Using Economic Evidence to Set Healthcare Priorities in Low‐Income and Lower‐Middle‐Income Countries: A Systematic Review of Methodological Frameworks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Policy makers in low-income and lower-middle-income countries (LMICs) are increasingly looking to develop 'evidence-based' frameworks for identifying priority health interventions. This paper synthesises and appraises the literature on methodological frameworks--which incorporate economic evaluation evidence--for the purpose of setting healthcare priorities in LMICs. A systematic search of Embase, MEDLINE, Econlit and PubMed identified 3968 articles with a further 21 articles identified through manual searching. A total of 36 papers were eligible for inclusion. These covered a wide range of health interventions with only two studies including health systems strengthening interventions related to financing, governance and human resources. A little under half of the studies (39%) included multiple criteria for priority setting, most commonly equity, feasibility and disease severity. Most studies (91%) specified a measure of 'efficiency' defined as cost per disability-adjusted life year averted. Ranking of health interventions using multi-criteria decision analysis and generalised cost-effectiveness were the most common frameworks for identifying priority health interventions. Approximately a third of studies discussed the affordability of priority interventions. Only one study identified priority areas for the release or redeployment of resources. The paper concludes by highlighting the need for local capacity to conduct evaluations (including economic analysis) and empowerment of local decision-makers to act on this evidence.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.060 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it