Comparing Predictive Power of Area-Level Socioeconomic Status Indices Across Health Outcomes and Geographic Levels
Bibliographic record
Abstract
BACKGROUND: Many 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. OBJECTIVES: Compare 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 DESIGN: Principal 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. RESULTS: All 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. CONCLUSIONS: Across 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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".