Median Home Value Research Articles

Components of Residential Neighborhood Deprivation and Their Impact on the Likelihood of Live-Donor and Preemptive Kidney Transplantation.

Adults residing in deprived neighborhoods face various socioeconomic stressors, hindering their likelihood of receiving live-donor kidney transplantation (LDKT) and preemptive kidney transplantation (KT). We quantified the association between residential neighborhood deprivation index (NDI) and the likelihood of LDKT/preemptive KT, testing for a differential impact by race and ethnicity. We studied 403937 adults (age ≥ 18) KT candidates (national transplant registry; 2006-2021). NDI and its 10 components were averaged at the ZIP-code level. Cause-specific hazards models were used to quantify the adjusted hazard ratio (aHR) of LDKT and preemptive KT across tertiles of NDI and its 10 components. Candidates residing in high-deprivation neighborhoods were more likely to be female (40.1%vs. 36.2%) and Black (41.9%vs. 17.7%), and were less likely to receive both LDKT (aHR=0.66, 95% confidence interval [CI]: 0.64-0.67) and preemptive KT (aHR=0.60, 95% CI: 0.59-0.62) than those in low-deprivation neighborhoods. These associations differedby race and ethnicity (Black: aHRLDKT=0.58, 95% CI: 0.55-0.62; aHRpreemptive KT=0.68, 95% CI: 0.63-0.73; Pinteractions: LDKT<0.001; Preemptive KT=0.002). All deprivation components were associated with the likelihood of both LDKT and preemptive KT (except median home value): for example, higher median household income (LDKT: aHR=1.08, 95% CI: 1.07-1.09; Preemptive KT: aHR=1.10, 95% CI: 1.08-1.11) and educational attainments (≥high school [LDKT: aHR=1.17, 95% CI: 1.15-1.18; Preemptive KT: aHR=1.23, 95% CI: 1.21-1.25]). Residence in socioeconomically deprived neighborhoods is associated with a lower likelihood of LDKT and preemptive KT, differentially impacting minority candidates. Identifying and understanding which neighborhood-level socioeconomic status contributes to these racial disparities can be instrumental in tailoring interventions to achieve health equity in LDKT and preemptive KT.

Sampling coverage of the Arkansas all-payer claims database by County's persistent poverty designation.

To evaluate the quality of Arkansas All-Payer Claims Database (APCD) for disparity research in persistent poverty areas by determining (1) its representativeness of Arkansas population, (2) variation by county, and (3) differences in coverage between persistent poverty and other counties. Cross-sectional study using 2019 Arkansas APCD member enrollment data and county-level data from various agencies. An alias identifier linked persons across insurance plans. County FIPS codes were used to extract county-level variables. Cohort 1 included individuals with ≥1 day of medical coverage in 2019. Cohort 2 included individuals with medical coverage in June, 2019. Cohort 3 included individuals with continuous medical coverage in 2019. Sampling proportions of a county's population in the three cohorts were compared between persistent poverty and other counties. Inverse-variance weighted linear regression was used to identify county-level socioeconomic and demographic characteristics associated with inclusion in each cohort. In 2019, 73.6% of Arkansans had medical coverage for ≥1 day (Cohort 1), 66.3% had coverage in June (Cohort 2), and 58.8% had continuous coverage (Cohort 3) in APCD. Sampling proportions varied by county (median[range]: Cohort 1, 78% [58%-95%]; Cohort 2, 71% [51%-88%]; and Cohort 3, 64% [44%-80%]), and were higher among persistent poverty counties than others for all three cohorts (mean [SD], persistent poverty vs. other: Cohort 1: 80.9% [6.4%] vs. 77.1% [6.3%], p = 0.04; Cohort 2: 74.0% [6.4%] vs. 70.1% [6.2%], p = 0.03; Cohort 3: 66.4% [6.1%] vs. 62.7% [6.0%], p = 0.03). In the 2019 APCD, larger counties and those with higher proportions of females or persons 65+ years had higher coverage, whereas counties with higher per capita household income, median home value, or disproportionately more persons of other races (non-White and non-Black) had lower coverage (p < 0.05 for all three cohorts). The Arkansas APCD had good coverage of Arkansas population. Coverage was higher in persistent poverty counties than others.

Extreme Heat Exposure Is Associated with Higher Socioeconomic Disadvantage and Elevated Youth Delinquency.

