Bridging an Information Divide: Clinically Relevant Models of Alcohol Consumption and Liver Disease to Inform Dynamic and Interpretable Community Alcohol Policies


Alcohol-attributable liver disease (ALD) rates have been on the rise globally and in the United States over the past two decades and harmful alcohol use contributes to more than 200 disease and injury conditions. Each disease event carries a significant cost for the individual, their community, and society at large. Due to the unobserved nature of early disease states and limited data on disease prevalence in the general population, most models of ALD and alcohol-use disorders focus on the treatment of late-stage disease including liver cirrhosis and liver cancer without the ability to consider harm reduction strategies available at the population and individual level.

This talk will focus on a clinically relevant model of ALD that incorporates the regenerative abilities of the liver, with the objective of projecting the impact of current drinking patterns on disease morbidity and mortality in the US population and the impact of societal and individual level interventions on projected disease burden and healthcare costs. The first portion of the talk will focus on building an alcohol consumption and liver disease model incorporating current consumption data that can be leveraged for decision analysis by stake holders at the individual, clinical, and population level. The second part will consider the potential of harm reduction strategies and alcohol policy to positively impact the future burden of disease in the United States. A data collection scheme will be proposed to parameterize a personalized disease progression and decision-making model.


Jovan Julien is currently on a postdoctoral fellowship at Massachusetts General Hospital's Institute for Technology Assessment and Harvard Medical School, where they are currently working on modeling the impact of breathing modulation and other meditation and mindfulness techniques on disease mitigation strategies. Their broader research interests are in predictive health models that can inform wellness interventions and policy at the individual, interpersonal, and systemic levels to limit and eventually reverse the growth rate in per capita spending on healthcare while improving long-term outcomes. Jovan received their B.Sc in Biomedical Engineering from Brown University, and a master’s in health systems engineering and PhD degree in Operations Research from Georgia Institute of Technology's H. Milton Stewart School of Industrial & Systems Engineering. Their studies were supported with a Health Policy Research Fellowship sponsored by the Robert Wood Johnson Foundation.