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Residents of low-income multifamily housing can have elevated exposures to multiple environmental pollutants known to influence asthma. Simulation models can characterize the health implications of changing indoor concentrations, but quantifying the influence of interventions on concentrations is challenging given complex airflow and source characteristics. In this study, we simulated concentrations in a prototype multifamily building using CONTAM, a multizone airflow and contaminant transport program. Contaminants modeled included PM2.5 and NO2, and parameters included stove use, presence and operability of exhaust fans, smoking, unit level, and building leakiness. We developed regression models to explain variability in CONTAM outputs for individual sources, in a manner that could be utilized in simulation modeling of health outcomes. To evaluate our models, we generated a database of 1000 simulated households with characteristics consistent with Boston public housing developments and residents and compared the predicted levels of NO2 and PM2.5 and their correlates with the literature. Our analyses demonstrated that CONTAM outputs could be readily explained by available parameters ( R2 between 0.89 and 0.98 across models), but that one-compartment box models would mischaracterize concentrations and source contributions. Our study quantifies the key drivers for indoor concentrations in multifamily housing and helps to identify opportunities for interventions. Practical Implications Many low-income urban asthmatics live in multifamily housing that may be amenable to ventilation-related interventions such as weatherization or air sealing, wall and ceiling hole repairs, and exhaust fan installation or repair, but such interventions must be designed carefully given their cost and their offsetting effects on energy savings as well as indoor and outdoor pollutants. We developed models to take into account the complex behavior of airflow patterns in multifamily buildings, which can be used to identify and evaluate environmental and non-environmental interventions targeting indoor air pollutants which can trigger asthma exacerbations. [ABSTRACT FROM AUTHOR]

Hydrazine belongs to a group of compounds for which there is evidence that the in vivo genotoxic effects become manifest only upon exposure to toxic dose levels. The present study was performed to investigate whether this phenomenon is also reflected in the pattern of DNA methylation. The induction of N7- and O6-methylguanine (MeGua) was studied in liver DNA of rats, 16 hr after treatment with various doses of hydrazine. After DNA isolation, the presence of N7-MeGua in DNA was assessed with an immunochemical method and with a physicochemical technique (HPLC with electrochemical detection). Application of these two methods resulted in almost identical patterns of dose-dependent induction of guanine N7-methylation in rats dosed orally with 0.1 to 10 mg hydrazine per kilogram of body weight, increasing from 1.1–1.3 to 39–45 N7-MeGua per 106 nucleotides. At lower dosages a constant adduct level was observed, equivalent to that in untreated rats (background level). The O6-MeGua level was analyzed by a combination of HPLC separation and competitive radioimmunoassay. A background level was observed for untreated rats and no increase was visible up to the 0.2 mg/kg dose group. After hydrazine doses from 0.2 to 10 mg/kg, O6-MeGua increased from 0.29 to 134 per 109 nucleotides. These data show that even at dosages below the maximum tolerated dose (0.6 mg/kg/day), for which carcinogenic effects have not been described, DNA adducts are formed. A comparison is made of the data obtained in this study with models that describe the mechanism of hydrazine-induced DNA methylation. [ABSTRACT FROM AUTHOR]

Abstract Background In the United States, asthma is the most common chronic disease of childhood across all socioeconomic classes and is the most frequent cause of hospitalization among children. Asthma exacerbations have been associated with exposure to residential indoor environmental stressors such as allergens and air pollutants as well as numerous additional factors. Simulation modeling is a valuable tool that can be used to evaluate interventions for complex multifactorial diseases such as asthma but in spite of its flexibility and applicability, modeling applications in either environmental exposures or asthma have been limited to date. Methods We designed a discrete event simulation model to study the effect of environmental factors on asthma exacerbations in school-age children living in low-income multi-family housing. Model outcomes include asthma symptoms, medication use, hospitalizations, and emergency room visits. Environmental factors were linked to percent predicted forced expiratory volume in 1 second (FEV1%), which in turn was linked to risk equations for each outcome. Exposures affecting FEV1% included indoor and outdoor sources of NO2 and PM2.5, cockroach allergen, and dampness as a proxy for mold. Results Model design parameters and equations are described in detail. We evaluated the model by simulating 50,000 children over 10 years and showed that pollutant concentrations and health outcome rates are comparable to values reported in the literature. In an application example, we simulated what would happen if the kitchen and bathroom exhaust fans were improved for the entire cohort, and showed reductions in pollutant concentrations and healthcare utilization rates. Conclusions We describe the design and evaluation of a discrete event simulation model of pediatric asthma for children living in low-income multi-family housing. Our model simulates the effect of environmental factors (combustion pollutants and allergens), medication compliance, seasonality, and medical history on asthma outcomes (symptom-days, medication use, hospitalizations, and emergency room visits). The model can be used to evaluate building interventions and green building construction practices on pollutant concentrations, energy savings, and asthma healthcare utilization costs, and demonstrates the value of a simulation approach for studying complex diseases such as asthma.

