shifting the paradigm - university of saskatchewan · 2009-07-23 · shifting the paradigm: how...

Shifting the Paradigm:How systems thinking can change the wayHow systems thinking can change the way

we address the obesity epidemic

Diane T. Finegood, PhD

Professor School of Biomedical Physiology &Professor, School of Biomedical Physiology & Kinesiology, Simon Fraser University

Executive Director, The CAPTURE Projectfi d@ [email protected]

U of S Dynamic Modelling for Health Policy, June 22, 2009

Take Home Messages

• Obesity is complex (not just complicated)

• We need solutions appropriate for complex problemsproblems

• We need to consider solutions at multiple levels of intervention

Conceptual Models of Obesity

GA Bray. Physiology & Behavior 82 (2004) 115– 121

INDIVIDUAL POPULATIONFACTORS

INTERNATIONAL WORK/SCHOOL/

HOME

COMMUNITYLOCALITY

NATIONAL/REGIONAL

LeisureActivity/Facilities

PublicTransport

TransportBiology

O

ITY

EnergyExpenditure

%

Globalizationof

markets

I f ti

LabourPublicSafety

Urbanization

OPREV

%OBESE

OR

UNDERWTF d

Development

Infections

Worksite Food & Activity

Health Care

Sanitation

Health

Social Security

ALE

Food intake :

Nutrient density

Activity

SystemManufactured/Imported Food

Family &Home

Media &CultureMedia

programs& advertising

SchoolFood &ActivityAgriculture/

G d /

p

Food & Nutrition

Education

ActivityGardens/Local markets

Nutrition

NationalNationalperspectiveperspective

Source: see Kumanyika Ann Rev Pub Health 2001; 22:293-308

“Causal Web”

Obesity System Map

http://kim.foresight.gov.uk/Obesity/Obesity.html

Simple / Complicated vs Complex

Homogeneity Heterogeneity/diversity

Linear

No feedback; no

Nonlinear dynamics

Feedback adaptationNo feedback; no learning/adaptation

Feedback, adaptation, learning, evolution

St h ti ithDeterministic

No connection between

Stochastic with concern for “tails”

Emicro and macro

Independence

Emergence

InterdependenceIndependence Interdependence

Causality and Complex Systems

“ lit“….causality can only be meaningfully defined for systems with linearlinear interactionsamong their

i bl ”variables.”

Wagner. Biology and Philosophy 14: 83–101, 1999.From flickr.com by nerovivo

Predominant research paradigm:p g“problem-oriented”

• Focus on etiologies of diseases and risk factors (“problems”)Focus on etiologies of diseases and risk factors ( problems )

• To identify causes and correlates of “problems”

• Goal: to generate hypotheses about potential treatment, prevention, and control strategies (“solutions”)

• Assumption: Knowing the cause of a problem is necessary (or at least helpful) in determining how to treat or prevent it

• Most comfortable for researchers (reductionist)

• Many successes: antibiotics for infectious diseases, chemo and di ti f C t ti f h l t l tradiation for Cancer, statins for cholesterol, etc.

• But often falls short

From T. Robinson Am J Prev Med. 2005 Feb;28(2 Suppl 2):194-201.

Alternative research paradigm:p g“solution-oriented”

• Focus on etiologies of health (“solutions”)

• Can be based on hypotheses generated by “problem-oriented” research, but not always

• Assumption: it is not always necessary (or even helpful) to first know • Assumption: it is not always necessary (or even helpful) to first know the causes and correlates of a problem to determine how to effectively prevent or treat it

E h h h k d h d ? ( • Emphasizes the question: what works and how to do it? (most relevant for clinical and public health practice and policy)

• Perceived to be higher risk by researchers…should be more comfortable for clinicians and public health practitioners

• Will shorten the cycle from research to improved population health

From T. Robinson Am J Prev Med. 2005 Feb;28(2 Suppl 2):194-201.

Solution-Oriented Research

An example:

1. Does advertising on children’s television lead to increased childhood obesity? (problem oriented)

2. Does eliminating advertising on children’s g gtelevision reduce obesity? (solution oriented)

Robinson and Sirard. Am J Prev Med 2005;28(2S2):194–201)

Take Home Messages


• We need solutions appropriate for complex problems


Common Responses to Complex ProblemsProblems

• Retreat

• Despair

• Believe the problem is beyond hopep y p

• Assign blame, figure out who is responsible

• Si l l ti• Simple solutions

• Galvanize our collective efforts and invest significant resourcesresources

Bar-Yam, Y. Making Things Work, 2004.

