Census Tract Level Research Articles

Drug Overdose Death among Residents of Urban Census Tracts: How Granular Geographical Analyses Uncover Socioenvironmental Correlates in Cuyahoga County, Ohio.

Geostatistical data aggregated at state, county, municipality, or ZIP code levels often are utilized for assessing drug overdose epidemic impact and planning resource distribution. Data aggregated at these levels may obscure critical disparities among populations experiencing high rates of drug-related mortality (DRM), especially in densely populated urban areas. Our research was centered on Cuyahoga County (Cleveland), OH, which ranks 15th in the USA for drug-related mortality. This study built on recent efforts that adopted a finer geographical lens by examining DRM rates at the census tract level. Our investigation used Cuyahoga County census tracts with high and low DRM rates and compared them with Cuyahoga County census tracts with high and low levels of opportunity as developed by a publicly available, statewide opportunity index. Analyzing DRM data from 2014 to 2022, we found that the odds of an individual experiencing DRM in low-opportunity areas were quadruple the odds for someone in high-opportunity zones. Our findings highlight the critical need for more granular geographic analysis in urban areas, where heterogenous socioenvironmental conditions appear to correlate with significant heterogeneity in the ways in which residents experience the risk of dying from a drug overdose. By focusing on smaller areas, this approach provides a clearer understanding of the DRM landscape that could facilitate the prioritization of more targeted, culturally centered, public health interventions.

Social determinants of health and variability in treatment for patients with early-stage Non-Small Cell Lung Cancer.

In non-small cell lung cancer (NSCLC), social determinants of health (SDOHs) influence treatment, but SDOHs with geographic precision are infrequently used in real-world research due to privacy considerations. This research aims to characterize the influence of census-tract level SDOHs on treatment for stage I and IIa NSCLC. Patients diagnosed between 1/1/17 and 9/30/22 with stages I and IIa NSCLC in the Syapse Learning Health Network had their addresses geocoded and linked to five census tract-level indicators of SDOH (social vulnerability index (SVI), percent (%) housing burden, % broadband internet access, primary care shortage area, and rurality). Clinical and demographic characteristics were ascertained from medical records. Nested multinomial logistic regression models estimated associations between SDOHs and initial treatment using two-sided Wald tests. The collective statistical significance of SDOHs was assessed with a likelihood ratio test (LRT) comparing nested models. Descriptive statistics described time-to-treatment-initiation. Among 3595 patients, 58% were initially treated with surgery, 29% with radiation, and 12% with "other." Two SDOH variables were associated with increased relative risk ratios (RRR) for radiation therapy compared to surgery: living in primary care shortage areas (RRR 1.61, 95% CI: (1.23-2.10)) and living in non-metropolitan areas (RRR 1.45 (1.02-2.07)). The LRT suggested that the five SDOH variables collectively improved the treatment model. Further, patients in areas with high SVI, low internet access, and high housing-burden initiated treatment later. When using precise estimates of geospatial SDOHs, these measures were associated with treatment, and should be considered in analyses of cancer outcomes.

Disparities in Anti-emetic Prophylaxis Care Processes are Predicted by Patient Neighborhood: A Retrospective Cohort and Geospatial Analysis.

