Biological Age for Prevention in Insurance

Biological age (BA) offers a promising approach to encapsulate complex health information into a single interpretable metric. This study evaluates BA methods as tools for prevention in insurance, focusing on their ability to predict mortality and disease incidence. Using NHANES data, we compare four BA calculation methods-multiple linear regression (MLR), Klemera-Doubal Method (KDM), PhenoAge, and Random Forest (RF). We include a practical application of estimating death counts from life tables. Our findings reveal that PhenoAge and RF consistently outperform other methods in mortality prediction and provide a better match with observed death counts after calibration. While MLR and KDM lag in predictive performance, they demonstrate interpretability that may be valuable for some applications. PhenoAge showed the greatest flexibility and adaptability for prevention-focused applications, particularly for estimating death counts. However, a key challenge remains in calibrating BA methods to align with absolute mortality risks, as highlighted by their initial biases in estimating death counts. We argue that BA's primary value lies in its dual role: a reliable risk estimator and an effective communication tool for promoting preventive health behaviors. By addressing calibration issues and tailoring BA methods to specific insurance contexts, this research underscores BA's potential to improve prevention programs, aligning health incentives for both policyholders and insurers.