Cancer burden for BOHS Nov '11
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A presentation given at the BOHS Autumn Scientific meeting in Leeds, November 2011
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Cancer burden for BOHS Nov '11
- 1. Estimation of the Burden of Cancer in Great Britain due to Occupation L Rushton 1 , T. Brown 2 , R Bevan 3 , J W Cherrie 4 , G Evans 2 , L Fortunato 1 , S Bagga 3 , P Holmes 3 , S Hutchings 1 , R Slack 3 , M Van Tongeren 4 , C Young 2 1 Dept. of Epidemiology and Biostatistics, Imperial College London; 2 Health and Safety Laboratory, Buxton, Derbyshire 3 Institute of Environment and Health, Cranfield University 4 Institute of Occupational Medicine This study was funded by the Health and Safety Executive
- 4. Cancer Site AF (%) Deaths (2005) Registrations (2004) M F Total M F Total M F Total Mesothelioma 97.0 82.5 94.9 1699 238 1937 1699 238 1937 Sinonasal 43.3 19.8 32.7 27 10 38 195 31 126 Lung 21.1 5.3 14.5 4020 725 4745 4627 815 5442 Nasopharynx 10.8 2.4 8.0 7 1 8 14 1 15 Bladder 7.1 1.9 5.3 215 30 245 496 54 550 Breast 4.6 4.6 555 555 1969 1969 NMSC 6.9 1.1 4.5 20 2 23 2513 349 2862 Larynx 2.9 1.6 2.6 17 3 20 50 6 56 Oesophagus 3.3 1.1 2.5 156 28 184 159 29 188 STS 3.4 1.1 2.4 11 3 13 22 4 27 Stomach 3.0 0.3 1.9 101 6 108 149 9 157 NHL 2.1 1.1 1.7 43 14 57 102 39 140 Melanoma (eye) 2.9 0.4 1.6 1 0 1 6 1 6 Total 8.2 (7.2, 9.9) 2.3 (1.7, 3.2) 5.3 (4.6, 6.6) 6355 (5640, 7690) 1655 (1249, 2287) 8010 (6888, 9977) 9988 (6938, 14794) 3611 (2370, 5412) 13598 (9308, 20206)
- 5. Carcinogen or Occupation Total Registrations (% of total burden) Cancer Sites Asbestos 4216 (30.8%) Larynx, Lung, Mesothelioma , Stomach Shift work (+ Flight Personnel) 1957 (14.3%) Breast Mineral oils 1730 Bladder, Lung , NMSC, Sinonasal Solar radiation 1541 (11.3%) NMSC Silica 907 (6.6%) Lung Diesel engine exhaust 801 (5.9%) Bladder, Lung PAHs - Coal tars and pitches 545 (4.0%) NMSC Painters 359 (3.2%) Bladder, Lung , Stomach Dioxins 316 (2.3%) Lung , NHL, STS Environmental Tobacco Smoke (non-smokers) 284 (2.1%) Lung Radon 209 (1.5%) Lung Welders 175 (1.3%) Lung , Melanoma (eye) Tetrachloroethylene 164 (1.2%) Cervix, NHL , Oesophagus Arsenic 129 (0.9%) Lung Strong inorganic-acid mists 122 (0.9%) Larynx, Lung Chromium 89 Lung , Sinonasal Non-arsenical insecticides 73 Brain, Leukaemia, Multiple myeloma, N HL
- 6. Industry Sector Attributable Registrations Male Female Total Exposures Construction 4573 64 4637 14 Painter + decorators 331 3 334 1 Roadmen + roofers 471 0 471 1 Total construction 5375 68 5442 16 Shift work (including flight personnel) 0 1969 1969 1 Metal workers 1083 169 1252 1 Personal + household services 256 403 659 17 Land Transport 454 42 497 9 Mining 283 12 296 10 Printing, publishing and allied trades 232 50 282 10 Public administration and defence 229 34 263 6 Wholesale + retail trades 51 136 187 11 Farming 180 39 220 5 Welders 165 16 181 2 Manufacture of instruments, etc 204 2 206 6 Manufacture of transport equipment 164 18 182 16 Non-ferrous metal basic industries 122 34 156 18
- 9. 2060 2020 1971-80 2001-10 1981-90 1991-00 REPs FTYs 2050 2030 2040 2021-30 2011-20 2031-40 2041-50 2010 1961-70 10 year estimation intervals ‘ Known’ exposure Forecast exposure Forecast Risk Exposure Periods – 10-50 year latency REP Risk exposure period FTY Forecast target year