Social Determinants of Health (SDoH) continue to drive persistent disparities in perioperative care. Our team has previously demonstrated racial and socioeconomic disparities in perioperative processes, notably in the administration of antiemetic prophylaxis, in several large perioperative registries. Given how neighborhoods are socially segregated in the US, we examined geospatial clustering of perioperative antiemetic disparities. We conducted a retrospective cohort study of anesthetic records from the University of Utah Hospital with 19,477 patients meeting inclusion criteria. We geocoded patient home addresses and combined them with the Census Block Group(CBG) level neighborhood disadvantage (ND), a composite index of from the National Neighborhood Data Archive (NaNDA). We stratified our patients by antiemetic risk score and calculated the number of anti-emetic interventions. We utilized Poisson Spatial Scan Statistics, implemented in SaTScan, to detect geographic clusters of under-treatment. We identified one significant cluster (p < .001) of undertreated perioperative antiemetic prophylaxis cases. The relative risk (RR) of the whole cluster is 1.44, implying that patients within the cluster are 1.44 times more likely to receive fewer antiemetics after controlling for antiemetic risk. Patients from more disadvantaged neighborhoods were more likely to receive below median antiemetic prophylaxis after controlling for risk. To our knowledge, this is the first geospatial cluster analysis of perioperative process disparities; we leveraged innovative geostatistical methods and identified a spatially defined, geographic cluster of patients whose home address census-tract level neighborhood deprivation index predicted disparities in risk adjusted antiemetic prophylaxis.

Social determinants of health and mild cognitive impairment in a diverse community sample.

The associations between community-wide social determinants of health and mild cognitive impairment (MCI) among individuals warrant investigation. Among 2830 dementia-free individuals aged 65+ years in a community-based US study, we examined cross-sectional associations of MCI (Clinical Dementia Rating = 0.5) with the following potential social determinants of health: at the census tract or block group level obtained from public sources: neighborhood disadvantage (Area Deprivation Index, ADI), air pollution with fine particulate matter (PM2.5), greenspace, Walkability Index, ambulatory healthcare availability per square mile, homicide rate; and at the individual participant level, birth/schooling in a southern US state. Unadjusted logistic regression models found higher odds ratios (OR, 95% CI) for MCI with higher ADI (1.01, 1.003-1.02), higher PM2.5 (1.16, 1.07-1.26), higher homicide rate (1.007, 1.001-1.012), lesser greenspace (0.99, 0.90-0.99), and southern schooling (2.06, 1.6-3.54). Adjusting for age, race, sex, and educational level, ADI remained statistically significant (1.04, 1.01-1.06), while PM2.5 and Southern state schooling interacted significantly with race and were more strongly affected in Black participants than in White participants. In this cohort, several community-wide social/environmental factors were associated with MCI. While clinicians should continue to encourage older adults to modify their individual risk factors, policy changes are needed to mitigate social determinants of health in the community.

Application of Machine Learning and Deep Neural Visual Features for Predicting Adult Obesity Prevalence in Missouri.

This research study investigates and predicts the obesity prevalence in Missouri, utilizing deep neural visual features extracted from medium-resolution satellite imagery (Sentinel-2). By applying a deep convolutional neural network (DCNN), the study aims to predict the obesity rate of census tracts based on visual features in the satellite imagery that covers each tract. The study utilizes Sentinel-2 satellite images, processed using the ResNet-50 DCNN, to extract deep neural visual features (DNVF). Obesity prevalence data, sourced from the CDC's 2022 estimates, is analyzed at the census tract level. The datasets were integrated to apply a machine learning model to predict the obesity rates in 1052 different census tracts in Missouri. The analysis reveals significant associations between DNVF and obesity prevalence. The predictive models show moderate success in estimating and predicting obesity rates in various census tracts within Missouri. The study emphasizes the potential of using satellite imagery and advanced machine learning in public health research. It points to environmental factors as significant determinants of obesity, suggesting the need for targeted health interventions. Employing DNVF to explore and predict obesity rates offers valuable insights for public health strategies and calls for expanded research in diverse geographical contexts.

