Modelling Alcohol Consumption Patterns to Enable Policy Impact Assessment

Objective To prevent harmful effects of alcohol use, various countries implement policies preventing excessive and heavy episodic drinking. To enable the evaluation of the impact of such policies on (future) drinking behaviour, we aimed to develop a model that predicts alcohol consumption patterns.

The model predicts alcohol use in three stages. First, a logistic submodel predicts probabilities of drinking any alcohol. Second, for drinkers, a submodel predicts the weekly consumption through a negative binomial distribution for the number of beverages. Finally, based on the predicted weekly consumption, a logistic submodel predicts probabilities of heavy episodic drinking. The distribution for the weekly consumption was calibrated, targeted to predict the prevalence of excessive and heavy episodic drinking accurately. Model parameters were estimated using Dutch individual-level cross-sectional survey data covering the years 2008-2022. The characteristics age, sex, education, calendar time and their interactions were used as predictors and the model accounts for trend breaks in the data. Model performance was assessed by comparing population-level predictions with observed data on which the model was calibrated (2014-2022).

A comparison between predictions of the calibrated model and observed data shows that the prevalences of excessive (error <0.2 percent point (pp)) and heavy episodic drinking (error <0.1 pp) align, averaged over the years 2014 to 2022. Visual inspection using qq-plots and within-sample validation over time further indicates that the model fits well for predicting excessive and heavy episodic drinking, based on the predicted distribution for the weekly consumption.

We developed a model for alcohol consumption patterns based on Dutch data. This model enables evaluation of the impact of interventions on the (future) prevalence of excessive and heavy episodic drinking. Competing Interest Statement The authors have declared that no competing interests exist. Funding Statement Yes Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics approval not applicable I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Footnotes Clinical trial number: not applicable Data Availability The National Health Survey (22) data that support the findings of this study are available through Data Archiving and Network Services (DANS) Data Station Life Sciences from the following year-specific DOIs: 2008-2010: https://doi.org/10.17026/dans-zrm-7r4z 2010, 2011: https://doi.org/10.17026/dans-z93-mj8s 2012: https://doi.org/10.17026/dans-zcc-5stc 2013: https://doi.org/10.17026/dans-zdk-dwmn 2014: https://doi.org/10.17026/dans-xcm-u69z 2015: https://doi.org/10.17026/dans-xwr-m26w 2016: https://doi.org/10.17026/dans-xxa-e3m7 2017: https://doi.org/10.17026/dans-xxd-j335 2018: https://doi.org/10.17026/dans-z5s-b7ve 2019: https://doi.org/10.17026/dans-z5s-b7ve 2020: https://doi.org/10.17026/dans-x58-3ayy 2021: https://doi.org/10.17026/dans-x2a-yw93 2022: https://doi.org/10.17026/LS/0OKUIT