Patterns of Cancer-Related Healthcare Access across Pennsylvania: Analysis of Novel Census Tract-Level Indicators of Persistent Poverty.

BACKGROUND: Persistent poverty census tracts have had ≥20% of the population living below the federal poverty line for 30+ years. We assessed the relationship between persistent poverty and cancer-related healthcare access across census tracts in Pennsylvania. METHODS: We gathered publicly available census tract-level data on persistent poverty, rurality, and sociodemographic variables, as well as potential access to healthcare (i.e., prevalence of health insurance, last-year check-up), realized access to healthcare (i.e., prevalence of screening for cervical, breast, and colorectal cancers), and self-reported cancer diagnosis. We used multivariable spatial regression models to assess the relationships between persistent poverty and each healthcare access indicator. RESULTS: Among Pennsylvania's census tracts, 2,789 (89.8%) were classified as non-persistent poverty, and 316 (10.2%) were classified as persistent poverty (113 did not have valid data on persistent poverty). Persistent poverty tracts had lower prevalence of health insurance [estimate = -1.70, standard error (SE) = 0.10], screening for cervical cancer (estimate = -4.00, SE = 0.17) and colorectal cancer (estimate = -3.13, SE = 0.20), and cancer diagnosis (estimate = -0.34, SE = 0.05), compared with non-persistent poverty tracts (all P < 0.001). However, persistent poverty tracts had higher prevalence of last-year check-up (estimate = 0.22, SE = 0.08) and screening for breast cancer (estimate = 0.56, SE = 0.15; both P < 0.01). CONCLUSIONS: Relationships between persistent poverty and cancer-related healthcare access outcomes differed in direction and magnitude. Health promotion interventions should leverage data at fine-grained geographic units (e.g., census tracts) to motivate focus on communities or outcomes. IMPACT: Future studies should extend these analyses to other states and outcomes to inform public health research and interventions to reduce geographic disparities.

Historical Redlining, Persistent Mortgage Discrimination, and Race in Breast Cancer Outcomes.

Importance: Inequities created by historical and contemporary mortgage discriminatory policies have implications for health disparities. The role of persistent mortgage discrimination (PMD) in breast cancer (BC) outcomes has not been studied. Objective: To estimate the race-specific association of historical redlining (HRL) with the development of BC subtypes and late-stage disease and a novel measure of PMD in BC mortality. Design, Setting, and Participants: This population-based cohort study used Georgia Cancer Registry data. A total of 1764 non-Hispanic Black and White women with a BC diagnosis and residing in an area graded by the Home Owners' Loan Corporation (HOLC) in Georgia were included. Patients were excluded if they did not have a known subtype or a derived American Joint Committee on Cancer stage or if diagnosed solely by death certificate or autopsy. Participants were diagnosed with a first primary BC between January 1, 2010, to December 31, 2017, and were followed through December 31, 2019. Data were analyzed between May 1, 2022, and August 31, 2023. Exposures: Scores for HRL were examined dichotomously as less than 2.5 (ie, nonredlined) vs 2.5 or greater (ie, redlined). Contemporary mortgage discrimination (CMD) scores were calculated, and PMD index was created using the combination of HRL and CMD scores. Main Outcomes and Measures: Estrogen receptor (ER) status, late stage at diagnosis, and BC-specific death. Results: This study included 1764 women diagnosed with BC within census tracts that were HOLC graded in Georgia. Of these, 856 women (48.5%) were non-Hispanic Black and 908 (51.5%) were non-Hispanic White; 1148 (65.1%) were diagnosed at 55 years or older; 538 (30.5%) resided in tracts with HRL scores less than 2.5; and 1226 (69.5%) resided in tracts with HRL scores 2.5 or greater. Living in HRL areas with HRL scores 2.5 or greater was associated with a 62% increased odds of ER-negative BC among non-Hispanic Black women (odds ratio [OR], 1.62 [95% CI, 1.01-2.60]), a 97% increased odds of late-stage diagnosis among non-Hispanic White women (OR, 1.97 [95% CI, 1.15-3.36]), and a 60% increase in BC mortality overall (hazard ratio, 1.60 [95% CI, 1.17-2.18]). Similarly, PMD was associated with BC mortality among non-Hispanic White women but not among non-Hispanic Black women. Conclusions and Relevance: The findings of this cohort study suggest that historical racist policies and persistent discrimination have modern-day implications for BC outcomes that differ by race. These findings emphasize the need for a more nuanced investigation of the social and structural drivers of disparate BC outcomes.

A Census Tract-Level Examination of Diagnosed HIV Infection and Social Vulnerability Themes Among Black/African American, Hispanic/Latino, and White Adults, 2019-USA.

