Cost-Effectiveness of Accelerated HIV Response Scenarios in Côte d'Ivoire
Bibliographic record
Abstract
BACKGROUND: Despite Côte d'Ivoire epidemic being labeled as "generalized," key populations (KPs) are important to overall transmission. Using a dynamic model of HIV transmission, we previously estimated the impact of several treatment-as-prevention strategies that reached-or missed-the UNAIDS 90-90-90 targets in different populations groups, including KP and clients of female sex workers (CFSWs). To inform program planning and resources allocation, we assessed the cost-effectiveness of these scenarios. METHODS: Costing was performed from the provider's perspective. Unit costs were obtained from the Ivorian Programme national de lutte contre le Sida (USD 2015) and discounted at 3%. Net incremental cost-effectiveness ratios (ICER) per adult HIV infection prevented and per disability-adjusted life-years (DALY) averted were estimated over 2015-2030. RESULTS: The 3 most cost-effective and affordable scenarios were the ones that projected current programmatic trends [ICER = $210; 90% uncertainty interval (90% UI): $150-$300], attaining the 90-90-90 objectives among KP and CFSW (ICER = $220; 90% UI: $80-$510), and among KP only (ICER = $290; 90% UI: $90-$660). The least cost-effective scenario was the one that reached the UNAIDS 90-90-90 target accompanied by a 25% point drop in condom use in KP (ICER = $710; 90% UI: $450-$1270). In comparison, the UNAIDS scenario had a net ICER of $570 (90% UI: $390-$900) per DALY averted. CONCLUSIONS: According to commonly used thresholds, accelerating the HIV response can be considered very cost-effective for all scenarios. However, when balancing epidemiological impact, cost-effectiveness, and affordability, scenarios that sustain both high condom use and rates of viral suppression among KP and CFSW seem most promising in Côte d'Ivoire.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".