BACKGROUND: Environmental hazards may influence health outcomes and be a driver of health inequalities. We sought to characterize the extent to which social-environmental inequalities were associated with surgical outcomes following a complex operation.
METHODS: In this cross-sectional study, patients who underwent abdominal aortic aneurysm repair, coronary artery bypass grafting, colectomy, pneumonectomy, or pancreatectomy between 2016 and 2021 were identified from Medicare claims data. Patient data were linked with social-environmental data sourced from Centers for Disease Control and Agency for Toxic Substances and Disease Registry data based on county of residence. The Environmental Justice Index social-environmental ranking (SER) was used as a measure of environmental injustice. Multivariable regression analysis was performed to assess the relationship between SER and surgical outcomes.
RESULTS: Among 1,052,040 Medicare beneficiaries, 346,410 (32.9%) individuals lived in counties with low SER, while 357,564 (33.9%) lived in counties with high SER. Patients experiencing greater social-environmental injustice were less likely to achieve textbook outcome (odds ratio 0.95, 95% confidence interval 0.94-0.96, P < 0.001) and to be discharged to an intermediate care facility or home with a health agency (odds ratio 0.97, 95% confidence interval 0.96-0.98, P < 0.001).
CONCLUSIONS: Cumulative social and environmental inequalities, as captured by the Environmental Justice Index SER, were associated with postoperative outcomes among Medicare beneficiaries undergoing a range of surgical procedures. Policy makers should focus on environmental, as well as socioeconomic injustice to address preventable health disparities.

Littering of cigarette butts is a major environmental challenge. In 2022, ~124 billion cigarette butts were littered in the United States. This litter may pose an environmental justice concern by disproportionately affecting human and environmental health in communities of color or communities of low socioeconomic status. However, the lack of data on the distribution and magnitude of cigarette butt littering prevents an environmental justice analysis and limits the ability to tackle this environmental challenge. We conducted an environmental justice assessment of tobacco product waste, specifically cigarette butts, through spatially-explicit, place-based estimates across the contiguous U.S. We built a bottom-up model by synthesizing census tract-level population and smoking prevalence, state-level cigarette consumption, and published littering data to assess the spatial pattern of cigarette consumption and littering, and its implications for environmental injustice in >71,600 U.S. census tracts. Further, we compared the model output to urbanicity (rural-urban commuting area) and Social-Environmental Risk (SER; CDC Environmental Justice Index). Cigarette butt density was not uniformly distributed across the U.S. and ranged from 0-45.5 butts/m2, with an area-weighted average of 0.019 ± 0.0005 butts/m2. Cigarette butt density was 96 times higher in metropolitan vs. rural areas. Cigarette butt density increased significantly with SER, with 5.6 times more littered cigarette butts, and a steeper response to population density, in census tracts with the highest SER vs. the lowest SER. These results demonstrate the relative influences of location, smoking prevalence, and population density, and show that cigarette butt littering is a potential environmental justice concern in the U.S. This study provides information that may help devise targeted strategies to reduce cigarette butt pollution and prevent disproportionate impacts. The spatial data layer with place-based cigarette consumption and butt density is a tool that can support municipal, state, and federal level policy work and future studies on associations among cigarette butt pollution and environmental health outcomes.

