Health and Safety Executive

The burden of occupational cancer in Great Britain Predicting the future burden of occupational cancer – Methodology

Prepared by Imperial College London for the Health and Safety Executive 2011

RR849 Research Report

Health and Safety Executive

The burden of occupational cancer in Great Britain Predicting the future burden of occupational cancer – Methodology

Sally Hutchings Lesley Rushton Imperial College London Faculty of Medicine St Mary’s Campus Norfolk Place London W2 1PG

The methodology developed to estimate the future burden of cancers in GB attributable to occupational exposure is summarised in this report. The method builds on the attributable fraction approach developed to estimate the current burden of occupational cancer in GB. For future estimates the risk exposure periods (REPs), for cancer latencies up to 50 years, have been projected forward in time to estimate attributable fractions for a series of forecast target years. The method takes into account past and projected trends in exposure and quantifies the impact of possible strategies to reduce the future burden of cancers (eg, introducing and achieving compliance with exposure limits). Adjustment factors are introduced to account for changes in exposed numbers and exposure levels, and are applied in estimation intervals within the REPs.

The report contains illustrative scenarios aimed at reducing future lung cancers due to occupational exposure to respirable crystalline silica (RCS). These examples suggest that attributable fractions for lung cancer due to RCS could be reduced from 2.07% in 2010 to nearly zero by 2060, depending on the timing and success of these interventions. The importance of achieving compliance with current exposure standards in small industries is highlighted and may be more effective than setting lower exposure standards. The method has been used to forecast future cancer burdens for other high-risk carcinogens and occupations identified by the HSE current cancer burden work. It is also adaptable for other countries and other exposure situations in the general environment and can accommodate socio-economic impact assessments.

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

© Crown copyright 2011

First published 2011

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ACKNOWLEDGEMENTS

Funding was obtained from the Health and Safety Executive (HSE). Andrew Darnton from the HSE was responsible for the work on mesothelioma. The contributions to the project and advice received from other HSE and Health and Safety Laboratory staff is gratefully acknowledged. Two workshops were held during the project bringing together experts from the UK and around the world. We would like to thank all those who participated and have continued to give advice and comment on the project. We would also like to thank Helen Pedersen and Gareth Evans for their help in editing and formatting the reports.

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EXECUTIVE SUMMARY

Imperial College London in collaboration with the Health and Safety Laboratory (HSL), the Institute of Environment and Health and the Institute of Occupational Health are carrying out a project to produce (i) an updated estimate of the current burden of occupational cancer in Great Britain and (ii) an estimate of the future occupational cancer burden in Great Britain based on recent and current exposures. Specifically the aims of the latter are to develop methodology: (1) to estimate and predict the future burden of occupational cancer in Great Britain, (2) to identify industries and occupations where best to target efforts to reduce future incidence, (3) to indicate when and how interventions to reduce exposures would have most impact, and (4) for monitoring achievement against targets set on the basis of the forecasting. This report describes the methodology developed for predicting future burden and illustrates this with the example of lung cancer due to occupational exposure to respirable crystalline silica.

Possible Approaches

There are three basic approaches to estimating the future burden of occupational cancer. (1) to estimate future attributable fractions (AFs), that is the proportions of cases that would not

have occurred in the absence of exposure, using an extension of the methodology developed to estimate current burden

(2) to estimate the ‘lifetime risk’ of a cohort of newly exposed workers based on national incidence rates applied to their future person-years-at risk and excess risk from their occupational exposure

(3) to estimate attributable numbers (ANs) directly from projected cancer numbers, with the projections based on a structural regression model in which the contributions of non-occupational and occupational risk factors can be estimated separately.

In all approaches the number of attributable cancers that could be avoided by reducing exposure to known carcinogens in the workplace is estimated by comparing estimates made for ‘baseline’ or exposure trend scenarios with estimates made for ‘intervention’ scenarios which are based on targeted reductions in exposure levels.

Approach adopted for this project

A workshop with an international team of invited experts was held in June 2008 to discuss the methodology, at which it was agreed that the first (attributable fraction) approach would be adopted. The attributable fraction approach depends on knowledge of the risk of disease due to exposure and the proportion of the GB population exposed. The approach developed here is an extension of the methods used to estimate the current burden of occupational cancer. Relative risks (RRs) that have been identified for specific cancer/exposure pairings for the current burden estimates, generally at ‘higher’ and ‘lower’ exposure levels, are also used to estimate future burden. In order to account for cancer latency, a risk exposure period (REP) is defined as the period during which exposure occurs that is relevant to the development of a cancer in the target year (i.e., 2005 for the current burden estimates). For predicting future burden these exposure windows are projected forward in time, with AFs and attributable numbers estimated for a series of forecast target years (FTYs) that stretch far enough into the future to account for the latency of cancers currently being initiated. For example, for solid tumours such as lung cancer, which are assumed to have latencies of between 10 and 50 years, FTYs up to 2060 are needed for an exposure occurring in 2010. Numbers of workers occupationally exposed are obtained from the CARcinogen EXposure database (CAREX), or numbers employed in specific exposed jobs are obtained from the Labour Force Survey (LFS) or Census of Employment, along with estimates of

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employment turnover and life expectancy. These are used to estimate numbers ever exposed in the projected REPs who are of an age to develop a cancer in the forecast target years.

Adjustment factors that represent intervention scenarios such as (1) change in employment structure in grouped main industry sectors (agriculture and fishing, utilities mining and manufacturing, construction, and services) and (2) change in the proportions of workers exposed at ‘high’, ‘medium’, ‘low’ and ‘background’ exposure levels, are applied to the numbers ever exposed to represent change over the forecast exposure periods. These adjustment factors can be set either to represent ‘baseline’ change, based on historic and forecast employment and exposure level trends, or to represent change resulting from a planned ‘intervention’, for example by reducing the numbers exposed (closing ‘higher’ exposed parts of the industry) or by re-estimating numbers exposed at the ‘higher’ levels after a reduced exposure limit has been enforced. Choice of intervention scenarios depends on policy needs and their potential usefulness for decision making.

All the adjustments are applied in 10 year estimation intervals, to allow for trends across time and to give reasonable flexibility to the timing of interventions. Numbers ever exposed during the forecast REPs are divided by an estimate of numbers in each FTY who would have been of working age during the REP, to obtain the proportion of the population exposed (Pr(E)). The standard current burden approach (Levin’s equation) is then used to estimate AF from RR and Pr(E). To estimate attributable numbers the AF is applied to a prediction of total numbers for the specific cancer for each FTY, which is based on current (2005) cancer rates and demographic change only (to represent the rising and aging GB population). All estimates are produced by industry/occupation, so that their relative contributions to attributable cancers can be assessed. Factors can also be applied by workplace size category, so that different trends can be applied in larger and smaller workplaces if required, and the effect of different enforcement success in these workplaces can also be assessed.

Assumptions made for the estimation include assuming standard latency periods for solid and for haematopoietic cancers of from 10 to 50 years and 0-20 years respectively, and that the latency times follow a lognormal distribution between these extremes around an average for solid cancers of 35 years. For estimating numbers ever exposed, standard (national) age distributions and recruitment of young (age 15-24) workers only is assumed. A single exposure cancer initiation and latency causal model has also implicitly been assumed, and RRs from current burden are assumed to be transferable to recent and future exposure situations. For the exposure level factors, assumptions may also have to be made about the distributions of exposure levels where good exposure data are not available.

The principal sources of uncertainty and bias in the estimates made for future burden are the same as for the current burden estimates. These being the possibility of inaccurate risk exposure periods, inappropriate distributions for cancer latency, unknown numbers exposed at the ‘higher’ and ‘lower’ exposure levels, and risk estimates that are (a) too high e.g. taken from studies of highly exposed workers and inappropriately applied to those exposed at lower levels, or (b) biased downwards by a healthy worker effect in the study from which the risk estimate is taken. Comparisons and rankings between industries and occupations, and also between alternative ‘intervention scenarios’ to assess the effectiveness of alternative interventions, are thus recommended.

In order to monitor the success of an intervention to reduce exposure, the most appropriate approach would be to monitor future exposure levels. Due to the effect of exposures in the past continuing to contribute to future burden no reducion in attributable cancer numbers is seen for

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the solid tumours until at least 2030. The value of a future achieved exposure level can be used to obtain an achieved attributable fraction using the same prediction methodology. This must be applied to forecasts of future cancer registrations made from the same baseline (in our case 2005) to obtain achieved attributable numbers that can then be compared with the target forecasts estimated for the intervention.

In summary, a method is presented here to estimate the future burden of occupational cancer that facilitates testing of the effect of a range of potential interventions. The method is adaptable to situations where data, in particular exposure level data, are sparse; it is most robust in allowing comparison between intervention effects, and where a broad estimate of future burden across exposures is required. However it can also be adapted to assess impacts of policy in specific industries, and can be adapted to use higher quality exposure data if available.

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CONTENTS

1. INTRODUCTION 1

2. APPROACHES FOR ESTIMATING THE FUTURE BURDEN OF OCCUPATIONAL CANCER 2

2.1 Attributable Fraction Approach 2 2.2 Lifetime Risk Approach 6 2.3 Cancer Projection Approach 9

3. MAIN ASSUMPTIONS OF THE ATTRIBUTABLE FRACTION AND LIFETIME RISK APPROACHES 11

4. OUTPUTS OF THE AF AND LR APPROACHES 12

5. MONITORING SUCCESS 13

6. CASE STUDY EXAMPLE: ATTRIBUTABLE FRACTION APPROACH 14

6.1: Choose Forecast Target Years And Identify REPs 14 6.2: Estimate Currently Exposed Numbers And Allocate To H/M/L/B Exposure Levels.. 14 6.3 Select Appropriate Employment Trend Factors (For Broad Industry Sector Groups) 16 6.4 Estimate GMs, GSDs And Change In Exposure Levels From Available Exposure Data 16 6.5 Choose Change Scenario(s) 17 6.6 Forecast Cancer Numbers For Estimating Attributable Numbers 20 6.7 Results, Attributable Fraction Approach 21

7. APPENDICES 29

: Attributable Fraction Method: Features Of The Estimation Process 29Appendix 1Appendix 2: Derivation Of The Exposure Level Factors, Attributable Fraction Approach 40

: Lifetime Risk Method: Details Of The Estimation Process 46Appendix 3Appendix 4: Data Required For Estimating Future Burden 49 Appendix 5: Steps In The Calculation Of Future Burden 50

: Data Tables For AF And LR Methods 52Appendix 6Appendix 7: Additional Equations Used In The Af Approach 61 Appendix 8: Data For CAREX And LFS/CoE Adjustment Factors .. 63 Appendix 9: Cancer Deaths And Registrations (1995-2005) 66 Appendix 10: Worked Calculation Example, Attributable Fraction Approach 68

8. REFERENCES 71

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1. INTRODUCTION

The currently used estimate of the proportion of cancer deaths in Great Britain due to occupational causes is 4%, with an uncertainty range of from 2% to 8%, based on the study of Doll and Peto (1981). This equates to approximately 6,000 deaths per annum (with a range of 3,000 to 12,000). The aim of this project was to update these figures and to estimate the burden of cancer due to occupation in Great Britain. Specific aims were:

(1) To estimate a current overall attributable fraction and attributable number of cancers due to occupation.

(2) To indicate the relative contributions of the occupational carcinogens to the current total burden of occupational cancer in Great Britain, thereby providing evidence on which to prioritise intervention.

(3) To estimate future burden resulting from current exposures, in order to indicate where to prioritise future intervention.

(4) To suggest areas for future data collection, to improve the quality of the estimates.

This technical report describes the methodology for aim (3).

The aim of this part of the project is to build on methodology developed for estimation of current burden and develop a methodology to estimate and predict the burden of occupational cancer in Great Britain over the next fifty years. The long time scale is needed to take account of the latency of some of the cancers involved. A principle aim is to identify, from epidemiological and other relevant data, industries and occupations where HSE might best target efforts to reduce the future incidence of occupational cancer. For this objective forecasts of future burden are required based on current exposure trends by exposure, industry and occupation. An additional aim is to assess the effects on future cancer numbers of interventions taking place at different times, or targeting different size workplaces or specific industries or occupations. If targets are set a further aim is to assess how achievement against these targets can be monitored.

Section 2 below describes three alternative methodological approaches to predicting future burden, of which the two that were thought to be the most useful have been developed in some detail. A brief description is given of each method; full details of the two approaches used are contained in technical Appendices 1-3. An example of the application of the approach chosen for predicting cancer burden, for lung cancer from exposure to crystalline silica, is given in Section 6. More details of the statistical and data aspects of the methodology are given in Appendices 4 to 10. An international workshop of experts was held in June 2008 to discuss the different methodological approaches and this document incorporates the advice and decisions made by workshop participants.

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2. APPROACHES FOR ESTIMATING THE FUTURE BURDEN OF OCCUPATIONAL CANCER

There are three basic approaches to estimating the future burden of occupational cancer:

1) Estimating future attributable fractions (AFs) i.e. the proportions of cases that would not have occurred in the absence of exposure, using the methodology previously developed for estimation of current burden with exposure windows projected forward in time. These are applied to projected cancer numbers based on current cancer rates and population projections by age and sex i.e., the attributable fraction approach (Armstrong BG and Darnton A: 2007i)

2) Estimating a ‘general population’ lifetime risk for a newly exposed workers cohort from national incidence rates applied to future ‘person-years-at-risk’, and applying an estimate of excess risk (relative risk minus 1) from current occupational exposures to this lifetime risk to obtain occupation attributable numbers (ANs) (termed the lifetime risk approach)

3) Estimating attributable numbers directly from projected cancer numbers based on epidemiological as well as demographic components of change. This requires an occupational exposure risk factor as well as non-occupational risks to be incorporated into a structural regression model for cancer incidence rates. This is then projected forward and applied to population projections by age and sex to obtain forecast cancer numbers that can be partitioned between occupational and non-occupational attribution (cancer projection approach, Mathers CD and Loncar D: 2006).

In all approaches the number of attributable cancers that could be avoided by reducing exposure to known carcinogens in the workplace is estimated by comparing estimates made for ‘baseline’ or exposure trend scenarios with estimates made for ‘intervention’ scenarios, which are based on targeted reductions in exposure levels.

2.1 Attributable Fraction Approach

This approach is an extension of the methods used to estimate the current burden of occupational cancer. The methodology has been described in detail elsewhere (Rushton L, Hutchings S, and Brown T: 2008; Rushton L, et al. 2010). In brief, estimation of the AF depends on knowledge of the risk of the disease due to the exposure of interest and the proportion of the population exposed. Risk estimates were obtained from published literature, and national data sources were used for estimating proportions exposed. Dose-response risk estimates were generally not available, nor were proportions available of those exposed at different levels of exposure over time for the working population in GB. Separate risk estimates were therefore generally extracted relating to overall ‘higher’, ‘medium’, ‘lower’ and ‘background’ levels. To take account of cancer latency a ‘risk exposure period’ (REP) was defined as the period during which exposure occurred that was relevant to the development of the cancer in the target year 2005. For solid tumours a latency of at least 10 and at most 50 years was assumed and for haematopoietic neoplasms 0-20 years latency was assumed. The proportion of the GB population exposed to the occupational carcinogens of concern over the REP was estimated taking into account changes in numbers employed and adjusting for employment turnover. For the current burden, estimates were made separately for each cancer/exposure pairing, and by exposed industry or occupation. The same approach is used for predicting future burden and separate estimates can be made for alternative scenarios of changes in exposure levels and proportions exposed. A ‘baseline trend scenario’ is based on the pattern of past and current exposure up to the present, and on forecast exposure levels into the future. ‘Intervention scenarios’ are also based on past and current exposures up to the present, and suitably chosen target exposure levels in the future. To assess their relative impact on reducing attributable numbers, the intervention scenario results may be compared to a ‘baseline scenario’, based similarly on past exposures but with current exposures held constant into the future.

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In order to estimate future attributable fractions, firstly a suitable range of forecast target years (FTYs) is chosen for which the AFs are to be estimated. These must take long latencies into account, for example 2010, 2020, 2030, 2040, 2050 and 2060 are used for a solid tumour such as lung cancer where latencies of between 10 and 50 years are assumed, as in the current burden methodology. For each FTY a risk exposure period (REP) is defined as for current burden (Hutchings, S, Rushton, L. and Brown, T: 2007), but now shifted forward in time, illustrated in Figure 1. These risk exposure periods take account of cancer latency, so that for an estimate of the burden of occupational cancer in 2020 for example, the REP is 1971-2010.

The forecast REPs are divided into ten year ‘estimation intervals’ so that appropriate adjustment factors for estimation of the proportion exposed (and separate relative risk estimates if appropriate) can be applied to each ten-year interval. Ten year intervals are chosen for convenience; any interval could be used up to the data handling capacity of the software used. To adjust for changing overall employment levels for the period up to 2001-10 these factors are based on estimates of numbers employed from the Labour Force Survey. To adjust for changing exposure levels a shift in the proportion of workers exposed from higher to lower levels of exposure is estimated using data on mean exposure levels and assumptions about the distribution of these levels. These adjustments are made where possible by grouped main industry sector and workplace size. For 2011-20 onwards the factors may be (1) held constant for a baseline scenario, (2) based on underlying trends projected forwards in time to represent a baseline trend scenario, or (3) set arbitrarily to represent target levels of exposure for an intervention scenario, e.g. by reduction of the proportion exposed at higher levels. In the case of the baseline trend scenario linear projections are used up to 20 years into the future, after which levels are assumed to remain constant due to the uncertainty of any predictions made beyond this point. See Appendices A1.1 and A1.2 for full details of the adjustment factors used.

Figure 1: Examples of forecast Risk Exposure Periods for a solid tumour with assumed latency of between 10 and 50 years

The intervention scenarios can be represented by changes in numbers exposed at different exposure levels and/or in different workplace sizes, and at different points in time (the choice is limited to the beginning of an estimation interval), or as a trend across time. Changes to exposure levels can also be reflected in the relative risk estimate for each estimation interval, based on a suitable model of exposure-response (a linear dose-response relationship is generally assumed). Choice of intervention scenarios depends on policy needs and their potential usefulness for decision-making (see Appendix A1.3.2). As an illustration, in the

REPsREPs FTYFTYss

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1971-80 2001-101981-90 1991-00

20502030 2040

2021-302011-20 2031-40 2041-50

2010

1961-70

‘Known’ exposure Forecast exposure20602020

1971-80 2001-101981-90 1991-00

20502030 2040

2021-302011-20 2031-40 2041-50

2010

1961-70

‘Known’ exposure Forecast exposure

1010 yyear estiear estimmatiationon iintnteervrvaallss

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example of lung cancer from exposure to crystalline silica, the effect of hypothetical interventions to halve the exposure limit from 0.1 to 0.05 mg/m3 in 2010 (or in 2020) in all workplaces, and changes to the levels of compliance achieved in different sizes of workplaces, or amongst all employers, are estimated in terms of reduced AFs and avoided attributable registrations.

The number of workers ever exposed is estimated for each 10-year estimation interval. As for current burden, the numbers are estimated from the CARcinogen EXposure database (CAREX) (for 1990-93) (Pannett B, et al, 1998) or UK Labour Force Survey (LFS) (LFS, 2009), Census of Employment or Annual Business Inquiry data (ONS, 2006), applying a turnover equation that takes into account life expectancy to the forecast target year. Note that in the estimation of numbers ever exposed the cohort of workers entering at the beginning of the REP is assumed to be of all working ages (the age distribution matching that of the general population of working age), and thereafter workers recruited into the ever exposed cohort through employment turnover in each estimation interval are assumed to be only aged 15-24. Exposure level change factors are applied only to the workers newly recruited during the estimation interval, both before and after 2005. The proportion of the population exposed, and occupation attributable fractions (AFs) are then estimated using the standard formulae (Levin’s or Miettenen’s formulae as for current burden4), and are then summed across the REP for each forecast target year.