Solutions to Complex Problems

• Support individuals / individuals matter

• Match capacity to capacity

• Distribute decision, action, & authorityy

• Establish networks and teams

• Set functional goals• Set functional goals

• Create competition and feedback loops

A ff ti t i l l• Assess effectiveness at various levels


Capacity and Complexity

Complexity ofO i

Survive

Organism orOrganization(Capacity)

Fail

Complexity of Environment

Bar-Yam, Y. Complexity Rising, www.necsi.org

New Approaches Are Needed

UK Design Council & Bolton Diabetes Centre

Reducing the Complexity

UK Design Council & Bolton Diabetes Centre

Heterogeneity of Individuals

Distribution of Individual Challenges

Principal Components Analysis

• Recognizes “patterns” of items varying together that explain g p y g g pmost of variance

Patterns (“Phenotypes”) from Kitchener Data

11% General feeling of support and doing better not a lot of worry or11% General feeling of support and doing better, not a lot of worry or fear, no problems with food, family support strong

8% Doing worse Family not supporting Hard to get know good food8% Doing worse. Family not supporting. Hard to get, know good food & remain only eating appropriate food, finds exercise boring, thinks may be in denial, bad habits of many sort, could use more support definitely not doing better or family supportsupport, definitely not doing better or family support.

7% Family supports but still lapse, feel that doing better and doing everything that can, but still tempted by food, not checking feet, male

6% Many food concerns, confused by medical terms, not worsening, good family supportgood family support

Cumulative Variance Explained by Patterns

Number of patterns

Obesity System Map


Foresight

Prevention is a cross government issue –glike climate change

Foresight Programme, B. Butland, unpublished observations

Trust as a System Variable

Concept Map Clusters

• Strategies1 Partnership behaviours

• Barriers1 Self interests 1. Partnership behaviours

2. Facilitative communication

1. Self interests2. Criticism and negativism3 Stereotypes and

3. Reciprocal knowledge4. Methods for

3. Stereotypes and misrepresentations

4. Knowledge controlcollaboration

5. Roles & expectations5. System barriers6. Competing values

6. Collaborative orientation7. Support & resources

Sh d l d hi

7. Rigid paradigms

8. Shared leadership

Solution Space

Solution Space

4

12 2

33

Obesity System Map


Reduced System Map

Top Variables By Number of Inputs

Top Variables by Number of Outputs

Feedback Loops

25

20

15

quen

cy

10Freq

0

5

02 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

Number of Variables in Loop

Knowledge to Action Cycle

• IndividualKnowledge

• OrganizationAction

• Community

• Nation

W ld

Data

• World

Canadian platform to increaseCanadian platform to increase usage of real-world evidence

Plateforme canadienne pour accroître l’usage de données

probantes du monde réelprobantes du monde réel

Canadian platform to increase usage of real-world evidence

CAPTURE

CAnadian Platform To increase Usage of

• CAPTURE will be a Knowledge

Real world Evidence

• CAPTURE will be a system that supports and enables the collection

Knowledge

Actionand use of more practice and policy-relevant, “real world” evidenceworld evidence

Data

KAD Cycle Steps (?)

Knowledge

Action

Data


Environmental Scan (sample)

• Metadata online Registry (METeOR) • Rapid Risk Factor Surveillance System (RRFSS) • European Community Health Indicators p y• OECD Health Care Quality Indicator Project • Behavioral Risk Factor Surveillance System • Common Community Measures for Obesity Prevention (Measures Project) • RE AIM• RE-AIM • Canadian Outcomes Research Institute • Canada Health Infoway • Yorkshire & Humber Public Health Observatory Indicator Search Tool • Canadian Institutes of Health Information • SRDC Evaluation of BC Healthy Living Alliance • Chronic Disease Infobase • Canadian Alliance for Regional Risk Factor Surveillance • YMCA Community Healthy Living Index • Pan Canadian Public Health Network of Surveillance and Information Expert Group• Arthritis Community Research and Evaluation Unit • PHAC-Centre for Excellence in Evaluation and Program Design


PHAC Centre for Excellence in Evaluation and Program Design • National Obesity Observatory for England

Scan and Steps

1 2 3 4 5 6 7 8 9

Identify

A D KK

Access knowledge

Identify and review

appropriate information

Adapt into practice

Determine indicators of

interest

Identify source,

methods and tools to

collect data

Collect and store data

Retrieve and use

data

Reach conclusions

Create knowledge products

ProjectsCBPPMCCMT Public Health +NCCMT Registry of KT Methods and Tools for Public HealthHealth-Evidence.ca WebsiteThe HPC and HPC NetworkTEIP ToolsNCCMT Dialogue PH

ACREU Evaluation of Chronic Disease Prevention and Management InitiativesCCHSRRFSSSHAPESSRDC Evaluation of BCHLACEEPDCancer Control P.L.A.N.E.T.The CORI ModelChronic Disease Infobase


