Evaluating COVID-19 Risk to Essential Workers by Occupational Group: A Case Study in Massachusetts.

Occupational exposure to SARS-CoV-2 varies by profession, but "essential workers" are often considered in aggregate in COVID-19 models. This aggregation complicates efforts to understand risks to specific types of workers or industries and target interventions, specifically towards non-healthcare workers. We used census tract-resolution American Community Survey data to develop novel essential worker categories among the occupations designated as COVID-19 Essential Services in Massachusetts. Census tract-resolution COVID-19 cases and deaths were provided by the Massachusetts Department of Public Health. We evaluated the association between essential worker categories and cases and deaths over two phases of the pandemic from March 2020 to February 2021 using adjusted mixed-effects negative binomial regression, controlling for other sociodemographic risk factors. We observed elevated COVID-19 case incidence in census tracts in the highest tertile of workers in construction/transportation/buildings maintenance (Phase 1: IRR 1.32 [95% CI 1.22, 1.42]; Phase 2: IRR: 1.19 [1.13, 1.25]), production (Phase 1: IRR: 1.23 [1.15, 1.33]; Phase 2: 1.18 [1.12, 1.24]), and public-facing sales and services occupations (Phase 1: IRR: 1.14 [1.07, 1.21]; Phase 2: IRR: 1.10 [1.06, 1.15]). We found reduced case incidence associated with greater percentage of essential workers able to work from home (Phase 1: IRR: 0.85 [0.78, 0.94]; Phase 2: IRR: 0.83 [0.77, 0.88]). Similar trends exist in the associations between essential worker categories and deaths, though attenuated. Estimating industry-specific risk for essential workers is important in targeting interventions for COVID-19 and other diseases and our categories provide a reproducible and straightforward way to support such efforts.

Assessing the impact of aircraft arrival on ambient ultrafine particle number concentrations in near-airport communities in Boston, Massachusetts.

Aircraft emissions contribute to overall ambient air pollution, including ultrafine particle (UFP) concentrations. However, accurately ascertaining aviation contributions to UFP is challenging due to high spatiotemporal variability along with intermittent aviation emissions. The objective of this study was to evaluate the impact of arrival aircraft on particle number concentration (PNC), a proxy for UFP, across six study sites 3-17 km from a major arrival aircraft flight path into Boston Logan International Airport by utilizing real-time aircraft activity and meteorological data. Ambient PNC at all monitoring sites was similar at the median but had greater variation at the 95th and 99th percentiles with more than two-fold increases in PNC observed at sites closer to the airport. PNC was elevated during the hours with high aircraft activity with sites closest to the airport exhibiting stronger signals when downwind from the airport. Regression models indicated that the number of arrival aircraft per hour was associated with measured PNC at all six sites, with a maximum contribution of 50% of total PNC at a monitor 3 km from the airport during hours with arrival activity on the flight path of interest (26% across all hours). Our findings suggest strong but intermittent contributions from arrival aircraft to ambient PNC in communities near airports.

Critical windows of susceptibility for the effects of prenatal exposure to heat and heat variability on gestational growth.

