Prevalence of a dual diagnosis of mental illness and substance use disorder in Ontario, Canada: a retrospective cohort study using linked administrative data
Bibliographic record
Abstract
ObjectivePopulation-based prevalence estimates of co-occurring mental illness and substance use disorder (herein dual diagnosis) are scarce and derived from a single source (e.g., survey data) which may lead to underestimation of prevalence. We linked administrative data to a representative mental health survey in Ontario, Canada to estimate dual diagnosis prevalence using a data triangulation method. Approach We retrospectively linked the 2002 and 2012 Canadian Community Health Survey on Mental Health (CCHS-MH) to emergency department, inpatient hospital, and outpatient physician records in Ontario, Canada. Mental illness and substance use disorder were ascertained though self-report, Composite International Diagnostic Interview screening, and linked administrative health records. We estimated 1-year, 5-year, and lifetime prevalence of dual diagnosis. ResultsOf the CCHS-MH survey participants, 14,790 (99.8%) were included in the study. The 1-year, 5-year, and lifetime prevalence of dual diagnosis was 2.3%, 4.8%, and 9.8%, respectively, which attenuated to 2.1%, 4.1%, and 8.4%, respectively, when tobacco use disorder was removed from substance use disorder ascertainment. ConclusionDual diagnosis is more common in the general population than previously estimated. Considering barriers to accessing care for both mental illness and substance use disorders among people with dual diagnosis annually, our 5-year prevalence estimate is likely most informative for health system planning. ImplicationsIn the context of increased health burden, barriers to care access, and challenges to effective treatment associated with dual diagnosis, our findings present a case for increased investment in integrated models of mental healthcare and addiction medicine.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".