Assessing the completeness of infant and childhood immunizations within a provincial registry populated by parental reporting: A study using linked databases in Ontario, Canada
Bibliographic record
Abstract
INTRODUCTION: In Ontario, Canada, parents have the responsibility to report their child's routine infant and childhood vaccines to the provincial immunization registry (the Digital Health Immunization Repository; DHIR) without healthcare provider validation. Despite its use in routine immunization coverage monitoring, no study has previously examined the completeness of immunization data within the DHIR. METHODS: We assessed the completeness of DHIR immunizations, as compared to immunizations within the Electronic Medical Records-Primary Care (EMRPC) database, also known as EMRALD, a network of family physician electronic medical records (EMRs). We linked client records from the DHIR and EMRPC to a centralized population file. To create the study cohort, we examined children born during 2005-2008 and further defined the cohort based on those rostered to an EMRPC physician, visit criteria to ensure ongoing care by an EMRPC provider, and school attendance in Ontario at age 7. We calculated up-to-date (UTD) immunization coverage at age 7 for individual vaccines and overall using data from the DHIR and EMRPC separately, and compared the estimates. RESULTS: The analytic cohort to assess DHIR data completeness included 2,657 children. Overall UTD coverage (all vaccines assessed) was 82.0% in the DHIR and 67.6% in EMRPC. UTD coverage was higher in the DHIR for all vaccines assessed individually, with the exception of meningococcal C conjugate vaccine (difference = 0.3%). After excluding two EMRPC sites with irregularities in immunization data, the difference in overall UTD coverage between systems decreased from 14.4% to 6.6% INTERPRETATION: These results validate the use of DHIR for coverage assessment but also suggest that bidirectional exchange of immunization information has the potential to increase immunization data completeness in both systems.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".