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Modelling alcohol consumption patterns to enable policy impact assessment.

Jasper Ten Dam1,2, A Jeroen Rodenburg1, Hendrik Koffijberg2

  • 1Department of Statistics Data Science and Modelling, National Institute for Public Health and the Environment (RIVM), Bilthoven, The Netherlands.

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Summary
This summary is machine-generated.

A new model accurately predicts alcohol consumption patterns, including excessive and heavy episodic drinking, using Dutch data. This tool aids in evaluating public health policies aimed at reducing alcohol-related harm.

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Area of Science:

  • Public Health
  • Epidemiology
  • Biostatistics

Background:

  • Alcohol consumption poses significant public health risks, necessitating effective policies to curb excessive and heavy episodic drinking.
  • Evaluating the impact of these policies requires accurate prediction of future drinking behaviors.

Purpose of the Study:

  • To develop and validate a statistical model for predicting alcohol consumption patterns.
  • The model aims to forecast the prevalence of excessive and heavy episodic drinking.

Main Methods:

  • A three-stage model was developed: predicting any alcohol consumption, estimating weekly consumption via a negative binomial distribution, and predicting heavy episodic drinking.
  • Model parameters were estimated using Dutch survey data (2008-2022), incorporating age, sex, education, and time trends.
  • Model performance was validated by comparing predictions with observed data (2014-2022).

Main Results:

  • The calibrated model accurately predicted the prevalence of excessive drinking (error <0.2 pp) and heavy episodic drinking (error <0.1 pp) between 2014-2022.
  • QQ-plots and within-sample validation confirmed the model's good fit for predicting these drinking patterns.

Conclusions:

  • A robust model for predicting alcohol consumption patterns in the Netherlands has been developed.
  • This model serves as a valuable tool for assessing the effectiveness of interventions targeting excessive and heavy episodic drinking.