Person:

Zigler, Corwin

Expanded use of reduced complexity approaches in epidemiology and environmental justice investigations motivates detailed evaluation of these modeling approaches. Chemical transport models (CTMs) remain the most complete representation of atmospheric processes but remain limited in applications that require large numbers of runs, such as those that evaluate individual impacts from large numbers of sources. This limitation motivates comparisons between modern CTM-derived techniques and intentionally simpler alternatives. We model population weighted PM2.5 source impacts from each of greater than 1,100 coal power plants operating in the United States in 2006 and 2011 using three approaches: 1) adjoint PM2.5 sensitivities calculated by the GEOS-Chem CTM; 2) a wind field-based Lagrangian model called HyADS; and 3) a simple calculation based on emissions and inverse source-receptor distance. Annual individual power plants’ nationwide population weighted PM2.5 source impacts calculated by HyADS and the inverse distance approach have normalized mean errors between 20% and 28% and root mean square error ranges between 0.0003 and 0.0005 µg m-3 compared to adjoint sensitivities. Reduced complexity approaches are most similar to the GEOS-Chem adjoint sensitivities nearby and downwind of sources, with degrading performance farther from and upwind of sources particularly when wind fields are not accounted for.

Sulfur oxides (SOx), particularly SO2 emitted by coal-fired power plants, produce long-term risks for cardiovascular disease (CVD). We estimated the relative risks of CVD and ischemic heart disease (IHD) attributable to SOx emission globally. National SOx reduction achieved by emissions control systems was defined as the average SOx reduction percentage weighted by generating capacities of individual plants in a country. We analyzed the relative risk of CVD incidence associated with national SOx reduction for 13,581 coal-fired power-generating units in 79 countries. A 10% decrease in SOx emission was associated with 0.28% (males; 95%CI = −0.39%~0.95%) and 1.69% (females; 95%CI = 0.99%~2.38%) lower CVD risk. The effects on IHD were > 2 times stronger among males than females (2.78%, 95%CI = 1.99%~3.57% vs. 1.18%, 95%CI = 0.19%~2.17%). Further, 1.43% (males) and 8.00% (females) of CVD cases were attributable to suboptimal SOx reduction. Thus, enhancing regulations on SOx emission control represents a target for national and international intervention to prevent CVD.

Coal-fired power plants release substantial air pollution, including over 60% of U.S. sulfur dioxide (SO2) emissions in 2014. Such air pollution may exacerbate asthma; however, direct studies of health impacts linked to power plant air pollution are rare. Here, we take advantage of a natural experiment in Louisville, Kentucky, where one coal-fired power plant retired and converted to natural gas and three others installed SO2 emission control systems between 2013 and 2016. Dispersion modeling indicated exposure to SO2 emissions from these power plants decreased after the energy transitions. We used several analysis strategies, including difference-in-differences, first-differences, and interrupted time-series modeling to show that the emissions control installations and plant retirements were associated with reduced asthma disease burden related to ZIP code-level hospitalizations and emergency room visits, and individual-level medication use as measured by digital medication sensors.

Confounder selection and adjustment are essential elements of assessing the causal effect of an exposure or treatment in observational studies. Building upon work by Wang et al. (2012, Biometrics 68, 661-671) and Lefebvre et al. (2014, Statistics in Medicine 33, 2797-2813), we propose and evaluate a Bayesian method to estimate average causal effects in studies with a large number of potential confounders, relatively few observations, likely interactions between confounders and the exposure of interest, and uncertainty on which confounders and interaction terms should be included. Our method is applicable across all exposures and outcomes that can be handled through generalized linear models. In this general setting, estimation of the average causal effect is different from estimation of the exposure coefficient in the outcome model due to noncollapsibility. We implement a Bayesian bootstrap procedure to integrate over the distribution of potential confounders and to estimate the causal effect. Our method permits estimation of both the overall population causal effect and effects in specified subpopulations, providing clear characterization of heterogeneous exposure effects that may vary considerably across different covariate profiles. Simulation studies demonstrate that the proposed method performs well in small sample size situations with 100-150 observations and 50 covariates. The method is applied to data on 15,060 US Medicare beneficiaries diagnosed with a malignant brain tumor between 2000 and 2009 to evaluate whether surgery reduces hospital readmissions within 30 days of diagnosis.