Accuracy of Alcohol Use Disorders Identification Test for Detecting Problem Drinking in 18–35 Year-Olds in England: Method Comparison Study

and females aged 18–35 in England.Methods: A method comparison study with 420 primary care patients aged 18–35. Test measures were AUDIT and AUDIT-C. Reference standard measures were (a) Time-Line Follow-Back interview for hazardous drinking; World Mental Health Composite International Diagnostic Interview for (b) DSM-IV alcohol abuse, (c) DSM-IV alcohol dependence, (d) DSM-5 alcohol use disorders. Results: Area under the curve (AUC) was (a) 0.79 (95 % CI 0.73–0.85; males) and 0.84 (0.79–0.88; females); (b) 0.62 (0.54–0.72; males) and 0.65 (0.57–0.72; females); (c) 0.77 (0.65–0.87; males) and 0.76 (0.67–0.74; females); (d) 0.70 (0.60–0.78; males) and 0.73 (CI 0.67–0.78; females). Identification of threshold cut-point scores from the AUC was not straightfor-ward. Youden J statistic optimal cut-point scores varied by 4–6 AUDIT scale points for each outcome according to whether sensitivity or specificity were prioritized. Using Bayes ’ Theorem, the post-test probability of drinking problems changed as AUDIT score increased, according to the slope of the probability curve. Conclusion: The full AUDIT scale showed good or very good accuracy for all outcome measures for males and females, except for alcohol abuse which had sufficient accuracy. In a screening scenario where sen-sitivity might be prioritized, the optimal cut-point is lower than established AUDIT cut-points of 8+ for men and 6+ for women. Bayes’ Theorem to calculate individual probabilities for problem drinking offers an alternative to arbitrary cut-point threshold scores in screen-ing and brief intervention programmes