US Census Tracts Research Articles

Comparative Analysis of Indices for Social Determinants of Health in Pediatric Surgical Populations

Conclusions vary substantially among studies examining associations between area-based social determinants of health (SDOH) and pediatric health disparities based on the selected patient population and SDOH index. Most national studies use zip codes, which encompass a wide distribution of communities, limiting the generalizability of findings. To characterize the distributions of composite SDOH indices for pediatric surgical patients within a national sample of academic children's hospitals and to assess SDOH index precision in classifying patients at similar levels of disadvantage. This multicenter retrospective cohort study included patients younger than 18 years who underwent surgery from January 1, 2016, to December 31, 2021, at 8 American College of Surgeons National Surgical Quality Improvement Program Pediatric children's hospitals. Data were analyzed November 15, 2023, to September 25, 2024. Exposures included the Social Vulnerability Index (SVI), Area Deprivation Index (ADI), and Child Opportunity Index (COI), which are composite scores of SDOH factors within a geographic area. A standardized, high-fidelity process was developed to link individual patients to SDOH indices at the US census tract and census block group level. The primary outcome was composite SDOH index distribution, which was assessed using interrater reliability scores. Substantial agreement was defined as a Cohen κ statistic higher than 0.60. Of 55 865 included patients, 54.6% were male; 34.8% were infants and toddlers (0-3 years of age), 39.0% were school age (4-12 years), and 26.2% were adolescents (13-17 years). A total of 3468 patients (6.2%) could not be matched to either an SVI, ADI, or COI. Patients with missing geocodes were more likely to be Hispanic (20.1% vs 17.1%; P < .001) and have Medicaid insurance (48.1% vs 44.6%; P < .001) compared with patients with addresses that could be geocoded. With all institutions grouped, SDOH indices showed only minor variations. However, within each institution and among institutions, indices varied considerably, especially the ADI. Indices had low-to-fair interrater reliability within institutions (κ range, 0.15-0.33), indicating that each index classified individual patients differently according to disadvantage. In this multicenter retrospective cohort study of 55 865 pediatric surgical patients, 3 different composite measures of SDOH classified disadvantage for individual patients differently. The SDOH index components should be understood and carefully considered prior to inclusion of a composite measure in the analysis of children's surgical outcomes.

Neighbourhood Deprivation and Gestational Diabetes Mellitus in Arizona From the AzPEARS Study.

The maternal population residing in rural areas, tribal lands or near the United States-Mexico border are at high risk for adverse pregnancy complications, notably gestational diabetes mellitus (GDM). Few studies have considered the socioeconomic status (SES) attributes of neighbourhoods on maternal health during gestation. Given that the national and global rate of GDM is rising, this analysis moves beyond individual-level factors and investigates the role of neighbourhood deprivation on GDM risk. To investigate the relationship between neighbourhood deprivation and risk of GDM using Arizona state birth records. This population-based study of singleton live births (N = 481,113) utilised birth certificates from 2014 to 2020 from the AzPEARS study. Using American Community Survey (5-year data) linked to 2010 US Census tracts, a composite neighbourhood deprivation index (NDI) score (continuous and quartiles) was derived from 8 socioeconomic indicators. Risk ratios (RR) and 95% confidence intervals (95% CI) were used to examine the association between NDI and GDM risk, adjusting for maternal age, maternal education, race/ethnicity, parity, rurality, and birth year. The state-wide incidence of GDM was 7.8% (n = 37,636) with variation by neighbourhoods (4% to 12%). GDM risk was highest for Native Americans (17.6%), Asian/Pacific Islanders (13.7%) and Hispanic/Latinas (8.3%). Compared to mothers living in areas with the lowest quartile of NDI, mothers living in neighbourhoods with the highest deprivation had an adjusted risk ratio of 1.21 for developing GDM (95% CI 1.18, 1.26). We observed that neighbourhood deprivation was positively associated with a higher risk of GDM for each increase in quartile. These results suggest that NDI, a proxy for neighbourhood socioeconomic status, may contribute to GDM risk. Identifying high-risk neighbourhoods for place-based interventions targeting the most vulnerable birthing populations may be an effective strategy in the prevention of GDM.

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.

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.

US federal resource allocations are inconsistent with concentrations of energy poverty.

