Multiple Social Vulnerabilities to Health Disparities and Hypertension and Death in the REGARDS Study
Bibliographic record
Abstract
Social vulnerabilities increase the risk of developing hypertension and lower life expectancy, but the effect of an individual’s overall vulnerability burden is unknown. Our objective was to determine the association of social vulnerability count and the risk of developing hypertension or dying over 10 years and whether these associations vary by race. We used the REGARDS study (Reasons for Geographic and Racial Differences in Stroke) and included participants without baseline hypertension. The primary exposure was the count of social vulnerabilities defined across economic, education, health and health care, neighborhood and built environment, and social and community context domains. Among 5425 participants of mean age 64±10 SD years of which 24% were Black participants, 1468 (31%) had 1 vulnerability and 717 (15%) had ≥2 vulnerabilities. Compared with participants without vulnerabilities, the adjusted relative risk ratio for developing hypertension was 1.16 (95% CI, 0.99–1.36) and 1.49 (95% CI, 1.20–1.85) for individuals with 1 and ≥2 vulnerabilities, respectively. The adjusted relative risk ratio for death was 1.55 (95% CI, 1.24–1.93) and 2.30 (95% CI, 1.75–3.04) for individuals with 1 and ≥2 vulnerabilities, respectively. A greater proportion of Black participants developed hypertension and died than did White participants (hypertension, 38% versus 31%; death, 25% versus 20%). The vulnerability count association was strongest in White participants ( P value for vulnerability count×race interaction: hypertension=0.046, death=0.015). Overall, a greater number of socially determined vulnerabilities was associated with progressively higher risk of developing hypertension, and an even higher risk of dying over 10 years.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".