An important issue with respect to the AF approach is the shape taken by the distribution of latencies between our assumed minima and maxima. In the estimation of current burden a uniform distribution was assumed implicitly where it was necessary to split the REP where for example an exposure had ceased. However, this assumes that cancers are equally likely to be initiated at any time across the REP to appear in the target year (this also assumes a single exposure cancer initiation model). In reality the uniform distribution assumption is unlikely and for solid tumours, for example, a peak of initiation at around 35 years latency is thought to be most likely (Magnani C et al: 2008) (Hodgson J, personal communication). For estimating the future burden, the shape of the distribution will determine the numbers of cancers assumed to be initiated before and after the intervention. Several different distributions are explored in Appendix A1.4.1. A lognormal distribution has been assumed for this project.

Attributable numbers of cancers for a given target year are estimated by weighting the contribution to attributable numbers by the proportion of cancers initiated in each ten year estimation interval of the REP using the chosen latency distribution. For example, if a uniform distribution holds and for a target year of 2050, all 10 year intervals from 2000 to 2040 are weighted equally. For the lognormal distribution, the weights are the values for the (probability mass function) distribution at each year, averaged across estimation intervals, from 2000 to 2040 representing latencies of 50 down to 10 years for solid tumours (see Figure 2). More details are given in Appendix A1.4.2. The weights are applied during the calculation of AF; a detailed example of the calculation of attributable fraction is in Appendix 10.

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Figure 2: Lognormal latency distribution aggregated across estimation intervals

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These AFs are then applied to projected numbers of deaths and/or registrations in each forecast target year to obtain occupation attributable numbers (ANs). For this a forecast of cancer deaths and registrations is needed for each FTY. These projected numbers can be estimated to take into account using one of the following options:

• Current death/registration rates only (assuming no demographic and non-occupational exposure change)

• Demographic structure change only. This is based on national population projections, to which current cancer rates are applied. No account then is taken of future changes in survival, or of non-occupational exposure trends.

• All future demographic and non-occupational exposure trends (see section 2.3 below). Existing projections of cancer numbers could be used, or numbers would be obtained by using a cancer projection approach based on either an age-period-cohort or structural forecasting model.

The calculation process for future burden estimates is summarised in Figure 3 and in detail in Appendices 1 & 2.

When forecasting future cancer burden, issues arise as regards the relative contributions of occupational, environmental, diet and lifestyle factors to overall numbers. To correctly estimate the occupational contribution, the projection element must assume and maintain a constant ratio of the other environmental, diet and lifestyle exposure factor levels. For these estimates therefore forecast cancer numbers have been estimated using constant (current) death/registration rates, applied to population projections by age and sex, to examine the change due to occupational exposures only (i.e. 2005 cancer rates by sex and age band have been applied to projected population by sex and age band). The projected AFs and attributable numbers can also then be compared directly with current burden estimates. Due to the increasing population but also in particular the increasing proportion of the elderly, this results in rising numbers of total deaths and registrations, so that attributable numbers may increase even though AFs are falling.

Full details of the estimation process for the attributable fraction approach are in Appendix 1.

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Figure 3: Calculation process for future burden (AF approach)

Choose baseline, trend and intervention

scenario(s) and adjustment factors for each

Choose forecast target years (FTYs)

and identify REPs and estimation intervals

List exposed industries /occupations, allocate to H/M/L/B

exposure levels to match RRs and estimate numbers exposed

in an ‘origin’ year

Identify RRs for H/M/L/B exposure levels

Estimate GMs, GSDs and change in exposure

levels from available exposure data

For each scenario

Select appropriate employment trend factors

Choose adjustment factors to characterise

each scenario

RR adjustment

factors

Calculate exposure level

factors

Employment level

adjustment factors

Workplace size

adjustment factors

Apply employment and exposure level factors

by estimation interval

Estimate AF for each estimation

interval

RR by exposure

level

Apply AF to projected deaths /registrations to get AN for each FTY.

ANB for baseline, ANS for intervention scenarios

Sum AFs across estimation intervals for each FTY’s REP

ANB - ANS = ‘avoided’ cancers

Assume suitable latency distribution e.g.

lognormal

Estimate projected cancer

deaths/registrations using current rates and FTY population projections

by age/sex

Estimate Pr(E) by estimation interval

for each FTY

Estimate numbers ever exposed by

estimation interval for each FTY’s REP

2.2 Lifetime Risk Approach

An individual exposed worker’s lifetime risk for a particular occupational cancer is the product of the lifetime risk for that cancer in the general population and the relative risk associated with the worker’s level of exposure. Thus for a cohort of workers currently in a

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particular age category the difference between the estimates of their lifetime risk assuming no change to current exposures and estimates assuming some change in exposure level occurs in the future, will give an indication of the benefit resulting from the change. In particular, the total number of cancer cases attributable to the exposure can be estimated for a specific birth cohort starting work after the exposure levels have changed, by estimating their lifetime risk of cancer with and without the change. This would indicate the numbers of cancers that could be avoided if, for example, exposure levels dropped.

The choice of birth cohort is important. Unlike the attributable fraction approach, no account is taken of the risk exposure period and no assumptions are made about latency. However, for any specific year all exposures previous to this will continue to produce cancers into the future. The most straightforward approach is therefore to use the youngest, previously unexposed, workers, e.g. aged 15-24 i.e. newly recruited. For a full picture of future risk however those currently exposed to a particular occupational carcinogen and of all working ages would be the correct cohort to use, presented in a way that allows the contribution of past exposures to be recognised. This would however require assumptions to be made about the time-response relationship between the exposure and attributed cancer, i.e. latency as for the attributable fraction approach. Cohorts of an age that can be assumed to have no previous exposure are therefore used in this approach.

The approach is therefore to estimate a ‘general population’ lifetime risk for an exposed cohort newly recruited in 2010-14, and assumed to be aged 15-24 at recruitment. The size of the initial cohort can be estimated using a simple turnover equation, multiplying currently exposed numbers by annual employment turnover and by number of years (five in this case). The ‘general population’ lifetime risk is estimated by multiplying current age-sex specific mortality or incidence rates for specific cancers by the cohort person-years-at-risk. The person years at risk are derived from the numbers of survivors using current life table data based on current life expectancy, up to 2079 for the 2010-14 intake. This gives the proportion of the initial cohort that will develop the specific cancers. Occupation attributable numbers for a specific carcinogen are then estimated by multiplying this proportion by the excess risk (RR-1) for the occupational carcinogen at a certain current level of occupational exposure; the relative risks are obtained from the current burden project. Multiplying these individual excess lifetime risks by an estimate of the numbers exposed then gives the numbers of attributable cancers that can be expected to occur over the lifetime of the cohort of currently exposed workers.

The numbers currently exposed can be adjusted in the same way as in the attributable fraction approach, by applying employment adjustment factors and proportions of the workforce exposed at different exposure levels (‘higher’ and ‘lower’), to CAREX, LFS or Annual Business Inquiry data for the cohort’s first window of exposure (for example 2010 for the 2010-14 cohort).

Lifetime attributable number estimates can be made for different intervention scenario cohorts and then compared with baseline or with baseline trend estimates (baseline, trend and intervention scenarios are as defined and implemented for the AF approach above). Avoided cancers are then obtained by subtraction of intervention from baseline scenario results.

It is possible to make a series of estimates for successive newly recruited cohorts (2010-14, 2015-19, 2020-24 etc), for example up to the expected maximum life expectancy of the first intake cohort (2070-74). Estimates can then be made for a forecast or intervention scenario involving change over time affecting cohorts beyond 2010, for 5 year time intervals up to 2070-74. Avoided cancer numbers can be estimated, but actual attributable cancer numbers in each time interval will represent only the cancers for these newly exposed cohorts. The numbers do not include any from previously exposed workers, and whole lifetime cancer numbers are only estimable for the first cohort if the 2070-74 cut-off is maintained, as

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subsequent cohorts have their older and highest cancer-risk years truncated. The results by time interval are not therefore comparable with the AF approach.

The lifetime attributable numbers for the 2010-14 cohort could however be compared with the results from the AF approach. Total attributable cancer numbers in one forecast target year for the LF approach are for the cohort of all workers from age 15-24 ever exposed and eligible to become a case in that year. This should equal the total attributable cancers appearing over the (adult) lifetime of the cohort of workers recruited to the same exposure in a single year. So for solid tumours the LR results for a single (2010-14) newly recruited cohort should equal five times the AF result for a single forecast target year, specifically 2060 as this represents only exposures from 2010 (the forecast REP for 2060 is 2010-50 for solid tumours in the AF approach). However, for the AF method the numbers ‘ever exposed’ are estimated assuming a 40 years risk exposure period (REP). This is equivalent to the ‘cohort person-years-at-risk’ (divided by 5) but only in proportion to length of REP / average cohort life expectancy (i.e. about 40/60 in the examples for men). This may indicate underestimation of attributable cancers in the AF approach.

Alternatively, the cohort lifetime attributable fraction can also be estimated as cohort lifetime attributable numbers (ANs) divided by (national population lifetime cancer numbers plus cohort ANs).

The main advantage of the lifetime risk approach is that it avoids the need for any assumptions about maximum and minimum latency and the timing and length of the risk exposure period. It is also easy to present and describe, as “the number of cancers attributable to work that those newly exposed to a workplace carcinogen can be expected to contract in their lifetimes”.

The main disadvantage is that the method can only be applied as full lifetime risk to a single newly recruited worker cohort with no previous exposure, and not the currently exposed cohort of all working ages unless strong assumptions are made about exposure-time-response relationships (not considered appropriate and therefore not attempted for this project). The approach is not therefore readily adaptable to forecasting or introducing intervention scenarios that involve changes over time (the compromise of introducing successive intake cohorts and time estimation intervals sacrifices the simplicity of presentation and interpretation). It also has to be assumed that the relative risk used for the ‘all adult’ age group in the current burden estimates are appropriate (portable) to young worker groups at the outset of their working life, and the same risks continue as they age (this is probably not an unreasonable assumption). In common with the AF method it is also assumed that exposure levels represented by the ‘higher’ and ‘lower’ allocations remain the same in the future as for the current burden estimation, that is for a risk exposure period of 1956-95 (for solid tumours such as lung cancer).

As noted above, in general the results for the lifetime risk approach will not be comparable to the attributable fraction and cancer projection approaches (see below), as both these estimate occupation attributable numbers for a specific forecast target year whereas the lifetime risk approach estimates attributable numbers over the lifetime of a specific cohort of exposed workers. This may be a further disadvantage of the approach in that there will be less scope to devise a method to monitor success in reducing cancer numbers, as these are not estimated for all exposed workers for a defined time period. However it is possible to estimate attributable numbers separately for 10-year intervals through the lifetime of one specific worker cohort.

Full details of the estimation process for the lifetime risk approach are given in Appendix 3.

8

2.3 Cancer Projection Approach

This approach uses current and past cancer rates to predict rates into the future. There are two standard methods.

The age-period-cohort model (Holford TR: 1983) uses historic cancer rates by age, sex and time period. Synthetic birth cohorts are constructed from 5-year age groups and 5-year calendar periods by subtracting age from period. The model in general takes the following form (Bray F and Moller B: 2006):

Rap = exp(Aa + Dp + Pp + Cc) where Rap= incidence rate for age group a in calendar period p

Dp = drift (average increase or decrease in rates over time) Aa = age component for age group a Pp = non-linear period component for period p Cc = non-linear cohort component for cohort c

The object is to estimate the drift, or average underlying trend over time, which can then be projected forward in time. The Poisson regression log function link in the above equation, which produces predictions that grow exponentially over time, can be replaced by a power function to reduce the growth in the predicted rates (Moller B et al: 2003). The age component represents the change in cancer rates with age. The period component may represent changes in cancer classification or diagnosis or the introduction of a new potent carcinogen. The cohort component accounts for cancer latency and also patterns of past exposure for a particular population cohort, as it represents factors, notably employment and lifestyle factors, shared by a particular generation (birth cohort) as they age together. The drift may also represent changing risk factors or factor levels over time. This method can account for past change in exposure levels but not for future changes.

The HSE use the age-period-cohort projection approach on mesothelioma register data to predict future mesothelioma burden (Hodgson JT et al: 2005).

The ‘structural approach’ uses the estimated relationship between cancer incidence and independent risk factors such as socio-economic status and smoking levels in a regression model. The Global Burden of Disease projections use this approach (Murray JL and Lopez AD: 1997; Mathers CD and Loncar D: 2006):

Raki = exp(Caki + β1lnY + β2lnHC + β3lnY2 +β4T+ β5lnSI)

where Raki = incidence rate for age a, sex k and cause i Caki = constant Y = GDP/capita, HC = human capital, T = time, SI = smoking impact

Projected cancer numbers are then obtained by projecting the rates forward, and applying these to projected national population estimates by age and sex; these projection methods are well established for deaths/registrations (Bray and Moller, 2006).

Both these methods can allow for • The effect of demographic changes on projected ANs, and • The continuing effect on ANs of latency and therefore past exposures.

In theory the structural model could be adapted to estimate separately the relative contributions of occupational exposures versus other non-occupational risk factors. This could

9

be achieved by fitting an appropriate UK model that included non-occupational and occupational exposure factors to the current cancer incidence data, and using projections of these risk factor levels to forecast non-occupational and occupational cancers. However this has not been attempted for this project in the absence of good quality risk-response information for non-occupational as well as occupational factors.

Simpler approaches additional to the age-period-cohort and structural models above which are used to predict future cancer rates are:

1) To assume constant rates 2) To use a straight regression on age and sex

As above, these rates are applied to projected national population estimates by age and sex. In the AF approach described in section 2.1 constant cancer rates by age and sex are applied to projected national population estimates to obtain the denominator for an estimate of attributable numbers.

A hybrid approach between the cancer projection approach and the attributable fraction approach described above would be to project cancer numbers directly, using either the age-period-cohort or the structural model (or use existing projections) that take account of changes in non-occupational exposures as well as demographic changes, and to apply the attributable fractions obtained from the AF approach to these projections to obtain attributable numbers (see Section 2.1 above). This would retain the main advantage of the AF approach (allowing estimation for alternative future scenarios).

However as already noted, for the future burden AF to be used to correctly estimate attributable numbers, it should be applied to a projected cancer estimate that maintains a constant ratio between the relative contributions of occupational, environmental, diet and lifestyle factors. Otherwise, if numbers of a specific cancer are increasing due to changes in a non-occupational factor e.g. diet, environmental pollution, the occupation AF applied to these projected numbers will overestimate the occupation attributable numbers. Similarly, as overall projected lung cancer numbers fall in the future due to fewer people smoking, the occupational AF applied to this total will underestimate the true occupation attributable number of lung cancers, except where (unmeasured) synergies between occupational exposures and smoking are operating.

For this reason, in the attributable fraction approach constant cancer rates projected to take into account demographic changes only have been used to estimate future attributable cancer numbers.

10

3. MAIN ASSUMPTIONS OF THE ATTRIBUTABLE FRACTION AND LIFETIME RISK APPROACHES

3.1 Attributable Fraction (AF) Approach

The maximum and minimum latency determines the duration and time period of the forecast risk exposure periods, and so they are central to estimating numbers ‘ever exposed’ and the periods when their exposure counts in determining attributable numbers. For example, where the standard assumption about bladder cancer (as a solid tumour) is a minimum latency of ten years and a maximum of 50 years, if the maximum latency is really 30 years rather than 50, then it follows that:

1) the numbers ever exposed will be lower (reflecting 20 not 40 years turnover) so that AFs and therefore ANs will be much lower,

2) interventions in 2010 to eliminate exposure for example will cause attributable numbers to reach zero by 2040 rather than 2060.

The shape of the latency distribution affects the number of cancers prevented by an intervention to reduce numbers employed, or to reduce exposure levels, during the REP. For example if a power distribution is assumed, where most cancers are close to the maximum latency, the impact in the target years we are considering (up to 2060) will be much lower because most of the cancers have already been initiated. On the other hand for a uniform distribution, where as many cancers would be initiated after as before an intervention in the middle of the REP for that forecast target year, the impact would be greater. The lognormal distribution was chosen because there is the best, albeit limited, evidence for this shape (Armenian HK and Lilienfeld AM: 1974). An indication of the size of the effect of using alternative distributions is shown in Figure 12 in Appendix A1.4.1

The AF approach also assumes that the current burden relative risks for the different exposure levels are applicable into the forecast period, and therefore that the nature of the exposures has not changed from those occurring in the current burden REP.

A single exposure cancer initiation and latency causal model has also implicitly been assumed. This model proposed by Iversen and Arley (Iversen S and Arley N, 1950) assumed cancer results from a single cell becoming initiated and then remaining in a latent state for a period of time subject to a random distribution. A lognormal distribution of latencies has generally been used to fit the model to epidemiologic data.

3.2 Life Time Risk (LR) Approach

No assumptions are required about cancer latency. The assumptions that are made are common to the AF approach, including:

• All workers were first exposed at age 15 • New recruits are all aged 15-24 • Past exposures match exposures in the forecast period

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4. OUTPUTS OF THE AF AND LR APPROACHES

Results from the AF approach is illustrated in Section 6 for lung cancer and silica exposure. The outputs as regards prediction of the attributable burden are compared below. The main difference between the approaches is that under the AF approach the estimates are annual and applied to a series of forecast target years, and under the LR approach they represent whole lifetime risk for a defined exposed cohort, or for forecast time periods.

AAF approachF approach •• Predicted attributable fPredicted attributable frractions (actions ( AAFsFs))

and attributable cancers (and attributable cancers ( AANsNs) f) foror fforecastorecast ffuture occupational exuture occupational exposureposure

•• Predicted AFsPredicted AFs andand ANsANs fforor selectedselected target reductions in occupationallytarget reductions in occupationallyexexposed nuposed nummbers atbers at ‘‘higherhigher’’ exexposureposurelevlevels, achieels, achievved byed by specifspecific datic dateses ((‘‘intervintervention scenariosention scenarios ’’))

•• ‘‘avavoidedoided ’’ cancers under selectedcancers under selected targettarget ‘‘intervintervention scenariosention scenarios ’’

•• AFsAFs andand ANsANs aare fre foor a series or a series off fforecast target yorecast target years (ears ( FFTTYYss)), i.e. they, i.e. theyare periodic annual ratesare periodic annual rates

LR approachLR approach •• ExExcess cancers due to currentcess cancers due to current

occupational exoccupational exposureposure –– oovverer ththe le liiffetetiimme ofe of aa sisinnggllee nenewwllyy

eexxpoposesed cd cohoohorrt ort or ––

rreecrcruuiiteted chd chohohortsorts •• ExExcess cancers fcess cancers for for forecast anorecast andd

selected target occupational exposureselected target occupational exposurelevleveellss

–– FForor aa nnewlewlyy popostst--iintnterervveentntiionon exexpoposseded cocohohorrt fot forr iittss lliifefetitimme ore or bbyy titimmee peperriiodod..

