Amandine Luquiens1,2,3, Anis Ben Said4, Haïm Sadik4, Emilio Ferrer Sánchez Del Villar4, Arthur Le Manach4, Benjamin Ambrosino4, Christophe Tzourio5, Amine Benyamina6,7, Henri-Jean Aubin6,7. 1. Addiction department, Hôpital Paul Brousse, APHP, 12 avenue Paul Vaillant-Couturier, 94804, Villejuif cedex, France. amandineluquiens@gmail.com. 2. Université Paris-Saclay, Univ. Paris-Sud, UVSQ, CESP, INSERM, Villejuif, France. amandineluquiens@gmail.com. 3. CMAP, Ecole Polytechnique, Palaiseau, France. amandineluquiens@gmail.com. 4. Ecole Centrale-Supélec Paris, Paris, France. 5. Team HEALTHY, Bordeaux Population Health Research Center, INSERM, Univ. Bordeaux, UMR 1219, CHU Bordeaux, 33000, Bordeaux, France. 6. Addiction department, Hôpital Paul Brousse, APHP, 12 avenue Paul Vaillant-Couturier, 94804, Villejuif cedex, France. 7. Université Paris-Saclay, Univ. Paris-Sud, UVSQ, CESP, INSERM, Villejuif, France.
Abstract
INTRODUCTION: The objective for this study was to combine drinking characteristics and two subjective measures, drinker identity and alcohol-related quality of life, i.e., negative impact of alcohol on quality of life, to determine relevant profiles for indicated prevention programs. In particular, we hypothesized that different profiles of students with high level of alcohol consumption exist when exploring subjectivity. METHODS: We performed an online survey among 16,930 students. We collected sociodemographics and environmental data, including alcohol-related quality of life, drinker identity, and drinking characteristics. We performed a hierarchical clustering on principal components. We described all variables in each cluster and explored between clusters differences by Kruskal-Wallis tests. RESULTS: We identified five clusters as regarding drinker identity, drinking characteristics, and alcohol-related quality of life. Among these five clusters, three clusters presented high drinking characteristics. A very vulnerable cluster showed high level of alcohol consumption, impact on quality of life and on academic results, and strong drinker identity. An egodystonic cluster showed high level of consumption, mild impact on quality of life and on academic results, but low drinker identity. A cluster seemed short-term super-adapted in heavy drinking environment, showing high level of alcohol consumption and drinker identity, but low impact on quality of life and on academic results (all between clusters p values < 0.001 with Kruskal-Wallis tests). CONCLUSION: The subjective experience of students from these clusters was significantly different (p value < 0.001), and could explain some inadequacy of certain prevention strategies, considering binge drinker student as a homogeneous group. Prospective studies are needed to explore changes over time of these clusters.
INTRODUCTION: The objective for this study was to combine drinking characteristics and two subjective measures, drinker identity and alcohol-related quality of life, i.e., negative impact of alcohol on quality of life, to determine relevant profiles for indicated prevention programs. In particular, we hypothesized that different profiles of students with high level of alcohol consumption exist when exploring subjectivity. METHODS: We performed an online survey among 16,930 students. We collected sociodemographics and environmental data, including alcohol-related quality of life, drinker identity, and drinking characteristics. We performed a hierarchical clustering on principal components. We described all variables in each cluster and explored between clusters differences by Kruskal-Wallis tests. RESULTS: We identified five clusters as regarding drinker identity, drinking characteristics, and alcohol-related quality of life. Among these five clusters, three clusters presented high drinking characteristics. A very vulnerable cluster showed high level of alcohol consumption, impact on quality of life and on academic results, and strong drinker identity. An egodystonic cluster showed high level of consumption, mild impact on quality of life and on academic results, but low drinker identity. A cluster seemed short-term super-adapted in heavy drinking environment, showing high level of alcohol consumption and drinker identity, but low impact on quality of life and on academic results (all between clusters p values < 0.001 with Kruskal-Wallis tests). CONCLUSION: The subjective experience of students from these clusters was significantly different (p value < 0.001), and could explain some inadequacy of certain prevention strategies, considering binge drinker student as a homogeneous group. Prospective studies are needed to explore changes over time of these clusters.
Authors: Angelo M DiBello; Mary Beth Miller; Chelsie M Young; Clayton Neighbors; Kristen P Lindgren Journal: Addict Behav Date: 2017-07-27 Impact factor: 3.913
Authors: Marc A Schuckit; Susan Tapert; Scott C Matthews; Martin P Paulus; Neil J Tolentino; Tom L Smith; Ryan S Trim; Shana Hall; Alan Simmons Journal: Alcohol Clin Exp Res Date: 2011-10-17 Impact factor: 3.455
Authors: Kristen P Lindgren; Clayton Neighbors; Bethany A Teachman; Reinout W Wiers; Erin Westgate; Anthony G Greenwald Journal: Psychol Addict Behav Date: 2012-03-19
Authors: A Luquiens; D Whalley; S R Crawford; P Laramée; L Doward; M Price; N Hawken; J Dorey; L Owens; P M Llorca; B Falissard; H J Aubin Journal: Qual Life Res Date: 2014-11-19 Impact factor: 4.147