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Background Polycystic ovary morphology (PCOM) is an ultrasonographic finding that can be present in women with ovulatory disorder and oligomenorrhea due to hypothalamic, pituitary, and ovarian dysfunction. While air pollution has emerged as a possible disrupter of hormone homeostasis, limited research has been conducted on the association between air pollution and PCOM. Methods We conducted a longitudinal cohort study using electronic medical records data of 5,492 women with normal ovaries at the first ultrasound that underwent a repeated pelvic ultrasound examination during the study period (2004–2016) at Boston Medical Center. Machine learning text algorithms classified PCOM by ultrasound. We used geocoded home address to determine the ambient annual average PM 2.5 exposures and categorized into tertiles of exposure. We used Cox Proportional Hazards models on complete data ( n  = 3,994), adjusting for covariates, and additionally stratified by race/ethnicity and body mass index (BMI). Results Cumulative exposure to PM 2.5 during the study ranged from 4.9 to 17.5 µg/m 3 (mean = 10.0 μg/m 3 ). On average, women were 31 years old and 58% were Black/African American. Hazard ratios and 95% confidence intervals (CI) comparing the second and third PM 2.5 exposure tertile vs. the reference tertile were 1.12 (0.88, 1.43) and 0.89 (0.62, 1.28), respectively. No appreciable differences were observed across race/ethnicity. Among women with BMI ≥ 30 kg/m 2 , we observed weak inverse associations with PCOM for the second (HR: 0.93, 95% CI: 0.66, 1.33) and third tertiles (HR: 0.89, 95% CI: 0.50, 1.57). Conclusions In this study of reproductive-aged women, we observed little association between PM 2.5 concentrations and PCOM incidence. No dose response relationships were observed nor were estimates appreciably different across race/ethnicity within this clinically sourced cohort.

Many studies have identified an inequitable distribution of exposure to PM2.5 (particulate matter less than 2.5 microns) by race. We investigated the association of PM2.5 and cardiovascular mortality considering both the decedents' race and neighborhood racial composition as potential modifiers. We obtained geocoded cardiovascular mortality records of all black and white decedents from urban block-groups in Massachusetts between 2001 and 2011 (n = 130,863). We examined the association between PM2.5 and cardiovascular mortality, and assessed effect modification by three types of racial modifiers: decedents' race, census block-group percent black and white, and two novel measures of racial segregation. The Racial Residential Segregation (RRS) quantifies the concentration of non-Hispanic blacks and whites in each block-group. The Index of Racial Dissimilarity measures dissimilarity in non-Hispanic black and white racial distribution between the smaller census block-group and larger tract. We found a 2.35%(95%CI: 0.92%;3.79%) increase in mortality for each 10μg/m3 increase in two-day average exposure to PM2.5. The effect was modified by the block-group racial composition, with higher risks in block-groups with the highest percentage of black residents (interaction p-value = 0.04), and in block-groups with the lowest RRS (i.e. higher black to white resident ratio, interaction p-value = 0.072). Racial dissimilarity did not modify the associations. Current levels of PM2.5 are associated with increased cardiovascular deaths in Massachusetts, with different risks between areas with different racial composition and segregation. This suggests that pollution reductions in neighborhoods with the highest percentage of non-Hispanic blacks would be most beneficial in reducing cardiovascular mortality and disparities.

Future impacts of climate change-driven increases in extreme heat exposure on population health will be driven both by the frequency and intensity of future heat, as well as changes in the size and vulnerability of affected communities. Given indications of spatial persistence of social vulnerability across the US, we explore the implications for heat exposures of the most vulnerable populations due to mid-century climate change. Population heat exposure is estimated by combining heat hazard with population growth projections. Specifically, median projected acute (95% maximum temperature), extreme (hot days, daily maximum heat index >40 °C), and chronic (cooling degree days) heat hazard are assessed in contemporary (1995–2014) and projected (2041–2060) epochs across 25+ climate models under three climate scenarios against 2020 social vulnerability index (SVI). By mid-century, increases in both chronic and extreme heat will be concentrated in the South region. Population is expected to grow faster in census tracts at the high and low extrema of the SVI. The intersection of these projected future trends suggests that the intensity and exacerbation of heat exposures due to climate change will not be evenly distributed across the population. Individuals who are most vulnerable today will face disproportionately larger heat exposures circa mid-century. Populations in high SVI areas will both increase in size and experience the largest increases in extreme heat exposure. Spatial aggregation masks fine scale changes. While cities and counties with concentrations of vulnerable populations will face rightward shifts in the tails of their temperature across the US, extreme and especially chronic population heat exposures will be concentrated in a swath stretching across southern US states. The magnitude of population heat exposure changes and their intersection with underlying vulnerability require action to focus adaptation resources on resilience to extreme heat.