Abstract 4134079: Neighborhood-Level Indices of Social Disadvantage and Cardiovascular-Kidney-Metabolic Health

Background: Several area-level indices exist that integrate a variety of social drivers of health. However, whether certain indices better capture neighborhood-level variability in cardiovascular-kidney-metabolic (CKM) outcomes is not well known. Aims: We sought to examine the associations of census tract-level indices of social disadvantage with CKM outcomes. Methods: Exposures for the analysis included seven indices of social disadvantage (ADI, COI, EJI, NDI, SDI, SREI, SVI) based on census tract-level data from the 2010-2019 American Community Survey (ACS) and census tract-level median household income from the 2015-2019 ACS data. Outcomes included the census tract-level prevalence of CKM outcomes (obesity, hypertension, diabetes, hyperlipidemia, chronic kidney disease, coronary heart disease [CHD], and stroke) from the 2019 Behavioral Risk Factor Surveillance System. All indices and median income were standardized, and we used linear regression to examine the associations between each census tract-level social measure and CKM outcomes, adjusted for population size and median age. Next, we calculated the △r 2 in adjusted models for the association of median income with each CKM outcome when each index was added. Results: Among 65,476 US census tracts, median (IQR) population size was 4068 (2969, 5374), age was 39 years (35, 44), and household income was $58,870 (44276, 79802). Median income as well as each index was significantly associated with census tract-level prevalence of each CKM outcome assessed (p &lt; 0.05). The r 2 (Panel A) and △r 2 values (Panel B) varied for each of the social measures and CKM outcomes, with higher r 2 values when each index was individually added to median income compared with income alone. For example, for CHD, the r 2 ranged between 0.39 to 0.67 and △r 2 ranged from 0.01 to 0.10. Conclusions: Neighborhood-level measures of social disadvantage are differentially associated with CKM outcomes, with a large proportion of the variability explained by median income. Identifying the advantages and disadvantages of each index and comparison with median income can inform the prioritization of measures for specific outcomes.

Gun Free Zones in Alcohol-Serving Establishments and Risk for Firearm Violence: A Cross-Sectional, Geospatial Study in Texas.

To date, there have been no peer-reviewed studies in the United Statesestimating the impact of gun-free zonepolicies in alcohol-serving establishments on rates of firearm violencein and aroundsuch establishments. In this study, we utilized a cross-sectional design to estimate the impact of Texas's 51% alcohol law, which prohibits the carrying of firearms in establishments that generate over half of their revenue from alcohol sales. The analysis focused on the difference in shooting incidents in and around establishments with and without firearm carrying prohibitions in 2021 and 2022. After adjusting for establishment type (bar/restaurant), alcohol sales volume, census tract level demographic factors, and the number of nearby restaurants and bars, results indicated that gun-prohibiting bars experienced significantly fewer shootings compared to those that allowed guns. Specifically, establishments that were gun-prohibited had 37% fewer shootings within 50m than those that were gun-allowing, with a 95% confidence interval ranging from 60% fewer to 0.2% fewer. This association was more prominent in bars than in restaurants. The protective association with gun-prohibited status diminished with increased distance from the establishments; results were not significant at 100m. Our study findings align with research suggesting that gun-free zones can reduce firearm violence. However, future studies using quasi-experimental designs that can better support causal inference are needed to support such a conclusion, as are studies exploring the efficacy of such policies in various settings and over longer periods.

The association between composite measures of social vulnerability and PICU admission for status asthmaticus.

Current knowledge of the impact of socioeconomic factors on the risk of admission to the pediatric intensive care unit (PICU) for asthma is limited. Using composite measures of social vulnerability-Social Vulnerability Index (SVI) and Child Opportunity Index (COI) 2.0-we compared patients admitted for status asthmaticus to the PICU and pediatric ward at Children's Hospital Los Angeles (CHLA). We hypothesized patients with a high SVI and low COI are at higher risk for PICU admission. Patients were identified using ICD-10 codes for asthma. Primary outcome was admission to PICU versus ward for status asthmaticus. Patient-registered residential street addresses were geocoded and spatially joined to SVI and COI 2.0 data at the census tract level. Univariate and regression analyses using the patient's SVI, COI 2.0, and admission location were conducted. From January 2017 to March 2022, there were 2458 admissions matched to addresses from 1983 distinct patients. The overall median SVI for all patients was 0.86 (IQR 0.6, 0.9). Overall median COI was 25.0 (IQR 10, 50). There was no difference in SVI or COI for admission to the PICU versus the ward. However, children requiring multiple hospital admissions for asthma were associated with higher SVI and lower COI. Children admitted to CHLA for asthma had an elevated SVI and low COI. There was no difference between admission locations based on SVI or COI scores. This indicates we care for children at increased socioeconomic risk, but this did not increase PICU use for asthma.

