Melissa R. Fiffer

Segregation measures over time likely mask the movement of Latinos into predominantly non-Hispanic Black (NHB) areas. Due to systematic disinvestment, the co-location of NHB and Latinos may correlate with environmental and social stressors. We construct a combined racial isolation (RI) measure for Blacks and Latinos for the 72 246 contiguous U.S. census tracts. We compare trends (1990-2015) in RI of NHB individuals and RI of NHB and Hispanic individuals (RI-NHB + H). We estimate correlations between RI-NHB + H and CDC Environmental Justice Index (EJI) components. We assess associations between EJI, RI-NHB + H, and birthweight percentiles using North Carolina detailed birth records (n = 504 363; 2015-2019). Between 1990-2015, RI-NHB + H and RI-NHB increased in most (88.7%) and the majority (69.1%) of U.S. census tracts, respectively. The largest local, spatial correlations between RI-NHB + H and EJI occurred in the southeast, southwest, and parts of the west coast, where both are high. For NHB and NHW mothers, the association between EJI and birthweight percentiles was increasingly negative at higher levels of RI-NHB + H. Among Hispanic mothers, the association was negative at mid RI levels, but not at high RI levels. Our results underscore the changing nature of segregation in the U.S. and illuminate cumulative impacts experienced by NHB and Latino populations on reproductive health.

Background: Epidemiologic studies have associated higher neighborhood greenness with lower type 2 diabetes (T2D) risk. However, more work is needed to assess interrelationships between greenness, T2D risk factors, and T2D. Our aim was to prospectively evaluate the association between greenness and T2D incidence, and investigate effect modifiers, in the Nurses’ Health Study (NHS) and Nurses’ Health Study II (NHSII) cohorts of US women. Methods: Greenness exposure was defined using the normalized difference vegetation index (NDVI), a quantitative indicator of photosynthetic vegetation. We obtained 30m² resolution Landsat satellite data and calculated average NDVI within 270 m and 1230 m radial buffers to represent residential exposure and exposure within a short walk/drive using addresses from 1992 to 2017. We used time-varying Cox proportional hazards models to assess summer average NDVI in the 2 years before diagnosis and self-reported, validated clinician T2D diagnosis through 2019. We adjusted for time-varying covariates including lifestyle factors, hormone use, individual and neighborhood socioeconomic status (nSES), population density, particulate matter (PM)_2.5 and nitrogen dioxide (NO₂) exposure, and baseline body mass index (BMI). Results from the two cohorts (n = 212,548) were meta-analyzed. We examined effect modification by time-varying BMI, physical activity, smoking, region, air pollution, population density, and nSES. Supplemental analyses explored mediation by physical activity and air pollution. Results: During the 27 years of follow-up, there were 18,527 incident T2D cases. In fully adjusted models, the meta-analyzed hazard ratio was 0.96 (95% confidence interval = 0.95, 0.97) for a 0.1 unit increase in 2-year summer average NDVI. In NHS, stronger associations were found among participants in the lowest PM_2.5 tertile, and in NHSII, among those with BMI <30 and those in neighborhoods above the lowest nSES quartile. Conclusions: In one of the first US nationwide prospective analyses of greenness and T2D, we found a protective association robust to air pollution co-exposure adjustment and persistent across subpopulations.

Research has detected associations between air pollution exposure and type 2 diabetes (T2DM), but findings from large cohort studies are needed to ascertain the most influential pollutants, susceptible subpopulations, and low-level exposure associations. Our aim was to prospectively evaluate the association between long-term exposure to fine particulate matter (PM_2.5) and nitrogen dioxide (NO₂) and T2DM incidence in the Nurses' Health Study (NHS) and Nurses' Health Study II (NHSII) cohorts of U.S. women. Monthly PM_2.5 and NO₂ exposures were predicted from spatiotemporal models and linked to participants' residential addresses. We used Cox proportional hazards models to assess the association between 24-month moving average PM_2.5 and NO₂ exposure and self-reported, clinician diagnosed T2DM from 1992-2019. We adjusted for time-varying lifestyle factors, reproductive hormonal factors, and individual and neighborhood socioeconomic status (SES). Results were meta-analyzed. We evaluated whether relationships persisted at levels below the current U.S. EPA National Ambient Air Quality Standards (NAAQS). Lastly, we examined multiplicative and additive interactions by body mass index (BMI), smoking status, physical activity, neighborhood SES, and region. Over follow-up, there were 19,083 incident T2DM cases among the 208,733 women in NHS and NHSII. In fully-adjusted single pollutant models, the HR for an interquartile range (IQR=4.9 µg/m³) higher 24-month average PM_2.5 exposure was 1.05 (95% CI: 1.02, 1.08) for incident T2DM. The HR for an IQR (7.3 ppb) higher NO₂ exposure was 1.05 (95% CI: 1.01, 1.09). Both associations were robust to co-adjustment. Associations remained stable when restricting to PM_2.5 levels below the NAAQS as compared to the full dataset. Stronger associations were observed in individuals who had a BMI ≥30, were physically active, and resided in the Northeast. Our results showed a positive association between T2DM and long-term exposure to PM_2.5 and NO₂, persisting even at levels below the current EPA NAAQS. https://doi.org/10.1289/EHP15673.

Ultrafine particles (UFPs) pose a human health risk as they can penetrate deep into the respiratory system. The Harvard supersite in Boston, MA provides one of the longest time series of UFP concentrations. This study examined the hypothesis that long-term reductions in PM_2.5 mass and sulfur have influenced UFP trends by limiting the ability of UFPs to coagulate onto the accumulation mode via polydisperse coagulation with larger particles. The study used Generalized Additive Models (GAMs) to assess whether changes in PM_2.5 mass and sulfur concentrations resulted in smaller than expected (assuming no change in PM_2.5 mass or sulfur) decreases in daily UFP trends over the 20-year period from 1999 to 2018. The impact of PM_2.5 mass and sulfur changes were represented as UFP penalties. Bootstrapping was applied to calculate standard errors for the different trend and penalty estimates. Results showed that PM_2.5 mass and sulfur concentrations declined significantly over the study period. The analysis found an estimated 7.3% (95% CI: 3.5, 11.1%) UFP penalty due to long-term PM_2.5 mass trends, and a 9.9% (95% CI: 6.2, 13.7%) UFP penalty due to long-term sulfur trends. Findings from this study suggest that future UFP control efforts should account for the role of PM_2.5 mass and sulfur changes.