A systematic review of scope and quality of health economic evaluations conducted in Ethiopia
Bibliographic record
Abstract
There has been an increased interest in health technology assessment and economic evaluations for health policy in Ethiopia over the last few years. In this systematic review, we examined the scope and quality of healthcare economic evaluation studies in Ethiopia. We searched seven electronic databases (PubMed/MEDLINE, EMBASE, PsycINFO, CINHAL, Econlit, York CRD databases and CEA Tufts) from inception to May 2021 to identify published full health economic evaluations of a health-related intervention or programme in Ethiopia. This was supplemented with forward and backward citation searches of included articles, manual search of key government websites, the Disease Control Priorities-Ethiopia project and WHO-CHOICE programme. The quality of reporting of economic evaluations was assessed using the Consolidated Health Economic Evaluation Reporting Standards (CHEERS) checklist. The extracted data were grouped into subcategories based on the subject of the economic evaluation, organized into tables and reported narratively. This review identified 34 full economic evaluations conducted between 2009 and 2021. Around 14 (41%) of studies focussed on health service delivery, 8 (24%) on pharmaceuticals, vaccines and devices, and 4 (12%) on public-health programmes. The interventions were mostly preventive in nature and focussed on communicable diseases (n = 19; 56%) and maternal and child health (n = 6; 18%). Cost-effectiveness ratios varied widely from cost-saving to more than US $37 313 per life saved depending on the setting, perspectives, types of interventions and disease conditions. While the overall quality of included studies was judged as moderate (meeting 69% of CHEERS checklist), only four out of 27 cost-effectiveness studies characterized heterogeneity. There is a need for building local technical capacity to enhance the design, conduct and reporting of health economic evaluations in Ethiopia.
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How this classification was reachedexpand
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 | 0.084 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".