2828 The influence of ethnicity and social disadvantage on frailty in a United Kingdom (UK) population
Bibliographic record
Abstract
Abstract Background Frailty has both health + health economic consequences. There are however few data concerning occurrence of frailty in different ethnic groups in the United Kingdom (UK). The aim of this analysis was to determine frailty prevalence across an ethnically diverse city and to explore the influence of age/social-disadvantage/ethnicity on occurrence. We looked also at frailty related risk of severe illness in relation to COVID-19 infection. Methods Using data from the Greater Manchester Health Record (GMCR), we defined frailty index based on the presence/absence of up to 36 deficits scaled 0–1. We defined frailty based on those with 9 or more deficits (out of total = 36) and electronic frailty index (eFi) as the total number of deficits present, divided by 36 (range 0–1). Results There were 534,567 people aged 60 + years on 1January2020 in Greater Manchester. There was noticeable variation in frailty prevalence across general practices. The majority were white (84%) with 4.7% self-describing as Asian/Asian British, and 1.3% Black/Black British. The prevalence of moderate to severe frailty (eFI > 0.24) was 22.1%. Prevalence was higher in women than men (25.3% vs 18.5%) and increased with age. Compared to the prevalence of frailty in Whites (22.5%) prevalence was higher in Asian/Asian British ethnicity people (28.1%) and lower in those of Black/Black British descent (18.7%). Prevalence increased with increasing social disadvantage (p = 0.002 for trend across disadvantage quintiles). Among those with a positive COVID-19 test those with frailty were more likely to require hospital admission within 28-days, with increased risk for Asian/Asian British descent (OR = 1.47; 95% CI 1.34–1.61) and Black/Black British descent (OR 1.86; 95% CI 1.56–2.20) people vs Whites. Conclusion There is marked variation in occurrence of frailty across Greater Manchester. Frailty is more common in Asian/Asian British people than Whites and less common among Black/Black British with a gradient that relates to social disadvantage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".