Global Patterns of Frailty and Multi-Morbidity
Bibliographic record
Abstract
BACKGROUND. Frailty is a syndrome characterized by a decreased resistance to stressors, leading to increased vulnerability to adverse outcomes, including mortality. Multi-morbidity refers to the presence of two or more chronic diseases, and is associated with increased risk of adverse health outcomes. Most of the literature in frailty is based on older people (65+ years) living in high income countries. OBJECTIVE. To compare the predictive ability of three frailty indices for all-cause and one-year mortality among high- (HIC), middle- (MIC), and low- income country (LIC) participants; and to assess the mortality risk associated with multi-morbidity. METHODS. Using data from the Prospective Urban and Rural Epidemiological (PURE) study, we developed three indices using different definitions of frailty (one phenotypic frailty index; two cumulative deficit indices). All indices were tested for predictive ability for mortality both individually and with multi-morbidity. RESULTS. Prevalence of phenotypic frailty was greatest in LIC (8%), intermediate in MIC (7%), and lowest in HIC (4%). Multi-morbidity was most prevalent in HIC (20%), intermediate in MIC (15%), and lowest in LIC (13%). Increased frailty was associated with greater mortality risk using all frailty indices (e.g. HR (95% CI) of 2.63 (2.35-2.95) for the phenotypically frail relative to the robust). At each frailty level, mortality risk was higher within one year of baseline measurement than afterwards, and increased if it was accompanied by concurrent multi-morbidity (e.g. HR of phenotypic frailty increases from 2.27 (1.96-2.62) to 5.08 (4.34-5.95) if accompanied by multi-morbidity). CONCLUSION. All frailty indices predicted mortality. This study is unique in evaluating the prognostic ability of frailty indices in middle-aged adults across HIC, MIC, and LICs.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".