performed

by E. Papageorgiou. Results were examined one by one for all the categories information

has been collected on, through the questionnaires and additional sources of information (i.e.

international organisations websites).

Dr Linos noted that we should inquire which of the datasets are collected from census and

which from surveys. Experts should be contacted to comment on how to form a health

indicator based on data derived from different methods of collection.

Prof. Kogevinas noted that more information should be collected on fatal accidents (if it is

routinely collected, if occupation/immigration/legal status is included in the statistics). He



moreover suggested that a categorization of proposed indicators should be considered (i.e.

category of labour force; of exposure/risk; of health outcomes; of health services; of health

promotion) Criteria for the indicators should be based on their availability, feasibility and

usefulness.

4. Presentation of the WP4 progress and outcomes

D. Kouimintzis presented an initial list of the identified risks as they are documented in the

literature review and summarized the most significant ones by subgroup and production

type.

5. Creation of an all-inclusive list of potential indicators

Dr. Linos presented the methodology for the development of indicators and Dr. Kogevinas

presented a draft of a working paper titled “Health indicators for agriculture and fishery”.

6. Planning for subsequent work task allocation

Dr. Kogevinas noted that the IMIM group would prepare the final list of the available data

sources as the WP3 deliverable. The sources for the proposed indicators will have to be

checked for availability and the produced material will also be circulated for evaluation and

contributions by the other partners.

D. Kouimintzis will check all proposed indicators for availability and provide the IMIM

team with all needed data sources and documentation for this task.


Development Of Public Health Indicators For Reporting Environmental/Occupational Risks Related To Agriculture And Fishery - DIRERAF

Work Package IV “Identification and categorization of production specific risks”

DIRERAF: Work package 4 2


Contents

Executive Summary ...............................................................................................................3 1. Introduction .....................................................................................................................4 2. The DIRERAF methodology for identifying health risks in agriculture and fisheries ....7 3.Estimating the occupational and environmental risks in the sector of agriculture in Europe.....................................................................................................................................9

3.1 Acute Health Effects .....................................................................................................9 3.1.1 Injuries ...................................................................................................................9 3.1.2 Acute intoxication ................................................................................................ 15

3.2 Chronic Health Effects ................................................................................................ 20 3.2.1 Musculoskeletal disorders .................................................................................... 20 3.2.2 Hearing loss.......................................................................................................... 23 3.2.3 Cancer................................................................................................................... 24 3.2.4 Immunologic disorders ........................................................................................ 34 3.2.5 Non malignant respiratory disorders .................................................................. 37 3.2.6 Reproductive / developmental disorders ............................................................ 44 3.2.7 Neuropsychological disorders .............................................................................. 47 3.2.8 Skin disorders (non-malignant) .......................................................................... 52 3.2.9 Cardiovascular diseases....................................................................................... 54 3.2.10 Infectious diseases ............................................................................................ 55

4.Estimating the occupational and environmental risks in the sector of fisheries in Europe................................................................................................................................... 57 5. References........................................................................................................................ 60



Executive Summary This work package attempts to explore the risks for acute and chronic health effects for the working population in the sectors of agriculture and fishery. The risks are identified by reviewing the recent medical and agronomic literature and then categorized by production technique, type and employment group. The results reveal various health risks of varying magnitude for different working populations. The subsets of the resulting data are encoded, using different codes by region, production type, activity, production technique and the associated risk for each category. For the output, the identifications of similarities and differences of occupations in the agriculture and fishery sectors across Europe are also considered. The deliverable of this work package is a list of categories of identified risks (health and accidents).



1. Introduction Agriculture is considered one of the most dangerous industries, along with mining and construction in terms of reported occupational accidents and fatalities. ILO estimates that almost 170,000 agricultural workers are killed every year [1]. In Europe alone, over 350,000 accidents and nearly 700 deaths have been reported between 1994 and 1999 in agriculture, hunting and forestry among a workforce of over 5 million workers employed in these sectors [2]. Yet, even these figures severely underestimate the occurance of accidents and injuries in agriculture. Several factors contribute to these fact: the self employed status of the majority of agricultural workers; the insurance protection is often inadequate or a significant portion of the farming workforce is uninsured; the surge of foreign workers employed in agriculture on a temporal status; lack of health and safety law provisions and enforcement in many countries, even among the developed nations; lack of mechanisms for monitoring and registering occupational diseases or, even worse, lack of definition of occupational disease in agriculture . On top of that, statistics rarely report the burden of chronic diseases on agricultural workforce health. That gap of information is necessary to confirm or seriously question the general belief that the farming population is considered healthier than the urban residents. The agricultural workforce in the European Union has some properties that need to be taken into account when attempting to assess the health risks associated with occupational and environmental hazards. As it happens with the rest of the developed countries, the total agricultural workforce is shrinking year by year and the traditional European farming population is aging [3]; foreign workers are employed more and more on a temporary basis for the labor-intensive and dangerous job tasks; family farms are a considerable percentage of the total holdings, where children, older adults and spouses also contribute to the family income, by working in the fields / stables [4]; lastly, from south to north there are significant variations as far as the production types and methods are concerned, making it difficult to predict common risk estimates under the job title “farmer”. The literature agrees that the farming population is a healthy one. All-cause and all cancer mortality is low, compared to the general population, attributed to a more active lifestyle and a smaller prevalence of smoking and drinking habits [5], although increased risks for specific cancers have been documented and they are presented in the next pages. Standarized mortality ratios due to cardiovascular diseases are a lot smaller than 100, although some exceptions exist [6-9]. Acute health effects such as accidents and injuries are most frequent among the farming population. Accidents could be lethal or non-lethal, can occur due to a wide range of factors (machinery, animals, tools, chemicals) and result to serious injuries, disabilities or even death. Several aggravating factors have been implicated such as (health status, age, gender, education, working hours, work environment, etc) [10, 11]. The risk of injury has been estimated by many studies for different populations and in various units (see table 4). The latest comprehensive review on the matter estimates the risk of injury to be 10% with a range of 0.5 to 16.6 per 100 person-years [11]. Intoxication from pesticides, due to intended and unintended absorption is a worldwide problem, and a heavily underreported one [12]. Some acute intoxication symptoms might also be crop-specific, for instance green tobacco sickness due to dermal absorption of nicotine in tobacco farmers [13]. Although hearing loss can not be considered as an acute health effect, it is a disability that affects a great proportion of the farming population, especially those using machinery or those, who work in large livestock facilities [14]. On the other hand, hearing loss is a risk factor for accidents and is significantly associated with the years of working as a farmer [10, 15, 16]. Special groups that face increased risk of accidents are elderly people, children and immigrant workers, each one for different reasons [17-20]. Although overall cancer mortality is low, the farming population is considered a high risk group for selected cancer types [5, 21]. Cancers of the stomach, skin, brain, and lymphatic and hematopoietic systems have been shown to be associated with a farming occupation. Also, various associations have been found among various chemical substances or compound categories and various types of cancer. Non Hodgkin lymphoma has been found to be persistently associated with farming across several studies, suggested to be linked to pesticides exposure and animal contact [21]. More recent cohort studies indicate a possible risk for prostate cancer among farmers and pesticide applicators [5]. Crop- or method-specific increased risks for specific cancers have also been



reported, such as lung cancer in sugar cane farmers or ovarian cancer in female greenhouse workers [22-24]. Tumors diagnosed in children raised in farms have also been studied. A significant proportion of these studies indicate an potential association of parental occupation as a farmer with brain tumours [25, 26]. Organic and inorganic dust is a common hazard in many agricultural settings and exposed farmers run a high risk of non malignant respiratory disorders [27]. Grain and potato farming, as well as dairy and swine farming in animal confinement buildings have been associated with an increased risk of chronic obstructive pulmonary disorder, lung volumes restriction and/or decrease in lung function tests [28]. Livestock farming, and particularly exposure to stable disinfectants and automatic dry feeding, has been consistently associated with higher levels of bronchial hyperresponsiveness. Asthma is also a respiratory disorder common among specific groups of farmers (swine; grain; flowers; those chronically exposed to some insecticides), initiated or aggravated by health conditions, such as atopy, smoking and history of family asthma. Pesticide applicators are also in an increased risk of developing asthma-like symptoms, such as wheezing, and associations with specific chemical compounds have been suggested by recent studies [29, 30]. Swine and grain farming is particularly associated with the onset of organic dust toxicity syndrome (ODTS) [31, 32]. The prevalence of this disease among this producers’ category exceeds 30% [27]. Sensitization is also increased among various crop and livestock producers (grapes; flowers; greenhouse workers), reaching 21% among greenhouse workers up to over 60% among grape farmers [33]. Mites are the main cause of sensitization in many production settings, such as citrus orchards and greenhouses [34-37]. A series of studies also suggest a protective effect of growing on a farm on the sensitization of farmers [38, 39]. Farmers are a high risk group for hip osteoarthritis and that is easily explained by the demanding physical tasks needed to be performed (regular heavy lifting; prolonged standing; vibration; prolonged joint compression activities) [40]. Knee osteoarthritis and low back pain are also some musculoskeletal disorders with elevated risk estimates for farmers [41-43]. Low back pain associated with tractor driving is highly associated, and a dose-response relationship seems to exist between the degree of vibration and low back pain / sciatica symptoms [44]. Increased risk for other musculoskeletal symptoms have also been reported for specific tasks, like milking, poultry farming, crop harvesting and other tasks associated with repetitive movements [45-47]. The effects of agricultural exposures to the reproductive system of both genders and the development of the fetus have been studied by numerous studies, mainly with regards to pesticides exposure. Farmers exposed to pesticides or other chemicals, such as solvents, might have a significant risk for infertility [48]. More specifically, semen quality seems to be affected by pesticide exposure in men, while in women there seems to be an increased risk of spontaneous abortions [49]. An increased risk of birth defects among farmers’ offspring is the conclusion of the majority of the studies in this field. While birth weight does not seem to be a problem, an increased risk of stillbirths among pesticide exposed parents has also been documented in a few studies. Still, this area of adverse health effects needs more carefully studies to verify the above mentioned results. Dermatological disorders among farmers are highly common, but their prevalence seems to be underestimated due to its mild and known to the farmer quick and benign physical course [50]. Dermatitis caused by pesticides in various production categories has been consistently documented [51]. Other types of contant and irritant dermatitis have also been associated with farming profession and sensitization to antigens existent in the farmer’s workplace or in the environment seems to aggravate their onset [52]. Infectious dermatological disorders (mucoses; anthrax; Orf nodules, etc) have long been associated with farming occupation [53]. Risk factors identified are hot wet environment, animal contact and lack of personal protective equipment. The risk of transmitting animal-borne diseases to humans, especially those occupationally exposed to known hosts is particularly elevated and known. Among the well documented diseases are leptospirosis, athrax, brucellosis, tuberculosis and various virii [54-59]. Lately, the risk of viral diseases has emerged as a serious threat not only for those occupationally exposed, but also for the general population and is regarded as a serious threat to public health worldwide. Most importantly, avian flu on poultry has been monitored intensively and the breach of the interspecies barrier is most concerning [60]. Recent literature confirms that occupationally exposed populations (poultry workers, open field traditional farmers, veterinarians) run a moderate risk of being infected with the disease [61, 62].



Mental problems among farmers are reported to be frequent, although recent cohort studies calculate standardized mortality ratios due to mental disorders to be below or close to 100 [6, 63]. An important indication on the magnitude of the problem is the high incidence of suicide among farmers reported in many studies worldwide [64-66]. Various conditions (geographic isolation; beraucracy; collapse of financial support via subsidies;) have been identified as risk factors [67, 68]. In addition, farmers have one of the highest rates of depression and anxiety [69, 70]. The problem is even larger for women, where the literature reports high prevalence of stress, depression and fatigue symptoms [71, 72]. Seasonal workers also deal with high levels of stress and mental health symptoms, attributed mainly to restricted access to health providers and unpredictable work future [67, 73, 74]. Furthermore, other neuropsychological effects, such as fatigue or decline in the cognitive functions, attributed to pesticide exposure, have been documented both in crop and livestock farmers [75-77]. Pesticide exposure is also considered to be associated with the onset of Parkinson’s disease among those who are chronically exposed [78, 79]. Studying the health and safety of workers employed in the fishing industry is probably one of the most challenging tasks in public health. Defining, tracing and monitoring the population of fishermen is difficult [80]. In fact, only a few countries retain law provisions that enforce health and safety regulations taking into account the fishermen and even fewer countries maintain accurate reporting systems for registering mortality and morbidity. Furthermore, the European fishing sector involves a wide range of fish farming, catching and handling activities, which vary according to the geography, the traditions and the capital invested from country to country. The scientific literature, as far as estimating the health risks involved in fishery, is scarse. Most studies report on acute health effects incidence, such as annual mortality due to accidents. The most recent review on health risks associated with fishing occupation has estimated the annual mortality rate to be between 1.3 to 5.7 cases per 1,000 fishermen mainly due to fatal accidents, diseases of the cardiovascular system, homicide and suicides [80]. Less information is available related to morbidity risk estimates. A limited number of studies report of increased risk of chronic respiratory, musculoskeletal, cancer, skin and eye-related disorders [45, 81-86]. Perhaps the most interesting finding is the pesistently highly elevated prevalence of smoking individuals among all population groups examined [83]. This health determinant attribute has been suggested to be associated with a series of findings in various studies (increased risk for lip, nasal or upper ariway cancer; liver cancer; chronic obstructive bronchitis; ischemic heart disease) [83, 84, 87, 88]. Furthermore, numerous studies provide contradicting estimations on prevalence of alcohol consumption among fishermen compared to the general population [89, 90].



2. The DIRERAF methodology for identifying health risks in agriculture and fisheries This work package attempts to explore the risks for acute and chronic health effects for the working population in the sectors of agriculture and fishery. The risks are identified by reviewing the recent medical and agronomic literature and then categorized by production technique, type and employment group. The results reveal various health risks of varying magnitude for different working populations. The subsets of the resulting data are encoded, using different codes by region, production type, activity, production technique and the associated risk for each category. For the output, the identifications of similarities and differences of occupations in the agriculture and fishery sectors across Europe are also considered. The following online electronic databases were used for the research of the relevant published literature:

• AGRICOLA (AGRICultural OnLine Access) • EMBASE.com • PubMed • Science Citation Index (Web of Science) • TOXNET

The databases used were queried for mainly English literature until early 2006. In the first stage of research a group of keywords, for each system/organ or disease were selected and used, in conjunction with group of keywords depicting the production type and the type of risk. These keywords included: Specific risk type keywords “occupation” , “occupational”, “work” , “work-related” - “environmental” + “exposure” , “risk”, “hazard”

Specific exposure group keywords “agriculture” , “farm” , “farming” , “farmer” , “worker” , “grower” , “applicator”, “migrant” , “immigrant” , “seasonal” , “children” , “elderly”

Specific hazards keywords “pollen” , “tractor” , “animals” , “motor vehicle”, “machinery”, “tools”, “noise” , “radiation” , “ultraviolet” , “sunburn” , “thermal” , “cold” , “heat”, “vibration” “diesel”, “exhaust” , “benzene” , “dust” , “solvent” “pesticides”, “insecticides”, “herbicides” , “fungicides” , “biocides” , “fertilizers” Specific production type keywords eg “greenhouse” OR “vineyard”, “wine-maker”, “grape” OR “citrus”, “orange” , “lemon”, etc System/organ/disease specific keywords “trauma” , “injury” , “wound” , “emergency” , “accident” , “fall” , “disease”, “illness”, “disorder” “musculoskeletal” , “fracture”, “strain” , “sprain”, “spine” , “hip” “cancer” , “malignancy” , “malignant” “leukemia” , “lymphoma” , “myeloma” “sarcoma” , “mesothelioma” “skin”, “dermatitis” , “melanoma” “respiratory” , “asthma” , “COPD” , “lung” , “nasal” , “laryngeal” , “tuberculosis” “sensitization” , “allergy” “kidney” , “renal” , “bladder” “hepatic” , “liver” , “hepatitis” , “cirrhosis”



“developmental”, “reproductive” , “semen” , “abortion” , “fertility”, “birthweight” “mental”, “nervous” , “neuropsychiatric”, “stress” “hearing loss” “poisoning” , “intoxication”

After running the first queries, this initial list of keywords was refined to become more efficient. Databases were queried with the keywords above during the first five months of 2006 in various combinations. Apart from the databases, a second round of search was conducted based on the reference articles cited in the papers retrieved at the first search stage. In the event of finding papers of European origin in a different language, attempts to retrieve and translate the full text in English were made, so that they could be included as well. All abstracts were read and assessed and the full text was retrieved, where available. After completing the summarization of the literature by production type, a general search on agriculture and farming activities was conducted on the same basic research methods. Inclusion and exclusion criteria The following criteria were taken into consideration for including a study in the literature review:

• Published in a peer-reviewed journal after 1995 • The study focused on the farming popupation, that is, farm workers, their spouses, children or other

people in close proximity of farms / the fishing population • One or more health effects were examined in relation to farming or fishing occupation

Studies were excluded because:

• The outcome of the study did not include a risk estimate figure (proportionate mortality ratio [PMR], standarized mortality ratio [SMR], prevalence/incidence rate, Odds Ratio [OR], Relative Risk [RR], Attributable Risk [AR]) of adverse health effects

• They were case reports or studies of ecological design or studies reffering to exposure assessment only. Tabulation and summarization of the literature First step was spotting the most comprehensive and recent review published for each adverse health effect. An executive summary of all selected review studies was written. Then, the relevant literature was evaluated and summarized, and subsequently a table was created that included the findings of each study. For each study, the estimates of relative risk, odds ratio, standardized incidence ratio, standardized mortality ratio or proportionate mortality ratios, where available, were tabulated, along with information on the population size and their confidence intervals, country of origin, and citation of the authors.



3.Estimating the occupational and environmental risks in the sector of agriculture in Europe 3.1 Acute Health Effects

3.1.1 Injuries On the issue of injuries in agriculture, we were able to identify 94 studies. 69 studies were fit for inclusion, while 25 were excluded, either because they provided no risk estimates (23) or because of study design restrictions (2).

Table 1. Main results presented on the review by McCurdy on Agriculture-Injuries Agriculture - Injuries Injury risk Appr. 10% (range 0.5/100 person-years – 16.6/100 person-years) Host risk factors

• Age Increased risk for <19 and >65 ; Increased mortality among elderly • Gender Male (up to 4.5fold increased risk vs female farmers) / Working hours possible confounder (men work more

and in more hazardous jobs) • Race 2.5fold increased risk for African American • Education 2fold increased chance to report an injury in the group of educated farmers (part college) • History of previous injury 3-4fold increased risk; current impairment: 2.4fold increased risk • Hearing impairment Increased risk (OR 1.59, 95%CI 0.95-2.67) • Visual impairment Increased risk (OR 1.42, 95%CI 0.76-2.63) • Consumption of prescribed drugs 2.8fold increased risk for stomach remedies/laxatives / 4.2fold increased risk for

hear/circulatory medications • Alcohol consumption >2fold increased risk for consumption of >200md/d • Farm ownership 3fold increased risk for farm owners • Employment type 10% increased risk for hired workers • Safety courses No obvious effect • Working hours Increased risk per hour worked for part-time workers / Overall risk higher for full-time workers • Carelessness 50% increased risk for farmers with previous history of injury characterizing themselves careless • Hurry 50-100% increased risk for farmers admitting being in a hurry in their jobs

Work environment risk factors

• Farm size 25% increased risk for farmers working in <49acres • Farm residence 6.3fold increased risk for farm residents vs nonfarm residents • Gross annual farm income Increased risk with income; Income >40,000$: OR 1.52 • Non-resident farm workers present >2fold increased risk for machinery- and animal- related animals • Farm machinery Up to 3.5fold increased risk for reporting an injury for tractor use and >15 pieces of machinery

available • Production type - Increased risk for fruit farms; - 2fold increased risk for beef and dairy workers; lower risk when

cows are confined (OR 0.28, 95%CI 0.12-.064) and when cows are registered (OR 0.36; 95%CI 0.17-0.79) • Seasonal/temporal factors Increased risk when working in fall/spring and in weekends

Machinery related injuries 18-35% of all injuries

• Types involved Loading equipment; power-takeoff devices; augers; corn pickers; hay balers; tractors; motor vehicles

• Mechanisms Entanglement; being run over or pinned • Risk factors Working hours on farm (RR 4.88, 95%CI 1.97-12.08 for 60-79h/w vs <20h/w); auger use (RR 2.36,

95%CI 1.17-4.76), male (RR 3.79, 95%CI 1.81-7.92); separated/widowed/divorced (RR 3.82, 95%CI 1.50-9.74) vs <16 and never married; crop farming (RR 2.13; 95%CI 1.07-4.25)

• Hospitalization In 5% of cases • Persistent impairment In 25% of cases

Farm vehicle injuries



• Risk factors Age: older>younger (40.4 vs 32.1 years median age); male; conviction for speeding; conviction for driving when intoxicated

Tractor-related injuries most common farm machines; cause of 69% of farm-related deaths (1980-85)

• Risk factors Age 3fold increased risk for drivers <25 years of age; lack of rollover protection structures (ROPS); not wearing a belt

Falls risk 0.75 per 100 person years • Tasks fruit harvesting; other • Prevalence 25% of all injury cases • Risk factors gender >2fold increased risk for males; hours worked 2% increased risk per hour; non-residents 2fold

increased risk Animals – 12-33% of all injury cases

• Risk factors production type cattle and horse farms; milking dairy >30h/w: 20fold increased risk; trimming or treating hooves: 4fold increased risk

Children agricultural injuries

• Risk factors gender male>female; age increased risk around 4 and 14-16 years old; season increased risk in summer months

McCurdy, S.A. and D.J. Carroll, Agricultural injury. American Journal of Industrial Medicine, 2000. 38(4): p. 463-480. [11].

Table 2. Summary on injuries and farming from 1995-2006 included in the literature review by country, author, year, design, population studied and risk estimate. Country Author Ref Year study design population risk estimate

USA Voaklander [91] 2006 case-control farmers stopped taking narcotic pain killers (OR = 9.37 [95% CI:4.95, 17.72]) and non-steroidal anti-inflammatories (OR = 2.40 [95% CI:1.43, 4.03]) 30 days prior to the date of injury; taking sedatives up until the date of injury (OR = 3.01 [95 CI:1.39, 6.52]) incontinence/urinary tract disorders (OR = 2.95 [95% CI:1.30, 6.71]) prior injury (OR = 1.42 [95% CI:1.04, 1.95])

UK Solomon [92] 2006 cohort farmers <1 year: rate 25.5/1000 person-years Incidence Rate Ratio 3.7 (95%CI 2.8-5.0)

Canada Brison [18] 2006 National case series

Children 1-6 The annual rate of fatal agricultural injury was substantially higher than that of all-cause, unintentional fatal injury among Canadian children aged 1-6 years (14.9 v. 8.7 per 100,000 person-years, respectively). Differences in risk were attributed to elevated fatal agricultural injury rates among boys

USA Cole [93] 2006 Cross-sectional Tractor drivers

Overturn rate: 9.1% of farms

USA Stallones [94] 2005 Cross - sectional

farmers sleep patterns associated with increased risk of injury

USA Choi [95] 2005 cohort farmers Hearing loss in the better ear: OR 1.62 (95%CI 1.03-2.55) ; hearing asymmetry: OR 1.67 (95%CI 1.14-2.44); fair/poor self-reported hearing: OR 1.96 (95%CI 1.26-3.05)

USA Blair [96] 2005 cohort farmers Work >50h/w: OR=1.65; 95%CI 1.23-2.21) ; large number of livestock: OR=1.77, 95%CI 1.24-2.51; >high school education OR = 1.61, 95%CI 1.21-2.12; regular medication use OR = 1.44, 95%CI 1.04-1.96; wearing a hearing aid OR=2.36, 95%CI 1.07-5.20

USA Atrubin [97] 2005 case-control farmers loss of balance or co-ordination: >3fold increased risk for reporting an injury (95%CI 1.68-5.81)

South Africa

Marais [98] 2005 Cohort Fruit farmers Injury rate: 10.4 injuries per farm per year. Half of these injuries were work-related. Workers aged 20-39 were most at risk. Injuries sustained were related to routine activities of fruit farming, occurred mostly in the orchards and involved cuts, bruises and abrasions to the hands, including the fingers, and the eyes.

USA Hendricks [99] 2005 Telephone survey

<20 years 15.7 injuries /1,000 household youth (2001) 18.8 injuries / 1,000 household youth (1998)



USA Spengler [100] 2004 case-control farmers sleep medication use (OR = 2.11, 95%CI = 1.01-4.40); presence of three sleep apnea symptoms (OR = 2.48, 95%CI = 1.13-5.41)

USA Rautiainen [101] 2004 case-control farmers injury rate: 42/100 person-years; severe injury requiring professional medical care rate: 15/100 person-years risk factors: Raising livestock, poor general health, and exposures to dust and gas, noise, chemicals and pesticides, and lifting

USA McCurdy [102] 2004 Cross - sectional study

farmers one-year cumulative incidence for any FWR injury of 6.9% (95% CI 5.8%-8.2%), or a mean 8.2 FWR injuries per 100 farmers in the preceding year (95% CI 6.8-9.7) RF: white ethnicity (OR 3.19; 95% CI 1.38-7.36), increased annual hours worked on the farm, low levels of administrative work, and increased percentage of time working with livestock

Canada Dimich-Ward

[103] 2004 hospital reporting system

farmers Males vs females: 11-fold elevated agriculture related fatalities for males - males: >60 years of age injured more often - males vs females for non-machinery hospitalizations: 3/1 ratio Causes: roll-over (32%) males; run-over (45%) females

USA Lim [104] 2004 hospital reporting system

farmers 302 farm machinery injuries, 14 fatal - causes: all terrain vehicles (76), tractors (72), power take-offs (15) - Injury rate: 119.9 / 100,000 (1990) fell to 50.7 / 100,000 (1999)

UK Lindsay [105] 2004 Cross - sectional study

cattle farmers

prevalence of reported injuries: 24%; tagging-related injuries: 12%; risk factors: Tagging: working alone; in open field; with a vehicle nearby. Clipping: working alone; beef cattle; younger age. Both: injuries from livestock in other circumstances

Greece Petridou [106] 2004 Surveillance data

farmers estimated countrywide injury incidence for farming and equestrian sports combined was 21 per 100,000 person-years. Farming injuries were more serious, with 25% requiring hospitalization

USA Miller [107] 2004 Survey farmers Rate of injury requiring medical treatment: 19.9%

Poland Bartoszcze [108] 2004 Hospital based survey

Farmers Facial skeleton injuries inflicte during agricultural tasks: more frequent in males, between 18-40. Causes: machinery (male); animal-inflicted, falls (female)

USA/Canada

Kingman [109] 2003 database survey corn workers 197 cases of silo engulfment (156 fatal; 41 nonfatal) from 1980-2001; rates: 7fatal and 2nonfatal cases/year

Finland Virtanen [110] 2003 registry farmers Risk factors: Men>women, except with regard to injuries caused by animals; Dairy and hog farming; number of dairy cows

USA Sprince [10] 2003 nested case-control

farmers weekly farming work hours (> or = 50 hours/week) (OR = 1.65; 95% CI = 1.23-2.21); presence of large livestock (OR = 1.77; 95% CI = 1.24-2.51); education beyond high school (OR = 1.61; 95% CI = 1.21-2.12); regular medication use (OR = 1.44; 95% CI = 1.04-1.96); wearing a hearing aid (OR = 2.36; 95% CI = 1.07-5.20); younger age

USA Sprince [10] 2003 nested case -control

farmers risk factors for machinery-related injuries: hours per week spent on farmwork (OR = 2.02; 95% CI 1.38-2.94); fewer years of farming experience (OR = 1.79; 95% CI 1.14-2.79); wearing a hearing aid (OR = 4.37; 95% CI 1.55-12.25); drinking problems (OR = 2.49; 95% CI 1.00-6.19)

Finland Rissanen [111] 2003 national case series

farmers 120/217 tractor related injuries (1988-2000); risk groups: elderly & children

USA Sprince [16] 2003 nested case - control

farmers Injuries due to falls: age (40-64): (OR = 2.21; 95% CI = 1.20-4.07); doctor-diagnosed arthritis/rheumatism (OR = 2.05; 95% CI = 1.11-3.79), difficulty hearing normal conversation (even with a hearing aid, in the case of those who used one) (OR = 1.82; 95% CI = 1.07-3.08); taking medications regularly (OR = 1.80; 95% CI = 1.02-3.18)

Australia Franklin [112] 2003 hospital reporting system

farmers Annual injury rate: 30 / 100 farms (males: 72.2%)

USA Sprince [16] 2003 nested case-control

livestock farmers

Animal-related injuries: hearing aid (odds ratio [OR] = 5.4 [95% CI, 1.6 to 18.0]), doctor-diagnosed arthritis or rheumatism (OR = 3.0 [95% CI, 1.7 to 5.2]); education beyond high school (OR = 1.8 [95% CI, 1.1 to 2.8]); younger age

USA Netto [113] 2003 cohort poultry workers (female)

accidents (PMR=1.5, 95%CI 1.0-2.2 for all females)

USA Perry [114] 2003 National data analysis

Children Annual fatality/non-fatality figure: 103/year (-32,000/y)

USA McCurdy [19] 2003 Cohort Migrant farm workers

Injury incidence rate: 9.3 /100 FTE



Denmark Hartman [115] 2003 Registry based farmers 10.2 sick leave claims per year per 100 farmers causes: MSD (61%)

USA Browning [116] 2003 Cross-sectional Children Farm-related injury rate: 2.8 per 100 children highest injury rate: 9.2 per 100 children among boys 16-18

UK Solomon [117] 2002 national case series

farmers crude annual death rate:8.8/100 000 (5fold elevated than any other industry combined); non-fatal: 633/100 000 per year highest rates: ages 16-19: 10.1/100,000; 55-64: 11.2/100,000; 65+: 16.7/100,000; highest risk in summer months

USA Carruth [118] 2002 cross sectional study

female farmers

1-year cumulative incidence rate:4.8% (95%CI = 3.7 - 6.3), primarily livestock farming -- (81.2%) occurred on beef cattle, dairy, horse, or hog farms.Injuries occurred most often in women younger than 50 years

USA Lee [119] 2002 mortality crop farmers agricultural machines PMR=891 (WM), PMR=8,524 (WF), PMR=1,244 (BM); drowning and submersion PMR=150 (WM), PMR=187 (BM); electric current PMR=219 (WM); fire or flames PMR=155 (WM), PMR=196 (BM); Motor vehicle PMR=129 (WM), PMR=196 (WF), PMR=163 (BM); natural and environmental factors PMR=180 (WM), PMR=362 (WF), PMR=170 (BM), PMR=248 (BF)

USA Lee [119] 2002 mortality livestock farmers

PMR=141 (WM), PMR=184 (WF) -injury-Motor vehicle ; PMR=140 (WM) - injury-fire or flames; PMR=148 (WM) - injury-drowning and submersion; PMR=231 (WM), PMR=583 (WF) - injury-natural and environmental factors; PMR=996 (WM), PMR=11,000 (WF) - injury-agricultural machines; PMR=265 (WM), PMR=4,380 (BM) - injury-electric current

USA Munshi [120] 2002 Cohort Farmers Annual injury rate: 25.9/100 FTE USA Sprince [121] 2002 Case-control farmers Risk factors for machinery-related injury: hours per week spent on farmwork

(OR = 2.02; 95% CI 1.38-2.94), fewer years of farming experience (OR = 1.79; 95% CI 1.14-2.79), wearing a hearing aid (OR = 4.37; 95% CI 1.55-12.25), a high CAGE score suggesting problem drinking (OR = 2.49; 95% CI 1.00-6.19)

USA Pryor [122] 2002 Cohort Farm children

32/177 children sustained injuries within one year

USA McCurdy [123] 2002 Cohort Migrant farmworker children

3.8 injuries/100 person-years

USA Hard [124] 2002 National data analysis

Farmers Fatality rate: 25.8/100,000 non-fatality farm injury rate: 7.5 per 100 workers

Australia Mather [125] 2001 Survey Farmers Injury incidence in two years: 38% injury burden among sheep breeders: 30.5%

Australia O’Connor [126] 2001 registry farmers risk of spinal cord injury: 17/million causes: falls 50%

USA Hwang [127] 2001 cohort farmers Male: OR 2.84 (95%CI 1.84-4.40) - Aged <25: OR 4.36 (95%CI 1.43-14.15); 25-34: OR 4.08 (95%CI 1.49-12.08); 35-44 OR 5.25 (95%CI 1.98-15.10) - Hearing loss: OR 1.67 (95%CI 1.13-2.45) - Joint trouble: OR 2.90 (95%CI 1.73-4.90) - Avg. hours work/d 4-8: OR 4.28 (95%CI 1.76-10.7) ; >8h/d: OR 9.54 (95%CI 4.48-21.2) - Being owner/operator: OR 2.00 (95%CI 1.41-2.83) - Doing tractor work: OR 2.18 (95%CI 1.54-3.08) - Doing milking: OR 2.09 (95%CI 1.48-2.95) - Dairy farming: OR 1.82 (95%CI 1.03-3.25)

USA Park [128] 2001 cohort farmers One-year cumulative incidence of farmwork-related injuries: 10.5%. Depressive symptoms (OR 3.22; 95%CI 1.04-9.99); and the number of hours working with animals (OR 2.14; 95%CI 1.04-4.44) were associated with the incidence of farmwork-related injuries

USA Gerberich [129] 2001 cohort farmers Farming-related injury rate: 1,683/100,000 persons Causes: animals (40%) Operate a tractor: RR 1.42 (95%CI 1.04-1.94) Work with dairy cattle: RR 1.60 (95%CI 1.19-2.14) Male: RR 1.63 (95%CI 1.15-2.30)

USA Carruth [130] 2001 Cross - sectional

female farmers

cumulative one-year incidence of farm injuries for women in this area was 5% (95%CI 3.7-6.3) -- spring/summer, working on large-animal farms, more time spent in farm work, persistent back pain or weakness during the previous 12 months, driving a tractor, and hauling farm goods to market

New Zealand

Horburgh [131] 2001 Cohort Male farmers Fatal injuries annual rate: 21.2/100,000



Australia Franklin [132] 2001 Retrospective data analysis

Farmers Fatality rate from unintentional work-related fatalities: 20.6/100,000 agricultural workers

USA McGwin [133] 2000 Cohort farmersr Injury rates were 2.9 times (95%CI 2.0-4.3) higher for African-American farm workers compared with Caucasian and African-American owners. Part-time farming (RR 2.0, 95%CI: 1.3-2.5), prior agricultural injury (RR 1.5, 95% CI 1.0-2.1), and farm machinery in fair/poor condition (RR = 1.8, 95% CI 1.2-2.7) were also independently associated with injury rates.

Denmark Rasmussen [134] 2000 cohort farmers injury rate associated with production; pigs>crop>mixed>dairy; 2fold increased risk for <50 years of age; 10.5% of injuries were to children aged < 15

USA Reed [135] 2000 Review Farm children

the literature continues to report primarily descriptive studies that rely on small samples focusing on the nature of the injury event and immediate consequences. Analysis of larger databases, such as worker compensation claims, trauma registries, and agricultural injury surveillance, still lacks valid denominators; thus, incidence rates cannot be calculated

USA McGwin [136] 2000 cohort farmers Risk of injury for African American farm workers: RR 2.9 (95%CI 2.0-4.3). Part-time farming RR 2.0 (95%CI 1.3-2.5), prior agricultural injury RR 1.5 (95%CI 1.0-2.1) , farm machinery in fair/poor condition RR 1.8 (95%CI 1.2-2.7) Livestock as primary commodity: RR 3.7 (95%CI 1.8-7.4)

China Xiang [137] 2000 Cross-sectional farmers 33% of the farmers reported at least 1 work-related injury in the 24 months before the survey. Major external causes of the injuries were hand tools (50%), falls (26%), and heavy falling objects (10%). The statistically significant risk factors for injury were low family income, 1 to 6 school years of education, self-reported pesticide exposure, tension in relationships with neighbors, and stress in life

Canada Bancej [138] 2000 Survey Farm children 1-4

Injury rate: 22.6/1,000 person years

USA DeMuri [20] 2000 Review Children Annual fatality rate: >100 deaths/y (USA) risk factors: Poor supervision, unreasonable expectations, financial difficulties and lack of safety devices

Canada Pickett [139] 1999 Surveillance data

Farmers Overall annual injury mortality rate: 11.6/100,000 farm population High rates were observed among men of all ages and among elderly people. Among the cases that listed the person involved, farm owner-operators accounted for 60.2% of the people killed

USA Gerberich [140] 1998 cohort farmers Risk factors: hours worked per week on the farm (40-59, 60-79, 80+); operation of an auger; field crops; male gender Hospitalization rate: 5%

USA Lewis [141] 1998 Case-control Farm operators

Injury reporting rate: 10.3% Risk factors: younger age (OR 3.1, CI 1.1-9.3), having an impairment or health problem that limits work (OR 2.4, CI 1.5-3.8), hand or arm exposure to acids or alkalis (OR 2.6, CI 1.1-5.9)

USA Crawford [142] 1998 Case-control Principal operators

overall injury rate: 5/100 person-years

USA Browning [143] 1998 Cohort Farmers >55 years old

Crudy injury rate: 9.03 per 100 farmers Risk factors: farmers working on farms with beef cattle (alone) (OR 1.90; 95%CI 1.02-3.55) or farms with beef cattle and tobacco (OR 2.15; 95%CI 1.00-4.59); prior injury

USA Stueland [144] 1997 Case-control Female farm workers

number of hours worked and the presence of bulls on the farm. Most (55%) of the women were injured while in a barn. A cow was the primary agent of injury in 17 (42.5%) of the cases

USA Rivara [145] 1997 National data analysis

Children Non-fatal injury rate: 1,717 per 100,000 resident farm children

USA Richardson [146] 1997 cohort Farmers Crude mortality rate Farm workers: 38 per 100,000 worker-years Farm labourers: 16 per 100,000 worker-years

USA Boyle [147] 1997 Case-control Dairy farmers

Risk factors for injury milking (correlated with hours spent at milking, 1-10h: rate ratio 2.3; 31-63: rate ratio 20.6); trimming/treating hooves (rate ratio = 4.2)

USA Stueland [148] 1996 Case-control Age <18 Overall injury rate: 18.27 per 1000 farm resident persons male injury rate: 23.47 per 1000 farm resident persons female injury rate: 12.66 per 1000 farm resident persons

USA Nordstrom [149] 1996 Case-control farmers Annual risk of farm fall injury: 7.5 (95% CI: 5.7, 10.0) per 1,000 person-years (crude rate: higher in men), (rate based on hours of farmwork: higher in women). The risk of fall injury increased 2% (95% CI: 1%, 4%) per hour worked. Residents of farms with some farm workers not living on the farm had a fall injury rate 2.5 (95% CI: 1.0, 6.2) times greater than residents of other farms. Residents of farms with registered cows had one-third (95% CI:



0.14, 0.93) the risk of residents of other farms.

Australia Low [150] 1996 Telephone survery

farmers At least one injury per year: 1/5 farms At least one serious injury per year: 1/12 farms Risk factors: age (and/or experience), previous injury status, body mass index, hours of sleep, a variable measuring daytime drowsiness and a variable measuring perceived stress.

Canada Pickett [151] 1995 survey Farmers Crude injury rate 5.8 per 100 person-years between 31-40: crude injury rate 12.2 per 100 person-years male 31-40 age group (12.2 per 100 persons per year). Spouses of farm owner-operators (1.7 per 100 persons per year); their children (2.0 per 100 persons per year)

USA Layde [152] 1995 Case-control farmers Hours worked per week (2% increased risk/nonresident workers on farm (OR 2.32; 95%CI 1.07 - 5.06), cows fed in barn in summer (OR 0.28, 95%CI 0.12 - 0.64), registered cows on farm (OR 0.36, 95%CI 0.17 - 0.79)

USA Zwerling [153] 1995 review farmers White male farmers: increased proportional mortality ratio for all injuries: 1.26 (95%CI 1.21-1.31). PMR for at-work injuries: 3.77 (95% CI 3.35-4.24); non occupational injuries suicides: PMR 1.20 (95% CI 1.09-1.32); motor vehicle crashes: 1.23 (95% CI 1.12-1.34); electrocutions: 1.78 (95% CI 1.08-2.95).



3.1.2 Acute intoxication On the issue of acute intoxication, we were able to identify 54 studies. 27 studies were fit for inclusion, while 27 were excluded, either because they provided no risk estimates (18) or because of study design restrictions (9). Table 3. Summary on acute intoxication and farming from 1995-2006 included in the literature review by country, author, year, design, population studied, risk estimate and topic.

Country Author Ref Year Study design Population Findings

Jordan Abdullat [154] 2006 Cohort farmers

Mean fatality rate: 0.68 cases / 100,000; male to female ratio: 1.03; 64.3% of all pesticide fatalities due to suicide (male:female ratio 1.37:1).

Bolivia Jors [155] 2006 Cross-sectional Farmers

Risk factors for experiencing symptoms of acute pesticide poisoning: spraying from 1-3 times with Ops past month: OR 1.91 (95%CI 0.58-6.30; >3 times past month: 5.97 (95%CI 1.63-21.96); taking less precautions increases the risk of the event (OR 5.15 to 13.88)

Brazil Recena [156] 2006 Cross-sectional farmers

59.6% reported intoxication symptoms; hand washing after pesticide application correlated with reporting symptoms (p=0.014)

USA Bell [157] 2006 Cohort Farmers

Risk factors: ever having another incident OR 3.8, 95%CI 2.7-5.3; days of exposure OR 1.4, 95%CI 0.9-2.2 for 6-10 days – OR 2.2, 95%CI 1.4-3.6 for >20 applications/y (compared to <5 applications/y) Incidence of high pesticide exposure event -> Iowa: 8.8/1,000 applicators; North Carolina: 2.0/1,000 applicators; spouses: 2.0/1,000 Medical care sought: 13% of applicators; 22% of spouses

USA Mehler [158] 2006 Surveillance data

General population

0.024 fatalities and 1.38 hospitalizations (95% confidence interval [CI] = 1.01-1.74) per 100,000 California population person-years

Philippines Lu [159] 2005 cross-sectional flower workers

prevalence of pesticide-related illnesses: 32% ; most often: weakness; fatigue; eye itching; blurry eyes - risk factors: farm use of pesticides; exposure; field re-entry after spraying

acute pesticide poisoning

Sri Lanka Van der Hoek [160] 2005

Hospital based study

Patients admitted for acute pesticide poisoning

Most cases occurred among young adults and the large majority (84%) was because of intentional self-poisoning. Case fatality was 18% with extremely high case fatality for poisoning with the insecticide endosulfan and the herbicide paraquat. Cases were generally younger than controls, of lower educational status and were more often unemployed. No agricultural risk factors were found but a family history of pesticide poisoning and having ended an emotional relationship in the past year was clearly associated with intentional self-poisoning.

India Mancini [161] 2005 Cross-sectional

Female farmers mixing pesticides

10% of the pesticide application sessions were associated with three or more neurotoxic/systemic signs and symptoms typical of poisoning by organophosphates

Various Litchfield [162] 2005 Review farmers

Occupational acute pesticide poisonings in less developed countries are a small proportion of overall reported poisoning and are associated with the more minor effects of pesticides. They are a small proportion (<1-4%) of the several million cases of occupational injuries and ill health in agricultural workers worldwide

USA Alarcon [163] 2005 Surveillance data

School children exposed to pesticides

7.4 cases of acute pesticide-related illnesses / 1,000,000 children ; 27.3 cases / 1,000,000 school employee FTE. Severity: high (0.1%); moderate (11%); low (89%). Cause: pesticides used at schools (69%), drift exposure (31%)



India Parikh [164] 2005 case-control tobacco farmers

GTS prevalence = 47% (male 42.66% - female 55.7%). The toxicity was mild and acute, not requiring hospitalization. On chronic effects: no statistically significant health results (BP, ECG, eye exams)

green tobacco sickness

USA Calvert [165] 2004 Surveillance data

General population

Overall acute occupational pesticide-related illness incidence: 1.17 per 100,000 FTE; in agriculture: 18.2/100,000 FTE – other industries: 0.53/100,000FTE Severity: low (69.7%); moderate (29.6%); high (0.4%) Main cause: insecticides(49%)

USA Garry [166] 2004 Review children

Elevated risk of pesticide poisoning among: children, non-whites, males and residents of southern US states; last 20 years: decrease in mortality in the US; mean age: 5 years (50% under 3)

Brazil Faria [167] 2004 Cross-sectional farmers

annual incidence of pesticide poisoning: 2.2 episodes per 100 exposed. Risk factors: pplying pesticide, reentering crop fields after spraying, and working with pesticides on more than one

Brazil Delgado [168] 2004 Cross-sectional farmers

62% of workers reported at least one illness associated with mixing or spraying pesticides

California (USA) Reeves [169] 2003

State dataseries farmers

500 pesticide poisonings/y (1997-2000) – most often: fumigation, grapes, oranges, cotton – cause: 51% pesticide drift; 25% pesticide residues – poisonings due to law violations: 41%

USA Trape-Cardoso [170] 2003 case-control

tobacco immigrant workers

15% of cases could be diagnosed as GTS. Using a stricter GTS case definition, the frequency fell to 4%. Non-smokers significantly more likely to report GTS-like symptoms.


UK Coggon [171] 2002 Review Farmers exposed to Ops

Unintentional fatal poisoning by pesticides is extremely rare in Britain. Documented reports of non-fatal acute poisoning are also uncommon, but there may be substantial under-ascertainment of minor incidents.

