Urban Census Tracts 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.

Geographic Variations in Urban‐Rural Particulate Matter (PM2.5) Concentrations in the United States, 2010–2019

AbstractFine particulate matter 2.5 (PM2.5) is a widely studied pollutant with substantial health impacts, yet little is known about the urban‐rural differences across the United States. Trends of PM2.5 in urban and rural census tracts between 2010 and 2019 were assessed alongside sociodemographic characteristics including race/ethnicity, poverty, and age. For 2010, we identified 13,474 rural tracts and 59,065 urban tracts. In 2019, 13,462 were rural and 59,055 urban. Urban tracts had significantly higher PM2.5 concentrations than rural tracts during this period. Levels of PM2.5 were lower in rural tracts compared to urban and fell more rapidly in rural than urban. Rural tract annual means for 2010 and 2019 were 8.51 [2.24] μg/m3 and 6.41 [1.29] μg/m3, respectively. Urban tract annual means for 2010 and 2019 were 9.56 [2.04] μg/m3 and 7.51 [1.40] μg/m3, respectively. Rural and urban majority Black communities had significantly higher PM2.5 pollution levels (10.19 [1.64] μg/m3 and 9.79 [1.10] μg/m3 respectively), in 2010. In 2019, they were: 7.75 [1.1] μg/m3 and 7.09 [0.78] μg/m3, respectively. Majority Hispanic communities had higher PM2.5 levels and were the highest urban concentration among all races/ethnicities (8.01 [1.73] μg/m3), however they were not the highest rural concentration among all races/ethnicities (6.22 [1.60] μg/m3) in 2019. Associations with higher levels of PM2.5 were found with communities in the poorest quartile and with higher proportions of residents age<15 years old. These findings suggest greater protections for those disproportionately exposed to PM2.5 are needed, such as, increasing the availability of low‐cost air quality monitors.

Quantifying urban park use in the USA at scale: empirical estimates of realised park usage using smartphone location data

A large body of evidence connects access to greenspace with substantial benefits to physical and mental health. In urban settings where access to greenspace can be limited, park access and use have been associated with higher levels of physical activity, improved physical health, and lower levels of markers of mental distress. Despite the potential health benefits of urban parks, little is known about how park usage varies across locations (between or within cities) or over time. We estimated park usage among urban residents (identified as residents of urban census tracts) in 498 US cities from 2019 to 2021 from aggregated and anonymised opted-in smartphone location history data. We used descriptive statistics to quantify differences in park usage over time, between cities, and across census tracts within cities, and used generalised linear models to estimate the associations between park usage and census tract level descriptors. In spring (March 1 to May 31) 2019, 18·9% of urban residents visited a park at least once per week, with average use higher in northwest and southwest USA, and lowest in the southeast. Park usage varied substantially both within and between cities; was unequally distributed across census tract-level markers of race, ethnicity, income, and social vulnerability; and was only moderately correlated with established markers of census tract greenspace. In spring 2019, a doubling of walking time to parks was associated with a 10·1% (95% CI 5·6-14·3) lower average weekly park usage, adjusting for city and social vulnerability index. The median decline in park usage from spring 2019 to spring 2020 was 38·0% (IQR 28·4-46·5), coincident with the onset of physical distancing policies across much of the country. We estimated that the COVID-19-related decline in park usage was more pronounced for those living further from a park and those living in areas of higher social vulnerability. These estimates provide novel insights into the patterns and correlates of park use and could enable new studies of the health benefits of urban greenspace. In addition, the availability of an empirical park usage metric that varies over time could be a useful tool for assessing the effectiveness of policies intended to increase such activities. Google.

Social inequities in food deserts and food swamps in a northeastern Brazilian capital.

