Frailty trajectories and associated factors in the years prior to death: evidence from 14 countries in the Survey of Health, Aging and Retirement in Europe
Bibliographic record
Abstract
Abstract Background Age-related changes in frailty have been documented in the literature. However, the evidence regarding changes in frailty prior to death is scarce. Understanding patterns of frailty progression as individuals approach death could inform care and potentially lead to interventions to improve individual’s well-being at the end of life. In this paper, we estimate the progression of frailty in the years prior to death. Methods Using data from 8,317 deceased participants of the Survey of Health, Ageing, and Retirement in Europe, we derived a 56-item Frailty Index. In a coordinated analysis of repeated measures of the frailty index in 14 countries, we fitted growth curve models to estimate trajectories of frailty as a function of distance to death controlling both the level and rate of frailty progression for age, sex, years to death and dementia diagnosis. Results Across all countries, frailty before death progressed linearly. In 12 of the 14 countries included in our analyses, women had higher levels of frailty close to the time of death, although they progressed at a slower rate than men (e.g. Switzerland (-0.008, SE = 0.003) and Spain (-0.004, SE = 0.002)). Older age at the time of death and incident dementia were associated with higher levels and increased rate of change in frailty, whilst higher education was associated with lower levels of frailty in the year preceding death (e.g. Denmark (0.000, SE = 0.001)). Conclusion The progression of frailty before death was linear. Our results suggest that interventions aimed at slowing frailty progression may need to be different for men and women. Further longitudinal research on individual patterns and changes of frailty is warranted to support the development of personalized care pathways at the end of life.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".