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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".