–– fforor a sa sereriieses ofof rrececrruiuitteed cod cohohorrttss ovoveerr ththeieirr lliifefetitimme ore or byby titimmee peperriioodd

•• ‘‘avavoidedoided’’ cancers under selectedcancers under selected targettarget ‘‘intervintervention scenariosention scenarios ’’ foforr aa single cohortsingle cohort

•• AF wAF wiithin cohortthin cohort •• AF as a fAF as a frraction oaction off total populatitotal populationon

liflifeetimtimee riskrisk •• EstimEstimates are fates are for cohort lifor cohort lifetimetime, or bye, or by

timtime period fe period for defor defined entryined entry cohortscohorts

12

5. MONITORING SUCCESS

It is important to note that if targets are to be set to reduce the incidence of occupational cancer, they must be based on reduced numbers (registrations or deaths) and not on the AF. If there are several risk factors contributing to the burden of a disease a change in attributable fraction for one factor will result in a change in the attributable fraction of the others. Therefore attributable numbers rather than AFs represent a more useful estimate of the future cancer burden due to occupation.

For the AF approach, the success of a given occupational-based intervention should be assessed by monitoring exposure levels in the future and using achieved numbers exposed at these levels to obtain an ‘achieved’ AF, using the same estimation methodology, that is then applied to registration forecasts from the same baseline (in our case 2005) to obtain ‘achieved’ attributable numbers that can then be compared with the target forecasts estimated for the intervention. For the LR approach, the success of an intervention would be measured in achieved attributable cancers as above but for a cohort’s lifetime or forecast periods rather than for FTYs.

It may be possible to use the cancer projection approach to estimate cancer numbers based on target occupational exposure levels and known non-occupational exposure levels and subtract these from ‘achieved’ numbers, but for this approach excellent all-exposure-response modelling is needed.

13

6. CASE STUDY EXAMPLE: ATTRIBUTABLE FRACTION APPROACH

Lung cancer and crystalline silica exposure In order to illustrate the application of the methodology, we forecast the future burden of lung cancer in Great Britain due to occupational exposure to respirable crystalline silica (RCS) under a range of different scenarios, shown in Table 7 below. The full range of scenarios estimated for RCS, together with full and more detailed results, is available in the accompanying future burden results report (The Burden of Occupational Cancer in Great Britain: Predicting Future Burden Technical Report: Results: In preparation).

Preliminary steps in the calculation

6.1: Choose Forecast Target Years And Identify REPs

The standard solid tumour REP is appropriate for cancer of the lung, with latency of between 10 and 50 years, and a ‘peak’ latency at about 35 years. A lognormal distribution with mean ln(35) and a range of 40 years translated to a standard deviation of (ln(40)/6 = 0.61) is assumed for the latency distribution.

Given this REP and latency assumption, a change made to exposed numbers or exposure levels in 2010 will be affecting cancer incidence until 2060. The target years chosen are therefore 2010, 2020, 2030, 2040, 2050 and 2060, to give an indication of how cancers attributable to a change occurring in 2010 will be distributed across the following 50 years. Additional estimates for RCS are given for 2070 and 2080 in the accompanying future burden results report to illustrate the effect of introducing reduced exposure standards in 2020 and 2030.

6.2: Estimate Currently Exposed Numbers And Allocate To H/M/L/B Exposure Levels, And Choose Relative Risks

For crystalline silica the numbers currently exposed were taken from CAREX estimates. The industries included and their allocations to high, medium, low and background exposure levels are in Table 1 below. From CAREX data, it is estimated that 554,244 men and 35,865 women were exposed to crystalline silica in 1990-93, with 445,431 men and 4,491 women exposed at ‘high’ level in construction (76% of exposed workers) and a further 16,544 men and 5,255 women exposed in the pottery industry at a ‘medium’ level of exposure.

For workers exposed at ‘high/medium’ level a relative risk was obtained from a meta-analysis by Kurihara and Wada, 2004 of 1.32 (95% CI 1.24 – 1.41). Other workers in manufacturing and in the services sector were judged to be exposed at a ‘low’ or ‘background’ level (see Table 1). A relative risk of 1.17 (1.12-1.22) was assumed for the low exposed group, from a study by Pelucchi et al, 2006, and 1.00 (95% CI 0.85-1.30) was assumed for the background exposed group, from a study by Steenland et al, 2001.

14

Table 1: Industry/Occupation classes and CAREX numbers exposed to crystalline silica

Agent Source Industry Exposure level

Sector code

Numbers exposed – total

Numbers exposed - Men

Numbers exposed - Women

Silica CAREX Air transport B G-Q 702 456 246 Silica CAREX Beverage industries B C-E 24 18 6 Silica CAREX Construction H F 449,930 445,431 4,499 Silica CAREX Crude petroleum and

natural gas production B C-E 1,670 1,269 401

Silica CAREX Education services B G-Q 610 397 214 Silica CAREX Electricity, gas and steam L C-E 3,382 2,570 812 Silica CAREX Food manufacturing B C-E 360 274 86 Silica CAREX Iron and steel basic

industries L C-E 3,853 2,928 925

Silica CAREX Land transport B G-Q 5,123 3,330 1,793 Silica CAREX Manufacture of fabricated

metal products, except machinery and equipment

L C-E 8,002 6,082 1,920

Silica CAREX Manufacture of footwear B C-E 100 76 24 Silica CAREX Manufacture of furniture

and fixture, except primary of metal

B C-E 398 302 96

Silica CAREX Manufacture of glass and glass products

L C-E 6,932 5,268 1,664

Silica CAREX Manufacture of industrial chemicals

L C-E 618 470 148

Silica CAREX Manufacture of instruments, photographic and optical goods

B C-E 1,567 1,191 376

Silica CAREX Manufacture of leather and products of leather or of its substitutes

B C-E 158 120 38

Silica CAREX Manufacture of machinery except electrical

L C-E 16,253 12,352 3,901

Silica CAREX Manufacture of miscellaneous products of petroleum and coal

L C-E 290 220 70

Silica CAREX Manufacture of other chemical products

L C-E 5,662 4,303 1,359

Silica CAREX Manufacture of other non-metallic mineral products

L C-E 24,406 18,549 5,857

Silica CAREX Manufacture of plastic products not elsewhere classified

B C-E 1,750 1,330 420

Silica CAREX Manufacture of pottery, china and earthenware

M C-E 21,769 16,544 5,225

Silica CAREX Manufacture of rubber products

B C-E 703 534 169

Silica CAREX Manufacture of textiles B C-E 338 257 81 Silica CAREX Manufacture of transport

equipment L C-E 6,420 4,879 1,541

Silica CAREX Manufacture of wearing apparel, except footwear

B C-E 494 375 119

Silica CAREX Metal ore mining L C-E 1,161 1,138 23 Silica CAREX Non-ferrous metal basic

industries L C-E 2,406 1,829 577

15

Agent Source Industry Exposure level

Sector code

Numbers exposed – total

Numbers exposed - Men

Numbers exposed - Women

Silica CAREX Other manufacturing industries

L C-E 1,316 1,000 316

Silica CAREX Other mining L C-E 16,240 15,915 325 Silica CAREX Petroleum refineries B C-E 114 87 27 Silica CAREX Printing, publishing and

allied industries B C-E 756 575 181

Silica CAREX Research and scientific institutes

B G-Q 880 572 308

Silica CAREX Sanitary and similar services

B G-Q 3,760 2,444 1,316

Silica CAREX Services allied to transport B G-Q 588 382 206 Silica CAREX Tobacco manufacture B C-E 1 1 0 Silica CAREX Water transport B G-Q 1,193 775 418 Silica Total 554,244 35,685

6.3 Select Appropriate Employment Trend Factors (For Broad Industry Sector Groups)

Data from the LFS indicate in general that employment levels in mining and manufacturing are falling and employment in the service sector and to a lesser degree construction is rising. Employment level adjustment factors were derived using the LFS data based on a linear trend, with an origin year of 1990-93. These factors were used to adjust the CAREX data to obtain proportions exposed at the high, medium, low and background levels in the estimation intervals (see Appendix 6 Table 13). The construction sector is dominated by small employers with 39% described as self-employed and a further 35% in workplaces employing less than 50 workers in 2005 (Appendix 6 Table 16). The data available on the proportions of small and medium sized enterprises from 1994 to 2006 combined with LFS estimates for the overall size of the construction industry indicate (using a linear fit) that these proportions will fall in the future (Appendix 8 Figure 19c).

6.4 Estimate GMs, GSDs And Change In Exposure Levels From Available Exposure Data

Creely et al, 2006 estimate that exposure levels, including crystalline silica, have in general been falling by about 8% a year since 1985. Average exposure levels were estimated to be 0.226 gm/m3 (geometric mean, with GSD=6.4) in 2003 based on exposures in construction in the Netherlands (IOM, personal communication, Lumen et al, 2001, Tjoe et al, 2003). This estimate is assumed to be indicative of the mean exposure levels to RCS and a probable fall in these levels across industry as a whole. GSD is held constant across the forecast period1.

Using these data 67% of workers were estimated to be exposed above the workplace exposure limit (WEL) of 0.1 mg/m3 in 2001-10, confirmed by evidence that levels of RCS in the UK construction industry greatly exceed the current WEL (Chisholm J: 1999). The boundaries between the high, medium, low and background exposure levels which were estimated from the allocation of exposed numbers to these categories are illustrated in Appendix 2 in Figure 16.

1 If the standard deviation of the assumed lognormal distribution (ln(GSD)) is allowed to fall in proportion to the mean (GM) across the forecast period, the proportions exposed at ‘high’ level rise to 100% rather than falling, due to the effect of a ‘tightening’ distribution of exposure levels as mean exposure falls. This effect is described in Appendix 2, and occurs where GSD is large with respect to distribution mean, and proportions estimated to be exposed a high level in 1975 are greater than 50%.

16

6.5 Choose Change Scenario(s)

The baseline and intervention scenarios tested are in Table 7 below with additional information on their implementation and interpretation summarized below. The scenarios in Table 7 are numbered as they appear in the RCS results section of the HSE Future Burden Results report. Not all of the scenarios are summarised in Table 7 and the full list and detail of the scenarios are given in the Future Burden results report (The Burden of Occupational Cancer in Great Britain: Predicting Future Burden: Technical Report). The first set of scenarios (3)-(5) test introducing a new exposure limit of half the current one, either in 2010, 2020 or 2030. Scenario (7) tests the introduction of a ‘dynamic’ exposure standard that is lowered gradually over three decades (new standards introduced in 2010, 2020 and 2030). Scenarios (9a)-(12a) test the effect of non-compliance varying by workplace size, with higher compliance (90%) assumed in larger workplaces; scenarios (15) and (13a) to (15a) test the introduction of a range of new exposure standards versus compliance at existing rates (15) and 90%. Scenarios (16), (17) and (19) test the effect of reducing the numbers exposed or the relative risk at higher exposure levels.

Baseline scenario (1) Current burden exposure levels and RRs for 1956-95 are assumed to be maintained, and (a) the 2005 total exposed numbers, and (b) the proportions exposed at ‘high/medium’, ‘low’ and ‘background’ levels are presumed to be unchanged from 2005 (use the 2001-10 estimation interval factors/proportions for 2011-2020 onwards).

Baseline (trend) scenario (2) As for (1) but (a) from 2005 onwards a continuation into the future of linear general employment trends as they affect total employed numbers is assumed, and (b) average exposure levels are assumed to follow a downward trend of 8% for crystalline silica so that the proportions exposed at ‘higher’ versus ‘lower’ levels also decline. These downward trends are assumed to have started in 1971-1980 and are continued through to 2021-2030, and the changing proportions at the different exposure levels are estimated as described in Appendix 2. The estimates are shown in Table 2 below as an example for this scenario. These proportions as estimated separately for each scenario determine the proportions of the population exposed by estimation interval from which AF is calculated.

17

Table 2: Baseline (trend) scenario proportions of exposed workers at high, medium, low and background exposure levels, crystalline silica

Proportions exposed (men plus women) Estimation Interval 1971-

1980* 1981-1990

1991-2000

2001-2010**

2011-2020

2021-2030

Exposure level High (H) 0.70 0.53 0.36 0.21 0.10 0.04 Medium (M) 0.05 0.06 0.06 0.04 0.03 0.02 Low (L) 0.22 0.33 0.41 0.43 0.38 0.28 Background (B) 0.03 0.08 0.18 0.32 0.49 0.66

Trend (annual) -8% Distribution Mean (GM,

mg/m3) 2.757 1.198 0.520 0.226 0.098 0.043 SD (GSD, mg/m3) 6.4 6.4 6.4 6.4 6.4 6.4

* Estimation interval that contains the origin year for the estimate of proportions exposed at H/M/L/B levels, usually 1975 (CAREX numbers are adjusted using the appropriate employment level adjustment factor, (Appendix 6 Table 13), but proportions can be estimated for any year ** Estimation interval that contains the baseline year at which GM was estimated

Intervention scenarios For dusty work involving crystalline silica, steps will be needed to control exposure above 0.05 mg/m3 by providing better local ventilation and if necessary respiratory protection, and air conditioned control cabs in dusty areas (Cherrie, J.W., 2008). This solution indicates a transfer of workers from exposure at ‘higher’ to ‘lower’ levels, rather than a reduction in overall numbers exposed. In particular the effect of reducing the exposure limit in the workplace from 0.1 to 0.05 mg/m3 is of interest.

Where there is an existing exposure standard:

Scenarios (3) to (5) test the time of introduction of a new exposure standard. The effect is estimated of introducing the lower standard in 2010, 2020 and 2030 and compared to maintaining the existing exposure standard.

Scenario (7) tests ‘dynamic’ exposure standards which are estimated as a fraction (e.g. half) of the exposure mean, forecast assuming no decline but taking into account the effect of earlier lowering of the exposure standard. Exposure means from 2011-20 onwards are as estimated for the estimation interval after introduction of each new standard

Compliance is assumed to be as for the existing exposure standard in 2001-10, remaining constant. The fraction is of each ‘current’ mean, i.e. of the 2001-10 mean for the exposure standard introduced in 2010, of the 2011-20 mean for the 2020 exposure standard etc.

In the case of this estimate, if non-compliance for the existing exposure standard in 2001-2010 exceeds 50% (i.e. mean exposure level is greater than the exposure standard), and the same level of non-compliance is maintained, mean exposure levels will only fall due to the new exposure standard (i.e. independently of the forecast decline). This will occur if the fraction of the exposure mean that is chosen to estimate a ‘dynamic’ standard produces an estimate lower than the existing standard (i.e. the fraction is less than existing standard / exposure mean). As an example, for the RCS existing standard / exposure mean = 0.1 / 0.226 = 0.4425, i.e., the fraction which produces no change in mean exposure levels in the forecast period. A fraction of 0.25 was therefore chosen. A fraction greater than existing standard / exposure mean (e.g. 0.5) will lead to less stringent exposure standards and rising rather than falling mean exposure levels. The mean exposure levels produced by

18

applying this fraction (intervention scenario 8) are shown for illustration only in Table 6 below, as for other exposures a halving of the exposure standard would normally be tested.

Scenario (7) is estimated as three separate interventions (i, ii and iii) in 2010, 2020 and 2030 respectively, to be considered as three parts of a single intervention taking place over time. As the effects are cumulative, only the final result is illustrated in the results below.

Scenarios (9a) to (12a) test the effect of compliance by workplace size, by assuming non-compliance remains as for the existing exposure standard in smaller workplaces (if it is greater than 10%) and is 10% in the larger workplaces. The four workplace size classes are entered into the lower non-compliance group in series.

Where there is no existing exposure standard (e.g. this is the case for DEE for example)

Scenarios (15) and (13a) to (15a) test the relative effect of introducing a range of new exposure standards. These may for example be standards used elsewhere, or fractions of a standard used elsewhere, or estimated from a threshold exposure level boundary.

In the case of RCS where there is an existing standard it is possible to test new standards assuming the same rate of compliance. Normally however in these cases non-compliance cannot be estimated as the proportion exposed above the existing exposure standard. New exposure standards are therefore tested assuming either 10% or 1% non-compliance. For the example for RCS, for which an exposure standard exists but with a current mean exposure level, which indicates high non-compliance (67%), new standards of 0.05 and 0.025 mg/m3 are tested with 90% compliance (scenarios (13a) to (15a). Note that ANs are determined by the rate of compliance as well as the exposure standard.

Scenarios (13a) to (15a) are also used to estimate the exposure standard that would be needed to produce a halving, or near zero, attributable numbers. To produce zero attributable numbers RR=1 (no excess risk) would be required at the lowest achievable exposure level (e.g. background level), and 100% compliance.

Where an exposure standard is not appropriate (asbestos, ETS, radon) the following apply:

Scenarios (16) to (17) test the effect of reducing the numbers (or proportions) exposed at higher levels by either (i) closing whole industries (numbers exposed for the industry become zero at intervention) or (ii) transferring increasing proportions (e.g. 25%, 50%, 75%) of workers exposed at high level to lower level exposure by transferring whole industries to lower level at intervention.

In the example for RCS, (i) all silica exposed manufacturing (sectors C-E) is closed down in 2010 (scenario 16), and (ii) all high exposed workers are transferred to low exposed in 2010 (scenario 17).

Scenario (19) tests the effect of reducing the relative risk at higher exposure levels by lowering (for example ‘halving’) excess risk (RR-1) and applying the new RR to existing exposure level bands. This implicitly assumes that workers have moved from higher to lower exposure levels but no attempt is made to identify the new boundary levels.

In the example for RCS, excess risk was halved at high, medium and low exposure levels in 2010 (scenario 19).

19

6.6 Forecast Cancer Numbers For Estimating Attributable Numbers

In the case of lung, the large number of these cancers has not fluctuated significantly over the last five years so that smoothing is not required (Appendix 9 Figure 20). Forecast cancer numbers are therefore based on rates in 2005 (2004 for registrations). There is a downward trend in both incidence and mortality in men to 2005, and an upward trend in women. Due to the predicted increase in GB population size and ageing of the population, forecast cancer numbers (not taking into account this already rising trend) are rising (see Table 3 and Figure 4 below).

Table 3: Forecast number of lung cancers in ages 25+, based on projected GB population

2005 2010 2020 2030 2040 2050 2060 2070 2080 Lung Deaths, Men 19,045 21,005 26,342 32,102 36,519 39,917 42,870 46,612 50,035 Registrations, Men 21,923 24,024 29,857 35,809 40,362 43,643 46,927 50,696 54,140

Deaths, Women 13,753 14,393 16,770 19,698 21,996 23,366 24,454 25,832 27,019 Registrations, Women 15,455 16,156 18,793 21,769 24,114 25,341 26,600 27,939 29,179

20

Figure 4: Forecast registrations for lung cancer and GB population, 2010-2080

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6.7 Results, Attributable Fraction Approach

In the following results, attributable numbers are based on cancer registrations. The cancer numbers presented using the attributable fraction (AF) approach are per year for the target years given, and are based on the all working age cohort of currently (2005) exposed workers. Attributable fractions and numbers are estimated for each of the scenarios (1) to (19) above.

Threshold exposure levels normally estimated from the proportions of workers exposed at the different exposure levels, according to the allocation of industries in Table 1, are given in Table 4. These remain constant for all scenarios and for men and women. Estimated mean (GM) exposure levels are shown in Table 5 below, and proportions exposed above the standards in Table 6, for the existing and the trial exposure standard described in the intervention scenarios. Data for a ‘dynamic’ estimate using a half rather than quarter mean is also shown in Tables 5 and 6 for illustration only (see comment under scenario (7) in Section 6.5 above).

The overall proportions estimated to be exposed at the different exposure levels under the various exposure scenarios are shown in Table 7 below.

21

Assuming a distribution of exposures to RCS based on the data set out above, and using the methods described in the main paper, it is estimated that 67% of workers were exposed above the workplace exposure limit (WEL) of 0.1 mg/m3 in 2001-10. Estimated proportions exposed above the WEL in earlier estimation intervals are in the first row of Table 6 below.

