Growth and capacity for cost‐effectiveness analysis in Africa
Bibliographic record
Abstract
As economic evaluation becomes increasingly essential to support universal health coverage (UHC), we aim to understand the growth, characteristics, and quality of cost-effectiveness analyses (CEA) conducted for Africa and to assess institutional capacity and relationship patterns among authors. We searched the Tufts Medical Center CEA Registries and four databases to identify CEAs for Africa. After extracting relevant information, we examined study characteristics, cost-effectiveness ratios, individual and institutional contribution to the literature, and network dyads at the author, institution, and country levels. The 358 identified CEAs for Africa primarily focused on sub-Saharan Africa (96%) and interventions for communicable diseases (77%). Of 2,121 intervention-specific ratios, 8% were deemed cost-saving, and most evaluated immunizations strategies. As 64% of studies included at least one African author, we observed widespread collaboration among international researchers and institutions. However, only 23% of first authors were affiliated with African institutions. The top producers of CEAs among African institutions are more adherent to methodological and reporting guidelines. Although economic evidence in Africa has grown substantially, the capacity for generating such evidence remains limited. Increasing the ability of regional institutions to produce high-quality evidence and facilitate knowledge transfer among African institutions has the potential to inform prioritization decisions for designing UHC.
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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 | 0.424 | 0.758 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.029 | 0.032 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".