Using routinely collected laboratory and health administrative data to assess influenza vaccine effectiveness: introducing the Flu and Other Respiratory Viruses Research (FOREVER) Cohort
Bibliographic record
Abstract
IntroductionAnnual evaluation of influenza vaccine effectiveness (VE) is required because of frequent changes to circulating and vaccine strains. Traditionally, VE studies enroll patients who fulfill case definitions for respiratory infections and are tested for influenza. VE estimates generated from convenience samples of routinely collected specimens might be biased. Objectives and ApproachWe assessed the validity of using data from respiratory specimens collected during clinical encounters to estimate VE. We created the Flu and Other Respiratory Viruses Research (FOREVER) Cohort by linking respiratory virus laboratory test results from 2009-2014 from 11 public health and 8 hospital laboratories across Ontario to health administrative databases, including databases with billing claims for physician- and pharmacist-administered influenza vaccines. We evaluated the presence of information and selection biases when using these data and estimated VE in community-dwelling older adults (>65) using the test-negative design under conditions that emulated the inclusion criteria in traditional VE studies. ResultsThe FOREVER Cohort included test results from 283,711 respiratory specimens obtained from 216,730 individuals. The overall linkage proportion to health administrative databases using deterministic and probabilistic linkage methods was 97.5%. Influenza positivity for older adults with unknown lag between illness onset and specimen collection was similar to those for whom illness onset date was documented to be ≤7 days before specimen collection, suggesting minimal outcome misclassification associated with information bias. The likelihood of influenza testing was similar between vaccinated and unvaccinated individuals, suggesting an absence of selection bias that could arise when a case definition for influenza testing is not employed. Lastly, VE estimates were similar under various conditions, demonstrating the robustness of using these data, and were comparable to published estimates. Conclusion/ImplicationsThe FOREVER Cohort can be used to estimate VE with negligible bias. Compared to traditional VE studies that are limited to recruited patients, routinely collected specimens create a larger, more generalizable sample. Linkage to health administrative databases can identify those with comorbidities and permit evaluation of VE in high-risk groups.
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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 | 0.049 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 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".