Advanced Stage Disease Progression and Mortality Rate Before and After the Implementation of the Universal Test and Treat Strategy (UTT) for HIV Patients in Ethiopia: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The universal test and treat (UTT) program, is a strategy for eliminating HIV and, it involves screening all populations at risk for HIV infection, initiating early treatment for those diagnosed HIV positive, monitoring and maintaining treatment, and Retaining patients in care. Therefore, this meta-analysis evaluates the impact of the Test-and-Treat strategy on HIV-positive patients in Ethiopia, focusing on mortality rates and disease progression. METHODS: A systematic literature search was conducted using databases such as PubMed, Embase, African Journals Online (AJOL), Google Scholar, and Web of Science. Data were classified into two periods: 2005 to 2015 (before test and treat era) and 2016-2024 (after test and treat strategy). Eligible studies included cohort and cross-sectional designs providing distinct data for these timeframes, irrespective of publication year, to assess reductions in mortality and disease progression (WHO Stage III or IV). Study quality and bias were assessed using the Newcastle-Ottawa Scale (NOS), ensuring rigorous evaluation across selection, comparability, and outcome domains. A random-effects model was employed for the meta-analysis. RESULTS: The pooled mortality rate decreased significantly from 21% (95% CI: 14-29%) in the before test and treat period to 9% (95% CI: 6-12%) after the test and treat period, representing a 57.14% reduction. The proportion of patients in WHO Stage III declined from 47% (95% CI: 39-54%) to 21% (95% CI: 16-26%), a reduction of 55.32%. Similarly, the prevalence of WHO Stage IV decreased from 14% (95% CI: 12-16%) to 8% (95% CI: 5-10%), reflecting a 42.86% reduction. CONCLUSION: The test and treat strategy in Ethiopia has substantial reductions in mortality and disease progression. These results underscore the effectiveness of early, universal treatment initiation in improving patient survival and reducing the burden of HIV-related complications.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.043 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".