Sociodemographic Patterns in Mood and Anxiety Disorders Among Youth and Young Adults in Canada: An Analysis of the 2015-2021 Surveillance Data
Bibliographic record
Abstract
Background and objective Mood and anxiety disorders are increasingly affecting youth and young adults in Canada, with significant sociodemographic disparities. Understanding these patterns is essential for guiding targeted mental health interventions. Hence, this study aimed to describe the trends in diagnosed mood and anxiety disorders among Canadian youth (12-29 years) from 2015 to 2021, and to compare prevalence across sociodemographic groups (sex, age, income, geography, and ethnocultural identity). Methods This study used cross-sectional surveillance data from the Canadian Community Health Survey (2015-2021), analyzing weighted prevalence estimates and 95% confidence intervals (CI) stratified by key sociodemographic factors using descriptive statistical methods. Results The overall prevalence of diagnosed mood and anxiety disorders increased from 12.9% (95% CI: 11.7-14.2) in 2015 to 17.3% (95% CI: 15.6-18.9) in 2021. Female youth consistently reported higher rates than males (22.8% vs. 12.0% in 2021). Young adults aged 18-25 had the highest burden compared to adolescents (13.4%) and older youth. The prevalence was disproportionately higher among indigenous populations (33.7%), low-income groups, and residents of Atlantic provinces. Immigrants and racialized groups reported lower prevalence, but this may be attributed to underdiagnosis or systemic barriers to care. Conclusions Mood and anxiety disorders among Canadian youth are on the rise, with significant disparities in terms of sex, age, income, and ethnicity. These findings underscore the urgent need for equitable, culturally competent mental health services, early intervention, and policy responses tailored to vulnerable groups. Population-level surveillance combined with clinical insights is crucial to inform targeted mental health strategies and promote resilience in Canada's youth and young adult populations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".