BACKGROUND Benzene is a known carcinogen. Occupational exposure to benzene is an established risk factor for leukaemia. Less is known about the effects of long-term exposure to low doses in the environment. Vulnerable groups, such as children, may be at risk. RESULTS DESIGN AND DATA Ecological study on wards within the area of London bounded by the M25 orbital motorway. Leukaemia incidences in children aged 0-14 between 1985 and 2001 obtained from national cancer register. Postcode-level benzene concentrations for two periods ( and ) estimated using an air dispersion model (ADMS- Urban) from road, traffic, emissions and meteorological data. Annual incidence (peak in Inner London, ) Smoothed standardised incidence ratios across London Leukaemia Traffic-related benzene Benzene-leukaemia association Slight increasing trend in smoothed SIRs with benzene, by ward / time period Big decrease since 1990 (cleaner engines, catalytic converters, cleaner petrol) Benzene concentrations by postcode, Peaks along roads and in inner London. BAYESIAN HIERARCHICAL MODELS Ideal framework for the intricacies of observational data in environmental epidemiology. Model 1: Poisson regression for standardised incidence of leukaemia y ij in area i and time period j, in terms of mean benzene exposure x ij. y ij ~ Poisson(E ij  ij ), log(  ij ) = α + β x ij Elaborate this basic model, step by step, to account for various sources of potential bias and uncertainty: Model 2: Between area heterogeneity in baseline risks log(  ij ) = α + β x ij + H i, with H i ~ N(0, σ 2 H ) Model 3: Spatially-correlated between area heterogeneity log(  ij ) = α + β x ij + S i + H i, with S i ~ CAR(σ 2 s ) Model 4: Ecological bias correction. Account for the within-area variance v ij of exposure. log(  ij ) = α + β x ij + S i + H i β 2 v ij Model 5: Varying coefficients between areas log(  ij ) = α + β x ij + S i + H i (B i + β) 2 v ij + B i x ij, with B i ~ CAR(σ 2 B ) Model 6: Exposure measurement error. log(  ij ) = α + β x ij * + S i + H i (B i + β) 2 v ij * + B i x ij *, with x ij * ~ N(  ij x, σ 2 ij x ) and v ij * ~ Gamma(a ij v, b ij v ) Various plausible measurement errors considered. IMPLEMENTATION MCMC simulation from posterior distributions using WinBUGS Estimated relative risks for benzene RR of 1.33 for 1 unit of benzene (cube root  g/m 3 )  15% reduction in risk associated with the reduction in mean benzene concentration from the to periods 7% increase in risk from a low pollution postcode (10% quantile of benzene concentration in London, ) to a high one (90% quantile) Conclusions robust to ecological bias (within-area exposure variability, model 4) and, similarly, to exposure measurement error and early-period extrapolation (model 6). DIC suggests little difference in fit between models 2-6. Calculation of DIC in WinBUGS Posterior distributions of random effects and their parameters are highly skewed, almost bimodal. Default WinBUGS method of calculating Dhat=“plug-in deviance at posterior means” using posterior means of stochastic parents of the response is unstable in this case. Better to calculate Dhat outside BUGS using posterior means of log(  ij ). (see Model fitted to two datasets Exposure and incidence Exposure and incidence , with exposures in estimated by extrapolation (right) Uncertainty about this extrapolation incorporated in measurement error model (6) WORK IN PROGRESS Confounders: Investigate suggested risk factors for childhood leukaemia: power lines, phone/radio masts, population “mixing”, other pollutants. Acute myelogenous leukaemia (AML) is most strongly linked with occupational benzene exposure. Isolate benzene effect on AML incidence. Threshold effect for low-level benzene exposure? BACKGROUND Benzene is a known carcinogen. Occupational exposure to benzene is an established risk factor for leukaemia. Less is known about the effects of long-term exposure to low doses in the environment. Vulnerable groups, such as children, may be at risk. Hierarchical modelling of traffic-related benzene and childhood leukaemia in London Chris Jackson, Nicky Best, Kees de Hoogh and Sylvia Richardson Department of Epidemiology and Public Health, Imperial College, London Residual relative risks (RRR) Spatially correlated, suggesting unobserved area-level risk factors. 95% posterior interval for RRR shortens from (0.91, 1.29) (model 4) to (0.99, 1.16) after accounting for varying coefficients (model 5). Map of posterior medians: Varying coefficients Map of posterior median benzene-specific relative risks, exp(B i + β) (model 5). Strong fitted spatial pattern in “susceptibility” to exposure. Economic and Social Research Council / Department of Health, UK Directed acyclic graph (Model 6)