Abstract Background There is growing concern in communities surrounding airports regarding the contribution of various emission sources (such as aircraft and ground support equipment) to nearby ambient concentrations. We used extensive monitoring of nitrogen dioxide (NO2) in neighborhoods surrounding T.F. Green Airport in Warwick, RI, and land-use regression (LUR) modeling techniques to determine the impact of proximity to the airport and local traffic on these concentrations. Methods Palmes diffusion tube samplers were deployed along the airport's fence line and within surrounding neighborhoods for one to two weeks. In total, 644 measurements were collected over three sampling campaigns (October 2007, March 2008 and June 2008) and each sampling location was geocoded. GIS-based variables were created as proxies for local traffic and airport activity. A forward stepwise regression methodology was employed to create general linear models (GLMs) of NO2 variability near the airport. The effect of local meteorology on associations with GIS-based variables was also explored. Results Higher concentrations of NO2 were seen near the airport terminal, entrance roads to the terminal, and near major roads, with qualitatively consistent spatial patterns between seasons. In our final multivariate model (R2 = 0.32), the local influences of highways and arterial/collector roads were statistically significant, as were local traffic density and distance to the airport terminal (all p < 0.001). Local meteorology did not significantly affect associations with principal GIS variables, and the regression model structure was robust to various model-building approaches. Conclusion Our study has shown that there are clear local variations in NO2 in the neighborhoods that surround an urban airport, which are spatially consistent across seasons. LUR modeling demonstrated a strong influence of local traffic, except the smallest roads that predominate in residential areas, as well as proximity to the airport terminal.

Widespread North American wildfires in 2023 led to exposure to ambient wildfire smoke outside of traditionally wildfire-prone regions. The objective was to evaluate levels of indoor air pollutants in relation to ambient wildfire smoke exposure in eastern Massachusetts. Using a real-time multipollutant sensor system in five Boston area households, this study assessed indoor fine particulate matter (PM 2.5 ), nitrogen dioxide (NO 2 ), and total volatile organic compound concentrations (TVOC) two days before and during days of hazardous wildfire smoke exposure (smoke days). The relationship between ambient PM 2.5 from regulatory monitors and indoor PM 2.5 before and during smoke days was investigated by mixed effects linear regression. During smoke days and the preceding non-smoke days, median indoor PM 2.5 was 9.9 µg/m 3 and 3.5 µg/m 3 ( p  < 0.001), respectively; median NO 2 was 20.5 ppb and 18.4 ppb ( p  = 0.11); median TVOC was 6,715 µg/m 3 and 5,361 µg/m 3 ( p  = 0.35). A 1% increase in ambient PM 2.5 was associated with a 0.93% increase in indoor PM 2.5 on smoke days (95% CI, 0.54%-1.32%) and a 0.34% increase on non-smoke days (95% CI, 0.17%-0.66%), though interaction testing of smoke day status was not statistically significant ( p  = 0.14). In Northeastern US homes, indoor PM 2.5 increased significantly during ambient wildfire smoke exposure, which may reflect increased infiltration and increased indoor particle-generating activities during smoke days. Implications : This study reports on household exposure to wildfire smoke in eastern Massachusetts, finding that indoor PM 2.5 more than doubled compared to preceding non-smoke days, while indoor NO 2 and TVOC did not significantly rise. Though the generalizability of this study is limited by the small number of homes studied, the findings suggest that more investigation is needed to understand indoor air pollution during future wildfire smoke exposure in regions not traditionally wildfire-prone and to inform mitigation efforts.

Objectives: Environmental risk factors may contribute to sleep-disordered breathing. We investigated the association between indoor particulate matter ≤2.5µm in aerodynamic diameter (PM 2.5 ) and sleep-disordered breathing in children in an urban US community., Methods: The sample consisted of children aged 6-12years living in predominantly low-income neighborhoods in Boston, Massachusetts. Indoor PM 2.5 was measured in participants' main living areas for 7days using the Environmental Multipollutant Monitoring Assembly device. High indoor PM 2.5 exposure was defined as greater than the sample weekly average 80th percentile level (≥15.6 μg/m 3 ). Sleep-disordered breathing was defined as an Apnea-Hypopnea-Index (AHI) or Oxygen-Desaturation-Index (ODI) (≥3% desaturation) of ≥5 events/hour. Habitual loud snoring was defined as caregiver-report of loud snoring (most or all the time each week) over the past 4weeks. We examined the associations of PM 2.5 with sleep-disordered breathing or snoring using logistic regression adjusting for potential confounders., Results: The sample included 260 children (mean age 9.6years; 41% female), with 32% (n = 76) classified as having sleep-disordered breathing. In a logistic regression model adjusted for socioeconomics and seasonality, children exposed to high indoor PM 2.5 levels (n = 53) had a 3.53-fold increased odds for sleep-disordered breathing (95%CI: 1.57, 8.11, p = .002) compared to those with lower indoor PM 2.5 . This association persisted after additional adjustments for physical activity, outdoor PM 2.5 , environmental tobacco smoke, and health characteristics. Similar associations were observed for snoring and indoor PM 2.5. CONCLUSIONS: Children with higher indoor PM 2.5 exposure had greater odds of sleep-disordered breathing and habitual loud snoring, suggesting that indoor air quality contributes to sleep disparities., Competing Interests: Declaration of conflicts of interest S. Redline has consulted for Eli Lilly Inc (unrelated to this project). W. Phipatanakul has consulted for Genentech, Novartis, Sanofi, Regeneron, GSK, Astra Zeneca for asthma-related therapeutics, unrelated to this project. All other authors declare no conflicts of interest., (Copyright © 2024 National Sleep Foundation. Published by Elsevier Inc. All rights reserved.)