USA Arcury [172] 2002 Cross-sectional

Migrant farmworkers

Risk factors for GTS: not wearing rain suits (OR 5.60, 95%CI 1.56-20.15; contract job (OR 4.20, 95%CI 1.22-14.51)

USA Quandt [173] 2001 Cross-sectional

Migrant farmworkers

Predicting factors: greater age, later-season work, wet working conditions, smoking, and work task (priming tobacco)

USA Arcury [174] 2001 cohort

tobacco immigrant workers

The green tobacco sickness prevalence was 24.2%, whereas the ID was 1.88 days per 100 days worked. Greater work experience (5+ years, ID = 0.87; first year ID = 2.41) and tobacco use (ID of 1.18 vs 2.39) were negatively associated with green tobacco sickness. Task (eg, priming ID, 4.04; topping ID, 1.86; barning ID, 0.62) and working in wet clothing (25% of workdays ID, 2.97; fewer than 25% of workdays ID, 1.29) had the largest effect


USA Quandt [175] 2000 cohort

tobacco immigrant workers GTS crude incidence rate: 41%


USA Chain-Castro [176] 1998

Cross-sectional

Migrant farmworkers Acute pesticide poisoning incidence: 20%

Korea Shin [177] 1998 Cross-sectional farmers

During summer farming 21.9% of the subjects experienced suspected pesticide poisoning. 18.8% mild poisoning, and 2% more serious poisoning. significantly associated: sex, days of consecutive pesticide use, hours of pesticide use per day, having received safety education (weakly associated), and compliance with safety guidelines for application. Not significant risk factors: age, education, wearing protective gear, and compliance with safety guidelines for personal hygiene after pesticide use were not significant risk factors to determine pesticide poisoning



Sri Lanka Van der Hoek [178] 1998

National data series Farmers

very high incidence of serious pesticide poisoning was observed, with 68% due to intentional ingestion of liquid pesticides

Spain Martin [179] 1996 Hospital based farmers

Eighty per cent of poisoning events were accidental in nature, most occurred in males, greenhouse workers, and the toxic agents was most commonly absorbed through the skin and airways

USA Ballard [180] 1995

hospital records survey

tobacco workers

66 cases of hospital-reported GTS in 1993. Crude incidence rate: 14/1,000


To examine the comparability of reported injury rates in the literature we examined the formulas used for this estimation we found that literature review reveals a great variation on the way the injury rate is estimated. The following table depicts studies from 1995 to 2006, calculating a type of injury rate indicator. Table 4. Tabulation of studies published on industry rates by author,country of the study, year of publication, type of indicator,estimated value and corresponding units of measurement.

Country Author year study design population type of indicator Value Canada Pickett [151] 1995 survey farmers crude injury rate 5.8 per 100 person-years

Canada Pickett [151] 1995 survey farmers (31-40) crude injury rate 12.2 per 100 person-years

USA Stueland [148] 1996 case-control age <18 overall injury rate 18.27 per 1000 farm resident persons

USA Stueland [148] 1996 case-control age <18 male injury rate 23.47 per 1000 farm resident persons

USA Stueland [148] 1996 case-control age <18 female injury rate 12.66 per 1000 farm resident persons

USA Crandall [181] 1997

forensic data survey farm workers mortality rate 21.3/100,000 worker-year

USA Richardson [146] 1997 cohort farm workers crude mortality rate 38 per 100,000 worker-years

USA Richardson [146] 1997 cohort farm laborers crude mortality rate 16 per 100,000 worker-years

USA Rivara [145] 1997 national data analysis children non-fatal injury rate

1,717 per 100,000 resident farm children

USA Browning [143] 1998 cohort

farmers >55 years old crude injury rate 9.03 per 100 farmers

USA Crawford [142] 1998 case-control

principal operators overall injury rate 5/100 person-years

USA Lewis [141] 1998 case-control farm operators injury reporting rate 10.3%

Canada Pickett [139] 1999 surveillance data farmers overall annual injury mortality rate 11.6/100,000 farm population

Canada Voaklander [182] 1999 surveillance data famers >60 overall mortality rate

32.8/100,000 population per year (men 98%)

Canada Bancej [138] 2000 survey farm children 1-4 age injury rate 22.6 /1000 person-years

Denmark Rasmussen [134] 2000 cohort farmers overall injury rate 23.6/100,000 working hours

Denmark Rasmussen [134] 2000 cohort swine farmers overall injury rate 33.1/100,000 working hours

Denmark Rasmussen [134] 2000 cohort dairy farmers overall injury rate 17.4/100,000 working hours

Denmark Rasmussen [134] 2000 cohort crop farmers overall injury rate 21.4/100,000 working hours

USA DeMuri [20] 2000 review children annual fatality rate >100 deaths / year (USA)



Australia Franklin [132] 2001

retrospective data analysis

unintentional work-related fatalities fatality rate 20.6/100,000 agricultural workers

Australia Mather [125] 2001 survey farmers 38% suffered at least one injury in 2 years

Australia Mather [125] 2001 survey sheep breeders injury burden 30.5%

Australia O’Connor [126] 2001 registry survey farmers spinal cord injury rate 17/1,000,000 persons

New Zealand Firth [14] 2001 cross-sectional farm workers injury prevalence 17.1%

New Zealand

Horsburgh [131] 2001 cohort male farmers fatal injuries annual rate 21.2/100,000

USA Carruth [130] 2001 cohort female farm workers

cumulative one-year incidence 0,05

USA Gerberich [129] 2001 survey

children 0-19 years old

farming sources injury rate 1,683/100,000

USA Gerberich [129] 2001 survey

children 0-19 years old

non-farming sources injury rate 6,980/100,000

USA Hwang [127] 2001 cohort agriculture severe farm injury incidence 0,09

USA Park [183] 2001 cohort farm operators annual work-related injury 10.5%

USA Carruth [130] 2001 cross-sectional study

female farmers

cumulative one-year incidence of farm injuries 5%

UK Solomon [117] 2002 survey

farmers in RIDDOR

nonfatal crude incidence rate 633/100 000 employed


farmers in RIDDOR crude annual death rate 8.8/100 00 employed

USA Hard [124] 2002 national data analysis agriculture fatality rate 25.8/100,000

USA Hard [124] 2002 national data analysis agriculture

non-fatality farm injury rate 7.5 per 100 workers

USA Munshi [120] 2002 cohort farmers annual injury rate 25.9/100 full time equivalents

USA Rautianen [184] 2002 review agriculture fatality rate 22/100,000 workers

USA Rautianen [184] 2002 review agriculture farm injury rate 0.5-16.6/100,000 workers

USA Carruth [118] 2002 cross-sectional study

female farmers

1-year cumulative incidence rate 4.8%

Australia Franklin [112] 2003

hospital reported data farmers annual rate 30 / 100 farms

Finland Virtanen [110] 2003 cohort

female full-time farmers injury incidence 5.8 per 100 person-years

Finland Virtanen [110] 2003 cohort

male full-time farmers injury incidence 9.1 per 100 person-years

Finland Virtanen [110] 2003 cohort hog farmers injury incidence 9.7 per 100 person-years

Finland Virtanen [110] 2003 cohort cattle farmers injury incidence 8.7 per 100 person-years

Finland Rissanen [111] 2003 registry survey

farmers nationwide farm-related fatalities 217 farmers

USA McCurdy [185] 2003 cohort

migrant farm workers injury incidence rate 9.3 / 100 full-time equivalent



USA Perry [114] 2003 national data analysis children

annual fatality/nonfatality figure 103/year -32,000/year

Canada Lim [104] 2004 survey children in farms annual injury rate 1990: 119.9 / 100,000

Canada Lim [104] 2004 survey children in farms annual injury rate 1999: 50.7 / 100,000

UK Lindsay [105] 2004 cross-sectional

livestock farmers prevalence rate 0,24

USA Rautianen [101] 2004 cohort farmers injury rate 42/100 person years

USA McCurdy [102] 2004 cross-sectional farmers

one-year cumulative farm-related injury rate 6.9% (95% CI 5.8%-8.2%)

UK Roberts [186] 2004 retrospective fishermen fatal accident rate 103.1 per 100 000 fishermen-years

Poland Jaremin [187] 2004 retrospective fishermen

average annual mortality rate 89 per 100 000 employees per year

Canada Brison [18] 2006 national case series children 1-6

agricultural injuries annual rate

14.9 per 100,000 person-years(all-cause: 8.7/100,000)


farmers <1 year of employment injury rate 25.5/1000 person-years



3.2 Chronic Health Effects

3.2.1 Musculoskeletal disorders On the issue of musculoskeletal disorders in agriculture, we were able to identify 26 studies. 19 studies were fit for inclusion, while 5 were excluded, either because they provided no risk estimates (3) or because of study design restrictions (2). The most comprehensive review was by Walker-Bone et al, published in 2002 [40]. A summary of the most important findings of this review is presented below: Table 5. Main findings of review article published by Walker-Bone K on Agriculture-Muscoloskeletal disordersusculoskeletal disorders.sculoskeletal Disorders

Agriculture - Musculoskeletal Disorders Osteoarthritis (OA) Hip OA - Farmers: high risk group (2-3fold elevated rates of total hip arthroplasty among surgically treated; 4fold elevated risk of admission among farmers; more pensions for hip OA than other occupations (OR=13.8, 95%CI 4.0-48.1) To tackle potential referral bias: studies among asymptomatic farmers showed: a) 10fold higher risk of OA in X-rays than control films from the general population b) 2fold increased risk for severe OA among farmers working >10 years than controls, c) 3fold increase in the rate of joint space narrowing and 6fold increased risk for labourers >30 years in farms Risk factors regular heavy lifting; prolonged standing; walking over rough ground; vibration from machines; hours of hip joint compression activities Knee OA – findings suggest an increased risk among farmers (OR 1.4-1.5; disability pension risk: OR 5.3) Risk factors heavy physical activity Low-back pain – less evidence but still increased risk among farmers. Simple LBP more prevalent among farm workers than white-collar controls – risk equal to other blue collar workers. Findings from compensation claims are mixed on the severity and disability status. LBP and tractor driving – positive studies show increased risk for recurrent LBP (OR = 2.0) and sciatica (OR=1.6); increased risk for long term sick leave due to intervertebral disc disorders linked with the vibration dose (OR=7.2, 95%CI 0.92-17.9); and other studies with similar results. Negative study (among tractor drivers): OR 0.9, 95%CI 0.8-1.1 and negative association with sciatica symptoms (OR=0.6, 95%CI 0.4-0.9) Risk factors exposure to whole body vibration; other ergonomic factors may pose as confounder Neck-upper limb complaints – few studies. Findings suggest an increased risk for neck pain (OR 1.96, 95%CI 1.48-2.59 among farmers) and arm pain (OR 1.3-1.8 among milkmaids compared to nurses) Risk factors working with arms elevated; static loading; forceful excertion; repetitive work; heavy lifting Hand-arm vibration syndrome (HAVS) – unclear findings Rheumatoid arthritis (RA) – more common in farmers (OR 1.8, 95%CI 1.0-3.5); linked with exposure to crops (OR=1.9, 95%CI 0.7-4.9), contact with pesticides (OR=1.6, 95%CI 0.5-5.6) but not with pesticides (OR 0.9) – association still unclear Walker-Bone K et al: Musculoskeletal disorders in farmers and farm workers. Occup Med 2002; 52:8,pp441-450



Table 6. Summary on musculoskeletal disorders and farming from 1995-2006 included in the literature review by country, author, year, design, population studied, risk estimate and site involved. Country Author Ref Year study

design population risk estimate

Netherlands Hartman [188] 2006 case-control

farmers LBP: Risk factors: increasing age (OR 1.06 per year, 95%CI 1.04-1.09)- BMI >27 (OR 1.93, 95%CI 1.2-3.2) - Smoking (OR 1.90, 95%CI 1.2-2.9) - Former pain (OR 3.28, 95%CI 2.1-5.1) - Tractor driving >1,000h/y (OR 2.44, 95%CI 1.0-6.4) - “high work pace and load” (OR 1.59, 95%CI 1.0-2.4)

USA Rosecrance [41] 2006 cross-sectional

corn and soybean farmers

Low back pain prevalence (self reported): 37.5%; shoulders (25.9%), knees (23.6%), and neck (22.4%)


farmers Neck: carrying, lifting or moving heavy materials and neck pain: OR 4.46 (95%CI 1.74-11.47)

Netherlands Hartman [188] 2006 case-control

farmers Neck-upper limp: Risk factors: - pig farming (OR 3.63, 95%CI 1.4-9.7) - mushroom farming (OR 6.14, 95%CI 1.4-27.2) - dairy/pig farming (OR 4.56, 95%CI 1.1-19.5) - age (OR 1.10, 95%CI 1.06-1.14) - smoking (OR 1.79, 95%CI 1.0-3.2) - former pain (OR 3.37, 95%CI 1.9-6.1)


farmers Shoulder pain prevalence (self reported): 37.5%; contact with animals and shoulder pain: OR 5.20 (95%CI 2.11-12.79)

USA Quandt [189] 2006 cross-sectional

poultry workers; immigrants

MSD problems: most common among poultry workers - prevalence of leg/foot problems: 23%; neck/back: 36%; arm/wrist/hand problems: 46%

Various Punnett [190] 2005 review general LBP: Farmers: RR 5.17 (95%CI 1.57-17.0) [based on data from Leigh and Sheetz (1989)] Exposure category used in comparative risk assessment: high RR 3.65 Farmers, fishermen and forestry workers: RR 4.3(a) – 3.6(b)

Sweden Stal [191] 2005 Cross-sectional

Pig farmers musculoskeletal morbidity is high among pig farmers. The women had significantly more problems than the men with respect to the upper extremities. Symptoms in the wrists and hands such as numbness, reduced muscle strength, aching fingers and wrists, and tendency to drop things were significantly more common among the women than the men. Occupational factors dealing with, for example, the size of the pig farm, were not related to the occurrence of symptoms.

Sweden Holmberg [42] 2005 Cross-sectional

farmers No difference in the msd prevalence between farmers and control group

Sweden Thelin [192] 2004 Cross-sectional

Farmers Farmers with larger dairy and swine confinement operations (sows) had an increased risk of acquiring osteoarthritis of the hip - milking >40 cows daily: (OR = 4.5, 95% CI 1.9-11.0) in relation to those who did not work in dairy production. - Work >5 hr daily in animal barns (OR = 13.3, 95% CI 1.2-145.0) in relation to those who did not work with animals. - large farm areas (>100 ha) (OR = 0.14, 95% CI 0.05-0.43) in relation to those who had smaller farm areas.

Sweden Pinzke [193] 2003 cohort dairy farmers Incidence of symptoms Females vs males: incidental shoulder symptoms (OR 2.8 95%CI 1.3-6.0), hand symptoms (OR 3.9 95%CI 1.8-8.3), feet symptoms (OR 2.3 95%CI 1.1-4.6) Above the median age (male): increased risk of knee symptoms (OR 2.2 95%CI 0.9-5.2), foot symptoms (OR 2.5 95%CI 1.0-5.9) Cows milked above the median value (female): elbow symptoms (OR 4.0 95%CI 1.1-15.2) Milking units above the median value (female): elbow symptoms (OR 4.3 95%CI 0.9-19.9) Left-handedness (female): increased risk for upper back symptoms (OR 14.7 95%CI 1.1-187.4) Persistence of symptoms Females vs males: persistent lower back symptoms (OR 0.5 95%CI 0.2-1.1) Risk factors of persistent symptoms



Body weight above the median value (female): OR 6.4 95%CI 0.9-43-2)

Sweden Toren [44] 2002 Cross-sectional

Farmers Not significant association between tractor driving and risk for low-back and hip symptoms

Sweden Holmberg [194] 2002 Case-control

Farmers reported significantly more symptoms affecting the hands and forearms, low back, and hips than did the non-farmers, and a non-significant trend in the same direction was found for symptoms from the neck, shoulders, and knees. However, the farmers did not seek medical advice more often than the referents, and they reported significantly less sick leave for these problems

Australia Scutter [195] 1997 Cross-sectional

farmers 77.7% of farmers experienced neck pain and 79.2% experienced headache

USA Sandmark [196] 2000 Case-control

Surgically treated OA patients

both male and female farmers ran the highest risk of knee OA. The men had considerably higher exposure to lifting at work, and also to jumps and vibration, than the women. Among the men there was an association between lifting at work [odds ratio (OR) 3.0, 95% confidence interval (95% CI) 1.6-5.5], squatting or knee bending (OR 2.9, 95% CI 1.7-4.9), kneeling (OR 2.1, 95% CI 1.4-3.3), and jumping (OR 2.7, 95% CI 1.7-4.1) with knee OA. Exposure to physically demanding tasks at home, such as taking care of an elderly or handicapped person, was associated with knee OA among the women (OR 2.2, 95% CI 1.3-3.6)

USA Reckner [197] 2001 Case-control

RA patients Increased risks were seen in men born into households with private wells (OR 2.8, 95% CI 1.5 to 5.2), residentially exposed to mould (OR 4.6, 95% CI 1.1 to 20.2), or exposed to farm animals (OR 3.3, 95% CI 0.7 to 16.6)

Japan Mirbod [198] 1997 Cross-sectional

farmers complaints of LBP among male and female green tea and strawberry farmers were most frequent. There was a large variation (16.0-72.2%) in the prevalence of LBP among subjects operating vibrating tools

Finland Manninen [199] 1995 Cohort farmers one-year prevalence rates of unspecified low-back pain (13.3%) and sciatic pain (9.6%). Full-time farmers had a significantly higher prevalence of sciatic pain than did part-time or retired farmers. OR 9.6 (95% CI: 2.7-65.2) for current smokers and 13.1 (95% CI: 1.7-53.0) for ex-smokers as compared to never smokers.

Various Maetzel [200] 1997 review General population

(1) A consistently positive relationship exists between work involving knee bending and knee OA in men (range of odds ratio: 1.4-6). (2) The evidence suggesting a relationship between knee OA and occupational exposure in women is inconclusive. (3) A consistently positive but weak relationship exists between work related exposure (i.e., farming in particular) and hip OA in men. We felt we could not conclude with confidence that this relationship is strong due to the potential biases that exist in each of these studies. (4) No study attempted to investigate occupational exposure and hip OA in women.

USA Kumar [201] 1999 Cohort farmers Regular work-related backache was more common among tractor-driving farmers (40%) than among non-tractor-driving farmers (18%, P = 0.015). no significant objective differences on clinical or magnetic resonance imaging evaluation were found between the two groups.

USA Holmberg [43] 2004 Case-control

farmers Farm work was not related to an increased risk for men. However, women who had worked for 11-30 years in farming tended to have an increased risk (OR 2.1, 95% CI 1.0-4.5).



3.2.2 Hearing loss On the issue of hearing loss we were able to identify 13 studies. 8 studies were fit for inclusion, while 5 were excluded, either because they provided no risk estimates (3) or because of study design restrictions (2). Few studies exist examining the issue of auditory impairement among farmers among the 8 studies identified and judged as meeting the criteria for inclusion. The main finding is that some exposure categories of farmers experience increased risk of hearing loss, especially in high frequencies. The main risk factors include: Driving a tractor without cab or hearing aid; Prolonged exposure to noisy environment; age; metal work; exposure to pesticides Table 7. Summary on hearing loss and farming from 1995-2006 included in the literature review by country, author, year, design, population studied and study findings. Country Author Ref Year Design Population Findings

USA Rabinowitz [202] 2005 Cross-sectional

Migrant farmers

More than half the subjects had some degree of hearing loss at audiometric frequencies between 500 and 6,000 Hz, especially in the higher frequencies. Risk factors for hearing loss included age and abnormal tympanometry

India Kumar [203] 2005 Cross-sectional

farmers hearing loss of more than 25 dB: 48% hearing loss among tractor-driving farmers; 30% in non tractor driving farmers (P<0.0393).

USA Perry [204] 2005 Review Young farmers

Population at risk: almost 2 million youths in farmsyoung people: more vulnerable to noise-induced hearing loss in high frequencies almost 50% among young farmers – 25% among non farmers; ototoxicity of: solvents only animal studies; pesticides exposure assessment studies only. Brazilian pesticide applicators: exposure to Ops and pyrethroids can induce central hearing loss (RR 6.8, 95%CI 2.27-20.93). Other studies -> risk factors: exposure to Ops and Pyrethroids; age; gender; high school education; firearms use; grain dryer operation – conclusion: scarse literature on risks

Poland Solecki [205] 2003 Cross-sectional

farmers The risk of hearing impairment due to exposure to noise (for the allowable value of hearing loss: 30 dB), which may cause an occupational acoustic trauma, reaches the value 9.4 %.

New Zealand

McBride [206] 2003 Cross-sectional

farmers Age, driving tractors without cabs (low frequency: OR 2.72, 95%CI 1.10-6.78; high frequency: OR 1.94, 95%CI 1.05–3.58), and working with metal (low frequency: OR 1.84, 95%CI 1.03-3.28; high frequency: OR 1.95, 95%CI 1.03-3.51) were important risk factors. The majority of farmers have a moderate risk of hearing loss, but a significant minority is at high risk

USA Kerr [207] 2003 Cross-sectional

farmers hearing loss greater than 25 dB: laborers 53%, farmers 67% more hearing loss in the left ear overall, significantly more so at 4,000 Hz for laborers and at 2,000 Hz for farmers.

USA Hwang [127] 2001 cross-sectional

farmers prevalence of self-reported hearing loss: 22%; risk factors: hours working with noisy farm equipment, having had a noisy nonfarm job. Confounders: age, gender, being from a livestock farm, loss of consciousness due to head trauma

USA Hwang [208] 2001 Review farmers high rate of hearing loss among farmers (self-report and audiometry) Missouri farmers: 47% reported hearing loss / 26% office



workers / 32% non-farmers (Thelin, 1983) New York dairy farmers: 65% hearing loss at 3,4,6kHz / 37% rural non-farmers (Marvel, 1991) Hearing loss starts at young age (Broste, 1989), at the higher frequencies (Karlovich, 1988), increases with age and number of years worked in farming (May, 1990; Marvel, 1991)

3.2.3 Cancer The literature review produced a significant amount of studies, investigating potential carcinogenic effects of a series of chemical, physical and biological factors to which the farming population is exposed. On the issue of cancer we were able to identify 144 studies. 119 studies were fit for inclusion, while 25 were excluded, either because they provided no risk estimates (22) or because of study design restrictions (3). Among them, there is no comprehensive review covering the full extent of potential carcinogenic health effects. Still, we could identify 5 site specific review articles and 9 meta-analysis papers. A tabulation of the most recent reviews and meta-analysis is displayed below: Table 8. Summary of published site specific review and meta-analysis papers on cancer risk and farming by authors, year of publication and summary of key findings. Author Year Type Site Summary of findings Descatha [21]

2005 Review LHC NHL & pesticide exposure: positive association across numerous studies Pesticides examined: organochlorines (no strong association found); carbamate pesticides (an increased risk is suggested in one study); phenoxyacetic acid may be associated with NHL or as others suggest because of dioxin contamination in their formulation; insecticide oils and triazine (associated with CLL too) CLL excess risk in various studies. Pesticides are the suggested cause (chlorinated only) Hairy cell leukaemia associated with occupational pesticide exposure (3 case-control studies) MDS: associated with pesticide exposure (one case-study) OR 3.66 (95%CI 1.9-7.0)

Valery [209] 2005 Meta-analysis

Ewing’s sarcoma

periconception and gestation periods: pooled OR = 2.3, 95%CI 1.3-4.1 for children whose fathers had worked on farms and OR = 3.9, 95%CI 1.6-9.9 for those whose mothers had farmed. For the periconception and gestation periods, there was a 3.5-fold increased risk for those with both parents having farmed and a doubling of risk for those with at least one parent having farmed

Von Maele-Fabry [210]

2004 Meta-analysis

prostate Pooled RR 1.24 (95%CI 1.06-1.45) RR 0.61-2.38 (5 to 566 cases)

Yeni-Komshian [211]

2000 Review Childhood brain tumours

Five of the seven studies examined childhood farm residence or exposure of mother or child to farm animals and, of these five, four reported elevated risk for CBT with odds ratios (OR) ranging from 0.9 to 2.5 for maternal exposures and from 0.6 to 6.7 for children's exposures. Later studies that were larger subsequently examined histological type and reported excess risk for primitive neuroectodermal tumours (PNETs) with farm residence prenatally (OR = 3.7, CI = 0.8, 24) or in childhood (OR = 5.0, CI = 1.1, 4.7). Increased risk of PNET was also associated with maternal exposure to pigs (OR = 12, CI = .1, 47) or poultry (OR = 4.0, CI = 1.2, 13).

Khuder [212]

1999 Meta-analysis

Hodgkin’s disease

The combined RR was 1.25 (95% CI 1.11-1.42) for all the studies, and 1.08 (95% CI 0.91-1.29) for the studies involving female farmers.

Khuder [213]

1999 Meta-analysis

lip combined RR=2.0 (95%CI, 1.74-2.30) (all studies); RR=1.28 (95%CI 0.79-2.08) for female farmers

Khuder [214]

1998 Meta-analysis

Brain tumors RR=1.30, 95% CI 1.09 - 1.56. Female farmers RR= 1.04, 95% CI 0.84 - 1.29). Central USA farmers RR = 1.25, 95%CI 1.09 - 1.44

Zahm [215] 1998 Review Childhood cancer

Leukemia, NHL, brain cancer, soft tissue sarcoma, Hodgkin’s disease in children associated with pesticide exposure – risk estimates much larger than those in adults

Khuder 1997 Meta-analysis

melanoma combined RR 0.88 (95% CI 0.74-1.05)

Khuder 1997 Meta-analysis

Non-melanotic

combined RR 1.0 (95% CI 0.89-1.14)

Khuder [216]

1997 Meta-analysis

Multiple Myeloma

RR=1.23 (95%CI 1.14-1.32); RR=1.23 (95%CI 1.17-1.29) (female) Central USA: RR=1.38 (95%CI 1.27-1.51)

Keller- 1997 Meta- prostate Positive associations between prostate cancer and farming were found by the analysis



Byrne [217] analysis including all studies and the analysis limited to the retrospective studies. No association was found with the analysis that included only studies reporting a standard mortality ratio.

Dich [218] 1997 Review STS, testicular, breast, stomach, lung, skin

Soft tissue sarcomas (STS): -case-control studies suggest an association of exposure to phenoxy acid herbicides and chlorophenols among forestry workers, farmers and pesticide applicators. In one case study: increased risk for STS due to dioxin contamination. Nested case-control studies (2): higher risk of STS for exposed to phenoxy acid herbicides and their contaminants. A cohort study reported no statistically significant increase in the risk for STS. - exposure to DDT was associated with STS (3 studies positive, 1 negative) - exposure to pentachlorophenols not associated with STS (4 studies); one positive study: 4fold increased risk for exposure >5years before diagnosis Testicular cancer findings unsupportive of potential association Breast cancer evidence do not support a causal association with exposure to DDT (based on the findings of a large nested case-control study) Stomach cancer excess risk found in one cohort study Lung cancer most cohort studies report decreased risk among farmers and pesticide applicators (two earlier cohort studies reported a 35% excess risk) Skin cancer contradicting results

Blair [219] 1995 Review All cancer types

Excess rates: leukemia, non-Hodgkin's lymphoma, multiple myeloma, soft-tissue sarcoma, and cancers of the skin, lip, stomach, brain, and prostate.Deficiency: lung, bladder, liver, colon, esophagus, rectum, kidney

Table 9. Summary on cancer and farming from 1995-2006 included in the literature review by country, author, year, design, population studied, risk estimate and site involved.

Country Author Ref Year study design population risk estimate topic

cancers of the brain

USA Lee [220] 2005 case-control

farmers Ever worked on a farm and duration of farming were associated with significantly increased risks of glioma (>55 years on a farm OR 3.9, 95%CI 1.8-8.6). Positive findings resticted only to proxy responders. Risk estimates were significantly increased both for self and proxy responders for some herbicides (metribuzin OR 3.4, 95%CI 1.2-9.7, paraquat OR 11.1, 95%CI 1.2-101) and insecticides (bufencarb OR 18.9, 95%CI 1.9-187, chlorpyrifos OR 22.6, 95%CI 2.7-191, coumaphos OR 5.9, 95%CI 1.1-32) –small number of cases

cancer-adult glioma

USA Van Wijngaarden

[221] 2003 Case-control

farmers Elevated risks of astrocytoma were found for paternal exposure (ever vs. never) to all four classes of pesticides (odds ratio (OR) = 1.4-1.6). An increased risk of PNET was observed for only herbicides (OR = 1.5). For mothers, odds ratios for astrocytoma were elevated for insecticides, herbicides, and nonagricultural fungicides (OR = 1.3-1.6) but not agricultural fungicides (OR = 1.0). No indication was found of an increased risk for PNET.

Childhood brain tumours

Various Efird [25] 2003 Case-control

Gen.population

Significantly elevated odds ratios (OR) for CBT were associated with children's personal and maternal prenatal exposure while living on a farm with pigs (child OR = 1.7, mother OR = 2.3), horses (child OR = 1.6, mother OR = 1.8), dogs (child OR = 1.5, mother OR = 1.5) and cats (child OR = 1.5, mother OR = 1.7). Children who were exposed to pigs, horses and cats combined, while living on a farm, had a threefold elevated OR for CBT. Increased ORs for primitive neuroectodermal tumours (PNET) were associated with children's farm exposure to dogs (OR = 1.9) and cats (OR = 2.2), and maternal farm exposure to pigs (OR = 4.2). The OR for CBT was elevated (OR = 2.3) for children of mothers who had preconception/prenatal farm- or agriculture-related employment involving potential contact with animals, relative to no farm- or agriculture-related employment. In particular, increased ORs for CBT were observed for children of mothers who were employed as general farmers (OR = 4.1) or general farm workers (OR = 3.8). During the 5 years preceding the index child's birth, maternal exposures were related to CBT, relative to no maternal exposure to agricultural chemicals or animal products: fertilisers (OR = 1.8), pesticides (OR = 2.0), animal manure (OR = 2.0) and unprocessed wool (OR = 3.0).




USA De Roos [222] 2003 case-control

farmers General farmers and farmworkers:- ever worked: OR 2.5 (95%CI 1.4-4.7)- worked >5 years total: OR 3.0 (95%CI 1.0-9.1)farmed crops: OR 1.4 (95%CI 0.6-3.5)raised farm animals: OR 1.7 (95%CI 0.7-3.9)mixed farming: OR 2.3 (95%CI 0.5-2.3)spent time in animal confinement building or egg-laying house: OR 4.9 (95%CI 1.0-25.3)used pesticides: OR 1.4 (95%CI 0.5-4.0)

cancer-adult glioma

USA De Roos [222] 2003 Case-control

Glioma patients – hospital ontrols

general farmers and farmworkers (OR 2.5; 95% CI 1.4-4.7 glioma

Various Menegoz [223] 2002 multicenter case-control

livestock farmers

reduced glioma risk among general farm workers:OR 0.66 (95%CI 0.5-0.9) -- living on a farm not a risk factor for glioma or meningioma

cancer-glioma

Various Cordier [26] 2001 Case controls

Gen. population

An increased risk in relation with agricultural work was seen for all CBT combined (OR 1.3, 95%CI 1.0-1.8) and for other glial tumors (OR 1.8, 95%CI 1.0-3.5)


USA Holly [224] 1998 Case-control

farmers Elevated risks for CBTs were observed in association with mothers' exposure to pigs [odds ratio (OR) = 3.8, 95% confidence interval (CI) = 1.2-12] and horses (OR = 2.2, 95% CI = 1.0-4.8) on a farm during the index pregnancy. Children diagnosed with primitive neuroectodermal tumors showed elevated risks for CBTs with personal and maternal prenatal exposure to pigs (child, OR = 4.0, 95% CI = 1.2-13; mother, OR = 11.9, 95% CI = 2.8-51) and poultry (child, OR = 3.0, 95% CI = 1.1-8.0; mother, OR = 4.0, 95% CI = 1.2-14). No other animal exposures of children or mothers were found to be consistently related to CBTs. Children diagnosed with primitive neuroectodermal tumors who were on a farm for > 1 year and were first on a farm when they were < 6 months of age also had increased risk for CBTs (OR = 3.9, 95% CI = 1.2-13). A somewhat increased risk for CBTs was found for children of mothers who ever had worked on livestock farms compared with mothers who never had worked on a farm (OR = 7.4, 95% CI = 0.86-64, based on five case mothers and one control mother who worked on livestock farms during the 5 years preceding the birth of the index child).


USA Khuder [214] 1998 Meta-analysis

farmers RR=1.30, 95% CI 1.09 - 1.56. Female farmers RR= 1.04, 95% CI 0.84 - 1.29). Central USA farmers RR = 1.25, 95%CI 1.09 - 1.44

Cancer-brain

Canada Morrison [225] 1995 cohort corn farmers growing >10-50 acres of corn, brain cancer SMR=1.47 (95%CI 1.13-1.89)

cancer-brain

cancers of lip/skin Norway Nordby [226] 2004 cohort farmers Incidence rate: 4.4 per 100,000 person-years

horses on the farm: RR 1.6, 95%CI 1.0-2.4- pesticide use: RR 0.7, 95%CI 0.4-1.0- grain production: RR 1.3, 95%CI 0.9-2.1- increasing levels of fungal forecasts: RR 1.6, 95%CI 0.9-2.8 in the highest two quartiles

cancer-lip

Canada Morrison [225] 1995 cohort vegetable farmers

growing >1 acre but <10 acres of vegetables, lip cancer SMR=7.18 (95%CI 1.44-20.97) - 3 obs.cases / 0.4 expected

cancer-lip

Various Khuder [213] 1999 meta-analysis

farmers combined RR=2.0 (95%CI, 1.74-2.30) (all studies); RR=1.28 (95%CI 0.79-2.08) for female farmers

cancer-lip

Iceland Rafnsson [227] 2006 cohort sheep farmers exposed to lindane while dipping

Male lip cancer SIR: 1.50 (95% CI 1.08-2.04); female lip cancer SIR: 9.09 (95% CI 1.02-32.82); male all-cause cancer SIR: 0.79 (95%CI 0.76-0.83); most cancer sites SIR <1

cancer-lip and other

USA Alavanja [5] 2005 cohort female farm spouses

SIR 1.64, 95% CI 1.24-2.09 cancer-melanoma

USA Khuder [213] 1999 meta-analysis

farmers combined RR 0.88 (95% CI 0.74-1.05) cancer-melanoma

USA Khuder [213] 1999 meta-analysis

farmers combined RR 1.0 (95% CI 0.89-1.14) cancer-non-melanotic



Sweden Linet [228] 1995 Cohort farmers Risk overall was not increased among farmers, despite a significant excess of melanoma of the face, neck, and scalp

melanoma

general USA Hou [229] 2006 Cohort farmers Overall cancer incidence did not increase with increasing

lifetime pendimethalin use The risk for rectal cancer rose with increasing lifetime pendimethalin exposure when using nonexposed as the reference (rate ratio = 4.3; 95% confidence interval = 1.5-12.7 for the highest exposed subjects; P for trend = 0.007)

General cancer

Italy Nanni [230] 2005 Cohort Female farmers

Total age standarized mortality ratio (ASR) was 0.86 (95% CI 0.80-0.92). Only gastric cancer was associated with a significant but declining excess mortality (ASR ratio 1.26; 95% CI 1.11-1.43). Total ASR ratio decreased from 1.07 (95% CI 0.95-1.20) in 1969-1976 to 0.74 (95% CI 0.66-0.82) in 1985-1993.

General cancer

USA Blair [6]

2005 Cohort farmers SMR for total cancer, and cancers of the esophagus, stomach, and lung were 0.6 or lower for both farmers and spouses. These deficits varied little by farm size, type of crops or livestock on the farm, years of handling pesticides, holding a non-farm job, or length of follow up.

general

USA Netto [113] 2003 cohort poultry workers

Increased but not statistically significant PMRs for these cancers: colon, pancreas, lung, cervix, brain and other CNS and of lymphatic tissue.

cancer-various

Sweden Rodvall [231] 2003 cohort Male pesticide applicators

Overall cancer standardised incidence ratio (SIR) 0.70, 95%CI 0.52-0.92. Reduced risk for leukaemia, NHL, testicular cancer

General cancer

USA Wang [232] 2002 Cohort farmers the female farm resident cohort experienced significantly lower cancer rates for all cancers combined, and for lung cancer, compared with rural nonfarm female residents. In addition, significantly low rates for colorectal cancer and ovarian cancer were found among the female cohort members. Nonsignificant excesses were found for thyroid and liver cancers.

various

Italy Settimi [233] 2001 Case-control

farmers Increased risks of cancer associated with agricultural work were found for stomach (OR = 1.4, 95%CI:0.9-2.0), rectum (OR = 1.5, 95%CI:0.8-2.7), larynx (OR = 1.4, 95%CI:0.8-2.5), and prostate (OR = 1.4, 95%CI:1.0-2.1). The excess of prostate cancer was specifically related to application of pesticides (OR = 1.7, 95%CI:1.2-2.6).

Stomach, rectum, larynx, prostate

USA Rusiecki [234] 2006 Cohort Pesticide applicators

No clear risk for any cancer subtype was found for exposure to metolachlor

Lung, prostate

USA Pahwa [235] 2003 Case-control

Gen.population

exposure to farm animals had minimal effect on risk Hodgkin lymphoma, MM, STS

USA Pahwa [236] 2006 Case-control

Gen.population

No additional risk from these combinations of exposures of developing these three types of tumor was found in contrast to non-Hodgkin lymphoma.

Hodgkin lymphoma, MM, STS

USA Lee [237] 2004 Case-control

farmers no association for either cancer with ever-use of insecticides (stomach OR 0.9, 95% CI 0.6 to 1.4; oesophagus OR 0.7, 95% CI 0.4 to 1.1) or herbicides (stomach OR 0.9, 95% CI 0.5 to 1.4; oesophagus OR 0.7, 95% CI 0.4 to 1.2)

Stomach/oesophageal cancer

USA Cerhan [238] 1998 PMR study

While male farmers >20

deficit PMRs for all-cause cancer mortality (PMR = 0.92, CI = 0.90-0.94) and for lung (PMR = 0.70, CI = 0.66-0.73), liver (PMR = 0.65, CI = 0.50-0.86), and other cancer sites strongly related to smoking and alcohol use. Farmers at all ages had excess deaths for cancers of the prostate (PMR = 1.26, CI = 1.19-1.33), rectum (PMR = 1.29, CI = 1.07-1.56), brain (PMR = 1.10, CI = 0.92-1.32), multiple myeloma (PMR = 1.17, CI = 0.98-1.40), non-Hodgkin's lymphoma (PMR = 1.09, CI = 0.96-1.23), and Hodgkin's disease (PMR = 1.62, CI = 1.04-2.54). Younger farmers (aged 20 to 64 years) had excess deaths for colon cancer (PMR = 1.52, CI = 1.26-1.85) and skin melanoma (PMR = 1.60, CI = 1.07-2.38), while older farmers (aged 65+ years) had excess deaths for cancers of the pancreas (PMR = 1.18, CI = 1.04-1.34), lip (PMR = 1.58, CI = 0.59-4.21), and leukemia (PMR = 1.26, CI = 1.09-1.46).

general



USA Johnson [239] 1997 Cohort Poultry workers

Statistically significant increased risks were observed for cancer of the oesophagus, liver cancer, tumours of the haemopoietic lymphatic system, and motor vehicle accidents, in the group of poultry workers as a whole or in particular race/sex subgroups

General cancer

Sweden Wiklund [240] 1995 cohort farmers Overall cancer SIR: 0.80 (95%CI 0.78-0.81) various

cancers of hematolymphatic system Italy Costantini [241] 2001 multicente

r case-control

grape and orchard farmers

- small cell lymphoma: OR=1.6; 95% CI 5 0.9 –2.9 (20 cases)- multiple myeloma: OR 5 2.1; 95% CI 5 1.0–4.2

cancer-lhc

USA Mills [242] 2005 case-control

vegetable farmers

OR 1.67, 95%CI 1.12-2.48 cancer-All LH cancers

Italy Assennato [243] 1997 Case-control

LHC patients leukemia and farmer: (OR 5.38; CI 95% = 1.31-21.99); OR observed for leukemia and breeder (OR 1.20; CI 95% = 0.73-2.00)

LHC cancers

Iceland Zhong [244] 1996 Cohort Licensed pesticide applicators

cancer of lymphatic and haematopoietic tissue: SIR = 5.56, 95%CI 1.12-16.23. rectal cancer: SIR = 2.94, 95% CI: 1.07-6.40), and this cancer was even more predominant among the licensed pesticides users (SIR = 4.63, 95% CI: 1.49-10.80

Lhc, rectum

Italy Assennato [245] 1995 Case-control

LHC patients The odds ratio (OR) of hematologic malignancies for agricultural workers was 1.63 [95% Confidence Intervals (95% CI): 0.69-4.34] in the whole sample and 6.00 (95% CI: 1.21-25.52) in the female group. Significative increased risks have been observed for exposure to DDT and creolin (OR = 4.11; 95% CI: 1.16-14.55) and, for leukemia, for cattle breeders (OR = 6.38; 95% CI: 1.46-27.83).

LHC cancers


farmers exposed to mancozeb

OR 2.35, 95%CI 1.12-4.95 cancer-leukaemia

USA Reynolds [247] 2005 Case-control

children Two commonly used pesticides were associated with higher leukemia risk when comparing the highest and lowest categories: metam sodium (OR=2.05; 95% confidence interval=1.01-4.17) and dicofol (1.83; 1.05-3.22).

leukemia

USA Freeman [248] 2004 cohort farmers exposed to diazinon

RR 3.36 (95%CI 1.08-10.49) for the highest exposure category (>38.8 exposure days)

cancer-leukaemia

Italy Miligi [249] 2003 case-control

farmers insecticide oils: OR 11.7 (95%CI 2.8-79.2) cancer-leukaemia

USA Blair [250] 2001 Case-control

Leukaemia patients

Employed >10 years in agriculture: OR 2.1 (95%CI 1.0/4.5) leukemia

Norway Kristensen [251] 1996 cohort dairy farmers dairy farming and acute leukemia among men [rate ratio 1.76, 95% CI 1.02-3.05]

Cancer – leukemia

USA Nordstrom [252] 1998 Case-control

farmers Elevated odds ratio (OR) was found for exposure to farm animals in general: OR 2.0, 95% confidence interval (CI) 1.2-3.2. The ORs were elevated for exposure to cattle, horse, hog, poultry and sheep. Exposure to herbicides (OR 2.9, CI 1.4-5.9), insecticides (OR 2.0, CI 1.1-3.5), fungicides (OR 3.8, CI 1.4-9.9) and impregnating agents (OR 2.4, CI 1.3-4.6) also showed increased risk. Certain findings suggested that recall bias may have affected the results for farm animals, herbicides and insecticides. Exposure to organic solvents yielded elevated risk (OR 1.5, CI 0.99-2.3), as did exposure to exhaust fumes (OR 2.1, CI 1.3-3.3)

HCL

France Clavel [253] 1996 case-control

HCL patients – hospital controls

Forage growing associated with HCL (OR 2.8, 95% CI 1.6-4.9), even among farmers who had never handled pesticides (OR 3.4, 95% CI 1.0-11.0). OR 2.8 (95% CI 1.4-5.6) for exposure to forage and 7.5 (95% CI 0.9-61.5) for nonsmokers exposed to organophosphorus insecticides.

Hairy cell leukemia

France Clavel [254] 1995 case-control

HCL patients – hospital controls

Agriculture: OR 1.7 (95% CI 1.1-2.4) for men; OR 2.7 (95% CI 1.1-6.7) for women

Hairy cell leukemia

France Nisse [255] 2001 Case-control

Gen.population

being an agricultural worker [odds ratio (OR) = 3.66; 95% confidence interval (CI) 1.9-7.0

MDS

Germany Mester [256] 2006 cohort farmers All-category exposure OR=9.2 (95%CI 2.6-33.1) cancer-mm



Japan Sonoda [257] 2005 Case-control

Gen.population

agriculture and fishery showed a significant association with increased risk (OR = 5.89, 95% CI = 1.24-28.04)

Multiple myeloma

USA Rusiecki [258] 2004 cohort farmers exposed to atrazine

RRLD 1.60, 95%CI 0.37-7.01, Ptrend 0.41RRIWLD 2.17, 95%CI 0.45-10.32, Ptrend 0.21.Comparisons of cancer incidence in applicators with the highest atrazine exposure and those with the lowest exposure, assessed by lifetime days (RRLD) and intensity-weighted lifetime days (RRIWLD) of exposure

cancer-mm

USA Baris [259] 2004 Case control

Mm patients Farmers and farm workers had odds ratios of 1.9 (95% CI 0.8-4.6) and 1.4 (95% CI 0.8-2.3), respectively. An odds ratio of 1.7 (95% CI 1.0-2.7) was observed for sheep farm residents or workers, whereas no increased risks were found for cattle, beef, pig, or chicken farm residents or workers.