BACKGROUND: Assessing HIV diagnosis and the social vulnerability index (SVI) by themes (socioeconomic status, household composition and disability, minority status and English proficiency, and housing type and transportation) might help to identify specific social factors contributing to disparities across census tracts with high rates of diagnosed HIV infection in the USA. METHODS: We examined HIV rate ratios in 2019 using data from CDC's National HIV Surveillance System (NHSS) for Black/African American, Hispanic/Latino, and White persons aged ≥ 18 years. NHSS data were linked to CDC/ATSDR SVI data to compare census tracts with the lowest SVI (Q1) and highest SVI (Q4) scores. Rates and rate ratios were calculated for 4 SVI themes by sex assigned at birth for age group, transmission category, and region of residence. RESULTS: In the socioeconomic theme analysis, we observed wide within-group disparity among White females with diagnosed HIV infection. In the household composition and disability theme, we observed high HIV diagnosis rates among Hispanic/Latino and White males who lived in the least socially vulnerable census tracts. In the minority status and English proficiency theme, we observed a high percentage of Hispanic/Latino adults with diagnosed HIV infection in the most socially vulnerable census tracts. In the housing type and transportation theme, we observed a high percentage of HIV diagnoses attributed to injection drug use in the most socially vulnerable census tracts. CONCLUSION: The development and prioritization of interventions that address specific social factors contributing to disparities in HIV across census tracts with high diagnosis rates are critical to reducing new HIV infections in the USA.

Evaluation of a Targeted COVID-19 Community Outreach Intervention: Case Report for Precision Public Health.

BACKGROUND: Cameron County, a low-income south Texas-Mexico border county marked by severe health disparities, was consistently among the top counties with the highest COVID-19 mortality in Texas at the onset of the pandemic. The disparity in COVID-19 burden within Texas counties revealed the need for effective interventions to address the specific needs of local health departments and their communities. Publicly available COVID-19 surveillance data were not sufficiently timely or granular to deliver such targeted interventions. An agency-academic collaboration in Cameron used novel geographic information science methods to produce granular COVID-19 surveillance data. These data were used to strategically target an educational outreach intervention named "Boots on the Ground" (BOG) in the City of Brownsville (COB). OBJECTIVE: This study aimed to evaluate the impact of a spatially targeted community intervention on daily COVID-19 test counts. METHODS: The agency-academic collaboration between the COB and UTHealth Houston led to the creation of weekly COVID-19 epidemiological reports at the census tract level. These reports guided the selection of census tracts to deliver targeted BOG between April 21 and June 8, 2020. Recordkeeping of the targeted BOG tracts and the intervention dates, along with COVID-19 daily testing counts per census tract, provided data for intervention evaluation. An interrupted time series design was used to evaluate the impact on COVID-19 test counts 2 weeks before and after targeted BOG. A piecewise Poisson regression analysis was used to quantify the slope (sustained) and intercept (immediate) change between pre- and post-BOG COVID-19 daily test count trends. Additional analysis of COB tracts that did not receive targeted BOG was conducted for comparison purposes. RESULTS: During the intervention period, 18 of the 48 COB census tracts received targeted BOG. Among these, a significant change in the slope between pre- and post-BOG daily test counts was observed in 5 tracts, 80% (n=4) of which had a positive slope change. A positive slope change implied a significant increase in daily COVID-19 test counts 2 weeks after targeted BOG compared to the testing trend observed 2 weeks before intervention. In an additional analysis of the 30 census tracts that did not receive targeted BOG, significant slope changes were observed in 10 tracts, of which positive slope changes were only observed in 20% (n=2). In summary, we found that BOG-targeted tracts had mostly positive daily COVID-19 test count slope changes, whereas untargeted tracts had mostly negative daily COVID-19 test count slope changes. CONCLUSIONS: Evaluation of spatially targeted community interventions is necessary to strengthen the evidence base of this important approach for local emergency preparedness. This report highlights how an academic-agency collaboration established and evaluated the impact of a real-time, targeted intervention delivering precision public health to a small community.

Associations between a Novel Measure of Census Tract-Level Credit Insecurity and Frequent Mental Distress in US Urban Areas, 2020.

Access to and utilization of consumer credit remains an understudied social determinant of health. We examined associations between a novel, small-area, multidimensional credit insecurity index (CII), and the prevalence of self-reported frequent mental distress across US cities in 2020. The census tract-level CII was developed by the Federal Reserve Bank of New York using Census population information and a nationally representative sample of anonymized Equifax credit report data. The CII was calculated for tracts in 766 cities displayed on the City Health Dashboard at the time of analysis, predominantly representing cities with over 50,000 residents. The CII combined data on tract-level participation in the formal credit economy with information on the percent of individuals without revolving credit, percent with high credit utilization, and percent with deep subprime credit scores. Tracts were classified as credit-assured, credit-likely, mid-tier, at-risk, or credit-insecure. We used linear regression to examine associations between the CII and a modeled tract-level measure of frequent mental distress, obtained from the CDC PLACES project. Regression models were adjusted for neighborhood economic and demographic characteristics. We examined effect modification by US region by including two-way interaction terms in regression models. In adjusted models, credit-insecure tracts had a modestly higher prevalence of frequent mental distress (prevalence difference = 0.38 percentage points; 95% CI = 0.32, 0.44), compared to credit-assured tracts. Associations were most pronounced in the Midwest. Local factors impacting credit access and utilization are often modifiable. The CII, a novel indicator of community financial well-being, may be an independent predictor of neighborhood health in US cities and could illuminate policy targets to improve access to desirable credit products and downstream health outcomes.