Background: Perinatal exposure to phthalates is associated with adverse health impacts for parents and children. The field of environmental health literacy seeks to measure how environmental health information is conceptualized and used to inform behaviors. We assessed whether scores on the validated Phthalate Environmental Reproductive Health Literacy (PERHL) scale were associated with biomarkers of phthalate exposure., Methods: 42 members of the Environmental Reproductive and Glucose Outcomes (ERGO) cohort completed the PERHL scale and provided spot urine samples. Phthalate summary measures for model outcomes were created by calculating molar sums of specific gravity-corrected metabolite concentrations representing exposure to parent phthalate, Di(2-ethylhexyl)phthalate (DEHP), personal care product (PCP)-associated phthalates, and parent butyl-phthalates. Linear regression models were used to estimate the associations of the PERHL scale scores with phthalate summary measures, controlling for educational attainment (college degree or higher vs. no college degree), age (years), and race and ethnicity (non-Hispanic White vs. non-White)., Results: Higher scores on the PERHL Scale and subscales were generally associated with lower ΣDEHP, Σbutyl, and ΣPCP metabolite concentrations. A one-point increase in the 'Protective Behavior/Risk Control' subscale score was significantly associated with a -30.3% (95% CI: -50.1, -2.6) decrease in ΣDEHP, and a -30.6% (95% CI: -51.5, -0.63) decrease in Σbutyl metabolite concentrations., (© 2024. The Author(s).)

PM 2.5 concentrations are higher during rush hours at background stations compared to the average concentration across these stations. Few studies have investigated PM 2.5 concentration and its spatial distribution during rush hours using machine learning models. This study employs a geospatial-artificial intelligence (Geo-AI) prediction model to estimate the spatial and temporal variations of PM 2.5 concentrations during morning and dusk rush hours in Taiwan. Mean hourly PM 2.5 measurements were collected from 2006 to 2020, and aggregated into morning (7 a.m.-9 a.m.) and dusk (4 p.m.-6 p.m.) rush-hour mean concentrations. The Geo-AI prediction model was generated by integrating kriging interpolation, land-use regression, machine learning, and a stacking ensemble approach. A forward stepwise variable selection method based on the SHapley Additive exPlanations (SHAP) index was used to identify the most influential variables. The performance of the Geo-AI models for morning and dusk rush hours had accuracy scores of 0.95 and 0.93, respectively and these results were validated, indicating robust model performance. Spatially, PM 2.5 concentrations were higher in southwestern Taiwan for morning rush hours, and suburban areas for dusk rush hours. Key predictors included kriged PM 2.5 values, SO 2 concentrations, forest density, and the distance to incinerators for both morning and dusk rush hours. These PM 2.5 estimates for morning and dusk rush hours can support the development of alternative commuting routes with lower concentrations., 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 © 2024 Elsevier Ltd. All rights reserved.)

Background: While the impacts of social and environmental exposure on cardiovascular risks are often reported individually, the combined effect is poorly understood., Methods and Results: Using the 2022 Environmental Justice Index, socio-environmental justice index and environmental burden module ranks of census tracts were divided into quartiles (quartile 1, the least vulnerable census tracts; quartile 4, the most vulnerable census tracts). Age-adjusted rate ratios (RRs) of coronary artery disease, strokes, and various health measures reported in the Prevention Population-Level Analysis and Community Estimates data were compared between quartiles using multivariable Poisson regression. The quartile 4 Environmental Justice Index was associated with a higher rate of coronary artery disease (RR, 1.684 [95% CI, 1.660-1.708]) and stroke (RR, 2.112 [95% CI, 2.078-2.147]) compared with the quartile 1 Environmental Justice Index. Similarly, coronary artery disease 1.057 [95% CI,1.043-1.0716] and stroke (RR, 1.118 [95% CI, 1.102-1.135]) were significantly higher in the quartile 4 than in the quartile 1 environmental burden module. Similar results were observed for chronic kidney disease, hypertension, diabetes, obesity, high cholesterol, lack of health insurance, sleep <7 hours per night, no leisure time physical activity, and impaired mental and physical health >14 days., Conclusions: The prevalence of CVD and its risk factors is highly associated with increased social and environmental adversities, and environmental exposure plays an important role independent of social factors.

