Estimating the Prevalence of Mental and Substance Use Disorders: A Systematic Approach to Triangulating Available Data to Inform Health Systems Planning
Bibliographic record
Abstract
OBJECTIVE: To estimate the prevalence of specific mental and substance use disorders (MSUDs), by age and sex, as a first step toward informing needs-based health systems planning by decision-makers. METHODS: We developed a conceptual framework and a systematic methodology for combining available data sources to yield prevalence estimates for specific MSUDs. Data sources used included published, peer-reviewed literature from Canada and comparable countries, Canadian population survey data, and health administrative data from British Columbia. Several well-established methodologies including systematic review and meta-analyses of published prevalence estimates, modelling of age- and sex-specific distributions, and the Global Burden of Disease severity distribution model were incorporated in a novel mode of triangulation. RESULTS: Using this novel approach, we obtained prevalence estimates for 10 MSUDs for British Columbia, Canada, as well as prevalence distributions across age groups, by sex. CONCLUSION: Obtaining reliable assessments of disorder prevalence and severity is a useful first step toward rationally estimating service need and plan health services. We propose a methodology to leverage existing information to obtain robust estimates in a timely manner and with sufficient granularity to, after adjusting for comorbidity and matching with severity-specific service bundles, inform need-based planning efforts for adult (15 years and older) mental health and substance use services.
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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 | 0.269 | 0.515 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.043 | 0.028 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| 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".