Family-Based Childcare in Chicago: Opportunities to Advance Environmental and Social Justice for Children.

Predictors of Neighborhood Risk for Late-Stage Melanoma: Addressing Disparities through Spatial Analysis and Area-Based Measures

INTRODUCTION: Low food access has been associated with an increased risk of obesity and cardiovascular disease, but little is known if food access increases the likelihood of adverse pregnancy outcomes. We aimed to determine if living in an area of low food access is associated with an increased prevalence of preterm birth in Florida. METHODS: We linked, at the census tract level, data on food access from the United States Department of Agriculture (USDA) Food Access Research Atlas to pregnancy outcome data from the Florida Community Health Assessment Research Tool Set. We defined a census tract as “low access to healthy food” if the tract was both low income and a significant number (≥500) or proportion (33%) of the population lives more than 1 mile (urban areas) or 10 miles (rural areas) from the nearest supermarket. Prevalence ratios and 95% confidence intervals were calculated using modified Poisson regression models. RESULTS: We analyzed 2,368,114 births in the state of Florida from 2008-2018 with nonmissing gestational age and food access designation. The preterm birth rate was 10.9% and 10.1% in areas with and without low food access, respectively. We calculated a prevalence ratio of 1.04 (95% CI 1.03–1.05) for the association between preterm birth and low food access after adjustment for maternal race/ethnicity. CONCLUSION: Living in an area of low food access was associated with a slight increase in prevalence of preterm birth in Florida. Food access and other measures of neighborhood deprivation may play a role in the risk of adverse perinatal outcomes.

Introduction: Violent crime (e.g., homicide, aggravated assault) is a major public health issue that disproportionately affects communities of color in large urban centers. Studies have reported that residents in high crime communities are less likely to engage in physical activity. There is limited understanding of how violent crime influences physical inactivity and obesity at the community level. We aimed to address this gap by examining differences in spatial relationships between violent crime rate, physical inactivity, and obesity by racial/ethnic composition of community residents in Chicago, IL. Hypothesis: We assessed the hypothesis that violent crime rate is associated with the prevalence of physical inactivity and obesity at the census tract level in Chicago, IL. Methods: We conducted an ecological assessment of 2018 census tract data obtained from various sources. We used data from the City of Chicago to calculate per capita violent crime rate (number of incidents per 1,000 residents) for all census tracts (N = 801). Data on physical inactivity and obesity prevalence (%) were acquired from the CDC. Socio-demographic data (i.e., % Non-Hispanic (NH) White, % NH Black, % Hispanic, median household income) were obtained from the census bureau. We examined spatial lag and error models to determine if violent crime rate is associated with % physical inactivity and % obesity after controlling for socio-demographic characteristics and amenity availability (i.e., per capita outdoor parks and grocery stores). Stratified models were examined to identify differences in associations among majority NH White, NH Black, and Hispanic census tracts (defined as ≥ 50% representation). Results: NH Black census tracts (n = 278) had significantly higher rates of violent crime, physical inactivity, and obesity than Hispanic (n = 169) and NH White tracts (n = 240). Overall, violent crime rate was positivity associated with % physical inactivity (p<0.001) but not % obesity (p=0.77) in Chicago after controlling for covariates. Stratified models revealed that violent crime rate was positively associated with % physical inactivity (p<0.001) and % obesity (p=0.01) among NH Black tracts. Violent crime rate was not associated with % physical inactivity or % obesity among Hispanic and NH White census tracts. Conclusions: Racial/ethnic composition of residents appears to influence census-tract level associations between violent crime rate, physical inactivity, and obesity. Violent crime appears to be more relevant to physical inactivity and obesity in Chicago’s NH Black communities compared to Hispanic and NH White communities.

