64th ISI World Statistics Congress - Ottawa, Canada

64th ISI World Statistics Congress - Ottawa, Canada

Leveraging microsimulation models for public health policy decision making

Conference

64th ISI World Statistics Congress - Ottawa, Canada

Format: CPS Abstract

Keywords: data_integration, evidenced-based decision-making, public-health, simulation

Session: CPS 43 - Statistical modelling VII

Tuesday 18 July 8:30 a.m. - 9:40 a.m. (Canada/Eastern)

Abstract

The Simulation of Alcohol Control Policies for Health Equity (SIMAH) project uses a novel microsimulation approach to investigate the extent to which alcohol use, socioeconomic status, and race/ethnicity contribute to unequal developments in United States life expectancy and how alcohol control interventions could reduce such inequalities. The application of microsimulation techniques to matters of public health is only recently picking up speed. As such, the SIMAH project is the first, to the best of our knowledge, to use microsimulation to i) model alcohol-attributable mortality on the population level and ii) estimate policy effects.
Representative, secondary data from several sources are being analyzed to empirically inform all model parameters and the synthetic population that is being simulated over time. Markov models are being used to inform transition intensities between levels of socioeconomic status and drinking patterns. Cause-specific mortality attributable to alcohol is being modelled dynamically by socioeconomic status, race/ethnicity, age, and sex. To investigate alcohol control intervention strategies, a baseline scenario will be contrasted with multiple counterfactual intervention scenarios.
The session will be showcasing the current state of the work and first preliminary results on mortality outcomes and policy modelling approaches. The preliminary results indicate that the crucial microsimulation component provides good fit to observed demographic changes in the population, providing a robust baseline model for further simulation work to dynamically model diverse intervention scenarios to inform public health policy for a more equitable future.