Evaluation of School Vaccine Coverage and Medical Vaccine Exemptions Following the Repeal of School Entry Nonmedical Vaccine Exemption Options in New York State
Notice bibliographique
Résumé
ABSTRACT Importance Following the repeal of nonmedical vaccine exemption options from school entry immunization laws in California, gains in vaccine coverage were partially offset by increases in medical vaccine exemptions. Although several U.S. states, including New York State (NYS), recently adopted similar legislation, the impact of these laws on school vaccine coverage and medical vaccine exemptions has not yet been examined. Objective To estimate the effects of NYS legislation eliminating nonmedical school entry vaccine exemptions on required vaccine coverage and the uptake of medical vaccine exemptions at NYS schools outside of New York City (NYC). Design Interrupted time-series analyses using generalized estimating equations to examine longitudinal school immunization compliance data from the 2012-13 through 2021-22 school years. Setting New York State public and nonpublic schools outside of NYC. Participants Schools that submitted ≥1 compliance report in the time periods before and after the legislative repeal of nonmedical exemptions with publicly available student enrollment data. Exposure NYS Senate Bill 2994A was passed in June 2019, eliminating school entry nonmedical vaccine exemptions; since compliance with the law was evaluated for most students during the following school year, we considered the 2019-20 school year as the law’s effective date. Main Outcomes and Measures Main outcomes examined were school required vaccine coverage, defined as the percentage of students at each school who completed all grade-appropriate NYS vaccine requirements, and the percentage of students with a medical vaccine exemption. Results Among 3,525 eligible schools, the implementation of NYS Senate Bill 2994A was associated with an increase in mean required vaccine coverage of 5% and 1% among nonpublic and public schools, respectively, with additional annual increases in coverage observed through the 2021-22 school year. The law’s implementation was also associated with a 0.1% (95% CI: 0.0%, 0.1%) decrease in medical vaccine exemptions at both public and nonpublic schools, and small, but significant mean annual declines in medical vaccine exemptions through the end of the study period. Conclusion and Relevance The NYS elimination of school entry nonmedical vaccine exemption options was effective to improve required vaccine coverage; coverage gains were not replaced by increases in medical vaccine exemptions. KEY POINTS Question Was the New York State (NYS) law eliminating nonmedical vaccine exemption options from school entry vaccine requirements effective to increase vaccine coverage among NYS schools (outside of New York City)? Findings Using interrupted time-series analyses, we found the implementation of the NYS law was associated with an increase in mean required vaccine coverage at NYS schools; small, but significant declines in medical exemptions were also observed in relation to the law. Meaning State legislation eliminating nonmedical vaccine exemption options from school entry vaccine laws can be effective to improve school vaccine coverage without replacement by medical vaccine exemptions.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,018 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».