Census Block Research Articles

Longitudinal Impacts of High Versus Low Greenness on Cardiovascular Disease Conditions.

Greenness-or vegetative presence-has been identified as a factor in chronic disease. The present study examines the longitudinal relationship between objective measures of greenness at the residential block level and incidence of 6 cardiovascular disease conditions. Analyses examined the impact of consistently high versus consistently low "precision" greenness at the Census block level on the 5-year incidence of cardiovascular disease conditions, including acute myocardial infarction, atrial fibrillation, heart failure, ischemic heart disease, stroke/transient ischemic attack, and hypertension, among 229 034 US Medicare beneficiaries in Miami-Dade County, Florida, USA. Zero-inflated Poisson regression was used to model the odds of developing any new cardiovascular disease and number of new cardiovascular disease conditions based on greenness tertiles computed across 2011 and 2016 Normalized Difference Vegetation Index values, adjusting for individual age, sex, race, ethnicity, baseline cardiovascular disease conditions, neighborhood income, and walkability in 2011 and 2016. When compared with individuals consistently in the low greenness tertile in 2011 and 2016, those consistently in the high greenness tertile in 2011 and 2016 had a 9% lower odds of having any new cardiovascular conditions (odds ratio [OR], 0.91 [95% CI, 0.84-0.99]; P=0.021). Over a 5-year period, consistently high greenness, when compared with consistently low greenness, was associated with lower odds of any new cardiovascular disease conditions. Identifying the role of greenness exposure in such a small geographic area, the Census block on which the older adult resides, allows for more precise, strategic decisions on where additional trees can be added-by selecting at-risk blocks rather than entire neighborhoods for tree-planting interventions.

Assessing access: Texting hotline app provides mental health crisis care for economically deprived youth

BackgroundDue to rapidly increasing youth suicides in the U.S state of Utah, the legislature funded creation of a 24/7 texting-based smartphone app in Spanish and English targeting Utah's school aged population. Recent research elsewhere (in the Netherlands) suggests cost inhibits help seeking among the economically disadvantaged. We evaluate the relationship between poverty and app usage during the onset of the COVID-19. MethodLocal demographics, social determinants of health and COVID-19 infection rates were modeled using a Bayesian spatio-temporal approach examining usage rates. ResultsWhen controlling for generally researched suicide crisis covariates, app usage is shown to vary depending on economic status of the population, with the largest relative increases in use among disadvantaged youth. DiscussionThis bilingual Spanish/English, texting (SMS) based, smart phone app crisis hotline proved effective at providing adolescents from certain populations access to mental health care. The groups discussed are in Census Block Groups (CBGs – neighborhoods) with higher poverty, and/or lower population density (rural areas). The usage of the crisis hotline by these populations increased relative to the overall population as the COVID-19 pandemic unfolded. However, adolescents from areas of higher mobility (our proxy for housing insecure) and those in areas with larger non-White populations had a relative decrease in usage.

Abstract PR001: A comparison of modeling approaches to assess the interplay of social, behavioral, and environmental factors on lung cancer racial disparities

Abstract Purpose: While risk factors for lung cancer incidence are well established, our understanding of factors driving an increased risk of lung cancer among Black/African American populations remains incomplete. Methods: We assembled data from five U.S. lung cancer case-control studies matched on race, gender and age: the Southern Community Cohort Study, the Inflammation, Health, Ancestry and Lung Epidemiology study, the Northern California Lung Cancer Study, three Detroit area lung studies, and the NCI-Maryland Lung Cancer Study. Incident lung cancers were ascertained from state and regional cancer registries or National Death Index mortality records. Social, behavioral, and environmental factors were ascertained by in-person interviews or mailed-in questionnaires. Participant's residential addresses were geocoded and area-level area deprivation and environmental justice variables were linked at the census block. We evaluated interactions between known lung cancer risk factors and race using multilevel logistic regression analyses and novel tree-based approaches to identify potential factors contributing to racial disparities. Tree-based approaches included a conditional matched classification tree analysis designed to detect complex interactions, as well as a specialized partially recursively induced structured moderation (PRISM) analysis to study variation in disparity in lung cancer risk across levels of educational status. Results: Among 5,829 lung cancer cases and 10,671 controls (64% self-identified Black/African American and 36% White), the average age was 60 years, 50% were female, and 24% had less than a high school education. Conditional logistic regression models fit after imputation of missing data identified increased risk of lung cancer associated with personal history of cancer, family history of lung cancer, current smoking status, smoking duration, smoking pack-years, and higher neighborhood deprivation; whereas, reduced lung cancer risk was associated with higher education and increased BMI. Similar patterns were observed among Black/African Americans only, with the addition of secondhand smoke exposure. Assessment for interactions with race identified significant interactions between race and body mass index (BMI) (p-value < 0.0001), PM2.5 (p= 0.010) and gender (p=0.001) and lung cancer risk. A conditional matched classification tree analysis identified both increased and decreased risk subgroups defined by multi-way interactions of similar variables that were identified as significant main effects in the conditional logistic regression analysis. Smoking pack-years and BMI were identified as the most important determinants of heterogeneity of disparity in lung cancer risk across educational levels in our study population. Conclusions: The use of both traditional and novel tree-based based approaches provide approaches for identifying factors contributing to racial disparities. Future studies should assess the interplay of lung cancer risk factors to inform development of targeted interventions. Citation Format: Melinda C. Aldrich, Chen Zhao, Christine M Lusk, Lucie McCoy, James Jaworski, Michael Mumma, Catherine Pichardo, Michael Betti, John K Wiencke, Brid Ryan, Ann G Schwartz, Sunil Rao. A comparison of modeling approaches to assess the interplay of social, behavioral, and environmental factors on lung cancer racial disparities [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr PR001.

