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.).

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.

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).

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.

Neighborhood greenness and long-term physical and psychosocial quality of life among prostate cancer survivors in the Health Professionals Follow-up Study.

INTRODUCTION: Neighborhood greenness may benefit long-term prostate cancer survivorship by promoting physical activity and social integration, and reducing stress and exposure to air pollution, noise, and extreme temperatures. We examined associations of neighborhood greenness and long-term physical and psychosocial quality of life in prostate cancer survivors in the Health Professionals Follow-up Study. METHODS: We included 1437 individuals diagnosed with non-metastatic prostate cancer between 2008 and 2016 across the United States. Neighborhood greenness within a 1230m buffer of each individual's mailing address was measured using the Landsat satellite image-based Normalized Difference Vegetation Index (NDVI). We fit generalized linear mixed effect models to assess associations of greenness (in quintiles) with longitudinal patient reported outcome measures on prostate cancer-specific physical and psychosocial quality of life, adjusting for time-varying individual- and neighborhood-level demographic factors and clinical factors. RESULTS: The greatest symptom burden was in the sexual domain. More than half of survivors reported good memory function and the lack of depressive signs at diagnosis. In fully adjusted models, cumulative average greenness since diagnosis was associated with fewer vitality/hormonal symptoms (highest quintile, Q5, vs lowest quintile, Q1: mean difference: 0.46, 95% confidence interval [CI]: 0.81, -0.12). Other domains of physical quality of life (bowel symptoms, urinary incontinence, urinary irritation, and sexual symptoms) did not differ by greenness overall. Psychosocial quality of life did not differ by greenness overall (Q5 vs Q1, odds ratio [95% CI]: memory function: 1.01 [0.61, 1.73]; lack of depressive signs: 1.10 [0.63, 1.95]; and wellbeing: 1.17 [0.71, 1.91]). CONCLUSION: During long-term prostate cancer survivorship, cumulative average 1230m greenness since diagnosis was associated with fewer vitality/hormonal symptoms. Other domains of physical quality of life and psychosocial quality of life did not differ by greenness overall. Limitations included potential non-differential exposure measurement error and NDVI's lack of time-activity pattern.

Gerrymandering and the Packing and Cracking of Medical Uninsurance Rates in the United States.

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.

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).

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.

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.

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.

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.

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.

Ultraviolet radiation and age at natural menopause in a nationwide, prospective US cohort.

BACKGROUND: Solar ultraviolet radiation (UV) is a critical environmental factor for dermal conversion of vitamin D, which is suggested to support reproductive health. However, current epidemiological studies have reported conflicting results on the associations between vitamin D levels and ovarian reserve. Further, few studies have considered UV exposure and reproductive aging, which is closely related to declined ovarian reserve. OBJECTIVES: We sought to examine the associations of long-term UV exposure and age at natural menopause in a large, nationwide, prospective cohort. METHODS: Participants in the Nurses' Health Study II (NHS II) who were premenopausal at age 40 were included and followed through 2015. Erythemal UV radiation from a high-resolution geospatial model was linked to the participants' residential histories. Early-life UV was estimated using the reported state of residence at birth, age 15, and age 30. We used time-varying Cox proportional hazards models to estimate the hazard ratio (HR) and 95% confidence intervals (CIs) for natural menopause, adjusting for potential confounders and predictors of menopause. RESULTS: A total of 63,801 women reported natural menopause across the 1,051,185 person-years of follow-up among 105,631 eligible participants. We found very modest associations with delayed menopause for long-term UV exposure (adjusted HR comparing highest to lowest quartile of cumulative average UV: 0.96, 95% CI: 0.94, 0.99). There was a suggestive inverse association between UV at age 30 with menopause (adjusted HR comparing highest to lowest quartile: 0.97, 95% CI: 0.95, 1.00) but not with UV at birth and age 15. CONCLUSIONS: Solar UV exposure in adulthood was modestly associated with later onset of menopause. Although consistent with previous findings on vitamin D intake and menopause in the same population, these weak associations found in this study may not be of clinical relevance.

Association of Tropical Cyclones With County-Level Mortality in the US.

Importance: Tropical cyclones have a devastating effect on society, but a comprehensive assessment of their association with cause-specific mortality over multiple years of study is lacking. Objective: To comprehensively evaluate the association of county-level tropical cyclone exposure and death rates from various causes in the US. Design, Setting, and Participants: A retrospective observational study using a Bayesian conditional quasi-Poisson model to examine how tropical cyclones were associated with monthly death rates. Data from 33.6 million deaths in the US were collected from the National Center for Health Statistics over 31 years (1988-2018), including residents of the 1206 counties in the US that experienced at least 1 tropical cyclone during the study period. Exposures: Tropical cyclone days per county-month, defined as number of days in a month with a sustained maximal wind speed 34 knots or greater. Main Outcomes and Measures: Monthly cause-specific county-level death rates by 6 underlying causes of death: cancers, cardiovascular diseases, infectious and parasitic diseases, injuries, neuropsychiatric conditions, and respiratory diseases. The model yielded information about the association between each additional cyclone day per month and monthly county-level mortality compared with the same county-month in different years, up to 6 months after tropical cyclones, and how these estimated associations varied by age, sex, and social vulnerability. The unit of analysis was county-month. Results: There were 33 619 393 deaths in total (16 691 681 females and 16 927 712 males; 8 587 033 aged 0-64 years and 25 032 360 aged 65 years or older) from the 6 causes recorded in 1206 US counties. There was a median of 2 tropical cyclone days experienced in total in included US counties. Each additional cyclone day was associated with increased death rates in the month following the cyclone for injuries (3.7% [95% credible interval {CrI}, 2.5%-4.9%]; 2.0 [95% CrI, 1.3-2.7] additional deaths per 1 000 000 for 2018 monthly age-standardized median rate [DPM]; 54.3 to 56.3 DPM), infectious and parasitic diseases (1.8% [95% CrI, 0.1%-3.6%]; 0.2 [95% CrI, 0.0-0.4] additional DPM; 11.7 to 11.9 DPM), respiratory diseases (1.3% [95% CrI, 0.2%-2.4%]; 0.6 [95% CrI, 0.1-1.1] additional DPM; 44.9 to 45.5 DPM), cardiovascular diseases (1.2% [95% CrI, 0.6%-1.7%]; 1.5 [95% CrI, 0.8-2.2] additional DPM; 129.6 to 131.1 DPM), neuropsychiatric conditions (1.2% [95% CrI, 0.1%-2.4%]; 0.6 [95% CrI, 0.1-1.2] additional DPM; 52.1 to 52.7 DPM), with no change for cancers (-0.3% [95% CrI, -0.9% to 0.3%]; -0.3 [95% CrI, -0.9 to 0.3] additional DPM; 100.4 to 100.1 DPM). Conclusions and Relevance: Among US counties that experienced at least 1 tropical cyclone from 1988-2018, each additional cyclone day per month was associated with modestly higher death rates in the months following the cyclone for several causes of death, including injuries, infectious and parasitic diseases, cardiovascular diseases, neuropsychiatric conditions, and respiratory diseases.