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

OBJECTIVE: 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. METHODS: 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. RESULTS: 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]. CONCLUSIONS: 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.

Association of Neighborhood Recreation Centers and Poststroke Outcomes in a Population-Based Cohort.

BACKGROUND: Higher neighborhood socioeconomic status has been favorably associated with stroke outcomes. This may be due to these areas having more beneficial resources such as recreational centers. We aimed to determine if neighborhood density of recreation centers is favorably associated with stroke outcomes. METHODS: We conducted analyses of data from the Brain Attack Surveillance in Corpus Christi project, a cohort of stroke survivors ≥45 years of age residing in Nueces County, TX (2009-2020). We included non-Hispanic White and Mexican American incident stroke survivors, who were not institutionalized prestroke and completed baseline and follow-up assessments (N=1392). We calculated the density of fitness and recreational sports centers within their residential census tract during the year of their stroke. Outcomes included function (self-ratings on activities of daily living and instrumental activities of daily living), cognition (modified mini-mental state exam), depression (Patient Health Questionnaire-8), and quality of life (abbreviated Stroke-Specific Quality of Life Scale). We fit confounder-adjusted gamma-distributed mixed generalized linear models with a log link for each outcome and considered interaction with stroke severity. RESULTS: On average, participants were 65 years old, 53% male, and 63% Mexican American. Median recreational centers were 1.60 per square mile (interquartile range, 0.41-3.06). Among moderate-severe stroke survivors, greater density of recreation centers (75th versus 25th percentile) was associated with more favorable function and possibly quality of life (activities of daily living/instrumental activities of daily living, 4.8% change [95% CI, -0.11% to -9.27%]; Stroke-Specific Quality of Life Scale, 3.7% change [95% CI, -0.7% to 8.2%]). Minimal nonsignificant differences were observed among the overall stroke population and those with mild stroke. CONCLUSIONS: The availability of recreation centers may be beneficial for poststroke function and quality of life among those with moderate-severe stroke. If further research confirms recreation centers to be beneficial, this could inform rehabilitation following stroke.

The associations of income and Black-White racial segregation with HIV outcomes among adults aged ≥18 years-United States and Puerto Rico, 2019.

OBJECTIVE(S): To examine associations between Index of Concentration at the Extremes (ICE) measures for economic and racial segregation and HIV outcomes in the United States (U.S.) and Puerto Rico. METHODS: County-level HIV testing data from CDC's National HIV Prevention Program Monitoring and Evaluation and census tract-level HIV diagnoses, linkage to HIV medical care, and viral suppression data from the National HIV Surveillance System were used. Three ICE measures of spatial polarization were obtained from the U.S. Census Bureau's American Community Survey: ICEincome (income segregation), ICErace (Black-White racial segregation), and ICEincome+race (Black-White racialized economic segregation). Rate ratios (RRs) for HIV diagnoses and prevalence ratios (PRs) for HIV testing, linkage to care within 1 month of diagnosis, and viral suppression within 6 months of diagnosis were estimated with 95% confidence intervals (CIs) to examine changes across ICE quintiles using the most privileged communities (Quintile 5, Q5) as the reference group. RESULTS: PRs and RRs showed a higher likelihood of testing and adverse HIV outcomes among persons residing in Q1 (least privileged) communities compared with Q5 (most privileged) across ICE measures. For HIV testing percentages and diagnosis rates, across quintiles, PRs and RRs were consistently greatest for ICErace. For linkage to care and viral suppression, PRs were consistently lower for ICEincome+race. CONCLUSIONS: We found that poor HIV outcomes and disparities were associated with income, racial, and economic segregation as measured by ICE. These ICE measures contribute to poor HIV outcomes and disparities by unfairly concentrating certain groups (i.e., Black persons) in highly segregated and deprived communities that experience a lack of access to quality, affordable health care. Expanded efforts are needed to address the social/economic barriers that impede access to HIV care among Black persons. Increased partnerships between government agencies and the private sector are needed to change policies that promote and sustain racial and income segregation.

Census Tracts Are Not Neighborhoods: Addressing Spatial Misalignment in Studies Examining the Impact of Historical Redlining on Present-day Health Outcomes.

BACKGROUND: Research examining the effects of historical redlining on present-day health outcomes is often complicated by the misalignment of contemporary census boundaries with the neighborhood boundaries drawn by the US Home Owners' Loan Corporation (HOLC) in the 1930s. Previous studies have used different approaches to assign historical HOLC grades to contemporary geographies, but how well they capture redlining exposure is unknown. METHODS: Our analysis included 7711 residences identified in the Multiple Listing Service database in Atlanta, Georgia (2017-2022). We evaluated the classification of HOLC grade assignment (A, B, C, D, or ungraded) when assigning exposure under four area-level approaches (centroid, majority land area, weighted score, and highest HOLC) compared with using complete address data (gold standard). We additionally compared approaches across three 2020 census geographies (tract, block group, and block). RESULTS: When comparing the use of census tracts to complete address data, sensitivity was highest for the weighted score approach, which correctly identified 77% of residences in truly A-D graded neighborhoods as compared with the majority land area (44%), centroid (54%), and highest HOLC (59%) approaches. Regarding specificity, the majority land area approach best-classified residences in truly ungraded neighborhoods (93%) as compared with the weighted score (65%), centroid (81%), and highest HOLC (54%) approaches. Classification improved regardless of approach when using census block compared with the census tract. CONCLUSIONS: Misclassification of historical redlining exposure is inevitable when using contemporary census geographies rather than complete address data. This study provides a framework for assessing spatial misalignment and selecting an approach for classification.

