Modelling Drug Policies Gabriele Bammer National Centre for Epidemiology and Population Health, The Australian National University Hauser Center for Nonprofit

Modelling Drug Policies

Gabriele BammerNational Centre for Epidemiology and Population Health,

The Australian National UniversityHauser Center for Nonprofit Organizations,

Harvard University

Presentation overview

Where does modelling fit? Modelling as a tool for

descriptionexplanationpredictionimputationintegrationevaluation

Problem framing and boundary setting Ignorance and uncertainty Capacity Integration and Implementation Sciences

Where does modelling fit?...1

Move away from narrowly defined and delimited problems to complex problems requiring multiple inputs – less well developed

Aid to thinking – conceptual ± quantification

“A schematic description of a system, theory, or phenomenon that accounts for its known or inferred properties and may be used for further study of its characteristics: a model of generative grammar; a model of an atom; an economic model.” (http://www.thefreedictionary.com/modelling).

http://www.thefreedictionary.com/modelling




How can modelling help drugs research? Modelling as a tool for

description

explanation

prediction

imputation

integration

evaluation

(usually more than one; illustrate multiple ways model can be used)


Broad overview - no specifics, but flavour, plus strengths of some particular types of modelling

Not typology Not comprehensive Bigger is not always better Never perfect representation of reality

- framing and boundaries- ignorance and uncertainty (capacity)

My approach- modelling as a view of a system- modelling sceptic





Modelling for description...1

Ability to show connections between elements in problem of interest

Appreciate system in which model is embedded

Example

Model of tobacco control developed by a US National Cancer Institute project “Initiative for the Study and Implementation of Systems”

Modelling for description...2

Funding for tobaccocontrol programs

Gov. incomefrom tabacco

taxes

Tobacco controlprograms

Smokers

Perceived importance tofocus on other health

programs

Public awarenessof tobaccohealth risk

Pressure on tobaccocompanies to reducemarketing activities

Tobacco marketingactivities

Taxrevenues

fromsmokers

+

+

+

-

+

People quittingsmoking

-

Fraction of peoplesmoking

Smoking as asocial norm

People startingsmoking

Tobaccorevenues

+

+ +

+

++

+

Health care costs

+

Health insurerscoverage of tobacco

quitting costs

+

+

Researchersawarness of

tobacco healthrisk

Funding fortobacco health

research+

+

Govt awarenessof tobaccohealth risk

+

-

Pro-tobaccocontituencies

Anti-tobaccoconstituencies

++ +

+

Tobacco productsavailability

+

Tobaccogrowers

+

+

+

++

+

Govt willingness tolegislate tobacco

control

- + +

Tobaccotaxes

Govt funding oftobacco control

--

Trend in tobaccocompany revenues

+

-

Anti-smokinglegislation

-

Modelling for description…3

Advantage of system dynamics models: considers what those linkages mean in terms of

increasing or decreasing the elements (in system dynamics terms ‘stocks’)



taxes


Smokers


programs




Taxrevenues

fromsmokers

+

+

+

-

+


-




Tobaccorevenues

+

+ +

+

++

+

Health care costs

+


quitting costs

+

+


tobacco healthrisk


research+

+


+

-



++ +

+


+

Tobaccogrowers

+

+

+

++

+


control

- + +

Tobaccotaxes


--


+

-


-



taxes


Smokers


programs




Taxrevenues

fromsmokers

+

+

+

-

+


-




Tobaccorevenues

+

+ +

+

++

+

Health care costs

+


quitting costs

+

+


tobacco healthrisk


research+

+


+

-



++ +

+


+

Tobaccogrowers

+

+

+

++

+


control

- + +

Tobaccotaxes


--


+

-


-



descriptionexplanationpredictionimputationevaluation


Modelling for explanation...1

Move beyond description to understanding what is going on

Examples: Revisit system dynamics model West’s synthetic model of addiction



taxes


Smokers


programs




Taxrevenues

fromsmokers

+

+

+

-

+


-




Tobaccorevenues

+

+ +

+

++

+

Health care costs

+


quitting costs

+

+


tobacco healthrisk


research+

+


+

-



++ +

+


+

Tobaccogrowers

+

+

+

++

+


control

- + +

Tobaccotaxes


--


+

-


-

Modelling for explanation...3

Move beyond description to understanding what is going on

Examples: Revisit system dynamics model West’s synthetic model of addiction

Responses (starting, modifying or stopping an action)

Impulses/inhibitions (urges)

Motives (wants/needs)

Evaluations (beliefs) PlansEmotional states (e.g. happiness

distress)

Drives (e.g. hunger)

Stimuli/information

Arousal

West’s synthetic model of addiction (2006)





Modelling for prediction...1

Help appreciate likely consequences of particular actions – huge benefit of models

But BEWARE!!!

