ISSUE: There is growing interest in going beyond conventional approaches to economic evaluation to understand whether costs and effects are distributed fairly, and to what extent trade-offs exist between efficiency and equity objectives. One clinical context in which inequities may impact on value is genome sequencing. Sequencing is being implemented in clinical practice at the population level globally, but initial analyses have highlighted unequal test access (impacting data diversity) and outcomes. These inequalities may be inequities, and could lead to bias in studies evaluating the cost-effectiveness of population sequencing programmes. This panel session will identify key challenges for health economic analysis related to diverse datasets (data sources, type, access, population coverage) and inequalities in a genomic testing context. The session will begin with an overview, then engage panellists with backgrounds in multiple jurisdictions in a structured discussion to consider evidence challenges and debate potential approaches and solutions. Audience input will complement the panel discussion.

OVERVIEW: Genome sequencing – the process of determining an individual’s entire DNA sequence – could transform care for people with cancer or rare diseases worldwide, improving diagnosis, prognosis and clinical management. Population sequencing initiatives (e.g. 100,000 Genomes Project (England), All of Us Program (USA), 1+Million Genomes initiative (EU)) have generated valuable evidence on the potential clinical benefits of implementing sequencing in routine care. Evidence on cost-effectiveness is more elusive, and equity issues may impact the value case; evidence suggests inequalities exist in both access to sequencing (impacting data diversity), and the health and cost outcomes arising from sequencing. Approaches such as distributional cost-effectiveness analysis could help quantify key trade-offs between improving total health and reducing inequality in outcomes. However, evidence gaps may challenge the implementation of such an approach. Alternatively, health systems modelling – underpinned by population-level data – could help to quantify issues around access and utilisation.