Impact of vaccine delays at the 2, 4, 6 and 12 month visits on incomplete vaccination status by 24 months of age in Quebec, Canada
Bibliographic record
Abstract
BACKGROUND: Timeliness in the administration of recommended vaccines is often evaluated using vaccine delays and provides more information regarding the susceptibility of children to vaccine-preventable diseases compared with vaccine coverage at a given age. The importance of on-time administration of vaccines scheduled at the first visit is well documented, but data are scarce about the impact of vaccine delays at other visits on vaccination status by 24 months of age. Using vaccine delays for the first three doses of DTaP-containing vaccines and for the first dose of measles-containing vaccines as markers of timeliness at the 2, 4, 6 and 12 month visits, we estimated the proportion of incomplete vaccination status by 24 months of age attributable to a vaccine delay at each of these visits. METHODS: We used the data from six cross-sectional coverage surveys conducted in the Province of Quebec from 2006 to 2016 which included 7183 children randomly selected from the universal health insurance database. A vaccine dose was considered delayed if received 30 days or more after the recommended age. The impact of new vaccine delays at each visit on incomplete vaccination status by 24 months of age was estimated with the attributable risk in the population. RESULTS: The proportion of children with vaccine delay was 5.4% at 2 months, 13.3% at 4 months, 23.1% at 6 months and 23.6% at 12 months. Overall, 72.5% of all 2-year-old children with an incomplete status by 24 months were attributable with a vaccine delay, of which 16.1% were attributable with a first vaccine delay at 2 months, 10.6% at 4 months, 14.0% at 6 months and 31.8% at 12 months. CONCLUSIONS: While great emphasis has been put on vaccine delays at the first vaccination visit, the prevalence of vaccine delays was greater with later visits and most children with an incomplete vaccination status by 24 months had a vaccine delay occurring during these later visits. Interventions to improve timeliness should address vaccine delays at each visit and not only focus on the first visit.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".