Paradox: surprising results – value and danger

Examples: Revisit system dynamics modelDPMP- Australian heroin market

Also more complex economic micro-simulation models


Youthnonsmokers

Adultnonsmokers

Youthsmokers

Adultsmokers

Youthformer

smokersAdultformer

smokers

Aging 1 Adult neversmokers dying

Youth smokingstarts

Adult smokingstarts

Youth quitsYouth formersmokers smoking Adult quits

Time to age 1Nonsmoking adult

lifespan

Norm yth uptake Norm adult uptake

Norm youth quitrate

Norm adult quitrate

Youth returning tosmoking

Adult returning tosmoking

Adult formersmokers smoking

Youth smokersaging

Former youthsmokers aging

Former adultsmokers dying

Smoking adultlifespan

Adult smokersdying

Norm former adultsmoker lifespan

Time to age 2

Time to age 3

Effect of public opinionon adult quit rate

Effect of public opinionon adult uptake

Effect of public opinionon youth uptake

Effect of public opinionon youth quit rate

<Total adults>

New youth

Norm birth rate

Note. Years are the unit of age. yth = youth.

Model of Aging Chains of Smokers (Birth to Death)


Adults smoking40 M

35 M

30 M

25 M

20 M

1980 1990 2000 2010 2020Time (Year)

Adults smoking : cut adult init in half peopleAdults smoking : cut teen init in half peopleAdults smoking : cut child init in half peopleAdults smoking : smoking chain base people


Help appreciate likely consequences of particular actions – huge benefit of models

But BEWARE!!!

Paradox: surprising results – value and danger

Examples: Revisit system dynamics modelDPMP- Australian heroin market (Moore et al, 2005)

Also more complex economic micro-simulation models


Economic return from

dealing =

Revenue from selling drugs

-

Cost of obtain-ing the drugs sold

-

Convent-ional

business costs

-Non-costs





Modelling for imputation

Problems in drugs area of lack of data

Imputation bridges data gaps – use in demography for mortality estimates in developing countries

Examples: DPMP, estimates of heroin users using truncated Poisson

and Poisson regression modelling for the former and the application of a Jolly-Seber type model (Dietze et al, 2005)

Good example of not trusting results





Modelling for integration...1

Model is an additional benefit – main benefit is that it provides a focus for bringing together different disciplinary and stakeholder perspectives

Examples:

Return to tobacco control

DPMP: complex adaptive systems



Model is an additional benefit – main benefit is that it provides a focus for bringing together different disciplinary and stakeholder perspectives

Examples:Return to tobacco controlDPMP: complex adaptive systems (Perez et al., 2005)

Drug use in Melbourne:Epidemiology, treatment, crime, economics

Includes dynamics







Modelling for evaluation...1

Theory-based Evaluation or Program Logic or Program Theory

Makes assumptions explicit Links program strategy to evaluation Systematic link between inputs and activities and

outcomes

Example:Evaluation of WA community crime reduction program

(Cummings 2005)

Modelling for evaluation...2

Modelling overall

No model can provide a perfect representation of reality

Every model has significant limitations

Move on now to three key limitations





Problem framing and boundary setting…1

Importance of framing political ‘spin’ ‘junkie’, ‘drug user’, ‘treatment client’ and ‘pretty

criminal’

Examples:Drug use as a problem or as a symptomDrugs and Public Policy Project: agent-host-vector

approachPollack: symbolism of needle-syringe exchange

Problem framing and boundary setting…2

Boundary setting : Attention to what is included, excluded and marginalised- link with framing- link with values

Example:Model of tobacco control:

Exclude industry perspectiveMarginalise smokers (assume advocates for tobacco industry, therefore not consulted)

Methods include:Scoping methodsUlrich’s Critical system heuristics

Scoping…1

Scoping is the preparatory stage of a project where we systematically think about what we can best do with the time, money and person-power we have at our disposal.

It involves considering:what we want to achieve and who will be affectedwho needs to be on sidewhat needs to be done to get therewhat are the likely blocks and how they can be overcome.

Scoping…2

Helps us: Broaden our view of the problem beyond what we

know and understand, recognising and respecting different points of view

Set boundaries Decide if we want to challenge the way the problem

is generally viewed, by paying more attention to something society sees as marginal or excluded

Work out who we need to have on-side to give the project legitimacy.

