Vending machines: a low-barrier method to deliver harm reduction services in the community

PROJECT SUMMARY/ABSTRACT Overdose deaths have increased, reaching all-time-highs recently. Harm reduction (HR) strategies can help reduce the number of these fatalities, while connecting individuals to substance use disorder (SUD) treatment when they are ready. HR-focused smart vending machines (sVMs) delivering naloxone, fentanyl test strips, and other public health items, represent a potential non-stigmatizing, low-barrier approach to HR that may increase access and engagement with underserved populations, reduce overdoses and related public health threats, and health disparities in SUD. As HR-focused VMs grow in popularity in the US and are implemented in communities with different characteristics and preferences, there is a need for an implementation science- driven, rigorous approach to implementation and evaluation of VMs’ impact on individuals and communities. This K23-supported study will build the foundation toward implementation and evaluation strategies for sVMs in communities impacted by overdoses and SUD. Using a Type 1 effectiveness-implementation hybrid design, the K23 study will test the hypothesis that application of evidence-based strategies to develop and test research protocols will promote future effective implementation and evaluation of HR-focused sVMs in diverse communities. The K23 will identify implementation barriers and facilitators to refine an early-phase protocol developed during a pilot study, using the Exploration, Preparation, Implementation and Sustainment (EPIS) framework and qualitative input from community partners. The refined protocol will guide sVM placement in a new community setting (Aim 1). The sVM’s reach, utilization, adoption, and acceptability will be evaluated in a convergent mixed methods design, informed by the Reach, Effectiveness, Adoption, Implementation, Maintenance (RE-AIM) framework (Aim 2). The sVM’s impact will also be explored using the convergent mixed methods approach and public health data from existing, local databases (Aim 2). Finally, the study will also use an explanatory sequential mixed methods design to assess the utility of sVM’s data to guide community resource allocation. The sVM-collected data on user interest in specific services, and interviews with key community stakeholders will help assess the community’s capacity to meet the expressed demand for specific services, informing resource allocation (Aim 3). By applying evidence-based approaches to HR-focused sVM and its evaluation, this research is a critical first step toward future rigorous implementation and dissemination of sVM as a non-stigmatizing, accessible path for HR and SUD in underserved populations. The mentored research career development award (K23) will also support PI’s in-depth training in addiction medicine, implementation science, mixed methods, community-engaged research, and large dataset analyses that will enable her to become a successful, independent physician-scientist who develops, implements, and evaluates innovative community-based interventions to reduce SUD-related harms and health disparities.