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Clinical Trial
. 2013 Jan-Feb;35(1):74-82.
doi: 10.1016/j.genhosppsych.2012.06.014. Epub 2012 Jul 21.

Evaluating brief screeners to discriminate between drug use disorders in a sample of treatment-seeking adults

Li-Tzy Wu  1 , Marvin S Swartz, Jeng-Jong Pan, Bruce Burchett, Paolo Mannelli, Chongming Yang, Dan G Blazer
Affiliations
Clinical Trial

Evaluating brief screeners to discriminate between drug use disorders in a sample of treatment-seeking adults

Li-Tzy Wu et al. Gen Hosp Psychiatry. 2013 Jan-Feb.

Abstract

Objective: The objective was to identify a potential core set of brief screeners for the detection of individuals with a substance use disorder (SUD) in medical settings.

Method: Data were from two multisite studies that evaluated stimulant use outcomes of an abstinence-based contingency management intervention as an addition to usual care (National Drug Abuse Treatment Clinical Trials Network trials 006-007). The sample comprised 847 substance-using adults who were recruited from 12 outpatient substance abuse treatment settings across the United States. Alcohol and drug use disorders were assessed by the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Checklist. Data were analyzed by factor analysis, item response theory (IRT), sensitivity and specificity procedures.

Results: Comparatively prevalent symptoms of dependence, especially inability to cut down for all substances, showed high sensitivity for detecting an SUD (low rate of false negative). IRT-defined severe (infrequent) and low discriminative items, especially withdrawal for alcohol, cannabis and cocaine, had low sensitivity in identifying cases of an SUD. IRT-defined less severe (frequent) and high discriminative items, including inability to cut down or taking larger amounts than intended for all substances and withdrawal for amphetamines and opioids, showed good-to-high values of area under the receiver operating characteristic curve in classifying cases and noncases of an SUD.

Conclusion: Findings suggest the feasibility of identifying psychometrically reliable substance dependence symptoms to develop a two-item screen for alcohol and drug disorders.

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Figures

Fig. 1
Fig. 1
Prevalence (12 months) of substance dependence symptoms among users of specific substance Note: Lines extending from bars indicate 95% confidence intervals of the estimates. If two error bars overlap, the difference between two groups is not statistically significant (P > 0.05).
Fig. 2
Fig. 2
Item response theory (IRT) analysis of substance dependence symptoms among users of specific substance: (a) alcohol, (b) cannabis, (c) cocaine, (d) amphetamines, and (e) opiates
Fig. 2
Fig. 2
Item response theory (IRT) analysis of substance dependence symptoms among users of specific substance: (a) alcohol, (b) cannabis, (c) cocaine, (d) amphetamines, and (e) opiates
Fig. 2
Fig. 2
Item response theory (IRT) analysis of substance dependence symptoms among users of specific substance: (a) alcohol, (b) cannabis, (c) cocaine, (d) amphetamines, and (e) opiates
Fig. 2
Fig. 2
Item response theory (IRT) analysis of substance dependence symptoms among users of specific substance: (a) alcohol, (b) cannabis, (c) cocaine, (d) amphetamines, and (e) opiates
Fig. 2
Fig. 2
Item response theory (IRT) analysis of substance dependence symptoms among users of specific substance: (a) alcohol, (b) cannabis, (c) cocaine, (d) amphetamines, and (e) opiates
Fig. 3
Fig. 3
(a) Sensitivity, (b) specificity, and (c) area under the receiver operating characteristic curve (ROC-AUC) of substance dependence symptoms among users of specific substance
Fig. 3
Fig. 3
(a) Sensitivity, (b) specificity, and (c) area under the receiver operating characteristic curve (ROC-AUC) of substance dependence symptoms among users of specific substance
Fig. 3
Fig. 3
(a) Sensitivity, (b) specificity, and (c) area under the receiver operating characteristic curve (ROC-AUC) of substance dependence symptoms among users of specific substance
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