Evidence-based drug policy – myth or reality? Alison Ritter, DPMP Director NDARC

1. Evidence-based drug policy – myth or reality?Alison Ritter, DPMP DirectorNDARC Presentation 6th Feb, 2007, Canberra

2. 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 (*)

3. 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%

5. 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)

6. 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

7. Evidence-based policy? • Simple question = what works best? • Research usually limited on this • Doesn’t take into account dynamic interactions between sectors • Doesn’t take into account different outcomes • Doesn’t take into account policy making processes

8. 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

9. 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

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

11. 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!

12. 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

13. 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

14. 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

15. 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.

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

17. “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

18. 1. PROBLEMS • Agenda setting • Indicators and monitoring • Focusing events • Symbols • Budgets • Interpretation • Problem recognition (“should do something”) • Need a solution/alternative • Rise and fade Coupling of 1 + 2 + 3 Policy entrepreneurs: join problem, solution and politics • 2. POLITICS • Agenda setting • Influenced by: • National mood • Organised political forces • Governmental phenomena • Consensus building through bargaining POLICY WINDOW Small/short and scarce. Predictable or Unpredictable “Problem” window (1) “Politics” window (2) • 3. POLICY PROCESSES • Alternatives • Policy community • Ideas as an evolutionary processes (mutation & recombination) • Criteria for success of an alternative (technical feasibility; values congruence; constraints manageable; public and political acceptability) • Softening up (years) • Emerging consensus (diffusion & tipping point) Kingdon, T. (2003) Agendas, Alternatives and Public Policies. (2nd Ed) NY: Longman “Garbage can” model

19. “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 (don’t pair too early)

20. 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.

21. 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.

22. 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 AC’s. • AC’s include: policy analysts, academics, journalists, advocates etc. • Policy change occurs when AC’s 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.

23. Advocacy Coalition Framework • Case example: Supervised Injecting Centre (Van Beek, 2004) • Players = local community, A&D service providers, local chamber of commerce, the churches, non-govt expert bodies, parliamentary processes, media, advocates. • Implications for researchers • Know the AC’s that exist • Provide briefings etc for significant players • Stakeholder engagement in the research from the start • Use advocacy strategies

24. 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

25. Don’t 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”) *

26. 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

27. 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

28. 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@unsw.edu.au T: + 61 (2) 9385 0236 DPMP Monographs: http://notes.med.unsw.edu.au/ndarcweb.nsf Research – current – Drug Policy Modelling Program