A Novel Interdisciplinary Approach to Understanding How Contextual Factors Influence Alcohol Consumption

While many high-risk drinkers express a desire to reduce their alcohol consumption, achieving meaningful reductions remains challenging. This project will advance understanding of how contextual factors influence alcohol reduction attempts and inform policy interventions addressing record-high alcohol-related harm. To achieve this, I will integrate two complementary computational techniques never before combined in public health research: one that sheds light on the cognitive processes underlying individual’s momentary choices (drift-diffusion model) and another that examines how individual-level behaviours and interactions result in population-level trends (agent-based model). The computational model will be calibrated and validated using drinking trend data from England (2005–2024) and then used to appraise how novel alcohol policies that alter drinking contexts might influence alcohol reduction attempts—both at the individual-level in real-time and across the population over the longer-term. Using demographic data, the modelled population can be stratified (e.g., by socioeconomic status) to investigate differential policy impacts and provide insights into health inequalities. While this project focuses on alcohol, the methodological tools developed will be adaptable to other public health challenges, including diet, vaping, and gambling.