Identifying low dose measurement error corrected effects of multiple pollutants using causal modeling

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Project Summary The Candidate will contribute to the goals of the parent grant by using updated hybrid air pollution exposure models and Medicare administrative data and link them with the newly acquired National Death Index (NDI) data. The NDI data identifies the specific cause of death allowing the Candidate to establish a complementary project that addresses the casual effect of air pollutants with cause-specific cardiovascular mortality both individually and jointly within the Medicare population. Specifically, the study will employ a quasi-experimental design (difference-in- differences) that controls for both measured and many unmeasured confounders by stratifying on either the subject (controlling for time invariant or slowly varying individual covariates) or the neighborhood (controlling for time invariant or slowly varying neighborhood level covariates). This is a major advantage of DID since many unmeasured and measured confounders are controlled for by designed. We will employ updated exposure models for the years 2000-2020 with an increased spatial resolution of 500m. By using a novel mixture technique, fast Bayesian Kernel Machine Regression (BKMR), that accelerates the processing speed of the traditional BKMR, the study can analyze big datasets such as Medicare. Fast BKMR identifies interactions between individual pollutants, nonlinearities at low concentrations, and the overall effect of the mixture. We will also produce estimates on a restricted subset of the Medicare population that lived in areas that never exceeded the current EPA standards. This will help draw conclusions on whether the current regulations are sufficient to protect cardiovascular health. With the support of this supplement, we will incorporate the Candidate’s background in cardiovascular epidemiology and mixture methods into our current Medicare study. Her proposed work for this supplement not only aligns well with the objectives of the primary R01 but has the potential to contribute to the ongoing efforts to tighten ambient air pollution regulations