Prevalence of frailty in 62 countries across the world: a systematic review and meta-analysis of population-level studies
Bibliographic record
Abstract
INTRODUCTION: The prevalence of frailty at population level is unclear. We examined this in population-based studies, investigating sources of heterogeneity. METHODS: PubMed, Embase, CINAHL and Cochrane Library databases were searched for observational population-level studies published between 1 January 1998 and 1 April 2020, including individuals aged ≥50 years, identified using any frailty measure. Prevalence estimates were extracted independently, assessed for bias and analysed using a random-effects model. RESULTS: In total, 240 studies reporting 265 prevalence proportions from 62 countries and territories, representing 1,755,497 participants, were included. Pooled prevalence in studies using physical frailty measures was 12% (95% CI = 11-13%; n = 178), compared with 24% (95% CI = 22-26%; n = 71) for the deficit accumulation model (those using a frailty index, FI). For pre-frailty, this was 46% (95% CI = 45-48%; n = 147) and 49% (95% CI = 46-52%; n = 29), respectively. For physical frailty, the prevalence was higher among females, 15% (95% CI = 14-17%; n = 142), than males, 11% (95% CI = 10-12%; n = 144). For studies using a FI, the prevalence was also higher in females, 29% (95% CI = 24-35%; n = 34) versus 20% (95% CI = 16-24%; n = 34), for males. These values were similar for pre-frailty. Prevalence increased according to the minimum age at study inclusion. Analysing only data from nationally representative studies gave a frailty prevalence of 7% (95% CI = 5-9%; n = 46) for physical frailty and 24% (95% CI = 22-26%; n = 44) for FIs. CONCLUSIONS: Population-level frailty prevalence varied by classification and sex. Data were heterogenous and limited, particularly from nationally representative studies making the interpretation of differences by geographic region challenging. Common methodological approaches to gathering data are required to improve the accuracy of population-level prevalence estimates. PROTOCOL REGISTRATION: PROSPERO-CRD42018105431.
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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 | 0.035 | 0.078 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.029 |
| Bibliometrics | 0.016 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".