Human exposure factors as potential determinants of the heterogeneity in city-specific associations between PM 2.5 and mortality

• Published in: Journal of exposure science & environmental epidemiology 2019-06, Vol.29 (4), p.557
• Main Authors: Baxter, Lisa K, Dionisio, Kathie, Pradeep, Prachi, Rappazzo, Kristen, Neas, Lucas
• Format: Article
• Language: English
• Subjects: Adult; Air Pollutants - analysis; Air Pollution - analysis; Cities; Environmental Exposure; Female; Heating; Humans; Mortality; Particulate Matter - analysis; Particulate Matter - toxicity; Transportation
• ISSN: 1559-064X
• DOI: 10.1038/s41370-018-0080-7

Multi-city population-based epidemiological studies of short-term fine particulate matter (PM ) exposures and mortality have observed heterogeneity in risk estimates between cities. Factors affecting exposures, such as pollutant infiltration, which are not captured by central-site monitoring data, can differ between communities potentially explaining some of this heterogeneity. This analysis evaluates exposure factors as potential determinants of the heterogeneity in 312 core-based statistical areas (CBSA)-specific associations between PM and mortality using inverse variance weighted linear regression. Exposure factor variables were created based on data on housing characteristics, commuting patterns, heating fuel usage, and climatic factors from national surveys. When survey data were not available, air conditioning (AC) prevalence was predicted utilizing machine learning techniques. Across all CBSAs, there was a 0.95% (Interquartile range (IQR) of 2.25) increase in non-accidental mortality per 10 µg/m increase in PM and significant heterogeneity between CBSAs. CBSAs with larger homes, more heating degree days, a higher percentage of home heating with oil had significantly (p < 0.05) higher health effect estimates, while cities with more gas heating had significantly lower health effect estimates. While univariate models did not explain much of heterogeneity in health effect estimates (R < 1%), multivariate models began to explain some of the observed heterogeneity (R = 13%).