- 12. Forecast lung cancers for Respirable Crystalline Silica 2010 Attributable Fraction Attributable registrations Avoided registrations 3.3 803 2060 Base-line: exposure limit 0.1 mg/m 3 , compliance 33% 1.08 794 Exposure limit 0.05 mg/m 3 , compliance 33% 0.80 592 202 Exposure limit 0.025 mg/m 3 , compliance 33% 0.56 409 385 Exposure limit 0.1 mg/m 3 , compliance 90% 0.14 102 693 Exposure limit 0.05 mg/m 3 , compliance 90% 0.07 49 745 Exposure limit 0.025 mg/m 3 , compliance 90% 0.03 21 773
- 14. Improvement in compliance by workplace size for Respirable Crystalline Silica 2010 Attributable Fraction % Attributable registrations Avoided registrations 3.3 803 2060 Base-line: exposure limit 0.1 mg/m 3 , compliance 33% 1.08 794 Exposure limit 0.05 mg/m 3 , compliance 33% 0.80 592 202 Exposure limit 0.05 mg/m 3 , % compliance changes by employed workplace size and self employed 33% < 250, self employed; 90% 250+ 0.68 499 295 33% < 50, self employed; 90% 50+ 0.61 451 344 33% self employed; 90% all sizes employed 0.35 261 533 90% all workplaces 0.07 49 745
- 15. Attributable Numbers of Cancer Registrations Scenarios All Base (1) Trend (2) (3) (4) (5) (6) Exposure Cancer Site 2010 2060 Exposure defined by agent; no appropriate exposure measurements ETS Lung 1465 0 0 67 156 Coal tars NMSC 489 800 877 602 475 433 402 Radon Lung 220 379 411 341 317 309 190 Solar radiation NMSC 1749 3069 3279 2552 2030 1503 163 Occupational circumstances, no specified carcinogen Painters Bladder, Lung, Stomach 461 640 639 481 383 347 321 Shift work Breast 1649 3062 3848 2134 1178 194 0 Welders Lung 189 140 63 105 83 76 70 Carcinogens for which exposure standards can be set Arsenic Lung 128 92 47 92 88 87 87 Asbestos Larynx, Lung Mesothelioma, Stomach 4281 2759 2864 2785 2689 2626 2307 Diesel Bladder, Lung 380 406 399 451 412 374 34 Silica Lung 837 794 442 102 49 21 10 Strong acids Larynx, Lung 122 39 7 19 12 10 12 TCDD (Dioxins) Lung, NHL, STS 286 123 30 22 8 5 6 Tetrachloro-ethylene Cervix, NHL, Oesophagus 139 135 119 123 118 117 119 Total 12050 12327 12938 9812 7944 6064 3705
- 17. Uncertainties and the impact on the burden estimation Source of Uncertainty Potential impact on burden estimate Exclusion of IARC group 2B and unknown carcinogens e.g. for electrical workers and leukaemia ↓ Inappropriate choice of source study for risk estimate ↑↓ Imprecision in source risk estimate ↑↓ Source risk estimate from study of highly exposed workers applied to lower exposed target population ↑ Risk estimate biased down by healthy worker effect, exposure misclassification in both study and reference population ↓ Inaccurate latency/risk exposure period, e.g. most recent 20 years used for leukaemia, up to 50 years solid tumours ↓ Effect of unmeasured confounders ↑↓ Unknown proportion exposed at different levels ↑↓
Notas del editor
- HSE is reviewing its priorities for chemicals including carcinogens. Wants to develop partnerships and practical measures to reduce occupational cancer in GB. May include better control, elimination and targeted enforcement To do this they need a sound evidence base including some baseline figures Doll and Peto’s analysis was focussed on the US mortality and their proportions (AFS) have been applied to the UK to give an estimation and used by the HSE. Our aim is to carry out an estimation of the current burden and to use a methodology that allows for some estimation for future burden to be predicted. The project will aid the HSE in prioritising some target occupation/industry/job/task and cancer type pairings that can then be focused on for intervention.
