MODELLING TO SUPPORT REVISED WHO HIV TREATMENT GUIDELINES: CHALLENGES OF SYNTHESIZING RESULTS ACROSS MULTIPLE MODELS

Purpose: To guide the 2013 revision of its HIV treatment guidelines, the World Health Organization commissioned the HIV Modelling Consortium to evaluate possible policy changes. Rather than rely on a single model, we engaged multiple HIV modeling groups to contribute to a synthetic policy analysis.

Method: We requested research groups with expertise in modeling HIV epidemiology to simulate the potential impact of standardized policy alternatives in four settings: India, Vietnam, South Africa, and Zambia. Competing policies involved expanding ART eligibility to individuals with higher CD4 cell counts, prioritizing high-risk groups, increasing treatment coverage. A standardized costing framework was developed. Policy options were compared in terms of epidemic impact, health system costs, and cost-effectiveness over 20 years. Research groups met in November 2012 to finalize the analytic approach.  Synthesized results from 12 mathematical models were reported to the WHO guideline committee in early 2013.

Result: All models predicted that broad expansions in ART eligibility and coverage would substantially reduce HIV transmission, reduce HIV-related mortality, and increase total costs. In generalized epidemics (South Africa, Zambia), most models estimated that expanding eligibility to CD4 ≤500 cells/µL—a key policy under consideration— would cost US$500-$1,500 per disability-adjusted life year averted, versus current guidelines. Other expansions in eligibility and coverage also produced favourable cost-effectiveness ratios. In concentrated epidemics (India, Vietnam), expanding ART eligibility and increasing coverage in high-risk populations appeared highly cost-effective compared to conventional benchmarks, but efforts to raise coverage in the general population did not. While common themes emerged, translating findings into policy messages was challenging: there was substantial variation on several modelled outcomes, models differed in the set of interventions they could simulate, costing approaches and reported outcomes had to be simplified to allow consistent application across models, and the project timeframe was constrained by predefined WHO policy-setting timelines, limiting the depth of analysis possible. Challenges were particularly acute when attempting to calculate the incremental cost-effectiveness of multiple competing interventions, and standard approaches for synthesising modelled evidence proved unsatisfying for summarizing results across multiple models.

 Conclusion: Modeled analyses provided useful input to guideline revisions, identifying policies that would likely be cost-effective in most settings. Synthesizing the results of multiple models is challenging, and must balance the transparency of reporting disparate outcomes with the need to identify summary messages.