PROJECT SUMMARY/ABSTRACT Methadone treatment (MT) for opioid use disorder (OUD) significantly reduces overdose risk, but remains underutilized in the U.S. Limited availability of Opioid Treatment Programs (OTPs) – the only facilities licensed to dispense methadone, as well as regulations that have often made accessing MT burdensome and stigmatiz- ing for patients, have led to calls for a significant overhaul of the MT system. For the first time in decades, US law makers are seriously considering reforms that could substantially change the nature of MT delivery. These reforms range from expanding MT through mobile units that operate out of existing OTPs, to more significant ones – such as making MT available through office-based prescribing and pharmacy dispensing, as proposed by the “Modernizing Opioid Treatment Access Act (MOTA),” currently being reviewed by members of Con- gress. As policy makers weigh such reforms, there remains much uncertainty about which changes will have the greatest health benefits while minimizing harms, and how these changes will affect different population groups. Empirical research is therefore urgently needed to help guide ongoing policy decisions. We propose to use an agent-based model (ABM) computer simulation approach to estimate the potential impacts of four alternative MT policy scenarios currently being considered in U.S. policy discussions: “OTP Only,” “Mobile Methadone,” “Addiction-Specialist Prescribing,” and “Primary Care Prescribing.” Specifically, we will construct these simulated scenarios using an existing ABM our team has already calibrated for 16 counties in NY State, and estimate how changing the environment and access points to MT within each scenario (e.g. OTP, mobile units, prescriber, pharmacy locations) subsequently influences the following population level out- comes: methadone initiation and six-month retention (Aim 1); fatal and non-fatal opioid overdose (Aim 2), and racial/ethnic and urban/rural disparities for Aim 1 and 2 outcomes (Aim 3). We will parametrize and calibrate each model using a combination of publicly and privately obtained data and existing literature, and test how outcomes vary based on differential adoption of MT at the program, prescriber, pharmacy, patient level. This innovative proposal is an excellent fit for this “Time-Sensitive” FOA as it can inform current policy deci- sions being considered by US law makers. It builds off of an existing ABM, which can ensure timely feasibility and dissemination of findings. Our team brings together experts in OUD treatment policy, overdose, and simu- lation modeling, and will be conducted in partnership with the NY State government’s Office of Addiction Ser- vices and Supports, ensuring direct translation of findings to local policy decisions. We will also bring together an Expert Advisory Board to help inform model inputs and disseminate findings to relevant stakeholders. Find- ings will be made widely available via a public interactive dashboard, and will provide a foundation for a subse- quent R01 to expand the ABM to other states. This work will critically inform national and local discussions to promote evidence-based policies that lead to the greatest reductions in overdose and improvements in health.

PROJECT NARRATIVE For the first time in decades, U.S. law makers are considering methadone policy reforms that could significantly change the landscape of opioid use disorder treatment, but uncertainty remains about which changes will lead to the greatest health benefits, while minimizing unintended harms. Using New York State as a case study, we propose to use an agent-based modeling approach to simulate four alternative methadone treatment policy scenarios currently being considered in U.S. policy discussions, and estimate their respective impact on population-level rates of methadone initiation and six-month retention, fatal and non-fatal opioid overdose, and racial/ethnic and urban/rural disparities in these outcomes. Through a unique collaboration between government partners and experts in opioid policy and simulation modeling, and dissemination of findings via a public dashboard and Expert Advisory Board, this work will inform and promote evidence-based policies that can reduce overdose and improve population health.