Model Comparisons of the Effectiveness and Cost-Effectiveness of Vaccination: A Systematic Review of the Literature
Bibliographic record
Abstract
OBJECTIVES: To describe all published articles that have conducted comparisons of model-based effectiveness and cost-effectiveness results in the field of vaccination. Specific objectives were to 1) describe the methodologies used and 2) identify the strengths and limitations of the studies. METHODS: We systematically searched MEDLINE and Embase databases for studies that compared predictions of effectiveness and cost-effectiveness of vaccination of two or more mathematical models. We categorized studies into two groups on the basis of their data source for comparison (previously published results or new simulation results) and performed a qualitative synthesis of study conclusions. RESULTS: We identified 115 eligible articles (only 5% generated new simulations from the reviewed models) examining the effectiveness and cost-effectiveness of vaccination against 14 pathogens (69% of studies examined human papillomavirus, influenza, and/or pneumococcal vaccines). The goal of most of studies was to summarize evidence for vaccination policy decisions, and cost-effectiveness was the most frequent outcome examined. Only 33%, 25%, and 3% of studies followed a systematic approach to identify eligible studies, assessed the quality of studies, and performed a quantitative synthesis of results, respectively. A greater proportion of model comparisons using published studies followed a systematic approach to identify eligible studies and to assess their quality, whereas more studies using new simulations performed quantitative synthesis of results and identified drivers of model conclusions. Most comparative modeling studies concluded that vaccination was cost-effective. CONCLUSIONS: Given the variability in methods used to conduct/report comparative modeling studies, guidelines are required to enhance their quality and transparency and to provide better tools for decision making.
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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 | 0.025 | 0.084 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.023 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".