Neighborhood-level Census Research Articles

Multilevel Risk Factors for Sleep-Disordered Breathing-Related Symptom Burden in an Urban Pediatric Community-Based Sample

Pediatric sleep-disordered breathing (SDB) disproportionately affects children with low socioeconomic status (SES). The multilevel risk factors that drive these associations are not well understood. What are the associations between SDB risk factors, including individual health conditions (obesity, asthma, and allergies), household SES (maternal education), indoor exposures (environmental tobacco smoke [ETS] and pests), and neighborhood characteristics (neighborhood disadvantage), and pediatric SDB symptoms? Cross-sectional analyses were performed on 303 children (aged 6-12 years) enrolled in the Environmental Assessment of Sleep Youth study from 2018 to 2022. Exposures were determined by caregiver reports, assays of measured settled dust from the child's bedroom, and neighborhood-level Census data (deriving the Childhood Opportunity Index to characterize neighborhood disadvantage). The primary outcome was the SDB-related symptom burden assessed by the OSA-18 questionnaire total score. Using linear regression models, we calculated associations between exposures and SDB-related symptom burden, adjusting for sociodemographic factors, then health conditions, indoor environment, and neighborhood factors. The sample included 303 children (39% Hispanic, Latino, Latina, or Spanish origin; 30% Black or African American; 22% White; and 11% other). Increasing OSA-18 total scores were associated with low household SES after adjustment for demographic factors, and with asthma, allergies, ETS, pests (mouse, cockroach, and rodents), and an indoor environmental index (sum of the presence of pests and ETS; 0-2) after adjusting for sociodemographic factors. Even after further adjusting for asthma, allergies, and neighborhood disadvantage, ETS and pest exposure were associated with OSA-18 (ETS: β = 12.80; 95% CI, 7.07-18.53, also adjusted for pest; pest exposure: β = 3.69; 95% CI, 0.44-6.94, also adjusted for ETS). In addition to associations with ETS, a novel association was observed for indoor pest exposure and SDB symptom burden. Strategies to reduce household exposure to ETS and indoor allergens should be tested as approaches for reducing sleep health disparities.

Development of a Multilevel Model to Identify Patients at Risk for Delay in Starting Cancer Treatment

Delays in starting cancer treatment disproportionately affect vulnerable populations and can influence patients' experience and outcomes. Machine learning algorithms incorporating electronic health record (EHR) data and neighborhood-level social determinants of health (SDOH) measures may identify at-risk patients. To develop and validate a machine learning model for estimating the probability of a treatment delay using multilevel data sources. This cohort study evaluated 4 different machine learning approaches for estimating the likelihood of a treatment delay greater than 60 days (group least absolute shrinkage and selection operator [LASSO], bayesian additive regression tree, gradient boosting, and random forest). Criteria for selecting between approaches were discrimination, calibration, and interpretability/simplicity. The multilevel data set included clinical, demographic, and neighborhood-level census data derived from the EHR, cancer registry, and American Community Survey. Patients with invasive breast, lung, colorectal, bladder, or kidney cancer diagnosed from 2013 to 2019 and treated at a comprehensive cancer center were included. Data analysis was performed from January 2022 to June 2023. Variables included demographics, cancer characteristics, comorbidities, laboratory values, imaging orders, and neighborhood variables. The outcome estimated by machine learning models was likelihood of a delay greater than 60 days between cancer diagnosis and treatment initiation. The primary metric used to evaluate model performance was area under the receiver operating characteristic curve (AUC-ROC). A total of 6409 patients were included (mean [SD] age, 62.8 [12.5] years; 4321 [67.4%] female; 2576 [40.2%] with breast cancer, 1738 [27.1%] with lung cancer, and 1059 [16.5%] with kidney cancer). A total of 1621 (25.3%) experienced a delay greater than 60 days. The selected group LASSO model had an AUC-ROC of 0.713 (95% CI, 0.679-0.745). Lower likelihood of delay was seen with diagnosis at the treating institution; first malignant neoplasm; Asian or Pacific Islander or White race; private insurance; and lacking comorbidities. Greater likelihood of delay was seen at the extremes of neighborhood deprivation. Model performance (AUC-ROC) was lower in Black patients, patients with race and ethnicity other than non-Hispanic White, and those living in the most disadvantaged neighborhoods. Though the model selected neighborhood SDOH variables as contributing variables, performance was similar when fit with and without these variables. In this cohort study, a machine learning model incorporating EHR and SDOH data was able to estimate the likelihood of delays in starting cancer therapy. Future work should focus on additional ways to incorporate SDOH data to improve model performance, particularly in vulnerable populations.