Background: Substantial evidence exists linking phthalate exposure to adverse reproductive health outcomes. Current US federal regulations of consumer product chemicals place the onus on individuals to mitigate their exposure to phthalates, with assumptions of sufficient environmental health literacy (EHL). Few validated scales for people of reproductive age exist to evaluate phthalate-specific EHL., Objectives: Our objective is to develop a multidimensional scale characterizing latent factors of phthalate knowledge, risk perception, and self-efficacy to inform individual-level interventions for reducing phthalate exposure., Methods: We distributed a survey with 31 items to 117 participants in the Environmental Reproductive and Glucose Outcomes (ERGO) cohort who gave birth within the last 5 years. Exploratory factor analysis (EFA) was used to identify underlying latent factors. Internal reliability was assessed via omega hierarchical coefficient. Average sum scores for each latent factor and the overall Phthalate Environmental Reproductive Health Literacy (PERHL) score were calculated. Associations between latent factors, overall PERHL score, and sociodemographic characteristics were explored using linear models., Results: Six latent factors were identified as follows: "Awareness of Phthalate Reproductive Health Impacts," "Uncertainty," "Protective Behavior/Risk Control," "Regulatory Interest," "Awareness of Phthalate Exposure Pathways," and "General Phthalate Knowledge." Each factor demonstrated acceptable to strong internal reliability, with coefficients ranging between 0.63 and 0.93. Non-white participants had lower scores for the "Awareness of Phthalate Reproductive Health Impacts" [ β : - 0.35 , 95% confidence interval (CI): - 0.63 , - 0.07 ], "Awareness of Phthalate Exposure Pathways" ( β : - 0.32 , 95% CI: - 0.57 , - 0.07 ), and "General Phthalate Knowledge" ( β : - 0.36 , 95% CI: - 0.66 , - 0.06 ), but no significant difference in scores on "Uncertainty" ( β : 0.17, 95% CI: - 0.16 , 0.50), "Protective Behavior/Risk Control" ( β : - 0.04 , 95% CI: - 0.36 , 0.28), or "Regulatory Interest" ( β : - 0.21 , 95% CI: - 0.51 , 0.09). No associations were seen for age or educational attainment and latent or sum factors., Discussion: Six latent factors were identified for the PERHL scale. Non-white race and ethnicity was associated with lower scores for knowledge-related scale factors. https://doi.org/10.1289/EHP13128.

Background: Federal housing assistance is an important policy tool to ensure housing security for low-income households. Less is known about its impact on residential environmental exposures, particularly lead., Objectives: We conducted a quasi-experimental study to investigate the association between federal housing assistance and blood lead levels (BLLs) in a nationally representative US sample age 6 y and older eligible for housing assistance., Methods: We used the 1999-2018 National Health and Nutrition Examination Survey (NHANES) linked with US Department of Housing and Urban Development (HUD) administrative records to assess BLLs of NHANES participants with concurrent HUD housing assistance (i.e., current recipients, n = 3 , 0 71 ) and those receiving assistance within 2 y after the survey (i.e., pseudo-waitlist recipients, n = 1,235 ). We estimated BLL least squares geometric means (LSGMs), odds ratio (OR) for BLL ≥ 3.5 μ g / dL , and percent differences in LSGMs by HUD housing assistance status adjusting for age, sex, family income-to-poverty ratio, education, country of birth, race/ethnicity, region, and survey year. We also examined effect modification using interaction terms and stratified analyses by program type [i.e., public housing, multifamily, housing choice vouchers (HCV)], and race/ethnicity., Results: Current HUD recipients had a significantly lower LSGM [ 1.07 μ g / dL ; 95% confidence interval (CI): 1.02, 1.12] than pseudo-waitlist recipients ( 1.21   μ g / dL ; 95% CI: 1.14, 1.28), with an adjusted OR of 0.60 (95% CI: 0.42, 0.87) for BLL ≥ 3.5 μ g / dL . Some effect modification were observed: The protective association of HUD assistance on BLL was strongest among public housing ( - 19.5 % LSGM; 95% CI: - 27.5 % , - 1 0 . 7 % ), multifamily ( - 12.5 % LSGM; 95% CI: - 2 0 . 7 % , - 3.5 % ), and non-Hispanic White ( - 2 0 . 6 % LSGM; 95% CI: - 29.8 % , - 1 0 . 3 % ) recipients. It was weaker to null among HCV ( - 5.7 % LSGM; 95% CI: - 12.7 , 1.7%), non-Hispanic Black ( - 1.6 % LSGM; 95% CI: - 8.1 % , 5.4%), and Mexican American (-12.5% LSGM; 95% CI: - 31.9 % , 12.5 % ) recipients., Discussion: Our research underscores the importance of social-structural determinants like federal housing assistance in providing affordable, stable, and healthy housing to very low-income households. More attention is needed to ensure housing quality and racial equity across HUD's three major housing assistance programs. https://doi.org/10.1289/EHP12645.