Multiple myeloma

Various Khuder [216] 1997 meta-analysis

farmers RR=1.23 (95%CI 1.14-1.32); RR=1.23 (95%CI 1.17-1.29) (female) Central USA: RR=1.38 (95%CI 1.27-1.51)

cancer-mm

Norway Kristensen [251] 1996 cohort potato farmers

Multiple myeloma was associated with pesticide indicators for both genders, mainly for subjects cultivating potatoes (risk estimation not available)

cancer-mm

Spain Van Balen [260] 2006 case-control farmers

General farmers: OR 1.8 (95%CI 1.1-2.0)Crop farmers / animal farmers: OR 2.8 (95%CI 1.3-5.8)Exposure to non arsenic pesticides over 9 years: OR 2.4 (95%CI 1.2-2.8)

cancer- lymphoma

Germany Mester [256] 2006 cohort farmers All-category exposure OR=2.4 (95%CI 0.7-6.1) Lymphoma

USA Svec [261] 2005 mortality occupations with animal contact

Exposure to animals: increased mortality for all six leukaemia histological subtypes ALL OR=1.54 (95%CI 1.26-1.89]; CLL OR=1.24 (95%CI 1.14-1.36]; AML OR=1.43 (95%CI 1.31-1.56]; CML OR=1.33 (95%CI 1.17-1.51]; other OR=1.34 (95%CI 1.15-1.57]; unknown OR=1.22 (95%CI 1.12-1.33]) and both NHL subtypes: diffuse NHL OR =1.46 (95%CI 1.27-1.69); follicular NHL OR=1.12 (95%CI 0.79-1.59)

cancer-leukaemia/ NHL

USA Samanic [262] 2006 cohort farmers exposed to dicamba

No association with NHL cancer-NHL

Italy Amadori [263] 1995 Case-control

LHC patients no occupation showed a significantly high risk. When the two job titles farmers only and farmer-breeders who are also involved in animal breeding are classified within the extremely varied occupation of agriculture or animal-breeding or fishing, a high risk for NHLs and CLLs is seen in the farmer-breeders (OR 1.79, 95% CI 1.22 - 2.63). Analyses according to histological type show that the risks are concentrated in CLLs and in low grade NHLs

NHL-CLL

Canada Fritschi [264] 2002 Case-control

farmers Compared to subjects without occupational exposure to animals, occupational exposure to beef cattle increased the risks of leukemia (odds ratio (OR) 2.0, 95% confidence interval (CI) 1.2–3.3) and NHL (OR 1.8, 95% CI 1.1–2.9). No other animal exposure was consistently associated with risk of lymphohematopoietic cancer.

Leukaemia-NHL

USA Lynch [265] 2006 cohort farmers exposed to cyanazine

RR 1.25, 95%CI, 0.47-3.35 cancer-NHL

Canada Fritschi [266] 2005 case-control

farmers Substancial exposure to any pesticide: OR 3.09 (95%CI 1.42-6.70) Non-significant risk for exposure to specific pesticide categories (organochlorines, organophosphates, “other pesticides”) No association with NHL for the exposure metrics.Follicular lymphoma: most common subtype

cancer-NHL


farmers exposed to 2,4D

OR 3.80, 95%CI 1.85-7.81 cancer-NHL


RRLD 1.61, 95%CI 0.62-4.16, Ptrend 0.35RRIWLD 1.75, 95%CI 0.73-4.20, Ptrend 0.14Comparisons of cancer incidence in applicators with the highest atrazine exposure and those with the lowest exposure, assessed by lifetime days (RRLD) and intensity-weighted lifetime days (RRIWLD) of exposure

cancer-NHL



USA Chiu [267] 2004 pooled case-control

farmers Statistical significant ORs for NHL: men with family history of nonhematopoietic cancer OR 1.5 (95%CI 1.3-1.8); men with family history of hematopoietic cancer among first-degree relatives OR 2.7 (95%CI 1.9-3.7)Statistical significant ORs for NHL: only for proxy responders (family history of hematopoietic cancer and animal insecticides: OR 4.6, 95%CI 1.9-11.2), crop insecticides (OR 4.7, 95%CI 1.6-13.4), herbicides (OR 4.9, 95%CI 1.7-14.2)

cancer-NHL

Italy Miligi [249] 2003 case-control

farmers no-crop specific risk (flower, vegetables, orchard, greenhouses)nitroderivatives OR 1.9 (95%CI 0.7-5.3), phenylimides OR 3.1 (95%CI 0.9-12.4)

cancer-NHL

USA McDuffie [268] 2002 case-control

farmers Statistically significant association with NHL->=13 swine: OR 1.96 (95%CI 1.21-3.18)-bison,elkostriches: OR 3.26 (95%CI 1.20-8.89)-personal history of cancer: OR 1.95 (95%CI 1.14-3.35)-exposure to diesel fuel or exhaust: OR 1.52 (95%CI 1.05-2.19)- farm residency between 4 and 15 years: OR 2.15 (95%CI 1.34-3.45)

cancer-NHL

USA Waddell [269] 2001 Case-control

farmers Use of organophosphate pesticides was associated with a statistically significant 50% increased risk of NHL, but direct interviews showed a significantly lower risk (OR = 1.2) than proxy interviews (OR = 3.0). Among direct interviews the risk of small lymphocytic lymphoma increased with diazinon use (OR = 2.8), after adjustment for other pesticide exposures.

NHL

USA Zheng [270] 2001 Pooled-analysis

farmers Compared with nonfarmers, farmers who had ever used carbamate pesticides had a 30% to 50% increased risk of NHL, whereas farmers without carbamate pesticide use showed no increased risk

NHL

Canada McDuffie [271] 2001 Case-control

Gen.population

the risk of NHL was statistically significantly increased by exposure to phenoxyherbicides [OR, 1.38; 95% confidence interval (CI), 1.06-1.81] and to dicamba (OR, 1.88; 95% CI, 1.32-2.68). Exposure to carbamate (OR, 1.92; 95% CI, 1.22-3.04) and to organophosphorus insecticides (OR, 1.73; 95% CI, 1.27-2.36), amide fungicides, and the fumigant carbon tetrachloride (OR, 2.42; 95% CI, 1.19-5.14) statistically significantly increased risk.

NHL

Italy Gambini [7] 1997 Cohort Rice farmers tendency toward an increased risk of nonHodgkin's lymphoma NHL

USA Metayer [272] 1998 nested case-control

workers in chicken-slaughtering plants

OR = 3.3, 95% CI 0.8-13.1 cancer-lymphoma

USA Metayer [272] 1998 nested CC workers in cattle/sheep/pig abattoirs

OR = 2.8, 95% CI 0.8-9.5 cancer-lymphoma

Canada Morrison [225] 1995 cohort corn farmers growing >10-50 acres of corn, NHL SMR=1.43 (95%CI 1.10-1.82)

cancer-NHL

Cancers of the airway / respiratory system


RR 0.52 (95%CI 0.22-1.25) cancer-lung

USA Samanic [262] 2006 cohort farmers exposed to dicamba

Trend for increased risk of lung cancer among low-exposed applicators (p-value: 0.02, RR 2.16, 95%CI 0.97-4.82) for the upper half of the highest tertile

cancer-lung

USA Bonner [273] 2005 cohort farmers exposed to carbofuran

RR 3.05, 95%CI 0.94-9.87 for those with > 109 days of lifetime exposure to carbofuran, compared with those with <9 lifetime exposure days, with a significant dose-response trend for both days of use per year and total years of use.

cancer-lung

USA Lee [274] 2004 cohort farmers exposed to chlorpyrifos

Lung cancer: exposure >56 days: RR 2.18 (95%CI 1.31-3.64) cancer-lung

USA Rusiecki [258] 2004 cohort farmers exposed to metachlor

RR 2.37 (95%CI 0.97-5.82) (with lifetime days exposure in the highest category)

cancer-lung


RRLD 1.91, 95% CI 0.93-3.94, Ptrend 0.08, and RRIWLD 1.37, 95% CI 0.65-2.86, Ptrend 0.19Comparisons of cancer incidence in applicators with the highest atrazine exposure and those with the lowest exposure, assessed by lifetime days (RRLD) and intensity-weighted lifetime days

cancer-lung



(RRIWLD) of exposure

USA Alavanja [275] 2004 cohort farmers exposed to pesticides

SIR 0.44 (95%CI 0.39-0.49)Herbicides metolachlor and pendimethalin and insecticides chlorpyrifos and diazinon showed some evidence of exposure response for lung cancer.

cancer-lung

USA Freeman [248] 2004 cohort farmers exposed to diazinon

RR 2.41 (95%CI 1.31-4.43) for the highest exposure category (>38.8 exposure days); lowest exposure category: RR 3.19 (95%CI 1.28-7.93)

cancer-lung

USA Coble [22] 2003 case-control

sugar cane farmers

OR=4.4 (95%CI 1.4-13.6) cancer-oral cavity

India Amre [23] 1999 case-control

sugar cane farmers

Ever employed on a sugar cane farm: OR 1.92 95%CI 1.08-3.40 Linear trend between years of employment and risk of lung cancer by an OR factor of 1.21 (1.02-1.40) (burning canes >210 days in their lifetime: OR 2.30 (95%CI 1.10-4.70)

cancer-lung

Cancers of the G/I system

Sweden Ji [88] 2005 follow-up farmers Farmers: Liver cancer total: SIR=0.94 (95%CI 0.86-1.01); primary liver: SIR=0.90 (95%CI 0.80-1.01); gallbladder: SIR=0.96 (95%CI 0.82-1.11)

cancer-liver

USA Lee [237] 2004 case-control

farmers Farming: OR=0.9 (95%CI 0.6-1.3) [stomach]; OR=0.7 (95%CI 0.5-1.2) [oesophagus]Using insectides: OR=0.9 (95%CI 0.6-1.4) [stomach]; OR=0.7 (95%CI 0.4-1.1) [oesophagus]

cancer-stomach

USA Ji B [276] 2001 case-control

farmers significant trend in risk with increasing exposure, low exposure level: OR 1.3 (95%CI 1.0-1.7); medium/high exposure: OR 1.4 (95%CI 1.0-2.0)- exposure to fungicides: OR 1.5 (95%CI 1.1-1.9) (low), OR 1.5 (95%CI 0.3-7.6) (moderate/high)- exposure to herbicides: OR 1.5 (95%CI 0.8-3.1) (low), OR 1.6 (95%CI 0.7-3.4) (moderate/high)- no production-specific risks (vegetable, fruit, tree nut, crop) statistically significant

cancer-pancreas

Spain Alguacil [277] 2000 case-control

farmers highest risk was observed for arsenical pesticides (OR 3.4, 95%CI 0.9-12.0) and ‘other pesticides’ (OR 3.17, 95%CI 1.1-9.2)

cancer-pancreas

Denmark Dossing [278] 1997 case-control

farmers Agriculture and hunting: OR 0.57 (95%CI 0.4-0.9) cancer-liver

Canada Morrison [225] 1995 cohort berries farmers

growing >10 acres of berries, colon cancer SMR=1.72 (95%CI 0.89-3.01)

cancer-colon

Canada Morrison [225] 1995 cohort vegetable farmers

growing >10 acres of vegetables, stomach cancer SMR=1.73 (95%CI 1.08-2.62)

cancer-stomach

Canada Morrison [225] 1995 cohort potato farmers

growning >10 acres of potates, pancreas SMR=1.55 (95%CI 0.89-2.52)

cancer-pancreas

Cancers of the renal/urinary system


RR 1.23 (95%CI, 0.87-1.70) cancer-prostate

USA Alavanja [5] 2005 cohort private applicators

(SIR 1.24, 95% CI 1.18-1.33) cancer-prostate

USA Alavanja [5] 2005 cohort commercial applicators

(SIR 1.37, 0.98-1.86) cancer-prostate


RRLD 0.88, 95% CI 0.63-1.23, Ptrend .26, and RRIWLD 0.89, 95% CI 0.63-1.25, Ptrend _ .35Comparisons of cancer incidence in applicators with the highest atrazine exposure and those with the lowest exposure, assessed by lifetime days (RRLD) and intensity-weighted lifetime days (RRIWLD) of exposure

cancer-prostate

Various Von Maele-Fabry

[210] 2004 meta-analysis

farmers Pooled RR 1.24 (95%CI 1.06-1.45)RR 0.61-2.38 (5 to 566 cases)

cancer-prostate

USA Fleming [9] 1999 Cohort Licensec pesticide

prostate cancer mortality (SMR 2.38; 95% CI 1.83-3.04) prostate



applicators

UK Ewings [279] 1996 Case-control

Hospital based

no association between farming and risk of prostatic cancer (OR 0.74, 95%CI 0.46-1.18)

prostate

Netherlands

Zeegers [280] 2004 cohort farmers RR longest = 0.70 (99%CI 0.36-1.36)RR baseline = 0.75 (99%CI 0.39-1.46)

cancer-prostate

USA Alavanja [281] 2003 cohort farmers Prostate cancer: SIR 1.14 (95%CI 1.05-1.24)Commercial applicators: SIR 1.41 (95%CI 0.89-2.11) – private applicators: SIR 1.13 (95%CI 1.04-1.24)- Chlorinated pesticides among applicators >50 years of age and methyl bromide were significantly associated with prostate cancer risk

cancer-prostate

Italy Settimi [282] 2003 case-control

farmers “ever been employed in agriculture”:OR 1.4 (95%CI 0.9-2.0)Exposure to organochlorine insecticides and acaricides: OR 2.5 (95%CI 1.4-4.2)Exposure to DDT: OR 2.1 (95%CI 1.2-3.8)Exposure to dicofol: OR 2.8 (95%CI 1.5-5.0)

cancer-prostate


farmers Grapes/tree fruit: OR 1.16 (95%CI 0.87-1.57); citrus fruit: OR 1.08 (95%CI 0.60-1.92); vegetables: OR 0.75 (95%CI 0.56-1.01); mushrooms OR=1.91 (95%CI 0.87-4.18); strawberries: OR 1.26 (95%CI 0.54-2.92)Increased risk when exposed to simazine, lindane. Heptachlor.

cancer-prostate

Sweden Sharma-Wagner

[284] 2000 registry survey

dairy workers

SIR 1.01 (95%CI 0.76-1.31) [55 cases] cancer-prostate

USA Parker [285] 1999 cohort farmers Men whose usual occupation was farmer were at an increased risk of prostate cancer after adjustment for age, smoking, alcohol, and dietary factors (RR = 1.7; 95% CI = 1.0-2.7). Exclusion of well-differentiated, localized tumors slightly strengthened the association (RR = 2.0; 95% CI = 1.1-3.6). Risk was confined to older (age 70+ years) farmers (RR = 2.2; 95% CI = 1.1-4.3); we found no evidence of an effect among younger farmers (RR = 1.0; 95% CI = 0.4-2.1)

Prostate cancer

Sweden Dich [286] 1998 Cohort farmers SIR: 1.13 (95%CI 1.02-1.24). Born after 1935: PR 2.03 (0.82-4.19). For those born earlier than 1935 the SIR was 1.12 (1.01-1.24)

prostate

Canada Parent [287] 2000 case-control

male farmers Farmers (any exposure): 1.6 (95%CI 1.0-2.6)Farmers (>10 years of exposure): 1.1 (95%CI 0.5-2.4)Nursery workers (gardening – any exposure): 4.1 (95%CI 1.7-10.3)

cancer-kidney renal cell carcinoma

USA Krstev [288] 1998 Case-control

Gen.population

Farming was related to prostate cancer (OR = 2.17; 95% CI = 1.18-3.98). Risk was restricted, however, to short-term workers and workers in crop production. Risk was not limited to those farming after 1950

Prostate cancer

Europe Mannetje [289] 1999 multicenter case-control

tobacco preparers female

OR=3.1 (95% CI 1.1-9.3) cancer-bladder

Europe Mannetje [289] 1999 multicenter case-control

field crop and vegetable workers female

OR=1.8 (95% CI 1.0-3.1) cancer-bladder

Female-specific cancers

USA Alavanja [5] 2005 cohort female applicators

SIR 2.97, 95% CI 1.28-5.85 cancer-ovarian

USA Young [290] 2005 case-control

exposed to triazine herbicides

1.34; 95% CI 0.77-2.33 cancer-ovarian

USA Engel [291] 2005 cohort farmers Breast cancer standardized incidence ratios were 0.87 (95% confidence interval: 0.74, 1.02) for women who reported ever applying pesticides and 1.05 (95% confidence interval: 0.89, 1.24) for women who reported never applying pesticides. There was some evidence of increased risk associated with use of 2,4,5-trichloro-phenoxypropionic acid (2,4,5-TP) and possibly use of dieldrin, captan, and 2,4,5-trichlorophenoxyacetic acid (2,4,5-TP), but small numbers of cases among those who had

cancer-breast



personally used the pesticides precluded firm conclusions.

Italy Settimi [24] 1998 cohort greenhouse farmers

Observed cases:2 SMR=20.0 (95%CI 3.5–63.0) P=0.1

cancer-ovarian

Male-specifc cancers

Norway Kristensen [292] 1996 Cohort farmers Specific fertilizer regimens on the farm were associated with testicular cancer (rate ratio = 2.44; 95% confidence interval = 1.66-3.56), in particular nonseminoma (rate ratio = 4.21; 95% confidence interval = 2.13-8.32). The rate ratio estimates were highest for boys ages 15-19 years

Testicular cancer

sarcomas

USA Valery [209] 2005 Meta-analysis

Farmers periconception and gestation periods: pooled OR = 2.3, 95%CI 1.3-4.1 for children whose fathers had worked on farms and OR = 3.9, 95%CI 1.6-9.9 for those whose mothers had farmed. For the periconception and gestation periods, there was a 3.5-fold increased risk for those with both parents having farmed and a doubling of risk for those with at least one parent having farmed

Cancer – Ewing’s sarcoma

USA Moore [293] 2005 Case-control

Farmers Risk of ES was increased with probable parental exposure to wood dusts during their usual occupation post pregnancy (OR 3.2; 95%CI 1.1–9.2). Exposure to pesticides and farm animals were not significantly associated with the disease. A history of household pesticide extermination was associated with Ewing’s sarcoma among boys aged 15 or younger (OR= 3.0; 95%CI 1.1– 8.1), but not among girls or older boys.

Cancer – Ewing’s sarcoma

USA Valery [294] 2002 Case-control

farmers excess of case mothers who worked on farms at conception and/or pregnancy (odds ratio (OR) = 2.3, 95% confidence interval (CI) 0.5–12.0) and a slightly smaller excess of farming fathers. the risk doubled for those who ever lived on a farm (OR = 2.0, 95% CI 1.0–3.9), and more than tripled for those with farming fathers at conception and/or pregnancy (OR = 3.5, 95% CI 1.0–11.9)

Ewing sarcoma

Various Kogevinas [295] 1995 Nested case-control

Workers exposed to herbicides

Excess risk of soft tissue sarcoma was associated with exposure to any phenoxy herbicide [odds ratio (OR) = 10.3; 95% confidence interval (CI) = 1.2-91] and to each of the three major classes of phenoxy herbicides

sarcoma

Italy Cottoni [296] 1997 Case-control

Gen.population

the risk of having classic Kaposi sarcoma was significantly increased in subjects farming cereals

sarcoma



3.2.4 Immunologic disorders On the issue of immunologic disorders we were able to identify 52 studies. 35 studies were fit for inclusion, while 17 were excluded, because of study design restrictions .Most studies in this area were cross-sectional, discovering a high prevalence of atopy and a potential protective effect of being raised on a farm. Table 10. Summary of results of articles reviewed on the issue of allergy and farming presented by country of study, author, year of publication, study design, population studied and risk estimate.

Country Author Ref Year Study design

Population Findings

Greece Zekveld [297] 2006 Cross-sectional

Rural schoolchildren

prevalence of atopy: 24% Atopy and seasonal rhinitis were significantly and independently more common among first-born children.

Various Schram-Bijkerk, D.

[36] 2006 Cross-sectional

children bell-shaped dose-response relationship between mite allergen exposure and sensitization to mite allergens

UK Perkin [298] 2006 Cross-sectional

Children farmers' children had significantly less current asthma symptoms (adjusted odds ratio (OR), 0.67; 95% CI, 0.49-0.91; P = .01) and current seasonal allergic rhinitis (adjusted OR, 0.50; 95%r CI, 0.33-0.77; P = .002) but not current eczema symptoms (adjusted OR, 0.91; 95% CI, 0.68-1.21; P = .53) or atopy (adjusted OR, 0.68; 95% CI, 0.40-1.16; P = .15). Unpasteurized milk consumption was associated with a 59% reduction in total IgE levels (P P = .02).

Canada Dimich-Ward [38] 2006

cross-sectional livestock farmers

living in a farm residence in comparison with a rural non-livestock area: OR 0.51 (95%CI 0.30-0.85) for allergic rhinitis - OR 0.45 (95% CI 0.24-0.84) for atopic dermatitis

Greece Chatzi [299] 2006 cross-sectional

Grape farmers six of the twelve predefined groups of major pesticides were significantly related with allergic rhinitis symptoms. The highest risks were observed for paraquat and other bipyridyl herbicides (OR, 2.2; 95%CI, 1.0-4.8), dithiocarbamate fungicides (OR, 2.5; 95%CI, 1.1-5.3) and carbamate insecticides (OR, 3.0; 95%CI, 1.4-6.5)

Denmark Portengen [300] 2005 CS-CC pig farmers (adult)

Low atopic sensitization in adult pig farmers, due to endotoxin exposure. End.exp and atopic sensitization to common allergens inversely associated [ OR 0.03 (95% CI, 0.0-0.34) for 2-fold increase of endotoxin

Sweden Kronqvist [301] 2005 cross-sectional

greenhouse workers Working >10 years in a greenhouse: OR=1.7 (95% CI 0.5–5.9)

Various Kim [302] 2005 Review General population

spider mites such as citrus red mite (CRM), European red mite (ERM), and two-spotted spider mite (TSM) are important allergens in the development of work-related asthma in fruit-cultivating farmers. Moreover, outdoor spider mites are not only common sensitizing allergens, but are also associated with the prevalence of asthma among children and nonfarming adults living in rural areas

Netherlands Heederik [303] 2005 review Farming population

it is very likely that the protective effect of exposure to PAMPs such as endotoxin is not limited to childhood age. The protective effects that probably developed during childhood can still be observed at adult age

Greece Chatzi [304] 2005 cross-sectional grape farmers prevalence of atopy: 64.2%

Germany Schafer [305] 2005 Cohort Gen. population Fulltime farmers were sensitised less frequently (22.0%, 8.4%).

Italy Sisinni [306] 2004 cross-sectional

grape and olive farmers

Risk of sensitization: same as the general population.Prevalence of respiratory disease not statistically significantly different between the two groups

Poland Waluziak [307] 2004 case-control farmers

cereal farmers: OR 13.75; 95% CI 2.39, 78.83positive SPT to cereals (OR 26.92; 95% CI 5.33, 135.9)storage mites (OR 44.07; 95% CI 8.40, 231.1)

USA Naleway [39] 2004 review Farming population

The majority of published prevalence surveys report that asthma and atopy are less common among children living in rural areas. While many exposures differ in rural and urban areas of the world, several recent studies have suggested that agricultural exposures in



early childhood may decrease the risk of developing atopic disease. Livestock exposure, in particular, seems to be important

Netherlands Doekes [308] 2004 Cross-sectional

Greenhouse workers

Exposure to microbial biopesticides may confer a risk of IgE-mediated sensitization.

Sweden Braback [309] 2004 cohort farmers farming vs non-farming families: OR 1.00 (95%CI 0.93—1.07), 0.94 (95%CI 0.88-1.01) and 0.85 (95%CI 0.79-0.91 [conscripts]

Sweden Johansson [310] 2003 Cross-sectional

Greenhouse workers

predatory mites P. persimilis and H. miles can cause IgE-mediated sensitization among greenhouse workers

New Zealand Wickens [311] 2002 Cross-sectional

Rural schoolchildren

at least weekly consumption of yoghurt with hayfever (odds ratio (OR) = 0.3, 95% confidence intervals (CI) 0.1-0.7) and allergic rhinitis (OR = 0.3, 95% CI 0.2-0.7); any unpasteurized milk consumption with atopic eczema/dermatitis syndrome (AEDS) (OR = 0.2, 95% CI 0.1-0.8); cats inside or outside with hayfever (OR = 0.4, 95% CI 0.1-1.0) and AEDS (OR = 0.4, 95% CI 0.2-0.8); dogs inside or outside with asthma (OR = 0.4, 95% CI 0.2-0.8); and pigs with SPT positivity (OR = 0.2, 95% CI 0.1-0.9). - protective effect of early-life animal exposures -greater prevalence of allergic disease on farms.

Denmark Portengen [312] 2002 Cohort Farming students both being a farmer (ORs 0.62-0.75) and having had a farm childhood (ORs 0.55-0.75) appeared to contribute independently to a lower risk of sensitization to common allergens as assessed by SPT and IgE serology

Korea Kim [313] 2002 Cross-sectional

Rural population positive skin response rates to TSM were 4.2% of 1563 metropolitan subjects, 3.8% of 5568 non-metropolitan subjects and 6.5% of 1464 subjects living nearby apple farms, and that to CRM 15.6% of 8029 living nearby citrus farms. environmental exposure to these mites may represent an important risk factor in the sensitization and the clinical manifestations of asthma and rhinitis in children and adolescents living in rural and urban areas

Netherlands Groenewoud


Greenhouse horticulture

Work-related symptoms were reported in 53.8% of all cases. Sensitization to the bell pepper plant was found in 35.4%.

Netherlands Groenewoud [315] 2002

cross-sectional

flower greenhouse farmers Sensitization to Chrysanthemum pollen: 20.2%

Various Von Mutius [316] 2002 Review children

Children raised on a farm also have a decreased prevalence of atopic diseases. The protective effect of contact with livestock and poultry is consistent among several studies. Although the pathophysiologic mechanisms involved remain undefined, studies suggest that exposure to endotoxin and other components of bacteria may play an important role in protecting against childhood atopic diseases.

Various Monso [317] 2002 Cross-sectional

Greenhouse flower workers

Bronchial provocation challenge confirmed occupational asthma in three workers (7.7%), all of them sensitized to workplace flowers or molds. No cases of occupational asthma were found among nonsensitized growers. Poor ventilation proved to be a marginal risk factor for wheezing (air velocity: odds ratio, 0.11; 95% confidence interval, 0.01-1.04). Sensitization to flowers or molds was found in 13 of 38 growers (34.2%)

Sweden Klintberg [318] 2001 cohort farmers

The risk ratio (RR) for ever having asthma and/or allergic rhinoconjunctivitis was significantly lower among children of farmers compared to children of nonfarmers (RR=0.38, confidence interval (CI) 95% 0.19-0.77). there was a reduced risk among children of farmers for having both respiratory symptoms and sensitization to any International Study on Asthma and Allergy in Childhood allergen (RR=0.28, CI 95% 0.09-0.88).

Italy Astarita [319] 2001 Cross-sectional

farmers prevalence of positive SPT to TU was 6%. TU-induced allergic/nonallergic complaints accounted for 65% of farmers with challenge-confirmed occupational disease. In all farmers, sensitization to common allergens was a risk factor for both current occupational and nonoccupational complaints, while TU sensitization was a prominent risk factor for occupational complaints

Korea Park [320] 2000 cross-sectional citrus workers

Prevalence of citrus red mite sensitive asthma: 8.1% (11 cases) Prevalence of citrus red mite sensitive rhinitis: 18.4% (25 cases)

Spain Navarro [321] 2000 Cross-sectional

Greenhouse workers

prevalence of a positive SPT to TU was of 25%. Forty-five workers (19%) were TU-allergic, occurring more often in atopic greenhouse workers (P < 0.0001)



Spain Sanchez-Guerrero


Greenhouse carnation farmers

Skin prick test responses with carnation extract were positive in 15 of the 16 patients and negative in all control subjects = IgE mediated occupational allergy

Sweden Kronqvist [323] 1999 Cross-sectional

Dairy farmers prevalence of atopy was 26.7% among the farmers. Risk factors for asthma and rhinoconjunctivitis: sensitization to mites (OR = 5.8 vs. OR = 3.8) and pollens (OR = 10.3 vs. OR=5.8) - significant relationship between sensitization to mites and working time (OR = 5.2).

Sweden Kronqvist [324] 1999 Cross-sectional

Farmers (87% dairy farmers) from a region in Sweden

Immediate onset hypersensitivity was present in 41.7% of the 1015 farmers studied - in 1984 (40.0%). The prevalence of asthma had increased significantly during the previous 12 years (5.3% vs 9.8%), as had asthma in combination with rhinoconjunctivitis (3.7% vs 7.0%). Rhinoconjunctivitis, on the other hand, had not changed significantly (36.5% vs 33.1%) and remained one of the most common symptoms. The prevalence of storage mite allergy in the farming population in 1996 was 6.5% and constituted an important cause of allergic symptoms

Koream Kim [35] 1999 Cross-sectional

Citrus farmers positive rate of skin responses to one or more of 11 common inhalant allergens excluding citrus red mite was 17.1%, and if citrus red mite was included, 25.9% of farmers had positive responses. On skin prick tests, citrus red mite (16.5%) was the most common sensitizing allergen. Among farmers with asthma and allergic rhinitis, the positive skin responses to citrus red mite were noted in 54.5 and 68.5%, respectively.

Korea Kim [34] 1999 Cross-sectional

Apple farmers European red mite (23.2%) was the most common sensitizing allergen, followed by Tyrophagus putrescentiae (21.2%), two- spotted spider mite (16.6%). positive skin response rates to European red mite and two-spotted spider mite were 40.4% and 27.0%, respectively

New Zealand Kimbell-Dunn [325] 1999

Cross-sectional farmers

Asthma prevalence was higher for horse breeders/groomers (16.5%), pig farmers (18.2%), poultry farmers (17.4%), and those working with oats (17.4%). Asthma was also significantly elevated among those working with cleaning powders (14.7%). Women were more likely to report current asthma than were men (OR 1.8, 95% CI 1.3-2.5).

Israel Goldberg [326] 1998 cross-sectional flower workers

52% sensitization to flower allergens, much higher than the general population - 15% left their jobs (severe sensitization)



3.2.5 Non malignant respiratory disorders On the issue of agricukture-non malignant respiratory disorders we were able to identify 80 studies. 57 studies were fit for inclusion, while 23 were excluded, either because they provided no risk estimates (2) or because of study design restrictions (21). The most comprehensive study to date on health effects of respiratory hazards is that of the American Thoracic Society in 1998 [27]. The review includes a thorough hazard mapping in agriculture and discusses in detail all respiratory disorders that have been reported in the literature (airway disorders, interstitial lung disease, respiratory infections) until then. We opted to present the review article by Omland et al in 2002, which focuses on risk factors and non malignant respiratory disorders[28]. A summary of the most important findings by Omland et al is presented on table #11: Table 11. Main conclusions of the review article by Omland O, et al on non-malignant respiratory disorders in agriculture Agriculture-Non Malignant Respiratory disorders.

Agriculture - Non Malignant Respiratory Disorders Sensitization Increased risk of sensitization in Scandinavian farmers due to mites – among storage mites, Lepidoglyphus Destructor is more common. Protective effect of being raised on a farm and sensitization to common inhalant allergens.[In Swiss children: sensitization to outdoor allergens OR 0.38, 95%CI 0.16-0.87) – to indoor allergens OR 0.15, 95%CI 0.04-0.57)] – contact with farm animals associated with reduced risk of atopic sensitization Exposure to animal housings and consumption of raw cow milk – protective factors. Chronic bronchitis Wide range of prevalence (2%-32% depending on the production type), most common among farmers with livestock production compared to grain farmers – cohort studies suggest that chronic bronchitis is a work-related disease in farming Risk factors: atopy; smoking; swine farming; From cross-sectional studies: eczema or rhinitis; former smoking; livestock production; horses; >4h in confinement buildings; crop handling; hay handling; male gender; age; altitude of farming From cohort studies: working as a farmer Asthma Wide range of prevalence (0.7-21%) Current epidemiological evidence indicate that the prevalence of self-reported asthma in the farming population may not be increased; need for longitudinal studies of incidence Risk factors: age; family asthma; asthma/atopy as a child; gender (both); low FEV1; smoking ; animal production; pig farming; grain farming; flower growing; organic dust at work; carbamate insecticides Bronchial hyperresponsiveness Livestock farming is significantly associated with higher levels of bronchial hyperresponsiveness – range of prevalence 10.3-23.5% - cohort studies suggest that bronchial hyperresponsiveness is a work-related disease in farming Risk factors: exposure to ammonia; whood shavings as bedding; automatic dry feeding - in cross-sectional studies: age; baseline FEV1; wheezing during work; pellet feeding; location of air exhaust - in a cohort study: farming

Lung function Base line FEV1 or FEV1/FVC significantly reduced in farmers (8/12 studies involving a control group) Most evidence are provided for swine, dairy and potato production – varies by production type Cohort studies data: pig farming in swine confinement building poses a significant risk for annual loss of FEV1 by as much as 20-40ml. Other exposures (dairy, grain) less dangerous. Risk factors for decreasing lung function: smoking; bronchial hyperresponsiveness; disinfectants; automated dry feed system; endotoxin. Omland O: Exposure and respiratory health in farming in temperate zones – a review of the literature. Ann Agric Environ Med 2002;9;119-136



Studies on non malignant respiratory diseases and farming from 1995-2006 On table 12. 56 studies meeting the inclusion criteria are presented. Not only do they provide an update of the current knowledge of current effects of farming in respiratory health of the agricultural workers, but they also cover a significant portion of the production types and methods of agriculture in Europe. Infectious respiratory diseases are not presented on this chapter since they are discussed along with the rest of infectious hazards. Table 12. Summary of results of articles reviewed on the issue of non-malignant respiratory disorders and farming presented by country of study, author, year of publication, study design, population studied and risk estimate.


asthma

Canada Dimich-Ward [38] 2006 cross-sectional

livestock farmers

living in a farm residence in comparison with a rural non-livestock area: OR 0.49 (95%CI 0.27-0.89) for diagnosed asthma,

USA Hoppin [327] 2006 cohort pesticide applicators

8/16 pesticides associated with wheeze- Herbicide chlorimuron-ethyl: OR 1.62, 95%CI 1.25-2.10 - Elevated ORs for terbufos, fonofos, chlorpyrifos, phorate - Dose-response trends observed for chlorpyrifos, phorate and chlorimuron-ethyl- Strongest OR: 2.40, 95%CI 1.24-4.65 for applying chlorpyrifos >40 days/year

Greece Chatzi [304] 2005 cross-sectional grape farmers

asthma prevalence: 6.7% (not st.diff. compared to control group)

Brazil Faria [29] 2005 cross-sectional farmers

history of pesticide poisoning: OR 1.54, 95%CI 1.04-2.58

Brazil Faria [29] 2005 cross-sectional

female farmers

exposed to pesticides: OR 1.51, 95%CI 1.07-2.14

Turkey Akpinar-Elci [328] 2004 cross-sectional florists

prevalence of work-related asthma-like symptoms: 14.1% (risk factors: work intensity; work duration; size; atopy; family history)

Norway Eduard [329] 2004 case-control livestock farmers

Prevalence of asthma: 3.7% (physician-diagnosed), 2.7% (current asthma)cattle farmers: adjusted OR 1.8, 95%CI 1.1-2.8) and pig farmers: adjusted OR 1.6, 95%CI 1.0-2.5)- non-atopic asthma significantly higher in pig farmers: adjusted OR 2.0, 95%CI 1.2-3.3 and in farmers with >=2 types of livestock: adjusted OR 1.9, 95%CI 1.1-3.3- atopic asthma less common in farmers with >=1 types of livestock: adjusted OR 0.32, 95%CI 0.11-0.97

Norway Wijnand [330] 2004 case-control farmers

risk of asthma:OR 0.52 (95% CI 0.36-0.75);atopic (OR 0.33 (95% CI 0.15-0.69)non-atopic asthma (OR 0.60 (95% CI 0.39-0.93)asthma prevalence: 4.0% among farmers, 5.7% in the rural, and 7.6% in the urban population

Norway Eduard [331] 2004 Cross-sectional farmers

The asthma prevalence was 4.0% among farmers, 5.7% in the rural, and 7.6% in the urban population. Atopy was similar (9-10%). Most asthmatics were not atopic, 67-75%. Farmers had asthma less often than the general population OR 0.52 (95% CI 0.36-0.75); both atopic (OR 0.33 (95% CI 0.15-0.69)) and non-atopic asthma (OR 0.60 (95% CI 0.39-0.93)).

Europe Monso [332] 2003 cross-sectional

greenhouse flowers adjusted OR 1.71, 95% CI: 1.06–2.77

USA Hoppin [333] 2003 cohort livestock farmers

Wheezing and animal production exposures:Dairy cattle: OR 1.26 (95%CI 1.08-1.48), hogs: OR 1.13 (95%CI 1.03-1.23), sheep OR 1.10



(95%CI 0.91-1.34), poultry OR 1.36 (95%CI 1.13-1.62), eggs OR 1.70 (95%CI 1.28-2.26), any animal OR 1.13 (95%CI 1.04-1.23)- A significant dose response was observed for both the number of poultry and the number of livestock on the farm

Germany Radon [334] 2003 cross-sectional

sheep breeders

prevalence OR=2.1, 95% CI 1.5 to 3.0risk factors: full time farming (POR 1.6, 95% CI 0.93-2.7); use of chemical footbaths (POR 2.1, 95% CI 1.2-3.7)

Finland Koskela [335] 2003 Cross-sectional farmers

Current farming was found to decrease the risks of pet- and pollen-induced upper airway symptoms, dose-dependently with the intensity and duration of animal husbandry.

Spain Borghetti [336] 2002 cross-sectional

poultry farmers asthma prevalence: 35.7%

USA Hoppin [30] 2002 cohort pesticide applicators

Paraquat (OR 1.27, 95%CI 1.04-1.56), parathion (OR 1.5, 95%CI 1.04-2.16), malathion (OR 1.14, 95%CI 1.02-1.28), chlorpyrifos (OR 1.12, 95%CI 1.01-1.25) and thiocarbamate (OR 1.32, 95%CI 1.05-1.65) had elevated ORs for wheeze.Significant dose-response trends for chlorpyrifos, thiocarbamate, paraquat and parathion. Atrazine (OR 1.20, 95%CI 1.07-1.34) and alachlor (OR 1.24, 95%CI 1.09-1.42) were associated with wheeze. Those applying atrazine >20days/year: OR 1.5 (95%CI 1.2-1.9)


flower and/or ornamental plant growers

5.1% prevalence of asthma - working with flowers and ornaments: statistically significant risk factor for asthma (adjusted OR 2.1, 95%CI 1.1–3.9)

New Zealand Kimbell-Dunn [325] 1999

cross-sectional farmers

Asthma prevalence: horse breeders/groomers (16.5%), pig farmers (18.2%), poultry farmers (17.4%), and those working with oats (17.4%). Asthma was also significantly elevated among those working with cleaning powders (14.7%). Women were more likely to report current asthma than were men (OR 1.8, 95% CI 1.3-2.5). Hay fever was significantly higher in deer and crop farmers, and farmers working with horses and goats; eczema was higher for goat and deer farmers

Sweden Larsson [338] 1999 cross-sectional

poultry farmers

Bronchial responsiveness to methacholine increased approximately fivefold in all groups following exposure - no difference in symptoms

Norway Melbostad [339] 1998 Cross-sectional farmers

The lifetime prevalence of self-reported asthma in the population was 6.3%. Significant risk factors for current asthma were asthma among next-of-kin, asthma as child or adolescent, animal production, and age. In a comparison with subjects with no family history of asthma and no animal production the adjusted odds ratio for current asthma in never smokers was 1.9 [95% confidence interval (95% CI) 0.4-8.9] for subjects with family history only, 2.2 (95% CI 1.1-4.2) for subjects with animal production only, and 6.3 (95% CI 3.1-13.1) for subjects with both factors. A combination of animal production, smoking, and a positive family history gave an odds ratio of 8.1 (95% CI 4.0-16.2) for current asthma

Chronic obstructive disorder

Finland Laasonen [340] 2006 CS pig farmers

Prevelance of chronic bronchitis: (16% vs 9% of the control group - p=0.019); work-related symptoms: (48% vs 25%, p<0.001). Chronic bronchitis associated with pig farming OR 2.0 (95%CI 1.1-3.5). Working >6h/d: OR 3.9 (95%CI 1.1-13.9); >20 years: OR 3.3 (95%CI 1.4-7.8) - Statistically significant decline in FEV1 and FVC compared to controls (p<0.001) after adjustment



for age, atopy and smoking.

Brazil Faria [29] 2005 cross-sectional farmers

history of pesticide poisoning: OR 1.57, 95%CI 1.08-2.28

USA Blair [6] 2005 cohort farmers

COPD men SMR=0.2 (95%CI 0.1–0.3) spouses SMR=0.3 (95%CI 0.2–0.7) total SMR=0.2 (95%CI 0.2–0.3)

Brazil Faria [29] 2005 cross-sectional

female farmers

exposed to pesticides: OR 1.34, 95%CI 1.00-1.81

USA Gomez [341] 2004 cohort farmers

Wheeze prevalence: 18.2%Risk factors for wheeze: smoking; reaction to allergy skin testing; immunotherapy; insect sting; reactivity to a pet; having goats; acreage in corn for silage


livestock farmers

COPD prevalence: 17.1%Dust and endotoxin showed a dose-response relationship with COPD

France Chaudemanche [343] 2003 cc dairy farmers prevalence: higher in dairy farmers (p=0.013)


greenhouse flower cultivators

toxic pneumonitis (adjusted OR 1.75, 95% CI: 1.29–2.38) - chronic bronchitis (adjusted OR 1.67, 95% CI: 1.22–2.27). Increased risk for nonsmokers (Europe): - chronic bronchitis (adjusted OR 1.59, 95% CI: 1.02–2.48)

Poland Skorska [344] 2003 cross-sectional hop farmers prevalence: 34.8%

Italy Mastrangelo [345] 2003 case-control farmers OR = 15.1 (95%CI 3.2-71.6)

USA Lee [119] 2002 mortality livestock farmers PMR=183, 95%CI 147-226 (WM)

USA Lee [119] 2002 mortality crop farmers PMR=133, 95%CI 118-149 (WM), PMR=147, 95%CI 111-192 (BM)

Switzerland Danuser [346] 2001 Cross-sectional farmers

the prevalence rate was 16.0% for chronic bronchitis, 15.4% for asthma symptoms, and 42.0% for reporting at least one work-related symptom. . In crop farmers, the prevalance for chronic bronchitis was increased [OR 2.32 (1.03-5.23)]. Over 4 hr spent per day in animal confinement buildings more than doubled the risk for reporting chronic bronchitis [OR 2.61 (1.01-6.76)] and phlegm [OR 2.3 (0.99-5.4)] independent of the type of farming. The comparison of Swiss farmers with the Swiss population showed a twofold elevated risk of reporting chronic bronchitis [OR 1.89 (1.32-2.95)] and a 4.5-fold elevated risk for bringing up phlegm regularly [OR 4.5 (3.25-6.69)] in farmers.

Zimbabwe Osim [347] 1998 Cross-sectional

Tobacco farmworkers

Restrictive lung defect attributed to long-term exposure to flue curing and stacking of tobacco leaves

Lung functions

Various Radon [348] 2002 Cross-sectional farmers

pig farmers were at high risk of asthma-like syndrome as compared to farmers keeping other kinds of animals. Among plant farmers, greenhouse workers were at higher risk for symptoms of asthma. The prevalence of symptoms of allergies were significantly lower among animal farmers as compared to the population of the European Community Respiratory Health Survey. In contrast, animal farmers had a significantly higher prevalence of



symptoms of chronic bronchitis. The major risk factor for respiratory symptoms was shown to be ventilation of the animal houses and greenhouses.

France Venier [349] 2006 cohort dairy farmers

declined lung functionsrisk factors: age, lack of modernization, male sexno association: altitude; climate

Greece Chatzi [304] 2005 cross-sectional grape farmers

Lower FEV1 than controls (p=0.03); FVC and FEV1/FVC not st.diff than controls

Sri-Lanka Peiris-John [350] 2005 case-control crop farmers

FVC ratio lower in farmers between exposure seasons (p<0.001), further decreased during exposure season (p=0.023) and, lower in both periods (p<0.05) than controls

Yugoslavia Djuricic [351] 2004 cross-sectional pig farmers

no differences in the average values of FEV1 (p=0.574) and FEV1 % predicted (p=0.653) between pig farmers and control subjects

Canada Kirychuk [352] 2003 cross-sectional grain farmers lower FVC and FEV1 values than nonfarmers

France Chaudemanche [343] 2003 case-control dairy farmers

FEV1/VC: lower in dairy farmers (p<0.025)- Spo2: lower in dairy farmers (-0.7%, p<0.01) than in controls- farming: decline in FEV1/VC (p<0.025) after adjustment for covariates

Canada Kirychuk [352] 2003 cross-sectional

poultry workers

Cage-based workers: lower FEV1, FEF25-75 and FEV1/FVC than floor-based workers and lower than nonfarming controls—increased prevalence of respiratory symptoms

Greece Likas [353] 2001 Cross-sectional

Greenhouse workers

Lung function tests in 42 workers showed that temporary work did not influence significantly the respiratory health status

Canada Melenka [354] 1999 Cross-sectional

Crop and livestock farmers

Ten years of exposure to a moderate dust level was associated with a deficit of 43 ml in the FEV1 and a deficit of 0.44% in the FEV1/FVC

Netherlands Post [355] 1998 Cross-sectional farmers

annual decline in FEV1 and maximal mid-expiratory flow (MMEF) were significantly related to occupational exposure to dust and endotoxin in the grain processing and animal feed industry

Respiratory symptoms

Iran Hashemi [356] 2006 Cross-sectional

Sheep breeders and crop farmers

The proportions of sheep breeders with wheezing (16.5%), asthma (14%), cough (29%), breathlessness (31.5%) and flu-like illness (38%) were higher than in agricultural farmers. A significant dose-response relationship among the daily hours worked with animals, the number of animals and the prevalence of symptoms was established for sheep farmers. Sheep shearing and the use of pesticide were associated with an increased risk of wheezing and phlegm


poultry workers; immigrants

prevalence of respiratory symptoms: 14.5% (cause of disability for 11 workers)

Canada Pahwa [357] 2006 cross-sectional

grain workers

reduction in the prevalence of chronic respiratory symptoms after improvement of dust control measures - initial increasing trend of the prevalence of these symptoms

USA King [358] 2006 cross-sectional

poultry workers exposed to chlorine by-products

Work-related wheezing, coughing, sneezing, watery eyes more common among poultry workers (statistically significant); sore throat, burning/stinging eyes, asthma symptoms more common (non statistically significant difference); shortness of breath, chest tightness, itchy runny nose, stuffy nose not different among groups

Sweden Rylander [359] 2006 cross-sectional

poultry workers

significantly higher airway responsiveness among the workers compared to controls (decrease 9.5 SD 7.5 vs 3.4, SD 3.3) - FEV1 significantly lower in poultry workers than in controls (p<0.001) - respiratory symptoms more frequent in poulty workers (p<0.05)



Greece Chloros [360] 2004 Cross-sectional

Tobacco workers

The prevalence of chronic bronchitis was 8.7 % versus 20.6 % in controls. Chronic obstructive pulmonary disease was found in 13 workers (1.3 %) and in 16 controls (3.4 %). FEV1 %pred, FVC %pred and the FEV1/FVC ratio were lower in controls, whereas FEF25-75 % %pred was lower in workers. There were no workers with bronchial asthma or extrinsic allergic alveolitis. Rhinitis was reported by 27.3 % of the workers versus 17.9 % of controls, whereas nasal flows were 563+/-211 versus 645 +/- 321 ml/sec, respectively.

