MERC: Our Work

The Modeling and Economic Resource Center (MERC) supports the Innovation and Acceleration Projects by providing guidance on the application of modeling methods, cost analyses, and behavioral economic strategies. Scientists from MERC develop tools to assist the projects in making informed decisions about costs of implementing evidence-based practices/interventions. They also conduct innovative research that highlights the complexities found in implementing evidence-based practices to address the overdose crisis. Through their work, the MERC team aims to contribute to knowledge about how evidence-based practices/interventions can be replicated across systems, communities, and circumstances.

Two of the MERC's innovative research projects are:

  • Innovative payment design for peer services research project. This project will develop innovative approaches to paying for peer services that support OUD treatment and recovery. We will conduct a scoping review of existing mechanisms that might be utilized to authorize Medicaid payment for peer support services for substance use disorder and the extent to which these mechanisms are being utilized. We will also identify current payment rates using state Medicaid or other guidelines about billing codes and modifiers used for peer support services. This analysis will support recommendations for using existing payment mechanisms, adjusting payment rates, or designing new payment mechanisms. We will then either adapt an existing bundled service payment model to include coverage of peer support services or design a new bundled payment that comprehensively covers a team-based approach, including peer support services. We will develop a payment design framework based on consultation with service provider and payer key informants. We will develop and implement transparent algorithms that calculate a payment rate given the design choices and the cost of providing peer support services.
  • OUD comparative and meta-modeling research project. This project will use innovative techniques to synthesize information from multiple OUD simulation models to improve efficiency and accuracy of model predictions of key OUD outcomes. We will address to what degree the structure of OUD simulation models creates bias or determines outcomes, an important methodological research question. We will use a comparative modeling approach similar to those used in other disease areas to consider how differences in modeling methods (compartmental model vs micro-simulation vs agent-based) and focus (venues vs communities vs individuals) affect the nature of the relationships between model inputs and outputs. We will then explore the advantages of using meta-models to improve the robustness and relevance of model projections. Meta-models, or emulators, are statistical models describing the association between inputs and outputs of complex simulation systems in a simplified form. The advantage of meta-modeling is that it takes a complex simulation model that may require expertise to use and extended time to run and turns it into a concrete tool that provides instant results, bringing the power of simulation model analysis directly to the end user.