Recent data from the US Energy Information Administration reveals that nearly one in three households in the United States report experiencing energy poverty, and this number is only expected to rise. Federal assistance programs exist, but allocations across states have been nearly static since 1984, while the distribution of energy poverty is dynamic in location and time. We implement a LASSO-based machine learning approach using sociodemographic and geographical information to estimate energy burden in each US census tract for 2015 and 2020. We then compare the allocation to states from the Low Income Home Energy Assistance Program to an optimized allocation. We allocate funds to the most burdened households, providing them with enough assistance to reduce their energy expenditures so that their household energy burden is equal to a new maximum allowable energy burden. This markedly shifts funds from the northern cold-weather states to the southern warm-weather states.

Abstract A015: Small area-estimation for low mammography screening: Geographic and racial disparities in mammography screening, 2015 – 2022

Abstract Background: Mammography screening is associated with at least a 20% reduction in breast cancer mortality. However, there are racial/ethnic differences in breast cancer mortality. For example, non-Hispanic Black (NHB) women are significantly more likely to die of breast cancer than non-Hispanic White (NHW) women. Few studies have reported small-area estimates for mammography screening among the entire US population; differences by race were either not examined or resulted in mixed findings. The American Cancer Society (ACS) and the United States Preventive Services Taskforce (USPSTF) recommend regular mammography screening from age 40 through 74 years. The current study aims to identify areas of up-to-date mammography screening and to elucidate whether mammography screening varied by geographical location, race, or ethnicity. Methods: Mammography screening within the past 12 months was retrieved from the 2015-2022 Behavioral Risk Factor Surveillance System data. The study population consisted of adult women aged 40 and older residing within the United States. 2020 US census tract population estimates were used to generate gender (all women), race/ethnicity (NHB, NHW, non-Hispanic Asian, and Hispanic/Latina), income, and age group weights for each US census tract. Synthetic estimates of mammography screening uptake at the census tract were conducted using our novel PARIS model (Probability of mammography screening within the past year, given age, race/ethnicity, income, and state) using a multilevel logistic regression model. Regional differences in mammography screening by US Census Bureau regions and divisions were examined. Results: Up-to-date mammography among all women in the sample was 55.98% (SD = 2.07; F (3, 72429) = 2886.81, p &amp;lt; .0001). Mammography screening examined by race indicated the greatest screening rates among non-Hispanic Black women (M = 60.78, SD = 3.11) and least among Asian women (M = 48.68, SD = 3.15). Among four US Census Bureau regions, the West region had the lowest rates of mammography screening among women (M = 54.74, SD = 1.85) while the South region had the highest rates (M = 56.55, SD = 2.18; F(3, 72429) = 2886.81, p &amp;lt; .0001). After further parsing geographical units, the West Pacific region, among the 9 US Census Bureau divisions, had the lowest mammography screening (M = 54.53, SD = 1.81) while the East South Central division had the highest rates of mammography screening (M = 56.94, SD = 2.10; F(8, 72429) = 1360.60, p &amp;lt; .0001).Conclusion: Using novel geospatial methods, the current study elucidated significant differences in mammography screening by race and place. These results suggest that Black women and women living in the South have higher up-to-date mammography screening compared to other women. Thus, the existing inequities in breast cancer outcomes may be explained by differences in the cancer care continuum (delays in follow-up care, treatment, and breast cancer survival). Further examination of the interplay between screening uptake, late-stage diagnosis, and death is needed. Citation Format: Sydney P. Howard, Justin X. Moore, Elinita Pollard, Ryan Johnson, Lovoria Williams, Adebola Adegboyega, Jie Chen, Jorge Cortes, Aimee James. Small area-estimation for low mammography screening: Geographic and racial disparities in mammography screening, 2015 – 2022 [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A015.

Disparities in Spatial Access to Sleep Health Care in the United States: A Population-Based Geospatial Analysis

