Ambient heat exposure patterns and emergency department visits and hospitalizations among medicare beneficiaries 2008-2019.

OBJECTIVE: To assess the association between ambient heat and all-cause and cause-specific emergency department (ED) visits and acute hospitalizations among Medicare beneficiaries in the conterminous United States. DESIGN: Retrospective cohort study. SETTING: Conterminous US from 2008 and 2019. PARTICIPANTS: 2% random sample of all Medicare fee-for-service beneficiaries eligible for Parts A, B, and D. MAIN OUTCOME MEASURES: All-cause and cause-specific (cardiovascular, renal, and heat-related) ED visits and unplanned hospitalizations were identified using primary ICD-9 or ICD-10 diagnosis codes. We measured the association between ambient temperature - defined as daily mean temperature percentile of summer (June through September) - and the outcomes. Hazard ratios and their associated 95% confidence intervals were estimated using multivariable Cox proportional hazards regression, adjusting for individual level demographics, comorbidities, healthcare utilization factors and zip-code level social factors. RESULTS: Among 809,636 Medicare beneficiaries (58% female, 81% non-Hispanic White, 24% <65), older beneficiaries (aged ≥65) exposed to >95th percentile temperature had a 64% elevated adjusted risk of heat-related ED visits (HR [95% CI], 1.64 [1.46,1.85]) and a 4% higher risk of all-cause acute hospitalization (1.04 [1.01,1.06]) relative to <25th temperature percentile. Younger beneficiaries (aged <65) showed increased risk of heat-related ED visits (2.69 [2.23,3.23]) and all-cause ED visits (1.03 [1.01,1.05]). The associations with heat related events were stronger in males and individuals dually eligible for Medicare and Medicaid. No significant differences were observed by climatic region. We observed no significant relationship between temperature percentile and risk of CV-related ED visits or renal-related ED visits. CONCLUSIONS: Among Medicare beneficiaries from 2008 to 2019, exposure to daily mean temperature ≥ 95th percentile was associated with increased risk of heat-related ED visits, with stronger associations seen among beneficiaries <65, males, and patients with low socioeconomic position. Further longitudinal studies are needed to understand the impact of heat duration, intensity, and frequency on cause-specific hospitalization outcomes.

Air pollution and serious bleeding events in high-risk older adults.

IMPORTANCE: Despite biological plausibility, very few epidemiologic studies have investigated the risks of clinically significant bleeding events due to particulate air pollution. OBJECTIVE: To measure the independent and synergistic effects of PM2.5 exposure and anticoagulant use on serious bleeding events. DESIGN: Retrospective cohort study (2008-2016). SETTING: Nationwide Medicare population. PARTICIPANTS: A 50% random sample of Medicare Part D-eligible Fee-for-Service beneficiaries at high risk for cardiovascular and thromboembolic events. EXPOSURES: Fine particulate matter (PM2.5) and anticoagulant drugs (apixaban, dabigatran, edoxaban, rivaroxaban, or warfarin). MAIN OUTCOMES AND MEASURES: The outcomes were acute hospitalizations for gastrointestinal bleeding, intracranial bleeding, or epistaxis. Hazard ratios and 95% CIs for PM2.5 exposure were estimated by fitting inverse probability weighted marginal structural Cox proportional hazards models. The relative excess risk due to interaction was used to assess additive-scale interaction between PM2.5 exposure and anticoagulant use. RESULTS: The study cohort included 1.86 million high-risk older adults (mean age 77, 60% male, 87% White, 8% Black, 30% anticoagulant users, mean PM2.5 exposure 8.81 µg/m3). A 10 µg/m3 increase in PM2.5 was associated with a 48% (95% CI: 45%-52%), 58% (95% CI: 49%-68%) and 55% (95% CI: 37%-76%) increased risk of gastrointestinal bleeding, intracranial bleeding, and epistaxis, respectively. Significant additive interaction between PM2.5 exposure and anticoagulant use was observed for gastrointestinal and intracranial bleeding. CONCLUSIONS: Among older adults at high risk for cardiovascular and thromboembolic events, increasing PM2.5 exposure was significantly associated with increased risk of gastrointestinal bleeding, intracranial bleeding, and epistaxis. In addition, PM2.5 exposure and anticoagulant use may act together to increase risks of severe gastrointestinal and intracranial bleeding. Thus, clinicians may recommend that high-risk individuals limit their outdoor air pollution exposure during periods of increased PM2.5 concentrations. Our findings may inform environmental policies to protect the health of vulnerable populations.

