Factor analysis and normative scores of Alcohol Use Disorders Identification Test (AUDIT) in a representative sample of the general population of Greece.

Abstract

The most widely used screening instrument for alcohol use disorders (AUD) is the Alcohol Use Disorders Identification Test (AUDIT) which, although initially developed for use in primary care, is increasingly used in general population studies. Previous studies that have assessed the screening properties and the factorial structure of AUDIT were mostly based on clinical samples and did not take into consideration the possible differences in AUDIT factorial properties between subgroups according to age, sex and mental health status. Aim of the current study was to explore the distribution of AUDIT and AUDIT-Consumption (AUDIT-C) scores and the factorial structure of AUDIT in subgroups of participants according to sex, age and the presence of mental health disorder. Descriptive statistics and Exploratory/Confirmatory Factor Analysis of AUDIT were extracted in a general population representative sample of 4,894 Greek participants. Different cut-offs are suggested in order to screen 10% of the population with the highest severity of AUD into the aforementioned subgroups. Generally, a cut-off between 10-12 at AUDIT score is suggested for screening the 10% with the highest severity of alcohol use problems in subgroups of frequent alcohol consumers (e.g. younger males) and a cut-off between 4-5 would screen the 5% with the highest severity of alcohol use problems in subgroups of low alcohol-consumers (e.g. older women). A cut-off of 3 in AUDIT-C score is suggested for screening 25% of individuals with the heaviest alcohol consumption. The traditional three-factor model does not explain better the factorial structure of AUDIT compared to the 2-factors model. The AUDIT is a reliable instrument for assessing AUD and heavy alcohol consumption in the Greek general population. Age, sex and the presence of mental health disorders should be taken into consideration when selecting cut-offs for screening purposes in non-clinical samples.