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In June 2006, the Centers for Disease Control and Prevention released a request for applications to identify, improve, and evaluate the effectiveness of nonpharmaceutical interventions (NPIs)-strategies other than vaccines and antiviral medications-to mitigate the spread of pandemic influenza within communities and across international borders (RFA-CI06-010). These studies have provided major contributions to seasonal and pandemic influenza knowledge. Nonetheless, key concerns were identified related to the acceptability and protective efficacy of NPIs. Large-scale intervention studies conducted over multiple influenza epidemics, as well as smaller studies in controlled laboratory settings, are needed to address the gaps in the research on transmission and mitigation of influenza in the community setting. The current novel influenza A (H1N1) pandemic underscores the importance of influenza research. [ABSTRACT FROM AUTHOR]

Background: Laboratory research studies indicate that aerosolized influenza viruses survive for longer periods at low relative humidity (RH) conditions. Further analysis has shown that absolute humidity (AH) may be an improved predictor of virus survival in the environment. Maintaining airborne moisture levels that reduce survival of the virus in the air and on surfaces could be another tool for managing public health risks of influenza.Methods: A multi-zone indoor air quality model was used to evaluate the ability of portable humidifiers to control moisture content of the air and the potential related benefit of decreasing survival of influenza viruses in single-family residences. We modeled indoor AH and influenza virus concentrations during winter months (Northeast US) using the CONTAM multi-zone indoor air quality model. A two-story residential template was used under two different ventilation conditions - forced hot air and radiant heating. Humidity was evaluated on a room-specific and whole house basis. Estimates of emission rates for influenza virus were particle-size specific and derived from published studies and included emissions during both tidal breathing and coughing events. The survival of the influenza virus was determined based on the established relationship between AH and virus survival.Results: The presence of a portable humidifier with an output of 0.16 kg water per hour in the bedroom resulted in an increase in median sleeping hours AH/RH levels of 11 to 19% compared to periods without a humidifier present. The associated percent decrease in influenza virus survival was 17.5 - 31.6%. Distribution of water vapor through a residence was estimated to yield 3 to 12% increases in AH/RH and 7.8-13.9% reductions in influenza virus survival.Conclusion: This modeling analysis demonstrates the potential benefit of portable residential humidifiers in reducing the survival of aerosolized influenza virus by controlling humidity indoors. [ABSTRACT FROM AUTHOR]

Prenatal exposures are associated with childhood asthma, and risk may increase with simultaneous exposures. Pregnant women living in lower-income communities tend to have elevated exposures to a range of potential asthma risk factors, which may interact in complex ways. We examined the association between prenatal exposures and the risk of childhood acute-care clinical encounters for asthma (hospitalizations, emergency department visits, observational stays) using conditional logistic regression with a multivariable smoothing term to model the interaction between continuous variables, adjusted for maternal characteristics and stratified by sex. All births near the New Bedford Harbor (NBH) Superfund site (2000-2006) in New Bedford, Massachusetts, were followed through 2011 using the Massachusetts Pregnancy to Early Life Longitudinal (PELL) Data System to identify children aged 5-11 years with acute-care clinical asthma encounters (265 cases among 7787 children with follow-up). Hazard ratios (HRs) were higher for children living closer to the NBH site with higher umbilical cord blood lead levels than in children living further away from the NBH site with lower lead levels (P <.001). HRs were higher for girls (HR = 4.17; 95% CI, 3.60-4.82) than for boys (HR = 1.72; 95% CI, 1.46-2.02). Our results suggest that prenatal lead exposure in combination with residential proximity to the NBH Superfund site is associated with childhood asthma acute-care clinical encounters. 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.)

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., (© 2023. The Author(s).)

Background: Extreme heat is a leading cause of morbidity and mortality during summer months in the United States. Risk of heat exposure and associated health outcomes are disproportionately experienced by people with lower incomes, people of color, and/or immigrant populations., Methods: As qualitative research on the experiences of residents in heat islands is limited, this community-based study examined barriers and coping strategies for keeping cool among residents of Chelsea and East Boston, Massachusetts-environmental justice (EJ) areas that experience the urban heat island effect-through semistructured interviews and qualitative content analysis., Results: Results indicate that all participants ( n  = 12) had air conditioning, but high energy bills contributed to low use. Eight participants were self-described heat-sensitive, with five experiencing poor health in heat. In addition, nine reported insufficient hydration due to work schedules, distaste of water, or perceptions of it being unsafe., Discussion: This research highlights the importance of understanding perceptions of residents in EJ communities to contextualize vulnerability and identify multipronged heat coping strategies and targeted interventions., Competing Interests: No competing financial interests exist., (© Alina M. McIntyre et al., 2023; Published by Mary Ann Liebert, Inc.)

Rising ambient temperatures due to climate change will impact both indoor temperatures and heating and cooling utility costs. In traditionally colder climates, there are potential tradeoffs in how to meet the reduced heating and increased cooling demands, and issues related to lack of air conditioning (AC) access in older homes and among lower-income populations to prevent extreme heat exposure. We modeled a typical multi-family home in Boston (MA) in the building simulation program EnergyPlus to assess indoor temperature and energy consumption in current (2020) and projected future (2050) weather conditions. Selected households were those without AC (no AC), those who ran AC sometimes (some AC), and those with sufficient resources to run AC always (full AC). We considered stylized cooling subsidy policies that allowed households to move between groups, both independently and in conjunction with energy efficiency retrofits. Results showed that future weather conditions without policy changes yielded an increase in indoor summer temperatures of 2.1 °C (no AC), increased cooling demand (range: 34-50%), but led to a decrease in net yearly total utility costs per apartment (range: - $21 to - $38). Policies that allowed households to move to greater AC utilization yielded average indoor summer temperature decreases (- 3.5 °C to - 6.2 °C) and net yearly total utility increases (range: + $2 to + $94) per apartment unit, with greater savings for retrofitted homes primarily due to large decreases in heating use. Our model results reinforce the importance of coordinated public policies addressing climate change that have an equity lens for both health and climate goals., (© 2023. The New York Academy of Medicine.)

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., Competing Interests: The authors declare no conflicts of interest relevant to this study., (© 2023 The Authors. GeoHealth published by Wiley Periodicals LLC on behalf of American Geophysical Union.)

Infectious disease surveillance frequently lacks complete information on race and ethnicity, making it difficult to identify health inequities. Greater awareness of this issue has occurred due to the COVID-19 pandemic, during which inequities in cases, hospitalizations, and deaths were reported but with evidence of substantial missing demographic details. Although the problem of missing race and ethnicity data in COVID-19 cases has been well documented, neither its spatiotemporal variation nor its particular drivers have been characterized. Using individual-level data on confirmed COVID-19 cases in Massachusetts from March 2020 to February 2021, we show how missing race and ethnicity data: (1) varied over time, appearing to increase sharply during two different periods of rapid case growth; (2) differed substantially between towns, indicating a nonrandom distribution; and (3) was associated significantly with several individual- and town-level characteristics in a mixed-effects regression model, suggesting a combination of personal and infrastructural drivers of missing data that persisted despite state and federal data-collection mandates. We discuss how a variety of factors may contribute to persistent missing data but could potentially be mitigated in future contexts., (© 2022. The Author(s).)

