O-42 A novel weighting approach to addressing healthy worker survivor bias

• Published in: Occupational and environmental medicine (London, England) Vol. 80; no. Suppl 1; p. A78
• Main Authors: Keil, Alexander, Li, Yi, Kelly-Reif, Kaitlin
• Format: Journal Article
• Language: English
• Published: London BMJ Publishing Group Ltd 01.03.2023; BMJ Publishing Group LTD
• Subjects: Abstracts; Bias; Carcinogenicity; Carcinogens; Computer applications; Confidence intervals; Estimates; Health risks; Ionizing radiation; Lung cancer; Miners; Mortality; Mortality risk; Occupational exposure; Occupational health; Radiation effects; Radon; Risk management; Risk reduction; Statistical analysis; Survival; Uncertainty; Uranium; Weighting
• ISSN: 1351-0711; 1470-7926
• DOI: 10.1136/OEM-2023-EPICOH.191

IntroductionRadon gas is a major source of ionizing radiation exposures in humans that contributes to the global burden of lung cancers. Human carcinogenicity of radon has been established, in part, in studies of exposed workers, including uranium miners. Impact estimates from occupational studies are subject to healthy worker survivor bias, which has been proposed to result in substantial underestimates of radon’s health effects. However, existing analytic methods for addressing bias due to healthy worker survivor bias are sensitive to model misspecification.Material and MethodsWe describe a new approach for estimating health effects of occupational exposures that addresses healthy worker survivor bias while reducing modeling assumptions. This approach utilizes inverse probability weighting and originates from the literature on dynamic treatment regimes. We use this approach to estimate impacts of hypothetical occupational standards on lung cancer mortality using data from 4124 miners from the Colorado Plateau Uranium Miners’ cohort followed through 2005.ResultsThe estimated cumulative lung cancer mortality risk at age 80 was 14.9% (95% confidence interval [CI] = 13.7%, 16.1%). Under a hypothetical intervention to limit exposure to 20 working levels, we estimated a risk reduction (at age 80) of 2.7% (95%CI = 3.6%, 1.7%). Estimates at lower exposure levels were larger but subject to greater uncertainty than previous analyses in this cohort using modeling-based estimators.ConclusionsOur approach offers substantial strengths when addressing healthy worker survivor bias, namely regarding computational simplicity and reduced reliance on modeling assumptions. Use within this highly exposed cohort also highlighted challenges with using our approach to estimate effects at low exposure levels: model-based extrapolation with the parametric g-formula can be used to reduce uncertainty under stronger assumptions. The proposed approach provides a simple approach to addressing healthy worker survivor bias that provides promise for reducing modeling assumptions in studies of occupational exposures.