Characterizing people with frequent emergency department visits and substance use: a retrospective cohort study of linked administrative data in Ontario, Alberta, and B.C., Canada
Bibliographic record
Abstract
Abstract Background Substance use is common among people who visit emergency departments (EDs) frequently. We aimed to characterize subgroups within this cohort to better understand care needs/gaps, and generalizability of characteristics in three Canadian provinces. Methods This was a retrospective cohort study (April 1 st , 2013 to March 31 st , 2016) of ED patients in Ontario, Alberta, and British Columbia (B.C.) We included patients ≥ 18 years with substance use-related healthcare contact during the study period and frequent ED visits, defined as those in the top 10% of ED utilization when all patients were ordered by annual ED visit number. We used linked administrative databases including ED visits and hospitalizations (all provinces); mental heath-related hospitalizations (Ontario and Alberta); and prescriptions, physician services, and mortality (B.C.). We compared to cohorts of people with (1) frequent ED visits and no substance use, and (2) non-frequent ED visits and substance use. We employed cluster analysis to identify subgroups with distinct visit patterns and clinical characteristics during index year, April 1 st , 2014 to March 31 st , 2015. Results In 2014/15, we identified 19,604, 7,706, and 9,404 people with frequent ED visits and substance use in Ontario, Alberta, and B.C (median 37–43 years; 60.9–63.0% male), whose ED visits and hospitalizations were higher than comparison groups. In all provinces, cluster analyses identified subgroups with “extreme” and “moderate” frequent visits (median 13–19 versus 4–6 visits/year). “Extreme” versus “moderate” subgroups had more hospitalizations, mental health-related ED visits, general practitioner visits but less continuity with one provider, more commonly left against medical advice, and had higher 365-day mortality in B.C. (9.3% versus 6.6%; versus 10.4% among people with frequent ED visits and no substance use, and 4.3% among people with non-frequent ED visits and substance use). The most common ED diagnosis was acute alcohol intoxication in all subgroups. Conclusions Subgroups of people with “extreme” (13–19 visits/year) and “moderate” (4–6 visits/year) frequent ED visits and substance use had similar utilization patterns and characteristics in Ontario, Alberta, and B.C., and the “extreme” subgroup had high mortality. Our findings suggest a need for improved evidence-based substance use disorder management, and strengthened continuity with primary and mental healthcare.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".