Multivariate analysis of outcome of mental health care using graphical chain models - The South-Verona outcome project 1

Background. Short-term outcome of mental health care was assessed in a mult idimensional perspective using graphical chain models, a new multivariate m ethod that analyses the relationship between variables conditionally, i.e. taking into account the effect of antecedent and intervening variables. Methods. GAF, BPRS, DAS (at baseline and after 6 months), LQL and VSSS (at follow-up only) were administered to 194 patients attending the South-Veron a community-based mental health service. Direct costs in the interval were also calculated. Graphical chain models were used to analyse: (1) the assoc iations between predictors (psychopathology, disability, functioning, asses sed at baseline); (2) the effects of predictors on costs; and (3) the effec t of predictors and costs on outcomes (psychopathology, disability, functio ning, quality of life and service satisfaction) as well as their correlatio n. Results. Psychopathology, disability and functioning scores at baseline pre dicted the corresponding scores at 6-month follow-up, with greater improvem ent in the more severely ill. Higher psychopathology and poorer functioning at baseline predicted higher costs and, in turn, costs predicted poorer fu nctioning at follow-up. Outcome indicators polarized in two groups: psychop athology, disability and functioning, which were highly correlated; and the dyad service satisfaction and quality of life. Service satisfaction was hi ghly related to quality of life and was predicted by low disability and hig h dysfunctioning. No predictors for quality of life were found. Conclusions. Graphical chain models were demonstrated to be a useful method ology to analyse process and outcome data. The results of the present study help in formulating specific hypotheses for future studies on outcome.