Purpose: When studying health risks across a large geographic region such as a state or province, researchers often assume that finer-resolution data on health outcomes and risk factors will improve inferences by avoiding ecological bias and other issues associated with geographic aggregation. However, coarser-resolution data (e.g., at the town or county-level) are more commonly publicly available and packaged for easier access, allowing for rapid analyses. The advantages and limitations of using finer-resolution data, which may improve precision at the cost of time spent gaining access and processing data, have not been considered in detail to date., Methods: We systematically examine the implications of conducting town-level mixed-effect regression analyses versus census-tract-level analyses to study sociodemographic predictors of COVID-19 in Massachusetts. In a series of negative binomial regressions, we vary the spatial resolution of the outcome, the resolution of variable selection, and the resolution of the random effect to allow for more direct comparison across models., Results: We find stability in some estimates across scenarios, changes in magnitude, direction, and significance in others, and tighter confidence intervals on the census-tract level. Conclusions regarding sociodemographic predictors are robust when regions of high concentration remain consistent across town and census-tract resolutions., Conclusions: Inferences about high-risk populations may be misleading if derived from town- or county-resolution data, especially for covariates that capture small subgroups (e.g., small racial minority populations) or are geographically concentrated or skewed (e.g., % college students). Our analysis can help inform more rapid and efficient use of public health data by identifying when finer-resolution data are truly most informative, or when coarser-resolution data may be misleading., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Extreme heat represents a growing threat to public health, especially across the densely populated, developed landscape of cities. Climate adaptation strategies that aim to manage urban microclimates through purposeful design can reduce the heat exposure of urban populations, however, it is unclear how the temperature impacts of urban green space and albedo vary across cities and background climate. This study quantifies the sensitivity of surface temperature to landcover characteristics tied to two widely used climate adaptation strategies, urban greening and albedo manipulation (e.g. white roofs), by combining long-term remote sensing observations of land surface temperature, albedo, and moisture with high-resolution landcover datasets in a spatial regression analysis at the census block scale across seven United States cities. We find tree cover to have an average cooling impact of -0.089 K per % cover, which is approximately four times stronger than the average grass cover cooling impact of -0.021 K per % cover. Variability in the magnitude of grass cover cooling impacts was primarily a function of vegetation moisture content, with the Land Surface Water Index (LSWI) explaining 89 % of the variability in grass cover cooling impacts across cities. Variability in tree cover cooling impacts was primarily a function of sunlight and vegetation moisture content, with solar irradiance and LSWI explaining 97 % of the cooling variability across cities. Albedo cooling impacts were consistent across cities with an average cooling impact of -0.187 K per increase of 0.01. While these interventions are broadly effective across cities, there are critical regional trade-offs between vegetation cooling efficiency, irrigation requirements, and the temporal duration and evolution of the cooling benefits. In warm, arid cities, high albedo surfaces offer multifaceted benefits such as cooling and water conservation, whereas temperate, mesic cities likely benefit from a combination of strategies, with greening efforts targeting highly paved neighborhoods., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

The growing frequency, intensity, and duration of extreme heat events necessitates interventions to reduce heat exposures. Local opportunities for heat adaptation may be optimally identified through collection of both quantitative exposure metrics and qualitative data on perceptions of heat. In this study, we used mixed methods to characterize heat exposure among urban residents in the area of Boston, Massachusetts, US, in summer 2020. Repeated interviews of N = 24 study participants ascertained heat vulnerability and adaptation strategies. Participants also used low-cost sensors to collect temperature, location, sleep, and physical activity data. We saw significant differences across temperature metrics: median personal temperature exposures were 3.9 °C higher than median ambient weather station temperatures. Existing air conditioning (AC) units did not adequately control indoor temperatures to desired thermostat levels: even with AC use, indoor maximum temperatures increased by 0.24 °C per °C of maximum outdoor temperature. Sleep duration was not associated with indoor or outdoor temperature. On warmer days, we observed a range of changes in time-at-home, expected given our small study size. Interview results further indicated opportunities for heat adaptation interventions including AC upgrades, hydration education campaigns, and amelioration of energy costs during high heat periods. Our mixed methods design informs heat adaptation interventions tailored to the challenges faced by residents in the study area. The strength of our community-academic partnership was a large part of the success of the mixed methods approach., (© 2022. The Author(s).)

We provide a novel method to assess the heat mitigation impacts of greenspace though studying the mechanisms of ecosystems responsible for benefits and connecting them to heat exposure metrics. We demonstrate how the ecosystem services framework can be integrated into current practices of environmental health research using supply/demand state-of-the-art methods of ecological modeling of urban greenspace. We compared the supply of cooling ecosystem services in Boston measured through an indicator of high resolution evapotranspiration modeling, with the demand for benefits from cooling measured as a heat exposure risk score based on exposure, hazard and population characteristics. The resulting evapotranspiration indicator follows a pattern similar to conventional greenspace indicators based on vegetation abundance, except in warmer areas such as those with higher levels of impervious surface. We identified demand-supply mismatch areas across the city of Boston, some coinciding with affordable housing complexes and long term care facilities. This novel ES-framework provides cross-disciplinary methods to prioritize urban areas where greenspace interventions can have the most impact based on heat-related demand., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Koen Tieskens reports a relationship with World Bank that includes: consulting or advisory., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Background: The Affordable Care Act expanded health coverage for low-income residents through Medicaid expansion and increased funding for Health Center Program New Access Points from 2009 to 2015, improving federally qualified health center (FQHC) accessibility. The extent to which these provisions progressed synergistically as intended when states could opt out of Medicaid expansion is unknown., Objective: To compare change in FQHC accessibility among census tracts in Medicaid expansion and nonexpansion states., Research Design: Tract-level FQHC accessibility scores for 2008 and 2016 were estimated applying the 2-step floating catchment area method to American Community Survey and Health Resources and Services Administration data. Multivariable linear regression compared changes in FQHC accessibility between tracts in Medicaid expansion and nonexpansion states, adjusting for sociodemographic and health system factors and accounting for state-level clustering., Subjects: In total, 7058 census tracts across 10 states., Results: FQHC accessibility increased comparably among tracts in Medicaid expansion and nonexpansion states (coef: 0.3; 95% CI: -0.3, 0.8; P -value: 0.36). FQHC accessibility increased more in tracts with higher poverty and uninsured rates, and those with lower proportions of non-English speakers and Black or African American residents., Conclusion: Similar gains in FQHC accessibility across Medicaid expansion and nonexpansion states indicate improvements progressed independently from Medicaid expansion, rather than synergistically as expected. Accessibility increases appeared consistent with HRSA's goal to improve access for individuals experiencing economic barriers to health care but not for those experiencing cultural or language barriers to health care., Competing Interests: M.P.C. provided a training on introduction to implementation science for Janssen Pharmaceuticals for which he received compensation. The other authors declare no conflict of interest., (Copyright © 2022 Wolters Kluwer Health, Inc. All rights reserved.)

