Multimorbidity and quality of life: longitudinal analysis of the European SHARE database
Bibliographic record
Abstract
Abstract Multimorbidity defined most commonly as co-existence of 2+ diseases is one of the major challenges of an ageing society. It is often accompanied with declining quality of life (QoL). The study aims to 1) assess the relationship between increasing number of diseases and QoL over time, 2) explore the differences between several European countries. Longitudinal data analysis performed on the relevant waves (2004 to 2017) of the Survey of Health, Ageing and Retirement in Europe (SHARE). Data were collected every two years among participants aged 50+. Health conditions were identified through an open-end questionnaire containing 17 prelisted conditions. QoL was evaluated by Control, Autonomy, Self-Realization and Pleasure questionnaire (CASP-12v). Maximum QoL score, describing the best state was 48; minimum, 12 points. Association between increasing number of diseases and QoL is being assessed with multilevel analysis accounting for time and clustering within household and country. Minimum follow-up is 2 time points. Confounding variables include age, sex, socio-economic status, social support and health care parameters. Preliminary findings show that 20 countries and 87,087 individuals participated in at least 2 waves; 80,041 answered CASP at least twice. Number of diseases when first reported was on average 1.65 (IQR=0,2) and increased to 1.88 (IQR=1,3) when last reported. Similarly, between first and last reported point QoL decreased on average by -0.32 (SD: ± 5.9); estimated by non-rescaled CASP scale. Greece showed the strongest decrease of -1.73 (SD: ± 6.36), while QoL increased in some countries, the most in Portugal for 0.76 (SD: ± 5.62). Our preliminary findings suggest high geographic variations in QoL, possibly driven by differential clustering of multimorbidity across Europe, design issues and other factors. This may underline the need for country-specific analysis and initiatives to address the growing burden of multimorbidity in our ageing populations. Key messages First longitudinal study to address this research questions across wide range of European countries using SHARE. Study accounts for large number of confounding factors owing to the abundance of collected information.
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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 | 0.004 | 0.008 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".