Introduction: The Theory of Planned Behaviour (TPB) describes how attitudes, norms and perceived behavioural control guide health behaviour, including alcohol consumption. Dual Process Theories (DPT) suggest that alongside these reasoned pathways, behaviour is influenced by automatic processes that are determined by the frequency of engagement in the health behaviour in the past. We present a computational model integrating TPB and DPT to determine drinking decisions for simulated individuals. We explore whether this model can reproduce historical patterns in US population alcohol use and simulate a hypothetical scenario, "Dry January", to demonstrate the utility of the model for appraising the impact of policy interventions on population alcohol use.

Method: Constructs from the TPB pathway were computed using equations from an existing individual-level dynamic simulation model of alcohol use. The DPT pathway was initialised by simulating individuals' past drinking using data from a large US survey. Individuals in the model were from a US population microsimulation that accounts for births, deaths and migration (1984-2015). On each modelled day, for each individual, we calculated standard drinks consumed using the TPB or DPT pathway. In each year we computed total population alcohol use prevalence, frequency and quantity. The model was calibrated to alcohol use data from the Behavioral Risk Factor Surveillance System (1984-2004).

Results: The model was a good fit to prevalence and frequency but a poorer fit to quantity of alcohol consumption, particularly in males. Simulating Dry January in each year led to a small to moderate reduction in annual population drinking.

Conclusion: This study provides further evidence, at the whole population level, that a combination of reasoned and implicit processes are important for alcohol use. Alcohol misuse interventions should target both processes. The integrated TPB-DPT simulation model is a useful tool for estimating changes in alcohol consumption following hypothetical population interventions.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8529781PMC
http://dx.doi.org/10.1016/j.addbeh.2021.107094DOI Listing

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