This study identified food deserts and swamps, investigating their associations with socioeconomic and demographic conditions. This ecological study was conducted using data from urban census tracts in the city of Recife, which were considered the unit of analysis. Information on food retail was obtained from government sources in 2019. Census tracts below the 25th percentile in the density of healthy food retail (i.e., those that predominantly sell natural or minimally processed foods, mixed businesses, and super- and hypermarkets) were classified as food deserts. Census tracts above the 25th percentile in the density of unhealthy food retail (i.e., those selling primarily ultra-processed foods) were considered food swamps. The socioeconomic and demographic conditions of the census tracts were evaluated using variables from the 2010 census (per capita income, average income, race, literacy of the head of household, and the availability of essential services) and the Health Vulnerability Index. Census tracts considered food deserts (28.5%) were more vulnerable, characterized by lower income and access to essential services, more illiterate residents and more minorities (Black/Indigenous/mixed race). Food swamps (73.47%) were more prevalent in less vulnerable neighbourhoods characterized by higher percentages of literate residents and Whites, greater purchasing power, and better basic sanitation. The characteristics of Recife's food deserts and swamps demonstrate social inequalities in the food environment. Public facilities could play a vital role in promoting healthy eating within food deserts. Additionally, future implementation of taxes on ultra-processed foods and the provision of tax subsidies to natural or minimally processed food sellers might contribute to fostering healthier dietary choices.

Features of the Physical and Social Neighborhood Environment and Neighborhood-Level Alzheimer's Disease and Related Dementia in South Carolina.

Studies are increasingly examining the relationship between the neighborhood environment and cognitive decline; yet, few have investigated associations between multiple neighborhood features and Alzheimer's disease and related dementias (ADRD). We investigated the relationship between neighborhood features and ADRD cumulative incidence from 2010 to 2014 in the South Carolina Alzheimer's Disease Registry (SCADR). Diagnosed ADRD cases of age were ascertained from the SCADR by ZIP code and census tract. Neighborhood features from multiple secondary sources included poverty, air pollution [particulate matter with a diameter of 2.5 micrometers or less ()], and rurality at the census-tract level and access to healthy food, recreation facilities, and diabetes screening at the county level. In addition to using Poisson generalized linear regression to estimate ADRD incident rate ratios (IRR) with 95% confidence intervals (CIs), we applied integrated nested Laplace approximations and stochastic partial differential equations (INLA-SPDE) to address disparate spatial scales. We estimated associations between neighborhood features and ADRD cumulative incidence. The average annual ADRD cumulative incidence was 690 per 100,000 people per census tract (95% CI: 660, 710). The analysis was limited to 98% of census tracts with a population old (i.e., 1,081 of 1,103). The average percent of families living below the federal poverty line per census tract was 18.8%, and of census tracts were considered rural. The average percent of households with limited access to healthy food was 6.4%. In adjusted models, every ) increase of was associated with 65% higher ADRD cumulative incidence (; 95% CI: 1.30, 2.09), where at or below is considered healthy. Compared to large urban census tracts, rural and small urban tracts had 10% (; 95% CI: 1.00, 1.23) and 5% (; 95% CI: 0.96, 1.16) higher ADRD, respectively. For every percent increase of the county population with limited access to healthy food, ADRD was 2% higher (; 95% CI: 1.01, 1.04). Neighborhood environment features, such as higher air pollution levels, were associated with higher neighborhood ADRD incidence. The INLA-SPDE method could have broad applicability to data collected across disparate spatial scales. https://doi.org/10.1289/EHP13183.

Opportunities to Improve Access to and Retention in the Child and Adult Care Food Program: Key Recommendations From Early Childcare Providers in Illinois, December 2020-July 2021.

Objectives. To identify recommendations to improve access to and retention in the Child and Adult Care Food Program (CACFP) as critical strategies to address health equity for low-income children. Methods. We conducted a qualitative key informant study of early childcare center and home providers (n = 35) in low-income urban and rural census tracts in Illinois between December 2020 and July 2021. Interviews with providers were organized and analyzed by the study team in MAXQDA Qualitative Data Analysis software. Themes were refined and finalized via member checking with an expert panel of providers and advocates. Results. Overall, providers spoke positively of the benefits of CACFP participation. Themes that centered around strategies to improve awareness of and access to CACFP included (1) conducting systematic statewide outreach, (2) improving technical assistance for enrollment, and (3) supporting positive sponsor-provider relationships. Themes related to retention included (1) alleviating procurement burdens, (2) extending reimbursement rates, and (3) expanding flexibilities. Conclusions. Policymakers looking to increase access to and retention in CACFP could consider state-level strategies such as systematic outreach and more targeted technical assistance. (Am J Public Health. 2024;113(S3):S231-S239. https://doi.org/10.2105/AJPH.2023.307433).