BACKGROUND: Studies have shown that prenatal heat exposure may impact fetal growth, but few studies have examined the critical windows of susceptibility. As extreme heat events and within season temperature variability is expected to increase in frequency, it is important to understand how this may impact gestational growth. OBJECTIVES: We investigated associations between various measures of weekly prenatal heat exposure (mean and standard deviation (SD) of temperature and heat index (HI), derived using temperature in °C and dew point) and term birthweight or odds of being born small for gestational age (SGA) to identify critical windows of susceptibility. METHODS: We analyzed data from mother-child dyads (n = 4442) in the Boston-based Children's HealthWatch cohort. Birthweights were collected from survey data and electronic health records. Daily temperature and HI values were obtained from 800 m gridded spatial climate datasets aggregated by the PRISM Climate Group. Distributed lag-nonlinear models were used to assess the effect of the four weekly heat metrics on measures of gestational growth (birthweight, SGA, and birthweight z-scores). Analyses were stratified by child sex and maternal homelessness status during pregnancy. RESULTS: HI variability was significantly associated with decreased term birthweight during gestational weeks 10-29 and with SGA for weeks 9-26. Cumulative effects for these time periods were -287.4 g (95% CI: -474.1 g, -100.8 g for birthweight and 4.7 (95% CI: 1.6, 14.1) for SGA. Temperature variability was also significantly associated with decreased birthweight between weeks 15 and 26. The effects for mean heat measures on term birthweight and SGA were not significant for any gestational week. Stratification by sex revealed a significant effect on term birthweight in females between weeks 23-28 and in males between weeks 9-26. Strongest effects of HI variability on term birthweight were found in children of mothers who experienced homelessness during pregnancy. Weekly HI variability was the heat metric most strongly associated with measures of gestational growth. The effects observed were largest in males and those who experienced homelessness during pregnancy. DISCUSSION: Given the impact of heat variability on birthweight and risk of SGA, it is important for future heat warnings to incorporate measure of heat index and temperature variability.

Fine particulate matter and polycystic ovarian morphology.

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 PM2.5 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 PM2.5 during the study ranged from 4.9 to 17.5 µg/m3 (mean = 10.0 µg/m3). 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 PM2.5 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/m2, 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 PM2.5 concentrations and PCOM incidence. No dose response relationships were observed nor were estimates appreciably different across race/ethnicity within this clinically sourced cohort.

Long-term Exposure to PM2.5 and Mortality for the Older Population: Effect Modification by Residential Greenness.

BACKGROUND: Although many studies demonstrated reduced mortality risk with higher greenness, few studies examined the modifying effect of greenness on air pollution-health associations. We evaluated residential greenness as an effect modifier of the association between long-term exposure to fine particles (PM2.5) and mortality. METHODS: We used data from all Medicare beneficiaries in North Carolina (NC) and Michigan (MI) (2001-2016). We estimated annual PM2.5 averages using ensemble prediction models. We estimated mortality risk per 1 µg/m3 increase using Cox proportional hazards modeling, controlling for demographics, Medicaid eligibility, and area-level covariates. We investigated health disparities by greenness using the Normalized Difference Vegetation Index with measures of urbanicity and socioeconomic status. RESULTS: PM2.5 was positively associated with mortality risk. Hazard ratios (HRs) were 1.12 (95% confidence interval (CI) = 1.12 to 1.13) for NC and 1.01 (95% CI = 1.00 to 1.01) for MI. HRs were higher for rural than urban areas. Within each category of urbanicity, HRs were generally higher in less green areas. For combined disparities, HRs were higher in low greenness or low SES areas, regardless of the other factor. HRs were lowest in high-greenness and high-SES areas for both states. CONCLUSIONS: In our study, those in low SES and high-greenness areas had lower associations between PM2.5 and mortality than those in low SES and low greenness areas. Multiple aspects of disparity factors and their interactions may affect health disparities from air pollution exposures. Findings should be considered in light of uncertainties, such as our use of modeled PM2.5 data, and warrant further investigation.

Time-varying associations between COVID-19 case incidence and community-level sociodemographic, occupational, environmental, and mobility risk factors in Massachusetts.

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 (PM2.5), 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.

Descriptive characterization of sound levels in an environmental justice city before and during a global pandemic.

