AUDIT‐C Scores as a Scaled Marker of Mean Daily Drinking, Alcohol Use Disorder Severity, and Probability of Alcohol Dependence in a U.S. General Population Sample of Drinkers

Background Brief alcohol screening questionnaires are increasingly used to identify alcohol misuse in routine care, but clinicians also need to assess the level of consumption and the severity of misuse so that appropriate intervention can be offered. Information provided by a patient's alcohol screening score might provide a practical tool for assessing the level of consumption and severity of misuse. Methods This post hoc analysis of data from the 2001 to 2002 N ational E pidemiologic S urvey on A lcohol and R elated C onditions ( NESARC ) included 26,546 U.S. adults who reported drinking in the past year and answered additional questions about their consumption, including Alcohol Use Disorders Identification Test—Consumption questionnaire (AUDIT‐C) alcohol screening. Linear or logistic regression models and postestimation methods were used to estimate mean daily drinking, the number of endorsed alcohol use disorder ( AUD ) criteria (“ AUD severity”), and the probability of alcohol dependence associated with each individual AUDIT ‐ C score (1 to 12), after testing for effect modification by gender and age. Results Among eligible past‐year drinkers, mean daily drinking, AUD severity, and the probability of alcohol dependence increased exponentially across increasing AUDIT ‐ C scores. Mean daily drinking ranged from < 0.1 to 18.0 drinks/d, AUD severity ranged from < 0.1 to 5.1 endorsed AUD criteria, and probability of alcohol dependence ranged from < 1 to 65% across scores 1 to 12. AUD severity increased more steeply across AUDIT ‐ C scores among women than men. Both AUD severity and mean daily drinking increased more steeply across AUDIT ‐ C scores among younger versus older age groups. Conclusions Results of this study could be used to estimate patient‐specific consumption and severity based on age, gender, and alcohol screening score. This information could be integrated into electronic decision support systems to help providers estimate and provide feedback about patient‐specific risks and identify those patients most likely to benefit from further diagnostic assessment.