Prevalence of Frailty Among Hospitalized Older Adults in New Brunswick, Canada: an Administrative Data Population-Based Study
Bibliographic record
Abstract
Background Characterizing the prevalence and distribution of frailty within a population can help guide decision-making and policy development by identifying health service resource needs. Here we describe the prevalence of frailty among hospitalized older adults in New Brunswick (NB), Canada. Methods NB administrative hospital claims data were used to identify hospitalized older adults aged 65 or older between April 1, 2017 and March 31, 2019. Frailty was quantified using the Hospital Frailty Risk Score (HFRS), a validated frailty tool derived from claims data. Individuals with a HFRS ranked as intermediate or high were categorized as frail. The distribution of frailty across sex and age are described. Crude prevalence estimates and corresponding 95% confidence intervals are presented. Results A total of 55,675 older adults (52% females) were hospitalized. The overall prevalence of frailty was 21.2% (95%CI: 20.9–21.6). Prevalence increased with age: 12.7% (95%CI: 12.3–13.1) in the 65–74 age group, 24.7% (95%CI: 24.1–25.3) in the 75–84 age group and 41.6% (95%CI: 40.6–42.7) for those aged 85 and over (p<.001). Discussion/Conclusion The distribution of frailty is in line with that reported in other jurisdictions. We demonstrate the feasibility of the HFRS to identify and characterize frailty in a large sample of older adults who were hospitalized, using administrative data.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".