ObjectivesTo examine spatial access to sleep health care in the United States and investigate associations with demographic and socioeconomic characteristics, thereby identifying high-risk communities with limited spatial access to sleep health service. DesignA cross-sectional population-based geospatial analysis. Settings and ParticipantsResidents in US Census tracts across the 48 contiguous states, Alaska, and Hawaii. MethodsThe 2020 American Community Survey 5-year estimates, 2010 rural-urban commuting area codes, 2020 Area Deprivation Index, and sleep care provider locations from the National Provider Identifier file were used to assess the spatial access and related demographic/socioeconomic characteristics. Spatial access was measured by spatial access ratio using enhanced 2-step floating catchment area methods. The associations were investigated using logistic regression analysis and multivariate linear regression analysis. ResultsA total of 45.8 million residents experienced low spatial access to sleep health care. Spatial access decreased in rural and high Area Deprivation Index areas, and in areas characterized by higher population with uninsured status, vehicle unavailability, internet unavailability, cognitive difficulties, and hearing difficulties. With a 10% increase in the percentage of the racial minority (non-white) population, metropolitan census tracts experienced an increase in spatial access (3.268%), whereas micropolitan (−1.526%) and rural (−4.493%) areas experienced a decrease in spatial access. Similar findings were observed within the ethnic minority (Hispanic or Latino) population. Conclusions and ImplicationsDisparities exist in spatial access to sleep health care across the United States, especially for disadvantaged individuals. Racial/ethnic minorities exhibit contrasting spatial access patterns in urban and rural areas, with those in rural areas facing more challenges in spatial access to sleep health care.

Childhood PM2.5 exposure and upward mobility in the United States

Although it is well documented that exposure to fine particulate matter (PM2.5) increases the risk of several adverse health outcomes, less is known about its relationship with economic opportunity. Previous studies have relied on regression modeling, which implied strict assumptions regarding confounding adjustments and did not explore geographical heterogeneity. We obtained data for 63,165 US census tracts (86% of all census tracts in the United States) on absolute upward mobility (AUM) defined as the mean income rank in adulthood of children born to families in the 25th percentile of the national income distribution. We applied and compared several state-of-the-art confounding adjustment methods to estimate the overall and county-specific associations of childhood exposure to PM2.5 and AUM controlling for many census tract-level confounders. We estimate that census tracts with a 1 μg/m3 higher PM2.5 concentrations in 1982 are associated with a statistically significant 1.146% (95% CI: 0.834, 1.458) lower AUM in 2015, on average. We also showed evidence that this relationship varies spatially between counties, exhibiting a more pronounced negative relationship in the Midwest and the South.

Neighborhood Child Opportunity and Preterm Birth Rates by Race and Ethnicity

In the US, infants born to non-Hispanic Black birthing parents are 50% more likely to be born preterm than those born to non-Hispanic White birthing parents, and individual-level factors do not fully account for this inequity. Neighborhood context, rooted in historic patterns of structural racism, may facilitate understanding patterns of inequity in preterm birth. To estimate the association between neighborhood opportunity level, measured by the Child Opportunity Index (COI), and preterm birth among infants in Massachusetts. In this cross-sectional, population-based study, Massachusetts birth certificates from 3 large metropolitan areas (Boston, Springfield, and Worcester) were linked to US Census tract-level data from the COI, and log binomial regression models and generalized estimating equations were fit to examine associations of different levels of opportunity with preterm birth. Singleton infants born in Massachusetts between February 1, 2011, and December 31, 2015, were included. Analyses were originally conducted in 2019 and updated in 2024. Level of child opportunity (measured by the COI) at the US Census tract level. Race and ethnicity were ascertained from the birth certificate, as reported by the birthing parent. Live birth before 37 completed weeks' gestation. The analytic dataset included 267 553 infants, of whom 18.9% were born to Hispanic, 10.1% to non-Hispanic Asian or Pacific Islander, 10.1% to non-Hispanic Black, and 61.0% to non-Hispanic White birthing parents. More than half of infants born to non-Hispanic Black and Hispanic birthing parents were born into very low opportunity neighborhoods, and in crude models, this was associated with greater prevalence of preterm birth relative to very high opportunity neighborhoods (prevalence ratio, 1.44; 95% CI, 1.37-1.52). After adjustment for covariates, infants born into very low opportunity neighborhoods still had a greater prevalence of preterm birth (prevalence ratio, 1.16; 95% CI, 1.10-1.23). In this cross-sectional study of neighborhood opportunity and preterm birth, elevated risk associated with exposure to a very low opportunity neighborhood, coupled with the disproportionate exposure by race and ethnicity, points to a modifiable factor that may contribute to racial and ethnic inequities in preterm birth. Future research should investigate interventions that seek to address neighborhood opportunity.

Mediators of racial and ethnic inequities in clinical trial participation among patients with cancer, 2011-2023.

