Impact of school vaccination mandates on pediatric vaccination coverage: a systematic review
Bibliographic record
Abstract
BACKGROUND: Mandated vaccination for school attendance is a growing strategy internationally. Our aim was to investigate the effects of implementing school vaccination mandates on pediatric population vaccine coverage. METHODS: In this systematic review, we searched MEDLINE, Embase, CINAHL, the Education Resources Information Center (ERIC) and the PAIS Index for empirical studies of implementation of a primary or secondary school vaccination requirement published in any language through March 2019 with vaccination rates as an outcome. We sought additional studies by consulting experts, reference lists and grey literature sources. Included studies were too heterogeneous for meta-analysis; thus, we extracted data using a standardized rubric and synthesized the results narratively. RESULTS: Among the 4232 citations obtained, 20 studies met the inclusion criteria. Eighteen were conducted with US data, 1 with Italian data, and 1 with Australian data. Four studies examined school-entry mandates, and 16 examined adolescent requirements. An uncontrolled before-after design was used in 10 studies, cross-sectional analysis in 7, a retrospective cohort design in 2, and a prospective cohort in 1. In many cases, increased documentation of coverage followed the addition of new requirements. The exception to this was human papillomavirus vaccination mandates, which were highly controversial, in the United States. The studies contained notable risks of bias, with cointerventions rarely acknowledged or accounted for, and subpopulations often excluded. A substantial risk of ecological fallacy existed for most studies. INTERPRETATION: Vaccination mandates appear largely associated with increased vaccination coverage, but it is not possible to attribute causality to the mandate in most studies. High-quality implementation research that uses whole-population coverage data and takes into consideration cointerventions, confounders, clustering of unvaccinated populations and context is required.
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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 | 0.015 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".