Evidence-based%20drug%20policy%20

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Evidence-based drug policy myth or reality?
Alison Ritter, DPMP DirectorNDARC

• Presentation 6th Feb, 2007, Canberra

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Illicit drug policy

• Drug policy is complicated
• Multiple perspectives
• Users, families, health professionals, police,
  politicians, community members
• Strong public opinions
• Significant government spending ()
• Complicated interventions ()

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Significant government spending

• Total spending 3.2 billion p.a.
• Direct 1.3 billion (41)
• Indirect/consequences 1.9 billion (59)
• Federal Govt 30
• State/Territory Govt 70
• Law enforcement 56

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Government spending (direct only)

• Law enforcement 553.9m (431 to 705)
• Interdiction 181.5m (149 to 351)
• Prevention 295.8m (88 to 534)
• Treatment 256.3m (204 to 279)
• Harm reduction 26.3m (19 to 44)

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Complicated responses

• Law enforcement, eg
• Legalisation of drugs
• Crop eradication programs
• Customs and border control
• Crackdowns and Raids
• Police discretion, diversion, drug courts
• Prevention, eg
• Mass media campaigns
• School-based drug education
• Treatment, eg
• Detoxification
• Methadone or buprenorphine maintenance
• Therapeutic communities
• Cognitive behavioural relapse prevention
• Harm reduction, eg
• Needle Syringe Programs
• Peer education for users
• Non-injecting routes of administration

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Evidence-based policy?

• Simple question what works best?
• Research usually limited on this
• Doesnt take into account dynamic interactions
  between sectors
• Doesnt take into account different outcomes
• Doesnt take into account policy making processes

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Policy making processes - relationship to
evidence?

• Uptake of evidence in policy-making
• Frustration by researchers
• Policy-makers feeling misunderstood
• Problems
• long (researchers) vs short (policymakers)
  timeframes
• ambiguity lack of certainty in much social
  science research
• inaccessibility of research results
• sheer bulk of research materials
• research career structures and the academic
  reward systems
• lack of clarity about roles (for example
  balancing objectivity and advocacy)
• rapid change in the policy environment
• problems of governmental capacity
• clash of cultures and
• communication failures between researchers and
  policy makers

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• Solutions?
• summary reports, bulletins, dot points
• personalised briefings
• use of mail outs
• respect the limited time of policy makers
• be patient
• maintain a reputation of objectivity
• think about and prepare good news angles to the
  research
• nurture political champions
• develop mutual understanding and respect
• But even with these, not much progress
• Solution may lie in understanding the
  policy-making processes better

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• The policy world is as alien to most researchers
  as a distant foreign land and most do not even
  realise it
• Michael Agar, 2002

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Models of policy making

• There is not one model of how policy is made
• Researchers usually assume that the process is
  linear
• Problem Options Solutions
  Implementation
• And that it is rational!

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So, models of policy making

• Technical/rational model
• Incrementalism model
• Power and pressure groups
• Interactive model
• Garbage can model
• Advocacy coalition framework
• Punctuated equilibrium
• etc

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Rational/technical approach

• Conventional image ID an issue, seek solutions
• Series of steps
• 1. identify problem
• 2. identify causes
• 3. develop options
• 4. analyse options
• 5. select an intervention
• 6. implement and evaluate
• Fundamental, exhaustive, rational, root approach

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Rational/technical model

• Case example improving pharmacotherapies -
  buprenorphine
• Implications for researchers
• Engage in steps 1-4 (ID problems, causes,
  options)
• Conduct research that is relevant, timely,
  credible
• Know which problems are on the agenda
• Have ready synthesised reports to feed into the
  problem, causes or options steps

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Incrementalism

• Policy making is not dramatic rather small
  incremental shifts
• Successive limited comparisons between existing
  policies (or alternatives)
• Comparing marginal values
• Better than to attempt (and fail) at big change
• Lindblom, C., E. (1959). The science of 'muddling
  through'. Public Administration Review, 19,
  79-88.
• Lindblom, C., E. (1979). Still muddling, not yet
  through. Public Administration Review, 39(26),
  517-526.