Climate change has led to an increase in the frequency and intensity of extreme heat events, a trend expected to continue. This poses significant health risks, particularly for vulnerable populations like children. While previous research has largely concentrated on the physical health impacts of extreme heat, less attention has been given to behavioral outcomes, such as delinquency. This study investigates the association between extreme heat exposure and delinquency among children, utilizing data from the Adolescent Brain Cognitive Development (ABCD) study. It also explores the potential mediating roles of neighborhood socioeconomic status (SES; measured by median home value), puberty, peer deviance, and financial difficulties. Data from the national ABCD study were analyzed to assess the relationship between extreme heat exposure (exposure) and delinquency (outcome). Covariates included race/ethnicity, sex, and age. Mediators examined were neighborhood SES, puberty, peer deviance, and financial difficulties. Structural equation modeling (SEM) was employed for data analysis. Overall, 11,878 children entered our analysis. The analysis revealed a significant association between extreme heat exposure and higher levels of delinquency among children. Children more exposed to extreme heat were more likely to be Black, reside in lower SES neighborhoods, experience greater financial difficulties, and have more advanced puberty status. The group facing the highest heat exposure was also economically disadvantaged. The findings suggest that children already disadvantaged by socio-economic factors are disproportionately affected by extreme heat, leading to increased delinquency. This highlights the need for targeted interventions to protect these vulnerable populations and address the mediators of extreme heat exposure. Future research should focus on longitudinal studies and evaluate the effectiveness of various mitigation strategies to address these disparities.

NEIGHBORHOOD AFFLUENCE AND RISK OF INCIDENT DEMENTIA: ROLE OF PHYSICAL ACTIVITY DESTINATIONS

Abstract Neighborhood affluence encompasses the degree of high educational, occupational, and wealth concentration within residential communities. Highly affluent neighborhoods may reduce dementia risk in part by providing amenities that promote physical activity. We examined associations between neighborhood affluence, and specifically the quantity of nearby physical activity (PA) destinations (e.g., gyms), with incident dementia. Participants were 2,942 adults ≥65 years old from the Cardiovascular Health Cognition Study. Baseline neighborhood measures included 1) a 1990 Census-derived affluence index (percent with college education, percent with professional/managerial occupation, median home value) and 2) the count of PA destinations from the 1990 National Establishment Time Series (NETS) database that fell within a 1-km Euclidean buffer around the home. A total of 469 dementia cases (240 Alzheimer’s, 208 mixed/vascular, 21 other) were clinically adjudicated from neurological/neuropsychological examinations (Mean=5.3±1.8 follow-up years). In Cox proportional hazards models adjusted for clinic site and individual demographics (age, gender, race, marital status), each 1-SD higher neighborhood affluence was associated with 15% reduced hazard of all-cause dementia (HR=0.85, 95%CI: 0.77-0.94), largely driven by mixed/vascular subtype (HR=0.73, 95%CI: 0.64-0.86), but not Alzheimer’s subtype (HR=0.93, 95%CI: 0.81-1.07). Further, participants with ≥2 (versus no) nearby PA destinations had 37% reduced hazard of specifically mixed/vascular dementia (HR=0.63, 95%CI: 0.41-0.99). After also adjusting for individual health, income, and education, neighborhood affluence remained protective against mixed/vascular dementia (HR=0.77, 95%CI: 0.65-0.90), but number of PA destinations was no longer associated. Neighborhood affluence may reduce dementia risk via improved vascular health and operate through additional mechanisms beyond nearby PA amenities.

The Impact of Non-Adherence to Antihypertensive Drug Therapy.

Medication non-adherence is a major healthcare barrier, especially among diseases that are largely asymptomatic, such as hypertension. The impact of poor medication adherence ranges from patient-specific adverse health outcomes to broader strains on health care system resources. The Centers for Disease Control (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) database was used to retrieve Centers for Medicare and Medicaid Services' data pertaining to blood pressure (BP) medication adherence, socio-economic variables, and cardiovascular (CV) outcomes across the United States. Multivariable linear regression models were used to estimate the change in total CV deaths as a function of non-adherence to BP medications. For every percent increase in the non-adherence rate, the total number of CV deaths increased by 7.13 deaths per 100,000 adults (95% CI: 6.34-7.92), even after controlling for the percentage of residents with access to insurance, the percentage of residents who were eligible for Medicaid, the percentage of residents without a college education, median home value, income inequality, and the poverty rate (p < 0.001). There is a significant association between non-adherence to BP medications and total CV deaths. Even a one percent increase in the adherence rate in the United States could result in tens of thousands of preventable CV deaths. Based on recently published CDC data, this could also have a tremendous impact on health care costs. This provides compelling evidence for increased efforts to improve adherence.

