Validation of influenza vaccination status using health administrative databases by integrating pharmacy claims and medical billing databases in Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Determining vaccination status among the population is key for vaccine research and surveillance. This study aimed to validate the combined use of Ontario Health Insurance Program (OHIP) physician billing claims and Ontario Drug Benefit program (ODB) pharmacist billing claims against data from the Canadian Community Health Survey (CCHS). METHODS: OHIP and ODB billing claims databases were linked to 2013-2014 CCHS data, which contain self-reported seasonal influenza vaccination status of respondents (the reference standard). Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), and their associated 95% confidence intervals (CIs) were estimated. Subgroup analyses were performed based on key respondent characteristics, including having a regular medical doctor and the presence of risk factors for influenza complications. RESULTS: There were 31,390 eligible CCHS respondents aged ≥ 12 years in Ontario who responded to the influenza vaccination questions and agreed to have their responses linked to health administrative databases. More than half (55%) were female, 29% were aged ≥ 65 years, 93% had a regular medical doctor, and 54% had one or more risk factors for influenza complications. The sensitivity for the combined administrative databases was 60.1% (95% CI, 59.3%-61.0%), specificity was 98.5% (95% CI, 98.3%-98.7%), PPV was 96.7% (95% CI, 96.3%-97.1%), and NPV was 76.9% (95% CI, 76.4%-77.5%). Sensitivity was higher among those aged ≥ 65 years (72.7%; 95% CI, 71.6%-73.7%), with a regular medical doctor (61.1%; 95% CI, 60.3%-62.0%), and those with at least one risk factor for influenza complications (65.8%; 95% CI, 64.9%-66.8%). CONCLUSION: Combining administrative physician and pharmacy claims data in Ontario results in moderate sensitivity but very high specificity and PPV, demonstrating that they can be a valid measure of influenza vaccination status.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".