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We calculate health improvements in Medicare beneficiaries associated with reduced coal exposure and PM2.5 concentrations in the United States

Replication material for: Ann Intern Med. 2017;166(5):324-331. DOI: 10.7326/M16-0185

National Studies on Air Pollution and Health

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages.

Identifying individuals who are at high risk of cancer due to inherited germline mutations is critical for effective implementation of personalized prevention strategies. Most existing models to identify these individuals focus on specific syndromes by including family and personal history for a small number of cancers. Recent evidence from multi-gene panel testing has shown that many syndromes once thought to be distinct are overlapping, motivating the development of models that incorporate family history information on several cancers and predict mutations for more comprehensive panels of genes. Once such class of models are Mendelian risk prediction models, which use family history information and Mendelian laws of inheritance to estimate the probability of carrying genetic mutations, as well as future risk of developing associated cancers. To flexibly model the complexity of many cancer-mutation associations, we present a new software tool called PanelPRO, a R package that extends the previously developed BayesMendel R package to user-selected lists of susceptibility genes and associated cancers. The model identifies individuals at an increased risk of carrying cancer susceptibility gene mutations and predicts future risk of developing hereditary cancers associated with those genes. Additional functionalities adjust for prophylactic interventions, known genetic testing results, and risk modifiers such as race and ancestry. The package comes with a customizable database with default parameter values estimated from published studies. The PanelPRO package is open-source and provides a fast and flexible back-end for multi-gene, multi-cancer risk modeling with pedigree data. The software enables the identification of high-risk individuals, which will have an impact on personalized prevention strategies for cancer and individualized decision making about genetic testing.

There is increasing focus on whether air pollution originating from different sources has different health implications. In particular, recent evidence suggests that fine particulate matter (PM2.5) with chemical tracers suggesting coal combustion origins is especially harmful. Augmenting this knowledge with estimates from causal inference methods to identify the health impacts of PM2.5 derived from specific point sources of coal combustion would be an important step towards informing specific, targeted interventions. We investigated the effect of high-exposure to coal combustion emissions from 783 coal-fired power generating units on ischemic heart disease (IHD) hospitalizations in over 19 million Medicare beneficiaries residing at 21,351 ZIP codes in the eastern United States. We used InMAP, a newly-developed, reduced-complexity air quality model to classify each ZIP code as either a high-exposed or control location. Our health outcomes analysis uses a causal inference method - propensity score matching - to adjust for potential confounders of the relationship between exposure and IHD. We fit separate Poisson regression models to the matched data in each geographic region to estimate the incidence rate ratio for IHD comparing high-exposed to control locations. High exposure to coal power plant emissions and IHD were positively associated in the Northeast (IRR = 1.08, 95% CI = 1.06, 1.09) and the Southeast (IRR = 1.06, 95% CI = 1.04, 1.08). No significant association was found in the Industrial Midwest (IRR = 1.02, 95% CI = 1.00, 1.04), likely the result of small exposure contrasts between high-exposed and control ZIP codes in that region. This study provides targeted evidence of the association between emissions from specific coal power plants and IHD hospitalizations among Medicare beneficiaries., Comment: 26 pages, 14 figures

Pollutant emissions from coal-burning power plants have been deemed to adversely impact ambient air quality and public health conditions. Despite the noticeable reduction in emissions and the improvement of air quality since the Clean Air Act (CAA) became the law, the public-health benefits from changes in emissions have not been widely evaluated yet. In terms of the chain of accountability (HEI Accountability Working Group, 2003), the link between pollutant emissions from the power plants (SO2) and public health conditions (respiratory diseases) accounting for changes in ambient air quality (PM2.5) is unknown. We provide the first assessment of the longitudinal effect of specific pollutant emission (SO2) on public health outcomes that is mediated through changes in the ambient air quality. It is of particular interest to examine the extent to which the effect that is mediated through changes in local ambient air quality differs from year to year. In this paper, we propose a Bayesian approach to estimate novel causal estimands: time-varying mediation effects in the presence of mediators and responses measured every year. We replace the commonly invoked sequential ignorability assumption with a new set of assumptions which are sufficient to identify the distributions of the natural indirect and direct effects in this setting.

