Trends in medical and nonmedical immunization exemptions to measles-containing vaccine in Ontario: an annual cross-sectional assessment of students from school years 2002/03 to 2012/13
Bibliographic record
Abstract
BACKGROUND: Under Ontario legislation, for select vaccine-preventable diseases nonimmunized or under-immunized students must undergo vaccination or provide a statement of exemption, or risk suspension from school. At the time of this assessment, these diseases included measles, mumps, rubella, diphtheria, tetanus and polio. METHODS: Exemptions data for the school years 2002/03 to 2012/13 were obtained from the Immunization Records Information System used in Ontario. Temporal trends were expressed for 7- and 17-year-old students by exemption classification (medical, prior immunity, religious or conscientious belief, total) at the provincial level, by school year and by birth cohort. Regional analysis was conducted for the 2012/13 school year. A temporal trend analysis of exemptions for measles-containing vaccines was performed by using a Poisson distribution with a 2-sided test (α = 5%). RESULTS: For both 7- and 17-year-old students, religious or conscientious exemptions for measles-containing vaccines significantly increased over the study period (p < 0.001 in both age groups), whereas medical exemptions decreased (p < 0.001 in both age groups). The trends were reproduced when examined by birth cohort. The percentage of Ontario students with any exemption classification (total exemptions) remained low (< 2.5%) during the study period, although considerable geographic variation was noted. INTERPRETATION: Ontario data suggest that nonmedical exemptions have increased during the last 11 years, consistent with trends reported elsewhere. The trend toward increasing religious or conscientious exemptions coupled with declining medical exemptions explains why total exemptions have remained stable or decreased at the provincial level. The prominent geographic variability in exemptions suggests that targeted interventions may be suitable for consideration.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".