How access to federal recovery credit assistance influences future wealth trajectories after a natural disaster: The case of Hurricane Harvey

Hurricane Harvey made landfall in Texas in August 2017. The Small Business Administration (SBA) provided direct-to-household loan aid for over $2.9 billion to assist recovery, twice as large as the Federal Emergency Management Agency's direct-to-household grant program to individuals impacted by the storm. The author examined the role of federal disaster home loan availability and wealth creation through changes in home values after Hurricane Harvey. The study linked SBA data to seasonally adjusted private market housing data. The author used a fixed-effect difference-in-difference regression with propensity score matching study design to estimate the effect of federal credit recovery assistance on log median home values over time. Communities receiving federal credit assistance saw a statistically significant 6.4 % faster increase in home values than communities receiving no federal credit assistance post-Harvey. In addition, communities with shorter (two weeks or less) wait times to receive SBA loans saw a 2 % faster rise in home value prices post-Harvey than communities with longer median wait times. While disaster damage influenced the receipt of assistance, pre-disaster wealth influenced the speed of the assistance received. Understanding at what points barriers to access arise within the delivery of aid, and the wealth outcomes of such inequities, point to clear ways policy changes may promote equitable recovery for all.

The Structure of Pandemic Vulnerability: Housing Wealth, Residential Segregation, and COVID-19 Mortality

The COVID-19 pandemic has been particularly devastating for those with limited economic resources. Extensive research demonstrates the negative relationship between wealth and mortality at both the individual and area levels. In addition, residential segregation has been linked to poor health and greater mortality. Home equity is the largest asset that many Americans own, but residential segregation devalues homes located in Black neighborhoods. Despite the interlocking relationships between wealth, residential segregation, and mortality, it remains unclear how wealth and residential segregation work to predict COVID-19 deaths. Using U.S. Census data and county-level COVID-19 data from Johns Hopkins University (n = 1164), I deploy median home value as a wealth proxy and negative binomial regression models to interrogate two questions. (1) What is the relationship between home value and COVID-19 deaths? (2) How does the relationship vary by level of residential segregation? Results indicate that COVID-19 mortality is 64 percent greater in the lowest wealth counties than in the wealthiest counties. At average median home value, the most segregated counties with the largest Black populations suffer 28 percent more COVID-19 deaths than similarly situated counties with low levels of residential segregation and small Black populations. This study underscores the importance of accounting for residential segregation in examinations of the well-established relationship between socioeconomic status and health and mortality.

Different Data, Different Measures: Comparing Alternative Indicators of Changes in Neighborhood Home Values

Urban scholars and practitioners have used changes in neighborhood-level home values to serve as indicators of neighborhood change, including gentrification and disinvestment. A common measure is the “median home value” variable from the American Community Survey (ACS). However, household-level research suggests that self-assessed home values, such as those of the ACS, differ significantly from market-based measures, and medians can be affected by changes in the mix of homes . Transaction-based home price indices are unaffected by such changes and are based on market sales rather than self-assessments, but also have limitations. Moreover, self-assessments of home values might be desired if the intention is to measure the value households place on their homes or to avoid potential biases baked into market values. Comparing changes in the ACS median home value to a common market-based home price index (HPI), we find that the ACS median tends to fall more slowly than the HPI when values are falling and increase more slowly than the HPI when values rise. The differences between the measures are large and are not randomly distributed across space, tending to be larger in neighborhoods where values fall or rise more steeply. They are also related to a variety of neighborhood characteristics.

Expanding the coverage and accuracy of parcel-level land value estimates.