Quantifying Multipollutant Health Impacts Using the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE): A Case Study in Atlanta, Georgia.

BACKGROUND: Air pollution risk assessments do not generally quantify health impacts using multipollutant risk estimates, but instead use results from single-pollutant or copollutant models. Multipollutant epidemiological models account for pollutant interactions and joint effects but can be computationally complex and data intensive. Risk estimates from multipollutant studies are therefore challenging to implement in the quantification of health impacts. OBJECTIVES: Our objective was to conduct a case study using a developmental multipollutant version of the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) to estimate the health impact associated with changes in multiple air pollutants using both a single and multipollutant approach. METHODS: BenMAP-CE was used to estimate the change in the number of pediatric asthma emergency department (ED) visits attributable to simulated changes in air pollution between 2011 and 2025 in Atlanta, Georgia, applying risk estimates from an epidemiological study that examined short-term single-pollutant and multipollutant (with and without first-order interactions) exposures. Analyses examined individual pollutants (i.e., ozone, fine particulate matter, carbon monoxide, nitrogen dioxide (NO2), sulfur dioxide, and particulate matter components) and combinations of these pollutants meant to represent shared properties or predefined sources (i.e., oxidant gases, secondary pollutants, traffic, power plant, and criteria pollutants). Comparisons were made between multipollutant health impact functions (HIF) and the sum of single-pollutant HIFs for the individual pollutants that constitute the respective pollutant groups. RESULTS: Photochemical modeling predicted large decreases in most of the examined pollutant concentrations between 2011 and 2025 based on sector specific (i.e., source-based) estimates of growth and anticipated controls. Estimated number of avoided asthma ED visits attributable to any given multipollutant group were generally higher when using results from models that included interaction terms in comparison with those that did not. We estimated the greatest number of avoided pediatric asthma ED visits for pollutant groups that include NO2 (i. e., criteria pollutants, oxidants, and traffic pollutants). In models that accounted for interaction, year-round estimates for pollutant groups that included NO2 ranged from 27.1 [95% confidence interval (CI): 1.6, 52.7; traffic pollutants] to 55.4 (95% CI: 41.8, 69.0; oxidants) avoided pediatric asthma ED visits. Year-round results using multipollutant risk estimates with interaction were comparable to the sum of the single-pollutant results corresponding to most multipollutant groups [e.g., 52.9 (95% CI: 43.6, 62.2) for oxidants] but were notably lower than the sum of the single-pollutant results for some pollutant groups [e.g., 77.5 (95% CI: 66.0, 89.0) for traffic pollutants]. DISCUSSION: Performing a multipollutant health impact assessment is technically feasible but computationally complex. It requires time, resources, and detailed input parameters not commonly reported in air pollution epidemiological studies. Results estimated using the sum of single-pollutant models are comparable to those quantified using a multipollutant model. Although limited to a single study and location, assessing the trade-offs between a multipollutant and single-pollutant approach is warranted. https://doi.org/10.1289/EHP12969.

1965 US Voting Rights Act Impact on Black and Black Versus White Infant Death Rates in Jim Crow States, 1959-1980 and 2017-2021.

Objectives. To investigate the impact of the US Voting Rights Act (VRA) of 1965 on Black and Black versus White infant deaths in Jim Crow states. Methods. Using data from 1959 to 1980 and 2017 to 2021, we applied difference-in-differences methods to quantify differential pre-post VRA changes in infant deaths in VRA-exposed versus unexposed counties, controlling for population size and social, economic, and health system characteristics. VRA-exposed counties, identified by Section 4, were subject to government interventions to remove existing racist voter suppression policies. Results. Black infant deaths in VRA-exposed counties decreased by an average of 11.4 (95% confidence interval [CI] = 1.7, 21.0) additional deaths beyond the decrease experienced by unexposed counties between the pre-VRA period (1959-1965) and the post-VRA period (1966-1970). This translates to 6703 (95% CI = 999.6, 12 348) or 17.5% (95% CI = 3.1%, 28.1%) fewer deaths than would have been experienced in the absence of the VRA. The equivalent differential changes were not significant among the White or total population. Conclusions. Passage of the VRA led to pronounced reductions in Black infant deaths in Southern counties subject to government intervention because these counties had particularly egregious voter suppression practices. (Am J Public Health. 2024;114(3):300-308. https://doi.org/10.2105/AJPH.2023.307518).

