Cost-effectiveness of measles treatment: a systematic review.
Bibliographic record
Abstract
OBJECTIVE: Measles is still common in many developing countries, and its outbreaks have been on the rise since 2009 even though the disease is almost entirely preventable through safe and effective vaccination. This paper aims to provide evidence about the systematic review of the cost-effectiveness of measles treatment in different regions worldwide. METHODS: The methodical search began on 10th January 2019 to look for all articles on the cost-effectiveness of measles treatment published from January 2019 to April 2019 in SCOPUS, Pubmed (www.ncbi.nlm.nih.gov) and Cochrane (www.cochrane.org).We summarised the articles by using a data table to extract all information using health economic evaluation methods. RESULTS: We identified 14 articles from the 69 total articles searched. These articles showed favourable costeffectiveness or cost-benefit ratios in high- and middle-income countries based on data organised by World Bank Income Level in 2018: the United States, Canada, Japan, India and Zambia. However, research is still limited in lowincome countries and thus the effectiveness of vaccination programmes cannot be conclusively identified. CONCLUSIONS: This review shows the overview of the research in health economic evaluations of measles in different places, years and using different methods of intervention. Overall, it evaluates the cost-effectiveness of measles treatment.
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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 | 0.008 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".