rr954 - a comparative analysis of self-reported and ... · a comparative analysis of self-reported...

Health and Safety Executive

A comparative analysis of self-reported and medically certified incidence data on work-related illness

Prepared by the University of Manchester for the Health and Safety Executive 2013

RR954 Research Report

© Crown copyright 2013

First published 2013

You may reuse this information (not including logos) free of charge in any format or medium, under the terms of the Open Government Licence. To view the licence visit www.nationalarchives.gov.uk/doc/open-government-licence/, write to the Information Policy Team, The National Archives, Kew, London TW9 4DU, or email [email protected].

Some images and illustrations may not be owned by the Crown so cannot be reproduced without permission of the copyright owner. Enquiries should be sent to [email protected].


A comparative analysis of self-reported and medically certified incidence data on work-related illness

A Money, L Hussey, M Carder, S Turner & R Agius Centre for Occupational and Environmental Health University of Manchester Ellen Wilkinson Building Oxford Road Manchester M13 9PL

The impact of work on health is of major importance to Government policy makers, employers and employees alike. Thus, it is important to be able to monitor the incidence and change in incidence of work-related ill-health (WRIH) over time. One (national) source of information relating to WRIH in the UK is the Self-reported Work-related Illness and Injury (SWI) survey which has been included as an annual module in the Labour Force Survey (LFS) since 2003/04. Earlier versions were run in 1990, 1995 and 2001/02. However, the Health and Safety Executive (HSE) acknowledges the limitations of the SWI data and, in particular, the possibility that over or under attribution to work may be a factor in its estimates. Furthermore, an expert workshop convened by the HSE in February 2009 concluded that the HSE should identify preferred data sources for different categories of WRIH, taking into account their respective strengths and weaknesses.

This report and the work it describes were funded by the Health and Safety Executive (HSE). Its contents, including any opinions and/or conclusions expressed, are those of the authors alone and do not necessarily reflect HSE policy.

HSE Books

EXECUTIVE SUMMARY

Introduction:

The impact of work on health is of major importance to Government policy

makers, employers and employees alike. Thus, it is important to be able to

monitor the incidence and change in incidence of work-related ill-health

(WRIH) over time. One (national) source of information relating to WRIH in the

UK is the Self-reported Work-related Illness and Injury (SWI) survey which

has been included as an annual module in the Labour Force Survey (LFS)

since 2003/04. Earlier versions were run in 1990, 1995 and 2001/02.

However, the Health and Safety Executive (HSE) acknowledges the

limitations of the SWI data and, in particular, the possibility that over or under

attribution to work may be a factor in its estimates. Furthermore, an expert

workshop convened by the HSE in February 2009 concluded that the HSE

should identify preferred data sources for different categories of WRIH, taking

into account their respective strengths and weaknesses.

The other main data sources drawn on by HSE for statistics on occupational

ill-health are: The Health and Occupation Reporting network (THOR –

previously ODIN until 2001) including The Health and Occupation Reporting

Network in General Practice (THOR-GP); Industrial Injuries Disability Benefit

scheme (IIDB); Reporting of Injuries, Diseases and Dangerous Occurrences

Regulations (RIDDOR); and Death Certificates (DC). Of these, THOR-GP,

which is a UK wide network of over 250 general practitioners trained to

diploma level in occupational medicine who report cases of work-related ill-

health as seen in general practice is arguably the most comparable with SWI

(in terms of concepts used, diagnostic categories and estimation of working

days lost). However, the degree to which the SWI and THOR-GP may be

complementary or may overlap requires critical comparative analysis.

2

Aims and methods:

The aim of this study was to make a detailed comparative analysis of the

methodology of the secular trends, frequency and demographic or

occupational distribution of WRIH from the SWI data and from THOR-GP.

The main limitation of the SWI data arises from a concern that it is the

individuals’ perception of the attribution of an illness being caused or made

worse by their occupation, rather than assessment of work attribution made

by a medical practitioner. Unlike SWI, THOR-GP reporting relies on medically

qualified practitioners with training in occupational medicine. THOR-GP

starting collecting data in June 2005, using methods developed from other

more long standing THOR (specialist) schemes. GPs report electronically to

THOR-GP via an on-line web-form. For each case submitted, information is

provided relating to diagnoses, demography, occupation, industry,

task/event/causative agent, sickness certification/fit note, referrals to other

health practitioners and symptom onset.

The incidence rates for THOR-GP used in this comparison were three year

averages based on 2006 to 2008 data. These were compared with the three

year average of the 2006/07, 2007/08 and 2008/09 SWI surveys, which were

deemed the most comparable for the time period covered. Both THOR-GP

and SWI incidence rates were adjusted to take account of the fact that data

was collected from a sample of GPs/households. The number of persons

employed each year according to the LFS was used as the denominator.

Differences in temporal variation over the period of overlap of the datasets

were also examined. Individual year incidence rates were also compared with

estimates of change in incidence of cases reported to THOR-GP obtained

from multi-level models (MLM). The MLM methodology to determine trends in

incidence (which took into account other factors which may cause variation in

incidence, either between reporters or within reporters over time) has been

routinely applied to THOR data since 2004.

3

SWI estimates of working days lost (WDL) were also compared with the

sickness absence data reported by GPs. These were not strictly comparable

because sickness absence data reported by GPs is more often than not the

number of days certified, as opposed to WDL [note also that the THOR-GP

figure is predominantly the duration of the initial period of certified sickness

absence]

Results:

Overall, the number of estimated cases and resulting GB incidence rates were

approximately 25% higher for the SWI than for THOR-GP. Musculoskeletal

disorders (MSDs) and mental ill-health diagnoses made up most of the cases

reported to both schemes. THOR-GP had a higher proportion of skin disease

reported than SWI, whereas self-reports result in a much larger proportion of

‘other’ types of WRIH. Despite SWI having an overall higher incidence rate of

WRIH (THOR-GP 1549, SWI 1920 per 100,000 person employed), THOR-GP

has higher rates of incidence for MSDs (THOR-GP 828, SWI 667) and skin

disease (THOR-GP 154, SWI 33). SWI results show higher rates for mental

ill-health (THOR-GP 484, SWI 788), and in particular, for ‘other’ cases with an

incidence rate 4 times higher than for GP reported cases (THOR-GP 60, SWI

254).

Variation by age was similar for both the SWI and THOR-GP with the highest

rates of incidence typically amongst the 45 to 54 year olds (THOR-GP 1756,

SWI 2336) although THOR-GP showed higher rates (1456) than the SWI

(1376) in the youngest age group. THOR-GP incidence rates for total WRIH

showed little variation by gender, whilst SWI rates were higher in females

(2092) compared to males (1771). Incidence rates showed considerable

variation by occupation. Although both THOR-GP and SWI had administrative

occupations as their second most frequently reported occupation (SOC)

codes, overall SWI incidence rates tended to be higher (than THOR-GP)

amongst ‘higher’ skilled occupations (SOC coding is based on the skills,

training and experience required to do the job) whereas THOR-GP had

increased incidence rates amongst ‘lower’ unskilled occupations. In contrast,

analysis by industry revealed a greater degree of consistency between

4

THOR-GP and SWI, with both reporting a relatively high incidence of WRIH in

the public administration and defence, and the health and social work sector.

Comparisons of specific categories of illness revealed that THOR-GP had a

much higher rate for upper limb disorders (453) than SWI (307) and increased

rates in younger age groups, and in males. For both schemes, incidence rates

for stress, anxiety and depression were higher in females (THOR-GP 648,

SWI 973) compared to males (THOR-GP 352, SWI 627) and in teaching and

research professionals compared to other occupations. THOR-GP reported a

higher incidence of skin disease than SWI and the pattern by age group

differed compared to other disease categories, with highest rates in the

younger age group that decreased with age.

Both THOR-GP and SWI incidence rates of total WRIH decreased over the

period 2006 to 2008, with a steeper decrease between 2006 and 2007 (7% for

THOR-GP and 11% for SWI), compared to between 2007 and 2008 (both

schemes 3-4%). Incidence rates for MSDs and mental ill-health also fell over

this time period, with typically larger drops between 2006 and 2007 and for

the SWI rates compared to THOR-GP rates. Typically, the MLM model (where

rates have been adjusted for other factors that might cause variation over

time) suggested a larger decrease in THOR-GP incidence rates over the

same time period (compared to the unadjusted THOR-GP incidence rates)

and therefore the change in incidence predicted by the MLM was more

consistent with that seen for the SWI.

Overall, the number of WDL estimated by the SWI is nearly twice that of

THOR-GP estimates (THOR-GP 42% fewer days than SWI), which are by

definition based on the narrower ‘GP certified’ criterion. Cases of work-

related mental ill-health comprise the highest proportion of WDL for both

schemes (THOR-GP 57%, SWI 47%). Males have the highest estimates of

WDL for both schemes; however as rates per workers, females have the

highest rate. The highest rates of WDL were reported for health and social

welfare professionals (SWI) and protective service occupations (THOR-GP).

5

Conclusion:

A number of differences and similarities between the two data sources have

been highlighted. As expected (since the SWI should in theory capture those

cases not considered severe enough to see a GP) SWI incidence rates are

commonly higher than the corresponding THOR-GP rates. However, for

MSDs and skin disease, the reverse is true. The higher THOR-GP incidence

rates for MSDs (THOR-GP 828, SWI 667) may be largely due to differences

in the categorisation of musculoskeletal injuries between the two schemes.

SWI injuries occurring in the previous 12 months are excluded at the interview

stage, whereas THOR-GP results include musculoskeletal injuries, as it can

be difficult to ascertain whether a musculoskeletal problem has been caused

by repeated exposure or a single traumatic event. The THOR-GP

musculoskeletal rate excluding cases that were coded as most likely to be

classed as an injury, is less than the SWI rate (625). The relatively high SWI

mental ill-health incidence rates may also be due to a preference to report

mental ill-health diagnoses to SWI over other diagnoses (only 1 episode can

be reported). Moreover an individual may be more ready to attribute a mental

ill-health issue to work than a GP, while the GP may be better at identifying

other, underlying causal factors, such as for dermatitis. The main age related

finding – that (converse to the pattern for the other illness categories)

incidence rates for skin disease were higher (both SWI and THOR-GP) in the

youngest age group, is probably at least partly explained by the high

incidence amongst hairdressers. With respect to job, the most striking finding

was the tendency for SWI incidence rates to be higher for the ‘higher’

occupational groups whilst the reverse was seen for THOR-GP. This is likely

confounded by the fact that these ‘higher’ occupations have higher

proportions of stress, depression and anxiety than other occupations, and

these psychological diagnoses have higher rates of incidence in the SWI. A

degree of consistency between SWI and THOR-GP was also seen in the

observed trends in incidence, although it is important not to over interpret

apparent changes in incidence over short time scales.

In summary, although a degree of overlap between the two data sources

certainly exists, THOR-GP is at least complementary to SWI and presents

6

some advantages. The main advantage of THOR-GP over SWI is that

reporters to THOR-GP are medically qualified with additional training in

occupational medicine and may be more accurate in their attribution. This

may partly account for THOR-GPs higher rate of reporting of occupational

dermatitis and possibly, in part, THOR-GPs lower rate of mental ill-health.

Unlike SWI, THOR-GP also has the ability to report co-morbidities (thus

avoiding the potential under-reporting or over-reporting of specific categories

of illness seen in the SWI). As such, THOR-GP, particularly in combination

with the other THOR schemes, provides a much richer, and more

authoritative, data source, for example, relating to skin and respiratory

diagnoses. THOR-GP also collects data on causal factors, referrals and

symptom onset, enabling further analyses investigating how these factors

vary by different conditions, jobs etc. SWI suggests higher rates of mental ill-

health partly because not all the cases present to their GP and partly because

the design of SWI allows for only one morbidity to be reported (which tends to

be the commonest).

The self reporting approach of SWI means that estimates based on this

source are influenced by respondents' awareness of work-related illness as a

concept, and by their readiness to attribute and report their condition in these

terms. Reports from GPs will be less influenced by these personal factors.

The clear trend for THOR-GP rates for manual occupations to be higher than

the corresponding SWI rates, with the opposite pattern for non-manual jobs

probably reflects some correction of these personal factors in GP reporting.

The main advantage of the SWI is its rigorously representative sampling and

consistent methodology. This may imply that the trend measures taken from

the SWI are probably more reliable than those derived from THOR-GP.

7

CONTENTS

Page number 1 Introduction 20

2 Aims and Objectives 21

3 Overview of Data Sources 21 3.1 The Self-reported Work-related Illness and Injury Survey 21

(SWI) 3.2 The Health and Occupation Reporting network in 24

General Practice (THOR-GP)

4 Methods 30 4.1 Incidence rates 30 4.2 Trends in incidence 33 4.3 Working days lost 34

5 Results 35 5.1 Descriptive results 35 5.2 Incidence Estimations 40 5.2.1 Total WRIH 40 5.2.2 Musculoskeletal Disorders (MSDs) 49 5.2.3 Stress, depression and anxiety 67 5.2.4 Respiratory disease 74 5.2.5 Skin disease 77 5.2.6 Hearing disorders 80 5.2.7 Other WRIH 80 5.3 Trends in incidence 82 5.4 Working days lost 90 5.4.1 All cause work-related ill-health 90 5.4.2 Musculoskeletal disorders 100 5.4.3 Stress, depression and anxiety 106

6 Discussion and conclusions 112

7 Recommendations 125

8

LIST OF TABLES

Table 1 Categories of illness reported to THOR-GP (as defined by HSE contract specification)

Table 2 Categories of illness compared between SWI and THOR-GP

Table 3 All cause work-related ill-health by major diagnostic category; 3 year average estimates and incidence rates, 2006-2008

Table 4 All cause work-related ill-health by age group; 3 year average estimates and incidence rates, 2006-2008

Table 5 All cause work-related ill-health by gender; 3 year average estimates and incidence rates, 2006-2008

Table 6 All cause work-related ill-health by occupational group (2-digit SOC 2000); 3 year average estimates and incidence rates, 2006-2008

Table 7 All cause work-related ill-health by industrial division (SIC 92); 3 year average estimates and incidence rates, 2006-2008

Table 8 Work-related musculoskeletal disease by age group; 3 year average estimates and incidence rates 20062008

Table 9 Work-related musculoskeletal disorders by gender; 3 year average estimates and incidence rates 20062008

Table 10 Work-related musculoskeletal disorders by occupational group (2-digit SOC 2000); 3 year average estimates and incidence rates 2006-2008

Table 11 Work-related musculoskeletal disease by industrial division (SIC 92); 3 year average estimates and incidence rates 2006-2008

Table 12 Work-related upper limb disorders by age group; 3 year average estimates and incidence rates 20062008

Table 13 Work-related upper limb disorders by gender; 3 year average estimates and incidence rates 2006-2008

Page number

31

32

42

43

44

45

47

53

54

55

57

59

60

9

Table 14 Work-related upper limb disorders by occupational group (2-digit SOC 2000); 3 year average estimates and incidence rates 2006-2008

61

Table 15 Work-related upper limb disorders by industrial division (SIC 92); 3 year average estimates and incidence rates 2006-2008

62

Table 16 Work-related spine / back disorders by age group; 3 year average estimates and incidence rates 20062008

63

Table 17 Work-related spine / back disorders by gender; 3 year average estimates and incidence rates 20062008

64

Table 18 Work-related spine / back disorders by occupational group (2-digit SOC 2000); 3 year average estimates and incidence rates 2006-2008

65

Table 19 Work-related spine / back disorders by industrial division (SIC 92); 3 year average estimates and incidence rates 2006-2008

66

Table 20 Work-related stress, depression and anxiety by age group; 3 year average estimates and incidence rates 2006-2008

68

Table 21 Work-related stress, depression and anxiety by gender; 3 year average estimates and incidence rates 2006-2008

69

Table 22 Work-related stress, depression and anxiety by occupational group (2-digit SOC 2000); 3 year average estimates and incidence rates 2006-2008

70

Table 23 Work-related stress, depression and anxiety by industrial division (SIC 92); 3 year average estimates and incidence rates 2006-2008

72

Table 24 Work-related respiratory disease by age group; 3 year average estimates and incidence rates 20062008

75

Table 25 Work-related respiratory disease by gender; 3 year average estimates and incidence rates 2006-2008

76

10

Table 26 Work-related skin disease by age group; 3 year 78 average estimates and incidence rates 2006-2008

Table 27 Work-related skin disease by gender; 3 year average 78 estimates and incidence rates 2006-2008

Table 28 Other work-related ill-health by gender; 3 year 81 average estimates and incidence rates 2006-2008

Table 29 Other work-related ill-health by age group; 3 year 81 average estimates and incidence rates 2006-2008

Table 30 Estimated THOR-GP and SWI incidence rates of total 83 WRIH, 2006-2008

Table 31 Estimated THOR-GP and SWI incidence rates of total 84 work-related musculoskeletal disorders, 2006-2008

Table 32 Estimated THOR-GP and SWI incidence rates of total 85 work-related stress, depression and anxiety, 20062008

Table 33 Change in incidence of total WRIH reported to SWI 87 and THOR-GP, and predicted by MLM

Table 34 Change in incidence of total musculoskeletal 88 disorders reported to SWI and THOR-GP, and predicted by MLM

Table 35 Change in incidence of stress, depression and 89 anxiety reported SWI and THOR-GP, and predicted by MLM

Table 36 All cause work-related ill-health, estimated number 91 of GB WDL and number of days per worker; THORGP and SWI 2006 to 2008

Table 37 All cause work-related ill-health, estimated number 92 of GB WDL and number of days per worker by age group; THOR-GP and SWI 2006 to 2008

Table 38 All cause work-related ill-health, estimated number 94 of GB WDL and number of days per worker by gender; THOR-GP and SWI 2006 to 2008

Table 39 All cause work-related ill-health, estimated number 95 of GB WDL and number of days per worker by sub-major SOC group; THOR-GP and SWI 2006 to 2008

11

Table 40 All cause work-related ill-health, estimated number 98 of GB WDL and number of days per worker by industrial division; THOR-GP and SWI 2006 to 2008

Table 41 Musculoskeletal disorders, estimated number of GB 100 WDL and number of days per worker by age group; THOR-GP and SWI 2006 to 2008

Table 42 Musculoskeletal disorders, estimated number of GB 101 WDL and number of days per worker by gender; THOR-GP and SWI 2006 to 2008

Table 43 Musculoskeletal disorders, estimated number of GB 104 sickness absence days lost and number of days per worker by major SOC group; THOR-GP and SWI 2006 to 2008

Table 44 Musculoskeletal disorders, estimated number of GB 105 WDL and number of days per worker by industrial sector; THOR-GP and SWI 2006 to 2008

Table 45 Stress, depression and anxiety, estimated number 106 of GB WDL lost and number of days per worker by age group; THOR-GP and SWI 2006 to 2008

Table 46 Stress, depression and anxiety, estimated number 108 of GB WDL and number of days per worker by gender; THOR-GP and SWI 2006 to 2008

Table 47 Stress, depression and anxiety, estimated number 110 of GB WDL and number of days per worker by major SOC group; THOR-GP and SWI 2006 to 2008

Table 48 Stress, depression and anxiety, estimated number 111 of GB WDL and number of days per worker by major SOC group; THOR-GP and SWI 2006 to 2008

LIST OF FIGURES

Page number

Figure 1 THOR-GP and SWI estimates of GB proportionate 36 distribution by diagnostic category, 2006-2008

Figure 2 THOR-GP and SWI estimates of number of GB 37 cases, all work-related ill-health by age group, 2006-2008

12

Figure 3 THOR-GP and SWI estimates of number of GB cases, all work-related ill-health by gender, 20062008

37

Figure 4 THOR-GP and SWI estimates of number of GB cases, all work-related ill-health by industrial division, 2006-2008

38

Figure 5 THOR-GP and SWI estimates of number of GB cases, all work-related ill-health by sub-major SOC group, 2006-2008

39

Figure 6 GB incidence rate per 100,000 persons employed by diagnostic category, 2006-2008

42

Figure 7 GB incidence rate per 100,000 persons employed by age group; 3 year average incidence rates, 2006-2008

43

Figure 8 GB incidence rate per 100,000 persons employed by gender; 3 year average incidence rates, 20062008