Background: Children are potentially more susceptible to the adverse effects of pesticides due to more sensitive organ systems and lower capacity to metabolize and eliminate chemicals compared to adults. The health risks are particularly concerning children with asthma, living in low-income neighborhoods in multi-family housing because of their impaired respiratory health, and factors associated with low-income, multi-family environments., Objective: To assess the association between pesticide exposure and asthma morbidity among children 7-12 years residing in low-income, multi-family housing., Methods: The concentrations of seven urinary pesticide biomarkers: 3,5,6-trichloro-2-pyridinol (TCPy), 2-isopropyl-4-methyl-6-hydroxypyrimidine, para-nitrophenol (PNP), 3-phenoxybenzoic acid (3-PBA), 4-fluoro-3-phenoxybenzoic acid, trans-3-(2,2-dichlorovinyl)-2,2-dimethyl-cyclopropane-1-carboxylic acid, and 2,4-dichlorophenoxyacetic acid (2,4-D) were measured. Children (n = 162) were followed for one year with three measures of pesticides biomarkers. Associations between individual biomarkers and asthma attack, asthma related health care utilization, and fraction of exhaled nitric oxide (FeNO), adjusting for demographic and household factors were examined with Generalized Estimating Equations (GEE). Weighted Quantile Sum (WQS) regression was used to examine the effect of pesticide mixture on asthma attacks and asthma-related health care utilization (HCU)., Results: In adjusted GEE models, positive non-significant associations were found between PNP and HCU (adjusted Odds Ratio(aOR):2.05 95% CI:0.76-5.52) and null associations for 3-PBA and HCU (aOR:1.07 95% CI: 0.88-1.29). Higher concentrations of PNP and 2,4-D were associated with significantly lower FeNO levels (PNP: -17.4%; 2,4-D:-19.74%). The mixture was positively associated with HCU in unadjusted (OR: 1.56 97.5% CI: 1.08-2.27) but not significant in adjusted models (aOR: 1.40 97.5% CI: .86-2.29). The non-specific pyrethroid biomarker 3-PBA at baseline contributed the greatest weight to the index (45%)., Significance: There were non-significant associations between pesticide biomarkers and respiratory outcomes in children with asthma. There was a suggestive association between urinary pesticide biomarkers and HCU. Further studies with larger sample sizes could help to confirm these findings., Impact Statement: Pesticide exposure among children in the urban environment is ubiquitous and there is a dearth of information on the impact of low-level chronic exposure in vulnerable populations. This study suggested that pesticide exposure at concentrations below the national average may not affect asthma morbidity in children. However, different biomarkers of pesticides showed different effects, but the mixture suggested increasing pesticide exposure results in asthma related HCU. The results may show that children with asthma may be at risk for negative health outcomes due to pesticides and the need to further examine this relationship., (© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Elevated levels of ground-level ozone (O 3 ) can have harmful effects on health. While previous studies have focused mainly on daily averages and daytime patterns, it's crucial to consider the effects of air pollution during daily commutes, as this can significantly contribute to overall exposure. This study is also the first to employ an ensemble mixed spatial model (EMSM) that integrates multiple machine learning algorithms and predictor variables selected using Shapley Additive exExplanations (SHAP) values to predict spatial-temporal fluctuations in O 3 concentrations across the entire island of Taiwan. We utilized geospatial-artificial intelligence (Geo-AI), incorporating kriging, land use regression (LUR), machine learning (random forest (RF), categorical boosting (CatBoost), gradient boosting (GBM), extreme gradient boosting (XGBoost), and light gradient boosting (LightGBM)), and ensemble learning techniques to develop ensemble mixed spatial models (EMSMs) for morning and evening commute periods. The EMSMs were used to estimate long-term spatiotemporal variations of O 3 levels, accounting for in-situ measurements, meteorological factors, geospatial predictors, and social and seasonal influences over a 26-year period. Compared to conventional LUR-based approaches, the EMSMs improved performance by 58% for both commute periods, with high explanatory power and an adjusted R 2 of 0.91. Internal and external validation procedures and verification of O 3 concentrations at the upper percentile ranges (in 1%, 5%, 10%, 15%, 20%, and 25%) and other conditions (including rain, no rain, weekday, weekend, festival, and no festival) have demonstrated that the models are stable and free from overfitting issues. Estimation maps were generated to examine changes in O 3 levels before and during the implementation of COVID-19 restrictions. These findings provide accurate variations of O 3 levels in commute period with high spatiotemporal resolution of daily and 50m * 50m grid, which can support control pollution efforts and aid in epidemiological studies., 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 © 2023 Elsevier Ltd. All rights reserved.)

