Innovative Data Synthesis Methodology and Academic-Agency Partnerships to Implement Evidence Based Programs Under the Affordable Care Act

The objective of this paper is to propose a cohesive plan for institutionalizing evidence based mental health and substance abuse programs in order to fulfill the National Prevention Strategies’ goal of promoting mental and emotional well-being.  To achieve this objective, we: 1) describe statistical methodology that can maximize the information gained through the quantitative synthesis of existing trials despite a somewhat limited field of literature, in order to make recommendations on what types of interventions work best for whom, and 2) present a framework for the development of partnerships between state and federal agencies in order to work toward the sustainable implementation of evidence based programs.

The Affordable Care Act (ACA) provides approximately $1.3 billion for state agencies, schools, and other organizations to deliver evidence based programs (EBPs), especially those recommended by The United States Preventive Services Task Force (USPSTF) for targeting violence prevention, workforce expansion, screening, prevention, and the  treatment of mental health and substance use disorders in both youth and adults (OMB, 2013).  The USPSTF is charged with reviewing the literature and providing recommendations on EBPs.  While scientifically rigorous, their reviews often provide quantitative summaries such as number needed to treat and outcome boundaries, opting to exclude formal decision analyses.  We discuss how such review methods may not fully capitalize on the potential knowledge to be gained from the literature on the extent of intervention benefits, thus reducing certainty and constraining recommendations.

Innovative methodology for synthesizing data, such as integrative data analysis (IDA), could provide useful insight into the existing literature on prevention interventions beyond what is readily available through traditional decision summaries, which primarily utilize published, study-level effect sizes.  Using the example of the Collaborative Data Synthesis for Adolescent Depression Trials (CDSADT) study, we illustrate the potential magnitude of higher statistical efficiency in using IDA over the traditional decision process for identifying evidence-based prevention programs for synthesizing 18 adolescent depression prevention trials, including increased insight into the moderators and mediators of intervention effects.  Further, as an alternative to depending upon the passive dissemination of these methods to key actors who are responsible for achieving the NPS goals, such as the USPSTF, we present a dissemination model based upon partnership building between researchers and these actors.  Such partnerships will aim to 1) facilitate the institutionalization of EBPs into community practice with fidelity, and 2) help programs function sustainably after the cessation of ACA funding.