Abstract A043: Improving our understanding of social determinants of health and neighborhood deprivation on survival in patients after radical cystectomy for urothelial carcinoma

Abstract Introduction: To determine the effects of Social Determinants of Health (SDoH) represented by neighborhood deprivation (ND) and ethnicity on bladder cancer survival by using a state-specific neighborhood deprivation index(the healthy places index, HPI) in measuring recurrence-free survival (RFS) and overall-survival (OS). Methods: We queried our IRB-approved institutional database for patients receiving radical cystectomy with diversion from 2013 to 2019. HPI values already linked to 5-digit zip codes via crosswalk method of census block association were obtained and assigned to our population in quartiles(Q1 representing most deprivation). Patients without associated HPI values were excluded. Multivariable (MVA) logistic regression and Cox models were fit to assess the relationship between HPI and OS and RFS when adjusting for age, race, sex, stage, node status, smoking, diabetes. Kaplan-meier(KM) curves were generated for OS and RFS. Results: 729 patients were included for analysis, of which 81% were male with a median age of 71 years (Range: 35 – 93) at the time of cystectomy. No differences were seen between quartiles for age. 82.99% were non-hispanic white and ethnic minorities comprising 17.01% of patients. Non-white patients were associated with more ND (Q1 41.94%, Q2 29.03%, Q3 16.94%, Q4 12.10%, p<000.1). There were 65 (8.92%) non-white hispanic and black patients (Q1 56.92%, Q2 23.08%, Q3 13.85%, Q4 6.15%, p<0.0001). On MVA Age (HR 95% 1.024 p=0.0026), extravesical disease (HR 95% 2.605 p <0.0001), Node positive status (HR 95% 2.948 p<0.0001), lymphovascular invasion (HR 95% 2.005 p<0.0001), and worse ND measured by HPI (Q4 vs Q1 HR 95% 1.759 p = 0.0055) were associated with worse overall survival(OS). Non-white race was associated with better OS (HR 95% 0.499 p = 0.0017) and RFS (HR 95% 0.581 p=0.0108). Kaplan-Meier demonstrated worse recurrence-free survival p=0.0118 and OS p=0.0109 for the most deprived quartile(Q1). Conclusion: More neighborhood deprivation is correlated with worse OS in UBC. Black and non-white hispanic patients receiving surgery for UBC experienced a higher proportion of deprivation, but non-white race is correlated with better OS in UBC. Higher powered studies are required to confidently identify disparities in care for non-white patients experiencing UBC. Additionally, analysis of individual SDoH included in the HPI is merited to investigate a more specific correlation with UBC. Citation Format: Anosh Dadabhoy, Chirag Doshi, Sejal Mehta, Farshad Sheybaee Moghaddam, Luis Santos Molina, Erika Wood, Leilei Xia, Hooman Djaladat, Anne Schuckman, Siamak Daneshmand. Improving our understanding of social determinants of health and neighborhood deprivation on survival in patients after radical cystectomy for urothelial carcinoma [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A043.

