A comparison of outcomes among patients with schizophrenia in two mental health systems: a health state approach.

This paper introduces a health state modeling approach using clustering and Markov analysis to compare short- and long-term outcomes among health care populations. We provide a comparison to more conventional mixed effects regression methods and show that discrete state modeling offers a richer portrait of patient outcomes than the standard univariate techniques. We demonstrate our approach using primary data from a three year observational study of patients treated for schizophrenia at a VA Medical Center (VA) and in a Community Mental Health Center (CMHC) in the same urban community. Randomly selected samples of outpatients treated for schizophrenia or schizoaffective disorder were interviewed every six months using standardized psychiatric assessments such as the Positive and Negative Syndrome Scale (PANSS). Items from the PANSS were used to define 7 discrete health states representing different levels of severity and diverse mixtures of psychiatric symptoms. Conventional analysis showed that VA patients exhibited increasingly severe symptoms, while CMHC patients remained more stable over the study period. Health state analysis reinforced these results but also identified which subpopulations of VA patients were deteriorating. In particular they showed that there was little change over time among VA patients in the best and worst health states. Instead the deterioration was caused by VA patients with: a) mild symptoms and hallucinations and b) serious positive and negative symptoms, being more likely to enter a state with severe positive and negative symptoms accompanied by moderate general distress.