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Background: Mapping health outcomes related to environmental health hazards at the county level can lead to a simplification of risks experienced by populations in that county. The Centers for Disease Control and Prevention's National Environmental Public Health Tracking Program has developed sub-county geographies that aggregate census tracts to allow for stable, minimally suppressed data to be displayed. This helps to highlight more local variation in environmental health outcomes and risk data. However, we wanted to understand whether the aggregation method used was aggregating sociodemographically similar or dissimilar areas with one another. This analysis attempts to explore whether the distributions of select people who may be at increased risk for exposure to environmental health hazards as identified by the Tracking Program are preserved in these sub-county geographies with the census tracts used as the foundation to create them. Methods: Mean values of three sociodemographic characteristics (persons aged 65 years and older, people from racial and ethnic minority groups, and population below the poverty level) for each sub-county geography in five states were calculated and placed into five break groups. Differences in break groups were determined and compared for each sub-county geography and census tract. Results: The sociodemographic characteristics among the census tracts and two aggregated sub-county geographies were similar. In some instances, census tracts with a low population or a highly skewed population (e.g., very high percentage of population aged 65 years and older) were aggregated with dissimilar census tracts out of necessity to meet the requirements set by the Tracking Program's aggregation methodology. This pattern was detected in 2.41-6.59% of census tracts within the study area, depending on the sociodemographic variable and aggregation level. Conclusions: The Tracking Program's sub-county aggregation methodology aggregates census tracts with similar characteristics. The two new sub-county geographies can serve as a potential option for health officials and policymakers to develop targeted interventions using finer resolution health outcome and environmental hazard data compared to coarser resolution county-level data. [ABSTRACT FROM AUTHOR]

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

Abstract Background Mapping health outcomes related to environmental health hazards at the county level can lead to a simplification of risks experienced by populations in that county. The Centers for Disease Control and Prevention’s National Environmental Public Health Tracking Program has developed sub-county geographies that aggregate census tracts to allow for stable, minimally suppressed data to be displayed. This helps to highlight more local variation in environmental health outcomes and risk data. However, we wanted to understand whether the aggregation method used was aggregating sociodemographically similar or dissimilar areas with one another. This analysis attempts to explore whether the distributions of select people who may be at increased risk for exposure to environmental health hazards as identified by the Tracking Program are preserved in these sub-county geographies with the census tracts used as the foundation to create them. Methods Mean values of three sociodemographic characteristics (persons aged 65 years and older, people from racial and ethnic minority groups, and population below the poverty level) for each sub-county geography in five states were calculated and placed into five break groups. Differences in break groups were determined and compared for each sub-county geography and census tract. Results The sociodemographic characteristics among the census tracts and two aggregated sub-county geographies were similar. In some instances, census tracts with a low population or a highly skewed population (e.g., very high percentage of population aged 65 years and older) were aggregated with dissimilar census tracts out of necessity to meet the requirements set by the Tracking Program’s aggregation methodology. This pattern was detected in 2.41-6.59% of census tracts within the study area, depending on the sociodemographic variable and aggregation level. Conclusions The Tracking Program’s sub-county aggregation methodology aggregates census tracts with similar characteristics. The two new sub-county geographies can serve as a potential option for health officials and policymakers to develop targeted interventions using finer resolution health outcome and environmental hazard data compared to coarser resolution county-level data.

Background Air pollution is positively associated with some childhood cancers, whereas greenness is inversely associated with some adult cancers. The interplay between air pollution and greenness in childhood cancer etiology is unclear. We estimated the association between early-life air pollution and greenness exposure and childhood cancer in Texas (1995 to 2011). Methods We included 6101 cancer cases and 109 762 controls (aged 0 to 16 years). We linked residential birth address to census tract annual average fine particulate matter <2.5 µg/m³ (PM2.5) and Normalized Difference Vegetation Index (NDVI). We estimated odds ratios (ORs) and 95% confidence intervals (CIs) between PM2.5/NDVI interquartile range increases and cancer. We assessed statistical interaction between PM2.5 and NDVI (likelihood ratio tests). Results Increasing residential early-life PM2.5 exposure was associated with all childhood cancers (OR = 1.10, 95% CI = 1.06 to 1.15), lymphoid leukemias (OR = 1.15, 95% CI = 1.07 to 1.23), Hodgkin lymphomas (OR = 1.27, 95% CI = 1.02 to 1.58), non-Hodgkin lymphomas (OR = 1.24, 95% CI = 1.02 to 1.51), ependymoma (OR = 1.27, 95% CI = 1.01 to 1.60), and others. Increasing NDVI exposure was inversely associated with ependymoma (0- to 4-year-old OR = 0.75, 95% CI = 0.58 to 0.97) and medulloblastoma (OR = 0.75, 95% CI = 0.62 to 0.91) but positively associated with malignant melanoma (OR = 1.75, 95% CI = 1.23 to 2.47) and Langerhans cell histiocytosis (OR = 1.56, 95% CI = 1.07 to 2.28). There was evidence of statistical interaction between NDVI and PM2.5 (P  < .04) for all cancers. Conclusion Increasing early-life exposure to PM2.5 increased the risk of childhood cancers. NDVI decreased the risk of 2 cancers yet increased the risk of others. These findings highlight the complexity between PM2.5 and NDVI in cancer etiology. [ABSTRACT FROM AUTHOR]