Planning for cost-effective conservation requires reliable estimates of land costs, spatially-differentiated at high resolution. Nolte (2020) provides a county-by-county, parcel-level estimation approach that dramatically improves estimates of fair market value for undeveloped land across the contiguous Unites States. Much undeveloped land of conservation interest is under threat of conversion to agricultural use or is already agricultural. This paper demonstrates the value of accounting for additional variables that affect agricultural productivity and demand for undeveloped land, as well as the benefit of modeling at scales corresponding to regional agricultural markets. We find that countywide median home value, climatic variables, and several parcel-level soil type variables contribute substantially to predictive power. Enlarging the set of predictors and the geographical scale of modeling improves accuracy by approximately 15 percent and, relative to a more restricted modeling benchmark adapted from Nolte (2020), extends coverage into 376 counties occupying 1.35 million km2. To assess the practical benefits of our modeling approach, we simulate the protection of 30 percent of US lands via government purchasing, modeled after the Biden administration's "30x30" initiative. Using our proposed modeling strategy, the purchasing agency saves approximately $15 million per year, or 4 percent of the USDA's annual land easement budget.

Developmental Trajectories of Autism.

The goal of this study was to describe the typical, longitudinal, developmental trajectories of communication and social functioning in individuals with autism spectrum disorder from childhood through adulthood and to determine the correlates of these trajectories. Children with autism spectrum disorder who were born in California from 1992 through 2016 and enrolled with the California Department of Developmental Services were identified. Subjects with <4 evaluations in the database were excluded, resulting in a sample of 71 285 individuals. Score sequences were constructed based on evaluative items for communication and social functioning. Typical trajectories were identified using group-based latent trajectory modeling, and logistic regression was used to determine the odds of classification into a social adolescent decline trajectory by individual-, family-, and zip code-level factors. Six typical patterns of communication functioning and 7 typical patterns of social functioning were identified. Whereas the majority of autistic individuals exhibit improved communication functioning as they age, the majority of individuals exhibit steady social functioning. A small group of individuals (5.0%) exhibits high social functioning in childhood that declines in adolescence. Membership in this adolescent decline group is associated with maternal non-Hispanic white race and ethnicity, female sex, moderate levels of maternal education, lower zip code-level median home values and population density, and higher zip code-level inequality. Most autistic individuals show improved communication and social functioning as they age, but not all do. Trajectory group membership is correlated with socioeconomic status. Future research should investigate what drives these correlations.

Spatial distribution of solar PV deployment: an application of the region-based convolutional neural network

Solar photovoltaic (PV) deployment plays a crucial role in the transition to renewable energy. However, comprehensive models that can effectively explain the variations in solar PV deployment are lacking. This study aims to address this gap by introducing two innovative models: (i) a computer vision model that can estimate spatial distribution of solar PV deployment across neighborhoods using satellite images and (ii) a machine learning (ML) model predicting such distribution based on 43 factors. Our computer vision model using Faster Regions with Convolutional Neural Network (Faster RCNN) achieved a mean Average Precision (mAP) of 81% for identifying solar panels and 95% for identifying roofs. Using this model, we analyzed 652,795 satellite images from Colorado, USA, and found that approximately 7% of households in Colorado have rooftop PV systems, while solar panels cover around 2.5% of roof areas in the state as of early 2021. Of our 16 predictive models, the XGBoost models performed the best, explaining approximately 70% of the variance in rooftop solar deployment. We also found that the share of Democratic party votes, hail and strong wind risks, median home value, the percentage of renters, and solar PV permitting timelines are the key predictors of rooftop solar deployment in Colorado. This study provides insights for business and policy decision making to support more efficient and equitable grid infrastructure investment and distributed energy resource management.

Social Determinants of Health and Racial Disparities in Cardiac Events in Breast Cancer.

Racial disparities have been reported for breast cancer and cardiovascular disease (CVD) outcomes. The determinants of racial disparities in CVD outcomes are not yet fully understood. We aimed to examine the impact of individual and neighborhood-level social determinants of health (SDOH) on the racial disparities in major adverse cardiovascular events (MACE; consisting of heart failure, acute coronary syndrome, atrial fibrillation, and ischemic stroke) among female patients with breast cancer. This 10-year longitudinal retrospective study was based on a cancer informatics platform with electronic medical record supplementation. We included women aged ≥18 years diagnosed with breast cancer. SDOH were obtained from LexisNexis, and consisted of the domains of social and community context, neighborhood and built environment, education access and quality, and economic stability. Race-agnostic (overall data with race as a feature) and race-specific machine learning models were developed to account for and rank the SDOH impact in 2-year MACE. We included 4,309 patients (765 non-Hispanic Black [NHB]; 3,321 non-Hispanic white). In the race-agnostic model (C-index, 0.79; 95% CI, 0.78-0.80), the 5 most important adverse SDOH variables were neighborhood median household income (SHapley Additive exPlanations [SHAP] score [SS], 0.07), neighborhood crime index (SS = 0.06), number of transportation properties in the household (SS = 0.05), neighborhood burglary index (SS = 0.04), and neighborhood median home values (SS = 0.03). Race was not significantly associated with MACE when adverse SDOH were included as covariates (adjusted subdistribution hazard ratio, 1.22; 95% CI, 0.91-1.64). NHB patients were more likely to have unfavorable SDOH conditions for 8 of the 10 most important SDOH variables for the MACE prediction. Neighborhood and built environment variables are the most important SDOH predictors for 2-year MACE, and NHB patients were more likely to have unfavorable SDOH conditions. This finding reinforces that race is a social construct.