Many environmental justice communities face elevated exposures to multiple stressors, given biases in urban and environmental policy and planning. This paper aims to evaluate sound level exposure in a densely populated environmental justice city in close proximity to major roadways, a nearby airport and high levels of industrial activity. In this study we collected various sound level metrics to evaluate the loudness and frequency composition of the acoustical environment in Chelsea, Massachusetts, USA. A total of 29 week-long sites were collected from October 2019 to June 2020, a time period that also included the influence of the COVID-19 pandemic, which drastically altered activity patterns and corresponding sound level exposures. We found that Chelsea is exposed to high levels of sound, both day and night (65 dB (A), and 80 dB and 90 dB for low frequency, and infrasound sound levels). A spectral analysis shows that 63 Hz was the dominant frequency. Distance to major roads and flight activity (both arrivals and departures) were most strongly correlated with all metrics, most notably with metrics describing contributing from lower frequencies. Overall, we found similar patterns during the COVID-19 pandemic but at levels up to 10 dB lower. Our results demonstrate the importance of noise exposure assessments in environmental justice communities and the importance of using additional metrics to describe communities inundated with significant air, road, and industrial sound levels. It also provides a snapshot of how much quieter communities can be with careful and intentional urban and environmental policy and planning.

Multimodal Recruitment to Study Ovulation and Menstruation Health: Internet-Based Survey Pilot Study.

BACKGROUND: Multimodal recruitment strategies are a novel way to increase diversity in research populations. However, these methods have not been previously applied to understanding the prevalence of menstrual disorders such as polycystic ovary syndrome. OBJECTIVE: The purpose of this study was to test the feasibility of recruiting a diverse cohort to complete a web-based survey on ovulation and menstruation health. METHODS: We conducted the Ovulation and Menstruation Health Pilot Study using a REDCap web-based survey platform. We recruited 200 women from a clinical population, a community fair, and the internet. RESULTS: We recruited 438 women over 29 weeks between September 2017 and March 2018. After consent and eligibility determination, 345 enrolled, 278 started (clinic: n=43; community fair: n=61; internet: n=174), and 247 completed (clinic: n=28; community fair: n=60; internet: n=159) the survey. Among all participants, the median age was 25.0 (SD 6.0) years, mean BMI was 26.1 kg/m2 (SD 6.6), 79.7% (216/271) had a college degree or higher, and 14.6% (37/254) reported a physician diagnosis of polycystic ovary syndrome. Race and ethnicity distributions were 64.7% (176/272) White, 11.8% (32/272) Black/African American, 7.7% (21/272) Latina/Hispanic, and 5.9% (16/272) Asian individuals; 9.9% (27/272) reported more than one race or ethnicity. The highest enrollment of Black/African American individuals was in clinic (17/42, 40.5%) compared to 1.6% (1/61) in the community fair and 8.3% (14/169) using the internet. Survey completion rates were highest among those who were recruited from the internet (159/174, 91.4%) and community fairs (60/61, 98.4%) compared to those recruited in clinic (28/43, 65.1%). CONCLUSIONS: Multimodal recruitment achieved target recruitment in a short time period and established a racially diverse cohort to study ovulation and menstruation health. There were greater enrollment and completion rates among those recruited via the internet and community fair.

Impacts of high temperature on adverse birth outcomes in Seoul, Korea: Disparities by individual- and community-level characteristics.

BACKGROUND: Few studies have examined temperature's effect on adverse birth outcomes and relevant effect modifiers. OBJECTIVES: We investigated associations between heat and adverse birth outcomes and how individual and community characteristics affect these associations for Seoul, Korea, 2004-2012. METHODS: We applied logistic regression to estimate associations between heat index during pregnancy, 4 weeks before delivery, and 1 week before delivery and risk of preterm birth and term low birth weight. We investigated effect modification by individual (infant's sex, mother's age, and mother's educational level) and community characteristics (socioeconomic status (SES) and percentage of green areas near residence at the gu level, which is similar to borough in Western countries). We also evaluated associations by combinations of individual- and community-level SES. RESULTS: Heat exposure during whole pregnancy was significantly associated with risk of preterm birth. An interquartile (IQR) increase (5.5 °C) in heat index during whole pregnancy was associated with an odds ratio (OR) of 1.033 (95% CI 1.005, 1.061) with NO2 adjustment, and 1.028 (95% CI 0.998, 1.059) with PM10 adjustment, for preterm birth. We also found significant associations with heat exposure during 4 weeks before delivery and 1 week before delivery on preterm birth. We did not observe significant associations with term low birth weight. Higher risk of heat on preterm birth was associated with some individual characteristics such as infants with younger or older mothers and lower community-level SES. For combinations of individual- and community-level SES, the highest and most significant estimated effect was found for infants with low educated mothers living in low SES communities, with suggestions of effects of both individual-and community-level SES. CONCLUSIONS: Our findings have implications for evaluating impacts of high temperatures on birth outcomes, estimating health impacts of climate change, and identifying which subpopulations and factors are most relevant for disparities in this association.

