Association between frailty, chronic conditions and socioeconomic status in community-dwelling older adults attending primary care: a cross-sectional study using practice-based research network data
Bibliographic record
Abstract
Objectives Frailty is a multidimensional syndrome of loss of reserves in energy, physical ability, cognition and general health. Primary care is key in preventing and managing frailty, mindful of the social dimensions that contribute to its risk, prognosis and appropriate patient support. We studied associations between frailty levels and both chronic conditions and socioeconomic status (SES). Design Cross-sectional cohort study Setting A practice-based research network (PBRN) in Ontario, Canada, providing primary care to 38 000 patients. The PBRN hosts a regularly updated database containing deidentified, longitudinal, primary care practice data. Participants Patients aged 65 years or older, with a recent encounter, rostered to family physicians at the PBRN. Intervention Physicians assigned a frailty score to patients using the 9-point Clinical Frailty Scale. We linked frailty scores to chronic conditions and neighbourhood-level SES to examine associations between these three domains. Results Among 2043 patients assessed, the prevalence of low (scoring 1–3), medium (scoring 4–6) and high (scoring 7–9) frailty was 55.8%, 40.3%, and 3.8%, respectively. The prevalence of five or more chronic diseases was 11% among low-frailty, 26% among medium-frailty and 44% among high-frailty groups (χ2=137.92, df 2, p<0.001). More disabling conditions appeared in the top 50% of conditions in the highest-frailty group compared with the low and medium groups. Increasing frailty was significantly associated with lower neighbourhood income (χ2=61.42, df 8, p<0.001) and higher neighbourhood material deprivation (χ2=55.24, df 8, p<0.001). Conclusion This study demonstrates the triple disadvantage of frailty, disease burden and socioeconomic disadvantage. Frailty care needs a health equity approach: we demonstrate the utility and feasibility of collecting patient-level data within primary care. Such data can relate social risk factors, frailty and chronic disease towards flagging patients with the greatest need and creating targeted interventions.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".