Outcomes monitoring and the testing of new psychiatric treatments: work therapy in the treatment of chronic post-traumatic stress disorder.
AUTOR(ES)
Rosenheck, R
RESUMO
OBJECTIVE: To evaluate the effectiveness of a work therapy intervention, the Department of Veterans Affairs (VA) Compensated Work Therapy program (CWT), in the treatment of patients suffering from chronic war-related post-traumatic stress disorder (PTSD); and to demonstrate methods for using outcomes monitoring data to screen previously untested treatments. DATA SOURCES/STUDY SETTING: Baseline and four-month follow-up questionnaires administered to 3,076 veterans treated in 52 specialized VA inpatient programs for treatment of PTSD at facilities that also had CWT programs. Altogether 78 (2.5 percent) of these patients participated in CWT during the four months after discharge. STUDY DESIGN: The study used a pre-post nonequivalent control group design. DATA COLLECTION/EXTRACTION METHODS: Questionnaires documented PTSD symptoms, violent behavior, alcohol and drug use, employment status, and medical status at the time of program entry and four months after discharge from the hospital to the community. Administrative databases were used to identify participants in the CWT program. Propensity scores were used to match CWT participants and other patients, and hierarchical linear modeling was used to evaluate differences in outcomes between treatment groups on seven outcomes. PRINCIPAL FINDINGS: The propensity scaling method created groups that were not significantly different on any measure. No greater improvement was observed among CWT participants than among other patients on any of seven outcome measures. CONCLUSIONS: Substantively this study suggests that work therapy, as currently practiced in VA, is not an effective intervention, at least in the short term, for chronic, war-related PTSD. Methodologically it illustrates the use of outcomes monitoring data to screen previously untested treatments and the use of propensity scoring and hierarchical linear modeling to adjust for selection biases in observational studies.
