The Drug Policy Modelling Program SimDrug Models

1. The Drug Policy Modelling ProgramSimDrug Models Presentation at the AIVL National meeting Wednesday 29th October, 2008

2. The DPMP Goal: To improve Australian drug policy, through • Developing the evidence base for policy • Translating the evidence (modelling) • Studying policy-making processes Approach • Collaborative, teams across Australia and internationally • Research and practice elements • Application of multiple disciplinary approaches

3. Why do we need DPMP? • Lack of evidence upon which to base policies • Evidence that does exist is not analysed and used in the best way to inform policy • A complex array of possible policy options • Models or tools to help policy-makers make good decisions are lacking • Provision of evidence alone is not enough – need to understand policy processes

4. Areas of work • Developing the evidence base for policy • Developing, implementing and evaluating dynamic policy-relevant models (translating the evidence) • Studying policy-making processes Projects in these 3 areas….

5. Developing the evidence-base for policy • Melbourne Injecting Drug User Cohort Study (MIX) • Structural analysis of the Australian heroin drought • The Australian (illicit) drug policy timeline • The influence of drug prices on the patterns of drug consumption of methamphetamine users • Problem-Oriented and Partnership Policing: An evaluation of the LEAPS (Liquor Enforcement & Proactive Strategies) • Reducing the Methamphetamine Problem in Australia: Evaluating Innovative Partnerships Between Police, Pharmacies and Other Third Parties • Developing a common metric to evaluate policy options (the Harm Index) • Australian drug policy: an overview report on use, harms and relationship to policy • A comparative analysis of research into illicit drugs in the European Union • A summary of diversion programs across Australia • Working estimates of the social costs per gram and per user for cannabis, cocaine, opiates and amphetamines

6. Developing and using dynamic policy-relevant models • Modelling cannabis diversion programs in Australia • Developing a model to assess the economic consequences of cannabis policy options • Examining the economic consequences of different types of law enforcement interventions directed towards methamphetamine • Opioid Pharmacotherapy Review Treatment in Australia • SimHero – modelling the impacts of policing strategies in the context of a major heroin shortage • SimDrugPolicing: An adaptation of SimDrug to explore three policing scenarios • Policy practice project: Working with Victoria Police on their Drug Harm Index • Policy practice project: Working with ACT Health on drug treatment service systems, including for the planned new ACT prison • Policy practice project: Working with NSW Police on responses to ecstasy users • Policy practice project: WA, licensed venues and harm: an agent-based model

7. Studying policy-making processes in Australia • Public opinion, the media, and illicit drug policy in Australia • Using Integration and Implementation Sciences to understand nexus between research and policy • An analysis of Australian illicit drug policy coordination • Where do policy makers source research evidence? • A review of Australian public opinion surveys on illicit drugs • The Australian (illicit) drug policy timeline • Dialogue methods for research integration • Trackmarks – documenting the contribution of drug user organisations to drug policy in Australia; developing a meaningful engagement kit.

8. Models - translating the evidence • New ways to provide “evidence” • When case studies / field trials difficult • Interesting and fun (can capture the imagination) • Can facilitate communications • Portray complexities and dynamics - Systems perspective - Dynamics between law enforcement, treatment, prevention, harm reduction

9. Modelling approaches Types of modelling approaches • Epidemic modelling • System dynamic models • Economic models – relative cost-effectiveness etc. • Soft systems models • Agent-based models Not one preferred modelling approach Not predictive, but tools to aid discussion, debate & decision-making

10. Agent-based models • SimDrug • SimDrugPolicing • SimHero • SimAmph (not DPMP funded) • ACT model in development Design team: Pascal Perez, Anne Dray Research team: AR, PD, LM etc.

11. Complex Systems Science • A bundle of theories and methods around ‘complex adaptive systems’ • Complex adaptive systems: • Emergence • Path dependency • Non-state equilibrium • Adaptation

12. Drugs as ‘complex adaptive systems’ • Street level drug markets fit the characteristics of a complex adaptive system • Emergent phenomena (eg heroin drought?) • Disequilibrium (between chaos and order) • Constant adaptation • Tools that help model CAS: Agent-based models, Dynamical Systems and Network Theory

13. “SimDrug”: An Agent Based Model • Agents • Users, dealers, wholesalers, outreach workers, police • Model of dynamic interactions over time • Archetypical representation of 5 suburbs • wealth, crime risk, conducivity to drug use • Variables within the model • Arrests, overdoses, treatment #’s, cash of dealers

14. wholesaler dealer user-dealer user friendship street dealing drug supply SimDrug - Network of influences

15. Social entities (agents) • Users • Drug need (3 levels in the model) • Cash (available money) • Overdose (rules) • Location and dealer location • Readiness to sell (user-dealer) • Dealers • 20:1 ratio users:dealers • Cash and profit • Drug stocks • Assess risk (police) and freeze • Wholesalers • Ratio 1 wholesaler to 15 dealers • Cash (profit) • Drug stocks (quantities, market prices, purities are externalities) • ‘Clients’ (dealers) – if arrested, all assoc dealers freeze

