title: Multivariate analysis of outcome of mental health care using graphical chain models. The South-Verona Outcome Project 1.
abstract: BACKGROUND
  Short-term outcome of mental health care was assessed in a multidimensional perspective using graphical chain models, a new multivariate method 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-Verona community-based mental health service. Direct costs in the interval were also calculated. Graphical chain models were used to analyse: (1) the associations between predictors (psychopathology, disability, functioning, assessed at baseline); (2) the effects of predictors on costs; and (3) the effect of predictors and costs on outcomes (psychopathology, disability, functioning, quality of life and service satisfaction) as well as their correlation.
  
  
  RESULTS
  Psychopathology, disability and functioning scores at baseline predicted the corresponding scores at 6-month follow-up, with greater improvement in the more severely ill. Higher psychopathology and poorer functioning at baseline predicted higher costs and, in turn, costs predicted poorer functioning at follow-up. Outcome indicators polarized in two groups: psychopathology, disability and functioning, which were highly correlated; and the dyad service satisfaction and quality of life. Service satisfaction was highly related to quality of life and was predicted by low disability and high dysfunctioning. No predictors for quality of life were found.
  
  
  CONCLUSIONS
  Graphical chain models were demonstrated to be a useful methodology to analyse process and outcome data. The results of the present study help in formulating specific hypotheses for future studies on outcome.
authors.affiliation: Servizio di Psicologia Medica, Università di Verona, Italy.
authors.forename: M
authors.initials: M
authors.lastname: Ruggeri
authors.forename: A
authors.initials: A
authors.lastname: Biggeri
authors.forename: P
authors.initials: P
authors.lastname: Rucci
authors.forename: M
authors.initials: M
authors.lastname: Tansella
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D003156:Community Mental Health Services / Q000191:economics / Q000706:statistics & numerical data*; D003196:Computer Graphics; D003258:Consumer Behavior / Q000191:economics; D003362:Cost-Benefit Analysis; D005260:Female; D006801:Humans; D007558:Italy; D008297:Male; D001523:Mental Disorders / Q000191:economics / Q000523:psychology / Q000534:rehabilitation*; D008875:Middle Aged; D010043:Outcome and Process Assessment, Health Care / Q000706:statistics & numerical data*; D017060:Patient Satisfaction / Q000191:economics; D011569:Psychiatric Status Rating Scales / Q000706:statistics & numerical data*; D011788:Quality of Life