Abstract A147: A ten-year overview of cancer genetic family history screening in Georgia’s Latina population

Abstract Introduction: Population-based cancer genetic family history screening to identify families at high risk for BRCA-associated cancers has been endorsed by national public health policies. This report aimed to describe the utilization of family history screening services from 2013 to 2022 according to rurality and socioeconomic deprivation among the Latina community in the state of Georgia. Methods: Self-identified Latina women who attended a medical appointment at participating Georgia Public Health Clinics were invited to utilize the cancer genetic family history screening services. Screening results and participant zip code were reviewed. Area deprivation index (ADI) was measured at the census block group level and assessed as a dichotomous variable (more deprived and less deprived). Rurality was measured through Rural-Urban Commuting Area (RUCA) codes and was assessed dichotomously (urban and rural). The ADI and RUCA codes were linked to participant data by zip code to characterize genetic service access among the Latina community. Results: We found 9,330 (3.31%) adult Latinas in Georgia completed cancer genetic family history screening from 2013-2022. In this sample, 9,066 (97.17%) women screened negative, and 264 (2.83%) women screened positive (i.e., family history suggestive of higher risk for carrying BRCA1/2 mutations compared to the general population). The family history screening program reached Latinas in 16 of 18 public health districts in Georgia. However, family history screening completion varied significantly across the 16 districts (mean=327 women, range: 2 - 1,337). FH screening completion was higher among Latinas in urban areas (n=7,871) than Latinas in rural areas (n=1,459). Latinas who lived in more socially deprived areas appeared to have a higher number of screening completion (n=5,207) than those living in less socially deprived areas (n=4,123) due to interaction with urbanicity. Conclusions: Georgia’s family history screening program was able to reach Latina women across the state of Georgia, particularly those living in urban, socially deprived areas. To ensure equitable cancer genetic screening dissemination, future efforts should prioritize tailored outreach in rural regions and a comprehensive evaluation to identify key determinants of screening trends among Georgia’s Latina population. Citation Format: Dayanna A. Ramírez León, Lauren E. Barber, Sheryl Gabram-Mendola, Cindy Snyder, Susan Vadaparampil, Lindsay Fuzzell, Lauren E. McCullough, Lynn Durham, Yue Guan. A ten-year overview of cancer genetic family history screening in Georgia’s Latina population [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A147.

Abstract B152: Joint Associations of Neighborhood Socioeconomic Status and Education on the Incidence of Colorectal Cancer: The Multiethnic Cohort Study

Abstract Background: Colorectal cancer (CRC) is the 3rd most diagnosed cancer worldwide. When considered separately, both neighborhood- and individual-level SES exposures have been associated with CRC incidence. However, the joint associations of neighborhood- and individual-level SES on CRC incidence have not been examined. Methods: In a prospective study of 153,725 Multiethnic Cohort (MEC) participants (24,764 African American, 44,447 Japanese American, 34,974 Latino, 11,511 Native Hawaiian, and 38,029 White participants) recruited from predominantly Los Angeles County, California (CA), and Hawaii (HI), we examined the joint association of neighborhood SES (nSES) and individual-level education with CRC incidence (CA: 2,519, HI: 2,210 cases). Education, other demographics, and health behaviors were self-reported in the baseline questionnaire (1993-1996). CRC diagnosis was determined via linkages with state cancer registries. nSES was derived using a composite score based on 1990 US Census block group data and assigned to geocoded baseline addresses. Our joint SES measure included four categories: high nSES (quintiles 4-5) and high education (> high school), high nSES and low education (≤ high school), low nSES (quintiles 1-3) and high education, and low nSES and low education. State-specific hazards ratios (HR) for CRC incidence were estimated for joint SES categories. Cox regression adjusted for age at cohort entry, CRC family history, polyp history, obesity, diabetes, diet, alcohol, smoking, physical activity, and NSAID, multivitamin, and hormone therapy use. Subgroup analyses were conducted by sex, race, and ethnicity. Results: For HI MEC participants, compared to individuals with high nSES and high education, risk for CRC was highest among those with low nSES and low education (HR=1.27; 95% CI:1.11-1.45) followed by high nSES and low education (HR=1.25; 95% CI:1.12-1.41), and low nSES and high education (HR=1.17; 95% CI: 1.03-1.31). This pattern of association was observed across sex but differed across race and ethnicity (p-heterogeneity=0.04). The overall pattern of association was observed among White participants in HI. However, for Japanese American individuals in HI, compared to those with high nSES and high education, risk for CRC was highest for low nSES and high education (HR=1.29; 95% CI:1.09-1.53) and high nSES and low education (HR= 1.28; 95% CI; 1.10-1.50). For Native Hawaiian individuals, compared to those with high nSES and high education, CRC risk was highest for those with low nSES and low education (HR=1.70; 95% CI: 1.20-2.42). Associations were not statistically significant between the joint SES measure and CRC risk in CA (HRs: 0.92-0.97). Conclusion: Among MEC participants in HI, CRC risk varied with the joint measure of nSES and education across racial and ethnic groups. These findings emphasize the importance of considering the joint associations of neighborhood- and individual-level SES in understanding racial and ethnic disparities in CRC. Citation Format: Catherine Walsh, Meera Sangaramoorthy, Songyi Park, Sung-shim (Lani) Park, Gertraud Maskarinec, Lynne Wilkens, Christopher Haiman, Anna Wu, Salma Shariff-Marco, Loic Le Marchand, Iona Cheng. Joint Associations of Neighborhood Socioeconomic Status and Education on the Incidence of Colorectal Cancer: The Multiethnic Cohort Study [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B152.

