F133. ARE WE UNDERESTIMATING THE INCIDENCE OF PSYCHOTIC DISORDER? ESTIMATES FROM POPULATION-BASED HEALTH ADMINISTRATIVE DATA FROM ONTARIO, CANADA
Bibliographic record
Abstract
Recent incidence estimates from population-based health administrative data in Ontario suggest an incidence rate of non-affective psychosis of 55.6 per 100,000 person-years in the general population. However, early psychosis intervention (EPI) programs across the province estimate that the treated incidence of first-episode psychosis is in the range of 12 to 13 per 100,000 per year, which corresponds to frequently cited estimates of the incidence of schizophrenia. This discrepancy between population-based estimates of incidence and the treated incidence reported by EPI programs suggests that there may be additional cases of psychotic disorder receiving services elsewhere in the health care system. Our objective was to estimate the incidence of non-affective psychosis in the catchment area of an EPI program, and compare this estimate to the EPI-treated incidence of psychotic disorder. We constructed a retrospective cohort of incident cases of non-affective psychosis in the catchment area from 1997 to 2015 using linked population-based health administrative data. Cases were identified by the presence either one hospitalization with a primary discharge diagnosis of non-affective psychosis, or two outpatient physician billings with a diagnosis of non-affective psychosis occurring within a 12-month period. We estimated cumulative incidence proportions of non-affective psychoses for the total sample meeting our case definition using denominator data obtained from the census. Using admission ratios from the EPI program (# admitted/# referred), we correct our population-based incidence estimate to yield an estimated “true incidence” of non-affective psychosis. Reslts: Our case definition identified 2,864 cases of incident non-affective psychosis over the 17-year time-period. We estimate that the “true incidence” of non-affective psychosis in the program catchment area is more than twice as high as the EPI-treated incidence estimates (final numbers forthcoming). Our findings suggest that incidence estimates obtained using case ascertainment strategies limited to specialized psychiatric services may substantially underestimate the incidence of non-affective psychotic disorders, relative to population-based estimates. We need accurate information on the epidemiology of psychotic disorders to allow service planners and administrators to more effectively resource EPI services and evaluate their coverage.
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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 | 0.006 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".