Study protocol for a multi-level cross-sectional study on the equitable reach and implementation of coordinated specialty care for early psychosis

BackgroundApproximately 115,000 young adults will experience their first episode of psychosis (FEP) each year in the USA. Coordinated specialty care (CSC) for early psychosis is an evidence-based early intervention model that has demonstrated effectiveness by improving quality of life and reducing psychiatric symptoms for many individuals. Over the last decade, there has significant increase in the implementation of CSC programs throughout the USA. However, prior research has revealed difficulties among individuals and their family members accessing CSC. Research has also shown that CSC programs often report the limited reach of their program to underserved populations and communities (e.g., ethnoracial minorities, rural and low socioeconomic neighborhoods). Dissemination and implementation research focused on the equitable reach and implementation of CSC is needed to address disparities at the individual level.MethodsThe proposed study will create a novel integrative multi-level geospatial database of CSC programs implemented throughout the USA that will include program-level data (e.g., geocoded location, capacity, setting, role availability), provider-level data (race, ethnicity, professional credentials), and neighborhood-level census data (e.g., residential segregation, ethnic density, area deprivation, rural-urban continua, public transit time). This database will be used to characterize variations in CSC programs by geographical location and examine the overall reach CSC programs to specific communities. The quantitative data will be combined with qualitative data from state administrators, providers, and service users that will inform the development of dissemination tools, such as an interactive dashboard, that can aid decision making.DiscussionFindings from this study will highlight the impact of outer contextual determinants on implementation and reach of mental health services, and will serve to inform the future implementation of CSC programs with a primary focus on equity.

70-OR: Association of Diabetes Screening with Demographic, Neighborhood Level Census, and Area Gentrification Characteristics

Research assessing predictors of preventive screening often focuses on individual patient factors, despite known relationships between neighborhood environment and health. Using a retrospective cohort study of 519,826 patients meeting US Preventive Service Task Force guideline recommendation for screening for prediabetes and type 2 diabetes (age 35-70 years and BMI ≥25) from Kaiser Permanente Northern California in 2018, we used logistic regression to assess the relationship between diabetes screening and census neighborhood tract-level measures, and area gentrification measures developed by the Urban Displacement Project, controlling for age, sex, race, ethnicity, and BMI. Thirty-eight percent of patients were screened for diabetes. Patients aged 60-70 years had higher odds of diabetes screening compared to patients aged 35-39 years (OR: 2.73, 95% CI: 2.67-2.78) . Men had lower odds of screening compared to women (OR: 0.91, 0.90-0.92) . Asian patients had higher odds of screening compared to White patients (OR 1.44, 1.42-1.46) . Patients with BMI ≥40 had higher odds of screening compared to patients with BMI 25-29.9 (OR: 1.34, 1.30-1.37) . Patients living in census tracts with the lowest median values of homes (OR: 0.93, 0.90-0.96) and with the lowest median household income (OR: 0.95, 0.91-0.99) had lower odds of diabetes screening compared to those in census tracts with the highest values. Patients living in census tracts classified as Ongoing Displacement/Low Income Susceptible to Displacement had lower odds of screening (OR: 0.96, 0.93-0.98) compared to those living in stable moderate mixed income tracts. We found insufficient screening rates and that gentrification and area-level socioeconomic factors were associated with lower diabetes screening, even after adjusting for individual demographic and clinical factors. Health care systems should consider addressing social needs and community resources as a path to improving preventive care. Disclosure T.Thomas: None. W.T.Dyer: None. L.A.Rodriguez: None. J.Schmittdiel: None. Funding Kaiser Permanente Northern California Community Benefit Programs; The Permanente Medical Group (TPMG) Delivery Science Fellowship Program; National Institute of Diabetes and Digestive and Kidney Diseases grant T32DK11668401. Dr. Schmittdiel received additional support from the NIDDK-funded Health Delivery Systems Center for Diabetes Translational Research (1P30 DK92924) .

