MétaCan
Menu
Back to cohort
Record W4403825041 · doi:10.1093/eurpub/ckae144.812

Long COVID prevalence differences across 27 European countries that participated in the 2021 Survey of Health, Ageing and Retirement in Europe

2024· article· en· W4403825041 on OpenAlexaff
Sarah Cuschieri, Magdalena Kozela, Agnieszka Chłoń‐Domińczak, Monika Oczkowska, Piotr Wilk

Bibliographic record

VenueEuropean Journal of Public Health · 2024
Typearticle
Languageen
FieldMedicine
TopicLong-Term Effects of COVID-19
Canadian institutionsWestern University
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)AgeingMedicineEnvironmental healthPandemicGerontologyVirologyDiseaseOutbreak

Abstract

fetched live from OpenAlex

Abstract The COVID-19 pandemic caused a significant burden in Europe, with over 60% of infected individuals developing Long COVID symptoms. Variations in Long COVID prevalence across countries are linked to individual-level factors, but the influence of population-based country-level characteristics remains unclear. Thus, our objective was to assess how country-level characteristics related to: (i) the way European countries responded to the COVID-19 pandemic, (ii) their population-level health status, (iii) provision and access to healthcare services, (iv) human development, governance, and environmental risk factors, are associated with Long COVID. We used a sample of 4,004 middle-aged and older adults from 27 European countries who, in summer 2021, participated in Corona Survey 2 (from Survey of Health, Ageing and Retirement in Europe) and who reported COVID-19 infection one year prior to the survey. To assess the potential role of country-level characteristics, while controlling for key individual-level factors, we estimated three sequential multilevel random intercept logistic regression models. Approximately 70% of respondents who had COVID-19 also experienced Long COVID symptoms and there were significant cross-country differences in Long COVID rates, ranging from 32% to 89%. About 13% of the total variance in the risk of having Long COVID can be attributed to cross-country differences and the remaining 87% to individual-level factors. The individual-level characteristics also accounted for 6% of the observed cross-country differences in Long COVID rates (compositional effects) while the country-level characteristics further reduced this variance by over 50%. Both individual and country-level factors influence Long COVID occurrence, emphasizing the need for tailored recovery plans, healthcare planning and resource allocation at the national level to address this condition.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.149
GPT teacher head0.395
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueEuropean Journal of Public HealthSame topicLong-Term Effects of COVID-19French-language works237,207