Background Industrial facilities are not located uniformly across communities in the United States, but how the burden of exposure to carcinogenic air emissions may vary across population characteristics is unclear. We evaluated differences in carcinogenic industrial pollution among major sociodemographic groups in the United States and Puerto Rico. Methods We evaluated cross-sectional associations of population characteristics including race and ethnicity, educational attainment, and poverty at the census tract level with point-source industrial emissions of 21 known human carcinogens using regulatory data from the US Environmental Protection Agency. Odds ratios and 95% confidence intervals comparing the highest emissions (tertile or quintile) to the referent group (zero emissions [ie, nonexposed]) for all sociodemographic characteristics were estimated using multinomial, population density–adjusted logistic regression models. Results In 2018, approximately 7.4 million people lived in census tracts with nearly 12 million pounds of carcinogenic air releases. The odds of tracts having the greatest burden of benzene, 1,3-butadiene, ethylene oxide, formaldehyde, trichloroethylene, and nickel emissions compared with nonexposed were 10%-20% higher for African American populations, whereas White populations were up to 18% less likely to live in tracts with the highest emissions. Among Hispanic and Latino populations, odds were 16%-21% higher for benzene, 1,3-butadiene, and ethylene oxide. Populations experiencing poverty or with less than high school education were associated with up to 51% higher burden, irrespective of race and ethnicity. Conclusions Carcinogenic industrial emissions disproportionately impact African American and Hispanic and Latino populations and people with limited education or experiencing poverty thus representing a source of pollution that may contribute to observed cancer disparities. [ABSTRACT FROM AUTHOR]

Background: Despite the growing increase in the utilization of hospice in the U.S, disparities exist in the utilization of hospice. Accumulating evidence has shown that neighborhood characteristics have an impact on availability of hospice agencies. Objective: To assess the association between neighborhood social vulnerability and hospice agency availability. Methods: Using the Medicare Post-Acute Care and Hospice Provider Utilization and Payment Public Use Files (PAC PUF) for 2019. Hospice agency addresses were geocoded to the census tract level. Multivariable Poisson regression models were used to assess the association between socioeconomic status SVI theme and hospice agency availability adjusting for number of home health agencies, primary care health profession shortage, per cent Black, and Percent Hispanic at the census tract level and rurality. Results: The socioeconomic status SVI subtheme was associated with decreased likelihood of hospice agency availability (adjusted IRR (aIRR),.56; 95% CI,.50-.63; P <.001). Predominantly Black, and predominantly Hispanic neighborhoods had lower rates of hospice agency availability (aIRR,.48; 95% CI,.39-.59; P <.001 and aIRR,.29; 95% CI,.24-.36; P <.001), respectively. Conclusion: Neighborhood socioeconomic disadvantage was associated with lower availability of hospice agencies. Policies aimed at increasing access to hospice should be cognizant of neighborhood socioeconomic disadvantage. [ABSTRACT FROM AUTHOR]

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. Methods and 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.

Abstract Background Communities in the United States (US) exist on a continuum of urbanicity, which may inform how individuals interact with their food environment, and thus modify the relationship between food access and dietary behaviors. Objective This cross-sectional study aims to examine the modifying effect of community type in the association between the relative availability of food outlets and dietary inflammation across the US. Methods Using baseline data from the REasons for Geographic and Racial Differences in Stroke study (2003–2007), we calculated participants’ dietary inflammation score (DIS). Higher DIS indicates greater pro-inflammatory exposure. We defined our exposures as the relative availability of supermarkets and fast-food restaurants (percentage of food outlet type out of all food stores or restaurants, respectively) using street-network buffers around the population-weighted centroid of each participant’s census tract. We used 1-, 2-, 6-, and 10-mile (~ 2-, 3-, 10-, and 16 km) buffer sizes for higher density urban, lower density urban, suburban/small town, and rural community types, respectively. Using generalized estimating equations, we estimated the association between relative food outlet availability and DIS, controlling for individual and neighborhood socio-demographics and total food outlets. The percentage of supermarkets and fast-food restaurants were modeled together. Results Participants (n = 20,322) were distributed across all community types: higher density urban (16.7%), lower density urban (39.8%), suburban/small town (19.3%), and rural (24.2%). Across all community types, mean DIS was − 0.004 (SD = 2.5; min = − 14.2, max = 9.9). DIS was associated with relative availability of fast-food restaurants, but not supermarkets. Association between fast-food restaurants and DIS varied by community type (P for interaction = 0.02). Increases in the relative availability of fast-food restaurants were associated with higher DIS in suburban/small towns and lower density urban areas (p-values

Much scientific work over the past few decades has linked health outcomes and disease risk to genomics, to derive a better understanding of disease mechanisms at the genetic and molecular level. However, genomics alone does not quite capture the full picture of one's overall health. Modern computational biomedical research is moving in the direction of including social/environmental factors that ultimately affect quality of life and health outcomes at both the population and individual level. The future of studying disease now lies at the hands of the social determinants of health (SDOH) to answer pressing clinical questions and address healthcare disparities across population groups through its integration into electronic health records (EHRs). In this perspective article, we argue that the SDOH are the future of disease risk and health outcomes studies due to their vast coverage of a patient's overall health. SDOH data availability in EHRs has improved tremendously over the years with EHR toolkits, diagnosis codes, wearable devices, and census tract information to study disease risk. We discuss the availability of SDOH data, challenges in SDOH implementation, its future in real-world evidence studies, and the next steps to report study outcomes in an equitable and actionable way.