Socioeconomically Disadvantaged Neighborhoods Face Increased Persistence of SARS-CoV-2 Clusters.

Objective: To investigate the association between socioeconomic deprivation and the persistence of SARS-CoV-2 clusters.Methods: We analyzed 3,355 SARS-CoV-2 positive test results in the state of Geneva (Switzerland) from February 26 to April 30, 2020. We used a spatiotemporal cluster detection algorithm to monitor SARS-CoV-2 transmission dynamics and defined spatial cluster persistence as the time in days from emergence to disappearance. Using spatial cluster persistence measured outcome and a deprivation index based on neighborhood-level census socioeconomic data, stratified survival functions were estimated using the Kaplan-Meier estimator. Population density adjusted Cox proportional hazards (PH) regression models were then used to examine the association between neighborhood socioeconomic deprivation and persistence of SARS-CoV-2 clusters.Results: SARS-CoV-2 clusters persisted significantly longer in socioeconomically disadvantaged neighborhoods. In the Cox PH model, the standardized deprivation index was associated with an increased spatial cluster persistence (hazard ratio [HR], 1.43 [95% CI, 1.28–1.59]). The adjusted tercile-specific deprivation index HR was 1.82 [95% CI, 1.56–2.17].Conclusions: The increased risk of infection of disadvantaged individuals may also be due to the persistence of community transmission. These findings further highlight the need for interventions mitigating inequalities in the risk of SARS-CoV-2 infection and thus, of serious illness and mortality.

Race and affluence shape spatio-temporal urbanization trends in Greater Houston, 1997 to 2016

Urbanization results in increasing impervious surfaces with the potential to threaten fragile environments and heighten flood risks. In the United States, research on the social processes driving urbanization has tended to focus on the twenty-first century, but less is known about how temporal trends arose from the spatial layout of the urban land upon which this growth was founded. To address this gap, we present a novel interdisciplinary synthesis using neighborhood-level census data in tandem with a satellite-derived annual land cover change time series to assess the role of race, affluence, and socioeconomic status in shaping spatio-temporal urbanization in the Houston metropolitan area from 1997−2016. Results from cross-sectional and temporal regression models indicate that while social dynamics associated with historical versus recent urbanization are related, they are not identical. Thus, while temporal change in Houston's urbanization is driven primarily by socioeconomic status, the social dynamics associated with spatial disparities in urbanization relate primarily to race, regardless of socioeconomic status. These results are noteworthy as urbanization in Houston does not fully comport with existing theoretical perspectives or with empirical findings nationally. Instead, we suggest these findings reflect the city’s politics and culture surrounding land use. Thus, beyond its important social and environmental implications, this study affirms the utility of fusing socio-demographic data with satellite remote sensing of urban growth, and highlights the value of the socioenvironmental succession framework for characterizing urbanization as a recursive process in space and time.