Ambient Heat and Risk of Serious Hypoglycemia in Older Adults With Diabetes Using Insulin in the U.S. and Taiwan: A Cross-National Case-Crossover Study.

OBJECTIVE: To measure the association between ambient heat and hypoglycemia-related emergency department visit or hospitalization in insulin users. RESEARCH DESIGN AND METHODS: We identified cases of serious hypoglycemia among adults using insulin aged ≥65 in the U.S. (via Medicare Part A/B/D-eligible beneficiaries) and Taiwan (via National Health Insurance Database) from June to September, 2016-2019. We then estimated odds of hypoglycemia by heat index (HI) percentile categories using conditional logistic regression with a time-stratified case-crossover design. RESULTS: Among ∼2 million insulin users in the U.S. (32,461 hypoglycemia case subjects), odds ratios of hypoglycemia for HI >99th, 95-98th, 85-94th, and 75-84th percentiles compared with the 25-74th percentile were 1.38 (95% CI, 1.28-1.48), 1.14 (1.08-1.20), 1.12 (1.08-1.17), and 1.09 (1.04-1.13) respectively. Overall patterns of associations were similar for insulin users in the Taiwan sample (∼283,000 insulin users, 10,162 hypoglycemia case subjects). CONCLUSIONS: In two national samples of older insulin users, higher ambient temperature was associated with increased hypoglycemia risk.

Air Pollution and Temperature: a Systematic Review of Ubiquitous Environmental Exposures and Sudden Cardiac Death.

PURPOSE OF REVIEW: Environmental exposures have been associated with increased risk of cardiovascular mortality and acute coronary events, but their relationship with out-of-hospital cardiac arrest (OHCA) and sudden cardiac death (SCD) remains unclear. SCD is an important contributor to the global burden of cardiovascular disease worldwide. RECENT FINDINGS: Current literature suggests a relationship between environmental exposures and cardiovascular disease, but their relationship with OHCA/SCD remains unclear. A literature search was conducted in PubMed, Embase, Web of Science, and Global Health. Of 5138 studies identified by our literature search, this review included 30 studies on air pollution, 42 studies on temperature, 6 studies on both air pollution and temperature, and 1 study on altitude exposure and OHCA/SCD. Particulate matter air pollution, ozone, and both hot and cold temperatures are associated with increased risk of OHCA/SCD. Pollution and other exposures related to climate change play an important role in OHCA/SCD incidence.

Associations between air pollution, residential greenness, and glycated hemoglobin (HbA1c) in three prospective cohorts of U.S. adults.

