Commentary: Improving the Effectiveness and Utility of the Helping to End Addiction Long-Term (HEAL) Prevention Cooperative: A Full Translational Framework

The Helping to End Addiction Long-term (HEAL) Prevention Collaborative (HPC) is designed to expedite the development of programs aimed at preventing opioid misuse and opioid use disorder (OUD) in older adolescents and young adults (ages 16–30). Funded by the National Institutes of Health Office of the Director (ODP-NIH), the HPC includes ten outcome studies that focus on distinct interventions to determine their effectiveness and real-world applicability. Also included is a coordinating center at RTI International that supports the individual projects. This commentary highlights the scientific and practical significance of this cooperative and its promise for facilitating the production and implementation of successful interventions. Attributes such as novel program designs, advanced methodologies, addressing unique characteristics of diverse populations, and real-time analysis of data and costs make this cooperative highly innovative. We note, however, that papers in this Supplemental Issue did not specifically address the persistent need to obtain stronger effect sizes than those achieved to date. Existing data captured earlier in development (< 16 years of age) are uncovering interactive neurocognitive and social-contextual mechanisms underlying the phenomena we wish to prevent. HPC projects could be guided by this information to incorporate developmentally appropriate measures of mechanisms shown previously to be influential in targeted outcomes and determine how they are impacted by specific components of their interventions. This mechanistic information can provide a roadmap for constructing interventions that are more precision-based and, thus, more likely to yield greater benefits for a larger number of recipients. Furthermore, an understanding of underlying mechanism(s) promises to shed light on the sources of heterogeneity in outcomes for further intervention refinement. It is quite possible, if not probable, that meaningful measures of underlying processes will reveal subtypes—some with very high effect sizes and others that are much lower—directly enabling program refinements to more directly target mechanisms that portend and explain less favorable outcomes. Described herein is a full-spectrum translational approach which promises to significantly boost effect sizes, a key objective that should be reached prior to scaling.