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A risk algorithm that predicts alcohol use disorders among college students


The first year of college may carry especially high risk for onset of alcohol use disorders. We assessed the one-year incidence of alcohol use disorders (AUD) among incoming first-year students, predictors of AUD-incidence, prediction accuracy and population impact. A prospective cohort study of first-year college students (baseline: N = 5843; response rate = 51.8%; 1-year follow-up: n = 1959; conditional response rate = 41.6%) at a large university in Belgium was conducted. AUD were evaluated with the AUDIT and baseline predictors with the Composite International Diagnostic Interview Screening Scales (CIDI-SC). The one-year incidence of AUD was 3.9% (SE = 0.4). The most important individual-level baseline predictors of AUD incidence were being male (OR = 1.53; 95% CI = 1.12–2.10), a break-up with a romantic partner (OR = 1.67; 95% CI = 1.08–2.59), hazardous drinking (OR = 3.36; 95% CI = 1.31–8.63), and alcohol use characteristics at baseline (ORs between 1.29 and 1.38). Multivariate cross-validated prediction (cross-validated AUC = 0.887) shows that 55.5% of incident AUD cases occurred among the 10% of students at highest predicted risk (20.1% predicted incidence in this highest-risk subgroup). Four out of five students with incident AUD would hypothetically be preventable if baseline hazardous drinking was to be eliminated along with a reduction of one standard deviation in alcohol use characteristics scores, and another 15.0% would potentially be preventable if all 12-month stressful events were eliminated. Screening at college entrance is a promising strategy to identify students at risk of transitioning to more problematic drinking and AUD, thus improving the development and deployment of targeted preventive interventions.

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The Leuven College Survey was carried out in conjunction with the World Health Organization World Mental Health (WMH) survey initiative and is a part of the World Mental Health International College Student project. A complete list of all within‐country and cross‐national WMH publications can be found at

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Corresponding author

Correspondence to C. Benjet.

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Conflict of interest

In the past 3 years, Dr. Kessler received support for his epidemiological studies from Sanofi Aventis, he was a consultant for Johnson & Johnson Wellness and Prevention, Shire and Takeda, and served on an advisory board for the Johnson & Johnson Services Inc. Lake Nona Life Project. Dr. Kessler is a co-owner of DataStat, Inc., a market research firm that carries out healthcare research. Dr. Demyttenaere has served on advisory boards for Boehringer Ingelheim Eli Lilly, Lundbeck, Johnson&Johnson, Livanova, Servier, and has research grants from Eli Lilly, foundation ‘ga voor geluk’, Fonds voor Wetenschappelijk Onderzoek Vlaanderen. Dr. Ebert reports to have received consultancy fees/served in the scientific advisory board from several companies, such as Sanofi, Novartis, Minddistrict, Lantern, Schoen Kliniken, and German health insurance companies (BARMER, Techniker Krankenkasse). He is also a stakeholder of the Institute for health training online (GET.ON), which aims to implement scientific findings related to digital health interventions into routine care. The other authors have no interests to declare.

Ethical approval

The study’s protocol was approved by the University Hospital Leuven Biomedical Ethical Board and complies with the ethical standards of the Helsinki Declaration of 1975, as revised in 2008.

Financial support

The Belgium survey was supported by the Belgian Fund for Scientific Research (11N0514N/11N0516N/1114717 N/1114719 N), the King Baudouin Foundation (2014‐J2140150‐102905), Eli Lilly (IIT‐H6U‐BX‐I002), and Fonds GaVoorGeluk/ Diepensteyn Foundation (2018-LUF-0070).

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Informed consent was obtained from all students.

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Benjet, C., Mortier, P., Kiekens, G. et al. A risk algorithm that predicts alcohol use disorders among college students. Eur Child Adolesc Psychiatry (2021).

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  • Alcohol use disorder
  • Hazardous drinking
  • University students
  • Risk algorithm
  • Incidence