BACKGROUND: While studies suggest impacts of individual environmental exposures on type 2 diabetes (T2D) risk, mechanisms remain poorly characterized. Glycated hemoglobin (HbA1c) is a biomarker of glycemia and diagnostic criterion for prediabetes and T2D. We explored associations between multiple environmental exposures and HbA1c in non-diabetic adults. METHODS: HbA1c was assessed once in 12,315 women and men in three U.S.-based prospective cohorts: the Nurses' Health Study (NHS), Nurses' Health Study II (NHSII), and Health Professionals Follow-up Study (HPFS). Residential greenness within 270 m and 1,230 m (normalized difference vegetation index, NDVI) was obtained from Landsat. Fine particulate matter (PM2.5) and nitrogen dioxide (NO2) were estimated from nationwide spatiotemporal models. Three-month and one-year averages prior to blood draw were assigned to participants' addresses. We assessed associations between single exposure, multi-exposure, and component scores from Principal Components Analysis (PCA) and HbA1c. Fully-adjusted models built on basic models of age and year at blood draw, BMI, alcohol use, and neighborhood socioeconomic status (nSES) to include diet quality, race, family history, smoking status, postmenopausal hormone use, population density, and season. We assessed interactions between environmental exposures, and effect modification by population density, nSES, and sex. RESULTS: Based on HbA1c, 19% of participants had prediabetes. In single exposure fully-adjusted models, an IQR (0.14) higher 1-year 1,230 m NDVI was associated with a 0.27% (95% CI: 0.05%, 0.49%) lower HbA1c. In basic component score models, a SD increase in Component 1 (high loadings for 1-year NDVI) was associated with a 0.19% (95% CI: 0.04%, 0.34%) lower HbA1c. CI's crossed the null in multi-exposure and fully-adjusted component score models. There was little evidence of associations between air pollution and HbA1c, and no evidence of effect modification. CONCLUSIONS: Among non-diabetic adults, environmental exposures were not consistently associated with HbA1c. More work is needed to elucidate biological pathways between the environment and prediabetes.

Retrospective cohort study investigating synergism of air pollution and corticosteroid exposure in promoting cardiovascular and thromboembolic events in older adults.

OBJECTIVE: To evaluate the synergistic effects created by fine particulate matter (PM2.5) and corticosteroid use on hospitalisation and mortality in older adults at high risk for cardiovascular thromboembolic events (CTEs). DESIGN AND SETTING: A retrospective cohort study using a US nationwide administrative healthcare claims database. PARTICIPANTS: A 50% random sample of participants with high-risk conditions for CTE from the 2008-2016 Medicare Fee-for-Service population. EXPOSURES: Corticosteroid therapy and seasonal-average PM2.5. MAIN OUTCOME MEASURES: Incidences of myocardial infarction or acute coronary syndrome (MI/ACS), ischaemic stroke or transient ischaemic attack, heart failure (HF), venous thromboembolism, atrial fibrillation and all-cause mortality. We assessed additive interactions between PM2.5 and corticosteroids using estimates of the relative excess risk due to interaction (RERI) obtained using marginal structural models for causal inference. RESULTS: Among the 1 936 786 individuals in the high CTE risk cohort (mean age 76.8, 40.0% male, 87.4% white), the mean PM2.5 exposure level was 8.3±2.4 µg/m3 and 37.7% had at least one prescription for a systemic corticosteroid during follow-up. For all outcomes, we observed increases in risk associated with corticosteroid use and with increasing PM2.5 exposure. PM2.5 demonstrated a non-linear relationship with some outcomes. We also observed evidence of an interaction existing between corticosteroid use and PM2.5 for some CTEs. For an increase in PM2.5 from 8 µg/m3 to 12 µg/m3 (a policy-relevant change), the RERI of corticosteroid use and PM2.5 was significant for HF (15.6%, 95% CI 4.0%, 27.3%). Increasing PM2.5 from 5 µg/m3 to 10 µg/m3 yielded significant RERIs for incidences of HF (32.4; 95% CI 14.9%, 49.9%) and MI/ACSs (29.8%; 95% CI 5.5%, 54.0%). CONCLUSION: PM2.5 and systemic corticosteroid use were independently associated with increases in CTE hospitalisations. We also found evidence of significant additive interactions between the two exposures for HF and MI/ACSs suggesting synergy between these two exposures.

Impacts of census differential privacy for small-area disease mapping to monitor health inequities.

The U.S. Census Bureau will implement a modernized privacy-preserving disclosure avoidance system (DAS), which includes application of differential privacy, on publicly released 2020 census data. There are concerns that the DAS may bias small-area and demographically stratified population counts, which play a critical role in public health research, serving as denominators in estimation of disease/mortality rates. Using three DAS demonstration products, we quantify errors attributable to reliance on DAS-protected denominators in standard small-area disease mapping models for characterizing health inequities. We conduct simulation studies and real data analyses of inequities in premature mortality at the census tract level in Massachusetts and Georgia. Results show that overall patterns of inequity by racialized group and economic deprivation level are not compromised by the DAS. While early versions of DAS induce errors in mortality rate estimation that are larger for Black than non-Hispanic white populations in Massachusetts, this issue is ameliorated in newer DAS versions.