Table 4: Threshold exposure levels estimated from the proportions exposed at H/M/L/B level in the baseline year

Threshold Units: mg/m3 Source H/M 1.03 Estimated from H/M/L/B % allocation M/L 0.78 Estimated from H/M/L/B % allocation L/B 0.09 Estimated from H/M/L/B % allocation

Table 5: Estimated mean (GM) exposure levels under existing and proposed exposure standards assuming constant non-compliance as for existing exposure standard, forecast and appropriate intervention scenarios

Scenario Exposure standard

1971-80 1981-90 1991-00 2001-10 2011-20 2021-30 2031-40 2041-50

Baseline (trend)2 0.10 2.76 1.20 0.52 0.23 0.098 0.043 0.043 0.043 (2)1 0.05 2.76 1.20 0.52 0.23 0.113 0.113 0.113 0.113

(10) 1 0.025 2.76 1.20 0.52 0.23 0.0565 0.0565 0.0565 0.0565 ‘Dynamic’

estimate, quarter mean

(7i) 0.057 2.76 1.20 0.52 0.23 0.13 0.13 0.13 0.13 (7ii) 0.032 2.76 1.20 0.52 0.23 0.13 0.07 0.07 0.07 (7iii) 0.018 2.76 1.20 0.52 0.23 0.13 0.07 0.04 0.04

‘Dynamic’ estimate (b), half mean 3

(8i) 0.113 2.76 1.20 0.52 0.23 0.26 0.26 0.26 0.26 (8ii) 0.128 2.76 1.20 0.52 0.23 0.26 0.29 0.29 0.29 (8iii) 0.144 2.76 1.20 0.52 0.23 0.26 0.29 0.33 0.33

1 means and proportions are estimated against a baseline scenario 2 from 2031-40 zero decline is assumed 3 shown to illustrate the effect of 50%+ non-compliance and rising mean exposure levels only

22

Table 6: Proportions exposed above the existing and proposed exposure standards (non-compliance), forecast and appropriate intervention scenarios

Scenario Exposure standard

1971-80 1981-90 1991-00 2001-10 2011-20 2021-30 2031-40 2041-50

Baseline (trend)2 0.10 0.96 0.91 0.81 0.67 0.50 0.32 0.32 0.32

(2)1 0.05 0.98 0.96 0.90 0.79 0.67 0.67 0.67 0.67 (10) 1 0.025 0.99 0.98 0.95 0.88 0.67 0.67 0.67 0.67

‘Dynamic’ estimate,

quarter mean (7i) 0.057 0.98 0.95 0.88 0.77 0.67 0.67 0.67 0.67 (7ii) 0.032 0.99 0.97 0.93 0.85 0.77 0.67 0.67 0.67 (7iii) 0.018 1.00 0.99 0.96 0.91 0.85 0.77 0.67 0.67

‘Dynamic’ estimate (b),

half mean 3

(8i) 0.113 0.96 0.90 0.79 0.65 0.67 0.67 0.67 0.67 (8ii) 0.128 0.95 0.89 0.78 0.62 0.65 0.67 0.67 0.67 (8iii) 0.144 0.94 0.87 0.76 0.60 0.62 0.65 0.67 0.67

If the 8% annual fall in exposure levels does not continue beyond 2001-2010 (baseline scenario 1), nearly 800 attributable cancers are forecast in 2060. Numbers of cancers tend to rise for the baseline scenario due to rising numbers of total projected lung cancers caused by an aging population. All the figures highlight the lack of any reduction in cancers until after 2030 due to the long latency of the cancer. Introducing a reduced exposure standard (half the current one of 0.1 mg/m3, scenario 2) gives a reduction of both the AF and cancer numbers compared to the baseline; Figures 5a and b illustrate the effect of delaying the introduction by 10 and 20 years respectively (scenarios 4-5). Scenario 7 where the exposure standard is reduced as the expected exposure levels fall until 2030 is more effective than just halving the current standard (Table 7).

Scenarios 9a to 12a represent the introduction of a halved exposure standard (0.05 mg/m3) in 2010 plus the effect of improving compliance to 90% in an increasing range of workplaces from only the largest (250+ employees, scenario 6) to all workplaces including the self-employed (scenario 12a). Results for these compared to the baseline scenario (1) are shown in Figures 6a and b. Attributable cancers do not disappear totally as low level exposure still occurs even with this level of compliance, but the improvement on scenario (3), where non-compliance rates are assumed to be the same as were occurring with respect to the existing exposure standard (0.1mg/m3), is considerable. The great improvement in cancers avoided when workplaces with less than 50 workers have an improved compliance rate (scenario 11a) compared to reduction in larger workplaces (scenario 10a) highlights the comparative predominance of small enterprises particularly in the construction industry which is the most important industry sector for potential silica exposure.

Scenarios 15 and 13a-15a introduce more options for lowering the exposure standard and simultaneously improving compliance. By comparing scenarios 1, 2 and 15 (33% compliance) with 13a-15a (90% compliance) for standards of 0.1, 0.05 and 0.025 mg/m3

respectively (Table 7 and Figures 7 a & b), the effectiveness of enforcement compared to lowering the standard is clearly demonstrated. The effectiveness of scenarios 16,17 and 19 (Table 7), closing all mining and manufacturing industries (16), transferring higher to lower level exposed (17), and halving excess risk (19), can also be compared.

An example of how the results were calculated, for a subset of the RCS exposed population (men self-employed in the construction industry), is in Appendix 10.

23

Table 7: Forecast Lung Cancers for 2060 Attributable to Occupational Exposure to Respirable Crystalline Silica and Avoidable Numbers for a Range of Interventions

Intervention scenario Attributable Attributable Cancer Fraction Cancer

registrations registrations avoided

2010 Current burden 2.07%

2060 Baseline Current (2005) employment and 1.08% scenario exposure levels are maintained (1) Trend Linear employment and exposure level scenario trends assumed to 2021-30, constant (2) thereafter 0.64%

To test timing of introduction of a reduced exposure standard (3)

(4)

(5)

Introduce exposure standard=0.05 0.80% mg/m3 in 2010, same compliance (33%) with new limit as old (0.1mg/m3), all workplaces Introduce exposure standard=0.05 mg/m3 in 2020, same compliance, all workplaces Introduce exposure standard=0.05 mg/m3 in 2030, same compliance, all workplaces

0.90%

1.02%

837

794

470

592 202

666 128

753 42

To test ‘dynamic exposure standard’ that reduces as previous intervention standards based on realized exposure levels take effect (7) Introduce exposure standard as (i)

quarter 2001-2010 forecast scenario mean in 2010, same compliance (33%) as for existing standard in 2001-10, then (ii) quarter 2011-2020 scenario mean as forecast under (i) in 2020, compliance as for (i) in 2011-2020, then (iii) quarter 2021-2030 scenario mean as forecast under (ii) in 2030, compliance as for (ii) in 2021-2030. No continuing 8% downward trend.

To test effect of compliance by workplace size (9a)

(10a)

(11a)

(12a)

Introduce exposure standard=0.05 mg/m3 in 2010, 33% compliance in workplaces employing 0-249, 90% compliance in workplaces employing 250+ Introduce exposure standard=0.05 mg/m3 in 2010, 33% compliance in workplaces employing 0-49, 90% compliance in workplaces employing 50+ Introduce exposure standard=0.05 mg/m3 in 2010, 33% compliance in self-employed, 90% compliance in other workplaces Introduce exposure standard=0.05 mg/m3 in 2010, 90% compliance in all workplaces

0.68%

0.68%

0.61%

0.35%

0.07%

499 295

499 295

451 344

261 533

49 745

To test effect of introducing lower exposure standards versus compliance rate (15) Introduce exposure standard=0.025 0.56% 409 385

mg/m3 in 2010, same compliance (33%) as for existing standard in 2001-10, all workplaces

(13a) Maintain exposure standard=0.1 mg/m3 0.14% 102 693 in 2010, compliance at 90%, all workplaces

24

Intervention scenario Attributable Attributable Cancer Fraction Cancer

registrations registrations avoided

Introduce exposure standard=0.05 0.07% 49 745 (14a)=(12a) mg/m3 in 2010, compliance at 90%, all

workplaces (15a) Introduce exposure standard=0.025 0.03% 21 773

mg/m in 2010, compliance at 90%, all workplaces

To test effect of closing industries, transferring high/medium exposed workers to low exposed, and lowering RR (16) Close all C-E industry in 2010 1.10% 808 4 (17) Transfer all high exposed (construction 0.35% 255 556

and potteries (H)) to low exposed (L) in 2010

(19) Halve excess risk in 2010 at high (H) and 0.56% 409 402 low (L) exposure levels

25

Figure 5:

A) B)

1,000 3.0

900

2010 2020 2030 2040 2050 2060 2070 2080

Attri

buta

ble

Reg

istra

tions

800

700

600

500

400

300

200

Attri

buta

ble

Frac

tion,

%

2.5

2.0

1.5

1.0

2010 2020 2030 2040 2050 2060 2070 2080

0.5 100

0 0.0

Forecast Year Forecast Year

Figure 5 a & b compare the effect of delaying introduction of a reduced exposure standard, using scenarios.

(1) Baseline: Current (2005) employment and exposure levels and exposure standard=0.1 mg/m3 maintained

(3) Introduce exposure standard=0.05 mg/m3 in 2010, same compliance (33%) with new limit as old (0.1mg/m3), all workplaces

(4) Introduce exposure standard=0.05 mg/m3 in 2020, same compliance, all workplaces

(5) Introduce exposure standard=0.05 mg/m3 in 2030, same compliance, all workplaces

26

Figure 6

A) B)

1,000 3.0 900

100 0.5

0 2010 2020 2030 2040 2050 2060 2070 2080 2010 2020 2030 2040 2050 2060 2070

0.0

Forecast Year Forecast Year

Attri

buta

ble

Frac

tion,

%

Attri

buta

ble

Reg

istra

tions

2.5 800

700

600

500

2.0

1.5

400

300

200

1.0

Figure 6 a & b compares the effect of improved compliance by workplace size in four size categories, using scenarios

(1) Baseline: exposure limit 0.1mg/m3, compliance 33%

(3) Exposure limit 0.05mg/m3 from 2010, compliance 33% all workplaces

(9a) Exposure limit 0.05mg/m3 from 2010, compliance 33% < 250, self employed; 90% 250+

(10a) Exposure limit 0.05mg/m3 from 2010, compliance 33% < 50, self employed; 90% 50+

(11a) Exposure limit 0.05mg/m3 from 2010, compliance 33% self employed; 90% all sizes employed

(12a) Exposure limit 0.05mg/m3 from 2010, compliance 90% all workplaces

27

2080

Figure 7

A) B)

1,000 3.0 900

Attri

buta

ble

Frac

tion,

%

Attri

buta

ble

Reg

istra

tions

100

0.0 0 2010 2020 2030 2040 2050 2060 2070 2080 2010 2020 2030 2040 2050 2060 2070 2080

Forecast Year Forecast Year

Figure 7 a & b compare the effect of reducing the exposure standard for RCS versus compliance, using scenarios

(1) Baseline: exposure limit 0.1mg/m3 maintained, compliance 33% (3) Exposure limit 0.05mg/m3 from 2010, compliance 33% (15) Exposure limit 0.025mg/m3 from 2010, compliance 33%

(13a) Exposure limit 0.1mg/m3 maintained, compliance 90% (14a) Exposure limit 0.05mg/m3 from 2010, compliance 90%

(15a) Exposure limit 0.025mg/m3 from 2010, compliance 90%

2.5 800

700

600

500

2.0

1.5

400

300

200

1.0

0.5

28

7. APPENDICES

APPENDIX 1: ATTRIBUTABLE FRACTION METHOD: FEATURES OF THE ESTIMATION PROCESS

A1.1 Forecast Target Years And Estimation Intervals

A range of target years from 2010 to 2060 has been chosen to cover the solid tumour standard REP which is based on assumed latencies of between 10 and 50 years. The REP for the target year of 2010 is therefore 1961-2000, for 2020 it is 1971-2010, etc. The forecast REPs are divided into 10 year intervals in the calculation process (1961-70, 1971-80, 1981-90, 1991-00 for example for the 1961-2000 REP) so that appropriate adjustment factors can be applied, see below, and so that separate relative risk estimates can be applied in each estimation interval if required. In order to make a proper comparison with results from the lifetime risk approach, in the example for lung cancer the forecast period has been extended to 2080.

A1.2 Estimating Numbers Exposed In The REP

The estimates of numbers ever exposed are based on data items 1 to 5 in Appendix 4 below. Point estimates of numbers exposed are obtained from CAREX data (which was for 1990-93), or LFS (available for 1979-2006) or Census of Employment (CoE) (ONS, 2006) data (available from 1971) for a suitable single year. Data for any year within the range of the risk exposure periods can be used where information is available on employment level trends (see below and Appendix 2 Section 2.3 (year of origin)). Turnover (TO) estimates for the correct mid-point in each 10-year interval of the REP are also required to estimate numbers ever exposed. In practice, for current burden an employment turnover rate averaged over the years for which data were available (1984, 1991 and 1998) was used to obtain a stable estimate. With an additional one year available (2003), an average taken over 1991, 1998 and 2003 resulted in very little change to the TO estimates, therefore for the sake of simplicity the 1984/1991/1998 average TO is applied across all forecast years. Separate yearly TO estimates and the two averages described above are shown in Table 12 in Appendix 6, for information only. The calculations for these factors are based on LFS data on length of employment and are described in the current burden Methodology Technical Report, Appendix 5 (The Burden of Occupational Cancer in Great Britain Technical Report: Methodology: “Estimating The Proportion In The Population Exposed”). Life expectancy tables for the mid-point year or as close as possible to it for each 10 year estimation interval are used to estimate the proportions of these numbers ‘ever exposed’ who will survive to be recorded as a cancer case (death or registration) in the target year. The calculation method is described in Appendix 7 of this report. The life tables were obtained from the Government Actuary’s Department (GAD – Cohort Life Expectancy Tables 1981-2054; http://www.gad.gov.uk/Life_Tables/Interim_Life_Tables.htm).

CAREX and LFS employment level adjustment factors are required to correct the CAREX and single year LFS/CoE data to each 10 year REP interval (see Tables 13, 14 & 15 in Appendix 6) to allow for known and forecast changes in the pattern of employment. These factors have been estimated for grouped main industry sectors, i.e. agriculture, hunting, forestry and fishing (A,B), mining and quarrying, utilities and manufacturing industry (C-E), construction (F) and service industries (G-Q). They are estimated using LFS employment data, linearly extrapolated to 2025, and for 1975 to 2005 the estimates are based on a smoothed (linear) time series as illustrated in Appendix 8 Figures 17 and 18. CAREX factors are based around 1990-1993; the LFS factors on

29

1979 for long latency REPs, and 1995 data is used for short latency REPs. Beyond 2025 the factors are assumed to remain constant.

Estimates of numbers ever exposed are then allocated by workplace size band. For the four workplace size categories ‘none’ (self-employed), 1-49, 50-249, and 250+ employees, the percentages employed are given in Appendix 6 Table 16. These are estimated from Small and Medium Enterprise (SME) statistics for the UK for 1994-2006 (http://stats.berr.gov.uk/ed/sme) using a linear extrapolation to 2025 and back to 1975 and estimates for 1995 and 2005 also based on the smoothed (linear) time series, for main industry sectors A, B; C-E, F, G-O (no data is available for the sectors L: Public administration and defence, P: Private households with employed persons and Q: Extra-territorial organizations, and is acknowledged to be unreliable in the VAT exempt areas of M: Education, N: Health and Social Work and O: Other community, Social and Personal Service Activities). The trends are illustrated in Appendix 8 Figures 19a to d. Beyond 2025 these percentages are also assumed to remain constant.

Finally, an exposure level adjustment factor is applied to each 10-year estimation interval. These factors are the proportions of workers exposed at the various exposure levels, as defined for current burden, or if suitable RR estimates are available at ‘higher’, ‘medium’, ‘lower’ and ‘background’ exposure levels. The proportions are estimated for each forecast target year. The estimates are based on the underlying trends measured as annual percentage fall in exposure levels in Great Britain, or relevant to the particular exposure if this underlying trend is not appropriate. There is evidence to suggest that workplace exposure levels are currently falling at a rate of between 6% and 8% a year, resulting in a decrease over a period of 20 years to a third or a quarter of the original level (Cherrie JW: 2008). The factors used may be based on this estimate or on a specific exposure trend estimate, but vary according to the proportions exposed at ‘higher’ levels in the year of origin, normally taken to be 1975 for the solid tumours (a midpoint in the REP), or 1990 for short latency cancers.

The method assumes that exposure levels are distributed lognormally with known, or assumed, distribution parameters (generally geometric means (GM) and geometric standard deviations (GSD)), and that the mean of the distribution shifts in proportion to the average annual decline. The proportions are estimated using the methods and derivation described in Appendix 2. The proportions are applied to adjust total numbers exposed at the various exposure levels to allow for shifting of exposed workers from higher to lower exposed groups. If the mean of a distribution is available, exposure levels representing the boundaries between high/medium, medium/low and low/background exposure categories can be estimated. Proportions are estimated separately for each exposure, for men plus women together. For current burden, separate industries/occupations have been allocated in their entirety to high, medium, low, or background exposures, and so proportions based only on these total numbers exposed are applied in the forecast years to estimate the proportions of workers within the higher exposed industries that are expected to shift to lower exposed categories. It is possible to assume different distribution parameters and decline rates for different industry groups or occupations. However, more than one exposure level must be represented overall so that threshold levels can be estimated separating the exposure level groups.

The data required for estimating numbers ever exposed in the forecast risk exposure periods are given in Tables 11a & b in Appendix 6.

30

A1.2.1 Exposure levels – exposure distribution assumptions

As noted above, to derive estimates of the proportions of workers exposed at higher exposure levels as the average exposure levels fall, a lognormal distribution for exposure levels is assumed. Estimates of changing proportions exposed can be made independently of knowledge of the distribution mean. However, if the distribution mean is known, the effects of reducing specific exposure limits can be estimated. Where no data to define the distribution parameters (GM and GSD) are available for a specific exposure, assumptions on the size of the standard deviation (SD), and its rate of decline with respect to the mean, are required. Assumptions about SD are, however, problematic; using for example one third of ln(mean) can lead to unusable negative SD estimates. The estimated proportions exposed at higher levels can also rise as mean levels fall, if SDs are also falling – as the distribution narrows in range lower proportions fall below the high/medium/low/baseline thresholds. A constant SD is therefore assumed across FTYs where this is a problem. See Appendix 2 for more details.

We have assumed that exposure can be summarized in ordered categories, for example as ‘high’, ‘medium’, ‘low’ and ‘background’ exposed groups. It would also be possible to assume a continuous distribution of exposure levels across an exposed population. The total AF would then be obtained by integrating, instead of summing, across the possible range of exposure levels with probability density functions for the exposures, with and without an intervention scenario replacing Pr(E) (Armstrong and Darnton, 2007).

A1.2.2 Exposure-response curve assumptions

Various exposure-response functions can be assumed. If a linear relationship is assumed, the AF depends only on the mean exposure level, and it reduces as exposure levels change in proportion to the change in the mean exposure level. This will not be the case however if, for example, a threshold, or non-linear, dose-response is assumed. Only the exposure-response assumptions used for current burden will be applied to future burden estimates of numbers exposed at the various levels for the forecast and intervention scenario estimates.

A1.3 Components of Future Burden Estimation

There are two separate components in estimation of future burden: (1) a component based on historic/current exposure estimates and realistic forecasts; and (2) a component based on the effects of intervening to reduce the forecast exposures.

