Trends in the prevalence of depression and anxiety disorders among working-age Canadian adults between 2000 and 2016
Bibliographic record
Abstract
BACKGROUND: Understanding the prevalence of major depressive episodes (MDEs) and anxiety disorders at the population level among different labour force segments is critical to assessing and planning equitable mental health policies for Canadians adults. This study quantified prevalence trends of annually reported MDEs, anxiety disorders, and comorbid MDEs and anxiety disorders among working-age Canadians by labour force status, between 2000 and 2016. DATA AND METHODS: This study used multiple cycles of the Canadian Community Health Survey. MDE prevalence was assessed using variants of the Composite International Diagnostic Interview and the Patient Health Questionnaire-9. Anxiety disorder prevalence captured the presence of an anxiety disorder diagnosed by a healthcare professional. Prevalence estimates were calculated in each survey cycle for three labour force groups: employed, unemployed and not participating in the labour force. A meta-analytic framework stratified by labour force status estimated prevalence trends. RESULTS: Between 2000 and 2016, MDE prevalence remained statistically stable over time at 5.4% (95% confidence interval [CI]: 4.7% to 6.0%), 11.7% (95% CI: 10.4% to 13.0%) and 9.8% (95% CI: 8.5% to 11.2%) among participants who were employed, unemployed, and not participating in the labour force, respectively. Anxiety prevalence ranged from 4.6% to 10.8%, and increased over time (employed: β=0.26%/year, 95% CI: 0.08% to 0.45%; unemployed: β=0.34%/year, 95% CI: -0.10% to 0.78%; not participating in the labour force: β=0.55%/year, 95% CI: 0.15% to 0.95%). Stable comorbid MDE and anxiety prevalence ranged from 1.2% to 4.1% between 2003 and 2016. DISCUSSION: Trends suggest that MDE prevalence has remained stable among all labour force groups since 2000, while anxiety disorder prevalence has modestly increased since 2003. Disorder prevalence increased as labour force attachment decreased across all outcomes studied.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".