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. STUDY DESIGN: 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

Background: Communities in the United States (US) exist on a continuum of urbanicity, which may inform how individuals interact with their food environment, and thus modify the relationship between food access and dietary behaviors. Objective: This cross-sectional study aims to examine the modifying effect of community type in the association between the relative availability of food outlets and dietary inflammation across the US. Methods: Using baseline data from the REasons for Geographic and Racial Differences in Stroke study (2003–2007), we calculated participants' dietary inflammation score (DIS). Higher DIS indicates greater pro-inflammatory exposure. We defined our exposures as the relative availability of supermarkets and fast-food restaurants (percentage of food outlet type out of all food stores or restaurants, respectively) using street-network buffers around the population-weighted centroid of each participant's census tract. We used 1-, 2-, 6-, and 10-mile (~ 2-, 3-, 10-, and 16 km) buffer sizes for higher density urban, lower density urban, suburban/small town, and rural community types, respectively. Using generalized estimating equations, we estimated the association between relative food outlet availability and DIS, controlling for individual and neighborhood socio-demographics and total food outlets. The percentage of supermarkets and fast-food restaurants were modeled together. Results: Participants (n = 20,322) were distributed across all community types: higher density urban (16.7%), lower density urban (39.8%), suburban/small town (19.3%), and rural (24.2%). Across all community types, mean DIS was − 0.004 (SD = 2.5; min = − 14.2, max = 9.9). DIS was associated with relative availability of fast-food restaurants, but not supermarkets. Association between fast-food restaurants and DIS varied by community type (P for interaction = 0.02). Increases in the relative availability of fast-food restaurants were associated with higher DIS in suburban/small towns and lower density urban areas (p-values < 0.01); no significant associations were present in higher density urban or rural areas. Conclusions: The relative availability of fast-food restaurants was associated with higher DIS among participants residing in suburban/small town and lower density urban community types, suggesting that these communities might benefit most from interventions and policies that either promote restaurant diversity or expand healthier food options. [ABSTRACT FROM AUTHOR]

At present, Canadian census tract boundaries are available in digital form for 1951 and at 5-year intervals for the 1976–2021 period; the 1956–66 census boundary files have not been digitized and associated data are not readily available for the pre-1971 period. This inhibits the mapping and analysis of neighbourhood change for a period of rapid urban and social transformation. To fill this gap, we digitized 1956–66 census tract boundaries from paper maps for all cities for which such data were disseminated. We adjusted 2006 boundaries to match georeferenced historical maps in concert with ancillary data, including topographic and cadastral maps. All decisions are documented in the files. Finally, printed profile tables for 1951 and 1956 were digitized for joining the boundary files. Researchers may use these datasets to explore, analyse and map geospatial trends in the Canadian population at the neighbourhood scale back to 1951. [ABSTRACT FROM AUTHOR]

Background Racial inequities exist in retention in human immunodeficiency virus (HIV) care and multilevel analyses are needed to contextualize and address these differences. Leveraging data from a multisite clinical cohort of people with HIV (PWH), we assessed the relationships between patient race and residential characteristics with missed HIV care visits. Methods Medical record and patient-reported outcome (PRO; including mental health and substance-use measures) data were drawn from 7 participating Center for AIDS Research Network of Integrated Clinical Systems (CNICS) sites including N = 20 807 PWH from January 2010 through December 2015. Generalized estimating equations were used to account for nesting within individuals and within census tracts in multivariable models assessing the relationship between race and missed HIV care visits, controlling for individual demographic and health characteristics and census tract characteristics. Results Black PWH resided in more disadvantaged census tracts, on average. Black PWH residing in census tracts with higher proportion of Black residents were more likely to miss an HIV care visit. Non-Black PWH were less likely to miss a visit regardless of where they lived. These relationships were attenuated when PRO data were included. Conclusions Residential racial segregation and disadvantage may create inequities between Black PWH and non-Black PWH in retention in HIV care. Multilevel approaches are needed to retain PWH in HIV care, accounting for community, healthcare setting, and individual needs and resources. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic caused many surgical providers to conduct outpatient evaluations using remote audiovisual conferencing technology (i.e., telemedicine) for the first time in 2020. We describe our year-long institutional experience with telemedicine in several general surgery clinics at an academic tertiary care center and examine the relationship between area-based socioeconomic measures and the likelihood of telemedicine participation. Methods: We performed a retrospective review of our outpatient telemedicine utilization among four subspecialty clinics (including two acute care and two elective surgery clinics). Geocoding was used to link patient visit data to area-based socioeconomic measures and a multivariable analysis was performed to examine the relationship between socioeconomic indicators and patient participation in telemedicine. Results: While total outpatient visits per month reached a nadir in April 2020 (65% decrease in patient visits when compared to January 2020), there was a sharp increase in telemedicine utilization during the same month (38% of all visits compared to 0.8% of all visits in the month prior). Higher rates of telemedicine utilization were observed in the two elective surgery clinics (61% and 54%) compared to the two acute care surgery clinics (14% and 9%). A multivariable analysis demonstrated a borderline-significant linear trend (p = 0.07) between decreasing socioeconomic status and decreasing odds of telemedicine participation among elective surgery visits. A sensitivity analysis to examine the reliability of this trend showed similar results. Conclusion: Telemedicine has many patient-centered benefits, and this study demonstrates that for certain elective subspecialty clinics, telemedicine may be utilized as the preferred method for surgical consultations. However, to ensure the equitable adoption and advancement of telemedicine services, healthcare providers will need to focus on mitigating the socioeconomic barriers to telemedicine participation. [ABSTRACT FROM AUTHOR]

