Long COVID prevalence differences across 27 European countries that participated in the 2021 Survey of Health, Ageing and Retirement in Europe
Bibliographic record
Abstract
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.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".