Neighborhood Ecologies, Cases, and Deaths during the Beginning of the COVID-19 Pandemic: Lessons for Current and Future Epidemics?

The coronavirus disease 2019 (COVID-19) pandemic has disproportionately affected Black and Latinx communities. Ecologic analyses have shown that counties with a higher percentage of Latinx and Black people have worse COVID-19 outcome rates. Few ecologic analyses have been published at the neighborhood (census tract) level. We sought to determine whether certain sociodemographic neighborhood ecologies were associated with COVID-19 case and death rates in metropolitan Atlanta, Georgia. We used census data and principal-component analysis to identify unique neighborhood ecologies. We then estimated correlation coefficients to determine whether the neighborhood profiles produced by a principal-component analysis were correlated with COVID-19 case and death rates. We conducted geographically weighted regression models to assess how correlation coefficients varied spatially for neighborhood ecologies and COVID-19 outcomes. We identified two unique neighborhood profiles: (1) high percentage of residents, Hispanic ethnicity, without a high school diploma, without health insurance, living in crowded households, and lower percentage older than 65 years; and (2) high percentage of residents, Black race, living in poverty, unemployed, and households receiving Supplemental Nutrition Assistance Program benefits. Profile 1 was associated with COVID-19 case rate (Pearson r = 0.462, P < 0.001) and profile 2 was associated with COVID-19 death rate (Spearman r = 0.279, P < 0.001). Correlations between neighborhood profiles and COVID-19 outcomes varied spatially. Neighborhoods were differentially at risk of COVID-19 cases or deaths depending on their sociodemographic ecology at the beginning of the COVID-19 pandemic. Prevention methods and interventions may need to consider different social determinants of health when addressing potential cases and deaths during future emergent epidemics.

Social factors related to gun violence in urban United States

IntroductionGun violence (GV), acknowledged as a public health crisis, disproportionately affects urban centers in the United States. However, the precise connections between social factors and GV are not fully understood. This study, leveraging the Cardiff Model approach, evaluates whether there is a statistical and geospatial relationship between social vulnerability index (SVI) and GV. MethodsIn this cross-sectional study, we combined Gun Violence Archive (GVA), a dataset of firearm violence (2019–2023), with corresponding census tract level SVIs for three selected cities—Atlanta, GA; St. Louis, MO; and Washington, DC. The GVA was created, due to gaps in government-based data, from daily public records and media by an independent data collection group. Also, the SVI, derived from a place-based index from Centers for Disease Control (CDC), was originally used to evaluate the resiliency of communities to recover from socio-environmental stressors. GV data from these three cities, representative of major urban centers in the United States, were used to conduct various analyses. We used Poisson regression to assess whether nine of the sixteen SVI measures were statistically associated with GV incidents. Furthermore, we examined whether spatial relationships between SVI and GV differ by level of concentrated disadvantage in urban neighborhoods. ResultsOut of all GV incidents (10,442), corresponding to 488 census tracts, teenagers ages 12–17 were found to have higher GV rates in St. Louis (12 %) than Atlanta (7.6 %) and Washington DC (10.5 %). Children, ages 0–11, in St. Louis (3.1 %) were involved in more GV incidents than the other two (Washington DC-1.2 % and Atlanta-2.3 %) cities. In the composite model, census tract-level SVIs were statistically significantly associated with GV. Some of the most prominent SVIs, viewed as predictors of GV, included racial-ethnic minority [Incidence Rate Ratio (IRR): 1.02, 95 % CI: 1.01–1.02, p<0.001], 150 % of Federal Poverty Level (FPL) [IRR: 1.00, 95 % CI: 1.00–1.01, p<0.001], and unemployment (IRR: 1.01, p<0.001). Also, many of the SVI variables were found to be significantly associated with GV incidents in each of the three selected cities. ConclusionsIn this original study, we found that there was a strong statistically significant association between SVI-related disadvantaged neighborhoods and increased GV incidents. Additionally, we found that the geospatial distribution of GV incidents were more concentrated in neighborhoods with increased vulnerability throughout the three (Atlanta, GA; St. Louis, MO; and Washington, DC) selected cities than those neighborhoods with decreased vulnerability. Unique approaches such as integration of the SVI with gun control legislation, which informs ownership, use, and access to firearms, provides a better strategy to inform the implementation of an interdisciplinary approach such as the Cardiff Model. GV policy reform, from the public health and criminology lens, would help reduce the prevalence of GV through the promotion of social resilience within communities.