Many techniques for estimating exposure to airborne contaminants do not account for building characteristics that can magnify contaminant contributions from indoor and outdoor sources. Building characteristics that influence exposure can be challenging to obtain at scale, but some may be incorporated into exposure assessments using public datasets. We present a methodology for using public datasets to generate housing models for a test cohort, and examined sensitivity of predicted fine particulate matter (PM 2.5 ) exposures to selected building and source characteristics. We used addresses of a cohort of children with asthma and public tax assessor's data to guide selection of floorplans of US residences from a public database. This in turn guided generation of coupled multi-zone models (CONTAM and EnergyPlus) that estimated indoor PM 2.5 exposure profiles. To examine sensitivity to model parameters, we varied building floors and floorplan, heating, ventilating and air-conditioning (HVAC) type, room or floor-level model resolution, and indoor source strength and schedule (for hypothesized gas stove cooking and tobacco smoking). Occupant time-activity and ambient pollutant levels were held constant. Our address matching methodology identified two multi-family house templates and one single-family house template that had similar characteristics to 60 % of test addresses. Exposure to infiltrated ambient PM 2.5 was similar across selected building characteristics, HVAC types, and model resolutions (holding all else equal). By comparison, exposures to indoor-sourced PM 2.5 were higher in the two multi-family residences than the single family residence (e.g., for cooking PM 2.5 exposure, by 26 % and 47 % respectively) and were sensitive to HVAC type and model resolution. We derived the influence of building characteristics and HVAC type on PM 2.5 exposure indoors using public data sources and coupled multi-zone models. With the important inclusion of individualized resident behavior data, similar housing modeling can be used to incorporate exposure variability in health studies of the indoor residential environment., Competing Interests: Declaration of competing interest The authors declare no conflict of interest for this work., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Purpose: Children may be exposed to numerous in-home environmental exposures (IHEE) that trigger asthma exacerbations. Spatially linking social and environmental exposures to electronic health records (EHR) can aid exposure assessment, epidemiology, and clinical treatment, but EHR data on exposures are missing for many children with asthma. To address the issue, we predicted presence of indoor asthma trigger allergens, and estimated effects of their key geospatial predictors., Methods: Our study samples were comprised of children with asthma who provided self-reported IHEE data in EHR at a safety-net hospital in New England during 2004-2015. We used an ensemble machine learning algorithm and 86 multilevel features (e.g., individual, housing, neighborhood) to predict presence of cockroaches, rodents (mice or rats), mold, and bedroom carpeting/rugs in homes. We reduced dimensionality via elastic net regression and estimated effects by the G-computation causal inference method., Results: Our models reasonably predicted presence of cockroaches (area under receiver operating curves [AUC] = 0.65), rodents (AUC = 0.64), and bedroom carpeting/rugs (AUC = 0.64), but not mold (AUC = 0.54). In models adjusted for confounders, higher average household sizes in census tracts were associated with more reports of pests (cockroaches and rodents). Tax-exempt parcels were associated with more reports of cockroaches in homes. Living in a White-segregated neighborhood was linked with lower reported rodent presence, and mixed residential/commercial housing and newer buildings were associated with more reports of bedroom carpeting/rugs in bedrooms., Conclusions: We innovatively applied a machine learning and causal inference mixture methodology to detail IHEE among children with asthma using EHR and geospatial data, which could have wide applicability and utility., (Copyright © 2022. Published by Elsevier Inc.)

Background: Foreign-born Black and Latina women on average have higher birthweight infants than their US-born counterparts, despite generally worse socioeconomic indicators and prenatal care access, i.e., "immigrant birthweight paradox" (IBP). Residence in immigrant enclaves and associated social-cultural and economic benefits may be drivers of IBP. Yet, enclaves have been found to have higher air pollution, a risk factor for lower birthweight., Objective: We investigated the association of immigrant enclaves and children's birthweight accounting for prenatal ambient air pollution exposure., Methods: In the Boston-based Children's HealthWatch cohort of mother-child dyads, we obtained birthweight-for-gestational-age z-scores (BWGAZ) for US-born births, 2006-2015. We developed an immigrant enclave score based on census-tract percentages of foreign-born, non-citizen, and linguistically-isolated households statewide. We estimated trimester-specific PM 2.5 concentrations and proximity to major roads based residential address at birth. We fit multivariable linear regressions of BWGAZ and examined effect modification by maternal nativity. Analyses were restricted to nonsmoking women and term births., Results: Foreign-born women had children with 0.176 (95% CI: 0.092, 0.261) higher BWGAZ than US-born women, demonstrating the IBP in our cohort. Immigrant enclave score was not associated with BWGAZ, even after adjusting for air pollution exposures. However, this association was significantly modified by maternal nativity (p interaction  = 0.014), in which immigrant enclave score was positively associated with BWGAZ for only foreign-born women (0.090, 95% CI: 0.007, 0.172). Proximity to major roads was negatively associated with BWGAZ (-0.018 per 10 m, 95% CI: -0.032, -0.003) and positively correlated with immigrant enclave scores. Trimester-specific PM 2.5 concentrations were not associated with BWGAZ., Significance: Residence in immigrant enclaves was associated with higher birthweight children for foreign-born women, supporting the role of immigrant enclaves in the IBP. Future research of the IBP should account for immigrant enclaves and assess their spatial correlation with potential environmental risk factors and protective resources., (© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Heating and cooling requirement differences across climates not only have carbon emissions and energy efficiency implications but also impact indoor air quality (IAQ) and health. Energy and IAQ building simulation models help understand tradeoffs or co-benefits, but these have not been applied to evaluate climate zone or multi-family home differences. We modeled a four-story multi-family home in six U.S. climate zones and quantified energy, IAQ, and health outcomes with EnergyPlus, CONTAM, and a pediatric asthma systems science model. Pollutant sources included cooking and ambient. Outputs were daily PM 2.5 and NO 2 indoor concentrations, infiltration, energy for heating and cooling, and asthma exacerbations, which were compared across climate zones, apartment units, and resident behaviors. Daily ambient-sourced PM 2.5 decreased and cooking-sourced PM 2.5 increased with higher ambient temperatures. Infiltration air changes per hour were higher on the first versus the fourth floor and in colder climates. Window opening during cooking led to decreases in total pollutant concentrations (11%-18% for PM 2.5 and 9%-15% for NO 2 ), 3%-4% decreases in asthma exacerbations within climate zones, and minimal impacts on cooling, but led to increased heating demand (4%-8%). Our results demonstrate the influence of meteorology, multi-family building characteristics, and resident behavior on IAQ, energy, and health, focused on multi-zone methodology., (© 2022 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Background: The COVID-19 pandemic has highlighted the need for targeted local interventions given substantial heterogeneity within cities and counties. Publicly available case data are typically aggregated to the city or county level to protect patient privacy, but more granular data are necessary to identify and act upon community-level risk factors that can change over time., Methods: Individual COVID-19 case and mortality data from Massachusetts were geocoded to residential addresses and aggregated into two time periods: "Phase 1" (March-June 2020) and "Phase 2" (September 2020 to February 2021). Institutional cases associated with long-term care facilities, prisons, or homeless shelters were identified using address data and modeled separately. Census tract sociodemographic and occupational predictors were drawn from the 2015-2019 American Community Survey. We used mixed-effects negative binomial regression to estimate incidence rate ratios (IRRs), accounting for town-level spatial autocorrelation., Results: Case incidence was elevated in census tracts with higher proportions of Black and Latinx residents, with larger associations in Phase 1 than Phase 2. Case incidence associated with proportion of essential workers was similarly elevated in both Phases. Mortality IRRs had differing patterns from case IRRs, decreasing less substantially between Phases for Black and Latinx populations and increasing between Phases for proportion of essential workers. Mortality models excluding institutional cases yielded stronger associations for age, race/ethnicity, and essential worker status., Conclusions: Geocoded home address data can allow for nuanced analyses of community disease patterns, identification of high-risk subgroups, and exclusion of institutional cases to comprehensively reflect community risk., (© 2021 The Authors. Influenza and Other Respiratory Viruses published by John Wiley & Sons Ltd.)

