How multimorbidity and socio-economic factors affect Long Covid: Evidence from European Countries
Bibliographic record
Abstract
Abstract Introduction An increasing number of individuals continue reporting symptoms following the acute stage of Covid-19 infection. Few studies have investigated the factors related to Long Covid. Our aim was to assess how multimorbidity, socio-economic factors (immigration, education, employment, and income), and country of residence affect the presence and number of persistent symptoms attributable to Covid-19 illness in Europe. Methods We used data from the SHARE Corona surveys collected in 2020 and 2021. The sample included 4,004 respondents aged 50 years and older who were affected by the Corona virus. The outcome was the number of persistent symptoms attributable to Covid-19 illness, including: fatigue; cough, congestion, shortness of breath; loss of taste or smell; headache; body aches, joint pain; chest or abdominal pain; diarrhoea, nausea; and confusion. We conducted a multilevel analysis for a hurdle model with negative binomial distribution. Results Overall, 73% of respondents were estimated to have at least one persistent symptom associated with Covid-19 illness and, on average, they had 2.73 symptoms. However, there were some statistically significant across country differences in the presence and number of symptoms. Respondents who were employed were more likely to report at least one symptom (OR = 1.40) and those with higher levels of education were less likely to report any symptoms (OR = 0.67). Respondents with multimorbidity had an increased risk of experiencing an additional symptom (RR = 1.12) while respondents who were employed had a decreased risk of experiencing an additional symptom (RR = 0.85). Discussion and conclusions Presence and number of persistent symptoms associated with Covid-19 illness was highly prevalent and varied significantly across European countries. Evidence from the present work underscores the need to target high-risk groups and those with multimorbidity to reduce long-term health consequences of Covid-19.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".