Abstract Background Within the United States (US), because racial/ethnic disparities in cannabis arrests continue, and cannabis legalization is expanding, understanding disparities in availability of legal cannabis services is important. Few studies report mixed findings regarding disparities in availability of legal cannabis services; none examined New York. We examined disparities in availability of medical cannabis services in New York. We hypothesized that New York census tracts with few Black or Hispanic residents, high incomes, high education levels, and greater urbanicity would have more medical cannabis services. Methods In this cross-sectional study, we used data from the 2018 US Census Bureau 5-year American Community Survey and New York Medical Marijuana Program. Main exposures were census tract characteristics, including urban–rural classification, percentage of Black and Hispanic residents, percentage of residents with bachelor’s degrees or higher, and median household income. Main outcomes were presence of at least one medical cannabis certifying provider and dispensary in each census tract. To compare census tracts’ characteristics with (vs. without) certifying providers and dispensaries, we used chi-square tests and t-tests. To examine characteristics independently associated with (vs. without) certifying providers, we used multivariable logistic regression. Results Of 4858 New York census tracts, 1073 (22.1%) had medical cannabis certifying providers and 37 (0.8%) had dispensaries. Compared to urban census tracts, suburban census tracts were 62% less likely to have at least one certifying provider (aOR = 0.38; 95% CI = 0.25–0.57). For every 10% increase in the proportion of Black residents, a census tract was 5% less likely to have at least one certifying provider (aOR = 0.95; 95% CI = 0.92–0.99). For every 10% increase in the proportion of residents with bachelor’s degrees or higher, a census tract was 30% more likely to have at least one certifying provider (aOR = 1.30; 95% CI = 1.21–1.38). Census tracts with (vs. without) dispensaries were more likely to have a higher percentage of residents with bachelor’s degrees or higher (43.7% vs. 34.1%, p

Purpose: Investigating the impact of social determinants of health (SDOHs) on cancer care in large populations relies on census estimates. Routine clinic SDOH screening provides timely patient-level information which could inform best practices. This study evaluated the correlation between patient-reported SDOH needs and population-level census tract measures., Methods: This was a retrospective cross-sectional study of a cohort of adult patients with GI malignancy screened for SDOHs such as financial insecurity, transportation, and food insecurity during initial outpatient evaluation at East Carolina University (formerly Vidant) Health Medical Center in Greenville, NC (November 2020-July 2021). Primary outcomes included number and severity of identified SDOH needs and area deprivation index (ADI) and census tract measures for each patient. Spearman rank correlations were calculated among patient-level needs and between patient-level needs and similar census tract measures., Results: Of 112 patients screened, 58.9% self-identified as White (n = 66) and 41.1% as Black (n = 46). A total of 50.5% (n = 54) resided in a rural county. The collective median state ADI rank was 7 (IQR, 5-9). The median household income was $38,125 in US dollars (USD) (IQR, $31,436-$48,934 [USD]). Only 12.5% (n = 14) reported a moderate or severe financial need. Among reported needs, financial need moderately correlated with food insecurity (coefficient, 0.46; P < .001) and transportation (coefficient, 0.45; P < .001). Overall, census tract measures and reported needs poorly correlated. Lack of transportation correlated with percentage of households without a vehicle (coefficient, 0.18; P = .03) and limited access to healthy foods (coefficient, 0.18; P = .04)., Conclusion: Given the poor correlation between reported and census needs, population-level measures may not accurately predict patient-reported needs. These findings highlight the importance of SDOH screening in the clinical setting to reduce health disparities and identify opportunities to improve care delivery.

Background Exercise‐based cardiac rehabilitation (CR) is known to reduce morbidity and mortality for patients with cardiac conditions. Sociodemographic disparities in accessing CR persist and could be related to the distance between where patients live and where CR facilities are located. Our objective is to determine the association between sociodemographic characteristics and geographic proximity to CR facilities. Methods and Results We identified actively operating CR facilities across Los Angeles County and used multivariable Poisson regression to examine the association between sociodemographic characteristics of residential proximity to the nearest CR facility. We also calculated the proportion of residents per area lacking geographic proximity to CR facilities across sociodemographic characteristics, from which we calculated prevalence ratios. We found that racial and ethnic minorities, compared with non‐Hispanic White individuals, more frequently live ≥5 miles from a CR facility. The greatest geographic disparity was seen for non‐Hispanic Black individuals, with a 2.73 (95% CI, 2.66–2.79) prevalence ratio of living at least 5 miles from a CR facility. Notably, the municipal region with the largest proportion of census tracts comprising mostly non‐White residents (those identifying as Hispanic or a race other than White), with median annual household income

This cross-sectional ecological study examined the relationship between neighborhood-level standard occupational groups in the USA and COVID-19 vaccine uptake using 774 census tract data, each consisting of approximately 1600 housing units. The neighborhood-level COVID-19 vaccination uptake data were retrieved from Harris County Public Health, Harris County, Texas. The standard occupational group data were from the US Census Bureau. We calculated the incidence rate ratios (IRRs) for vaccine uptake using bivariate and multivariable Poisson regression models. In the adjusted models, we found that the healthcare practitioner/technician (IRR: 1.008; 95% CI: 1.003–1.014; p = 0.001), business/management/legal (IRR: 1.011; 95% CI: 1.008–1.013; p < 0.001), computer/engineering/life/physical/social science (IRR: 1.018; 95% CI: 1.013–1.023; p < 0.001), and arts/design/entertainment/sports/media (IRR: 1.031; 95% CI: 1.018–1.044; p < 0.001) occupational groups were more likely to have received the full regimen of a COVID-19 vaccine. On the contrary, the building/installation/maintenance/repair (IRR: 0.991; 95% CI: 0.987–0.995; p < 0.001), construction/extraction/production (IRR: 0.991; 95% CI: 0.988–0.995; p < 0.001), transportation/material moving (IRR: 0.992; 95% CI: 0.987–0.997; p = 0.002), food preparation/serving related (IRR: 0.995; 95% CI: 0.990–0.999; p = 0.023), and personal care/services (IRR: 0.991; 95% CI: 0.985–0.998; p = 0.017) groups were less likely to have received the complete dose of a COVID-19 vaccine. White-collar workers were more likely to be vaccinated than blue-collar workers. We adjusted for age, sex, and race/ethnicity in the multivariable analysis. The low vaccine uptake among certain occupational groups remains a barrier to pandemic control. Engaging labor-centered stakeholders in the development of vaccination interventions may increase uptake. [ABSTRACT FROM AUTHOR]

