Ethnic and age disparities in incidence of stroke over time: analysis from the South London Stroke Register over 16 years: (Oral presentation at: 22nd European Stroke Conference, London, May 28–31, 2013)
Bibliographic record
Abstract
Objectives: To investigate ethnic and age disparities in stroke incidence over time from an inner-city population-based stroke register. Methods: Trends in stroke incidence and prior-to-stroke risk factors were investigated with the South London Stroke Register (SLSR), a population-based register, covering a multi-ethnic population of 271,817 inhabitants in South London with 63% white, 28% black, and 9% of other ethnic group. Age-, ethnicity- and sex- specific incidence rates with 95% confidence intervals were calculated assuming a Poisson distribution and their trends over time tested by the Cochran-Armitage test. Results: 4251 patients with first-ever stroke were registered between 1995 and 2010. Total stroke incidence reduced by 34.4% over the 16-year period from 202.6 to 132.9 per 100,000 population (p<0.0001). Similar declines in stroke incidence were observed in men (228.2 to 143.6) and women (177.9 to 121.4), white (190.6 to 126.2) and black (264.1 to 148.2), and patients aged over 55 years (658.4 to 419.5) (all significant with p<0.0001), but not in patients aged under 55 years (24 to 20.7, not significant with p>0.05). The mean age at stroke decreased significantly from 71.6 years in 1995-1998 to 69.4 years in 2007 2010 (p=0.0002). The proportion of all strokes aged < 55 years increased from 10% in 1995 to 20.4% in 2010. The reduction in prevalence of prior-to-stroke risk factors was mostly seen in white patients over 55 years, while an increase in diabetes was observed in younger black patients aged under 55 years. Conclusions: Total stroke incidence decreased over a 16-year time period. However, this was not seen in younger age groups. Stroke risk increased in younger age and black groups. The advances in risk factor reduction observed in white groups over age 55 years failed to be transferred to younger age groups for both black and white.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".