44

Figure 9 GB incidence rate per 100,000 persons employed by sub-major SOC group; 3 year average incidence rates, 2006-2008

46

Figure 10 GB incidence rate per 100,000 persons employed by industrial division; 3 year average incidence rates, 2006-2008

48

Figure 11 Number of GB musculoskeletal cases by anatomical site, THOR-GP and SWI, 2006-2008

52

Figure 12 Musculoskeletal GB incidence rate per 100,000 persons employed disorders by anatomical site THOR-GP and SWI 2006 to 2008

52

Figure 13 GB incidence rates for work-related musculoskeletal disorders per 100,000 persons employed by age group; 3 year average incidence rates 2006-2008

53

Figure 14 GB incidence rate for work-related musculoskeletal disorders per 100,000 persons employed by gender; 3 year average incidence rates 2006-2008

54

Figure 15 GB incidence rate for work-related musculoskeletal disorders per 100,000 persons

56

13

employed by sub-major SOC group; 3 year average incidence rates 2006-2008

Figure 16 GB incidence rates for work-related musculoskeletal disorders per 100,000 persons employed by industrial division; 3 year average incidence rates 2006-2008

58

Figure 17 GB incidence rates for work-related upper limb disorders per 100,000 persons employed by age group; 3 year average incidence rates 2006-2008

59

Figure 18 GB incidence rates for work-related upper limb disorders per 100,000 persons employed by gender; 3 year average incidence rates 2006-2008

60

Figure 19 GB incidence rates for work-related spine / back disorders per 100,000 persons employed by age group; 3 year average incidence rates 2006-2008

63

Figure 20 GB incidence rates for work-related spine / back disorders per 100,000 persons employed by gender; 3 year average incidence rates 2006-2008

64

Figure 21 GB incidence rates for work-related stress, depression and anxiety per 100,000 persons employed by age group; 3 year average incidence rates 2006-2008

68

Figure 22 GB incidence rate for work-related stress, depression and anxiety per 100,000 persons employed by gender; 3 year average incidence rates 2006-2008

69

Figure 23 GB incidence rates for work-related stress, depression and anxiety per 100,000 persons employed by sub-major SOC group; 3 year average incidence rates 2006-2008

71

Figure 24 GB incidence rates for work-related stress, depression and anxiety per 100,000 persons employed by industrial division; 3 year average incidence rates 2006-2008

73

Figure 25 Work-related respiratory disease by gender; incidence rates THOR-GP and SWI 2006-2008

75

Figure 26 GB incidence rate for work-related respiratory disease per 100,000 persons employed by gender; 3 year average incidence rates 2006-2008

76

14

Figure 27 Work-related skin disease by age group; incidence rates THOR-GP and SWI 2006-2008

78

Figure 28 GB incidence rate for work-related skin disease per 100,000 persons employed by gender; 3 year average incidence rates 2006-2008

79

Figure 29 Estimated THOR-GP and SWI incidence rates of total WRIH, 2006-2008

83

Figure 30 Estimated THOR-GP and SWI incidence rates of total work-related musculoskeletal disorders, 2006-2008

84

Figure 31 Estimated THOR-GP and SWI incidence rates of total work-related stress, depression and anxiety, 2006-2008

85

Figure 32 Relative rates by year (2006 estimate = 1), with 95% confidence intervals, total WRIH, THOR-GP, 2006-2008

87

Figure 33 Relative rates by year (2006 estimate = 1), with 95% confidence intervals, total musculoskeletal disorders, THOR-GP, 2006-2008

88

Figure 34 Relative rates by year (2006 estimate = 1), with 95% confidence intervals, stress, depression and anxiety, THOR-GP, 2006-2008

89

Figure 35 All cause work-related ill-health, estimated number of GB WDL; THOR-GP and SWI 2006 to 2008

91

Figure 36 WDL per worker; THOR-GP (adjusted estimate) and SWI 2006 to 2008

92

Figure 37 All cause work-related ill-health number of GB WDL; THOR-GP estimate) and SWI 2006 to 2008

estimated (adjusted

93

Figure 38 All cause work-related ill-health estimated number of GB WDL per worker; THOR-GP (adjusted estimate) and SWI 2006 to 2008

93

Figure 39 All cause work-related ill-health estimated number of GB WDL ; THOR-GP (adjusted estimate) and SWI 2006 to 2008

94

15


94

Figure 41 All cause work-related ill-health estimated number of GB WDL per worker by sub-major SOC group; THOR-GP (adjusted estimate) and SWI 2006 to 2008

97

Figure 42 All work-related ill-health estimated number of GB WDL per worker by industrial division; THORGP (adjusted estimate) and SWI 2006 to 2008

99

Figure 43 Musculoskeletal disorders estimated number of GB WDL; THOR-GP (adjusted estimate) and SWI 2006 to 2008

101

Figure 44 Musculoskeletal disorders estimated number of GB WDL per worker; THOR-GP (adjusted estimate) and SWI 2006 to 2008

102

Figure 45 Musculoskeletal disorders, estimated number of GB WDL by gender; THOR-GP (adjusted estimate) and SWI 2006 to 2008

102

Figure 46 Musculoskeletal disorders, estimated number of GB WDL per case by gender; THOR-GP (adjusted estimate) and SWI 2006 to 2008

103

Figure 47 Stress, depression and anxiety, estimated number of GB WDL; THOR-GP (adjusted estimate) and SWI 2006 to 2008

107

Figure 48 Stress, depression and anxiety, estimated number of GB WDL per worker; THOR-GP (adjusted estimate) and SWI 2006 to 2008

107

Figure 49 Stress, depression and anxiety, estimated number of GB WDL by gender; THOR-GP (adjusted estimate) and SWI 2006 to 2008

108

Figure 50 Stress, depression and anxiety, estimated number of GB WDL per case by gender; THORGP (adjusted estimate) and SWI 2006 to 2008

109

16

LIST OF APPENDICES

Appendix A Self-reported work-related illness module from 2006/07 LFS taken directly from HSE website

Appendix B Distribution of THOR-GP reporters

Appendix C Industry and occupation categories included in the study

Appendix D Estimated incidence and rates of injuries reported to THOR-GP and SWI 2006 to 2008

Appendix E Distribution of THOR-GP and GB practices

17

GLOSSARY OF TERMS

COEH the Centre for Occupational and Environmental Health at the University of Manchester engages in research and postgraduate education. The centre mainly investigates the epidemiology of occupational and environmental ill-health.

CORE REPORTER a reporter who reports cases continuously on a monthly basis.

EPIDERM Occupational Skin Surveillance. A surveillance scheme that started in 1993 and which collects information on cases of work-related skin disease reported by consultant dermatologists.

HARVESTING the potential for new reporters to a surveillance scheme to include, in their first months return, cases seen prior to their becoming a reporter.

INTERNATIONAL STATISTICAL CLASSIFICATION OF DISEASES AND RELATED HEALTH PROBLEMS (ICD-10) is a coding system for diseases and signs, symptoms, social circumstances and external causes of injury and illness as classified by the World Health Organization.

LABOUR FORCE SURVEY (LFS) is a quarterly panel survey conducted by the Office for National Statistics, with a sample of households in order to collect data on the labour market with which to formulate and manage UK labour market policies.

MEMBERSHIP TIME for a given month – the length of time the reporter has been a member of the scheme.

MUSCULOSKELETAL DISORDERS (MSDs) include problems such as low back pain, joint injuries and repetitive strain injuries of various sorts.

MULTI-LEVEL STATISTICAL MODEL (MLM) a model which assumes a hierarchical data structure with data units at lower levels (e.g. time) nested within units at a higher level (e.g. reporter).

OPRA Occupational Physicians Reporting Activity. A surveillance scheme that started in 1996 and which collects information on cases of all work-related ill-health reported by occupational physicians.

REPORT CARD/WEB-FORM the card/web-form on which reporters record new cases of work-related disease seen that month.

REPORTING FATIGUE any tendency whereby reporters become less interested in participation as time since joining the scheme increases.

18

SAMPLE REPORTER a reporter who reports on only one, randomly sampled month per year. Only cases seen during the sample month should be reported.

SEASONAL VARIATION refers to the variation in reporting cases within a year, the pattern for which tends to be repeated from one year to the next.

SICKNESS ABSENCE RETURN the number of days certified as sick (including days self-certified to consultation) associated with a case of work-related ill-health reported to THOR-GP.

STANDARD INDUSTRIAL CLASSIFICATION (SIC) the coding system maintained by the Office for National Statistics for classifying industrial sectors in the UK.

STANDARD OCCUPATIONAL CLASSIFICATION (SOC) the coding system maintained by the Office for National Statistics for classifying occupational groups in the UK.

SELF REPORTED WORK-RELATED ILLNESS AND INJURY SURVEY (SWI) modules in the national Labour Force Survey sponsored by the Health and Safety Executive conducted in order to collect data on and provide an indication of the prevalence and incidence of work-related ill-health (as self-reports by individuals) in GB.

SWORD Surveillance of Work-related and Occupational Respiratory Disease. A surveillance scheme that started in 1989 and which collects information on cases of work-related respiratory disease reported by consultant chest physicians.

THOR-GP a project which uses a research network of General Practitioners with training in Occupational Medicine to determine the incidence of occupational disease, work-related ill-health and sickness absence burden in the UK.

THOR NETWORK The Health and Occupation Reporting network which runs several surveillance schemes for work-related disease. Took over from the Occupational Disease Intelligence Network (ODIN), which had the same role as THOR until 2001.

WORK-RELATED ILL-HEALTH (WRIH) is any episode of ill-health that has been caused or aggravated by an individuals’ occupation.

19

1 INTRODUCTION

There is no cast-iron source of national information on the work-related ill-

health (WRIH) burden in the UK. However it is possible to refine estimates

through ‘triangulation’ of various sources [1]. Moreover an expert workshop

convened by the Health and Safety Executive (HSE) in February 2009

concluded that HSE should identify preferred data sources for different

categories of WRIH, taking into account their respective strengths and

weaknesses. The Self Reported Work Related Illness and Injury Surveys

(SWI) conducted annually since 2003 by HSE as a module in the Labour

Force Survey (LFS) [2, 3] is one such data source. However, HSE

acknowledges the limitations of the SWI data and, in particular, the possibility

that over or under attribution to work may be a factor in its estimates.

The other main data sources drawn on by HSE for statistics on occupational

ill-health are: The Health and Occupation Reporting network (THOR –

previously ODIN) and The Health and Occupation Reporting Network in

General Practice (THOR-GP); Industrial Injuries Disability Benefits scheme

(IIDB) [4]; Reporting of Injuries, Diseases and Dangerous Occurrences

Regulations (RIDDOR), however, the HSE does not use RIDDOR data for

occupational diseases [5]; and Death Certificates (DC) [6]. Of these, THOR

GP, which is a UK wide network of over 250 general practitioners trained to

diploma level in occupational medicine, who report medically verified cases of

WRIH as seen in general practice [7, 8] is probably the most comparable (in

terms of concepts used, diagnostic categories and estimation of working days

lost). Indeed, the HSE recently observed [9] “THOR and THOR-GP provide

alternative data sources to the LFS for estimating the extent of WRIH and can

be used to investigate the level of misattribution in self-reported data’’.

However, the degree to which the SWI and THOR-GP data may be

complementary or may overlap, or may have relative strengths and

weaknesses, requires critical comparative analysis.

20

2 AIMS AND OBJECTIVES

The aim of this study was to make a detailed comparative analysis of the

methodology of the secular trends, frequency and demographic or

occupational distribution of WRIH from the SWI data and from THOR-GP.

The specific objectives were to:

o Compare the overall incidence estimates for major categories of illness

from the two sources;

o Investigate whether any differences vary by major industry or

occupational group or by demography;

o Establish whether any differences show temporal variation over the

period of overlap of the datasets.

o Discuss how differences may be explained.

3 OVERVIEW OF DATA SOURCES

3.1 THE SELF-REPORTED WORK RELATED ILLNESS SURVEY (SWI)

Introduction and data collected

In order to collect data on, and obtain a picture of, individually reported work-

related illness and workplace injury in the UK, the HSE sponsors two modules

in the LFS: self-reported work-related illness and the workplace injury survey.

The LFS is a survey of a sample of UK households and the purpose of the

survey is to collect data on the UK labour market in order to formulate and

manage labour market policies [10, 11, 12]. The LFS, carried out by the

Office for National Statistics, first started in 1973 and until 1983 the survey

was conducted every 2 years. From 1984 – 1991, the survey was conducted

on an annual basis, and from 1992 onwards the LFS adopted a panel survey

design, which is conducted on a quarterly basis [10].

The SWI module was first included in the LFS in 1990 and has continued on

an annual basis since 2003/04. The aim of this module is to provide the HSE

with an indication of the prevalence and incidence of self-reported WRIH by

individuals in Great Britain. The SWI module [see appendix A for full module

21

question list] is conducted (via face-to-face or telephone interviews) with those

individuals sampled in the LFS who are aged 16 or over and who are currently

employed, or who had been employed in the past. These individuals are then

asked the following screening question:

Within the last 12 months have you suffered from any illness, disability or

other physical or mental problem that was caused or made worse by your job

or by work you have done in the past?

Individuals who respond positively to this screening question are then asked

how many work-related illnesses they have experienced in the last 12 months.

If they report having more than one work-related illness, respondents are

asked which one they consider to be the most serious, and it is this (most

serious) illness only that the following questions relate to: diagnostic category

(coded by the interviewer as musculoskeletal; breathing / lung; skin; hearing;

stress, depression or anxiety; headache / eye strain; heart disease / attack /

circulatory problem; infectious; other); first awareness of ill-health; whether

the respondent has had any time off work for ill-health, how many working

days lost; any action their employer / organisation had taken in response to

the ill-health; job title; qualifications and training required for the job [13].

Data on WRIH is made available via the HSE website as estimates and

incidence rates of the number of people who have conditions that they self

report (not medically verified) as being caused or made worse by their

occupation. Estimates are provided by diagnostic types (including

musculoskeletal; stress; skin; respiratory; hearing problems; heart disease /

attacks; infectious disease) and by industrial and occupational groupings.

Calculation of incidence rates

As the LFS collects data on a sample of UK households, the data requires

weighting in order for estimates to be provided for the UK population. The

weighting procedure that the ONS carries out on the LFS data takes account

of non-response among different groups of the population and allows for

adjustments for age, sex and sub-regions [14]. However the weighting factors

22

applied to the main LFS data do not take into account non-response to the

SWI module in the LFS, therefore HSE statisticians apply an adjustment to

the SWI data in order to factor in such non-response [see 14 for further

information].

The HSE publishes detailed technical notes outlining the survey design and

methods used in the SWI [15], for the purpose of the estimates and rates

used in the study the following definitions apply:

• Estimated incidence – refers to the number of new cases of WRIH

that occurred in the 12 month reference period

• Incidence rate – refers to the estimated incidence divided by the

population at risk of experiencing a new case of WRIH during the

reference period (denominator used is the LFS data) and multiplied

by 100,000 [15].

Working days lost

The LFS also provides estimates and rates for the total number of working

days lost to WRIH. Estimates are expressed as full day equivalent (FDE)

which allows for any variation in hours worked, e.g. part-time workers.

Individuals are asked about how much time they had taken off in the previous

12 month reference period as a result of their ill-health and their responses

are assigned to one of ten categories of days, weeks or months [16]. Full-day

equivalent working days are calculated by adjusting the days lost estimates

using the ratio of the individuals usual weekly hours to the average usual

weekly hours of all full-time workers estimated using the LFS.

Rates are presented as annual average working days lost (FDE) per case of

WRIH, and annual average working days lost (FDE) per full-time equivalent

[17]. Full-time equivalent workers are calculated as the sum of the ratio of

individuals’ usual weekly hours to the average usual weekly hours of all full-

time workers estimated using the LFS for current workers i.e. those working in

the reference period [17].

23

Critical review of the SWI as a data source

Statistics from the SWI are often widely quoted as evidence of the burden /

scale of WRIH in the UK. However, there are a number of well established

difficulties / limitations with the data that are acknowledged by researchers in

the field of occupational ill-health, [18, 19] and by HSE [20]. The main

limitation of the SWI data comes from a concern that it is the individuals’

perception of the attribution of an illness being caused or made worse by their

occupation, rather than verification of work attribution made by a medical

practitioner [18, 21]. Studies that have sought to investigate the validity of the

SWI for estimating WRIH [21] have concluded that the self-report method

adopted by the SWI can result in an overestimation in the number of cases of

a disease that are attributable to work, and that this overestimation may vary

by certain factors (such as age and mental health status) at the time of

questioning [21], thereby ‘calling into question’ the use of the SWI to quantify

trends in WRIH both nationally and internationally. Other limitations of the

SWI data concern the latency period of certain diseases, particularly in

measuring short term targets [12], the potential effect of social and cultural

factors on the reporting of certain symptoms, and the impact of disease

specific campaigns [20].

3.2 THE HEALTH AND OCCUPATION REPORTING NETWORK IN

GENERAL PRACTICE (THOR-GP)

Introduction and data collected

THOR-GP [7] is a surveillance scheme based at The Centre for Occupational

& Environmental Health at The University of Manchester, collecting data on

WRIH as reported by 250 to 300 GPs trained to Faculty of Occupational

Medicine diploma level in occupational medicine.

THOR-GP starting collecting data in June 2005, using methods developed

from other, more long standing (specialist) schemes within THOR. These

surveillance schemes started with SWORD [22] (Surveillance of Work-Related

& Occupational Respiratory Disease) in 1989. Of all the schemes within

24

THOR, THOR-GP methods most closely follow those of OPRA [23]

(Occupational Physicians Reporting Activity) which collects data from

occupational physicians. Like OPRA, all categories of WRIH are reported,

unlike schemes (such as SWORD) where clinical specialists report just one

major disease category of WRIH.

All participating GPs report electronically via an on-line webform which can be

accessed via the THOR-GP home page [24]. The first time a GP sees a

patient in their general practice clinic with ill-health that the GP believes to

have been caused or made worse by the patient’s work, they should report

the case to THOR-GP. Details on each case submitted via the webform are

as follows:

• Diagnoses / symptoms

• Demographic data – age, gender, postcode district

• Employment details – occupation and industry

• Task / event / causative agent

• Sickness certification / fit note data – if the patient was issued with

sickness certification, for how many days, any days off prior to

consultation and expected return to work. GPs are also able to

submit information on any adjustments recommended to help a

patient remain in the workplace in line with the fit note (which

replaced the ‘sick note’ in April 2010). GPs are also able to submit

information on further sickness absence issued to a previously

reported case should the patient need more time away from work (at

the end of the initial period of absence).

• Referrals – if the patient had any onward referrals to other health

practitioners and if referred to a hospital specialist, the type of

specialist to whom they were referred.

• Other data – symptom onset, caused or aggravated by a single or

repeated exposure.

25

THOR-GP reporters who joined the scheme in 2005 started reporting on a

continual month by month basis (core reporting), and by the end of 2006 there

were 302 core reporters. From 2007 new participants were randomly

assigned (in equal numbers) to report as core (i.e. reporting every month of

the year) or on a sample basis (i.e. one randomly allocated month per year).

Sample reporting has shown to reduce the risk of reporter fatigue [25] and

involves a reporter being assigned just one randomly assigned month to

report cases. Cases from sample reporters are multiplied by 12 and added to

the reports from core participants to give an annual estimate of cases. From

January 2010, all participants in THOR-GP were randomly assigned into core

or sample reporting groups at a ratio of 1:4.

If a reporter has not seen any relevant cases in any reporting month they are

asked to submit a ‘I have nothing to report’ return.

Case information is coded using ICD 10 (diagnosis / symptoms) Standard

Occupational Classification (SOC) [26], Standard Industrial Classification

(SIC) [27] and Government region and county coding. The agent, task and

event data is coded using systems developed within THOR and HSE.

Calculation of incidence rates

The THOR-GP numerator is based on reports from 250 to 300 GPs, this

constitutes approximately 1% of GPs in Great Britain (GB). In order to

extrapolate the THOR-GP cases up to GB figures, the data are adjusted by

response rate (66%) and part-time practice (THOR-GPs work about 71% of

full-time equivalence in general practice) and then the number of cases

reported per participating GP is multiplied by the number of GB GPs. This

estimate of GB cases is then divided by the number of persons employed

according to the LFS and multiplied by 100,000 to give incidence rates per

100,000 persons employed.