Background: The 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., Objective: To quantify the impact of NDVI spatial resolution on greenness exposure misclassification., Methods: Greenness exposure was assessed for 31,328 children in the Greater Boston Area in 2016 using NDVI from MODIS (250 m 2 ), Landsat 8 (30 m 2 ), Sentinel-2 (10 m 2 ), and the National Agricultural Imagery Program (NAIP, 1 m 2 ). 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., Results: Greenness 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%)., Significance: Greenness 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., (© 2022. The Author(s), under exclusive licence to Springer Nature America, Inc.)

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., (© 2021. The Author(s).)

We conducted a retrospective case-control study of 1,097 women in Massachusetts and Rhode Island, USA, to examine the association between stillbirth related to placental abruption or placental insufficiency and maternal exposure to traffic-related air pollution. We utilized distance to nearest roadway proximity metrics as a proxy for traffic-related air pollution exposure. No meaningful increase in the overall odds of placental-associated stillbirths was observed (adjusted OR: 1.1, 95% CI: 0.5-2.8). However, mothers living within 50 m of a roadway had a 60% increased odds of experiencing a stillbirth related to placental abruption compared to mothers living greater than 200 m away. This suggestive finding was imprecise due to the small case number in the highest exposure category (95% CI: 0.6-4.0). Future studies of placental abruption with more precise exposure assessments are warranted.

Background: Extensive data available in electronic health records (EHRs) have the potential to improve asthma care and understanding of factors influencing asthma outcomes. However, this work can be accomplished only when the EHR data allow for accurate measures of severity, which at present are complex and inconsistent., Objective: Our aims were to create and evaluate a standardized pediatric asthma severity phenotype based in clinical asthma guidelines for use in EHR-based health initiatives and studies and also to examine the presence and absence of these data in relation to patient characteristics., Methods: We developed an asthma severity computable phenotype and compared the concordance of different severity components contributing to the phenotype to trends in the literature. We used multivariable logistic regression to assess the presence of EHR data relevant to asthma severity., Results: The asthma severity computable phenotype performs as expected in comparison with national statistics and the literature. Severity classification for a child is maximized when based on the long-term medication regimen component and minimized when based only on the symptom data component. Use of the severity phenotype results in better, clinically grounded classification. Children for whom severity could be ascertained from these EHR data were more likely to be seen for asthma in the outpatient setting and less likely to be older or Hispanic. Black children were less likely to have lung function testing data present., Conclusion: We developed a pragmatic computable phenotype for pediatric asthma severity that is transportable to other EHRs., (Copyright © 2020 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.)

Background: Many vulnerable populations experience elevated exposures to environmental and social stressors, with deleterious effects on health. Multi-stressor epidemiological models can be used to assess benefits of exposure reductions. However, requisite individual-level risk factor data are often unavailable at adequate spatial resolution., Objective: To leverage public data and novel simulation methods to estimate birthweight changes following simulated environmental interventions in two environmental justice communities in Massachusetts, USA., Methods: We gathered risk factor data from public sources (US Census, Behavioral Risk Factor Surveillance System, and Massachusetts Department of Health). We then created synthetic individual-level data sets using combinatorial optimization, and probabilistic and logistic modeling. Finally, we used coefficients from a multi-stressor epidemiological model to estimate birthweight and birthweight improvement associated with simulated environmental interventions., Results: We created geographically resolved synthetic microdata. Mothers with the lowest predicted birthweight were those identifying as Black or Hispanic, with parity > 1, utilization of government prenatal support, and lower educational attainment. Birthweight improvements following greenness and temperature improvements were similar for all high-risk groups and were larger than benefits from smoking cessation., Significance: Absent private health data, this methodology allows for assessment of cumulative risk and health inequities, and comparison of individual-level impacts of localized health interventions.

Background: Associations between community-level risk factors and COVID-19 incidence are 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. We examined town-level demographic variables, including z-scores of percent Black, Latinx, over 80 years and undergraduate students, as well as factors related to occupation, housing density, economic vulnerability, air pollution (PM 2.5 ), and institutional facilities., 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 Black residents (IRR=1.12 CI=(1.12-1.13) in spring, IRR=1.01 CI=(1.00-1.01) in fall). The association with number of long-term care facility beds per capita also decreased over time (IRR=1.28 CI=(1.26-1.31) in spring, IRR=1.07 CI=(1.05-1.09)in fall). Controlling for other factors, towns with higher percentages of essential workers experienced elevated incidence of COVID-19 throughout the pandemic (e.g., IRR=1.30 CI=(1.27-1.33) in spring, IRR=1.20, CI=(1.17-1.22) in fall). Towns with higher percentages of Latinx residents also had sustained elevated incidence over time (e.g., IRR=1.19 CI=(1.18-1.21) in spring, IRR=1.14 CI=(1.13-1.15) in fall)., Conclusions: Town-level COVID-19 risk factors vary with time. In Massachusetts, racial (but not ethnic) disparities in COVID-19 incidence have decreased over time, 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.

Background: Pediatric asthma is currently the most prevalent chronic disease in the United States, with children in lower income families disproportionately affected. This increased health burden is partly due to lower-quality and insufficient maintenance of affordable housing. A movement towards 'green' retrofits that improve energy efficiency and increase ventilation in existing affordable housing offers an opportunity to provide cost-effective interventions that can address these health disparities., Methods: We combine indoor air quality modeling with a previously developed discrete event model for pediatric asthma exacerbation to simulate the effects of different types of energy retrofits implemented at an affordable housing site in Boston, MA., Results: Simulation results show that retrofits lead to overall better health outcomes and healthcare cost savings if reduced air exchange due to energy-saving air tightening is compensated by mechanical ventilation. Especially when exposed to indoor tobacco smoke and intensive gas-stove cooking such retrofit would lead to an average annual cost saving of over USD 200, while without mechanical ventilation the same children would have experienced an increase of almost USD 200/year in health care utilization cost., Conclusion: The combination of indoor air quality modeling and discrete event modeling applied in this paper can allow for the inclusion of health impacts in cost-benefit analyses of proposed affordable housing energy retrofits.

