Mortality along the continuum of HIV care in Rwanda: a model-based analysis
Bibliographic record
Abstract
BACKGROUND: HIV is the leading cause of death among adults in sub-Saharan Africa. However, mortality along the HIV care continuum is poorly described. We combine demographic, epidemiologic, and health services data to estimate where are people with HIV dying along Rwanda's care continuum. METHODS: We calibrated an age-structured HIV disease and transmission stochastic simulation model to the epidemic in Rwanda. We estimate mortality among HIV-infected individuals in the following states: untested, tested without establishing care in an antiretroviral therapy (ART) program (unlinked), in care before initiating ART (pre-ART), lost to follow-up (LTFU) following ART initiation, and retained in active ART care. We estimated mortality among people living with HIV in Rwanda through 2025 under current conditions, and with improvements to the HIV care continuum. RESULTS: In 2014, the greatest portion of deaths occurred among those untested (35.4%), followed by those on ART (34.1%), reflecting the large increase in the population on ART. Deaths among those LTFU made up 11.8% of all deaths among HIV-infected individuals in 2014, and in the base case this portion increased to 18.8% in 2025, while the contribution to mortality declined among those untested, unlinked, and in pre-ART. In our model only combined improvements to multiple aspects of the HIV care continuum were projected to reduce the total number of deaths among those with HIV, estimated at 8177 in 2014, rising to 10,659 in the base case, and declining to 5,691 with combined improvements in 2025. CONCLUSION: Mortality among those untested for HIV contributes a declining portion of deaths among HIV-infected individuals in Rwanda, but the portion of deaths among those LTFU is expected to increase the most over the next decade. Combined improvements to the HIV care continuum might be needed to reduce the number of deaths among those with HIV.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".