The Role of Social and Demographic Factors in Shaping Frailty in Europe
Bibliographic record
Abstract
Abstract Background Using data from the Survey of Health, Ageing, and Retirement in Europe (SHARE), this study aims to examine the prevalence and progression of frailty among older adults across Europe, explore regional disparities, and identify key sociodemographic and social factors, such as loneliness, social engagement, and family support, associated with frailty status. Methods A cross-sectional analysis of SHARE data from Waves 6 and 8 (release 9.0.0) was conducted, including non-institutionalised individuals aged 50 or older at both time points, across 17 European countries. Frailty was operationalised using variables defined by Santos-Eggimann (muscle weakness, exhaustion, unintentional weight loss, slowness, and low physical activity). One point was assigned for each criterion met, and participants were categorised as: Non-frail (0 points), Pre-frail (1-2 points), Frail (3-5 points). Adjusted Multilevel Multinomial Logistic regression models were then estimated for the frail and pre-frail groups, using the non-frail group as the reference category, to identify the explanatory factors that underlie individual and country-level effects, focusing on welfare characteristics as a country-level predictor of frailty. Results Significant differences in frailty prevalence were observed across countries. Multilevel multinomial logistic regression indicated that approximately 20% of the variability in frailty prevalence could be attributed to differences at the national level. Frailty and pre-frailty were more common among older adults, women, and individuals with lower educational attainment. Perceived loneliness and limited participation in social activities emerged as the most significant social variables associated with frailty. Notably, the poverty index accounted for about 8% of this between-country variability. Conclusion Our findings reveal substantial variability in frailty prevalence across European countries, where significantly higher rates of frailty and pre-frailty are observed in Southern Europe. These geographic disparities suggest that psychosocial and cultural factors, along with the structure and strength of national welfare characteristics, may play a critical role in influencing frailty outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".