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Record W4390813961 · doi:10.1186/s12877-023-04623-5

Uptake of COVID-19 and influenza vaccines in relation to preexisting chronic conditions in the European countries

2024· article· en· W4390813961 on OpenAlexaff
Shangfeng Tang, Lü Ji, Ghose Bishwajit, Shuyan Guo

Bibliographic record

VenueBMC Geriatrics · 2024
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsUniversity of Ottawa
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsMedicineChronic bronchitisVaccinationInfluenza vaccinePopulationCross-sectional studyPublic healthLogistic regressionEnvironmental healthDiabetes mellitusEuropean unionDemographyImmunologyInternal medicinePathology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.047
Threshold uncertainty score0.384

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.103
GPT teacher head0.406
Teacher spread0.303 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations10
Published2024
Admission routes1
Has abstractyes

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