Routine vaccination coverage at ages 2 and 7, before, during, and after the COVID-19 pandemic: Results from the STARVAX surveillance system
Bibliographic record
Abstract
OBJECTIVE: A decline in routine vaccination coverage in children has been observed after the onset of the COVID-19 pandemic, but it is unclear whether these declines were sustained over time. This analysis assessed changes in vaccination coverage at ages 2 and 7 years for routinely administered vaccinations before, during, and after the COVID-19 pandemic. METHODS: Vaccination coverage was measured using yearly data from the Standardized Reporting on Vaccination (STARVAX) surveillance system between December 31, 2019, and December 31, 2023. Four provinces (Alberta, Saskatchewan, Manitoba, and New Brunswick) and one territory (Yukon) produced reports from their immunization registries and provided the Public Health Agency of Canada with the numbers of vaccinated 2-year-old and 7-year-old children. Population estimates from Statistics Canada and the Yukon Bureau of Statistics were used as the denominators. RESULTS: There was a decline in vaccination coverage in 2023 compared to 2019. In 2-year-olds, coverage for one dose of the measles, mumps, and rubella (MMR) vaccine and four doses of the diphtheria, tetanus, and acellular pertussis (DTaP) vaccine decreased from 88.9% to 84.3% and from 79.6% to 73.3%, respectively. Among 7-year-olds, DTaP (up-to-date) and MMR (two doses) vaccination coverage decreased from 77.3% to 68.2% and 85.4% to 74.9%, respectively. CONCLUSION: These declines are of concern and suggest that health care professionals should provide parents with accurate information regarding vaccines and encourage them to have their children vaccinated and keep vaccinations up to date.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".