Because of poverty, children and families in low- and middle-income countries often face significant impediments to health and well-being. Centre-based day care services may influence the development of children and the economic situation of parents by providing good quality early childhood care and by freeing parents to participate in the labour force. To assess the effects of centre-based day care without additional interventions (e.g. psychological or medical services, parent training) on the development, health and well-being of children and families in low- and middle-income countries (as defined by the World Bank 2011). In April 2014, we searched CENTRAL, Ovid MEDLINE, EMBASE, PsycINFO, ERIC and 16 other sources, including several World Health Organization (WHO) regional databases. We also searched two trials registers, websites of government and non-government agencies and reference lists of relevant studies. We included randomised and quasi-randomised controlled trials and prospective non-randomised studies with contemporaneous control groups and assessments both before and after intervention. We considered non-randomised controlled trials, as centre-based care in low- and middle-income countries is unlikely to be studied using randomised controlled trials (Higgins 2011). We included the following outcomes: child intellectual development, child psychosocial development, maternal and family outcomes and incidence of infectious diseases. Two review authors independently assessed risk of bias and extracted data from the single included study. Only one trial, involving 256 children, met the inclusion criteria for this review. This study was assessed as having high risk of bias because of non-random allocation, incomplete outcome data and insufficient control of confounding factors. Results from this study suggest that centre-based day care may have a positive effect on child cognitive ability compared with no treatment (care at home) (assessed using a modified version of the British Ability Scale-II (BAS-II) (standardised mean difference (SMD) 0.74, 95% confidence interval (CI) 0.48 to 1.00, 256 participants, 1 study, very low-quality evidence). This study did not measure other variables relevant to this review. The single study included in this review provides limited evidence on the effects of centre-based day care for children younger than five years of age in low- and middle-income countries. This study was at high risk of bias and may have limited generalisability to other low- and middle-income countries. Many of the studies excluded from this review paired day care attendance with co-interventions that are unlikely to be provided in normal day care centres. Effectiveness studies on centre-based day care without these co-interventions are few, and the need for such studies is significant. In future studies, comparisons might include home visits or alternative day care arrangements.

IntroductionKing County, Washington, fares well overall in many health indicators. However, county-level data mask disparities among subcounty areas. For disparity-focused assessment, a demand exists for examining health data at subcounty levels such as census tracts and King County health reporting areas (HRAs).MethodsWe added a “nearest intersection” question to the Behavioral Risk Factor Surveillance System (BRFSS) and geocoded the data for subcounty geographic areas, including census tracts. To overcome small sample size at the census tract level, we used hierarchical Bayesian models to obtain smoothed estimates in cigarette smoking rates at the census tract and HRA levels. We also used multiple imputation to adjust for missing values in census tracts.ResultsDirect estimation of adult smoking rates at the census tract level ranged from 0% to 56% with a median of 10%. The 90% confidence interval (CI) half-width for census tract with nonzero rates ranged from 1 percentage point to 37 percentage points with a median of 13 percentage points. The smoothed-multiple–imputation rates ranged from 5% to 28% with a median of 12%. The 90% CI half-width ranged from 4 percentage points to 13 percentage points with a median of 8 percentage points.ConclusionThe nearest intersection question in the BRFSS provided geocoded data at subcounty levels. The Bayesian model provided estimation with improved precision at the census tract and HRA levels. Multiple imputation can be used to account for missing geographic data. Small-area estimation, which has been used for King County public health programs, has increasingly become a useful tool to meet the demand of presenting data at more granular levels.