Urban environment as an independent predictor of insulin resistance in a South Asian population.

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.

Urban vegetation and heat-related mortality in Seoul, Korea.

Urban areas are particularly vulnerable to heat-related health outcomes. Simultaneous trends of climate change and urbanization may increase the urban heat-related health burden. We investigated the effects of urban vegetation on heat-related mortality, and evaluated whether different levels of vegetation and individuals' characteristics affect the temperature-mortality associations within Seoul, Korea 2000-2009. We used Normalized Difference Vegetation Index (NDVI) to assess the urban vegetation within Seoul. We applied an overdispersed Poisson generalized linear model with interaction term between temperature and indicator of NDVI group (categorized in 3 levels) to assess the effect modification of the temperature-mortality association by urban vegetation. We conducted stratified analysis to explore whether associations are affected by individual characteristics of sex and age. The association between total mortality and a 1°C increase in temperature above the 90th percentile (25.1°C) (the "heat effect") was the highest for gus with low NDVI. The heat effect was a 4.1% (95% confidence interval (CI) 2.3, 5.9%), 3.0% (95% CI 0.2, 5.9%), and 2.2% (95% CI -0.5, 5.0%) increase in mortality risk for low, medium, and high NDVI group, respectively. Estimated risks showed similar effects by sex and age. Our findings suggest a higher mortality effect of high temperature in areas with lower vegetation in Seoul, Korea.

Climate stress and anxiety, environmental context, and civic engagement: A nationally representative study.

There is increasing recognition that people are experiencing stress and anxiety around climate change, and that this climate stress/anxiety may be associated with more pro-environmental behavior. However, less is known about whether people's own environmental exposures affect climate stress/anxiety or the relationship between climate stress/anxiety and civic engagement. Using three waves of survey data (2020-2022) from the nationally representative Tufts Equity in Health, Wealth, and Civic Engagement Study of US adults (n = 1071), we assessed relationships among environmental exposures (county-level air pollution, greenness, number of toxic release inventory sites, and heatwaves), self-reported climate stress/anxiety, and civic engagement measures (canvasing behavior, collaborating to solve community problems, personal efficacy to solve community problems, group efficacy to solve community problems, voting behavior). Most participants reported experiencing climate stress/anxiety (61%). In general, the environmental exposures we assessed were not significantly associated with climate stress/anxiety or civic engagement metrics, but climate stress/anxiety was positively associated with most of the civic engagement outcomes (canvassing, personal efficacy, group efficacy, voter preference). Our results support the growing literature that climate stress/anxiety may spur constructive civic action, though do not suggest a consistent relationship between adverse environmental exposures and either climate stress/anxiety or civic engagement. Future research and action addressing the climate crisis should promote climate justice by ensuring mental health support for those who experience climate stress anxiety and by promoting pro-environmental civic engagement efforts.

The Exposure Peaks of Traffic-Related Ultrafine Particles Associated with Inflammatory Biomarkers and Blood Lipid Profiles.

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.

Nationwide estimation of daily ambient PM2.5 from 2008 to 2020 at 1 km2 in India using an ensemble approach.

