2024 SOA ImpACT Conference - Session 2F: Relative risk curves, population attributable fractions, and the burden of proof for evidence 10.28.24 Recording

"The purpose of science is not to analyze or describe but to make useful models of the world. A model is useful if it allows us to get use out of it.”Edward de Bono At the conclusion of this session, attendees will be equipped to apply relative risk curves in their actuarial work, including the ability to examine dose-response relationships, apply population attributable fractions (PAFs), and develop customized risk models for cause-specific mortality and morbidity outcomes based on individual-level exposures to selected risk factors. These skills are becoming increasingly important as actuaries adopt novel applications of big data and dynamic health information to refine risk assessments and keep pace with global health trends. Personalized risk models require a solid understanding of both the strength and application of evidence related to emerging risks—such as metabolic and behavioral risks—that are driving deteriorating health outcomes. As we examine these risks, the session will underscore the importance of evidence-based approaches in developing effective personalized risk models. This session will also emphasize the critical bridge between theory and practice, particularly the role of integrating science and technology to create actionable solutions. Cross-disciplinary collaboration, especially with technology teams, is essential as consumer-friendly, data-driven insurance products demand that actuaries bridge the gap between technical solutions and business objectives. The Institute for Health Metrics and Evaluation (IHME) will present Global Burden of Disease research measuring the rlationships between modifiable risk factors and health outcomes, and the Burden of Proof method for quantifying the overall strength of scientific evidence of these risk-outcome relationships. A case study from Vitality will highlight how the use of risk data can enable personalized risk models delivered through user-friendly digital platforms, and how actuaries can benefit from working in interdisciplinary teams with health experts, data scientists, and technology specialists.