Exploring the relationship between national economic indicators and relative fitness and frailty in middle-aged and older Europeans
Bibliographic record
Abstract
BACKGROUND: on an individual level, lower-income has been associated with disability, morbidity and death. On a population level, the relationship of economic indicators with health is unclear. OBJECTIVE: the purpose of this study was to evaluate relative fitness and frailty in relation to national income and healthcare spending, and their relationship with mortality. DESIGN AND SETTING: secondary analysis of data from the Survey of Health, Ageing and Retirement in Europe (SHARE); a longitudinal population-based survey which began in 2004. SUBJECTS: a total of 36,306 community-dwelling people aged 50 and older (16,467 men; 19,839 women) from the 15 countries which participated in the SHARE comprised the study sample. A frailty index was constructed as the proportion of deficits present in relation to the 70 deficits available in SHARE. The characteristics of the frailty index examined were mean, prevalence of frailty and proportion of the fittest group. RESULTS: the mean value of the frailty index was lower in higher-income countries (0.16 ± 0.12) than in lower-income countries (0.20 ± 0.14); the overall mean frailty index was negatively correlated with both gross domestic product (r = -0.79; P < 0.01) and health expenditure (r = -0.63; P < 0.05). Survival in non-frail participants at 24 months was not associated with national income (P = 0.19), whereas survival in frail people was greater in higher-income countries (P < 0.05). CONCLUSIONS: a country's level of frailty and fitness in adults aged 50+ years is strongly correlated with national economic indicators. In higher-income countries, not only is the prevalence of frailty lower, but frail people also live longer.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".