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. 2003 Feb;33(2):357-62.
doi: 10.1017/s0033291702006700.

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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The Performance of the K6 and K10 Screening Scales for Psychological Distress in the Australian National Survey of Mental Health and Well-Being

T A Furukawa et al. Psychol Med. .

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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