Interventions to Support People With HIV Following Hospital Discharge: A Systematic Review
Bibliographic record
Abstract
Background: Individuals hospitalized with HIV-related complications face high post-discharge mortality and morbidity, particularly in resource-limited settings. This systematic review evaluated the impact of interventions to reduce post-hospital mortality, lower readmissions, and improve linkage to care. Methods: We searched the PubMed, Embase, and Cochrane databases up to 1 October 2024 for studies reporting outcomes of post-discharge interventions. Two independent reviewers performed study selection, extracted data, and assessed risk of bias. We pooled data using random effects meta-analysis. Results: We included 4 randomized controlled trials (conducted in Spain, South Africa, Tanzania, and the United States) and 6 observational studies (Canada, Thailand, Zambia, and the United States). Interventions included pre-discharge counseling, medication review, referral to care, and goal setting, as well as post-discharge follow-up via home visits, telephone calls, and support from social workers or community health workers. Pooled data from randomized controlled trials showed no difference between post-discharge interventions and usual care in mortality, but the estimate was imprecise (relative risk [RR], 0.98; 95% CI, .59-1.63). However, interventions may reduce readmissions (RR, 0.82; 95% CI, .52-1.30) and may slightly improve linkage/retention in care (RR, 1.10; 95% CI, .95-1.27). Observational studies reported similar results, with no mortality effect but potential reductions in readmissions (RR, 0.77; 95% CI, .48-1.25) and improved linkage/retention (RR, 1.42; 95% CI, 1.11-1.81). Interventions were largely feasible, acceptable, and low cost. Conclusions: Interventions that include pre-discharge care planning and post-discharge follow-up, such as telephone contact and home visits, may improve linkage to care and reduce readmissions. However, interventions were not associated with reduced post-discharge mortality.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".