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.