- Studies could be: population or industry based single or pooled study meta-analysis Selected studies with comparable exposures to GB: Large sample size Clear case definition Appropriate comparison population Controlled for confounders where possible Adequate exposure assessment National data sources used to get the numbers ever exposed. CAREX – CARcinogen Exposure database – gives the estimated numbers exposed by country, carcinogen and industry. Included 139 agents evaluated by IARC as Groups 1,2A and some 2B across 55 industrial classes of the UN system ISIC. However, the prevalences were largely based on US and Finnish (FINJEM) rates and applied to numbers employed in the industry of other countries. LFS series of 2% household based samples from 1973 Census of employment etc employer based surveys from 1971 – gives numbers by sex/ full and part time/ 4 digit SIC code. Numbers ever worked from UK population of numbers of working age over the REP – gave denominator for the proportion. Adjusted for turnover, new workers and people retiring and dying + change in broad trends in employment patterns e.g. Service industries going up, manufacturing going down Three international workshops held during the project to discuss and develop the methodology. Helped to focus the assumptions we had to make to take account of inherent limitations in available data. These included: Pragmatic decision about REP and cancer latency Decision to assign industry sectors to ‘higher’ and ‘lower’ i.e. had proportions exposed over the REP at these two levels and then used published literature to select appropriate risk estimates for these levels Decision to use IARC group 1 and 2A. NB study carried out using classifications in place at end of 2008. In 2009 IARC reviewed all class 1 carcinogens so if we estimated burden now more cancer sites would be included e.g. asbestos and cervical cancer, colorectal cancer
- Note nos do not always add up due to rounding. Ranked by total AF (column 3). AF tells us what proportions of the total numbers of that disease are due to occupation. So for mesothelioma, caused by asbestos, we estimate 95% (includes occupational and para-occupational). Looking at the bottom line we estimate that approximately 5.5% of all cancers are attributable to occupational carcinogens. Note the 95% confidence interval (random error). We have this for everything but this is the only slide I’m showing them due to lack of space. These AFs translate into considerable numbers of cancer deaths and registrations. Note these are annual. On the whole everything, AFs, deaths and registrations are greater for men. Overall 8.2% of all cancers in 2005 in men due to occupation. Similar figure to Doll and Peto’s top numbers. Female 2.3 similar to bottom of D&P’s range. Allows decisions to be made on a number of different measures e.g. Focus on cancers with high AFs i.e. for which most of the cancers are due to occupation (could be total, males, females) Focus on reducing cancers with large numbers of attributable deaths e.g. Lung cancer, mesothelioma breast cancer or Those with large numbers of attributable cancer registrations e.g. First 3 same as deaths but also NMSC which rarely kills but might incur considerable health costs
- 42 in total. Top 17 ranked on total registrations. From this slide we can see that several carcinogens affect multiple cancer sites, notably on this slide, lung cancer. Some affect as many as 4 cancer sites e.g. mineral oils. The ones in blue are those priority substances/occupations that we then went on to consider what would happen in future. Mineral oils not considered as thought that these have changes so much no longer going to be a problem in future. Cover 85.7% of all the exposures we looked at with a total of 11724 cancer registrations out of 13679. Also multiple carcinogens for each cancer site. Many had several carcinogens classified as 1 or 2A. Most complex was lung cancer with 32 carcinogenic agents or occupations classified as IARC 1 or 2A carcinogens – we estimated AFs separately for 21 – next slide.
