Neighbourhood-level social capital, marginalisation, and the incidence of schizophrenia and schizoaffective disorder in Toronto, Canada: a retrospective population-based cohort study
Bibliographic record
Abstract
BACKGROUND: Studies have shown mixed results regarding social capital and the risk of developing a psychotic disorder, and this has yet to be studied in North America. We sought to examine the relationship between neighbourhood-level marginalisation, social capital, and the incidence of schizophrenia and schizoaffective disorder in Toronto, Canada. METHODS: We used a retrospective population-based cohort to identify incident cases of schizophrenia and schizoaffective disorder over a 10 year period and accounted for neighbourhood-level marginalisation and a proxy indicator of neighbourhood social capital. Mixed Poisson regression models were used to estimate adjusted incidence rate ratios (aIRRs). RESULTS: = 649 020) we identified 4841 incident cases of schizophrenia and schizoaffective disorder. A 27% variation in incidence was observed between neighbourhoods. All marginalisation dimensions, other than ethnic concentration, were associated with incidence. Compared to areas with low social capital, areas with intermediate social capital in the second [aIRR = 1.17, 95% confidence interval (CI) 1.03-1.33] and third (aIRR = 1.23, 95% CI 1.08-1.40) quintiles had elevated incidence rates after accounting for marginalisation. There was a higher risk associated with the intermediate levels of social capital (aIRR = 1.18, 95% CI 1.00-1.39) when analysed in only the females in the cohort, but the CI includes the possibility of a null effect. CONCLUSIONS: The risk of developing schizophrenia and schizoaffective disorder in Toronto varies by neighbourhood and is associated with socioenvironmental exposures. Social capital was not linearly associated with risk, and risk differs by sex and social capital quintile. Future research should examine these relationships with different forms of social capital and examine how known individual-level risk factors impact these findings.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".