Although racially and ethnically minoritized populations are less likely to participate in cancer trials, it is unknown whether social determinants of health (SDOH) explain these inequities. Here we identify SDOH factors that contribute to racial and ethnic inequities in clinical trial participation among patients with 22 common cancers. This retrospective cohort study used electronic health record data (2011-2023) linked to neighborhood (US Census tract) data from multiple sources. Patients were followed from diagnosis to clinical study drug receipt (proxy for trial participation), death, or last recorded activity. Associations were assessed using Cox proportional hazards models adjusted for clinical factors (year of diagnosis, age, sex, performance status, disease stage, cancer type). To elucidate which area-level SDOH underlie racial and ethnic inequities, mediation analysis was performed using nonlinear multiple additive regression tree models. This study included 250 105 patients (64.7% non-Latinx White, 8.9% non-Latinx Black, 5.2% Latinx). Black and Latinx patients were more likely to live in economically or socially marginalized areas (eg, disproportionately minoritized [measure of segregation], limited English proficiency, low vehicle ownership) than White patients. Black (3.7%; hazard ratio = 0.55, 95% confidence interval [CI] = 0.52 to 0.60) and Latinx patients (4.4%; hazard ratio = 0.63, 95% CI = 0.58 to 0.69) were less likely to participate in trials than White patients (6.3%). Fewer patients in economically or socially marginalized neighborhoods participated in trials. Mediators explained 62.2% (95% CI = 49.5% to 74.8%) of participation inequities between Black and White patients; area-level SDOH-including segregation (29.9%, 95% CI = 21.2% to 38.6%) and vehicle ownership (11.6%, 95% CI = 7.0% to 16.1%)-were the most important mediators. Similarly, Latinx-White participation inequities were mediated (65.1%, 95% CI = 49.8% to 80.3%) by area-level SDOH, such as segregation (39.8%, 95% CI = 28.3% to 51.3%), limited English proficiency (11.6%, 95% CI = 2.8% to 20.4%), and vehicle ownership (9.6%, 95% CI = 5.8% to 13.5%). To improve racial and ethnic diversity in cancer trials, efforts to address barriers related to adverse neighborhood SDOH factors are necessary.

Social Vulnerability May Underlie Racial Disparities in Peripartum Cardiomyopathy Outcomes.

Black patients have disproportionately more cases of peripartum cardiomyopathy (PPCM) and more severe disease. To better understand these disparities, we examined the geographic distribution of patients with PPCM by race and evaluated associations between race and social vulnerability. We hypothesized that Black patients with PPCM are more likely than White patients to live in socially vulnerable communities. A retrospective cohort study of patients with PPCM defined by the National Institutes of Health, National Heart, Lung, and Blood Institute was conducted at a single center from January 2000 to November 2017. The US census tract for each patient was identified, and social vulnerability was assessed using the Centers for Disease Control and Prevention Social Vulnerability Index (SVI). Higher SVI values represent a more vulnerable community. SVI and select subcomponents were compared by self-reported race. Among 90 patients with PPCM (47 White, 43 Black), the ejection fraction at diagnosis was similar between groups, although Black patients were more likely to have an ejection fraction of ≤40% at 6 to 12 months postpartum. Black race was associated with living in areas of greater social vulnerability; mean SVI was significantly higher among Black individuals than among White individuals (.56 versus .33, P=.0003). Black patients lived in areas with more people living in poverty, higher unemployment, and more single-parent households. Black patients with PPCM were more likely to have persistent left ventricular dysfunction and live in areas of greater social vulnerability. Strategies to achieve equitable social determinants of health are needed to improve health outcomes in Black patients with PPCM.