USA Netto [113] 2003 cohort poultry workers

Statistically significant PMR results in white males: non-malignant respiratory diseases (PMR=1.9, 95%CI 1.0-3.5)

Various Linaker [361] 2002 Review farmers

The most important diseases are rhinitis and asthma, which, although common, are not usually fatal. Some non-allergic conditions, e.g. asthma-like syndrome and organic toxic dust syndrome, are not yet fully understood, but appear to be common among farm workers. The most serious respiratory diseases are hypersensitivity pneumonitis and respiratory infections, but these are rare.

USA Sprince [362] 2000 Surveillance data analysis farmers

Among farmers, applying pesticides to livestock was associated with significantly increased odds of phlegm (OR = 1.91, 95% CI 1.02-3.57), chest ever wheezy (OR = 3.92, 95% CI 1.76-8.72), and flu-like symptoms (OR = 2.93, 95% CI 1.69-5.12) in models adjusting for age and smoking. Conventional vertical silos were significantly associated with increased odds of chest ever wheezy (OR = 2.75, 95% CI 1.23-6.12) and flu-like symptoms (OR = 2.40, 95% CI 1.31-4.37). There were also significant associations between several respiratory symptoms and the presence of animal confinement facilities on the farm.

USA Wilkins [363] 1999 cross-sectional grain farmers

prevalences: 9.4% (7.6-11.1%) for chronic cough, 10.8% (9.0-12.6%) for chronic phlegm, 16.2% (14.1-18.3%) for dyspnea, and 8.1% (6.4-9.8%) for non-cold wheezerisk factors: smoking, age, pet allergy. lifetime hours of cab tractor operation (cough); percent time spent farming (phlegm); having livestock other than cattle, cows, and calves (dyspnea); acres of corn for silage or green chop (cough); acres of alfalfa hay (non-cold wheeze); and personal involvement with pesticides (cough)

Poland Skorska [364] 1998 cross-sectional grain farmers

work-related symptoms during harvesting and threshing: 44.7% (dry cough [26.3%]; dyspnoea [19.7%]; tiredness [15.7%]; chest tightness [10.5%]; plugging of nose and hoarseness [6.5%] = high response of grain farmers to inhalant microbial allergens

South Africa Rees [365] 1998 cross-sectional

poultry farmers

More poultry workers than controls had symptoms consistent with asthma (e.g. 3%, 4%, 13% and 11% in controls and subjects with low, medium and high exposure, respectively) - More poultry workers than controls had positive immunodiffusion test reactions to chicken feed, feathers and serum, and IgE to chicken faeces

France Dalphin [366] 1998 cohort Dairy farmers

Dairy farmers consistently had more respiratory symptoms and lower levels of respiratory function than did control subjects. In the study populations as a whole, the mean annual decline in vital capacity (VC) and forced expiratory volume in one second (FEV1) was slightly, but nonsignificantly, higher in farmers than in control subjects: in mL x yr(-1) (SD), -43.1 (68.2) versus -37.9 (60.2) for VC and -32.8 (56.7) versus -30 (47.2) for FEV1. There was a positive interaction between farming and age (i.e. duration of exposure in this cohort) on respiratory function



decline, and in male subjects aged > or = 45 yrs, dairy farming was associated with an accelerated loss in VC

USA Garcia [367] 1996 Cross-sectional

Migrant farmworkers

Chronic respiratory symptoms (cough, wheezing, sputum production) in adult workers (n = 354) were elevated (8.5%, 6.2%, 6.5%, respectively) and were accompanied by physiologic abnormalities as determined by pulmonary function testing. Over 15% of the adult cohort exhibited a FEV1/FVC < 75, and over 14% had FEF25-75 values which were less than 60% of predicted. The observed airflow obstruction of both large and small airways was not explained by cigarette usage (43%) in the adult cohort (current/past smokers). Tuberculin skin tests (TST) were positive (> or = 10 mm) in 55/195 men and 35/123 women for a total prevalence of 28.3%

Various Do Pico [368] 1996 Review farmers

At risk are farmworkers and those involved in the processing, stocking, transportation, handling, and inspection of unprocessed agricultural, animal, and forestry products; veterinarians; gardeners; game, river, and forest keepers; persons involved in building, supplying, or servicing farm operations; and residents of rural communities

USA McCurdy [369] 1996 Cross-sectional Rice farmers

Chronic cough was reported by 7.1% of the subjects, chronic bronchitis by 6.3%, persistent wheeze by 8.8%, and hay fever by 25% of the subjects. Chronic cough was significantly associated with reported hours per year spent burning rice stubble. FEV1s were significantly, inversely associated with reported years spent working with heated rice dryers. Midexpiratory flows (FEF25-75) were significantly decreased.

Organic dust toxic syndrome / hay fever

Netherlands Smit [370] 2006 cross-sectional

livestock farmers

3-fold lower prevalence of hay fever in livestock farmers than crop farmers (OR 0.3, 95%CI 0.1-0.5)

Netherlands Smit [370] 2006 cross-sectional

organic farmers

higher prevalence of organic fever vs conventional farmers (9.3% vs 6.9) - but non statistical significant (OR=1.2, 95%CI 0.8-1.7)

Germany Von Ehrenstein [371] 2000 Cross-sectional

Farmers’ children

Farmers' children had lower prevalences of hay fever (adjusted odds ratio = 0.52, 95% CI 0.28-0.99), asthma (0.65, 0.39-1.09), and wheeze (0.55, 0.36-0.86) than their peers not living in an agricultural environment. Among farmers' children increasing exposure to livestock was related to a decreasing prevalence of atopic diseases (aOR = 0.41, 95% CI 0.23-0.74).

Netherlands Vogelzang [372] 1999 cross-sectional pig farmers

Prevalence of ODTS significantly greater than controls (6.4% vs. 2.6%, P , 0.05).

USA Von Essen [373] 1999 Cross sectional farmers ODTS prevalence: 36%



3.2.6 Reproductive / developmental disorders On the issue of reproductive/developmental disorders we were able to identify 24 studies. 22 studies were fit for inclusion, while 2 were excluded, either because they provided no risk estimates (1) or because of study design restrictions (1). The most extensive review was done by Hanke et al, published in 2004, focusing mainly on the effects of pesticides on the reproductive system and developmental disorders of the fetus. A summary of the most important findings of this review is presented below: Table 13. Summary results of review paper by Hanke W et al on Agriculture-Reproductive/developmental Disorders

Agriculture - Reproductive/Developmental Disorders Time to pregnancy (time needed to conceive) Only 3 out of 7 relevant studies reported statistically significant associations, other found no association

• application of pesticides solely by the male: fecundability ratio=0.46 (95%CI 0.28-0.77) • spraying season: fecundability ratio=0.42 (95%CI 0.20-0.92) • applying pesticides >100h/y in greenhouses: over 6 month delay of conception OR=2.4 (95%CI1.2-5.1) • Exposure to pyrethroids: fecundability ratio=0.40 (95%CI 0.19-0.85)

Semen quality -Working as a farmer: increased risk for specific morphological abnormalities of the sperm and decreased sperm count per ejaculate and percentage of viable sperm - no effects of pesticide exposure on sex hormones Infertility Farmers exposed to pesticides might have a significant risk for infertility - women in agriculture-related industries: OR=7.0 (95%CI 2.3-20.8) - women residing in a farm: OR=1.8 (95%CI 1.2-2.7) - reduced sperm count: OR 2.13 (95%CI 1.18-3.88) - fertilization rate: OR 0.22 (95%CI 0.06-0.80) for high pesticide paternal exposure Sex ratio Recent studies (5 out of 7) suggest a decreased male/female birth ratio. Other studies suggest opposite or non-existing effects. Spontaneous abortion Excessive risk of spontaneous abortion among female farmers and pesticide applicators (nine studies) Only one study (Indonesia) provided different results. Pesticides associated: phenoxy acetic acid, triazine herbicide, 2,4-D, 2,4-DB (early abortions), sulfonylurea, imidizolinone, 9100 mixture; glyphosate, thiocarbamate, fungicide (late abortion) Birth defects Increased risk of birth defects related to pesticide exposure. Increased risk of limb anomalies, orofacial cleft or birthmarks Infant of gardeners in Finland: musculoskeletal malformations OR=5.0 p<0.05 ; developmental defects in Quebec farmers: RR 4.5 p<0.05; increased hemangiomas in Bogota: OR=6.6 p<0.05; Washington state farmers: risk of limb defects OR=2.6 (95%CI 1.1-5.8) Spain: congenital malformations: OR 3.1 (95%CI 1.1-9.0) Finland: orofacial cleft: OR 1.9 (95%CI 1.1-3.5) Minnesota / cohort / pesticide applicators children with anomalies: OR 1.57 (95%CI 1.22-2.01) Increased risk for specific pesticides (trifluralin, atrazine, 2,4D, MCOA) or categories (fumigants, herbicides, phenoxyherbicides) Preterm delivery No relation between agricultural employment and increased risk of preterm delivery Birthweight / small-for-gestation-age



No increased risk for low birth weight and small-for-gestation-age. In Indonesia, among female pesticide applicators lower birth weight: RR=3.6 (95%CI 2.4-5.2) Stillbirth Most associations were positive:

• Canada: OR 3.1 (95%CI 1.1-8.6) for stillbirth without major malformations • Norway: OR 2.4 (95%CI 1.0-5.9) for stillbirth due to congenital anomalies • California: RR 1.4 (95%CI 1.0-1.7) for stillbirth due to all causes of death

Hanke W et al: The risk of adverse reproductive and developmental disorders due to occupational pesticide exposure. IJOMH 2004;17(2):223-243

Table 14. Summary of results of articles reviewed on the issue of reproductive-developmental disorders and farming presented by country of study, author, year of publication, study design, population studied and risk estimate.

Country Author Ref Year study design

population risk estimate topic

USA Farr [374] 2006 cohort

Premenopausal women 35-55

the median time to menopause increased by approximately 3 months for women who used pesticides (hazard ratio = 0.87, 95% confidence interval: 0.78, 0.97) and by approximately 5 months for women who used hormonally active pesticides (hazard ratio = 0.77, 95% confidence interval: 0.65, 0.92).

Repr - menopause

Norway Kristensen [375] 2000 Cohort Female farmers

Categories of high exposure were associated with reproductive outcomes and cancer among female farmers, the strongest occurring for late-term abortion (ratio 2.6, 95% confidence interval (95% CI) 1.6-4.3). abortion

Netherlands Bretvend [376] 2006 Case control

Female greenhouse workers

The crude fecundability ratio was statistically significant and indicate that work in greenhouse may impaired fecundability, but the correction for confounding changed the fecundability ratio crude fecundability ratio OR= 1.18 95%CI (1.03-1.35) correction for confounding OR= 1.11 95%CI (0.96-1.29) restricted analyses to full-time worker OR= 0.89 95%CI (0.67-1.19 restricted analyses to first pregnancies OR= 0.90 95%CI (0.62-1.32) gathering flowers OR= 0.46 95%CI (0.18-1.19)

Repr – fecundability ratio

USA Sallmen [48] 2006 cohort farm families

Exposure to solvents and subfertility: Female (OR 1.42, 95% CI 1.15-1.75); (OR 1.21, 95%CI 0.93-1.57) for monthly exposure - 1.40 (95% CI 0.97 to 2.03) for daily or weekly exposure Parental exposure: one parent (OR 1.62, 95%CI 1.20-2.17); both parents: OR 2.10 (95%CI 1.22-3.60)

repr-subfertility

Denmark Abell [377] 2000

Cross-sectional

Greenhouse flower workers

the median values of sperm concentration and the proportion of normal spermatozoa were 60% and 14% lower, respectively, in the high-level exposure group (N=13) than in the low-level group (N=44). The median sperm concentration was 40% lower for the men with > 10 years' experience in a greenhouse than for those with < 5 years' experience.

Semen quality

Italy Lauria [378] 2006 Case control

Female greenhouse flower workers

No relation between time-to-pregnancy and working in greenhouses (with potentially high exposure to pesticides) HR = 0.96 (95%CI: 0.81, 1.13)

Repr – time to pregnancy

Denmark Abell [379] 2000

Cross-sectional

Greenhouse female workers

Adjusted fecundability ratio for workers in flower greenhouses versus other union members was 1.11 [95% confidence interval (95% CI) 0.90-1.36]. Among workers in flower greenhouses the handling




of cultures many hours per week, the spraying of pesticides, and the nonuse of gloves was related to reduced fecundability [adjusted fecundability ratio 0.69 (95% CI 0.47-1.03), 0.78 (95% CI 0.59-1.06), and 0.67 (95% CI 0.46-0.98), respectively].

Finland Sallmen [380] 2003

Cross-sectional

Greenhouse male workers

Fecundability was suggestively decreased for exposed greenhouse workers who were inefficiently protected, with FDR values of 0.67 [95% confidence interval (95% CI) 0.33-1.35], 0.92 [95% CI 0.45-1.88] and 0.77 [95% CI 0.46-1.29] for high exposure, moderate exposure and low exposure, respectively, as compared with unexposed greenhouse workers


Italy Petrelli [381] 2001 Case-control

Greenhouse male workers

increase in the risk of conception delay among the greenhouse workers with high exposure (OR:2.4; 95% CI: 1.2–5.1).


France, Denmark Thonneau [382] 1999 Cohort farmers

In France, the adjusted fecundability ratio was 1.17 (95% confidence interval (CI) 0.89-1.55) for exposed and nonexposed agricultural workers. In Denmark, it was 1.09 (95% CI 0.82-1.43) for exposed and nonexposed farmers and 0.83 (95% CI 0.69-1.18) for greenhouse workers and nonexposed farmers.


Poland Jurewicz [383] 2005 Case control

Female greenhouse workers under 45

The mean birth weight of infants whose mothers worked in greenhouse during pregnancy (heavy work) was 177 g lower than that of those whose mothers worked out of greenhouse (light work) (p = 0.05). Potential exposure to pesticides was not a statistically significant factor for the lower birth weight observed (-70g)

Repr- birth weight

Mexico Idrovo [384] 2005 Cohort

Female floriculture workers

Work in flower production, irregural relationship, illness and tabacco exposure would be associated with impaired fecundability - irregural relationship with partner OR= 0.82 95%CI (0.73-0.91) - illness in the year prior to pregnancy OR= 0.78 95%CI (0.62-0.98) - tobacco smoke OR= 0.71 95%CI (0.59-0.85) - work in flower production <24 months OR= 0.86 95%CI (0.75-0.98) - work in flower production >24 months OR= 0.73 95%CI (0.63-0.84)

Repr- fecundability

Norway Norby [385] 2005 cohort farmers

Neural tube defects (131 cases, prevalence 12.8/10 000 births) was moderately associated with potato cultivation (PR 1.6, 95% CI 1.1-2.3) and paternal work of > 500 hours/year (PR 1.6, 95% CI 1.1-2.5).

Dev – neural tube defects

USA Farr [386] 2004 cohort pesticide users

Longer menstrual cycles and increased odds of missed periods (OR = 1.5, 95%CI 1.2-1.9) - for probable hormonally active pesticides: 60-100% increased chance of long cycles, missed periods, and intermenstrual bleeding compared to women never used pesticides

repr.-menstruation disorders

USA Rull [387] 2006

pooled case-control

pesticide exposed

Neural tube defects: increased risk among immigrant Latina mothers, of lower education, unemployed during pregnancy - in single-pesticide logistic models, elevated Ors for NTDs were observed for several pesticides - in multi-pesticide model: benomyl: OR 2.3 (95%CI 0.9-5.6), naled: OR 2.7 (95%CI 0.9-8.2) - spina bifida and chlorpyrifos (OR 1.5, 95%CI 0.9-2.7) - anencephaly and naled (OR 2.0, 95%CI 0.9-4.3)

repr.-developmental disorders

Mexico Lacasana [388] 2006 Case-control

Female farmers

The children of mothers who worked in agriculture in the acute risk periodhad a greater risk of dev



anencephaly (OR = 4.57, 95% CI 1.05 to 19.96). The risk of fathers having a child with anencephaly was greater in those who applied pesticides irrespective of whether it was done in the ARP or the non-acute risk period (OR = 2.50, 95% CI 0.73 to 8.64; and OR = 2.03, 95% CI 0.58 to 7.08, respectively

South Africa Heeren [389] 2003 Case control

Rural women

Babies with birth defects were seven times more likely to be born to women exposed to chemicals used in gardens and fields compared to no reported exposure (Odds Ratio 7.18, 95% CI 3.99, 13.25); and were almost twice as likely to be born to women who were involved in dipping livestock used to prevent ticks (OR 1.92, 95% CI 1.15, 3.14). They were also 6.5 times more likely to be born to women who were using plastic containers for fetching water (OR 6.5, 95% CI 2.2, 27.9).

Dev – child birth defects

USA Engel [390] 2000 Cohort farmers

Elevated risk of limb defects was observed for the exposed group in comparison with both the nonagricultural and paternal agriculture groups, with ethnicity-adjusted prevalence ratios of 2.6 [95% confidence interval (95% CI) 1.1-5.8] and 2.6 (95% CI 0.7-9.5), respectively.

Dev – mat.exposure to pesticides

USA Shaw [391] 1999

Cross-sectional Farmers

Paternal occupational exposure to pesticides, as reported by the mother, revealed elevated ORs for only two of the cleft phenotypes inverted question markOR = 1.7 [95% confidence interval (CI) = 0.9-3.4] for multiple cleft lip with/without cleft palate and OR = 1.6 [95% CI = 0.7-3.4] for multiple cleft palate inverted question mark dev

Norway Kristensen [392] 1997 Cohort Female farmers

moderate increases in risk for spina bifida and hydrocephaly, the associations being strongest for exposure to pesticides in orchards or greenhouses [spina bifida: 5 exposed cases, odds ratio (OR) = 2.76, 95% confidence interval (CI) = 1.07-7.13; hydrocephaly: 5 exposed cases, OR = 3.49, 95% CI = 1.34-9.09]. Exposure to pesticides, in particular in grain farming, was also associated with limb reduction defects (OR = 2.50; 95% CI = 1.06-5.90). dev

USA Garry [393] 1996 cohort

Pesticide applicators

The birth defect rate for all birth anomalies was significantly increased in children born to private appliers birth anomalies per/1000 live births: 30.0 for private appliers versus 26.9 for the general population of the same region. The lowest rates, 23.7/1000 for private appliers versus 18.3/1000 for the general population, occurred in noncorp regions. The male/female sex ratio of high phenoxy herbicide/fungicide use is 2.8 for appliers versus 1.5 for the general population of the same region (p = 0.05). In minimal use regions, this ratio is 2.1 for appliers versus 1.7 for the general population. various

3.2.7 Neuropsychological disorders On the issue of agriculture-mental disorders we were able to identify 39 studies. 32 studies were fit for inclusion, while 6 were excluded, either because they provided no risk estimates (4) or because of study design restrictions (2). Among the most imortant findings, we have observerd excess risk of psychiatric mortality and morbidity; potential association with Parkinson’s disease; decline in cognitive and neurological function attributed to pesticide exposure. Among them most extensive review was done by Fraser et al, published in 2005. A summary of the most important findings of this review is presented below:



Table 15. Summary of conclusions of review article by Fraser et al on Agriculture-Mental disorders. Agriculture - Neuropsychological Disorders Farmers: one of tthe highest rate of suicides, increased risk of mental health problems. Studies have found higher rates of depression and anxiety among farmers compared to the general population, but there also contradicting studies on the same subject. No date to estimate the risk of psychiatric morbidity. Risk factors identified for stress and mental illness among farmers:

• economic concerns and pressures • bureaucracy • geographic isolation

Farmers live in close proximity with relatives; blurred roles between work, home and family; tensions and increased responsibilities; Shortness of communication among family members Younger generation and daughter-in-laws in farms experience the highest levels of stress Depression symptoms may be attributed mainly to family problems -Other studies suggest that financial issues contribute more significantly in suicide and depression than family problems. Children: may develop mental health problems due to accidents/injuries -farm life: develops positive qualities Men: farming men tend to have more financial problems and work-related stress than non-farmers -higher suicide rates in Australia and UK than the national average; but lower prevalence of psychiatric morbidity but still higher frequency to report of a non-worth living life (OR 2.56, 95%CI 1.39-4.69) - Easer access to more effective means of suicide; firearms are the weapon of choice, significantly higher than in the general population Women: literature reports on high prevalence of stress, depression and fatigue - women: higher levels of anxiety than men - heavier burden due to financial problems Seasonal farm workers: high level of stress and mental health symptoms; factors includes less access to health providers, unpredictable work future less access to mental health professionals is considered, but the same rate of mental health usage among farm and nonfarm communities has been reported. Fraser C et al : Farming and mental health problems and mental illness. Int. J Social Psychiatry 2005;51(4) [68]

Table 16. Summary of studies examining the relationship of neural and mental disorders with agricultural occupation by country, author, year, study design, population exposed and findings.

Country Author Ref Year Study design

Population Findings

Australia Judd [65] 2006 cross-sectional farmers

No evidence that farmers have more often psychological problems. Risk factors for suicide: individual personality; gender (male>female); help-seeking attitude

UK Stark [394] 2006

death registry survey farmers

overall suicide rate= 31.4/100,000 per year (95% CI 28.1-35.1); main weapon: firearms (29%); no association between farmer suicide area and suicide rate among general population in that area

USA Blair [6] 2005 cohort farmers

males: SMR=0.6 (95%CI 0.5–0.9) spouses: SMR=0.7 (95%CI 0.3–1.5) total: SMR=0.6 (95%CI 0.5–0.8)

UK Sanne [70] 2004 cross-sectional farmers

Compared with non-farmers, farmers had higher levels and prevalences of depression, particularly the male farmers, who also had higher anxiety levels.

Japan Nishimura [395] 2004

National data analysis

Farmers and fishermen

suicide rate was positively associated with primary industry percentage with significant tendency while it was significantly and negatively associated with annual total sunshine.

USA

Van Winjngaarden [63] 2003

death registry survey farmers

Mortality for psychiatric disorders and occupations with pesticides exposure: OR=1.46 (95%CI 1.33-1.6)



USA Kamel [396] 2003 cross-sectional

general farmers

Ever having done farm work was associated with poor performance on four tests—digit span [odds ratio (OR) = 1.90; 95% confidence interval (CI), 1.02–3.53], tapping (coefficient = 4.13; 95% CI, 0.00–8.27), Santa Ana test (coefficient = 1.34; 95% CI, 0.29–2.39), and postural sway (coefficient = 4.74; 95% CI, –2.20 to 11.7). Long-term experience of farm work is associated with measurable deficits in cognitive and psychomotor function

UK Thomas [397] 2003 Cross-sectional farmers

only 6% of farmers reported clinically relevant psychiatric morbidity. Psychiatric morbidity was not significantly associated with farm type or size in this study. Farmers reported a lower prevalence of psychiatric morbidity than the general population but were more likely to report thinking that life is not worth living, particularly after the low prevalence of psychiatric morbidity had been taken into account (odds ratio 2.56, 95% CI 1.39 to 4.69). When restricting the comparison to only rural or semirural householders, this increased risk was even more pronounced (odds ratio 3.26, 95% CI 1.51 to 7.02)

Australia Page [398] 2002

National data analysis farmers

Estimated age standardized rates per 100 000 for male farm managers for the 10-year period ranged from 24.8 to 51.4. For male agricultural labourers these rates ranged from 23.5 to 41.9. Analyses also revealed a negative correlation (r = -0.75, p < 0.01) between farm manager suicide rates and farmers' terms of trade. Male farm manager and agricultural labourer suicide rates are higher than male national rates and rates in the wider rural population, particularly in the later years of the period investigated

USA Hovey [73] 2002 Cross-sectional

Migrant farmers

Migrant farm workers with heightened levels of acculturative stress were more likely to report high levels of anxiety and depression. Family dysfunction, ineffective social support, low self-esteem, lack of agreement with the decision to migrate, high education levels, high levels of acculturative stress, and high levels of anxiety were significantly associated with high depression levels. The overall findings suggest that migrant farm workers who experience elevated levels of acculturative stress may be at risk for experiencing high levels of anxiety and depression.

UK Gregoire [67] 2002 Review farmers

Farmers are subject to a number of unique occupational stressors, many of which have been aggravated in recent years by changes in farming practice and by economic factors. These are probably part of the explanation for the high rates of suicide in farmers and farm workers, which in the UK account for the largest number of suicides in any occupational group. Suicide is usually associated with mental illness, which, in farming communities, appears to be particularly stigmatized and poorly understood.

USA Lee [119] 2002 mortality farmers

All mental disorders (excluding schizophrenia and retardation): PMR=115, 95%CI 103-127 (Black Male) Specific nonpsychotic mental disease following organic brain damage: PMR=113, 95%CI 104-123 (White Male), PMR=162, 95%CI 101-245 (Black Female)

USA Carruth [72] 2002 cross-sectional

female farmers

24% of the 657 farm women reported depressive symptoms (95% C.I. 20.9–27.5). >65 years of age had the highest prevalence of depressive symptoms compared to the other age groups

UK Booth [69] 2000 Cross-sectional farmers

high levels of occupational stress in farming families. 35% of respondents scored positively on the General Health Questionnaire (GHQ) with female respondents showing significantly higher scores than males. A significant proportion of respondents also showed elevated levels of anxiety and depression as measured by the Hospital Anxiety and Depression Scale (HAD)

UK Booth [399] 2000 Case-control

Farmers having committed suicide

Farmers were significantly more likely to use firearms to kill themselves (42% of farmers v 11% controls). They were less likely to use a car exhaust or to die by poisoning (9% farmers v 50% controls). Farmers were significantly less likely to leave a suicide note (21% farmers v 41% controls).

UK Eisner [400] 1999

Comparative analysis

Beef farmers

the overall rates of depression and anxiety fell in both groups between 1994 and 1996, with the rates falling significantly more in the control group. However, the farmers were still more depressed and anxious than the controls, and those farmers that had been depressed or anxious in 1994 were more likely to be depressed or anxious in 1996.

USA Kupersmidt [401] 1997

Cross-sectional

Farmworker children

66% of the children had one or more psychiatric diagnoses based on mother or child reports, with anxiety disorders being the most prevalent diagnosis

Parkinson’s disease



USA Ascherio [78] 2006 cohort farmers

All (exposed vs not exposed): RR 1.8 (95%CI 1.3-2.5; p=0.0003) after adjustment Non-farmers (exposed vs non-exposed: RR 1.7 (95%CI 1.2-2.5) Farmers (exposed vs non exposed non farmers): RR 1.6 (95%CI 0.9-2.7)

USA Kamel [79] 2005 cohort farmers

Self-reported neurologic symptoms Lifetime days of insecticide use: 1-50 days OR 1.64 (95%CI 1.36-1.97), 51-500 days OR 1.89 (95%CI 1.58-2.25), >500 days OR 2.50 (95%CI 2.00-3.13) (compared to never users) - Modest association with fumigants (OR 1.50 (95%CI 1.24-1.81) for >50 days), and weaker relationships for herbicides (OR 1.32, 95%CI 0.99-1.75 for >500days) and fungicides (OR 1.23, 95%CI 1.00-1.50 for >500days) - Associations were strongest for organophosphates and organochlorines

USA Lee [119] 2002 mortality livestock farmers PMR=119, 95%CI 105-134 (white male)

USA Petrovitch [402] 2002 cohort

plantation workers

Men ever worked in a plantation vs never worked: RR 1.9 (95%CI 1.0-3.5) Non-significant positive trend between exposure to pesticides and risk for PD

Various Priyadarshi [403] 2000

meta-analysis farmers

exposed to pesticides: OR=1.85 (95%CI 1.31-2.60) (all studies); OR=2.16 (95%CI 1.95-2.39) (US studies) farmers : OR=1.42 (95%CI 1.05-1.91) (all studies); OR=1.72 (95%CI 1.20-2.46) (US studies) rural residents: OR=1.56 (95%CI 1.18-2.07) (all studies); OR=2.17 (95%CI 1.54-3.06) (US studies) well water drinkers: OR=1.26 (95%CI 0.97-1.64) (all studies); OR=1.44 (95%CI 0.92-2.24) (US studies) farmers: combined OR=1.94 (95% CI 1.49-2.53) (all studies); OR=2.15 (95% CI 1.14-4.05) (US studies).

Pesticide related mental effects

Spain Roldan-Tapia [77] 2005

case-control

greenhouse pesticide applicators

Subjects with >10 years of using pesticides: - worsened perceptive function performance OR=6.93 (95% CI:1.52-31.51)- visuomotor praxis OR=5.00 (95% CI:1.22-20.40) - integrative task performance time OR=4.12 (95% CI:1.18-14.39)

UK Tahmaz [75] 2003 case control

sheep farmers exposed to Ops

Higher chronic fatigue scores were associated with higher exposure to organophosphate pesticides

USA Stallones [404] 2002 Cross-sectional Farmers

Symptoms that were significantly associated with a previous poisoning were difficulty concentrating (OR 2.07, 95%CI 1.22-3.50); relatives noticing person had trouble remembering things (OR 2.54, 95%CI 1.47,-4.39); making notes to remember things (OR 2.18, 95%CI 1.20-3.97); finding it hard to understand the meaning of newspapers, magazines, and books (OR 1.90, 95%CI 1.01,-3.60); felt irritable (OR 1.84, 95%CI 1.08-3.12); felt depressed (OR 2.82, 95%CI 1.65-4.81); had heart palpitations without exertion (OR 2.83, 95%CI 1.22-6.54); sleeping more 6than usual (OR 3.58, 95%CI 1.95-6.58); difficulty moving fingers or grasping things (OR 2.08, 95%CI 1.06-3.24); and headaches at least once a week (OR 1.85, 95%CI 1.06-3.24). Increased odds of illness were being male, being depressed, sleeping too much, and using crop organophosphates

USA Stallones [405] 2002 Cross-sectional Farmers

Depression associated with pesticide related illness OR: 5.87, 95%CI 2.56-13.44)

UK Pilkington [406] 2001 cross-sectional

sheep farmers exposed to OPs

strong association between exposure to OP concentrate and neurological symptoms; weak evidence of a chronic effect of low dose cumulative exposure to Ops

USA Stallones [404] 2002 Cross-sectional farmers

increased odds of illness: being male, being depressed, sleeping too much, and using crop organophosphates

France Helmer [407] 2001 Cohort farmers

the risk of dementia with parkinsonism seemed to he increased in farmers in comparison with professionals and managerials, particularly among women (RR 7.47; 95%CI, 1.80-31.07)

US Beach [408] 1996 Cross-sectional

Sheep breeders exposed

differences in neurological examination detected between groups were subtle and their clinical significance was unclear



to OPs

Poland Bazylewicz-Walczak [409] 1999

Cross-sectional

Female greenhouse workers

exposed female workers were characterized by longer reaction times and reduced motor steadiness compared to the unexposed workers. In addition, increased tension, greater depression and fatigue, more frequent symptoms of CNS disturbances were observed in the exposed women compared to the controls.

South Africa London [410] 1998

Cross-sectional

Pesticide applicators

Compared with nonapplicators, current applicators reported significantly more dizziness, sleepiness, and headache and had a higher overall neurological symptom score (OR 2.25, 95%CI 1.15-4.39. Previous pesticide poisoning was significantly associated with the neurological scores (OR 4.08, 95% CI 1.48-11.22)

US Stephens [411] 1995 Cross-sectional

Sheep farmers exposed to OPs

farmers performed significantly worse than controls in tests to assess sustained attention and speed of information processing. These effects remained after adjustment for covariates. The farmers also showed greater vulnerability to psychiatric disorder than did the controls as measured by the General Health Questionnaire. There were no observed effects on short-term memory and learning



3.2.8 Skin disorders (non-malignant) On the issue of skin disorders (non-malignant) we were able to identify 25 studies. 19 studies were fit for inclusion, while 6 were excluded, either because they provided no risk estimates (4) or because of study design restrictions (2). Table 17. Summary of studies examining the relationship of Skin disorders (non-malignant) with agricultural occupation by country, author, year, study design and population.



poultry workers; immigrants prevalence of skin symptoms: 21.5%

Greece Chatzi [412] 2006 case-control grape farmers

Significantly higher in grape farmers than in controls:- self-reported itchy rash (adjusted OR=2.31, 95% CI 1.10-4.84, p<0.05) - work-related itchy rash (adjusted OR=4.08, 95% CI 1.01-20.33, p<0.05)

Greece Chatzi [412] 2006 Cross-sectional Grape farmers

Self-reported itchy rash (OR, 2.31; 95%CI, 1.10-4.84, P<0.05) within the last 12 months, and work-related itchy rash (OR, 4.08; 95%CI, 1.01-20.33, P<0.05) were significantly higher in grape farmers than in controls, after adjusting for age and sex

USA McCall [413] 2005 claims filed survey

general farming Estimated claim rate: 13.10 / 100,000 workers (95%CI 11.83-14.37)

USA McCall [413] 2005 Sick claim records farmers

Employees in the farming, forestry, and fishing occupations and industries had significantly higher claim rates compared with employees in other occupations.

USA Susitaival [414] 2004 cross-sectional

crop farm operators

Self-reported dermatitis: 8.9% male; 15.8% femaleRisk factors: femage gender (OR 2.0, 95%CI 1.3-3.0); respiratory atopy (OR 1.4, 95%CI 1.01-1.90)

USA Susitaival [414] 2004 cross-sectional grape farmers

Male grape farmers had the highest prevalence of self-reported dermatitis (15.5% for small scale and 9.8% for large-scale grape farming).

Poland Spiewak [415] 2003 survey of claims filed

general farming

Occupational dermatosis incidence:1992: 0.006/10,000/year; 1999: 0.189/10,000/yearMost common: allergic contact dermatitis (86%), infectious skin diseases (10%), irritant contact dermatitis (3%) and urticaria (2%); Causes: most common plant dusts (38%)

Poland Spiewak [416] 2001 Cross-sectional farmers

14 farmers (19.2%) complained of work-related skin symptoms, caused most often by hops (11%), followed by grain (5.6%), hay (5.5%) and straw (4.1%). Five farmers (6.8%) complained of hand dermatitis, four (5.5%) of airborne dermatitis, and eight (11.0%) of pruritus. The skin symptoms were mostly mild, however, one case of severe invalidating airborne dermatitis to hops was found.

USA Park [183] 2001 Cross-sectional farmers

9.6% of male farmers and 14.4% of wives of farmers reported dermatitis during the previous 12-month period. Risk factors among male farmers: history of allergy (OR 8.2; 95%CI 2.0-33.3). Risk factors among female farmers: some college education (OR, 3.4; 95% CI, 1.1 to 9.9); exposure to petroleum products (OR, 3.1; 95% CI, 1.3 to 7.0)

Various Spiewak [51] 2001 Review

Farmers exposed to pesticides

Contact dermatitis [Japan] Matsushita (1980) / general population / 33.6% prevalence of pesticide related contact dermatitis [Taiwan] Guo (1996) / fruit farmers / 30% prevalence of pesticide related contact dermatitis [Spain] Garcia-Perez (1984) / farmers exposed to carbamates and mercury / 3fold elevated prevalence [Poland] Luty (2000) / hop growers / 25.1% prevalence of contact allergy Urticaria Case reports only Erythema multiforme Case reports only Parakeratosis variegate Case reports only Porphyria cutanea tarda Case reports and description of an outbreak



after eating contaminated seeds Chloracne Related to pesticide spraying: only case reports Hypopigmentation of the skin Case reports only Hair loss Case report (DDT exposure) Nail dystrophy Described in pesticide sprayers exposed to herbicides, insecticides diquat, paraquat and dinitroorthocresol Skin cancer 8 of 12 studies of cancer among farmers: excess in melanoma incidence (attributed to sunlight exposure and to pesticides) Increased risk for lip and skin cancer among pesticide applicators Increased risk for arsenic pesticides – carcinogenic effect may appear decades later from exposure

USA Park [320] 2000 cross-sectional

general farming

Prevalence of dermatitis: male (9.6%), female (14.4%)Risk factors:- History of allergy (OR 8.2, 95%CI 2.0-33.3)Dermatitis among farmer wives:- some college education: OR 3.4; 95%CI 1.1-9.9- exposure to petroleum products: OR 3.1, 95%CI 1.3-7.0

New Zealand

Kimbell-Dunn [325] 1999

cross-sectional grain farmers

Prevalence of self-reported eczema/skin allergy: Grain handling: 28% (p<0.05) compared to other farmers

USA Burnett [417] 1998

Analysis of employer reports farmers

(1993): estimated 8,835 cases of occupational dermatitis – rate: 1.12/10,000 workers. The largest number of cases was in health services, while the highest rate was in agricultural crops.

Poland Spiewak [418] 1998 review farmers mycoses are the most prevalent skin diseases in farmers, and may be present even in over 20% of the population.

Denmark Paulsen [419] 1998 Cross-sectional

Gardeners and greenhouse workers

lifetime prevalence of occupational dermatitis: 19.6%. risk factors Occupational mucosal symptoms; working with Compositae family of plants; training as a gardener in females (significantly associated with an increased prevalence of occupational eczema)

Equador Cole [420] 1997 Cross-sectional Potato farmers

OR for dermatitis with years of pesticide use equaled 1.12 for field workers and applicators. For dermatitis, ORs of 1.42 and 1.49 were calculated for the application practices used by field workers and applicators, respectively

Australia Pirkis [421] 1997 Cross-sectional

Animal farmers

over 21% of respondents had experienced dermatitis symptoms within the previous two years; 14.8% within the previous 12 months: and 6.7% had current symptoms

Finland Kanerva [422] 1996 Register analysis farmers

The occupations with the highest numbers of occupational contact urticaria were farmers (341 cases out of 815 reported)

Taiwan Guo [423] 1996 Cross-sectional Fruit farmers

40% of farmers were sensitive to agricultural chemical allergens, which was about twofold higher than that of the comparison group. Farmers were most frequently sensitive to Captofol, Folpet, and Captan which were associated with dermatitis on the volar aspects of the hands.

Finland Susitaival [424] 1995 Cross-sectional farmers

(8.6%) reported hand or forearm dermatoses in a self-administered questionnaire. Delayed contact allergy to cow dander was found in 27/41

Finland Susitaival [425] 1995 Follow-up farmers

Dermatitis excarbation risk factors: continuation of farm work, history of skin atopy, symptoms of metal allergy, and age under 45 years. Handling cattle, e.g., milking, was considered an exacerbating factor of the dermatosis by 37% of those who had milked sometimes in their lives. In this group, 75% of hand dermatoses in those who had finished milking work had healed.



3.2.9 Cardiovascular diseases On the issue of cardiovascular diseases we were able to identify 5 studies. 4 studies were fit for inclusion, while 1 was excluded due to study design restrictions. Table 18. Summary of studies examining the relationship of cardiovascular diseases with agricultural occupation by country, author, year, study design and population. Country Author Ref Year Study

design population Findings

USA Blair [6] 2005 cohort farmers men: SMR= 0.5 (95%CI0.5–0.6) spouses: SMR= 0.4 (95%CI 0.3–0.5) total: SMR= 0.5 (95%CI 0.5–0.6)

USA Lee [119] 2002 mortality livestock farmers

AMI: PMR=120, 95%CI 118-122 (WM), PMR=148, 95%CI 104-204 (BM)Cerebrovascular disease: PMR=108, 95%CI 105-112 (WM), PMR=298, CI 110-650 (BF)Chronic rheumatic heart disease: PMR=135, 95%CI 110-164 (WM)Polyarteritis nodosa: PMR: 196, 95%CI 118-307 (WM)Diseases of veins and lymphatics: PMR=146, 95%CI 110-190 (WM)

USA Lee [119] 2002 mortality crop farmers AMI: PMR=118, 95%CI 117-119 (WM), PMR=122, 95%CI 119-126 (BM)Cerebrovascular disease: PMR=116, 95%CI 114-117 (WM), PMR=137, 95%CI 133-142 (BM), PMR=137, 95%CI 127-148 (BF)Disease of arteries, arterioles and capillaries: PMR=121, 95%CI 103-142 (WF)

Italy Gambini [7] 1997 cohort rice farmers Myocardial infraction: SMR=72.4 (95%CI 56.1-92.0)Other ischemic heart diseases: SMR=41.0 (95%CI 32.1-51.7)

USA McCarty [426] 2002 Cross-sectional

Female farmers

The percentage of women with 3 or more modifiable risk factors was 26.1% (95% CL = 23.9, 28.4). he prevalence of current cigarette smoking was significantly higher in the non-farm residents, while the prevalence of hypertension and obesity was significantly higher in the farm residents. Overall, obesity prevalence is significantly higher in the study cohort than US women in general (35% versus 23%). Only 5 (0.7%) of the farm residents and 10 (1.2%) of the non-farm residents reported a previous myocardial infarction.



3.2.10 Infectious diseases On the issue of infectious diseases we were able to identify 37 studies. 24 studies were fit for inclusion, while 13 were excluded, either because they provided no risk estimates (4) or because of study design restrictions (9). Table 19. Summary of studies examining the relationship of infectious diseases with agricultural occupation by country, author, year, study design and population. Country Author Ref Year study

design population risk estimate Topic

New Zealand Baker [427] 2006 Case-control

Sheep breeders Salmonellosis was significantly associated with occupational contact with sheep and having a household member who had occupational contact with sheep, during the 3 days prior to illness or interview

Salmonellosis

USA Myers [428] 2006 CS pig farmers Exposure to swine H1N1 virus infection (OR 35.3; 95%CI 7.7-161.8); exposure to swine H1N2 virus (OR 13.8; 95%CI 5.4-35.4)

infections-H1N1

Various Swayne [429] 2006 review Poultry workers

Poultry workers at increased risk for H5N1 infection H5N1

Netherlands Voss [430] 2005 CS pig farmers methicillin-resistant Staphylococcus aureus prevalence rate >760 times greater than the rate of patients admitted to Dutch hospitals

infections-MRSA

Germany Jansen [431] 2005 national case series

general population

increase in disease incidence to 0.06 per 100,000 (1998-2003) - occupational causation estimated to 30%

infections-leptospirosis

Germany Kern [432] 2004 case-control

farm workers Being a farmer: OR = 4.7 ; lived in a farmhouse (OR=6.4); growing leaf or root vegetables: OR=2.5 No CI provided

infections-alv.echino coccosis

Various Wilson [433] 2004 case report / review

poultry workers

poultry workers were three times more likely than the general population to suffer campylobacteriosis

infections-campylo bacteriosis

Netherlands Koopmans [59] 2004 cross-sectional

poultry workers

19 H7N7 infections reported among 453 poultry workers -- reported symptoms: 349/453 reported conjunctivitis, 90/453 influenza-like illness, 67/453 other complaints -- detected A/H7 detected in conjunctival samples (26.4%) among people with conjunctivitis only, (9.4%) with influenza-like illness and conjunctivitis, (5.4%) with influenza-like illness only, and in four (6%) who reported other symptoms

infections-H7N7

Italy Castiglia [434] 2004 Hospital discharge records

Farmers Cystic echinococcosis incidence: farmers-shepherdess (68.6 per 100,000) and pensioners (59.6 per 100,000)

echinococcosis

Greece Bikas [435] 2003 case-control

rural residents Adjusted ORs after multi-variative analysis:Living in lowlands: OR 1.82 (95%CI 1.09-3.05)Professional occupation with animals: OR 2.4, 95%CI 1.2-4.8 Absense of stables: OR 9.1 (95%CI 2.2-38.7)Trauma during animal delivery: OR 11.2 (95%CI 3.2-39.1) - after limiting to only those in contact with animals: milking OR 2.0 (95%CI 1.1-3.7), trauma in animal delivery OR 5.9 (95%CI 2.9-11.9)

infections-brucellosis

Spain Roman-Sanchez

[436] 2003 cross-sectional

farm workers Prevalence of strongyloidiasis: 12.4% (95%CI 8.4-16.4) F4

infections-strongyloidiasis

Czech Republic

Brhel [437] 2003 Survey farmers Occupational infectious diseases prevalence in agriculture: 20% (2nd in order after health professionals)



China Bridges [438] 2002 cross-sectional

poultry workers

10% H5 seroprevalence rate among poultry workers - increased risk due to occupational exposure suggested

infections-H5N1

India Thakur [439] 2002 Cross-sectional

Gen.population prevalence rate of 4.97 per cent in samples that included specimens from persons occupationally exposed to animals.

brucellosis

Turkey Kurcer [440] 2002 Cross-sectional

farmers HBV infection (=anti HBc+) was independently associated with the age group of 21 years and older (OR=3.7, 95% CI=1.884-7.494), in illiterate subjects (OR=2.1, 95% CI=1.180-3.326), in farmers and labourers (OR=2.8, 95% CI=1.042-7.953) and in these with multiple sexual partners (OR=2.1, 95% CI=1.574-8.168)

HBV

USA Schulte [441] 2001 Reporting center survey

Migrant farm workers

Migrant farm workers represented 1 percent of all reported tuberculosis cases from 1993 to 1997

TB

Various Levett [58] 2001 review farmers Occupational exposure high risk groups: farmers; fish farmers; rice field workers; taro farmers; banana farmers; sugar cane cutters Fish workers because of the high mortality rates associated with Icterohaemorrhagieae infections, it is considered an important occupational risk group, despite the very small absolute number of workers affected Livestock farming major occupational risk group worldwide. -Highest risk dairy farming (serovar hardjo) – milking dairy cattle (cattle are maintainance hosts of serovar hardjo); also pig farming in temperate climates

leptospirosis

Australia Drobeniuc [442] 2001 CS pig farmers The prevalence of HEV infection was higher among swine farmers than among the comparison group (51.1% vs. 24.7%; prevalence ratio, 2.07; 95% CI, 1.62-2.64). HEV infection associated with:-occupational history of cleaning barns or assisting sows at birth (odds ratio [OR], 2.46; 95% CI, 1.52-4.01), - years of occupational exposure (OR, 1.04 per year; 95% CI, 1.01-1.07)- history of drinking raw milk (OR, 1.61; 95% CI, 1.08-2.40)

infections-HEV

USA Much [443] 2000 Cross-sectional

Nonmigrant farm workers

TB prevalence rate: 15% (lower in comparison to other similar studies)

TB

Denmark Holk [444] 2000 national case series

general population

incidence: 0.09/100,000 inhabitants/y; 93% of the cases 18-64y old; 90% men; 63% occupational exposure (41% fish farmers, 28% farmers). Fatality rate: 7%

infections-leptospirosis

Hong Kong Mounts [445] 1999 Case-control

Poultry farmers

Exposure to live poultry (by visiting either a retail poultry stall or a market selling live poultry) in the week before illness began was significantly associated with H5N1 disease (64% of cases vs. 29% of controls, odds ratio, 4.5, P=.045).