High-resolution assessment of historical levels is essential for assessing the health effects of ambient air pollution in the large Indian population. The diversity of geography, weather patterns, and progressive urbanization, combined with a sparse ground monitoring network makes it challenging to accurately capture the spatiotemporal patterns of ambient fine particulate matter (PM2.5) pollution in India. We developed a model for daily average ambient PM2.5 between 2008 and 2020 based on monitoring data, meteorology, land use, satellite observations, and emissions inventories. Daily average predictions at each 1 km × 1 km grid from each learner were ensembled using a Gaussian process regression with anisotropic smoothing over spatial coordinates, and regression calibration was used to account for exposure error. Cross-validating by leaving monitors out, the ensemble model had an R2 of 0.86 at the daily level in the validation data and outperformed each component learner (by 5-18%). Annual average levels in different zones ranged between 39.7 µg/m3 (interquartile range: 29.8-46.8) in 2008 and 30.4 µg/m3 (interquartile range: 22.7-37.2) in 2020, with a cross-validated (CV)-R2 of 0.94 at the annual level. Overall mean absolute daily errors (MAE) across the 13 years were between 14.4 and 25.4 µg/m3. We obtained high spatial accuracy with spatial R2 greater than 90% and spatial MAE ranging between 7.3-16.5 µg/m3 with relatively better performance in urban areas at low and moderate elevation. We have developed an important validated resource for studying PM2.5 at a very fine spatiotemporal resolution, which allows us to study the health effects of PM2.5 across India and to identify areas with exceedingly high levels.

Positional error and time-activity patterns in near-highway proximity studies: an exposure misclassification analysis.

BACKGROUND: The growing interest in research on the health effects of near-highway air pollutants requires an assessment of potential sources of error in exposure assignment techniques that rely on residential proximity to roadways. METHODS: We compared the amount of positional error in the geocoding process for three different data sources (parcels, TIGER and StreetMap USA) to a "gold standard" residential geocoding process that used ortho-photos, large multi-building parcel layouts or large multi-unit building floor plans. The potential effect of positional error for each geocoding method was assessed as part of a proximity to highway epidemiological study in the Boston area, using all participants with complete address information (N = 703). Hourly time-activity data for the most recent workday/weekday and non-workday/weekend were collected to examine time spent in five different micro-environments (inside of home, outside of home, school/work, travel on highway, and other). Analysis included examination of whether time-activity patterns were differentially distributed either by proximity to highway or across demographic groups. RESULTS: Median positional error was significantly higher in street network geocoding (StreetMap USA = 23 m; TIGER = 22 m) than parcel geocoding (8 m). When restricted to multi-building parcels and large multi-unit building parcels, all three geocoding methods had substantial positional error (parcels = 24 m; StreetMap USA = 28 m; TIGER = 37 m). Street network geocoding also differentially introduced greater amounts of positional error in the proximity to highway study in the 0-50 m proximity category. Time spent inside home on workdays/weekdays differed significantly by demographic variables (age, employment status, educational attainment, income and race). Time-activity patterns were also significantly different when stratified by proximity to highway, with those participants residing in the 0-50 m proximity category reporting significantly more time in the school/work micro-environment on workdays/weekdays than all other distance groups. CONCLUSIONS: These findings indicate the potential for both differential and non-differential exposure misclassification due to geocoding error and time-activity patterns in studies of highway proximity. We also propose a multi-stage manual correction process to minimize positional error. Additional research is needed in other populations and geographic settings.

Highway proximity associations with blood markers of inflammation: evidence for a role for IL-1ß.

Cardiovascular disease is known to be associated with proximity to major roadways and highways. Thus, blood samples from 20 near highway and 20 urban background residents were analyzed for presence of cytokines and other biomarkers. Near-highway participants displayed significantly lower socioeconomic status (SES) and significantly higher occupational vehicle exhaust exposure and higher low-density lipoprotein (LDL) levels. Controlling for exposure to vehicle exhaust on the job, interleukin-6 (IL-6) was numerically higher in near highway participants. Using logistic regression analyses, IL-1ß was significantly elevated near highway. It is interesting that elevations were found in IL-1ß, a key cytokine linked to inflammation from particulate matter (PM). More studies are needed with larger sample sizes to assess the possible role of IL-1ß.

Impacts of the choice of distance measurement method on estimates of access to point-based resources.

