Trends of multimorbidity in 15 European countries: a population-based study in community-dwelling adults aged 50 and over
Bibliographic record
Abstract
BACKGROUND: The objective of this work was to analyse the prevalence trends of multimorbidity among European community-dwelling adults. METHODS: A temporal series study based on waves 1, 2, 4, 5, 6 and 7 of the Survey of Health, Ageing and Retirement in Europe (SHARE) was conducted, and community-dwelling participants aged 50+ (n = 274,614) from 15 European countries were selected for the period 2004-2017. Prevalence, adjusted by age, Average Annual Percentage Change (APC) and 95% confidence interval (95% CI) were all calculated. Trend analyses were realised by period, age groups and groups of diseases. RESULTS: The results showed a large variability in the prevalence of multimorbidity in adults aged 50 and over among European countries. Increase in the prevalence of multimorbidity in the countries of central Europe (Austria, Belgium, Czech Republic, France, Germany and Switzerland) and Spain in both sexes, and in the Netherlands among men. Stability was observed in northern and eastern European countries. Musculoskeletal and neurodegenerative groups showed more significant changes in the trend analyses. CONCLUSIONS: This information can be useful for policy makers when planning health promotion and prevention policies addressing modifiable risk factors in health.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.001 | 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".