Measures of Neighborhood Opportunity and Adherence to Recommended Pediatric Primary Care.

Importance: Neighborhood conditions are known to broadly impact child health. Research to date has not examined the association of the Childhood Opportunity Index (COI), a multidimensional indicator of neighborhood environment conditions, specifically with pediatric primary care outcomes. Objective: To determine the association of neighborhood opportunity measured by the COI with health metrics commonly captured clinically in pediatric primary care, reflecting both access to preventive care and child well-being. Design, Setting, and Participants: This cross-sectional observational study used electronic health record data from a large pediatric primary care network in the northeastern US. Participants included patients aged 0 to 19 years who were active in the primary care network between November 2020 and November 2022. Data were analyzed in December 2022. Exposure: Census tract-level COI overall score (in quintiles). Main Outcomes and Measures: Outcomes included up-to-date preventive care and immunization status and presence of obesity, adolescent depression and suicidality, and maternal depression and suicidality. Multivariable mixed-effects logistic regressions estimated associations of these outcomes with COI quintiles, adjusted for age, sex, race and ethnicity, and insurance type. Results: Among 338 277 patients (mean [SD] age, 9.8 [5.9] years; 165 223 female [48.8%]; 158 054 [46.7%] non-Hispanic White, 209 482 [61.9%] commercially insured), 81 739 (24.2%) and 130 361 (38.5%) lived in neighborhoods of very low and very high COI, respectively. Living in very high COI neighborhoods (vs very low COI) was associated with higher odds of being up-to-date on preventive visits (odds ratio [OR], 1.40; 95% CI, 1.32-1.48) and immunizations (OR, 1.77; 95% CI, 1.58-2.00), and with lower odds of obesity (OR, 0.55; 95% CI, 0.52-0.58), adolescent depression (OR, 0.78; 95% CI, 0.72-0.84) and suicidality (OR, 0.79; 95% CI, 0.73-0.85), and maternal depression (OR, 0.78; 95% CI, 0.72-0.86) and suicidality (OR, 0.71; 95% CI, 0.61-0.83). Conclusions and Relevance: This cross-sectional study of electronic health record data found that neighborhood opportunity was associated with multiple pediatric primary care outcomes. Understanding these associations can help health systems identify neighborhoods that need additional support and advocate for and develop partnerships with community groups to promote child well-being. The findings underscore the importance of improving access to preventive care in low COI communities.

The Association Between Census Tract Healthy Food Accessibility and Life Expectancy in the United States.

Accessibility of healthy food is an important predictor for several health outcomes, but its association with life expectancy is unclear. We evaluated the association between U.S. Department of Agriculture's Food Research Atlas measures of healthy food accessibility and life expectancy at birth across contiguous U.S. census tracts using spatial modeling analysis. Both income and healthy food accessibility were associated with life expectancy at birth, as indicated by shorter life expectancy in low-income census tracts when comparing tracts with similar healthy food accessibility level, and in low-access tracts when comparing tracts with similar income level. Compared to high-income/high-access census tracts, life expectancy at birth was lower in high-income/low-access (- 0.33 years; 95% confidence interval - 0.42, - 0.28), low-income/high-access (- 1.45 years; - 1.52, - 1.38), and low-income/low-access (- 2.29 years; - 2.38, - 2.21) tracts after adjusting for socio-demographic characteristics and incorporating vehicle availability. Effective interventions to increase healthy food accessibility may improve life expectancy.

Assessing performance of ZCTA-level and Census Tract-level social and environmental risk factors in a model predicting hospital events.

Predictive analytics are used in primary care to efficiently direct health care resources to high-risk patients to prevent unnecessary health care utilization and improve health. Social determinants of health (SDOH) are important features in these models, but they are poorly measured in administrative claims data. Area-level SDOH can be proxies for unavailable individual-level indicators, but the extent to which the granularity of risk factors impacts predictive models is unclear. We examined whether increasing the granularity of area-based SDOH features from ZIP code tabulation area (ZCTA) to Census Tract strengthened an existing clinical prediction model for avoidable hospitalizations (AH events) in Maryland Medicare fee-for-service beneficiaries. We created a person-month dataset for 465,749 beneficiaries (59.4% female; 69.8% White; 22.7% Black) with 144 features indexing medical history and demographics using Medicare claims (September 2018 through July 2021). Claims data were linked with 37 SDOH features associated with AH events from 11 publicly-available sources (e.g., American Community Survey) based on the beneficiaries' ZCTA and Census Tract of residence. Individual AH risk was estimated using six discrete time survival models with different combinations of demographic, condition/utilization, and SDOH features. Each model used stepwise variable selection to retain only meaningful predictors. We compared model fit, predictive performance, and interpretation across models. Results showed that increasing the granularity of area-based risk factors did not dramatically improve model fit or predictive performance. However, it did affect model interpretation by altering which SDOH features were retained during variable selection. Further, the inclusion of SDOH at either granularity level meaningfully reduced the risk that was attributed to demographic predictors (e.g., race, dual-eligibility for Medicaid). Differences in interpretation are critical given that this model is used by primary care staff to inform the allocation of care management resources, including those available to address drivers of health beyond the bounds of traditional health care.