UNASSIGNED: Previous studies have linked exposure to concentrated animal feeding operations (CAFOs) with various health outcomes. However, relatively few studies evaluated the impacts of CAFOs on adverse birth outcomes, despite significant public health concerns regarding maternal and child health.
UNASSIGNED: This cross-sectional study investigated the risk of adverse birth outcomes associated with CAFOs exposure and evaluated disparities in exposure to CAFOs and associated health outcomes.
UNASSIGNED: We obtained individual-level birth records from 2003 to 2020 from the Pennsylvania Department of Health. We considered two adverse birth outcomes: (1) preterm birth (PTB); and (2) low birth weight (LBW). Exposure was considered as a binary indicator (presence or absence of CAFO) and as categories based on level of exposure. Logistic regression was applied to estimate the association between CAFOs exposure and adverse birth outcomes. Models were adjusted for infant\'s sex, maternal demographics (age, race/ethnicity, education), prenatal BMI, prenatal care, smoking status, marital status, plurality, WIC status, and urban/rural indicator. We examined both disparities in exposure and in health response.
UNASSIGNED: Presence of CAFOs was associated with higher risk of PTB, with an increasing trend with higher levels of CAFOs exposure. Compared to the no CAFO exposure group, the odds ratios for PTB were 1.022 (95 % confidence interval 1.003, 1.043), 1.066 (1.034, 1.100), 1.069 (1.042, 1.097) for low, medium, and high CAFOs exposure groups, respectively. Some maternal characteristics were associated with a higher CAFO-related risk of PTB. Similar associations were observed for LBW for some characteristics such as mother\'s race/ethnicity, education, WIC status, and urbanicity, although some findings were not statistically significant.
UNASSIGNED: Our findings suggest that presence of CAFOs increases risk of preterm birth. Our results indicate that some maternal characteristics may be associated with higher risk of CAFO-related PTB or LBW. This study can inform future research on disparities in CAFO exposure and associated health burden.

Many local agencies in the United States and other countries are tasked to install air pollution monitoring systems of highly accurate sensors that have high acquisition, operating, and maintenance costs. The need for expanded coverage of air quality measurements across Salt Lake County (SLCO), Utah is being met by mounting air quality and temperature sensors on an expanding fleet of battery electric buses (BEBs). Monitoring air quality from a mobile sensor network provides real-time insights into air pollution patterns at high temporal and spatial resolution. Mobile measurements contribute to assessing residents\' exposure to air pollution, facilitating the implementation of cost-effective public health policies and highlighting disparities. The Electric Bus Air Quality Observation Project was launched in SLCO during July 2021 and has collected millions of observations to date. A BEB traveling at typical traffic speeds (~10 m s-1) can provide multiple measurements along city block lengths of up to ~200 m. With careful analysis that factors in the time response of the differing sensors, variability from block-to-block may be attributed to fine-scale factors (e.g., pollution and heat sources, tree shading and urban vegetation, etc.). Preliminary findings showcase the value of increased coverage and resolution. During an extreme heat event in July 2023, both the morning and afternoon temperature readings showed differences of over 6.5 °C (12 °F), primarily as an east-west gradient with similar gradients in ozone. We conclude that temperature and pollutant concentration readings, at fine spatial and temporal resolutions, will facilitate future health studies and equitable policy and mitigation strategies. Our study demonstrates that our partnerships established with governmental, non-profit, and transit agencies facilitate the successful transfer of research and development to operational real-time mobile air quality monitoring.

