COVID-19 pandemic impact on childhood vaccination coverage in Quebec, Canada
Bibliographic record
Abstract
Response measures to mitigate the coronavirus disease 2019 pandemic impacted access to routine vaccination services. We evaluate the impact of the pandemic on routine infant vaccination uptake by comparing vaccination coverage, vaccine delays and doses administered in 2019 and 2020, in Quebec, Canada. Using a population-based vaccination registry, we compared vaccination coverage at 3, 5, 13 and 19 months of age between 2019 and 2020 cohorts each month from January to November. For vaccine delays, we measured the cumulative proportion vaccinated in each targeted cohort monthly. We also compared the measles-containing vaccines administered before 24 months of age between the same period in 2019 and 2020. A decline in vaccination coverage and children vaccinated on time was observed in all cohorts during the first months of the pandemic. The greatest impact was observed for the 18-month vaccination visit with a difference in vaccination coverage between both cohorts of 30.9% in May. Measles-containing doses administered during the first months of the pandemic were lower in 2020 compared with 2019: -21.1% in March (95%CI-21.6;-20.4), and -39.2% in April (95%CI-40.0;-38.2). After May, the coverage increased for all cohorts to reach pre-pandemic levels after a few months for most target ages. Routine childhood vaccinations were affected during the first months of the pandemic, but catch-up occurred thereafter and vaccination coverage in affected cohorts were very close to levels of 2019 after a few months of follow-up. Real-time monitoring of childhood vaccination is essential but also for other vaccination programs, severely affected by the pandemic.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".