Acute mental health service use following onset of the COVID-19 pandemic in Ontario, Canada: a trend analysis
Bibliographic record
Abstract
BACKGROUND: The extent to which heightened distress during the COVID-19 pandemic translated to increases in severe mental health outcomes is unknown. We examined trends in psychiatric presentations to acute care settings in the first 12 months after onset of the pandemic. METHODS: This was a trends analysis of administrative population data in Ontario, Canada. We examined rates of hospitalizations and emergency department visits for mental health diagnoses overall and stratified by sex, age and diagnostic grouping (e.g., mood disorders, anxiety disorders, psychotic disorders), as well as visits for intentional self-injury for people aged 10 to 105 years, from January 2019 to March 2021. We used Joinpoint regression to identify significant inflection points after the onset of the pandemic in March 2020. RESULTS: Among the 12 968 100 people included in our analysis, rates of mental health-related hospitalizations and emergency department visits declined immediately after the onset of the pandemic (peak overall decline of 30% [hospitalizations] and 37% [emergency department visits] compared to April 2019) and returned to near prepandemic levels by March 2021. Compared to April 2019, visits for intentional self-injury declined by 33% and remained below prepandemic levels until March 2021. We observed the largest declines in service use among adolescents aged 14 to 17 years (55% decline in hospitalizations, 58% decline in emergency department visits) and 10 to 13 years (56% decline in self-injury), and for those with substance-related disorders (33% decline in emergency department visits) and anxiety disorders (61% decline in hospitalizations). INTERPRETATION: Contrary to expectations, the abrupt decline in acute mental health service use immediately after the onset of the pandemic and the return to near prepandemic levels that we observed suggest that changes and stressors in the first 12 months of the pandemic did not translate to increased service use. Continued surveillance of acute mental health service use is warranted.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".