BACKGROUND: The United States Environmental Protection Agency (USEPA) regulates over 80 contaminants in community water systems (CWS), including those relevant to infant health outcomes. Multi-cohort analyses of the association between measured prenatal public water contaminant concentrations and infant health outcomes are sparse in the US.
OBJECTIVE: Our objectives were to (1) develop Zip Code Tabulation Area (ZCTA)-level CWS contaminant concentrations for participants in the Environmental Influences on Child Health Outcomes (ECHO) Cohort and (2) evaluate regional, seasonal, and sociodemographic inequities in contaminant concentrations at the ZCTA-level. The ECHO Cohort harmonizes data from over 69 extant pregnancy and pediatric cohorts across the US.
METHODS: We used CWS estimates derived from the USEPA\'s Six-Year Review 3 (2006-2011) to develop population-weighted, average concentrations for 10 contaminants across 7640 ZCTAs relevant to the ECHO Cohort. We evaluated contaminant distributions, exceedances of regulatory thresholds, and geometric mean ratios (with corresponding percent changes) associated with ZCTA sociodemographic characteristics via spatial lag linear regression models.
RESULTS: We observed significant regional variability in contaminant concentrations across the US. ZCTAs were most likely to exceed the maximum contaminant level for arsenic (n = 100, 1.4%) and the health-protective threshold for total trihalomethanes (n = 3584, 64.0%). A 10% higher proportion of residents who were American Indian/Alaskan Native and Hispanic/Latino was associated with higher arsenic (11%, 95% CI: 7%, 15%; and 2%, 95% CI: 0%, 3%, respectively) and uranium (15%, 95% CI: 10%, 21%; and 9%, 95% CI: 6%, 12%, respectively) concentrations.
CONCLUSIONS: Nationwide epidemiologic analyses evaluating the association between US community water system contaminant concentration estimates and associated adverse birth outcomes in cohort studies are sparse because public water contaminant concentration estimates that can be readily linked to participant addresses are not available. We developed Zip Code Tabulation Area (ZCTA)-level CWS contaminant concentrations that can be linked to participants in the Environmental Influences on Child Health Outcomes (ECHO) Cohort and evaluated regional, seasonal, and sociodemographic inequities in contaminant concentrations for these ZCTAs. Future epidemiologic studies can leverage these CWS exposure estimates in the ECHO Cohort to evaluate associations with relevant infant outcomes.

Neglecting indoor air quality in exposure assessments may lead to biased exposure estimates and erroneous conclusions about the health impacts of exposure and environmental health disparities. This study assessed these biases by comparing two types of personal exposure estimates for 100 individuals: one derived from real-time particulate matter (PM2.5) measurements collected both indoors and outdoors using a low-cost portable air monitor (GeoAir2.0) and the other from PurpleAir sensor network data collected exclusively outdoors. The PurpleAir measurement data were used to create smooth air pollution surfaces using geostatistical methods. To obtain mobility-based exposure estimates, both sets of air pollution data were combined with the individuals\' GPS tracking data. Paired-sample t-tests were then performed to examine the differences between these two estimates. This study also investigated whether GeoAir2.0- and PurpleAir-based estimates yielded consistent conclusions about gender and economic disparities in exposure by performing Welch\'s t-tests and ANOVAs and comparing their t-values and F-values. The study revealed significant discrepancies between GeoAir2.0- and PurpleAir-based estimates, with PurpleAir data consistently overestimating exposure (t = 5.94; p < 0.001). It also found that females displayed a higher average exposure than males (15.65 versus. 8.55 μg/m3) according to GeoAir2.0 data (t = 4.654; p = 0.055), potentially due to greater time spent indoors engaging in pollution-generating activities traditionally associated with females, such as cooking. This contrasted with the PurpleAir data, which indicated higher exposure for males (43.78 versus. 46.26 μg/m3) (t = 3.793; p = 0.821). Additionally, GeoAir2.0 data revealed significant economic disparities (F = 7.512; p < 0.002), with lower-income groups experiencing higher exposure-a disparity not captured by PurpleAir data (F = 0.756; p < 0.474). These findings highlight the importance of considering both indoor and outdoor air quality to reduce bias in exposure estimates and more accurately represent environmental disparities.

