Trends and socioeconomic inequalities in acute mental health service use in Canada, 2004–2019: a nationally representative retrospective cohort study
Bibliographic record
Abstract
BACKGROUND: Acute mental health service use (AMHSU), that is, hospitalisations and emergency department (ED) visits for mental health, have been rising in the Canadian province of Ontario and globally; however, national-level estimates are not available. We examine trends and socioeconomic inequalities in AMHSU in the Canadian adult population between 2004 and 2019. METHODS: Using the Canadian Community Health Survey linked to tax and health administrative datasets, we reported prevalence rates of AMHSU using negative binomial regression models. Income-based absolute inequalities in AMHSU were reported using the Slope Index of Inequality. RESULTS: Over the study period, hospitalisations for mood disorders decreased from 144.8 (95% CI: 116.0-173.7) to 67.5 (95% CI: 54.5-80.4) per 100 000, while those for substance-related disorders (SRD) increased. Rates of ED visits increased for all conditions, with the largest increase for anxiety disorders, from 252.3 (95% CI: 210.9-293.6) to 434.1 (95% CI: 382.2-486.1) per 100 000. Females had higher rates of hospitalisations and ED visits for all conditions except SRD. We found pronounced income-based inequalities in both hospitalisations and ED visits for mental health, comparing those at the top versus bottom of the income distribution. Absolute inequalities for hospitalisations widened for SRD, from -93.6 (95% CI: -131.1 to -56.1) to -135.2 (95% CI: -203.4 to -67.1) per 100 000, and decreased for mood disorders, from -309.5 (95% CI: -443.8 to -175.3) to -126.0 (95% CI: -182.0 to -69.9) per 100 000. Additionally, absolute inequalities increased for ED visits across all mental health conditions. CONCLUSION: Interventions aimed at improving access to preventive services could mitigate observed inequalities in AMHSU.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".