Abstract Background The impact of socioeconomic status (SES) has been described for screening and accessing treatment for colon cancer. However, little is known about the “downstream” effect in patients who receive guideline‐concordant treatment. This study assessed the impact of SES on cancer‐specific survival (CSS) and overall survival (OS) for stage III colon cancer patients. Methods The SEER Census Tract‐Level SES Dataset from 2004 to 2015 was used to identify stage III colon adenocarcinoma patients who received curative‐intent surgery and adjuvant chemotherapy. The predictor variable was census tract SES. SES was analyzed as quintiles. The outcome variables were OR and CSS. Statistical analysis included chi square tests for association, Kaplan–Meier, Cox, Fine and Gray regression for survival analysis. Results In total, 27,222 patients met inclusion criteria. Lower SES was associated with younger age, Black or Hispanic race/ethnicity, Medicaid/uninsured, higher T stage, and lower grade tumors. CSS at the 25th percentile was 54 months for the lowest SES quintile and 80 for the highest. Median OS was 113 months for the lowest SES quintile and not reached for highest. The 5‐year CSS rate was 72.4% for the lowest SES quintile compared to 78.9% in the highest (p

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. 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. 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. 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. 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. • Identifying the most vulnerable neighborhoods based on social determinants of health. • Employing latent class analysis (LCA) to separate census tracts into 4 latent classes. • LCA can identify unique profiles for neighborhoods vulnerable to adverse events. • GIS was used to visualize the distribution of the most vulnerable neighborhoods. • Setting up the potential for differential intervention strategies for communities. [ABSTRACT FROM AUTHOR]

Background: Within the United States (US), because racial/ethnic disparities in cannabis arrests continue, and cannabis legalization is expanding, understanding disparities in availability of legal cannabis services is important. Few studies report mixed findings regarding disparities in availability of legal cannabis services; none examined New York. We examined disparities in availability of medical cannabis services in New York. We hypothesized that New York census tracts with few Black or Hispanic residents, high incomes, high education levels, and greater urbanicity would have more medical cannabis services.Methods: In this cross-sectional study, we used data from the 2018 US Census Bureau 5-year American Community Survey and New York Medical Marijuana Program. Main exposures were census tract characteristics, including urban-rural classification, percentage of Black and Hispanic residents, percentage of residents with bachelor's degrees or higher, and median household income. Main outcomes were presence of at least one medical cannabis certifying provider and dispensary in each census tract. To compare census tracts' characteristics with (vs. without) certifying providers and dispensaries, we used chi-square tests and t-tests. To examine characteristics independently associated with (vs. without) certifying providers, we used multivariable logistic regression.Results: Of 4858 New York census tracts, 1073 (22.1%) had medical cannabis certifying providers and 37 (0.8%) had dispensaries. Compared to urban census tracts, suburban census tracts were 62% less likely to have at least one certifying provider (aOR = 0.38; 95% CI = 0.25-0.57). For every 10% increase in the proportion of Black residents, a census tract was 5% less likely to have at least one certifying provider (aOR = 0.95; 95% CI = 0.92-0.99). For every 10% increase in the proportion of residents with bachelor's degrees or higher, a census tract was 30% more likely to have at least one certifying provider (aOR = 1.30; 95% CI = 1.21-1.38). Census tracts with (vs. without) dispensaries were more likely to have a higher percentage of residents with bachelor's degrees or higher (43.7% vs. 34.1%, p < 0.005).Conclusions: In New York, medical cannabis services are least available in neighborhoods with Black residents and most available in urban neighborhoods with highly educated residents. Benefits of legal cannabis must be shared by communities disproportionately harmed by illegal cannabis. [ABSTRACT FROM AUTHOR]

Background: The COVID‐19 pandemic has highlighted the need for targeted local interventions given substantial heterogeneity within cities and counties. Publicly available case data are typically aggregated to the city or county level to protect patient privacy, but more granular data are necessary to identify and act upon community‐level risk factors that can change over time. Methods: Individual COVID‐19 case and mortality data from Massachusetts were geocoded to residential addresses and aggregated into two time periods: "Phase 1" (March–June 2020) and "Phase 2" (September 2020 to February 2021). Institutional cases associated with long‐term care facilities, prisons, or homeless shelters were identified using address data and modeled separately. Census tract sociodemographic and occupational predictors were drawn from the 2015–2019 American Community Survey. We used mixed‐effects negative binomial regression to estimate incidence rate ratios (IRRs), accounting for town‐level spatial autocorrelation. Results: Case incidence was elevated in census tracts with higher proportions of Black and Latinx residents, with larger associations in Phase 1 than Phase 2. Case incidence associated with proportion of essential workers was similarly elevated in both Phases. Mortality IRRs had differing patterns from case IRRs, decreasing less substantially between Phases for Black and Latinx populations and increasing between Phases for proportion of essential workers. Mortality models excluding institutional cases yielded stronger associations for age, race/ethnicity, and essential worker status. Conclusions: Geocoded home address data can allow for nuanced analyses of community disease patterns, identification of high‐risk subgroups, and exclusion of institutional cases to comprehensively reflect community risk. [ABSTRACT FROM AUTHOR]

