Effects of the COVID-19 pandemic on self-reported 12-month pneumococcal vaccination series completion rates in Canada: An interrupted time-series analysis
Bibliographic record
Abstract
Abstract Background Routine childhood vaccination improves health and prevents morbidity and mortality from vaccination preventable diseases. There are indications that the COVID-19 pandemic has negatively impacted vaccination rates globally, but systematic studies on this are still lacking in Canada. This study aims to add knowledge on the effect of the pandemic on pneumococcal vaccination rates of children using self-reported immunization data entered into the CANImmunize digital vaccination tool. Methodology An interrupted time series analysis was conducted on aggregated monthly enrollment of children on the platform (2016-2021) and their pneumococcal immunization series completion rates (2016-2020). Predicted trends before and after the onset of the COVID-19 related restriction (March 1, 2020) were compared by means of an Autoregressive Integrated Moving Average (ARIMA). Results Pandemic restrictions were associated with changes in self-reported pneumococcal immunization rates amongst the users of the CANImmunize platform. The monthly enrollment of children on the platform decreased by – 1177.52 records (95% CI: –1865.47, – 489.57), with a continued decrease of 80.84 records each month. Self-reported pneumococcal immunization series completion rates had an immediate increase of 14.57% (95% CI 4.64, 24.51) followed by a decrease of –3.54% each month. Conclusion The onset of the COVID-19 related restrictions impacted enrollment of children in the CANImmunize digital immunization platform, and an overall decrease in self-reported pneumococcal immunization series completion rates. Our findings support that efforts to increase catch-up immunization campaigns so that children who could not get scheduled immunization during the pandemic are not missed.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".