Census Tract Patterns and Contextual Social Determinants of Health Associated with COVID-19 in a Hispanic Population from South Texas (Preprint)

Introduction: We describe the California Healthy Places Index (HPI) and its performance relative to other indexes for measuring community well-being at the census-tract level. The HPI arose from a need identified by health departments and community organizations for an index rooted in the social determinants of health for place-based policy making and program targeting. The index was geographically granular, validated against life expectancy at birth, and linked to policy actions. Materials and Methods: Guided by literature, public health experts, and a positive asset frame, we developed a composite index of community well-being for California from publicly available census-tract data on place-based factors linked to health. The 25 HPI indicators spanned 8 domains; weights were derived from their empirical association with tract-level life expectancy using weighted quantile sums methods. Results: The HPI’s domains were aligned with the social determinants of health and policy action areas of economic resources, education, housing, transportation, clean environment, neighborhood conditions, social resources, and health care access. The overall HPI score was the sum of weighted domain scores, of which economy and education were highly influential (50% of total weights). The HPI was strongly associated with life expectancy at birth ( r = 0.58). Compared with the HPI, a pollution-oriented index did not capture one-third of the most disadvantaged quartile of census tracts (representing 3 million Californians). Overlap of the HPI’s most disadvantaged quartile of census tracts was greater for indexes of economic deprivation. We visualized the HPI percentile ranking as a web-based mapping tool that presented the HPI at multiple geographies and that linked indicators to an action-oriented policy guide. Practice Implications: The framing of indexes and specifications such as domain weighting have substantial consequences for prioritizing disadvantaged populations. The HPI provides a model for tools and new methods that help prioritize investments and identify multisectoral opportunities for policy action.

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A Census Tract–Level Examination of Social Determinants of Health among Black/African American Men with Diagnosed HIV Infection, 2005–2009—17 US Areas

PLoS ONE ◽

10.1371/journal.pone.0107701 ◽

2014 ◽

Vol 9 (9) ◽

pp. e107701 ◽

Cited By ~ 12

Author(s):

Zanetta Gant ◽

Larry Gant ◽

Ruiguang Song ◽

Leigh Willis ◽

Anna Satcher Johnson

Keyword(s):

African American ◽

Hiv Infection ◽

Social Determinants Of Health ◽

Social Determinants ◽

Census Tract ◽

African American Men ◽

Determinants Of Health ◽

Black African ◽

Census Tract Level

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Social determinants of health and hospital readmissions: can the HOSPITAL risk score be improved by the inclusion of social factors?

BMC Health Services Research ◽

10.1186/s12913-020-05989-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Shirlene Obuobi ◽

Rhys F. M. Chua ◽

Stephanie A. Besser ◽

Corey E. Tabit

Keyword(s):

Social Determinants Of Health ◽

Risk Score ◽

Social Determinants ◽

Census Tract ◽

Predictive Accuracy ◽

Hospital Readmissions ◽

Tertiary Care ◽

Determinants Of Health ◽

Census Tract Level ◽

C Statistic

Abstract Background The HOSPITAL Risk Score (HRS) predicts 30-day hospital readmissions and is internationally validated. Social determinants of health (SDOH) such as low socioeconomic status (SES) affect health outcomes and have been postulated to affect readmission rates. We hypothesized that adding SDOH to the HRS could improve its predictive accuracy. Methods Records of 37,105 inpatient admissions at the University of Chicago Medical Center were reviewed. HRS was calculated for each patient. Census tract-level SDOH then were combined with the HRS and the performance of the resultant “Social HRS” was compared against the HRS. Patients then were assigned to 1 of 7 typologies defined by their SDOH and a balanced dataset of 14,235 admissions was sampled from the larger dataset to avoid over-representation by any 1 sociodemographic group. Principal component analysis and multivariable linear regression then were performed to determine the effect of SDOH on the HRS. Results The c-statistic for the HRS predicting 30-day readmission was 0.74, consistent with published values. However, the addition of SDOH to the HRS did not improve the c-statistic (0.71). Patients with unfavorable SDOH (no high-school, limited English, crowded housing, disabilities, and age > 65 yrs) had significantly higher HRS (p < 0.05 for all). Overall, SDOH explained 0.2% of the HRS. Conclusion At an urban tertiary care center, the addition of census tract-level SDOH to the HRS did not improve its predictive power. Rather, the effects of SDOH are already reflected in the HRS.