Objectives: Understanding how environmental and social stressors cluster is critical to explaining and addressing health disparities. It remains unclear how these stressors cluster at fine spatial resolution in low to medium-income, urban households. We explored patterns of environmental and social exposures at the household-level and potential predictors of these joint exposures in two environmental justice communities in the Greater Boston area., Methods: We recruited 150 households in Chelsea, MA and the Dorchester neighborhood of Boston, MA, between 2016 and 2019 and collected data on two domains: environmental and social stressor. For each domain, we fit Latent Class Analysis (LCA) models to exposure data to assess intra-domain variability, and cross-classified the resultant classes to identify joint exposure profiles. We compared differences in the distribution of these profiles by participants' demographic and household characteristics using χ2, Fisher's exact, Analysis of Variance, and Kruskal-Wallis tests., Results: We identified two latent classes in each domain: High environmental (n = 90; 60.4%), Low environmental (n = 59; 39.6%), High Social (n = 31; 20.8%), and Low Social (n = 118; 79.2%). Cross-classification yielded four joint exposure profiles: Both Low (n = 46, 30.9%); Both High (n = 18, 12.1%); High environmental-Low Social (n = 72, 48.3%); and Low environmental-High Social (n = 13, 8.7%). Significant group differences were found by housing type (e.g., single-family vs. multi-family) (Fisher's exact p = 0.0016), housing tenure (p = 0.0007), and study site (p < 0.0001). We also observed differences by race/ethnicity, income, and education: households that were Hispanic/Latinx, below the poverty level, and with lower education were more likely to be in the Both High group., Conclusions: Our analyses confirmed that environmental and social stressors cluster in socially disadvantaged households. Housing type, housing tenure, and location of the residence were also strong predictors of cluster membership, with renter and multi-family residents at risk of high exposures to environmental and social stressors., 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 © 2023 Elsevier Inc. All rights reserved.)

Background: Pediatric sleep-disordered breathing (SDB) disproportionately affects children with low socioeconomic status (SES). The multilevel risk factors that drive these associations are not well understood., Research Question: What are the associations between SDB risk factors, including individual health conditions (obesity, asthma, and allergies), household SES (maternal education), indoor exposures (environmental tobacco smoke [ETS] and pests), and neighborhood characteristics (neighborhood disadvantage), and pediatric SDB symptoms?, Study Design and Methods: Cross-sectional analyses were performed on 303 children (aged 6-12 years) enrolled in the Environmental Assessment of Sleep Youth study from 2018 to 2022. Exposures were determined by caregiver reports, assays of measured settled dust from the child's bedroom, and neighborhood-level Census data (deriving the Childhood Opportunity Index to characterize neighborhood disadvantage). The primary outcome was the SDB-related symptom burden assessed by the OSA-18 questionnaire total score. Using linear regression models, we calculated associations between exposures and SDB-related symptom burden, adjusting for sociodemographic factors, then health conditions, indoor environment, and neighborhood factors., Results: The sample included 303 children (39% Hispanic, Latino, Latina, or Spanish origin; 30% Black or African American; 22% White; and 11% other). Increasing OSA-18 total scores were associated with low household SES after adjustment for demographic factors, and with asthma, allergies, ETS, pests (mouse, cockroach, and rodents), and an indoor environmental index (sum of the presence of pests and ETS; 0-2) after adjusting for sociodemographic factors. Even after further adjusting for asthma, allergies, and neighborhood disadvantage, ETS and pest exposure were associated with OSA-18 (ETS: β = 12.80; 95% CI, 7.07-18.53, also adjusted for pest; pest exposure: β = 3.69; 95% CI, 0.44-6.94, also adjusted for ETS)., Interpretation: In addition to associations with ETS, a novel association was observed for indoor pest exposure and SDB symptom burden. Strategies to reduce household exposure to ETS and indoor allergens should be tested as approaches for reducing sleep health disparities.

Background: Personal care products are a notable source of exposure to endocrine-disrupting chemicals (EDCs). Racial/ethnic differences in the use of hair products containing EDCs are reported, with women and children of color more commonly using hair products that are hormonally active and contain EDCs than other racial/ethnic groups. There is limited research examining the neighborhood-level social and economic factors that may contribute to these reported disparities., Objectives: We aimed to examine the safety of hair products across sociodemographically diverse neighborhoods in Boston, Massachusetts., Methods: Eight neighborhoods were identified based on indicators of race/ethnicity and socioeconomic status (SES). We randomly selected 50 stores and collected data on the hair products for sale and their corresponding Environmental Working Group (EWG) Skin Deep hazard score. The association between neighborhood and EWG hazard category (low, moderate, high) was examined using a multinomial logistic regression., Results: A total of 14,019 hair products were identified in the eight neighborhoods. When considering products with EWG hazard scores, Roxbury, a lower income community of color, and Mission Hill, a lower income community, were reported to have a higher percentage of high-hazard hair products in comparison with Beacon Hill [12.2% (163/1,332), 11.4% (65/571) vs. 7.9% (30/382), respectively]. Differences between the safety of hair products were observed, with Roxbury and Mission Hill reporting more than a 2-fold higher risk ratio (RR) of finding hair products with high vs. low EWG scores in comparison with that of Beacon Hill [RR for Roxbury: 2.3, 95% confidence interval (CI): 1.1, 4.6; RR for Mission Hill: 2.3, 95% CI: 1.0, 5.4]. Other neighborhoods were also observed to have an increased RR in comparison with Beacon Hill, however, with 95% CIs that extended beyond the null., Discussion: Retail stores in neighborhoods with a higher percentage of residents of color and lower SES were found to be more likely to sell products with high hazard scores than stores in a higher SES and predominately non-Hispanic White neighborhood. https://doi.org/10.1289/EHP10653.