Association of Community Vulnerability and State Gun Laws With Firearm Deaths in Children and Adolescents Aged 10 to 19 Years.

Importance: Firearm-related injuries are the leading cause of death among children and adolescents in the US. For youths aged 10 to 19 years, 64% of firearm-related deaths are due to assault. Understanding the association between the rate of death due to assault-related firearm injury and both community-level vulnerability and state-level gun laws may inform prevention efforts and public health policy. Objective: To assess the rate of death due to assault-related firearm injury stratified by community-level social vulnerability and state-level gun laws in a national cohort of youths aged 10 to 19 years. Design, Setting, and Participants: This national cross-sectional study used the Gun Violence Archive to identify all assault-related firearm deaths among youths aged 10 to 19 years occurring in the US between January 1, 2020, and June 30, 2022. Exposure: Census tract-level social vulnerability (measured by the Centers for Disease Control and Prevention social vulnerability index [SVI]; categorized in quartiles as low [<25th percentile], moderate [25th-50th percentile], high [51st-75th percentile], or very high [>75th percentile]) and state-level gun laws (measured by the Giffords Law Center gun law scorecard rating; categorized as restrictive, moderate, or permissive). Main Outcomes and Measures: Youth death rate (per 100 000 person-years) due to assault-related firearm injury. Results: Among 5813 youths aged 10 to 19 years who died of an assault-related firearm injury over the 2.5-year study period, the mean (SD) age was 17.1 (1.9) years, and 4979 (85.7%) were male. The death rate per 100 000 person-years in the low SVI cohort was 1.2 compared with 2.5 in the moderate SVI cohort, 5.2 in the high SVI cohort, and 13.3 in the very high SVI cohort. The mortality rate ratio of the very high SVI cohort compared with the low SVI cohort was 11.43 (95% CI, 10.17-12.88). When further stratifying deaths by the Giffords Law Center state-level gun law scorecard rating, the stepwise increase in death rate (per 100 000 person-years) with increasing SVI persisted, regardless of whether the Census tract was in a state with restrictive gun laws (0.83 in the low SVI cohort vs 10.11 in the very high SVI cohort), moderate gun laws (0.81 in the low SVI cohort vs 13.18 in the very high SVI cohort), or permissive gun laws (1.68 in the low SVI cohort vs 16.03 in the very high SVI cohort). The death rate per 100 000 person-years was higher for each SVI category in states with permissive compared with restrictive gun laws (eg, moderate SVI: 3.37 vs 1.71; high SVI: 6.33 vs 3.78). Conclusions and Relevance: In this study, socially vulnerable communities in the US experienced a disproportionate number of assault-related firearm deaths among youths. Although stricter gun laws were associated with lower death rates in all communities, these gun laws did not equalize the consequences on a relative scale, and disadvantaged communities remained disproportionately impacted. While legislation is necessary, it may not be sufficient to solve the problem of assault-related firearm deaths among children and adolescents.

Income and rural-urban status moderate the association between income inequality and life expectancy in US census tracts.

BACKGROUND: A preponderance of evidence suggests that higher income inequality is associated with poorer population health, yet recent research suggests that this association may vary based on other social determinants, such as socioeconomic status (SES) and other geographic factors, such as rural-urban status. The objective of this empirical study was to assess the potential for SES and rural-urban status to moderate the association between income inequality and life expectancy (LE) at the census-tract level. METHODS: Census-tract LE values for 2010-2015 were abstracted from the US Small-area Life Expectancy Estimates Project and linked by census tract to Gini index, a summary measure of income inequality, median household income, and population density for all US census tracts with non-zero populations (n = 66,857). Partial correlation and multivariable linear regression modeling was used to examine the association between Gini index and LE using stratification by median household income and interaction terms to assess statistical significance. RESULTS: In the four lowest quintiles of income in the four most rural quintiles of census tracts, the associations between LE and Gini index were significant and negative (p between < 0.001 and 0.021). In contrast, the associations between LE and Gini index were significant and positive for the census tracts in the highest income quintiles, regardless of rural-urban status. CONCLUSION: The magnitude and direction of the association between income inequality and population health depend upon area-level income and, to a lesser extent, on rural-urban status. The rationale behind these unexpected findings remains unclear. Further research is needed to understand the mechanisms driving these patterns.

A Census Tract-Level Examination of HIV Care Outcomes and Social Vulnerability Among Black/African American, Hispanic/Latino, and White Adults in the Southern United States, 2018.