Structural Influences on Methamphetamine Use Among Black Sexual Minority Men (HISTORY Study): Protocol for a Longitudinal Cohort Study.

Sexual minority men are disproportionately affected by methamphetamine use, with recent studies suggesting an increase in use specifically among Black sexual minority men. Black sexual minority men face unique structural barriers to achieving optimal health. Given its harmful effects, and in light of existing health disparities, an increase in methamphetamine use among Black sexual minority men poses a significant public health concern. The Health Impacts and Struggles to Overcome the Racial Discrimination of Yesterday (HISTORY) study is investigating the potential impacts of exposure to the census tract-level structural racism and discrimination (SRD) on methamphetamine use among Black sexual minority men in Atlanta, Georgia, and will identify intervention targets to improve prevention and treatment of methamphetamine use in this population. This study uses a mixed methods and multilevel design over a 5-year period and incorporates participatory approaches. Individual-level quantitative data will be collected from a community-based cohort of Black sexual minority men (N=300) via periodic assessment surveys, ecological momentary assessments, and medical record abstractions. Census tract-level measures of SRD will be constructed using publicly available administrative data. Qualitative data collection will include longitudinal, repeated in-depth interviews with a subset (n=40) of study participants. Finally, using a participatory group model-building process, we will build on our qualitative and quantitative data to generate causal maps of SRD and methamphetamine use among Black sexual minority men, which in turn will be translated into actionable recommendations for structural intervention. Enrollment in the HISTORY study commenced in March 2023 and is anticipated to be completed by November 2024. The HISTORY study will serve as a crucial background upon which future structural interventions can be built, to mitigate the effects of methamphetamine use and SRD among Black sexual minority men. DERR1-10.2196/63761.

Understanding the zero-emission vehicle market spatial diffusion and its determinants from 2019 to 2022 using spatial econometric models

Due to the rapid rise in the acceptance of zero-emission vehicles (ZEVs), much research has been conducted on the market evolution of alternative fuel technology. The research reported in this paper builds on prior studies by incorporating several new directions. Firstly, the spatial distribution of ZEV purchases in California is explored, then seven models (two nonspatial models and five spatially explicit models) are developed to predict regional ZEV sales. The predictions are based on fundamental and widely accessible characteristics such as infrastructure and demographic statistics. Next, a spatial econometric model known as the “spatial error model” is chosen to distribute ZEV sales from their original postal code (ZIP) level to the more often utilized US census tract level. This allows for carrying out a comprehensive analysis that includes studying the correlation of ZEV sales with local incentives and disadvantaged communities. Results reveal that ZEV sales in California increased significantly between 2019 and 2022, as spatial autocorrelation decreased. Vulnerable groups were less likely to have access to ZEVs, particularly during the initial stage of the ZEV market penetration in 2019. As expected, regions with considerable ZEV sales tended to also experience substantial purchase rebates and lower disadvantaged population, especially in 2019.

