Racial and Ethnic Disparities in Antihypertensive Medication Prescribing Patterns and Effectiveness
Bibliographic record
Abstract
Variability in drug effectiveness and provider prescribing patterns have been reported in different racial and ethnic populations. We sought to evaluate antihypertensive drug effectiveness and prescribing patterns among self-identified Hispanic/Latino (Hispanic), Non-Hispanic Black (Black), and Non-Hispanic White (White) populations that enrolled in the NIH All of Us Research Program, a US longitudinal cohort. We employed a self-controlled case study method using electronic health record and survey data from 17,718 White, Hispanic, and Black participants who were diagnosed with essential hypertension and prescribed at least one of 19 commonly used antihypertensive medications. Effectiveness was determined by calculating the reduction in systolic blood pressure measurements after 28 or more days of drug exposure. Starting systolic blood pressure and effectiveness for each medication were compared for self-reported Black, Hispanic, and White participants using adjusted linear regressions. Black and Hispanic participants were started on antihypertensive medications at significantly higher SBP than White participants in 13 and 7 out of 19 medications, respectively. More Black participants were prescribed multiple antihypertensive medications (58.46%) than White (52.35%) or Hispanic (49.9%) participants. First-line HTN medications differed by race and ethnicity. Following the 2017 American College of Cardiology and the American Heart Association High Blood Pressure Guideline release, around 64% of Black participants were prescribed a recommended first-line antihypertensive drug compared with 76% of White and 82% of Hispanic participants. Effect sizes suggested that most antihypertensive drugs were less effective in Hispanic and Black, compared with White, participants, and statistical significance was reached in 6 out of 19 drugs. These results indicate that Black and Hispanic populations may benefit from earlier intervention and screening and highlight the potential benefits of personalizing first-line medications.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".