Place-Based Measures of Inequity and Vision Difficulty and Blindness

Known social risk factors associated with poor visual and systemic health in the US include segregation, income inequality, and persistent poverty. To investigate the association of vision difficulty, including blindness, in neighborhoods with measures of inequity (Theil H index, Gini index, and persistent poverty). This cross-sectional study used data from the 2012-2016 American Community Survey and 2010 US census tracts as well as Theil H index, Gini index, and persistent poverty measures from PolicyMap. Data analysis was completed in July 2023. The main outcome was the number of census tract residents reporting vision difficulty and blindness (VDB) and the association with the Theil H index, Gini index, or persistent poverty, assessed using logistic regression. In total, 73 198 census tracts were analyzed. For every 0.1-unit increase in Theil H index and Gini index, there was an increased odds of VDB after controlling for census tract-level median age, the percentage of the population that identified as female sex, the percentage of the population that identified as a member of a racial or ethnic minority group, state, and population size (Theil H index: odds ratio [OR], 1.14 [95% CI, 1.14-1.14; P < .001]; Gini index: OR, 1.15 [95% CI, 1.15-1.15; P < .001]). Persistent poverty was associated with an increased odds of VDB after controlling for census tract-level median age, the percentage of the population that identified as female sex, the percentage of the population that identified as a member of a racial or ethnic minority group, state, and population size compared with nonpersistent poverty (OR, 1.36; 95% CI, 1.35-1.36; P < .001). In this cross-sectional study, residential measures of inequity through segregation, income inequality, or persistent poverty were associated with a greater number of residents living with VDB. It is essential to understand and address how neighborhood characteristics can impact rates of VDB.

505 Use of Expanded Access at Michigan Medicine and Associations with Neighborhood Factors

OBJECTIVES/GOALS: Socioeconomic status (SES) affects risk of disease and access to therapies. The expanded access (EA) pathway allows for the clinical use of investigational products for patients who have serious illness but no Food and Drug Administration (FDA)-approved therapeutic options. The SES of patients who receive EA is unknown. METHODS/STUDY POPULATION: We reviewed the patients who were approved for treatment through a single-patient EA pathway between 2018 and 2023. Using Michigan Medicine (MM) DataDirect software linked to the MM electronic medical record system, we linked the EA pathway patients to neighborhood data from the National Neighborhood Data Archive (NaNDA) to compare neighborhood related markers of affluence among EA patients and others treated at MM. We used descriptive statistics to compare variables between EA pathway patients and residents of the state of Michigan or the local county surrounding MM (Washtenaw County), using US Census tract data to provide context for these findings. RESULTS/ANTICIPATED RESULTS: MM patients who received EA treatments were more likely to come from neighborhoods that showed markers of high SES compared to residents of the state of Michigan but not Washtenaw County. This includes the proportion of persons living in poverty (12.5% EA / 13.4% Michigan / 12.4% Washtenaw) and education in the form of a bachelor’s degree or higher (32.2% / 30.6% / 57.2%). This varied by the disease being treated. Oncology patients were more likely to be from areas with less poverty and more education (12.4% / 76.8%) than the EA average. EA patients being treated for infectious diseases were from areas with more poverty and less education (13.5% / 26.7%). DISCUSSION/SIGNIFICANCE: Patients treated at Michigan Medicine using treatments obtained through the EA pathway came from areas that were, on average, more affluent than residents of the state of Michigan as a whole. This finding warrants more research to ensure equitable access to these therapies for patients in disadvantaged neighborhoods.

Neighborhood Deprivation, Race and Ethnicity, and Prostate Cancer Outcomes Across California Health Care Systems

Non-Hispanic Black (hereafter, Black) individuals experience worse prostate cancer outcomes due to socioeconomic and racial inequities of access to care. Few studies have empirically evaluated these disparities across different health care systems. To describe the racial and ethnic and neighborhood socioeconomic status (nSES) disparities among residents of the same communities who receive prostate cancer care in the US Department of Veterans Affairs (VA) health care system vs other settings. This cohort study obtained data from the VA Central Cancer Registry for veterans with prostate cancer who received care within the VA Greater Los Angeles Healthcare System (VA cohort) and from the California Cancer Registry (CCR) for nonveterans who received care outside the VA setting (CCR cohort). The cohorts consisted of all males with incident prostate cancer who were living within the same US Census tracts. These individuals received care between 2000 and 2018 and were followed up until death from any cause or censoring on December 31, 2018. Data analyses were conducted between September 2022 and December 2023. Health care setting, self-identified race and ethnicity (SIRE), and nSES. The primary outcome was all-cause mortality (ACM). Cox proportional hazards regression models were used to estimate hazard ratios for associations of SIRE and nSES with prostate cancer outcomes in the VA and CCR cohorts. Included in the analysis were 49 461 males with prostate cancer. Of these, 1881 males were in the VA cohort (mean [SD] age, 65.3 [7.7] years; 833 Black individuals [44.3%], 694 non-Hispanic White [hereafter, White] individuals [36.9%], and 354 individuals [18.8%] of other or unknown race). A total of 47 580 individuals were in the CCR cohort (mean [SD] age, 67.0 [9.6] years; 8183 Black individuals [17.2%], 26 206 White individuals [55.1%], and 13 191 individuals [27.8%] of other or unknown race). In the VA cohort, there were no racial disparities observed for metastasis, ACM, or prostate cancer-specific mortality (PCSM). However, in the CCR cohort, the racial disparities were observed for metastasis (adjusted odds ratio [AOR], 1.36; 95% CI, 1.22-1.52), ACM (adjusted hazard ratio [AHR], 1.13; 95% CI, 1.04-1.24), and PCSM (AHR, 1.15; 95% CI, 1.05-1.25). Heterogeneity was observed for the racial disparity in ACM in the VA vs CCR cohorts (AHR, 0.90 [95% CI, 0.76-1.06] vs 1.13 [95% CI, 1.04-1.24]; P = .01). No evidence of nSES disparities was observed for any prostate cancer outcomes in the VA cohort. However, in the CCR cohort, heterogeneity was observed for nSES disparities with ACM (AHR, 0.82; 95% CI, 0.80-0.84; P = .002) and PCSM (AHR, 0.86; 95% CI, 0.82-0.89; P = .007). Results of this study suggest that racial and nSES disparities were wider among patients seeking care outside of the VA health care system. Health systems-related interventions that address access barriers may mitigate racial and socioeconomic disparities in prostate cancer.