Thirty-Day Unplanned Hospital Readmissions in Patients With Cancer and the Impact of Social Determinants of Health: A Machine Learning Approach.

Develop a cancer-specific machine learning (ML) model that accurately predicts 30-day unplanned readmissions in patients with solid tumors. The initial cohort included patients 18 years or older diagnosed with a solid tumor. Two distinct cohorts were generated: one with and one without detailed social determinants of health (SDOHs) data. For each cohort, data were temporally partitioned in 70% (training), 20% (validation), and 10% (testing). Tree-based ML models were developed and validated on each cohort. The metrics used to evaluate the model's performance were receiver operating characteristic curve (ROC), area under the ROC curve, precision, recall (R), accuracy, and area under the precision-recall curve. We included 13,717 patients in this study in two cohorts (5,059 without SDOH data and 8,658 with SDOH data). Unplanned 30-day readmission occurred in 21.3% of the cases overall. The five main non-SDOH factors most highly associated with an unplanned 30-day readmission (R, 0.74; IQR, 0.58-0.76) were: number of previous unplanned readmissions; higher Charlson comorbidity score; nonelective index admission; discharge to anywhere other than home, hospice, or nursing facility; and higher anion gap during the admission. Neighborhood crime index, neighborhood median home values, annual income, neighborhood median household income, and wealth index were the main five SDOH factors important for predicting a high risk for an unplanned hospital readmission (R, 0.66; IQR, 0.56-0.72). The models were not directly comparable. Key drivers of unplanned readmissions in patients with cancer are complex and involve both clinical factors and SDOH. We developed a cancer-specific ML model that with reasonable accuracy identified patients with cancer at high risk for an unplanned hospital readmission.

An ecological analysis of walkability and housing affordability in Canada: Moderation by city size and neighbourhood property type composition.

The neighbourhood built environment can support the physical activity of adults regardless of their individual-level socioeconomic status. However, physical activity supportive (walkable) neighbourhoods may not be accessible to those with lower incomes if homes in walkable neighbourhoods are too expensive. The objectives of this study were: 1) to estimate the associations between neighbourhood walkability and home values in Canadian cities, and 2) to test whether these associations differ by city size and residential property type composition within neighbourhoods. We linked built environment data from the 2016 Canadian Active Living Environments (Can-ALE) index with neighbourhood-level structural home characteristics and sociodemographic data from the 2016 Canadian census for 33,026 neighbourhoods across 31 Census Metropolitan Areas. We used multilevel linear regression models to estimate covariate-adjusted associations between neighbourhood walkability and natural-log median home values and tested city size and neighbourhood property type composition as moderators. There were no statistically significant associations between walkability and home values overall. The associations between neighbourhood walkability and home values were jointly moderated by city size and property type composition. For small and medium sized cities, within neighbourhoods containing a high proportion of detached homes, walkability was negatively associated with home values (b = -0.05, 95% CI: -0.10, -0.01; and, b = -0.04, 95% CI: -0.06, -0.02, for small and medium cities, respectively). However, for extra-large cities, within neighbourhoods containing a high proportion of detached homes, walkability was positively associated with home values (b = 0.06, 95% CI: 0.01, 0.10). Our findings suggest that, based on housing affordability, higher walkable neighbourhoods are likely accessible to lower income households that are situated in small and medium Canadian cities. In larger cities, however, municipal interventions (e.g., inclusionary zoning or targeted development of subsidized or social housing) may be needed to ensure equitable access to walkable neighbourhoods for lower income households.

