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February 1, 2003Psychological Medicine1,614 citations

The performance of the K6 and K10 screening scales for psychological distress in the Australian National Survey of Mental Health and Well-Being

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TFToshi A. FurukawaTokyo University of Social Welfare
Ronald C. Kessler
Ronald C. KesslerHarvard University Press
TSTim SladeThe University of Sydney

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Abstract

Background. Two new screening scales for psychological distress, the K6 and K10, have been developed but their relative efficiency has not been evaluated in comparison with existing scales. Method. The Australian National Survey of Mental Health and Well-Being, a nationally representative household survey, administered the WHO Composite International Diagnostic Interview (CIDI) to assess 30-day DSM-IV disorders. The K6 and K10 were also administered along with the General Health Questionnaire (GHQ-12), the current de facto standard of mental health screening. Performance of the three screening scales in detecting CIDI/DSM-IV mood and anxiety disorders was assessed by calculating the areas under receiver operating characteristic curves (AUCs). Stratum-Specific Likelihood Ratios (SSLRs) were computed to help produce individual-level predicted probabilities of being a case from screening scale scores in other samples. Results. The K10 was marginally better than the K6 in screening for CIDI/DSM-IV mood and anxiety disorders (K10 AUC: 0·90, 95%CI: 0·89–0·91 versus K6 AUC: 0·89, 95%CI: 0·88–0·90), while both were significantly better than the GHQ-12 (AUC: 0·80, 95%CI: 0·78–0·82). The SSLRs of the K10 and K6 were more informative in ruling in or out the target disorders than those of the GHQ-12 at both ends of the population spectrum. The K6 was more robust than the K10 to subsample variation. Conclusions. While the K10 might outperform the K6 in screening for severe disorders, the K6 is preferred in screening for any DSM-IV mood or anxiety disorder because of its brevity and consistency across subsamples. Precision of individual-level prediction is greatly improved by using polychotomous rather than dichotomous classification.

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Furukawa et al. (2003) studied this question.

synapsesocial.com/papers/698268d6e578c79f9d65bc39https://doi.org/10.1017/s0033291702006700
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