Uptake of COVID-19 and influenza vaccines in relation to preexisting chronic conditions in the European countries
Bibliographic record
Abstract
Abstract Background The suboptimal uptake of COVID-19 and influenza vaccines among those with non-communicable chronic diseases is a public health concern, because it poses a higher risk of severe illness for individuals with underlying health conditions, emphasizing the need to address barriers to vaccination and ensure adequate protection for this vulnerable population. In the present study, we aimed to identify whether people with chronic illnesses are more likely to get vaccinated against COVID-19 and influenza in the European Union. Methods Cross-sectional data on 49,253 men (n = 20,569) and women (n = 28,684) were obtained from the ninth round of the Survey of Health, Ageing and Retirement in Europe (June – August, 2021). The outcome variables were self-reported COVID-19 and influenza vaccine uptake status. The association between the uptake of the vaccines and six preexisting conditions including high blood pressure, high blood cholesterol, chronic lung disease, diabetes, chronic bronchitis, and asthma was estimated using binary logistic regression methods. Results The vaccination coverage for COVID-19 ranged from close to 100% in Denmark (98.2%) and Malta (98.2%) to less than 50% in Bulgaria (19.1%) and Romania (32.7%). The countries with the highest percentage of participants with the influenza vaccine included Malta (66.7%), Spain (63.7%) and the Netherlands (62.5%), and those with the lowest percentage included Bulgaria (3.7%), Slovakia (5.8%) and Poland (9.2%). Participants with high blood pressure were 3% less likely [Risk difference (RD) = -0.03, 95% CI = -0.04, -0.03] to report taking COVID-19 and influenza [RD = -0.03, 95% CI= -0.04, -0.01] vaccine. Those with chronic lung disease were 4% less likely [RD = -0.04, 95% CI= -0.06, -0.03] to report taking COVID-19 and 2% less likely [RD= -0.02, 95% CI = -0.04, -0.01] to report taking influenza vaccine. Men and women with high blood pressure were 3% less likely to have reported taking both of the vaccines. Conclusions Current findings indicate a suboptimal uptake of COVID-19 and influenza vaccines among adult men and women in the EU countries. Those with preexisting conditions, including high blood pressure and chronic lung disease are less likely to take the vaccines.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".