Cost-Utility Analysis of Dolutegravir- Versus Efavirenz-Based Regimens as a First-Line Treatment in Adult HIV/AIDS Patients in Ethiopia
Bibliographic record
Abstract
BACKGROUND: In several countries, the dolutegravir (DTG)-based regimen is generally preferred as first-line antiretroviral therapy (ART) over the efavirenz (EFV)-based regimen, but the evidence in low-income countries is limited. OBJECTIVE: Our study aimed to evaluate the cost effectiveness of DTG- versus EFV-based first-line human immunodeficiency virus (HIV) treatment in Ethiopia. METHODS: We developed a microsimulation model for the progression of HIV/acquired immune deficiency syndrome (AIDS) to examine the cost effectiveness of DTG-based first-line ART compared with an EFV-based regimen from a healthcare payer perspective. We used a lifetime horizon with a 1-month cycle length and a 3% annual discount rate. The primary outcomes were a lifetime cost in US dollars ($), quality-adjusted life-months (QALMs) that converted to QALYs using the formula QALY = QALM/12, and incremental cost-effectiveness ratio (ICER). Deterministic sensitivity analysis was conducted to account for parameter uncertainty. RESULTS: Compared with the EFV-based regimen, the DTG-based regimen was associated with an expected lifetime cost of $12,709 (vs. $12,701) and expected QALYs of 15.3 (vs. 14.7 QALYs) per patient, resulting in an ICER value of $13.33 per QALY. From an alternative analysis with a 5-year time horizon, DTG-based ART was found to be dominant, with expected gains of 0.17 QALYs at a lower cost of $1 per patient. The deterministic sensitivity analysis depicted that the maximum increase in ICER value was $72 per QALY, and all ICER values were below the estimated threshold value. CONCLUSIONS: The DTG-based first-line regimen appears to be cost effective compared with the EFV-based regimen for the treatment of HIV/AIDS patients in an Ethiopian setting.
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 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".