Cost-Effectiveness of the ‘One4All’ HIV Linkage Intervention in Guangxi Zhuang Autonomous Region, China
Bibliographic record
Abstract
BACKGROUND: In Guangxi Zhuang Autonomous Region, China, an estimated 80% of newly-identified antiretroviral therapy (ART)-eligible patients are not engaged in ART. Delayed ART uptake ultimately translates into high rates of HIV morbidity, mortality, and transmission. To enhance HIV testing receipt and subsequent treatment uptake in Guangxi, the Chinese Center for Disease Control and Prevention (CDC) executed a cluster-randomized trial to assess the effectiveness and cost-effectiveness of a streamlined HIV testing algorithm (the One4All intervention) in 12 county-level hospitals. OBJECTIVE: To determine the incremental cost-effectiveness of the One4All intervention delivered at county hospitals in Guangxi, China, compared to the current standard of care (SOC). PERSPECTIVE: Health System. TIME HORIZON: 1-, 5-and 25-years. METHODS: We adapted a dynamic, compartmental HIV transmission model to simulate HIV transmission and progression in Guangxi, China and identify the economic impact and health benefits of implementing the One4All intervention in all Guangxi hospitals. The One4All intervention algorithm entails rapid point-of-care HIV screening, CD4 and viral load testing of individuals presenting for HIV screening, with same-day results and linkage to counselling. We populated the model with data from the One4All trial (CTN-0056), China CDC HIV registry and published reports. Model outcomes were HIV incidence, mortality, costs, quality-adjusted life years (QALYs), and the incremental cost-effectiveness ratio (ICER) of the One4All intervention compared to SOC. RESULTS: The One4All testing intervention was more costly than SOC (CNY 2,182 vs. CNY 846), but facilitated earlier ART access, resulting in delayed disease progression and mortality. Over a 25-year time horizon, we estimated that introducing One4All in Guangxi would result in 802 averted HIV cases and 1629 averted deaths at an ICER of CNY 11,678 per QALY gained. Sensitivity analysis revealed that One4All remained cost-effective at even minimal levels of effectiveness. Results were robust to changes to a range of parameters characterizing the HIV epidemic over time. CONCLUSIONS: The One4All HIV testing strategy was highly cost-effective by WHO standards, and should be prioritized for widespread implementation in Guangxi, China. Integrating the intervention within a broader combination prevention strategy would enhance the public health response to HIV/AIDS in Guangxi.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".