Long-term Exposure to PM2.5 and Mortality for the Older Population: Effect Modification by Residential Greenness
Version 3 2024-05-27, 18:52Version 3 2024-05-27, 18:52
Version 2 2024-05-27, 05:21Version 2 2024-05-27, 05:21
Version 1 2023-12-08, 14:41Version 1 2023-12-08, 14:41
journal contribution
posted on 2024-05-27, 18:52authored byF. Dominici, J. Schwartz, J.Y. Son, K.J Lane, M.B. Sabath, Marie Lynn MirandaMarie Lynn Miranda, M.L. Bell, Q. Di
Background: Although many studies demonstrated reduced mortality risk with higher greenness, few studies examined the modifying effect of greenness on air pollution–health associations. We evaluated residential greenness as an effect modifier of the association between long-term exposure to fine particles (PM2.5) and mortality. Methods: We used data from all Medicare beneficiaries in North Carolina (NC) and Michigan (MI) (2001–2016). We estimated annual PM2.5 averages using ensemble prediction models. We estimated mortality risk per 1 μg/m3 increase using Cox proportional hazards modeling, controlling for demographics, Medicaid eligibility, and area-level covariates. We investigated health disparities by greenness using the Normalized Difference Vegetation Index with measures of urbanicity and socioeconomic status. Results: PM2.5 was positively associated with mortality risk. Hazard ratios (HRs) were 1.12 (95% confidence interval (CI) = 1.12 to 1.13) for NC and 1.01 (95% CI = 1.00 to 1.01) for MI. HRs were higher for rural than urban areas. Within each category of urbanicity, HRs were generally higher in less green areas. For combined disparities, HRs were higher in low greenness or low SES areas, regardless of the other factor. HRs were lowest in high-greenness and high-SES areas for both states. Conclusions: In our study, those in low SES and high-greenness areas had lower associations between PM2.5 and mortality than those in low SES and low greenness areas. Multiple aspects of disparity factors and their interactions may affect health disparities from air pollution exposures. Findings should be considered in light of uncertainties, such as our use of modeled PM2.5 data, and warrant further investigation.