Impacts of Long-term Exposure to Fine Particulate Matter on Mortality Among the Elderly

Abstract

Many studies link long-term fine particle (PM2.5) exposure to mortality, even at levels below current US air quality standards (12 μg/m3). These findings have been disputed citing traditional approaches do not guarantee evidence of causality. We obtained open cohort data for over 68.5 million Medicare enrollees (65 years or older) from 2000-2016. We assigned PM2.5 zip code concentrations based on an ensemble prediction model. We implemented five statistical approaches to estimate the effect of PM2.5 exposure on mortality, accounting for potential confounders. The two traditional regression approaches for confounding adjustment: 1) Cox proportional hazards model, and 2) Poisson regression. We also considered three causal inference modeling approaches that rely on the potential outcomes framework and generalized propensity scores (GPS). These approaches adjust for confounding using 1) matching by GPS; 2) weighting by GPS, and 3) including GPS as a covariate in the health outcome model (adjustment by GPS). For the period 2000-2016, we found that all statistical approaches provide consistent results.