Rachel C. Nethery’s research while affiliated with Beverly Hospital, Boston MA and other places

Flooding greatly endangers public health and is an urgent concern as rapid population growth in flood-prone regions and more extreme weather events will increase the number of people at risk. However, an exhaustive analysis of mortality following floods has not been conducted. Here we used 35.6 million complete death records over 18 years (2001–2018) from the National Center for Health Statistics in the United States, highly resolved flood exposure data and a Bayesian conditional quasi-Poisson model to estimate the association of flooding with monthly county-level death rates for cancers, cardiovascular diseases, infectious and parasitic diseases, injuries, neuropsychiatric conditions and respiratory diseases up to 3 months after the flood. During the month of flooding, very severe heavy rain-related floods were associated with increased infectious disease (3.2%; 95% credible interval (CrI): 0.1%, 6.2%) and cardiovascular disease (2.1%; 95% CrI: 1.3%, 3.0%) death rates and tropical cyclone-related floods were associated with increased injury death rates (15.3%; 95% CrI: 12.4%, 18.1%). During the month of very severe tropical cyclone-related flooding, increases in injury death rate were higher for those ≥65 years old (24.9; 95% CrI: 20.0%, 29.8%) than for those aged <65 years (10.2%; 95% CrI: 6.6%, 13.8%) and for females (21.2%; 95% CrI: 16.3%, 26.1%) than for males (12.6%; 95% CrI: 9.1%,16.1%). Effective public health responses are critical now and with projected increased flood severity driven by climate change.

Background: Climate change adversely affects human health, resulting in higher demand for health care services. However, the impact of climaterelated environmental exposures on medical imaging utilization is currently unknown. Purpose: To determine associations of short-term exposures to ambient heat and particulate air pollution with utilization of emergency department medical imaging. Materials and Methods: In this retrospective time-stratified case-crossover study, daily imaging utilization counts from four emergency departments were linked to local daily environmental data—including fine particulate matter with 2.5-μm or smaller aerodynamic diameter (PM2.5) and ambient temperature—over 10 years (January 2013 to December 2022). Conditional Poisson regression models were used to evaluate the associations between daily imaging utilization and environmental exposures on the same day and each of the 7 days preceding imaging, lag days 0–7, controlling for day of the week, month, and year. Moving averages of mean daily PM2.5 and temperature were calculated to account for lagged exposure effects. Imaging counts were also stratified by modality (CT, radiography, US, and MRI). Results: In an analysis of 1 666 420 emergency department imaging studies, a rise of 10 °C in the 2-day moving average of mean daily temperature and a rise of 10 μg/m3 in the 3-day moving average of mean daily PM2.5 were associated with overall imaging utilization increases of 5.1% (incidence rate ratio [IRR], 1.051; 95% CI: 1.045, 1.056) and 4.0% (IRR, 1.040; 95% CI: 1.035, 1.046), respectively. Heat exposure days (mean temperature >20 °C) and air pollution exposure days (mean PM2.5 >12 μg/m3) were associated with same-day excess absolute risk of 5.5 and 6.4 imaging studies per 1 million people at risk per day, respectively. Heat exposure days and air pollution exposure days were associated with increased utilization of radiography (excess relative risk, 2.7% [P < .001] and 2.1% [P < .001], respectively) and CT (excess relative risk, 2.0% [P = .001] and 2.7% [P < .001]) but not US (P = .14 and P = .14) or MRI (P = .70 and P = .65). Conclusion: Short-term exposures to ambient heat and particulate air pollution were associated with increased utilization of radiography and CT but not US or MRI.

Although some pollutants emitted in vehicle exhaust, such as benzene, are known to cause leukaemia in adults with high exposure levels, less is known about the relationship between traffic-related air pollution (TRAP) and childhood haematologic cancer. In the 1990s, the US EPA enacted the reformulated gasoline program in select areas of the U.S., which drastically reduced ambient TRAP in affected areas. This created an ideal quasi-experiment to study the effects of TRAP on childhood haematologic cancers. However, existing methods for quasi-experimental analyses can perform poorly when outcomes are rare and unstable, as with childhood cancer incidence. We develop Bayesian spatio-temporal matrix completion methods to conduct causal inference in quasi-experimental settings with rare outcomes. Selective information sharing across space and time enables stable estimation, and the Bayesian approach facilitates uncertainty quantification. We evaluate the methods through simulations and apply them to estimate the causal effects of TRAP on childhood leukaemia and lymphoma.

