Impact of the COVID-19 pandemic on vaccine coverage for early childhood vaccines in Alberta, Canada: a population-based retrospective cohort study
Bibliographic record
Abstract
OBJECTIVE: To assess the impact of the COVID-19 pandemic on early childhood vaccination coverage in Alberta, Canada. SETTING: Alberta, a western Canadian province, which has a population of 4.4 million and approximately 50 000 births annually. DESIGN: In this retrospective cohort study, population-based administrative health data were analysed to determine the vaccination coverage for measles-containing, pertussis-containing and rotavirus vaccines. PRIMARY OUTCOME MEASURE: We measured monthly and cumulative vaccine coverage. We assessed the absolute difference in monthly and cumulative coverage for each vaccine dose by comparing children due for vaccination in each month of 2019 and 2020, with follow-up to determine if missed doses were caught up later. PARTICIPANTS: We included 114 178 children in the 2019 analysis cohort and 106 530 children in the 2020 analysis cohort. RESULTS: Monthly vaccination coverage in 2020 was higher than 2019 until March, when coverage significantly declined. Comparing April 2020 to 2019, coverage was 9.9% (95% CI 7.9% to 12.0%) lower for measles vaccine; 4.9% (95% CI 3.3% to 6.5%), 7.1% (95% CI 5.2% to 9.1%), 5.2% (95% CI 3.1% to 7.4%) and 8.8% (95% CI 6.6% to 10.9%) lower for first, second, third and fourth doses of pertussis-containing vaccine, respectively; and 4.0% (95% CI 2.3% to 5.7%), 7.1% (95% CI 5.1% to 9.2%) and 4.6% (95% CI 2.4% to 6.7%) lower for first, second and third doses of rotavirus vaccine, respectively. Monthly coverage improved during May to July 2020; however, some doses experienced a second decline during September to October 2020. The cumulative coverage analysis showed that the measles-containing vaccine had the largest difference in coverage at the end of follow-up. CONCLUSIONS: Children who were due for vaccination early in the pandemic and in Fall 2020, especially those due for measles vaccination, may require additional catch-up.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".