Practical metrics for establishing the health benefits of research to support research prioritisation

Perspectives on the use of modelling and economic analysis to guide HIV programmes in sub-Saharan Africa

HIV modelling and economic analyses have had a prominent role in guiding programmatic responses to HIV in sub-Saharan Africa. However, there has been little reflection on how the HIV modelling field might develop in future. HIV modelling should more routinely align with national government and ministry of health priorities, recognising their legitimate mandates and stewardship responsibilities, for HIV and other wider health programmes. Importance should also be placed on ensuring collaboration between modellers, and that joint approaches to addressing modelling questions, becomes the norm rather than the exception. Such an environment can accelerate translation of modelling analyses into policy formulation because areas where models agree can be prioritised for action, whereas areas over which uncertainty prevails can be slated for additional study, data collection, and analysis. HIV modelling should increasingly be integrated with the modelling of health needs beyond HIV, particularly in allocative efficiency analyses, where focusing on one disease over another might lead to worse health overall. Such integration might also enhance partnership with national governments whose mandates extend beyond HIV. Finally, we see a need for there to be substantial and equitable investment in capacity strengthening within African countries, so that African researchers will increasingly be leading modelling exercises. Building a critical mass of expertise, strengthened through external collaboration and knowledge exchange, should be the ultimate goal.

Concomitant health benefits package design and research prioritisation: development of a new approach and an application to Malawi

Health benefits packages (HBPs) are increasingly used in many countries to guide spending priorities on the path towards universal health coverage. Their design is, however, informed by an uncertain evidence base but research funds available to address this are limited. This gives rise to the question of which piece of research relating to the cost-effectiveness of interventions would most contribute to improving resource allocation. We propose to incorporate research prioritisation as an integral part of HBP design. We have, therefore, developed a framework and a freely available companion stand-alone tool, to quantify in terms of net disability-adjusted life-years (DALYs) averted, the value of research for the interventions considered for inclusion in a package. Using the tool, the framework can be implemented using sensitivity analysis results typically reported in cost-effectiveness studies. To illustrate the framework, we applied the tool to the evidence base that informed the Malawi Health Sector Strategic Plan 2017–2022. Out of 21 interventions considered, 8 investment decisions were found to be uncertain and three showed strong potential for research to generate large health gains: ‘male circumcision’, ‘community-management of acute malnutrition in children’ and ‘isoniazid preventive therapy in HIV +individuals’, with a potential to avert up to 65 762, 36 438 and 20 132 net DALYs, respectively. Our work can help set research priorities in resource-constrained settings so that research funds are invested where they have the largest potential to impact on the population health generated via HBPs.

A Review of Web-Based Tools for Value-of-Information Analysis

Value-of-information analysis (VOI) is a decision-theoretic approach that is used to inform reimbursement decisions, optimise trial design and set research priorities. The application of VOI analysis for informing policy decisions in practice has been limited due, in part, to the perceived complexity associated with the calculation of VOI measures. Recent efforts have resulted in the development of efficient methods to estimate VOI measures and the development of user-friendly web-based tools to facilitate VOI calculations. We review the existing web-based tools including Sheffield Accelerated Value of Information (SAVI), the web interface to the BCEA (Bayesian Cost-Effectiveness Analysis) R package (BCEAweb), Rapid Assessment of Need for Evidence (RANE), and Value of Information for Cardiovascular Trials and Other Comparative Research (VICTOR). We describe what each tool is designed to do, the inputs they require, and the outputs they produce. Finally, we discuss how tools for VOI calculations might be improved in the future to facilitate the use of VOI analysis in practice.