Impact of Co-occurring Mental Disorders and Chronic Physical Illnesses on Frequency of Emergency Department Use and Hospitalization for Mental Health Reasons
Bibliographic record
Abstract
Background: Patients with mental disorders (MD) are at high risk for a wide range of chronic physical illnesses (CPI), often resulting in greater use of acute care services. This study estimated risk of emergency department (ED) use and hospitalization for mental health (MH) reasons among 678 patients with MD and CPI compared to 1,999 patients with MD only. Methods: Patients visiting one of six Quebec (Canada) ED for MH reasons and at onset of a MD in 2014–15 (index year) were included. Negative binomial models comparing the two groups estimated risk of ED use and hospitalization at 12-month follow-up to index ED visit, controlling for clinical, sociodemographic, and service use variables. Results: Patients with MD, more severe overall clinical conditions and those who received more intensive specialized MH care had higher risks of frequent ED use and hospitalization. Continuity of medical care protected against both ED use and hospitalization, while general practitioner (GP) consultations protected against hospitalization only. Patients aged 65+ had lower risk of ED use, whereas risk of hospitalization was higher for the 45–64- vs. 12–24-year age groups, and for men vs. women. Conclusion: Strategies including assertive community treatment, intensive case management, integrated co-occurring treatment, home treatment, and shared care may improve adequacy of care for patients with MD-CPI, as well as those with MD only whose clinical profiles were severe. Prevention and outreach strategies may also be promoted, especially among men and older age groups.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".