Background: Early life exposure to neurotoxicants and non-chemical psychosocial stressors can impede development of prefrontal cortical functions that promote behavioral regulation and thereby may predispose to adolescent risk-taking related behaviors (e.g., substance use or high-risk sexual activity). This is particularly concerning for communities exposed to multiple stressors., Methods: This study examined the relation of exposure to mixtures of chemical stressors, non-chemical psychosocial stressors, and other risk factors with neuropsychological correlates of risk-taking. Specifically, we assessed psychometric measures of both adverse behavioral regulation and adaptive attributes among adolescents (age ∼ 15 years) in the New Bedford Cohort (NBC), a sociodemographically diverse cohort of 788 children born 1993-1998 to mothers residing near the New Bedford Harbor Superfund site. The NBC includes biomarkers of prenatal exposure to organochlorines and metals; sociodemographic, parental and home characteristics; and periodic neurodevelopmental assessments. We modelled exposure mixtures using multi-dimensional smooths within generalized additive models., Results: Children of younger mothers with lower IQ who were exposed prenatally to higher polychlorinated biphenyls and lead had poorer anger control. This pattern was not apparent for children of older mothers with higher IQs. Direction of associations between increased hyperactivity and prenatal levels of organochlorine mixtures differed by maternal age and depression symptoms. Higher cord blood Pb levels, in conjunction with poorer HOME scores, were associated with poorer self-esteem when mothers had fewer depression symptoms., Conclusions: Analyses suggest that prenatal chemical exposures and non-chemical factors interact to contribute to neuropsychological correlates of risk-taking behaviors in adolescence. By simultaneously considering multiple factors associated with adverse behavioral regulation, we identified potential high-risk combinations that reflect both chemical and psychosocial stressors amenable to intervention., (Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Intrauterine growth has health implications both in childhood and adulthood. Birthweight is partially determined by prenatal environmental exposures. We aim to identify important predictors of birthweight out of a set of environmental, built environment exposures, and socioeconomic environment variables during pregnancy (i.e., fine particulate matter (PM 2.5 ), temperature, greenness, walkability, noise, and economic indices). We included all singleton live births of mothers who resided in urban census block-groups and delivered in Massachusetts between 2001 and 2011 ( n = 640,659). We used an elastic-net model to select important predictors of birthweight and constructed a multivariate model including the selected predictors, with adjustment for confounders. We additionally used a weighted quantile sum regression to assess the contribution of each exposure to differences in birthweight. All exposures were selected as important predictors of birthweight. In the multivariate model, lower birthweight was significantly associated with lower greenness and with higher temperature, walkability, noise, and segregation of the "high income" group. Treating the exposures individually, nighttime noise had the highest weight in its contribution to lower birthweight. In conclusion, after accounting for individual confounders, maternal environmental exposures, built environment exposures, and socioeconomic environment during pregnancy were important predictors of birthweight, emphasizing the role of these exposures in fetal growth and development.

Background: 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., Methods: 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., Results: 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., Conclusion: 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., Competing Interests: The authors have declared that no competing interests exist.

While residential energy and ventilation standards aim to improve the energy performance and indoor air quality (IAQ) of homes, their combined impact across diverse residential activities and housing environments has not been well-established. This study demonstrates the insights that a recently-developed, freely-available coupled IAQ-energy modeling platform can provide regarding the energy and IAQ trade-offs of weatherization (i.e., sealing and insulation) and ventilation retrofits in multifamily housing across varied indoor occupant activity and mechanical ventilation scenarios in Boston, MA. Overall, it was found that combined weatherization and improved ventilation recommended by design standards could lead to both energy savings and IAQ-related benefits; however, ventilation standards may not be sufficient to protect against IAQ disbenefits for residents exposed to strong indoor sources (e.g., heavy cooking or smoking) and could lead to net increases in energy costs (e.g., due to the addition of continuous outdoor air ventilation). The modeling platform employed in this study is flexible and can be applied to a wide range of building typologies, retrofits, climates, and indoor occupant activities; therefore, it stands as a valuable tool for identifying cost-effective interventions that meet both energy efficiency and ventilation standards and improve IAQ across diverse housing populations.

Neighborhood demographic polarization, or the extent to which a privileged population group outnumbers a deprived group, can affect health by influencing social dynamics. While using birth records from 2001 to 2013 in Massachusetts ( n = 629,675), we estimated the effect of two demographic indices, racial residential polarization (RRP) and economic residential polarization (ERP), on birth weight outcomes, which are established predictors of the newborn's future morbidity and mortality risk. Higher RRP and ERP was each associated with higher continuous birth weight and lower odds for low birth weight and small for gestational age, with evidence for effect modification by maternal race. On average, per interquartile range increase in RRP, the birth weight was 10.0 g (95% confidence interval: 8.0, 12.0) higher among babies born to white mothers versus 6.9 g (95% CI: 4.8, 9.0) higher among those born to black mothers. For ERP, it was 18.6 g (95% CI: 15.7, 21.5) higher among those that were born to white mothers versus 1.8 g (95% CI: -4.2, 7.8) higher among those born to black mothers. Racial and economic polarization towards more privileged groups was associated with healthier birth weight outcomes, with greater estimated effects in babies that were born to white mothers than those born to black mothers., Competing Interests: The authors declare no conflict of interest.

New methods are needed to efficiently characterize built environment attributes and residential behaviors to improve exposure assessment in epidemiologic research, given limitations of available databases and approaches. Window-opening and presence of air conditioning (AC) units predict indoor air quality and thermal comfort, but data are not widely available. In this study, we tested the utility of a GIS-based tool for rapidly assessing open windows and window/wall AC units in the city of Chelsea, Massachusetts using georeferenced street-level photographs and crowdsourced online surveys. We characterized open windows and window/wall AC units for 969 parcels in the winter and 1213 parcels in the summer, requiring ~40 person hours per season. In the winter, 21% of parcels surveyed had a window or wall AC unit and 19% had an open window. In the summer, 69% had a window or wall AC unit and 53% had an open window. We demonstrated an efficient method for rapidly characterizing open windows and window/wall AC units across an entire city. This tool can help to characterize exposures for epidemiological research, engage community members, and inform local land use planning and decision-making.

Tetrachloroethylene (PCE) is a common contaminant in both occupational and community settings. High exposure levels in the workplace have been shown to have adverse impacts on reproduction and development but few epidemiological studies have examined these effects at the lower levels commonly seen in community settings. We were presented with a unique opportunity to examine the reproductive and developmental effects of prenatal exposure to PCE-contaminated drinking water resulting from the installation of vinyl-lined water pipes in Massachusetts and Rhode Island from the late 1960s through 1980. This review describes the methods and findings of two community-based epidemiological studies, places their results in the context of the existing literature, and describes the strengths and challenges of conducting epidemiological research on a historical pollution episode. Our studies found that prenatal exposure to PCE-contaminated drinking water is associated with delayed time-to-pregnancy, and increased risks of placental abruption, stillbirths stemming from placental dysfunction, and certain birth defects. No associations were observed with pregnancy loss, birth weight, and gestational duration. Important strengths of this research included the availability of historical data on the affected water systems, a relatively high exposure prevalence and wide range of exposure levels, and little opportunity for recall bias and confounding. Challenges arose mainly from the retrospective nature of the exposure assessments. This research highlights the importance of considering pregnant women and their developing fetuses when monitoring, regulating, and remediating drinking water contaminants.

Air pollution exposure during pregnancy has been associated with impaired fetal growth and postnatal weight gain, but few studies have examined the effect on weight growth trajectories. We examine the association between validated 1 km 2 resolution particulate matter (PM 2.5 ) concentrations, averaged over pregnancy, and sex-specific growth trajectories from birth to age six of participants in the Boston-based Children's HealthWatch cohort (4797 participants, 84,283 measures). We compared weight trajectories, predicted using polynomial splines in mixed models, between prenatal PM 2.5 above or below the median (9.5 µg/m 3 ), and examined birth weight as an effect modifier. Females exposed to average prenatal PM 2.5 ≥ 9.5 µg/m 3 had higher weights compared to females exposed to < 9.5 µg/m 3 throughout the study period (0.16 kg at 24 months, 0.61 kg at 60 months). In males, higher prenatal PM 2.5 exposure was associated with significantly lower weights after 24 months of age, with differences increasing with time (-0.17 at 24 months, -0.72 kg at 60 months). Associations were more pronounced among low birth weight (<2500 g) females, but did not differ by birth weight status in males. Our findings demonstrate the complex association between air pollution exposures and childhood weight trajectories and emphasize the importance of sex-stratified analyses.