Association between Outdoor Light at Night and Prostate Cancer in the Health Professionals Follow-up Study.

BACKGROUND: Circadian disruption is a potential risk factor for advanced prostate cancer, and light at night (LAN) exposure may disrupt circadian rhythms. We evaluated whether outdoor LAN increases the risk of prostate cancer. METHODS: We prospectively followed 49,148 participants in the Health Professionals Follow-up Study from 1986 through 2016. We estimated baseline and cumulative time-varying outdoor LAN with ∼1 km2 resolution using data from the US Defense Meteorological Satellite Program's Operational Linescan System, which was assigned to participants' geocoded addresses. Participants reside in all 50 U.S. states and reported a work or home address. We used multivariable Cox models to estimate HRs and 95% confidence intervals (CI) for the association between outdoor LAN and risk of overall (7,175 cases) and fatal (915 cases) prostate cancer adjusting for individual and contextual factors. RESULTS: There was no association between the interquartile range increase in cumulative LAN and total (HR, 1.02; 95% CI, 0.98-1.06) or fatal (HR, 1.05; 95% CI, 0.96-1.15) prostate cancer in adjusted models. However, there was a positive association between baseline LAN and total prostate cancer among non-movers (HR, 1.06; 95% CI, 1.00-1.14) including among highly screened participants (HR, 1.11; 95% CI, 1.01-1.23). CONCLUSIONS: There was a suggestive positive association between baseline outdoor LAN and total prostate cancer. Additional studies with different measures of outdoor LAN and in more diverse populations are necessary. IMPACT: To our knowledge, this is the first longitudinal cohort study exploring the relationship between outdoor LAN and prostate cancer.

Estimating a Causal Exposure Response Function with a Continuous Error-Prone Exposure: A Study of Fine Particulate Matter and All-Cause Mortality.

Numerous studies have examined the associations between long-term exposure to fine particulate matter (PM2.5) and adverse health outcomes. Recently, many of these studies have begun to employ high-resolution predicted PM2.5 concentrations, which are subject to measurement error. Previous approaches for exposure measurement error correction have either been applied in non-causal settings or have only considered a categorical exposure. Moreover, most procedures have failed to account for uncertainty induced by error correction when fitting an exposure-response function (ERF). To remedy these deficiencies, we develop a multiple imputation framework that combines regression calibration and Bayesian techniques to estimate a causal ERF. We demonstrate how the output of the measurement error correction steps can be seamlessly integrated into a Bayesian additive regression trees (BART) estimator of the causal ERF. We also demonstrate how locally-weighted smoothing of the posterior samples from BART can be used to create a more accurate ERF estimate. Our proposed approach also properly propagates the exposure measurement error uncertainty to yield accurate standard error estimates. We assess the robustness of our proposed approach in an extensive simulation study. We then apply our methodology to estimate the effects of PM2.5 on all-cause mortality among Medicare enrollees in New England from 2000-2012.

Air Pollution and Cardiovascular and Thromboembolic Events in Older Adults With High-Risk Conditions.

Little epidemiologic research has focused on pollution-related risks in medically vulnerable or marginalized groups. Using a nationwide 50% random sample of 2008-2016 Medicare Part D-eligible fee-for-service participants in the United States, we identified a cohort with high-risk conditions for cardiovascular and thromboembolic events (CTEs) and linked individuals with seasonal average zip-code-level concentrations of fine particulate matter (particulate matter with an aerodynamic diameter ≤ 2.5 µm (PM2.5)). We assessed the relationship between seasonal PM2.5 exposure and hospitalization for each of 7 CTE-related causes using history-adjusted marginal structural models with adjustment for individual demographic and neighborhood socioeconomic variables, as well as baseline comorbidity, health behaviors, and health-service measures. We examined effect modification across geographically and demographically defined subgroups. The cohort included 1,934,453 individuals with high-risk conditions (mean age = 77 years; 60% female, 87% White). A 1-µg/m3 increase in PM2.5 exposure was significantly associated with increased risk of 6 out of 7 types of CTE hospitalization. Strong increases were observed for transient ischemic attack (hazard ratio (HR) = 1.039, 95% confidence interval (CI): 1.034, 1.044), venous thromboembolism (HR = 1.031, 95% CI: 1.027, 1.035), and heart failure (HR = 1.019, 95% CI: 1.017, 1.020). Asian Americans were found to be particularly susceptible to thromboembolic effects of PM2.5 (venous thromboembolism: HR = 1.063, 95% CI: 1.021, 1.106), while Native Americans were most vulnerable to cerebrovascular effects (transient ischemic attack: HR = 1.093, 95% CI: 1.030, 1.161).