Working days lost

The figure used to calculate sickness absence rates is the sum of the number

of days prior to consultation, days certified at consultation, and days issued on

26

subsequent consultations. This figure is then extrapolated to GB figures using

the same method used to calculate the incidence rates, this is then divided by

the number of persons employed to produce the number of sickness absence

days per worker.

Although this method of capturing sickness absence data would allow capture

of the total period of sickness absence, it seems clear that most GPs do not

provide this longitudinal data, especially in the case of the long-term sick.

Therefore, the sickness absence data are audited on an annual basis. These

audits collect data retrospectively a year after the initial cases are reported; a

sample of GPs are asked how long the patient had away from work in total

and if they had returned to work. The retrospective data collection shows the

total number of days associated with the cases selected for audit is

consistently about 60% more than those data collected prospectively,

therefore as well as the unadjusted figures, sickness absence rates adjusted

(increased by 60% [n/40 x 100]) are also provided.

Critical review of THOR-GP as a data source

Data collected within THOR-GP (as in the whole of THOR) originate from

surveillance methods that are practical, uniform, and rapid. They allow early

identification of new workplace hazards (perhaps from emerging industrial

sectors) for which interventions can be identified before large numbers of

employees have sustained problems from work exposures. THOR-GP

participants are recruited from sources such as Occupational Medicine

Diploma courses [28] and national on-line databases [29], producing a wide

distribution of reporters throughout the UK who provide data from a range of

geographical areas (Appendix B).

Unlike SWI, THOR-GP reporting relies on medically qualified practitioners

with training in occupational medicine. These reporters have skills and

knowledge to provide objective evidence-based decisions, enabling them to

consider work and its attribution in relation to ill-health. A GP may recognise

that dermatitis was likely to have been caused by work when a patient has not

27

realised this. Conversely, a GP might not necessarily share a patient’s

conclusion that work has caused mental ill-health.

However, there are limitations to these surveillance methods, including case

definition. THOR-GP reports are based on physicians’ opinions about the

work-relatedness of the condition [30], and although reporting guidelines are

available on the scheme website, opinions are likely to differ.

As THOR-GP reporters are trained to occupational medicine diploma level

they may differ from other GPs, not only in the cases that they see, but also in

their reporting preferences and patterns. THOR-GPs may also differ from

other GPs in their working timetables (and therefore tasks) within their general

practices. Ongoing work to understand the THOR-GP denominator will enable

a study of the demographics of patients registered with THOR-GP practices,

which is essential to assess possible biases and make valid comparisons in

incidence rates. Preliminary analysis of work in progress on the geographical

distribution and industrial employment of the THOR-GP population has shown

it to be proportionally very similar to that of the whole GB [7].

The method of estimating the annual number of cases by multiplying the

sample cases by 12 and adding these reports to those from the core reporters

is possibly a source of error. The use henceforth of estimated cases or

estimated incidence refers to this method. This method for sampling

physicians’ practice was established to try to minimise reporter fatigue and

encourage participation; its merits and disadvantages have been formally

investigated in a randomised control trial examining ‘core’ and ‘sample’

reporting behaviour [25]. However, any variations in monthly case reporting

are less likely to be a methodological problem for categories of commonly

presenting WRIH, in comparison to rarer occupational diseases.

The retrospective sickness absence data audits have shown that the

prospective data collected via the web-form does not give a complete picture

of the period of sickness absence, however it is extremely valuable when

comparing the number of sickness absence episodes and length of time away

28

from work between different sectors of the employed population.

Unsurprisingly, the data collected retrospectively give a more accurate

longitudinal picture of how long patients are having away from work due to

WRIH.

29

4 METHODS

4.1 INCIDENCE RATES

Data period

The incidence rates for THOR-GP used in the comparison were three year

averages based on 2006 to 2008 data. These were compared with the three

year average of the 2006/07, 2007/08 and 2008/09 SWI surveys, which were

deemed the most comparable in terms of time period covered. For example,

the SWI survey published as ‘2006/07’ relates to respondents who were

asked in interview as part of the 2007 Q1 LFS (i.e. Jan,

Feb, Mar 2007) about work-related illness that occurred in the last 12

months. Hence, the results reflect an average of 12 month periods

ending in the period Jan 2007 - Mar 2007.

Categories of illness

The categories of illness reportable to SWI are described in Appendix A. Any

type of WRIH can be reported to THOR-GP and reported cases are then

assigned to one (or more) of the categories shown in Table 1. The categories

compared between the two data sources are shown in Table 2.

Age and gender

For each category of illness, SWI and THOR-GP incidence rates were

compared by gender and by age group. The age groups were: 16 to 24 years,

25 to 34 years, 35 to 44 years, 45 to 54 years, 55 to 64 years, and 65+ years

(this last category for THOR-GP only).

Occupation and industry

For each category of illness, SWI and THOR-GP incidence rates were

compared by major industry group – using SIC divisions A – Q [26] (Appendix

C) and by occupation – using SOC to 2-digits [27] (Appendix C)

The approach adopted in this study was to compare THOR-GP and SWI

incidence rates for all of the categories of ill-health, age, gender, occupation

and industry as described. Some of these estimates of GB incidence (and

30

hence corresponding incidence rates) are based on small sample sizes and

as such are too small to provide reliable estimates; therefore any figures

based on fewer than 20 cases/respondents are not shown in the tables. In

addition, to avoid a misleading impression of precision (particularly in the

absence of confidence intervals) figures are rounded. Incidence estimates are

rounded to thousands and rates to 3 significant figures.

Table 1 Categories of illness reported to THOR-GP (as defined by HSE contract specification)

CATEGORY DESCRIPTION

Respiratory Cases are assigned to one (or more) of 10 different categories:

asthma, inhalation accidents, allergic alveolitis, bronchitis /

emphysema, infectious disease, non-malignant pleural disease,

mesothelioma, lung cancer, pneumoconiosis, ‘other’ respiratory

disease.

Skin Cases are assigned to one (or more) of 8 different categories:

contact dermatitis, contact urticaria, folliculitus / acne, infective,

mechanical, nail, neoplasia, ‘other’ dermatoses.

Musculoskeletal Cases are assigned to one (or more) of 8 different categories:

‘hand / wrist / arm’, ‘elbow’, ‘shoulder’, ‘neck / thoracic spine’,

‘spine / back’, ‘hip / knee’, ‘ankle / foot’, and ‘other’

musculoskeletal.

Mental ill-health Cases are assigned to one (or more) of 6 different categories:

‘anxiety and depression’, ‘post traumatic stress disorder’, ‘other

work stress’, ‘alcohol / drug abuse’, ‘psychotic episode’, and

‘other psychiatric disorders’.

Audiological Cases are assigned to one (or more) of 5 different categories:

‘noise induced hearing loss’, ‘tinnitus’, ‘balance problems’,

‘tympanic disorders’ and ‘other’ audiological.

Infectious Cases are assigned to one (or more) of 10 different categories:

‘brucellosis’, ‘hepatitis’, ‘legionellosis’, ‘leptospirosis’, ‘ornithosis’,

‘pulmonary tuberculosis’, ‘Q fever’, ‘diarrhoeal disease’,

‘scabies’ and ‘other’ infectious.

Other Cases not assigned to any of the categories outlined above are

assigned to the ‘other’ category

31

Table 2 Categories of illness compared between SWI and THOR-GP

SWI THOR-GP

Total cases Total cases

Breathing or lung problems Total respiratory

Skin problems Total skin

Musculoskeletal disorders (MSDs) Total musculoskeletal

MSDs mainly affecting the upper

limbs or neck

Cases assigned to the musculoskeletal

categories: ‘hand/wrist/arm’, ‘elbow’,

‘shoulder’ and ‘neck/thoracic spine’

categories

MSDs mainly affecting the back Cases assigned to the musculoskeletal

category: ‘spine / back’

Stress, depression or anxiety Cases assigned to the mental ill-health

categories: ‘anxiety and depression’,

‘other work stress’ and ‘other stress

related symptoms’

Hearing loss Total audiological

Other (than those above or

infectious disease) e.g. headache /

eye strain, heart disease / attack /

circulatory problems

Cases assigned to the THOR-GP

category ‘other’. e.g. lacerations,

headaches, eye injuries etc.

NOTE: A comparison between cases of infectious disease reported to SWI and THOR-GP was not made because only 18 actual infection cases were reported to THOR-GP during the time period and the majority of these were included in other categories e.g. scabies “infestations” classified as skin disease.

32

4.2 TRENDS IN INCIDENCE

Differences in temporal variation over the period of overlap of the datasets

were examined. THOR-GP and SWI incidence rates for the years 2006, 2007

and 2008 (separately) were compared for total WRIH, and for total MSDs, and

stress, depression and anxiety. Temporal variation in the incidence of

respiratory and skin diagnoses was not investigated separately due to the

comparatively small numbers of cases.

Incidence rates were also compared with estimates of change in incidence of

cases reported to THOR-GP obtained from multi-level models (MLM). The

MLM methodology to determine trends in incidence of THOR data has been

described in full previously [31, 32, 33, 34, 35]. Briefly, to estimate changes in

incidence over time from reporter data, it is important not only to allow for

variation in the number of reporters over time, but also to allow for possible

changes over time in the reporter ‘populations’: e.g. some reporters may

cover much larger populations than others, and changes in this characteristic

over time could in itself produce a false time trend. To allow for this, MLM

(also called random effects models) were used to analyse the data. The MLM

also took into account other factors that may cause variation in incidence

either between reporters, or within reporters over time. These included

reporter type (core or sample), harvesting (it is conceivable that, in the first

month/s of reporting, a new entrant to a surveillance scheme might include

cases first seen before being recruited as a reporter) and seasonal effects

(either because of underlying seasonal variation in illness or because of

seasonality in reporting behaviour). A further potentially important factor to

account for is ‘fatigue’ which refers to the fact that, as membership time

increases, a reporter might become less interested in participation, but still

retain membership. The question of how to investigate, and deal with,

possible fatigue is a major methodological challenge for the investigation of

trends in incidence. A number of different approaches have been adopted

previously but none have been entirely satisfactory [36, 37]. In view of these

difficulties, the trend estimates presented here have not been adjusted for the

33

potential effect of ‘fatigue’, while work continues to further characterise and

adjust for this phenomenon.

Thus, the STATA software command ‘xtnbreg’ was used to fit longitudinal,

negative binomial (i.e. over-dispersed) Poisson models with random effects.

Two different approaches were taken; a non-parametric approach where the

model contained separate indicator variables for different years and relative

risks (2006 set as 1) estimated, and a parametric approach where the annual

percentage change was estimated (assuming that there is a constant time

trend throughout the period).

4.3 WORKING DAYS LOST

SWI estimates of working days lost (WDL) are compared with the prospective

sickness absence data reported by GPs. Data reported by GPs is more often

than not the number of days certified (plus self-certified time absent prior to

medical certification), as opposed to WDL so they are not strictly comparable,

however differences in sickness absence patterns can be observed. For the

sake of simplicity, sickness absence data for both schemes will be referred to

as WDL.

Results for WDL are shown as the estimated number of WDL (3 year

average) and this number as a rate per persons employed. THOR-GP figures

are shown adjusted and unadjusted by 60%. The data illustrated in the tables

are not shown graphically if there are too many SWI data cells missing

through small sample sizes. Also due to small numbers, sickness

absence/work days lost data are only shown for all WRIH, all MSDs and

stress, anxiety and depression. Age groups are collapsed and SOC codes

grouped to major group level for the separate diagnostic categories.

34

5 RESULTS

5.1 DESCRIPTIVE RESULTS

The most frequently reported categories of illness to both the SWI and THOR

GP are mental ill-health and musculoskeletal diagnoses, with GPs most

frequently reporting musculoskeletal diagnoses and SWI reports most

frequently being mental ill-health (Figure 1). A similar proportion of respiratory

diagnoses were reported to the two schemes but a higher proportion of the

cases reported to THOR-GP were skin diagnoses, compared to the SWI. The

proportion of cases reported as ‘other’ was much higher for the SWI

compared to THOR-GP.

Cases reported to both THOR-GP and SWI followed a very similar pattern

and were most frequently reported in the 35-44 years of age group (Figure 2).

The estimated number of cases by gender was similar for both schemes

however THOR-GP had slightly more cases reported in males whereas SWI

had more female reports (Figure 3).

A breakdown of the cases reported to THOR-GP and SWI by industrial

division and occupational group is provided in Figures 4 and 5, respectively.

Cases from health & social work, manufacturing, and wholesale and retail

trade were the 3 most frequently reported industrial sectors in both schemes,

however, THOR-GP cases were most frequently reported in manufacturing,

and SWI cases most frequently reported in health and social work. The most

frequently reported occupational groups for THOR-GP were SOC group 92:

elementary administrative and service occupations and SOC group 41:

administrative occupations. The most frequently reported groups for SWI were

SOC group 11: corporate managers and SOC group 41.

35

Figure 1 THOR-GP and SWI estimates of GB proportionate distribution by diagnostic category, 2006-2008

Musculoskeletal 53%

THOR-GP Estimated cases N=435,734

SWI Estimated cases N=575,214

Skin 2%

Musculoskeletal 35%

Stress/anxiety/ depression

31%

Respiratory 2%

Skin 10%

Hearing <1% Other

4%

Hearing 1%

Respiratory 3%

Other 19%


40%

Figure 2 THOR-GP and SWI estimates of number of GB cases, all work-related ill-health by age group, 2006-2008

180000

Nu

mb

er o

f G

B c

ases

160000

140000

120000

100000

80000

60000

40000

20000

0

THOR-GP

SWI

16 to 25 25 to 34 35 to 44 45 to 54 55 to 64 65 plus

Figure 3 THOR-GP and SWI estimates of number of GB cases, all work-related ill-health by gender, 2006-2008

350000

Nu

mb

er o

f G

B c

ases

300000

250000

200000

150000

100000

50000

0

Male

Female

THOR-GP SWI

Figure 4 THOR-GP and SWI estimates of number of GB cases, all work-related ill-health by industrial division, 2006-2008

100,000 50,000 0 50,000 100,000

SWI THOR-GP C

B

A

D

E

F

G

H

I

J

K

L

M

N

O

P

Q

Number of GB cases

SIC 92 Division A Agriculture, hunting & forestry B Fishing C Mining & quarrying D Manufacturing E Electricity, gas & water supply F Construction G Wholesale & retail trade; repair of motor vehicles & personal & household goods H Hotels & restaurants I Transport, storage & communication J Financial intermediation K Real estate, renting & business activities L Public administration & defence M Education N Health & social work O Other community, social & personal service activities P Private households with employed persons Q Extra-territorial organisations & bodies

38

-

Figure 5 THOR-GP and SWI estimates of number of GB cases, all work-related ill-health by sub-major SOC group, 2006-2008

92

91

82

81

72

71

62

61

54

53

52

51

42

41

35

34

33

32

31

24

23

22

21

12

11

SWI THOR-GP

2 Digit SOC code 11 Corporate managers 12 Managers & proprietors in agriculture & services 21 Science & technology professionals 22 Health professionals 23 Teaching & research professionals 24 Business & public service professionals 31 Science & technology associate professionals 32 Health & social welfare associate professionals 33 Protective service occupations 34 Culture, media & sports occupations 35 Business & public service associate professionals 41 Administrative occupations 42 Secretarial & related occupations 51 Skilled agricultural trades 52 Skilled metal & electrical trades 53 Skilled construction & building trades 54 Textiles, printing & other skilled trades 61 Caring personal service occupations 62 Leisure & other personal service occupations 71 Sales occupations 72 Customer service occupations 81 Process, plant & machine operatives 82 Transport & mobile machine drivers & operatives 91 Elementary trades, plant & storage related occupations 92 Elementary administration & service occupations 88 Missing

50,000 30,000 10,000 0 10,000 30,000 50,000 70,000

Number of GB cases 39

5.2 INCIDENCE ESTIMATIONS

5.2.1 TOTAL WRIH

Three year average GB incidence rates for SWI and THOR-GP by major

diagnostic category are shown in Table 3 and Figure 6. For all WRIH the SWI

incidence rate was slightly higher at 1900 per 100,000 compared to 1550 per

100,000 for THOR-GP. Comparing categories of illness, SWI incidence rates

were typically higher than THOR-GP rates with the exception of skin disease

and MSDs. However, it should be noted that annual SWI rates for skin,

respiratory and hearing are typically not published on the HSE website as

sample numbers are considered too small to provide reliable estimates.

Similarly, respiratory and hearing cases reported to THOR-GP are also

relatively small and any estimated rates should therefore be treated with

caution, however, THOR-GP captures larger numbers of skin cases (10% of

all THOR-GP cases) therefore estimates are considered more reliable.

Three year average GB incidence rates for SWI and THOR-GP by age group

are shown in Table 4 and Figure 7. For both THOR-GP and SWI, the highest

incidence was in the 45 to 54 years of age group, followed by the 35 to 44

years of age group. With the exception of the 16 to 25 years of age group,

SWI incidence rates were higher than THOR-GP incidence rates. There was

little difference in the incidence by gender for THOR-GP, whilst SWI incidence

rates were slightly higher for females compared to males (Figure 8).

The highest incidence of WRIH reported to THOR-GP was in SOC group 91:

elementary trades, plant & storage related occupations whilst the highest

incidence of WRIH reported to the SWI was in SOC group 32: Health & social

welfare associate professionals (Table 6 and Figure 9). Overall, SWI reported

a higher incidence (compared to THOR-GP) in SOC groups 1 (managers and

senior officials), 2 (professional occupations), 3 (associate professional and

technical occupations), 6 (personal service occupations) and 7 (sales and

customer service occupations). THOR-GP reported a higher incidence

(compared to SWI) in SOC groups 4 (administrative and secretarial

40

occupations), 5 (skilled trades occupations), 8 (process, plant and machine

operatives) and 9 (elementary occupations).

SWI and THOR-GP incidence rates by industrial division are shown in Table 7

and Figure 10. For both groups, high incidence rates were reported in ‘fishing’

and in ‘mining and quarrying’ (although it should be noted that cases reported

to both SWI and THOR-GP in these industrial divisions are relatively small

and any estimated rates should therefore be treated with caution). Both

THOR-GP and SWI reported a high incidence of WRIH in the public

administration and defence sector, and the health and social work sector.

