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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".