Self-driving vehicles’ impacts on Americans’ long-distance domestic travel choices

ABSTRACT This research estimated models for long-distance domestic passenger trips before and after the introduction of autonomous vehicles (AVs) and their application to a 10% synthetic US population. The authors synthesized 12.1M households and 28.1M individuals across 73,056 US census tracts. To generate disaggregated passenger trips, travel demand models, including trip frequency, season, purpose, party size, mode choice, and destination choice, and vehicle ownership models were estimated. Different datasets, including a 2021 long-distance AV survey, 2016/17 National Household Travel Survey (NHTS) survey, EPA Smart Location data, and FHWA rJourney dataset were used for model estimation. The model applications indicated AV ownership to be 0.33 per capita after the introduction of AVs within the marketplace with a $3500 AV technology cost premium in the year 2040. Total person-miles traveled per capita in long-distance trips was also estimated to rise 35% (from 280 to 379 miles per month).

Neighborhood Environmental Burden and Cardiovascular Health in the US

Cardiovascular disease is the leading cause of death in the US. However, little is known about the association between cumulative environmental burden and cardiovascular health across US neighborhoods. To evaluate the association of neighborhood-level environmental burden with prevalence of cardiovascular risk factors and diseases, overall and by levels of social vulnerability. This was a national cross-sectional study of 71 659 US Census tracts. Environmental burden (EBI) and social vulnerability indices from the US Centers for Disease Control and Prevention (CDC) and Agency for Toxic Substances and Disease Registry were linked to the 2020 CDC PLACES data set. Data were analyzed from March to October 2023. The EBI, a measure of cumulative environmental burden encompassing 5 domains (air pollution, hazardous or toxic sites, built environment, transportation infrastructure, and water pollution). Neighborhood-level prevalence of cardiovascular risk factors (hypertension, diabetes, and obesity) and cardiovascular diseases (coronary heart disease and stroke). Across the US, neighborhoods with the highest environmental burden (top EBI quartile) were more likely than those with the lowest environmental burden (bottom EBI quartile) to be urban (16 626 [92.7%] vs 13 414 [75.4%]), in the Midwest (5191 [28.9%] vs 2782 [15.6%]), have greater median (IQR) social vulnerability scores (0.64 [0.36-0.85] vs 0.42 [0.20-0.65]), and have higher proportions of adults in racial or ethnic minority groups (median [IQR], 34% [12-73] vs 12% [5-30]). After adjustment, neighborhoods with the highest environmental burden had significantly higher rates of cardiovascular risk factors than those with the lowest burden, including hypertension (mean [SD], 32.83% [7.99] vs 32.14% [6.99]; adjusted difference, 0.84%; 95% CI, 0.71-0.98), diabetes (mean [SD], 12.19% [4.33] vs 10.68% [3.27]; adjusted difference, 0.62%; 95% CI, 0.53-0.70), and obesity (mean [SD], 33.57% [7.62] vs 30.86% [6.15]; adjusted difference, 0.77%; 95% CI, 0.60-0.94). Similarly, neighborhoods with the highest environmental burden had significantly higher rates of coronary heart disease (mean [SD], 6.66% [2.15] vs 6.82% [2.41]; adjusted difference, 0.28%; 95% CI, 0.22-0.33) and stroke (mean [SD], 3.65% [1.47] vs 3.31% [1.12]; adjusted difference, 0.19%; 95% CI, 0.15-0.22). Results were consistent after matching highest and lowest environmentally burdened neighborhoods geospatially and based on other covariates. The associations between environmental burden quartiles and cardiovascular risk factors and diseases were most pronounced among socially vulnerable neighborhoods. In this cross-sectional study of US neighborhoods, cumulative environmental burden was associated with higher rates of cardiovascular risk factors and diseases, although absolute differences were small. The strongest associations were observed in socially vulnerable neighborhoods. Whether initiatives that address poor environmental conditions will improve cardiovascular health requires additional prospective investigations.

