39 Impact of the COVID-19 pandemic on routine immunization coverage in Canada: Results from a novel national surveillance system
Bibliographic record
Abstract
Abstract Background The COVID-19 pandemic has posed unprecedented challenges to routine immunization programs globally. There is a growing body of evidence highlighting the impact of the pandemic on other routinely administered public health services. Understanding vaccination trends for routinely administered vaccinations during the pandemic is crucial for addressing potential gaps in protection and devising targeted strategies to enhance routine immunization programs. Objectives This study investigates the impact of the pandemic on routine immunization coverage in Canada, focusing on DTaP (Diphtheria, Tetanus, and Pertussis) with at least 4 doses and MMR (Measles, Mumps, and Rubella) with at least 1 dose, among children at age 2. Design/Methods Data were collected from the Routine Childhood Vaccination Coverage Surveillance system (RCVCSS) a novel national surveillance based on standardized detailed reports prepared by participating provinces and territories using data from their immunization registries. This analysis covers the years 2019 to 2022, capturing pre-pandemic, pandemic, and post-pandemic phases with data from 4 reporting provinces and territories (Alberta, Saskatchewan, New Brunswick and Yukon). The primary objective was to assess any changes in vaccination coverage at age 2 for routinely administered immunizations from 2019 to 2022, i.e., before, during and after the COVID-19 pandemic. Results Analysis reveals a decline in routine immunization coverage from 2019 to 2022. DTaP vaccination coverage at age 2 decreased from 82.0% in 2019 to 71.8% in 2022. Likewise, MMR vaccination coverage at two years showed a decline from 90.8% in 2019 to 81.7% in 2022. Similar downward trends in coverage were observed for other sets of antigens vaccinated against at age 2 including Varicella, Rotavirus, Polio, Pneumococcal and Hepatitis B. No major differences were observed when looking at vaccination coverage over time by sex. There were regional variations, with certain jurisdictions experiencing more pronounced declines than others and an increase in coverage in 2022 the post pandemic year. Conclusion There was a sustained decline in DTaP and MMR vaccination coverage at age 2 from 2019 to 2022. Further research is imperative to ascertain whether there has been a catch-up in vaccination coverage and to identify the root causes of the observed declines, so these can be addressed, and targeted interventions can be undertaken to allow coverage level to recover to their pre-pandemic levels.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 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".