Optimizing DSM-IV-TR classification accuracy: A Brief Biosocial Screen for detecting current gambling disorders among gamblers in the general household population


Objective: To develop a pathological gambling {(PG)} screen for efficient application to the household population and for clinicians to use with treatment seekers. Method: We applied a series of multivariate discriminant functions to past-12-month Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision {(DSM-IV-TR)–based}, gambling-related problems; the National Epidemiologic Survey on Alcohol and Related Conditions {(NESARC)} measured and collected this data. The {NESARC} conducted computer-assisted personal interviews with 43 093 households and identified the largest sample of pathological gamblers drawn from the general household population. Results: We created a 3-item, brief biosocial gambling screen {(BBGS)} with high sensitivity {(Sensitivity} = 0.96; 76 of 79 pathological gamblers correctly identified) and high specificiy {(Specificity} = 0.99; 10 892 of 11 027 nonpathological gamblers correctly identified). Conclusions: Major {US} studies reveal extensive comorbidity of {PG} with other mental illnesses. The {BBGS} features psychometric advantages for health care providers that should encourage clinicians and epidemiologists to consider current {PG} along with other problems. The {BBGS} is practical for clinical application because it uses only 3 items and they are easy to ask, answer, and include in all modes of interviewing, including self-administered surveys. The {BBGS} has a strong theoretical foundation because it includes 1 item from each of the addiction syndrome 3 domains: neuroadaptation (for example, withdrawal); psychosocial characteristics (for example, lying); and adverse social consequences of gambling (for example, obtaining money from others).

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