Cardiac rehabilitation in low- and middle-income countries: a review on cost and cost-effectiveness
Bibliographic record
Abstract
BACKGROUND: By 2030, more than 80% of cardiovascular disease-related deaths and disability-adjusted life years will occur in the 139 low- and middle-income (LMIC) countries. Cardiac rehabilitation (CR) has been demonstrated to be effective and cost-effective mainly based on data from high-income countries. The purpose of this paper was to review the literature for cost and cost-effectiveness data on CR in LMICs. METHODS: MEDLINE (Ovid) and EMBASE (Ovid) electronic databases were searched for CR 'cost' and 'cost-effectiveness' data in LMICs. RESULTS: Five CR publications with cost and cost-effectiveness data from middle-income countries were identified with none from low-income countries. Studies from Brazil demonstrated mean monthly savings of US$190 for CR, with a US$48 increase in a control group with mean costs of US$503 for a 3-month CR program. Mean costs to the public health care system of US$360 and US$540 when paid out-of-pocket were reported for a 3-month CR program in seven Latin American middle-income countries. Cardiac rehabilitation is reported to be cost-effective in both Brazil and Colombia. CONCLUSIONS: Cardiac rehabilitation for patients with heart failure in Brazil and Colombia was estimated to be cost-effective. However, given the limited health care budgets in many LMICs, affordable CR models will need to be developed for LMICs, particularly for low-income countries.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".