Background and aims: Short screenings for alcohol use disorder (AUD) are crucial for public health purposes, but current self-reported measures have several pitfalls and may be unreliable. The main aim of our study was to provide empirical evidence on the psychometric performance of self-reports currently used. Our research questions were: compared with a gold standard clinical interview, how accurate are (1) self-reported AUD, (2) self-reported alcohol use over time and (3) biomarkers of alcohol use among Swiss men? Finally, we aimed to identify an alternative screening tool.
Design: A single-center study with a cross-sectional design and a stratified sample selection.
Setting: Lausanne University Hospital (Switzerland) from October 2017 to June 2018.
Participants: We selected participants from the French-speaking participants of the ongoing Cohort Study on Substance Use and Risk Factors (n = 233). The sample included young men aged on average 27.0 years.
Measurements: We used the Diagnostic Interview for Genetic Studies as the gold standard for DSM-5 AUD. The self-reported measures included 11 criteria for AUD, nine alcohol-related consequences, and previous 12 months' alcohol use. We also assessed biomarkers of chronic excessive drinking (ethyl glucuronide and phosphatidylethanol).
Findings: None of the self-reported measures/biomarkers taken alone displayed both sensitivity and specificity close to 100% with respect to the gold standard (e.g. self-reported AUD: sensitivity = 92.3%, specificity = 45.8%). The best model combined eight self-reported criteria of AUD and four alcohol-related consequences. Using a cut-off of three, this screening tool yielded acceptable sensitivity (83.3%) and specificity (78.7%).
Conclusions: Neither self-reported alcohol use disorder nor heavy alcohol use appear to be adequate to screen for alcohol use disorder among young men from the Swiss population. The best screening alternative for alcohol use disorder among young Swiss men appears to be a combination of eight symptoms of alcohol use disorder and four alcohol-related consequences.
Keywords: Alcohol; community-based sample; epidemiology; machine learning; psychometrics; public health.
© 2019 Society for the Study of Addiction.