A1.3.1 Historic/current and forecast data

Historic/current data (1960 - 2007) are used to estimate the part of the AF coming from exposures in the REPs up to the present time. For this part of the estimate, where estimated trends in numbers employed and in exposure levels are available (or can be assumed) they are used for broad industry sectors (i.e. A, B; C-E; F; G-Q) by workplace size classes (i.e. none (self-employed), 1-49, 50-249, 250+employees). This is in order to model general changes in industrial processes and working conditions rather than attempting to model in detail carcinogen exposure levels. These three factors (CAREX/LFS employment level, exposure level proportions and workplace size percents) are applied to the numbers ever exposed estimated separately for each 10 year estimation interval by the turnover equation, to get more realistic estimates of numbers exposed by industry sector by workplace size cell. The factors are estimated for the years 1975, 1985, 1995, 2005, and forecast for the years 2015 and 2025 to represent the estimation intervals

31

up to 1980, 1981-90, 1991-00, 2001-10, 2011-20 and 1021-30 respectively. No change is assumed beyond 2025 for the 2031-40 and 2041-50 intervals.

The factors for the forecast period estimation intervals in Tables 13, 14 and 15 in Appendix 6, and the exposure level adjustment factors described above are used for forecast scenario estimates. They are also then adjusted to represent realistic target exposure levels or exposure scenarios for hypothetical interventions.

There will be areas where this general approach is not applicable, i.e. in new industries and for agents where exposure level and prevalence may have increased over the past 20 to 30 years for which suitable trends are needed.

A1.3.2 Choice of intervention scenarios

For the application of the methods to selected carcinogens the choice of intervention scenarios has been selected after discussion with the HSE.

For forecasting where no deliberate changes are to be made, in most cases, in the absence of any better exposure trend data, the forecast adjustment factors in Appendix 6, Tables 13, 14, 15 and as estimated from the methods in Appendix 2 can be applied to CAREX or LFS/CoE exposed numbers by grouped main industry sector. The forecasts may be estimated by assuming either

1. No change in current total exposed numbers and the proportions exposed at the ‘higher’ exposure levels, i.e. using the 2005 factors for 2015 to 2045 (baseline scenario), or

2. From 2005 onwards assuming a continuation into the future of linear employment level trends fitted to the 1978-2007 data, for the factors for 2015 to 2025 and constant thereafter, and trends in average exposure levels (baseline trend scenario).

If, for specific exposures, current and recent data or estimates are available of numbers employed at a range of exposure levels, then dose-response RRs that are also likely to be available can be applied to estimate future AFs and ANs.

For testing intervention scenarios, adjustments are made to the two factors described below as they affect attributable fraction estimates in future estimation intervals. Relative risks may also be adjusted.

The options include:

1. Eliminating all exposure in a particular forecast target year (e.g. closing down or exporting the hazardous industry/process), or reducing the overall numbers exposed across all industries or in certain sectors only.

2. Intervention to reduce exposures to an appropriate level, for example by introducing, or lowering, an occupational exposure standard. This is assumed to lead to exposed workers shifting from high to low exposure levels. A new mean of the (shifted) distribution of exposure levels is estimated, based on the proportions assumed to remain exposed above the exposure standard (non-compliance), set for example at the same proportion as above an existing exposure standard, or 10% or 1% above where no previous standard existed.

3. Intervention to reduce exposures as above but with varying compliance according to workplace size. Compliance can be measured as different proportions remaining exposed (such as previous compliance levels or 1%) above the exposure standard.

32

4. Introducing a new or lower exposure standard as a step change at alternative points in time (e.g. 2010, 2020, 2030)

5. Intervention to reduce exposure standards gradually in response to the decline in mean exposure levels over time.

6. Intervention to reduce exposures through improved hygiene, affecting RR by lowering exposure dose

In each case the estimated attributable numbers are compared to the results for the baseline, or baseline trend, scenarios to obtain an estimate of avoided cancers by subtraction. Examples of the above interventions have been used in the future burden analysis for respirable crystalline silica (see Section 6).

A1.3.3 Allowing for a change in numbers exposed during the forecast REP

The multiplication factor that represents change in exposed numbers over the forecast period (the CAREX/LFS adjustment factors) can be adapted to represent the change in overall numbers exposed, by industry or occupation, and applied to the forecast numbers ever exposed estimated for any of the 10 year estimation intervals.

A1.3.4 Allowing for a change in exposure level during the forecast REP

The effect of a change in exposure level during the REP was handled for the current burden estimation by splitting the numbers exposed at the point of change and applying separate RRs, with numbers exposed remaining constant, but moved from e.g., a high to a low exposure category. For the future burden, where exposure levels change during the REP, the numbers ever exposed (Ne(REP)) are obtained using the turnover equation (Appendix 7.1), and are estimated separately for each exposure level category, and each 10 year estimation interval. The appropriate RRs for the exposure levels are then applied to the numbers exposed by estimation interval to calculate an AF for each industry, each exposure level, and for each estimation interval.

A1.3.5 Allowing for a change in relative risk during the forecast REP

If exposure levels are required to change gradually (but in practice in 10-year steps) across the forecast period, but no information is available to define the exposure level distribution the, a factor is applied to the RR for each exposure level to obtain the new RR for each estimation interval. This requires an assumption generally of linear dose-responses as the exposures reduce from ‘higher’ to ‘lower’ levels.

A1.3.6 Estimating the AF

Levin’s equation for the attributable fraction (Levin, 1953) is used to estimate AF from the relative risks (RR) and the proportions newly exposed in each estimation interval, by industry/occupation and exposure level. The AFs are then summed across estimation intervals for each industry by exposure level, using equation (7.1) from the current burden methodology modified as:

(A1.1) AFhip = {Σp Pr(Ehip)(RRhp - 1)}/{1+{Σh Σi Σp Pr(Ehip)(RRhp - 1)} where h = exposure level

i = industry /occupation p = estimation interval

33

Minimum latency 10 yearsMaximum latency 50 years‘Peak latency’ 35 years

A total AF due to the occupational exposure in each forecast target year is obtained by summing across industries and exposure levels. Attributable fractions and numbers can then be presented for separate industries if required as well as by exposure level and for total exposure.

A1.4 Latency Distribution

An important issue in forecasting attributable cancer numbers in that, although cancers may originate at any time across a REP to appear in the forecast target year, the number induced is unlikely to be evenly distributed by year across the period. For example, although for solid tumours we have assumed that all latencies between 10 and 50 years are possible, around 35 years is thought to be most likely. Therefore, a probability distribution for cancer latency is needed.

A1.4.1 Choice of latency distribution

We have considered four possible models, illustrated in Figure 8 below

(1) Uniform distribution, range = maximum latency – minimum latency

(2) Normal distribution, mean = ‘peak latency’, SD = (max. latency - min. latency) /6

(3) Lognormal model, mean = ln(peak latency), SD = ln(max. latency - min. latency) /6

(4) Power model (Hodgson et al, 2005, Health Effects Institute, 1991):

R ∼ D*(t-10)k

where R = risk

D = cumulative exposure (here taken to be constant)

t = time since first exposure (lagged by 10 years)

2 <= k <= 3

Figure 8: Alternative latency models for the solid tumour standard REP

PPPrrrob

aob

aob

abilibili

bilittt

yyy dedede

nsnsnsititityyy

0.00.00.0888

0.00.00.0777

0.00.00.0666

0.00.00.0555

0.00.00.0444

0.00.00.0333

0.00.00.0222

0.00.00.0111

0.00.00.0000

LoLoLoLoLognognognognognorrrrrmmmmmalalalalalNormalPowerUniform

NormalPowerUniform

NormalPowerUniform

NormalPowerUniform

Normal Power Uniform

0102030405060 0102030406060 50 01020304050

LaLaLatetetencncncyyy (((yyyeeeaaarsrsrs)))

Minimum latency == 10 years Maximum latency == 50 years ‘Peak latency’ == 35 years

34

Whichever model is chosen, the numbers of cancers appearing in the target year will be the same; they will be proportional to the area under the curve, which is assumed to be constant; initiation dates vary according to the distribution of latency, but the overall numbers of cancers ‘initiated’ in the REP is assumed to remain the same2. An exception would be if there is a change in the level of exposure during the REP. The affect this would have on the numbers of cancers initiated for a reduction in exposure levels assumed to occur in 2010 and resulting in a halving of the numbers of attributable cancers ‘initiated’ is illustrated for the lognormal latency distribution (Figure 9), for the uniform distribution (Figure 10) and the power distribution (Figure 11).

2 Note that although the numbers of cancers ‘initiated’ is assumed to be the same, the number of exposed individuals with an ‘initiated’ cancer surviving due to normal life expectancy to the target year, and therefore also the number of cancers recorded in the target year will not be the same for the different latency distributions. For example, there will be more survivors and therefore cancers appearing if a uniform distribution is assumed than if a lognormal, or even more evidently a power distribution is assumed where most cancers are ‘initiated’ in the early years of the REP.

35

Figure 9: '' AAttrittributabbutablele numnumbbeers brs byy yyear oear off iinniitiatiotiation'n' ffoor er eaachch tatargetrget yyeeaarr (targe(target yt yeear 2ar 2050050 hihighlghlighightteedd),), lolognognormrmalal didistristributiobution wn wiith eth exxppoosuresure rereducduceedd in 20in 2010 so10 so thathatt attribuattributtaabblele nnuummbers abers arre hale halvveded

PrProp

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0.050.05 TTTYYY===201020102010

TTTYYY===202020202020 0.040.04 TTTYYY===203020302030

0.030.03 TTTYYY===204020402040

TTTYYY===205020502050 0.020.02

TTTYYY===206020602060

0.010.01

0.000.00 19601960 19701970 19801980 19901990 20002000 20102010 20202020 20302030 20402040 20502050

YearYear

Figure 10: AAttrittributabbutablele nunummbersbers bbyy yyear ofear of '' iinniitiatiotiation'n' ffoor eachr each targtargetet yyeear (taar (tarrget yget year 20ear 2050 hi50 highlghlighightteedd),), uunnififormorm

lalatetencyncy distribdistribuutiontion wwiith eth exxpoposursuree redreduucedced iinn 202010 so that attri10 so that attributabbutable nule nummbbers areers are hhaalvlveedd

TTTYYY===201020102010

PrProopp

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n o

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0.0.0303

0.0.0303 TTTYYY===202020202020

TTTYYY===203020302030

TTTYYY===204020402040

0.0.0202

0.0.0202 TTTYYY===205020502050

TTTYYY===206020602060

0.0.0101

0.0.0101

0.0.0000 19601960 19701970 19801980 19901990 20002000 20102010 20202020 20302030 20402040 20502050 20602060

YearYear

Figure 11:

AAttrittributabbutablele nnuummbbeerrss bbyy yyeearar ofof ''iinitianitiattiion'on' ffoor eachr each tatarget yrget yeeaarr (ta(tarrget yget year 2ear 2005500 hihighlghlighightteedd) po) powweerr didistristributiobution (k=2n (k=2), w), wiithth eexxposuposurree redureduced inced in 22010010 soso thathatt attribuattributtaabblele nnuummbbeerrss aarre hale halvveedd

PrPropop

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n o

tion

off c

ance

rca

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s 'i

s 'inn

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tedd'' TTTYYY===201020102010

0.080.080.08 TTTYYY===202020202020 0.070.070.07 TTTYYY===203020302030 0.060.060.06 TTTYYY===204020402040

0.050.050.05 TTTYYY===205020502050

0.040.040.04 TTTYYY===206020602060

0.030.030.03

0.020.020.02

0.010.010.01

0.000.000.00 19601960 19701970 19801980 19901990 20002000 20102010 20202020 20302030 20402040 20502050 20602060

YearYear

36

UniformLognormalPowerNormal

The effect of this change in exposure rate on the numbers of cancers appearing in the target year is illustrated in Figure 12 below. In this figure as above a reduction in exposure levels resulting in a halving of the numbers of attributable cancers ‘initiated’ has been assumed to occur in 2010. In these circumstances, from target year 2020 onwards cancer numbers appearing in a given target year are highest as the effect works through if latencies are assumed to follow a power distribution (most cancers having the longest latencies, k=2 was used here), and lowest if a uniform distribution is assumed (all latencies equally likely). The lognormal, and normal distribution up to target year 2040, lie between these two.

There is evidence from data on mesothelioma and time since first exposure to asbestos to support the power distribution for mesothelioma, and evidence for the lognormal distribution from a range of cancers including lung cancer in asbestos workers, bladder cancer from occupational exposures including in dyestuff workers and leukaemia and radiation exposure (Armenian HK and Lilienfeld AM: 1974), and possibly mesothelioma in gas mask workers (McDonald JC, Harris JM and Berry, G., 2006), but evidence for no other cancers and distributions. The lognormal has the most supporting evidence and has therefore been chosen for these estimates of future burden.

Figure 12: Cancer numCancer numbers ibers in targetn target yyeaearr accordiaccording to an asng to an assumsumed latened latenccyy disdisttributionribution wwith tith the ehe exxposposureure reduced ireduced inn 2010 s2010 so that attribuo that attributtable caable cancers are halncers are halvveded

0.0

0.2

0.4

0.6

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n R

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UniformLognormalPowerNormal

Uniform Lognormal Power Normal

20120100 20202020 22030030 20420400 22050050 20620600

ForecaForecast targetst target yyearear

A1.4.2 Applying a latency distribution Having selected the preferred latency distribution (see section A1.4.1), the attributable number of cancers for a given target year is estimated by weighting the contribution to attributable numbers according to the proportion of cancers initiated by year across the REP for the given distribution. For example, for a uniform distribution with a target year of 2050, all years from 2000 to 2040 are weighted equally. For the lognormal distribution, the weights are the values for the (probability mass function) distribution at each year from 2000 to 2040 representing latencies of 49 down to 10 years for solid tumours, scaled to sum to 40 (see Figure 8). The weights can be applied to the summations in the equation for numbers ever exposed during the REP (Ne(REP), the summations are the l(adj15)i s in equation A7.1, the turnover equation, see Appendix 7) If this is done the result for estimation intervals is no longer an estimate only of these numbers, although the sum across estimation intervals remains correct.

37

Alternatively the distribution weights can be applied in an aggregated form for each estimation interval (e.g., over ten year) during calculation of the AF (before summation of the Pr(E)*(RR-1) across the estimation intervals, see equation (A1.1). The (single year) weights are summed over the estimation interval and divided by the number of years in the interval to obtain the aggregated weight (see Figure 13 below). This is the method used in the examples estimated in this project.

Note that although the weights for all distributions sum to one (*40 in practice for the standard solid tumour REP, or *4 for the 10 year estimation interval weights, to equate to a weight of unity for each latency year for the uniform distribution used in current burden estimates), the total weighting factors for both the original cohort size (at the beginning of the REP) and for those recruited to the cohort during the REP differ between distributions if applied by year to the equations for numbers ever exposed. This is because numbers surviving to the target year from the early part of the REP are lower than for later years, so that higher proportions here will result in lower ever exposed numbers overall surviving to the forecast target year. The proportion of the population exposed (Pr(E)), attributable fraction (Levin’s equation) and hence attributable numbers are then obtained as for current burden (see equation (A1.1) in this report).

The equations for the single year weights and aggregated weights are given in Appendix 7. The weights used in this project are in Table 8 below.

Table 8: Weights used for cancer initiation by estimation interval

Year 1-10 Year 11-20 Year 21-30 Year 31-40 Solid tumour (long latency) REP

1.30 1.84 0.83 0.03 Blood cancers (short latency) REP

1.49 0.51

38

Figure 13: Lognormal latency distribution aggregated across estimation intervals

LoLognognormal distribution latenrmal distribution latenccyy wweeightsights La

tenc

Late

ncyy

wweei

gightht

2.02.0

2.52.5 SSSiiingngngllleee yyyeeeararar weweweiiiggghththtsss EstiEstiEstimmmaaatttiiiononon iiinnnteteterrrvvvalalal wwweeeighighightttsss

1.51.5

1.01.0

0.50.5

0.00.0 6060 5050 4040 20203030

LaLatetencyncy ((yyeeaarrss))

1010 00

A1.5 Forecast Population Estimates

These are taken from ONS population projections for GB by 5 year age bands (in Population projections, GB, 2006-.xls), interpolating between years where necessary, and including only the relevant age bands exposed in the REP and surviving to the target year (ages 25+ for the solid tumour REP, ages 15-84 (men) or 15-79 (women) for short latency cancers). The estimates for the standard solid tumour and short latency REPs are in Appendix 6, Table 11a.

A1.6 Projected Cancer Numbers

Future total cancer numbers are estimated by multiplying current (2005) deaths (2004 registrations) in each five year age band from 15 upwards, for England, Wales and Scotland by ONS (mid-year age at last birthday) GB projected numbers in that age band for each target year (2010, 2020 etc), divided by current (2005) population numbers in that age band from current ONS mid-year population estimates for GB, and then summing across age bands and countries. The estimates for lung cancer for example are in Section 6 Table 7. Trends in cancer deaths and registrations for nasal (a relatively rare) and lung (a common) cancer are in Appendix 9. Instability in numbers from year to year is not a significant problem, except for the rare cancers such as nasal cancer (Figure 21), whereas longer term upward and downward trends in cancer deaths and registrations are a problem (Figures 20 and 22). Future cancer numbers are generally therefore estimated using a single, most recent, year’s data for consistency across cancers, and in the case of a rare cancer deaths and registrations are taken as the average over the most recent five years for which GB data are available (2000-2004 for registrations, 2001-2005 for deaths). Prediction of future cancer numbers following current trends is not required for this analysis as these trends are likely to be linked overwhelmingly to non-occupational exposure and other survival and diagnosis issues, including the effect of an aging population, which is already accounted for in the method adopted to project future cancer numbers. As for forecast population estimates, only cancers in ages eligible to have been ‘initiated’ in the risk exposure period are included, that is in ages 25+ for solid tumours, and 15-84 (men) or 15-79 (women) for short latency cancers.

39

APPENDIX 2: DERIVATION OF THE EXPOSURE LEVEL FACTORS, ATTRIBUTABLE FRACTION APPROACH

It has been estimated that exposure levels are falling by between 6% and 8% a year on average in Great Britain, resulting in a decrease over a period of 20 years to a third or a quarter of the original level (Cherrie JW: 2008). To obtain an estimate of the proportions of workers exposed at the current burden ‘high’ and other exposure levels from this the procedure isas follows.

A2.1 Assume that this trend can be applied across the forecast risk exposure periods from 1975 to 2025, to obtain exposure trend factors calculated to a base of 1 in 1975 (1975 factors are applied to estimation intervals before 1970, and levels after 2025 are assumed constant). The trends are illustrated in Figure 14 below. If a trend for a specific exposure is available from Creely et al, 2006, (in Tables 1, 19 or Appendix 1 in that report, see Table 10 below) it should be used in preference.

Figure 14 TTrenrendds ins in expexpososurure lee levveels: 19ls: 1975 to75 to 22025025

FaFact

oct

or b

ar b

asedsed

oonn 1

91975

=75

=11

AverAverageage –– CCherherrriiee ((2008)2008): H: Hiigh 8%gh 8%1.1.22 AverAverAverageageage ––– CCCherherherrrriiie (e (e (2008)2008)2008)::: CCCentententrrralalal 777%%%

1.1.00

0.0.88

0.0.66

0.0.44

0.0.22

0.0.00

Average – Cherrie (2008): Low 6%DEE - Armstrong and Darnton (2008): 2.3%Average – Cherrie (2008): Low 6% DEE - Armstrong and Darnton (2008): 2.3%

19719755 19851985 19951995 20052005 20120155 20252025

YearYear

A2.2 Assume that exposure levels are distributed lognormally with a geometric mean (GM) and a geometric standard deviation (GSD) estimated for the specific exposure, with future mean levels represented by applying this average yearly reduction and a constant standard deviation. If the data are not available to estimate a GSD for a particular exposure, it could be estimated either as one third of the difference between the range maximum and the estimated geometric mean (GM) exposure level; or as one sixth of the exposure range (where the minimum exposure level is not zero). In both cases estimates are on a natural log scale as follows:

ln(GSD) = [ln(max) – ln(min)]/6, where min > 0, or

(A2.1) ln(GSD) = [ln(max) - ln(GM)]/3

These estimates are based on the characteristic of a normal distribution that 99% of the range of values falls within 3 standard deviations of the mean. In the absence of data on the range or mean levels for a particular exposure, a distribution GSD can be estimated by assuming a ratio between maximum and mean exposure levels. If this ratio is constant across years, the GSD is constant. Possible ratios, based on the data from Creely et al (2006) for toluene, respirable flour, wood,

40

quarry and rubber process dusts, quartz and rubber fume, range from 100 (wood dust) to about 1,750 (toluene). See Table 10 below.