Monitoring changes in walkability over time: An environmental exposure change detection framework with implications for equity and social justice

Evidence suggests that walkability and greenspace impact travel related activity patterns and vehicle emissions which affect sustainability, public health, and equity. Resulting levels of physical activity, active, or sedentary travel time impact obesity, diabetes, and heart disease which impact COVID-19 mortality. It is now possible to track changes in locally controlled land use characteristics known to impact sustainability and health. This information can provide decisionmakers with feedback required to spatially prioritize and better link state and nationally funded transportation investments with locally sanctioned land use actions. Linking the achievement of established benchmarks of health equity-based indicators with funding establishes a more performance-based approach connecting land use with transportation investment. This study longitudinally tracks neighborhood-level walkability features at the census tract level for 2013 and 2020 for the entire USA. Longitudinal volatility-based change detection models are developed to examine how changes in walkability over time correlate with racialization and social justice. Walkability tends to increase over time with significant variations across metro regions and the urban-rural continuum. Largest and smallest increases in walkability were observed in Western Pacific and Northwest states, respectively. Increased racial and social justice disparities were observed in access to more walkable infrastructure by marginalized populations (such as less-educated, older, unemployed, and black individuals). Significant heterogeneity in the spatial distribution of walkability was observed, over the variation captured by observed sociodemographic, regional, and urban/rural factors. The findings highlight the potential for an “environmental surveillance” system to support a “performance-based” approach to transportation funding that prioritizes resource allocation consistent with Justice40 and United Nation's Sustainable Development Goals.

The role of ethnic enclaves and neighborhood socioeconomic status in invasive breast cancer incidence rates among Asian American, Native Hawaiian, and Pacific Islander females in California.

Few studies have examined whether the incidence rates of invasive breast cancer among Asian American, Native Hawaiian, and Pacific Islander (AANHPI) populations differ by the neighborhood social environment. Thus, we examined associations of ethnic enclave and neighborhood socioeconomic status (nSES) with breast cancer incidence rates among AANHPI females in California. A total of 14,738 AANHPI females diagnosed with invasive breast cancer in 2008-2012 were identified from the California Cancer Registry. AANHPI ethnic enclaves (culturally distinct neighborhoods) and nSES were assessed at the census tract level using 2007-2011 American Community Survey data. Breast cancer age-adjusted incidence rates and incidence rate ratios (IRRs) were estimated for AANHPI ethnic enclave, nSES, and their joint effects. Subgroup analyses were conducted by stage of disease. The incidence rate of breast cancer among AANHPI females living in lowest ethnic enclave neighborhoods (quintile (Q)1) were 1.21 times (95% Confidence Interval (CI) 1.11, 1.32) that of AANHPI females living highest ethnic enclave neighborhoods (Q5). In addition, AANHPI females living in highest vs. lowest SES neighborhoods had higher incidence rates of breast cancer (Q5 vs. Q1 IRR = 1.30, 95% CI 1.22 to 1.40). The incidence rate of breast cancer among AANHPI females living in low ethnic enclave + high SES neighborhoods was 1.32 times (95% CI 1.25, 1.39) that of AANHPI females living in high ethnic enclave + low SES neighborhoods. Similar patterns of associations were observed for localized and advanced stage disease. For AANHPI females in California, incidence rates of breast cancer differed by nSES, ethnic enclave, when considered independently and jointly. Future studies should examine whether the impact of these neighborhood-level factors on breast cancer incidence rates differ across specific AANHPI ethnic groups and investigate the pathways through which they contribute to breast cancer incidence.

Spatial and Racial/Ethnic Variation in the Prevalence of Cesarean Delivery in a South Carolina Medical Center.

