Cost-effectiveness of self-management of blood pressure in hypertensive patients over 70 years with suboptimal control and established cardiovascular disease or additional cardiovascular risk diseases (TASMIN-SR)
Bibliographic record
Abstract
BACKGROUND: A previous economic analysis of self-management, that is, self-monitoring with self-titration of antihypertensive medication evaluated cost-effectiveness among patients with uncomplicated hypertension. This study considered cost-effectiveness of self-management in those with raised blood pressure plus diabetes, chronic kidney disease and/or previous cardiovascular disease. DESIGN AND METHODS: A Markov model-based economic evaluation was undertaken to estimate the long-term cost-effectiveness of self-management of blood pressure in a cohort of 70-year-old 'high risk' patients, compared with usual care. The model used the results of the TASMIN-SR trial. A cost-utility analysis was undertaken from a UK health and social care perspective, taking into account lifetime costs of treatment, cardiovascular events and quality adjusted life years. A subgroup analysis ran the model separately for men and women. Deterministic sensitivity analyses examined the effect of different time horizons and reduced effectiveness of self-management. RESULTS: Base-case results indicated that self-management was cost-effective compared with usual care, resulting in more quality adjusted life years (0.21) and cost savings (-£830) per patient. There was a 99% chance of the intervention being cost-effective at a willingness to pay threshold of £20,000 per quality adjusted life year gained. Similar results were found for separate cohorts of men and women. The results were robust to sensitivity analyses, provided that the blood pressure lowering effect of self-management was maintained for more than a year. CONCLUSION: Self-management of blood pressure in high-risk people with poorly controlled hypertension not only reduces blood pressure, compared with usual care, but also represents a cost-effective use of healthcare resources.
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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 | 0.006 | 0.011 |
| 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) | 0.002 | 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".