A2.3 Assume the allocations to ‘high’ (H), ‘medium’ (M), ‘low’ (L) and ‘baseline’ (B) exposure levels of the exposed worker numbers using data from CAREX and LFS/CoE used for the current burden estimation (updated to allow for more exposure categories) are correct for a year of origin of 1975; these represent exposure levels for the standard risk exposure period of 1956-1995 assumed for solid tumours (1986-2005 for short latency cancers). An allocation of H/M/L/B applicable to any year of origin can be used, see section A2.4 below.

A2.4 The boundary levels separating ‘high’ and ‘medium’, ‘medium’ and ‘low’, and ‘low’ and ‘background’ exposed are represented by the exposure levels on the horizontal axis in Figures 15a and b and Figure 16 that separate the proportions allocated to the various exposure levels in the year of origin (1975) lognormal distribution curve.

The proportions of the distributions that move across a series of boundaries represent the numbers of exposed workers moving from higher to lower exposure levels in the future. Here we define the term ‘boundary’ as the estimated average exposure level separating a ‘higher’ from a ‘lower’ exposed group as the distributions shift with time along an exposure level axis. The boundaries are determined by the ‘high’, ‘medium, ‘low’ and ‘background’ exposed proportions in the REP that applies to the current burden of cancer, and are estimated as

(A2.2) Th = LOGINV[1-ph0, ln(GM0), ln(GSD0)]

where ph0 is the proportion exposed at or above the specific level, in the year of origin for the current burden estimate (usually 1975), and GM0 and GSD0 are the mean and standard deviation of the distribution of exposures for that year. GM0, and values of GMj for forecast years j that represent subsequent estimation intervals, are obtained by applying an estimate of the annual (x) percentage change in exposure levels to an estimate of GM (GMB) obtained for a specific baseline year B, as

(A2.3) GMj = GMB * [(1-x)t(j)]/[(1-x)t(B)]

where t(j) = time in years from origin.

A2.5 The proportion of workers exposed at the higher levels in a particular year are then determined by taking the proportion of the lognormal distribution curve that represents workers’ exposure levels in that year and which fall above the boundary exposure levels. These proportions, for the years 1975, 1985 to 2025 (to represent each 10 year estimation interval), are estimated separately for each exposure. Proportions are assumed to remain constant before and after these years. The proportion of workers (phj) exposed at level h as represented in the exposure level distribution for each forecast year j is estimated as:

(A2.4) phj = 1-LOGNORMDIST[Th, ln(GMj), ln(GSDj)]

This proportion is the exposure level factor for a forecast scenario.

A2.6 To calculate results by separate industries/occupations, the proportion used and therefore the factor to apply for each forecast year j is given by the ratio of the proportions exposed at each level in the forecast year to the proportion in the year of origin:

41

1975 1985 1995 2005 2015

Ratio max:mean

GSD

100 4.64 H 0.40 0.35 0.26 0.15 0.12 M 0.30 0.30 0.29 0.25 0.23 L 0.20 0.22 0.26 0.29 0.29 B 0.10 0.13 0.19 0.31 0.36 1,500 11.4

5 H

0.40 0.37 0.31 0.23 0.20 M 0.30 0.30 0.30 0.28 0.28 L 0.20 0.21 0.24 0.27 0.28 B 0.10 0.12 0.15 0.22 0.24

(A2.5) f2j = phj/ph0

The concept is illustrated in Figures 15a and b and Figure 16 below. Two alternative estimates of GSD are used in Figures 15 a and b in which

1. 40%, 30%, 20% and 10% of workers were subjected to theoretical high (H), medium (M), low (L) and background (B) level exposures in 1975,

2. the level of exposure is assumed to be falling at 2% a year since 1975, and 3. the distribution GSD is (a) based on a maximum to mean ratio of 100 (GSD=4.64), and (b)

based on a maximum to mean ratio of 1,500 (GSD=11.45); the GM is set at 1 in 1975.

The results are sensitive to the choice of standard deviation for the distributions. For example, the proportions at the ‘high’ exposure level for the assumed distribution GSDs in Figures 15a and b are shown in Table 9 below. A greater spread of exposure levels (higher GSD) results in a delayed shift from higher to lower exposure categories.

Table 9 2025

Average exposure levels falling at 2% per year

0.10 0.20 0.29 0.41

0.18 0.27 0.29 0.27

42

Figure 15a&b: Theoretical distributions for exposure levels falling at 2% a year, and standard deviations of the distribution estimated as one third of the difference between maximum and mean exposure levels (natural log scale).

15a) Maximum: mean ratio = 100 AAssumssumeedd ddistribuistributitionsons ooff exexpoposuresure levleveels at 1ls at 100 yyeeaarr inintetervrvaals, bals, basedsed on aon ann eequaqual dil distristributiobutionn bebetwtweeneen eexxppoosuresure lelevveelsls inin 1971975, an5, and ad a 2%2% avaveerrageage anannuanuall ffaalll il inn mmeeanan (GM(GM)) levleveelsls acrossacross ththee peperiodriod wwiithth GGMM==11 (197(1975)5) andand GGSSDD == eexxp((lnp((ln((100100))/3))/3)) coconstantnstant acrossacross yyeeaarrs.s.

H/MH/M L/BL/BM/M/LL WoWorrkkeerrss rreprepresentesented byed by tthe prhe proporoportitionsons H=9H=9%,%,

MM==16%,16%,underunder thesthesee L=L= 197525%,25%, 1975ccururvves to the les to the lefteft BB==49%49%ofof tthe the thhrreesholshol 1985dsds 1985 iin 2025n 2025arare exe exposposeded 1995atat 1995

tthe hihe highergher llevevelelss HH==40%40% 2005,, 2005MM==30%,30%,asas iindindicatcated,ed, andand 20152015L=L=20%,20%,prproporoportitions to theons to the BB== 202510%10% 2025 rriightght atat llowowerer iin 1975n 1975lelevveelsls

0.0.801.001.201.401.601.802.002.00 0.0.801.001.201.401.601.80 0.6060 0. 0.0.204040 0.0.20 0000

H/MH/M,, MM//L, L/BL, L/B threthresholshold ed exxpoposusure lere levveelsls wwiith exth exppoosuresure lleevveell aatt ththe 199e 1995 m5 meanean == 00..222626 mmgg//mm33

15b) Maximum : mean ratio = 1,500

The lognormal distribution curves for silica are illustrated in Figure 16.

AAssumssumeed distribd distribuutiontionss ooff exexppoossuurere lleevveels atls at 1100 yyear inear intetervrvals, bals, baasesed on and on an eequaqual distribl distribuutiontion bbeetwtweeeenn exexppoosuresure lleevveellss iinn 1971975,5, 22%% averaaverage ange annuanual fl faallll iinn mmeanean (GM(GM)) levleveels across the pels across the periodriod, GM, GM=1 (19=1 (1975),75), anand GSDd GSD==eexxpp((ln(1((ln(1500500))/3))/3)) conconsstantantt acroacrossss yyeearsars

..

H/MH/M M/M/LL L/L/BB

WWoorrkkererss rreprepresentesented byed by thethe prproporoporttiionsons underunder tthese chese cururvveses to the lefto the leftt of the throf the thresholesholds ards aree HH==14%,14%, exexposed at the hiposed at the highergher lleveevellss asas MM==20%20%,, 19751975 indiindiccated,ated, andand prproporoporttiionsons ttoo the rthe riight at lght at lowowerer lleevelsvels

L=L=26%,26%, B=B=40%40% iin 2025n 2025

19851985 19951995 20052005 20152015 20252025

2.2. 1.1.601.800000 1.1.601.80 0.1.001.204040 0.1.001.20 0.8080 0. 0.6060 0. 0.0.204040 0.0.20 0000

H/MH/M,, MM//L, L/BL, L/B threthresholshold ed exxpoposusure lere levveellss wwiith exth exposuposurre leve leveel at thl at the 199e 1995 m5 meanean == 00..222626 mmgg//mm33))

43

2021-2030

Figure 16: Lognormal exposure level distributions shifting with time as average levels decline, illustrated for lung cancer due to occupational exposure to crystalline silica, baseline trend scenario, based on an 8% average annual fall in mean (GM) levels across the period, and GSD = 6.4 (IOM data) constant across years.

Workers represented by the proportions under these curves to the left of the

H/M M/L thresholds are exposed at the higher levels L/B as indicated, and proportions to the right at lower levels

70% 5% workers workers 2011-2020 exposed at exposed at 22% workers 2001-2010 'high' (H) 'medium' exposed at level in (M) level in 3% men 1991-2000 'low' (L) level in 1971-80 1971-80 exposed at

1971-80 1981-1990 'backgound' (B) level in 1971-1980

1971-80

Boundary exposure levels: H/M = HighMedium M/L = Medium/Low L/B = Low/Background

1.2 1.1 1.0 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0.0

Exposure level in mg/m3(2005 mean = 0.226 mg/m3) If exposure levels remain constant, the proportion exposed at higher levels also remains constant. The method requires at least two exposure levels to be represented in the industries or occupations where the exposure occurs, otherwise a boundary between exposure levels cannot be estimated. It is possible to have different rates of decline by industry sector or industry/occupation, but again only if more than one exposure level is represented within the sector or industry/occupation. In general, as it is only possible to allocate whole industries/occupations to a single exposure category, proportions shifting into lower categories are assumed to be distributed between the industries/occupations originally in the higher category in proportion to numbers exposed in the industry/occupation, so that phj/ph0 remains constant. A2.7 To implement an intervention scenario for a change in exposure limit, the procedure is as follows. Estimate the proportions exposed across time as described above for the baseline trend scenario. For the baseline scenario set the proportions to their 2005 value. Estimate proportions exposed above the old and new limits for each forecast estimation interval as one minus the proportion of the lognormal distribution, with GM and GSD as for the forecast estimate, that is below each limit respectively. The proportion exposed above an existing exposure limit L1 for a forecast year j is given by (A2.6) pL1j = 1 –LOGNORMDIST[L1, ln(GMj), ln(GSDj)] To test the effect of reducing a workplace exposure limit to L2, a new distribution GM (GML2j) can be estimated which represents a shift downwards from the existing GMj equal to the shift from the old (L1) to the new (L2) limit on the natural log scale:

New scenario GML2j = exp{ln(old GMj) – [ln(L1) – ln(L2)]}which is equivalent to

44

(A2.7) GM L2j = exp{ln(L2)-z(1- pL1j)*ln(GSDj)}

where z(p) is the inverse of the standard normal cumulative distribution. The new high exposed proportions are estimated by substituting this new scenario GML2j for the original forecast scenario GMj in equation (A2.4) for each forecast estimation interval j from when the intervention is applied (e.g. for the 2011-2020 interval). These proportions now represent a shift in the exposure distribution corresponding to the shift in the limit, from the estimation interval succeeding the change onwards. The same proportion that was exposed above the old limit (pL1j) continues to be exposed above the new limit.

If the new scenario only applies, for example, in the larger workplaces, these adjustment factors are applied in the larger workplaces, and different (baseline or trend scenario) adjustment factors are applied elsewhere.

For an intervention scenario where it is assumed that the proportion exposed above a new limit is close to zero, the new scenario GM is estimated by setting the proportion exposed above the new exposure limit in equation (A2.7) above (pL1j) to a low figure, for example 0.01. Scenarios 13a-15a and 13b-15b in the crystalline silica example in Section 6 illustrate this.

Table 10: Data from Creely et al, 2006 for a range of exposures

Substance Source GM GSD Min. Max. Max:GM ratio Unit

Toluene NEDB 5.0 10.3 0.007 8698 1740 ppm

Flour dust data NEDB /Industry 3.4 4.1 0.005 1149 338 mg/m3

Wood dust NEDB 5.2 3.6 0.010 502 96 mg/m3

Respirable quarry dust IOM 0.6 2.9 <0.01 554 923 mg/m3

Respirable quartz IOM 0.03 4.0 <0.01 5 150 mg/m3

Rubber process dust BRMA 0.59 4.55 0.02 325 551 mg/m3

NEDB 1.4 4.29 0.06 190 136 mg/m3

Rubber fume BRMA 169 3.26 7.07 23020 136 mg/m3

NEDB 642 3.37 7.07 120,000 187 mg/m3

45

   

APPENDIX 3: LIFETIME RISK METHOD: DETAILS OF THE ESTIMATION PROCESS

A3.1: The method is to apply sex- and age-specific mortality or incidence rates to person years at risk derived from the numbers of survivors from life table data. The proportion of the current general population cohort, either of working age or subject to no previous workplace exposure (aged 15-24 say), that will develop the disease (ONS, Cancer statistics registrations: 2004) is then obtained.

Lifetime risk for the general population (LRGP) per person is estimated as follows. The risk rate for the GB population currently of age i for the period when they will be at age j is:

(A3.1) λij = rj * PYARij

where rj = Dj / Nj = current death or incidence rate for specific cancer in age group j, males and females treated separately, from national

mortality/registration data (Dj).

PYARij = Ni * [pyar]ij

= future lifetime person years at risk for the population aged i in the current year 2005, for the period when they will be in age class j (i.e. from (2005 + jl -15) to (2005 + ju – 15), where jl and ju are the lower and upper age class limits), and therefore subject to the general population cancer rates rj . [pyar]ij is the matrix of years spent by an individual of age i in 2005 at each of the ages j = i to 100, truncated according to the current life expectancy for age i (from Life Tables, GB, 2004-2006.xls).

Ni = Current population aged i from GB mid-year estimates (GB Mid-year Population Estimates 2005.xls)

(A3.2) λij

where l = lower age limit of a working age cohort, e.g. 15

u = upper age limit of cohort, e.g. retirement age (64) for all working ages, or 24 for young workers only.

LRGP can be estimated separately for the periods (2005+jl -15) to (2005+ ju -15) where jl and ju are lower and upper age band limits. Five-year bands (2005-09, 2010-14 etc) have been used in the examples given below.

A3.2: This proportion is then multiplied by the excess risk (RR-1) for the occupational carcinogen at the current exposure level, to obtain the ‘baseline’ lifetime excess risk for an individual worker currently exposed to the occupational carcinogen at that level (LRexph).

(A3.3) LRexph = LRGP * (RRh-1) where RRh = relative risk from current burden at exposure level h

A3.3: Applying these individual lifetime excess risks to an estimate of numbers exposed then gives the numbers of attributable cancers that can be expected to occur over the lifetimes of this cohort of exposed workers. Separate RR estimates are applied to the parts of the cohort exposed

46

at ‘higher’ and at ‘lower’ level, and the result summed to obtain an overall lifetime risk result for the exposed cohort. This cohort can also be subdivided into its industry or occupation components, and apportioned between different workplace size categories.

A3.4: The estimates of numbers exposed are obtained as for the Attributable Fraction approach by multiplying the estimated cohort size (see equation A3.7 below) by the employment level adjustment factor for 2005 (see Table 13 in Appendix 6, CAREX data only, to update the point estimate from 1990-93), and the proportion exposed at ‘higher’ level in 2005 and its complement for the ‘lower’ exposure level estimate (see Appendix 2 for the derivation). Attributable numbers for a baseline scenario (ANB) are therefore estimated as

(A3.4) ANB = Σhnrh *f1 *f2 * LRexph

where nrh = cohort size estimate of exposed numbers at exposure level h

f1 = employment level adjustment factor (CAREX data only)

f2 = proportion at ‘higher’ exposed level

To obtain an estimate for a forecast or intervention change in exposure level, change scenario employment adjustment factors and adjusted exposure level proportions are applied as for the Attributable Fraction approach to the numbers in the cohort.

ANS = Σhnrh *f1S *f2S * LRexph

where f1S and f2S are the factors/proportions used to represent an alternative forecast or intervention scenario.

A3.5: Avoided cancer numbers are then obtained by subtraction.

(A3.5) ANsaved = ANB - ANS

where ANB = attributable numbers resulting from current exposure levels

ANS = attributable numbers resulting from reduced (forecast or intervention scenario) exposure levels

A3.6: The attributable fraction is the attributable cancer numbers as a proportion of total expected cancer numbers (attributable cancers plus those arising from the general background rate), either within the worker cohort, or to be comparable to the AF approach as a proportion of GB projected cancer numbers.

(A3.6) AF = AN / (AN + GPN)

where GPN = Number of cancers expected in the GB general population of the exposed cohort’s age structure.

The method assumes implicitly that the exposed workers have the same (current – 2005) age structure and life expectancy as the general population in the cohort’s age range.

A3.7: CAREX or LFS/CoE estimates of numbers exposed include all working ages for the point estimates of the numbers exposed (n0). All of this (currently exposed) cohort has been subject to previous exposure of some duration. In order to avoid having to take account of this previous

47

exposure in estimating attributable numbers due to forecast or intervention changes in exposure levels, only the lifetime risk of cohorts of new recruits to the exposed workplaces (nr) is considered. The size of these cohorts is estimated using a turnover equation on n0. As for current burden, for estimating their lifetime risks the recruits are assumed to be in the age range 15-24.

(A3.7) nr = n0 * TO * t

where n0 = numbers currently employed in the exposed job/industry

TO = employment turnover rate, as for current burden updated to 2003 (the most recent available date, see Table 12 in Appendix 6)

t= length of recruitment period the cohort represents in years (usually 5)

Equations (A3.4) are then applied to these cohorts (nrh for exposure level h) to obtain baseline or forecast or intervention estimates of attributable numbers for the cohorts’ expected lifetime.

To estimate when the cancers that are ascribed to current work exposure can be expected to appear (as registrations or deaths), the future risk is partitioned into five year periods corresponding to the age bands for which cancer rates are estimated as they move through the forecast period, which for 15-19 year olds in 2005 extends to 2065-69 (for men, 2070-74 for women as they have longer life expectancy). Occupation attributable and avoided cancers are estimated for the lifetime of the cohort and for 5 year forecast intervals.

48

APPENDIX 4: DATA REQUIRED FOR ESTIMATING FUTURE BURDEN

A4.1 Attributable Fraction Approach

For the AF approach the following data are required: 1. Point estimates of numbers exposed during the forecast REP, from CAREX, LFS or CoE.

2. Estimates of employment turnover from LFS data

3. Survival to the target year, from life table estimates.

4. Estimates of employment trends across the forecast REPs, from LFS employment time series data and extrapolated forecasts (the CAREX and LFS/CoE employment level adjustment factors).

5. Estimates of proportions of employed workers by workplace size category (Small and Medium Enterprise data from Government Statistics (the Department for Business, Enterprise and Regulatory Reform)).

6. Estimates of proportions by year across the forecast REP of cases appearing in the target year based on an appropriate latency distribution. These proportions can be applied to the life table data (see section A1.4.2). Alternatively, the proportions can be applied to the AF estimates by say 10-year estimation intervals, during the calculation of AF, as done for this project.