41

-

Table 3 All cause work-related ill-health by major diagnostic category; 3 year average estimates and incidence rates 2006-2008

Diagnostic category THOR GP SWI Estimate Rate per 100,000 Estimate Rate per 100,000

Respiratory 10100 39 16000 54*

Skin 43000 150 10000 33* Musculoskeletal 233000 830 200000 670

o Upper limb 127000 450 91000 300 o Spine/back 72000 250 81000 270

Stress / anxiety / depression 136000 450 236000 790 Hearing * * * * Infectious * * 32020 100 Other 17000 60 76000 250 Total 436000 1550 575000 1900

* Sample numbers too small to provide reliable estimates

Figure 6 GB incidence rate per 100,000 persons employed by diagnostic category, 2006-2008

Musculoskeletal

Stress/anxiety/depression

Skin

Respiratory

Hearing

Other

SWI THOR-GP

0 200 400 600 800 1000

Incidence rate per 100,000 persons employed

42

-

Table 4 All cause work-related ill-health by age group; 3 year average estimates and incidence rates 2006-2008

Age group THOR GP SWI Estimate Rate per

100,000 Estimate Rate per

100,000 16 to 24 years 55000 1460 60000 1380 25 to 34 years 88000 1490 111000 1750 35 to 44 years 122000 1700 158000 2100 45 to 54 years 108000 1760 154000 2300 55 to 64 years 57000 1490 74000 2000 65 years plus 4000 700 / /

Figure 7

2500

2000

1500

1000

500

0

Inci

den

ce r

ate

per

100

,00

per

son

sem

plo

yed

GB incidence rate per 100,000 persons employed by age group; 3 year average incidence rates 2006-2008

THOR-GP

SWI

16 to 25 25 to 34 35 to 44 45 to 54 55 to 64 65 plus

Age group

43

-

Table 5 All cause work-related ill-health by gender; 3 year average estimates and incidence rates 2006-2008

Gender THOR GP SWI Estimate Rate per Estimate Rate per

100,000 100,000 Male 233000 1560 283000 1770 Female 203000 1570 292000 2100

Figure 8

Inci

den

ce r

ate

per

100

,000

per

son

sem

plo

yed

GB incidence rate per 100,000 persons employed by gender; 3 year average incidence rates 2006-2008

2500

2000

1500

1000

500

0 THOR-GP SWI

Male

Female

44

- -

Table 6 All cause work-related ill-health by occupational group (2digit SOC 2000); 3 year average estimates and incidence rates 2006-2008

2 Digit SOC code THOR GP SWI Estimate Rate per


100,000 11 Corporate managers 19000 550 62000 1730 12 Managers & proprietors in agriculture & services 8000 840 14000 1590 21 Science & technology professionals 3000 300 17000 1540 22 Health professionals * * 6000 1670 23 Teaching & research professionals 17000 1290 32000 2220 24 Business & public service professionals 7000 700 17000 1670 31 Science & technology associate professionals 5000 1100 8000 1490 32 Health & social welfare associate professionals 16000 1520 37000 3350 33 Protective service occupations 8000 2530 8000 2470 34 Culture, media & sports occupations 5000 780 14000 2080 35 Business & public service associate professionals 8000 540 28000 1700 41 Administrative occupations 37000 1490 44000 1690 42 Secretarial & related occupations 14000 1770 10000 1210 51 Skilled agricultural trades 7000 2360 7000 2380 52 Skilled metal & electrical trades 26000 2290 22000 1830 53 Skilled construction & building trades 36000 3260 20000 1680 54 Textiles, printing & other skilled trades 14000 2640 8000 1480 61 Caring personal service occupations 27000 1540 44000 2310 62 Leisure & other personal service occupations 14000 2500 14000 2450 71 Sales occupations 21000 1200 23000 1220 72 Customer service occupations 6000 1400 9000 2060 81 Process, plant & machine operatives 36000 3800 18000 1770 82 Transport & mobile machine drivers & operatives 18000 1670 18000 1550 91 Elementary trades, plant & storage related occupations 37000 3870 16000 1500 92 Elementary administration & service occupations 43000 1900 26000 1030 88 Missing * * * *


45

Figure 9 GB incidence rate per 100,000 persons employed by sub-major SOC group; 3 year average incidence rates 2006-2008

92

91

82

81

72

71

62

61

54

53

52

51

42

41

35

34

33

32

31

24

23

22

21

12

11

SWI

THOR-GP

4000 4000 2000 0 2000


See Table 6 for SOC Groups

46

-

Table 7 All cause work-related ill-health by industrial division (SIC 92); 3 year average estimates and incidence rates 2006-2008

SIC 92 Division THOR GP SWI Estimate Rate per


100,000 A Agriculture, hunting & forestry 9000 2560 7000 1680 B Fishing * * * * C Mining & quarrying 3000 3350 * * D Manufacturing 68000 1940 55000 1550 E Electricity, gas & water supply 6000 2980 4000 1900 F Construction 46000 2020 37000 1530 G Wholesale & retail trade; repair of motor vehicles & personal & household goods 53000 1280 62000 1400 H Hotels & restaurants 20000 1660 13000 920 I Transport, storage & communication 34000 1750 37000 1850 J Financial intermediation 14000 1100 20000 1550 K Real estate, renting & business activities 26000 780 54000 1450 L Public administration & defence 38000 1910 51000 2480 M Education 29000 1120 49000 1760 N Health & social work 63000 1820 99000 2700 O Other community, social & personal service activities 25000 1570 30000 1730 P Private households with employed persons * * * * Q Extra-territorial organisations & bodies * * * *


47

Figure 10 GB incidence rate per 100,000 persons employed by industrial division; 3 year average incidence rates 20062008

SWI THOR-GP

Q

P

O

N

M

L

K

J

I

H

G

F

E

D

C

B

A

8000 6000 4000 2000 0 2000 4000


See Table 7 for SIC Groups

48

5.2.2 MUSCULOSKELETAL DISORDERS (MSDs)

The largest broad category of musculoskeletal disorders (MSDs) reported to

both THOR-GP and SWI was upper limb disorders (which encompasses the

diagnoses of hand/wrist/arm, elbow, shoulder and neck/thoracic spine),

followed by back disorders (spine/back) (Figure 11). Annual average THOR

GP and SWI incidence rates by anatomical site are shown in Figure 12. A

higher incidence of upper limb disorders was reported to THOR-GP whilst the

incidence rate for back disorders was similar between the two data sources.

According to data from both THOR-GP and the SWI, the highest incidence of

MSDs was in the 45 to 54 years of age groups (Table 8 and Figure 13).

Overall, THOR-GP incidence rates were higher for all age groups, but this

difference was more apparent in the younger age groups. THOR-GP

incidence rates of MSDs were higher for males compared to females whilst

little difference by gender was observed for SWI derived rates (Table 9 and

Figure 14).

THOR-GP and SWI incidence rates by occupation are shown in Table 10 and

Figure 15. Similar to the patterns observed for total WRIH, SWI incidence

rates for MSDs tended to be higher than THOR-GP rates for the ‘higher’

occupational groups (for example, managers and senior officials and

professional/technical occupations) whilst the converse was true for the ‘lower’

occupational groups. The highest THOR-GP incidence rate was observed for

SOC sub-group 91: ‘elementary trades, plant and storage related

occupations’, whilst the highest SWI incidence rate was observed for SOC

sub-group 51: skilled agricultural trades.

High incidence rates were observed for the industrial division: ‘fishing’ (Table

11 and Figure 16), but as already discussed, these rates are based on small

sample sizes and should be treated with caution. Similarly, THOR-GP

reported a high incidence rate for the ‘electricity, gas and water supply sector’,

but again this rate is based on relatively small numbers. Restricting to those

incidence rates based on larger sample sizes (i.e. excluding those HSE

49

considered to be based on too small a sample size to provide reliable

estimates: denoted by a ‘*’ in the results), THOR-GP incidence rates tended to

be higher than SWI rates. The exceptions to this were ‘health and social care

sector’ and ‘real estate, renting and business activities’, for which SWI rates

were higher.

UPPER LIMB DISORDERS

Incidence rates of upper limb disorders by age are shown in Table 12 and

Figure 17 and by gender in Table 13 and Figure 18. The pattern observed is

similar to that observed for total MSDs. It should be noted that SWI incidence

rates for upper limb disorders by age and by gender are not published on the

HSE website (only prevalence data are given).

Incidence rates for upper limb disorders by occupation are shown in Table 14.

At this level of disaggregation it is difficult to make meaningful comparisons

between the two datasets due to small sample sizes. Restricting the

comparison to those SOC sub-groups with larger sample sizes showed similar

THOR-GP and SWI incidence rates for SOC sub-group 41: administrative

occupations, whilst THOR-GP reported a higher incidence rate in all other

SOC sub-groups (SOC sub-groups:52, 53, 71, 81, 82 and 93). Similarly, a

comparison of incidence rates by industry (Table 15) - restricted to those

divisions with larger sample sizes - suggests similar THOR-GP and SWI

incidence rates for the health and social care sector but higher THOR-GP

rates for manufacturing, construction, and wholesale and retail trade.

BACK DISORDERS

THOR-GP incidence rates by age (Table 16) show the highest incidence to be

for the 16 to 24 years of age group, with rates declining by age thereafter. In

contrast, the highest SWI incidence rate is for the 35 to 44 years of age group.

However, these rates are based on relatively small sample sizes. The pattern

by gender (Table 17 and Figure 20) is similar to that observed for total MSDs

and upper limb disorders.

50

THOR-GP and SWI incidence rates by occupation (Table 18) and industry

(Table 19) are included for completeness but need to be interpreted with

caution due to the small sample sizes (particularly for the occupational

groups).

51

Figure 11 Number of GB musculoskeletal cases by anatomical site, THOR-GP and SWI, 2006-2008

Other Other 14% 15%

Upper limb 54%

Back 31%

Upper limb 46%

Back 40%

Figure 12 Musculoskeletal GB incidence rate per 100,000 persons employed disorders by anatomical site THOR-GP and SWI 2006 to 2008

THOR-GP

SWI

0 100 200 300 400 500

Back Upper limb

THOR-GP Estimated cases N=233,079

SWI Estimated cases N=199,889

52

-

Table 8 Work-related musculoskeletal disease by age group; 3 year average estimates and incidence rates 2006-2008



100,000 16 to 24 years 28000 740 16000 370 25 to 34 years 47000 790 35000 550 35 to 44 years 64000 900 58000 780 45 to 54 years 59000 960 55000 840 55 to 64 years 33000 840 28000 750 65 years plus * * * *


Figure 13 GB incidence rates for work-related musculoskeletal disorders per 100,000 persons employed by age group; 3 year average incidence rates 2006-2008

Inci

den

ce r

ate

per

100

,000

per

son

sem

plo

yed

1200

1000

800

600

400

200

0

THOR-GP

SWI

16 to 25 25 to 34 35 to 44 45 to 54 55 to 64 65 plus

Age group

53

-

Table 9 Work-related musculoskeletal disorders by gender; 3 year average estimates and incidence rates 2006-2008


100,000 100,000 Male 141000 940 106000 660 Female 92000 720 94000 670

Figure 14 GB incidence rate for work-related musculoskeletal disorders per 100,000 persons employed by gender; 3 year average incidence rates 2006-2008

Inci

den

ce r

ate

per

100

,000

p

erso

ns

emp

loye

d 1000

900 800 700 600 500 400 300 200 100

0 THOR-GP SWI

Male

Female

54

- -

Table 10 Work-related musculoskeletal disorders by occupational group (2-digit SOC 2000); 3 year average estimates and incidence rates 2006-2008



100,000 11 Corporate managers 3000 95 14000 400 12 Managers & proprietors in agriculture & services * * 5000 600 21 Science & technology professionals * * 5000 500 22 Health professionals * * * * 23 Teaching & research professionals * * 6000 430 24 Business & public service professionals * * 4000 410 31 Science & technology associate professionals 20000 400 3000 670* 32 Health & social welfare associate professionals 6000 600 11000 970 33 Protective service occupations 3000 940 * * 34 Culture, media & sports occupations 4000 650 7000 980 35 Business & public service associate professionals * * 7000 450 41 Administrative occupations 13000 500 13000 510 42 Secretarial & related occupations 9000 1070 * * 51 Skilled agricultural trades 4000 1370 5000 1610 52 Skilled metal & electrical trades 18000 1550 9000 750 53 Skilled construction & building trades 26000 2330 11000 930 54 Textiles, printing & other skilled trades 9000 1620 * * 61 Caring personal service occupations 14000 780 15000 800 62 Leisure & other personal service occupations 7000 1210 4000 710 71 Sales occupations 14000 770 8000 420 72 Customer service occupations * * 2000 470* 81 Process, plant & machine operatives 23000 2410 10000 1000 82 Transport & mobile machine drivers & operatives 14000 1330 10000 880 91 Elementary trades, plant & storage related Occupations 28000 2900 7000 700 92 Elementary administration & service occupations 28000 1240 13000 500 88 Missing * * * *


55

Figure 15 GB incidence rates for work-related musculoskeletal disorders per 100,000 persons employed by sub-major SOC group; 3 year average incidence rates 2006-2008

92

91

82

81

72

71

62

61

54

53

52

51

42

41

35

34

33

32

31

24

23

22

21

12

11

SWI THOR-GP

2000 1000 0 1000


See Table 10 for SOC groups

56

2000 3000

-

Table 11 Work-related musculoskeletal disease by industrial division (SIC 92); 3 year average estimates and incidence rates 20062008



100,000 A Agriculture, hunting & forestry 5000 1420 * * B Fishing * * * * C Mining & quarrying * * * * D Manufacturing 42000 1190 25000 700 E Electricity, gas & water supply 3000 1670 * * F Construction 33000 1470 19000 800 G Wholesale & retail trade; repair of motor vehicles & personal & household goods 34000 810 24000 540 H Hotels & restaurants 10000 830 6000 400 I Transport, storage & communication 22000 1170 14000 700 J Financial intermediation 3000 240 5000 380 K Real estate, renting & business activities 15000 450 16000 440 L Public administration & defence 16000 780 11000 540 M Education 7000 270 11000 400 N Health & social work 25000 740 30000 830 O Other community, social & personal service activities 15000 920 14000 800 P Private households with employed persons * * * * Q Extra-territorial organisations & bodies * * * *


57

Figure 16 GB incidence rates for work-related musculoskeletal disorders per 100,000 persons employed by industrial division; 3 year average incidence rates 2006-2008

Q

P

O

N

M

L

K

J

I

H

G

F

E

D

C

B

A

SWI

THOR-GP

8000 6000 4000 2000 0 2000 4000


See Table 11 for SIC groups

58

-

Table 12 Work-related upper limb disorders by age group; 3 year average estimates and incidence rates 2006-2008

Age group THOR GP SWI1

Estimate Rate per 100,000


16 to 24 years 12000 310 6000 130 25 to 34 years 23000 390 15000 230 35 to 44 years 37000 520 24000 320 45 to 54 years 36000 580 29000 440 55 to 64 years 18000 470 14000 360 65 years plus * *

1 Prevalence data only (not incidence) provided on HSE website

Figure 17 GB incidence rates for work-related upper limb disorders per 100,000 persons employed by age group; 3 year average incidence rates 2006-2008

600

500

400

300

200

100

0 16 to 25 25 to 34 35 to 44 45 to 54 55 to 64 65 plus

Age groups

THOR-GP

SWI

Inci

den

ce r

ate

per

100

,000

per

son

sem

plo

yed

59

-

Table 13 Work-related upper limb disorders by gender; 3 year average estimates and incidence rates 2006-2008

Gender THOR GP SWI1



Male 70000 470 44000 270 Female 57000 440 47000 340

1 Prevalence data only (not incidence) provided on HSE website

Figure 18 GB incidence rates for work-related upper limb disorders per 100,000 persons employed by gender; 3 year average incidence rates 2006-2008

Inci

den

ce r

ate

per

100

,000

per

son

s em

plo

yed

600

400

200

0 THOR-GP SWI

Male

Female

60

- -

Table 14 Work-related upper limb disorders by occupational group (2-digit SOC 2000); 3 year average estimates and incidence rates 2006-2008



100,000 11 Corporate managers * * 9000 250 12 Managers & proprietors in agriculture & services

* * * *

21 Science & technology professionals * * * * 22 Health professionals * * * * 23 Teaching & research professionals * * * * 24 Business & public service professionals

* * * *

31 Science & technology associate professionals

* * * *

32 Health & social welfare associate professionals 3000 250 5000 450

33 Protective service occupations * * * * 34 Culture, media & sports occupations

* * * *

35 Business & public service associate professionals

* * 4000 220

41 Administrative occupations 9000 360 8000 320 42 Secretarial & related occupations 7000 930 * * 51 Skilled agricultural trades * * 52 Skilled metal & electrical trades 9000 800 4000 350 53 Skilled construction & building trades 16000 1400 6000 470

54 Textiles, printing & other skilled trades 6000 1120 * *

61 Caring personal service occupations 6000 360 * *

62 Leisure & other personal service occupations 4000 800 * *

71 Sales occupations 6000 340 4000 210 72 Customer service occupations * * * * 81 Process, plant & machine operatives 13000 1410 5000 510

82 Transport & mobile machine drivers & operatives 5000 460 4000 370

91 Elementary trades, plant & storage related occupations

14000 1410 * *

92 Elementary administration & service occupations 13000 560 4000 150

88 Missing * * * *


61

-

Table 15 Work-related upper limb disorders by industrial division (SIC 92); 3 year average estimates and incidence rates 20062008

SIC 92 Division THOR GP SWI1



A Agriculture, hunting & forestry 3000 900

* *

B Fishing * * * * C Mining & quarrying * * * * D Manufacturing 25000 710 14000 390 E Electricity, gas & water supply 2000 940

* *

F Construction 17000 740 9000 370 G Wholesale & retail trade; repair of motor vehicles & personal & household goods

18000 420 10000 230

H Hotels & restaurants 6000 480 * * I Transport, storage & communication 9000 470 5000 240

J Financial intermediation 2000 190 * * K Real estate, renting & business activities 11000 310 8000 220

L Public administration & defence 9000 470 6000 310

M Education 4000 160 6000 220 N Health & social work 12000 350 12000 340 O Other community, social & personal service activities

8000 520 7000 390

P Private households with employed persons

* * * *

Q Extra-territorial organisations & bodies

* * * *

1 Prevalence data only (not incidence) provided on HSE website * Sample numbers too small to provide reliable estimates

62

-

Table 16 Work-related spine / back disorders by age group; 3 year average estimates and incidence rates 2006-2008

Age group THOR GP SWI1



16 to 24 years 12000 310 8000 190 25 to 34 years 16000 270 16000 260 35 to 44 years 19000 270 28000 370 45 to 54 years 15000 240 18000 280 55 to 64 years 9000 230 8000 200 65 years plus * * * *


Figure 19 GB incidence rates for work-related spine / back disorders per 100,000 persons employed by age group; 3 year average incidence rates 2006-2008

Inci

den

ce r

ate

per

100

,000

per

son

sem

plo

yed

400

350

300

250

200

150

100

50

0

THOR-GP

SWI

16 to 25 25 to 34 35 to 44 45 to 54 55 to 64 65 plus

Age group

63

-

Table 17 Work-related spine / back disorders by gender; 3 year average estimates and incidence rates 2006-2008

Gender THOR GP SWI1



Male 46000 310 45000 280 Female 26000 200 36000 260


Figure 20 GB incidence rates for work-related spine / back disorders per 100,000 persons employed by gender; 3 year average incidence rates 2006-2008

Inci

den

ce r

ate

per

100

,000

per

son

s em

plo

yed

400

300

200

100

0 THOR-GP SWI

Male

Female

64

- -

Table 18 Work-related spine / back disorders by occupational group (2-digit SOC 2000); 3 year average estimates and incidence rates 2006-2008



100,000 11 Corporate managers * * 4000 120 12 Managers & proprietors in agriculture & services

* * * *

21 Science & technology professionals * * * * 22 Health professionals * * * * 23 Teaching & research professionals * * * * 24 Business & public service professionals

* * * *

31 Science & technology associate professionals

* * * *

32 Health & social welfare associate professionals 4000 320 5000 470*

33 Protective service occupations * * * * 34 Culture, media & sports occupations

* * * *

35 Business & public service associate professionals

* * * *

41 Administrative occupations * * 4000 160 42 Secretarial & related occupations * * * * 51 Skilled agricultural trades * * * * 52 Skilled metal & electrical trades 5000 470 4000 340* 53 Skilled construction & building trades 5000 440 * *

54 Textiles, printing & other skilled trades

* * * *

61 Caring personal service occupations 6000 330 10000 550

62 Leisure & other personal service occupations

* * * *

71 Sales occupations 6000 320 * * 72 Customer service occupations * * 81 Process, plant & machine operatives 6000 640 * *

82 Transport & mobile machine drivers & operatives 7000 640 4000 390

91 Elementary trades, plant & storage related occupations

10000 1010 4000 370

92 Elementary administration & service occupations 11000 510 5000 190

88 Missing * * * *


65

-

Table 19 Work-related spine / back disorders by industrial division (SIC 92); 3 year average estimates and incidence rates 20062008

SIC 92 Division THOR GP SWI1



A Agriculture, hunting & forestry * * * * B Fishing * * * * C Mining & quarrying * * * * D Manufacturing 11000 310 9000 240 E Electricity, gas & water supply * * * * F Construction 9000 380 7000 290 G Wholesale & retail trade; repair of motor vehicles & personal & household goods 11000 270 9000 210

H Hotels & restaurants 23000 230 * * I Transport, storage & communication 9000 500 7000 340 J Financial intermediation * * * * K Real estate, renting & business activities 4000 130 6000 170 L Public administration & defence 3000 170 * * M Education 5000 160 N Health & social work 11000 330 16000 420 O Other community, social & personal service activities 5000 320 5000 300

P Private households with employed persons

* * * *

Q Extra-territorial organisations & bodies * * * * 1 Prevalence data only (not incidence) provided on HSE website * Sample numbers too small to provide reliable estimates

66

5.2.3 STRESS, DEPRESSION AND ANXIETY

The highest THOR-GP incidence rate for stress, depression and anxiety was

for the 35 to 44 years of age group whilst for the SWI it was for the 45 to 54

years of age group (Table 20 and Figure 21). SWI incidence rates for all age

categories were higher than THOR-GP rates. Both THOR-GP and SWI

derived rates were much higher for females compared to males (Table 21 and

Figure 22).