H5N1

Greece Hadjichri- stodoulou

[446] 1999 cross-sectional

rural residents Occupation: RR 5.81 (p<0.00001), consumption of raw milk: RR 1.98 (p<0.002)Consumption of unpasteurised fresh cheese: RR 2.13 (p<0.01)

infections-brucellosis

Poland Cisak [447] 1998 Cross-sectional

farmers Seroprevalence of tick-bone encephalitis virus: forestry workers 19.8%; farmers 32.0%. the frequency of seropositive reactions in forestry workers and farmers was significantly greater compared to control group (p < 0.001 and p < 0.05, respectively).

TBE virus

USA McCurdy [448] 1997 Cross-sectional

Migrant farm workers

16.6% positive tuberculin skin tes TB



4.Estimating the occupational and environmental risks in the sector of fisheries in Europe The scientific literature, as far as estimating the health risks involved in fishery, is scarse. Most studies report on acute health effects incidence, such as annual mortality due to accidents.

Fishery General comments:

- fishermen is a population difficult to monitor, both in defining and in tracing - registration systems for calculating mortality is generally deficient - scientific literature is rare -

Mortality studies Average annual mortality rated calculated between 1.3-5.7 cases / 1000 (except one study which calculated an all-cause mortality for fishermen >55 to be 0.6/1000) Causes:

- fatal accidents (rates x20 than those of coal miners); missing on board; weather conditions; poisoning - circulatory diseases and acute myocardial infarction account for almost half of the fatalities documented - homicide and suicide rate (3.1%)

Non-European studies report a high occupation mortality rate (19% in Alaska) related to commercial fishing; in Canada the age-specific standard mortality ratio (ASMR) for licensed fishermen <55 of age was between 1.4-1.7 and for >55: 0.6 for all causes. Cause-specific mortality studies: respiratory intoxication due to increased levels of carbon dioxide and hydrogen sulphide (Denmark); increased risk for liver and lung cancer due to high rates of smoking among fishermen Morbidity studies (Italy) Higher prevalence of solar keratosis (OR 22.5), obstructive bronchitis (OR 3.56). Work injury was associated with musculoskeletal dysfunction (RR 13.8) and chronic bronchitis with deck hands (RR 4.40) (Spain) Prevalence of digestive disorders (29.7% vs 8.6%), respiratory disorders (21.6% vs 15.1%), ophthalmological problems (41.8% vs 4.1%) increased in a cohort of fishermen compared to the reference group. (Poland) repatriation causes in ocean fishing: circulatory system disease; mental/nervous disorders; genito-urinary conditions. All-disease rate: 13.48cases/1000 men per year; all-injury rate: 3.26 cases / 1000 men per year (Denmark) injury rate: 20.4 / 100 persons per year Health determinants studies Alcoholism: higher rate (2,5fold) in fishermen, 4fold rate in the 20-29 year group (UK); alcohol dependency: 5% (Poland) Smoking: 72.9% of fishermen were smokers (Poland) Review: Matheson C et al: “The health of fishermen in the catching sector of the fishing industry: a gap analysis”. Occup.Med.;2001; 51(5),p305-311 [80]

Table 20. Summary on health adverse effects in fisheries from 1995-2006 included in the literature review by country, author, year, design, population studied and study findings. Country Author Ref Year study

design Populatio n

risk estimate Topic

Spain Marco [83] 1995 cross-sectional & mortality

fishermen first cause of mortality (34.9%), most common sites: trachea, bronchials and lung (13.3%)

cancer-all causes

Sweden Ji [88] 2005 cohort fishermen SIR 1.90 (95%CI 1.37–2.51) cancer-lip

Sweden Ji [88] 2005 follow-up fishermen Liver cancer total: SIR=1.17 (95%CI 0.75-1.69); primary liver: SIR=0.79 (95%CI 0.34-1.44); gallbladder: SIR=1.90 (95%CI 0.90-3.26)

cancer-liver

Japan Sonoda [257] 2005 case control

agriculture & fishery

OR = 5.89, 95% CI = 1.24-28.04 cancer-mm

Sweden Hemelt [87] 2004 cohort fishermen SIR 2.50 (95%CI 0.99–5.18) cancer-



nasal

Sweden Ji [85] 2006 cohort fishermen SIR 1.22 (95%CI 1.00–1.47) Cancer-stomach

Sweden Ji [84] 2005 cohort fishermen SIR 1.72 (95%CI 1.34–2.16) cancer-upper airway tract

Poland Jaremin [449] 2003 retrospective

male deep sea fishermen

work-site MI incidence: not higher than among general population of Polish males in the same decade - pre-hospital and one-month mortality due to MI were higher in the group of seafarers as compared to the group of general male population

Cardiovascular

Denmark Jensen [86] 1996 cohort fishermen Death of ischemic heart disease (SMR = 1.27, 95%CI 1.01-1.57)

Cardiovascular

UK Lawrie [90] 2004 cross-sectional

fishermen increased prevalence of smoking & passive smokinglower rates of alcohol consumption compared to general population

Health determinants


fishermen smoking prevalence: 47.5% Health determinants

UK Roberts [186] 2004 retrospective

fishermen fatal accident rate=103.1 per 100 000 fishermen-years, (1976-1995), 52.4 times higher (95% CI 42.9 to 63.8) than for all workers in Great Britain during the same period, and this relative risk increased through the 1980s up to 76.6 during 1991-95. Relative risks with the construction (12.3) and manufacturing (46.0) industries were higher than 5 and 20 respectively, during 1959-68- 454/616 (74%) occupational, and 394 (87%) deaths of drowning - most common fatality cause: trawlers foundering in adverse weather; 82/145 accidents involved nets

Injuries

Australia O'Connor [450] 2006 database survey

commercial fishermen

46 fishermen fatalities between 1992-1998 Injuries

Denmark Jensen [451] 2006 case-control

open sea fishermen

preparing, shooting and hauling of the fishing gear and nets =56.8% of all injuries causes - OR for these jobs 2.4 (95%CI 2.10-2.77) (varies depending on fishing type), OR for traffic on board 15.3 (95%CI 12.0-19.4)

Injuries

Denmark Jensen [86] 1996 cohort fishermen deaths by accident other than road accidents (SMR = 5.76, 95%CI 3.09-7.46)

Injuries

Poland Jaremin [187] 2004 retrospective

fishermen average annual mortality rate: 89 per 100 000 employees per year -- significantly higher in boats <13 m in length - causes: sea catastrophes (60%), alcohol implicated in 45% of cases seen by a coroner

Injuries

Canada Brooks [452] 2005 retrospective

fishermen 133 fatalities of immersion between 1976-2002 Injuries

UK Roberts [453] 2004 longitudinal study

fishermen homicide rate 0.4 per 100,00 worker-years among fishermen in British fishing (1976-2002)

Mental

USA Lipscomb [82] 2004 cohort fishermen MSD symptoms prevalence: 38.5% - causes of work impairment: low back pain (17.7%), hands,wrists,shoulders (7%) - factors: weather, type of boat, gear, crew size, and level of experience

msd-various




deep sea fishermen

compared to coastal fishermen: lower Tiffeneau index (FEV/FVC), greater prevalence of smoking, chronic bronchitis, history of tuberculosis and laboral disability, number of medical consultations and admissions due to respiratory problemshigh sea fishermen: laboral disability due to respiratory disorders (22%-primary cause)

Respiratory


fishermen Asthma prevalence: 9.8% Respiratory-asthma


fishermen Chronic bronchitis prevalence: 18.3%, smoking prevalence: 47.5%

Respiratory-COPD

Denmark Jensen [86] 1996 cohort fishermen SMR due to bronchitis and emphysema among 35-64 years old crew members: 1.96, 95%CI 1.01-3.45

Respiratory-COPD

(not specified)

Siracusa [454] 2003 cross-sectional

fishermen Sensitization to live fishing bait was found in 24/64 workers (31.6%) - sensitization strongly associated with work-related symptoms

Respiratory-sensitization

USA McCall [413] 2005 claims filed survey

forestry and fishing

Estimated claim rate 64.53 / 100,000 workers (95%CI 18.79-110.27)

Skin



5. References 1. ILO. ILO warns on farm safety. 1997 [cited 10-1-2007]. 2. European Community, Key data on Health 2002, in Health Statistics. 2002, Eurostat: Luxembourg. 3. Eurostat, Special issue: Farm Structure Survey 2005, in Agricultural Statistics. 2005, European

Commission: Luxembourg. 4. Frank, A.L., et al., Issues of agricultural safety and health. Annu Rev Public Health, 2004. 25: p. 225-45. 5. Alavanja, M.C., et al., Cancer incidence in the agricultural health study. Scand J Work Environ Health,

2005. 31 Suppl 1: p. 39-45; discussion 5-7. 6. Blair, A., et al., Mortality among participants in the agricultural health study. Ann Epidemiol, 2005.

15(4): p. 279-85. 7. Gambini, G.F., et al., Cancer Mortality among Rice Growers in Novara Province, Northern Italy.

American Journal of Industrial Medicine, Vol. 31, No. 4, pages 435-441, 23 references, 1997, 1997. 8. Lee, E., et al., Proportionate mortality of crop and livestock farmers in the United States, 1984-1993.

American Journal of Industrial Medicine, 2002. 42(5): p. 410-420. 9. Fleming, L.E., et al., Mortality in a cohort of licensed pesticide applicators in Florida. 1999. 56(1): p. 14-

21. 10. Sprince, N.L., et al., Risk factors for agricultural injury: a case-control analysis of Iowa farmers in the

Agricultural Health Study. J Agric Saf Health, 2003. 9(1): p. 5-18. 11. McCurdy, S.A. and D.J. Carroll, Agricultural injury. American Journal of Industrial Medicine, 2000.

38(4): p. 463-480. 12. McCauley, L.A., et al., Studying health outcomes in farmworker populations exposed to pesticides.

Environ Health Perspect, 2006. 114(6): p. 953-60. 13. McBride, J.S., et al., Green tobacco sickness. Tob Control, 1998. 7(3): p. 294-298. 14. Firth, H., et al., Health of farmers in southland: an overview. N Z Med J, 2001. 114(1140): p. 426-8. 15. Choi, S.-W., et al., Hearing loss as a risk factor for agricultural injuries. American Journal of Industrial

Medicine, 2005. 48(4): p. 293-301. 16. Sprince, N.L., et al., Risk factors for animal-related injury among Iowa large-livestock farmers: a case-

control study nested in the Agricultural Health Study. J Rural Health, 2003. 19(2): p. 165-73. 17. Voaklander, D.C., et al., Pain, medication, and injury in older farmers. American Journal of Industrial

Medicine, 2006. 49(5): p. 374-382. 18. Brison, R.J., et al., Fatal agricultural injuries in preschool children: risks, injury patterns and strategies

for prevention. Cmaj, 2006. 174(12): p. 1723-6. 19. McCurdy, S.A., et al., Agricultural injury in California migrant Hispanic farm workers. American

Journal of Industrial Medicine, 2003. 44(3): p. 225-235. 20. DeMuri, G.P. and M.A. Purschwitz, Farm injuries in children: a review. Wmj, 2000. 99(9): p. 51-5. 21. Descatha, A., et al., Occupational Exposures and Haematological Malignancies: Overview on Human

Recent Data. Cancer Causes and Control, 2005. 16(8): p. 939-953. 22. Coble, J.B., et al., Sugarcane farming, occupational solvent exposures, and the risk of oral cancer in

Puerto Rico. J Occup Environ Med, 2003. 45(8): p. 869-74. 23. Amre, D.K., et al., Case-control study of lung cancer among sugar cane farmers in India. 1999. 56(8): p.

548-552. 24. Settimi, L., et al., Cancer among greenhouse owners and their relatives: Results of a pilot study.

American Journal of Industrial Medicine, 1998. 33(1): p. 88-89. 25. Efird, J.T., et al., Farm-related exposures and childhood brain tumours in seven countries: results from

the SEARCH International Brain Tumour Study. Paediatric and Perinatal Epidemiology, 2003. 17(2): p. 201-211.

26. Cordier, S., et al., Parental occupations and childhood brain tumors: results of an international caseβ€“control study. Cancer Causes and Control, 2001. 12(9): p. 865-874.

27. American Thoracic Society, Respiratory Health Hazards in Agriculture. Am. J. Respir. Crit. Care Med., 1998. 158(5): p. 1S-76S.

28. Omland, O., Exposure and respiratory health in farming in temperate zones--a review of the literature. Ann Agric Environ Med, 2002. 9(2): p. 119-36.



29. Faria, N.M., et al., Pesticides and respiratory symptoms among farmers. Rev Saude Publica, 2005. 39(6): p. 973-81.

30. Hoppin, J.A., et al., Chemical Predictors of Wheeze among Farmer Pesticide Applicators in the Agricultural Health Study. Am. J. Respir. Crit. Care Med., 2002. 165(5): p. 683-689.

31. Spurzem, J.R., D.J. Romberger, and S.G. Von Essen, Agricultural lung disease. Clin Chest Med, 2002. 23(4): p. 795-810.

32. Vogelzang, P.F.J., et al., Organic dust toxic syndrome in swine confinement farming. American Journal of Industrial Medicine, 1999. 35(4): p. 332-334.

33. Monso, E., Occupational asthma in greenhouse workers. Curr Opin Pulm Med, 2004. 10(2): p. 147-50. 34. Kim, Y.K., et al., Spider mite allergy in apple-cultivating farmers: European red mite (Panonychus ulmi)

and two-spotted spider mite (Tetranychus urticae) may be important allergens in the development of work-related asthma and rhinitis symptoms. Journal of Allergy and Clinical Immunology, 1999. 104(6): p. 1285-1292.

35. Kim, Y.K., et al., Citrus red mite (Panonychus citri) is the most common sensitizing allergen of asthma and rhinitis in citrus farmers. Clinical and Experimental Allergy, 1999. 29(8): p. 1102-1109.

36. Schram-Bijkerk, D., et al., Nonlinear relations between house dust mite allergen levels and mite sensitization in farm and nonfarm children. Allergy, 2006. 61(5): p. 640-647.

37. Fernandez-Caldas, E. and V.I. Calvo, Mite allergens. Current Allergy and Asthma Reports, 2005. 5(5): p. 402-410.

38. Dimich-Ward, H., et al., Contact with livestock - a protective effect against allergies and asthma? Clinical & Experimental Allergy, 2006. 36(9): p. 1122-1129.

39. Naleway, A.L., Asthma and atopy in rural children: is farming protective? Clin Med Res, 2004. 2(1): p. 5-12.

40. Walker-Bone, K. and K.T. Palmer, Musculoskeletal disorders in farmers and farm workers. Occup Med (Lond), 2002. 52(8): p. 441-450.

41. Rosecrance, J., G. Rodgers, and L. Merlino, Low back pain and musculoskeletal symptoms among Kansas farmers. Am J Ind Med, 2006. 49(7): p. 547-56.

42. Holmberg, S., et al., Low back pain comorbidity among male farmers and rural referents: a population-based study. Ann Agric Environ Med, 2005. 12(2): p. 261-8.

43. Holmberg, S., A. Thelin, and N. Thelin, Is there an increased risk of knee osteoarthritis among farmers? A population-based case-control study. Int Arch Occup Environ Health, 2004. 77(5): p. 345-50.

44. Toren, A., et al., Tractor-driving hours and their relation to self-reported low-back and hip symptoms. Appl Ergon, 2002. 33(2): p. 139-46.

45. Madeleine, P., et al., The effects of neck-shoulder pain development on sensory-motor interactions among female workers in the poultry and fish industries. A prospective study. Int Arch Occup Environ Health, 2003. 76(1): p. 39-49.

46. Pugh, K.J., D. Pienkowski, and J.T. Gorczyca, Musculoskeletal trauma in tobacco farming. Orthopedics, 2000. 23(2): p. 141-143.

47. Redondo, V.L., et al., Olive collector's paralysis. Neurologia, 1997. 12(9): p. 427-8. 48. Sallmen, M., et al., Fertility and exposure to solvents among families in the Agricultural Health Study.

Occup Environ Med, 2006. 63(7): p. 469-475. 49. Hanke, W. and J. Jurewicz, The risk of adverse reproductive and developmental disorders due to

occupational pesticide exposure: an overview of current epidemiological evidence. Int J Occup Med Environ Health, 2004. 17(2): p. 223-43.

50. English, J.S.C., Occupational dermatoses: overview. Occup Med (Lond), 2004. 54(7): p. 439-440. 51. Spiewak, R., Pesticides as a cause of occupational skin diseases in farmers. Ann Agric Environ Med,

2001. 8(1): p. 1-5. 52. Paulsen, E., J. Sogaard, and K. Andersen, Occupational dermatitis in Danish gardeners and greenhouse

workers (I). Prevalence and possible risk factors. Contact Dermatitis, 1997. 37(6): p. 263-70. 53. Harries, M.J. and J.T. Lear, Occupational skin infections. Occup Med (Lond), 2004. 54(7): p. 441-449. 54. Yuen, K.Y. and S.S. Wong, Human infection by avian influenza A H5N1. Hong Kong Med J, 2005. 11(3):

p. 189-99. 55. Sauret, J.M. and N. Vilissova, Human brucellosis. J Am Board Fam Pract, 2002. 15(5): p. 401-406.



56. Reid, A.J., Brucellosis--a persistent occupational hazard in Ireland. Int J Occup Environ Health, 2005. 11(3): p. 302-4.

57. Nienhaus, A., A. Schablon, and R. Diel, Tuberculosis in occupational medicine - Review. Zentralblatt fur Arbeitsmedizin, Arbeitsschutz und Ergonomie, 2006. 56(5): p. 124-131.

58. Levett, P.N., Leptospirosis. Clin Microbiol Rev, 2001. 14(2): p. 296-326. 59. Koopmans, M., et al., Transmission of H7N7 avian influenza A virus to human beings during a large

outbreak in commercial poultry farms in the Netherlands. The Lancet, 2004. 363(9409): p. 587-593. 60. WHO. Cumulative Number of Confirmed Human Cases of Avian Influenza A/(H5N1) Reported to WHO.

2006 [cited 2006 20061205]; Available from: http://www.who.int/csr/disease/avian_influenza/country/cases_table_2006_11_29/en/index.html.

61. Halpin, J., Avian flu from an occupational health perspective. Archives of Environmental Health, 2006. 60(2): p. 62-69.

62. ECDC, "Who is at risk of getting “Bird Flu” - Highly pathogenic H5N1 avian influenza?" 2006(Technical Report).

63. van Wijngaarden, E., Mortality of mental disorders in relation to potential pesticide exposure. J Occup Environ Med, 2003. 45(5): p. 564-8.

64. Stark, C., et al., Male suicide and occupation in Scotland. Health Stat Q, 2006. 29(29): p. 26-9. 65. Judd, F., et al., Understanding suicide in Australian farmers. Soc Psychiatry Psychiatr Epidemiol, 2006.

41(1): p. 1-10. 66. Malmberg, A., S. Simkin, and K. Hawton, Suicide in farmers. Br J Psychiatry, 1999. 175: p. 103-5. 67. Gregoire, A., The mental health of farmers. Occup Med (Lond), 2002. 52(8): p. 471-476. 68. Fraser, C.E., et al., Farming and Mental Health Problems and Mental Illness. International Journal of

Social Psychiatry, 2005. 51(4): p. 340-349. 69. Booth, N.J. and K. Lloyd, Stress in farmers. Int J Soc Psychiatry, 2000. 46(1): p. 67-73. 70. Sanne, B., et al., Farmers are at risk for anxiety and depression: the Hordaland Health Study. Occup

Med (Lond), 2004. 54(2): p. 92-100. 71. Elger, U., et al., [Stresses and health risks of farm women]. Soz Praventivmed, 1995. 40(3): p. 146-56. 72. Carruth, A.K. and C.A. Logan, Depressive symptoms in farm women: effects of health status and farming

lifestyle characteristics, behaviors, and beliefs. J Community Health, 2002. 27(3): p. 213-28. 73. Hovey, J.D. and C.G. Magana, Exploring the mental health of Mexican migrant farm workers in the

Midwest: psychosocial predictors of psychological distress and suggestions for prevention and treatment. J Psychol, 2002. 136(5): p. 493-513.

74. Hansen, E. and M. Donohoe, Health issues of migrant and seasonal farmworkers. J Health Care Poor Underserved, 2003. 14(2): p. 153-64.

75. Tahmaz, N., A. Soutar, and J.W. Cherrie, Chronic Fatigue and Organophosphate Pesticides in Sheep Farming: A Retrospective Study Amongst People Reporting to a UK Pharmacovigilance Scheme. Ann Occup Hyg, 2003. 47(4): p. 261-267.

76. Salvi, R.M., et al., Neuropsychiatric evaluation in subjects chronically exposed to organophosphate pesticides. Toxicol Sci, 2003. 72(2): p. 267-71.

77. Roldan-Tapia, L., T. Parron, and F. Sanchez-Santed, Neuropsychological effects of long-term exposure to organophosphate pesticides. Neurotoxicology and Teratology, 2005. 27(2): p. 259-266.

78. Ascherio, A., et al., Pesticide exposure and risk for Parkinson's disease. Annals of Neurology, 2006. 60(2): p. 197-203.

79. Kamel, F. and J.A. Hoppin, Association of pesticide exposure with neurologic dysfunction and disease. Environ Health Perspect, 2004. 112(9): p. 950-8.

80. Matheson, C., et al., The health of fishermen in the catching sector of the fishing industry: a gap analysis. Occup Med (Lond), 2001. 51(5): p. 305-311.

81. Chow, W.-H., et al., Esophageal Cancer and Occupation in a Cohort of Swedish Men. American Journal of Industrial Medicine,, 1995 27(5): p. 749-757.

82. Lipscomb, H.J., et al., Musculoskeletal symptoms among commercial fishers in North Carolina. Applied Ergonomics, 2004. 35(5): p. 417-426.

83. Marco Jordan, L., et al., [Respiratory disorders among fishermen]. Med Clin (Barc), 1995. 105(6): p. 211-5.



84. Ji, J. and K. Hemminki, Occupation and upper aerodigestive tract cancers: a follow-up study in Sweden. J Occup Environ Med, 2005. 47(8): p. 785-95.

85. Ji, J. and K. Hemminki, Socio-economic and occupational risk factors for gastric cancer: A cohort study in Sweden. European Journal of Cancer Prevention, 2006. 15(5): p. 391-397.

86. Jensen, O.C., Mortality in Danish fishermen. Bull Inst Marit Trop Med Gdynia, 1996. 47(1-4): p. 5-10. 87. Hemelt, M., C. Granstrom, and K. Hemminki, Occupational risks for nasal cancer in Sweden. J Occup

Environ Med, 2004. 46(10): p. 1033-40. 88. Ji, J. and K. Hemminki, Variation in the risk for liver and gallbladder cancers in socioeconomic and

occupational groups in Sweden with etiological implications. International Archives of Occupational and Environmental Health, 2005. 78(8): p. 641-649.

89. Wang, J.Y., et al., [Study on the relationship between smoking, alcohol intake and hyperlipidemia in fishermen]. Zhonghua Liu Xing Bing Xue Za Zhi, 2003. 24(12): p. 1115-7.

90. Lawrie, T., et al., The health and lifestyle of Scottish fishermen: a need for health promotion. Health Educ. Res., 2004. 19(4): p. 373-379.

91. Voaklander, D.C. and K.D.K.B.H.R.D.P.S.L.S.N.Y.W. Pickett, Pain, medication, and injury in older farmers. American Journal of Industrial Medicine, 2006. 49(5): p. 374-382.

92. Solomon, C., et al., Non-fatal occupational injuries in British agriculture. Occup Environ Med, 2006: p. oem.2005.024265.

93. Cole, H.P., M.L. Myers, and S.C. Westneat, Frequency and severity of injuries to operators during overturns of farm tractors. J Agric Saf Health, 2006. 12(2): p. 127-38.

94. Stallones, L., C. Beseler, and P. Chen, Sleep Patterns and Risk of Injury Among Adolescent Farm Residents. American Journal of Preventive Medicine, 2006. 30(4): p. 300-304.

95. Seong-Woo Choi, C.P.-A.N.L.S.R.H.R.K.J.D.G.A.F.P.S.W.C.Z., Hearing loss as a risk factor for agricultural injuries. American Journal of Industrial Medicine, 2005. 48(4): p. 293-301.

96. Blair, A., et al., Disease and injury among participants in the Agricultural Health Study. J Agric Saf Health, 2005. 11(2): p. 141-50.

97. Atrubin, D., et al., Self-reported symptoms of neurotoxicity and agricultural injuries among Ohio cash-grain farmers. American Journal of Industrial Medicine, 2005. 47(6): p. 538-549.

98. Marais, S. and A. Kritzinger, Farm worker injuries on Western Cape fruit farms: the role of the lay health worker. Curationis, 2005. 28(4): p. 86-92.

99. Hendricks, K.J., et al., Injuries to youth living on U.S. farms in 2001 with comparison to 1998. J Agromedicine, 2005. 10(4): p. 19-26.

100. Spengler, S.E., S.R. Browning, and D.B. Reed, Sleep deprivation and injuries in part-time Kentucky farmers: impact of self reported sleep habits and sleep problems on injury risk. Aaohn J, 2004. 52(9): p. 373-82.

101. Rautiainen, R.H., et al., Injuries in the Iowa Certified Safe Farm Study. J Agric Saf Health, 2004. 10(1): p. 51-63.

102. McCurdy, S.A., et al., Nonfatal occupational injury among California farm operators. J Agric Saf Health, 2004. 10(2): p. 103-19.

103. Dimich-Ward, H., et al., Gender differences in the occurrence of farm related injuries. Occup Environ Med, 2004. 61(1): p. 52-56.

104. Gerald W. Lim, K.L.B.W.P.D.P.S.D.C.V., Fatal and non-fatal machine-related injuries suffered by children in Alberta, Canada, 1990-1997. American Journal of Industrial Medicine, 2004. 45(2): p. 177-185.

105. Lindsay, S., et al., Injuries to Scottish farmers while tagging and clipping cattle: a cross-sectional survey. Occup Med (Lond), 2004. 54(2): p. 86-91.

106. Petridou, E., et al., The mosaic of equestrian-related injuries in Greece. J Trauma, 2004. 56(3): p. 643-7. 107. Miller, R.L., J.K. Webster, and S.C. Mariger, Nonfatal injury rates of Utah agricultural producers. J

Agric Saf Health, 2004. 10(4): p. 287-95. 108. Bartoszcze-Tomaszewska, M., et al., [Facial skeleton traumas in farmers in east-central region of Poland

(part I)]. Wiad Lek, 2004. 57(5-6): p. 201-5. 109. Kingman, D.M., G.R. Deboy, and W.E. Field, Contributing factors to engulfments in on-farm grain

storage bins: 1980 through 2001. J Agromedicine, 2003. 9(1): p. 39-63.



110. Virtanen, S.V., et al., Work injuries among Finnish farmers: A National Register Linkage study 1996-1997. American Journal of Industrial Medicine, 2003. 43(3): p. 314-325.

111. Rissanen, P. and K. Taattola, Fatal injuries in Finnish agriculture, 1988-2000. J Agric Saf Health, 2003. 9(4): p. 319-26.

112. Franklin, R.C. and J.N. Davies, FARM-RELATED INJURY PRESENTING TO AN AUSTRALIAN BASE HOSPITAL. Australian Journal of Rural Health, 2003. 11(6): p. 292-302.

113. Netto, G.F. and E.S. Johnson, Mortality in workers in poultry slaughtering/processing plants: the Missouri poultry cohort study. Occup Environ Med, 2003. 60(10): p. 784-788.

114. Perry, M.J., Children's agricultural health: traumatic injuries and hazardous inorganic exposures. J Rural Health, 2003. 19(3): p. 269-78.

115. Hartman, E., et al., Sick leave analysis among self-employed Dutch farmers. Occup Med (Lond), 2003. 53(7): p. 461-468.

116. Browning, S.R., et al., Agricultural tasks and injuries among Kentucky farm children: Results of the farm family health and hazard surveillance project. Southern Medical Journal, 2003. 96(12): p. 1203-1212.

117. Solomon, C., Accidental injuries in agriculture in the UK. Occup Med (Lond), 2002. 52(8): p. 461-466. 118. Carruth, A.K., et al., Nonfatal Injury Experiences Among Women on Family Farms. Clin Nurs Res, 2002.

11(2): p. 130-148. 119. Lee, E. and C.A.B.N.L.L.L.C.J.P. Sestito, Proportionate mortality of crop and livestock farmers in the

United States, 1984-1993. American Journal of Industrial Medicine, 2002. 42(5): p. 410-420. 120. Munshi, K., et al., Causes, nature, and outcomes of work-related injuries to adolescents working at farm

and non-farm jobs in rural Minnesota. Am J Ind Med, 2002. 42(2): p. 142-9. 121. Sprince, N.L., et al., Risk factors for machinery-related injury among Iowa farmers: a case-control study

nested in the Agricultural Health Study. Int J Occup Environ Health, 2002. 8(4): p. 332-8. 122. Pryor, S.K., A.K. Caruth, and C.A. McCoy, Children's injuries in agriculture related events: the effect of

supervision on the injury experience. Issues Compr Pediatr Nurs, 2002. 25(3): p. 189-205. 123. McCurdy, S.A., et al., Injury risks in children of California migrant Hispanic farm worker families.

American Journal of Industrial Medicine, 2002. 42(2): p. 124-133. 124. Hard, D.L., J.R. Myers, and S.G. Gerberich, Traumatic injuries in agriculture. J Agric Saf Health, 2002.

8(1): p. 51-65. 125. Mather, C. and T. Lower, Farm injury in Tasmania. Australian Journal of Rural Health, 2001. 9(5): p.

209-215. 126. O'Connor, P., Work related spinal cord injury, Australia 1986-97. Inj Prev, 2001. 7(1): p. 29-34. 127. Hwang, S.-A. and M.I.G.A.D.S.T.L.S.J.J.J.M.E.M. Hallman, Severe farm injuries among New York

farmers. American Journal of Industrial Medicine, 2001. 40(1): p. 32-41. 128. Park, H., et al., Risk factors for work-related injury among male farmers in Iowa: a prospective cohort

study. J Occup Environ Med, 2001. 43(6): p. 542-7. 129. Gerberich, S.G., et al., Injuries among children and youth in farm households: Regional Rural Injury

Study-I. Inj Prev, 2001. 7(2): p. 117-122. 130. Carruth, A.K., et al., Women in agriculture: risk and injury experiences on family farms. J Am Med

Womens Assoc, 2001. 56(1): p. 15-8. 131. Horsburgh, S., A.M. Feyer, and J.D. Langley, Fatal work related injuries in agricultural production and

services to agriculture sectors of New Zealand, 1985-94. Occup Environ Med, 2001. 58(8): p. 489-495. 132. Franklin, R.C., et al., Agricultural work-related fatalities in Australia, 1989-1992. J Agric Saf Health,

2001. 7(4): p. 213-27. 133. McGwin, G.J., R. Enochs, and J.M. Roseman, Increased Risk of Agricultural Injury among African-

American Farm Workers from Alabama and Mississippi. Am. J. Epidemiol., 2000. 152(7): p. 640-650. 134. Rasmussen, K. and O.C.J.M. Lauritsen, Incidence of unintentional injuries in farming based on one year

of weekly registration in Danish farms. American Journal of Industrial Medicine, 2000. 38(1): p. 82-89. 135. Reed, D.B. and D.T. Claunch, Nonfatal farm injury incidence and disability to children: A systematic

review. American Journal of Preventive Medicine, 2000. 18(4, Supplement 1): p. 70-79. 136. McGwin, G., Jr., R. Enochs, and J.M. Roseman, Increased Risk of Agricultural Injury among African-

American Farm Workers from Alabama and Mississippi. Am. J. Epidemiol., 2000. 152(7): p. 640-650.



137. Xiang, H., et al., Agricultural work-related injuries among farmers in Hubei, People's Republic of China. Am J Public Health, 2000. 90(8): p. 1269-76.

138. Bancej, C. and T. Arbuckle, Injuries in Ontario farm children: a population based study. Inj Prev, 2000. 6(2): p. 135-40.

139. Pickett, W., et al., Fatal work-related farm injuries in Canada, 1991-1995. Canadian Agricultural Injury Surveillance Program. Cmaj, 1999. 160(13): p. 1843-8.

140. Gerberich, S.G., et al., Machinery-related injuries:: Regional Rural Injury Study--I (RRIS--I). Accident Analysis & Prevention, 1998. 30(6): p. 793-804.

141. Lewis, M.Q., et al., Work-related injuries among Iowa farm operators: an analysis of the Iowa Farm Family Health and Hazard Surveillance Project. Am J Ind Med, 1998. 33(5): p. 510-7.

142. Crawford, J., et al., A cross-sectional case control study of work-related injuries among Ohio farmers. American Journal of Industrial Medicine, 1998. 34(6): p. 588-599.

143. Browning, S.R., et al., Agricultural injuries among older Kentucky farmers: The Farm Family Health and Hazard Surveillance Study. Am J Ind Med, 1998. 33(4): p. 341-53.

144. Stueland, D.T., et al., Case-control study of agricultural injuries to women in central Wisconsin. Women Health, 1997. 25(4): p. 91-103.

145. Rivara, F.P., Fatal and non-fatal farm injuries to children and adolescents in the United States, 1990-3. Inj Prev, 1997. 3(3): p. 190-4.

146. Richardson, D., et al., Fatal agricultural injuries in North Carolina by race and occupation, 1977-1991. American Journal of Industrial Medicine, 1997. 31(4): p. 452-458.

147. Boyle, D., et al., Injury from dairy cattle activities. Epidemiology, 1997. 8(1): p. 37-41. 148. Stueland, D.T., et al., Case control study of agricultural injuries to older farmers in central Wisconsin. J

Am Geriatr Soc, 1996. 44(4): p. 475-6. 149. Nordstrom, D.L., et al., Fall-related occupational injuries on farms. Am J Ind Med, 1996. 29(5): p. 509-

15. 150. Low, J.M., G.R. Griffith, and C.L. Alston, Australian farm work injuries: incidence, diversity and

personal risk factors. Aust J Rural Health, 1996. 4(3): p. 179-89. 151. Pickett, W., et al., Nonfatal farm injuries in Ontario: a population-based survey. Accid Anal Prev, 1995.

27(4): p. 425-33. 152. Layde, P.M., et al., Machine-related occupational injuries in farm residents. Ann Epidemiol, 1995. 5(6):

p. 419-26. 153. Zwerling, C., L.F. Burmeister, and C.M. Jensen, Injury mortality among Iowa farmers, 1980-1988:

comparison of PMR and SMR approaches. Am J Epidemiol, 1995. 141(9): p. 878-82. 154. Abdullat, E.M., et al., Agricultural and horticultural pesticides fatal poisoning; The Jordanian experience

1999-2002. Journal of Clinical Forensic Medicine, 2006. 13(6-8): p. 304-307. 155. Jors, E., et al., Occupational pesticide intoxications among farmers in Bolivia: a cross-sectional study.

Environ Health, 2006. 5(10): p. 10. 156. Recena, M.C., et al., Pesticides exposure in Culturama, Brazil--knowledge, attitudes, and practices.

Environ Res, 2006. 102(2): p. 230-6. 157. Bell, E.M., D.P. Sandler, and M.C. Alavanja, High pesticide exposure events among farmers and spouses

enrolled in the Agricultural Health Study. J Agric Saf Health, 2006. 12(2): p. 101-16. 158. Mehler, L.N., et al., California surveillance for pesticide-related illness and injury: coverage, bias, and

limitations. J Agromedicine, 2006. 11(2): p. 67-79. 159. Lu, J.L., Risk factors to pesticide exposure and associated health symptoms among cut-flower farmers. Int

J Environ Health Res, 2005. 15(3): p. 161-9. 160. van der Hoek, W. and F. Konradsen, Risk factors for acute pesticide poisoning in Sri Lanka. Trop Med

Int Health, 2005. 10(6): p. 589-96. 161. Mancini, F., et al., Acute pesticide poisoning among female and male cotton growers in India. Int J Occup

Environ Health, 2005. 11(3): p. 221-32. 162. Litchfield, M.H., Estimates of acute pesticide poisoning in agricultural workers in less developed

countries. Toxicol Rev, 2005. 24(4): p. 271-8. 163. Alarcon, W.A., et al., Acute illnesses associated with pesticide exposure at schools. Jama, 2005. 294(4):

p. 455-65.



164. Parikh, J.R., et al., Acute and chronic health effects due to green tobacco exposure in agricultural workers. Am J Ind Med, 2005. 47(6): p. 494-9.

165. Calvert, G.M., et al., Acute occupational pesticide-related illness in the US, 1998-1999: surveillance findings from the SENSOR-pesticides program. Am J Ind Med, 2004. 45(1): p. 14-23.

166. Garry, V.F., Pesticides and children. Toxicology and Applied Pharmacology, 2004. 198(2): p. 152-163. 167. Faria, N.M., et al., [Rural work and pesticide poisoning]. Cad Saude Publica, 2004. 20(5): p. 1298-308. 168. Delgado, I.F. and F.J. Paumgartten, [Pesticide use and poisoning among farmers from the county of Paty

do Alferes, Rio de Janeiro, Brazil]. Cad Saude Publica, 2004. 20(1): p. 180-6. 169. Reeves, M. and K.S. Schafer, Greater risks, fewer rights: U.S. farmworkers and pesticides. International

Journal of Occupational and Environmental Health, 2003. 9(1): p. 30-39. 170. Trape-Cardoso, M., et al., Shade tobacco and green tobacco sickness in Connecticut. J Occup Environ

Med, 2003. 45(6): p. 656-61. 171. Coggon, D., Work with pesticides and organophosphate sheep dips. Occup Med (Lond), 2002. 52(8): p.

467-70. 172. Arcury, T.A., et al., A clinic-based, case-control comparison of green tobacco sickness among minority

farmworkers: clues for prevention. South Med J, 2002. 95(9): p. 1008-11. 173. Quandt, S.A., et al., Environmental and behavioral predictors of salivary cotinine in Latino tobacco

workers. J Occup Environ Med, 2001. 43(10): p. 844-52. 174. Arcury, T.A., et al., The incidence of green tobacco sickness among Latino farmworkers. J Occup

Environ Med, 2001. 43(7): p. 601-9. 175. Quandt, S.A., et al., Migrant farmworkers and green tobacco sickness: new issues for an understudied

disease. Am J Ind Med, 2000. 37(3): p. 307-15. 176. Chain-Castro, T.J., R. Barron-Aragon, and L. Haro-Garcia, Pesticide poisoning in Mexican seasonal farm

workers. Int J Occup Environ Health, 1998. 4(3): p. 202-3. 177. Shin, D.C., et al., Pesticide poisoning and its related factors among Korean farmers. Med Lav, 1998. 89

Suppl 2(2): p. S129-35. 178. Van der Hoek, W., et al., Pesticide poisoning: a major health problem in Sri Lanka. Soc Sci Med, 1998.

46(4-5): p. 495-504. 179. Martin, R.J., et al., [Poisoning caused by organophosphate insecticides. Study of 506 cases]. Rev Clin

Esp., 1996. 196(3): p. 145-9. 180. Ballard, T., et al., Green tobacco sickness: occupational nicotine poisoning in tobacco workers. Arch

Environ Health, 1995. 50(5): p. 384-9. 181. Crandall, C.S., et al., Farm-related injury mortality in New Mexico, 1980-91. Accid Anal Prev, 1997.

29(2): p. 257-61. 182. Voaklander, D.C., et al., Work-related mortality among older farmers in Canada. Can Fam Physician,

1999. 45: p. 2903-10. 183. Park, H., et al., Farm-related dermatoses in Iowa male farmers and wives of farmers: a cross-sectional

analysis of the Iowa Farm Family Health and Hazard Surveillance Project. J Occup Environ Med, 2001. 43(4): p. 364-9.

184. Rautiainen, R.H. and S.J. Reynolds, Mortality and morbidity in agriculture in the United States. J Agric Saf Health, 2002. 8(3): p. 259-76.

185. McCurdy, S.A. and S.J.S.D.J.C.J.J.B.L.A. Morrin, Agricultural injury in California migrant Hispanic farm workers. American Journal of Industrial Medicine, 2003. 44(3): p. 225-235.

186. Roberts, S.E., Occupational mortality in British commercial fishing, 1976-95. Occup Environ Med, 2004. 61(1): p. 16-23.

187. Jaremin, B. and E. Kotulak, Mortality in the Polish small-scale fishing industry. Occup Med (Lond), 2004. 54(4): p. 258-260.

188. Hartman, E., et al., Risk factors for sick leave due to musculoskeletal disorders among self-employed Dutch farmers: A case-control study. American Journal of Industrial Medicine, 2006. 49(3): p. 204-214.

189. Quandt, S.A., et al., Illnesses and injuries reported by Latino poultry workers in western North Carolina. American Journal of Industrial Medicine, 2006. 49(5): p. 343-351.

190. Punnett, L., et al., Estimating the global burden of low back pain attributable to combined occupational exposures. American Journal of Industrial Medicine, 2005. 48(6): p. 459-469.



191. Stal, M. and J.E. Englund, Gender difference in prevalence of upper extremity musculoskeletal symptoms among Swedish pig farmers. J Agric Saf Health, 2005. 11(1): p. 7-17.

192. Thelin, A., E. Vingard, and S. Holmberg, Osteoarthritis of the hip joint and farm work. Am J Ind Med, 2004. 45(2): p. 202-9.

193. Pinzke, S., Changes in working conditions and health among dairy farmers in southern Sweden. A 14-year follow-up. Ann Agric Environ Med, 2003. 10(2): p. 185-95.

194. Holmberg, S., et al., Musculoskeletal symptoms among farmers and non-farmers: a population-based study. Int J Occup Environ Health, 2002. 8(4): p. 339-45.

195. Scutter, S., K.S. Turker, and R. Hall, Headaches and neck pain in farmers. Aust J Rural Health, 1997. 5(1): p. 2-5.

196. Sandmark, H., C. Hogstedt, and E. Vingard, Primary osteoarthrosis of the knee in men and women as a result of lifelong physical load from work. Scand J Work Environ Health, 2000. 26(1): p. 20-5.

197. Reckner Olsson, A., T. Skogh, and G. Wingren, Comorbidity and lifestyle, reproductive factors, and environmental exposures associated with rheumatoid arthritis. Ann Rheum Dis, 2001. 60(10): p. 934-9.

198. Mirbod, S.M., R. Inaba, and H. Iwata, Low back pain among different groups of subjects exposed to hand-arm transmitted vibration. Ind Health, 1997. 35(2): p. 212-21.

199. Manninen, P., H. Riihimak, and M. Heliovaara, Incidence and risk factors of low-back pain in middle-aged farmers. Occup Med (Lond), 1995. 45(3): p. 141-6.

200. Maetzel, A., et al., Osteoarthritis of the hip and knee and mechanical occupational exposure--a systematic overview of the evidence. J Rheumatol, 1997. 24(8): p. 1599-607.

201. Kumar, A., et al., Effect of whole-body vibration on the low back. A study of tractor-driving farmers in north India. Spine, 1999. 24(23): p. 2506-15.