The Neighborhood Atlas Area Deprivation Index For Measuring Socioeconomic Status: An Overemphasis On Home Value.

The Area Deprivation Index (ADI), popularized by the Neighborhood Atlas, is a multifaceted proxy measure for assessing socioeconomic disadvantage that captures social risk factors that are not available in typical clinical registries and that are related to adverse health outcomes. In applying the ADI to New York State, we found that the downstate regions (New York City and its suburbs) were as deprived as or more deprived than the other regions for thirteen of the seventeen ADI variables (all but the ones measured in dollars), but the Neighborhood Atlas-computed overall ADI deprivation was much less in the downstate areas. Numerous census block groups with high home values (indicating low deprivation) accompanied by high deprivation in the other ADI variables had overall ADI scores as computed by the Neighborhood Atlas in the same or contiguous deciles as the home values. We concluded that Neighborhood Atlas-computed ADI scores for New York block groups are mainly representative of median home value. This can be especially problematic when considering quality assessment, funding, and resource allocation in regions with large variations in cost of living, and it may result in underresourcing for disadvantaged communities with high housing prices. We conclude that the Neighborhood Atlas ADI would be more accurate for comparing block groups if variables were standardized before computing the overall index.

The Neighborhood Atlas Area Deprivation Index And Recommendations For Area-Based Deprivation Measures.

Edward Hannan and colleagues found that a lack of standardization of input variables to the Area Deprivation Index (ADI) available from the Neighborhood Atlas overweights the index for median home value and thus potentially further disadvantages already disadvantaged communities. We recommend that a revised ADI be created that addresses the error, and we offer five steps to avoid future errors in the creation of area deprivation measures.

Impact of Patient Demographics and Neighborhood Socioeconomic Variables on Clinical Trial Participation Patterns for Non-Hodgkin Lymphoma at a Major Academic Institution

Background: There have been tremendous advancements in Non-Hodgkin Lymphoma (NHL) therapies and outcomes in large part due to clinical trials. Prior studies have demonstrated that certain populations including the elderly, certain racial and/or ethnic minority groups, and women are underrepresented in cancer clinical trials. Better understanding of how social context impacts clinical trial enrollment will allow us to better design tailored interventions to address barriers to clinical trial participation. We performed a retrospective analysis of patients with NHL seen at MD Anderson Cancer Center (MDACC) to systematically investigate the association between clinical trial participation patterns and sociodemographic context measured by neighborhood socioeconomic factors based on place of residence. Examining our practice patterns may then inform strategies to enhance trial recruitment of underrepresented groups. Methods: We examined 3448 patients, age ≥18 years with a diagnosis of NHL seen at MDACC between January 2017 and December 2020. Using patient address, geolocation software and American Community Survey (2015 - 2020) and 2020 census data available through data.Census.gov we ascertained 35 neighborhood level socioeconomic status (nSES) variables corresponding to each census tract including the domains of educational attainment, neighborhood median income, poverty, crowding, race composition and home values. We performed descriptive analysis to understand the demographics of the total populations and those enrolled in clinical trials. We performed univariate analysis of clinical trial enrollment patterns by sex, race, age, histology type, insurance type and nSES variables using 2-sample t-test and Mann-Whitney tests (MHT). Using exhaustive variable selection based upon minimizing the Akaike Information Criteria, including 2-way interactions, we found the set of variables that generated an optimal generalized additive logistic regression model for incidence of participation in clinical trials. We repeated this procedure for the diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL) subgroups. We adjusted alpha 0.05/3=0.017 as the criteria for significance in these models to address multiple testing. Results: We excluded 130 patients with international patient addresses and 427 patients without complete address information. and found no significant difference in baseline characteristic or clinical trial enrollment between the 572 excluded patients and the study cohort of 2943 patients. The study population was 59% male, 74.6% Non-Hispanic white (NHW), 13.2% Hispanic, 6.1% black, 3.7% Asian; 29.5% had DLBCL, 22% FL, 13.2% mantle cell, 9% marginal zone, 8.3% T-cell, and 6.5% high grade B cell lymphoma. The most common insurance was managed care 47% and Medicare 45.7%; 2.5% of patients were self-pay, 1.2% had Medicaid, and 2.1% had other government insurance. There was a high overall clinical trial participation rate of 30.5% with 20.9% enrolled in therapeutic trials and participation differed by diagnosis (Table). In univariate analyses, lower participation rates were associated with lower nSES including: increasing % of populations living below poverty level, increasing % living in crowded households, increasing % above age 25 years with less than high school diploma, and decreasing median household income. Racial composition of census tract was not associated with differences in clinical trial participation. Participation did not differ by insurance type. In multivariable analysis, incidence of participation declined nonlinearly with age in the overall, FL, and DLBCL models. In the DLBCL subset Hispanic patients had lower odds of participation than Whites (odds ratio 0.34, p=0.0003). No nSES variable predicted clinical trial participation in these models. Conclusion: In a large academic cohort, age and diagnosis not race, gender, insurance type, or nSES were independent predictors of trial participation. One major limitation to the generalizability of these results is the select patient population seen at our institution are relatively well insured. Examination of multi-center datasets including community practices when underinsured patients received care and evaluation of interventions to influence access to clinical trials are needed. Figure 1View largeDownload PPTFigure 1View largeDownload PPT Close modal

