Determinants of emergency department use and hospitalization among people who inject drugs: A systematic review and meta-analysis
Bibliographic record
Abstract
Background The present study aimed to identify sociodemographic characteristics, risky behaviors, type of drug use, and service use variables associated with emergency department (ED) use and hospitalization among people who inject drugs (PWID).Methods Studies in English published from January 1, 1995, to December 15, 2021, were searched for on PubMed, Scopus, Cochrane, and Web of Science to identify primary studies on ED use and hospitalization among PWID.Results After a detailed assessment of 17,348 outputs, a total of 19 studies met the eligibility criteria for inclusion in the analysis. Greater risks of ED use and hospitalization among PWID were associated with (i) a history of homelessness, (ii) HIV-positive status, and (iii) injecting drugs more than four times per day. Individuals were more likely to use the ED if they (i) had a history of physical abuse, (ii) were using cocaine and methamphetamine, and (iii) had used primary care services. Women and individuals with chronic physical illnesses were more likely to be hospitalized.Conclusions The present study is the first to integrate determinants related to ED use and hospitalization based on sociodemographic characteristics, risky behaviors, type of drug, and service use determinants among PWID. To reduce ED use and hospitalization among PWID, the paper also recommends various strategies could be implemented.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.025 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".