2795 Using the dynamics of the frailty index to assess population health across different countries
Bibliographic record
Abstract
Abstract Background Worldwide population ageing is motivating how to measure the health of ageing populations. One approach is to compare dynamics of frailty, assessed by the cumulative-deficit frailty index, across different populations. We aim to compare the frailty distribution, mortality risk, and change in frailty over time between 18 countries. Methods Using data from five harmonised international surveys (HRS, SHARE, ELSA, CHARLS and MHAS) we assessed frailty with a 40-item frailty index (baseline, 2-, 4- and 6-year follow-up), along with mortality status. We constructed separate regression models for participants with the fewest baseline health deficits (‘zero-state’—assessing ambient health of the population) and the rest of the population (‘non-zero-state’). Using logistic and negative binomial, respectively, we assessed the odds of mortality and the rate of deficit accumulation (i.e. change in frailty index) between countries, adjusted for baseline frailty, age, and sex. Results Highest baseline frailty, mortality risk, and the most rapid increases in frailty were observed in Mexico, followed by China. Differences in mortality risk and deficit accumulation were similar regardless of baseline frailty. Lowest mortality risk and the slowest rates of deficit accumulation were observed in Scandinavian countries and in Switzerland. Differences between Central/Southern European countries, USA and UK varied when comparing zero-state with non- zero-state models. For example, mortality rates and deficit accumulation were relatively lower among the healthiest subset of the USA (and to a lesser extent UK) population. However, when modelling those with some degree of baseline frailty, mortality and deficit accumulation in the USA were relatively higher compared to European countries. Conclusion Dynamics of the frailty index can provide insights into population-level differences in health across different settings. For some, but not all, countries, findings are sensitive to the degree of frailty present at baseline, which may reflect inequalities in healthcare provision or access.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".