- Top industry is construction: 16 different lung carcinogens not all lead to any registrations. But other industry groups are also potentially exposed to several lung carcinogens e.g. manufacture of transport equipment (11, including chromium, nickel, radon, ETS), manufacture of machinery except electrical (notably nickel and chromium, plus silica and also radon) and seven each for land transport (especially diesel engine exhaust) and personal and household services (chromium in the lead plus diesel, PAHs and asbestos, and also ETS and radon and a small number exposed to cadmium . For men in the construction industry also are at risk of multiple cancer sites in this case 9 total registrations. By far the largest are meso, lung and NMSC but there also a few for stomach (47, 12 lead, 35 asbstos), bladder (41 DEE), Larynx ( 7 asbestos), Oesophagus (10 Tetra) Sinonsal (21 wood dust). Meso 1060 asbestos, NMSC 787 solar radiation and lung 2461 (painter 213, arsenic 15, asbestos 1221, cobalt 4, diesel 247, ETS 34, lead 19, silica 703, radon 5). Note personal and household services includes repair trades, laundries and dry cleaners, domestic services, hairdressing and beauty etc.
- To predict future burden we extend the methods forward in time We make predictions every 10 years (could do this for more years or extend the methods for continuous forecast) Use the same REPs as for current burden. As we move forward we get less past exposure and more future exposure (show graph in next slide) Main extension is to increase the numbers of levels of exposure from H/L in current burden to H/M/L/B so reallocated industry sectors and found suitable RRs Forecast takes account of employment turnover and industry sector trends e.g. service sector is expected to rise in future Once we’ve estimated future AFs we apply these to forecast numbers of deaths and cancer registration based on demographic projections only i.e. assuming all non-occupational risk factors such as smoking stay the same as 2004/5. The forecast numbers of cancers go up because the population is forecast to go as is the proportion of the elderly and cancer is generally a disease of the elderly.
- Example using silica. We know from exposure data that currently compliance to the current limit of 0.1mg/m 3 is only about 33%. Table shows the impact of improving compliance compared with lowering a standard. Could vary both of these and the timing of introducing the standard. Can also express this in terms of DALYs which can be fed into economic analyses Decisions can be made on the scenarios, the AFs, ANs, DALYs etc
- Right hand graph shows the AFs for each scenario. No difference between them until after 2030 Left hand graph shows the same scenarios for each forecast year Shows no difference in attributable cancers between the scenarios up to 2030. Also because the total nos of lung cancers will rise anyway due to the rise population and rising proportion of the elderly the numbers of attributable cancers rises until after 2020. General conclusion is that whatever the intervention there is no impact until after 2030 because of the legacy of past exposures.
- This example assumes we have halved the current limit and then tests how effective improving compliance is by workplace size. The most effective interventions are the last 2 when a) compliance improves in those companies employing less than 50 employees (200 more saved compared with previous intervention when compliance is improved in only those companies employing more than 50 employees) b) All workplaces have improved compliance including the self-employed (another 200). This is because silica exposure now occurs largely in the construction industry which is largely small companies and the self-employed.
- Will rise to nearly 13,000 by 2060 given current trends in employment and exposure levels (>12,300 if current levels maintained). Aging population is a factor. No impact seen until 2030 because of general increase in cancers due to aging population With modest intervention (e.g. scenario 3) over 2,000 cancers can be avoided (including 376 lung, 928 breast cancers, 432 NMSC) With stronger interventions (e.g. scenario 6) nearly 8,500 can be avoided (including 1,732 lung, 3,062 breast and 3,287 NMSC) Effective interventions Silica - improve compliance DEE - need for v. low exposure limit indicated Shift work – If increasing risk with duration of exposure is valid then limiting years of night work reduces burden Intervention scenarios ETS: Compliance (3) 98% services 90% other (4) 95%/80% Radon: Reduce exposed numbers by 10% in (3) 2010, (4) 2020, (5) 2030, (6) 50% in 2010 Solar radiation: Move (3) 1/3, (4) 2/3, (5) all to next lower exposure category resp., (6) move all to lowest exposure category Shift Work: Restrictions on length of employment result in (3) 20% 30% 50%, (4) 10% 20% 70%, (5) 0% 10% 90%, at 15+ years, 5-14 years and <5 years resp. (6) 100% at <5 years For occupations (and coal tars), excess risk reduced to: (3) 75%, (4) 50%, (5) 25% of current risk