Effectiveness of influenza vaccination in working-age adults with diabetes: a population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Guidelines recommend influenza vaccinations in all diabetic adults, but there is limited evidence to support vaccinating working-age adults (<65 years) with diabetes. We examined the effectiveness of influenza vaccine in this subgroup, compared with elderly adults (≥ 65 years) for whom vaccination recommendations are well accepted. METHODS: We identified all adults with diabetes, along with a sample of age-matched and sex-matched comparison subjects without diabetes, from 2000 to 2008, using administrative data from Manitoba, Canada. With multivariable Poisson regression, we estimated vaccine effectiveness (VE) on influenza-like illnesses (ILIs), pneumonia and influenza (PI) hospitalisations and all-cause (ALL) hospitalisations during periods of known circulating influenza. Analyses were replicated outside of influenza season to rule out residual confounding. RESULTS: We included 543 367 person-years of follow-up, during which 223 920 ILI, 5422 PI and 94 988 ALL occurred. The majority (58%) of adults with diabetes were working age. In this group, influenza vaccination was associated with relative reductions in PI (43%, 95% CI 28% to 54%) and ALL (28%, 95% CI 24% to 32%) but not ILI (-1%, 95% CI -3% to 1%). VE was similar in elderly adults for ALL (33-34%) and PI (45-55%), although not ILI (12-13%). However, similar estimates of effectiveness were also observed for all three groups during non-influenza control periods. CONCLUSIONS: Working-age adults with diabetes experience similar benefits from vaccination as elderly adults, supporting current diabetes-specific recommendations. However, these benefits were also manifest outside of influenza season, suggesting residual bias. Vaccination recommendations in all high-risk adults would benefit from randomised trial evidence.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".