PD05-01 IMPAIRED ACCESS TO HEALTHY FOOD AND LOWER SOCIOECONOMIC STATUS ARE ASSOCIATED WITH HIGHER STONE BURDEN

Several integrative indices at the neighborhood level have been developed in the US, but direct comparisons across these indices for the prevalence of cardiovascular-kidney-metabolic (CKM) conditions are limited. Moreover, it is not known whether certain indices better capture place-based variability in CKM conditions or provide additional information when compared with a single measure of income. To determine how much variability is explained by neighborhood indices in the prevalence of CKM conditions at the census tract level and the incremental value of each index when added to median household income. This cross-sectional study of US national surveillance data (Behavioral Risk Factor Surveillance System and American Community Survey) included all census tracts with complete data for exposures, covariates, and outcomes from 2010 to 2022. Analyses were completed between October 2024 and July 2025. Seven neighborhood indices available at the census tract level (Area Deprivation Index, Child Opportunity Index, Environmental Justice Index, Neighborhood Deprivation Index, Social Deprivation Index, Social Vulnerability Index, Structural Racism Effect Index) and median household income at the census tract level from the American Community Survey. The primary outcome was prevalence of CKM conditions (coronary heart disease [CHD], stroke, and chronic kidney disease [CKD]) at the census tract level. Differences in the exposures and outcomes were visualized by mapping index scores, median household income, and prevalences of CKM conditions for all census tracts. To assess index agreement, pairwise correlations were computed with Spearman rank correlation coefficients, and to assess the incremental variability explained by each index when added to median household income, change in r2 was calculated. Of the 65 476 US census tracts included, median (IQR) prevalence was 5.9% (4.7%-7.4%) for CHD, 3.3% (2.6%-4.1%) for stroke, and 2.9% (2.5%-3.4%) for CKD. The indices and income were modestly to highly correlated (range, 0.46-0.94), with some discordance in how each measure classified census tracts by quartiles of index scores. All indices and income were significantly associated with CKM condition prevalence at the census tract level in multivariable linear regression models, adjusted for median population size and age. The r2 values of the indices with CHD, stroke, and CKD ranged from 0.379 (SE = 0.033) for the correlation between the Environmental Justice Index and stroke to 0.688 (SE = 0.002) for the correlation between the Structural Racism Effect Index and stroke. Additionally, the improvement in variability for CHD, stroke, and CKD (change in r2) explained by the addition of each index to income ranged from 0.014 (SE = 0.001) for the correlation between the Environmental Justice Index and CHD to 0.195 (SE = 0.002) for the correlation between Structural Racism Effect Index and stroke. In this cross-sectional study, there were similar associations across neighborhood indices and income with the prevalence of CKM conditions. These findings inform how different place-based measures can be applied in public health research and policy.

BackgroundExperiencing violence, both exposure and victimization, has been associated with negative health outcomes including increased mental health problems and sexual risk behaviors. This ecological analysis aimed to explore the ecologic...

(1) Background: exposure to heavy metals is associated with adverse health effects and disproportionately impacts low-income communities and communities of color. We carried out a community-based participatory research study to examine the distribution of heavy metal concentrations in the soil and social vulnerabilities to soil heavy metal exposures across Census tracts in Santa Ana, CA. (2) Methods: soil samples (n = 1528) of eight heavy metals including lead (Pb), arsenic (As), manganese (Mn), chromium (Cr), nickel (Ni), copper (Cu), cadmium (Cd), and zinc (Zn) were collected in 2018 across Santa Ana, CA, at a high spatial resolution and analyzed using XRF analysis. Metal concentrations were mapped out and American Community Survey data was utilized to assess metals throughout Census tracts in terms of social and economic variables. Risk assessment was conducted to evaluate carcinogenic and non-carcinogenic risk. (3) Results: concentrations of soil metals varied according to landuse type and socioeconomic factors. Census tracts where the median household income was under $50 000 had 390%, 92.9%, 56.6%, and 54.3% higher Pb, Zn, Cd, and As concentrations compared to high-income counterparts. All Census tracts in Santa Ana showed hazard index >1, implying the potential for non-carcinogenic health effects, and nearly all Census tracts showed a cancer risk above 10−4, implying a greater than acceptable risk. Risk was predominantly driven by childhood exposure. (4) Conclusions: findings inform initiatives related to environmental justice and highlight subpopulations at elevated risk of heavy metal exposure, in turn underscoring the need for community-driven recommendations for policies and other actions to remediate soil contamination and protect the health of residents.

