Data-driven Huntington's disease progression modelling and estimation of societal cost in the UK.

Abstract

We develop a Huntington's disease (HD) progression model and integrate this with a novel economic model, accounting for the major factors of the HD's societal cost. Data from the Enroll-HD observational study were used to fit a continuous-time hidden Markov disease progression model, which identified five distinct states. The number of disease states was determined using a cross-validated maximum likelihood approach. A novel data augmentation method was used to correct the biased life expectancy of the progression model. Multiple sources of cost data were then mapped to Enroll-HD variables using expert experience. A simulation of a synthetic patient population was used to show the feasibility of the approach in estimating population costs and the impact of hypothetical intervention scenarios. Our results confirm that early cognitive decline, which is not captured by the total functional capacity score currently used by clinicians but flagged up in HD integrated staging system, can be quantified from participants' visits. Finally, the results of the UK cost modelling show that indirect costs of HD such as state benefits and lost gross domestic product contribution could be the driving factors for the societal cost, over and above health and social care costs.