The highest THOR-GP incidence rate was observed for the occupational

group protective service occupations whilst for SWI it was the group health

and social welfare associate professionals (Table 22 and Figure 23). Both

THOR-GP and SWI reported high incidence rates for teaching and research

professionals. SWI derived incidence rates were typically higher than THOR

GP rates, the exceptions being: protective service occupations, administrative

occupations, secretarial and related occupations, skilled agricultural trades,

customer service occupations and process, plant and machine operatives, for

which the converse was true.

Incidence rates by industrial division are shown in Table 23 and Figure 24.

High THOR-GP incidence rates were observed for fishing, and for mining and

quarrying, but both of these are based on relatively small sample sizes. Both

THOR-GP and SWI incidence rates suggested a high incidence of stress,

anxiety and depression in health and social work, public administration and

defence and financial intermediation whilst a relatively low incidence was seen

in construction and manufacturing.

67

-

Table 20 Work-related stress, depression and anxiety by age group; 3 year average estimates and incidence rates 2006-2008



100,000 16 to 24 years 11000 280 20000 460 25 to 34 years 28000 470 47000 750 35 to 44 years 43000 600 66000 880 45 to 54 years 36000 590 68000 1030 55 to 64 years 17000 440 30000 800 65 years plus * * * *


Figure 21 GB incidence rates for work-related stress, depression and anxiety per 100,000 persons employed by age group; 3 year average incidence rates 2006-2008

0

200

400

600

800

1000

1200

Inci

den

ce r

ate

per

100

,000

per

son

sem

plo

yed

THOR-GP

SWI

16 to 25 25 to 34 35 to 44 45 to 54 55 to 64 65 plus

Age group

68

-

Table 21 Work-related stress, depression and anxiety by gender; 3 year average estimates and incidence rates 2006-2008


100,000 100,000 Male 53000 350 100000 630 Female 84000 650 136000 970

Figure 22 GB incidence rate for work-related stress, depression and anxiety per 100,000 persons employed by gender; 3 year average incidence rates 2006-2008

Inci

den

ce r

ate

per

100

,000

p

erso

ns

emp

loye

d 1000

800

600

400

200

0 THOR-GP SWI

Male

Female

69

- -

Table 22 Work-related stress, depression and anxiety by occupational group (2-digit SOC 2000); 3 year average estimates and incidence rates 2006-2008



100,000 11 Corporate managers 15000 430 33000 930 12 Managers & proprietors in agriculture & services 5000 520 6000 700 21 Science & technology professionals * * 8000 720 22 Health professionals * * 2000 570 23 Teaching & research professionals 15000 1100 18000 1260 24 Business & public service professionals 5000 510 9000 850 31 Science & technology associate professionals 3000 510

* *

32 Health & social welfare associate professionals 7000 650 16000 1480 33 Protective service occupations 5000 1390 * * 34 Culture, media & sports occupations

* * * *

35 Business & public service associate professionals 6000 420 13000 810 41 Administrative occupations 24000 940 23000 880 42 Secretarial & related occupations 5000 670 5000 560 51 Skilled agricultural trades 52 Skilled metal & electrical trades 3000 250 5000 440 53 Skilled construction & building trades

* * * *

54 Textiles, printing & other skilled trades

* * * *

61 Caring personal service occupations 11000 610 16000 840 62 Leisure & other personal service occupations

* * 5000 960

71 Sales occupations 6000 360 10000 520 72 Customer service occupations 4000 1100 4000 920 81 Process, plant & machine operatives 4000 460

* *

82 Transport & mobile machine drivers & operatives

* * * *

91 Elementary trades, plant & storage related occupations 3000 290 5000 470 92 Elementary administration & service occupations 6000 280 9000 350 88 Missing * * * *


70

Figure 23 GB incidence rates for work-related stress, depression and anxiety per 100,000 persons employed by sub-major SOC group; 3 year average incidence rates 2006-2008

SWI

THOR-GP

92

91

82

81

72

71

62

61

54

53

52

51

42

41

35

34

33

32

31

24

23

22

21

12

11

2000 1000 0 1000 2000


See Table 22 for SOC groups

71

-

Table 23 Work-related stress, depression and anxiety by industrial division (SIC 92); 3 year average estimates and incidence rates 2006-2008



100,000 A Agriculture, hunting & forestry * * * * B Fishing * * * * C Mining & quarrying * * * * D Manufacturing 11000 320 17000 470 E Electricity, gas & water supply * * * * F Construction 3000 150 7000 300 G Wholesale & retail trade; repair of motor vehicles & personal & household goods 14000 330 24000 550 H Hotels & restaurants 4000 330 6000 410 I Transport, storage & communication 7000 380 14000 700 J Financial intermediation 10000 840 10000 800 K Real estate, renting & business activities 9000 260 24000 660 L Public administration & defence 20000 1000 27000 1300 M Education 20000 770 25000 890 N Health & social work 30000 880 43000 1170 O Other community, social & personal service activities 3000 200 8000 500 P Private households with employed persons

* * * *

Q Extra-territorial organisations & bodies * * * *


72

Figure 24 GB incidence rates for work-related stress, depression and anxiety per 100,000 persons employed by industrial division; 3 year average incidence rates 2006-2008

Q

P

O

N

M

L

K

J

I

H

G

F

E

D

C

B

A

SWI THOR-GP

3000 2000 1000 0 1000 2000


See Table 23 for SIC codes

73

5.2.4 RESPIRATORY DISEASE

Relatively few respiratory diagnoses are reported to THOR-GP or the SWI.

For completeness, incidence rates by age and gender only have been

included although these need to be treated with caution. Results at the level

of disaggregation by occupation and industry are not routinely published on

the HSE website as sample sizes are deemed too small to obtain reliable

estimates and have not been included here for either SWI or THOR-GP.

74

-

Table 24 Work-related respiratory disease by age group; 3 year average estimates and incidence rates 2006-2008



100,000 16 to 24 years * * * * 25 to 34 years 3000 40 * * 35 to 44 years * * 5000 60* 45 to 54 years * * * * 55 to 64 years * * * * 65 years plus * * * *


Figure 25 Work-related respiratory disease by age group; incidence rates THOR-GP and SWI 2006-2008

Inci

den

ce r

ate

per

100

,000

per

son

sem

plo

yed

160

140

120

100

80

60

40

20

0

THOR-GP

SWI

16 to 25 25 to 34 35 to 44 45 to 54 55 to 64 65 plus

Age group

75

-

Table 25 Work-related respiratory disease by gender; 3 year average

estimates and incidence rates 2006-2008


100,000 100,000 Male 8000 60 9000 60* Female 2000 20 7000 50*

* Sample numbers too small to provide reliable estimates / not published on HSE website

Figure 26 GB incidence rate for work-related respiratory disease per 100,000 persons employed by gender; 3 year average incidence rates 2006-2008

Inci

den

ce r

ate

per

100

,000

per

son

sem

plo

yed

60

40

20

0 THOR-GP SWI

Male

Female

76

5.2.5 SKIN DISEASE

A comparison has been made between THOR-GP and SWI incidence rates

for skin disease. Results at the level of disaggregation by occupation and

industry are not routinely published on the HSE website as sample sizes are

deemed too small to obtain reliable estimates and have not been included

here for either SWI or THOR-GP.

Both THOR-GP and SWI incidence rates suggest a higher incidence in the 16

to 24 years of age group, generally decreasing with age thereafter (Table 26).

There was little variation in incidence rates by gender, with slightly higher

incidence rates in females compared to males (Table 27 and Figure 28).

77

-

-

Table 26 Work-related skin disease by age group; 3 year average estimates and incidence rates 2006-2008



100,000 16 to 24 years 14000 370 * * 25 to 34 years 8000 140 * * 35 to 44 years 10000 140 * * 45 to 54 years 7000 120 * * 55 to 64 years 4000 90 * * 65 years plus * * * *

* Sample numbers too small to provide reliable estimates / not published on HSE website

Figure 27 Work-related skin disease by age group; incidence rates THOR-GP and SWI 2006-2008

Inci

den

ce r

ate

per

100

,000

per

son

sem

plo

yed

400

350

300

250

200

150

100

50

0 16 to 25 25 to 34 35 to 44 45 to 54 55 to 64 65 plus

THOR-GP

SWI

Age group

Table 27 Work-related skin disease by gender; 3 year average estimates and incidence rates 2006-2008


100,000 100,000 Male 22000 150 5000 30 Female 21000 160 5000 40

78

Figure 28 GB incidence rate for work-related skin disease per 100,000

persons employed by gender; 3 year average incidence rates 2006-2008

Inci

den

ce r

ate

per

100

,000

per

son

sem

plo

yed

180

160

140

120

100

80

60

40

20

0 THOR-GP SWI

Male

Female

79

5.2.6 HEARING DISORDERS

Sample numbers are too small to provide reliable estimates for hearing

disorders for either THOR-GP or SWI data.

5.2.7 OTHER WRIH

4% of THOR-GP cases and 19% of SWI cases (2006-2008) were reported as

‘other types of WRIH complaint’. The ‘other WRIH’ category in THOR-GP

includes the following types of diagnoses: cuts / wounds / lacerations;

migraines / headaches; hypertension. SWI ‘other WRIH’ category includes

heart disease / attack / circulatory problems and other, however, this other

category is not further specified.

Three year average incidence rates for both schemes report higher incidence

rates for other WRIH in males than females (Table 28). THOR-GP data show

the highest rate reported in the 25-34 year age group whereas SWI data

shows the highest incidence rate in the 55-64 year age group (Table 29).

80

-

-

Table 28 Other work-related ill-health by gender; 3 year average estimates and incidence rates 2006-2008


100,000 100,000 Male 11000 70 44000 280 Female 6000 50 32000 230

Table 29 Other work-related ill-health by age group; 3 year average estimates and incidence rates 2006-2008



100,000 16 to 24 years * * 9000 200 25 to 34 years 4000 80 15000 240 35 to 44 years 4000 60 18000 240 45 to 54 years 4000 70 19000 280 55 to 64 years * * 12000 320 65 years plus * * * *

* sample numbers too small to provide reliable estimates

81

5.3 TRENDS IN INCIDENCE

Table 30 and Figure 28 show the estimated THOR-GP and SWI incidence

rates for total WRIH, separately for the years 2006, 2007 and 2008. Both

THOR-GP and SWI data suggest a fall in incidence over this time period, with

a steeper drop between 2006 and 2007 compared to between 2007 and 2008.

Between 2006 and 2007, the THOR-GP incidence rate fell by 7% compared to

11% for the SWI rate over the same time period. Between 2007 and 2008, the

apparent fall in incidence was similar for the two data sources (3 to 4%).

Both THOR-GP and SWI incidence rates for total MSDs fell between 2006

and 2007, but the percentage drop seemed to be almost double for SWI

compared to THOR-GP incidence rates (Table 31 and Figure 29). Between

2007 and 2008, THOR-GP incidence rates fell again (but less steeply)

whereas SWI incidence rates increased slightly between these two years.

THOR-GP incidence rates for stress, depression and anxiety fell between

2006 and 2008 but less steeply than that observed for MSDs (Table 32 and

Figure 30). For THOR-GP, there was an approximate 1% decrease in the

incidence rate per year whilst for SWI a slightly larger decrease of 6%

between 2006 and 2007 and 3% between 2007 and 2008 was observed.

82

Table 30 Estimated THOR-GP and SWI incidence rates of total WRIH, 2006-2008

THOR-GP SWI

2006 1590 2090

2007 1480(7% ↓)* 1860 (11% ↓)*

2008 1430 (4% ↓)** 1810 (3% ↓)**

*Percentage change from 2006 to 2007 ** Percentage change from 2007 to 2008

Figure 29 Estimated THOR-GP and SWI incidence rates of total WRIH, 2006-2008

Total WRIH

0

500

1000

1500

2000

2500

2006 2007 2008

Year

Inci

denc

e ra

te p

er 1

00,0

00 e

mpl

oyed

THORGP

SWI

83

Table 31 Estimated THOR-GP and SWI incidence rates of total work-related musculoskeletal disorders, 2006-2008

THOR-GP SWI

2006 880 790

2007 770 (12% ↓)* 590 (25% ↓)*

2008 750 (3% ↓)** 630 (6% ↑)**


Figure 30 Estimated THOR-GP and SWI incidence rates of total work-related musculoskeletal disorders, 2006-2008

Total musculoskeletal

0

100

200

300

400

500

600

700

800

900

1000

Inci

denc

e ra

te p

er 1

00,0

00 e

mpl

oyed

THORGP

SWI

2006 2007 2008

Year

84

Table 32 Estimated THOR-GP and SWI incidence rates of total work-related stress, depression and anxiety, 2006-2008

THOR-GP SWI

2006 470 830

2007 470 (<1% ↓) 780 (6% ↓)

2008 460 (1% ↓) 760 (3% ↓)


Figure 31 Estimated THOR-GP and SWI incidence rates of total work-related stress, depression and anxiety, 2006-2008

Stress, anxiety and depression

0

100

200

300

400

500

600

700

800

900

Inci

denc

e ra

te p

er 1

00,0

00 e

mpl

oyed

THORGP

SWI

2006 2007 2008

Year

85

The percentage change per year in the incidence of total WRIH, MSDs and

stress, depression and anxiety (obtained from the MLM - adjusted for season,

‘harvesting’ or being a new reporter, and type of reporter), are shown in

Figures 31, 32 and 33. Results are presented as both (i) change in each year,

without any assumptions about constancy of trend and (ii) as an annual

percentage change assuming that there is a constant time trend throughout

the period.

The results from the MLM suggest a fall in the incidence of total WRIH of

approximately 13% per year, with a steeper drop between 2006 and 2007,

compared to 2008 (Figure 31). A comparison of the change in incidence as

shown by the incidence rates (which have not been adjusted for reporter

numbers, harvesting, season etc) and as obtained from the MLM models

(which have been adjusted for these factors) suggests the MLM model

predicts a steeper fall in incidence compared to that shown by the incidence

rates (Table 33).

The decrease in incidence of MSDs suggested by the incidence rates is also

shown by the results from the MLM, with both suggesting a bigger decrease

between 2006 and 2007 compared to between 2007 and 2008 (Figure 32 and

Table 34). However, the MLM suggested twice as big a decrease (24%

compared to 12%) during this time period (which was similar to that observed

for the SWI).

Overall, the MLM showed a decrease in the incidence of stress, anxiety and

depression of 9% per year (Figure 33). This was larger than the annual

decrease suggested by the THOR-GP incidence rates but again more

consistent with the change suggested by the SWI (Table 35). However, both

the MLM model results and the incidence rates suggested that the magnitude

of change between 2006 and 2007 was similar to that observed between

2007 and 2008.

86

Figure 32 Relative rates by year (2006 estimate = 1), with 95% confidence intervals, total WRIH, THOR-GP, 2006-2008

0

0.2

0.4

0.6

0.8

1

1.2

2006

2007

2008

Year

Rel

ativ

e ri

sk (

od

ds

rati

o)

Estimated change per year (2006-2008): -13% (95% CI: -17%, -9%)

Table 33 Change in incidence of total WRIH reported to SWI and THOR-GP, and predicted by MLM

Change in incidence SWI THOR-GP THOR-GP MLM

2006 to 2007 11% ↓ 7% ↓ 19%↓ 2007 to 2008 3% ↓ 4%↓ 6%↓

87

2006

2007

2008

Figure 33 Relative rates by year (2006 estimate = 1), with 95% confidence intervals, total musculoskeletal disorders, THOR-GP, 2006-2008

0

0.2

0.4

0.6

0.8

1

1.2

Rel

ativ

e ri

sk (

od

ds

rati

o)

Year

Estimated change per year (2006-2008): -16% (95% CI: -21%, -11%)

Table 34 Change in incidence of total musculoskeletal disorders reported to SWI and THOR-GP, and predicted by MLM


2006 to 2007 25% ↓ 12% ↓ 24% ↓

2007 to 2008 6% ↑ 3% ↓ 5% ↓

88

2006

2007

2008

Figure 34 Relative rates by year (2006 estimate = 1), with 95% confidence intervals, stress, depression and anxiety, THORGP, 2006-2008

0

0.2

0.4

0.6

0.8

1

1.2

Rel

ativ

e ri

sk (

od

ds

rati

o)

Year

Estimated change per year (2006-2008): -9% (95 CIs:-16%, -1%)

Table 35 Change in incidence of stress, depression and anxiety reported SWI and THOR-GP, and predicted by MLM


2006 to 2007 6% ↓ <1% ↓ 9%↓

2007 to 2008 3% ↓ 1% ↓ 8% ↓

89

5.4 WORKING DAYS LOST

5.4.1 All work-related ill-health

For both THOR-GP and SWI, cases of work-related mental ill-health make up

the highest proportion of working days lost (WDL) (Table 36 & Figure 34),

particularly in THOR-GP where the days due to these psychological

diagnoses make up the majority of days lost (57%). The number of WDL

estimated by SWI is almost twice that of THOR-GP (THOR-GP 42% fewer

days than SWI), however this difference in estimated days lost shows different

patterns when diagnostic categories are analysed separately (musculoskeletal

THOR-GP 41% fewer days, mental ill-health 30% fewer days). The

differences in the number of WDL between the two schemes are reflected in

the number of days lost per worker (Table 36 & Figure 35).

Analysis by age group shows that THOR-GP and SWI data follow a similar

pattern for the estimated number of days, which increases with age until the

45 to 54 age group, followed by a decrease in 55 to 64 year olds. However, a

different pattern emerges when the number of days is shown as a rate per

worker, with SWI results describing highest rates in 55-64 year olds (Table 37,

and Figures 36 & 37). Males had the highest estimates of WDL for both

schemes; however females showed the highest rates per worker (Table 38

and Figures 38 & 39).

SWI estimates showed highest rates of WDL per worker for health & social

welfare associate professionals (SOC 32, 1.92 WDL per worker) and for those

individuals working within health & social work (SIC division N, 1.63 WDL per

worker). THOR-GP estimates showed highest rates for protective service

occupations (SOC 33, 1.43 WDL per worker) and for workers employed within

fishing, mining & quarrying industries (SIC division B & C, 1.61 & 1.60

respectively) however estimates for these two industrial sectors are based on

fairly small numbers and results should be treated with even more caution.

(Tables 39 & 40, Figures 40 & 41).