Introduction: Alcohol is the major chemical risk factor for hepatocellular carcinoma (HCC) around the world, however other toxins including Arsenic have been shown to promote hepatocarcinogenesis in animals, though the exact mechanism is poorly understood. This ecological study assesses neighborhood-level HCC burden in Texas relative to arsenic exposure. Methods: Using data from the Texas Cancer Registry, we identified a cohort of individuals diagnosed with HCC between 2011 and 2015. The primary exposure of interest is Arsenic pollution as reported in the 2011 National Air Toxics Assessment (NATA) inventory, this national screening assessment by the Environmental Protection Agency (EPA) uses emissions data to estimate health risks from toxic air pollutants. NATA calculates the concentrations of toxic air pollutants at the census tract (neighborhood) level; the inhalation exposure concentrations of Arsenic are in units of micrograms per cubic meter, however, for analysis, exposure concentrations were divided into deciles. Arsenic concentrations, demographic data, and the Area Deprivation Index (composite measure of neighborhood socioeconomic disadvantage that relies on 17 census variables drawn from these categories: poverty, housing, employment, and education) were included in multivariable Poisson-based modeling using negative binomial regression to evaluate the association between Arsenic exposure and HCC incidence in Texas. Results: In a univariable model, the association between Arsenic inhalation exposure concentrations and HCC was not significant (IRR = 1.06 [95% CI, 0.98-1.16]). Whereas, in a multivariable model that included selected demographic and socioeconomic factors, results show that variation in census tract HCC incidence across Texas is significantly associated with the inhalation exposure concentrations of Arsenic. Based on our findings, in a typical Texas census tract, a 10-unit increase in decile classification of Arsenic inhalation exposure concentration increases the risk of HCC incidence by a factor of 1.30, while holding other explanatory variables constant (IRR = 1.30 [95% CI, 1.19-1.42]) (Table). Conclusion: Variation in HCC incidence across Texas’s census tracts is significantly associated with the inhalation exposure concentrations of Arsenic at the census tract level, higher Arsenic concentrations are associated with an increased incidence. This ecological finding needs to be further examined in direct association studies. Table 1. - Relationships between HCC incidence in Texas (2011 to 2015) and inhalation exposure concentrations of Arsenic (2011 estimate). Texas census tracts; N = 5,205 Univariable Models a Multivariable Model b IRR 95% CI p value IRR 95% CI p value Arsenic concentrations c 1.06 0.98-1.16 0.159 1.302 1.194-1.419 0.001 Area Deprivation Index 1.17 1.16-1.20 <0.001 1.618 1.444-1.813 <0.001 % Hispanic or Latino (NH) 1.08 1.07-1.09 <0.001 1.092 1.080-1.105 <0.001 % Non-Hispanic Asians 0.68 0.65-0.71 <0.001 0.892 0.853-0.934 <0.001 % Non-Hispanic African American 1.06 1.05-1.07 <0.001 1.098 1.080-1.116 <0.001 % Others; 2+ races d 0.58 0.54-0.63 <0.001 - % Population ≥ 60 y.o. 1.19 1.15-1.22 <0.001 1.406 1.359-1.455 <0.001 % Population male 1.05 1.00-1.11 0.064 1.171 1.117-1.226 <0.001 % Non-Hispanic White e 0.91 0.90-0.92 <0.001 aUnivariable models where Arsenic exposure concentrations was regressed on the HCC incidence separately. Also, each covariate was regressed on the HCC incidence separately.bMultivariable model where Arsenic concentrations and the covariates (P value ≤ 0.10) were regressed on the HCC incidence simultaneously.cThe 2011 National Air Toxics Assessment (NATA) inhalation exposure concentrations for Arsenic are in units of micrograms per cubic meter; for analysis, concentrations were divided into deciles.dVariable dropped from the multivariable model run because p value > 0.10.eTo avoid model overfitting from multicollinearity among race/ethnicity, Non-Hispanic White excluded from multivariable model.

