A case mix procedure for matching the clinical characteristics of patients with dual disorders to planned treatment and treatment costs

Case-mix adjustment of adolescent mental health treatment outcomes

Case-mix adjustment methods are needed to account for differences between providers when the youth they treat have characteristics that adversely affect treatment success. This study explores variables for adjusting mental health treatment outcomes for adolescents and the differential effects of case-mix adjustment on providers' performance. Linear regression modeling was used to identify case-mix variables for five outcomes. Predictive equations for each outcome were developed for models based on intake clinical data alone, clinical data plus administrative data, and clinical data plus data describing youth history and family environment. Variance explained by intake clinical data alone did not increase appreciably with the addition of administrative data or data describing youth history and family environment. Adjusting outcomes changed the relative performance of certain individual providers substantially, but had a more moderate impact on the overall interpretation of providers' performance.

Toward a level playing field: predictive factors for the outcomes of mental health treatment for adolescents

OBJECTIVE

To understand better the effectiveness of routine treatment for emotional and behavioral problems experienced by adolescents, methods are needed to control for between-provider differences in the distribution of factors that adversely affect treatment success. Such methods are necessary to fairly compare providers' outcomes and to aid clinicians in identifying adolescents for whom routine care may need to be altered. As a preliminary step toward developing a model to adjust treatment outcomes to account for predictive factors, findings from studies of treated samples of adolescents were reviewed to identify the factors that influence the likelihood of treatment success for this population.

METHOD

Medline and PSYCInfo databases were searched for studies of treated adolescents that reported the association between expert-nominated predictive factors and outcomes. Thirty-four studies met inclusion criteria.

RESULTS

Significant predictors identified in these studies include diagnosis, baseline severity of symptoms and functional impairment, family dysfunction, and previous treatment. Several expert-nominated factors have not been adequately studied in treated samples.

CONCLUSIONS

Much basic work is needed before a convincing body of empirical evidence can explain predictive factors for adolescent mental health treatment outcomes. Future efforts should determine a reduced set of predictive factors that can be measured with minimal burden to providers.

Using incarceration rates to measure mental health program performance

This article introduces a more refined conceptualization of the criminal justice involvement of clients of mental health programs than is evident in the existing literature, and demonstrates a research methodology that provides program administrators with standardized measures of program performance in this area. The conceptualization of the criminal justice involvement of people served by community mental health programs distinguishes between three distinct areas of concern: (1) program accessibility to people with a history of criminal justice involvement, (2) criminal justice outcomes, and (3) quality of program performance. The methodology makes use of existing data resources to provide a valid and reliable measure of program performance in these three areas.