Background Associations between community-level risk factors and COVID-19 incidence have been used to identify vulnerable subpopulations and target interventions, but the variability of these associations over time remains largely unknown. We evaluated variability in the associations between community-level predictors and COVID-19 case incidence in 351 cities and towns in Massachusetts from March to October 2020. Methods Using publicly available sociodemographic, occupational, environmental, and mobility datasets, we developed mixed-effect, adjusted Poisson regression models to depict associations between these variables and town-level COVID-19 case incidence data across five distinct time periods from March to October 2020. We examined town-level demographic variables, including population proportions by race, ethnicity, and age, as well as factors related to occupation, housing density, economic vulnerability, air pollution (PM 2.5 ), and institutional facilities. We calculated incidence rate ratios (IRR) associated with these predictors and compared these values across the multiple time periods to assess variability in the observed associations over time. Results Associations between key predictor variables and town-level incidence varied across the five time periods. We observed reductions over time in the association with percentage of Black residents (IRR = 1.12 [95%CI: 1.12–1.13]) in early spring, IRR = 1.01 [95%CI: 1.00–1.01] in early fall) and COVID-19 incidence. The association with number of long-term care facility beds per capita also decreased over time (IRR = 1.28 [95%CI: 1.26–1.31] in spring, IRR = 1.07 [95%CI: 1.05–1.09] in fall). Controlling for other factors, towns with higher percentages of essential workers experienced elevated incidences of COVID-19 throughout the pandemic (e.g., IRR = 1.30 [95%CI: 1.27–1.33] in spring, IRR = 1.20 [95%CI: 1.17–1.22] in fall). Towns with higher proportions of Latinx residents also had sustained elevated incidence over time (IRR = 1.19 [95%CI: 1.18–1.21] in spring, IRR = 1.14 [95%CI: 1.13–1.15] in fall). Conclusions Town-level COVID-19 risk factors varied with time in this study. In Massachusetts, racial (but not ethnic) disparities in COVID-19 incidence may have decreased across the first 8 months of the pandemic, perhaps indicating greater success in risk mitigation in selected communities. Our approach can be used to evaluate effectiveness of public health interventions and target specific mitigation efforts on the community level.

This cross-sectional study examined the presence of food stores and availability of fruits and vegetables in food stores with weight among urban public housing residents. While there was no association between average number of fruits or vegetables in food stores and weight, there were positive associations between number of convenience stores and weight and between number of general merchandise stores and weight.

Background Developing countries, such as India, are experiencing rapid urbanization, which may have a major impact on the environment: including worsening air and water quality, noise and the problems of waste disposal. We used health data from an ongoing cohort study based in southern India to examine the relationship between the urban environment and homeostasis model assessment of insulin resistance (HOMA-IR). Methods We utilized three metrics of urbanization: distance from urban center; population density in the India Census; and satellite-based land cover. Restricted to participants without diabetes (N = 6350); we built logistic regression models adjusted for traditional risk factors to test the association between urban environment and HOMA-IR. Results In adjusted models, residing within 0–20 km of the urban center was associated with an odds ratio for HOMA-IR of 1.79 (95% CI 1.39, 2.29) for females and 2.30 (95% CI 1.64, 3.22) for males compared to residing in the furthest 61–80 km distance group. Similar statistically significant results were identified using the other metrics. Conclusions We identified associations between urban environment and HOMA-IR in a cohort of adults. These associations were robust using various metrics of urbanization and adjustment for individual predictors. Our results are of public health concern due to the global movement of large numbers of people from rural to urban areas and the already large burden of diabetes.

Urbanization is associated with higher prevalence of cardiovascular disease worldwide. Aortic stiffness, as measured by carotid-femoral pulse wave velocity is a validated predictor of cardiovascular disease. Our objective was to determine the association between urbanization and carotid-femoral pulse wave velocity. The analysis included 6166 participants enrolled in an ongoing population-based study (mean age 42 years; 58% female) who live in an 80 × 80 km region of southern India. Multiple measures of urbanization were used and compared: 1) census designations, 2) satellite derived land cover (crops, grass, shrubs or trees as rural; built-up areas as urban), and 3) distance categories based on proximity to an urban center. The association between urbanization and carotid-femoral pulse wave velocity was tested in sex-stratified linear regression models. People residing in urban areas had significantly (p < 0.05) elevated mean carotid-femoral pulse wave velocity compared to non-urban populations after adjustment for other risk factors. There was also an inverse association between distance from the urban center and mean carotid-femoral pulse wave velocity: each 10 km increase in distance was associated with a decrease in mean carotid-femoral pulse wave velocity of 0.07 m/s (95% CI: -0.09, -0.06 m/s). The association was stronger among older participants, among smokers, and among those with other cardiovascular risk factors. Further research is needed to determine which components in the urban environment are associated with higher carotid-femoral pulse wave velocity.