Prior studies on the association between fine particulate matter with diameters 2.5 μm or smaller (PM2.5) and probability of death have not applied multilevel analysis disaggregating data for US census tract, states, and counties, nor tested its interaction by socioeconomic status (SES). Such an approach could provide a more refined identification and targeting of populations exposed to increased risk from PM2.5. To assess the association between PM2.5 and age-specific mortality risk (ASMR) using disaggregated data at the census tract level and evaluate such association according to census tract SES. This nationwide cross-sectional study used a linkage of 3 different data sets. ASMR for the period of 2010 to 2015 was obtained from the National Center for Health Statistic, SES data covering a period from 2006 to 2016 came from the American Community Survey, and mean PM2.5 exposure levels from 2010 to 2015 were derived from well-validated atmospheric chemistry and machine learning models. Data were analyzed in April 2021. The main exploratory variable was mean census tract-level long-term exposure to PM2.5 from 2010 to 2015. The primary outcome was census tract-level ASMR. Multilevel models were used to quantify the geographic variation in ASMR at levels of census tract, county, and state. Additional analysis explored the interaction of SES in the association of ASMR with PM2.5 exposure. Data from 67 148 census tracts nested in 3087 counties and 50 states were analyzed. The association between exposure to PM2.5 and ASMR varied substantially across census tracts. The magnitude of such association also varied across age groups, being higher among adults and older adults. Census tracts accounted for most of the total geographic variation in mortality risk (range, 77.0%-94.2%). ASMR was higher in deciles with greater PM2.5 concentration. For example, ASMR for age 75 to 84 years was 54.6 per 1000 population higher in the decile with the second-highest PM2.5 concentration than in the decile with the lowest PM2.5 concentration. The ASMR, PM2.5 concentrations, and magnitude of the association between both were higher in the census tracts with the lowest SES. This cross-sectional study found that census tracts with lower SES presented higher PM2.5 concentrations. ASMR and air pollution varied substantially across census tracts. There was an association between air pollution and ASMR across all age groups in the United States. These findings suggest that equitable public policies aimed at improving air quality are needed and important to increase life expectancy.

MP40-10 SOCIOECONOMIC STATUS AND HEALTHY FOOD ACCESS IMPACT 24-HOUR URINE PARAMETERS

Health consequences of intensive livestock industry and implications for environmental justice are of great concern in Iowa, USA, which has an extensive history of animal feeding operations (AFOs). We examined disparities in exposure to AFOs including concentrated AFOs (CAFOs) with several environmental justice metrics and considered exposure intensity based on animal units (AUs). Using data on permitted AFOs from the Iowa Department of Natural Resources, we evaluated environmental disparities by multiple environmental justice metrics (e.g. race/ethnicity, socio-economic status (SES), income inequality (Gini index), racial isolation, and educational isolation) using 2010 Census tract-level variables. We used an exposure metric incorporating the density and intensity as the sum of AUs within each Census tract. We investigated exposure disparities by comparing distributions of environmental justice metrics based on operation type (e.g. confinement, open feedlot, large CAFOs), animal type, and Census tract-level AFOs exposure intensity categories (i.e. from low exposure (quartile 1) to high exposure (quartile 4)). AFOs in Iowa were located in areas with lower percentages of racial/ethnic minority persons and high SES communities. For example, the percent of the population that is non-Hispanic Black was over 9 times higher in Census tracts without AFOs than tracts with AFOs (5.14% vs. 0.55%). However, when we considered AFO exposure intensity within the areas having AFO exposure, areas with higher AFO exposure had higher percentages of racial/ethnic minority persons (e.g. Hispanic) and low SES communities (e.g. higher educational isolation) compared to areas with lower AFO exposure. Findings by AFO type (e.g. large CAFO, medium CAFO) showed similar patterns of the distribution of environmental justice metrics as the findings for AFOs overall. We identified complex disparities with higher exposure to non-disadvantaged subpopulations when considering areas with versus without AFOs, but higher exposure to disadvantaged communities within areas with AFOs.

