Assessing Adverse Health Effects of Long-Term Exposure to Low Levels of Ambient Air Pollution: Implementation of Causal Inference Methods.
Francesca Dominici, Antonella Zanobetti, John H. Schwartz, Danielle Braun, Benjamin M. Sabath, Xiangwei Wu
Abstract
) at high spatial resolution (1 km × 1 km) for the continental United States over the period 2000-2016 and linking these predictions to health data. Second, it developed new causal inference methods for estimating exposure-response (ER) curves (ERCs) and adjusting for measured confounders. Third, it applied these methods to claims data from Medicare and Medicaid beneficiaries to estimate health effects associated with short- and long-term exposure to low levels of ambient air pollution. Finally, it developed pipelines for reproducible research, including approaches for data sharing, record linkage, and statistical software. Our HEI-funded work has supported an extensive portfolio of analyses and the development of statistical methods that can be used to robustly understand the health effects of short- and long-term exposure to low levels of ambient air pollution. Our Phase 1 report (Dominici et al. 2019) provided a high-level overview of our statistical methods, data analysis, and key findings, grouped into the following five areas: (1) exposure prediction, (2) epidemiological studies of ambient exposures to air pollution at low levels, (3) sensitivity analysis, (4) methodological contributions in causal inference, and (5) an open access research data platform. The current, final report includes a comprehensive overview of the entire research project. exposure is likely to be causally related to mortality. This conclusion assumes that the causal inference assumptions hold and, more specifically, that we accounted adequately for confounding bias. We explored various modeling approaches, conducted extensive sensitivity analyses, and found that our results were robust across approaches and models. This work relied on publicly available data, and we have provided code that allows for reproducibility of our analyses. , and mortality among Medicare beneficiaries (ages 65 and older). This work relies on newly developed causal inference methods for continuous exposure. (1.23 [95% CI, 1.18 to 1.28] to 1.37 [95% CI, 1.34 to 1.40]). remained almost unchanged. and adverse health outcomes at low exposure levels. Importantly, the curve was almost linear at exposure levels lower than the current national standards, indicating aggravated harmful effects at exposure levels even below these standards. exposures on all-cause mortality are nonlinear with statistical uncertainty. exposures of 50 ppb versus those with 30 ppb. institutions, including those that support the Health Effects Institute; therefore, it may not reflect the views or policies of these parties, and no endorsement by them should be inferred. A list of abbreviations and other terms appears at the end of this volume.