We examined the association between social vulnerability and HIV diagnoses, linkage to HIV medical care, and viral suppression among adults in the Southern U.S. Data from CDC's National HIV Surveillance System (NHSS) were used to determine census tract-level HIV diagnosis rates and percentages of persons linked to care within one month and with viral suppression within six months of diagnosis among Black/African American, Hispanic/Latino, and White adults aged ≥ 18 years residing in the Southern U.S. in 2018. Census tract-level social vulnerability data were obtained from the 2018 CDC Social Vulnerability Index (SVI). Rate and proportion ratios were used to determine the difference between the lowest quartile of SVI scores (Q1) and the highest quartile (Q4) by age group, transmission category, and region of residence and stratified by sex assigned at birth. Areas with the highest social vulnerability (Q4) had the highest rates of HIV diagnoses (Black: 56.5, Hispanic/Latino: 27.2, and White: 10.3). Those in Q4 also had the lowest percentages of adults linked to care (Black: 76.1%, Hispanic/Latino: 81.2%, and White: 77.8%), and the lowest percentages of adults with viral suppression (Black: 59.8%, Hispanic/Latino: 68.4%, and White: 65.7%). This ecological study found an association between social vulnerability, HIV diagnoses, and poorer care outcomes among Black/African American, Hispanic/Latino, and White adults. Tailoring interventions and improving access for persons residing in areas with the highest social vulnerability is necessary to reduce HIV transmission and improve health outcomes in the Southern U.S.

A Census Tract-Level Examination of Diagnosed HIV Infection and Social Vulnerability among Black/African American, Hispanic/Latino, and White Adults, 2018: United States.

BACKGROUND: To reduce health disparities and improve the health of Americans overall, addressing community-level social and structural factors, such as social vulnerability, may help explain the higher rates of HIV diagnoses among and between race/ethnicity groups. METHODS: Data were obtained from CDC's National HIV Surveillance System (NHSS) and the CDC/ATSDR social vulnerability index (SVI). NHSS data for Black, Hispanic/Latino, and White adults with HIV diagnosed in 2018 were linked to SVI data. To measure the relative disparity, rate ratios (RRs) with 95% CIs were calculated to examine the relative difference comparing census tracts with the lowest SVI scores (quartile 1, Q1) to those with the highest SVI scores (quartile 4, Q4) by sex assigned at birth for age group and region of residence. Differences in the numbers of diagnoses across the quartiles were analyzed by sex assigned at birth and transmission category. RESULTS: There were 13,807 Black, 8747 Hispanic/Latino, and 8325 White adults who received a diagnosis of HIV infection in the United States in 2018-with the highest HIV diagnosis rates among adults who lived in census tracts with the highest vulnerability (Q4). For each race/ethnicity and both sexes, the rate of HIV diagnoses increased as social vulnerability increased. The highest disparities in HIV diagnosis rates by SVI were among persons who inject drugs, and the highest within-group RRs were typically observed among older persons and persons residing in the Northeast. CONCLUSION: To reach the goals of several national HIV initiatives, efforts are needed to address the social vulnerability factors that contribute to racial and ethnic disparities in acquiring HIV and receiving care and treatment.

Associations Between Age of Onset of Pediatric Overweight/Obesity, a Child's Sociodemographic Characteristics, and Characteristics of a Child's Home Census Tract.

PURPOSE: To identify associations between age of onset of overweight/obesity, a child's sociodemographic characteristics, and characteristics of a child's home census tract. DESIGN: Retrospective electronic health record review of children with overweight/obesity. SETTING: Three primary care centers associated with a free-standing, tertiary-care pediatric institution in Cincinnati. SUBJECTS: Patients born between August 1, 2013 and July 31, 2014, who had a body mass index (BMI) ≥85th percentile before 5 years of age (n = 794). MEASURES: Primary outcome was the patient's age at the first encounter when BMI was ≥85th percentile. Patient-level predictors were sex, age, race/ethnicity, health insurance, and number of moves captured in the health record. Census tract-level predictors were density of bus stops, presence of grocery stores, and a Socioeconomic Deprivation Index. ANALYSIS: Multivariable linear regression models assessed for independent associations between age of onset of overweight/obesity and predictors. RESULTS: Patients were 55.8% female, 73.6% black, and 79.1% publicly insured. Each additional move per year was associated with onset of overweight/obesity occurring 4.05 months earlier (P < .0001). No significant associations between age of onset of overweight/obesity and census tract-level density of bus stops (P = .82), presence of grocery stores (P = .39), and socioeconomic deprivation (P = .53) were demonstrated. CONCLUSION: Public policy efforts toward improving access to grocery stores or public transportation may not be sufficient to prevent childhood obesity. Population-level interventions related to improving housing may also reduce obesity.