Risk factors for Lyme disease among residents of rural, suburban, and urban areas in the United States: A Case-Control Study

Abstract lyme disease (LD) is the most common vector-borne disease in the United States, with 476,000 cases estimated each year. It is unclear how LD risk factors vary by residential setting. We conducted a case-control study on LD risk by rural, urban, and suburban residential settings. Individuals from 15 high-incidence states and the District of Columbia in the Optum Research Database were identified as cases (LD medical claim) or controls (no LD medical claim, matched by county of residence and census block group population density). Participants were surveyed about LD history, outdoor activities, and residential characteristics. The final analytic dataset had 750 LD cases and 965 controls. Residence in a rural setting had increased LD risk (OR 1.41, 95% CI 1.16, 1.72). In multivariable analyses, activities associated with LD were hiking/walking/running or having an occupation in forests, wooded areas, or areas of tall grass (all respondents), and spending time in a yard (rural and urban residents only). Public health interventions can help prevent LD in high-incidence jurisdictions by reinforcing the nearuniversal LD risk for rural residents, and highlighting activities that lead to increased LD risk for those in areas with less ubiquitous tick exposure like in urban and suburban settings.

The Impact of Area Deprivation Index, Geography, and Mechanism on Incidence of Ballistic Injury to the Upper Extremity.

This study investigates the intersection of ballistic injuries, geography, and Area Deprivation Index (ADI). We hypothesized that both ADI and geography are correlated with incidence of upper extremity ballistic injuries. Further, we characterize and compare 2 distinct upper extremity gunshot injury populations presenting to our institution: those sustaining violent ballistic injuries and those who suffer an accidental, self-inflicted injury. Our purpose is to evaluate the impact of geography and ADI on the pattern of upper extremity gunshot injuries in Illinois and Missouri. This was a retrospective review of adult patients sustaining ballistic injury to the upper extremity at a single urban level I trauma center over 10 years (n = 797). Seven hundred thirty patients had home addresses in Illinois or Missouri; these addresses were geocoded and included for analysis. Mechanism of injury was self-reported. ADI was measured from the 2019 Neighborhood Atlas, in which deprivation increases from 1 to 100. Comparisons between groups were conducted with unpaired t tests, Fisher exact test, or χ2 testing, where appropriate. Addresses constituted 259 unique census tracts, and the average number of upper extremity gunshot wound incidents per tract was 3, with a maximum of 22; 15.4% of census block tracts made up almost half (48.4%) of the total ballistic injuries in the study period; 97.7% of violent injuries occurred in Urban areas, as compared with only 60% of accidental injuries (P < 0.05). ADI and incidence of upper extremity ballistic injury were positively correlated. ADI varied significantly between patients sustaining violent (median, 94; mean, 86.1) versus accidental self-inflicted (median, 79; mean, 70.9) injuries (P < 0.05). Fifty percent of violent injuries in our data set occurred in block groups from the 2 most deprived quintiles. Upper extremity gunshot wounds in general are concentrated in census blocks with high ADI. Violent injuries in particular are more likely to occur in urban areas with high ADI, whereas patients with accidental, self-inflicted injuries are more geographically and socioeconomically diverse. These differing populations require unique approaches to reduce incidence and morbidity.

Recreational response patterns by sociodemographic groups in a pandemic

ABSTRACT The COVID-19 pandemic has reinforced the importance of recreation as people flocked outdoors for socially distant activities. However, gaps remain in our understanding of how pandemics impact recreation and the unequal distribution of impacts across communities. Our study used cell phone data from Safegraph to explore census block level changes to local park visitation in California, USA, during the first 27 weeks of the pandemic. We utilized Latent Class Analysis to examine visitation based on sociodemographic groupings of race/ethnicity, income, and age. We found that African American communities experienced the largest decline in visitation and that communities with more Hispanic and Asian populations did not revert to prior visitation levels. Our work demonstrates the importance of recreation in communities and provides an easily interpretable method for evaluation to understand how humans use their environment.

