Cardiac Rehabilitation Availability and Density around the Globe
Bibliographic record
Abstract
Background Despite the epidemic of cardiovascular disease and the benefits of cardiac rehabilitation (CR), availability is known to be insufficient, although this is not quantified. This study ascertained CR availability, volumes and its drivers, and density. Methods A survey was administered to CR programs globally. Cardiac associations and local champions facilitated program identification. Factors associated with volumes were assessed using generalized linear mixed models, and compared by World Health Organization region. Density (i.e. annual ischemic heart disease [IHD] incidence estimate from Global Burden of Disease study divided by national CR capacity) was computed. Findings CR was available in 111/203 (54.7%) countries; data were collected in 93 (83.8% country response; N = 1082 surveys, 32.1% program response rate). Availability by region ranged from 80.7% of countries in Europe, to 17.0% in Africa (p < .001). There were 5753 programs globally that could serve 1,655,083 patients/year, despite an estimated 20,279,651 incident IHD cases globally/year. Volume was significantly greater where patients were systematically referred (odds ratio [OR] = 1.36, 95% confidence interval [CI] = 1.35–1.38) and programs offered alternative models (OR = 1.05, 95%CI = 1.04–1.06), and significantly lower with private (OR = .92, 95%CI = .91–.93) or public (OR = .83, 95%CI = .82–84) funding compared to hybrid sources. Median capacity (i.e., number of patients a program could serve annually) was 246/program (Q25-Q75 = 150–390). The absolute density was one CR spot per 11 IHD cases in countries with CR, and 12 globally. Interpretation CR is available in only half of countries globally. Where offered, capacity is grossly insufficient, such that most patients will not derive the benefits associated with participation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".