Kin location and racial disparities in exiting and entering poor neighborhoods

Blacks and Latinos/as are less likely than Whites to move from a poor neighborhood to a non-poor neighborhood and are more likely to move in the reverse direction. Using individual-level data from the Panel Study of Income Dynamics (1980–2013) and neighborhood-level census data, this study explores the role that the spatial location of familial kin networks plays in explaining these racially and ethnically disparate mobility patterns. Blacks and Latinos/as live closer than Whites to nuclear kin, and they are also more likely than Whites to have kin members living in poor neighborhoods. Close geographic proximity to kin and higher levels of kin neighborhood poverty inhibit moving from a poor to a non-poor neighborhood, and increase the risk of moving from a non-poor to a poor tract. Racial/ethnic differences in kin proximity and kin neighborhood poverty explain a substantial portion of racial gaps in exiting and entering poor neighborhoods.

Immigrants’ access to care under the Affordable Care Act: An examination of California

ABSTRACT Background: Welfare provision for noncitizens has been at the center of political debates. Understanding how Californian noncitizens and undocumented immigrants access health care would provide useful information to guide health policies. Method: The neighborhood-level Census data were used to learn the factors related to insurance coverage. LA County is identified as a case study to understand the status quo of health care access for noncitizen immigrants. Data from the Center for Migration Studies was used to derive the unmet demand of My Health LA, a county program that aims to cover the uninsured. Result: Two main factors that predict the extent of local insurance coverage, especially for noncitizens, are the percentage of Latino foreign-born and their chance of naturalization. The paper also finds that in 2014, approximately 61% of undocumented immigrants and 49% of noncitizens were uninsured in LA county, compared with less than 20% for citizens. Conclusion: Improving health care access among the undocumented would effectively reduce overall uninsured rates. This study advocates expanding county indigent programs to achieve higher health care coverage of the undocumented. Using lower-bound estimates, if My Health LA were to cover the entire potential patient pool, the cap must be increased by approximately 60%.

Using Neighborhood-Level Census Data to Predict Diabetes Progression in Patients with Laboratory-Defined Prediabetes.

Research on predictors of clinical outcomes usually focuses on the impact of individual patient factors, despite known relationships between neighborhood environment and health. To determine whether US census information on where a patient resides is associated with diabetes development among patients with prediabetes. Retrospective cohort study of all 157,752 patients aged 18 years or older from Kaiser Permanente Northern California with laboratory-defined prediabetes (fasting plasma glucose, 100 mg/dL-125 mg/dL, and/or glycated hemoglobin, 5.7%-6.4%). We assessed whether census data on education, income, and percentage of households receiving benefits through the US Department of Agriculture's Supplemental Nutrition Assistance Program (SNAP) was associated with diabetes development using logistic regression controlling for age, sex, race/ethnicity, blood glucose levels, and body mass index. Progression to diabetes within 36 months. Patients were more likely to progress to diabetes if they lived in an area where less than 16% of adults had obtained a bachelor's degree or higher (odds ratio [OR] =1.22, 95% confidence interval [CI] = 1.09-1.36), where median annual income was below $79,999 (OR = 1.16 95% CI = 1.03-1.31), or where SNAP benefits were received by 10% or more of households (OR = 1.24, 95% CI = 1.1-1.4). Area-level socioeconomic and food assistance data predict the development of diabetes, even after adjusting for traditional individual demographic and clinical factors. Clinical interventions should take these factors into account, and health care systems should consider addressing social needs and community resources as a path to improving health outcomes.

Relationships Between Neighborhood Social Capital and The Occurrence of Outdoor Falls in Canadian Older Adults: A Multilevel Analysis.

The aim of this study was to examine whether neighborhood-level social capital is a risk factor for falls outside of the home in older adults. Health questionnaires were completed by community-dwelling Canadians aged +65 years living in Kingston (Ontario) and St-Hyacinthe (Quebec), supplemented by neighborhood-level census data. Multilevel logistic regression models with random intercepts were fit. Variations in the occurrence of falls across neighborhoods were quantified by median odds ratio and 80% interval odds ratio. Between-neighborhood differences explained 7% of the variance in the occurrence of falls; this variance decreased to 2% after adjustment for neighborhood-level variables. In the fully adjusted models, higher levels of social capital increased the odds of falls by almost 2 times: (odds ratio [OR] = 2.10, 95% confidence interval [CI] = [1.19, 3.71]). Living in neighborhoods with higher levels of social capital was associated with higher risk of falling in older adults, possibly through more involvement in social activities.