7. Forecast population estimates by age in target year for the Pr(E) denominator, and for death/registration projections (ONS population projections).

8. Projected numbers of deaths and registrations by cancer for each target year taking into account demographic changes (national Mortality and Cancer Incidence data and population projections).

9. Current or past exposure level data where available, to estimate an average (geometric mean – GM) exposure level and a (geometric) standard deviation (GSD), in order to characterize the distribution of exposure levels. This distribution is assumed to be lognormal. An estimate of average change (usually decline) in mean exposure levels is also useful if available. When these data are available it is possible to estimate the effect of introducing exposure standards and compliance to current standards.

A4.2 Additional Data Required for the ‘Lifetime Risk’ Approach

10. Current sex and age specific cancer registration (or death) rates

11. Numbers of survivors from (current) life table estimates to derive person years at risk to which the death rates are applied, to obtain the probability of a member of the general population currently aged 15+ getting that cancer during his/her lifetime

12. The estimated number of workers currently exposed.

49

APPENDIX 5: STEPS IN THE CALCULATION OF FUTURE BURDEN

A5.1: AF Approach

The steps in the calculation process are illustrated in Figure 3 in the main report. All estimates are made by industry/occupation, and finally summed to obtain an overall cancer/exposure estimate.

A5.1.1 For each cancer /exposure combination

1. Choose forecast target years (FTY) to suit cancer latency e.g. 2010, 2020 etc., identify the correct risk exposure period (REP) for each FTY and divide the forecast REPs into 10 year estimation intervals. For example for a solid tumour, the REP for FTY=2050 is 2001-2040, with estimation intervals 2001-10, 2011-20, 2021-30, 2031-40.

2. Obtain an estimate of exposed numbers by industry or occupation (from CAREX, LFS, ABI) and allocate these to high, medium, low or background exposure levels, matched to portable relative risks identified from the epidemiological literature for similar work exposures. Any year can be chosen to which these estimates apply, as the employment level adjustment factors for each estimation interval will be used to adjust the numbers and hence proportions obtained to a point of origin that correctly represents the H/M/L/B allocation (e.g. 1975 for a solid tumour REP for current burden).

3. Select appropriate employment level adjustment factors (Appendix 8)

4. Estimate parameters for a distribution of exposure levels in a baseline year (GM, GSD for an assumed lognormal distribution), and annual change in exposure levels, from available data. These will be used to calculate the exposure level factors (Appendix 2).

5. Choose suitable baseline, trend and intervention scenarios, and identify the adjustment factors that will be used to characterise these scenarios.

A5.1.2 For each scenario

6. Estimate the numbers ever exposed for the REP for each FTY, by 10 year estimation interval taking into account the change scenario factors

• Adjust the point estimate of numbers exposed for change in employment structure in GB (employment level factors, Tables 13, 14 and 15 in Appendix 6)

• Adjust proportions exposed at the different exposure levels by estimation interval (exposure level factors, Appendix 2). If exposure levels are declining (trend scenario) or are reduced with compliance to a trial standard for an intervention scenario, numbers ever exposed are estimated separately for exposed workers at H/M/L/B level as they move into lower exposure classes across estimation intervals i.e. in 10 classes H_H, H_M, H_L, H_B, M_M, M_L, M_B, L_L, L_B, B_B. In this nomenclature the first letter represents the original H/M/L/B allocation and the second the level into which the exposed workers have moved (or remained).

• Allocate proportions exposed to workplace size classes

• Take account of annual employment turnover (TO) and survival to the FTY (using the current burden methodology turnover equation, Appendix 7 equation A7.1, using data in Tables 11a and 11b in Appendix 6).

7. Estimate proportions of the population exposed (Pr(E)) for each estimation interval using projected population estimates by age in forecast target years as denominator.

50

8. Estimate forecast attributable fractions (AFs) from Pr(E) and RR (Levin’s equation), by estimation interval in the REP for each forecast target year.

• Use relative risks (RRs) applied to forecast numbers at the various exposure levels. The RRs may be adjusted (RR adjustment factor) to reflect reduced risk where exposure levels cannot be estimated for example where an exposure is defined by occupation.

• Use a suitable distribution of cancer latency to apply the probability of becoming a case in the FTY from first exposure in each 10 year REP interval

9. Sum across REP estimation intervals for each FTY.

10. Apply AFs to projected deaths and registrations in the age range appropriate to the REP to obtain forecast attributable numbers (AN)

• Use current death and registration rates applied to population projections for the FTY by age and sex (only demographic change is assumed for projected deaths/registrations, no adjustment is made for non-occupational exposure trends).

11. To predict the effect of an intervention in terms of avoided cancers, estimate ANs for baseline and intervention scenarios separately and subtract.

A5.2 LR Approach

For the calculation process see Section 2.2 and Appendix 3.

51

APPENDIX 6: DATA TABLES FOR AF AND LR METHODS

Data for estimating numbers exposed in the forecast REP (AF approach) or numbers currently exposed in newly recruited cohorts (LR approach) are summarised in the tables below.

52

Table 11A: Data for calculating the numbers (turnover equation) and proportions ever exposed (AF approach)3

Forecast target year (FTY) 2005 (current burden)

2010 2020 2030 2040 2050 2060 2070 2080

Solid tumour REP (latency 10-50 yrs) REP 1956-1995 1961-

2000 1971-2010

1981-2020

1991-2030

2001-2040

2011-2050

2021-2060

2031-2070

Estimation intervals included 1956-70 1971-80 1981-90 1991-95

1961-70 1971-80 1981-90 1991-00

1971-80 1981-90 1991-00 2001-10

1981-90 1991-00 2001-10 2011-20

1991-00 2001-10 2011-20 2021-30

2001-10 2011-20 2021-30 2031-40

2011-20 2021-30 2031-40 2041-50

2021-30 2031-40 2041-50 2051-60

2031-40 2041-50 2051-60 2061-70

‘Peak’ latency year (TY-35) 1970 1975 1985 1995 2005 2015 2025 2035 2045 Ever of working age in REP** M, F

19.4 M 21.0 F

20.3 21.8

22.5 23.6

24.0 25.1

25.5 26.5

26.8 27.7

27.9 28.6

29.1 29.6

30.3 30.7

Ages included M, F

25-90+ 25-90+

25-90+ 25-90+

25-90+ 25-90+

25-90+ 25-90+

25-90+ 25-90+

25-90+ 25-90+

25-90+ 25-90+

25-90+ 25-90+

25-90+ 25-90+

Estimation interval

Life table used

Original cohort ages (a – b, equation A7.1)

1961-70 1980-82* 65-100 65-100 1971-80 1980-82* 65-100 1981-90 1984-86 65-100 1991-00 1994-96 65-100 2001-10 2004-06 65-100 2011-20 2015# 65-100 2021-30 2025# 65-100 2031-40 2025# 65-100

Recruited to cohort ages (c – d, equation A7.1)

1961-70 1980-82* 55-64 55-64

3 Appendix 5 in Current Burden Methodology Technical Report

53

Forecast target year (FTY) 2005 (current burden)

2010 2020 2030 2040 2050 2060 2070 2080

Solid tumour REP (latency 10-50 yrs) REP 1956-1995 1961-

2000 1971-2010

1981-2020

1991-2030

2001-2040

2011-2050

2021-2060

2031-2070

1971-80 1980-82* 45-54 45-54 55-64 1981-90 1984-86 35-44 35-44 45-54 55-64 1991-00 1994-96 25-34 25-34 35-44 45-54 55-64 2001-10 2004-06 25-34 35-44 45-54 55-64 2011-20 2015# 25-34 35-44 45-54 55-64 2021-30 2025# 25-34 35-44 45-54 55-64 2031-40 2025# 25-34 35-44 45-54 55-64 2041-50 2025# 25-34 35-44 45-54 2051-60 2025# 25-34 35-44 2061-70 2025# 25-34 Forecast target year (FTY) 2005

(current burden) 2010 2020 2030 2040 2050 2060 2070 2080

Short Latency REP (latency 0-20 yrs) REP 1986-2005 1991-

2010 2001-2020

2011-2030

2021-2040

2031-2050

2041-2060

2051-2070

2061-2080

Estimation intervals included 1986-90 1991-00 1991-00 2001-10 2001-10 2001-05 2011-20 2011-20

2021-30 2021-30 2031-40 2031-40

2041-50 2041-50 2051-60

2051-60 2061-70

2061-70 2071-80

‘Peak’ latency year (TY-15) 1990 1995 2005 2015 2025 2035 2045 2055 2065 Ever of working age in REP** M, F

23.0 M 23.1 F

23.9 23.9

25.5 25.0

27.0 26.1

28.2 27.0

29.0 27.4

30.0 28.3

31.1 30.7

31.8 31.3

Ages included M, F 15-84 15-79

15-84 15-79

15-84 15-79

15-84 15-79

15-84 15-79

15-84 15-79

15-84 15-79

15-84 15-79

15-84 15-79

Estimation interval

Life table used

Original cohort ages (a – b, equation A7.1)

54

Short Latency REP (latency 0-20 yrs) REP 1986-2005 1991-

2010 2001-2020

2011-2030

2021-2040

2031-2050

2041-2060

2051-2070

2061-2080

1986-90 1984-86 35-84/79 1991-00 1994-96 35-

84/79 2001-10 2004-06 35-

84/79 2011-20 2015# 35-

84/79 2021-30 2025# 35-

84/79 2031-40 2025# 35-

84/79 2041-50 2025# 35-

84/79 2051-60 2025# 35-

84/79 2061-70 2025# 35-

84/79 Recruited to cohort ages (c – d, equation A7.1)

1986-90 1984-86 30-34 1991-00 1994-96 20-29 25-34 2001-10 2004-06 15-19 15-24 25-34 2011-20 2015# 15-24 25-34 2021-30 2025# 15-24 25-34 2031-40 2025# 15-24 25-34 2041-50 2025# 15-24 25-34 2051-60 2025# 15-24 25-34 2061-70 2025# 15-24 25-34 2071-80 2025# 15-24

4

3 Appendix 5 in Current Burden Methodology Technical Report

55

Table 11B: Data for calculation of the numbers ever exposed (turnover equation, AF approach)

Solid tumour REP (latency 10-50 yrs) Estimation interval 1961-70

1956-70 1971-80 1981-90 1991-00

1991-95 2001-10 2011-20 2021-30 2031-40 2041-50 2051-60 2061-70

Interim life table to use

1980-82* 1980-82* 1984-86 1994-96 2004-06 2015# 2025# 2025# 2025# 2025# 2025#

TO factor (year) 1984/1991/1998 average Adjustment factors (year)- CAREX, LFS, WSP

1975 1975 1985 1995 2005 2015 2025 2025 2025 2025 2025

Short Latency REP (latency 0-20 yrs) Estimation interval 1986-90 1991-00

1991-95 2001-10 2011-20 2021-30 2031-40 2041-50 2051-60 2061-70 2071-80

Interim life table to use

1984-86 1994-96 2004-06 2015# 2025# 2025# 2025# 2025# 2025# 2025#

TO factor (year) 1984/1991/1998 average Adjustment factors (year)- CAREX, LFS, WSP

1985 1995 2005 2015 2025 2025 2025 2025 2025 2025

#Estimated from ONS death rates used for period life expectancy tables. Mortality rate data was from file ‘Age sex mortality rates for 2006 life expectancy projections.xls’ downloaded from the GAD website. Calculated proportions living to age x (the lx) were calculated using an ONS template downloaded from the GAD website to ‘ONS Life Table Templates.xls’.

*Earliest /latest available

**Estimated by interpolation between years e.g. 2036/41 for 2040

56

Table 12: Employment turnover factors for forecast numbers exposed Main Industry Sector Annual TO based on new starters employed for at least 1 year based on data for:

1984 1991 1998 2003 Average 1984/1991/1998

Average 1991/1998/2003

Men A,B Agriculture, hunting and forestry; fishing 0.09 0.07 0.07 0.06 0.07 0.07 C,D,E Mining and quarrying, electricity, gas and water;

manufacturing industry 0.08 0.09 0.10 0.09 0.09 0.10

F Construction 0.14 0.08 0.14 0.11 0.12 0.12 G-Q Service industries 0.10 0.11 0.12 0.12 0.11 0.12 Total 0.09 0.09 0.11 0.11 0.10 0.11 Women A,B Agriculture, hunting and forestry; fishing 0.16 0.08 0.09 0.10 0.10 0.10 C,D,E Mining and quarrying, electricity, gas and water;

manufacturing industry 0.13 0.13 0.15 0.11 0.14 0.14

F Construction 0.16 0.14 0.16 0.13 0.15 0.15 G-Q Service industries 0.14 0.14 0.16 0.13 0.15 0.15 Total 0.14 0.14 0.15 0.13 0.14 0.15

Table 13: CAREX employment level adjustment factors for forecast numbers exposed (adjusted to 1990-1993)

Main Industry Sector (factors from 2015 are for forecast trends) CAREX adjustment factor to:

1975 1985 1995 2005 2015 2025 2035 2045 Men A,B Agriculture, hunting and forestry; fishing 1.3 1.1 0.9 0.8 0.6 0.5 0.5 0.5 C-E Mining and quarrying, electricity, gas and water; manufacturing industry 1.4 1.2 0.9 0.7 0.4 0.2 0.2 0.2 F Construction 1.0 1.0 1.0 1.0 1.0 1.1 1.1 1.1 G-Q Service industries 0.8 0.9 1.0 1.2 1.3 1.4 1.4 1.4 Total 1.0 1.0 1.0 1.0 1.0 1.1 1.1 1.1 Women A,.B Agriculture, hunting and forestry; fishing 1.2 1.1 1.0 0.9 0.7 0.6 0.6 0.6 C-E Mining and quarrying, electricity, gas and water; manufacturing industry 1.5 1.2 0.9 0.6 0.3 0.1 0.1 0.1 F Construction 0.9 0.9 1.0 1.1 1.2 1.3 1.3 1.3 G-Q Service industries 0.7 0.9 1.1 1.2 1.4 1.6 1.6 1.6 Total 0.8 0.9 1.0 1.2 1.3 1.4 1.4 1.4

57

Table 14: LFS and CoE employment level adjustment factors for forecast numbers exposed (adjusted to 1979)

Main Industry Sector (factors from 2015 are for forecast trends) LFS/CoE adjustment factor to:

1975 1985 1995 2005 2015 2025 2035 2045 Men A,.B Agriculture, hunting and forestry; fishing 1.1 0.9 0.8 0.6 0.5 0.4 0.4 0.4 C-E Mining and quarrying, electricity, gas and water; manufacturing industry 1.1 0.9 0.7 0.5 0.3 0.2 0.2 0.2 F Construction 1.0 1.0 1.0 1.0 1.1 1.1 1.1 1.1 G-Q Service industries 0.9 1.1 1.2 1.4 1.5 1.7 1.7 1.7 Total 1.0 1.0 1.0 1.1 1.1 1.1 1.1 1.1 Women A,.B Agriculture, hunting and forestry; fishing 1.0 0.9 0.8 0.8 0.7 0.6 0.6 0.6 C-E Mining and quarrying, electricity, gas and water; manufacturing industry 1.1 0.9 0.7 0.5 0.2 0.0 0.0 0.0 F Construction 1.0 1.1 1.1 1.2 1.3 1.4 1.4 1.4 G-Q Service industries 0.9 1.1 1.4 1.6 1.8 2.0 2.0 2.0 Total 0.9 1.1 1.2 1.4 1.5 1.7 1.7 1.7

58

Table 15: LFS and CoE employment level adjustment factors for forecast numbers exposed (adjusted to 1995)

Main Industry Sector (factors from 2015 are for forecast trends) LFS/CoE adjustment factor to:

1975 1985 1995 2005 2015 2025 2035 2045 Men A,.B Agriculture, hunting and forestry; fishing 1.3 1.2 1.0 0.8 0.7 0.5 0.5 0.5 C-E Mining and quarrying, electricity, gas and water; manufacturing industry 1.5 1.3 1.0 0.7 0.5 0.2 0.2 0.2 F Construction 1.0 1.0 1.0 1.0 1.0 1.1 1.1 1.1 G-Q Service industries 0.8 0.9 1.0 1.1 1.2 1.4 1.4 1.4 Total 1.0 1.0 1.0 1.0 1.0 1.1 1.1 1.1 Women A,.B Agriculture, hunting and forestry; fishing 1.2 1.1 1.0 0.9 0.8 0.7 0.7 0.7 C-E Mining and quarrying, electricity, gas and water; manufacturing industry 1.6 1.3 1.0 0.7 0.4 0.1 0.1 0.1 F Construction 0.8 0.9 1.0 1.1 1.2 1.2 1.2 1.2 G-Q Service industries 0.7 0.8 1.0 1.2 1.3 1.5 1.5 1.5 Total 0.8 0.9 1.0 1.1 1.2 1.4 1.4 1.4

59

Size (number of employees) None (1)

1-49

50-249

250+

Table 16: Percentage employment by workplace size class for forecast numbers exposed

Industry Sector 1975 1985 1995 2005 2015 2025 2035 2045

All industries 10.0 11.6 13.2 14.8 16.4 18.0 18.0 18.0 A, B 58.3 51.7 45.1 38.5 31.9 25.3 25.3 25.3 C, D, E 1.6 3.2 4.8 6.4 8.0 9.6 9.6 9.6 F 55.3 49.8 44.3 38.8 33.3 27.8 27.8 27.8 G - O 8.5 10.1 11.7 13.3 14.9 16.5 16.5 16.5

All industries 32.8 32.2 31.6 31.0 30.4 29.8 29.8 29.8 A, B 34.1 42.0 49.9 57.8 65.7 73.6 73.6 73.6 C, D, E 18.2 20.3 22.4 24.5 26.6 28.8 28.8 28.8 F 34.0 34.3 34.6 34.9 35.2 35.4 35.4 35.4 G - O 39.0 36.4 33.9 31.3 28.7 26.1 26.1 26.1

All industries 14.7 13.6 12.6 11.5 10.5 9.4 9.4 9.4 A, B 3.4 3.2 2.9 2.6 2.3 1.1 1.1 1.1 C, D, E 19.2 19.8 20.4 21.0 21.6 22.2 22.2 22.2 F 7.0 7.7 8.3 9.0 9.6 10.3 10.3 10.3 G - O 12.1 11.4 10.6 9.9 9.2 8.5 8.5 8.5

All industries 42.5 42.6 42.6 42.7 42.7 42.8 42.8 42.8 A, B 4.1 3.1 2.1 1.1 0.1 0.0 0.0 0.0 C, D, E 61.0 56.7 52.4 48.1 43.8 39.4 39.4 39.4 F 3.7 8.2 12.8 17.3 21.9 26.5 26.5 26.5 G - O 40.4 42.1 43.8 45.5 47.2 48.9 48.9 48.9

60

APPENDIX 7: ADDITIONAL EQUATIONS USED IN THE AF APPROACH

A7.1 Turnover Equation5 To Estimate Numbers Ever Employed During The REP

i=b

(A7.1) Ne(REP) = ∑ l(adj15)i * n0/(R-15)} i=a

j=d +kk =(age(u)−age(1))

+ ∑ ∑ {l(adj15)j *n0 * TO /(age(u)-age(l)+1)} k =0 j=c+k

This equation is derived from the simple turnover equation:

Ne(REP) = n0 + {n0 * TO * t}

where n0 = numbers employed in the exposed job/industry at a mid-point in the REP,

TO = employment turnover rate,

t= length of risk exposure period in years.

been aged from 65 upwards by 2005 ( , a=65, b=100), An even distribution of ages across the original cohort is assumed. Retirement age is taken as 65 for men, 60 for women. This proportion is then applied to the original cohort size (n0) to get an estimate of the numbers from this original cohort who would still be alive in 2005.