Abstract 105: Rural-Urban Differences for Integrated First Responder and Drone Automated External Defibrillator Delivery in North Carolina

Background: Early defibrillation for out-of-hospital cardiac arrest (OHCA) can improve survival significantly, but timely access to AEDs remains a barrier especially in rural areas. We analyzed whether differences exist between urban and rural areas for a proposed program of first responder (FR) and drone AED delivery in North Carolina. Methods: Using CARES registry data, we identified OHCAs in 48 counties between Jan. 2013-Dec. 2019. We applied a hypothetical intervention in which 1) all FRs were provided AEDs and 2) a network of AED-carrying drones was optimized within each county to maximize <5-minute response times (defined as the interval from 9-1-1 call receipt to arrival of an AED via EMS, FR, or drone). Within each county, we classified census tracts by rural population with <25% as urban, 25-75% as mixed, and >75% as rural. We included only counties with ≥10 OHCAs per year in both rural and urban census tracts to compare resource allocation for counties with large geographic variation. Historical and intervention response times stratified by rurality were compared via the Wilcoxon signed-rank test. Results: We included 19 counties and 8,955 OHCAs (5,754 urban, 3,201 rural). The historical median response time was 6.9 mins [IQR: 5.1-8.7] in urban census tracts and 9.4 mins [IQR: 7.0-12.1] in rural tracts. The FR + drone intervention reduced estimated median response times by 42% to 4.0 mins [IQR: 3.1-5.0] in urban areas and by 24% to 7.1 mins [IQR: 5.2-9.4] in rural areas. Five-minute coverage improved from 24% to 77% for urban areas and 10% to 23% for rural areas. All counties showed significant improvement in response time for both urban and rural populations (p<0.01). Conclusion: Deployment of AEDs by FR and drones optimized for 5-minute coverage was estimated to improve AED response more so in urban areas than rural areas. Other optimization parameters are needed to reduce inequality between urban and rural response times in areas with large geographic variation.

Theoretical framework to develop an urban health index using built environment variables: the case of Ferrara, Italy

The quality of our habitat strongly determines the well-being of both our society and us as individuals. The Urban Health (UH) index is an emerging tool for decision-makers to bridge the disparities in the quality of life in cities. Our study assesses the quality of the built environment as a proxy for urban health and proposes a theoretical framework for constructing a UH index. We first conducted a literature review and statistical analyses to select and screen a comprehensive array of urban health indicators, and then used Principal Component Analysis (PCA) to obtain the indicators’ weights and build the UH index. On applying the framework on the city of Ferrara, Italy, we obtained promising results with four interpretable principal components explaining the contextual conditions. The autocorrelation of the UH index (Moran’s I = 0. 795) demonstrated strong clustering, with very healthy urban census tracts located within the city centre and decreasing overall urban health in peripheral census tracts.

Racial and economic segregation in Brazil: a nationwide analysis of socioeconomic and socio-spatial inequalities

This article aims to analyze residential segregation by race (racial segregation) and income (economic segregation) in Brazil and explore its relationship with socioeconomic and socio-spatial factors. Residential segregation was assessed using the dissimilarity index based on the 2010 demographic census and considering urban census tracts since segregation is sociologically considered an urban problem. The results for racial segregation showed that it is more evident in cities in the South and Southeast of Brazil and mainly affects the self-declared black population. The approach used to calculate economic segregation involved examining the income level of different low-income groups. Therefore, we consider families that earned between 0 and 1 minimum wage as the group with the greatest social vulnerability. We did not find significant correlations between racial and income segregation indices with aspects such as urbanization (urban population size). Finally, we present the racial segregation indices stratifying families by income thresholds for the 27 Brazilian capitals and conclude that per capita household income is a preponderant factor for the segregation of the poorest, especially in families whose residents self-identify as black.

