Comparison of PM2.5 air pollution exposure estimates at WHIMS participant locations based on different modelling approaches and impact on health effects estimation

Background and Aim: Many approaches to quantifying air pollution exposures for use in epidemiologic analyses have been developed. However, whether exposure estimates from different approaches are comparable and whether choice of approach affects reported associations with health-related outcomes remains unclear. We compare PM2.5 concentrations at addresses from participants of the Women’s Health Initiative Memory Study (WHIMS) derived using multiple estimation approaches and assess differences in associated health effects estimates. Methods: We linked annual PM2.5 exposure estimates from 1999 to 2004 from 12 different approaches to WHIMS participant addresses from across the continental United States and restricted analyses to data from participants with stable addresses within each calendar year. Approaches included geo-statistical interpolation approaches, geographic information system (GIS)-based statistical models, air dispersion and chemical transport models, and hybrid models. We used descriptive statistics and visualizations to assess relative and absolute agreement of exposure estimates from different approaches across space and time and examined the impact of estimation approach on associations between PM2.5 and non-accidental mortality, CVD-related mortality, and incident CVD events. Results: Annual PM2.5 exposure estimates were reasonably similar across approaches, although the specific shapes and ranges of the distributions varied. Interquartile ranges tended to be similar, though ranges were more variable. Relative agreement was consistently higher than absolute agreement. After controlling for putative confounders, associations of PM2.5 with mortality and CVD were similar regardless of estimation approach. Conclusions: Agreement across different air pollution estimation approaches for PM2.5 was generally high when considering participants drawn from a broad geographic area, suggesting that all models predicted similar between-area differences. The implications of fine-scale variability between exposure estimation approaches warrants further investigation. Keywords: Air pollution, exposure assessment