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Record W4386090657 · doi:10.1101/2023.08.17.23294227

Evaluation of School Vaccine Coverage and Medical Vaccine Exemptions Following the Repeal of School Entry Nonmedical Vaccine Exemption Options in New York State

2023· preprint· en· W4386090657 on OpenAlexaff
John W. Correira, Rhiannon Kamstra, Nanqing Zhu, Margaret K. Doll

Bibliographic record

VenuemedRxiv · 2023
Typepreprint
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsMD Precision (Canada)
FundersAlbany College of Pharmacy and Health Sciences
KeywordsRepealLegislationLegislatureMedicineState legislatureImmunizationFamily medicineEnvironmental healthPolitical scienceLawImmunology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.225

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.069
GPT teacher head0.360
Teacher spread0.291 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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