90

-

Table 36 All cause work-related ill-health, estimated number of GB WDL and number of days per worker; THOR-GP and SWI 2006 to 2008

Diagnostic category

THOR GP SWI

Number of Adjusted♦ WDL Adjusted♦ Number of WDL per worker WDL number of per WDL per WDL

WDL worker worker

Musculoskeletal 2260000 5646000 0.08 0.20 9605000 0.41 Stress / anxiety / depression

3601000 9003000 0.13 0.32 12909000 0.55

Total 6332000 15831000 0.23 0.56 27408000 1.16 ♦n/40 x 100

Figure 35 All cause work-related ill-health, estimated number of GB WDL; THOR-GP and SWI 2006 to 2008

Other 7%

Musculoskeletal 36%


57%

THOR-GP Adjusted estimated

work days lost= 15,831,000

Other 18%

SWI Estimated

work days lost = 27,408,000

Musculoskeletal 35%


47%

91

-

Figure 36 WDL per worker; THOR-GP (adjusted estimate) and SWI

2006 to 2008 S

ickn

ess

WD

L p

er w

ork

er

1.2

1

0.8

0.6

0.4

0.2

0 All work-related ill- Musculoskeletal Mental ill-health

health

THOR-GP

SWI

Table 37 All cause work-related ill-health, estimated number of GB WDL and number of days per worker by age group; THORGP and SWI 2006 to 2008

Age group THOR GP SWI Number of Adjusted♦ WDL Adjusted♦ Number of WDL per worker WDL number of per WDL per WDL

WDL worker worker

16 to 24 years 514000 1285000 0.14 0.34 1513000 0.55 25 to 34 years 1158000 2895000 0.20 0.50 4177000 0.79 35 to 44 years 1695000 4237000 0.2 0.60 7417000 1.19 45 to 54 years 1940000 4850000 0.32 0.79 8198000 1.47 55 to 64 years 1000000 2500000 0.26 0.65 6103000 1.66** 65 years plus 23000 56000 0.04 0.10 Missing 3100 8000 * * Total 6332000 15831000 0.23 0.56 27408000 1.16

♦n/40 x 100

92

Figure 37 All cause work-related ill-health estimated number of GB WDL; THOR-GP (adjusted estimate) and SWI 2006 to 2008

Est

imat

ed n

um

ber

of

WD

L p

erE

stim

ated

nu

mb

er o

f G

B W

DL

9000000

8000000

7000000

6000000

5000000

4000000

3000000

2000000

1000000

0

THOR-GP

SWI

16 to 25 25 to 34 35 to 44 45 to 54 55 to 64 65 plus

Age group


wo

rker

1.8

1.6

1.4

1.2

1

0.8

0.6

0.4

0.2

0

THOR-GP

SWI

16 to 25 25 to 34 35 to 44 45 to 54 55 to 64 65 plus

Age group

93

-

Table 38 All cause work-related ill-health, estimated number of GB WDL and number of days per worker by gender; THOR-GP and SWI 2006 to 2008

Gender THOR GP SWI Number of Adjusted ♦ WDL Adjusted ♦ Number of WDL per worker WDL number of per WDL per WDL

WDL worker worker

Male 3194000 7986000 0.21 0.53 14507000 1.02 Female 3138000 7845000 0.24 0.61 12900000 1.38 Total 6332000 15831000 0.23 0.56 27408000 1.16 ♦n/40 x 100

Figure 39 All cause work-related ill-health estimated number of GB WDL; THOR-GP (adjusted estimate) and SWI 2006 to 2008

16000000

Est

imat

ed n

um

ber

of

GB

WD

L

14000000

12000000

10000000

8000000

6000000

4000000

2000000

0

Male

Female

THOR-GP SWI


Est

imat

ed n

um

ber

of

WD

L p

erw

ork

er

1.5

1

0.5

Male

Female

0 94 THOR-GP SWI

- -

Table 39 All cause work-related ill-health, estimated number of GB WDL and number of days per worker by sub-major SOC group; THOR-GP and SWI 2006 to 2008

2 Digit SOC code THOR GP SWI Number of WDL

Adjusted ♦♦♦♦ number of WDL

WDL per worker

Adjusted♦♦♦♦ WDL per worker

Number of WDL

WDL per worker

11 Corporate managers 441000 1102000 0.13 0.32 2508000 0.75 12 Managers & proprietors in agriculture & services 113000 282000 0.13 0.33 589000 0.68 21 Science & technology professionals 21000 52000 0.02 0.05

506000 0.84

22 Health professionals 30000 76000 0.09 0.23 * * 23 Teaching & research professionals 574000 1434000 0.42 1.06 1476000 1.36 24 Business & public service professionals 160000 401000 0.17 0.42 554000 0.61 31 Science & technology associate professionals 96000 239000 0.19 0.48 * * 32 Health & social welfare associate professionals 270000 675000 0.25 0.62 1580000 1.92 33 Protective service occupations 190000 470000 0.57 1.43 402000 1.09

34 Culture, media & sports occupations 32000 81000 0.05 0.13 235000 0.46 35 Business & public service associate professionals 178000 446000 0.12 0.29 1128000 1.04 41 Administrative occupations 658000 1645000 0.26 0.65 1726000 0.92

42 Secretarial & related occupations 196000 491000 0.25 0.62 507000 0.93 51 Skilled agricultural trades 57000 143000 0.20 0.49 * * 52 Skilled metal & electrical trades 298000 745000 0.26 0.65 977000 0.87 53 Skilled construction & building trades 333000 832000 0.30 0.75 981000 0.86 54 Textiles, printing & other skilled trades 148000 370000 0.28 0.71 445000 1.02 61 Caring personal 492000 1231000 0.28 0.71 170100 1.37

95

- -

2 Digit SOC code THOR GP SWI Number of WDL

Adjusted ♦♦♦♦ number of WDL

WDL per worker

Adjusted♦♦♦♦ WDL per worker

Number of WDL

WDL per worker

service occupations 62 Leisure & other personal service occupations 73000 182000 0.13 0.33 558000 1.50

71 Sales occupations 242000 604000 0.14 0.34

684000 0.67

72 Customer service occupations 134000 334000 0.34 0.84 400000 1.35

81 Process, plant & machine operatives 436000 1091000 0.46 1.15 1234000 1.45 82 Transport & mobile machine drivers & operatives 261000 653000 0.24 0.61 1184000 1.13 91 Elementary trades, plant & storage related occupations 444000 1110000 0.46 1.16 1114000 1.38 92 Elementary administration & service occupations 414000 1036000 0.18 0.46 1903000 1.35 88 Missing 43000 107000 * *

Total 6332000 15831000 0.23 0.56 23290000 0.99 ♦n/40 x 100

96

Figure 41 All cause work-related ill-health estimated number of GB WDL per worker by sub-major SOC group; THOR-GP (adjusted estimate) and SWI 2006 to 2008

0

1.35

1.38

1.13

1.45

1.35

0.67

1.5

1.37

1.02

0.86

0.87

0.93

0.92

1.04

0.46

1.09 1.92

0.61

1.36

0.84

0.68

0.75

0.46

1.16

0.61

1.15

0.84

0.34

0.33

0.71

0.71

0.75

0.65

0.49

0.62

0.65

0.29

0.13 1.43

0.62

0.48

0.42

1.06

0.23

0.05

0.33

0.32

92

91

82

81

72

71

62

61

54

53

52

51

42

41

35

34

33

32

31

24

23

22

21

12

11

SWI THOR-GP

1.5 1.0 0.5 0 0.5 1.0 1.5 2.0

Estimates number of WDL per worker

97

-

Table 40 All cause work-related ill-health, estimated number of GB WDL and number of days per worker by industrial division; THOR-GP and SWI 2006 to 2008

SIC 92 THOR GP SWI Number of WDL

Adjusted♦ number of WDL

WDL per worker

Adjusted♦ WDL per worker

Number of WDL

WDL per worker

A Agriculture, hunting & forestry 66000 166000 0.18 0.45 * * B Fishing 8000 20000 0.64 1.61 * * C Mining & quarrying 67000 168000 0.64 1.60 * * D Manufacturing 920000 2300000 0.26 0.65 2480000 0.80 E Electricity, gas & water supply 99000 248000 0.49 1.22 * * F Construction 534000 1334000 0.24 0.59 2004000 0.90 G Wholesale & retail trade; repair of motor vehicles & personal & household goods 565000 1412000 0.14 0.34 2309000 0.75 H Hotels & restaurants 156000 389000 0.13 0.32 661000 0.75 I Transport, storage & communication 515000 1289000 0.27 0.67 2093000 1.18 J Financial intermediation 281000 703000 0.23 0.57 1016000 0.95 K Real estate, renting & business activities 300000 750000 0.09 0.22 1862000 0.62 L Public administration & defence 733000 1832000 0.37 0.92 2498000 1.46 M Education 771000 1927000 0.30 0.75 2277000 1.19 N Health & social work 1176000 2941000 0.34 0.86 4364000 1.63 O Other community, social & personal service activities 141000 351000 0.09 0.22 1127000 0.92 P Private households with employed persons 530 1330 0.00 0.01 * * Q Extra-territorial organisations & bodies 0 0 0.00 0.00 * *

Total 6332000 15831000 0.23 0.56 23290000 0.99 ♦n/40 x 100

98

Figure 42 All work-related ill-health estimated number of GB WDL per worker by industrial division; THOR-GP (adjusted estimate) and SWI 2006 to 2008

0

0.92

1.63

1.19

1.46

0.62

0.95

1.18

0.75

0.75

0.9

0

0.8

0.01

0.22

0.86

0.75

0.92

0.22

0.57

0.67

0.32

0.34

0.59

1.22

-.65

1.6

1.61

0.45

Q

P

O

N

M

L

K

J

I

H

G

F

E

D

C

B

A

SWI THOR-GP

2.0 1.5 1.0 0.5 0 0.5 1.0 1.5 2.0

Estimated number of WDL per case

99

-

5.4.2 Musculoskeletal disorders

SWI data show a marked increase with age in the number of WDL per worker,

whereas GP reported rates are fairly similar between the age groups (Table

41 & Figure 42). Both schemes have a higher number of days lost in males,

and according to THOR-GP data a higher rate per worker (Figure 43). SWI

data shows the rate for males and females to be the same (Table 42, Figures

44 & 45). SWI estimates show highest rates of WDL per worker for process,

plant & machine operatives (SOC group 8, 0.74 WDL per worker) and for

those individuals working within transport, storage & communication (SIC

division I, 0.53 WDL per worker). THOR-GP estimates show highest rates for

elementary occupations (SOC group 9, 0.02 WDL per worker) and for workers

employed within mining & quarrying (SIC division C, 0.59 WDL per worker),

however estimates are based on fairly small numbers and results should be

treated with caution. (Tables 43 & 44).

Table 41 Musculoskeletal disorders, estimated number of GB WDL and number of days per worker by age group; THOR-GP and SWI 2006 to 2008

Age group THOR GP SWI Number Adjusted♦ WDL per Adjusted♦ Number of WDL per worker of WDL number of worker WDL per WDL

WDL worker

16 to 34 years 742000 1855000 0.08 0.19 1601000 0.20 35 to 44 years 538000 1345000 0.08 0.19 2629000 0.36 45 to years plus 979000 2447000 0.09 0.23 5376000 0.58 Missing 110 270 * * Total 226000

0 5646000 0.08 0.20 ♦n/40 x 100

100

-

Figure 43 Musculoskeletal disorders estimated number of GB WDL; THOR-GP (adjusted estimate) and SWI 2006 to 2008

6000000

5000000

4000000

3000000

2000000

1000000

0 16 to 34 35 to 44 45 plus

Age group

THOR-GP

SWI

Table 42 Musculoskeletal disorders, estimated number of GB WDL and number of days per worker by gender; THOR-GP and SWI 2006 to 2008

Est

imat

ed n

um

ber

of

WD

L

Gender THOR GP SWI Number of Adjusted♦ WDL per Adjusted♦ Number of WDL per worker WDL number of worker WDL per WDL

WDL worker

Male 1548000 3870000 0.10 0.26 5801000 0.41 Female 711000 1777000 0.06 0.14 3804000 0.41 Total 2259000 5646000 0.08 0.20 9605000 0.41

♦n/40 x 100

101

Figure 44 Musculoskeletal disorders estimated number of GB WDL per worker; THOR-GP (adjusted estimate) and SWI 2006 to 2008

Est

imat

ed n

um

ber

of

WD

L p

erw

ork

er

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

THOR-GP

SWI

16 to 34 35 to 44 45 plus

Age group

Figure 45 Musculoskeletal disorders, estimated number of GB WDL by gender; THOR-GP (adjusted estimate) and SWI 2006 to 2008

6000000

Est

imat

ed n

um

ber

of

WD

L

5000000

4000000

3000000

2000000

1000000

0

Male

Female

THOR-GP SWI

102

Figure 46 Musculoskeletal disorders, estimated number of GB WDL

per case by gender; THOR-GP (adjusted estimate) and SWI 2006 to 2008

Est

imat

ed n

um

ber

of

WD

L p

er c

ase 0.5

0.4

0.3

0.2

0.1

0 THOR-GP SWI

Male

Female

103

-

Table 43 Musculoskeletal disorders, estimated number of GB WDL and number of days per worker by major SOC group; THOR-GP and SWI 2006 to 2008

1 digit SOC THOR GP Number of WDL


WDL per worker


Number of WDL

WDL per worker

1 Managers and senior officials 35000 88000 0.00 0.003 396000 0.10 2 Professional occupations 47000 118000 0.00 0.004 388000 0.12 3 Associate professional and technical occupations 151000 376000 0.01 0.013 1080000 0.30 4 Administrative and secretarial occupations 129000 323000 0.00 0.011 543000 0.22 5 Skilled trades occupations 513000 1281000 0.02 0.046 1347000 0.45 6 Personal service occupations 163000 407000 0.01 0.014 858000 0.59 7 Sales and customer service occupations 141000 353000 0.01 0.013 366000 0.28 8 Process, plant and machine operatives 459000 1149000 0.02 0.041 1408000 0.74 9 Elementary occupations 619000 1548000 0.02 0.055 1464000 0.66 Total 2259000 5646000 0.08 0.201 7845000 0.33 ♦n/40 x 100

104

-

Table 44 Musculoskeletal disorders, estimated number of GB WDL and number of days per worker by industrial sector; THORGP and SWI 2006 to 2008

SIC 92 THOR GP Number of WDL


WDL per worker


Number of WDL

WDL per worker

A Agriculture, hunting & forestry 45000 112000 0.12 0.31 * *

B Fishing 0 0 0.00 0.00 * * C Mining & quarrying 25000 62000 0.24 0.59 * * D Manufacturing 422000 1056000 0.12 0.30 1242000 0.40 E Electricity, gas & water supply 23000 59000 0.12 0.29 * * F Construction 421000 1052000 0.19 0.46 1123000 0.51 G Wholesale & retail trade; repair of motor vehicles & personal & household goods 323000 808000 0.08 0.20 969000 0.31 H Hotels & restaurants 57000 144000 0.05 0.12 * * I Transport, storage & communication 245000 613000 0.13 0.32 943000 0.53 J Financial intermediation 9000 23000 0.01 0.02 * * K Real estate, renting & business activities 139000 346000 0.04 0.10 421000 0.14 L Public administration & defence 139000 347000 0.07 0.17 564000 0.33 M Education 75000 187000 0.03 0.07 298000 0.16 N Health & social work 254000 634000 0.07 0.18 1316000 0.50 O Other community, social & personal service activities 79000 199000 0.05 0.12 * * P Private households with employed persons 530 1330 0.00 0.01 * * Q Extra-territorial organisations & bodies 0 0 0.00 0.00 * * Total 2259000 5646000 0.08 0.20 7845000 0.33 ♦n/40 x 100

105

-

5.4.3 Stress, depression and anxiety

For cases of work-related stress, depression and anxiety reported by both

schemes, the estimated number of WDL and the rate per worker increase with

age and also for females (Tables 45 & 46, Figures 46 to 49). SWI estimates

show highest rates of WDL per worker for associate professional & technical

occupations (SOC group 3, 0.64 WDL per worker) and for those individuals

working within public administration defence (SIC division L, 0.98 WDL per

worker). THOR-GP estimates show highest rates for administrative &

secretarial occupations (SOC group 4, 0.063 WDL per worker) and for

workers employed within electricity, gas & water supply, and public

administration & defence industries (SIC division E & L, both 0.72 WDL per

worker) (Tables 47 & 48).

Table 45 Stress, depression and anxiety, estimated number of GB WDL and number of days per worker by age group; THORGP and SWI 2006 to 2008

Age group THOR GP SWI Number of Adjusted♦ WDL per Adjusted♦ Number of WDL per worker WDL number of worker WDL per WDL

WDL worker

16 to 34 years 860000 2150000 0.09 0.22 2956000 0.37 35 to 44 years 1057000 2642000 0.15 0.37 3557000 0.57 45 plus 1751000 4377000 0.17 0.41 6397000 0.69 Missing 2300 7000 * * Total 3601000 9004000 0.13 0.32 12909000 0.55 ♦n/40 x 100

106

Est

imat

ed n

um

ber

of

WD

L

Figure 47 Stress, depression and anxiety, estimated number of GB WDL; THOR-GP (adjusted estimate) and SWI 2006 to 2008

7000000

6000000

5000000

4000000

3000000

2000000

1000000

0

THOR-GP

SWI

16 to 34 35 to 44 45 plus

Age group

Figure 48 Stress, depression and anxiety, estimated number of GB WDL per worker; THOR-GP (adjusted estimate) and SWI 2006 to 2008

Est

imat

ed n

um

ber

of

WD

L p

erw

ork

er

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

THOR-GP

SWI

16 to 34 35 to 44 45 plus

Age group

107

-

Table 46 Stress, depression and anxiety, estimated number of GB WDL and number of days per worker by gender; THOR-GP and SWI 2006 to 2008

Gender THOR GP SWI Number of Adjusted♦ WDL per Adjusted♦ Number of WDL per worker WDL number of worker WDL per WDL

WDL worker

Male 1329000 3323000 0.09 0.22 5773000 0.41 Female 2272000 5681000 0.18 0.44 7135000 0.76 Total 3601000 9003000 0.13 0.32 12909000 0.55 ♦n/40 x 100

Figure 49 Stress, depression and anxiety, estimated number of GB WDL by gender; THOR-GP (adjusted estimate) and SWI 2006 to 2008

Est

imat

ed n

um

ber

of

WD

L

8000000

6000000

4000000

2000000

0 THOR-GP SWI

Male

Female

108

Figure 50 Stress, depression and anxiety, estimated number of GB WDL per case by gender; THOR-GP (adjusted estimate) and SWI 2006 to 2008

0.8

0.6

0.4

0.2

0 THOR-GP SWI

Male

Female

Est

imat

ed n

um

ber

of

WD

L p

er c

ase

109

-

Table 47 Stress, depression and anxiety, estimated number of GB WDL and number of days per worker by major SOC group; THOR-GP and SWI 2006 to 2008

1 digit SOC THOR GP Number of WDL


WDL per worker


Number of WDL

WDL per worker

1 Managers and senior officials 494000 1235000 0.02 0.044 1790000 0.42 2 Professional occupations 724000 1811000 0.03 0.064 1936000 0.59 3 Associate professional and technical occupations 546000 1366000 0.02 0.049 2231000 0.64 4 Administrative and secretarial occupations 704000 1761000 0.03 0.063 1421000 0.58 5 Skilled trades occupations 189000 471000 0.01 0.017 640000 0.22 6 Personal service occupations 356000 891000 0.01 0.032

999000 0.62

7 Sales and customer service occupations 229000 574000 0.01 0.020 429000 0.33 8 Process, plant and machine operatives 170000 424000 0.01 0.015 * * 9 Elementary occupations 185000 464000 0.01 0.016 1198000 0.54 Total 3601000 9003000 0.13 0.320 11172000 0.48

♦n/40 x 100

110

-

Table 48 Stress, depression and anxiety, estimated number of GB WDL and number of days per worker by major SOC group; THOR-GP and SWI 2006 to 2008

SIC 92 THOR GP SWI Number of WDL


WDL per worker


Number of WDL

WDL per worker

A Agriculture, hunting & forestry 10000 24000 0.03 0.07 * * B Fishing 4000 9000 0.29 0.73 * * C Mining & quarrying 24000 60000 0.23 0.58 * * D Manufacturing 306000 766000 0.09 0.22 737000 0.24 E Electricity, gas & water supply 59000 147000 0.29 0.72 * * F Construction 73000 184000 0.03 0.08 * * G Wholesale & retail trade; repair of motor vehicles & personal & household goods 226000 564000 0.05 0.14 861000 0.28 H Hotels & restaurants 88000 220000 0.07 0.18 * * I Transport, storage & communication 249000 622000 0.13 0.32

764000 0.43

J Financial intermediation 269000 672000 0.22 0.55 863000 0.83 K Real estate, renting & business activities 159000 397000 0.05 0.12 1100000 0.36 L Public administration & defence 576000 1441000 0.29 0.72 1681000 0.98 M Education 668000 1670000 0.26 0.65 1527000 0.80 N Health & social work 842000 2104000 0.24 0.61 * * O Other community, social & personal service activities 136000 340000 0.08 0.21 * * P Private households with employed persons 0 0 0.00 0.00 * * Q Extra-territorial organisations & bodies 0 0 0.00 0.00 * * Total 3601000 9003000 0.13 0.32 12909000 0.55 ♦n/40 x 100

111

6 DISCUSSION AND CONCLUSIONS

Summary of main findings

Overall, the number of estimated cases and resulting GB incidence rates are

approximately 25% higher for the SWI than for THOR-GP. MSDs and mental

ill-health make up most of the cases of WRIH reported to both schemes. GPs

report more musculoskeletal cases than mental ill-health, whereas the SWI

survey results in more mental ill-health cases being recorded. THOR-GP also

has a higher proportion of skin disease reported than SWI, whereas self-

reports result in a much larger proportion of ‘other’ types of WRIH (e.g. heart

attack / circulatory problems; headaches / eye strain; other types of

complaint). These proportional diagnostic differences are reflected in the

resulting incidence rates (despite SWI having an overall higher incidence rate

of WRIH); THOR-GP has higher rates of incidence for MSDs and skin

disease, whereas SWI results show higher rates for mental ill-health, and in

particular, for ‘other’ diagnoses (4 times higher).

