Global assessment of national mandatory vaccination policies and consequences of non-compliance
Bibliographic record
Abstract
BACKGROUND: Declining vaccination coverage and increasing hesitancy is a worldwide concern. Many countries have implemented mandatory vaccination policies to promote vaccination. However, mandatory vaccination policies differ significantly by country. Beyond case studies, no comprehensive study has compared these policies or the penalties for non-compliance on a global scale. METHODS: We conducted extensive keyword, policy, and literature searches to identify mandatory national vaccination policies globally and develop a comprehensive database. A mandatory national vaccination policy was defined as a policy from a national authority that requires individuals to receive at least one vaccination based on age or to access a service. Two reviewers independently evaluated evidence for a mandate and whether non-compliance penalties were incorporated. We categorized penalties into four types, based on the nature of the penalty. These penalties impact an individual's financial, parental rights, educational (i.e., child's school entry and access), and liberty status. We rated the severity within each category. RESULTS: Of 193 countries investigated, 54% (n = 105) had evidence of a nationwide mandate as of December 2018. The frequency, types, and severity of penalties varied widely across all regions. We found that 59% (n = 62) of countries with national mandates defined at least one penalty for non-compliance with a vaccine mandate. Among those, educational penalties (i.e., limiting a child's entry or ongoing access to school) were the most common (69%; n = 43), with most countries with educational penalties refusing school enrollment until vaccination requirements are met (81%; n = 35). CONCLUSION: We undertook a comprehensive assessment of national mandatory vaccination policies and identified a diversity of penalties in place to promote compliance. Our results highlight the need to critically evaluate the implementation of non-compliance penalties in order to determine their effectiveness and to define best practices for sustaining high vaccination uptake worldwide.
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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 | 0.091 | 0.197 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".