ABOUT THE PROJECT

SIMAH is a major alcohol policy modeling project funded by the US National Institute on Alcohol Abuse and Alcoholism (NIAAA). Project number 5R01AA028009.

The project is an international collaboration between researchers at three world-leading centers in alcohol research and policy modeling:

• the Institute for Mental Health Policy Research at the Centre for Addiction and Mental Health (Toronto, ON, Canada)

• the Alcohol Research Group at the Public Health Institute (Emeryville, CA, US)

• the Sheffield Alcohol Research Group at the University of Sheffield (Sheffield, UK)

Life expectancy in the United States (US) has been stagnating and declining since about 2010. This trend is a result of increased mortality from specific causes of death (including alcohol-attributable mortality), especially among specific demographic subgroups. However, there have been no comprehensive evaluations of alcohol use as an underlying factor for mortality increases in specific socioeconomic and racial/ethnic groups in the US. SIMAH aims to fill this gap in knowledge.

SIMAH will integrate existing evidence with rich and diverse data sources to develop a unified computer model of mortality and alcohol use across different socioeconomic and racial/ethnic groups. This computer model will allow for a detailed investigation of the underlying causes for the recent declines in life expectancy in the US and offer novel insights to reverse these trends.

More specifically, SIMAH will:

1. Investigate whether (and the extent to which) alcohol use has more harmful effects among disadvantaged socioeconomic and racial/ethnic groups.

2. Develop a unified computer model to identify population-level trends in mortality and their causes based on individual life trajectories.

3. Inform public health policy by investigating different scenarios of alcohol-control interventions, with regard to their ability to reverse current decreases in life expectancy.

The computer model developed will be made publicly available and will allow knowledge users to obtain robust estimates of the direction and magnitude of intervention effects over time. Importantly, it will facilitate a fine-tuned knowledge translation platform that can be tailored to the needs of public health authorities on the state level. Instead of providing results in the form of a single point estimate, the computer model has the potential to flexibly analyze intervention scenarios upon request of stakeholders.