Prevalence of Co-Occurring Substance Use and other Mental Disorders in the Canadian Population
Bibliographic record
Abstract
OBJECTIVE: Population health surveys around the world have studied the epidemiology of comorbid substance use disorders (SUDs) and other mental disorders as part of larger efforts to assess needs and direct integrated planning and delivery of services. This study presents the first national assessment in Canada of the prevalence of co-occurring SUDs and other mental disorders, with attention to differences by substance problem severity, sex, age, and region. METHODS: This work is a secondary analysis of data from the 2002 Canadian Community Health Survey: Mental Health and Well-Being. The sample was obtained using a multistage stratified cluster design (n = 36,984, response rate = 77%). RESULTS: The 12-month population prevalence of co-occurring disorders was 1.7%. The 12-month prevalence of other mental disorders was higher among those with illicit drug, relative to alcohol, problems and among those with dependence, compared with those with less severe problems. Sex and age differences mirrored population differences in pure disorders. Salient regional differences included the higher rate of co-occurring disorders in British Columbia and the lower rates in Quebec. CONCLUSIONS: Cross-study comparisons are hampered by methodological differences; however, these Canadian rates are at the lower end of the range reported internationally. This might have resulted from the exclusion of several disorders known to be highly comorbid with SUDs. Nonetheless, prevalence is high in certain subgroups, and efforts under way to improve Canada's substance abuse and mental health services should continue to ensure that adequate attention is directed to the needs of people with co-occurring 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 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) | 0.003 | 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".