A Longitudinal Mixed Logit Model for Estimation of Push and Pull Effects in Residential Location Choice

We develop a random effects discrete choice model for the analysis of households’ choice of neighborhood over time. The model is parameterized in a way that exploits longitudinal data to separate the influence of neighborhood characteristics on the decision to move out of the current area (“push” effects) and on the choice of one destination over another (“pull” effects). Random effects are included to allow for unobserved heterogeneity between households in their propensity to move, and in the importance placed on area characteristics. The model also includes area-level random effects. The combination of a large choice set, large sample size, and repeated observations mean that existing estimation approaches are often infeasible. We, therefore, propose an efficient MCMC algorithm for the analysis of large-scale datasets. The model is applied in an analysis of residential choice in England using data from the British Household Panel Survey linked to neighborhood-level census data. We consider how effects of area deprivation and distance from the current area depend on household characteristics and life course transitions in the previous year. We find substantial differences between households in the effects of deprivation on out-mobility and selection of destination, with evidence of severely constrained choices among less-advantaged households. Supplementary materials for this article are available online.

Race, Neighborhood, and Drug Court Graduation

This research examines the possibility that racial disparities in drug court graduation are attributable to individual-level employment or education or to neighborhood-level disadvantage. Individual-level data on 455 drug court clients and neighborhood-level census and police incident data are joined geographically. Drug court graduation is modeled using multilevel logistic regression. In a model with no neighborhood-level indicators, client race, employment, and education all predicted drug court graduation. When neighborhood-level variables are introduced, client-level race drops from significance but employment and education remain significant predictors of graduation. Client race, then, appears to be an indirect indicator of neighborhood disadvantage, while client employment and education remain important individual-level predictors of drug court graduation. These results support further analysis of neighborhood-based barriers to drug court graduation and the development of drug court programming that can address neighborhood-based challenges.

Subjective and objective neighborhood characteristics and adult health

This study examines both objective and subjective assessments of neighborhood conditions, exploring the overlap between different sources of information on neighborhoods and the relative strength of their association with adult self-rated health. Data on perceived neighborhood quality from Wave IV (2001/2002) of the nationally representative U.S. Americans Changing Lives study are merged with neighborhood-level census tract data to measure subjective and objective neighborhood constructs. Structural equation models indicate that subjective and objective constructs are both related to health. However, the subjective construct (perceived neighborhood quality) is most strongly associated with health and mediates associations between health and the objective constructs (neighborhood disadvantage and affluence). Additionally, individual characteristics play an important role in shaping the contribution of neighborhood conditions through selection and mediation. Our results demonstrate the independent associations between both objective and perceived neighborhood quality and health, and highlight the particularly strong association between perceived neighborhood quality and health.

Neighborhood-based in-library use performance measures for public libraries: A nationwide study of majority–minority and majority white/low income markets using personal digital data collectors

The practice of aggregating public library use data to a system-wide level (central library and branches) can mask the library needs of more specific groups of users. This article introduces a study that addressed this need, by identifying libraries serving majority White/low income and majority–minority markets, and surveying those populations to identify types and levels of use. The study is critical for current library research and practices for these reasons: (1) the increasing diversity in race/ethnicity and languages spoken in U.S. communities; (2) low circulation rates exacerbated by increased Internet use; (3) mere existence of a library is critical to optimize use by populations without the library and reading experience; and (4) the recent release of the U.S. Public Library Geographic Database ( http://www.geolib.org/PLGDB.cfm) with neighborhood level census and library use data for all U.S. library jurisdictions. The methodologies developed offer potential for the collection of critical data for the public librarian of today.