Exposure measurement error is a ubiquitous but often overlooked challenge in causal inference with observational data. Existing methods accounting for exposure measurement error largely rely on restrictive parametric assumptions, while emerging data-adaptive estimation approaches allow for less restrictive assumptions but at the cost of flexibility, as they are typically tailored towards rigidly-defined statistical quantities. There remains a critical need for assumption-lean estimation methods that are both flexible and possess desirable theoretical properties across a variety of study designs. In this paper, we introduce a general framework for estimation of causal quantities in the presence of exposure measurement error, adapted from the control variates approach of Yang and Ding (2019). Our method can be implemented in various two-phase sampling study designs, where one obtains gold-standard exposure measurements for a small subset of the full study sample, called the validation data. The control variates framework leverages both the error-prone and error-free exposure measurements by augmenting an initial consistent estimator from the validation data with a variance reduction term formed from the full data. We show that our method inherits double-robustness properties under standard causal assumptions. Simulation studies show that our approach performs favorably compared to leading methods under various two-phase sampling schemes. We illustrate our method with observational electronic health record data on HIV outcomes from the Vanderbilt Comprehensive Care Clinic.

The substantial effect of air pollution on cardiovascular disease and mortality burdens is well-established. Emissions-reducing interventions on coal-fired power plants -- a major source of hazardous air pollution -- have proven to be an effective, but costly, strategy for reducing pollution-related health burdens. Targeting the power plants that achieve maximum health benefits while satisfying realistic cost constraints is challenging. The primary difficulty lies in quantifying the health benefits of intervening at particular plants. This is further complicated because interventions are applied on power plants, while health impacts occur in potentially distant communities, a setting known as bipartite network interference (BNI). In this paper, we introduce novel policy learning methods based on Q- and A-Learning to determine the optimal policy under arbitrary BNI. We derive asymptotic properties and demonstrate finite sample efficacy in simulations. We apply our novel methods to a comprehensive dataset of Medicare claims, power plant data, and pollution transport networks. Our goal is to determine the optimal strategy for installing power plant scrubbers to minimize ischemic heart disease (IHD) hospitalizations under various cost constraints. We find that annual IHD hospitalization rates could be reduced in a range from 20.66-44.51 per 10,000 person-years through optimal policies under different cost constraints.

Context Technological innovation and access to big data have allowed partisan gerrymandering to increase dramatically in recent redistricting cycles. Objective To understand whether and how partisan gerrymandering, including “packing” and “cracking” (ie, respectively concentrating within or dividing specified social groups across political boundaries), distorts understanding of public health need when health statistics are calculated for congressional districts (CDs). Design Cross-sectional study using 2020 CDs and nonpartisan simulated districts. Setting United States, 2017-2021. Participants United States residents. Main Outcome Measure Percent with no medical insurance (uninsured), within-district variance of percent uninsured, and between-district variance of percent uninsured. Results At the state level, states where partisan redistricting plans showed greater evidence of partisan gerrymandering were more likely to contain CDs with more extreme values of uninsurance rates than districts in states with less evidence for gerrymandering (association between z-scores for gerrymandering and between-district variation in uninsurance = 0.25 (−0.04, 0.53), P = .10). Comparing variation in uninsurance rates for observed CDs vs nonpartisan simulated districts across all states with more than 1 CD, in analyses stratified by state gerrymander status (no gerrymander, Democratic gerrymander, and Republican gerrymander), we found evidence of particularly extreme distortion of rates in Republican gerrymandered states, whereby Republican-leaning districts tended to have lower uninsurance rates (the percentage of Republican-leaning districts that were significantly lower than nonpartisan simulated districts was 5.1 times that of Democratic-leaning districts) and Democrat-leaning districts had higher uninsurance rates (the percentage of Democrat-leaning districts that were significantly higher than nonpartisan simulated districts was 3.0 times that of Republican-leaning districts). Conclusions Partisan gerrymandering can affect determination of CD-level uninsurance rates and distort understanding of public health burdens.

Purpose Prostate cancer survivors may benefit from a supportive social environment. We investigated associations of social integration and long-term physical and psychosocial quality of life among prostate cancer survivors who were participants in the Health Professionals Follow-up Study. Methods We included 1,428 individuals diagnosed with non-metastatic prostate cancer between 2008 and 2016. Social integration was measured by the Berkman-Syme Social Network Index (SNI) and marital status. We fit generalized linear mixed effect models for associations of SNI and marital status with patient reported outcome measures on physical and psychosocial quality of life captured between 2008 and 2020, adjusting for age, race, employment status, body mass index, comorbidities, smoking history, and clinical factors. Results Among those with baseline SNI (N = 1,362), 46.4% were socially integrated, 20.3% were moderately integrated, 27.4% were moderately isolated, and 5.9% were socially isolated. Among those reporting baseline marital status (N = 1,428), 89.5% were married. Socially integrated survivors (vs. socially isolated) reported fewer depressive signs and better psychosocial wellbeing. Physical quality of life did not differ by social integration. Married survivors (vs. not married) reported fewer urinary symptoms, but there were no differences in bowel, sexual, or vitality/hormonal symptoms. Conclusions Among prostate cancer survivors, being socially integrated was associated with fewer depressive signs and better psychosocial wellbeing, and married prostate cancer survivors had fewer urinary symptoms. Implications for Cancer Survivors This study highlighted aspects of long-term physical and psychosocial quality of life that are more favorable among prostate cancer survivors with a supportive social environment.