Health effects of air pollutant mixtures on overall mortality among the elderly population using Bayesian kernel machine regression (BKMR)

Abstract

It is well documented that fine particles matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) are associated with a range of adverse health outcomes. However, most epidemiologic studies have focused on understanding their additive effects, despite that individuals are exposed to multiple air pollutants simultaneously that are likely correlated with each other. Therefore, we applied a novel method - Bayesian Kernel machine regression (BKMR) and conducted a population-based cohort study to assess the individual and joint effect of air pollutant mixtures (PM2.5, O3, and NO2) on all-cause mortality among the Medicare population in 15 cities with 656 different ZIP codes in the southeastern US. The results suggest a strong association between pollutant mixture and all-cause mortality, mainly driven by PM2.5. The positive association of PM2.5 with mortality appears stronger at lower percentiles of other pollutants. An interquartile range change in PM2.5 concentration was associated with a significant increase in mortality of 1.7 (95% CI: 0.5, 2.9), 1.6 (95% CI: 0.4, 2.7) and 1.4 (95% CI: 0.1, 2.6) standard deviations (SD) when O3 and NO2 were set at the 25th, 50th, and 75th percentiles, respectively. BKMR analysis did not identify statistically significant interactions among PM2.5, O3, and NO2. However, since the small sub-population might weaken the study power, additional studies (in larger sample size and other regions in the US) are in need to reinforce the current finding.