Associations between PM2.5 and O3 exposures and new onset type 2 diabetes in regional and national samples in the United States

BackgroundExposure to particulate matter ≤2.5 μm in diameter (PM2.5) and ozone (O3) has been linked to numerous harmful health outcomes. While epidemiologic evidence has suggested a positive association with type 2 diabetes (T2D), there is heterogeneity in findings. We evaluated exposures to PM2.5 and O3 across three large samples in the US using a harmonized approach for exposure assignment and covariate adjustment. MethodsData were obtained from the Veterans Administration Diabetes Risk (VADR) cohort (electronic health records [EHRs]), the Reasons for Geographic and Racial Disparities in Stroke (REGARDS) cohort (primary data collection), and the Geisinger health system (EHRs), and reflect the years 2003–2016 (REGARDS) and 2008–2016 (VADR and Geisinger). New onset T2D was ascertained using EHR information on medication orders, laboratory results, and T2D diagnoses (VADR and Geisinger) or report of T2D medication or diagnosis and/or elevated blood glucose levels (REGARDS). Exposure was assigned using pollutant annual averages from the Downscaler model. Models stratified by community type (higher density urban, lower density urban, suburban/small town, or rural census tracts) evaluated likelihood of new onset T2D in each study sample in single- and two-pollutant models of PM2.5 and O3. ResultsIn two pollutant models, associations of PM2.5, and new onset T2D were null in the REGARDS cohort except for in suburban/small town community types in models that also adjusted for NSEE, with an odds ratio (95% CI) of 1.51 (1.01, 2.25) per 5 μg/m3 of PM2.5. Results in the Geisinger sample were null. VADR sample results evidenced nonlinear associations for both pollutants; the shape of the association was dependent on community type. ConclusionsAssociations between PM2.5, O3 and new onset T2D differed across three large study samples in the US. None of the results from any of the three study populations found strong and clear positive associations.

Gentrification as a Factor in the Incidence of Firearm Injuries

Firearm injuries are an epidemic in the US; more than 45 000 fatal injuries were recorded in 2020 alone. Gaining a deeper understanding of socioeconomic factors that may contribute to increasing firearm injury rates is critical to prevent future injuries. To explore whether neighborhood gentrification is associated with firearm injury incidence rates over time. This cross-sectional study used nationwide, urban US Census tract-level data on gentrification between 2010 and 2019 and firearm injuries data collected between 2014 and 2019. All urban Census tracts, as defined by Rural Urban Commuting Area codes 1 to 3, were included in the analysis, for a total of 59 379 tracts examined from 2014 through 2019. Data were analyzed from January 2022 through April 2023. Gentrification, defined to be an area in a central city neighborhood with median housing prices appreciating over the median regional value and a median household income at or below the 40th percentile of the median regional household income and continuing for at least 2 consecutive years. The number of firearm injuries, controlling for Census tract population characteristics. A total of 59 379 urban Census tracts were evaluated for gentrification; of these tracts, 14 125 (23.8%) were identified as gentrifying, involving approximately 57 million residents annually. The firearm injury incidence rate for gentrifying neighborhoods was 62% higher than the incidence rate in nongentrifying neighborhoods with similar sociodemographic characteristics (incidence rate ratio [IRR], 1.62; 95% CI, 1.56-1.69). In a multivariable analysis, firearm injury incidence rates increased by 57% per year for low-income Census tracts that did not gentrify (IRR, 1.57; 95% CI, 1.56-1.58), 42% per year for high-income tracts that did not gentrify (IRR, 1.42; 95% CI, 1.41-1.43), and 49% per year for gentrifying tracts (IRR, 1.49; 95% CI, 1.48-1.50). Neighborhoods undergoing the gentrification process experienced an additional 26% increase in firearm injury incidence above baseline increase experienced in neighborhoods not undergoing gentrification (IRR, 1.26; 95% CI, 1.23-1.30). Results of this study suggest that gentrification is associated with an increase in the incidence of firearm injuries within gentrifying neighborhoods. Social disruption and residential displacement associated with gentrification may help explain this finding, although future research is needed to evaluate the underlying mechanisms. These findings support use of targeted firearm prevention interventions in communities experiencing gentrification.

