Impact of “Active Ingredients” of an Integrated Service Model on Reducing Post-Treatment Substance Use
Methods: Survey data were collected from 1995-97 for the National Treatment Improvement Evaluation Study (NTIES), a prospective, longitudinal, multisite study of substance abuse treatment programs and their clients in the U.S.. Survey data on treatment organizations were collected on both health and social services received as part of substance abuse treatment as well as substance use at treatment entry, treatment exit, and 12-months post-treatment. The analytic sample for this paper consists of 486 Latinos, 339 men and 147 women. A comprehensive service delivery model was developed with four components: substance abuse counseling, need-service matching, and client-provider relationship. A variance decomposition approach was used to estimate the relative importance of each components in explaining post-treatment substance use.
Results: As expected, the greatest proportion of post-treatment drug use was accounted for by severity of pre-treatment drug use and length of time spent in treatment. However, after controlling for these factors as well client demographic factors, need-service ratio and client-provider relationship accounted for 6% and 7% of variance respectively. Both of these service delivery measures accounted for more outcome variance than the substance abuse counseling measure.
Conclusions and implications: Results show that two components of the comprehensive integrated service delivery model related to (1) insuring clients receive targeted services or the services they report that they need, as well as (2) the quality of the client provider relationship have a significant impact on substance abuse treatment outcomes. Results point to the development of integrated service models that include careful assessment and targeting of services as well as that insure service delivery within the context of positive client-provider relationship. Results also point to the value of analytic models designed to assign shares of “relative importance” in a set of regressors as a strategy for model development in this area.