Safer or Endangered at Home?: An Examination of Neighborhood Effects on Family Violence Before, During, and After the COVID-19 Safer-at-Home Order

AbstractResearch has begun to document the impact of COVID-19, and corresponding restrictions associated with the pandemic, to several unintended consequences including increased rates of family violence. Given these findings, there have been calls for research to understand predictors of family violence, and how such predictors may shift during differing periods of the pandemic. Rooted in the family stress model and social disorganization theory, this study used call for service data from two police departments located in the Midwest of the United States, to examine whether the relationship between sociostructural indices on the number of family violence incidents varied in the times before, during, and after COVID-19 Safer-at-Home orders. Based on a series of negative binomial regression models, results show that the association between neighborhood (i.e., Census block group) concentrated poverty, the percentage of the population between 15 and 24 years of age, and the percentage of the population that identify as male on family violence was amplified during and/or after the adoption of the Safer-at-Home order. Results also indicated that the Safer-at-Home order eradicated the once significant effect of residential mobility and family violence incidents. Lastly, although neighborhoods located within the more populous and metropolitan city experienced fewer family violence incidents, compared to the mixed rural-suburban city, the gap in the number of incidents per neighborhood decreased in the times following the enactment of the Safer-at-Home order. Based on these findings, proactive and reactive policies regarding resource dissemination and increasing neighboring activities are discussed.

Comparison and theoretical conceptualization analysis of statistical methods used to develop heat vulnerability indices in urban areas

In view of the impact of extreme temperatures on physical and psychological health, particularly in urban areas, several studies have focused on assessing social vulnerability using quantitative indexing approaches with the aim of creating a heat vulnerability index (HVI). In this context, this study employs three statistical methodologies frequently used to construct HVIs on the territory of the Toulouse Métropole, France, at the census block (IRIS) scale to assess the efficiency of this type of approach for evaluating social vulnerability in urban environments considering the current theoretical conceptualization. The three HVIs show the same general trends, with a spatial configuration in which high levels of vulnerability are concentrated in the downtown and suburbs of the Toulouse municipality. Vulnerability gradually decreases away from the urban core, becoming moderate in the inner suburbs and low on the outskirts. However, a spatial analysis of the clusters reveals variability in the boundaries of the vulnerability hotspots. Value class matching indicates that a significant number of census blocks are classified differently according to the method considered. These results raise questions concerning the ability of HVIs to provide reliable vulnerability assessments, given their geostatistical and conceptual limitations. Indexing approaches therefore appear to contradict current theoretical conceptualizations promoting the concept of vulnerability as being complex and multifactorial.

Digital discrimination under disparate impact: A legal and economic analysis

The lack of broadband in many rural and Tribal communities in the U.S. is widely recognized, but there are also claims of a lack of broadband availability in predominantly Minority and urban communities, sometimes labeled digital redlining or digital discrimination. Motivated by such claims, the bi-partisan Infrastructure Investment and Jobs Act of 2021 includes a provision addressing digital discrimination and directing the Federal Communications Commission to write rules implementing the statutory provision. The Commission's rules adopt two legal discrimination standards including intentional discrimination (differential treatment) and disparate impact (differential effect). Using data from the Commission's new broadband fabric data, we test for differences in broadband availability between predominantly minority and majority neighborhoods (measured as census block groups) and find no evidence of digital discrimination against minorities.

Geographic Spines in the 2020 Census Disclosure Avoidance System

The 2020 Census Disclosure Avoidance System (DAS) is a formally private mechanism that first adds independent noise to cross tabulations for a set of pre-specified hierarchical geographic units, which is known as the geographic spine. After post-processing these noisy measurements, DAS outputs a formally private database with fields indicating location in the standard census geographic spine, which is defined by the United States as a whole, states, counties, census tracts, block groups, and census blocks. This paper describes how the geographic spine used internally within DAS to define the initial noisy measurements impacts accuracy of the output database. Specifically, tabulations for geographic areas tend to be most accurate for geographic areas that both 1) can be derived by aggregating together geographic units above the block geographic level of the internal spine, and 2) are closer to the geographic units of the internal spine. After describing the accuracy tradeoffs relevant to the choice of internal DAS geographic spine, we provide the settings used to define the 2020 Census production DAS runs.