Scoping…3

Eight questions useful for scoping:1. What do we know about the problem?2. What can different interest groups and academic

disciplines contribute to addressing this problem?3. What areas are contentious?4. What are the big picture issues? In other words,

what are the political, social and cultural aspects of the problem?

These tackle the dimensions of the problem.

Scoping…4

5. Why is this problem on the agenda now?

6. What support and resources are likely to be available for tackling the problem?

7. What parts of the problem are already well covered and where are the areas of greatest need?

8. Where can the most strategic interventions be made?

These tackle the priorities.





Ignorance and uncertainty…1

Understanding is comparatively unsophisticated

No effective handles

Different disciplines and practice areas

- different approaches

- different importance

Nobody’s mandate to pull different approaches together


Primary level

Meta-level

Known Unknown

Known Known knowns

Unknown knowns (tacit knowledge)

Unknown Known unknowns (conscious ignorance)

Unknown unknowns (meta-ignorance)


STATISTICS - probability theory

Intelligence – gaps or overloadHistory – moral dimension

Music – essential for creativity

Art – certainty and uncertainty are a continuum, not opposites

Futures – unknown unknownsComplexity - irreducible

Religion – desirable vs fundamentalism


Typologies eg Smithson, 1989





Capacity…1

Watch out – modelling tends to take on a life of its own

Need to worry about limited research capacity and how to use it wisely

especially on a global level

Institute of Scientific Information publications listings

Capacity…2

Country Affiliations of Authors of Papers Listed in Web of Science (Jan. 1998 - Mar. 2003)

0

5,000

10,000

15,000

20,000

25,000

Low-Income Lower-Middle-Income

Upper-Middle-Income

Upper-Income

Economies

Au

tho

rs (

Me

dia

n)

High: 104,865 (India)

Median: 236

High: 153,753 (Russian

Federation)Median: 737

High: 60,207 (Poland)Median: 1,375

High: 2,060,522 (USA)Median: 19,169





Integration and Implementation Sciences…1

Modelling as an integrative tool Modelling for decision support – all of the

above Problem framing and boundary setting Ignorance and uncertainty (Capacity)

Integration and Implementation Sciences...2

Concepts and methods to improve: the generation of knowledge spanning disciplines

and practice,


Concepts and methods to improve: Knowledge generation,

the application of that knowledge in decision making

in policy, business, professional practice and community activism,


Concepts and methods to improve: knowledge generation, decision making, and

the implementation of those decisions to bring about effective change and social improvement.


Adequate description Methods Key concepts

Integration and Implementation Sciences?...6

No existing toolkit for methods to improve integration

ModelsDialogue

Common metric

Product

‘Vision’


Research to improve decision support - all forms of modelling are useful

Modelling as a tool for

description

explanation

prediction

imputation

integration

evaluation

Key concepts

Better ways of dealing with:

Ignorance and uncertainty Problem framing and boundary setting Values definition Collaboration

Why do we need it?

Recognised urgency to tackle complex problems

Learning by doing

No cross-fertilisation (fragmentation)

Reinventing of the wheel

Lack of quality control

No formal methodology or training (statistics analogy)

Need clear and systematic way for building integration into research projects

Cross-cutting specialisation

Application in a specific sector

Methodological

development with respect to a single sector

ENVIRONMENTHEALTH

INNOVATION & BUSINESS

RISK & SECURITY

INTERNATIONAL DEVELOPMENT

EDUCATION

POLICY & GOVERNMENT

SOCIETY, HUMAN BEHAVIOUR & CULTURE

Theory and

methods

Conclusions

Modelling has a lot to offer drugs research: description, explanation, prediction, imputation, integration, evaluation

Different modelling methods have different strengths

Every model has limitations

Case for a new specialisation – Integration and Implementation Sciences

What do you think?

Would this help you in your work?

Acknowledgements

Funding: Fulbright, Colonial Foundation Trust, NHMRC, LWA, Hauser Center, NCI (ISIS), GECAFS

Tobacco control: George RichardsonImputation: Terry HullEvaluation: David McDonald

DPMP – Alison Ritter, Gerald Midgley, Wendy Gregory, Pascal Perez, Anne Dray

Uncertainty – Michael Smithson

Documents

Modelling Drug Policies Gabriele Bammer National Centre for Epidemiology and Population Health, The Australian National University Hauser Center for Nonprofit