To make adjustment for loss from the cohort from deaths occurring before the target year of say 2005, n0 is replaced in this equation by an estimate of only those surviving to the target year, estimated using GB life table data (http://www.gad.gov.uk/Life_Tables/Interim_Life_Tables.htm). For the original cohort recruited at the beginning of the REP, the estimate is obtained as the sum of the proportions of survivors in GB at all the ages that would have been represented in the cohort at the outset divided by the number of ages originally represented (R-15). The cohort consists of ages from 15 to retirement age (R) in 1956 (for the solid tumour standard REP and a target year of 2005), who would have

For the part of the cohort recruited through employment turnover, t in the simple turnover equation is replaced by the sum of the proportions of survivors in the age range achieved by the turnover of recruited cohort members by 2005 (i.e. all those aged 25 to 64 for the solid tumour REP, Σj=c

j=d l(adj15)j , c=25, d=64). This proportion is then applied to the original cohort size n0, multiplied by the percent turnover (TO). Summing the proportions over this age range is equivalent to multiplying by the number of years in the REP (t). However, it would not be realistic to assume that all newly recruited members of the cohort enter at age 15, as this summation of survivor proportions would assume. Therefore to allow recruitment across the age range u to l, a further summation is introduced into the turnover part of the equation so that the range of ages is counted for the survivor proportions, divided by the length of the age range, giving an ‘average’ survivor proportion for the age range. An entry age range of 15 to 24 was used in this study, giving an age range length of 10 years. This seemed more realistic than

5 See Appendix 6* in Current Burden Methodology Technical Report

61

 

assuming entry at all ages from 15 to retirement, but more conservative than allowing entry at age 15 only.

The proportions of survivors to age i (the li) were obtained from GB life tables, adjusting to include only those still alive at age 15 by taking (l(adj15)i) = li / l15. The li are the proportions of survivors per live birth at age i, assuming sex-specific death rates current in the period that the life tables are estimated for (1980-82, the earliest tables available, for example were used for the solid tumour REP of 1956-1995 for the target year of 2005). The life tables used are given in Table 11B in Appendix 6).

For estimating future burden, the REP is divided into ten year estimation intervals. For the first interval, both parts of the cohort, the original all working age cohort recruited at the beginning of the REP and the first ten years of turnover recruited workers, are included. Equation (A7.1) above is used, with c=55 and d=64 for this group. In subsequent estimation intervals, only the turnover recruited cohort is included, using the second part of equation (A7.1) above, with the values of c and d given in Table 11A.

A7.3 Equations for the latency weights

Annual weights to apply to l(adj15) in equation (A7.1) above are calculated as

Lognormal For each latency year t, ranging from tl = lower latency limit to tu = upper limit (10 and 49 for solid tumours)

(A7.2) xt = NORMDIST(ln(t),ln(m),ln(s),FALSE) where t = latency year

m = 35 s = exp{[(ln(tu) – ln(tl)]/6} = 0.27

Annual weights are given by

(A7.3) for year t

Uniform Annual weights are given by

1/ (tu – tl +1) * 40

Normal As lognormal with xt = NORMDIST(t,m,s,FALSE)

where m = 35 s = (tu – tl +1)/6 = 6.67

Power As lognormal with xt = (t-10) k

where t = latency year (tl=10 to tu=49 for solid tumours) k = 2

The estimation interval weights to apply to the AF numerator are calculated as the sum of the annual weights (from equation (A7.3) above) over the latencies t within each estimation interval divided by the number of years in that estimation interval.

62

1975

1975

19

819

800

19198585

19

9019

90

1995

1995

220

00000

2005

2005

20

1020

10

201

20155

20

2020

20

2025

2025

19197755

1919

8080

19198585

1199

9900

19199955

2200

0000

20200505

2020

1010

20201515

2020

2020

20202255

APPENDIX 8: DATA FOR CAREX AND LFS/COE ADJUSTMENT FACTORS AND PERCENTAGE OF EMPLOYEES BY WORKPLACE SIZE

A8.1 CAREX and LFS/COE Adjustment Factors

Figure 17: LFS employed to 2007, and linear extrapolation to 2025, UK, men

LFSLFS MaMalele eempmpllooyymmeent bnt byy ggrrououpeped Mad Main Inin Indusdustrtryy SSeecctotorr

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A8.2 Percentage Employed By Workplace Size For all sectors, numbers in years outside 1994-2006 are based on extrapolated workplace size percentages applied to LFS totals. Numbers in years 1994-2006 are based on total employed obtained from SME data.

63

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2005

APPENDIX 9: CANCER DEATHS AND REGISTRATIONS (1995-2005)

Trends in numbers of cancer deaths and registrations are obtained from national data (ONS, 2004, ONS, 2005) Table 17 and Figures 20 and 21 illustrate these trends for lung and sinonasal cancers (a common and a relatively rare cancer respectively).

Table 17: Number of lung and sinonasal cancer registrations in England 1995-2004, and deaths in England and Wales, 1999-2005.

Men Women Sinonasal cancer Lung cancer Sinonasal cancer Lung cancer

Year Deaths (E&W)

Registrations (England)

Deaths (E&W)

Registrations (England)

Deaths (E&W)

Registrations (England)

Deaths (E&W)

Registrations (England)

1995 185 21060 116 11668 1996 188 20047 139 11349 1997 194 19846 133 11439 1998 190 19363 19515 135 11589 11812 1999 60 181 18736 19283 34 150 11232 11854 2000 74 199 17993 19035 43 153 11120 12055 2001 68 190 17579 18577 66 142 11149 11963 2002 69 179 17447 18056 44 118 11359 11922 2003 77 191 17155 17549 57 143 11610 12253 2004 63 195 16862 18105 53 131 11466 12354 2005 56 16852 48 11940

Average 67

189 17748 19107 49 136 11433 11867

Figure 20: Deaths and registrations for lung cancer, 1995-2005

0

5000

10000

15000

20000

25000

Num

bers

Men, Deaths (E&W)Men, Registrations (England)Women, Deaths (E&W)Women, Registrations (England)

Deaths and registrations for lung cancer, 1995-2005

0

5000

10000

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25000

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Men, Deaths (E&W)Men, Deaths (E&W) Men, Registrations (England)Men, Registrations (England) Women, Deaths (E&W) Women, Registrations (England)Women, Registrations (England)

YearYear

Figure 21 DDeateathshs and reand reggiiststratrations fions foor sr sinonasainonasal cal cancer, 1ncer, 1995995--22000055

0

50

100

150

200

250

Num

bers

Men, Deaths (E&W)Men, Registrations (England)Women, Deaths (E&W)Women, Registrations (England)

0

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bers

Men, Deaths (E&W)Men, Registrations (England)Women, Deaths (E&W)Women, Registrations (England)

Men, Deaths (E&W)Men, Deaths (E&W) Men, Registrations (England)Men, Registrations (England) Women, Deaths (E&W) Women, Registrations (England)Women, Registrations (England)

YeYearar 66

Figure 22: Percentage change in the age-standardised (European) incidence rates, major cancers, UK, 1995-2004*

All malignant neoplasms Mesothelioma

Malignant Melanoma Prostate

Liver Oral

Uterus Breast

Kidney Oesophagus

Non-Hodgkin Lymphoma Multiple Myeloma

Colorectal Pancreas

Brain + CNS MalesLeukaemia Females Ovary

Lung Cervix

Stomach Bladder

-50% -30% -10% 10% 30% 50% % change in incidence rates

* Cancer Research UK

67

APPENDIX 10: WORKED CALCULATION EXAMPLE, ATTRIBUTABLE FRACTION APPROACH

The calculation process for lung cancer due to exposure to crystalline silica, for men self-employed in a single industry (construction) is set out in Tables 18a and b below. Table 1 in Section 6 shows that there were 445,431 men exposed to crystalline silica in ‘construction’ in 1990-93 (estimated from CAREX). Construction is considered to be a ‘higher’ level exposure, for which the relative risk from current burden was 1.32. The estimates are for baseline trend scenario (2) described in Section 6. Where results do not sum correctly, this is due to rounding error.

68

Table 18 a & b: Example of the calculation of AF and attributable numbers for a single industry: Lung cancer from exposure to crystalline silica, self-employed male workers only in construction

Table 18a CALCULATION OF NUMBERS EVER EXPOSED Numbers ever exposed

summed across the estimation intervals

CAREX numbers exposed 445,431

Estimation interval: 1961-70 1971-80 1981-90 1991-00 2001-10 2011-20 2021-30 2031-40 2041-50 2051-60 2061-70 Numbers ever of working age during forecast REP [from equation 6.2.3]

CAREX adjustment factor [from Table 13] 1 1 1 1 1 1 1.1 1.1 1.1 1.1 1.1

Exposure level adjustment factor: proportion exposed at H/M/L/B levels ( f2j = (phj/ph0 from equation A2.5) H 1.000 1.000 0.758 0.508 0.295 0.146 0.061 0.061 0.061 0.061 0.061 M 0.000 0.000 0.082 0.079 0.063 0.041 0.022 0.022 0.022 0.022 0.022 L 0.000 0.000 0.160 0.413 0.615 0.540 0.396 0.396 0.396 0.396 0.396 B 0.000 0.000 0.000 0.000 0.028 0.273 0.521 0.521 0.521 0.521 0.521 Percentage of this workplace size [from Table 16]

55.3 55.3 49.8 44.3 38.8 33.3 27.8 27.8 27.8 27.8 27.8

Numbers exposed, multiplied by employment level factor, and exposure level and workplace size proportions H 246,323 246,323 168,232 100,223 50,969 21,716 8,377 8,377 8,377 8,377 8,377 M 0 0 18,136 15,636 10,852 6,034 2,935 2,935 2,935 2,935 2,935 L 0 0 35,456 81,467 106,216 80,083 53,908 53,908 53,908 53,908 53,908 B 0 0 0 0 4,790 40,495 70,994 70,994 70,994 70,994 70,994 Staff turnover per year [from Table 12] 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12

FTY Numbers ever exposed during the REP [from turnover equation A7.1]

2005 405,348 267,980 257,175 116,021 1,046,524

19,400,00 0

2010 274,731 267,980 257,175 232,751 1,032,637 20,300,000 2020 274,731 243,495 228,727 204,354 951,307 22,500,000 2030 254,176 219,135 201,057 176,160 850,528 24,000,000 2040 242,528 193,781 173,678 162,063 772,050 25,500,000 2050 227,785 167,902 160,032 162,063 717,782 26,800,000 2060 203,063 155,227 160,032 162,063 680,386 27,900,000 2070 191,202 155,227 160,032 162,063 668,524 29,100,000 2080 191,202 155,227 160,032 162,063 668,524 30,300,000

69

FTY: 2005 2010 2020 2030 2040 2050 2060 2070

Deaths from lung cancer in Forecast Target Year [current rates * projected population by age] 19,045 21,005 26,342 32,102 36,519 39,917 42,870 46,612Registrations for lung cancer in Forecast Target Year [current rates * projected population by age] 21,923 24,024 29,857 35,809 40,362 43,643 46,927 50,696Attributable deaths [AF * deaths in FTY] 324 337 338 313 255 183 121 100Attributable registrations [AF * registrations in FTY] 373 385 383 349 282 200 133 109

Table 18b CALCULATION OF ATTRIBUTABLE FRACTION

Relative risk (RR) RR_H = 1.32; RR_M = 1.32; RR_L = 1.17; RR_B = 1.00 Estimation interval:FTY : 1961-70 1971-80 1981-90 1991-00 2001-10 2011-20 2021-30 2031-40 2041-50 2051-60 2061-70

Sum (Pr(E)*(RR-1)) across REP

Sum of ‘across REP’ (Pr(E)*(RR-1)) across all industries (and workplace sizes) with silica exposure (A)

Calculate AF [Levin's equation A1.1; denominator is (1+A) by forecast REP

Pr(E)*(RR-1); Pr(E) = Numbers ever exposed / Numbers ever of working age; by FTY and estimation interval, (summed across H/M/L/B levels)

2005 0.007 0.004 0.004 0.002

2010 0.004 0.004 0.004 0.003

2020 0.004 0.003 0.003 0.002

2030 0.003 0.002 0.002 0.001

2040 0.002 0.002 0.001 0.001

2050 0.002 0.001 0.001 0.001 2060 0.001 0.001 0.001 0.001 2070 0.001 0.001 0.001 0.001 2080 0.001 0.000 0.000 0.001

Latency distribution factor (lognormal)

REP year: 1-10 11-20 21-30 31-40

1.30 1.84 0.83 0.03

Multiply Pr(E)*(RR-1) above by latency factor; by FTY and estimation interval

2005 0.010 0.007 0.001 0.000 0.018 0.036 0.0170

2010 0.006 0.008 0.003 0.000 0.017 0.035 0.0160

2020 0.005 0.006 0.002 0.000 0.013 0.029 0.0128

2030 0.00 0.004 0.002 0.000 0.010 0.023 0.0097

2040 0.003 0.003 0.001 0.000 0.007 0.018 0.0070

2050 0.002 0.002 0.000 0.000 0.005 0.013 0.0046

2060 0.001 0.001 0.000 0.000 0.003 0.009 0.0028 2070 0.001 0.001 0.000 0.000 0.002 0.008 0.0021

2080 0.001 0.001 0.000 0.000 0.002 0.007 0.0021

CALCULATION OF ATTRIBUTABLE NUMBERS 2080

50,035

54,140

103

112

70

8. REFERENCES

Armenian, H. K. and Lilienfeld, A. M: The distribution of incubation periods of neoplastic disease. Am. J Epidemiology (1974) 99: 92-100.

Armstrong, B. G. and Darnton, A: Estimating reduction in occupational disease burden following reduction in exposure. Occup. Environ. Med (2007) 65: 592-596.

Bray, F. and Moller, B: Predicting the future burden of cancer. Nature Reviews, Cancer, (2006) 6: 63-74.

Cherrie, J.W: We can eliminate occupational cancer from chemicals. Occupational Medicine (2008) 58: 314–315

Chisholm J: Respirable dust and respirable silica concentrations from construction activities. Indoor Built Environ. (1999) 8: 94-106.

Creely KS, Van Tongeren M, While D, Soutar AJ, Tickner J, Agostini M, de Vocht F, Kromhout H, Graham M, Bolton A, Cowie H, Cherrie JW (2006). Trends in inhalation exposure: mid 1980s till present. HSE Research Report No. 460. http://www.hse.gov.uk/research/rrpdf/rr460.pdf

Department for Business, Enterprise and Regulatory Reform website at http://stats.berr.gov.uk/ed/sme

Government Actuary’s Department, Interim Life Tables, at http://www.gad.gov.uk/Life_Tables/Interim_Life_Tables.htm

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Hodgson, J. T., McElvenny, D.M., Darnton, A. J., Price, M. J. and Peto, J: The expected burden of mesothelioma mortality in Great Britain from 2002 to 2050. British Journal of Cancer (2005) 92 (3): 587 – 593

Holford, T. R., The estimation of age, period and cohort effects for vital rates. Biometrics (1983) 39: 311–324.

Hutchings, S., Rushton, L. and Brown, T: Estimation of the burden of cancer in Great Britain due to Occupation. (2007) Technical Report: Methodology. HSE website http://www.hse.gov.uk/research/rrpdf/rr595meth.pdf

Iversen, S., Arley, N. 1950. On the mechanism of experimental carcinogenesis. Acta Path. Microb. Scand. 27:773-803 quoted in: Thomas, D., 1988. Models for exposure-time-response relationships with applications to cancer epidemiology. Ann. Rev. Public Health, 9, 451-482.

Kurihara N, Wada O: Silicosis and smoking strongly increase lung cancer risk in silica-exposed workers. Industrial Health. (2004) 42: 303-314.

Levin M. The occurrence of lung cancer in man. Acta Unio Internationalis Contra Cancrum. 1953; 9:531-541.

Lumens MEGL, Spee T: Determinants of exposure to respirable quartz dust in the construction industry. Ann Occup Hyg. (2001) 45 (7): 585–595.

Magnani, C., Ferrante, D., Barone-Adesi, F., Bertolotti, M., Todesco, A., Mirabelli, D. and Terracini, B: Cancer risk after cessation of asbestos exposure: a cohort study of Italian asbestos cement workers. Occup. Environ. Med (2008) 65: 164-170.

Mathers, C.D and Loncar, D: Projections of global mortality and burden of disease from 2002 to 2030. PLoS Medicine Open Access Journal, (2006) 3, 11, 2011-2030.

McDonald, J. C., Harris, J. M. and Berry, G: Sixty years on: the price of assembling military

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gasmasks in 1940. Occup Environ Med (2006) 63, 852–855

Moller, B., Fekjaer, H., Hakulinen, T., Sigvaldason, H., Storm, H.H., Talback, M and Haldorsen, T: Prediction of cancer incidence in the Nordic countries: empirical comparison of different approaches. Statist. Med (2003) 22: 2751–2766.

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72

GLOSSARY OF TERMS

Attributable fraction (AF): The proportion of cases that would not have occurred in the absence of occupational exposure.

Attributable numbers (AN): Deaths, or cancer registrations, that would not have occurred in the absence of occupational exposure.

Cancer/exposure pairing: This is a single carcinogen occupational exposure and a site at which cancer is induced by this exposure.

Exposure scenario: The industry process, job or occupation in which a worker is exposed to a carcinogenic substance.

Estimation intervals: The time intervals, normally of 10 years, that the risk exposure period is divided into in order to apply forecast and intervention exposure level factors at specific times or as trends across time.

Forecast target year (FTY): A year for which attributable numbers are being estimated

Forecast period : From the current year (2005) to the furthest forecast target year

Baseline scenario: Attributable fractions and numbers are estimated assuming no change in exposure levels in the forecast period

Forecast scenario: Attributable fractions and numbers are estimated based on predicted trends in exposed numbers and exposure levels through the forecast period

Intervention scenario : Attributable fractions and numbers are estimated based on a target change in exposure patterns in the forecast period

Latency : In this study it is defined as the period between first exposure (causal action) and the cancer appearing as a registration or death.

Risk Exposure Period: The period during which exposure to a causal factor is able to result in a cancer appearing at a certain time. It can also be called a Relevant Latency Window.

73

Published by the Health and Safety Executive 04/11

Health and Safety Executive

The burden of occupational cancer in Great Britain Predicting the future burden of occupational cancer – Methodology

The methodology developed to estimate the future burden of cancers in GB attributable to occupational exposure is summarised in this report. The method builds on the attributable fraction approach developed to estimate the current burden of occupational cancer in GB. For future estimates the risk exposure periods (REPs), for cancer latencies up to 50 years, have been projected forward in time to estimate attributable fractions for a series of forecast target years. The method takes into account past and projected trends in exposure and quantifies the impact of possible strategies to reduce the future burden of cancers (eg, introducing and achieving compliance with exposure limits). Adjustment factors are introduced to account for changes in exposed numbers and exposure levels, and are applied in estimation intervals within the REPs.

The report contains illustrative scenarios aimed at reducing future lung cancers due to occupational exposure to respirable crystalline silica (RCS). These examples suggest that attributable fractions for lung cancer due to RCS could be reduced from 2.07% in 2010 to nearly zero by 2060, depending on the timing and success of these interventions. The importance of achieving compliance with current exposure standards in small industries is highlighted and may be more effective than setting lower exposure standards. The method has been used to forecast future cancer burdens for other high-risk carcinogens and occupations identified by the HSE current cancer burden work. It is also adaptable for other countries and other exposure situations in the general environment and can accommodate socio-economic impact assessments.

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.

RR849

www.hse.gov.uk