202. Rabinowitz, P.M., et al., Hearing loss in migrant agricultural workers. J Agromedicine, 2005. 10(4): p. 9-17.

203. Kumar, A., et al., Effect of tractor driving on hearing loss in farmers in India. American Journal of Industrial Medicine, 2005. 47(4): p. 341-348.

204. Perry, M.J. and J.J. May, Noise and chemical induced hearing loss: special considerations for farm youth. J Agromedicine, 2005. 10(2): p. 49-55.

205. Solecki, L., Preliminary evaluation of occupational hearing loss risk among private farmers. Ann Agric Environ Med, 2003. 10(2): p. 211-5.

206. McBride, D.I., H.M. Firth, and G.P. Herbison, Noise exposure and hearing loss in agriculture: a survey of farmers and farm workers in the Southland region of New Zealand. J Occup Environ Med, 2003. 45(12): p. 1281-8.

207. Kerr, M.J., et al., Perceived and measured hearing ability in construction laborers and farmers. 2003. 44(4): p. 431-437.

208. Hwang, S.A., et al., Predictors of hearing loss in New York farmers. Am J Ind Med, 2001. 40(1): p. 23-31.

209. Valery, P.C., et al., Parental occupation and Ewing's sarcoma: Pooled and meta-analysis. International Journal of Cancer, 2005. 115(5): p. 799-806.

210. Van Maele-Fabry, G. and J.L. Willems, Prostate cancer among pesticide applicators: a meta-analysis. International Archives of Occupational and Environmental Health, 2004. 77(8): p. 559-570.

211. Yeni-Komshian, H. and E.A. Holly, Childhood brain tumours and exposure to animals and farm life: a review. Paediatr Perinat Epidemiol, 2000. 14(3): p. 248-56.

212. Khuder, S.A., et al., Meta-analysis of Hodgkin's disease among farmers. Scand J Work Environ Health, 1999. 25(5): p. 436-41.

213. Khuder, S.A., Etiologic clues to lip cancer from epidemiologic studies on farmers. Scand J Work Environ Health, 1999. 25(2): p. 125-30.

214. Khuder, S.A., A.B. Mutgi, and E.A. Schaub, Meta-analyses of brain cancer and farming. Am J Ind Med, 1998. 34(3): p. 252-60.

215. Zahm, S.H. and M.H. Ward, Pesticides and childhood cancer. Environ Health Perspect, 1998. 106 Suppl 3: p. 893-908.

216. Khuder, S.A. and A.B. Mutgi, Meta-analyses of multiple myeloma and farming. Am J Ind Med, 1997. 32(5): p. 510-6.



217. Keller-Byrne, J.E., S.A. Khuder, and E.A. Schaub, Meta-analyses of prostate cancer and farming. American Journal of Industrial Medicine, 1997. 31(5): p. 580-586.

218. Dich, J., et al., Pesticides and cancer. Cancer Causes Control, 1997. 8(3): p. 420-43. 219. Blair, A. and S.H. Zahm, Agricultural exposures and cancer. Environ Health Perspect, 1995. 103 Suppl

8: p. 205-8. 220. Lee, W.J., et al., Agricultural pesticide use and risk of glioma in Nebraska, United States. Occup Environ

Med, 2005. 62(11): p. 786-792. 221. van Wijngaarden, E., et al., Parental Occupational Exposure to Pesticides and Childhood Brain Cancer.

Am. J. Epidemiol., 2003. 157(11): p. 989-997. 222. De Roos, A.J., et al., Occupation and the risk of adult glioma in the United States. Cancer Causes and

Control, 2003. 14(2): p. 139-150. 223. Menegoz, F., et al., Contacts with animals and humans as risk factors for adult brain tumours. An

international case-control study. European Journal of Cancer, 2002. 38(5): p. 696-704. 224. Holly, E.A., et al., Farm and animal exposures and pediatric brain tumors: results from the United States

West Coast Childhood Brain Tumor Study. Cancer Epidemiol Biomarkers Prev, 1998. 7(9): p. 797-802. 225. Morrison, H.I., et al., Mortality among Canadian Fruit and Vegetable Farmers. Agricultural Health and

Safety: Workplace, Environment, Sustainability, H. H. McDuffie, J. A. Dosman, K. M. Semchuk, S. A. Olenchock, and A. Senthilselvan, Editors; Lewis Publishers, Boca Raton, pages 175-180, 11 references, 1995, 1995.

226. Nordby, K.C., A. Andersen, and P. Kristensen, Incidence of Lip Cancer in the Male Norwegian Agricultural Population. Cancer Causes and Control, 2004. 15(6): p. 619-626.

227. Rafnsson, V., Cancer incidence among farmers exposed to lindane while sheep dipping. Scand J Work Environ Health, 2006. 32(3): p. 185-9.

228. Linet, M.S., et al., Occupational risks for cutaneous melanoma among men in Sweden. J Occup Environ Med, 1995. 37(9): p. 1127-35.

229. Hou, L., et al., Pendimethalin exposure and cancer incidence among pesticide applicators. Epidemiology, 2006. 17(3): p. 302-7.

230. Nanni, O., et al., Relative and absolute cancer mortality of women in agriculture in northern Italy. Eur J Cancer Prev, 2005. 14(4): p. 337-44.

231. Rodvall, Y., J. Dich, and K. Wiklund, Cancer risk in offspring of male pesticide applicators in agriculture in Sweden. 2003. 60(10): p. 798-801.

232. Wang, Y., et al., Cancer incidence among a cohort of female farm residents in New York State. Arch Environ Health, 2002. 57(6): p. 561-7.

233. Settimi, L., et al., Cancer risk among male farmers: a multi-site case-control study. Int J Occup Med Environ Health, 2001. 14(4): p. 339-47.

234. Rusiecki, J.A., et al., Cancer incidence among pesticide applicators exposed to metolachlor in the Agricultural Health Study. International Journal of Cancer, 2006. 118(12): p. 3118-3123.

235. Pahwa, P., et al., Exposure to animals and selected risk factors among Canadian farm residents with Hodgkin's disease, multiple myeloma, or soft tissue sarcoma. J Occup Environ Med, 2003. 45(8): p. 857-68.

236. Pahwa, P., et al., Hodgkin lymphoma, multiple myeloma, soft tissue sarcomas, insect repellents, and phenoxyherbicides. J Occup Environ Med, 2006. 48(3): p. 264-74.

237. Lee, W.J., et al., Agricultural pesticide use and adenocarcinomas of the stomach and oesophagus. Occup Environ Med, 2004. 61(9): p. 743-749.

238. Cerhan, J.R., et al., Cancer Mortality among Iowa Farmers: Recent Results, Time Trends, and Lifestyle Factors (United States). Cancer Causes and Control, 1998. 9(3): p. 311-319.

239. Johnson, E.S., et al., Mortality from cancer and other diseases in poultry slaughtering/processing plants. Int J Epidemiol, 1997. 26(6): p. 1142-50.

240. Wiklund, K. and J. Dich, Cancer risks among male farmers in Sweden. Eur J Cancer Prev, 1995. 4(1): p. 81-90.

241. Costantini, A.S., et al., A multicenter case-control study in Italy on hematolymphopoietic neoplasms and occupation. Epidemiology, 2001. 12(1): p. 78-87.



242. Mills, P.K. and R. Yang, Breast cancer risk in Hispanic agricultural workers in California. Int J Occup Environ Health, 2005. 11(2): p. 123-31.

243. Assennato, G., et al., [Hemo-lymphopoietic tumors in agriculture. Case-control study in an epidemiologic area of southern Bari]. G Ital Med Lav Ergon, 1997. 19(1): p. 26-9.

244. Zhong, Y. and V. Rafnsson, Cancer incidence among Icelandic pesticide users. Int J Epidemiol, 1996. 25(6): p. 1117-24.

245. Assennato, G., et al., [Tumors of the hemolymphopoietic tract and employment in agriculture: a case-control study carried out in an epidemiologic area in southern Italy]. G Ital Med Lav, 1995. 17(1-6): p. 91-7.

246. Mills, P.K., R. Yang, and D. Riordan, Lymphohematopoietic Cancers in the United Farm Workers of America (UFW), 1988β€“2001. Cancer Causes and Control, 2005. 16(7): p. 823-830.

247. Reynolds, P., et al., Agricultural pesticide use and childhood cancer in California. Epidemiology, 2005. 16(1): p. 93-100.

248. Freeman, B., et al., Cancer Incidence among Male Pesticide Applicators in the Agricultural Health Study Cohort Exposed to Diazinon. Am. J. Epidemiol., 2005. 162(11): p. 1070-1079.

249. Miligi, L., et al., Non-Hodgkin's lymphoma, leukemia, and exposures in agriculture: Results from the Italian multicenter case-control study. American Journal of Industrial Medicine, 2003. 44(6): p. 627-636.

250. Blair, A., et al., Occupation and leukemia: A population-based case - control study in Iowa and Minnesota. American Journal of Industrial Medicine, 2001. 40(1): p. 3-14.

251. Kristensen, P., et al., Incidence and risk factors of cancer among men and women in Norwegian agriculture. Scand J Work Environ Health, 1996. 22(1): p. 14-26.

252. Nordstrom, M., et al., Occupational exposures, animal exposure and smoking as risk factors for hairy cell leukaemia evaluated in a case-control study. Br J Cancer, 1998. 77(11): p. 2048-52.

253. Clavel, J., et al., Farming, pesticide use and hairy-cell leukemia. Scand J Work Environ Health, 1996. 22(4): p. 285-93.

254. Clavel, J., et al., Hairy cell leukaemia, occupation, and smoking. Br J Haematol, 1995. 91(1): p. 154-61. 255. Nisse, C., et al., Occupational and environmental risk factors of the myelodysplastic syndromes in the

North of France. British Journal of Haematology, 2001. 112(4): p. 927-935. 256. Mester, B., et al., Occupation and malignant lymphoma: a population based case control study in

Germany. Occup Environ Med, 2006. 63(1): p. 17-26. 257. Sonoda, T., et al., A case-control study of multiple myeloma in Japan: association with occupational

factors. Asian Pac J Cancer Prev, 2005. 6(1): p. 33-6. 258. Rusiecki, J.A., et al., Cancer Incidence Among Pesticide Applicators Exposed to Atrazine in the

Agricultural Health Study. J Natl Cancer Inst, 2004. 96(18): p. 1375-1382. 259. Baris, D., et al., Occupation, pesticide exposure and risk of multiple myeloma. Scand J Work Environ

Health, 2004. 30(3): p. 215-22. 260. van Balen, E.C., et al., Exposure To Non Arsenic Pesticides Is Associated With Lymphoma Among

Farmers In Spain. Occup Environ Med, 2006: p. oem.2005.024026. 261. Svec, M., et al., Risk of lymphatic or haematopoietic cancer mortality with occupational exposure to

animals or the public. Occup. Environ. Med., 2005. 62: p. 726-735. 262. Samanic, C., et al., Cancer Incidence among Applicators Exposed to Dicamba in the Agricultural Health

Study. Environmental Health Perspectives, 2006. 263. Amadori, D., et al., Chronic lymphocytic leukaemias and non-Hodgkin's lymphomas by histological type

in farming-animal breeding workers: a population case-control study based on job titles. Occup Environ Med, 1995. 52(6): p. 374-9.

264. Fritschi, L., et al., Animal-related occupations and the risk of leukemia, myeloma, and non-Hodgkin's lymphoma in Canada. Cancer Causes and Control, 2002. 13(6): p. 563-571.

265. Lynch, S.M., et al., Cancer Incidence among Pesticide Applicators Exposed to Cyanazine in the Agricultural Health Study. Environ Health Perspect, 2006. 114(8): p. 1248-1252.

266. Fritschi, L., et al., Occupational exposure to pesticides and risk of non-Hodgkin's lymphoma. Am J Epidemiol, 2005. 162(9): p. 849-57.

267. Chiu, B.C.H., et al., Agricultural Pesticide Use, Familial Cancer, and Risk of Non-Hodgkin Lymphoma. Cancer Epidemiol Biomarkers Prev, 2004. 13(4): p. 525-531.



268. McDuffie, H.H., et al., Canadian male farm residents, pesticide safety handling practices, exposure to animals and non-Hodgkin's lymphoma (NHL). American Journal of Industrial Medicine, 2002. 42(S2): p. 54-61.

269. Waddell, B., et al., Agricultural use of organophosphate pesticides and the risk of non-Hodgkin's lymphoma among male farmers (United States). Cancer Causes and Control, 2001. 12(6): p. 509-517.

270. Zheng, T., et al., Agricultural exposure to carbamate pesticides and risk of non-Hodgkin lymphoma. J Occup Environ Med, 2001. 43(7): p. 641-9.

271. McDuffie, H.H., et al., Non-Hodgkin's Lymphoma and Specific Pesticide Exposures in Men: Cross-Canada Study of Pesticides and Health. Cancer Epidemiol Biomarkers Prev, 2001. 10(11): p. 1155-1163.

272. Metayer, C., E.S. Johnson, and J.C. Rice, Nested case-control study of tumors of the hemopoietic and lymphatic systems among workers in the meat industry. Am J Epidemiol, 1998. 147(8): p. 727-38.

273. Bonner, M.R., et al., Occupational exposure to carbofuran and the incidence of cancer in the Agricultural Health Study. Environ Health Perspect, 2005. 113(3): p. 285-9.

274. Lee, W.J., et al., Cancer Incidence Among Pesticide Applicators Exposed to Chlorpyrifos in the Agricultural Health Study. J Natl Cancer Inst, 2004. 96(23): p. 1781-1789.

275. Alavanja, M.C.R., et al., Pesticides and Lung Cancer Risk in the Agricultural Health Study Cohort. Am. J. Epidemiol., 2004. 160(9): p. 876-885.

276. Ji, B., et al., Occupational exposure to pesticides and pancreatic cancer. American Journal of Industrial Medicine, 2001. 39(1): p. 92-99.

277. Alguacil, J., et al., Risk of pancreatic cancer and occupational exposures in Spain. Ann Occup Hyg, 2000. 44(5): p. 391-403.

278. Dossing, M., et al., Liver cancer among employees in Denmark. Am J Ind Med, 1997. 32(3): p. 248-54. 279. Ewings, P. and C. Bowie, A case-control study of cancer of the prostate in Somerset and east Devon. Br J

Cancer, 1996. 74(4): p. 661-6. 280. Zeegers, M.P., et al., A prospective study of occupation and prostate cancer risk. J Occup Environ Med,

2004. 46(3): p. 271-9. 281. Alavanja, M.C.R., et al., Use of Agricultural Pesticides and Prostate Cancer Risk in the Agricultural

Health Study Cohort. Am. J. Epidemiol., 2003. 157(9): p. 800-814. 282. Settimi, L. and A.M.A.A.O. Axelson, Prostate cancer and exposure to pesticides in agricultural settings.

International Journal of Cancer, 2003. 104(4): p. 458-461. 283. Mills, P.K. and R. Yang, Prostate cancer risk in California farm workers. J Occup Environ Med, 2003.

45(3): p. 249-58. 284. Sharma-Wagner, S., et al., Occupation and prostate cancer risk in Sweden. J Occup Environ Med, 2000.

42(5): p. 517-25. 285. Parker, A.S., et al., A cohort study of farming and risk of prostate cancer in Iowa. Epidemiology, 1999.

10(4): p. 452-5. 286. Dich, J. and K. Wiklund, Prostate cancer in pesticide applicators in Swedish agriculture. The Prostate,

1998. 34(2): p. 100-112. 287. Parent, M.E., Y. Hua, and J. Siemiatycki, Occupational risk factors for renal cell carcinoma in Montreal.

Am J Ind Med, 2000. 38(6): p. 609-18. 288. Krstev, S., et al., Occupational risk factors and prostate cancer in U.S. Blacks and Whites. American

Journal of Industrial Medicine, 1998. 34(5): p. 421-430. 289. Mannetje, A., et al., Occupation and bladder cancer in European women. Cancer Causes Control, 1999.

10(3): p. 209-17. 290. Young, H.A., et al., Triazine herbicides and epithelial ovarian cancer risk in central California. J Occup

Environ Med, 2005. 47(11): p. 1148-56. 291. Engel, L.S., et al., Pesticide Use and Breast Cancer Risk among Farmers' Wives in the Agricultural

Health Study. Am. J. Epidemiol., 2005. 161(2): p. 121-135. 292. Kristensen, P., et al., Testicular cancer and parental use of fertilizers in agriculture. Cancer Epidemiol

Biomarkers Prev, 1996. 5(1): p. 3-9. 293. Moore, L.E., et al., Parental occupational exposures and Ewing's sarcoma. International Journal of

Cancer, 2005. 114(3): p. 472-478.



294. Valery, P.C., et al., Farm exposures, parental occupation, and risk of Ewing's sarcoma in Australia: a national case-control study. Cancer Causes and Control, 2002 13(3): p. 263-270.

295. Kogevinas, M., et al., Soft tissue sarcoma and non-Hodgkin's lymphoma in workers exposed to phenoxy herbicides, chlorophenols, and dioxins: two nested case-control studies. Epidemiology, 1995. 6(4): p. 396-402.

296. Cottoni, F., et al., The role of occupation and a past history of malaria in the etiology of classic Kaposi's sarcoma: a case-control study in north-east Sardinia. Br J Cancer, 1997. 76(11): p. 1518-20.

297. Zekveld, C., et al., The effects of farming and birth order on asthma and allergies. Eur Respir J, 2006. 28(1): p. 82-88.

298. Perkin, M.R. and D.P. Strachan, Which aspects of the farming lifestyle explain the inverse association with childhood allergy? Journal of Allergy and Clinical Immunology, 2006. 117(6): p. 1374-1381.

299. Chatzi, L., et al., Association of allergic rhinitis with pesticides use among grape farmers in Crete, Greece. Occup Environ Med, 2006.

300. Portengen, L., et al., Endotoxin exposure and atopic sensitization in adult pig farmers. Journal of Allergy and Clinical Immunology, 2005. 115(4): p. 797-802.

301. Kronqvist, M., et al., IgE-sensitization to predatory mites and respiratory symptoms in Swedish greenhouse workers. Allergy, 2005. 60(4): p. 521-526.

302. Kim, Y.K. and Y.Y. Kim, Outdoor spider mites as asthma allergens - Important in farming and nonfarming communities. Allergy and Clinical Immunology International, 2005. 17(5): p. 193-197.

303. Heederik, D. and T. Sigsgaard, Respiratory allergy in agricultural workers: recent developments. Curr Opin Allergy Clin Immunol, 2005. 5(2): p. 129-34.

304. Chatzi, L., et al., Allergic rhinitis, asthma, and atopy among grape farmers in a rural population in Crete, Greece. Chest, 2005. 127(1): p. 372-8.

305. Schafer, T., et al., Allergies in adults. Gesundheitswesen, 2005. 67(SUPPL. 1): p. S187-S192. 306. Sisinni, A.G., et al., Respiratory and skin allergies among vine and olive growers in the province of

Siena. Giornale Italiano di Medicina del Lavoro ed Ergonomia, 2004 26(4 SUPPL): p. 344-345. 307. Walusiak, J., et al., Small nonspecialized farming as a protective factor against immediate-type

occupational respiratory allergy? Allergy, 2004. 59(12): p. 1294-1300. 308. Doekes, G., et al., IgE sensitization to bacterial and fungal biopesticides in a cohort of Danish

greenhouse workers: The BIOGART Study. American Journal of Industrial Medicine, 2004. 46(4): p. 404-407.

309. Braback, L., A. Hjern, and F. Rasmussen, Trends in asthma, allergic rhinitis and eczema among Swedish conscripts from farming and non-farming environments. A nationwide study over three decades. Clinical & Experimental Allergy, 2004. 34(1): p. 38-43.

310. Johansson, E., et al., IgE-mediated sensitization to predatory mites in Swedish greenhouse workers. Allergy, 2003. 58(4): p. 337-341.

311. Wickens, K., et al., Farm residence and exposures and the risk of allergic diseases in New Zealand children. Allergy, 2002. 57(12): p. 1171-1179.

312. Portengen, L., et al., Low prevalence of atopy in young Danish farmers and farming students born and raised on a farm. Clinical & Experimental Allergy, 2002. 32(2): p. 247-253.

313. Kim, Y.K., et al., Role of environmental exposure to spider mites in the sensitization and the clinical manifestation of asthma and rhinitis in children and adolescents living in rural and urban areas. Clinical and Experimental Allergy, 2002. 32(9): p. 1305-1309.

314. Groenewoud, G.C.M., et al., Prevalence of occupational allergy to bell pepper pollen in greenhouses in the Netherlands. Clinical & Experimental Allergy, 2002. 32(3): p. 434-440.

315. Groenewoud, G.C.M., et al., Prevalence of occupational allergy to Chrysanthemum pollen in greenhouses in the Netherlands. Allergy, 2002. 57(9): p. 835-840.

316. von Mutius, E., Environmental factors influencing the development and progression of pediatric asthma. J Allergy Clin Immunol, 2002. 109(6 Suppl): p. S525-32.

317. Monso, E., et al., Occupational Asthma in Greenhouse Flower and Ornamental Plant Growers. Am. J. Respir. Crit. Care Med., 2002. 165(7): p. 954-960.

318. Klintberg, B., et al., Fewer allergic respiratory disorders among farmers' children in a closed birth cohort from Sweden. 2001. 17(6): p. 1151-1157.



319. Astarita, C., et al., Epidemiology of allergic occupational diseases induced by Tetranychus urticae in greenhouse and open-field farmers living in a temperate climate area. Allergy, 2001. 56(12): p. 1157-1163.

320. Park, H.S., et al., Specific IgG1 and IgG4 antibodies to citrus red mite in citrus farmers: A study of their relationship to respiratory symptoms. Journal of Korean Medical Science, 2000. 15(4): p. 407-412.

321. Navarro, et al., Prevalence of sensitization to Tetranychus urticae in greenhouse workers. Clinical & Experimental Allergy, 2000. 30(6): p. 863-866.

322. Sanchez-Guerrero, I., et al., Occupational allergy caused by carnation (Dianthus caryophyllus). J Allergy Clin Immunol., 1999. 104(1): p. 181-5.

323. Kronqvist, M., et al., Risk factors associated with asthma and rhinoconjunctivitis among Swedish farmers. Allergy, 1999. 54(11): p. 1142-9.

324. Kronqvist, et al., Increasing prevalence of asthma over 12 years among dairy farmers on Gotland, Sweden: storage mites remain dominant allergens. Clinical & Experimental Allergy, 1999. 29(1): p. 35-41.

325. Kimbell-Dunn, M., et al., Asthma and allergy in New Zealand farmers. American Journal of Industrial Medicine, 1999. 35(1): p. 51-57.

326. Goldberg, A., R. Confino-Cohen, and Y. Waisel, Allergic responses to pollen of ornamental plants: high incidence in the general atopic population and especially among flower growers. J Allergy Clin Immunol, 1998. 102(2): p. 210-4.

327. Hoppin, J.A., et al., Pesticides associated with Wheeze among Commercial Pesticide Applicators in the Agricultural Health Study. Am. J. Epidemiol., 2006. 163(12): p. 1129-1137.

328. Akpinar-Elci, M., O.C. Elci, and A. Odabasi, Work-related asthma-like symptoms among florists. Chest, 2004. 125(6): p. 2336-9.

329. Eduard, W., et al., Do farming exposures cause or prevent asthma? Results from a study of adult Norwegian farmers. Thorax, 2004. 59(5): p. 381-386.

330. Wijnand Eduard, E.O.P.S.B.J.D.D.H., Atopic and non-atopic asthma in a farming and a general population. American Journal of Industrial Medicine, 2004. 46(4): p. 396-399.

331. Eduard, W., et al., Atopic and non-atopic asthma in a farming and a general population. Am J Ind Med, 2004. 46(4): p. 396-9.

332. Monso, E., et al., Region-related risk factors for respiratory symptoms in European and Californian farmers. Eur Respir J, 2003. 21(2): p. 323-331.

333. Hoppin, J.A., et al., Animal production and wheeze in the Agricultural Health Study: interactions with atopy, asthma, and smoking. Occup Environ Med, 2003. 60(8): p. 3e-.

334. Radon, K. and C. Winter, Prevalence of respiratory symptoms in sheep breeders. Occup Environ Med, 2003. 60(10): p. 770-773.

335. Koskela, H.O., et al., Pet- and pollen-induced upper airway symptoms in farmers and in nonfarmers. Eur Respir J, 2003. 22(1): p. 135-140.

336. Borghetti, C., et al., [Sensitization and occupational asthma in poultry workers]. Med Clin (Barc), 2002. 118(7): p. 251-5.

337. Monso, E., et al., Respiratory Symptoms of Obstructive Lung Disease in European Crop Farmers. Am. J. Respir. Crit. Care Med., 2000. 162(4): p. 1246-1250.

338. Britt-Marie Larsson, K.L.P.M.L.M.L.P., Airway responses in naive subjects to exposure in poultry houses: Comparison between cage rearing system and alternative rearing system for laying hens. American Journal of Industrial Medicine, 1999. 35(2): p. 142-149.

339. Melbostad, E., W. Eduard, and P. Magnus, Determinants of asthma in a farming population. Scand J Work Environ Health, 1998. 24(4): p. 262-9.

340. Laasonen, K., Respiratory symptoms and lung function in pig farmers, in Lääketieteen laitos - Medical School. 2006, Tampere University: Tampere

341. Gomez, M.I., et al., Prevalence and predictors of respiratory symptoms among New York farmers and farm residents. Am J Ind Med, 2004. 46(1): p. 42-54.

342. Monso, E., et al., Chronic obstructive pulmonary disease in never-smoking animal farmers working inside confinement buildings. American Journal of Industrial Medicine, 2004. 46(4): p. 357-362.



343. Chaudemanche, H., et al., Respiratory status in dairy farmers in France; cross sectional and longitudinal analyses. Occup Environ Med, 2003. 60(11): p. 858-863.

344. Skorska, C., et al., Health effects of inhalation exposure to organic dust in hops farmers. Ann Univ Mariae Curie Sklodowska [Med], 2003. 58(1): p. 459-65.

345. Mastrangelo, G., et al., Ascertaining the risk of chronic obstructive pulmonary disease in relation to occupation using a case-control design. Occup Med (Lond), 2003. 53(3): p. 165-172.

346. Danuser, B., et al., Respiratory symptoms in Swiss farmers: An epidemiological study of risk factors. American Journal of Industrial Medicine, 2001. 39(4): p. 410-418.

347. Osim, E.E., C.T. Musabayane, and J. Mufunda, Lung function of Zimbabwean farm workers exposed to flue curing and stacking of tobacco leaves. S Afr Med J, 1998. 88(9): p. 1127-31.

348. Radon, K., et al., Prevalence and risk factors for airway diseases in farmers--summary of results of the European Farmers' Project. Ann Agric Environ Med, 2002. 9(2): p. 207-13.

349. Venier, A.G., et al., Influence of occupational factors on lung function in french dairy farmers. A 5-year longitudinal study. Am J Ind Med, 2006. 49(4): p. 231-7.

350. Peiris-John, R.J., et al., Low-level exposure to organophosphate pesticides leads to restrictive lung dysfunction. Respiratory Medicine, 2005. 99(10): p. 1319-1324.

351. Djuricic, S., et al., [Basic spirometry measurements in workers on pig farmers]. Srp Arh Celok Lek, 2004. 132(3-4): p. 85-91.

352. Kirychuk, S.P., et al., Respiratory symptoms and lung function in poultry confinement workers in Western Canada. Can Respir J, 2003. 10(7): p. 375-80.

353. Likas, C., et al., Noxious gases in greenhouses. Ann Agric Environ Med, 2001. 8(1): p. 99-101. 354. Melenka, L.S., et al., Lung health in Alberta farmers. Int J Tuberc Lung Dis, 1999. 3(10): p. 913-9. 355. Post, W., D. Heederik, and R. Houba, Decline in lung function related to exposure and selection

processes among workers in the grain processing and animal feed industry. Occup Environ Med, 1998. 55(5): p. 349-55.

356. Hashemi, N., et al., Prevalence of work-related respiratory symptoms in Iranian farmers. Can Respir J, 2006. 13(4): p. 198-202.

357. Pahwa, P., H.H. McDuffie, and J.A. Dosman, Longitudinal Changes in Prevalence of Respiratory Symptoms Among Canadian Grain Elevator Workers. Chest, 2006. 129(6): p. 1605-1613.

358. Bradley S. King, E.H.P.C.A.M.D.D.D.K.E.G.A.M.W., Eye and respiratory symptoms in poultry processing workers exposed to chlorine by-products. American Journal of Industrial Medicine, 2006. 49(2): p. 119-126.

359. Rylander, R. and M.F. Carvalheiro, Airways inflammation among workers in poultry houses. Int Arch Occup Environ Health, 2006. 79(6): p. 487-90.

360. Chloros, D., et al., Respiratory effects in workers processing dried tobacco leaves. Allergol Immunopathol (Madr), 2004. 32(6): p. 344-51.

361. Linaker, C. and J. Smedley, Respiratory illness in agricultural workers. Occup Med (Lond), 2002. 52(8): p. 451-459.

362. Sprince, N.L., et al., Respiratory symptoms: Associations with pesticides, silos, and animal confinement in the Iowa Farm Family Health and Hazard Surveillance Project. American Journal of Industrial Medicine, 2000. 38(4): p. 455-462.

363. Wilkins, J., et al., Prevalence of chronic respiratory symptoms among Ohio cash grain farmers. American Journal of Industrial Medicine, 1999. 35(2): p. 150-163.

364. Skorska, C., et al., Effects of exposure to grain dust in Polish farmers: work-related symptoms and immunologic response to microbial antigens associated with dust. Ann Agric Environ Med, 1998. 5(2): p. 147-53.

365. Rees, D., et al., Respiratory health and immunological profile of poultry workers. S Afr Med J, 1998. 88(9): p. 1110-7.

366. Dalphin, J.C., et al., Six year longitudinal study of respiratory function in dairy farmers in the Doubs province. Eur Respir J, 1998. 11(6): p. 1287-1293.

367. Garcia, J.G., K.S. Matheny Dresser, and A.D. Zerr, Respiratory health of Hispanic migrant farm workers in Indiana. Am J Ind Med, 1996. 29(1): p. 23-32.



368. do Pico, G.A., Lung (agricultural/rural). Otolaryngology - Head and Neck Surgery, 1996. 114(2): p. 212-216.

369. McCurdy, S.A., et al., Respiratory Health of California Rice Farmers. American Journal of Respiratory and Critical Care Medicine, Vol. 153, No. 5, Cooperative Agreement No. U07-CCU-906162, pages 1553-1559, 31 references, 1996, 1996.

370. Smit, L.A.M., et al., Hay fever and asthma symptoms in conventional and organic farmers in The Netherlands. Occup Environ Med, 2006: p. oem.2006.028167.

371. Von Ehrenstein, et al., Reduced risk of hay fever and asthma among children of farmers. 2000. 30(2): p. 187-193.

372. Peter F.J. Vogelzang, J.W.J.v.d.G.H.F.C.P.v.S., Organic dust toxic syndrome in swine confinement farming. 1999. p. 332-334.

373. Von Essen, S., et al., Respiratory Symptoms and Farming Practices in Farmers Associated With an Acute Febrile Illness After Organic Dust Exposure. Chest, 1999. 116(5): p. 1452-1458.

374. Farr, S.L., et al., Pesticide Exposure and Timing of Menopause: The Agricultural Health Study. Am. J. Epidemiol., 2006. 163(8): p. 731-742.

375. Kristensen, P., A. Andersen, and L.M. Irgens, Hormone-dependent cancer and adverse reproductive outcomes in farmers' families--effects of climatic conditions favoring fungal growth in grain. Scand J Work Environ Health, 2000. 26(4): p. 331-7.

376. Bretveld, R., G.A. Zielhuis, and N. Roeleveld, Time to pregnancy among female greenhouse workers. Scand J Work Environ Health, 2006. 32(5): p. 359-67.

377. Abell, A., S. Juul, and J.P. Bonde, Time to pregnancy among female greenhouse workers. Scandinavian Journal Of Work, Environment & Health, 2000. 26(2): p. 131-136.

378. Lauria, L., et al., Exposure to pesticides and time to pregnancy among female greenhouse workers. Reproductive Toxicology, 2006. In Press, Corrected Proof.

379. Abell, A., E. Ernst, and J. Bonde, Semen quality and sexual hormones in greenhouse workers. Scand J Work Environ Health, 2000. 26(6): p. 492-500.

380. Sallmen, M., et al., Time to pregnancy among the wives of Finnish greenhouse workers. Scandinavian Journal Of Work, Environment & Health, 2003. 29(2): p. 85-93.

381. Petrelli, G. and I. FigΓ -Talamanca, Reduction in fertility in male greenhouse workers exposed to pesticides. European Journal of Epidemiology, 2001. 17(7): p. 675-677.

382. Thonneau, P., et al., Effects of pesticide exposure on time to pregnancy: results of a multicenter study in France and Denmark. ASCLEPIOS Study Group. Am J Epidemiol., 1999. 150(2): p. 157-63.

383. Jurewicz, J., et al., Exposure to pesticides and heavy work in greenhouses during pregnancy: does it effect birth weight? International Archives of Occupational and Environmental Health, 2005. 78(5): p. 418-426.

384. Idrovo, A.J., et al., Time to first pregnancy among women working in agricultural production. Int Arch Occup Environ Health, 2005. 78(6): p. 493-500.

385. Nordby, K.C., et al., Indicators of mancozeb exposure in relation to thyroid cancer and neural tube defects in farmers' families. Scand J Work Environ Health, 2005. 31(2): p. 89-96.

386. Farr, S.L., et al., Pesticide Use and Menstrual Cycle Characteristics among Premenopausal Women in the Agricultural Health Study. Am. J. Epidemiol., 2004. 160(12): p. 1194-1204.

387. Rull, R.P., B. Ritz, and G.M. Shaw, Neural Tube Defects and Maternal Residential Proximity to Agricultural Pesticide Applications. Am. J. Epidemiol., 2006. 163(8): p. 743-753.

388. Lacasana, M., et al., Maternal and paternal occupational exposure to agricultural work and the risk of anencephaly. Occup Environ Med, 2006. 63(10): p. 649-56.

389. Heeren, G.A., J. Tyler, and A. Mandeya, Agricultural chemical exposures and birth defects in the Eastern Cape Province, South Africa: a case-control study. Environ Health, 2003. 2(1): p. 11.

390. Engel, L.S., E.S. O'Meara, and S.M. Schwartz, Maternal occupation in agriculture and risk of limb defects in Washington State, 1980-1993. Scand J Work Environ Health, 2000. 26(3): p. 193-8.

391. Shaw, G.M., et al., Maternal pesticide exposure from multiple sources and selected congenital anomalies. Epidemiology, 1999. 10(1): p. 60-6.

392. Kristensen, P., et al., Birth defects among offspring of Norwegian farmers, 1967-1991. Epidemiology, 1997. 8(5): p. 537-44.



393. Garry, V., et al., Pesticide appliers, biocides, and birth defects in rural Minnesota. Environ Health Perspect., 1996. 104(4): p. 394-399.

394. Stark, C., et al., Suicide in farmers in Scotland. Rural Remote Health, 2006. 6(1): p. 509. 395. Nishimura, M., et al., Suicide and occupation: further supportive evidence for their relevance. Progress in

Neuro-Psychopharmacology and Biological Psychiatry, 2004. 28(1): p. 83-87. 396. Kamel, F., et al., Neurobehavioral performance and work experience in Florida farmworkers.

Environmental Health Perspectives, 2003. 111(14): p. 1765-1772. 397. Thomas, H.V., et al., Mental health of British farmers * COMMENTARY. Occup Environ Med, 2003.

60(3): p. 181-186. 398. Page, A.N. and L.J. Fragar, Suicide in Australian farming, 1988-1997. Australian and New Zealand

Journal of Psychiatry, 2002. 36(1): p. 81-85. 399. Booth, N., M. Briscoe, and R. Powell, Suicide in the farming community: methods used and contact with

health services. Occup Environ Med, 2000. 57(9): p. 642-644. 400. Eisner, C.S., R.D. Neal, and B. Scaife, The effect of the 1996 'beef crisis' on depression and anxiety in

farmers and non-farming controls. Br J Gen Pract, 1999. 49(442): p. 385-6. 401. Kupersmidt, J.B. and S.L. Martin, Mental health problems of children of migrant and seasonal farm

workers: a pilot study. J Am Acad Child Adolesc Psychiatry, 1997. 36(2): p. 224-32. 402. Petrovitch, H., et al., Plantation work and risk of Parkinson disease in a population-based longitudinal

study. Arch Neurol, 2002. 59(11): p. 1787-92. 403. Priyadarshi, A., et al., A meta-analysis of Parkinson's disease and exposure to pesticides.

Neurotoxicology, 2000. 21(4): p. 435-40. 404. Stallones, L. and C. Beseler, Pesticide illness, farm practices, and neurological symptoms among farm

residents in Colorado. Environ Res, 2002. 90(2): p. 89-97. 405. Stallones, L. and C. Beseler, Pesticide poisoning and depressive symptoms among farm residents. Ann

Epidemiol, 2002. 12(6): p. 389-94. 406. Pilkington, A., et al., An epidemiological study of the relations between exposure to organophosphate

pesticides and indices of chronic peripheral neuropathy and neuropsychological abnormalities in sheep farmers and dippers. Occup Environ Med, 2001. 58(11): p. 702-710.

407. Helmer, C., et al., Occupation during life and risk of dementia in French elderly community residents. Journal of Neurology Neurosurgery and Psychiatry, 2001. 71(3): p. 303-309.

408. Beach, J.R., et al., Abnormalities on neurological examination among sheep farmers exposed to organophosphorous pesticides. Occup Environ Med, 1996. 53(8): p. 520-5.

409. Bazylewicz-Walczak, B., W. Majczakowa, and M. Szymczak, Behavioral effects of occupational exposure to organophosphorous pesticides in female greenhouse planting workers. Neurotoxicology, 1999. 20(5): p. 819-26.

410. London, L., et al., Effects of long-term organophosphate exposures on neurological symptoms, vibration sense and tremor among South African farm workers. Scand J Work Environ Health, 1998. 24(1): p. 18-29.

411. Stephens, R., et al., Neuropsychological effects of long-term exposure to organophosphates in sheep dip. Lancet, 1995. 345(8958): p. 1135-9.

412. Chatzi, L., et al., Skin symptoms and work-related skin symptoms among grape farmers in Crete, Greece. Am J Ind Med, 2006. 49(2): p. 77-84.

413. McCall, B.P., et al., Incidence Rates, Costs, Severity, and Work-Related Factors of Occupational Dermatitis: A Workers' Compensation Analysis of Oregon, 1990-1997. Arch Dermatol, 2005. 141(6): p. 713-718.

414. Susitaival, P.R.B.S.J.S.M.B.S., Self-reported dermatitis and skin cancer in California farm operators. American Journal of Industrial Medicine, 2004. 46(2): p. 136-141.

415. Spiewak, R., Occupational dermatoses among Polish private farmers, 1991-1999. American Journal of Industrial Medicine, 2003. 43(6): p. 647-655.

416. Spiewak, R., A. Gora, and J. Dutkiewicz, Work-related skin symptoms and type I allergy among eastern-Polish farmers growing hops and other crops. Ann Agric Environ Med, 2001. 8(1): p. 51-6.

417. Burnett, C., et al., Occupational dermatitis causing days away from work in U.S. private industry, 1993. American Journal of Industrial Medicine, 1998. 34(6): p. 568-573.



418. Spiewak, R., Zoophilic and geophilic fungi as a cause of skin disease in farmers. Ann Agric Environ Med, 1998. 5(2): p. 97-102.

419. Paulsen, E., J. Sogaard, and K. Andersen, [Occupational eczema in gardeners and greenhouse workers in the county of Funen]. Ugeskr Laeger., 1998. 160(48): p. 6972-5.

420. Cole, D.C., et al., Dermatitis in Ecuadorean Farm Workers. Contact Dermatitis, 24 references, 1997. 37(1): p. 1-8.

421. Pirkis, J.E., K.K. O'Regan, and R. Bailie, Contact dermatitis and in-feed exposure to antibiotics among pig feed handlers. Aust J Rural Health, 1997. 5(2): p. 76-9.

422. Kanerva, L., et al., Statistical data on occupational contact urticaria. Contact Dermatitis, 1996. 35(4): p. 229-33.

423. Guo, Y., et al., Prevalence of dermatoses and skin sensitisation associated with use of pesticides in fruit farmers of southern Taiwan. Occup Environ Med, 1996. 53(6): p. 427-31.

424. Susitaival, P., et al., Hand eczema in Finnish farmers. A questionnaire-based clinical study. 1995. 32(3): p. 150-155.

425. Susitaival, P. and M. Hannuksela, The 12-year prognosis of hand dermatosis in 896 Finnish farmers. 1995. 32(4): p. 233-237.

426. McCarty, C., et al., A comparison of cardiovascular disease risk factors in farm and non-farm residents: the Wisconsin Rural Women's Health Study. Wmj, 2002. 101(7): p. 34-9.

427. Baker, M.G., et al., A recurring salmonellosis epidemic in New Zealand linked to contact with sheep. Epidemiol Infect, 2006. 2: p. 1-8.

428. Myers, K.P., et al., Are swine workers in the United States at increased risk of infection with zoonotic influenza virus? Clin Infect Dis, 2006. 42(1): p. 14-20.

429. Swayne, D.E., Occupational and consumer risks from avian influenza viruses. Dev Biol (Basel), 2006. 124: p. 85-90.

430. Voss, A., et al., Methicillin-resistant Staphylococcus aureus in pig farming. Emerg Infect Dis, 2005. 11(12): p. 1965-6.

431. Jansen, A., et al., Leptospirosis in Germany, 1962-2003. Emerging Infectious Diseases, 2005. 11(7): p. 1048-1054.

432. Kern, P., et al., Risk factors for alveolar echinococcosis in humans. Emerg Infect Dis, 2004. 10(12): p. 2088-93.

433. Wilson, I.G., Airborne Campylobacter infection in a poultry worker: case report and review of the literature. Commun Dis Public Health, 2004. 7(4): p. 349-53.

434. Castiglia, P., et al., Epidemiology of hydatidosis in the province of Sassari, Italy. Parassitologia, 2004. 46(4): p. 371-3.

435. Bikas, C., et al., Epidemiology of human brucellosis in a rural area of north-western Peloponnese in Greece. Eur J Epidemiol, 2003. 18(3): p. 267-74.

436. Roman-Sanchez, P., et al., HIGH PREVALENCE OF STRONGYLOIDES STERCORALIS AMONG FARM WORKERS ON THE MEDITERRANEAN COAST OF SPAIN: ANALYSIS OF THE PREDICTIVE FACTORS OF INFECTION IN DEVELOPED COUNTRIES. Am J Trop Med Hyg, 2003. 69(3): p. 336-340.

437. Brhel, P. and M. Bartnicka, Occupational infectious diseases in the Czech Republic. Medycyna Pracy, 2003. 54(6): p. 529-533.

438. Bridges, C.B., et al., Risk of influenza A (H5N1) infection among poultry workers, Hong Kong, 1997-1998. J Infect Dis, 2002. 185(8): p. 1005-10.

439. Thakur, S.D. and D.C. Thapliyal, Seroprevalence of brucellosis in man. J Commun Dis, 2002. 34(2): p. 106-9.

440. Kurcer, M.A. and E. Pehlivan, Hepatitis B seroprevalance and risk factors in urban areas of Malatya. Turk J Gastroenterol, 2002. 13(1): p. 1-5.

441. Schulte, J.M., et al., Tuberculosis cases reported among migrant farm workers in the United States, 1993-97. J Health Care Poor Underserved, 2001. 12(3): p. 311-22.

442. Drobeniuc, J., et al., Hepatitis E virus antibody prevalence among persons who work with swine. J Infect Dis, 2001. 184(12): p. 1594-7.



443. Much, D.H., J. Martin, and I. Gepner, Tuberculosis among Pennsylvania migrant farm workers. J Immigr Health, 2000. 2(1): p. 53-6.

444. Holk, K., N. Susanne Vinther, and R. Tove, Human Leptospirosis in Denmark 1970-1996: An Epidemiological and Clinical Study. Scandinavian Journal of Infectious Diseases, 2000. 32(5): p. 533-538.

445. Mounts, A.W., et al., Case-control study of risk factors for avian influenza A (H5N1) disease, Hong Kong, 1997. J Infect Dis, 1999. 180(2): p. 505-8.

446. Hadjichristodoulou, C., et al., Epidemiological study of brucellosis in eight Greek villages using a computerised mapping programme. Eur J Epidemiol, 1999. 15(7): p. 671-80.

447. Cisak, E., et al., Seroepidemiologic study on tick-borne encephalitis among forestry workers and farmers from the Lublin region (eastern Poland). Ann Agric Environ Med, 1998. 5(2): p. 177-81.

448. McCurdy, S., D. Arretz, and R. Bates, Tuberculin reactivity among California Hispanic migrant farm workers. American Journal of Industrial Medicine, 1997. 32(6): p. 600-605.

449. Jaremin, B. and E. Kotulak, Myocardial infarction (MI) at the work-site among Polish seafarers. The risk and the impact of occupational factors. Int Marit Health, 2003. 54(1-4): p. 26-39.

450. O'Connor, P.J. and N. O'Connor, Work-related maritime fatalities. Accid Anal Prev, 2006. 38(4): p. 737-41.

451. Jensen, O.C., Injury risk at the work processes in fishing: A case-referent study. Eur J Epidemiol, 2006. 19: p. 19.

