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  • Title: Detecting differences in diagnostic assessment of bipolar disorder.
    Author: Zimmerman M, Ruggero CJ, Galione JN, McGlinchey JB, Dalrymple K, Chelminski I, Young D.
    Journal: J Nerv Ment Dis; 2010 May; 198(5):339-42. PubMed ID: 20458195.
    Abstract:
    During the past 25 years, semistructured diagnostic interviews have been the standard for diagnostic evaluations in research relying on reliable and valid psychiatric assessment and diagnosis. However, the use of semistructured interviews still requires interpretation of the diagnostic criteria and does not preclude the application of different diagnostic thresholds. The goal of this report from the Rhode Island Methods to Improve Diagnostic Assessment and Services project is to illustrate how a self-report scale can be used to detect systematic differences in the application of diagnostic criteria for bipolar disorder and to demonstrate the wide variation in how broadly different groups tend to diagnose bipolar disorder. We compared the frequency of bipolar diagnoses in 4 studies that examined the performance of mood disorders questionnaire (MDQ) with the Structured Clinical Interview for DSM-IV (SCID). We also compared the prevalence rate of MDQ cases and the ratio of SCID diagnoses with MDQ cases. The frequency of bipolar disorder in the 4 studies ranged from 10.9% to 76.2%-a 7-fold difference in prevalence rates. The frequency of MDQ-positive cases ranged from 17.8% to 31.2%, less than a 2-fold difference in prevalence rates. Thus, there was much less variability in MDQ rates than diagnosis rates. Moreover, the rank order of the prevalence of MDQ cases differed from the rank order of the prevalence of SCID diagnoses. The SCID/MDQ ratio significantly differed between the studies. These findings demonstrate how systematic differences in diagnostic practice might be detected using a self-administered scale such as the MDQ. The results also underscore that wide variation exists in the bias toward diagnosing bipolar disorder, even after controlling for differences in prevalence among samples.
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