Jason Bond1, Jane Witbrodt2, Yu Ye2, Cheryl J Cherpitel2, Robin Room3, Maristela G Monteiro4. 1. Alcohol Research Group, Emeryville, CA, USA jbond@arg.org. 2. Alcohol Research Group, Emeryville, CA, USA. 3. Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia Centre for Social Research on Alcohol and Drugs, Stockholm University, Stockholm, Sweden Centre for Alcohol Policy Research, Turning Point Centre, Melbourne, Australia. 4. Pan American Health Organization, Washington, DC, USA.
Abstract
AIMS: Although the relationship between the Y90 (blood alcohol concentration, BAC) and Y91 (clinician intoxication assessment) ICD-10 codes has received attention recently, the role of 10 signs of intoxication in the Y91-Y90 relationship has not been studied yet. This work examines these signs in the estimation of alcohol intoxication levels of patients in medical settings. METHODS: Collected and analyzed were data on 1997 injured emergency room patients from 17 countries worldwide reporting drinking prior to injury or presenting with a non-zero BAC from 17 countries worldwide. A model is estimated describing how the 10 signs inform the Y91, Y90 prediction with the goal of the use of observations on patients in place of a biological measure. RESULTS: Signs were consistent with a single underlying construct that strongly predicted Y91. Smell of alcohol on breath predicted Y91 above its contribution through the construct and was stronger for those with tolerance to alcohol than for those without. Controlling for Y91, no sign further contributed to prediction of Y90 indicating that Y91 incorporated all intoxication sign information in predicting Y90. Variance explained was high for Y91 (R(2) = 0.84) and intoxication signs (above 0.72 for all but smell on the breath, 0.57) and lower for Y90 (0.38). CONCLUSION: Intoxication assessments are well predicted by overall intoxication severity, which itself is well represented by intoxication signs along with differential emphasis on smell of alcohol on breath, especially for those with alcohol tolerance. However, BAC levels remain largely unexplained by intoxication signs with a clinician's assessment serving as the primary predictive measure.
AIMS: Although the relationship between the Y90 (blood alcohol concentration, BAC) and Y91 (clinician intoxication assessment) ICD-10 codes has received attention recently, the role of 10 signs of intoxication in the Y91-Y90 relationship has not been studied yet. This work examines these signs in the estimation of alcohol intoxication levels of patients in medical settings. METHODS: Collected and analyzed were data on 1997 injured emergency room patients from 17 countries worldwide reporting drinking prior to injury or presenting with a non-zero BAC from 17 countries worldwide. A model is estimated describing how the 10 signs inform the Y91, Y90 prediction with the goal of the use of observations on patients in place of a biological measure. RESULTS: Signs were consistent with a single underlying construct that strongly predicted Y91. Smell of alcohol on breath predicted Y91 above its contribution through the construct and was stronger for those with tolerance to alcohol than for those without. Controlling for Y91, no sign further contributed to prediction of Y90 indicating that Y91 incorporated all intoxication sign information in predicting Y90. Variance explained was high for Y91 (R(2) = 0.84) and intoxication signs (above 0.72 for all but smell on the breath, 0.57) and lower for Y90 (0.38). CONCLUSION: Intoxication assessments are well predicted by overall intoxication severity, which itself is well represented by intoxication signs along with differential emphasis on smell of alcohol on breath, especially for those with alcohol tolerance. However, BAC levels remain largely unexplained by intoxication signs with a clinician's assessment serving as the primary predictive measure.
Authors: Jürgen Rehm; Dolly Baliunas; Guilherme L G Borges; Kathryn Graham; Hyacinth Irving; Tara Kehoe; Charles D Parry; Jayadeep Patra; Svetlana Popova; Vladimir Poznyak; Michael Roerecke; Robin Room; Andriy V Samokhvalov; Benjamin Taylor Journal: Addiction Date: 2010-03-15 Impact factor: 6.526
Authors: Jürgen Rehm; Robin Room; Maristela Monteiro; Gerhard Gmel; Kathryn Graham; Nina Rehn; Christopher T Sempos; David Jernigan Journal: Eur Addict Res Date: 2003-10 Impact factor: 3.015
Authors: Susan D Cochran; Jack Drescher; Eszter Kismödi; Alain Giami; Claudia García-Moreno; Elham Atalla; Adele Marais; Elisabeth Meloni Vieira; Geoffrey M Reed Journal: Bull World Health Organ Date: 2014-06-17 Impact factor: 9.408