Mapeando as disparidades socioeconômicas de saúde urbana: um estudo comparativo entre seis capitais brasileiras / Mapping socioeconomic disparities in urban health: a comparative study of six Brazilian capitals / Mapeando las disparidades socioeconómicas de salud urbana: un estudio comparativo entre seis capitales brasileñas

Resumo A cidade é um modo de viver, pensar e sentir. O modo de vida urbano é capaz de produzir ideias, comportamentos, valores e conhecimentos, mas também pode acirrar disparidades socioeconômicas e de saúde da população que ali reside. Este artigo examina as disparidades em saúde urbana em seis capitais brasileiras: São Paulo, Rio de Janeiro, Salvador, Fortaleza, Belo Horizonte e Manaus. Para quantificar e mapear as disparidades intraurbanas nesses espaços, foram utilizados os dados do Censo Demográfico de 2010 para a aplicação do índice de saúde urbana (ISU), uma métrica que sintetiza oito diferentes variáveis socioeconômicas e de saneamento desagregadas por setores censitários. Os resultados são discutidos à luz de três vertentes teóricas: a diferenciação centro-periferia; abordagem econômica da saúde; e epidemiologia social. As descobertas desse estudo revelam que os setores censitários que abrangem populações com maior status socioeconômico e melhores condições de saneamento apresentaram índices de saúde urbana mais elevados do que os da periferia da cidade. Há indícios de melhores indicadores de saúde urbana para o Rio de Janeiro e São Paulo, em comparação com as demais capitais analisadas. No entanto, há importantes nuances em cada uma das seis cidades estudadas, especialmente quando se atribuem diferentes pesos às variáveis que compõem o ISU, apesar da marcada segregação espacial comum a todas elas. Considerar as distinções dentro do espaço urbano é uma estratégia fundamental para a compreensão desses aspectos sociais e econômicos e seus potenciais desdobramentos nas condições de saúde da população.

Abstract A city is a way of living, thinking, and feeling. The urban lifestyle can produce ideas, behaviors, values, and knowledge. Still, it can also intensify socioeconomic and health disparities in the population. This article examines urban health disparities in six Brazilian capitals: São Paulo, Rio de Janeiro, Salvador, Fortaleza, Belo Horizonte, and Manaus. To quantify and map intra-urban disparities in these spaces, data from the 2010 Demographic Census are used to apply the Urban Health Index, a metric that synthesizes eight different socio-economic and sanitation variables disaggregated by census tracts. The results are discussed in light of three theoretical perspectives: center-periphery differentiation, the economic approach to health, and social epidemiology. The findings of this study reveal that census tracts covering populations with higher socio-economic status and better sanitation conditions exhibited higher urban health index scores than those in the city's periphery. Results indicate better urban health indicators for Rio de Janeiro and São Paulo, compared to the other capitals analyzed. However, there are important nuances in each of the six cities, especially when assigning different weights to the variables that compose the Urban Health Index, despite the marked spatial segregation common to all. Considering distinctions within urban space is a fundamental strategy to understand these social and economic aspects and their potential implications for population health conditions.

Resumen La ciudad es una forma de vivir, pensar y sentir. El modo de vida urbano es capaz de producir ideas, comportamientos, valores y conocimientos, pero también lo es de intensificar las disparidades socioeconómicas y de salud de la población que reside en ella. Este artículo examina las disparidades en salud urbana en seis capitales brasileñas: São Paulo, Río de Janeiro, Salvador, Fortaleza, Belo Horizonte y Manaus. Para cuantificar y mapear las disparidades intraurbanas en estos espacios, se utilizan datos del censo demográfico de 2010 para aplicar el índice de salud urbana, una métrica que sintetiza ocho diferentes variables socioeconómicas y de saneamiento desagregadas por sectores censales. Los resultados se discuten a la luz de tres perspectivas teóricas: la diferenciación centro-periferia, el enfoque económico de la salud y la epidemiología social. Los hallazgos de este estudio revelan que los sectores censales que abarcan poblaciones con un mayor estatus socioeconómico y mejores condiciones de saneamiento presentaron puntajes más altos en el índice de salud urbana que los de la periferia de la ciudad. Hay indicios de mejores indicadores de salud urbana para Río de Janeiro y São Paulo, en comparación con las demás capitales analizadas. Sin embargo, se observan matices importantes en cada una de las seis ciudades analizadas, especialmente al asignar diferentes pesos a las variables que componen el pindice de salud urbana, a pesar de la marcada segregación espacial común a todas ellas. Considerar las distinciones dentro del espacio urbano es una estrategia fundamental para comprender estos aspectos sociales y económicos y sus posibles implicaciones en las condiciones de salud de la población.

A Framework for Interfacing and Partnering with Environmental Justice Communities as a Prelude to Human Health and Hazard Identification in the Vulnerable Census Tracts of Columbus, Ohio.

Columbus, Ohio is one of the more prosperous, well-educated, and progressive cities in the United States. However, it ranks as the second worst life expectancy at birth, has a census tract wealth gap (27-year disparity), and one of the higher infant mortality rates in the country. These data suggest that there are likely several high-risk, vulnerable neighborhoods in Columbus with residents experiencing disparate and adverse outcomes. Illustrative of this fact are studies that have examined the social processes and mechanisms through which neighborhood contexts are at the forefront, including exposures to chemical stressors such as particulate matter (PM2.5) as well as non-chemical stressors including violence, social determinants of health, zoning, and land use policies. It is documented that disparate and adverse outcomes are magnified in the vulnerable neighborhoods on the Near East Side as compared to Columbus city proper, Franklin County and/or the state of Ohio. As such, we developed a nuanced community engagement framework to identify potential environmental hazards associated with adverse pregnancy outcomes in those census tracts. The refined framework uses a blended version of traditional community-based participatory research (CBPR) models and is referred to as E6, Enhancing Environmental Endeavors via e-Equity, Education, and Empowerment.

