Synthesis through Simulation: Insights on the Epidemiology of Mood and Anxiety Disorders in Canada
Bibliographic record
Abstract
OBJECTIVE: Prevalence estimates for mood and anxiety disorders in Canada are available, but various methodological approaches have produced inconsistent results. Simulation studies involve careful examination of available data by an expert modelling team working together with subject matter experts. Simulation can integrate datasets and literature-based estimates from various sources into a coherent mathematical representation of the underlying total population epidemiology. METHODS: Supported by the Mental Health Commission of Canada, a simulation modelling project for mental disorders in Canada was recently undertaken. The modelling was carried out by RiskAnalytica using their Life at Risk platform. Specification and calibration of the model occurred in consultation with national and international experts. RESULTS: To reconcile estimates of incidence and prevalence, recall bias needed to be represented in the model. This suggests that the population prevalence of mood and anxiety disorders has been underestimated by population surveys and may explain a discrepancy observed in the age-specific prevalence in population surveys as compared with studies using administrative data. The number of Canadians with mood and anxiety disorders is projected to increase in upcoming decades as a result of population growth, but, based on conservative assumptions, an increased prevalence proportion is not anticipated. CONCLUSIONS: Simulation models can act as a platform for economic analyses and epidemiologic projections and can support the rapid exploration of what-if scenarios, thereby informing policy decisions. This first national-level simulation provides a high level overview of mood and anxiety disorder epidemiology in Canada.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".