Predicting Alcohol Consumption Patterns for Individuals with a User-Friendly Parsimonious Statistical Model

Wenbin Liang (), HuiJun Chih and Tanya Chikritzhs
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Wenbin Liang: School of Public Health, Fujian Medical University, Fuzhou 350108, China
HuiJun Chih: Curtin School of Population Health, Faculty of Health Sciences, Curtin University, GPO U1987, Perth, WA 6845, Australia
Tanya Chikritzhs: National Drug Research Institute, Faculty of Health Sciences, Curtin University, GPO U1987, Perth, WA 6845, Australia

IJERPH, 2023, vol. 20, issue 3, 1-10

Abstract: Many studies on the relationship between alcohol and health outcome focus primarily on average consumption over time and do not consider how heavy per-occasion drinking may influence apparent relationships. Improved methods concerning the most recent drinking occasion are essential to inform the extent of alcohol-related health problems. We aimed to develop a user-friendly and readily replicable computational model that predicts: (i) an individual’s probability of consuming alcohol ≥2, 3, 4… drinks; and (ii) the total number of days during which consumption is ≥2, 3, 4… drinks over a specified period. Data from the 2010 and 2011 National Survey on Drug Use and Health (NSDUH) were used to develop and validate the model. Predictors used in model development were age, gender, usual number of drinks consumed per day, and number of drinking days in the past 30 days. Main outcomes were number of drinks consumed on the last drinking occasion in the past 30 days, and number of days of risky levels of consumption. The area under ROC curves ranged between 0.86 and 0.91 when predicting the number of drinks consumed. Coefficients were very close to 1 for all outcomes, indicating closeness between the predicted and observed values. This straightforward modelling approach can be easily adopted by public health behavioral studies.

Keywords: prediction modelling; alcohol drinking; risky consumption; predictive accuracy; ROC curve (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2023
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