Several South Carolina (SC) communities are overburdened by pollution and may suffer from environmental health disparities. They may also lack access to resiliency buffers (community assets) that can counteract negative environmental exposures. To effectively address this double disparity, improvements in screening approaches and cumulative risk assessment (CRA) methods are needed to better understand and mitigate risk. The purpose of this study was to develop a cumulative stressors and resiliency index (CSRI) to rank human health and environmental risks at the census tract level in SC. We performed Principal Component Analysis (PCA) on variable subcategories to reduce the proposed indicators to 20 that reflected environmental stress and resiliency in communities impacted by environmental injustice. CSRI scores (0 - 100) were computed at the census tract level and high-risk (HR) census tracts were identified as CSRI scores in the 90th percentile. We performed a one-way analysis of variance (ANOVA) on CSRI scores by Environmental Affairs (EA) region and linear regression for percent non-white and CSRI scores. Choropleth maps were developed in ArcMap 10.5 using natural breaks to visualize spatial relationships. CSRI scores ranged from 7.4 &amp;#8211; 64.0. The mean CSRI score for SC was 29.1, which was lower than the mean score for Upstate (35.2) and Midlands (31.7) regions. The one-way ANOVA results indicated a statistically significant difference in CSRI scores by EA region (p &amp;lt;0.0001) except between the Lowcountry and Pee Dee regions [95% CI: -1.53, 2.68]. Based on the regression results, a one-unit increase in the percentage of non-white populations per census tract increased CSRI scores by roughly 6.1%. The results of our study provide a blueprint for targeting low resiliency communities for public health interventions and supports the inclusion of resilience factors in environmental justice (EJ) assessments.

Introduction and Study Purpose: Cancer screening is one of the most important preventive modifiers of cancer outcome. The general correlation between negative socioeconomic influences (SEIs) and poorer cancer screening rates is known. However, variations on SEI correlation with cancer screening need to be confirmed at a smaller local level since there could be significant local variations. CDC PLACES is a high resolution dataset that provides data on cancer screening rates at a census tract level with coverage across the US. It is made up of small area estimates constructed with demographic data in CDC BRFSS, poverty data from ACS, and other sources. With CDC PLACES and SEI data readily available on a census tract level, it is possible to calculate associations between cancer screening compliance and SEIs on a local level to better identify communities potentially benefiting from increased cancer screening. We hypothesize that cancer screening rates will be positively correlated with economic resources, education, insurance coverage, and decreased minority population. Methodology: We performed a retrospective cross-sectional study of cervical and colon cancer screening rates at a census tract level using data from CDC PLACES. This study included 378 of the 443 census tracts in Cuyahoga County, Ohio. 65 census tracts were excluded due to missing data. SEI census tract-level variables including race, gender, age, income, education, and health insurance coverage were obtained from ACS. Cancer screening rates were obtained from CDC PLACES. Linkage across datasource was conducted at the census tract level. Pearson correlation coefficients were calculated to examine associations between the selected SEI variables and cancer screening rates across all 378 census tracts. Results: Within the 378 census tracts examined, cervical and colon cancer screening rates in Cuyahoga County were correlated with a similar set of SEI variables. Positive correlations were observed between cervical and colon cancer screening rates with greater percent White population (r=0.35, p&amp;lt;0.001; r=0.36, p&amp;lt;0.001; respectively), formal education achievement (r=0.75, p&amp;lt;0.001; r=0.79, p&amp;lt;0.001; respectively), and private health insurance coverage (r=0.75, p&amp;lt;0.001; r=0.81, p&amp;lt;0.001; respectively). Conversely, cervical and colon cancer screening rates were negatively correlated with greater percent of Black population (r=-0.24, p&amp;lt;0.001; r=-0.27, p&amp;lt;0.001; respectively), lower English language fluency (r=-0.47, p&amp;lt;0.001; r=-0.42, p&amp;lt;0.001; respectively), higher unemployment (r=-0.47, p&amp;lt;0.001; r=-0.51, p&amp;lt;0.001; respectively), and publicly insured healthcare coverage (r=-0.65, p&amp;lt;0.001; r=-0.67, p&amp;lt;0.001; respectively). Conclusion: Small area correlations between cancer screening rates and SEIs has the potential to inform local policy and research that more accurately serves the health needs of smaller community regions. Citation Format: Zherui Xuan, Jennifer Cullen. Area-level socioeconomic influences on rates of cervical and colon cancer screenings [abstract]. In: Proceedings of the 16th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2023 Sep 29-Oct 2;Orlando, FL. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2023;32(12 Suppl):Abstract nr B092.