Abstract 13999: The Relationship Between Neighborhood Deprivation, Diabetes, and Extracellular Vesicles in African American Women at Risk for CVD: Data From a Pilot Study of the Step It Up Physical Activity Intervention

Background: African American women (AA) are at disproportionally high risk for diabetes and cardiovascular disease (CVD) which is accelerated when residing in resource-limited neighborhoods. Extracellular vesicles (EV) are emerging to be of importance in CVD development and progression, but little is known about a potential impact of neighborhood socioeconomic deprivation (NSD). In this study we examined associations between NSD, plasma EV, and markers of diabetes risk in a community-based cohort of AA women at risk for CVD from resource-limited neighborhoods in Washington, DC. Methods: Participants were enrolled in pilot testing of Step It Up, a community engaged physical activity intervention. Blood samples were drawn to measure clinical labs during participants’ baseline visit to the NIH Clinical Center. EV were isolated from heparinized plasma using size exclusion chromatography with size and numbers determined by nanoparticle tracking analysis. NSD was calculated using 2018 US Census tract data as a measure of neighborhood deprivation. Associations between NSD, EV, and diabetes-related clinical measures were analyzed using multivariable regressions adjusted for BMI and ASCVD 10-year risk score. Results: The study cohort was a convenience sample of AA women at risk for CVD (N=24, Age: 57±12, BMI: 35±6, ASCVD: 9±5). NSD associated with fasting glucose (β=0.54, p=0.007) and Hemoglobin A1c (HA1c) (β=0.51, p=0.007). Additionally, NSD associated with lower EV size (β=-0.42, p=0.03), with no association with EV concentration. Fasting glucose and HA1c associated negatively with EV size (β=-0.48, p=0.03 and β=-0.50, p=0.02, respectively) but no association was found with EV concentration. Conclusions: Thus, our data show that higher neighborhood deprivation is associated with lower EV size which, in turn, is associated with higher diabetes risk markers. More research is needed to understand the role of EV in diabetes and CVD development and progression. While larger studies further evaluating these observations are needed, our data highlight the importance of increased diabetes screening and multi-level interventions in patients residing in under-resourced communities to reduce existing health disparities.

Abstract 14103: Neighborhood Environmental Burden and Cardiovascular Health in the United States

Introduction: Little is known about the association between cumulative environmental burden and cardiovascular health. Methods: This national study of 71,659 US census tracts linked the CDC environmental burden index and social vulnerability index (SVI) to 2020 PLACES. Mixed-effects linear regression models were used to determine associations between cumulative environmental burden (air pollution, hazardous/toxic sites, built environment, transportation infrastructure, water pollution) and cardiovascular risk factors (hypertension, diabetes, obesity) and diseases (coronary heart disease, stroke) after adjustment for age, sex, SVI, rurality, region, and health care access. Results: Neighborhoods with the highest environmental burden (quartile 4), compared to those with the lowest burden (quartile 1), had greater SVI scores (0.64 vs 0.42) and a higher proportion of racial/ethnic minorities (34% vs 12%). After adjustment, quartile 4 neighborhoods had significantly higher rates of cardiovascular risk factors than quartile 1 neighborhoods, including hypertension (32.8% vs 32.1%, adjusted difference: 0.84% [95% CI: 0.71-0.98]), diabetes (12.2% vs 10.7%, 0.62% [0.53-0.70]), and obesity (33.6% vs 30.9%, 0.77% [0.60-0.94]). Similarly, quartile 4 neighborhoods had higher rates of cardiovascular diseases (Figure) . Results were consistent after geospatial- and covariate-based matching of quartile 4 and 1 neighborhoods. Associations between increasing environmental burden quartiles and cardiovascular risk factors and diseases were significantly more pronounced in most versus least socially vulnerable neighborhoods. Conclusion: Across US neighborhoods, cumulative environmental burden was dose-dependently associated with higher rates of cardiovascular risk factors and diseases, particularly in socially vulnerable communities. Efforts addressing poor environmental conditions may improve cardiovascular health and reduce inequities.