Few studies examined the impact of maternal socioeconomic status and of its combined effects with environmental exposures on birthweight. Our goal was to examine the impact of maternal homelessness (mothers ever homeless or who lived in shelters during pregnancy) and participation in the Special Supplemental Nutrition Program for Women, Infants and Children (WIC) during pregnancy in conjunction with air pollution exposure on birthweight in the Boston-based Children's HealthWatch cohort from 2007 through 2015 (n = 3366). Birthweight was obtained from electronic health records. Information on maternal homelessness and WIC participation during pregnancy were provided via a questionnaire. Prenatal fine particulate matter (PM 2.5 ) exposures, estimated at the subject's residential address, were calculated for each trimester. We fit linear regression models adjusting for maternal and child characteristics, seasonality, and block-group-level median household income and examined the interactions between PM 2.5 and each covariate. Prenatal maternal homelessness was associated with reduced birthweight (-55.7 g, 95% CI: -97.8 g, -13.7 g), while participating in WIC was marginally associated with increased birthweight (36.1 g, 95% CI: -7.3 g, 79.4 g). Only average PM 2.5 during the second trimester was marginally associated with reduced birthweight (-8.5 g, 95% CI: -19.3, 2.3) for a 1 µg/m 3 increase in PM 2.5 . The association of PM 2.5 during the second trimester with reduced birthweight was stronger among non-Hispanic Black mothers and trended toward significance among immigrants and single mothers. Our study emphasizes the independent and synergistic effects of social and environmental stressors on birthweight, particularly the potentially protective effect of participating in WIC for vulnerable populations.

Blood pressure is a leading risk factor for cardiovascular disease, influenced by chemical and non-chemical stressors. Exposure reduction strategies can potentially improve public health, but there are analytical challenges in developing quantitative models of health benefits, including the need for detailed multi-stressor exposure models, corresponding health evidence, and methods to simulate changes in exposure and resultant health benefits. These challenges are pronounced in low-income urban communities, where residents are often simultaneously exposed to numerous chemical and non-chemical stressors. For New Bedford (Massachusetts, USA), a low-income community near a Superfund site, we simulated geographically-resolved individual data, and applied previously published structural equation models developed from National Health and Nutrition Examination Survey (NHANES) data. These models simultaneously predict exposures to multiple chemicals (e.g., lead (Pb), cadmium (Cd), and polychlorinated biphenyls (PCBs)) and non-chemical factors (e.g., socioeconomic status), and determine their combined effects on blood pressure. We then modeled counterfactual scenarios reducing exposures and estimated the resulting changes in blood pressure distribution in the community. Results indicated small shifts in mean blood pressure and percentage of normotensive individuals with a reduction of Pb and/or PCB exposure. For example, a reduction in PCB to the lowest 10th percentile exposure in the NHANES resulted in a 2.4 mm Hg shift in systolic blood pressure (SBP), corresponding with 3% fewer individuals with SBP in the Stage 2 hypertension category [SBP ≥140]. Our model also emphasized the importance of the multi-stressor framework by simulating benefits of reductions in smoking rates, given positive associations with Pb and Cd but inverse associations with body mass index and blood pressure. This research demonstrates the ability to jointly consider chemical and non-chemical exposures and their impact on cardiovascular health, using approaches generalizable to other cumulative risk assessment applications., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Background: Children born near New Bedford, Massachusetts, have been prenatally exposed to multiple environmental chemicals, in part due to an older housing stock, maternal diet, and proximity to the New Bedford Harbor (NBH) Superfund site. Chemical exposure measures are not available for all births, limiting epidemiologic investigations and potential interventions., Objective: We linked biomonitoring data from the New Bedford Cohort (NBC) and birth record data to predict prenatal exposures for all contemporaneous area births., Methods: We used prenatal exposure biomarker data from the NBC, a population-based cohort of 788 mother-infant pairs born during 1993–1998 to mothers living near the NBH, linked to their corresponding Massachusetts birth record data, to build predictive models for cord serum polychlorinated biphenyls (expressed as a sum, [Formula: see text]), [Formula: see text] (DDE), hexachlorobenzene (HCB), cord blood lead (Pb), and maternal hair mercury (Hg). We applied the best fit models (highest pseudo [Formula: see text]), with multivariable smooths of continuous variables, to predict exposure biomarkers for all 10,270 births during 1993–1998 around the NBH. We used 10-fold cross validation to validate the exposure models and the bootstrap method to characterize sampling variability in the exposure predictions., Results: The 10-fold cross-validated [Formula: see text] for the [Formula: see text], DDE, HCB, Pb, and Hg exposure models were 0.54, 0.40, 0.34, 0.46, and 0.40, respectively. For each exposure model, multivariable smooths of continuous variables improved the fit compared with linear models. Other variables with significant effects on exposure estimates were paternal education, maternal race/ethnicity, and maternal ancestry. The resulting exposure predictions for all births had variability consistent with the NBC measured exposures., Conclusions: Predictive models using multivariable smoothing explained reasonable amounts of variance in prenatal exposure biomarkers. Our analyses suggest that prenatal chemical exposures can be predicted for all contemporaneous births in the same geographic area by modeling available biomarker data for a subset of that population. https://doi.org/10.1289/EHP4849.

Objective: To examine geographic access to community health centers (CHC accessibility) before and after Health Center Program expansion in three Southern states., Data Sources: Community health center data were from the Health Resources and Services Administration (1967-2016). Population estimates and sociodemographic characteristics were from the American Community Survey (2006-2015)., Study Design: We used the two-step floating catchment area method to calculate CHC accessibility for census tracts in 2008 and 2016. We mapped census tract-level variation and used spatial regression to assess to what extent indicators of potential CHC need were associated with change in accessibility from 2008 to 2016., Principal Findings: Community health center accessibility increased by 192 percent overall, and the proportion of tracts with no accessibility decreased by 65 percent. Indicators of potential need were not associated with greater gains in CHC accessibility from 2008 to 2016, but census tracts with less accessibility at baseline saw larger accessibility increases., Conclusions: Community health center accessibility substantially increased from 2008 to 2016, but increases did not differentially impact groups with greater potential need. This approach for measuring CHC accessibility offers significant improvement in granularity over traditional CHC accessibility measures., (© Health Research and Educational Trust.)

Individual housing characteristics can modify outdoor ambient air pollution infiltration through air exchange rate (AER). Time and labor-intensive methods needed to measure AER has hindered characterization of AER distributions across large geographic areas. Using publicly-available data and regression models associating AER with housing characteristics, we estimated AER for all Massachusetts residential parcels. We conducted an exposure disparities analysis, considering ambient PM 2.5 concentrations and residential AERs. Median AERs (h - 1 ) with closed windows for winter and summer were 0.74 (IQR: 0.47-1.09) and 0.36 (IQR: 0.23-0.57), respectively, with lower AERs for single family homes. Across residential parcels, variability of indoor PM 2.5 concentrations of ambient origin was twice that of ambient PM 2.5 concentrations. Housing parcels above the 90th percentile of both AER and ambient PM 2.5 (i.e., the leakiest homes in areas of highest ambient PM 2.5 )-vs. below the 10 percentile-were located in neighborhoods with higher proportions of Hispanics (20.0% vs. 2.0%), households with an annual income of less than $20,000 (26.0% vs. 7.5%), and individuals with less than a high school degree (23.2% vs. 5.8%). Our approach can be applied in epidemiological studies to estimate exposure modifiers or to characterize exposure disparities that are not solely based on ambient concentrations.