Air Pollution and Mortality at the Intersection of Race and Social Class.

BACKGROUND: Black Americans are exposed to higher annual levels of air pollution containing fine particulate matter (particles with an aerodynamic diameter of ≤2.5 µm [PM2.5]) than White Americans and may be more susceptible to its health effects. Low-income Americans may also be more susceptible to PM2.5 pollution than high-income Americans. Because information is lacking on exposure-response curves for PM2.5 exposure and mortality among marginalized subpopulations categorized according to both race and socioeconomic position, the Environmental Protection Agency lacks important evidence to inform its regulatory rulemaking for PM2.5 standards. METHODS: We analyzed 623 million person-years of Medicare data from 73 million persons 65 years of age or older from 2000 through 2016 to estimate associations between annual PM2.5 exposure and mortality in subpopulations defined simultaneously by racial identity (Black vs. White) and income level (Medicaid eligible vs. ineligible). RESULTS: Lower PM2.5 exposure was associated with lower mortality in the full population, but marginalized subpopulations appeared to benefit more as PM2.5 levels decreased. For example, the hazard ratio associated with decreasing PM2.5 from 12 µg per cubic meter to 8 µg per cubic meter for the White higher-income subpopulation was 0.963 (95% confidence interval [CI], 0.955 to 0.970), whereas equivalent hazard ratios for marginalized subpopulations were lower: 0.931 (95% CI, 0.909 to 0.953) for the Black higher-income subpopulation, 0.940 (95% CI, 0.931 to 0.948) for the White low-income subpopulation, and 0.939 (95% CI, 0.921 to 0.957) for the Black low-income subpopulation. CONCLUSIONS: Higher-income Black persons, low-income White persons, and low-income Black persons may benefit more from lower PM2.5 levels than higher-income White persons. These findings underscore the importance of considering racial identity and income together when assessing health inequities. (Funded by the National Institutes of Health and the Alfred P. Sloan Foundation.).

Investigating Use of Low-Cost Sensors to Increase Accuracy and Equity of Real-Time Air Quality Information.

U.S. Environmental Protection Agency (EPA) air quality (AQ) monitors, the "gold standard" for measuring air pollutants, are sparsely positioned across the U.S. Low-cost sensors (LCS) are increasingly being used by the public to fill in the gaps in AQ monitoring; however, LCS are not as accurate as EPA monitors. In this work, we investigate factors impacting the differences between an individual's true (unobserved) exposure to air pollution and the exposure reported by their nearest AQ instrument (which could be either an LCS or an EPA monitor). We use simulations based on California data to explore different combinations of hypothetical LCS placement strategies (e.g., at schools or near major roads), for different numbers of LCS, with varying plausible amounts of LCS device measurement errors. We illustrate how real-time AQ reporting could be improved (or, in some cases, worsened) by using LCS, both for the population overall and for marginalized communities specifically. This work has implications for the integration of LCS into real-time AQ reporting platforms.

Evaluation of Model-Based PM2.5 Estimates for Exposure Assessment during Wildfire Smoke Episodes in the Western U.S.

Investigating the health impacts of wildfire smoke requires data on people's exposure to fine particulate matter (PM2.5) across space and time. In recent years, it has become common to use machine learning models to fill gaps in monitoring data. However, it remains unclear how well these models are able to capture spikes in PM2.5 during and across wildfire events. Here, we evaluate the accuracy of two sets of high-coverage and high-resolution machine learning-derived PM2.5 data sets created by Di et al. and Reid et al. In general, the Reid estimates are more accurate than the Di estimates when compared to independent validation data from mobile smoke monitors deployed by the US Forest Service. However, both models tend to severely under-predict PM2.5 on high-pollution days. Our findings complement other recent studies calling for increased air pollution monitoring in the western US and support the inclusion of wildfire-specific monitoring observations and predictor variables in model-based estimates of PM2.5. Lastly, we call for more rigorous error quantification of machine-learning derived exposure data sets, with special attention to extreme events.