OBJECTIVE: Contamination in U.S. public drinking water systems (PWS) is estimated to cause millions of illnesses and billions of dollars in medical expenditures annually. Few prior studies have explored intervention strategies, including environmental enforcement, to reduce estimated health-related exposure disparities (exposure disparity) in PWS, which are driven partially by socioeconomic status (SES), racism, and PWS characteristics.
METHODS: This study used a longitudinal measurement method to estimate the annual health-related exposure level (health level) of each PWS in Michigan, based on data from the Enforcement and Compliance Online (ECHO) and U.S. Census Bureau databases. Using a decomposition model with four strategies, we analyzed how eliminating disparities in SES, proportion minority, environmental enforcement, and PWS characteristics across communities would affect adjusted exposure disparities.
RESULTS: This study found that adjusted race- and poverty-based exposure disparities have existed since the 1980s but might have decreased in the last one or two decades. PWS characteristics strongly impacted the crude and adjusted exposure disparity. Environmental enforcement, although less effective in minority-concentrated communities, reduced the adjusted race-based exposure disparity by 10%-20% in the 1980s, 8% in the 1990s, and 0.012% in the 2010s. Equalizing the poverty rate distribution reduced the adjusted race-based exposure disparity by 0.72% in the 1980s and 6.8% in the 2010s. However, equalizing racial and ethnic composition distribution increased the adjusted poverty-based exposure disparity in the 2000s.
CONCLUSIONS: These findings indicate that economically disadvantaged or minority-concentrated communities in Michigan disproportionately suffer from poorer PWS quality. Enhanced environmental enforcement, increased household income, PWS investment, and other actions are needed to address these exposure disparities effectively.

High resolution exposure surfaces are essential to capture disparities in exposure to traffic-related air pollution in urban areas. In this study, we develop an approach to downscale Chemical Transport Model (CTM) simulations to a hyperlocal level (∼100m) in the Greater Toronto Area (GTA) under three scenarios where emissions from cars, trucks and buses are zeroed out, thus capturing the burden of each transportation mode. This proposed approach statistically fuses CTMs with Land-Use Regression using machine learning techniques. With this proposed downscaling approach, changes in air pollutant concentrations under different scenarios are appropriately captured by downscaling factors that are trained to reflect the spatial distribution of emission reductions. Our validation analysis shows that high-resolution models resulted in better performance than coarse models when compared with observations at reference stations. We used this downscaling approach to assess disparities in exposure to nitrogen dioxide (NO2) for populations composed of renters, low-income households, recent immigrants, and visible minorities. Individuals in all four categories were disproportionately exposed to the burden of cars, trucks, and buses. We conducted this analysis at spatial resolutions of 12, 4, 1 km, and 100 m and observed that disparities were significantly underestimated when using coarse spatial resolutions. This reinforces the need for high-spatial resolution exposure surfaces for environmental justice analyses.

Extreme weather events are becoming more severe due to climate change, increasing the risk of contaminant releases from hazardous sites disproportionately located in low-income communities of color. We evaluated contaminant releases during Hurricanes Rita, Ike, and Harvey in Texas and used regression models to estimate associations between neighborhood racial/ethnic composition and residential proximity to hurricane-related contaminant releases. Two-to-three times as many excess releases were reported during hurricanes compared to business-as-usual periods. Petrochemical manufacturing and refineries were responsible for most air emissions events. Multivariable models revealed sociodemographic disparities in likelihood of releases; compared to neighborhoods near regulated facilities without a release, a one-percent increase in Hispanic residents was associated with a 5 and 10% increase in the likelihood of an air emissions event downwind and within 2 km during Hurricanes Rita and Ike (odds ratio and 95% credible interval= 1.05 [1.00, 1.13], combined model) and Harvey (1.10 [1.00, 1.23]), respectively. Higher percentages of renters (1.07 [1.03, 1.11], combined Rita and Ike model) and rates of poverty (1.06 [1.01, 1.12], Harvey model) were associated with a higher likelihood of a release to land or water, while the percentage of Black residents (0.94 [0.89, 1.00], Harvey model) was associated with a slightly lower likelihood. Population density was consistently associated with a decreased likelihood of a contaminant release to air, land, or water. Our findings highlight social inequalities in the risks posed by natural-technological disasters that disproportionately impact Hispanic, renter, low-income, and rural populations.

Pediatric pulmonologists have the expertise to be advocates in many areas that affect the respiratory health of children. This article provides an overview of selected advocacy topics related to health equity and provides key examples that can improve child respiratory health in the clinical encounter and beyond.