S292 Socioeconomic Status: An Important Determinant for Follow-Up for Surveillance Colonoscopy After Endoscopic Mucosal Resection

Introduction: Alabama had significantly higher incidence of colorectal cancer (CRC)at 43.5 compared to the US rate 39.5, and African Americans have higher rate of CRC compared to Caucasian individuals. There is compelling evidence that surveillance colonoscopy after high-risk adenoma polypectomy can reduce the risk of developing CRC.Our objective was to identify risk factors associated with poor follow up after undergoing colonoscopy with endoscopic mucosal resection (EMR). Methods: We performed retrospective chart review of patients who had EMR at our facility between June 2016-March 2022.Patient demographics, referral, procedural and census data were collected. Census data obtained was used to evaluate income, median household income, and educational attainment. Patients were divided into two groups; poor and good follow up. Poor follow up was defined as those who failed to follow up and/or presented for surveillance colonoscopy >1 month after the recommended timeframe. Results: Initial chart review included 520 eligible patients;73 were excluded due to lack of referral data, and 59 with recent EMR whose recommended surveillance colonoscopy has not passed. Of the included 388 patients, 293 (75.5%) had poor follow up and 95 (24.5%) had good follow up. Descriptive statistics of the various characteristics and comparisons of the of two groups is presented in Table. Compared to individuals with good follow up, there was a significant association of poor follow up with patients whose index colonoscopy was performed at an outside hospital (p=0.003) and if the index colonoscopy was their first colonoscopy (p=0.04). Additionally, census data revealed lower median home values were associated with poor follow up ($168,382) when compared to good follow up ($185,034). There was a trend to poor follow up in patients with obesity (80.5%), former alcohol use (88.9%), patients insured by Medicaid (88.9%), and minority populations including African American, and Hispanic/other ethnicities (77.1%,90.9%, respectively). Conclusion: Our study illustrates that social economic status has significant impact on surveillance colonoscopy after EMR, and outside hospital referral are more likely to have poor follow up compared to in house referrals. Given the potential impact this data has for reducing risk of developing CRC in lower social economic individuals, further studies are needed to validate this finding and stratify predictors of poor follow up. Table 1. - Descriptive characteristics and comparison of poora vs goodb follow up in patients who underwent EMR at UAB between June 2016-March 2022 Overall Follow-up p value N=388 Poor n=293 (75.5%) Good n=95 (24.5%) Age (years), mean (SD) 63 (10.4) 64 (10.4) 62 (10.4) 0.23c Sex Male 52.1% 75.3% 24.8% 0.89d Female 47.9% 75.8% 24.2% Race/Ethnicity Caucasian 67.8% 73.8% 26.2% 0.38d African American 28.1% 77.1% 22.9% Other 2.8% 90.9% 9.1% Type of insurance Private 45.9% 74.7% 25.3% 0.39d Medicare 47.4% 75.0% 25.0% Medicaid 4.6% 88.9% 11.1% Uninsured 2.1% 75.0% 25.0% BMI less than 24.9 – Underweight/ Normal 24.8% 72.8% 27.2% 0.11d between 25.0 and 29.9 – Overweight 32.4% 70.0% 30.0% more than 30.0 – Obese 42.9% 80.5% 19.5% ASA classification 1 0.8% 66.7% 33.3% 0.84d 2 60.2% 75.5% 24.5% 3 38.5% 75.8% 24.2% 4 0.5% 50.0% 50.0% Smoking Current 25.0% 76.3% 23.7% 0.79d Former 26.8% 73.1% 26.9% Never 47.7% 76.2% 23.8% Alcohol Current 46.1% 72.6% 27.4% 0.25d Former 6.9% 88.9% 11.1% Never 46.4% 76.1% 23.9% Drugs Current 3.4% 84.6% 15.4% 0.85d Former 4.4% 70.6% 29.4% Never 87.9% 75.4% 24.6% Employment Employed 29.9% 74.1% 25.9% 0.99d Unemployed 6.9% 74.1% 25.9% Retired 28.4% 75.5% 24.6% Disabled 10.1% 76.9% 23.1% First colonoscopy Yes 17.0% 75.8% 24.2% 0.04d* No 44.6% 69.9% 30.1% Outside Hospital Referral Yes 31.7% 86.2% 13.8% 0.003d* No 67.8% 70.7% 29.3% Time of procedure Morning 57.9% 72.9% 27.1% 0.16d Afternoon 42.0% 79.1% 20.9% ASA=American Society of Anesthesiologists; BMI=body mass index; EMR= endoscopic mucosal resection; SD=standard deviation; UAB=University of Alabama at Birmingham.*Statistically significant at 0.05 level (two-tailed test).aPoor follow up included patients that did not receive surveillance colonoscopy within recommended timeframe or patients who were lost to follow up.bGood follow up is defined as undergoing surveillance colonoscopy within the recommended timeframe.cUnpaired t-test.dChi-square test.