Addressing spatial misalignment in population health research: a case study of US congressional district political metrics and county health data.

Areal spatial misalignment, which occurs when data on multiple variables are collected using mismatched boundary definitions, is a ubiquitous obstacle to data analysis in public health and social science research. As one example, the emerging sub-field studying the links between political context and health in the United States faces significant spatial misalignment-related challenges, as the congressional districts (CDs) over which political metrics are measured and administrative units, e.g., counties, for which health data are typically released, have a complex misalignment structure. Standard population-weighted data realignment procedures can induce measurement error and invalidate inference, which has prompted the development of fully model-based approaches for analyzing spatially misaligned data. One such approach, atom-based regression models (ABRM), holds particular promise but has scarcely been used in practice due to the lack of appropriate software or examples of implementation. ABRM use "atoms", the areas created by intersecting all sets of units on which variables of interest are measured, as the units of analysis and build models for the atom-level data, treating the atom-level variables (generally unmeasured) as latent variables. In this paper, we demonstrate the feasibility and strengths of the ABRM in a case study of the association between political representatives' voting behavior (CD-level) and COVID-19 mortality rates (county-level) in a post-vaccine period. The adjusted ABRM results suggest that more conservative voting record is associated with an increase in COVID-19 mortality rates, with estimated associations smaller in magnitude but consistent in direction with those of standard realignment methods. The results also indicate that ABRM may enable more robust confounding adjustment and more realistic uncertainty estimates, properly representing the uncertainties arising from all analytic procedures. We also implement the ABRM in modern optimized Bayesian computing programs and make our code publicly available, which may enable these methods to be more widely adopted.

Mobile Source Benzene Regulations and Risk of Childhood and Young Adult Hematologic Cancers in Alaska: A Quasi-experimental Study.

BACKGROUND: We aimed to evaluate the impact of the EPA's Mobile Source Air Toxics rules (MSAT), which targeted benzene emissions, on childhood and young adult leukemia and lymphoma incidence in Alaska. METHODS: MSAT was implemented in 2011 and produced a dramatic decline in ambient benzene in Alaska. Due to previous benzene-related regulations enacted in the continental United States, MSAT had relatively modest impacts in other states. This created quasi-experimental conditions leveraged in this study. Using 2-year state-level incidence rates of childhood and young adult leukemia and lymphoma for each US state 2001-2018, we examined MSAT-attributable changes in incidence by applying a difference-in-differences approach. RESULTS: We found evidence of a substantial reduction associated with MSAT in incidence of childhood and young adult lymphoma (-1.23 [-1.84, -0.62] cases per 100,000), but not in leukemia (-0.13 [-0.77, 0.51] cases per 100,000). CONCLUSIONS: Our findings are consistent with the hypothesis that MSAT, which reduced benzene levels in Alaska, led to a decline in lymphoma incidence in children and young adults.

Gestational thyroid hormone concentrations and risk of attention-deficit hyperactivity disorder in the Norwegian Mother, Father and Child Cohort Study.

