ArticleMedical care2026
Comparing Predictive Power of Area-Level Socioeconomic Status Indices Across Health Outcomes and Geographic Levels.
Article in Medical care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed.
- Association between social vulnerability index and cataract surgery care in medicare beneficiaries: a retrospective cohort study.BMC ophthalmology · 2026Observational
- Rural-urban comparisons of area-based socioeconomic disadvantage and vulnerability indices.Health affairs scholar · 2026Article
- Incorporating polygenic risk scores and social determinants of health across populations: Considerations and best practices in research.American journal of human genetics · 2026Review
- COVID-19 Vaccine Effectiveness Against Mortality in the Omicron Period: Evidence from Linked Mortality and Vaccination Records.Vaccines · 2025Article
- Childhood Suicide Risk in the Emergency Department.JAMA network open · 2025Article
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Authors and funding
9 authors.
Funding
Abstract
backgroundMany researchers want to control for both individual-level demographic/health variables and area-level socioeconomic status (area-SES) when studying health outcomes. However, comparative assessments of area-SES indices across geographic levels and a range of health outcomes are scarce.
objectivesCompare predictive power for 3 commonly used area-SES indices: the Graham Social Deprivation Index (SDI), the Area Deprivation Index (ADI), and the CDC Social Vulnerability Index (SVI), for a variety of health outcomes, at different geographic levels (county, 5-digit zip-code, census tract, and census block group). Also compare these indices to the simpler Townsend Deprivation Index (TDI) and population percent in poverty (area-Poverty). RESEARCH
designPrincipal research methods are logistic and ordinary least squares regression. SUBJECTS: Medicare fee-for-service beneficiaries, COVID-19 decedents, and drug overdose decedents. MEASURES: SDI, SVI, ADI, TDI, area-Poverty. HEALTH OUTCOMES STUDIED: All-cause mortality, diabetes incidence and prevalence, hypertension, renal disease, and 30-day hospital readmission for Medicare beneficiaries; COVID-19 mortality; overdose mortality; Medicare fee-for-service spending.
resultsAll measures predict the health outcomes, controlling for age, gender, race/ethnicity, and comorbidities, at zip code, tract, and block-group levels. Predictive power is comparable for SDI, SVI, and a standardized version of ADI, and generally superior to TDI, area-Poverty, and non-standardized ADI. Predictive power is highest at tract level, similar at block-group; reasonably strong at zip code, but weaker at county level.
conclusionsAcross a range of health outcomes, we find similar predictive power for SDI, SVI, and standardized ADI, ideally measured at census tract level. SDI has the value of being more parsimonious, with similar performance. Non-standardized ADI cannot be recommended.
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