Features of the environment may modify the effect of particulate matter ≤2.5 µm in aerodynamic diameter (PM 2.5 ) on health. Therefore, we investigated how neighborhood sociodemographic and land-use characteristics may modify the association between PM 2.5 and cardiovascular mortality. We obtained residence-level geocoded cardiovascular mortality cases from the Massachusetts Department of Public Health ( n = 179,986), and PM 2.5 predictions from a satellite-based model (2001⁻2011). We appended census block group-level information on sociodemographic factors and walkability, and calculated neighborhood greenness within a 250 m buffer surrounding each residence. We found a 2.54% (1.34%; 3.74%) increase in cardiovascular mortality associated with a 10 µg/m³ increase in two-day average PM 2.5 . Walkability or greenness did not modify the association. However, when stratifying by neighborhood sociodemographic characteristics, smaller PM 2.5 effects were observed in greener areas only among cases who resided in neighborhoods with a higher population density and lower percentages of white residents or residents with a high school diploma. In conclusion, the PM 2.5 effects on cardiovascular mortality were attenuated by higher greenness only in areas with sociodemographic features that are highly correlated with lower socioeconomic status. Previous evidence suggests health benefits linked to neighborhood greenness may be stronger among lower socioeconomic groups. Attenuation of the PM 2.5 ⁻mortality relationship due to greenness may explain some of this evidence.

Purpose of Review: Multiple respiratory diseases, including asthma and chronic obstructive pulmonary disease (COPD), display significant socioeconomic and racial/ethnic disparities. The objective of this review is to evaluate the evidence supporting a link between disproportionate environmental exposures and these health disparities., Recent Findings: Studies suggest that various co-occurring factors related to the home environment, neighborhood environment, non-modifiable individual factors, and individual behaviors and attributes can increase or modify the risk of adverse respiratory outcomes among socioeconomically-disadvantaged and racially/ethnically diverse populations. Pollutants in the home environment, including particulate matter, nitrogen dioxide, and pesticides, are elevated among lower socioeconomic status populations and have been implicated in the development or exacerbation of respiratory-related conditions. Neighborhood crime and green space are socioeconomically patterned and linked with asthma outcomes through psychosocial pathways. Non-modifiable individual factors such as genetic predisposition cannot explain environmental health disparities but can increase susceptibility to air pollution and other stressors. Individual behaviors and attributes, including obesity and physical activity, contribute to worse outcomes among those with asthma or COPD., Summary: The root causes of these multifactorial exposures are complex, but many likely stem from economic forces and racial/ethnic and economic segregation that influence the home environment, neighborhood environment, and access to healthy foods and consumer products. Critical research needs include investigations that characterize exposure to and health implications of numerous stressors simultaneously, both to guard against potential confounding in epidemiological investigations and to consider the cumulative impact of multiple elevated environmental exposures and sociodemographic stressors on health disparities.

Residential energy efficiency and ventilation retrofits (eg, building weatherization, local exhaust ventilation, HVAC filtration) can influence indoor air quality (IAQ) and occupant health, but these measures' impact varies by occupant activity. In this study, we used the multizone airflow and IAQ analysis program CONTAM to simulate the impacts of energy retrofits on indoor concentrations of PM 2.5 and NO 2 in a low-income multifamily housing complex in Boston, Massachusetts (USA). We evaluated the differential impact of residential activities, such as low- and high-emission cooking, cigarette smoking, and window opening, on IAQ across two seasons. We found that a comprehensive package of energy and ventilation retrofits was resilient to a range of occupant activities, while less holistic approaches without ventilation improvements led to increases in indoor PM 2.5 or NO 2 for some populations. In general, homes with simulated concentration increases included those with heavy cooking and no local exhaust ventilation, and smoking homes without HVAC filtration. Our analytical framework can be used to identify energy-efficient home interventions with indoor retrofit resiliency (ie, those that provide IAQ benefits regardless of occupant activity), as well as less resilient retrofits that can be coupled with behavioral interventions (eg, smoking cessation) to provide cost-effective, widespread benefits., (© 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Mounting evidence over the past several decades has demonstrated inequitable distribution of pollutants of ambient origin between sociodemographic groups in the United States. Most environmental inequality studies to date are cross-sectional and used proximity-based methods rather than modeled air pollution concentrations, limiting the ability to examine trends over time or the factors that drive exposure inequalities. In this paper, we use 1km 2 modeled PM 2.5 and NO 2 concentrations in Massachusetts over an 8-year period and Census demographic data to quantify inequality between sociodemographic groups and to develop a more nuanced understanding of the drivers and trends in longitudinal air pollution inequality. Annual-average population-weighted PM 2.5 and NO 2 concentrations were highest for urban non-Hispanic black populations (11.8µg/m 3 in 2003 and 8.4µg/m 3 in 2010, vs. 11.3µg/m 3 and 8.1µg/m 3 for urban non-Hispanic whites) and urban Hispanic populations (15.9 ppb in 2005 and 13.0 ppb in 2010, vs. 13.0 ppb and 10.2 ppb for urban non-Hispanic whites), respectively. While population groups experienced similar absolute decreases in exposure over time, disparities in population-weighted concentrations increased over time when quantified by the Atkinson Index, a relative inequality measure. Exposure inequalities were approximately one order of magnitude greater for NO 2 compared to PM 2.5 , were more pronounced in urban compared to rural geographies, and between racial/ethnic groups compared to income and educational attainment groups. Our results also revealed similar longitudinal PM 2.5 and NO 2 inequality trends using Census 2000 and Census 2010 data, indicating that spatio-temporal shifts in air pollution may best explain observed trends in inequality. These findings enhance our understanding of factors that contribute to persistent inequalities and underscore the importance of targeted exposure reduction strategies aimed at vulnerable populations and neighborhoods., (Copyright © 2017 Elsevier Inc. All rights reserved.)

New Bedford Harbor (NBH) in Massachusetts was listed as a Superfund site in 1983 due to extensive contamination with polychlorinated biphenyls (PCBs) from industrial pollution. Since 1999, the U.S. Environmental Protection Agency (EPA) has posted signage and fencing in areas around NBH to discourage fishing activity, along with distributing consumption advisories locally. This exploratory study combined qualitative and geographic information system methods to assess fishing activity, signage, and awareness of the PCB contamination and consumption advisories among people fishing at NBH. We conducted interviews with fishermen along a portion of NBH ( n  = 12), and we recorded and geocoded observations and locations of fishing activity ( n  = 54) and fishing related signage ( n  = 53). Despite our limited sample size, our findings are similar to those of larger quantitative studies: advisories are not reaching vulnerable fishing populations. While all participants stated that their reasons for fishing were recreational, two-thirds reported that their catch was eaten. People who ate or shared their catch for consumption were less aware of signage, the advisory, and the contamination than nonconsumers. Most signage lacked visuals, the consumption messaging was not consistent, and was limited to English, Spanish, and Portuguese, which may not be sufficient to reach newer immigrants. This pilot study identified successes and shortcomings of the current efforts by EPA to reduce consumption of PCB contaminated seafood in this environmental justice community. Finally, we offer lessons learned and suggestions for future research on a larger scale., Competing Interests: No competing financial interests exist.

