Emergency department use for mental and substance use disorders: descriptive analysis of population-based, linked administrative data in British Columbia, Canada
Bibliographic record
Abstract
OBJECTIVES: Information on emergency department (ED) visits for mental and substance use disorders (MSUDs) is important for planning services but has not been explored in British Columbia (BC), Canada. We describe all MSUD ED visits for people ages 15 and older in the province of BC in 2017/2018 and document trends in MSUD ED visits between 2007/2008 and 2017/2018 by disorder group. DESIGN: Population-based linked administrative data comprised of ED records and physician billings capturing all MSUD ED visits in BC. SETTING: BC is Canada's westernmost province with a population of approximately 5 million. Permanent residents receive first-dollar coverage for all medically necessary services provided by licensed physicians or in hospitals, including ED services. POPULATION: All people age >15 with MSUD ED visits during the study period. MEASURES: All claims with a service location in the ED or corresponding to fee items billed only in the ED were examined alongside ED visits reported through a national reporting system. Patient characteristics (sex/gender, age, location of residence, income, treated disorders and comorbidities) and previous outpatient service use for all ED visits by visit diagnosis are also described. RESULTS: A total of 72 363 people made 134 063 visits to the ED in 2017/2018 for needs related to MSUD. MSUD ED visits have increased since 2010, particularly visits for substance use and anxiety disorders. People with more frequent visits were more likely to be male, on public prescription drug plans for income assistance, prescribed psychiatric medications, and living in lower-income neighbourhoods. They used more community-based primary care and psychiatry services and had lower continuity of primary care. CONCLUSIONS: MSUD ED visits are substantial and growing in BC. Findings underscore a need to strengthen and target community healthcare services and adequately resource and support EDs to manage growing patient populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.015 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".