Identifying mental health and substance use disorders using emergency department and hospital records: a population-based retrospective cohort study of diagnostic concordance and disease attribution
Bibliographic record
Abstract
OBJECTIVES: Administrative data are increasingly being used for surveillance and monitoring of mental health and substance use disorders (MHSUD) across Canada. However, the validity of the diagnostic codes specific to MHSUD is unknown in emergency departments (EDs). Our objective was to determine the concordance, and individual-level and hospital-level factors associated with concordance, between diagnosis codes assigned in ED and at discharge from hospital for MHSUD-related conditions. DESIGN: Population-based retrospective cohort study. SETTING: EDs and hospitals within Vancouver Coastal Health Authority (VCH), British Columbia, Canada. PARTICIPANTS: 16 926 individuals who were admitted into a VCH hospital following an ED visit from 1 April 2009 to 31 March 2017, contributing to 48 116 pairs of ED and hospital discharge diagnoses. PRIMARY AND SECONDARY OUTCOME MEASURES: We examined concordance in identifying MHSUD between the primary discharge diagnosis codes based on the International Statistical Classification of Diseases, 9th and 10th Revisions (Canada) assigned in the ED and those assigned in the hospital among all ED visits resulting in a hospital admission. We calculated the percent overall agreement, positive agreement, negative agreement and Cohen's kappa coefficient. We performed multiple regression analyses to identify factors independently associated with discordance. RESULTS: We found a high level of concordance for broad categories of MH conditions (overall agreement=0.89, positive agreement=0.74 and kappa=0.67), and a fair level of concordance for SUDs (overall agreement=0.89, positive agreement=0.31 and kappa=0.27). SUDs were less likely to be indicated as the primary cause in ED as opposed to in hospital (3.8% vs 11.7%). In multiple regression analyses, ED visits occurring during holidays, weekends and overnight (21:00-8:59 hours) were associated with increased odds of discordance in identifying MH conditions (adjusted OR 1.47, 95% CI 1.11 to 1.93; 1.27, 95% CI 1.16 to 1.40; 1.30, 95% CI 1.19 to 1.42, respectively). CONCLUSIONS: ED data could be used to improve surveillance and monitoring of MHSUD. Future efforts are needed to improve screening for individuals with MHSUD and subsequently connect them to treatment and follow-up care.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".