Relationship between Neighborhood-Level Social Risk Factor Measures and Presenting Glaucoma Severity Utilizing Multilevel Modeling

PurposeThe neighborhood and built environment social determinant of health domain has several social risk factors (SRF) that are modifiable through policy efforts. We investigated the impact of neighborhood-level SRFs on presenting glaucoma severity at a tertiary eye care center. DesignA cross-sectional study from August 2012 to May 2022 in the University of Michigan (UM) electronic health record (EHR). ParticipantsPatients with a diagnosis of any open angle glaucoma with ≥1 eye care visit at the UM Kellogg Eye Center and ≥1 reliable visual field. MethodsParticipants who met inclusion criteria were identified by ICD9/10 codes (365.x/H40.x). Data extracted from the EHR included patient demographics, address, presenting mean deviation (MD), and visual field reliability. Addresses were mapped to SRF measures at the census tract, block group, and county levels. Multilevel linear regression models were used to estimate the fixed effects of each SRF on MD, after adjusting for patient-level demographic factors and a random effect for neighborhood. Interactions between each SRF measure with patient-level race and Medicaid status were tested for an additive effect on MD. Main Outcome MeasuresThe main outcome measure was the effect of SRF on presenting MD. ResultsIn total, 4428 patients were included in the analysis who were, on average, 70.3 years old (standard deviation=11.9), 52.6% self-identified as female, 75.8% self-identified as White, and 8.9% had Medicaid. The median value of presenting MD was -4.94 dB (interquartile range=-11.45 to -2.07 dB). Neighborhood differences accounted for 4.4% of the variability in presenting MD. Neighborhood-level measures, including worse area deprivation (estimate, β=-0.31 per 1-unit increase; p<0.001), increased segregation (β=-0.92 per 0.1-unit increase in Theil’s H index; p<0.001), and increased neighborhood Medicaid (β=-0.68; p<0.001) were associated with worse presenting MD. Significant interaction effects with race and Medicaid status were found in several neighborhood-level SRF measures. ConclusionsAlthough patients’ neighborhood SRF measures accounted for a minority of the variability in presenting MD, most neighborhood-level SRFs are modifiable and were associated with clinically meaningful differences in presenting MD. Policies that aim to reduce neighborhood inequities to address allocation of resources could have lasting impacts on vision outcomes.

Neighborhood-Level Social Determinants of Health and Presenting Characteristics for Rhegmatogenous Retinal Detachments

Functional outcomes after repair of rhegmatogenous retinal detachments (RRDs) are highly dependent on baseline visual acuity and foveal status. Adverse social determinants of health (SDOH) can present barriers to timely presentation for repair and limit vision outcomes. To evaluate the association between neighborhood-level SDOH with baseline severity (visual acuity and fovea status) of RRD. This was a retrospective cohort study that included adult patients 18 years and older who underwent primary repair of uncomplicated RRD at the Wilmer Eye Institute from January 2008 to December 2018. Study data were analyzed from December 2023 to April 2024. The census block group of patient home addresses were matched to multiple neighborhood-level SDOH including the Area Deprivation Index (ADI), per capita income, percentage of renters, percentage of rent burden, percentage of people using a food assistance program, percentage of uninsured individuals, mode of transportation to work, distance to the nearest transit stop, total road density, National Walkability Index, Index of Medical Underservice score, and aggregate cost of medical care. Odds of presenting with vision worse than 20/40 or fovea-involving RRD using multivariable logistic regression adjusting for age, sex, race and ethnicity, and insurance. A total of 700 patients (mean [SD] age, 57.9 [12.4] years; 432 male [61.7%]) were included. Every decile increase in ADI, indicating more socioeconomic disadvantage, was associated with an increased odds of presenting with worse visual acuity and fovea-involving RRD (odds ratio [OR], 1.14; 95% CI, 1.04-1.24; P = .004 and OR, 1.13; 95% CI, 1.04-1.22; P = .005, respectively). Each $1000 increase in per capita income was associated with lower odds of presenting with worse vision (OR, 0.99; 95% CI, 0.98-0.99; P = .001). Every 1% increase in percentage of workers who drove to work was associated with an increased odds of presenting with worse vision and fovea-involving RRD (OR, 1.02; 95% CI, 1.01-1.03; P = .005 and OR, 1.01; 95% CI, 1.00-1.03; P = .04, respectively). Results of this cohort study suggest that patients with a residence in neighborhoods with more socioeconomic deprivation or a higher percentage of workers who drove to work were more likely to present with more severe RRD even after accounting for multiple individual-level characteristics. These findings support consideration of public policy changes to address the barriers faced by patients residing in certain neighborhoods who seek prompt surgical intervention for RRD to reduce health disparities in RRD outcomes.

Exposure to fine particulate matter in the New York City subway system during home-work commute.