Does racism have inertia? A study of historic redlining’s impact on present-day associations between development and air pollution in US cities

We explore how Home Owners’ Loan Corporation (HOLC) scores of the 1930s impact 2010 and 2015 inhalable particulate matter (PM10) concentrations for 15,232 census tracts, clustered in 196 cities throughout the contiguous United States. Using areal apportionment, we assign a HOLC score to housing tracts and construct hierarchical linear models to examine the relationship between the policy practice of redlining, PM pollution, and urban economic development. We find that redlining is associated with higher PM10 concentrations, and that higher HOLC grades also intensify the association of per capita income, median rent, median home values, and racial composition with PM10. These findings suggest that historical policy programs that were grounded in racial logics—such as the HOLC practice of ‘redlining’—have an inertia that results in them influencing development pathways and environmental outcomes of built environments for decades.

Orthopaedic Surgeon Distribution in the United States.

There is limited research on the supply and distribution of orthopaedic surgeons in the United States. The goal of this study was to analyze the association of orthopaedic surgeon distribution in the United States with geographic and sociodemographic factors. County-level data from the US Department of Health and Human Services Area Health Resources Files were used to determine the density of orthopaedic surgeons across the United States on a county level. Data were examined from 2000 to 2019 to analyze trends over time. Bivariate and multivariable negative binomial regression models were constructed to identify county-level sociodemographic factors associated with orthopaedic surgeon density. In 2019, 51% of the counties in the United States did not have an orthopaedic surgeon. Metropolitan counties had a mean of 22 orthopaedic surgeons per 100,000 persons while nonmetropolitan and rural counties had a mean of 2 and 0.1 orthopaedic surgeons per 100,000 persons, respectively. Over the past 2 decades, there was a significant increase in the percentage of orthopaedic surgeons in metropolitan counties (77% in 2000 vs 93% in 2019, P < 0.001) and in the proportion of orthopaedic surgeons 55 years and older (32% in 2000 vs 39% in 2019, P < 0.001). Orthopaedic surgeon density increased with increasing median home value (P < 0.001) and median household income (P < 0.001). Counties with a higher percentage of persons in poverty (P < 0.001) and higher unemployment rate (P < 0.001) and nonmetropolitan (P < 0.001) and rural (P < 0.001) counties had a lower density of orthopaedic surgeons. On multivariable analysis, a model consisting of median home value (P < 0.001), rural counties (P < 0.001), percentage of noninsured persons (P < 0.001), and percentage of foreign-born persons (P < 0.001) predicted orthopaedic surgeon density. Access to orthopaedic surgeons in the United States in rural areas is decreasing over time. County-level socioeconomic factors such as wealth and urbanization were found to be closely related with surgeon density.