A spot of bother: Why varicella vaccine programs matter
Bibliographic record
Abstract
OBJECTIVE: To evaluate Ontario's provincial varicella vaccination program through analysis of aggregate varicella cases in order to determine whether there has been a decrease in reportable disease burden; and to assess varicella vaccine adverse events following immunization (AEFIs). METHODS: Aggregate varicella cases (1993-2013) were extracted from the reportable disease databases. Pre-program (1993-2004) and post-program (2007-2013) periods were chosen according to implementation of the publicly funded vaccination program. AEFIs following administration of varicella vaccines (2010-2013) were also extracted. Reporting rates were calculated using net doses distributed as the denominator. Serious AEFIs were defined using World Health Organization standards. RESULTS: The incidence of aggregate varicella reports decreased significantly over the study period (from 311.4 to 22.2 cases per 100,000 population in 1993 and 2013, respectively). Incidence also decreased significantly in all age groups between the pre- and the post-program periods with a shift in age distribution towards older individuals in the post-program period. A total of 162 AEFIs following varicella vaccine were reported between 2010 and 2013 for an annualized reporting rate of 14.6 per 100,000 doses distributed. The most common events were rash (37.3%), including eight reports of varicella-like rash (0.7 per 100,000 doses distributed). Ten serious events were reported (0.9 per 100,000 doses distributed), and all vaccine recipients recovered. CONCLUSION: Significant reductions in varicella disease incidence and low AEFI reporting rates were observed with the introduction of the publicly funded varicella vaccine program in Ontario. Continued surveillance is indicated to further assess trends in varicella disease and vaccine safety.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".