The association between PM2.5 and mortality is well established; however, confounding by unmeasured factors is always an issue. In addition, prior studies do not tell us what the effect of a sudden change in exposure on mortality is. We consider the sub-population of Medicare enrollees who moved residence from one ZIP Code to another from 2000 to 2012. Because the choice of new ZIP Code is unlikely to be related with any confounders, restricting to the population of movers allows us to have a study design that incorporates randomization of exposure. Over 10 million Medicare participants moved. We calculated change in exposure by subtracting the annual exposure at original ZIP Code from exposure at the new ZIP Code using a validated model. We used Cox proportional hazards models stratified on original ZIP Code with inverse probability weights (IPW) to control for individual and ecological confounders at the new ZIP Code. The distribution of covariates appeared to be randomized by change in exposure at the new locations as standardized differences were mostly near zero. Randomization of measured covariates suggests unmeasured covariates may be randomized also. Using IPW, per 10 µg/m3 increase in PM2.5, the hazard ratio was 1.21 (95% confidence interval [CI] = 1.20, 1.22] among whites and 1.12 (95% CI = 1.08, 1.15) among blacks. Hazard ratios increased for whites and decreased for blacks when restricting to exposure levels below the current standard of 12 µg/m3. This study provides evidence of likely causal effects at concentrations below current limits of PM2.5.

BACKGROUND: Emissions from coal power plants have decreased over recent decades due to regulations and economics affecting costs of providing electricity generated by coal vis-a-vis its alternatives. These changes have improved regional air quality, but questions remain about whether benefits have accrued equitably across population groups. OBJECTIVES: We aimed to quantify nationwide long-term changes in exposure to particulate matter (PM) with an aerodynamic diameter

British Medical Journal, 367, ISSN:0007-1447, ISSN:0959-535X, ISSN:0959-8146, ISSN:0959-8138, ISSN:1468-5833

BACKGROUND AND PURPOSE: Structural brain networks possess a few hubs, which are not only highly connected to the rest of the brain but are also highly connected to each other. These hubs, which form a rich-club, play a central role in global brain organization. To investigate whether the concept of rich-club sheds new light on poststroke recovery, we applied a novel network-theoretical quantification of lesions to patients with stroke and compared the outcomes with what lesion size alone would indicate. METHODS: Whole-brain structural networks of 73 patients with ischemic stroke were reconstructed using diffusion-weighted imaging data. Disconnectomes, a new type of network analyses, were constructed using only those fibers that pass through the lesion. Fugl-Meyer upper extremity scores and their changes were used to determine whether the patients show natural recovery or not. RESULTS: Cluster analysis revealed 3 patient clusters: small-lesion-good-recovery, midsized-lesion-poor-recovery (MLPR), and large-lesion-poor-recovery (LLPR). The small-lesion-good-recovery consisted of subjects whose lesions were small, and whose prospects for recovery were relatively good. To explain the nondifference in recovery between the MLPR and LLPR clusters despite the difference (LLPR>MLPR) in lesion volume, we defined the metric to be the sum of the entries in the disconnectome and, more importantly, the to be the sum of all entries in the disconnectome corresponding to edges with at least one node in the rich-club. Unlike lesion volume and corticospinal tract damage (MLPRLLPR) or showed no difference for L-1. CONCLUSIONS: Smaller lesions that focus on the rich-club can be just as devastating as much larger lesions that do not focus on the rich-club, pointing to the role of the rich-club as a backbone for functional communication within brain networks and for recovery from stroke.

Importance: Hospitals that serve poorer populations have higher readmission rates. It is unknown whether these hospitals effectively lowered readmission rates in response to the Hospital Readmissions Reduction Program (HRRP). Objective: To compare pre-post differences in readmission rates among hospitals with different proportion of dual-eligible patients both generally and among the most highly penalized (ie, low performing) hospitals. Design: Retrospective cohort study using piecewise linear model with estimated hospital-level risk-standardized readmission rates (RSRRs) as the dependent variable and a change point at HRRP passage (2010). Economic burden was assessed by proportion of dual-eligibles served. Setting: Acute care hospitals within the United States. Participants: Medicare fee-for-service beneficiaries aged 65 years or older discharged alive from January 1, 2003 to November 30, 2014 with a principal discharge diagnosis of acute myocardial infarction (AMI), congestive heart failure (CHF), and pneumonia. Main Outcome and Measure: Decrease in hospital-level RSRRs in the post-law period, after controlling for the pre-law trend. Results: For AMI, the pre-post difference between hospitals that service high and low proportion of dual-eligibles was not significant (-65 vs. -64 risk-standardized readmissions per 10000 discharges per year, P=0.0678). For CHF, RSRRs declined more at high than low dual-eligible hospitals (-79 vs. -75 risk-standardized readmissions per 10000 discharges per year, P=0.0006). For pneumonia, RSRRs declined less at high than low dual-eligible hospitals (-44 vs. -47 risk-standardized readmissions per 10000 discharges per year, P=0.0003). Among the 742 highest penalized hospitals and all conditions, the pre-post decline in rate of change of RSRRs was less for high dual-eligible hospitals than low dual-eligible hospitals (-68 vs. -74 risk-standardized readmissions per 10000 discharges per year for AMI, -88 vs. -97 for CHF, and -47 vs. -56 for pneumonia, P