🤖 AI Summary
This study systematically evaluates the economic value and its heterogeneity of multi-cancer early detection (MCED) technologies in population-based screening from the perspective of the UK National Health Service (NHS). The authors develop a decision-analytic model that innovatively decomposes overall health benefit—measured in quality-adjusted life years (QALYs)—into distinct components attributable to cancer detection, false positives, overdiagnosis, and misclassification, with analyses further stratified by cancer type. Simulations of annual Galleri screening yield a net population benefit of 0.135 QALYs per person, with colorectal, lung, and ovarian cancers collectively accounting for more than 50% of this gain. This framework elucidates the heterogeneous sources and distribution of health value, offering a robust foundation for priority setting and informed health policy decisions.
📝 Abstract
Introduction: We present a framework to assess the economic value of healthcare interventions by disaggregating value and examining heterogeneity. We applied it to an early health-economic model of population screening in England with a multi-cancer early detection (MCED) test. Value for such technologies often includes benefits, such as those associated with earlier detection, alongside potential harms from, for example, false positives or overdiagnosis. Value also varies between individuals, including across cancer types and stages. Understanding these components and heterogeneity is crucial for assessing overall value and prioritising future research. Methods: We adapted an existing decision-analytic model to simulate annual Galleri screening in an asymptomatic population, measuring outcomes in Quality Adjusted Life Years (QALYs) from an English NHS perspective (year of 2024). We disaggregate headroom value (assuming no cost to the test) into key components: cancer identification (pre- and post-diagnosis), false positives, overdiagnosis, and misclassification. Post diagnosis value was further disaggregated by cancer type to explore heterogeneity. Results: Our analysis predicts an overall estimated headroom value of 0.135 QALYs per individual. Early cancer identification was a major contributor, driven by health gains rather than cost savings. Cancers of the colon/rectum, lung and ovary were the largest contributors, accounting for 50% of overall value. These results remained robust across scenario analyses. Conclusion: The value disaggregation can guide decision making by clarifying value drivers, assessing plausibility of overall estimates, exploring heterogeneity, and prioritising future model and evidence development activities.