Comparative Cost-Effectiveness of Hypertension Treatment in Non-Hispanic Blacks and Whites According to 2014 Guidelines: A Modeling Study
Bibliographic record
Abstract
BACKGROUND: We compared the cost-effectiveness of hypertension treatment in non-Hispanic blacks and non-Hispanic whites according to 2014 US hypertension treatment guidelines. METHODS: The cardiovascular disease (CVD) policy model simulated CVD events, quality-adjusted life years (QALYs), and treatment costs in 35- to 74-year-old adults with untreated hypertension. CVD incidence, mortality, and risk factor levels were obtained from cohort studies, hospital registries, vital statistics, and national surveys. Stage 1 hypertension was defined as blood pressure 140-149/90-99mm Hg; stage 2 hypertension as ≥150/100mm Hg. Probabilistic input distribution sampling informed 95% uncertainty intervals (UIs). Incremental cost-effectiveness ratios (ICERs) < $50,000/QALY gained were considered cost-effective. RESULTS: Treating 0.7 million hypertensive non-Hispanic black adults would prevent about 8,000 CVD events annually; treating 3.4 million non-Hispanic whites would prevent about 35,000 events. Overall 2014 guideline implementation would be cost saving in both groups compared with no treatment. For stage 1 hypertension but without diabetes or chronic kidney disease, cost savings extended to non-Hispanic black males ages 35-44 but not same-aged non-Hispanic white males (ICER $57,000/QALY; 95% UI $15,000-$100,000) and cost-effectiveness extended to non-Hispanic black females ages 35-44 (ICER $46,000/QALY; $17,000-$76,000) but not same-aged non-Hispanic white females (ICER $181,000/QALY; $111,000-$235,000). CONCLUSIONS: Compared with non-Hispanic whites, cost-effectiveness of implementing hypertension guidelines would extend to a larger proportion of non-Hispanic black hypertensive patients.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".