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Incrementalism

• Case example prevention programs in schools
  (education/information - competency approach)
• Implications for researchers
• Prepare for long time frame (tobacco 20yrs)
• Tight simple comparative analyses (within budget)
  are highly valued.

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Garbage can model

• Three independent streams
• Problems
• Politics
• Policy processes/solutions, alternatives
• Sloshing around, waiting to be matched up
• Policy window opens task to match problems and
  solutions
• Kingdon, T. (2003) Agendas, Alternatives and
  Public Policies. (2nd Ed). NY Longman

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Garbage can model
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Garbage can model

• Case example NCADA problem IDU and/or AIDS
  politics Hawke policy processes/solutions
  various (academics, drug treatment community, gay
  community).
• Implications for researchers
• Policy processes component key role in
  presenting alternatives (and data on problems)
• Look for when policy windows open
• Match up problems and solutions creatively (dont
  pair too early)

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Power pressure groups

• Three forces that determine policy
• Ideology (philosophy, values)
• Interests (primarily self-interests)
• Information (multiple sources)
• The distribution of power determines whose I-I-I
  will be dominant.
• Weiss, C. H. (1983). Ideology, interests and
  information the basis of policy positions. In D.
  Callahan B. Jennings (Eds.), Ethics, Social
  Sciences and Policy Analysis. NY Plenum Press.

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Power pressure groups

• Case example Diversion initiative
• Different constructions of the problem. Different
  Ideology, Interests and Information.
• Implications for researchers
• Information component
• Be aware of all information types and
  influences
• Strategic dissemination mailouts, briefings etc.

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Advocacy Coalition Framework

• Policy subsystem interaction of diverse actors
  interested in same policy area.
• Illicit drugs as a policy subsystem.
• Within each policy subsystem, advocacy coalitions
  form (because diversity of views across the whole
  subsystem). Usually 2-4 ACs.
• ACs include policy analysts, academics,
  journalists, advocates etc.
• Policy change occurs when ACs are in conflict
  and one AC rises to power specifies the
  agenda, and the policies
• Sabatier, P. A. (1988). An advocacy coalition
  framework of policy change and the role of
  policy-oriented learning therein. Policy
  Sciences, 21, 129-168.

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Advocacy Coalition Framework

• Case example Supervised Injecting Centre (Van
  Beek, 2004)
• Players local community, AD service providers,
  local chamber of commerce, the churches, non-govt
  expert bodies, parliamentary processes, media,
  advocates.
• Implications for researchers
• Know the ACs that exist
• Provide briefings etc for significant players
• Stakeholder engagement in the research from the
  start
• Use advocacy strategies

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Summary

• Different models apply at different times
• Models overlap they describe/focus on different
  components of the same processes
• No one way to ensure uptake of evidence

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Dont despair..

• Role of evidence in above models have mainly
  been looking at research as instrumental to a
  direct policy decision.
• Knowledge-driven (new science)
• Problem-solving (to answer a policy question)
• But other ways in which research evidence is
  used
• Interactive (iteration among multiple players)
• Political (to support a position ammunition)
• Tactical (to delay, deflect criticism, show
  responsibility)
• Enlightenment (new ideas permeate over time,
  backdrop of ideas)

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Where to from here?

• DPMP aims to
• develop the evidence-base for policy
• develop, implementing and evaluating dynamic
  policy-relevant models of drug issues and
• study policy-making processes in Australia
• Challenges
• Further work on models and what they mean for
  drug policy
• Comparisons of policy options
• Policy analysis rather than descriptive research
• Improving the evidence AND the intersection
  between researchers and decision-makers

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Acknowledgements

• This work forms part of the Drug Policy Modelling
  Program (DPMP). Funded by
• Colonial Foundation Trust
• NHMRC Career Development Award
• Thanks to
• The DSS study group (at the ANU, led by Prof
  Bammer)
• RegNet, the ANU

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Further information

• Assoc Prof Alison Ritter
• Drug Policy Modelling Program, Director
• National Drug and Alcohol Research Centre
• UNSW, Sydney, NSW, 2052, Australia
• E alison.ritter_at_unsw.edu.au
• T 61 (2) 9385 0236
• DPMP Monographs
• http//notes.med.unsw.edu.au/ndarcweb.nsf
• Research current Drug Policy Modelling
  Program