Although GPs report more cases in males, incidence rates are similar for

males and females. SWI reports more cases in females, resulting in a higher

incidence rate. Both schemes follow a similar pattern by age, with highest

rates of incidence among the 45 to 54 year olds (with the exception that

THOR-GP shows higher rates than the SWI in the youngest age group). The

occupations most frequently reported to THOR-GP and SWI were from

opposite ends of the occupational coding scales with SWI reporting cases

most frequently in corporate managers and GPs reporting cases in

elementary administration and service occupations. Both schemes had

administrative occupations as their second most frequently reported SOC

group. Overall SWI had higher incidence rates than THOR-GP amongst

‘higher’ occupations (SOC coding is based on the skills, training and

experience required to do the job) whereas THOR-GP had higher incidence

rates amongst ‘lower’ occupations.

Both schemes had the highest incidence rate in the fishing industry; however

these figures are based on small numbers. Additionally, THOR-GP and SWI

112

reported a relatively high incidence of WRIH in the public administration and

defence, and the health and social work sectors.

When work-related MSDs were analysed separately THOR-GP had a much

higher rate for upper limb disorders than SWI, with increased rates in younger

age groups, and in males. THOR-GP showed high incidence rates in

elementary trades, plant & storage occupations and skilled construction &

building trades; SWI had highest rates in skilled agricultural workers. SWI and

THOR-GP results showed different patterns in incidence for back problems by

age group, with THOR-GP showing highest rates in the 16-24 age group and

SWI having highest rates in the 35 to 44 age group.

Both SWI and THOR-GP had much higher rates for stress, depression and

anxiety in females. SWI reported highest rates in health & social welfare

professionals. GP reports resulted in highest rates for protective service

occupations. Both schemes also showed high rates in teaching & research

professionals. Both schemes had high rates in workers employed within

public administration & defence.

Respiratory results are based on small numbers; however there are a couple

of points of note; in THOR-GP males show a much higher rate, and there is

an extremely high rate in the THOR-GP 65 plus age group.

For skin disease, although a comparison between THOR-GP and SWI

incidence rates has been made, SWI incidence rates are not routinely

published on the HSE website as sample numbers are deemed too small to

provide reliable estimates. Overall the incidence of work-related skin disease

was higher in THOR-GP. Incidence rate results for both schemes showed

highest rates in the younger (16-24) age group and decrease in incidence with

increase in age. Both THOR-GP and SWI had highest rates in leisure & other

personal service occupations; the SOC sub-group that includes hairdressers

and beauticians. THOR-GP rates by industrial division were generally higher

than SWI rates with the highest rates reported in the following industries:

113

agriculture, hunting and forestry; hotels and restaurants; other community,

social and personal service activities.

Both schemes show higher incidence rates for other WRIH in males than

females. THOR-GP data show the highest rate reported in the 25-34 year

age group whereas SWI data shows the highest incidence rate in the 55-64

year age group. THOR-GP showed high rates of incidence in skilled

agricultural trades and the SWI had highest rates in culture, media and sports

occupations. Both schemes had high rates in workers employed in mining

and quarrying; the industry division with the highest incidence rate shown by

THOR-GP data was fishing (although this is based on small numbers)

whereas SWI shows the highest rate in private households with employed

persons.

As with incidence rates for skin and respiratory disease, SWI incidence rates

for other WRIH are not routinely published on the HSE website as sample

numbers are deemed too small to provide reliable estimates.

Looking at incidence rates over time, THOR-GP and SWI incidence rates (of

total WRIH) decreased over the period 2006 to 2008, with a steeper decrease

between 2006 and 2007 (7% for THOR-GP compared to 11% for SWI),

compared to between 2007 and 2008 (both schemes 3-4%). By comparison,

the MLM (where rates have been adjusted for other factors that might cause

variation over time) suggested a decrease in THOR-GP incidence rates of

19% between 2006 and 2007 and 6% between 2007 and 2008.

Both THOR-GP and SWI incidence rates for total MSDs fell between 2006

and 2007, but the percentage drop was almost double for SWI compared to

THOR-GP. Between 2007 and 2008, THOR-GP incidence rates fell again (but

less steeply) whereas SWI incidence rates increased slightly. The MLM also

suggested a larger decrease in incidence between 2006 and 2007 (compared

to between 2007 and 2008), and the estimated change between these two

years was twice that suggested by the incidence rates (24% compared to

12%).

114

THOR-GP incidence rates for stress, depression and anxiety fell between

2006 and 2008 but much less steeply than had been observed for MSDs. For

THOR-GP, there was an approximate 1% decrease in the incidence rate per

year whilst for SWI a slightly larger decrease (3-6%) was observed. The MLM

model also suggested a similar decrease in THOR-GP incidence rates

between 2006 and 2007 and between 2007 and 2008 but the magnitude of

the drop was 8-9%.

Overall, the number of WDL estimated by the SWI is nearly twice that of

THOR-GP estimates (THOR-GP 42% fewer days than SWI). Cases of work-

related mental ill-health comprise the highest proportion of WDL for both

schemes. Similar patterns of estimated numbers of WDL by age groups are

observed for both schemes, with an increase in days lost until 45-54 year age

group, then a decrease in WDL lost in the 55-64 age group; however, when

analysed as rates per worker, the SWI data show highest rates in the older

age group. Males have the highest estimates of WDL for both schemes;

however as rates per workers, females have the highest rate. SWI estimates

show the occupational group with the highest rates of WDL was health and

social welfare professionals, and for industry sector, the highest rate was for

those working in health and social care. THOR-GP estimates show the

highest rates were reported in the protective service occupations, and the

highest industry rate was for those employed in fishing and mining and

quarrying (although these estimates are based on small numbers and should

therefore be treated with some caution).

For MSDs, SWI data show an increase in WDL per worker with age, whereas

THOR-GP data show similar rates between the age groups. Both SWI and

THOR-GP have a higher number of days lost in males. For rates per worker,

THOR-GP has higher rates among males, whereas SWI rates are the same

for males and females. SWI estimates show highest rates of WDL for SOC

group 8, (process, plant and machine operatives) and for SIC division I, i.e.

those employed in the transport, storage and communication industry. THOR

GP estimates show highest rates for SOC group 9, (elementary occupations),

and for SIC division C i.e. workers in mining and quarrying industries

115

(although these estimates are based on small numbers and should therefore

be treated with some caution).

For diagnoses of stress, depression and anxiety, both schemes report WDL

per worker as increasing with age, and being higher for females. SOC group

3 (associate professional and technical workers), and those working in SIC

division L i.e. public administration and defence had the highest rates of WDL

per worker in SWI estimates. THOR-GP estimates also show the highest

rates for workers in public administration and defence (as well as those

employed in electricity, gas and water supply); by occupation, THOR-GP

estimates show highest rates of WDL per worker in SOC group 4

(administrative and secretarial occupations).

Discussion

The SWI survey by definition takes a sample of perceived ill-health regardless

of self referral to a GP, so it is closer to the base of the ‘surveillance pyramid’

than is THOR-GP. However, THOR-GP is based on more objective medically

certified reports of WRIH. In summary, as further discussed below, the

findings in this report are consistent with the conclusion that THOR-GP is less

biased toward mental ill-health and is a better reflection of the incidence of

more objectively determined work-related ill-health / occupational disease

such as occupational contact dermatitis.

The main observation in this study is the higher overall incidence rates from

the SWI survey in comparison to THOR-GP, with some variation when

diagnostic categories are analysed separately. It is unsurprising that SWI

rates are higher as it is not necessary for an individual to be seen by a

medical practitioner to be included as a SWI case, but there are reasons as to

why this differs by diagnostic category. First, the design of SWI and its mode

of administration emphasising only one illness may bias toward the cause

‘mental ill-health’. Secondly, trained GPs in THOR-GP may be better at

recognising occupational disease such as contact dermatitis, and possibly

less likely to be biased by psychosocial perception. Finally, it is possible that

there may be a response bias in SWI favouring those workers who socially or

116

occupationally are more likely to have mental ill-health and who are less likely

to be in manufacturing and / or to recognise the more classical occupational

diseases.

Overall the SWI rate is 1.23 times higher than the THOR-GP rate, and shows

similar increased rates for mental ill-health (1.63), respiratory (1.38) and

hearing (2.14) diagnoses. The rate for ‘other’ WRIH in SWI is increased at 4

times that of THOR-GP rates. Conversely, SWI rates for musculoskeletal and

skin disease are less than THOR-GP rates (1.24 and 4.67 times less,

respectively). The main difference between SWI and THOR-GP methodology

is that SWI is based on self-reports whereas THOR-GP is based on reports

from GPs, not only with medical training, but with additional vocational training

in the specific area of the interaction between work and health. The increased

SWI incidence rates could therefore be simply because the scheme is

capturing the less severe forms of WRIH that individuals didn’t consult their

GP about, but it could also be due to an over attribution of a work-related

cause [18,21] as it is based on a non-medically and non-occupationally

trained individual’s opinion about work-relatedness. However, this lack of

training might also lead to a work-related cause or aggravation being

unreported in the SWI data, especially for diseases about which the general

public has less awareness / knowledge.

Another difference in methodology between the schemes that is likely to have

an influence on rates between diagnostic categories is the fact that although

up to 8 episodes of WRIH can be recorded, only the episode considered the

most serious by the respondent has further questions related to it, and

subsequently only this ‘most serious episode’ is included in the SWI results. In

comparison, within THOR-GP, any number of diagnoses can be included in

co-morbid cases. For example, a patient may consider a case of WRIH as

more serious if it has resulted in a period away from work, and previous work

in THOR-GP has shown that 79% of work-related mental ill-health cases were

issued with a sick note compared to just 42% of MSDs and 15% of skin cases

[7]. Thus, it is possible that skin diagnoses are under-reported to the SWI,

which may partly explain why higher incidence rates were seen for THOR-GP.

117

The THOR-GP data described in this report include injury cases, therefore the

higher musculoskeletal rates in THOR-GP are likely to be largely explained by

the differences in the way injury data is handled in the two schemes. GPs

report all cases that they feel have been caused or attributed to work,

therefore this includes both work-related ill-health and workplace injury.

However the first question the interviewer asks at the start of the SWI survey

is “(Apart from the accident you have told me about,) within the last twelve

months have you suffered from any illness, disability or other physical or

mental problem that was caused or made worse by your job or work done in

the past?” Therefore accident / injury data is excluded from the start of SWI

data collection, and is, instead, recorded in the Workplace Injuries Survey.

Musculoskeletal injury cases reported to THOR-GP are included in the overall

musculoskeletal diagnostic category as it is not always possible to

conclusively determine whether a MSD was caused by an injury (single

exposure) or from repeated exposure. However, although musculoskeletal

injury cases have been included in the THOR-GP musculoskeletal category,

those thought likely to be caused by a single rather than a repeated exposure

are assigned an additional ‘injury’ code. As a sensitivity analysis, these MSDs

with an injury code were excluded and the THOR-GP MSD rate recalculated.

This resulted in a reduction in the incidence rate from 828 to 625 per 100,000

persons employed.

Apart from MSD single exposure / injuries, most other injury cases will fall

within the THOR-GP ‘other’ category and will include injuries such as burns,

lacerations, foreign objects in the eye, needle stick injuries etc. These injury

cases make up 40% of the ‘other’ cases in THOR-GP. These cases are not

included in SWI as they are excluded from the interview at outset. However,

as the Workplace Injury Survey data results only include musculoskeletal

injuries, it is unclear where these types of injury lie within the SWI data.

Between 2006 and 2008 there were 614 injury cases (15% of total cases)

reported to THOR-GP. An analysis of this injury data is outside the remit of

this report but an example of how THOR-GP data can be used to analyse

injuries is illustrated in Appendix D, where data has been coded to the same

118

classifications as SWI and rates calculated. THOR-GP rates are lower than for

SWI, however they follow a similar pattern by type of injury.

As already discussed, SWI mental ill-health incidence rates may be higher

than THOR-GP both because the SWI should be capturing cases not seen by

a GP and also because there may be a preference to report mental ill-health

diagnoses to SWI over other diagnoses. A further explanation is that an

individual may be more ready to attribute a mental ill-health issue to work than

a THOR-GP; as a GP with training in occupational medicine may be better at

identifying / balancing other, non-work-related underlying causal factors.

Comparatively few respiratory diagnoses were reported to either THOR-GP or

the SWI and as such it was not really possible to make any meaningful

comparisons. In addition to the SWI only allowing the most serious episode to

be recorded, work-related respiratory cases may also be missed by the SWI

as it may be less likely (than say for mental ill-health) that an individual will

associate his or her respiratory symptoms with work. For example, it is

recognised that in some cases of occupational asthma the symptoms may be

delayed in onset and may manifest in the evenings or at night at first and this

might not be immediately associated with work by the patient. In addition,

there may be difficulties in the diagnosis of some work-related respiratory

disease (e.g. occupational asthma) even for specialist respiratory physicians

and occupational physicians [38], therefore a GP might be reluctant to label a

case as such. Although comparatively few diagnoses of work-related

respiratory disease are reported to THOR-GP, this category of illness is

captured by SWORD (chest physicians) and OPRA (OPs) [22, 23]. Since

2002, chest physicians have reported (on average) 900 actual cases of

respiratory disease a year (approximately 2600 a year when adjusted for

sample reporting) whilst OPs reported approximately 50 cases per year over

the same time period (400 a year after adjusting for sample reporting).

A marked difference between the schemes was for the ‘other’ WRIH category

where SWI rates were 4 times higher than THOR-GP rates. For the purpose

of this study, this category refers to all WRIH other than respiratory, skin,

119

musculoskeletal, mental-ill health, hearing or infectious disease. It could be

possible that individuals self-reporting may assign a work-related cause to ill-

health (such as circulatory problems) whereas a THOR-GP attributes these to

lifestyle choices (such as diet and exercise and also family history), and

therefore cases are not reported to THOR-GP. A further explanation could be

that there may a higher proportion of poorly defined diagnoses reported to the

SWI than to THOR-GP which consequently get assigned to the ‘other’

category in the SWI.

Both THOR-GP and SWI incidence rates exhibited some variation by gender.

The most striking difference was for stress, depression and anxiety with both

THOR-GP and SWI rates being higher for females compared to males. It is

generally accepted that the overall incidence of stress, depression and anxiety

(work-related or otherwise) is reported earlier in the disease process in

females, which may extend to perceptions about work-related mental ill-

health.

In general, similar variations in incidence rates by age were observed for both

THOR-GP and the SWI, with rates typically increasing with age, reaching a

maximum around 35 to 44 years, then decreasing. The exception to this was

the incidence rates for skin disease which were highest for the youngest age

group (16 to 25 years), decreasing thereafter, with a similar pattern seen for

both THOR-GP and the SWI. This is driven by the high incidence of work-

related skin disease amongst hairdressers and beauticians, who tend to be

young and female [39]. A further point to note is the relatively high THOR-GP

incidence rate reported for respiratory disease in the 65+ years of age group.

This rate may reflect long latency diseases, so it is possible that it may be

artificially inflated due to incorrect denominators (in terms of the time period

covered) being applied. However, this result should be interpreted with

extreme caution due to the very small sample size on which the estimate was

based.

There was considerable variation in incidence rates for occupation and

industry, both within each data source and between the two data sources.

120

Interestingly, SWI incidence rates tended to be higher for the occupations at

the ‘top’ end of the occupational scale (for example, corporate managers)

whilst THOR-GP incidence rates tended to be higher for the ‘lower’ end of the

occupational scale (for example, elementary administration and service

occupations). This could be because SWI has a response bias acting against

such categories of occupations. An additional explanation may be that the

conditions suffered by those workers, associated with their reduced

awareness, results in under-reporting in SWI. On the other hand, once they

present to a trained THOR-GP, the association is made and reported by the

physician.

There was more consistency by industry, with both THOR-GP and SWI having

higher rates in public administration and defence and in the health and social

work sectors. As explained throughout this document, some of the figures,

especially when analysed by occupation and industry, are based on small

sample sizes and should be interpreted with caution. For example, fishing is

repeatedly shown as the industry sector with the highest rates of ill-health, but

it is difficult to draw conclusions from this as the results are based on very few

cases (8 cases were reported to THOR-GP during the study period).

However, published data have found fishing to be one of the most hazardous

occupations [40].

This study also sought to investigate trends in incidence over the 3 year time

period. The results suggest an overall fall in incidence of both the THOR-GP

incidence rates and the SWI rates over this time period. It is also encouraging

that, although the magnitude of the fall is generally different (steeper fall for

SWI compared to THOR-GP), there was some consistency between data

sources with a steeper decrease between 2006 and 2007 compared to

between 2007 and 2008. However, as noted by HSE (during an analysis of

trends in SWI incidence), it is important not to place too much emphasis on

changes over a single year but rather look at the long-term trend [41] and to

what degree three years’ worth of data can reveal anything about long term

trends is debatable.

121

This study additionally sought to compare the annual THOR-GP incidence

rates with the change in incidence as predicted by the MLM model (which

adjusts for other factors which might cause variation in incidence). The main

finding was that the MLM predicted a larger decrease in incidence year on

year compared to simply looking at ‘unadjusted’ THOR-GP incidence rates.

THOR data have been used to study trends in incidence over intervals to the

order of 10 years [31]. Using THOR-GP over smaller interval of 3 years for

this purpose has to be accompanied by considerable caution in interpretation.

In theory, the change in incidence predicted by the MLM should be more

‘accurate’, because it has adjusted for other potentially confounding factors,

but some of the resulting year-on-year change estimated look implausibly

large. The MLM results presented here were not adjusted for the potential

effect of ‘fatigue’, partly because initial investigations seemed to suggest that

this factor did not have an independent effect on incidence [32]. However, it is

difficult to separate out the two time variables (calendar time and participation

time), and further work investigating whether the large decrease in trend

observed for THOR-GP is being (partly) driven by fatigue, or some other as

yet unidentified factor, is ongoing.

In addition to incidence rates, this study also compared the number of WDL

between the two data sources. Unsurprisingly, for both schemes, diagnoses

of work-related mental ill-health comprise the highest proportion of WDL, with

a higher rate per worker amongst females compared to males. Although there

was some variation in the number of WDL between the two data sources, it is

important to remember (when making any comparisons) that the methodology

varied slightly between the two schemes. SWI data are based on an

individual’s recall and then converted to Full Day Equivalent. THOR-GP data

are based on the sickness certification information provided by the GPs that

is, for example, often described as 2 weeks. This is counted as 14 days but it

could be 10 days if weekends were not included. Also as discussed

previously, it is known that the THOR-GP prospective data collection does not

collect information on the full length of time that a patient has been away from

work, particularly in the case of the long-term sick. Retrospective data

collection has shown THOR-GP WDL information to be approximately a 60%

122

underestimation, and therefore data has been shown adjusted for this in this

study. This is a crude adjustment and should be interpreted accordingly.

Continued retrospective auditing of THOR-GP sickness absence data will

continue to improve these estimates and also has provided interesting data on

GPs ability to predict the length of time a patient will be away from work.

Moreover, in line with the introduction of the fit note in April 2010, adjustments

have been made to the THOR-GP web form to enable collection of data about

patients’ return to work and workplace adjustments, helping to build a better

picture of the interaction between work and health.

