A new statistical model for binge drinking pattern classification in college-student populations.

Area of Science:

• Alcohol consumption research
• Behavioral economics
• Public health

Background:

• Binge drinking (BD) is a prevalent and consequential alcohol consumption pattern among students.
• Defining and characterizing BD patterns presents a significant challenge in scientific research.
• Existing methods may not fully capture the nuances of BD across diverse student populations.

Purpose of the Study:

• To identify homogeneous drinking groups within a student population.
• To develop a novel model for assessing the severity of BD.
• To create a gender-independent tool for evaluating BD severity and its associated factors.

Main Methods:

• Employed a K-means clustering algorithm to identify distinct drinking profiles.
• Utilized a partial proportional odds model (PPOM) to estimate group probabilities.
• Validated findings using two independent samples of French university students (N=3,630 total).

Main Results:

• Four homogeneous drinking groups were identified: low-risk, hazardous, binge, and high-intensity BD.
• The PPOM successfully assigned probabilities for individuals belonging to these groups.
• Significant differences were observed in consumption consequences and behavioral economic demand indices across the four groups.

Conclusions:

• The developed model offers a progressive severity scale for drinking patterns and consequences.
• This approach provides a refined method for characterizing BD in university student samples.
• The frequency of drinking and level of drunkenness are identified as key features in the BD model.