A system dynamics model of Australian opioid pharmacotherapy maintenance treatment Jenny Chalmers*, Alison Ritter*, Mark Heffernan** and Geoff McDonnell*** * National Drug and Alcohol Research Centre, UNSW; ** Evans & Peck Pty Ltd; *** Adaptive Care Systems Pty Ltd.

Introduction Motivation- Is there potential for pharmacotherapy programs to better meet demand in relation to availability, accessibility and affordability of treatment Aim of research - develop a system dynamics model as a tool for policy makers to illustrate how treatment system as a whole responds to possible policy reforms Research questions - explore 3 areas of concern 80% of patients pay dispensing fees of $5/day. What would it cost the Government to cover them? Can system cope with increased demand for treatment? What would happen if supply of treatment places fell?

Why use this approach? Why a modeling approach? Why system dynamics? Difficult to explore the impacts of policy changes in real world. Simulation models are a safe, risk-free environment for experimenting with future policy options and gaining consensus among stakeholders. Why system dynamics? Rather than focus on the complex behaviour patterns of individuals, system dynamics elicits the structures, policies and “local” informal rules of the system from a range of qualitative and quantitative sources. Pattern-oriented approach to model development and calibration with pooled data. System dynamics 101 A computable representation of stocks (accumulations), flows (rates) and information feedbacks that drive overall behaviour over time.

Background Australia’s pharmacotherapy system is over 20 years old Close to 40,000 in treatment 70% treated with methadone Treatment system managed by state governments and funded by Commonwealth and state governments plus patient

The model

Model assumptions Model is Australia-wide Daily dispensing Each episode of treatment identical Each between treatment episode identical

Systems of care

Issue 1: Monthly methadone dispensing costs borne by Government

Issue 2: Demand increase: treatment novices vs treatment experienced

Issue 3: Impact on methadone patient numbers of limited entry to prescribers

Conclusion The model has the potential to serve as a spring-board for debates about policy directions In this paper we: Demonstrate that source of increased demand matters; Simulate the financial implications for Government of subsidising dispensing of methadone; and explore the implications of limiting entry of treatment seekers to prescribers.