br C Marant Micallef et al International Journal of

C. Marant Micallef et al. International Journal of Hygiene and Environmental Health 222 (2019) 22–29
Table 1
Data sources for exposure to occupational carcinogens.
Data source Description/Data available Occupational exposures Dates
Random sample of 50,000 salaried workers/Prevalence of exposure to Polycyclic aromatic hydrocarbons 1994 agents 1,3-Butadiene
Bis(chloromethyl)ether
Beryllium and compounds
Aromatic amines 2003
Arsenic and inorganic arsenic
compounds
Asbestos
Benzene
Cadmium compounds
Chromium (VI) compounds
Dichloromethane
Engine exhaust, diesel
Ethylene oxide
Formaldehyde
Nickel compounds
Polychlorinated biphenyls
Wood dust
Cobalt metal with tungsten carbide
Lead compounds, inorganic
Silica dust
Trichloroethylene
Perchlorethylene
Based on active gamma-Glu-Cys data: estimation of numbers of workers Acid mists, strong inorganic 1990–1993 exposed by sector/Numbers of salaried workers exposed to agents
National labour force survey (Direction des statistiques démographiques et sociales 2016) 
Representative survey of people aged ≥15/Numbers of workers by Iron and steel founding 2007 occupation or sector in 2007 Rubber manufacturing industry
Art glass, glass containers and
pressed ware (manufacture of)
Painter
Hairdresser or barber
Shiftwork that involves circadian
disruption
AGRICAN study, (Leveque-Morlais et al., 2015) French cohort of 180,000 farmers/Lifetime exposure to selected Lindane 2005 pesticides Diazinon
Malathion
SISERI database, (Institut de Radioprotection et de Sûreté Nucléaire, 2015)
Annual reports on occupational exposure to ionizing radiation in France  Exhaustive collection of individual radiation doses received by Ionizing radiation 1996 to 2015 workers exposed to ionizing radiation/Number of workers and mean radiation dose received for monitored workers
Job history of 10,000 representative French persons, combined with a Asbestos 2007 job-exposure matrix/Cross-sectional and lifetime prevalence of Benzene
exposure Silica dust
Trichloroethylene
Perchlorethylene
Dichloromethane
Leather dust
force surveys (see Tables S1 for the detailed categories and corre-sponding adjustment factors), i.e. we multiplied the cross-sectional prevalences by the adjustment factors to get the prevalence over the long exposure period. The adjustment factors were developed based on lifetime estimates of occupational exposures from the Matgéné program (Fevotte et al., 2011) as compared to the cross-sectional prevalences from the SUMER survey (Arnaudo et al., 2006) for the agents which were ascertained in both surveys. It was assumed that these adjustment factors accounted for all factors that may influence the difference be-tween cross-sectional prevalence estimates and estimates over the long REP, including exposures changes over time. For the agents ascertained in both surveys, the actual age and sex-specific ratios between the two existing estimates were used to estimate the prevalences of exposure over the long REP. 
2.3. Relative risk estimates
RR estimates of developing cancer for people ever exposed to the agent compared to people never exposed were obtained from meta-analyses, large cohorts or pooled occupational studies matching French exposure data in terms of exposure levels (see the review by Marant Micallef and colleagues explaining the rationale for the selection of the RRs used (Marant Micallef et al., 2018). In cases where RR estimates were only reported for stratified levels of exposures, a fixed effects meta-analysis was performed to pool the RRs estimates (Fleiss, 1993), as this method was applicable even in the absence of exposure pre-valence. The RR estimates related to ionizing radiation were derived from the BEIRVII dose-risk models (National Research Council, 2006), based on the average years of exposure and cumulated doses for workers exposed to ionizing radiation.