J Hazart1, M Blanquet1,2, A Debost-Legrand2,3, A Perreve4, S Leger5,6, V Martoia7, S Maurice8, G Brousse9,10, L Gerbaud1,2. 1. Service de Santé Publique, CHU de Clermont-Ferrand, 63058 Clermont-Ferrand CEDEX 1 France. 2. Université Clermont Auvergne, CNRS-UMR 6602, Institut Pascal, Axe TGI, Groupe PEPRADE, 63 000 Clermont-Ferrand, France. 3. Pôle Femme Et Enfant, Centre Hospitalier Universitaire de Clermont-Ferrand, Clermont-Ferrand, France. 4. Service de Santé Universitaire, 63000 Clermont-Ferrand. 5. Université Clermont Auvergne, Université Blaise Pascal, Laboratoire de Mathématiques BP 10448 F-63000, Clermont-Ferrand, France. 6. CNRS, UMR 6620, Laboratoire de Mathématiques, F-63171 Aubière, France. 7. Centre de santé interuniversitaire, Université Grenoble Alpes, 38400 Saint Martin d'Hères, France. 8. ISPED, Université de Bordeaux, 33076 Bordeaux CEDEX. 9. Service Psychiatrie et Addictologie de l'Adulte CMP B, CHU de Clermont-Ferrand, Clermont-Ferrand, France. 10. EA 7280 UFR Médecine, Université Clermont 1, Clermont-Ferrand, France.
Abstract
INTRODUCTION: Students overestimate alcohol consumption of those around them and underestimate their own, so that quantitative approach may not be the most relevant to assess students' drinking. The main objective was to provide an appropriate tool for screening for students with potential drinking problems. METHODS: A multicentre cross-sectional survey was conducted by internet between February and June, 2013 in France. Thirteen questions explored alcohol consumption, including 8 concerning after-effects of drinking episodes (4 items of the AUDIT) and alcohol behaviour (CAGE test). A multiple correspondence analysis (MCA) was conducted to identify profiles of student's alcohol consumption. Partitioning methods were used to group students by mode of alcohol use. The most relevant items included in the MCA were identified. Three questions were identified as most pertinent among the students with potential drinking problems and ranked by a decision tree with the Chi-square Automatic Interaction Detector method. Finally, we assessed the generalisation of the model. RESULTS: A total of 36,427 students participated in the survey: 25,679 were women (70.5% of respondents), sex ratio 0.42 and mean aged 21.2 (sd 3.7 years). Among those who had experimented with alcohol (N = 33,113), three consumption profiles were identified: "simple/non-use" (66.9%), "intermediate consumption" (25.9%) and "problem drinking" (7.2%). For the latter group, the three most relevant items were (Q20) "not able to stop drinking after starting", (Q21) "failed to do what was normally expected", and (Q23) "unable to remember what happened the night before". CONCLUSIONS: These results provide healthcare professionals with a 3-item screening tool for students "problem drinking".
INTRODUCTION: Students overestimate alcohol consumption of those around them and underestimate their own, so that quantitative approach may not be the most relevant to assess students' drinking. The main objective was to provide an appropriate tool for screening for students with potential drinking problems. METHODS: A multicentre cross-sectional survey was conducted by internet between February and June, 2013 in France. Thirteen questions explored alcohol consumption, including 8 concerning after-effects of drinking episodes (4 items of the AUDIT) and alcohol behaviour (CAGE test). A multiple correspondence analysis (MCA) was conducted to identify profiles of student's alcohol consumption. Partitioning methods were used to group students by mode of alcohol use. The most relevant items included in the MCA were identified. Three questions were identified as most pertinent among the students with potential drinking problems and ranked by a decision tree with the Chi-square Automatic Interaction Detector method. Finally, we assessed the generalisation of the model. RESULTS: A total of 36,427 students participated in the survey: 25,679 were women (70.5% of respondents), sex ratio 0.42 and mean aged 21.2 (sd 3.7 years). Among those who had experimented with alcohol (N = 33,113), three consumption profiles were identified: "simple/non-use" (66.9%), "intermediate consumption" (25.9%) and "problem drinking" (7.2%). For the latter group, the three most relevant items were (Q20) "not able to stop drinking after starting", (Q21) "failed to do what was normally expected", and (Q23) "unable to remember what happened the night before". CONCLUSIONS: These results provide healthcare professionals with a 3-item screening tool for students "problem drinking".
Entities:
Keywords:
Alcohol drinking; Alcohol misuse; College students; Prevention; Screening tool
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