Many health risk factors are intervention targets within communities, but information regarding high-risk subpopulations is rarely available at a geographic resolution that is relevant for community-scale interventions. Researchers and community partners in New Bedford, Massachusetts (USA) collaboratively identified high-priority behaviors and health outcomes of interest available in the Behavioral Risk Factor Surveillance System (BRFSS). We developed multivariable regression models from the BRFSS explaining variability in exercise, fruit and vegetable consumption, body mass index, and diabetes prevalence as a function of demographic and behavioral characteristics, and linked these models with population microdata developed using spatial microsimulation to characterize high-risk populations and locations. Individuals with lower income and educational attainment had lower rates of multiple health-promoting behaviors (e.g., fruit and vegetable consumption and exercise) and higher rates of self-reported diabetes. Our models in combination with the simulated population microdata identified census tracts with an elevated percentage of high-risk subpopulations, information community partners can use to prioritize funding and intervention programs. Multi-stressor modeling using data from public databases and microsimulation methods for characterizing high-resolution spatial patterns of population attributes, coupled with strong community partner engagement, can provide significant insight for intervention. Our methodology is transferrable to other communities., Competing Interests: The authors declare no conflict of interest.

Exposure to air pollution may adversely impact placental function through a variety of mechanisms; however, epidemiologic studies have found mixed results. We examined the association between traffic exposure and placental-related obstetric conditions in a retrospective cohort study on Cape Cod, MA, USA. We assessed exposure to traffic using proximity metrics (distance of residence to major roadways and length of major roadways within a buffer around the residence). The outcomes included self-reported ischemic placental disease (the presence of at least one of the following conditions: preeclampsia, placental abruption, small-for-gestational-age), stillbirth, and vaginal bleeding. We used log-binomial regression models to estimate risk ratios (RR) and 95% confidence intervals (CI), adjusting for potential confounders. We found no substantial association between traffic exposure and ischemic placental disease, small-for-gestational-age, preeclampsia, or vaginal bleeding. We found some evidence of an increased risk of stillbirth and placental abruption among women living the closest to major roadways (RRs comparing living <100 m vs. ≥200 m = 1.75 (95% CI: 0.82-3.76) and 1.71 (95% CI: 0.56-5.23), respectively). This study provides some support for the hypothesis that air pollution exposure adversely affects the risk of placental abruption and stillbirth; however, the results were imprecise due to the small number of cases, and may be impacted by non-differential exposure misclassification and selection bias., Competing Interests: The authors declare no conflict of interest. The founding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

Attention-deficit/hyperactivity disorder (ADHD) has an uncertain etiology, with potential contributions from different risk factors such as prenatal environmental exposure to organochlorines and metals, social risk factors, and genetics. The degree to which geographic variability in ADHD is independent of, or explained by, risk factors may provide etiological insight. We investigated determinants of geographic variation in ADHD-related behaviors among children living near the polychlorinated biphenyl-contaminated New Bedford Harbor (NBH) Superfund site in Massachusetts. Participants were 573 children recruited at birth (1993-1998) who were born to mothers residing near the NBH site. We assessed ADHD-related behaviors at age 8 years using Conners' Teacher Rating Scale-Revised: Long Version. Adjusted generalized additive models were used to smooth the association of pregnancy residence with ADHD-related behaviors and assess whether prenatal organochlorine or metal exposures, sociodemographic factors, or other factors explained spatial patterns. Models that adjusted for child's age and sex displayed significantly increased ADHD-related behavior among children whose mothers resided west of the NBH site during pregnancy. These spatial patterns persisted after adjusting for prenatal exposure to organochlorines and metals but were no longer significant after controlling for sociodemographic factors. The findings underscore the value of spatial analysis in identifying high-risk subpopulations and evaluating candidate risk factors., (© The Author 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health.)

Secondhand exposure to environmental tobacco smoke (ETS) in multifamily housing remains a health concern despite strong recommendations to implement non-smoking policies. Multiple studies have documented exposure to ETS in non-smoking units located in buildings with smoking units. However, characterizing the magnitude of ETS infiltration or measuring the impact of building interventions or resident behavior on ETS is challenging due to the complexities of multifamily buildings, which include variable resident behaviors and complex airflows between numerous shared compartments (e.g., adjacent apartments, common hallways, elevators, heating, ventilating and air conditioning (HVAC) systems, stack effect). In this study, building simulation models were used to characterize changes in ETS infiltration in a low income, multifamily apartment building in Boston which underwent extensive building renovations targeting energy savings. Results suggest that exterior wall air sealing can lead to increases in ETS infiltration across apartments, while compartmentalization can reduce infiltration. The magnitude and direction of ETS infiltration depends on apartment characteristics, including construction (i.e., level and number of exterior walls), resident behavior (e.g., window opening, operation of localized exhaust fans), and seasonality. Although overall ETS concentrations and infiltration were reduced post energy-related building retrofits, these trends were not generalizable to all building units. Whole building smoke-free policies are the best approach to eliminate exposure to ETS in multifamily housing.

For many policy analyses, including but not limited to cumulative risk assessments, it is important to characterize the individual and joint health effects of multiple stressors. With an increasing focus on psychosocial and other nonchemical stressors, this often includes epidemiological meta-analysis. Meta-analysis has limitations if epidemiological studies do not include all of the stressors of interest or do not provide multivariable outputs in a format necessary for risk assessment. Given these limitations, novel analytical methods are often needed to synthesize the published literature or to build upon available evidence. In this article, we discuss three recent case studies that highlight the strengths and limitations of meta-analytic approaches and other research synthesis techniques for human health risk assessment applications. First, a literature-based meta-analysis within a risk assessment context informed the design of a new epidemiological investigation of the differential toxicity of fine particulate matter constituents. Second, a literature synthesis for an effects-based cumulative risk assessment of hypertension risk factors led to a decision to develop new epidemiological associations using structural equation modeling. Third, discrete event simulation modeling was used to simulate the impact of changes in the built environment on environmental exposures and associated asthma outcomes, linking literature meta-analyses for key associations with a simulation model to synthesize all of the model components. These case studies emphasize the importance of conducting epidemiology with a risk assessment application in mind, the need for interdisciplinary collaboration, and the value of advanced analytical methods to synthesize epidemiological and other evidence for risk assessment applications., (© 2014 Society for Risk Analysis.)

High blood pressure is associated with exposure to multiple chemical and non-chemical risk factors, but epidemiological analyses to date have not assessed the combined effects of both chemical and non-chemical stressors on human populations in the context of cumulative risk assessment. We developed a novel modeling approach to evaluate the combined impact of lead, cadmium, polychlorinated biphenyls (PCBs), and multiple non-chemical risk factors on four blood pressure measures using data for adults aged ≥ 20 years from the National Health and Nutrition Examination Survey (1999-2008). We developed predictive models for chemical and other stressors. Structural equation models were applied to account for complex associations among predictors of stressors as well as blood pressure. Models showed that blood lead, serum PCBs, and established non-chemical stressors were significantly associated with blood pressure. Lead was the chemical stressor most predictive of diastolic blood pressure and mean arterial pressure, while PCBs had a greater influence on systolic blood pressure and pulse pressure, and blood cadmium was not a significant predictor of blood pressure. The simultaneously fit exposure models explained 34%, 43% and 52% of the variance for lead, cadmium and PCBs, respectively. The structural equation models were developed using predictors available from public data streams (e.g., U.S. Census), which would allow the models to be applied to any U.S. population exposed to these multiple stressors in order to identify high risk subpopulations, direct intervention strategies, and inform public policy., (Copyright © 2014 Elsevier Inc. All rights reserved.)