Multi-hazard assessments are increasingly vital to communities as exposures continue to change. Their data can provide a nuanced understanding of hazard interactions and their contribution to risk reduction. In previous work, the Patterns of Risk using an Integrated Spatial Multi-Hazard (PRISM) approach produces composite measures of vulnerability and risk at the county level for this purpose. The approach has the flexibility to be applied at a local level to benefit hazard vulnerability assessments and community sustainability planning activities. In this article, we use U.S. census tracts as proxy for neighbourhoods for a localized PRISM approach. The goal is to demonstrate the applicability of the PRISM approach at the community level and identify vulnerabilities in social, cultural, economic, and built systems for use in comparative analyses and mitigation planning. Exposure estimates are created using spatial extents and modelled data from 12 natural and 4 technological hazards. Land area, population counts, and property values are used to estimate vulnerability. National patterns of exposure, vulnerability, and risk reveal distinct regional trends that point to the importance of how impacts are defined. Eight case study examples demonstrate how the data can be analyzed to compare drivers of community vulnerability and risk.

Existing classifications of community type do not differentiate urban cores from surrounding non-rural areas, an important distinction for analyses of community features and their impact on health. Inappropriately classified community types can introduce serious methodologic flaws in epidemiologic studies and invalid inferences from findings. To address this, we evaluate a modification of the United States Department of Agriculture's Rural Urban Commuting Area codes at the census tract, propose a four-level categorization of community type, and compare this with existing classifications for epidemiologic analyses. Compared to existing classifications, our method resulted in clearer geographic delineations of community types within urban areas.

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., Methods and 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: HIV disproportionately affects Hispanic/Latino people in the United States, and factors other than individual attributes may be contributing to these differences. We examined differences in the distribution of HIV diagnosis and social determinants of health (SDH) among US-born and non–US-born Hispanic/Latino adults in the United States and Puerto Rico. Methods: We used data reported to the Centers for Disease Control and Prevention's National HIV Surveillance System (NHSS) to determine US census tract–level HIV diagnosis rates and percentages among US-born and non–US-born Hispanic/Latino adults aged ≥18 for 2017. We merged data from the US Census Bureau's American Community Survey with NHSS data to examine regional differences in federal poverty level, education, median household income, employment, and health insurance coverage among 8648 US-born (n = 3328) and non–US-born (n = 5320) Hispanic/Latino adults. Results: A comparison of US-born and non–US-born men by region showed similar distributions of HIV diagnoses. The largest percentages occurred in census tracts where ≥19% of residents lived below the federal poverty level, ≥18% did not finish high school, the median household income was <$40 000 per year, ≥6% were unemployed, and ≥16% did not have health insurance. A comparison of US-born and non–US-born women by region showed similar distributions. Conclusion: The findings of higher numbers of HIV diagnoses among non–US-born Hispanic/Latino adults than among US-born Hispanic/Latino adults, regional similarities in patterns of SDH and HIV percentages and rates, and Hispanic/Latino adults faring poorly in each SDH category are important for understanding SDH barriers that may be affecting Hispanic/Latino adults with HIV in the United States. [ABSTRACT FROM AUTHOR]

Much scientific work over the past few decades has linked health outcomes and disease risk to genomics, to derive a better understanding of disease mechanisms at the genetic and molecular level. However, genomics alone does not quite capture the full picture of one’s overall health. Modern computational biomedical research is moving in the direction of including social/environmental factors that ultimately affect quality of life and health outcomes at both the population and individual level. The future of studying disease now lies at the hands of the social determinants of health (SDOH) to answer pressing clinical questions and address healthcare disparities across population groups through its integration into electronic health records (EHRs). In this perspective article, we argue that the SDOH are the future of disease risk and health outcomes studies due to their vast coverage of a patient’s overall health. SDOH data availability in EHRs has improved tremendously over the years with EHR toolkits, diagnosis codes, wearable devices, and census tract information to study disease risk. We discuss the availability of SDOH data, challenges in SDOH implementation, its future in real-world evidence studies, and the next steps to report study outcomes in an equitable and actionable way.

Background: The impact of socioeconomic status (SES) has been described for screening and accessing treatment for colon cancer. However, little is known about the “downstream” effect in patients who receive guideline-concordant treatment. This study assessed the impact of SES on cancer-specific survival (CSS) and overall survival (OS) for stage III colon cancer patients. Methods: The SEER Census Tract-Level SES Dataset from 2004 to 2015 was used to identify stage III colon adenocarcinoma patients who received curative-intent surgery and adjuvant chemotherapy. The predictor variable was census tract SES. SES was analyzed as quintiles. The outcome variables were OR and CSS. Statistical analysis included chi square tests for association, Kaplan–Meier, Cox, Fine and Gray regression for survival analysis. Results: In total, 27,222 patients met inclusion criteria. Lower SES was associated with younger age, Black or Hispanic race/ethnicity, Medicaid/uninsured, higher T stage, and lower grade tumors. CSS at the 25th percentile was 54 months for the lowest SES quintile and 80 for the highest. Median OS was 113 months for the lowest SES quintile and not reached for highest. The 5-year CSS rate was 72.4% for the lowest SES quintile compared to 78.9% in the highest (p < 0.001). The 5-year OS rate was 66.5% for the lowest SES quintile and 74.6% in the highest (p < 0.001). Conclusion: This is the first study to evaluate CSS and OS in an incidence-based cohort of stage III colon cancer patients using a granular, standardized measure of SES. Despite receipt of guideline-based treatment, SES was associated with disparities in CSS and OS. [ABSTRACT FROM AUTHOR]

