The impact of long-term exposure to fine particulate matter on incident dementia among U.S. older adults

BACKGROUND AND AIM: Growing studies link long-term fine particle (PM2.5) exposures to increased risk of Alzheimer’s disease and other dementias, but there is a lack of comprehensive evaluation of these findings using causal inference modeling approaches. METHODS: Based on the Medicare Chronic Conditions Warehouse (2000-2018), we constructed two nationwide U.S. population-based cohorts of enrollees aged ≥65 for dementia and Alzheimer’s disease (AD), respectively. Coupled with high-resolution population-weighted air pollution exposure data, we estimated the effect of long-term exposure to PM2.5 on dementia and AD incidence using five distinct statistical approaches. Two traditional regression approaches for confounding adjustment: 1) Cox proportional hazards model, and 2) Poisson regression; two causal inference modeling approaches rely on the potential outcomes framework and generalized propensity scores (GPS). These approaches adjust for measured confounders (demographic characteristics, Medicaid eligibility, and ZIP-level covariates) using 1) matching by GPS, and 2) weighting by GPS. We further applied the difference-in-differences approach that controls for unmeasured confounders by design. RESULTS: We identified ~5.0 million incident dementia cases (N=18,442,459; dementia cohort) and ~2.4 million incident AD cases (N=19,092,115; AD cohort). Using the five distinct approaches, we found that an increase in annual PM2.5 by 5 μg/m³ leads to a statistically significant 6-10% and 10-16% increase in dementia and AD risk, respectively. CONCLUSIONS: Our study provides comprehensive evidence of the link between long-term PM2.5 exposure and increased incidence of dementia and AD, raising awareness of the continued importance of assessing the impact of air pollution exposure on neurological disorders among older adults. KEYWORDS: fine particulate matter, Alzheimer’s disease and dementias, causal inference modeling