Patient and practice level factors associated with seasonal influenza vaccine uptake among at-risk adults in England, 2011 to 2016: An age-stratified retrospective cohort study
Bibliographic record
Abstract
We sought to gain insights into the determinants of seasonal influenza vaccine (SIV) uptake by conducting an age-stratified analysis (18–64 and 65+) of factors associated with SIV uptake among at-risk adults registered to English practices. Records for at-risk English adults between 2011 and 2016 were identified using the Clinical Practice Research Datalink database. SIV uptake was assessed annually. The associations of patient, practice, and seasonal characteristics with SIV uptake were assessed via cross-sectional and longitudinal analyses, using mixed-effects and general estimating equation logistic regression models. Overall SIV uptake was 35.3% and 74.0% for adults 18–64 and 65+, respectively. Relative to white patients, black patients were least likely to be vaccinated (OR18-64: 0.82 (95% CI: 0.80, 0.85); OR65+: 0.59 (95% CI: 0.56, 0.62)), while Asian patients among 18–64 year olds were most likely to be vaccinated (OR18-64: 1.10 (95% CI: 1.07, 1.13)). Females were more likely than males to be vaccinated among 18–64 year olds (OR18-64: 1.19 (95% CI: 1.18, 1.20)). Greater socioeconomic deprivation was associated with decreased odds of uptake among older patients (OR65+: 0.74 (95% CI: 0.71, 0.77)). For each additional at-risk condition, odds of uptake increased (OR18-64: 2.33 (95% CI: 2.31, 2.36); OR65+: 1.39 (95% CI: 1.38, 1.39)). Odds of uptake were highest among younger patients with diabetes (OR18-64: 4.25 (95% CI: 4.18, 4.32)) and older patients with chronic respiratory disease (OR65+: 1.60 (95% CI: 1.58, 1.63)), whereas they were lowest among morbidly obese patients of all ages (OR18-64: 0.68 (95% CI: 0.67, 0.70); OR65+: 0.97 (95% CI: 0.94, 0.99)). Prior influenza season severity and vaccine effectiveness were marginally predictive of uptake. Our age-stratified analysis uncovered SIV uptake disparities by ethnicity, sex, age, socioeconomic deprivation, and co-morbidities, warranting further attention by GPs and policymakers alike.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".