BACKGROUND/OBJECTIVE: Lack of access to resources such as medical facilities and grocery stores is related to poor health outcomes and inequities, particularly in an environmental justice framework. There can be substantial differences in quantifying "access" to such resources, depending on the geospatial method used to generate distance estimates. METHODS: We compared three methods for calculating distance to the nearest grocery store to illustrate differential access at the census block-group level in the Atlanta metropolitan area, including: Euclidean distance estimation, service areas incorporating roadways and other factors, and cost distance for every point on the map. RESULTS: We found notable differences in access across the three estimation techniques, implying a high potential for exposure misclassification by estimation method. There was a lack of nuanced exposure in the highest- and lowest-access areas using the Euclidean distance method. We found an Intraclass Correlation Coefficient (ICC) of 0.69 (0.65, 0.73), indicating moderate agreement between estimation methods. SIGNIFICANCE: As compared with Euclidean distance, service areas and cost distance may represent a more meaningful characterization of "access" to resources. Each method has tradeoffs in computational resources required versus potential improvement in exposure classification. Careful consideration of the method used for determining "access" will reduce subsequent misclassifications.

School Greenness and Student-Level Academic Performance: Evidence From the Global South.

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.

Relationship between traffic-related air pollution and inflammation biomarkers using structural equation modeling.

BACKGROUND: Evidence suggests that exposure to traffic-related air pollution (TRAP) and social stressors can increase inflammation. Given that there are many different markers of TRAP exposure, socio-economic status (SES), and inflammation, analytical approaches can leverage multiple markers to better elucidate associations. In this study, we applied structural equation modeling (SEM) to assess the association between a TRAP construct and a SES construct with an inflammation construct. METHODS: This analysis was conducted as part of the Community Assessment of Freeway Exposure and Health (CAFEH; N = 408) study. Air pollution was characterized using a spatiotemporal model of particle number concentration (PNC) combined with individual participant time-activity adjustment (TAA). TAA-PNC and proximity to highways were considered for a construct of TRAP exposure. Participant demographics on education and income for an SES construct were assessed via questionnaires. Blood samples were analyzed for high sensitivity C-reactive protein (hsCRP), interleukin-6 (IL-6), and tumor necrosis factor-α receptor II (TNFRII), which were considered for the construct for inflammation. We conducted SEM and compared our findings with those obtained using generalized linear models (GLM). RESULTS: Using GLM, TAA-PNC was associated with multiple inflammation biomarkers. An IQR (10,000 particles/cm3) increase of TAA-PNC was associated with a 14 % increase in hsCRP in the GLM. Using SEM, the association between the TRAP construct and the inflammation construct was twice as large as the associations with any individual inflammation biomarker. SES had an inverse association with inflammation in all models. Using SEM to estimate the indirect effects of SES on inflammation through the TRAP construct strengthened confidence in the association of TRAP with inflammation. CONCLUSION: Our TRAP construct resulted in stronger associations with a combined construct for inflammation than with individual biomarkers, reinforcing the value of statistical approaches that combine multiple, related exposures or outcomes. Our findings are consistent with inflammatory risk from TRAP exposure.

Calculating access to parks and other polygonal resources: A description of open-source methodologies.

Public health studies routinely use simplistic methods to calculate proximity-based "access" to greenspace, such as by measuring distances to the geographic centroids of parks or, less frequently, to the perimeter of the park area. Although computationally efficient, these approaches oversimplify exposure measurement because parks often have specific entrance points. In this tutorial paper, we describe how researchers can instead calculate more-accurate access measures using freely available open-source methods. Specifically, we demonstrate processes for calculating "service areas" representing street-network-based buffers of access to parks within set distances and mode of transportation (e.g., 1-km walk or 20-minute drive) using OpenRouteService and QGIS software. We also introduce an advanced method involving the identification of trailheads or parking lots with OpenStreetMap data and show how large parks particularly benefit from this approach. These methods can be used globally and are applicable to analyses of a wide range of studies investigating proximity access to resources.