Comparing traditional and causal inference methodologies for evaluating impacts of long-term air pollution exposure on hospitalization with Alzheimer disease and related dementias

Abstract

Alzheimer disease and related dementias (ADRDs) present a growing public health burden in the United States. One actionable risk factor for ADRDs is air pollution: multiple studies have found associations between air pollution and exacerbation of ADRDs. Our study builds on previous studies by applying modern statistical causal inference methodologies-generalized propensity score (GPS) weighting and matching-on a large, longitudinal data set. We follow 50 million Medicare enrollees to investigate impacts of 3 air pollutants-fine particular matter (PM2.5), nitrogen dioxide (NO2), and summer ozone (O3)-on elderly patients' rate of first hospitalization with an ADRD diagnosis. Similar to previous studies using traditional statistical models, our results found increased hospitalization risks due to increased PM2.5 and NO2 exposure, with less conclusive results for O3. In particular, our GPS weighting analysis finds IQR increases in PM2.5, NO2, or O3 exposure result in hazard ratios of 1.108 (95% CI, 1.097, 1.119), 1.058 (1.049-1.067), or 1.045 (1.036-1.054), respectively. GPS matching results are similar for PM2.5 and NO2 with attenuated effects for O3. Our results strengthen arguments that long-term PM2.5 and NO2 exposure increases risk of hospitalization with an ADRD diagnosis. Additionally, we highlight strengths and limitations of causal inference methodologies in observational studies with continuous treatments. This article is part of a Special Collection on Environmental Epidemiology.

Keywords: Alzheimer disease and related dementias; Medicare; air pollution; causal inference; generalized propensity score.