Search

Racial/ethnic disparities in the association between short-term (eg, days, weeks), ambient fine particulate matter (PM2.5) and temperature exposures and stillbirth in the United States have been understudied. A time-stratified, case-crossover design using a distributed lag nonlinear model (0- to 6-day lag) was used to estimate stillbirth odds due to short-term increases in average daily PM2.5 and temperature exposures among 118 632 Medicaid recipients from 2000 to 2014. Disparities by maternal race/ethnicity (Black, White, Hispanic, Asian, American Indian) and zip code-level socioeconomic status (SES) were assessed. In the temperature-adjusted model, a 10 μg m-3 increase in PM2.5 concentration was marginally associated with increased stillbirth odds at lag 1 (0.68%; 95% CI, -0.04% to 1.40%) and lag 2 (0.52%; 95% CI, -0.03 to 1.06) but not lag 0-6 (2.80%; 95% CI, -0.81 to 6.45). An association between daily PM2.5 concentrations and stillbirth odds was found among Black individuals at the cumulative lag (0-6 days: 9.26% 95% CI, 3.12%-15.77%) but not among other races or ethnicities. A stronger association between PM2.5 concentrations and stillbirth odds existed among Black individuals living in zip codes with the lowest median household income (lag 0-6: 14.13%; 95% CI, 4.64%-25.79%). Short-term temperature increases were not associated with stillbirth risk among any race/ethnicity. Black Medicaid enrollees, and especially those living in lower SES areas, may be more vulnerable to stillbirth due to short-term increases in PM2.5 exposure. This article is part of a Special Collection on Environmental Epidemiology., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: Mounting evidence has shown that long-term exposure to fine particulate matter [PM ≤ 2.5 μ m in aerodynamic diameter ( PM 2.5 )] and ozone ( O 3 ) can increase mortality. However, the health effects associated with long-term exposure to nitrogen dioxide ( NO 2 ) are less clear, in particular the evidence is scarce for NO 2 at low levels that are below the current international guidelines., Methods: We constructed a population-based full cohort comprising all Medicare beneficiaries (aged ≥ 65 , N = 13,590,387 ) in the southeastern United States from 2000 to 2016, and we then further defined the below-guideline cohort that included only those who were always exposed to low-level NO 2 , that is, with annual means below the current World Health Organization guidelines (i.e., ≤ 21  ppb ). We applied previously estimated spatially and temporally resolved NO 2 concentrations and assigned annual means to study participants based on their ZIP code of residence. Cox proportional hazards models were used to examine the association between long-term exposure to low-level NO 2 and all-cause mortality, adjusting for potential confounders., Results: About 71.1% of the Medicare beneficiaries in the southeastern United States were always exposed to low-level NO 2 over the study period. We observed an association between long-term exposure to low-level NO 2 and all-cause mortality, with a hazard ratio  ( HR ) = 1.042 (95% CI: 1.040, 1.045) in single-pollutant models and a HR = 1.047 (95% CI: 1.045, 1.049) in multipollutant models (adjusting for PM 2.5 and O 3 ), per 10 -ppb increase in annual NO 2 concentrations. The penalized spline indicates a linear exposure-response relationship across the entire NO 2 exposure range. Medicare enrollees who were White, female, and residing in urban areas were more vulnerable to long-term NO 2 exposure., Conclusion: Using a large and representative cohort, we provide epidemiological evidence that long-term exposure to NO 2 , even below the national and global ambient air quality guidelines, was approximately linearly associated with a higher risk of mortality among older adults, independent of PM 2.5 and O 3 exposure. Improving air quality by reducing NO 2 emissions, therefore, may yield significant health benefits. https://doi.org/10.1289/EHP9044.

Inhaling radon and its progeny is associated with adverse health outcomes. However, previous studies of the health effects of residential exposure to radon in the United States were commonly based on a county-level temporally invariant radon model that was developed using measurements collected in the mid- to late 1980s. We developed a machine learning model to predict monthly radon concentrations for each ZIP Code Tabulation Area (ZCTA) in the Greater Boston area based on 363,783 short-term measurements by Spruce Environmental Technologies, Inc., during the period 2005-2018. A two-stage ensemble-based model was developed to predict radon concentrations for all ZCTAs and months. Stage one included 12 base statistical models that independently predicted ZCTA-level radon concentrations based on geological, architectural, socioeconomic, and meteorological factors for each ZCTA. Stage two aggregated the predictions of these 12 base models using an ensemble learning method. The results of a 10-fold cross-validation showed that the stage-two model has a good prediction accuracy with a weighted R 2 of 0.63 and root mean square error of 22.6 Bq/m 3 . The community-level time-varying predictions from our model have good predictive precision and accuracy and can be used in future prospective epidemiological studies in the Greater Boston area.

Background: Exposure to ionizing radiation increases the risk of chronic metabolic disorders such as insulin resistance and type 2 diabetes. Internal ionizing radiation from inhaled radioactive aerosol may contribute to the associations between fine particulate matter (PM 2.5 ) and gestational diabetes mellitus (GDM)., Methods: We used the Massachusetts Registry of Vital Records to study 1,061,937 pregnant women from 2001 to 2015 with a singleton pregnancy without pre-existing diabetes. Gross β activity measured by seven monitors of the U.S. Environmental Protection Agency's RadNet monitoring network was utilized to represent ambient particle radioactivity (PR). We obtained GDM status from birth certificates and used logistic regression analyses adjusted for socio-demographics, maternal comorbidities, PM 2.5 , temperature and relative humidity. We also examined effect modification by smoking habits., Results: Ambient particle radioactivity exposure during first and second trimester of pregnancy was associated with higher odds of GDM (OR: 1.18 (95% CI 1.10 to 1.22). Controlling for PM 2.5 did not substantially change the effects of PR on GDM. In women that reported being former or current smokers, the association between PR and GDM was null. In the full cohort, the overall effect of PM 2.5 on GDM without adjusting for PR was not significant., Conclusion: This is the first population-based study to examine the association between particle radioactivity and gestational diabetes mellitus - one of the most common pregnancy-related diseases with lifelong effects for the mother and the fetus. This finding has important public health policy implications because it enhances our understanding about the toxicity of PR, a modifiable risk factor, which to date, has been considered only as an indoor and occupational air quality risk., Competing Interests: Declaration of competing interest The authors have no conflicts of interest to declare., (Copyright © 2020 Elsevier B.V. All rights reserved.)