Greenspace in schools might enhance students' academic performance. However, the literature—dominated by ecological studies at the school level in countries from the Northern Hemisphere—presents mixed evidence of a beneficial association. We evaluated the association between school greenness and student‐level academic performance in Santiago, Chile, a capital city of the Global South. This cross‐sectional study included 281,695 fourth‐grade students attending 1,498 public, charter, and private schools in Santiago city between 2014 and 2018. Student‐level academic performance was assessed using standardized test scores and indicators of attainment of learning standards in mathematics and reading. School greenness was estimated using Normalized Difference Vegetation Index (NDVI). Linear and generalized linear mixed‐effects models were fit to evaluate associations, adjusting for individual‐ and school‐level sociodemographic factors. Analyses were stratified by school type. In fully adjusted models, a 0.1 increase in school greenness was associated with higher test scores in mathematics (36.9 points, 95% CI: 2.49; 4.88) and in reading (1.84 points, 95% CI: 0.73; 2.95); as well as with higher odds of attaining learning standards in mathematics (OR: 1.20, 95% CI: 1.12; 1.28) and reading (OR: 1.07, 95% CI: 1.02; 1.13). Stratified analysis showed differences by school type, with associations of greater magnitude and strength for students attending public schools. No significant associations were detected for students in private schools. Higher school greenness was associated with improved individual‐level academic outcomes among elementary‐aged students in a capital city in South America. Our results highlight the potential of greenness in the school environment to moderate educational and environmental inequalities in urban areas. Plain Language Summary Vegetation in the school environment might help students learn and perform better in cognitive‐demanding tasks. We linked standardized test records of 281,695 students in 1,498 schools in Santiago, Chile with measurements of vegetation around schools derived from satellite data, in order to investigate the association between school greenness and students' academic performance. We found that students who attended schools with more greenery had higher test scores in both mathematics and reading and were more likely to achieve learning standards in these subjects, even after taking into account differences between schools and students' socioeconomic contexts. The study also found that these beneficial associations were more substantial for students attending public schools than for those in private schools. These results highlight the potential benefits of having green spaces in schools, especially for students in urban areas, and suggest that this could be a way to address educational and environmental inequalities. Key Points Higher school greenness was associated with improved individual‐level academic performance in a capital city of the Global South Associations of greater magnitude and strength for students in public schools. No significant associations detected in private schools Greenness around schools might aid in reducing educational and environmental inequalities in urban areas

In this article, we explored the effects of ultrafine particle (UFP) peak exposure on inflammatory biomarkers and blood lipids using two novel metrics—the intensity of peaks and the frequency of peaks. We used data previously collected by the Community Assessment of Freeway Exposure and Health project from participants in the Greater Boston Area. The UFP exposure data were time-activity-adjusted hourly average concentration, estimated using land use regression models based on mobile-monitored ambient concentrations. The outcome data included C-reactive protein, interleukin-6 (IL-6), tumor necrosis factor-alpha receptor 2 (TNF-RII), low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides and total cholesterol. For each health indicator, multivariate regression models were used to assess their associations with UFP peaks (N = 364–411). After adjusting for age, sex, body mass index, smoking status and education level, an increase in UFP peak exposure was significantly (p < 0.05) associated with an increase in TNF-RII and a decrease in HDL and triglycerides. Increases in UFP peaks were also significantly associated with increased IL-6 and decreased total cholesterol, while the same associations were not significant when annual average exposure was used. Our work suggests that analysis using peak exposure metrics could reveal more details about the effect of environmental exposures than the annual average metric.

BackgroundThe Normalized Difference Vegetation Index (NDVI) is a measure of greenness widely used in environmental health research. High spatial resolution NDVI has become increasingly available; however, the implications of its use in exposure assessment are not well understood.ObjectiveTo quantify the impact of NDVI spatial resolution on greenness exposure misclassification.MethodsGreenness exposure was assessed for 31,328 children in the Greater Boston Area in 2016 using NDVI from MODIS (250 m2), Landsat 8 (30 m2), Sentinel-2 (10 m2), and the National Agricultural Imagery Program (NAIP, 1 m2). We compared continuous and categorical greenness estimates for multiple buffer sizes under a reliability assessment framework. Exposure misclassification was evaluated using NAIP data as reference.ResultsGreenness estimates were greater for coarser resolution NDVI, but exposure distributions were similar. Continuous estimates showed poor agreement and high consistency, while agreement in categorical estimates ranged from poor to strong. Exposure misclassification was higher with greater differences in resolution, smaller buffers, and greater number of exposure quantiles. The proportion of participants changing greenness quantiles was higher for MODIS (11–60%), followed by Landsat 8 (6–44%), and Sentinel-2 (5–33%).SignificanceGreenness exposure assessment is sensitive to spatial resolution of NDVI, aggregation area, and number of exposure quantiles. Greenness exposure decisions should ponder relevant pathways for specific health outcomes and operational considerations.