International Journal of Mental Health Systems (Sep 2011)
Validation aspects of the health of the nation outcome scales
Abstract
Abstract Background The purpose of the current study was the psychometric evaluation of the Health of the Nation Outcome Scales (HoNOS), an instrument developed to meet the necessity of a clinically acceptable outcome scale for routine use in mental illness services. Methods The study participants included 2,162 outpatients and residential inpatients (rated on the HoNOS on three occasions during the year 2000) with a range of mental illnesses in different diagnostic groups from ten Mental Health Departments, located in the area of Milan (Italy). Principal Component Analysis, Confirmatory Factor Analysis, Discriminant Analysis and Partial Credit Rasch Model were used to assess two sources of validity: the internal structure and the relationships with other variables. Results The results of the 12-item HoNOS demonstrate a significant departure from uni-dimensionality, confirmed by the Rasch analysis (which identified three misfitting items). However, HoNOS scores demonstrate stability and precision of item difficulties over time. Discriminant analysis showed that HoNOS scores have an acceptable level of discriminatory power in predicting the severity of patients' conditions (as represented by setting). Conclusions It was concluded that the Italian version of the HoNOS does not measure a single, underlying construct of mental health status. The internal structure validity analysis recommends a note of caution to use a summary index of the HoNOS scores, given the presence of multidimensionality and misfit. Nonetheless, the finding that the instrument is more multidimensional than unidimensional does not preclude the use of the HoNOS as a clinically valid tool for routine outcome assessment. In fact, item scores have demonstrated sufficient reliability (over diagnostic groups and care settings) and high precision in time, indicating that HoNOS items can be utilized as valid measurement instruments in longitudinal analyses.