The Incidence of First-Episode Schizophrenia-Spectrum Psychosis in Adolescents and Young Adults in Montreal: An Estimate from an Administrative Claims Database
Bibliographic record
Abstract
OBJECTIVE: There has been increasing interest in the psychiatric literature on research and service delivery focused on first-episode psychosis (FEP), and accurate information on the incidence of FEP is crucial for the development of services targeting patients in the early stages of illness. We sought to obtain a population-based estimate of the incidence of first-episode schizophrenia-spectrum psychosis (SSP) among adolescents and young adults in Montreal. METHODS: Population-based administrative data from physician billings, hospitalizations, pharmacies, and public health clinics were used to estimate the incidence of first-episode SSP in Montreal. A 3-year period (2004-2006) was used to identify patients with SSP aged 14 to 25 years. We used a 4- to 6-year clearance period to remove patients with a history of any psychotic disorder or prescription for an antipsychotic. RESULTS: We identified 456 patients with SSP, yielding a standardized annual incidence of 82.9 per 100 000 for males (95% CI 73.7 to 92.1), and 32.2 per 100 000 for females (95% CI 26.7 to 37.8). Using ecologic indicators of material and social deprivation, we found a higher-incidence proportion of SSP among people living in the most deprived areas, relative to people living in the least deprived areas. CONCLUSIONS: Clinical samples obtained from psychiatric services are unlikely to capture all treatment-seeking patients, and epidemiologic surveys have resource-intensive constraints, making this approach challenging for rare forms of psychopathology; therefore, population-based administrative data may be a useful tool for studying the frequency of psychotic disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".