Retention in care among people living with human immunodeficiency virus (HIV) in a low-resource setting
Bibliographic record
Abstract
BACKGROUND: This study aimed to evaluate the retention in care among a cohort of Egyptian people living with HIV (PLWHIV). METHOD: The study was conducted on PLWHIV attending Kasr Alainy HIV and Viral Hepatitis Centre, Cairo, Egypt, from January 1, 2019, to March 31, 2023. PLWHIV were considered not retained in care if there was no documented clinical visit or HIV viral load (VL) or CD4 count test for more than 6 months from their last recorded visit or test. Multivariable logistic regression analysis was used to test factors associated with retention in care. RESULTS: After excluding those who died and were referred, 369 PLWHIV were included in the analysis, and retention in care was observed in 325 (88%). The majority were males (81.8%) with a median age of 34 [29-41] years. Undetectable VL (OR: 3.555; 95% CI: 1.49-8.47), hepatitis B vaccination (OR: 2.835; 95% CI: 1.07-7.48), CD4 test availability (OR: 2.604; 95% CI: 1.02-6.64), receiving dolutegravir based antiretroviral therapy (OR: 2.429; 95% CI: 1.06-5.537), and longer duration of know HIV infection (OR: 1.025; 95% CI: 1.01- 1.04) were correlated with retention in care. Surprisingly, higher education levels were negatively correlated with retention in care (OR: 0.195, 95%: CI: 0.071-0.533), suggesting the need for further research to explore this relationship. CONCLUSION: These results are invaluable for developing targeted interventions and informing health policies to improve retention in HIV care in Egypt. Enhancing access to VL and CD4 testing, promoting VL suppression, and focusing on specific groups at risk of dropping out of care are essential strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".