Emergency Department Use, Hospitalization, and Their Sociodemographic Determinants among Patients with Substance-Related Disorders: A Worldwide Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Identifying the determinants of emergency department (ED) use and hospitalization among patients with substance-related disorders (SRD) can help inform healthcare services and case management regarding their unmet health needs and strategies to reduce their acute care. Objectives: The present study aimed to identify sociodemographic characteristics, type of used drug, and risky behaviors associated with ED use and hospitalization among patients with SRD. Methods: Studies in English published from January 1st, 1995 to April 30th, 2022 were searched from PubMed, Scopus, Cochrane Library, and Web of Science to identify primary studies on ED use and hospitalization among patients with SRD. Results: Of the 17,348 outputs found, a total of 39 studies met the eligibility criteria. Higher ED use and hospitalization among patients with SRD were associated with a history of homelessness (ED use: OR = 1.93, 95%CI = 1.32–2.83; hospitalization: OR = 1.53, 95%CI = 1.36–1.73) or of injection drug use (ED use: OR = 1.34, 95%CI = 1.13–1.59; hospitalization: OR = 1.42, 95%CI = 1.20–1.69). Being female (OR = 1.24, 95%CI = 1.14–1.35), using methamphetamine (OR = 1.99, 95%CI = 1.24–3.21) and tobacco (OR = 1.25, 95%CI = 1.11–1.42), having HIV (OR = 1.70, 95%CI = 1.47–1.96), a history of incarceration (OR = 1.90, 95%CI = 1.27–2.85) and injury (OR = 2.62, 95%CI = 1.08–6.35) increased ED use only, while having age over 30 years (OR = 1.40, 95%CI = 1.08–1.81) and using cocaine (OR = 1.60, 95%CI = 1.32–1.95) increased hospitalization only among patients with SRD. Conclusions: The finding outline the necessity of developing outreach program and primary care referral for patients with SRD. Establishing a harm reduction program, incorporating needle/syringe exchange programs, and safe injection training with the aim of declining ED use and hospitalization, is likely be another beneficial strategy for patients with SRD.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.031 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".