African Ancestry, Social Factors, and Hypertension Among Non-Hispanic Blacks in the Health and Retirement Study
Bibliographic record
Abstract
The biomedical literature contains much speculation about possible genetic explanations for the large and persistent black-white disparities in hypertension, but profound social inequalities are also hypothesized to contribute to this outcome. Our goal is to evaluate whether socioeconomic status (SES) differences provide a plausible mechanism for associations between African ancestry and hypertension in a U.S. cohort of older non-Hispanic blacks. We included only non-Hispanic black participants (N = 998) from the Health and Retirement Study who provided genetic data. We estimated percent African ancestry based on 84,075 independent single nucleotide polymorphisms using ADMIXTURE V1.23, imposing K = 4 ancestral populations, and categorized into quartiles. Hypertension status was self-reported in the year 2000. We used linear probability models (adjusted for age, sex, and southern birth) to predict prevalent hypertension with African ancestry quartile, before and after accounting for a small set of SES measures. Respondents with the highest quartile of African ancestry had 8 percentage points' (RD = 0.081; 95% CI: -0.001, 0.164) higher prevalence of hypertension compared to the lowest quartile. Adjustment for childhood disadvantage, education, income, and wealth explained over one-third (RD = 0.050; 95% CI: -0.034, 0.135) of the disparity. Explanations for the residual disparity remain unspecified and may include other indicators of SES or diet, lifestyle, and psychosocial mechanisms.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".