Effects of low-level air pollution exposures on hospital admission for myocardial infarction using multiple causal models

Myocardial infarctions have been associated with PM_2.5, and more recently with NO₂ and O₃, however counterfactual designs have been lacking and argument continues over the extent of confounding control. Here we introduce a doubly robust, counterfactual-based approach that deals with nonlinearity and interactions in associations between confounders and both outcome and exposure, as well as a double negative controls approach that capture omitted confounders. We used data from over 4 million admissions for myocardial infarction in the US Medicare population between 2000 and 2016 and linked them by ZIP code of residence to high resolution predictions of annual PM_2.5, NO_2, and O₃. We computed the counts of admissions for each ZIP code-year. In the doubly robust approach, we divided each pollutant into deciles, and for each decile, we fitted a gradient boosting machine model to estimate the effects of covariates, including the co-pollutants, on the counts. We used these models to predict, for all ZIP code-years, the expected counts had everyone be exposed in that decile. We also estimated the probability of being in that decile given all covariates, again with a gradient boosting machine, and used inverse probability weights to compute the weighted average rate of MI admission in each decile. In the negative control approach, for each pollutant, we fitted a quasi-Poisson model to estimate the exposure effect, adjusting for covariates including the co-pollutants, and negative exposure and outcome controls to control for unmeasured confounding. Each 1-μg/m³ increase in annual PM_2.5 increased the admission for MI by 1.37 cases per 10,000 person-years (95% CI: 1.20, 1.54) in the doubly robust approach, and by 0.69 cases (95% CI 0.60, 0.78) using the negative control approach. Elevated risks were seen even below annual PM_2.5 level of 8 μg/m³. Results for NO₂ and O₃ were inconsistent.