Scaling hypertension treatment in 24 low-income and middle-income countries: economic evaluation of treatment decisions at three blood pressure cut-points
Bibliographic record
Abstract
OBJECTIVE: Estimate the incremental costs and benefits of scaling up hypertension care in adults in 24 select countries, using three different systolic blood pressure (SBP) treatment cut-off points-≥140, ≥150 and ≥160 mm Hg. INTERVENTION: Strengthening the hypertension care cascade compared with status quo levels, with pharmacological treatment administered at different cut-points depending on the scenario. TARGET POPULATION: Adults aged 30+ in 24 low-income and middle-income countries spanning all world regions. PERSPECTIVE: Societal. TIME HORIZON: 30 years. DISCOUNT RATE: 4%. COSTING YEAR: 2020 USD. STUDY DESIGN: DATA SOURCES: Institute for Health Metrics and Evaluation's Epi Visualisations database-country-specific cardiovascular disease (CVD) incidence, prevalence and death rates. Mean SBP and prevalence-National surveys and NCD-RisC. Treatment protocols-WHO HEARTS. Treatment impact-academic literature. Costs-national and international databases. OUTCOME MEASURES: Health outcomes-averted stroke and myocardial infarction events, deaths and disability-adjusted life-years; economic outcomes-averted health expenditures, value of averted mortality and workplace productivity losses. RESULTS OF ANALYSIS: Across 24 countries, over 30 years, incremental scale-up of hypertension care for adults with SBP≥140 mm Hg led to 2.6 million averted CVD events and 1.2 million averted deaths (7% of expected CVD deaths). 68% of benefits resulted from treating those with very high SBP (≥160 mm Hg). 10 of the 12 highest-income countries projected positive net benefits at one or more treatment cut-points, compared with 3 of the 12 lowest-income countries. Treating hypertension at SBP≥160 mm Hg maximised the net economic benefit in the lowest-income countries. LIMITATIONS: The model only included a few hypertension-attributable diseases and did not account for comorbid risk factors. Modelled scenarios assumed ambitious progress on strengthening the care cascade. CONCLUSIONS: In areas where economic considerations might play an outsized role, such as very low-income countries, prioritising treatment to populations with severe hypertension can maximise benefits net of economic costs.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".