Impact of Differential Privacy and Census Tract Data Source (Decennial Census Versus American Community Survey) for Monitoring Health Inequities.

Objectives. To investigate how census tract (CT) estimates of mortality rates and inequities are affected by (1) differential privacy (DP), whereby the public decennial census (DC) data are injected with statistical "noise" to protect individual privacy, and (2) uncertainty arising from the small number of different persons surveyed each year in a given CT for the American Community Survey (ACS).Methods. We compared estimates of the 2008-2012 average annual premature mortality rate (death before age 65 years) in Massachusetts using CT data from the 2010 DC, 2010 DC with DP, and 2008-2012 ACS 5-year estimate data.Results. For these 3 denominator sources, the age-standardized premature mortality rates (per 100 000) for the total population respectively equaled 166.4 (95% confidence interval [CI] = 162.2, 170.6), 166.4 (95% CI = 162.2, 170.6), and 166.3 (95% CI = 162.1, 170.5), and inequities in the range from best to worst quintile for CT racialized economic segregation were from 103.4 to 260.1, 102.9 to 258.7, and 102.8 to 262.4. Similarity of results across CT denominator sources held for analyses stratified by gender and race/ethnicity.Conclusions. Estimates of health inequities at the CT level may not be affected by use of 2020 DP data and uncertainty in the ACS data.

A joint spatial factor analysis model to accommodate data from misaligned areal units with application to Louisiana social vulnerability.

With the threat of climate change looming, the public health community has an interest in identifying communities at the highest risk of devastation based not only on geographic features but also on social characteristics. Indices of community social vulnerability can be created by applying a spatial factor analysis to a set of relevant social variables measured for each community; however, current spatial factor analysis methodology is ill-equipped to handle spatially misaligned data. We introduce a joint spatial factor analysis model that can accommodate spatial data from two distinct partitions of a geographic space and identify a common set of latent factors underlying them. By defining the latent factors over the intersection of the two partitions, the model minimizes loss of information. Using simulated data constructed to mimic the spatial structure of our real data, we confirm the reliability of the model and demonstrate its superiority over competing ad hoc methods for dealing with misaligned data in spatial factor analysis. Finally, we construct an index of community social vulnerability for each census tract in Louisiana, a state prone to environmental disasters, which could be exacerbated by climate change, by applying the joint spatial factor analysis model to a set of misaligned social indicator data from the state. To demonstrate the utility of this index, we integrate it with Louisiana flood insurance claims data to identify communities that may be at particularly high risk during natural disasters, based on both social and geographic features.

Flavored e-cigarette liquids reduce proliferation and viability in the CALU3 airway epithelial cell line.

E-cigarettes are generally thought of as a safer smoking alternative to traditional cigarettes. However, little is known about the effects of e-cigarette liquids (e-liquids) on the lung. Since over 7,000 unique flavors have been identified for purchase in the United States, our goal was to conduct a screen that would test whether different flavored e-liquids exhibited different toxicant profiles. We tested the effects of 13 different flavored e-liquids [with nicotine and propylene glycol/vegetable glycerin (PG/VG) serving as controls] on a lung epithelial cell line (CALU3). Using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay as an indicator of cell proliferation/viability, we demonstrated a dose-dependent decrease of MTT metabolism by all flavors tested. However, a group of four flavors consistently showed significantly greater toxicity compared with the PG/VG control, indicating the potential for some flavors to elicit more harmful effects than others. We also tested the aerosolized "vapor" from select e-liquids on cells and found similar dose-dependent trends, suggesting that direct e-liquid exposures are a justifiable first-pass screening approach for determining relative e-liquid toxicity. We then identified individual chemical constituents for all 13 flavors using gas chromatography-mass spectrometry. These data revealed that beyond nicotine and PG/VG, the 13 flavored e-liquids have diverse chemical constituents. Since all of the flavors exhibited some degree of toxicity and a diverse array of chemical constituents with little inhalation toxicity available, we conclude that flavored e-liquids should be extensively tested on a case-by-case basis to determine the potential for toxicity in the lung and elsewhere.

Social integration and long-term physical and psychosocial quality of life among prostate cancer survivors in the Health Professionals Follow-up Study.

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.