While racial/ethnic disparities in cesarean delivery have been noted in the literature, less is known about the intersection between individual-level race/ethnicity and community-level social vulnerability as factors in cesarean delivery. The goal was to use medical record data from a large medical center combined with census tract-level data to examine patterns of social vulnerability, racial population distribution, and prevalence of cesarean delivery. Data were obtained from electronic medical records of patients from a large medical center in South Carolina from 2019 to 2020. The outcome variable was cesarean delivery (yes/no), and covariates included the year of delivery; age of patient; race/ethnicity; spoken language; BMI categories; clinical indications of anemia, hypertension, preeclampsia, and diabetes; and census tract Social Vulnerability Index (SVI). Generalized linear mixed models for multilevel binary logistic regression were used to test the main hypothesis that the census tract level Social Vulnerability Index is positively associated with cesarean delivery. Among a total of 5011 patients, we found that non-Hispanic Black mothers were more likely to have cesarean deliveries compared with non-Hispanic White mothers. After controlling for census tract-level SVI, the individual-level race/ethnicity association was no longer significant. Significant spatial autocorrelation across census tracts was evident for cesarean delivery prevalence, non-Hispanic Black population, and SVI. A high prevalence of cesarean delivery tended to cluster with high SVI and a high non-Hispanic Black population. We found that non-Hispanic Black mothers were more likely to have cesarean deliveries, which was explained by census tract differences in the SVI.

A Disparate Disaster: Spatial Patterns of Building Damage Caused by Hurricane Ian and Associated Socio-Economic Factors

The literature shows that communities under different socio-economic conditions suffer different levels of damage in disasters. In addition to the physical intensity of hazards, such differences are also related to the varying abilities of communities to prepare for and respond to disasters. This study analyzes the spatial patterns of building damage in Hurricane Ian in 2022 and investigates the socio-economic disparities related to the damage. Specifically, this study employs NASA’s Damage Proxy Map (DPM2) to analyze spatial patterns of building damage caused by the hurricane. Then, it uses statistical analysis to assess the relationships between building damage and hurricane intensity, building conditions, and socio-economic variables at the building and census tract levels. Furthermore, the study applies geographically weighted regression (GWR) to examine the spatial variation of the damage factors. The results provide valuable insights into the potential factors related to building damage and the spatial variation in the factors. The results also reveal the uneven distribution of building damage among different population groups, implying socio-economic inequalities in disaster adaptation and resilience. Moreover, the study provides actionable information for policymakers, emergency responders, and community leaders in formulating strategies to mitigate the impact of future hurricanes by identifying vulnerable communities and population groups.

Energy burden and mental health: A national study in the United States

The prevalence of mental health issues in the US has significantly risen over the past decade, and it is presumably linked to an energy burden issue that has recently gained attention as a critical social determinant of mental health. Utilizing extensive nationwide datasets at the census tract, we found that the census tract level energy burden is positively associated with two key mental health indicators even after accounting for living, housing, and sociodemographic characteristics: the prevalence of frequent mental distress and physician-diagnosed depression, across all US urban areas. We also observe that these associations are consistent across various climate regions. The findings highlight that energy burden has a detrimental impact on mental health, and that it should be e considered a significant social determinant of health in future studies. Lastly, our study advocates for national policies to achieve energy justice and address disparities in mental health.

Association between outdoor artificial light at night and low birth weight in California.

Artificial light at night (ALAN) is a growing environmental issue associated with adverse health consequences, but the association with low birth weight remains unknown. We aim to explore associations between outdoor ALAN and low birth weight across 7,808 census tracts in California. We use data from the California Communities Environmental Health Screening Tool (CalEnviroScreen) at the census tract level. VIIRS Nighttime Day/Night Band Composites Version 1 dataset within the Google Earth Engine platform was used to assess outdoor ALAN. Multivariable linear regression was used to analyze the data. Our findings showed that an interquartile range increase in outdoor ALAN resulted in an increase of 0.42% (95% CI: 0.37, 0.46) in low birth weight rate on average, after adjusting for all potential covariates. The results demonstrate a significant association between outdoor ALAN and low birth weight. These associations remained consistent in sensitivity analyses.

