Communities of color have been exposed to a disproportionate burden of air pollution across the United States for decades. Yet, the inequality in exposure to known toxic elements of air pollution is unclear. Here, we find that populations living in racially segregated communities are exposed to a form of fine particulate matter with over three times higher mass proportions of known toxic and carcinogenic metals. While concentrations of total fine particulate matter are two times higher in racially segregated communities, concentrations of metals from anthropogenic sources are nearly ten times higher. Populations living in racially segregated communities have been disproportionately exposed to these environmental stressors throughout the past decade. We find evidence, however, that these disproportionate exposures may be abated though targeted regulatory action. For example, recent regulations on marine fuel oil not only reduced vanadium concentrations in coastal cities, but also sharply lessened differences in vanadium exposure by segregation.

Several studies have provided strong evidence that long-term exposure to air pollution, even at low levels, increases risk of mortality. As regulatory actions are becoming prohibitively expensive, robust evidence to guide the development of targeted interventions to protect the most vulnerable is needed. In this paper, we introduce a novel statistical method that (i) discovers subgroups whose effects substantially differ from the population mean, and (ii) uses randomization-based tests to assess discovered heterogeneous effects. Also, we develop a sensitivity analysis method to assess the robustness of the conclusions to unmeasured confounding bias. Via simulation studies and theoretical arguments, we demonstrate that hypothesis testing focusing on the discovered subgroups can substantially increase statistical power to detect heterogeneity of the exposure effects. We apply the proposed denovo method to the data of 1,612,414 Medicare beneficiaries in the New England region in the United States for the period 2000 to 2006. We find that seniors aged between 81-85 with low income and seniors aged 85 and above have statistically significant greater causal effects of long-term exposure to PM2.5 on 5-year mortality rate compared to the population mean.

The positive relationship between airborne fine particulate matter (PM_2.5) and cardiovascular disease (CVD) is established. Little is known about effect size heterogeneity across distinct CVD outcomes. We conducted a multi-outcome case-crossover study of Medicare beneficiaries aged >65 years residing in the mainland USA from 2000 through 2012. The exposure was two-day average PM_2.5 in each individual’s residential zipcode. The outcomes were hospitalization for 432 distinct CVDs defined by the International Classification of Diseases, Revision 9. Our dataset included almost 24 million CVD hospitalizations. We analyzed the data using multi-outcome regression with tree-structured shrinkage (MOReTreeS), a novel method that enables: (1) borrowing of strength across outcomes; (2) data-driven discovery of outcome groups that are similarly affected by the exposure; (3) estimation of a single effect for each group. MOReTreeS grouped 420 outcomes together; for this group, the odds ratio [OR] for hospitalization associated with a 10 μg m^{− 3} increase in PM_2.5 was 1.011 (95% credible interval [CI] = 1.011–1.012). The model identified congestive heart failure as having the strongest positive association with PM_2.5 (OR = 1.019; 95%CI = 1.017–1.022). Some outcomes exhibited negative associations with PM_2.5, including aortic dissection, subarachnoid and intracerebral hemorrhage, abdominal aneurysm, and essential hypertension; further research is needed to understand these counterintuitive findings. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

The positive relationship between airborne fine particulate matter (PM_2.5) and cardiovascular disease (CVD) is established. Little is known about effect size heterogeneity across distinct CVD outcomes. We conducted a multi-outcome case-crossover study of Medicare beneficiaries aged >65 years residing in the mainland USA from 2000 through 2012. The exposure was two-day average PM_2.5 in each individual’s residential zipcode. The outcomes were hospitalization for 432 distinct CVDs defined by the International Classification of Diseases, Revision 9. Our dataset included almost 24 million CVD hospitalizations. We analyzed the data using multi-outcome regression with tree-structured shrinkage (MOReTreeS), a novel method that enables: (1) borrowing of strength across outcomes; (2) data-driven discovery of outcome groups that are similarly affected by the exposure; (3) estimation of a single effect for each group. MOReTreeS grouped 420 outcomes together; for this group, the odds ratio [OR] for hospitalization associated with a 10 μg m^{− 3} increase in PM_2.5 was 1.011 (95% credible interval [CI] = 1.011–1.012). The model identified congestive heart failure as having the strongest positive association with PM_2.5 (OR = 1.019; 95%CI = 1.017–1.022). Some outcomes exhibited negative associations with PM_2.5, including aortic dissection, subarachnoid and intracerebral hemorrhage, abdominal aneurysm, and essential hypertension; further research is needed to understand these counterintuitive findings. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

We develop a causal inference approach to estimate the number of adverse health events that were prevented due to changes in exposure to multiple pollutants attributable to a large-scale air quality intervention/regulation, with a focus on the 1990 Clean Air Act Amendments (CAAA). We introduce a causal estimand called the total events avoided (TEA) by the regulation, defined as the difference in the number of health events expected under the no-regulation pollution exposures and the number observed with-regulation. We propose matching and machine learning methods that leverage population-level pollution and health data to estimate the TEA. Our approach improves upon traditional methods for regulation health impact analyses by formalizing causal identifying assumptions, using population-level data, minimizing parametric assumptions, and collectively analyzing multiple pollutants. To reduce model-dependence, our approach estimates cumulative health impacts in the subset of regions with projected no-regulation features lying within the support of the observed with-regulation data, thereby providing a conservative but data-driven assessment to complement traditional parametric approaches. We analyze the health impacts of the CAAA in the U.S. Medicare population in the year 2000, and our estimates suggest that large numbers of cardiovascular and dementia-related hospitalizations were avoided due to CAAA-attributable changes in pollution exposure. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

We develop a causal inference approach to estimate the number of adverse health events that were prevented due to changes in exposure to multiple pollutants attributable to a large-scale air quality intervention/regulation, with a focus on the 1990 Clean Air Act Amendments (CAAA). We introduce a causal estimand called the total events avoided (TEA) by the regulation, defined as the difference in the number of health events expected under the no-regulation pollution exposures and the number observed with-regulation. We propose matching and machine learning methods that leverage population-level pollution and health data to estimate the TEA. Our approach improves upon traditional methods for regulation health impact analyses by formalizing causal identifying assumptions, using population-level data, minimizing parametric assumptions, and collectively analyzing multiple pollutants. To reduce model-dependence, our approach estimates cumulative health impacts in the subset of regions with projected no-regulation features lying within the support of the observed with-regulation data, thereby providing a conservative but data-driven assessment to complement traditional parametric approaches. We analyze the health impacts of the CAAA in the U.S. Medicare population in the year 2000, and our estimates suggest that large numbers of cardiovascular and dementia-related hospitalizations were avoided due to CAAA-attributable changes in pollution exposure. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.