# Simulating the Impact of Office-Based Methadone Prescribing and Pharmacy Dispensing on OUD Treatment and Overdose in New York State: An Agent-Based Modeling Approach > **NIH NIH R21** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2024 · $227,861 ## Abstract 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 int... ## Key facts - **NIH application ID:** 11009694 - **Project number:** 1R21DA061660-01 - **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE - **Principal Investigator:** Ashly Elizabeth Jordan - **Activity code:** R21 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $227,861 - **Award type:** 1 - **Project period:** 2024-08-01 → 2026-07-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/11009694 ## Citation > US National Institutes of Health, RePORTER application 11009694, Simulating the Impact of Office-Based Methadone Prescribing and Pharmacy Dispensing on OUD Treatment and Overdose in New York State: An Agent-Based Modeling Approach (1R21DA061660-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/11009694. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*