The Incidence of Psychotic Disorders and Area-level Marginalization in Ontario, Canada: A Population-based Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: There is limited Canadian evidence on the impact of socio-environmental factors on psychosis risk. We sought to examine the relationship between area-level indicators of marginalization and the incidence of psychotic disorders in Ontario. METHODS: We conducted a retrospective cohort study of all people aged 14 to 40 years living in Ontario in 1999 using health administrative data and identified incident cases of psychotic disorders over a 10-year follow-up period. Age-standardized incidence rates were estimated for census metropolitan areas (CMAs). Poisson regression models adjusting for age and sex were used to calculate incidence rate ratios (IRRs) based on CMA and area-level marginalization indices. RESULTS: There is variation in the incidence of psychotic disorders across the CMAs. Our findings suggest a higher rate of psychotic disorders in areas with the highest levels of residential instability (IRR = 1.26, 95% confidence interval [CI], 1.18 to 1.35), material deprivation (IRR = 1.30, 95% CI, 1.16 to 1.45), ethnic concentration (IRR = 1.61, 95% CI, 1.38 to 1.89), and dependency (IRR = 1.35, 95% CI, 1.18 to 1.54) when compared to areas with the lowest levels of marginalization. Marginalization attenuates the risk in some CMAs. CONCLUSIONS: There is geographic variation in the incidence of psychotic disorders across the province of Ontario. Areas with greater levels of marginalization have a higher incidence of psychotic disorders, and marginalization attenuates the differences in risk across geographic location. With further study, replication, and the use of the most up-to-date data, a case may be made to consider social policy interventions as preventative measures and to direct services to areas with the highest risk. Future research should examine how marginalization may interact with other social factors including ethnicity and immigration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".