16. Social entities (2) • Police • Arrest users, dealers and wholesalers • Random movement (non-strategic policing) • Targeted policing : crackdown (based on suburb protest). 10% chance of arresting dealer; 40% chance of arresting user-dealer. Wholesalers 0.25% chance. • Outreach workers • Sent to blocks with ↑ OD • Readiness for treatment (each encounter changes readiness

17. Spatial environment • Treatment centre • Three programs: detox, TC and MMP • Attend: when readiness for treatment reached set level (outreach and OD encounters) • LOS and success rate from Australian data • Street blocks • Crime committed • Overdoses • Wealth (cash available for crime) • Conducivity (attractiveness for dealing) • Suburb – average risk of blocks. Suburb protest (activates police)

18. Other features • Scaling – created 1/10th model (prevalence of users, police, treatment numbers etc.) • Open system (replaced users, dealers and wholesalers) • Output variables: • Locations of dealing and using • Crime rate • Arrest numbers • Overdose • Treatment access • Profit (cash of dealers)

20. Results • Hotspots and displacement • Impact of outreach workers • Impact of police

21. Hotspots

22. 1200 1000 800 treated users 600 10_OW 20_OW 50_OW 400 1_OW 100_OW 200 0 0 200 400 600 800 1000 1200 1400 time step Impact of outreach workers on treatment

23. Impact of police on arrest rate 1800 1600 1 Police 10 Police 1400 50 Police 100 Police 1200 1000 Arrest numbers 800 600 400 200 0 0 200 400 600 800 1000 1200 1400 Time Step

24. Impact of police on dealers’ cash 1200000 1 Police 1000000 10 Police 50 Police 100 Police 800000 $ Value 600000 400000 200000 0 0 200 400 600 800 1000 1200 1400 Time Step

25. SimDrugPolicing:Testing Police Options Three different scenarios: • Random policing • Hotspots policing • Problem-oriented policing Different rules for the police agents

26. Total number of arrested dealers

27. Total monthly street crime committed by users Dray, A., Mazerolle, L., Perez, P. & Ritter, A. Drug Law Enforcement in an Agent-Based Model: Simulating the Disruption to Street-Level Drug Markets. A chapter submitted to Dr Lin Liu and Dr John Eck (Eds) Artificial Crime Analysis Systems: Using Computer Simulations and Geographic Information Systems. University of Cincinnati, Ohio, USA

28. Number of users under treatment

29. Conclusions • DPMP is a multi-focussed endeavour • Models have potential to significantly improve policy discussion and decision-making • Agent-based models are powerful simulation tools • Process of model development is important • DPMP has completed models that demonstrate relevance

30. 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 Website: http://www.dpmp.unsw.edu.au

31. SimAmph Exploring the consequences of party-drug use among young Australians & social responses to intervention strategies Design Team: Pascal Perez and Anne Dray Research Team: David Moore, Paul Dietze, Lisa Maher, Rebecca Jenkinson, Christine Sokiou, Rachael Green, Suzie Hudson This project is supported by an NHMRC grant

32. Key features • Tribes /social rites (to which agents belong) • Venues: public + private (to which agents attend) • Agents ‘move’ between the tribes: increasing their drug use + harm or decreasing their drug use + harm • Based on: • Peer pressure • Health impacts • Media influence

33. Peer Pressure Health Experience Tribes and social rites A: alcohol - B: cannabis - E: ecstasy - S: speed - C: cocaine

34. MyFriendHealth Health Experience MyHealth Friends MyFriendNorm Media Peer Pressure MyStage Venue MyEnvironmentNorm AttendancelNorm MediaNorm VenueNorm MediaInfluence AttendancelHealth Network of influences

35. Input variables

36. Base scenario honey moon period transition period normalization period

37. Behavioural patterns (% of agents) Agent outcomes : mental, physical (% of agents) Societal consequences (% of agents) Consequences

38. Policy scenario testing • Prevention • Impact of mass-media campaigns on party-drug users • Harm reduction • Impact of peer-based and targeted interventions • Pill-testing • Law enforcement • Impact of sniffer dog interventions in public venues • Impact of drug bus interventions outside public venues

39. Pill testing scenario Specify: % of the market invaded by the “dodgy” pills % of major medical complications arising # of weeks of “dodgy” pills % availability of the pill-testing

40. Pill testing scenario Scenario: On time step 52, 30% of the ecstasy market is invaded by “dodgy pills”. The situation remains the same during 10 weeks. A user has 100% to develop a major medical condition if he uses one of these pills. Question: What percentage of users need to access a pill testing facility in order to contain the crisis when the market penetration ranges from 0 to 50%?

41. Pill testing scenario results Comments: A harm reduction program aiming at keeping the prevalence of major medical conditions among users at usual levels (< 5%), should provide access to pill testing facilities to, at least, 40% of users when the market penetration is only of 10%. This percentage jumps to 80% when the market penetration reaches 20%. Beyond that point, nearly the entire population of users needs to have access to a facility.