A Planner's quest for identifying spatial (in)justice in local communities: A case study of urban census tracts in North Carolina, USA

For the past several decades, spatial justice has been presented as a conceptual framework to understand and address geographic inequalities. To date, most work associated with spatial justice has been qualitative and case study based. This paper seeks to explore the issue of spatial justice through the development of a Spatial Justice Index (SJI). The SJI quantitatively explores geographic based variables of urban census tracts in North Carolina to apply the underlying concepts of spatial justice in the real world. Using a principal components analysis approach, the SJI incorporates variables related to the following categories that comprise the concept of spatial justice: Public Goods, Basic Services, Cultural Goods, Economic Opportunities and Healthy Environments and are explored across the following spatial measures: spatial density, spatial proximity, spatial diversity, and spatial connectivity. The results highlight the benefits of dense, mixed use development patterns, that are well connected in achieving higher levels of spatial justice. The development of a Spatial Justice Index can be applied by urban planners and government officials across the entire Country to help communities comprehend, accept, and combat spatially injustices.

Ethnic Disparities in Early-Onset Gastric Cancer Persist across Rural-Urban Geographies.

The incidence of noncardia gastric cancer is increasing in adults ages less than 50 years old. Early-onset gastric cancer (EOGC) is characterized by ethnic disparities occurring more in Hispanic persons than non-Hispanic persons. It is unknown whether rural-urban disparities exist in EOGC and if this intersects with ethnic disparities. We utilized the Surveillance Epidemiology and End Results 17 Census Tract-level Socioeconomic Status and Rurality Database from 2006 to 2018 to calculate incidence rates and incidence rate ratios of EOGC among Hispanic and non-Hispanic persons by census tract rural-urban location, age, gender, persistent poverty, and stage of disease. We used the Tiwari Method to estimate incidence rate ratios with 95% confidence intervals (CI). Hispanic persons had higher incidence rates of EOGC compared with non-Hispanic persons in both rural [incident rate ratios (IRR), 2.12; 95% confidence interval (CI), 1.64-2.73] and urban census tracts (IRR, 2.03; 95% CI, 1.91-2.16). Similar findings were seen when comparing Hispanic to non-Hispanic persons in rural and urban census tracts by age, stage of disease, and persistent poverty. Higher incidence rates of EOGC among Hispanic persons persist across rural-urban locations. Further research is needed to understand the etiology of this elevated risk in young Hispanics and interventions that may help to modify their outcome. While other cancers have ethnic disparities which may differ by rural-urban location, the ethnic disparity in EOGC among Hispanic and non-Hispanic persons does not differ by rural-urban residence.

US Census Tracts with Higher Proportions of Hispanics, Individuals with Disabilities, and Rural Location have Worse Geospatial Access to Neurological Care (P5-9.002)

