Objectives We propose a Bayesian method to adjust for the component of the healthy worker effect that arises from selection of healthier individuals into workforce to allow correct estimation of the standardised mortality ratio (SMR) and associated credible intervals.

Method Information on general populations is typically used to generate expected counts for outcomes in SMR calculations but an occupational cohort is not a random sample of the general population. The alternative is to use the expected number of outcomes from industrial cohorts known to experience the outcome of interest but free of the exposures that defined the observed cohort. In Bayesian terms, we can view “expected counts of outcomes given the observed age-sex-period structure” as the target of inference for which we seek a posterior distribution. We show that the problem reduces to elucidation of a prior distribution: we propose using expert opinions about relative rates of mortality outcomes of interest in the observed cohort relative to general population rates and direct estimation of reference rates from occupational cohort studies.

Results Data from DuPont on 320 000+ active and former employees with work histories in the US from 1955 will be used. This registry allows for the calculation of expected mortality counts using adjusted rates for national and regional DuPont worker populations. Robust specification of priors will be sought. Implementation of the calculations will be developed in common software.

Conclusions We plan to develop a method for SMR calculation that accounts for the healthy worker selection effect both in the point estimate and uncertainty interval.