Análise espacial de casos prováveis de dengue no município de São Luís, Maranhão, Brasil / Spatial analysis of probable dengue cases in the municipality of São Luís, Maranhão, Brazil / Análisis espacial de los casos probables de dengue en el municipio de São Luís, Maranhão, Brasil

INTRODUÇÃO: A dengue é considerada uma das principais arboviroses mundiais, caracterizada no Brasil pelo aumento de casos graves e óbitos. OBJETIVO: realizar análise espacial dos casos prováveis de dengue em São Luís - MA. MÉTODOS: Estudo ecológico de base populacional dos casos prováveis de dengue, notificados no Sistema de Informação de Agravos de Notificação (SINAN) em 2015 e 2016, ocorridos no município de São Luís ­ MA. Foram georreferenciados 4.681 casos prováveis de dengue por setores censitários, calculadas as taxas de incidência e ajustadas através do estimador bayesiano empírico local. Foi utilizado o estimador de densidade de Kernel e Moran Global e Local para a análise espacial. RESULTADOS: Evidenciou-se através do estimador de densidade de Kernel, áreas quentes (alta-densidade) nos setores censitários da região noroeste do município. As taxas de incidência foram ajustadas pela aplicação do método bayesiano empírico local, identificando-se maior quantidade de setores com média e alta incidência. A partir do índice de Moran global foi evidenciada autocorrelação espacial positiva estatisticamente significativa para as taxas de incidência de dengue (I=0,69; p<0,001) e para as taxas de incidência ajustadas pelo método bayesiano (I=0,80; p<0,001). De acordo com o índice de Moran local, identificou-se clusters de setores de alta incidência de dengue em áreas com alta densidade populacional na região nordeste e noroeste do município. CONCLUSÃO: A pesquisa demonstrou que os estimadores bayesianos ajudaram a minimizar os problemas de subnotificação e da influência do tamanho populacional nos setores censitários.

INTRODUCTION: Dengue is considered one of the main arboviruses in the world, characterized in Brazil by the increase in severe cases and deaths. OBJECTIVE: to perform spatial analysis of probable dengue cases in São Luís - MA. METHODS: Population-based ecological study of probable dengue cases, reported in the Notifiable Diseases Information System (SINAN) in 2015 and 2016, which took place in the city of São Luís - MA. 4,681 probable dengue cases were georeferenced by census sectors, incidence rates were calculated and adjusted using the local empirical Bayesian estimator. The Kernel and Moran Global and Local density estimator was used for spatial analysis. RESULTS: Hot areas (high-density) in the census sectors of the northwest region of the municipality were evidenced through the Kernel density estimator. Incidence rates were adjusted by applying the local empirical Bayesian method, identifying a greater number of sectors with medium and high incidence. From the global Moran index, statistically significant positive spatial autocorrelation was evidenced for the dengue incidence rates (I = 0.69; p <0.001) and for the incidence rates adjusted by the Bayesian method (I = 0.80; p <0.001). According to the local Moran index, clusters of sectors with a high incidence of dengue were identified in areas with high population density in the northeast and northwest regions of the municipality. CONCLUSION: The research demonstrated that Bayesian estimators helped to minimize the problems of underreporting and the influence of population size on census tracts.

INTRODUCCIÓN: El dengue es considerado una de las principales arbovirosis a nivel mundial, caracterizada en Brasil por el aumento de casos graves y muertes. OBJETIVO: Realizar un análisis espacial de los casos probables de dengue en São Luís - MA. MÉTODOS: Estudio ecológico de base poblacional de los casos probables de dengue, notificados en el Sistema de Informação de Agravos de Notificação (SINAN) en 2015 y 2016, ocurridos en el municipio de São Luís - MA. Se georreferenciaron 4.681 casos probables de dengue por sectores censales, se calcularon las tasas de incidencia y se ajustaron mediante el estimador empírico bayesiano local. Para el análisis espacial se utilizó el estimador de densidad Kernel y Moran global y local. RESULTADOS: Se evidenció a través del estimador de densidad Kernel, áreas calientes (de alta densidad) en los sectores censales de la región noroeste del municipio. Las tasas de incidencia se ajustaron mediante la aplicación del método bayesiano empírico local, identificándose una mayor cantidad de setores con incidencia media y alta. A partir del índice global de Moran se evidenció una autocorrelación espacial positiva estadísticamente significativa para las tasas de incidencia de dengue (I=0,69; p<0,001) y para las tasas de incidencia ajustadas por el método bayesiano (I=0,80; p<0,001). Según el índice local de Moran, se identificaron clusters de sectores de alta incidencia de dengue en áreas con alta densidad de población en las regiones noreste y noroeste del municipio. CONCLUSIÓN: La investigación demostró que los estimadores bayesianos ayudaron a minimizar los problemas de infradeclaración y la influencia del tamaño de la población en los sectores censales.