OBJECTIVE: Direct primary care (DPC) critics are concerned that the periodic fee precludes participation from vulnerable populations. The purpose is to describe the demographics and appointments of a, now closed, academic DPC clinic and determine whether there are differences in vulnerability between census tracts with and without any clinic patients.
METHODS: We linked geocoded data from the DPC\'s electronic health record with the social vulnerability index (SVI). To characterize users, we described their age, sex, language, membership, diagnoses, and appointments. Descriptive statistics included frequencies, proportions or medians, and interquartile ranges. To determine differences in SVI, we calculated a localized SVI percentile within Harris County. A t test assuming equal variances and Mann-Whitney U Tests were used to assess differences in SVI and all other census variables, respectively, between those tracts with and without any clinic patients.
RESULTS: We included 322 patients and 772 appointments. Patients were seen an average of 2.4 times and were predominantly female (58.4%). More than a third (37.3%) spoke Spanish. There was a mean of 3.68 ICD-10 codes per patient. Census tracts in which DPC patients lived had significantly higher SVI scores (ie, more vulnerable) than tracts where no DPC clinic patients resided (median, 0.60 vs 0.47, p-value < 0.05).
CONCLUSIONS: This academic DPC clinic cared for individuals living in vulnerable census tracts relative to those tracts without any clinic patients. The clinic, unfortunately, closed due to multiple obstacles. Nevertheless, this finding counters the perception that DPC clinics primarily draw from affluent neighborhoods.

BACKGROUND: Stroke survivors believe neighborhood resources such as community centers are beneficial; however, little is known about the influence of these resources on stroke outcomes. We evaluated whether residing in neighborhoods with greater resource density is associated with favorable post-stroke outcomes.
RESULTS: We included Mexican American and non-Hispanic White stroke survivors from the Brain Attack Surveillance in Corpus Christi project (2009-2019). The exposure was density of neighborhood resources (eg, community centers, restaurants, stores) within a residential census tract at stroke onset. Outcomes included time to death and recurrence, and at 3 months following stroke: disability (activities of daily living/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 multivariable Cox regression and mixed linear models. We considered interactions with stroke severity, ethnicity, and sex. Among 1786 stroke survivors, median age was 64 years (interquartile range, 56-73), 55% men, and 62% Mexican American. Resource density was not associated with death, recurrence, or depression. Greater resource density (75th versus 25th percentile) was associated with more favorable cognition (Modified Mini-Mental State Exam mean difference=0.838, 95% CI=0.092, 1.584) and among moderate-severe stroke survivors, with more favorable functioning (activities of daily living/instrumental activities of daily living=-0.156 [95% CI, -0.284 to 0.027]) and quality of life (abbreviated Stroke-Specific Quality of Life scale=0.194 [95% CI, 0.029-0.359]).
CONCLUSIONS: We observed associations between greater resource density and cognition overall and with functioning and quality of life among moderate-severe stroke survivors. Further research is needed to confirm these findings and determine if neighborhood resources may be a tool for recovery.

