Economic evaluations of scaling up strategies of evidence-based health interventions: a systematic review
Bibliographic record
Abstract
BACKGROUND: Scaling seeks to extend the benefits of evidence-based interventions (EBIs) to larger populations, and often with the hope of achieving economies of scale. However, little is known about scaling costs. Our goal was to find scaling studies that focused on economic evaluations of scaling, their characteristics and the methods they applied. METHODS: We performed a systematic review informed by the Joanna Briggs Institute and PRISMA reporting guidelines. We included all studies that conducted a full or partial economic evaluation of scaling an EBI in healthcare, applicable to any individual or organization in any country and setting. We included all study designs and imposed no restrictions on language. We conducted searches in Medline, Web of Science, Embase, Cochrane Library Database, PEDE, EconLIT, INHATA from their inception until November 12, 2024, including grey literature. Pairs of independent reviewers identified eligible studies and extracted data on study characteristics, scaling strategies, characteristics of economic evaluations and methods used. The methodological quality of included studies was evaluated using the British Medical Journal Checklist. Results were summarized using narrative synthesis. RESULTS: Of 8,936 unique citations, thirteen studies meet our inclusion criteria: ten cost-effectiveness and three cost-analysis studies. Studies were performed in lower- or middle-income countries (LMIC) as well as in high-income countries and covered EBIs for infectious diseases, mental health, and colorectal cancer. All reported direct costs (e.g., health professional training costs) and indirect costs (e.g., capital costs) associated with scaling strategies. Four studies were of high quality, eight of moderate quality and one of poor quality. CONCLUSION: With the increased interest in scaling EBIs in health, there is an urgent need for more evaluations of costs associated with scaling, both in LMIC and in high-income countries, and a need for rigour in how these evaluations are performed.
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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 | 0.097 | 0.322 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.018 | 0.023 |
| Bibliometrics | 0.021 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".