<h3>Objective:</h3> To examine neurological care spatial access disparities among various demographic segments of the U.S. population. <h3>Background:</h3> Incidence of neurological conditions in the U.S. is rising. A shortage of neurology clinicians and poorly understood disparities may hinder access to neurological care. There is a critical need to define community characteristics that may contribute to care access. <h3>Design/Methods:</h3> Neurologist practice locations from 2022 CMS Care Compare physician data were used to compute spatial access to neurological care measures for all U.S. census tracts. Census tract-level community characteristics (sex, age, race, ethnicity, education, income, insurance, % with computer, % without a vehicle, % with limited English % with hearing, vision, cognitive and ambulatory difficulty) were obtained from 2020 American Community Survey 5-year estimates. Rural-urban status was obtained from 2010 rural-urban commuting areas codes. Generalized additive models were built using 60-mile enhanced two-step floating catchment area spatial access ratios and three-step floating catchment area spatial access ratios as outcomes and community characteristics as predictors. <h3>Results:</h3> Using results from 73,057 U.S. census tracts, we found that access to neurological care (% change in ratio of neurologists to individuals) was significantly lower for rural (−80.8%) and micropolitan (−61.1%) areas. Additionally, tracts with a higher percentage of Hispanic populations (−0.5%), males (−0.9%), public insurance (−0.3%), uninsured (−0.9%), individuals with hearing difficulty (−5.2%), vision difficulty (−1.2%), and ambulatory difficulty (−1.9%) (p&lt;0.001) had lower access. Urban census tracts and tracts with greater Black (0.3%) and older (0.8%) populations were found to have higher spatial access to neurologists <h3>Conclusions:</h3> Access to neurological care appears worse in rural regions and in regions with higher proportions of Hispanics, and individuals with disabilities. For urban and Black communities proximity to neurologists is less likely a significant contributor to poor access. To improve neurological care disparities, future interventions will need to target unique access drivers for specific populations. <b>Disclosure:</b> Dr. McGinley has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Octave. Dr. McGinley has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Genentech. Dr. McGinley has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for EMD Serono. The institution of Dr. McGinley has received research support from Novartis. The institution of Dr. McGinley has received research support from Biogen. The institution of Dr. McGinley has received research support from Genentech. The institution of Dr. McGinley has received research support from NIH. Mr. Harvey has nothing to disclose. Dr. Ontaneda has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Novartis. Dr. Ontaneda has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Genentech/Roche. Dr. Ontaneda has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Biogen Idec. Dr. Ontaneda has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for Genentech/Roche. The institution of Dr. Ontaneda has received research support from NIH. The institution of Dr. Ontaneda has received research support from PCORI. The institution of Dr. Ontaneda has received research support from NMSS. The institution of Dr. Ontaneda has received research support from Genetech. Dr. Buchalter has stock in Pfizer Inc. Dr. Buchalter has stock in Viatris Inc. Dr. Buchalter has received personal compensation in the range of $100,000-$499,999 for serving as a Predoctoral and Postdoctoral Trainee with NIH.

Drive Time to Addiction Treatment Facilities Providing Contingency Management across Rural and Urban Census Tracts in 6 US States.

We examined drive times to outpatient substance use disorder treatment providers that provide contingency management (CM) and those that integrate CM with medication for opioid use disorder (MOUD) services in 6 US states. We completed cross-sectional geospatial analysis among census tracts in Delaware, Louisiana, Massachusetts, North Carolina, New York, and West Virginia. We excluded census tracts with a population of zero. Using data from the 2020 Shatterproof substance use treatment facility survey, our outcome was the minimum drive time in minutes from the census tract mean center of population to the nearest outpatient CM provider, outpatient CM provider with MOUD services, and federally qualified health centers (FQHC). We stratified census tracts by 2010 Rural-Urban Commuting Area codes and by state. The population was greater than zero in 11,719 of 11,899 census tracts. The median drive time to the nearest CM provider was 12.2 [interquartile range (IQR), 7.0-23.5) minutes and the median drive time to the nearest CM provider increased from 9.7 (IQR, 6.0-15.0) minutes in urban census tracts to 38.8 (IQR, 25.4-53.0) minutes in rural ( H = 3683, P < 0.001). The median drive time increased to the nearest CM provider with MOUD services [14.2 (IQR, 7.9-29.5) minutes, W = 18,877, P < 0.001] and decreased to the nearest FQHC [7.9 (IQR, 4.3-13.6) minutes, W = 11,555,894, P < 0.001]. These results suggest limited availability of CM, particularly within rural communities and for patients needing concurrent CM and MOUD treatment. Our results suggest greater adoption of CM within FQHCs could reduce urban-rural disparities in CM availability.

A novel machine-learning tool to identify community risk for firearm violence: The Firearm Violence Vulnerability Index.

