Evaluating the impact of achieving cascade equality in Eswatini: a modeling study on the prevention impacts of antiretroviral therapy
Bibliographic record
Abstract
OBJECTIVE: Inequalities in the antiretroviral therapy (ART) cascade across subpopulations remain an ongoing challenge in the global HIV response. Eswatini achieved the UNAIDS 95-95-95 ART cascade targets by 2020, with differentiated programs to minimize inequalities across subpopulations, including for female sex workers (FSWs) and their clients. We sought to estimate the impacts of this achievement, through a retrospective impact evaluation of ART scale-up in Eswatini. DESIGN: Drawing on population-level and FSW-specific surveys, we developed a compartmental model of heterosexual HIV transmission, and calibrated it to observed HIV prevalence, incidence, and ART cascade scale-up in Eswatini. METHODS: We defined four counterfactual scenarios in which the population overall reached only 80-80-90 by 2020, but where FSW, clients, both, or neither were disproportionately left behind, reaching only 60-40-80. We estimated additional HIV infections by 2020 in counterfactual vs. observed scenarios, and identified epidemic conditions which maximized differences. RESULTS: Compared with observed cascade scale-up in Eswatini, leaving behind neither FSW nor their clients led to median (95% confidence interval, 95% CI) 8.8 (6.3-10.9) additional infections by 2020 vs. 14.3 (10.8-18.6) if both were left behind, a 63 (31-128) increase. The impact of leaving behind FSW and/or clients was largely determined by their population sizes and HIV incidence ratio among clients vs. men overall. CONCLUSION: Inequalities in the ART cascade across subpopulations can undermine the anticipated prevention impacts of cascade scale-up. As Eswatini has shown, addressing inequalities in the ART cascade that intersect with transmission risk can maximize incidence reductions from cascade scale-up.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".