Evaluation of School Vaccine Coverage and Medical Vaccine Exemptions Following the Repeal of School Entry Nonmedical Vaccine Exemption Options in New York State
Bibliographic record
Abstract
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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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".