BACKGROUND: The increasingly precise conceptualization of Binge Drinking (BD), along with the rising incidence of this pattern of intake amongst young people, make it necessary to review the usefulness of instruments used to detect it. Little evidence exists regarding effectiveness of the AUDIT, AUDIT-C and AUDIT-3 in the detection of BD. This study evaluates their utility in a sample of university students, revealing the most appropriate cut-off points for each sex. METHODS: All students self-administered the AUDIT and completed a self-report of their alcohol consumption. A Two-step cluster analysis differentiated 5 groups of BD in terms of: the quantity consumed, the frequency of BD over the past six months and gender. A ROC curve adjusted cut-off points for each case. RESULTS: 862 university students (18-19 years-old/59.5% female), 424 (49.2%) from Valencia and 438 (50.8%) from Madrid, had cut-off points of 4 in AUDIT and 3 in AUDIT-C as a better fit. In all cases, the best classifier was AUDIT-C. Neither version properly classifies students with varying degrees of BD. CONCLUSIONS: All versions differentiate BD from non-BD, but none are able to differentiate between types of BD.
BACKGROUND: The increasingly precise conceptualization of Binge Drinking (BD), along with the rising incidence of this pattern of intake amongst young people, make it necessary to review the usefulness of instruments used to detect it. Little evidence exists regarding effectiveness of the AUDIT, AUDIT-C and AUDIT-3 in the detection of BD. This study evaluates their utility in a sample of university students, revealing the most appropriate cut-off points for each sex. METHODS: All students self-administered the AUDIT and completed a self-report of their alcohol consumption. A Two-step cluster analysis differentiated 5 groups of BD in terms of: the quantity consumed, the frequency of BD over the past six months and gender. A ROC curve adjusted cut-off points for each case. RESULTS: 862 university students (18-19 years-old/59.5% female), 424 (49.2%) from Valencia and 438 (50.8%) from Madrid, had cut-off points of 4 in AUDIT and 3 in AUDIT-C as a better fit. In all cases, the best classifier was AUDIT-C. Neither version properly classifies students with varying degrees of BD. CONCLUSIONS: All versions differentiate BD from non-BD, but none are able to differentiate between types of BD.
Authors: Manuel Herrero-Montes; Cristina Alonso-Blanco; María Paz-Zulueta; Amada Pellico-López; Laura Ruiz-Azcona; Carmen Sarabia-Cobo; Víctor Fradejas-Sastre; Ester Boixadera-Planas; Paula Parás-Bravo Journal: J Clin Med Date: 2021-12-23 Impact factor: 4.241
Authors: Laura Ballester; Itxaso Alayo; Gemma Vilagut; José Almenara; Ana Isabel Cebrià; Enrique Echeburúa; Andrea Gabilondo; Margalida Gili; Carolina Lagares; José A Piqueras; Miquel Roca; Victoria Soto-Sanz; Maria Jesús Blasco; Pere Castellví; Carlos G Forero; Philippe Mortier; Jordi Alonso Journal: Int J Environ Res Public Health Date: 2021-05-14 Impact factor: 3.390
Authors: José Alberto Benítez-Andrades; Alejandro Rodríguez-González; Carmen Benavides; Leticia Sánchez-Valdeón; Isaías García Journal: Int J Environ Res Public Health Date: 2018-10-31 Impact factor: 3.390