Millions of households in rich and poor countries alike are at risk of being unwilfully displaced from their homes or the land on which they live (i.e., lack secure tenure), and the urban poor are most vulnerable. Improving housing tenure security may be an intervention to improve housing and environmental conditions and reduce urban health inequalities. Building on stakeholder workshops and a narrative review of the literature, we developed a conceptual model that infers the mechanisms through which more secure housing tenure can improve housing, environmental quality, and health. Empirical studies show that more secure urban housing tenure can boost economic mobility, improve housing and environmental conditions including reduced exposure to pollution, create safer and more resourced communities, and improve physical and mental health. These links are shared across tenure renters and owners and different economic settings. Broader support is needed for context-appropriate policies and actions to improve tenure security as a catalyst for cultivating healthier homes and neighbourhoods and reducing urban health inequalities in cities., Competing Interests: No competing interests were disclosed., (Copyright: © 2023 Baumgartner J et al.)

Background: With rapid urbanization, the urban environment, especially the neighborhood environment, has received increasing global attention. However, a comprehensive overview of the association between neighborhood risk factors and human health remains unclear due to the large number of neighborhood risk factor-human health outcome pairs., Method: On the basis of a whole year of panel discussions, we first obtained a list of 5 neighborhood domains, containing 33 uniformly defined neighborhood risk factors. We only focused on neighborhood infrastructure-related risk factors with the potential for spatial interventions through urban design tools. Subsequently, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic meta-review of 17 infrastructure-related risk factors of the 33 neighborhood risk factors (e.g., green and blue spaces, proximity to major roads, and proximity to landfills) was conducted using four databases, Web of Science, PubMed, OVID, and Cochrane Library, from January 2000 to May 2021, and corresponding evidence for non-communicable diseases (NCDs) was synthesized. The review quality was assessed according to the A MeaSurement Tool to Assess Systematic Reviews (AMSTAR) standard., Results: Thirty-three moderate-and high-quality reviews were included in the analysis. Thirteen major NCD outcomes were found to be associated with neighborhood infrastructure-related risk factors. Green and blue spaces or walkability had protective effects on human health. In contrast, proximity to major roads, industry, and landfills posed serious threats to human health. Inconsistent results were obtained for four neighborhood risk factors: facilities for physical and leisure activities, accessibility to infrastructure providing unhealthy food, proximity to industry, and proximity to major roads., Conclusions: This meta-review presents a comprehensive overview of the effects of neighborhood infrastructure-related risk factors on NCDs. Findings on the risk factors with strong evidence can help improve healthy city guidelines and promote urban sustainability. In addition, the unknown or uncertain association between many neighborhood risk factors and certain types of NCDs requires further research., (© 2023. The Author(s).)

Given the increased use of air cleaners as a prevention measure in classrooms during the COVID-19 pandemic, this study aimed to investigate the effects of portable air cleaners with HEPA filters and window A/C fans on real-time (1 minute) concentrations of PM less than 2.5 microns (PM2.5) or less than 1 microns (PM1.0) in two classrooms in a non-urban elementary school in Rhode Island. For half of each school day, settings were randomized to "high" or "low" for the air cleaner and "on" or "off" for the fan. Descriptive statistics and linear mixed models were used to evaluate the impacts of each set of conditions on PM2.5 and PM1.0 concentrations. The mean half-day concentrations ranged from 3.4-4.1 μg/m3 for PM2.5 and 3.4-3.9 μg/m3 for PM1.0. On average, use of the fan when the air cleaner was on the low setting decreased PM2.5 by 0.53 μg/m3 [95% CI: -0.64, -0.42] and use of the filter on high (compared to low) when the fan was off decreased PM2.5 by 0.10 μg/m3 [95% CI: -0.20, 0.005]. For PM1.0, use of the fan when the air cleaner was on low decreased concentrations by 0.18 μg/m3 [95% CI: -0.36, -0.01] and use of the filter on high (compared to low) when the fan was off decreased concentrations by 0.38 μg/m3 [95% CI: -0.55, -0.21]. In general, simultaneous use of the fan and filter on high did not result in additional decreases in PM concentrations compared to the simple addition of each appliance's individual effect estimates. Our study suggests that concurrent or separate use of an A/C fan and air cleaner in non-urban classrooms with low background PM may reduce classroom PM concentrations., Competing Interests: The authors have declared that no competing interests exist., (Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.)

