Change in incidence rates for psychosis in different ethnic groups in south London: findings from the Clinical Record Interactive Search-First Episode Psychosis (CRIS-FEP) study
Bibliographic record
Abstract
BACKGROUND: A higher incidence of psychotic disorders has been consistently reported among black and other minority ethnic groups, particularly in northern Europe. It is unclear whether these rates have changed over time. METHODS: We identified all individuals with a first episode psychosis who presented to adult mental health services between 1 May 2010 and 30 April 2012 and who were resident in London boroughs of Lambeth and Southwark. We estimated age-and-gender standardised incidence rates overall and by ethnic group, then compared our findings to those reported in the Aetiology and Ethnicity of Schizophrenia and Other Psychoses (ÆSOP) study that we carried out in the same catchment area around 10 years earlier. RESULTS: From 9109 clinical records we identified 558 patients with first episode psychosis. Compared with ÆSOP, the overall incidence rates of psychotic disorder in southeast London have increased from 49.4 (95% confidence interval (CI) 43.6-55.3) to 63.1 (95% CI 57.3-69.0) per 100 000 person-years at risk. However, the overall incidence rate ratios (IRR) were reduced in some ethnic groups: for example, IRR (95% CI) for the black Caribbean group reduced from 6.7 (5.4-8.3) to 2.8 (2.1-3.6) and the 'mixed' group from 2.7 (1.8-4.2) to 1.4 (0.9-2.1). In the black African group, there was a negligible difference from 4.1 (3.2-5.3) to 3.5 (2.8-4.5). CONCLUSIONS: We found that incidence rates of psychosis have increased over time, and the IRR varied by the ethnic group. Future studies are needed to investigate more changes over time and determinants of change.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".