This cross-sectional ecological study examined the relationship between neighborhood-level standard occupational groups in the USA and COVID-19 vaccine uptake using 774 census tract data, each consisting of approximately 1600 housing units. The neighborhood-level COVID-19 vaccination uptake data were retrieved from Harris County Public Health, Harris County, Texas. The standard occupational group data were from the US Census Bureau. We calculated the incidence rate ratios (IRRs) for vaccine uptake using bivariate and multivariable Poisson regression models. In the adjusted models, we found that the healthcare practitioner/technician (IRR: 1.008; 95% CI: 1.003–1.014; p = 0.001), business/management/legal (IRR: 1.011; 95% CI: 1.008–1.013; p < 0.001), computer/engineering/life/physical/social science (IRR: 1.018; 95% CI: 1.013–1.023; p < 0.001), and arts/design/entertainment/sports/media (IRR: 1.031; 95% CI: 1.018–1.044; p < 0.001) occupational groups were more likely to have received the full regimen of a COVID-19 vaccine. On the contrary, the building/installation/maintenance/repair (IRR: 0.991; 95% CI: 0.987–0.995; p < 0.001), construction/extraction/production (IRR: 0.991; 95% CI: 0.988–0.995; p < 0.001), transportation/material moving (IRR: 0.992; 95% CI: 0.987–0.997; p = 0.002), food preparation/serving related (IRR: 0.995; 95% CI: 0.990–0.999; p = 0.023), and personal care/services (IRR: 0.991; 95% CI: 0.985–0.998; p = 0.017) groups were less likely to have received the complete dose of a COVID-19 vaccine. White-collar workers were more likely to be vaccinated than blue-collar workers. We adjusted for age, sex, and race/ethnicity in the multivariable analysis. The low vaccine uptake among certain occupational groups remains a barrier to pandemic control. Engaging labor-centered stakeholders in the development of vaccination interventions may increase uptake.

The number of people living in a household is associated with infrastructure demand such as transportation and water usage. As a result, infrastructure planners often require detail about the number of households by size-of-household at very small levels of geography (census tract or smaller) to calibrate their models. In addition, these data must also be projected into the future in order to support planning efforts. This paper documents a statistical technique for estimating and forecasting the distribution of households by size using a modified application of the Poisson distribution. This technique is valuable to demographers as it provides a simple and reliable tool for estimating the distribution of household sizes at nearly any level of geography for a given point in time using only one input parameter—average household size. There are a wide variety of applications of the Poisson distribution in biology and engineering. However, there are only few documented applications in demography. This article puts forth two key advancements over prior published work: (1) an entirely new, and greatly simplified method for applying the distribution, and (2) evidence of the reliability of the technique for estimating household size distributions in small geographic areas (e.g., counties and census tracts). Tests of the model based on U.S. Census Bureau data for 2010 suggest that the model is suitable for use in estimating the distribution of households by size at the county and census tract level. In addition, practitioners may want to consider adjustments for households of size one and size three, which are consistently under- and over-predicted, respectively. [ABSTRACT FROM AUTHOR]

This study compares the relationship between official crime rates in census tracts and resident perceptions of crime. Using a unique data set that links household-level data from the American Housing Survey metro samples over 25 years (1976-1999) with official crime rate data for census tracts in selected cities during selected years, this study finds that tract violent crime is the strongest predictor of residents' perception of crime. This standardized coefficient was.71 on average over the seven waves. Models simultaneously taking into account both violent and property crime found a consistently strong positive effect for violent crime but a consistently negative effect for property crime. Among types of violent crime, robbery and aggravated assault have the strongest effect on the perception of crime in the tract. Burglary showed a stronger effect on perceptions of crime in the 1970s but a steadily weakening effect since then. There was little evidence that the racial/ethnic composition of the tract affected these perceptions. © The Author(s) 2013.

Purpose: 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)., Conclusion: 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., Competing Interests: Conflict of interest: University of Houston contracted with Elation. Please note that the opinions of Dr. Olaisen do not reflect the opinion of his employer, the Agency for Healthcare Research and Quality., (© Copyright 2024 by the American Board of Family Medicine.)

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., (©2024 American Association for Cancer Research.)

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., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Multi-hazard assessments are increasingly vital to communities as exposures continue to change. Their data can provide a nuanced understanding of hazard interactions and their contribution to risk reduction. In previous work, the Patterns of Risk using an Integrated Spatial Multi-Hazard (PRISM) approach produces composite measures of vulnerability and risk at the county level for this purpose. The approach has the flexibility to be applied at a local level to benefit hazard vulnerability assessments and community sustainability planning activities. In this article, we use U.S. census tracts as proxy for neighbourhoods for a localized PRISM approach. The goal is to demonstrate the applicability of the PRISM approach at the community level and identify vulnerabilities in social, cultural, economic, and built systems for use in comparative analyses and mitigation planning. Exposure estimates are created using spatial extents and modelled data from 12 natural and 4 technological hazards. Land area, population counts, and property values are used to estimate vulnerability. National patterns of exposure, vulnerability, and risk reveal distinct regional trends that point to the importance of how impacts are defined. Eight case study examples demonstrate how the data can be analyzed to compare drivers of community vulnerability and risk. [ABSTRACT FROM AUTHOR]