Firearm violence in the United States is a public health crisis, but accessing accurate firearm assault data to inform prevention strategies is a challenge. Vulnerability indices have been used in other fields to better characterize and identify at-risk populations during crises, but no tool currently exists to predict where rates of firearm violence are highest. We sought to develop and validate a novel machine-learning algorithm, the Firearm Violence Vulnerability Index (FVVI), to forecast community risk for shooting incidents, fill data gaps, and enhance prevention efforts. Open-access 2015 to 2022 fatal and nonfatal shooting incident data from Baltimore, Boston, Chicago, Cincinnati, Los Angeles, New York City, Philadelphia, and Rochester were merged on census tract with 30 population characteristics derived from the 2020 American Community Survey. The data set was split into training (80%) and validation (20%) sets; Chicago data were withheld for an unseen test set. XGBoost, a decision tree-based machine-learning algorithm, was used to construct the FVVI model, which predicts shooting incident rates within urban census tracts. A total of 64,909 shooting incidents in 3,962 census tracts were used to build the model; 14,898 shooting incidents in 766 census tracts were in the test set. Historical third grade math scores and having a parent jailed during childhood were population characteristics exhibiting the greatest impact on FVVI's decision making. The model had strong predictive power in the test set, with a goodness of fit ( D2 ) of 0.77. The Firearm Violence Vulnerability Index accurately predicts firearm violence in urban communities at a granular geographic level based solely on population characteristics. The Firearm Violence Vulnerability Index can fill gaps in currently available firearm violence data while helping to geographically target and identify social or environmental areas of focus for prevention programs. Dissemination of this standardized risk tool could also enhance firearm violence research and resource allocation. Prognostic and Epidemiological; Level IV.

Association of Neighborhood Racial and Ethnic Composition and Historical Redlining With Built Environment Indicators Derived From Street View Images in the US

Racist policies (such as redlining) create inequities in the built environment, producing racially and ethnically segregated communities, poor housing conditions, unwalkable neighborhoods, and general disadvantage. Studies on built environment disparities are usually limited to measures and data that are available from existing sources or can be manually collected. To use built environment indicators generated from online street-level images to investigate the association among neighborhood racial and ethnic composition, the built environment, and health outcomes across urban areas in the US. This cross-sectional study was conducted using built environment indicators derived from 164 million Google Street View images collected from November 1 to 30, 2019. Race, ethnicity, and socioeconomic data were obtained from the 2019 American Community Survey (ACS) 5-year estimates; health outcomes were obtained from the Centers for Disease Control and Prevention 2020 Population Level Analysis and Community Estimates (PLACES) data set. Multilevel modeling and mediation analysis were applied. A total of 59 231 urban census tracts in the US were included. The online images and the ACS data included all census tracts. The PLACES data comprised survey respondents 18 years or older. Data were analyzed from May 23 to November 16, 2022. Model-estimated association between image-derived built environment indicators and census tract (neighborhood) racial and ethnic composition, and the association of the built environment with neighborhood racial composition and health. The racial and ethnic composition in the 59 231 urban census tracts was 1 160 595 (0.4%) American Indian and Alaska Native, 53 321 345 (19.5%) Hispanic, 462 259 (0.2%) Native Hawaiian and other Pacific Islander, 17 166 370 (6.3%) non-Hispanic Asian, 35 985 480 (13.2%) non-Hispanic Black, and 158 043 260 (57.7%) non-Hispanic White residents. Compared with other neighborhoods, predominantly White neighborhoods had fewer dilapidated buildings and more green space indicators, usually associated with good health, and fewer crosswalks (eg, neighborhoods with predominantly minoritized racial or ethnic groups other than Black residents had 6% more dilapidated buildings than neighborhoods with predominantly White residents). Moreover, the built environment indicators partially mediated the association between neighborhood racial and ethnic composition and health outcomes, including diabetes, asthma, and sleeping problems. The most significant mediator was non-single family homes (a measure associated with homeownership), which mediated the association between neighborhoods with predominantly minority racial or ethnic groups other than Black residents and sleeping problems by 12.8% and the association between unclassified neighborhoods and asthma by 24.2%. The findings in this cross-sectional study suggest that large geographically representative data sets, if used appropriately, may provide novel insights on racial and ethnic health inequities. Quantifying the impact of structural racism on social determinants of health is one step toward developing policies and interventions to create equitable built environment resources.