Racial and Ethnic Disparities in HIV Diagnosis Rates by Social Determinants of Health at the Census Tract Level among Adults in the United States and Puerto Rico, 2021.

To compare racial and ethnic disparities in HIV diagnosis rates among adults in census tracts with most disadvantaged vs advantaged levels of social determinants of health (SDOH). In this ecological analysis, we used the National HIV Surveillance System data in 2021 and SDOH data from 2017-2021 American Community Survey. We measured racial and ethnic disparities stratified by sex in the most disadvantaged quartiles and advantaged quartiles for: 1) Poverty 2) Education level 3) Median household income and 4) Insurance coverage. We calculated 8 relative disparity measures (Black-to-White rate ratio [RR], Hispanic/Latino-to-White RR, Index of Disparity [ID], population-weighted ID, Mean Log Deviation, Theil Index, Population Attributable Proportion, Gini coefficient) and 4 absolute disparity measures (Black-to-White rate difference [RD], Hispanic/Latino-to-White RD, absolute ID, and population-weighted absolute ID). Comparing the most disadvantaged quartiles to the most advantaged quartiles, all four absolute disparity measures decreased, but 7 of the 8 relative disparity measures increased: the median percentage decrease in the absolute measures for males and females respectively was 38.1% and 47.6% for poverty, 12.4% and 42.6% for education level, 43.6% and 44.0% for median household income, and 44.2% and 45.4% for insurance coverage. The median percentage increases for the relative measures for males and females respectively were 44.3% and 61.3% for poverty, 54.9% and 95.3% for education level, 19.6% and 90.0% for median household income, and 32.8% and 46.4% for insurance coverage. Racial and ethnic disparities in the most disadvantaged and advantaged quartiles highlight the need for strategies addressing the root causes of disparities.

Are new pediatric trauma centers located close to the high-risk populations? A geolocation study.

Higher rates of injury occur in children who live in low socioeconomic areas. Since 2010, the number of verified Level I and Level II pediatric trauma centers (PTCs) has doubled. The purpose of this study is to look at the location of new verified PTC in relation to children living in high-risk areas. Historical and recent data about verified Level I and Level II PTCs were obtained from the American College of Surgeons (ACS), the American Trauma Society, and State Data where available. Census data were obtained from the US Census Bureau's American Community Survey for 2010 and 2020 for children younger than 18 years. The pediatric population was stratified by (1) poverty threshold and (2) living within 30 miles of PTC. The census data and trauma center locations were geocoded using the ArcGIS Pro program. Data analysis was performed at the census tract level. A total of 55 Level I or Level II ACS-verified PTCs were in the United States in 2010 and 116 in 2020. In 2010, there were 14.5 million children (19.6%) below the poverty threshold and 12.9 million (17.8%) in 2020. In 2010, 23 states did not have an ACS-verified PTC either in the state or in a neighboring state within 30 miles. By 2020, only nine states did not have an ACS-verified PTC or a center in a neighboring state within 30 miles. When state level data were included, this dropped to four. The 19 states who had new verified PTCs covered between 14% and 21% of children below the poverty level. The percentage of children below the poverty threshold and less than 30-mile access to a Level I or Level II PTC in 2010 was 74.56% compared with children above poverty and less than 30-mile access, which was 70.34% (p < 0.0001). In 2020, children below poverty without 30-mile access had been reduced to 52% and 45.54% for children above poverty level (p < 0.0001). Thus, there was a greater increase in 30-mile access for children above the poverty level than for those below the poverty level (24.8% vs. 22.6%, p < 0.001). On a national level, despite doubling the number of PTC, it has not improved 30-mile access for high-risk children. Alternatively, 19 states now have better access. Retrospective Cohort Study, Level III.