Multimorbidity patterns and quality of life across European populations: Results from SHARE database
Bibliographic record
Abstract
Abstract An increasing number of people living with multimorbidity may receive suboptimal care since health systems are not well prepared to respond to their complex needs. Identifying which conditions most commonly group together could support better care for patients with multiple diseases. This is particularly critical for conditions that have the most deteriorating effect on quality of life (QoL). The aim of the study was to: 1) identify multimorbidity patterns in Europe and 2) assess their impact on QoL. This was a cross-sectional analysis performed on the Survey of Health, Ageing and Retirement in Europe (SHARE) among adults aged 50+, in eighteen countries (n = 67,179). The Control, Autonomy, Self-Realization and Pleasure (CASP-12v1) scale assessed QoL. Exploratory factor analysis (using 17 conditions) based on tetra-choric correlations, was applied to identify multimorbidity patterns. Associations between patterns and QoL were estimated with multilevel mixed-effects linear regression. The analyses were adjusted for socio-economic, clinical and psycho-social factors, and stratified by sex. Three multimorbidity patterns were found: 1) cardio-metabolic [frequency in men (27.7%); women (25.9%)], 2) psycho-geriatric [1.4%; 0.3%] and 3) mixed [11.7%; 17.4%]. Sample adequacy was confirmed by the Kaiser-Meyer-Olkin test [0.81; 0.84, for men and women, respectively]. The patterns showed slight sex differences. The frequency of all patterns increased with age, while patterns overlapped significantly in the population. The psycho-geriatric pattern had the most deteriorating effect on QoL [-4.5(95%CI:-6.2;-2.8) for men; -5.0(95%CI: -9.5; -0.5) for women]. Recognizing the most common disease patterns may allow more targeted planning and provision of care, including development of clinical guidelines, enhancing collaboration between health professionals, and creation of prevention plans to reduce complications and preserve the best QoL for patients with multimorbidity. Key messages First large population-based study on multimorbidity patterns and their impact on QoL across Europe, using SHARE database. The findings can serve to support better care for multimorbid patients.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".