Adherence to country-specific guidelines among economic evaluations undertaken in three high-income and middle-income countries: a systematic review
Bibliographic record
Abstract
OBJECTIVE: To assess the adherence of economic evaluations to the recommendations on principles of economic evaluation as stated in the country-specific guidelines for three countries across different income groups, namely, Canada, South Africa, and Egypt. METHODS: Searches were undertaken in three databases to identify economic evaluations meeting predefined inclusion criteria. Methodological and reporting standards listed in the country-specific guidelines were converted into discrete binary variables to calculate mean adherence scores. Quality appraisal was done using Drummond's checklist. Stratified analysis was undertaken to identify independent variables affecting adherence. RESULTS: We identified forty-four, seventy-nine, and sixteen economic evaluations for Canada, South Africa, and Egypt, respectively. The mean adherence score was the highest for Canada (71%), followed by South Africa (65%) and Egypt (60%). Adherence to guidelines was positively correlated with quality of studies, r = .72. Furthermore, the mean adherence score was significantly (p < .05) higher for studies using a cost-utility analysis design (72%), having local/national funding aid (72%), undertaken by a health economist (71%) and for pharmacoeconomic evaluations (70%). CONCLUSION: The quality of economic evaluations improves with adherence to country-specific guidelines. Locally funded and health-economist led health technology assessments (HTAs) should be encouraged for greater adherence to the guidelines. The HTA researchers and the HTA bodies should lay emphasis on adherence to the country-specific guidelines for improving the quality of HTA evidence.
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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 | 0.159 | 0.481 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.019 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".