Creating a Classification System of Opioid Use Disorder Treatment Policies: A Research Protocol

Abstract

The opioid-related overdose crisis remains a public health priority in the United States. A key component of initiatives to mitigate this crisis are policies that aim to improve equitable access to effective treatments for opioid use disorder (OUD). To facilitate this goal, it is crucial to effectively use and build upon evidence from existing studies evaluating the effectiveness of OUD treatment policies, though assessing the effectiveness of policies is challenging in part due to bespoke differences in how researchers define and categorize policies. This project aimed to improve addiction research through the development of an evidence- and consensus-based classification system for OUD treatment policies. The development of this classification system will take place in 5 steps. The first step will involve a review of existing policy classification systems to create a synthesized list of labels, definitions, and relational structure for OUD treatment policies. The second step will involve a refinement of this list through examining policy labels and definitions in existing empirical studies of OUD treatment policies, with discussion and revision of the list throughout this process. The third step will involve an online expert feedback exercise on the clarity, uniqueness, and completeness of the refined classification system. The fourth step will involve reliability testing of the classification system on existing policies to examine the interrater reliability across different areas of OUD treatment policy. The fifth step will involve a sorting task to place the OUD treatment policies into final categories. A unified classification system of OUD treatment policies can facilitate comprehensive and systematic assessments of what we know from existing empirical research, identify gaps in policy approaches, inform data collection efforts, improve future scientific evaluations, and help policymakers make more informed decisions about which policies are high-value for specific outcomes in specific populations and contexts.