Estimating the incidence of first-episode psychosis using population-based health administrative data to inform early psychosis intervention services
Bibliographic record
Abstract
BACKGROUND: Discrepancies between population-based estimates of the incidence of psychotic disorder and the treated incidence reported by early psychosis intervention (EPI) programs suggest additional cases may be receiving services elsewhere in the health system. Our objective was to estimate the incidence of non-affective psychotic disorder in the catchment area of an EPI program, and compare this to EPI-treated incidence estimates. METHODS: We constructed a retrospective cohort (1997-2015) of incident cases of non-affective psychosis aged 16-50 years in an EPI program catchment using population-based linked health administrative data. Cases were identified by either one hospitalization or two outpatient physician billings within a 12-month period with a diagnosis of non-affective psychosis. We estimated the cumulative incidence and EPI-treated incidence of non-affective psychosis using denominator data from the census. We also estimated the incidence of first-episode psychosis (people who would meet the case definition for an EPI program) using a novel approach. RESULTS: Our case definition identified 3245 cases of incident non-affective psychosis over the 17-year period. We estimate that the incidence of first-episode non-affective psychosis in the program catchment area is 33.3 per 100 000 per year (95% CI 31.4-35.1), which is more than twice as high as the EPI-treated incidence of 18.8 per 100 000 per year (95% CI 17.4-20.3). CONCLUSIONS: Case ascertainment strategies limited to specialized psychiatric services may substantially underestimate the incidence of non-affective psychotic disorders, relative to population-based estimates. Accurate information on the epidemiology of first-episode psychosis will enable us to more effectively resource EPI services and evaluate their coverage.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".