OBJECTIVE: Public Health officials are often challenged to effectively allocate limited resources. Social determinants of health (SDOH) may cluster in areas to cause unique profiles related to various adverse life events. The authors use the framework of unintended teen pregnancies to illustrate how to identify the most vulnerable neighborhoods.
METHODS: This study used data from the U.S. American Community Survey, Princeton Eviction Lab, and Connecticut Office of Vital Records. Census tracts are small statistical subdivisions of a county. Latent class analysis (LCA) was employed to separate the 832 Connecticut census tracts into four distinct latent classes based on SDOH, and GIS mapping was utilized to visualize the distribution of the most vulnerable neighborhoods. GEE Poisson regression model was used to assess whether latent classes were related to the outcome. Data were analyzed in May 2021.
RESULTS: LCA\'s results showed that class 1 (non-minority non-disadvantaged tracts) had the least diversity and lowest poverty of the four classes. Compared to class 1, class 2 (minority non-disadvantaged tracts) had more households with no health insurance and with single parents; and class 3 (non-minority disadvantaged tracts) had more households with no vehicle available, that had moved from another place in the past year, were low income, and living in renter-occupied housing. Class 4 (minority disadvantaged tracts) had the lowest socioeconomic characteristics.
CONCLUSIONS: LCA can identify unique profiles for neighborhoods vulnerable to adverse events, setting up the potential for differential intervention strategies for communities with varying risk profiles. Our approach may be generalizable to other areas or other programs.
CONCLUSIONS: What is already known on this topic Public health practitioners struggle to develop interventions that are universally effective. The teen birth rates vary tremendously by race and ethnicity. Unplanned teen pregnancy rates are related to multiple social determinants and behaviors. Latent class analysis has been applied successfully to address public health problems. What this study adds While it is the pregnancy that is not planned rather than the birth, access to pregnancy intention data is not available resulting in a dependency on teen birth data for developing public health strategies. Using teen birth rates to identify at-risk neighborhoods will not directly represent the teens at risk for pregnancy but rather those who delivered a live birth. Since teen birth rates often fluctuate due to small numbers, especially for small neighborhoods, LCA may avoid some of the limitations associated with direct rate comparisons. The authors illustrate how practitioners can use publicly available SDOH from the Census Bureau to identify distinct SDOH profiles for teen births at the census tract level. How this study might affect research, practice or policy These profiles of classes that are at heightened risk potentially can be used to tailor intervention plans for reducing unintended teen pregnancy. The approach may be adapted to other programs and other states to prioritize the allocation of limited resources.

BACKGROUND: The Environmental Justice Index is a tool released by the Centers for Disease Control and Prevention that quantifies and ranks the environmental burden and social vulnerability of each census tract. Racial and ethnic disparities in adverse pregnancy outcomes are well established. The relative contributions of individual (person-level) and environmental (neighborhood-level) risk factors to disease prevalence remain poorly understood.
OBJECTIVE: This study aimed to determine whether the Environmental Justice Index is associated with adverse pregnancy outcomes after adjustment for individual clinical and sociodemographic risk factors.
METHODS: This was a retrospective cross-sectional study of all patients who delivered a singleton newborn at ≥23 weeks of gestation between January 2019 and February 2022 at 7 hospitals within a large academic health system in New York. Patients were excluded if their home address was not available, if the address could not be geocoded to a census tract, or if the census tract did not have corresponding Environmental Justice Index data. Patients were also excluded if they had preexisting diabetes or hypertension. For patients who had multiple pregnancies during the study period, only the first pregnancy was included for analysis. Clinical and demographic data were obtained from the electronic medical record. Environmental Justice Index score, the primary independent variable, ranges from 0 to 1. Higher Environmental Justice Index scores indicate communities with increased cumulative environmental burden and increased social vulnerability. The primary outcome was adverse pregnancy outcome, defined as the presence of ≥1 of any of the following conditions: hypertensive disorders of pregnancy, gestational diabetes, preterm birth, fetal growth restriction, low birthweight, small for gestational age newborn, placental abruption, and stillbirth. Multivariable logistic regression was performed to investigate the relationship between Environmental Justice Index score and adverse pregnancy outcome, adjusting for potential confounding variables, including body mass index group, race and ethnicity group, advanced maternal age, nulliparity, public health insurance, and English as the preferred language.
RESULTS: A total of 65,273 pregnancies were included for analysis. Overall, adverse pregnancy outcomes occurred in 37.6% of pregnancies (n=24,545); hypertensive disorders of pregnancy (13.4%) and gestational diabetes (12.2%) were the most common adverse pregnancy outcome conditions. On unadjusted analysis, the strongest associations between Environmental Justice Index score and individual adverse pregnancy outcome conditions were observed for stillbirth (odds ratio, 1.079; 95% confidence interval, 1.025-1.135) and hypertensive disorders of pregnancy (odds ratio, 1.052; 95% confidence interval, 1.042-1.061). On multivariable logistic regression, every 0.1 increase in Environmental Justice Index score was associated with 1.4% higher odds of adverse pregnancy outcome (adjusted odds ratio, 1.014; 95% confidence interval, 1.007-1.021). The strongest associations with adverse pregnancy outcomes were observed with well-established clinical and social risk factors, including class 3 obesity (adjusted odds ratio, 1.710; 95% confidence interval, 1.580-1.849; reference: body mass index <25 kg/m2) and certain race and ethnicity groups (reference: non-Hispanic White), particularly Asian and Pacific Islander (adjusted odds ratio, 1.817; 95% confidence interval, 1.729-1.910), and non-Hispanic Black (adjusted odds ratio, 1.668; 95% confidence interval, 1.581-1.760) people.
CONCLUSIONS: Environmental Justice Index score is positively associated with adverse pregnancy outcomes, and most strongly associated with stillbirth and hypertensive disorders of pregnancy. Geospatial analysis with Environmental Justice Index may help to improve our understanding of health inequities by identifying neighborhood characteristics that increase the risk of pregnancy complications.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: Estrogen receptor (ER) status, late stage at diagnosis, and BC-specific death.
UNASSIGNED: 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.
UNASSIGNED: 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.

UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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).
UNASSIGNED: 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.
UNASSIGNED: Future studies should extend these analyses to other states and outcomes to inform public health research and interventions to reduce geographic disparities.

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.

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.

Cigarette smoking significantly contributes to preventable illness, death, and economic costs. Despite overall reduction in national smoking rates, disparities persist between demographic groups and geographic regions. While some studies have explored urban-rural differences in smoking prevalence, gaps exist in understanding localized patterns. This study focuses on examining smoking rates and related factors at the census tract level in McLennan County, Texas, a county that contains a mixture of urban, peri-urban, and rural areas. This study uses census tract level aggregate sociodemographic, smoking, and health-related data from the American Community Survey and the PLACES Project City Health Dashboard. Geospatial analyses mapped co-occurrence of high prevalence of smoking, mental and physical distress, and co-occurrence of lower routine medical check-ups, household income, and education. Multiple linear regression modeled associations between smoking and sociodemographic, and health-related factors. Geospatial analyses identified census tracts with co-occurring high prevalence of smoking, mental and physical distress, and co-occurrence of lower routine medical check-ups, household income, and education level in McLennan County. Regression analyses identified that smoking rates were positively correlated with frequent physical distress (p < 0.0001) and negatively correlated with the proportion of routine medical check-ups (p < 0.0001) and the proportion living in poverty (p = 0.0002). This study found significant variations in smoking rates, physical and mental distress, medical check-ups, and sociodemographic factors between neighboring census tracts which geospatial analyses examining larger geographic units may have overlooked. Future research should focus on obtaining individual-level and community-level data to develop more targeted interventions sensitive to specific community contexts.

UNASSIGNED: To examine the associations between child and neighborhood characteristics and incidence of COVID-19 infection during the first 19 months of the pandemic.
UNASSIGNED: We utilized individual electronic health record data and corresponding census tract characteristics for pediatric SARS-CoV-2 cases (age <18 years) from March 23, 2020 to September 30, 2021 with molecular tests resulted at a children\'s health system in Colorado. We compared associations between individual SARS-CoV-2 cases and census tract SARS-CoV-2 positivity rates over three time periods (TP1: March-September 2020; TP2: October 2020-March 2021; TP3: April-September 2021) using multinomial logistic regression for individual associations and negative binomial regression for census tract associations.
UNASSIGNED: We included 7498 pediatric SARS-CoV-2 cases and data from 711 corresponding census tracts. Spanish preferred health care language was associated with SARS-CoV-2 positivity for TP1 (odds ratio [OR] 4.9, 95% confidence interval [CI] 3.7-6.5) and TP2 (OR 2.01, 95% CI 1.6-2.6) compared with TP3. Other non-English preferred health care language was associated with SARS-CoV-2 positivity in TP1 (OR 2.4, 95% CI 1.4-4.2). Increasing percentage internationally born in a census tract was associated with SARS-CoV-2 positivity for TP1 (multivariable incident rate ratio [IRR]=1.040, p<0.0001), TP2 (multivariable IRR=1.028, p<0.0001), and in all TP combined (multivariable IRR=1.024, p<0.0001).
UNASSIGNED: Our study is notable for the identification of COVID-19 disparities among children in immigrant families and communities, particularly early in the pandemic. Addressing disparities for immigrant communities requires targeted investments in public health infrastructure.