Analyzing the risk factors of residential fires in urban and rural census tracts of Ohio using panel data analysis

Analyzing the risk factors of residential fires in urban and rural census tracts of Ohio using panel data analysis

Evidence of Geospatial and Socioeconomic Disparities in Access to Online Grocery Shopping for Fresh and Frozen Produce in North Carolina

Online grocery shopping is a rapidly growing food procurement approach in the United States with the potential to improve food access. Limited research has focused on understanding differential access to online grocery shopping that provides healthier items such as fresh or frozen fruits and vegetables. The study aim was to understand geospatial, socioeconomic, and racial disparities in the availability of healthy online grocery shopping and online Supplemental Nutrition Assistance Program (SNAP) acceptance in North Carolina. A cross-sectional study was conducted during spring2021. A listing of grocery stores with online shopping was generated using a systematic search strategy. Stores were geocoded and spatially joined to relevant contextual (rural/urban [based on US Department of Agriculture Rural Urban Continuum codes]), broadband Internet availability, socioeconomic variables (ie, percent poverty and Social Vulnerability Index), and demographic variables (ie, percent racial minority) in geographic information systems software. Prevalence rate ratios (PRRs) of healthy online grocery shopping (availability of curbside pickup or home delivery of fresh and frozen produce), and online SNAP acceptance (ie, availability of online SNAP), at the census tract level (n= 2,162). PRRs for availability of healthy online grocery shopping and SNAP online acceptance at the census tract level (n= 2,162) were modeled using Poisson regression with robust standard errors. This study found disparities in access to healthy online grocery shopping and SNAP online shopping availability in North Carolina. Healthy online shopping availability rates were higher in urban census tracts (PRR 1.68, 95% CI 1.47 to 1.92), areas with lower Social Vulnerability Index scores (PRR 0.99, 95% CI 0.98 to 0.99), higher Internet Availability Index scores (PRR 1.21, 95% CI 1.17 to 1.25), and lower percent poverty (PRR 0.94, 95% CI 0.90 to 0.98). SNAP online shopping availability rates were higher in urban census tracts (PRR 1.41, 95% CI 1.16 to 1.65), areas with higher Social Vulnerability Index scores (PRR 1.02, 95% CI 1.01 to 1.04), higher Internet Availability Index scores (PRR 1.15, 95% CI 1.10 to 1.20), and higher percent minority (PRR 1.02, 95% CI 1.0001 to 1.03). SNAP online shopping availability rates were lower in areas with higher percent poverty (PRR 0.90, 95% CI 0.85 to 0.95). This study found disparities in access to healthy online grocery shopping and SNAP online shopping for rural areas, and areas with higher poverty, and lower broadband Internet access in North Carolina. Further research is needed to identify effective strategies for addressing these disparities.

Racial/ethnic disparities in nationwide PM2.5 exposure: perils of assuming a linear relationship

BACKGROUND AND AIM Studies of racial/ethnic disparities in air pollution exposure assume an equal magnitude of disparity across the demographic distribution, which may under- or over-estimate the true disparity. Further, historic practices such as racist zoning policies could plausibly drive non-linear relationships between racial/ethnic composition and air pollution exposure. We aimed to conduct a nationwide analysis in urban areas allowing for non-linearity in the relationship between racial/ethnic composition and ambient particulate matter less than 2.5 micrometers (PM₂.₅), and test whether racial segregation modified this relationship. METHODS We obtained tract-level demographic information from the 2010 Census and computed the multi-group dissimilarity index, a metropolitan-area level measure of racial segregation. Ambient PM₂.₅ data was obtained from a publicly available source. We evaluated the relationship between percent non-Hispanic Black and percent non-Hispanic White residents and ambient PM₂.₅ using natural splines. RESULTS In 58,029 U.S. urban census tracts in 2010, we observed first a very steep and then a plateauing relationship between percent non-Hispanic Black residents and ambient PM₂.₅ concentration. Moving from the 25th percentile (1%) to the median (5%) of non-Hispanic Black residents was associated with a 0.58 &amp;#x3bc;g/m³ (95% CI: 0.56, 0.60) increase in PM₂.₅, while moving from the median to the 75th percentile (16% non-Hispanic Black) was associated with a 0.005 &amp;#x3bc;g/m³ (95% CI: -0.012, 0.020) increase. Reciprocally, in predominantly White census tracts, increases in the proportion of White residents was associated with a rapid decrease in ambient PM₂.₅. Segregation modified these relationships in a non-monotonic manner. CONCLUSIONS Our results suggest larger than previously reported Black-White disparities in exposure to PM₂.₅, especially in communities with a modest Black population, which make up the majority of urban census tracts in the US. KEYWORDS Racial/ethnic disparities; Air pollution; Particulate Matter; Environmental justice