The New York City (NYC) subway system accommodates 5.5 million daily commuters, and the environment within the subway is known to have high concentrations of fine particulate matter (PM2.5) pollution. Naturally, subway air pollution varies among individuals according to their mobility patterns, introducing the possibility of inequality in PM2.5 exposure. This study aims to evaluate individual and community-level exposure to subway PM2.5. We simulated the intracity home-to-work trip patterns using the Longitudinal Employer-Household Dynamics (LEHD) records of 3.1 million working commuters across 34,169 census blocks in four boroughs (Manhattan, Brooklyn, Queens, and the Bronx) of NYC. We incorporated the on-platform and on-train measured PM2.5 concentration data for the entire subway system. The mean underground platform concentration in the city was 139 μg/m3 with a standard deviation of 25 μg/m3, while the on-train concentration when underground was 99 μg/m3 with a standard deviation of 21 μg/m3. Using a network model, we determined the exposure of individual commuters during their daily home-work trips. We quantified the mean per capita exposure at the census block level by considering the proportion of workers within the blocks who rely on the subway for their work commute. Results indicate statistically significant weak positive correlation between elevated subway PM2.5 exposure and economically disadvantaged and racial minority groups.

The influence of socioeconomic characteristics on active travel in US metropolitan areas and the contribution to health inequity.

The prevalence of chronic disease in the US adult population varies across socioeconomic groups in the USA where approximately six in 10 adults have a chronic condition. Walking or cycling reduces the risk to many of these diseases and is influenced by the built environment, accessibility, and safety. We performed multivariate logistic and linear regression on the Health-Oriented Transportation model parameters using the 2009 and 2017 US National Household Transportation surveys, restricted to adults in major metropolitan areas. Model covariates included socioeconomic and environmental characteristics. Using odds ratios (OR) adjusted for model covariates, we observe several significant variables in 2009 and 2017. Residents of households with no cars were more likely to walk or cycle than those with two cars; OR=5.4 (4.8, 6.0). Residents of households in a census block with population density greater than 2,5000 persons/square mile were more likely to walk or cycle than those with a population density of 2000-3999; OR=2.6 (2.3, 2.8). Individuals with a graduate or professional degree were more likely to walk or cycle than those with a high school degree; OR=2.1 (1.9, 2.2). Individuals that self-report as Black or African American, or Asian are less likely to walk or cycle than White; OR=0.60 (0.56, 0.66), OR=0.70 (0.65, 0.75). The proportional increase in all-cause mortality from estimated reductions in physical activity for African American, Asian, and Hispanic populations were 1.0%, 0.7%, 0.8%, respectively. Access to automobiles and the surrounding population density are primary factors in the decision to walk or cycle. After adjusting for these and other factors, members of low-income, low-education, Black or African American, and Asian populations in US metropolitan areas are less likely to walk or cycle than high-income, high-education, or White populations and the discrepancy in physical activity is likely to contribute to health inequity.

Correlation Among Neighborhood-level Measures of the Tobacco Retail Environment.

Tobacco retailer density and distance to tobacco retailers are understood to influence tobacco-related behaviors; however, there is no general agreement on how to best characterize or measure the tobacco retail environment (TRE). In this data-driven analysis, we examine similarities among neighborhood-level measures of the TRE and assess how geographic resolution of the neighborhood units may affect them. We used locations of likely tobacco retailers in the United States (US) to calculate multiple retailer count, density, and distance measures. Measures were calculated at the Census block group, tract, and county (including county equivalents) levels of geographic aggregation. Spearman's correlation was used to evaluate similarity among the TRE measures. At the block group and tract level, correlation among all TRE measures ranged from slightly negative (ρ = -0.03) to nearly perfect (ρ = 0.99). At both levels of aggregation, distance-based TRE measures were highly correlated (ρ > 0.76). At the block group level, simple count of retailers was highly correlated with the density measures (ρ > 0.83), and at the tract level, simple count was moderately to highly correlated with the density measures (ρ > 0.5). Findings were generally similar at the county level; a notable deviation was that retailers per person was negatively correlated with all other TRE measures (range from ρ = -0.08 to ρ = -0.32). Some common measures were not correlated, suggesting they capture different aspects of the TRE; similarity among the various measures also varied by level of geographic aggregation. Because the tobacco retail environment shapes people's tobacco-related behaviors, using appropriate measures to characterize it at a neighborhood-level is paramount. Our work highlights both the similarities and differences among a set of common measures, thereby suggesting the measures may be capturing different aspects of the overall retail environment. Our findings regarding geographic level of aggregation underscore the importance of neighborhood definition in any tobacco retail environment analysis.