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FACETS: using open data to measure community social determinants of health

Journal of the American Medical Informatics Association ◽

10.1093/jamia/ocx117 ◽

2017 ◽

Vol 25 (4) ◽

pp. 419-422 ◽

Cited By ~ 5

Author(s):

Michael N Cantor ◽

Rajan Chandras ◽

Claudia Pulgarin

Keyword(s):

Social Determinants Of Health ◽

Social Determinants ◽

Census Tract ◽

National Level ◽

Open Data ◽

Community Level ◽

Determinants Of Health ◽

Data Sources ◽

Open Architecture ◽

Census Tract Level

Abstract Objective To develop a dataset based on open data sources reflective of community-level social determinants of health (SDH). Materials and Methods We created FACETS (Factors Affecting Communities and Enabling Targeted Services), an architecture that incorporates open data related to SDH into a single dataset mapped at the census-tract level for New York City. Results FACETS (https://github.com/mcantor2/FACETS) can be easily used to map individual addresses to their census-tract-level SDH. This dataset facilitates analysis across different determinants that are often not easily accessible. Discussion Wider access to open data from government agencies at the local, state, and national level would facilitate the aggregation and analysis of community-level determinants. Timeliness of updates to federal non-census data sources may limit their usefulness. Conclusion FACETS is an important first step in standardizing and compiling SDH-related data in an open architecture that can give context to a patient’s condition and enable better decision-making when developing a plan of care.

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Association between Influenza-like Illnesses and Social Determinants of Health by Census Tract in Houston/Harris County

Online Journal of Public Health Informatics ◽

10.5210/ojphi.v6i1.5046 ◽

2014 ◽

Vol 6 (1) ◽

Author(s):

Biru Yang ◽

Nathan Wang ◽

Wesley McNeely ◽

Salma Khuwaja ◽

Raouf Arafat

Keyword(s):

Social Determinants Of Health ◽

Social Determinants ◽

Census Tract ◽

Determinants Of Health ◽

Harris County

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Social Determinants of Health and Disparities in Linkage to Care Among Newly Diagnosed HIV Cases – South Carolina, 2009–2011

Open Forum Infectious Diseases ◽

10.1093/ofid/ofx163.1052 ◽

2017 ◽

Vol 4 (suppl_1) ◽

pp. S419-S420 ◽

Cited By ~ 1

Author(s):

Harmeet Gill ◽

Oluwole Babatunde ◽

Sharon Weissman

Keyword(s):

South Carolina ◽

Racial Disparities ◽

Social Determinants Of Health ◽

Social Determinants ◽

Census Tract ◽

Linkage To Care ◽

Male Gender ◽

Determinants Of Health ◽

Newly Diagnosed ◽

Hiv Diagnosis

Abstract Background Key to improved HIV outcomes is early diagnosis, linkage to care (LTC), retention in care (RIC) and viral load (VL) suppression. As treatment for HIV has become more effective, the gap in racial disparities has widened for LTC, RIC and VL. Social determinants of health (SDH) are conditions such as poverty level, income, education, employment that are responsible for most health inequities. SDH are drivers of disparities in HIV prevalence. The objective of this study is to evaluate the impact of SDH on racial disparities on time to LTC for newly diagnosed HIV infected individuals in South Carolina (SC). Methods Data was obtained from the SC enhanced HIV/AIDS Reporting System. Analysis includes individuals diagnosed with HIV in SC from 2009–2011. LTC was calculated as the time from HIV diagnosis to first CD4 or VL test. Early LTC was defined as within 30 days. Late LTC was >30 to 365 days. Individuals not LTC by 365 days were considered to have never been linked to care (NLTC). Census tract data was used to determine SHD (poverty, education, income, and unemployment) based on residence at the time of HIV diagnosis. Descriptive analysis was performed on data from newly infected individuals. Factors potentially associated with late LTC and NLTC including patient demographics, behavioral risk, residence at diagnosis and SDH were investigated. Results From 2009–2011, 2151 individuals were newly diagnosed with HIV. Of these 1636 (76.1%) were LTC early, 285 (13.2%) were LTC late and 230 (10.7%) were NLTC. NLTC was associated with male gender, lower initial CD4 count, and earlier stage of HIV at time of diagnoses (P <0.01). In multivariable analysis early HIV stage at HIV diagnosis (aOR: 1.82; 95% CI 1.3, 2.5) and living in census tracts with lower income (aOR 0.65; 95% CI 0.44, 0.97) are associated with late LTC. Male gender (aOR 2.66; 95% CI 1.49, 4.76) unknown HIV risk group (aOR 2.03; 95% CI 1.11, 2.74) and early HIV stage at diagnosis (aOR 4.59; 95% CI 2.33, 9.04) are associated with NLTC. Conclusion In SC, almost ¼ of newly diagnosed HIV infected individuals from 2009–2011 were LTC late or NLTC. SDH were not associated with late LTC or NLTC. Living in a low income census tract was associated with a lower risk for late LTC, possible because of access to Ryan White Services. Male gender and earlier HIV stage were factors with greatest association with late LTC and NLTC. Disclosures All authors: No reported disclosures.

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