Uptake rates of influenza vaccination in over 65s in Denmark: a comparison between Danish-born and migrant populations, 2015–21
Bibliographic record
Abstract
WHO's Immunization Agenda 2030 has placed renewed focus on life-course vaccination, including among migrants. Despite the availability of a seasonal vaccine, influenza remains a key contributor to winter excess mortality in Northern Europe, yet limited data on influenza vaccination uptake in migrants has been published. We analyzed Danish national registry data to determine influenza vaccine uptake across six flu seasons (2015/16-2020/21) among migrants (asylum-pathway and quota refugees, family reunified migrants) ≥65 years matched 1:6 on age and gender to Danish-born individuals. We used multivariate logistic regression models controlling for migrant status (immigration status, time in Denmark) and other sociodemographic variables (age, gender, nationality, urban/rural residence) to identify factors associated with influenza vaccination uptake. All analyses were done in R v4.2.1. Across all six seasons, overall flu vaccination uptake was 49.3% (Danish-born: 50.9%; migrant cohort: 39.4%). Migrants were less likely [odds ratio (OR): 0.66; 95% confidence interval (CI): 0.64-0.67] to receive an influenza vaccine across all seasons, with this gap widening from 2015/16 (OR: 0.78; 95% CI: 0.74-0.84) to the 2020/21 season (OR: 0.44; 95% CI: 0.42-0.46). Family-reunified migrants were less likely to receive an influenza vaccine across the study period than asylum-pathway and quota refugees and those from the Sub-Saharan Africa region had the lowest uptake in terms of area of origin. This large and unique dataset shows that migrant groups have lower uptake rates for influenza vaccination compared with Danish-born individuals, with the gap widening over time. Going forward, developing tailored interventions, co-developed in collaboration with communities themselves, will be key.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".