452. Brooks, C.J., K.A. Howard, and S.K. Neifer, How much did cold shock and swimming failure contribute to drowning deaths in the fishing industry in British Columbia 1976-2002? Occup Med (Lond), 2005. 55(6): p. 459-462.

453. Roberts, S.E., Work-related homicides among seafarers and fishermen. Int Marit Health, 2004. 55(1-4): p. 7-18.

454. Siracusa, A., et al., Prevalence of occupational allergy due to live fish bait. Clinical and Experimental Allergy, 2003. 33(4): p. 507-510.



Work Package V “Development of indicators”

DIRERAF: work package 5 2


Table of contents

Table of contents.......................................................................................................................... 2 Executive summary...................................................................................................................... 3 1. Introduction.............................................................................................................................. 4 2. Methodology............................................................................................................................ 4 3. Presentation of the indicators................................................................................................... 5

3.1 The short list of indicators ................................................................................................. 7 Category C indicators .......................................................................................................... 7

3.2 Detailed presentation ......................................................................................................... 9 Category A........................................................................................................................... 9 Category B ......................................................................................................................... 21 Category C ......................................................................................................................... 26

4. Annex..................................................................................................................................... 30 4.1 Working paper by IMIM and NKUA on “Health indicators for agriculture and fisheries”................................................................................................................................................ 32



Executive summary

The aim of this work package is to identify and describe the most important functional indicators related to occupational and environmental health risks associated with agriculture and fisheries, as they were decided and produced during the course of the project.

The foundation of the methodology used for the development of these indicators is the systematization of information resulting from the previous work packages: a) by identifying policies and practices with regards to data collection in the fields of health and safety in agriculture and fisheries and the environment (work package two), b) by identifying the minimal common dataset among European Member States based on the previous findings (work package three), c) by identifying health risks associated with agriculture and fisheries (work package four).

The development of indicators has been further facilitated by the session of the panel of experts organized in February, 2007, in Athens, Greece. The outcome of this session was an initial set of indicators proposed by the experts, which was used as a starting point for proposing the full set of indicators presented in this deliverable. The experts had received work packages 2,3 and 4, as well as additional informational material, so that it was taken into account during the discussions. The agenda and minutes of this session are included in the annex of the interim report [section (i)].

The deliverable of this work package is a list of indicators and guidelines for their application.



1. Introduction

The aim of this work package is to identify and describe the most important functional indicators that describe the occupational and environmental health risks associated with agriculture and fisheries.

This report includes the first version of the list of occupational and environmental health indicators, which are to be proposed for this purpose. This list is the outcome of an extensive search over national and international data sources collecting relevant information and a critical review of the recent medical literature, in order to identify and quantify the health risks associated with agriculture and fisheries for the exposed populations and the environment. Additionally, a significant contribution to this process was made by the experts feedback on the project, both through the directory of contacts with national and international experts and authorities we have established over the course of the project, as well as through a two-day brainstorming session we have organized with distinguished experts in Athens, February 2007.

The list of developed indicators will be further refined after the pilot testing of a selected group based on the proposed indicators. In addition, the list of developed indicators are already in the process of being evaluated not only among the project partners, but by a wide range of relevant stakeholders, which will hopefully contribute to the development of the final list of indicators to be proposed.

2. Methodology Attempting to propose a set of indicators, which could effectively monitor the health risks associated with agriculture and fisheries is a difficult task, as the proposed indicators should meet specific requirements in order to be ready for adoption at a European level. Firstly, any public health indicator, which aims at monitoring specific health risks, should be easy to implement. This means that employing already available data or already established data collection methods, the data required for the calculation of such an indicator would be easily and affordably available. The quality of the required data is another critical issue: such an indicator should be directly comparable in time and across Member States, thus requiring homogeneous data for its calculation. Three other factors are also important: significance; efficiency; policy relevance. Significance means that such an indicator should be able to directly or indirectly measure a significant and serious health threat, assessing on the magnitude of the risk and the extent to which it has an impact on the exposed population. Efficiency means that the indicator should be able to adequately monitor what it is supposed to monitor. Lastly, policy relevance means that such an indicator should not only be sensitive enough to detect changes as they evolve but should also monitor something amendable for change, for example, enforcing a policy to reduce a specific exposure. In order to meet the above criteria, the project had adopted a concise methodology, which is based on the systematization of already available knowledge. Initially, an exhaustive effort took place to identify already available policies and practices with regards to data collection in the fields of health and safety in agriculture, fisheries and the environment. The successful outcome of this attempt (as it is presented in detail in the deliverable of work package two) has enabled the project team to shape a critical view on the availability and quality of data sources on a national and international level, and to identify a minimal set of already available datasets (presented in



the deliverable of work package three). A decisive step forward was to identify which health risks associated with agriculture and fisheries are most important and what part of the population is at such risk. To do this, a systematic review of the most recent medical literature was conducted, in order to identify which health risks are associated with which production types or methods in agriculture and fisheries and to assess the magnitude of such risk. It should be stressed that the DIRERAF methodology considers public health indicators to be more than just an exact or approximate measure of health, economic, social or other characteristics of the target populations. Public health indicators, therefore, serve an additional role: the one of providing the link between an underlying disease or condition and the political will to monitor these in time and across different backgrounds. A critical review of the above findings was first conducted during a two-day session between researchers from NKUA and IMIM institutes in September 2006. Having examined the available datasets and the health risks identified by the literature, the two teams produced an introductory paper on a theoretical approach on indicators and a first set of already available indicators to take into account. This working paper is included in the annex of this report. The development of indicators has been further facilitated by the session of the panel of experts organized in February, 2007, in Athens, Greece. The panel of experts, having taken into account the results of the work completed so far, proposed a set of indicators during a two-day brainstorming session, which was used as a starting point for proposing the full set of indicators presented in this deliverable. The participants, the agenda and the minutes of this session are included in the annex of this report.

3. Presentation of the indicators The proposed indicators are grouped in three categories, based on their significance and the availability of collected data.

A. Category A (definite indicators): These are a set of important indicators, measuring something which could be associated with one or more health risks pertaining to agriculture, fisheries and the environment. For these indicators, datasets are mostly available from national and international authorities, which could be used immediately or require slight modifications, in order to calculate each indicator.

B. Category B: Good indicators, but of limited data and/or difficult to implement. These are a set of indicators, which could be characterized as significant and relevant, but for which already available data are limited or collecting such data on a regular basis is difficult to implement.

C. Category C (Indicators of limited value): Independently of their significance or the availability of data, these indicators may suffer from issues of data quality and generalizability, in particular, when original data sources are derived from surveys conducted among a limited number of participants.

Furthermore, the indicators in each category fall into five distinctive subcategories:



1. Population at risk: These indicators measure the number of workers in agriculture and fisheries. These indicators serve as measurements of the exposed population (eg population at risk) as well as denominators for calculating other indicators.

2. Exposure levels and types: These indicators estimate the level of exposure to various hazards, existing in the agricultural or fisheries work setting.

3. Environmental burden: These indicators estimate the level of the burden of the environment due to agriculture or fishing.

4. Health outcomes: These indicators measure the outcome of the health risks pertaining to the population exposed in agriculture and fisheries.

5. Health policies and practices: These indicators measure various aspects of policies and practices with regards to health and safety in agriculture and fisheries.

Each indicator proposed is described with the following: a) the definition of the indicator, b) a short description of how the indicator is calculated, c) the basic and optimal level of aggregation. As basic we characterize the absolutely necessary level, whereas optimal is the desirable. The reason of this disaggregating is mainly to provide enough information to meet the needs of equal protection. The usual levels of aggregation are gender, age and immigration status, d) the basic and optimal level of data collection, e) the significance of what the indicator attempts to monitor) and f) why this indicator is important (rationale) based on the findings on the work package 4, g) limitations of the indicator, h) potential data sources based on the work packages 2 and 3.



3.1 The short list of indicators In the following tables (1-3) the proposed indicators are listed. Table 1: List of proposed indicators belonging to category A Population at risk

A1_01. Number of persons occupied in agriculture A1_02. Number of persons occupied in fisheries A1_03. Number of working hours in agriculture A1_04. Percent of persons occupied in agriculture of the total active population

Exposure levels and types

A2_01. Number of animals per farmer A2_02. Number of animals per holding A2_03. Number of tractors per farmer A2_04. Average farm size A2_05. Greenhouses: area under glass

Environmental burden

A3_01. Fertilizers: use of fertilizers per hectare of cultivated land / per total country size A3_02. Number of samples of drinking water screened for nitrate levels A3_03. Percentage of samples exceeding set nitrate limits A3_04. Percentage of organic farming A3_05. Pesticides: Sales of pesticides per hectare/per capita A3_06. Number of samples of drinking water screened for pesticide residue levels A3_07. Percentage of samples exceeding set pesticide residue limits

Health outcomes

A4_01. Rate of fatal accidents in agriculture A4_02. Rate of fatal accidents in fisheries A4_03. Reported cases of pesticide poisoning A4_04. Number of reported zoonoses

Health policies and practices

A5_01. Rate of occupational diseases in agriculture A5_02. Rate of occupational diseases in fisheries A5_03. Pesticides: policy conformity A5_04. Health Services Utilization A5_05. Insurance coverage Composite Indicator

Table 2: List of proposed indicators belonging to category B Population at risk

B1_01. Number of immigrants occupied in agriculture B1_02. Percent of immigrants occupied in agriculture of the total farming population B1_03. Number of immigrants occupied in fisheries B1_04. Percent of immigrants occupied in fisheries of the total fishermen population

Exposure levels and types

B2_01. Exposure to UV radiation B2_02. Work organization index B2_03. Pesticides: kg of active ingredient / tons of pesticides sold per farmer

Health outcomes

B4_01. Rate of non-fatal accidents in agriculture B4_02. Rate of non-fatal accidents in fisheries


B5_01. Number of doctors/nurses per capita B5_02. Expenditure for prescribed medicine for chronic diseases B5_03. Expenditure for prescribed medicine for non-chronic diseases

Category C indicators Table 3: List of proposed indicators belonging to category C Exposure levels and types

C2_01. Self-reported noise exposure C2_02. Self-reported dust exposure C2_03. Self-reported chemical exposure



C2_04. Hours of work per week Health outcomes

C4_01. Self-reported hearing problems in farmers/fishermen C4_02. Self-reported musculoskeletal problems in farmers/fishermen C4_03. Self-reported work-related respiratory problems in farmers/fishermen C4_04. Self-reported skin disorders in farmers C4_05. Self-reported work-related stress


C5_01. Pesticides Safe Use Indicator C5_02. Farmers Health Promotion Composite Indicator C5_03. Health services availability - Distance from health services



3.2 Detailed presentation In the following tables, each indicators is presented in detail. The numbers and names of the indicators are the same as in tables 1-3.

Category A

A1_01. Number of persons occupied in agriculture Definition Labour force includes everyone (over the legal age limit) having provided an agricultural

work on and for the holding during the last 12 months. Included as regular labour force is every member of the holder's family working on the holding are taken as regular labour force (holder included) and non-family regularly employed labour force

Computation The total number of persons occupied in agriculture Aggregation (basic) Age groups: -19,20-44,45+

Gender Aggregation (optimal)

Age groups: 5-year bands Region: rural / semi-urban / urban Employment type (full-time / part-time) (owner / employed) (all-year round/seasonal) Production type Immigrant status

Time period [basic]:5-year [optimal]: annual Significance Agricultural workers constitute 5% of the total labour force of the European Union. Its

diverse makeup reflects a diverse health status of the farming population among countries and within each country. Changes over time may affect the overall health of a country’s population.

Rationale There is ample scientific evidence that the magnitude of risk for injury or illness is associated with the demographic characteristics of the agricultural workforce. Mapping the demographics of the agricultural workforce is the very first step for incorporating health risk management policies. This indicator serves as denominator for other indicators.

Limitations of the indicator

This serves as a proxy for the estimation of exposure of a portion of the workforce population.

Potential data sources National census data; national Farm Structure Survey; national Labour Force Survey

A1_02. Number of persons occupied in fisheries Definition This is the total number of the fisheries workforce Computation The total number of persons occupied in fisheries Aggregation (basic) Age groups: -19,20-44,45+


Age groups: 5-year bands Employment type (full-time / part-time) (owner / employed) Production type Immigrant status

Time period [basic]:5-year [optimal]: annual Significance The fishing industry is one of the most dangerous occupations in the EU, with one of the

highest injury and mortality rates. Mapping the demographics of this workforce is the very first step for incorporating health risk management policies. This indicator serves as denominator for other indicators.

Rationale This indicator reflects the workforce in risk for sustaining one of the highest injury and mortality rates in the EU



Potential data sources National census data; national Labour Force Survey; national Fisheries Statistics



A1_03. Number of working hours in agriculture Definition Number of working hours derived from agriculture Computation It is calculated as the Annual Work Units (AWU) derived from agriculture. The number of

hours comprising an AWU should correspond to the number of hours actually worked in a full-time job within agriculture (1 AWU = 1800 hours). Therefore it does not include public holiday, paid annual holidays, sick-leave, breaks for meals, etc. This unit has been extensively used by EUROSTAT.

Aggregation (basic) Age groups: -19,20-44,45+ Gender

Aggregation (optimal)


Time period [basic]:5-year [optimal]: annual Significance Agricultural work time ranges from a few hours to extreme overtime, during harvesting

conditions. The type of employment can also be all year round or seasonal, affecting the risk especially for accidents

Rationale This is an indicator estimating the level of aggregate exposure. Limitations of the indicator

This serves as a proxy for the estimation of exposure among the agricultural workforce, as well as a proxy for overtime employment - it is an indirect risk measurement.

Potential data sources National census data; Eurostat; Farm Structure Survey; Labour Force Survey

A1_04. Percentage of the total active workforce employed in agriculture Definition The percentage of active workforce employed in agriculture Computation The total number of workers employed in agriculture divided by the total number of active

labour force times 100 Aggregation (basic) Age groups: -19,20-44,45+



Time period [basic]:5-year [optimal]: annual Significance The percentage of agricultural workers varies from country to country across Europe.

Mapping this variation is the first step to identify countries with an increased population of agricultural workers.

Rationale This is an indirect indicator which shows the percentage of the total population exposed to the hazards of agricultural occupation


This serves as a proxy for the estimation of exposure among the agricultural workforce - it is an indirect risk measurement.

Potential data sources National census data; Eurostat; Farm Structure Survey; Labour Force Survey



A2_01. Number of animals per farmer Definition Number of animals per farmer Computation The total number of animals considered for production divided by the number of total farmers

in a Member State Aggregation Production type [optimal] (eg cattle, pigs, etc) Time period [basic]:5-year [optimal]: annual Significance Breeding animals is associated with increased risk of accidents, respiratory disorders,

transmitted zoonoses and even hearing impairment Rationale This indicator evaluates the level of risk due to contact with animals, which is associated with

increased exposure to various hazards. Limitations of the indicator

Proxy estimator of exposure;

Potential data sources Farm Structure Survey, Eurostat, national data sources

A2_02. Number of animals per holding Definition Number of animals per holding Computation The total number of animals considered for production divided by the number of total

holdings Aggregation Production type (e.g. beef) [optimal] Time period [basic]:5-year [optimal]: annual Significance There are indications that the number of animals handled per unit affects the risk of certain

health effects (accidents, respiratory disorders, transmitted zoo noses and even hearing impairment). It also expresses the level of automation / mechanization within the farm that may affect the risk.

Rationale This indicator estimates the concentration of livestock in the holdings, indirectly estimating the likelihood of exposure to the associated with large number of livestock risks


Proxy estimator of exposure;

Potential data sources Farm Structure Survey, Eurostat, national data sources

A2_03. Number of tractors sold per farmer Definition Number of tractors sold per number of farmers Computation The number of tractors sold divided by the total number of farmers Aggregation Production type [optimal] Time period [basic]: 10-year (the interval may seem broad, but it is used to accommodate for very recent

changes in the mechanization of a country. Significance Over 50% of fatal and non-fatal accidents in agriculture involve some kind of machinery. Rationale This indicator estimates the use of heavy machinery in agricultural works, which is associated

with increased injury mortality and morbidity. Limitations of the indicator

Proxy estimator of exposure (since some may not be used in production)

Potential data sources Eurostat; national data sources

A2_04. Average farm size Definition Mean farm size in hectares Computation Total arable land divided by the total number of active holdings employing at least one

agricultural worker Aggregation production [type] Time period Annual Significance Farm size is associated with the number of workers employed. Additionally, there is evidence

in the literature that links large farms with increased risk of accidents in farmers Rationale This is an indirect estimator of working conditions in farms Limitations of the Proxy estimation



indicator Potential data sources FSS, Eurostat

A2_05. Area under glass Definition Total area of greenhouses in a country per total arable land or per total number of farmers Computation The total number of hectares cultivated under glass divided by the total arable area or by the

total number of farmers Aggregation Production type [optimal] Time period [basic]:5-year [optimal]: annual Significance Working in greenhouses is associated with increased risk of skin, respiratory, malignant and

reproductive disorders. Rationale This indicators is an indirect exposure estimator Limitations of the indicator

This serves as a proxy for the estimation of exposure to the conditions of the greenhouse environment.

Potential data sources National data sources, EUROSTAT



A3_01. Tons of fertilizers sold per hectare of cultivated land / total country size Definition Total amount of fertilizers sold per hectare of cultivated land / per total country size Computation The total amount in metric tons of fertilizers sold divided by the total number of hectares of

total arable land / by the total country size Aggregation Production type [optimal] Time period Annual Significance A great proportion of the EU arable land is enhanced with additives in the form of

commercially available fertilizers. Compounds of the fertilizer material have been found to infiltrate underground, river and drinking waters. There is some scientific evidence linking excess of fertilizer compounds with various diseases. Also, a great impact on the environment has been noted, mainly due to the effects on the vulnerable water ecosystems.

Rationale This indicator measures the intensity of using fertilizers for crop production. It is considered in indirect indicator of environmental burden.


Fertilizer consumption is heavily dependent on the type of crop, climatic conditions and irrigation.

Potential data sources EUROSTAT

A3_02. Number of samples of drinking water screened for nitrate levels Definition The number of officially tested samples of drinking water for the whole of the country region

per year Computation The actual number of officially tested drinking water samples is acquired from the

corresponding national authority. Aggregation National/regional level [optimal] Time period Annual data Significance The vast majority of EU arable land is enhanced with additives in the form of commercially

available fertilizers. Compounds of the fertilizer material have been found to infiltrate underground, river and drinking waters.

Rationale The number of drinking water samples screened for nitrate levels reflects the level of awareness and policy orientation to monitor the drinking water quality and to promote sound agricultural practices. There is ample scientific evidence linking excess of fertilizer compounds, like nitrates, with various diseases. Susceptible population groups, such as infants, are in danger of developing serious adverse effects. Also, a great impact on the environment has been noted, mainly due to the effects on the vulnerable water ecosystems.


The number of samples depends on national monitoring mechanisms and relevant legal provisions. Fertilizer consumption is heavily dependent on the type of crop, climatic conditions and irrigation. Therefore, one should be very cautious when interpreting this indicator.

Potential data sources National data sources

Α3_03. Percentage of samples of drinking water exceeding set nitrate limits Definition The proportion of drinking water samples exceeding the predefined maximum nitrate limit

per year Computation Nominator is the actual number of drinking water samples exceeding the predefined

maximum nitrate limit in one year. Denominator is the total number of drinking water samples monitored in the same year

Aggregation National/regional level [optimal] Time period Annual data Significance The vast majority of EU arable land is enhanced with additives in the form of commercially

available fertilizers. Compounds of the fertilizer material have been found to infiltrate underground, river and drinking waters.

Rationale The number of samples of drinking water screened for nitrate levels reflects the level of awareness and policy orientation to monitor the drinking water quality and to promote sound agricultural practices. The proportion of positive samples is a strong indicator of long-term problems encountered in water quality.

Limitations of the The number of samples depends on national monitoring mechanisms and relevant legal



indicator provisions. Fertilizer consumption is heavily dependent on the type of crop, climatic conditions and irrigation. Therefore, one should be very cautious when interpreting this indicator.


A3_04. Percentage of area engaged in organic farming1 Definition Percentage of crop and livestock area engaged in organic farming Computation The sum of organic crop and livestock area divided by the total utilized arable area Aggregation production type Time period Annual Significance Organic farming causes less burden to the environment and is also safer for farmers. Rationale This indicator measures the trend of converting to organic farming, an environmentally

allegedly friendlier method of crop and livestock farming Limitations of the indicator

proxy estimator

Potential data sources EUROSTAT

A3_05. Sales of pesticides per hectare/per capita Definition Amount of pesticides used in agriculture per hectare/per capita Computation The amount of pesticides in tons used in agriculture divided by the country size or by the total

population. Aggregation production type, category of active ingredient Time period 5y [basic] – annual [optimal] Significance Pesticides are considered a serious burden to the environment and human health, as they have

been involved in a series of diseases. Rationale This is an indicator of pesticide use intensity, correlated with suggested adverse health effects Limitations of the indicator

Proxy estimator of exposure, since one pesticide sold may not be used in agriculture.

Potential data sources EUROSTAT-ECPA

A3_06. Number of samples of drinking water screened for pesticide residues levels Definition The number of officially tested samples of drinking water screened for pesticide residues

levels Computation The actual number of officially tested drinking water samples is acquired from the

corresponding national authority. Aggregation National/regional level [optimal] Time period Annual data Significance Pesticides are used in agriculture to enhance and protect production output. Active

compounds of the pesticide formulations may infiltrate underground, river and drinking waters, while residues exist in the food.

Rationale The number of drinking water samples screened for pesticide residues levels reflects the level of awareness and policy orientation to monitor the drinking water quality and promote sound agricultural practices. Pesticides have been involved in a series of human adverse effects as well as disruption of the ecological chain.


The number of samples depends on national monitoring mechanisms and relevant legal provisions. Available data is usually not easy to retrieve.


Α3_07. Percentage of samples of drinking water exceeding set pesticide residue limits

1 Farming is considered to be organic if it complies with “Council Regulation (EEC) No 2092/91 of 24 June

1991 (OJ No L 198/1991) on organic production of agricultural products and indications referring thereto on agricultural products and foodstuffs”, amended by "Council Regulation (EC) 392/2004 of 24 February 2004 (OJ No L 65/2004)".



Definition The proportion of drinking water samples exceeding the predefined maximum pesticide residues limit

Computation Nominator is the actual number of drinking water samples exceeding the predefined maximum pesticide residues limit according to EU regulations. Denominator is the total number of drinking water samples monitored in the same year

Aggregation National/regional level [optimal] Time period Annual data Significance Pesticides are used in agriculture to enhance and protect production output. Active

compounds of the pesticide formulations may infiltrate underground, river and drinking waters, while residues exist in the food.

Rationale The number of samples of drinking water screened for pesticide residues levels reflects the level of awareness and policy orientation to monitor the drinking water quality and to promote sound agricultural practices. Pesticides have been implicated in a series of human adverse effects as well as disruption of the ecological chain. Exceeding the maximum pesticide residue limit may be a warning sign, expressing the level of contamination of the environment. It may predict serious threats for the general population.


The number of samples depends on national monitoring mechanisms and relevant legal provisions, but the number of samples exceeding limits is independent of the number of measurements, unless all numbers are very small (large variability). Available data is usually not easy to retrieve.




A4_01. Rate of fatal accidents in agriculture Definition Incidence of fatal accidents in agriculture Computation The number of work-related fatalities in agriculture per year divided by the total number of

farmers Aggregation gender, age [basic]

production type [optimal] cause of accident [optimal] immigrant status [optimal] Employment type (full-time / part-time) (owner / employed) (all-year round/seasonal) [optimal]

Time period Annual Significance Accidents are the leading cause of premature death in agriculture. Rationale This indicator measures the number of work-related fatalities in agriculture Limitations of the indicator

depends on the quality of the primary data

Potential data sources national data sources, ILO (of questionable quality)

A4_02. Rate of fatal accidents in fisheries Definition Incidence of fatal accidents in fisheries Computation The number of work-related fatalities in fisheries divided by the total number of fishermen Aggregation gender, age [basic]

fishing method [optimal] cause of accident [optimal] immigrant status [optimal]

Time period Annual Significance Accidents are the leading cause of premature death in fisheries. Rationale This indicator measures the number of work-related fatalities in fisheries Limitations of the indicator

depends on the quality of the primary data

Potential data sources national data sources, ILO (of questionable quality)

A4_03. Reported cases of pesticide poisoning Definition Incidence of pesticide poisoning Computation the total number of pesticide-related poisoning incidents reported to the national poisoning

center divided by the total population Aggregation gender, age (age groups under 5 are also included) [basic]

region [optimal] immigrant status [optimal] Employment type (full-time / part-time) (owner / employed) (all-year round/seasonal) [optimal]

Time period Annual Significance Pesticide poisoning is a frequent cause of poisoning among children and adults. Rationale This indicator is an indirect estimator of adoption of safe pesticide practices Limitations of the indicator

proxy indicator; reliability of poisoning center data questionnable

Potential data sources national data sources

A4_04. Rate of reported zoonoses Definition Incidence of zoonoses reported to the responsible focal point at a national level Computation the total number of officially diagnosed zoonoses reported for the first time within a year to

the responsible focal point at a national level per total population per year Aggregation gender, age, diagnosis [basic]

occupation [optimal]



immigrant status [optimal] Employment type (full-time / part-time) (owner / employed) (all-year round/seasonal) [optimal]

Time period Annual Significance Livestock breeders and farmers in general are in great risk of acquiring animal-borne diseases Rationale This indicator measures the burden of animal-borne diseases transmitted to the general

population Limitations of the indicator

depends on the reliability of data – a quality assurance scheme should be enforced




A5_01. Rate of Occupational diseases in agriculture Definition Incidence of legally recognized occupational diseases in agriculture. Computation This indicator is the actual number of diseases recognized as occupational in the sector of

agriculture by legal authorities in one year divided by the total number of farmers in the same year the same country.

Aggregation Gender, age [basic] immigrant status [optimal] Employment type (full-time / part-time) (owner / employed) (all-year round/seasonal) [optimal]

Time period Annual Significance There has been a clear and evident association between a certain exposure (like the farming

occupation) and certain health outcomes. These health outcomes are considered of occupational causation in some countries and those persons are eligible for filing sick claims.

Rationale The number of legally recognized occupational diseases reflects the level of awareness and policy orientation to monitor the health status of farmers, compensate for the lost income and promote safe agricultural health practices. At the current stage, we cannot state that it reflects the real effect of the farming occupation on farmers’ health.


There are great differences among farmers regarding the employment type (self-employed, employed, uninsured family farm labour force), with farmers being insured even in different funds. In many countries there is also great distance between the actual legal provisions and how these provisions are implemented to the benefit of the farmer. This indicator should be interpreted with caution.

Potential data sources National data sources, such as national insurance data

A5_02. Rate of Occupational diseases in fisheries Definition Incidence of legally recognized occupational diseases in fisheries Computation This indicator is the actual number of diseases recognized as occupational in the sector of

fisheries by the legal authorities in one year divided by the total number of fishermen in the same year


Time period Annual Significance There has been a clear and evident association between a certain exposure (like the fishing

occupation) and certain health outcomes. These health outcomes are considered of occupational causation in some countries and those persons are eligible for filing sick claims.

Rationale The number of legally recognized occupational diseases reflects the level of awareness and policy orientation to monitor the health status of fishermen, compensate for the lost income and promote safe health practices.


There are great differences among fishermen regarding the employment type (self-employed, employed), with fishermen being insured even in different funds. In many countries there is also great distance between the actual legal provisions and how these provisions are implemented to the benefit of the fisherman. This indicator should be interpreted with caution.

Potential data sources National data sources, such as national insurance data



A5_03. Pesticides: policy comformity Definition This is a composite indicator measures the extend by which national authorities conform to

EU standards on pesticides sales and use Computation This is a composite indicator which takes into account the following: a) the existence of a

national authority in charge of regulating sales and use of pesticides, b) if new pesticides need licensing in order to go to the market, c) if existing pesticides need relicensing, d) if there are legal provisions for the certification of pesticide applicators, e) if it is obligatory for renewing the license of pesticide applicators, f) if there is a minimum of 10 hour training for the applicators upon licensing or renewal g) if pesticides are bought only by certified applicators, h) if there is a mechanism to monitor the concentration of pesticides in the water and the food, i) if there are official recommendations for the PPEs needed for applying pesticides for each production method or type, j) if there are mechanisms for collecting and safely handling pesticide containments. A “yes” answer to any of the above gives a score of 1, while a “no” answer a score of 0. The maximum potential score is 10.

Aggregation Region Time period Annual Significance An important indicator to detect the level of citizen protection. Rationale This indicator measures the conformity of each country to international regulations and

recommendations related to pesticide sales and use. Limitations of the indicator

Data not readily available


A5_04. Health services utilization Definition Utilization of health services Computation The total number of hospital discharges per year of individuals declaring agriculture as their

current or longest occupation divided by the total number of farmers during the same year Aggregation age, gender [b]

production type [o] cause of discharge [o] Time period Annual Significance Severe health problems (malignancies, respiratory, musculoskeletal, accidents, etc) often

require admission to hospital for diagnosis and treatment. Rationale This indicator measures the overall serious morbidity of farmers (requiring hospitalization). Limitations of the indicator

quality of data questionable


A5_05. Insurance coverage Composite Indicator Definition This is a composite indicator measures the extend and quality of the insurance coverage of the

farming population. Computation This is a composite indicator which takes into account the following: a) whether farmers are

insured, b) whether a family coverage scheme is available, c) whether farmers are entitled to health care benefits (hospital care; medicine; outpatient care), d) is insurance comparable to that of the general population, e) whether any scheme for self-employed exists, f) whether the insurance company offers additional specialized occupational health services for farmers, g) whether farmers are entitled to a pension, h) whether farmers are entitled to compensation, following sickness or accident (sickness leave). A “yes” answer to any of the above gives a score of 1, while a “no” answer a score of 0. The maximum potential score is 8.

Aggregation none Time period Annual Significance Farming is one of the most dangerous occupations. The insurance coverage of the farming

population is affected by the type of employment of the farmer, but also by national policies regarding protecting this vulnerable group from disability and social exclusion. It has been shown that non adequate insurance or feeling of insecurity may affect the health status of a



farmer, especially his mental health. Rationale This is a composite indicator measures the extend and quality of the insurance coverage of the

farming population. Limitations of the indicator

Data may be hard to acquire




Category B

B1_01. Immigrants employed in agriculture Definition Number of foreign-born workers occupied in agriculture on a seasonal or full-time basis Computation The total number of foreign-born workers occupied in agriculture on a seasonal or full-time

basis at any specific time in a country Aggregation (basic) Age groups: -19,20-44,45+


Age groups: 5-year bands Region: rural / semi-urban / urban Employment type (full-time / part-time) (owner / employed) (all-year round/seasonal) Production type

Time period [basic]:5-year [optimal]: annual Significance The number of foreign-born agricultural workers increases in the EU. There is significant

evidence that immigrants have less access to health services, receive less or no training related to health and safety practices, work significantly more hours than the average farm worker and perform heavier tasks. Furthermore, difficulties in communication and uncertainty over their future create a stressful environment for them and their accompanying families. All these constitute a profile of a high risk worker prone to injuries and illnesses.

Rationale This indicator estimates the number of immigrant workers, a high risk population difficult to trace, monitor and intervene.


This serves as a proxy for the estimation of exposure among the foreign-born agricultural workforce. This is an indirect risk measurement.

Potential data sources National data sources, EUROSTAT, LFS

B1_02. Immigrants as percentage of the total farming population Definition The percent of foreign-born workers occupied in agriculture as part of the total farming

population Computation The total number of foreign-born workers occupied in agriculture to the total number of

agricultural workers times 100. Both numbers are measured at the same point in time. Aggregation (basic) Age groups: -19,20-44,45+,



Time period [basic]:5-year [optimal]: annual Significance The number of foreign-born agricultural workers increases in the EU. There is significant

evidence that immigrants have less access to health services, receive less or no training related to health and safety practices, work significantly more hours than the average farm worker and perform heavier tasks. Furthermore, difficulties in communication and uncertainty over their future create a stressful environment for them and their accompanying families. All these constitute a profile of a high risk worker prone to injuries and illnesses.

Rationale This indicator estimates the percentage of a high risk population – immigrant farmers – a population difficult to trace, monitor and intervene, among the total farming population. Therefore, it is considered an estimation of increased hazard exposure. Knowing the proportion this group occupies within the total farmers group within a country and its trend over time helps in the direction of designing specialized health promotion and health care services for this vulnerable group, while it increases equality in the population.


This serves as a proxy for the estimation of exposure among the foreign-born agricultural workforce. This is an indirect risk measurement.




B1_03. Immigrants employed in fisheries Definition Number of foreign-born workers occupied in fisheries on a seasonal or full-time basis Computation The total number of foreign-born workers occupied in fisheries on a seasonal or full-time

basis Aggregation (basic) Age groups: -19,20-44,45+


Age groups: 5-year bands Employment type (full-time / part-time) (owner / employed) (all-year round/seasonal) type of fishing

Time period [basic]:5-year [optimal]: annual Significance There is significant evidence that immigrants have less access to health services, receive less

or no training related to health and safety practices, work significantly more hours and perform heavier tasks. All these constitute a profile of a high risk worker prone to injuries and illnesses.

Rationale This indicator estimates the number of immigrant workers, a high risk population difficult to trace, monitor and intervene.


This serves as a proxy for the estimation of exposure among the foreign-born fisheries workforce. This is an indirect risk measurement.


B1_04. Immigrants as percent of the total fishermen population Definition The percent of foreign-born workers occupied in fisheries as part of the total fishermen

population Computation The total number of foreign-born workers occupied in fisheries to the total number of

fisheries workers times 100. Both numbers are measured at the same point in time. Aggregation (basic) Age groups: -19,20-44,45+


Age groups: 5-year bands Employment type (full-time / part-time) (owner / employed) (all-year round/seasonal) type of fishing

Time period [basic]:5-year [optimal]: annual Significance There is significant evidence that immigrants have less access to health services, receive less

or no training related to health and safety practices, work significantly more hours and perform heavier tasks. All these constitute a profile of a high risk worker prone to injuries and illnesses.

Rationale This indicator estimates the percentage of a high risk population – immigrant farmers – a population difficult to trace, monitor and intervene, among the total fisheries population. Therefore, it is considered an estimation of increased hazard exposure. Knowing the proportion this group occupies within the total farmers group within a country and its trend over time helps in the direction of designing specialized health promotion and health care services for this vulnerable group, while it increases equality in the population.


This serves as a proxy for the estimation of exposure among the foreign-born fisheries workforce. This is an indirect risk measurement.


B2_01. Exposure to UV radiation Definition Yearly mean level of UV exposure Computation UV level at various locations in each member state divided by the total number of locations

and measurements per year Aggregation Area Time period Annual Significance UV exposure is associated with increased risk of skin melanoma. Farmers and fishermen are

known to be in higher risk due to their outdoor tasks Rationale This is an estimator of UV radiation to which employers are exposed when outdoors Limitations of the It does not take into account the number of hours working outdoors; therefore the AWUs



indicator should also be taken into account in order to evaluate the total exposure burden to the population

Potential data sources ELDOnet network

B2_02. Work organization index (WOI) Definition A composite indicator Computation This indicator is calculated as follows:

WOI = (number of employed in agriculture / total number of holdings) + (Annual Work Units in agriculture / total number of farmers) + (total number of farmers/self-employed+contract workers)

Aggregation none Time period Annual Significance Organization of work can affect the health status of farmers and the incidence of injuries.

Farmers usually work only part of the year and work overtime during seeding or harvesting periods. Also the amount of work among self-employed and contract farm workers is not equal, but both self-employed and those employed under contract enjoy a different level of job security than those employed without a contract.

Rationale This indicator measures the probability of work overload, the level of job insecurity and the social working conditions in the farm environment.


Composite indicator – maybe hard to understand

Potential data sources National data sources, Eurostat

B2_03. Pesticides sold per farmer Definition Amount of active ingredients sold or Amount of pesticides sold per farmer Computation The total amount of active pesticide ingredients sold in a formulation in kgrs or The total

amount of pesticide formulations sold in tons divided by the total number of farmers Aggregation category of ingredient Time period Annual Significance certain pesticide categories have been associated with increased risk of adverse health effects Rationale This indicator measures the extent of pesticide use. Farmers exposed to pesticides are at risk

for developing a series of adverse health effects. Limitations of the indicator

depends on the quality of the data

Potential data sources Eurostat-European Crop Protection Association (ECPA)

B4_01. Rate of non-fatal injuries in agriculture Definition Incidence of non-fatal accidents reported in agriculture Computation The number of non-fatal accidents which required hospital treatment and were reported to an

injury surveillance system within a year divided by the total number of farmers in the same year.

Aggregation age, gender [basic] production type [optimal] cause of accident [optimal] immigrant status [optimal] Employment type (full-time / part-time) (owner / employed) (all-year round/seasonal) [optimal]

Time period Annual Significance Injuries are the first cause of morbidity in agriculture. It can be of interest, due to serious

consequences, potentially causing temporal or constant disability and requiring medication and rehabilitation

Rationale This indicator measures the number of reported non-fatal accidents, which stand for over 50% of the total work-related morbidity in agriculture

Limitations of the depends on the quality and responsiveness of the reporting mechanism



indicator Potential data sources national injury surveillance systems

B4_02. Rate of non- fatal injuries in fisheries Definition Incidence of non-fatal accidents reported in fisheries Computation The number of non-fatal accidents which required hospital treatment and were reported to an

injury surveillance system within a year divided by the total number of fishermen in the same year.

Aggregation age, gender [basic] production type [optimal] cause of accident [optimal] immigrant status [optimal] Employment type (full-time / part-time) (owner / employed) (all-year round/seasonal) [optimal]

Time period Annual Significance Injuries are the first cause of morbidity in fisheries. It can be of serious consequences,

potentially causing temporal or constant disability and requiring medication and rehabilitation Rationale This indicator measures the number of reported non-fatal accidents, which stand for over 50%

of the total work-related morbidity in fisheries Limitations of the indicator

depends on the quality and responsiveness of the reporting mechanism

Potential data sources national injury surveillance systems

B5_01. Number of general practitioners/nurses per capita Definition Number of general practitioners/nurses in a given area per capita Computation The number of general practitioners/nurses in a given area divided by the total number of

population in that area Aggregation By rural/urban population [basic] Time period Annual Significance The number of general practitioners/nurses in an area as percentage of the total population is

loosely associated with the quality of health care provided and the availability of health care services

Rationale This indicator is a proxy estimator of the availability of health care services for the farming population in an area, when given separately for rural and urban areas


proxy indicator


B5_02. Expenditure for prescribed medicine for chronic diseases Definition Amount of money spent by the insurance companies or privately if data are available of the

farmers for paying for prescribed medicine taken on a chronic basis divided by the total number of farmers.

Computation The amount of money spent by the companies that insure the farmers or their families paid for prescribed medicine taken on a chronic basis divided by the number of insured farmers

Aggregation Gender; age [basic] health condition [optimal]

Time period Annual Significance Health disorders, like musculoskeletal and respiratory problems often require long-term

medication Rationale This indicator measures the burden of chronic diseases on the farming population indirectly Limitations of the indicator

Farmers are usually insured as a family and not strictly by occupation.

Potential data sources National data sources (insurance companies)



B5_03. Expenditure for prescribed medicine for non-chronic diseases Definition Amount of money spent by the farmers’ insurance companies on prescribed medication, taken

on a non-chronic basis divided by the total number of farmers Computation The amount of money spent by the insurance companies of the farmers or their families on

prescribed medication, taken on a non-chronic basis, divided by the number of insured farmers

Aggregation gender, age [basic] health condition [optimal]

Time period Annual Significance Health conditions, such as infections and skin disorders, require treatment for only a small

period of time Rationale This indicator measures the burden of non-chronic diseases on the farming population

indirectly Limitations of the indicator

Farmers are usually insured as a family and not strictly by occupation.

Potential data sources National data sources (insurance companies)



Category C

C2_01. Self-reported noise exposure Definition Percentage of farmers reporting frequent exposure to noisy environment Computation This indicator measures the percentage of positive answers given by farmers to the question

“Are you exposed at work to noise so loud that you would have to raise your voice to talk to people?” Positive answers are considered: “all of the time, almost all of the time, around ¾ of the time, around half of the time”. Numerator is the number of “yes” answered divided by the total number of farmers having answered this question.

Aggregation Production type (optimal) Time period Annual Significance There is evidence in the literature suggesting an increased risk of hearing loss among farmers

exposed to increased levels of noise for a long time. The source of noise could be machinery or animals in animal confinement buildings.

Rationale This indicators tackles the problem of inadequate noise level assessment on a regular basis – it can be considered as an estimate of work-related noise exposure, which can lead to hearing impairment


Self-reported; based on survey data

Potential data sources ELWC survey; recommendation for a special survey especially for farmers

C2_02. Self-reported dust exposure Definition Percentage of farmers reporting frequent exposure to dusty environments Computation This indicator measures the percentage of positive answers given by farmers to the question

“Are you exposed at work to breathing in smoke, fumes (such as welding or exhaust fumes), powder or dust (such as wood dust or mineral dust) etc?” Positive answers are considered: “all of the time, almost all of the time, around ¾ of the time, around half of the time”. Numerator is the number of “yes” answered divided by the total number of farmers having answered this question.

Aggregation Production type (optimal) Time period Annual Significance Some categories of agricultural workers (eg grain handling, greenhouse workers, livestock

breeders in animal confinement buildings) are frequently exposed to increased levels of organic and inorganic dust.

Rationale This indicators tackles the problem of inadequate dust level assessment on a regular basis – it can be considered as an estimate of work-related dust exposure, which could lead to respiratory disorders.




C2_03. Self-reported chemical exposure Definition Percentage of farmers reporting frequent exposure to chemical products Computation This indicator measures the percentage of positive answers given by farmers to the questions

“Are you exposed at work to breathing in vapours such as solvents and thinners” and “Are you exposed at work to handling or being in skin contact with chemical products or substances”. Positive answers are considered: “all of the time, almost all of the time, around ¾ of the time, around half of the time”. Numerator is the number of “yes” answered divided by the total number of farmers having answered this question.

Aggregation Production type (optimal) Time period Annual Significance Some categories of agricultural workers (eg pesticide applicators, farmers repairing their

equipment, etc) are frequently exposed to increased levels of chemical substances, some of which may be hazardous to their health (neuropsychological, respiratory, reproductive,



malignant disorders) Rationale This indicator can be considered as an estimate of work-related chemical substances

exposure, which could lead to a variety of health disorders. Limitations of the indicator



C2_04. Hours of work per week Definition Average hours of agricultural work per week Computation The total number of hours per weeks as an answer to the question “how many hours do you

usually work in your main paid job?” divided by the total number of agricultural workers responding to this question.

Aggregation gender, age [b] production type [o]

Time period Annual Significance Increased working hours have been associated with increased risk for non-intended accidents

at work Rationale this indicator measures the average level of work intensity Limitations of the indicator

proxy estimator of exposure, survey based

Potential data sources ELWC survey

C4_01. Percentage of farmers/fishermen with self reported hearing problems Definition Percentage of farmers/fishermen reporting work-related hearing problems Computation The number of farmers/fishermen who answered yes to the question “Does your work affect

your health?” and mentioned hearing problems to the question “how does it affect your health?” divided by the total number of agricultural workers responding to this question.


Time period Annual Significance Many agricultural tasks produce or are performed under high levels of noise. Chronic

exposure to these high levels might cause hearing impairment, as it is suggested by the literature

Rationale This indicator measures the perceived level of noise and its impact on the health of the farmer/fisherman.


Based on survey; self-reported


C4_02. Percentage of farmers/fishermen with self reported musculoskeletal problems Definition Percentage of farmers/fishermen reporting work-related musculoskeletal problems Computation The number of farmers/fishermen who answered yes to the question “Does your work affect

your health?” and mentioned backache and/or muscular pains in shoulders, neck and/or upper/lower limps to the question “how does it affect your health?” divided by the total number of agricultural workers responding to this question.


Time period Annual Significance Many agricultural tasks involve moving or lifting heavy loads, awkward positions, bending,

kneeling, etc. Performing these movements on a chronic basis might affect the musculoskeletal system of the farmer.

Rationale This indicator measures the perceived level of musculoskeletal strain and its impact on the health of the farmer/fisherman.






C4_03. Percentage of farmers/fishermen with self-reported respiratory problems Definition Percentage of farmers/fishermen reporting work-related respiratory problems Computation The number of farmers/fishermen who answered yes to the question “Does your work affect

your health?” and mentioned respiratory difficulties to the question “how does it affect your health?” divided by the total number of agricultural workers responding to this question.


Time period Annual Significance Many agricultural tasks produce or are performed under high levels of organic and inorganic

dust. Chronic exposure to these high levels might cause respiratory disorders, as it is suggested by the literature.

Rationale This indicator measures the perceived level of dust exposure and its impact on the health of the farmer/fisherman.




C4_04. Percentage of farmers/fishermen with self reported skin problems Definition Percentage of farmers/fishermen reporting work-related skin problems Computation The number of farmers/fishermen who answered yes to the question “Does your work affect

your health?” and mentioned skin problems to the question “how does it affect your health?” divided by the total number of agricultural workers responding to this question.


Time period Annual Significance Direct contact with plants, soil, chemicals or personal protective equipment may lead to skin

disorders, which are usually benign. However these disorders sometimes require treatment and affect the ability of a farmer to perform his tasks. A series of work-related skin disorders have also been reported for fishermen, due to contact with solvents, gas and constant exposure to a moisture environment.

