Validity of the 32-item Hypomania Checklist (HCL-32) in a clinical sample with mood disorders in China

BMC Psychiatry. 2011 May 15:11:84. doi: 10.1186/1471-244X-11-84.

Abstract

Background: The 32-item Hypomania Checklist (HCL-32), a questionnaire for screening bipolar disorders, has been utilised in several countries, but it unclear if the Chinese version of the HCL-32 is valid.

Methods: Consecutive patients with bipolar disorders (BP, N = 300) and unipolar major depression (UP, N = 156) completed the Chinese version of the HCL-32. The subjects underwent a structured clinical interview for DSM-IV Axis-I disorders (SCID).

Results: The eigenvalues for the first three factors in the HCL-32 were calculated as 5.16 (active/elated), 2.72 (risk-taking) and 2.48 (irritable) using factor analysis. Cronbach's alpha for the HCL-32 was calculated to be 0.88. Positive responses to twenty-eight items were significantly more frequent by patients with BP than those with UP, and the other four items (7th, 21st, 25th and 32nd) showed no such trend. Fourteen was the optimal cut-off for discriminating between BP and UP. The HCL-32 distinguished between BP-II and UP, with 13 being the optimal cut-off. A cut-off of 13 yielded a sensitivity of 0.77 and a specificity of 0.62 between BP and UP.

Conclusions: This study demonstrated that the simplified Chinese version of HCL-32 was valid for patients with mood disorders. The optimal cut-off of 13 for distinguishing between BP-II and UP was valid and could be used to improve the sensitivity of screening BP-II patients when the HCL-32 is used in psychiatric settings in China.

Publication types

  • Clinical Trial
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adolescent
  • Adult
  • Bipolar Disorder / diagnosis*
  • Checklist / methods
  • Checklist / statistics & numerical data*
  • China
  • Depressive Disorder, Major / diagnosis*
  • Factor Analysis, Statistical
  • Female
  • Humans
  • Male
  • Middle Aged
  • Predictive Value of Tests
  • Psychiatric Status Rating Scales / statistics & numerical data*
  • ROC Curve