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Rotavirus caused an estimated 151,714 deaths from diarrhea among children under 5 in 2019. To reduce mortality, countries are considering adding rotavirus vaccination to their routine immunization program. Cost-effectiveness analyses (CEAs) to inform these decisions are not available in every setting, and where they are, results are sensitive to modeling assumptions, especially about vaccine efficacy. We used advances in meta-regression methods and estimates of vaccine efficacy by location to estimate incremental cost-effectiveness ratios (ICERs) for rotavirus vaccination in 195 countries.Beginning with Tufts University CEA and Global Health CEA registries we used 515 ICERs from 68 articles published through 2017, extracted 938 additional one-way sensitivity analyses, and excluded 33 ICERs for a sample of 1,418. We used a five-stage, mixed-effects, Bayesian metaregression framework to predict ICERs, and logistic regression model to predict the probability that the vaccine was cost-saving. For both models, covariates were vaccine characteristics including efficacy, study methods, and country-specific rotavirus disability-adjusted life-years (DALYs) and gross domestic product (GDP) per capita. All results are reported in 2017 United States dollars.Vaccine efficacy, vaccine cost, GDP per capita and rotavirus DALYs were important drivers of variability in ICERs. Globally, the median ICER was $2,289 (95% uncertainty interval (UI): $147-$38,993) and ranged from $85 per DALY averted (95% UI: $13-$302) in Central African Republic to $70,599 per DALY averted (95% UI: $11,030-$263,858) in the United States. Among countries eligible for support from Gavi, The Vaccine Alliance, the mean ICER was $255 per DALY averted (95% UI: $39-$918), and among countries eligible for the PAHO revolving fund, the mean ICER was $2,464 per DALY averted (95% UI: $382-$3,118).Our findings incorporate recent evidence that vaccine efficacy differs across locations, and support expansion of rotavirus vaccination programs, particularly in countries eligible for support from Gavi, The Vaccine Alliance.

Cost-effectiveness analysis (CEA) is a well-known, but resource intensive, method for comparing the costs and health outcomes of health interventions. To build on available evidence, researchers are developing methods to transfer CEA across settings; previous methods do not use all available results nor quantify differences across settings. We conducted a meta-regression analysis of published CEAs of human papillomavirus (HPV) vaccination to quantify the effects of factors at the country, intervention, and method-level, and predict incremental cost-effectiveness ratios (ICERs) for HPV vaccination in 195 countries. We used 613 ICERs reported in 75 studies from the Tufts University’s Cost-Effectiveness Analysis (CEA) Registry and the Global Health CEA Registry, and extracted an additional 1,215 one-way sensitivity analyses. A five-stage, mixed-effects meta-regression framework was used to predict country-specific ICERs. The probability that HPV vaccination is cost-saving in each country was predicted using a logistic regression model. Covariates for both models included methods and intervention characteristics, and each country’s cervical cancer burden and gross domestic product per capita. ICERs are positively related to vaccine cost, and negatively related to cervical cancer burden. The mean predicted ICER for HPV vaccination is 2017 US$4,217 per DALY averted (95% uncertainty interval (UI): US$773–13,448) globally, and below US$800 per DALY averted in 64 countries. Predicted ICERs are lowest in Sub-Saharan Africa and South Asia, with a population-weighted mean ICER across 46 countries of US$706 per DALY averted (95% UI: $130–2,245), and across five countries of US$489 per DALY averted (95% UI: $90–1,557), respectively. Meta-regression analyses can be conducted on CEA, where one-way sensitivity analyses are used to quantify the effects of factors at the intervention and method-level. Building on all published results, our predictions support introducing and expanding HPV vaccination, especially in countries that are eligible for subsidized vaccines from GAVI, the Vaccine Alliance, and Pan American Health Organization.

Objective To examine the clinical and economic value of point-of-care CD4 (POC-CD4) or viral load monitoring compared with current practices in Mozambique, a country representative of the diverse resource limitations encountered by HIV treatment programs in sub-Saharan Africa. Design/methods We use the Cost-Effectiveness of Preventing AIDS Complications-International model to examine the clinical impact, cost (2014 US$), and incremental cost-effectiveness ratio [$/year of life saved (YLS)] of ART monitoring strategies in Mozambique. We compare: monitoring for clinical disease progression [clinical ART monitoring strategy (CLIN)] vs. annual POC-CD4 in rural settings without laboratory services and biannual laboratory CD4 (LAB-CD4), biannual POC-CD4, and annual viral load in urban settings with laboratory services. We examine the impact of a range of values in sensitivity analyses, using Mozambique's 2014 per capita gross domestic product ($620) as a benchmark cost-effectiveness threshold. Results In rural settings, annual POC-CD4 compared to CLIN improves life expectancy by 2.8 years, reduces time on failed ART by 0.6 years, and yields an incremental cost-effectiveness ratio of $480/YLS. In urban settings, biannual POC-CD4 is more expensive and less effective than viral load. Compared to biannual LAB-CD4, viral load improves life expectancy by 0.6 years, reduces time on failed ART by 1.0 year, and is cost-effective ($440/YLS). Conclusion In rural settings, annual POC-CD4 improves clinical outcomes and is cost-effective compared to CLIN. In urban settings, viral load has the greatest clinical benefit and is cost-effective compared to biannual POC-CD4 or LAB-CD4. Tailoring ART monitoring strategies to specific settings with different available resources can improve clinical outcomes while remaining economically efficient.

The research agenda towards an HIV cure is building rapidly. In this article, we discuss the reasons for and methodological approach to using mathematical modelling and cost-effectiveness analysis in this agenda. We provide a brief description of the proof of concept for cure and the current directions of cure research. We then review the types of clinical economic evaluations, including cost analysis, cost-benefit analysis and cost-effectiveness analysis. We describe the use of mathematical modelling and cost-effectiveness analysis early in the HIV epidemic as well as in the era of combination antiretroviral therapy. We then highlight the novel methodology of Value of Information (VOI) analysis and its potential role in the planning of clinical trials. We close with recommendations for modelling and cost-effectiveness analysis in the HIV cure agenda.