Rationale This indicator measures the perceived impact of work-related skin disorders on the health of the farmer/fisherman.




C4_05. Self-reported work-related stress Definition Percentage of farmers/fishermen with increased work-related stress Computation The number of fishermen who answered yes to the question “Does your work affect your

health?” and mentioned stress to the question “how does it affect your health?” divided by the total number of agricultural workers responding to this question.


Time period Annual Significance Farming is a stressful occupation, as it is suggested in the literature. Rationale This indicator measures the perceived stress that farmers/fishermen experience. Limitations of the indicator





C5_01. Pesticides: safe use indicator Definition This is a composite indicator measuring the safe use of pesticides Computation It is the total score of pesticide applicators answering the following questions:

a) do you wear personal protective equipment while spraying? (1) never to (5) always) b) have you been officially trained to handle pesticides? (1) never (2) once (3) recently c) are you licensed to handle pesticides? (1) no (2) used to but expired (3) currently licensed d) does your license need renewal? (1) no license (2) no renew required (3) renewal required e) do you purchase officially registered pesticides? (1) never to (3) only

Aggregation age, gender Time period Annual Significance Pesticide applicators run a great risk when coming in contact with pesticides while preparing

the mix or spraying. Literature suggests increased risk for respiratory, skin, malignant and reproductive disorders.

Rationale This indicator measures the average safe use of pesticides by the pesticide applicators and the existing policies to train and license them


survey based, not readily available

Potential data sources it should be incorporated in a special survey

C5_02. Farmers Health Promotion Composite Indicator Definition This composite indicator measures the extend of existing health promotion activities reaching

the farming population Computation This indicator takes into account the following: a) if there are occupational health services

specifically addressing the farmers, b) if there are general or specific health promotion campaigns targeting the farmers, c) if there are existing screening programmes targeting the farmers, d) if there are health promotion programmes on dental hygiene addressing the farming population, e) if there are health programmes on immunization addressing the farming population. A “yes” answer to any of the above gives a score of 1, while a “no” answer a score of 0. The maximum potential score is 5.

Aggregation None Time period Annual Significance Health promotion campaigns and educational programmes incorporating risk assessment

outcomes can influence health and work-related conditions. Rationale This indicator measures the existence and accessibility of farmers to health promotion

programmes and campaigns – which are dictated by national policies and priorities Limitations of the indicator

survey based;

Potential data sources Future survey

C5_03. Distance from health services Definition Percentage of farmers living more than 20 minutes away from the closest hospital Computation Percentage of farmers answering “yes” to the question “Do you live more than 20 minutes

away from the next hospital?” Aggregation Rural/urban area Time period Annual Significance Access to health care services is crucial when accidents or acute health problems occur. Rationale This indicator measures the proximity of agricultural workers to health care facilities. Limitations of the indicator

based on survey data

Potential data sources EurLife indicator (Eurobarometer – every 3 years)



4. Annex

1. Working paper by IMIM & NKUA on health indicators in agriculture and fisheries



ANNEX 1

Working paper by IMIM & NKUA on

health indicators in agriculture and fisheries



4.1 Working paper by IMIM and NKUA on “Health indicators for agriculture and fisheries” Introducton This paper is the outcome of a critical review of the findings of the work packages 2,3 and 4, which was conducted during a two-day working session between researchers from NKUA and IMIM in September 2006. Having examined the available datasets and the health risks identified by the literature, the two teams produced an introductory paper on a theoretical approach for the development of indicators. A preliminary set of already available indicators that should be taken into account was also compiled. The paper was presented to the panel of experts meeting in Athens, February 2007 as background material. Definitions Health indicators can be defined as measures that identify major exposures and the health status of a population and that are based on routinely collected data. Health indicators should be easily available, should not be biased and should apply to wide sectors of the population of interest or to specific subgroups of interest. Indicators may refer to the total population of EU or to subpopulations if these are related to different health problem exposures. Within Europe, primary subpopulations of interest are those defined by country. Other subpopulations of interest are, sex, age groups including minors and the elderly, groups by type of employment (e.g. self employed), migrants, and also occupational subsectors, e.g. deep sea fishing or coastal fishing. Labour force and specific exposures indicators. Labour force indicators provide major statistics of employment in agriculture and fisheries overall and in specific subpopulations. Although on their own they could be used as proxy for known and unknown exposure indicators, and provide the basis for the use of other indicators that evaluate the risk and health related indicators. Exposure indicators identify specific exposures that have been associated with potential health problems and also major employment patterns or specific occupational subpopulations that may influence exposure. Indicators of health These indicators identify the overall health status of a population and specific major health problems overall or in specific subpopulations e.g. pregnant women. Health problems identified by specific indicators of a population have to be previously clearly identified as health problems associated with the population of interest. Indicators related to health promotion These indicators identify health promotion activities that are related with the prevention of exposures, with health behaviours or with the identification of early disease. These indicators should be, as much as possible, specific to the population of interest. Indicators related to health services These indicators identify health services related activities associated with the early detection or treatment of diseases that are specific to the population of interest.



Health indicators for populations working in agriculture and fisheries Health indicators are presented in three major groups (exposure; health status and general health determinants; health promotion and health care services). They are symptoms subsequently ranked by availability of the data throughout Europe and by feasibility in collection of the data. Exposure Agriculture Workers in agriculture This indicator measures the number of workers in agriculture. It provides information on number of persons potentially at risk of exposures to agricultural work. It is expressed as number of workers in thousands. EUROSTAT receives this type of information. Pesticides used (in tons, total and in major subgroups) per number of employed persons in agriculture in each EU country This indicator provides a general indication of pesticide use that can be used as a proxy for pesticide exposure. It is weighted by number of persons employed in agriculture. Information can be derived from routinely collected statistics derived from FAO and ECPA. The latter provides information that covers about 90% of pesticides used in EU countries and provides amounts of total pesticides and of major subgroups (herbicides, insecticides, fungicides, rodenticides) Pesticides used (in tons, total and in major subgroups) per acres cultivated in each EU country. This indicator provides a general indication of pesticide use that can be used as a proxy for pesticide exposure. It is weighted by acres cultivated or by country size. Information can be derived from routinely collected statistics derived from FAO and ECPA. The latter provides information that covers about 90% of pesticides used in EU countries and provides amounts of total pesticides and of major subgroups (herbicides, insecticides, fungicides, rodenticides) Per acres cultivated; if available by type of product for specific potentially high risk pesticides that are currently used. Agricultural workers employed in flower and fruit cultivation This indicator provides an overview of the weight of special cultivations that are known to be associated with a higher exposure to pesticides. The cultivations of interest include flower and fruit cultivation. The indicator is expressed as number of workers working principally in these cultivations. Information will be derived from EUROSTAT. Agricultural workers employed in greenhouses. This indicator provides an overview of the weight of a special type of cultivation, greenhouses, that is known to be associated with a higher exposure to pesticides. The indicator is expressed as number of workers working in greenhouses. Information will be derived from EUROSTAT. Agricultural products produced in greenhouses. The output of the greenhouse establishments per total area under glass is correlated with the cultivation intensity and consequently, with the level of human involvement (and exposure) to greenhouse environment hazards. Both output and the area under glass are derived from national data sources and EUROSTAT. Use of machinery This indicator measures use of machinery and particularly tractors in agriculture and identifies an activity in agriculture that has been associated with injuries. It is expressed as tractors per persons



in agriculture adjusted for acres of cultivation. Information will be derived from EUROSTAT (employment data) and data on tractor sales Contact with animals, number of animals This indicator provides an estimate contact with animals that has been associated with zoonoses and with the occurrence of asthma and allergies. The indicator is expressed as number of animals (or alternatively with weight of animals if this information is available) by total number of agricultural workers employed in animal farming. Information will be derived from national data sources and EUROSTAT Contact with animals, number of animal farms This indicator provides an estimate contact with animals that has been associated with zoonoses and with the occurrence of asthma and allergies. The indicator is expressed as number of animal farms by total number of farms. Information will be derived from national data sources and EUROSTAT Contact with animals, modernisation of animal farming This indicator provides an estimate contact with animals that has been associated with zoonoses and with the occurrence of asthma and allergies. Within animal husbandry it focuses on an evaluation of modernisation of techniques used, as a proxy of potential contact with animal products. The indicator is expressed as number of animal farms using modern techniques (automatic animal feeding) by total number of animal farms. Information will be derived from national data sources and EUROSTAT Licensed applicators, proportion This indicator measures the number of licensed applicators of pesticides and provides information on controlled use of pesticides. The indicator is expressed as the ratio of the total number of licensed applicators on the total number of agricultural workers by total number of farmers. Information on licensed applicators is retrieved from national sources and on the total number of agricultural workers from EUROSTAT. Licensed applicators, per pesticides used This indicator measures the number of licensed applicators of pesticides taking into account the amount of pesticides used, and provides information on controlled use of pesticides. The indicator is expressed as the ratio of the total amount of pesticides used to the total number of licensed applicators. Information on licensed applicators is retrieved from national sources and on total amount of pesticides used from European industry sources. Licensed applicators, per pesticides used, acre adjusted This indicator measures the number of licensed applicators of pesticides taking into account the amount of pesticides used weighted by total acres of cultivated land, It provides information on controlled use of pesticides. The indicator is expressed as the ratio of the total amount of pesticides used divided by the product of the acres of cultivated land by the total number of licensed applicators. Information on licensed applicators is retrieved from national sources, cares by EUROSTAT, and on total amount of pesticides used from European industry sources. Type of application of pesticides This indicator measures the number of agricultural workers employing different ways of spraying, namely personal spraying; tractor spraying, air-application and application by



specialised personnel. It provides information on degree of exposure to pesticides during spraying. Information is retrieved from special surveys and information on licensed applicators from national sources Fisheries Type of fishing, number: This indicator measures the number of workers in fishing involved in different types of fishing namely, deep sea, coastal, fresh water. It provides information on potential risk of accidents that has been associated with type of fishing. It is measured as number of workers. Information is retrieved from EUROSTAT. Type of fishing, proportion: This indicator measures the number of workers in fishing involved in different types of fishing namely, deep sea, coastal, fresh water. It provides information on potential risk of accidents that has been associated with type of fishing. It is measured as the proportion of workers in fishing employed in different types of fishing, divided by the total number of workers in fishing. Information is retrieved from EUROSTAT. Fish farms, numbers: This indicator measures the number of workers employed in fish farms. It provides information on potential risk to allergies and asthma caused by fish foods, It is measured as number of workers employed in fish farms. Information is retrieved from national sources. Fish farms, numbers and catches: This indicator measures the number of workers employed in fish farms weighted by the catches of fish. It provides information on potential risks to allergies and asthma from fish foods. It is measured as number of workers employed in fish farms divided by tons of fish catches. Information is retrieved from national sources. Fish processing: This indicator measures the number of workers employed in fish processing. It provides information on potential risk of accidents. It is measured as number of workers employed in fish processing. Information is retrieved from national sources. Exposure to UV light This indicator measures the number of workers potentially exposed to UV light, taking into account latitude and type of fishing. It provides and indication of exposure to UV light and to diseases associated with this exposure among fishermen, namely skin cancer, malignant melanoma and lip cancer. It is measured as number of workers employed in fishing, weighted by latitude (multiplied by a score indicating UV light) and by type of fishing (proportion of fishing workers in deep sea fishing).. Information is retrieved from EUROSTAT, national sources and from meteorological data.



Health indicators Mortality total This indicator measures total mortality of workers in agriculture and fisheries. It. provides information on general health status of workers in agriculture and fisheries. It is measured as number of deaths among these workers divided by workers employed in this sector, and is provided separately for agriculture and for fisheries. Such data are not currently available and it is recommended to include information on longest held occupation in death certificates. Information on number of workers employed is retrieved from EUROSTAT. Mortality, cause specific: injuries This indicator measures cause specific mortality from injuries of workers in agriculture and fisheries. It. provides information on specific risks of workers in agriculture and fisheries. It is measured as number of deaths from injuries among these workers divided by workers employed in this sector, and is provided separately for agriculture and for fisheries. Such data are not currently available and it is recommended to include information on longest held occupation in death certificates. Information on number of workers employed is retrieved from EUROSTAT. Mortality, cause specific: suicide in fishermen This indicator measures cause specific mortality from suicide in fishermen. It. provides information about the specific risk of workers in fisheries. It is measured as number of deaths from suicides among these workers divided by workers employed in this sector. Such data are not currently available and it is recommended to include information on longest held occupation in death certificates. Information on number of workers employed is retrieved from EUROSTAT. Mortality, cause specific: cancer. This indicator measures cause specific mortality from cancer in agricultural workers and fishermen. It measures mortality from lymphomas and multiple myeloma and malignant melanoma in agricultural workers, and from malignant melanoma in fishermen. It. provides information on specific risk from specific diseases which have been previously described to have increased among these workers. It is measured as number of deaths from these causes among these workers divided by workers employed in this sector. Such data are not currently available and it is recommended to include information on longest held occupation in death certificates. Information on number of workers employed is retrieved from EUROSTAT. Cancer incidence, cause specific: cancer. This indicator measures cause specific incidence from cancer in agricultural workers and fishermen. It measures incidence from lymphomas and multiple myeloma, malignant melanoma, non-melanoma skin cancer and cancer of the lip in agricultural workers, and from malignant melanoma, non-melanoma skin cancer and cancer of the lip in fishermen. It. provides information on specific risk from specific diseases that have been previously described to have increased in these workers. It is measured as number of cases from these causes among these workers divided by workers employed in this sector. Such data are not currently available and it is recommended to include information on longest held occupation from cancer registry data. These data will only become available for areas of Europe that has cancer registries. Information on number of workers employed is retrieved from EUROSTAT. Cancer Registries: (occupation not generally available); all cancers, leukaemia, lymphomas, multiple Myeloma, melanoma, skin, lip. A recommendation is made to request occupation status in cancer registries. Occupational disease reporting: available data not complete



Musculoskeletal: survey on osteoarthritis; hip replacements Asthma/respiratory diseases (use existing surveillance systems, but not complete) + : suggest survey Infectious diseases (zoonoses)- leptospirosis, brucelosis EUROCAT. Congenital malformations by mother occupation; migrant status General health determinant indicators: Several general health determinants should be measured specifically for the occupations of interest including smoking, obesity, and alcohol. Health promotion and use Health services License for pesticide use PPE: sales of PPEs probably not possible to evaluate through routinely collected data; propose survey Policy oriented indicator: course available; number of workers attending + some kind of ratio. Musculoskeletal: hip replacements Accessibility and use of health services: probably not informative enough. Look at OECD data.

Panel of experts meeting

Agenda “Panel of experts meeting on the development of indicators” “PRONOE Boardroom” - ARION Hotel – ASTERAS Vouliagmenis Sunday, 25th February 2007 10:30 – 10:45: Welcome, A. Linos 10:45 – 11:15: Presentation of participants 11:15 – 11:30: Presentation of DIRERAF project, I. Kotsioni 11:30 – 11:45: The role of indicators, A. Linos 11:45 – 12:30: Discussion: Criteria for proposing, adopting and evaluating indicators 13:30 – 16:00: A. Brainstorming session to identify potentially useful/necessary indicators for

measuring: a) Population at risk, b) Exposure levels and types, c) Environmental burden

16:15 – 17:30: B. Brainstorming session to identify potentially useful/necessary indicators for

measuring outcomes 17:30 - 18:30: C. Brainstorming session to identify potentially useful/necessary indicators for

measuring health services Monday, 26th February 2007 09:30 – 11:00: Evaluation of proposed indicators based on data availability 11:00 – 12:30: Evaluation of proposed indicators based on public/environmental health

significance 13:30 – 15:30: Finalization of list of most important indicators 15:30 – 16:30: Conformity with existing indicators or indicators proposed by other bodies 16:30 – 18:30: Development of definition and formulas for the chosen indicators 18:30 – 19:00: Closing remarks

DIRERAF: Annex, Section (i) - Panel of experts meeting 2


Participants

Rokho Kim, MD, DrPH, PhD Acting Manager, Occupational Health Programme Scientist, Noise and Housing Burden of Disease WHO/EURO Centre for Environment and Health Shelia Hoar Zahm, Sc.D. Deputy Director Division of Cancer Epidemiology and Genetics National Cancer Institute, USA

Dr. Kari Kurppa Leading Advisor Department of Health and Work Ability Finnish Institute of Occupational Health Prof. Jovanka Bislimovska-Karadziska Head of Institute of Occupational Health University of "St. Cyril and Methodius" Republic of Macedonia Dr. Olaf Jensen Researcher – Expert on fishermen occupational health, University of Southern Denmark and Legekontoret for Sjømen, Bergen, Norway Researcher – Expert on fishermen occupational health Danish Maritime Institute, Denmark Dr. Vladimir Bencko Professor of Epidemiology Charles University of Prague First Faculty of Medicine Czech Republic

From the University of Athens, DIRERAF team:

Dr. Athena Linos, MD, PhD Dr. Christos Chatzis, MD Dimitris Kouimintzis, MD From PROLEPSIS Institute Ioanna Kotsioni, MSc



Minutes In the first day of the panel of experts meeting following the presentation of the project and its

methodology, the criteria for proposing, adopting and evaluating indicators were discussed.

It was widely agreed that it is of great importance to use the existing knowledge on the

development of indicators and to propose indicators applicable for testing. Moreover the need to

follow a strict epidemiologic approach was stressed as well as the need to use a Health Impact

Assessment perspective. In addition the European Healthy Place Initiative and WHO Healthy

Workplace notion should also be taken into account, as well as the ICOH concept on advancing

or emerging risks. The WHO approach of using composite or action indicators as a

benchmarking tool was also discussed as well as the inclusion of qualitative indicators.

The need to relate indicators to the formulation of policies was also stressed and the following

stages for the development of indicators were identified: a) State of the art / knowledge base, b)

Assess impact of determinants, c) 3 different scopes for indicators: - exposure, - effect, - public

health risk.

The issues of reliability, availability and applicability of the proposed indicators were discussed.

The comparability of proposed indicators at the European level emerged also as an issue that

potentially would be very difficult to address.

The criteria that were discussed for the selection of the indicators were: whether the indicator

can lead to policy making and therefore have impact on the health of farmers, whether it would

make sense (in economic terms as well) to collect the data that will be used for the calculation of

the indicator, whether available data are reliable, and whether data are comparable. The

discussion resulted in the proposal of the following selection criteria:

- policy relevance(including amenability),

- data availability(feasibility),

– data quality,

- comparability

The discussion on the population at risk concentrated on the following issues:



“Capturing” the population at risk in agriculture and especially fisheries was agreed to be a

difficult task that ideally could only be addressed by special censuses.

The definition of farmers and of subpopulations and the precise number of farmers and

subpopulations (including spouses and children working on the farm) are required to calculate the

denominators. People living in rural areas should also be considered as a population at risk

(environmental, public health component). The precise definition of seasonality should also be

agreed upon as well as the definition of part-time farmers/fishermen. Another difficulty is to

define the number of migrants working in agriculture, especially when these are undocumented

migrants.

The terminology often being different from country to country it was decided that the

EUROSTAT definitions should be used, however proposals for their amendment should be put

forward; i.e. use of working hours.

In general it was agreed that both the number of persons and the number of person hours should

be used for the population at risk calculation. The use of surveys was also discussed as a very

useful tool that should be proposed at the European level.

Regarding exposure measurements it was agreed that existing indicators; i.e. no. of licences for

pesticides use, no. of tractors sold per year, data on ultraviolet exposure (ELDONET) should be

taken into account. All these exposures are linked to types of production. What could be a useful

approach is to combine the content of exposure with the type of production type, the size of

production and the occupational title within the production unit to calculate more precisely the

exposure.

Measuring access to health care services could be addressed using insurance data, data from

surveys; i.e. European Health Survey, and proxy indicators such as the existence of legal

requirements. Examples of indicators could be: the existence of health promotion campaigns

focusing on agricultural workers or fishermen, the number of doctors or nurses per capita in rural

areas, the level of training of physicians on the occupational health problems of farmers and

fishermen.



A survey on the use of health services for farmers and fishermen could be proposed. Moreover a

recommendation to include the occupation variable to existing surveys should be put forward.

Regarding age, aggregation data should be requested as follows: a) <18, b) 19-44, c) >45.

However the optimal aggregation would be in 5-year bands.

An example of an exposure indicator that should be proposed is the sales of pesticides or the

consumption of pesticides (in kg of active ingredient per capita and per hectare)

What is very important but can only be done by means of survey is the aggregation by production

type. An idea was to propose that the Dublin survey could also be applied for agriculture and

fisheries, so that the type and level of exposure could be estimated. Another indicator could be

the percentage of overtime work.

An example of an indicator regarding the use of machineries could be the number of tractors per

farmer which could also be used as a proxy for noise exposure by means of measuring the source

of noise.

Another issue discussed was the usefulness of hazard indicators as opposed to outcome

indicators, taking into consideration the great time lapse between a hazard and its effect.

What is most important is that the indicators should be standardized, so that they could be

measured again in the same way therefore making possible the measurement in results.

An example of an indicator, both public health and environmental, could be the consumption of

antibiotics for veterinary use per capita.

Some general recommendations that were stated in the round-up section of the meeting and that

would be very useful for the monitoring of farmers’ and fishermen’ health would be to include

occupation as a variable in all diseases’ statistics coded by the ICD-11 code, as well as in

mortality statistics, injuries’ statistics, and cancer statistics.

A burden of disease study for the agriculture and fisheries sector would also be a very useful

proposition and DALYs could then be recommended as indicators.



Work Package VI “Development of socioeconomically and demographically specific indicators relevant to public health policies”



Table of contents

Introduction ............................................................................................................................................... 3 Methodology.............................................................................................................................................. 3 Population groups at risk ........................................................................................................................... 3

Immigrants.............................................................................................................................................. 3 Elderly .................................................................................................................................................... 4 Children .................................................................................................................................................. 5 Female farm workers.............................................................................................................................. 6 Seasonal/temporary farm workers.......................................................................................................... 6



Introduction

The aim of this work package is to disaggregate proposed occupational risk indicators by gender, major age group and nationality or citizenship status. The availability of data for different population groups, usually more vulnerable than the average population, is crucial for informing policy makers on the necessity of policies targeting particular populations based on their vulnerability to specific risks.

Methodology

“Fighting social exclusion is close to the top of the policy agenda of most EU countries. However, the very concept of social exclusion remains quite vague. [...] Lack of full-time employment, low educational qualifications, lone parenthood, non-EU citizenship and bad health are found to be positively and significantly associated with increased risk of social exclusion in most EU countries.” 1

Selecting for which specific populations disaggregated data would be useful has been based both on the findings of the literature on hazards and relative health risks and on the notion of social exclusion. Social exclusion components that define the populations we should be targeting are: type of employment (precarious, seasonal), age (both children and senior citizens are affected more severely by social exclusion), nationality (minority groups are more severely affected by social exclusion) and female gender. Using the findings from the literature review of work package 4 we have also been able to identify vulnerable population groups, which face an increased risk of accidents and illnesses. We therefore propose the level of aggregation needed, in order for these groups to be adequately monitored, in relation to the adverse health effects they are in particular risk for.

Population groups at risk

Immigrants

The number of foreign-born agricultural workers is continuously increasing in the EU. There is significant evidence that immigrants have less access to health services, receive less or no training related to health and safety practices, work significantly more hours than the average farm worker and perform heavier tasks. Furthermore, difficulties in communication and uncertainty over their future create a stressful environment for them and their families. This situation constitutes a profile of a high risk worker prone to injuries and illnesses. Having taken into account the above, we have considered immigrant status as a level of aggregation for 1 Abstract of the study “SOCIAL EXCLUSION IN THE EUA capability-based approach” by the London School of Economics and the European Institute (authors: F.Papadopoulos & P.Tsakloglou – source: http://www.lse.ac.uk/ collections/europeanInstitute/pdfs/EI%20working%20paper_Panos%20Tsakloglou.pdf)”



the following developed indicators: Category A indicators A1_01. Number of persons occupied in agriculture A1_02. Number of persons occupied in fisheries A1_03. Number of working hours in agriculture A4_01. Rate of fatal accidents in agriculture A4_02. Rate of fatal accidents in fisheries A4_03. Reported cases of pesticide poisoning A4_04. Number of reported zoonoses A5_01. Rate of occupational diseases in agriculture A5_02. Rate of occupational diseases in fisheries Category B indicators B4_01. Rate of non-fatal accidents in agriculture B4_02. Rate of non-fatal accidents in fisheries Additionally, a set of indicators calculate the absolute number and the percentage of immigrant labour force in agriculture and fisheries: B1_01. Number of immigrants occupied in agriculture B1_02. Percent of immigrants occupied in agriculture of the total farming population B1_03. Number of immigrants occupied in fisheries B1_04. Percent of immigrants occupied in fisheries of the total fishermen population

Elderly

Elderly people occupied in agriculture and fisheries run a serious risk of accidents, as seen by statistics which present with an increased rate of injury mortality. Additionally, old age is associated with a wide range of diseases (such as respiratory, musculoskeletal and mental disorders), which could be related to hazards in the workplace. Having taken into account the above, we have considered age over 45 as a basic level of aggregation and 5 year age bands as optimal aggregation for the following developed indicators: Category A indicators A1_01. Number of persons occupied in agriculture A1_02. Number of persons occupied in fisheries A1_03. Number of working hours in agriculture A1_04. Percent of persons occupied in agriculture of the total active population A4_01. Rate of fatal accidents in agriculture A4_02. Rate of fatal accidents in fisheries A4_03. Reported cases of pesticide poisoning A4_04. Number of reported zoonoses



A5_01. Rate of occupational diseases in agriculture A5_02. Rate of occupational diseases in fisheries Category B indicators B1_01. Number of immigrants occupied in agriculture B1_02. Percent of immigrants occupied in agriculture of the total farming population B1_03. Number of immigrants occupied in fisheries B1_04. Percent of immigrants occupied in fisheries of the total fishermen population B4_01. Rate of non-fatal accidents in agriculture B4_02. Rate of non-fatal accidents in fisheries

Children

Children living and working in farms have one of the highest mortality rates. Young age is associated with an increased risk for fatal and non-fatal accidents, due to a variety of factors intrinsic to the farm work environment (tractors, silos, tools, pesticides, etc). Having taken into account the above, we have considered age under 14 as a basic level of aggregation and 5 year age bands as optimal aggregation for the following developed indicators: Category A indicators A1_01. Number of persons occupied in agriculture A1_02. Number of persons occupied in fisheries A1_03. Number of working hours in agriculture A1_04. Percent of persons occupied in agriculture of the total active population A4_01. Rate of fatal accidents in agriculture A4_02. Rate of fatal accidents in fisheries A4_03. Reported cases of pesticide poisoning A4_04. Number of reported zoonoses A5_01. Rate of occupational diseases in agriculture A5_02. Rate of occupational diseases in fisheries Category B indicators B1_01. Number of immigrants occupied in agriculture B1_02. Percent of immigrants occupied in agriculture of the total farming population B1_03. Number of immigrants occupied in fisheries B1_04. Percent of immigrants occupied in fisheries of the total fishermen population B4_01. Rate of non-fatal accidents in agriculture B4_02. Rate of non-fatal accidents in fisheries



Female farm workers

Women occupied in agriculture is a population group difficult to monitor, as they are often considered as part of the family farm labor force and only a small part of the agricultural holding owners are female. However, they have a significant contribution to the farm output. To do that, they usually work intensively during harvesting periods and take up strenuous and even dangerous tasks. Additionally, women run a greater risk of psychiatric morbidity (stress, anxiety) and they are also affected by chemical exposure in the workplace (eg pesticides), which is presumably associated with an increased risk of reproductive and developmental disorders. Having taken into account the above, we have considered gender as a basic level of aggregation for the following developed indicators: Category A indicators A1_01. Number of persons occupied in agriculture A1_03. Number of working hours in agriculture A4_01. Rate of fatal accidents in agriculture A4_03. Reported cases of pesticide poisoning A4_04. Number of reported zoonoses A5_01. Rate of occupational diseases in agriculture Category B indicators B4_01. Rate of non-fatal accidents in agriculture

Seasonal/temporary farm workers

Seasonal and temporary workers are occupied on an irregular basis in agriculture, mainly employed during seeding, harvesting or in other periods where extra hands are needed in the field. They have been associated with an increased risk of accidents and injuries, a higher frequency of stress and anxiety due to job insecurity and a wide variety of work-related morbidity related with low adherence to health and safety measures. Having taken into account the above, we have considered employment type as an optimal level of aggregation for the following developed indicators: Category A indicators A1_01. Number of persons occupied in agriculture A1_03. Number of working hours in agriculture A1_04. Percent of persons occupied in agriculture of the total active population A4_01. Rate of fatal accidents in agriculture A4_02. Rate of fatal accidents in fisheries A4_03. Reported cases of pesticide poisoning A4_04. Number of reported zoonoses A5_01. Rate of occupational diseases in agriculture



A5_02. Rate of occupational diseases in fisheries Category B indicators B1_01. Number of immigrants occupied in agriculture B1_02. Percent of immigrants occupied in agriculture of the total farming population B1_03. Number of immigrants occupied in fisheries B1_04. Percent of immigrants occupied in fisheries of the total fishermen population B4_01. Rate of non-fatal accidents in agriculture B4_02. Rate of non-fatal accidents in fisheries We also consider monitoring this vulnerable population subgroup with the following indicators: A1_03. Number of working hours in agriculture B2_02. Work organization index C2_04. Hours of work per week


Development Of Public Health Indicators For Reporting Environmental/Occupational Risks Related To Agriculture And Fishery - DIRERAF

Work Package VII

“Pilot testing of the indicators”

Survey Protocol List of pilot indicators

DIRERAF: Unified report (draft) 2

DIRERAF: unified report Date: 7/12/2007 3:54:38 PM 2

Survey Protocol

Objectives

The main objective of this survey is to test a group of selected indicators for the feasibility of

the data collection. The survey also collects feedback on the effectiveness and scientific

validity of each indicator from the parties involved in the testing phase, in order to assess its

applicability. The analysis of the survey results will enable the project partners, in collaboration

with an already established panel of experts, to classify the pilot indicators based on their

appropriateness to be proposed for monitoring the occupational and environmental risks of the

agricultural population and those working in the fishing industry.

Which indicators to test

The testing phase of the project intends to evaluate a sample of indicators, which have been

selected after internal meetings among some of the responsible project partners. The rationale

behind this selection is to test indicators, which are considered of a different level of difficulty.

The level of difficulty for each indicator was estimated, based on data already collected from

the project’s previous work packages, and having mainly in mind the availability of existing

data, their accessibility, method of collection and aggregation. For more information, please

refer to the report of Work Package 2 “Identification of policies and practices”.

Participating countries

The testing phase of the pilot indicators runs in five countries: Greece (which is also the

project co-ordinator), Germany, Finland, Italy and Poland. For each country, the same protocol

is adopted and followed, so that the results would be as comparable as possible. In each

country, the partner of the project is in charge of completing the allocated tasks as efficiently

as possible.



Timetable and project management

Involved parties will have 8 weeks to contact national authorities, enquiry data sources, collect

relevant information, complete the questionnaire forms, return them to the project co-

ordinator and, if necessary, provide additional information or clarification, when requested.

The eight-week period will begin as soon as the current document is mailed to the project

partners.

At the end of every week, the co-ordinator will request information on the progress of

allocated tasks, discuss the results with collaborating parties and disseminate early results

among the team.

At the end of this period, each partner is expected to return a fully-completed questionnaire,

as well as additional comments on the findings and the survey itself.

Data to be collected

The survey questionnaires serve two purposes. One is to collect as much information as

possible on the availability of the data sources that are needed for the computation of the

indicator and on the quality of these data. The second purpose is to collect feedback from the

project teams on a general assessment of each indicator, particularly related to its policy

relevance, its scientific validity and its sensitivity to changes. The involved parties are also

expected to comment on the availability of basic and optimal levels of aggregation discussed in

each indicator table.

Criteria for evaluation

A set of criteria has been set, in order to rate each pilot indicator in terms of its feasibility,

applicability and usefulness. The following table depicts a short summary of each criterion.



Criterion Description Rating scale

Availability Data sources are available for computing the indicator, as well as for each level of aggregation 1 (poor) to 5 (excellent)

Quality Refers to the quality of the collection method of the original data required for the computation of the indicator 1 (poor) to 5 (excellent)

Policy relevance

Refers to the capacity of the indicator to diagnose health threats which could be managed by a policy intervention 1 (poor) to 5 (excellent)

Validity The indicator can measure what is supposed to measure and is easy to understand 1 (poor) to 5 (excellent)

Discernment The indicator is sensitive enough to monitor trends in time 1 (poor) to 5 (excellent)

Comparability The value of the indicator can be compared across the EU countries 1 (poor) to 5 (excellent)

Results of the evaluation process

The returned questionnaires will be analysed, in order to test the comparability of the results

among countries, the availability and accessibility of the data needed and the capacity of the

indicators to convey the required message, dictate needs for new policy decisions and monitor

changes in time. Initially, results on each indicator will be presented descriptively (means and

distribution) and evaluation of the indicators will follow.



Presentation of the indicators selected for the pilot survey

List of pilot indicators

A1_01. Number of persons occupied in agriculture Definition Labour force includes everyone (over the legal age limit) having provided

an agricultural work on and for the holding during the last 12 months. Included as regular labour force is every member of the holder's family working on the holding are taken as regular labour force (holder included) and non-family regularly employed labour force

Computation The total number of persons occupied in agriculture Aggregation (basic)

Age groups: -19,20-44,45+ Gender



Immigrant status Time period [basic]:5-year [optimal]: annual Significance Agricultural workers constitute 5% of the total labour force of the

European Union. Its diverse makeup reflects a diverse health status of the farming population among countries and within each country. Changes over time may affect the overall health of a country’s population.

Rationale There is ample scientific evidence that the magnitude of risk for injury or illness is associated with the demographic characteristics of the agricultural workforce. Mapping the demographics of the agricultural workforce is the very first step for incorporating health risk management policies. This indicator serves as denominator for other indicators.



Potential data sources

National census data; national Farm Structure Survey; national Labour Force Survey

A1_02. Number of persons occupied in fisheries Definition This is the total number of the fisheries workforce Computation The total number of persons occupied in fisheries Aggregation (basic)



Age groups: 5-year bands Employment type (full-time / part-time) (owner / employed) Production type



Immigrant status Time period [basic]:5-year [optimal]: annual Significance The fishing industry is one of the most dangerous occupations in the EU,

with one of the highest injury and mortality rates. Mapping the demographics of this workforce is the very first step for incorporating health risk management policies. This indicator serves as denominator for other indicators.

Rationale This indicator reflects the workforce in risk for sustaining one of the highest injury and mortality rates in the EU




National census data; national Labour Force Survey; national Fisheries Statistics

A1_03. Number of working hours in agriculture Definition Number of working hours derived from agriculture Computation It is calculated as the Annual Work Units (AWU) derived from agriculture.

The number of hours comprising an AWU should correspond to the number of hours actually worked in a full-time job within agriculture (1 AWU = 1800 hours). Therefore it does not include public holiday, paid annual holidays, sick-leave, breaks for meals, etc. This unit has been extensively used by EUROSTAT.

Aggregation (basic)




Time period [basic]:5-year [optimal]: annual Significance Agricultural work time ranges from a few hours to extreme overtime,

during harvesting conditions. The type of employment can also be all year round or seasonal, affecting the risk especially for accidents

Rationale This is an indicator estimating the level of aggregate exposure. Limitations of the indicator

This serves as a proxy for the estimation of exposure among the agricultural workforce, as well as a proxy for overtime employment - it is an indirect risk measurement.


National census data; Eurostat; Farm Structure Survey; Labour Force Survey

A3_01. Tons of fertilizers sold per hectare of cultivated land / total country size Definition Total amount of fertilizers sold per hectare of cultivated land / per total

country size



Computation The total amount in metric tons of fertilizers sold divided by the total number of hectares of total arable land / by the total country size

Aggregation Production type [optimal] Time period Annual Significance A great proportion of the EU arable land is enhanced with additives in the

form of commercially available fertilizers. Compounds of the fertilizer material have been found to infiltrate underground, river and drinking waters. There is some scientific evidence linking excess of fertilizer compounds with various diseases. Also, a great impact on the environment has been noted, mainly due to the effects on the vulnerable water ecosystems.

Rationale This indicator measures the intensity of using fertilizers for crop production. It is considered in indirect indicator of environmental burden.


Fertilizer consumption is heavily dependent on the type of crop, climatic conditions and irrigation.


EUROSTAT

A3_02. Number of samples of drinking water screened for nitrate levels Definition The number of officially tested samples of drinking water for the whole of

the country region per year Computation The actual number of officially tested drinking water samples is acquired

from the corresponding national authority. Aggregation National/regional level [optimal] Time period Annual data Significance The vast majority of EU arable land is enhanced with additives in the form

of commercially available fertilizers. Compounds of the fertilizer material have been found to infiltrate underground, river and drinking waters.

Rationale The number of drinking water samples screened for nitrate levels reflects the level of awareness and policy orientation to monitor the drinking water quality and to promote sound agricultural practices. There is ample scientific evidence linking excess of fertilizer compounds, like nitrates, with various diseases. Susceptible population groups, such as infants, are in danger of developing serious adverse effects. Also, a great impact on the environment has been noted, mainly due to the effects on the vulnerable water ecosystems.




National data sources

Α3_03. Percentage of samples of drinking water exceeding set nitrate limits



Definition The proportion of drinking water samples exceeding the predefined maximum nitrate limit per year

Computation Nominator is the actual number of drinking water samples exceeding the predefined maximum nitrate limit in one year. Denominator is the total number of drinking water samples monitored in the same year

Aggregation National/regional level [optimal] Time period Annual data Significance The vast majority of EU arable land is enhanced with additives in the form

of commercially available fertilizers. Compounds of the fertilizer material have been found to infiltrate underground, river and drinking waters.

Rationale The number of samples of drinking water screened for nitrate levels reflects the level of awareness and policy orientation to monitor the drinking water quality and to promote sound agricultural practices. The proportion of positive samples is a strong indicator of long-term problems encountered in water quality.




National data sources

A5_01. Rate of Occupational diseases in agriculture Definition Incidence of legally recognized occupational diseases in agriculture. Computation This indicator is the actual number of diseases recognized as occupational

in the sector of agriculture by legal authorities in one year divided by the total number of farmers in the same year the same country.


Time period Annual Significance There has been a clear and evident association between a certain

exposure (like the farming occupation) and certain health outcomes. These health outcomes are considered of occupational causation in some countries and those persons are eligible for filing sick claims.

Rationale The number of legally recognized occupational diseases reflects the level of awareness and policy orientation to monitor the health status of farmers, compensate for the lost income and promote safe agricultural health practices. At the current stage, we cannot state that it reflects the real effect of the farming occupation on farmers’ health.


There are great differences among farmers regarding the employment type (self-employed, employed, uninsured family farm labour force), with farmers being insured even in different funds. In many countries there is also great distance between the actual legal provisions and how these provisions are implemented to the benefit of the farmer. This indicator should be interpreted with caution.

Potential data National data sources, such as national insurance data



sources

A5_02. Rate of Occupational diseases in fisheries Definition Incidence of legally recognized occupational diseases in fisheries Computation This indicator is the actual number of diseases recognized as occupational

in the sector of fisheries by the legal authorities in one year divided by the total number of fishermen in the same year


Time period Annual Significance There has been a clear and evident association between a certain

exposure (like the fishing occupation) and certain health outcomes. These health outcomes are considered of occupational causation in some countries and those persons are eligible for filing sick claims.

Rationale The number of legally recognized occupational diseases reflects the level of awareness and policy orientation to monitor the health status of fishermen, compensate for the lost income and promote safe health practices.


There are great differences among fishermen regarding the employment type (self-employed, employed), with fishermen being insured even in different funds. In many countries there is also great distance between the actual legal provisions and how these provisions are implemented to the benefit of the fisherman. This indicator should be interpreted with caution.


National data sources, such as national insurance data

Minutes from the 2nd meeting of partners

DIRERAF: 2nd meeting of partners 1


DIRERAF: Minutes from 2nd meeting of partners

Agenda 09:40 – 09:50 – Welcome, Dr Athena Linos

09:50 – 10:00 – Welcome by the host, Prof. Marco Maroni

10:00 – 10:30 Identification and Review of existing policies and practices

10:00 – 10:30 Presentation of the work so far, what needs to be done [ICPS]

10:30 – 10:45 Identification of the minimal common dataset currently applied at the EU level and its level of aggregation 10:45 – 11:00 Coffee break

11:00 – 11: 20 Identification of health risks and accident by production type

11:00 – 11: 20 Task progress – time schedule [ UoA ]

11:20 – 11: 40 Risk profiles in agriculture (ICPS)

11:40 – 11: 50 Methodology for developing the indicators [UoA]

11:50 – 12:00 The panel of experts – participants, aim and tasks [UoA] 12:00 – 12:45 Discussion

12:45 – 13:00 Dissemination of the project [Prolepsis] 13:00 – 14:00 Lunch 14:00 – 15:00 Review of tasks to do – Draft of future meeting’s agenda – Closure of meeting

List of participants attending ICPS Prof. Marco Maroni, Teresa Mammone, Patrizia Vida, Francesca Metruccio National and Kapodistrian University of Athens Prof. Athena Linos; Dr. Christos Chatzis; Mr. D.Kouimintzis; Ms Evi Chronopoulou IMIM Dr. Maria Mirabelli FIOH Ms Milja Makinen Ms Kirsti Taattola Dresden University of Technology Prof. Kirch Dr. N.Schmitt Nofer Institute of Occupational Health Dr. Joanna Jurewicz Department of Public Health, Erasmus University Dr. Alex Burdoff Medical University of Sofia



Prof. Vera Koytcheva Charles University of Prague Prof. Vladimir Bencko Dr. Alena Slamova Institute of Preventive Medicine, Environmental and Occupational Health Ms Ioanna Kotsioni

Presentations

• Identification and Review of existing policies and practices – presentation of the work so far, what need to be done (Patrizia Vida)

• Identification of health risks and accidents by production type – Task progress, time schedule

(Dimitris Kouimintzis)

• Risk profiles in agriculture (Francesca Metruccio)

• Methodology for developing the indicators (Athena Linos)

• Dissemination of the project – Financial issues (Ioanna Kotsioni)

Discussion on the work packages Work package 2 Information are still missing for the following countries:

• Austria • Belgium • Estonia • France • Portugal

• The responsibility of ICPS in contacting countries that did not answer was stressed. Other partners could help ICPS providing contacts.

• Prof. Bencko offered to contact Fishery Authorities in Czech Republic, and to help

with the collection of data for Slovakia.

• Dr. Kirch provided a list of contacts for Austria and offered to provide contacts in Belgium and Portugal.

Work package 4



• Prof. Bencko suggested to look at the historical cases such as exposure to arsenic in vineyards and tobacco workers (i.e. in California), or organic mercury exposure, where there is a clear evidence of disease among workers. He also offered to prepare a report on fresh water fishery.

• Looking at data which show evidence of a certain disease among workers, Prof. Kirch

suggested that focus should be placed on the scientific literature rather than on statistical data that might not be as reliable.

• Prof. Linos stressed the importance of having some simple indicators concerning the

exposure assessment.

• A suggestion was made from Teresa Mammone to add rice as a production type of special interest

Work package 5

• Measure the risk: effect on health and on the environment

• Magnitude of the risk: consider the most frequent diseases (even if not severe, as asthma vs mesothelioma) because they are of public health concern.

• 3 major concerns as expressed in the discussion: -Tumors: tumor registry is not occupation specific. Hp: introduce a question into the registry – is the person affected a farmer or a fisherman? - Muskoloskeletal diseases - Asthma/allergies (dermatitis) • Criteria for the selection of indicators: looking at the recent literature and at the most

frequent health problems among farmers.

• Indicators that were suggested in the course of discussion refer to: - Existence of training courses for farmers with regards to the use of pesticides - Acute poisoning cases - Existence of European general practitioners/rural practitioners associations

• 2 very important parameters stressed out by Prof. Kirch and Dr Burdorf respectively are: - timeline for data collection - possibility of intervention, as an additional criteria for the selection of the final indicators

• For the forming of the panel of experts several propositions were discussed, concluding that it would be a good idea to include experts in the following topics:

- public health



- indicators development - agricultural medicine - fishery - occupational medicine - environment - epidemiology - injuries - biological agents

And possibly experts from WHO and Eurostat.

Dissemination

• Prof. Kirch made a very generous offer to include a special issue on the DIRERAF project in the “European Journal of Public Health” where he is editor. Therefore during the meeting a discussion on the possible papers to be included in the special issue was initiated and concluded in the following topics and proposed leaders.

Topics discussed for being included in a special issue of the “European Journal of Public Health”

• Health risks associated with farming in greenhouses • Methodology of developing indicators for monitoring the health of populations involved in

agriculture and fishery • Health risks associated with Southern European Crop Production [2 papers] • Health risks associated with Northern European Crop Production • Health risks associated with animal farming • Respiratory diseases and agriculture • Acute poisonings in agriculture • Health risks associated with tobacco production • Best practices in promoting farmers’ health • Farmers’ social insurance across Europe • The case of the Lombardi region in the development of risk indicators in Agriculture

This report was produced by a contractor for Health & Consumer Protection Directorate General and represents the views of thecontractor or author. These views have not been adopted or in any way approved by the Commission and do not necessarilyrepresent the view of the Commission or the Directorate General for Health and Consumer Protection. The EuropeanCommission does not guarantee the accuracy of the data included in this study, nor does it accept responsibility for any use madethereof.