Methodological changes implemented over time to support accurate and timely COVID-19 vaccine coverage estimates: Ontario, Canada
Bibliographic record
Abstract
The COVID-19 vaccination program implementation in Ontario, Canada has spanned multiple years and is ongoing. To meet the challenges of the program, Ontario developed and implemented a new electronic COVID-19 immunization registry, COVaxON, which captures individual-level data on all doses administered in the province enabling comprehensive coverage assessment. However, the need for ongoing COVID-19 vaccine coverage assessments over a multi-year vaccination program posed challenges necessitating methodological changes. This paper describes Ontario's COVID-19 immunization registry, the methods implemented over time to allow for the ongoing assessment of vaccine coverage by age, and the impact of those methodological changes. Throughout the course of the vaccination program, four different methodological approaches were used to calculate age-specific coverage estimates using vaccination data (numerator) obtained from COVaxON. Age-specific numerators were initially calculated using age at time of first dose (method A), but were updated to the age at coverage assessment (method B). Database enhancements allowed for the exclusion of deceased individuals from the numerator (method C). Population data (denominator) was updated to 2022 projections from the 2021 national census following their availability (method D). The impact was most evident in older age groups where vaccine uptake was high. For example, coverage estimates for individuals aged 70-79 years of age for at least one dose decreased from 104.9 % (method B) to 95.0 % (method D). Thus, methodological changes improved estimates such that none exceeded 100 %. Ontario's COVID-19 immunization registry has been transformational for vaccine program surveillance. The implementation of a single registry for COVID-19 vaccines was essential for comprehensive near real-time coverage assessment, and enabled new uses of the data to support additional components of vaccine program surveillance. The province is well positioned to build on what has been achieved as a result of the COVID-19 pandemic and expand the registry to other routine vaccination programs.
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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 | 0.211 | 0.277 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".