Modeling Disability-Adjusted Life-Years for Policy and Decision Analysis

This study outlines methods for modeling disability-adjusted life-years (DALYs) in common decision-modeling frameworks. Recognizing the wide spectrum of experience and programming comfort level among practitioners, we outline 2 approaches for modeling DALYs in its constituent parts: years of life lost to disease (YLL) and years of life lived with disability (YLD). Our beginner approach draws on the Markov trace, while the intermediate approach facilitates more efficient estimation by incorporating non-Markovian tracking elements into the transition probability matrix. Drawing on an existing disease progression discrete time Markov cohort model, we demonstrate the equivalence of DALY estimates and cost-effectiveness analysis results across our methods and show that other commonly used “shortcuts†for estimating DALYs will not, in general, yield accurate estimates of DALY levels nor incremental cost-effectiveness ratios in a modeled population. Highlights This study introduces 2 DALY estimation methods—beginner and intermediate approaches—that produce similar results, expanding the toolkit available to decision modelers. These methods can be adapted to estimate other outcomes (e.g., QALYs, life-years) and applied to other common decision-modeling frameworks, including microsimulation models with patient-level attributes and discrete event simulations that estimate YLDs and YLLs based on time to death and disease duration. Our findings further reveal that commonly used shortcut methods for DALY calculations may lead to differing results, particularly for DALY levels and incremental cost-effectiveness ratios.