A system dynamics model of Australian opioid pharmacotherapy maintenance treatment

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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.

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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?

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Why use this approach?

• Why a modeling approach?
• 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.
  

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Background

• Australias 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

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The model
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Model assumptions

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

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Systems of care
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Issue 1Monthly methadone dispensing costs borne
by Government
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Issue 2 Demand increase treatment novices vs
treatment experienced
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Issue 3 Impact on methadone patient numbers of
limited entry to prescribers
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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.