Impact of depression and recreational drug use on emergency department encounters and hospital admissions among people living with HIV in Ontario: A secondary analysis using the OHTN cohort study
Bibliographic record
Abstract
INTRODUCTION: Nearly half of HIV-positive patients experience mental health and substance use problems, but many do not receive adequate or ongoing mental health or addiction care. This lack of ongoing care can result in the use of costly acute care services. Prospective evaluations of the relationship between psychiatric and substance use disorders and acute care services use are lacking, and this information is needed to understand unmet needs and improve access to appropriate services. METHODS: We conducted a secondary data analysis from a multicenter, longitudinal, prospective cohort study (n = 3,482 adults) between October 1, 2007 and March 31, 2013. We used explanatory extended Cox proportional hazard regression models to examine the impact of current depression and recreational drug use on acute care services use, and to explore whether current depression and recreational drug use were associated with potentially avoidable acute care services use. RESULTS: Over our 5.5 year study period, HIV-positive participants with current depression-only (aHR [95% CI]:1.2[1.1-1.4]), recreational drug use-only (1.3[1.1-1.6]), or co-occurring depression and recreational drug use (1.4[1.2-1.7]) were associated with elevated hazard of emergency department (ED) encounters compared to participants without these conditions. Over half of ED encounters were potentially avoidable. Participants with current depression-only (1.3[1.1-1.5];1.3[1.03-1.6]), recreational drug use-only (1.3[1.04-1.6];1.5[1.1-1.9]), or co-occurring depression and recreational drug use (1.3[1.04-1.7];1.4[1.06-1.9]) were associated with elevated hazard of low-acuity or repeated ED encounters respectively. CONCLUSIONS: We found a significant increase in ED services use and potentially avoidable ED encounters (including low-acuity or repeated ED encounters), particularly among those with either current depression or recreational drug use. These findings emphasize the challenges in managing HIV and mental health/addiction co-morbidities in the current HIV care model. Future research should evaluate integrated and collaborative care programs for improving the coordination of care and effectively treat mental health and addiction problems among HIV-positive patients in Ontario.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.001 | 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".