Empleando los datos del Censo de Población y Viviendas del año 2012, con el distrito censal como unidad de análisis, y considerando los efectos espaciales de autocorrelación y heterogeneidad, en el presente artículo se aborda desde la dimensión espacial una aproximación al proceso de envejecimiento en los distritos censales de una parte del área de la capital,1 sin considerar otros factores de carácter económico o social. Con la aplicación de algunas técnicas del análisis exploratorio de datos espaciales se pretende contrastar la localización del segmento de población de 65 y más años, conocer cómo se relaciona en el espacio geográfico e identificar el tipo de distribución que lo caracteriza, como una forma de incentivar la toma de decisiones diferenciadas en la capital con un soporte espacial. Se comprueba que el proceso de envejecimiento no es homogéneo al interior de los municipios de la capital y que existen fragmentaciones y polarizaciones. El 23,5% de los distritos que abarcan el área de estudio están envejecidos, y el municipio Plaza de la Revolución es el que dentro de la provincia presenta en grado más acusado esta característica. Based on data from the Population and Housing Census of 2012, with the census tract as a unit of analysis and considering autocorrelation and heterogeneity spatial effects, this article addresses from the spatial dimension, the aging process in the census tracts of a part of the capital, without considering other economic or social factors. With the application of some techniques of the Exploratory Analysis of Spatial Data, it is intended to contrast the location of the population segment of 65 years and over, to know how it relates to the geographic space and to identify the type of distribution that characterizes it, as a way to promote differentiated decision making in the capital with a space support. It is verified that the aging process is not homogeneous within the municipalities of the capital and that there are fragmentations and polarizations. The 23.5% of the districts that cover the study area are aged, where the municipality Plaza de la Revolucion is the most critical in the province.

Background: The relationships between socioeconomic status and domestically acquired salmonellosis and leading Salmonella serotypes are poorly understood.Methods: We analyzed surveillance data from laboratory-confirmed cases of salmonellosis from 2010-2016 for all 10 Foodborne Disease Active Surveillance Network (FoodNet) sites, having a catchment population of 47.9 million. Case residential data were geocoded, linked to census tract poverty level, and then categorized into 4 groups according to census tract poverty level. After excluding those reporting international travel before illness onset, age-specific and age-adjusted salmonellosis incidence rates were calculated for each census tract poverty level, overall and for each of the 10 leading serotypes.Results: Of 52 821geocodable Salmonella infections (>96%), 48 111 (91.1%) were domestically acquired. Higher age-adjusted incidence occurred with higher census tract poverty level (P < .001; relative risk for highest [≥20%] vs lowest [<5%] census tract poverty level, 1.37). Children <5 years old had the highest relative risk (2.07). Although this relationship was consistent by race/ethnicity and by serotype, it was not present in 5 FoodNet sites or among those aged 18-49 years.Conclusion: Children and older adults living in higher-poverty census tracts have had a higher incidence of domestically acquired salmonellosis. There is a need to understand socioeconomic status differences for risk factors for domestically acquired salmonellosis by age group and FoodNet site to help focus prevention efforts. [ABSTRACT FROM AUTHOR]

Neighborhoods with high poverty rates have limited resources to support residents' health. Using census data, we calculated the proportion of each Women's Interagency HIV Study participant's census tract (neighborhood) living below the poverty line. We assessed associations between neighborhood poverty and (1) unsuppressed viral load [VL] in HIV-seropositive women, (2) uncontrolled blood pressure among HIV-seropositive and HIV-seronegative hypertensive women, and (3) uncontrolled diabetes among HIV-seropositive and HIV-seronegative diabetic women using modified Poisson regression models. Neighborhood poverty was associated with unsuppressed VL in HIV-seropositive women (> 40% versus ≤ 20% poverty adjusted prevalence ratio (PR), 1.42; 95% confidence interval (CI) 1.04–1.92). In HIV-seronegative diabetic women, moderate neighborhood poverty was associated with uncontrolled diabetes (20–40% versus ≤ 20% poverty adjusted PR, 1.75; 95% CI 1.02–2.98). Neighborhood poverty was associated with neither uncontrolled diabetes among HIV-seropositive diabetic women, nor uncontrolled hypertension in hypertensive women, regardless of HIV status. Women living in areas with concentrated poverty may need additional resources to control health conditions effectively. [ABSTRACT FROM AUTHOR]

Objective: Recent years have witnessed growing interest in the role of the social environment in the development and outcomes of schizophrenia. We investigated whether neighborhood characteristics are associated with two important prognostic factors in early-course psychosis, age at onset of psychosis (AOP) and duration of untreated psychosis (DUP).Methods: Data were collected from patients admitted to the hospital for first-episode schizophrenia-spectrum disorder. We collected data on perceived neighborhood disorder during childhood/adolescence and extracted data on 13 neighborhood characteristics from the American Community Survey based upon individual addresses. Four neighborhood-level factors were derived from factor analysis. Multiple logistic regression analyses assessed the association between specific neighborhood characteristics and the two prognostic factors (earlier AOP and longer DUP) in early-course psychosis.Results: 143 participants had valid addresses geo-coded. Neighborhood-level residential instability was associated with an earlier AOP (OR = 1.760; p = 0.022) even after controlling for known risk factors (OR = 2.026; p = 0.020) and also after controlling for individual-level residential instability (OR = 1.917; p = 0.037). The general socioeconomic status neighborhood factor (OR = 1.119; p = 0.019) and perceived neighborhood disorder (OR = 1.075; p = 0.005) were associated with a longer DUP. But only perceived neighborhood disorder (OR = 1.146; p = 0.011) remained significant, and general socioeconomic status was close to significant (OR = 1.215; p = 0.062), after controlling for individual-level predictors and socioeconomic status.Conclusions: This study found evidence that neighborhood-level characteristics (in this case, residential instability) may be associated with earlier AOP, and perceptions of neighborhood disorder are associated with a longer treatment delay. Socioenvironmental factors should be more consistently considered going forward in research on early psychotic disorders. [ABSTRACT FROM AUTHOR]