Evaluation of the 2018–2019 vaccine effectiveness against medically attended influenza-like illness using medical records and claims data
Bibliographic record
Abstract
BACKGROUND: Healthcare administrative databases are a rich source of information that could be leveraged to estimate real-world influenza vaccine effectiveness (VE). We aimed to evaluate the VE of standard egg-based influenza vaccines and determine if administrative healthcare data provide accurate VE estimates compared to the US CDC data. METHODS: This retrospective cohort study was conducted during the 2018-2019 influenza season. Individuals who had at least one relevant record per year between 2015 and 2019 in their electronic medical record were included. Individuals were considered protected 14 days after receiving an influenza vaccine. The outcome was the occurrence of medically attended influenza-like illness (MA-ILI) defined by clinical diagnostic codes. Adjusted odds ratios (aORs) were derived from multivariate logistic regression and adjusted VE (aVEs) were calculated using 100 × (1-aORs). RESULTS: A total of 5,066,980 individuals were included in the analysis with 1,307,702 (25.8%) considered vaccinated. Overall, the median age was 54 (IQR, 32-66) and 58.1% were female. Vaccine protection against MA-ILI was moderate in children and low in adults. All estimates were lower than VEs reported by the CDC for the 2018-2019 influenza season. Our results were robust to potential loss to follow up, but misclassification bias and residual confounding led to underestimation of the 2018-2019 aVE. When stratified by the number of primary care visits, aVE estimates and vaccination coverage increased with the number of primary care visits, reaching estimates similar to those obtained by the US CDC and US national vaccination coverage among those with ≥ 6 primary care visits, resulting in significant positive vaccine protection in frequent healthcare users. CONCLUSIONS: Moderate and low aVEs were observed during the 2018-2019 season using administrative healthcare data, which was likely due to detection and misclassification biases, correlated with healthcare seeking behaviour, leading to an underestimation of the 2018-2019 influenza VE.
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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 | 0.022 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".