There are a number of other limitations to the two schemes. As discussed,

SWI is likely to collect more data from the lower level of the surveillance

pyramid but these might be over estimates, and assessments of work-

relatedness are based on a non-medical person’s opinion. However, research

that has shown that GPs rely on a patient’s assessment, suggests that a

patients’ opinion of their ill-health is usually valid [42]. THOR-GP reports rely

on the opinions of GPs trained to diploma level in occupational medicine who

should be well placed to judge the work-relatedness of a case, however

opinions between GPs may differ.

SWI data could be subject to some recall bias as individuals are asked to

report on any WRIH episodes in the last 12 months. However as individuals

are only asked in detail about the most severe episode (likely to be the one

that would be retained in their memory the most), this might lead to some

inaccuracy with the amount of WDL reported. Furthermore, approximately a

third of the LFS interviews are collected via proxy interviews (i.e. partner,

sibling etc) which could lead to increased uncertainty in the accuracy of the

information collected [10]. However, LFS do have a number of strategies in

place to monitor the quality of the interviews and subsequent coding of the

data [10]. THOR-GPs generally submit a case to THOR-GP as and when they

see the patient or just after, at the end of a clinic, so recall bias should be

limited. In addition, all coding of THOR-GP data is carried out by two

independent coders with any differences reconciled by a third.

123

Both schemes have a nationwide distribution; SWI has a formalised stratified

sampling strategy in that it has a geographically stratified sampling strategy to

ensure complete and representative national coverage. Although the majority

of GPs participating in THOR-GP are recruited from a distance learning

course, an early assessment of the location of reporters in THOR-GP shows

an excellent regional distribution similar to all GB GPs (Appendix E), and more

work is ongoing to analyse this further.

This comparative study has shown some differences according to major

diagnostic group. SWI diagnostic categories are assigned by the interviewer

based on the problem described by the respondent. THOR-GP cases are

diagnosed by a medical practitioner and then coded using ICD10 codes by

THOR-GP researchers. There are some problems associated with this, for

example, as previously highlighted, is a back strain a disease (and therefore

assigned to the MSD category) or an injury (and therefore assigned to the

‘other’ category)? Other examples where this may be a problem are illustrated

within skin diagnoses, e.g. should a burn be classed as skin disease or as an

injury, and should scabies be classed as skin disease or as an infection?

In addition to THOR-GP and SWI, there are a number of other sources of

WDL data from employer organisations, for example, Confederation of British

Industry (CBI) and Chartered Institute of Personnel and Development (CIPD).

However, the statistics provided by these sources tend to differ greatly to the

statistics resulting from the SWI surveys. The HSE undertook a study in 2008

to investigate the feasibility of comparing sickness absence surveys to the

SWI [43]. The main finding arising from the study was that none of the surveys

was directly comparable with the SWI, mainly due to differences in survey

design; this therefore, limited the ability to make comparisons between the

variety of reports on sickness absence.

There may also be errors associated with the weighting up of the incidence

rates and WDL to GB estimates. At present, THOR-GP estimates are

weighted up to GB estimates based on a number of assumptions such as the

proportion of GB GPs reporting to THOR-GP. However, the THOR team is

124

7

engaged in an ongoing study to determine the size of the populations covered

by the reporters in THOR-GP and hence to provide even more accurate

estimates of incidence rates.

In conclusion, although a degree of overlap between the two data sources

certainly exists, THOR-GP data should be viewed in the least equal and

complementary to SWI data, and at best superior. The main advantage of

THOR-GP over SWI arises from the fact that reporters to THOR-GP are

medically qualified, with additional training in occupational medicine. This

results in more reliable reporting of occupational disease (e.g. dermatitis).

THOR-GP judgements are less likely to be influenced by psychosocial and

cultural perception and are more likely to be objective. Moreover, THOR-GP

is more likely to be stable and comparable with other (e.g. EU) data sources,

especially in respect of the more ‘classical’ occupational diseases and is likely

to be less biased by social background than the respondents to SWI. The

findings discussed above are consistent with this interpretation and

conclusion. Unlike SWI, there is also the ability to report co-morbidities (thus

avoiding the potential under-reporting or over-reporting of specific categories

of illness seen in the SWI). As such, THOR-GP, particularly in combination

with the other THOR schemes, provides a much richer data source relating to

skin and respiratory diagnoses. THOR-GP also collects data on causal

factors, referrals and symptom onset enabling further analyses investigating

how these factors vary by different conditions, jobs etc.

MAIN CONCLUSIONS

o THOR-GP and the SWI give a broadly similar picture of the

incidence of work related illness. The generally higher estimates

from SWI are consistent with the fact that the SWl can include

cases not seen by GPs, and the other main differences by

occupation and individual diagnostic group are consistent with a

view that GPs will be less affected by the differences in awareness

and willingness to report that inevitably affect the SWI.

125

o Methodological development in THOR-GP is needed to complete

the characterisation of the denominator, to audit and improve the

working days lost statistics and to assess trends over a longer

timescale and with more refined methods.

o It would be interesting to investigate whether a synthesis of the

two data sets could produce a more robust overall statistical

picture in which patterns of illness by occupation and diagnosis

would be drawn from THOR-GP and time trends from the SWI.

Better understanding of respondent biases in SWI and variations

in GP reporting over time would help validate such an approach.

Possible respondent biases in SWI need to be investigated more

critically.

126

REFERENCES

[1] Agius, RM, Carder, M, Hussey, L, Money, A & Turner, S, Determining best national estimates of the incidence of occupational and work related disease through triangulation of medically reported data. International Congress of Occupational Health (ICOH) conference proceedings 2009

[2] Hodgson JT, Jones JR, Clegg TA & Elliott RC (1998) Self-Reported Work-Related Illness in 1995: Results from a Household Survey, London, Health and Safety Executive

[3] Hodgson JT, Jones JR, Huxtable CS, & Price MJ (2003) Self-reported work-related illness in 2001/02: Results from a household survey. London, Health and Safety Executive

[4] Industrial Injuries Scheme available at www.hse.gov.uk/statistics/sources.htm#iidb (accessed August 2010)

[5] RIDDOR overview available at www.hse.gov.uk/statistics/sources.htm#riddor (accessed August 2010)

[6] Achieving the Revitalising Health and Safety Targets, Statistical Note on Progress Measurement; June 2001; HSE Epidemiology and Medical Statistics Unit and Economic and Statistical Analysis Unit.

[7] Hussey, L, Turner, S, Thorley, K, McNamee, R and Agius, R. Work-related ill-Health in general practice, as reported to a UK-wide surveillance scheme, British Journal of General Practice, 2008, 58, p.637-640

[8] Hussey, L, Turner, S, Thorley, K, McNamee, R & Agius, RM. Surveillance of work-related ill-health; a comparison of occupational physicians’ and general practitioners’ reporting. Occupational Medicine, In press.

[9] Assessment of compliance with the Code of Practice for Official Statistics: Statistics on Health and Safety at Work, HSE, Assessment report 42, May 2010.

[10] Labour Force Survey User Guide volume 1: Background and Methodology, 2007. http://www.statistics.gov.uk/downloads/theme_labour/LFSUG_vol1_20 07.pdf

[11] Labour Force Survey. Available at http://www.statistics.gov.uk/statbase/Source.asp?vlnk=358 (accessed July 2010)

127

[12] Labour Force Survey overview available at http://www.hse.gov.uk/statistics/lfs/about.htm (accessed July 2010)

[13] Self-reported work-related illness and workplace injuries survey; technical note http://www.hse.gov.uk/statistics/lfs/measures.htm Last accessed November 2009

[14] SWI Incidence Rate Calculation. Available online at http://www.hse.gov.uk/statistics/lfs/weighting.htm (accessed July 2010)

[15] SWI Definition of Estimates and Rates. Available online at http://www.hse.gov.uk/statistics/lfs/technicalnote.htm (accessed July 2010)

[16] SWI Working Days Lost Estimates. Available online at http://www.hse.gov.uk/statistics/lfs/calculation.htm (accessed July 2010)

[17] SWI Full Time Equivalent Definition. Available online at http://www.hse.gov.uk/statistics/lfs/dayslost.htm (accessed July 2010)

[18] Coggon, D. Monitoring trends in occupational illness. Occup Environ Med 2001 58 691-692

[19] Gerr, F. Surveillance of work-related musculoskeletal disorders. Occup Environ Med 2008 65; 298-299

[20] Spence, A, Hodgson, J, Osman, J. Monitoring trends in occupational illness: response. Occup Environ Med 2001 58 692-693

[21] Palmer, K T, Reading, I, Calnan, M, Coggon, D. How common is repetitive strain injury? Occup Environ Med 2008 65 331-335

[22] Meredith SK, Taylor VM, McDonald JC. Occupational respiratory disease in the United Kingdom 1989: a report to the British Thoracic Society and the Society of Occupational Medicine by the SWORD project group. Br J Ind Med 1991; 48(5):292-298.

[23] Cherry NM, Meyer JD, Holt DL, Chen Y, McDonald JC. Surveillance of work-related diseases by occupational physicians in the UK: OPRA 1996-1999. Occup Med (Lond) 2000; 50(7):496-503.

128

[24] THOR-GP website [online]. Available at: www.medicine.manchester.ac.uk/coeh/thorgp/ [Accessed 19th March 2008]

[25] McNamee R, Chen Y, Hussey L, Agius R. Randomised Controlled Trial comparing time-sampled versus continuous time reporting for measuring incidence. Epidemiology 2010; 21(3): 376-378.

[26] Office of Population Censuses and Surveys. Standard Occupational Classification. London: HMSO, 1990

[27] Central Statistical Office. Indexes to the Standard Industrial Classification of Economic Activities 1992. London: HMSO, 1993.

[28] Donnelly AB, Agius RM. The Distance Learning Courses in Occupational Medicine--20 years and onwards. Occup Med (Lond) 2005; 55(4):319-323

[29] Specialist Info.com [online]. Available at: www.specialistinfo.com/index.php [Accessed 19th March 2008]

[30] Chen Y, Agius R, McNamee R, Turner S, Taylor S, Fulluck L et al. Physicians' beliefs in the assessment of work attribution when reporting musculoskeletal disorders. Occup Med (Lond) 2005; 55(4):298-307

[31] McNamee R, Carder M, Chen Y, Agius R. Measurement of trends in incidence of work-related skin and respiratory diseases, UK 1996–2005 Occup Environ Med 2008; 65: 808-814

[32] McNamee R, Carder M, Money A, Agius R. Time trends in the incidence of work-related disease in the UK, 1996-2008: estimation from ODIN/THOR surveillance data. Report to HSE submitted September 2009.

[33] McNamee R, Carder M, Chen Y, Agius R. Time trends in the incidence of work-related disease in the UK, 1996-2007: estimation from ODIN/THOR surveillance data. Report to HSE submitted September 2008.

[34] McNamee R, Carder M, Money A, Agius R. Time trends in the incidence of work-related disease in the UK, 1996-2006: estimation from ODIN/THOR surveillance data. Report to HSE submitted September 2007.

[35] McNamee R, Carder M, Chen Y, Agius R. Time trends in the incidence of work-related disease in the UK, 1996-2004: estimation from ODIN/THOR surveillance data. Available at http://www.hse.gov.uk/statistics/pdf/trendsinthor.pdf. Last accessed September 2007

129

[36] McNamee R. Fatigue discussion. Document sent to HSE via email 12th March 2007.

[37] McNamee R. Surveillance and reporter fatigue. Report sent to HSE via email 8th May 2007.

[38] Turner S, McNamee R, Roberts C, Bradshaw L, Curran A, Francis M, Fishwick D, Agius R. Agreement in diagnosing occupational asthma by occupational and respiratory physicians who report to surveillance schemes for work-related ill-health. Occupational and Environmental Medicine, in press.

[39] Cherry, N, Meyer, J.D, Adisesh, A, Brooke, R, Owen-Smith, V, Swales, C & Beck, M,H. Surveillance of occupational skin disease: EPIDERM and OPRA. Br J Derm, 2000: 142: 1128-1134

[40] Roberts, S. Hazardous Occupations in Great Britain. The Lancet, 2002: 360: 9332: 543-544

[41] Analysis of the trends in self-reported work-related ill health with special reference to the trend reversal in 2006/07. Health and Safety Executive Statistics Branch, October 2008. Available at http://www.hse.gov.uk/statistics/pdf/lfsissue1.pdf Accessed August 2010

[42] Mowlam, A & Lewis, J. Exploring how General Practitioners work with patients on sick leave. 2005. Research report carried out by the National Centre for Social Research for the Department for Work and Pensions.

[43] Holmes, E, 2008. Research report RR673 - The feasibility of comparing sickness absence surveys and the Labour Force Survey. Health and Safety Laboratory prepared for the Health and Safety Executive

130

Appendix A – self-reported work-related illness module from 2006/07 LFS taken directly from HSE website http://www.hse.gov.uk/statistics/lfs/annex2.htm

Questions commissioned by the HSE were only applicable if respondents were:

• working during reference week, (WRKING = 'yes')

• or temporarily away from a job, (JBAWAY = 'yes')

• or working for their own or a family business, (OWNBUS = 'yes' or RELBUS = 'yes')

• or on an employment training scheme, (YTETMP = 1,2,4)

• or on the New Deal employment schemes (NEWDEA4 = 3,4,5,7)

• or on other New Deal options (study-based schemes, Gateway or Follow Through

options) and have additional paid work. (NEWDEA4 = 1, 6, 8, 9, or 19 & YTETJB =

'yes')

• or ever worked (EVERWK= 1)

ILLWRK

(Apart from the accident you have told me about,) within the last twelve months have you suffered from any illness, disability or other physical or mental problem that was caused or made worse by your job or work done in the past?

1. Yes

2. No

3. Don't know [*]

[*] this option only available for proxy interviews

The questions refer to any illness, disability or problem that was caused or made worse by

their work. It is only asked of people who have ever been employed. The illness, disability or

problem must be one caused or made worse by work, but the original cause, or worsening of

the condition due to work, could have been work before the 12 month period. However to be

eligible for these questions the respondent must have suffered from the effects or symptoms

of this work-related illness, disability or other physical/mental problem at some point during

the past 12 months.

NUMILL

Applies if ILLWRK=1 (Respondent suffered from an illness in last 12 months caused/made

worse by work)

How many illnesses have you had (in the last twelve months) that have been caused or

been made worse by your work?

131

Enter a numeric value between 1 and 8 (for 8 or more illnesses, code as 8).

The remainder of the questions refer to the respondent's most serious illness.

TYPILL


worse by work)

How would you describe this illness?

1. Bone, joint or muscle problems which mainly affect (or is mainly connected with)

arms, hands, neck or shoulder,


hips, legs or feet,


back,

4. Breathing or lung problems,

5. Skin problems,

6. Hearing problems,

7. Stress, depression or anxiety,

8. Headache and/or eyestrain,

9. Heart disease/attack, other circulatory system,

10. Infectious disease (virus, bacteria),

11. Other

TYPILL is still referring to the illness or disability in the last 12 months that was caused or made worse by the respondent's work. If more than one code applies, the respondent's illness has more than one effect. Code the one which the respondent says is the most serious, or affects them the most.

AWARE


worse by work)

When were you first aware of this illness? Please confirm the year and month.

1. Within the last 12 months

2. More than one year ago

The interviewer will read the question, but not prompt the person for an answer. The question

allows the interviewer to calculate whether the response from the interviewee was in the last

12 months or not.

132

TMEOFF

Applies IF IllWRK=1 (Respondent suffered from an illness in last 12 months caused/made

worse by work) and Respondent currently in work or left last job in previous 12 months

In the last twelve months, how much time off work have you had because of this illness

?

1. No time off work,

2. less than 1 day,

3. 1 to 3 days (Work days),

4. 4 to 6 days (Work days),

5. At least 1 week but less than 2 weeks,

6. At least 2 weeks but less than 1 month,

7. At least 1 month but less than 3 months,

8. At least 3 months but less than 6 months,

9. At least 6 months but less than 9 months,

10. At least 9 months but less than a year.

WCHJB3


worse by work)

May I just check, was the job that caused or made your illness worse the one you

previously mentioned as...

1. [Occupation title - main job]

2. [Occupation title - second job]

3. or was it some other job?

WIND


worse by work) and either WCHJB3=3 (some other job caused or made illness worse) OR

both OCCT=EMPTY and OCCT2=EMPTY (information on occupation for both main and

second job missing)

Thinking about the job which caused or made your illness worse, what did the

firm/organisation you worked for mainly make or do?

Enter a text of at most 80 characters

WINDT



133


second job missing)

Enter a short text for the industry


WOCCT




second job missing)

What was your job?


WOCCD




second job missing)

What did you mainly do in your job?


The interviewer will check special qualifications / training needed to do the job

134

APPENDIX B Distribution of THOR-GP reporters

135

APPENDIX C – Industry and occupation categories included in the study

SIC Description A Agriculture, hunting and forestry B Fishing C Mining and quarrying D Manufacturing E Electricity, gas and water supply F Construction G Wholesale and retail trade; repair of motor vehicles, motorcycles and

personal and household goods H Hotels and restaurants I Transport, storage and communication J Financial intermediation K Real estate, renting and business activities L Public administration and defence; compulsory social security M Education N Health and social work O Other community, social and personal service activities P Private households with employed persons Q Extra-territorial organisations and bodies

SOC Description 11 Corporate managers 12 Managers & proprietors in agriculture & services 21 Science & technology professionals 22 Health professionals 23 Teaching & research professionals 24 Business & public service professionals 31 Science & technology associate professionals 32 Health & social welfare associate professionals 33 Protective service occupations 34 Culture, media & sports occupations 35 Business & public service associate professionals 41 Administrative occupations 42 Secretarial & related occupations 51 Skilled agricultural trades 52 Skilled metal & electrical trades 53 Skilled construction & building trades 54 Textiles, printing & other skilled trades 61 Caring personal service occupations 62 Leisure & other personal service occupations 71 Sales occupations 72 Customer service occupations 81 Process, plant & machine operatives 82 Transport & mobile machine drivers & operatives 91 Elementary trades, plant & storage related occupations 92 Elementary administration & service occupations 88 Missing

136

-

APPENDIX D Estimated incidence and rates of injuries reported to THOR-GP and SWI 2006 to 2008

Injury category THOR GP SWI Estimate Rate per


100,000 Contact with moving machinery 4042 14 13000 47 Hit by moving, flying, falling object 7021 25 25000 87 Hit something fixed or stationary 3085 11 7000 24 Injured while handling, lifting or carrying 18829 67 71000 250 Slipped, tripped or fell on the same level 11915 42 57000 200 Fell from a height 6276 22 25000 91 Trapped by something collapsing or overturning 106 0 5000 17 Physically assaulted by a person 3191 11 15000 52 Other kinds of accident 10532 37 54000 190 Missing 532 2 0 0 Total 65530 233 273 970

137

APPENDIX E Distribution of THOR-GP and GB practices

4% 15%

13%

10%

9% 7%

11%

9%

9%

8% 6%

3%

5% 5%

20%

6%

10%

7% 8%

15%

8%

12%

South East London South West

West Midlands East Midlands North West

North East East of England Yorkshire & Humber

Wales Scotland

GB GPs THOR-GP practices

138

Published by the Health and Safety Executive 03/13


A comparative analysis of self-reported and medically certified incidence data on work-related illness The impact of work on health is of major importance to Government policy makers, employers and employees alike. Thus, it is important to be able to monitor the incidence and change in incidence of work-related ill-health (WRIH) over time. One (national) source of information relating to WRIH in the UK is the Self-reported Work-related Illness and Injury (SWI) survey which has been included as an annual module in the Labour Force Survey (LFS) since 2003/04. Earlier versions were run in 1990, 1995 and 2001/02. However, the Health and Safety Executive (HSE) acknowledges the limitations of the SWI data and, in particular, the possibility that over or under attribution to work may be a factor in its estimates. Furthermore, an expert workshop convened by the HSE in February 2009 concluded that the HSE should identify preferred data sources for different categories of WRIH, taking into account their respective strengths and weaknesses.

This report and the work it describes were funded by the Health and Safety Executive (HSE). Its contents, including any opinions and/or conclusions expressed, are those of the authors alone and do not necessarily reflect HSE policy.

RR954

www.hse.gov.uk

rr954 - a comparative analysis of self-reported and ... · a comparative analysis of self-reported...

Documents