BACKGROUND: Maternal thyroid function plays an important role in foetal brain development; however, little consensus exists regarding the relationship between normal variability in thyroid hormones and common neurodevelopmental disorders, such as attention-deficit hyperactivity disorder (ADHD). OBJECTIVE: We sought to examine the association between mid-pregnancy maternal thyroid function and risk of clinically diagnosed ADHD in offspring. METHODS: We conducted a nested case-control study in the Norwegian Mother, Father and Child Cohort Study. Among children born 2003 or later, we randomly sampled singleton ADHD cases obtained through linkage with the Norwegian Patient Registry (n = 298) and 554 controls. Concentrations of maternal triiodothyronine (T3), thyroxine (T4), T3-Uptake, thyroid-stimulating hormone (TSH) and thyroid peroxidase antibody (TPO-Ab) were measured in maternal plasma, collected at approximately 17 weeks' gestation. Indices of free T4 (FT4i) and free T3 (FT3i) were calculated. We used multivariable adjusted logistic regression to calculate odds ratios and accounted for missing covariate data using multiple imputation. We used restricted cubic splines to assess non-linear trends and provide flexible representations. We examined effect measure modification by dietary iodine and selenium intake. In sensitivity analyses, we excluded women with clinically significant thyroid disorders (n = 73). RESULTS: High maternal T3 was associated with increased risk of ADHD (5th vs 1st quintile odds ratio  2.27, 95% confidence interval 1.21, 4.26). For FT4i, both the lowest and highest quintiles were associated with an approximate 1.6-fold increase in risk of ADHD, with similar trends found for T4. The FT4i association was modified by dietary iodine intake such that the highest risk strata were confined to the low intake group. CONCLUSIONS: Both high and low concentrations of maternal thyroid hormones, although within population reference ranges, increase the risk of ADHD in offspring. Increased susceptibility may be found among women with low dietary intake of iodine and selenium.

Environmental Exposures and Anti-Müllerian Hormone: A Mixture Analysis in the Nurses' Health Study II.

BACKGROUND: Previous studies have linked environmental exposures with anti-Müllerian hormone (AMH), a marker of ovarian reserve. However, associations with multiple environment factors has to our knowledge not been addressed. METHODS: We included a total of 2,447 premenopausal women in the Nurses' Health Study II (NHSII) who provided blood samples during 1996-1999. We selected environmental exposures linked previously with reproductive outcomes that had measurement data available in NHSII, including greenness, particulate matter, noise, outdoor light at night, ultraviolet radiation, and six hazardous air pollutants (1,3-butadiene, benzene, diesel particulate matter, formaldehyde, methylene chloride, and tetrachloroethylene). For these, we calculated cumulative averages from enrollment (1989) to blood draw and estimated associations with AMH in adjusted single-exposure models, principal component analysis (PCA), and hierarchical Bayesian kernel machine regression (BKMR). RESULTS: Single-exposure models showed negative associations of AMH with benzene (percentage reduction in AMH per interquartile range [IQR] increase = 5.5%, 95% confidence interval [CI] = 1.0, 9.8) and formaldehyde (6.1%, 95% CI = 1.6, 10). PCA identified four major exposure patterns but only one with high exposure to air pollutants and light at night was associated with lower AMH. Hierarchical BKMR pointed to benzene, formaldehyde, and greenness and suggested an inverse joint association with AMH (percentage reduction comparing all exposures at the 75th percentile to median = 8.2%, 95% CI = 0.7, 15.1). Observed associations were mainly among women above age 40. CONCLUSIONS: We found exposure to benzene and formaldehyde to be consistently associated with lower AMH levels. The associations among older women are consistent with the hypothesis that environmental exposures accelerate reproductive aging.

Integrated causal-predictive machine learning models for tropical cyclone epidemiology.

Strategic preparedness reduces the adverse health impacts of hurricanes and tropical storms, referred to collectively as tropical cyclones (TCs), but its protective impact could be enhanced by a more comprehensive and rigorous characterization of TC epidemiology. To generate the insights and tools necessary for high-precision TC preparedness, we introduce a machine learning approach that standardizes estimation of historic TC health impacts, discovers common patterns and sources of heterogeneity in those health impacts, and enables identification of communities at highest health risk for future TCs. The model integrates (i) a causal inference component to quantify the immediate health impacts of recent historic TCs at high spatial resolution and (ii) a predictive component that captures how TC meteorological features and socioeconomic/demographic characteristics of impacted communities are associated with health impacts. We apply it to a rich data platform containing detailed historic TC exposure information and records of all-cause mortality and cardiovascular- and respiratory-related hospitalization among Medicare recipients. We report a high degree of heterogeneity in the acute health impacts of historic TCs, both within and across TCs, and, on average, substantial TC-attributable increases in respiratory hospitalizations. TC-sustained windspeeds are found to be the primary driver of mortality and respiratory risks.