Black and low-income neighborhoods tend to have higher concentrations of fast-food restaurants and low produce supply stores. Limited access to and consumption of nutrient-rich foods is associated with poor health outcomes. Given the realities of food access, many members within the Black communities grow food as a strategy of resistance to food apartheid, and for the healing and self-determination that agriculture offers. In this paper, we unpack the history of Black people, agriculture, and land in the United States. In addition to our brief historical review, we conduct a descriptive epidemiologic study of community food-growing spaces, food access, and neighborhood racial composition in present day Philadelphia. We leverage one of the few existing datasets that systematically documents community food-growing locations throughout a major US city. By applying spatial regression techniques, we use conditional autoregressive models to determine if there are spatial associations between Black neighborhoods, poverty, food access, and urban agriculture in Philadelphia. Fully adjusted spatial models showed significant associations between Black neighborhoods and urban agriculture (RR: 1.28, 95% CI = 1.03, 1.59) and poverty and urban agriculture (RR: 1.27, 95% CI = 1.1, 1.46). The association between low food access and the presence of urban agriculture was generally increased across neighborhoods with a higher proportion of Black residents. These results show that Philadelphia neighborhoods with higher populations of Black people and neighborhoods with lower incomes, on average, tend to have more community gardens and urban farms. While the garden data is non-temporal and non-causal, one possible explanation for these findings, in alignment with what Philadelphia growers have claimed, is that urban agriculture may be a manifestation of collective agency and community resistance in Black and low-income communities, particularly in neighborhoods with low food access., Competing Interests: Declaration of competing interest Soil Generation, Growing from the Root Project Team, Philadelphia Food Policy Advisory Council., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Although there is increasing interest in reporting results of environmental research efforts back to participants, evidence-based tools have not yet been applied to developed materials to ensure their accessibility in terms of literacy, numeracy, and data visualization demand. Additionally, there is not yet guidance as to how to formally assess the created materials to assure a match with the intended audience., Methods: Relying on formative qualitative research with participants of an indoor air quality study in Dorchester, Massachusetts, we identified means of enhancing accessibility of indoor air quality data report-back materials for participants. Participants (n = 20) engaged in semi-structured interviews in which they described challenges they encountered with scientific and medical materials and outlined written and verbal communication techniques that would help facilitate engagement with and accessibility of environmental health report-back materials. We coupled these insights from participants with best practice guidelines for written materials by operationalizing health literacy tools to produce accessible audience-informed data report-back materials., Results: The resulting data report-back materials had a 7th -grade reading level, and between a 4th -8th grade level of overall document complexity. The numeracy skills required to engage with the material were of the lowest demand, and we incorporated best practices for risk communication and facilitating understanding and actionability of the materials. Use of a rigorous assessment tool provides evidence of accessibility and appropriateness of the material for the audience., Conclusions: We outline a process for developing and evaluating environmental health data reports that are tailored to inspire risk-reduction actions, and are demonstrably accessible in terms of their literacy, numeracy, and data visualization demand. Adapting health literacy tools to create and evaluate environmental data report-back materials is a novel and evidence-based means of ensuring their accessibility., (© 2022. The Author(s).)

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.)

Background: Substandard housing conditions and hazardous indoor environmental exposures contribute to significant morbidity and mortality worldwide. Housing indices that capture the multiple dimensions of healthy housing are important for tracking conditions and identifying vulnerable households. However, most indices focus on physical deficiencies and repair costs and omit indoor environmental exposures, as few national data sources routinely collect this information., Methods: We developed a multidimensional Housing and Environmental Quality Index (HEQI) based on the World Health Organization's Housing and Health Guidelines and applied it to the 2019 American Housing Survey (AHS). The HEQI consisted of ten domains associated with poor health: household fuel combustion, dampness and mold, pests and allergens, lead paint risk, high indoor temperatures, low indoor temperatures, household crowding, injury hazards, inadequate water and sanitation, and ventilation. We evaluated the validity and performance of the HEQI against three housing characteristics (i.e., year built, monthly rent costs, unit satisfaction rating) and two established indices (i.e., Adequacy Index, Poor Quality Index)., Results: Approximately 79% (92 million) of U.S. households reported at least one HEQI domain associated with poor health (mean per household: 1.3; range: 0,8). Prevalent domains included household fuel combustion (61.4%), dampness and mold (15.9%), inadequate water and sanitation (14.3%), and injury hazards (11.9%). Pests and allergens, low indoor temperatures, and injury hazards were consistently associated with older homes, lower rent costs, and lower unit satisfaction. Compared to established housing indices, the HEQI captured four new environmental domains which enabled the identification of 57.7 million (63%) more households with environmental risk factors like mold, cockroaches, crowding, household fuel combustion, and higher building leakage., Conclusions: Indoor environmental exposures are prevalent in U.S. households and not well-captured by existing housing indices. The HEQI is a multidimensional tool that can be used to monitor indoor environmental exposures and housing quality trends in the U.S. Some domains, including radon, pesticides, asbestos, noise, and housing accessibility could not be assessed due to the lack of available data in the AHS. The mounting evidence linking residential environmental exposures with adverse health outcomes underscore the need for this data in the AHS and other national surveys., (© 2022. The Author(s).)