A data-driven approach to identifying PFAS water sampling priorities in Colorado, United States.

Per and polyfluoroalkyl substances (PFAS), a class of environmentally and biologically persistent chemicals, have been used across many industries since the middle of the 20th century. Some PFAS have been linked to adverse health effects. Our objective was to incorporate known and potential PFAS sources, physical characteristics of the environment, and existing PFAS water sampling results into a PFAS risk prediction map that may be used to develop a PFAS water sampling prioritization plan for the Colorado Department of Public Health and Environment (CDPHE). We used random forest classification to develop a predictive surface of potential groundwater contamination from two PFAS, perfluorooctane sulfonate (PFOS) and perfluorooctanoate (PFOA). The model predicted PFAS risk at locations without sampling data into one of three risk categories after being "trained" with existing PFAS water sampling data. We used prediction results, variable importance ranking, and population characteristics to develop recommendations for sampling prioritization. Sensitivity and precision ranged from 58% to 90% in the final models, depending on the risk category. The model and prioritization approach identified private wells in specific census blocks, as well as schools, mobile home parks, and public water systems that rely on groundwater as priority sampling locations. We also identified data gaps including areas of the state with limited sampling and potential source types that need further investigation. This work uses random forest classification to predict the risk of groundwater contamination from two per- and polyfluoroalkyl substances (PFAS) across the state of Colorado, United States. We developed the prediction model using data on known and potential PFAS sources and physical characteristics of the environment, and "trained" the model using existing PFAS water sampling results. This data-driven approach identifies opportunities for PFAS water sampling prioritization as well as information gaps that, if filled, could improve model predictions. This work provides decision-makers information to effectively use limited resources towards protection of populations most susceptible to the impacts of PFAS exposure.

Social determinants of health and outcome disparities in spine tumor surgery. Part 2: Neighborhood disadvantage and long-term outcomes.

Neighborhood-level resource disadvantage has been previously shown to predict extent of resection, oncological follow-up, adjuvant treatment, and clinical trial participation for malignancies, including glioblastoma. The authors aimed to characterize the association between neighborhood disadvantage and long-term outcomes after spine tumor surgery. The authors analyzed all patients who underwent surgery for primary or secondary (all metastatic pathologies) spine tumors at a single spinal oncology specialty center in the United States from 2015 to 2022. The Area Deprivation Index (ADI), a validated metric compositing 17 social determinants of health variables that ranges continuously from 0% (higher advantage) to 100% (higher disadvantage), was used to quantify neighborhood disadvantage. Patient addresses were matched to ADI on the basis of the census block of residence. Subsequently, the study population was dichotomized into advantaged (ADI 0%-33%) and disadvantaged (ADI 34%-100%) cohorts. The primary endpoint was functional status, as defined by Eastern Cooperative Oncology Group (ECOG) Performance Status Scale grade, with secondary endpoints including inpatient outcomes, mortality, readmissions, reoperations, and clinical research participation. Multivariable logistic, gamma log-link, and Cox regression adjusted for 14 confounders, including patient and oncological characteristics, general and tumor-related presenting severity, and treatment. In total, 237 patients underwent spine tumor surgery from 2015 to 2022, with an average age of 53.9 years, and 57.0% had primary tumors whereas 43.0% had secondary tumors; 55.3% (n = 131) were classified by ADI into the disadvantaged cohort. This cohort had higher rates of ambulation deficits on presentation (39.1% vs 23.5%, p = 0.015) and nonelective surgery (35.1% vs 23.6%, p = 0.030). Postoperatively, disadvantaged patients exhibited higher odds of residual tumor (OR 2.55, p = 0.026), especially for secondary tumors (OR 4.92, p = 0.045). Patients from disadvantaged neighborhoods additionally exhibited significantly higher odds of poor functional status at follow-up (OR 3.94, p = 0.002). Postoperative survival was 74.7% (mean follow-up 17.6 months), with the disadvantaged cohort experiencing significantly shorter survival (HR 1.92, p = 0.049). Moreover, this population had higher odds of readmission (OR 1.92, p = 0.046) and, for primary tumors, reoperation (OR 9.26, p = 0.005). Elective participation in prospective clinical research was lower among the disadvantaged cohort (OR 0.45, p = 0.016). Neighborhood disadvantage predicts higher rates of residual tumor, readmission, and reoperation, as well as poorer functional status, shorter postoperative survival, and decreased elective research participation. The ADI may be used to risk stratify spine oncology patients and guide targeted interventions to ameliorate neurosurgical disparities and to reduce barriers to research participation.