Coverage disparities and delayed immunization: Assessing 12-month vaccination completion in Canadian children using data from a digital immunization platform
Bibliographic record
Abstract
On-time vaccination is essential for community protection against vaccine-preventable diseases. Insufficient on-time coverage and immunization series completion can put the population at risk. This study described individual coverage and series completion rates of the 12-month dose of pneumococcal, meningococcal, and measles-mumps-rubella (MMR) vaccines, as well as predictors of these rates in Canada. We conducted a cross-sectional study with immunization data from the pan-Canadian digital vaccination tool (CANImmunize) to evaluate coverage of the 12-month dose for single and multiple-dose vaccines. We conducted bivariate and multivariate logistic regression analyses to investigate associations between factors such as account holder gender, active integration of vaccine record with a public health unit (PHU) and inclusion of linked records in CANImmunize, with vaccination coverage. We calculated crude and adjusted odds ratios and 95% confidence intervals. We analyzed 60,890 records from children aged 13 months to 18 years. Coverage of the 12-month's dose of multiple-dose vaccines was significantly lower compared to single-dose vaccines. 14.28% versus 17.23% of children who had reported the first dose of pneumococcal and meningococcal vaccines, respectively, have not received the 12-month dose of the same vaccine. Additionally, 10-13.2% of children who received the 12-month dose of pneumococcal, meningococcal, and MMR vaccines received the vaccine late (13+ months). Based on our data, there is a gap in the coverage of the 12-month dose between single and multiple-dose vaccines, and the 12-month dose was delayed for a proportion of children. Reminder interventions targeting populations with identified predictors of sub-optimal coverage may be valuable.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".