Summary Estimates Derived from a Multi-state Non-Markov Framework to Characterize the Course of Heart Disease

Multi-state Markov models have been used to model the course of chronic disease. However, they are unsuitable to chronic disease where past and present states interplay and affect future states, and the estimated transition probabilities are time-specific which are not straightforward for public health interpretation. We have proposed a multi-state non-Markov framework that splits disease states into substates conditioning on past states. As the substates track past states and indicate multimorbidity, the estimated transition rates can be used to derive two summary estimates: Disease path which shows path of state transition, and multimorbidity-adjusted life year (MALY) which represents the adjusted life year in full health. In this paper, we showed the derivation of the two summary estimates and applied them to characterize the course of heart disease using data from the Atherosclerosis Risk in Communities Study (ARIC) study. The course of heart disease was modeled in five states, namely, healthy, at metabolic risk, coronary heart disease (CHD), heart failure, and mortality. In this mid- to old-age population, the estimated MALY was 24.13 (95% CI: 16.55, 32.06) years. For healthy participants at baseline, the most likely disease paths were: "Healthy [->] at metabolic risk [->] mortality" (37%), "Healthy [->] mortality" (21%), "Healthy [->] at metabolic risk [->] heart failure [->] mortality" (19%), and "Healthy [->] at metabolic risk [->] CHD [->] mortality" (8%). The MALY was higher among women than men and higher among Whites than Blacks. The distribution of disease path was similar across sex and race subgroups. In summary, MALY and disease path characterize the disease course in a summary manner and have potential use in chronic disease prevention.