Routine childhood vaccination rates in an academic family health team before and during the first wave of the COVID-19 pandemic: a pre–post analysis of a retrospective chart review
Bibliographic record
Abstract
BACKGROUND: There has been concern about declining routine vaccination rates during the COVID-19 pandemic. We evaluated the impact of the COVID-19 pandemic on early childhood vaccination rates at 2 sites of an academic family health team in the Greater Toronto Area, Ontario, serving both an urban and suburban patient population. METHODS: We conducted a pre-post analysis of vaccination records from Jan. 1, 2018, to Nov. 30, 2020, for a cohort of children born between Jan. 1, 2018, and Aug. 31, 2020, from the electronic medical record (EMR) of the Mount Sinai Academic Family Health Team (including an urban academic site in Toronto and a suburban community site in Vaughan, Ontario). We estimated the proportion of children receiving timely, delayed or no vaccination for 10 publicly funded vaccines in the Ontario immunization schedule for the pre-COVID-19 (Jan. 1, 2018, to Mar. 16, 2020) and COVID-19 (Mar. 17 to Nov. 30, 2020) pandemic periods. We determined timeliness in accordance with the recommended age of administration, with a 28-day window; we considered vaccines administered after this window to be delayed. We estimated the median time to vaccination for each vaccine and present cumulative incidence curves. RESULTS: The patient population was balanced between boys (52.4%) and girls (47.6%), with an average age of 18.5 months and representation across low-, middle- and high-income groups. Of the 506 children in our cohort, 422 were up to date with vaccinations (83.4%) by the end of the study period. Comparatively, 308 (83.2%) of the 370 eligible patients were up to date for all required vaccinations by the end of the pre-COVID-19 period. Among children younger than 12 months, vaccination rates were similar in the pre-COVID-19 and COVID-19 pandemic periods. Lower rates of timely vaccination for children between 12 and 18 months of age were amplified during the pandemic. Cumulative incidence curves were suggestive of a decrease in the timeliness of vaccinations in the COVID-19 period for the vaccines administered at 12, 15 and 18 months, compared with the pre-COVID-19 period. INTERPRETATION: Our local findings suggest a deterioration in the uptake of routine childhood vaccines in children aged 12 to 18 months in the first year of the COVID-19 pandemic. Further study is needed to determine the extent of the vaccination gap in children across Canada, including the impact of subsequent waves of the COVID-19 pandemic.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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".