Abstract 14640: Community Socioeconomic Distress is Associated With Poor Access to Heart Failure Clinical Trials in the United States

Introduction: Clinical trials have historically lacked representation from minorities and individuals from lower socioeconomic backgrounds. Little is known about the role geographic access plays in recruiting diverse populations for clinical trials. Objectives: Given disparities in clinical trial enrollment, we sought to analyze how access to heart failure trials varies across demographic groups. Methods: We queried clinicaltrials.gov for trials classified under “heart failure” that are recruiting and enrolling adult patients. Trial site addresses were obtained using Google Maps. We calculated drive times from each US census tract to the closest trial site using ArcGIS Pro v3.1. Demographic information and urban / rural statuses for census tracts were obtained from the US Census Bureau. Distressed Community Index (DCI) scores (0 = no distress, 100 = severe distress) based on metrics including education level, poverty, and unemployment were collected. Data were analyzed using Python v3.8. Results: We identified 954 heart failure trials with 644 unique sites in the US. About 54% of the rural population lives more than 30 minutes from a trial site compared to 22% of the urban population (OR 0.23; p &lt; 0.001). Analysis of DCI scores covering 328 million people revealed that drive times increase with higher socioeconomic distress ( p &lt; 0.001; Figure). In both urban and rural settings, median drive times for individuals in the top 10% of distressed communities (56 min urban and 79 rural) are more than three times those of individuals in the lowest 10% of distressed communities (18 min urban and 23 rural). A larger proportion of non-white minorities live less than 30 minutes from a trial site compared to the white population (77% vs. 60%; OR 2.23; p &lt; 0.001). Conclusions: Increased socioeconomic distress is associated with worse access to heart failure trial sites for both urban and rural populations. Geographic barriers should be addressed to improve diversity in trials.

Abstract 12346: Atherosclerotic Cardiovascular Disease Risk Associated With Social Determinants of Health at Individual and Area Levels

Introduction: Adverse social determinants of health (SDOH) are associated with an increased risk for atherosclerotic cardiovascular disease (ASCVD). However, it is unclear if the ASCVD risks associated with area-level SDOH are similar to those with individual-level measures. Further, SDOH are not included in the Pooled Cohort Equations (PCEs) when estimating 10-year ASCVD risk. Studies have shown that PCEs systematically underestimate risk for socially deprived individuals. Aims To examine the association of individual- and area-level SDOH including education, income, and employment with incident ASCVD. Also, we assessed if adding SDOH to the PCEs improves risk prediction accuracy. Methods: We pooled data from four cohorts: Jackson Heart Study, Multi-Ethnic Study of Atherosclerosis, Framingham Heart Study Offspring Cohort, and Reasons for Geographic and Racial Differences in Stroke Study. We excluded participants with a history of ASCVD. Individual-level SDOH were based on participants’ self-report and area-level SDOH were derived by linking participants’ addresses to US Census tract data. We used Cox proportional hazards models to assess the associations between SDOH and ASCVD. We also assessed the changes in discrimination (Harrell’s C-index) and calibration (Integrated Brier Score) after adding SDOH to risk prediction models. Results: We analyzed data from 27,603 participants (mean age 61.4 years, 59% women, and 49% White). During a median follow-up of 13 years, 2,839 incident ASCVD events occurred. Both individual- and area-level SDOH were associated with ASCVD ( Figure, Panel A ). Adding both individual- and area-level SDOH to risk prediction modestly improved model discrimination and calibration in Black men and women ( Figure, Panels B &amp; C ), and also improved calibration in non-Black women. Conclusions: The findings suggest that SDOH may be considered in the development of future ASCVD risk assessment tools, particularly among Black individuals.