in 2010, 2020, 2030 resp., (6) 50% of current risk in 2010 For chemicals: (3) = existing (asbestos, RCS) or proposed standard, 90% compliance (4) = half this standard (5) = quarter of standard (except asbestos, DEE where 10%) (6) = existing/proposed standard, 99% compliance (except asbestos, DEE where 1% of standard, 90% compliance) Intervention (3) for the chemical agents represents 90% compliance to an existing (RCS, asbestos) or possible standard, e.g. 0.1 mg/m**3 for DEE based on a standard used in Austria or our estimated H/L boundary exposure levels for arsenic, strong acids and tetrachloroethylene (L/B for TCDD). H/L was chosen as these carcinogens are either genotoxic or possibly genotoxic there is no recognised threshold below which excess risk can be assumed to be zero (background exposed). For the other agents it represents a 25% reduction in RR for the occupations and for coal tars and a modest limit on night shift work from 30% 40% 30% working 15+, 5-14 and <5 years respectively to 20% 30% 50% in these categories. For radon exposed numbers are reduced by 10%, and for solar radiation a third of workers are moved into the next lowest category of time spent outdoors. Together these interventions would avoid over 2,000 cancers a year by 2060, highlighted green are >100. Intervention (6) for the chemical agents represents 99% compliance to an existing (RCS) or possible standard, e.g. our estimated H/L boundary exposure levels for arsenic, strong acids and tetrachloroethylene (L/B for TCDD). For asbestos and DEE where it represents 90% compliance to a stringent 1/100 th of the current exposure standard . For the other agents it represents a 50% reduction in RR for the occupations and for coal tars and a limit on night shift work to <5 years duration. For radon exposed numbers are reduced by 50% immediately, and for solar radiation all workers are moved into the lowest category of time spent outdoors. Together these interventions would avoid over 8,000 cancers a year by 2060, highlighted blue are >100.
- The method should be used for comparing the effect of alternative interventions, or comparing avoidable numbers of attributable cancers between exposures. NB don’t apply achieved AF to real 2030 cancer numbers as these will have increased because of the increasing proportions of the elderly. Note: there will probably have been many changes in the contribution of other environment and lifestyle risk factors.
- Group 2B carcinogens or other carcinogens not yet considered; potential underestimation of the burden Time and resources did not allow for systematic review; epidemiological studies from which risk estimates taken have inherent biases; could be portability issues Estimates based on past exposures that may have been much higher than current Difference in distribution of confounders between source and target Most risk estimates were low (< 2); large proportions exposed at low levels (hence low RR) can give high AF Lack of information on numbers exposed to agents and at different levels of exposure; assignment to high/low assumed similarity of intensity and duration of exposures For very low/background levels RR = 1 used if no information; gives zero AF May not have accounted fully for overlapping/ multiple exposures 2 workshops held during the project. Advice was to first prioritise on IARC group 1 and 2A. Group 2B, often regulated as if human carcinogens not yet looked at. Also unknown occupational carcinogens which may not be detected using current epi methods e.g. promoters rather than early stage carcinogens Focused on meta-analyses, key studies etc. had to be pragmatic in choice of key studies including portability to GB situation. Many exposures will have been a lot higher than currently or may not exist now. Long latency means that numbers of cancers will carry on being high in the future for some exposures. For some exposure on going high levels of exposures e.g. wood dust. For occupations e.g. painting, welding multiple exposures. Difficult to attribute risk to single agent. E.g. in painting many toxic paints have been replaced. However, silica, asbestos etc may remain Good data in the UK on nos. employed in broad industry groups but little information on exactly who in these industries is exposed to carcinogens and at what levels of exposure. We have no UK JEM Exposure misclassification could have occurred in using the CAREX industry classifications to assign low/high categories. In situations where no risk estimate could be identified for very low/background/environmental levels of exposure, used RR = 1. Assumes threshold exists. Didn’t evaluate shape of the exposure-risk relationships Could not account fully for all interactions.
- The patterns of our results are similar to those of other studies although the magnitude of the estimates may differ due to numbers of exposures included, methodology etc. Our estimates for only 6 cancers are overall higher than Doll and Peto with males at the higher end of their estimate and females at the lower end