Alcohol and drug dependence symptom items as brief screeners for substance use disorders: Results from the Clinical Trials Network.
This CTN ancillary investigation aimed to address an urgent need for screening of substance use problems in medical settings by examining substance-specific dependence criteria as potential brief screeners for the detection of patients with a substance use disorder (SUD). Researchers examined data from intake assessments of substance use disorders completed by 920 opioid dependent adults from outpatient treatment settings at 11 programs in 10 U.S. cities (part of protocol CTN-0003, which investigated taper schedules for buprenorphine/naloxone dosing). Data were analyzed by factor analysis, item response theory (IRT), sensitivity, and specificity procedures. Across all substances (alcohol, amphetamines, cannabis, cocaine, sedatives), “withdrawal” was among the least prevalent symptoms, while “taking large amounts” and “inability to cut down” were among the more prevalent. Items closely related to the latent trait of a substance use disorder showed good-to-high values of area under the receiver operating characteristic curve in identifying cases of SUD; IRT-defined severe and less discriminative items exhibited low sensitivity in identifying cases of SUD (“withdrawal” for all substances; “time using” for alcohol and sedatives; “giving up activities” for sedatives).
Conclusions: Study results suggest that “withdrawal” and “time using” are much less reliable indicators for a substance use disorder than “taking larger amounts than intended” and “inability to cut down,” and that the latter two items should be studied further for consideration in developing a simplified tool for screening patients for SUD in medical settings. These findings have implications for the use of common health indicators in electronic health records systems to improve patient care. This study also demonstrates the unique value of IRT analysis in determining the underlying trait of a set of measures and elucidating item-level psychometric information to help interpret results of sensitivity and specificity; researchers should consider incorporating IRT approaches into their analysis plans to support the psychometric quality of patient-reported items.
Supported by the Duke Clinical Research Institute (CTN DSC 1).
Related protocols: CTN-0003