The prevalence and geographic distribution of complex co-occurring disorders: a population study
Bibliographic record
Abstract
AIMS: A subset of people with co-occurring substance use and mental disorders require coordinated support from health, social welfare and justice agencies to achieve diversion from homelessness, criminal recidivism and further health and social harms. Integrated models of care are typically concentrated in large urban centres. The present study aimed to empirically measure the prevalence and distribution of complex co-occurring disorders (CCD) in a large geographic region that includes urban as well as rural and remote settings. METHODS: Linked data were examined in a population of roughly 3.7 million adults. Inclusion criteria for the CCD subpopulation were: physician diagnosed substance use and mental disorders; psychiatric hospitalisation; shelter assistance; and criminal convictions. Prevalence per 100 000 was calculated in 91 small areas representing urban, rural and remote settings. RESULTS: 2202 individuals met our inclusion criteria for CCD. Participants had high rates of hospitalisation (8.2 admissions), criminal convictions (8.6 sentences) and social assistance payments (over $36 000 CDN) in the past 5 years. There was wide variability in the geographic distribution of people with CCD, with high prevalence rates in rural and remote settings. CONCLUSIONS: People with CCD are not restricted to areas with large populations or to urban settings. The highest per capita rates of CCD were observed in relatively remote locations, where mental health and substance use services are typically in limited supply. Empirically supported interventions must be adapted to meet the needs of people living outside of urban settings with high rates of CCD.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".