System variables we need to learn to measureto measure• Complexity• Capacity• Connectivity• Connectivity• Heterogeneity • Interdependence• Trust• Trust

Take Home Messages


• We need solutions appropriate for complex problems


McGill Health Challenge: Specific Actions to Address Childhood ObesityActions to Address Childhood Obesity

Places to Intervene in a complex system

1. The power to transcend paradigms2. The paradigm that the system arises out of2. The paradigm that the system arises out of3. The goal of the system4. The power to add, change, evolve, or self-organize system

structurestructure5. The rules of the system 6. The structure of information flow

Th i d d i i iti f db k lctiv

enes

s

iffic

ulty

7. The gain around driving positive feedback loops8. The strength of negative feedback loops9. The length of delays

Effe D

10. The structure of material stocks and flows 11. The size of buffers and other stabilizing stocks12. Constants, parameters, numbers12. Constants, parameters, numbers

D. Meadows. Thinking in Systems, A Primer, Chelsea Green, 2009.

Intervention Level Framework (adapted from Meadows)(adapted from Meadows)• Paradigm

1 system’s deepest beliefs1. system s deepest beliefs

• Goals1 what the system is trying to achieve1. what the system is trying to achieve

• Structure1. As a whole, eg enhancing connections across most of the1. As a whole, eg enhancing connections across most of the

system

• Feedback and delays1. Self-regulation, self-reinforcement, adaptation

• Structural Elements1. Subsystems, actors, and the physical structure of the system

McGill Health Challenge: Specific actions to address childhood obesityaddress childhood obesity

Coalitions Linking Science And Prevention

• “Specific actions we can take together inSpecific actions we can take together in Canada that will increase the prevention of cancer and other major chronic diseasescancer and other major chronic diseases should include….”

• 497 ideas were generated and reduced to 114 statements

CLASP: Specific actions, take together, to increase prevention chronic diseaseincrease prevention chronic disease

Optimizing Investments?

CLASP: Concept Map

217

2131

37

7886 93

103113

1

3

5

911

14

17

1820

22

27

2829

3132 34

4753

576061

6670

7476

7786

90

93

94

96

98 104107

109110

111

113

4

51628

35

4445 46

50

565

59

6775 7984

8587

88

9096

101

102106

610

12 1524

25

26

30

33

36

40

48

54 5558

59

63

64

6869 7172

73

8091

95

101

112114

7810

13 1923

253038

39414243

49 5152

62

64

65

7381

828389 92

97

99100 105108

112

“Specific actions we can take together in Canada that will increase the prevention of cancer and other major chronic diseases should include…”

CLASP: Concept Map Clusters

37

C2

1721

3178 103113

B

C

E1

3

5

911

14

17

182022

27

29

3132 34

4753

57606166

70

7476

7786

90

9394

96

98103

104107

109110

111

113

AF

D 4

516

26

2835

4445

46

50

5657

59

61

6775 7984

8587

88

9096

102106

111

69

I2

J

G 6

12 1524

26

30

33

36

40

4850

54 5558

59

63

64

687172

73

8091

95

101

112114

23

H

I

7810

13 19

253038

39414243

49 5152

62

64

65

7381

8283

8992

95

97

99100 105

108112

23 97

“Specific actions we can take together in Canada that will increase the prevention of cancer and other major chronic diseases should include…”

CLASP: Cluster Labels

Knowledge Exchange Partnerships

Common Ground M l i lEvidence Based Primary Care and Population Health

Common Ground Multisectoral Approaches and Healthy Public Policy

Information SystemsLearning and Innovations

Mobilization

Inequity, Access and Exposure 2Built Environmentand Food Systems

Policy InstrumentsInequity Access and ExposureInequity, Access and Exposure

CLASP: Concept Map by Intervention Level

2

1721

37

78 103

Paradigm

Goals

1

39

11

14

17

1820

22

27

29

3132 34

4753

6066

70

74

7677

7886 93

94

98

103

104

107109

110

113Structure

Feedback

Elements

4

516

28

3545

46

5657

6061

67

70

79

8587

9096

102

111

12 1524

26

33

36

40

44

4850

58

59

636869

7172

75 79

80

84 88

91

101106

6

7810

13 19

253038

4349 5152

54 55

62

64

65

7381

8389

92

95

99

100 105108

112

114

13 19

233941

42 626582

97

CLASP: Clusters by Intervention Level

Paradigm

Goals37

2 2178Structure

Feedback

Elements B

C

E1

2

3911

14

17

1820

21

22

27

29

3132 34

4753

6066

70

7476

77

7886

9394

98103

104

107109

110

113

AF

D

1

4

516

22

2835

4546

56576061

67

70

75 7984

8587

88

9096

102106

111

69

I2

J

6

12 1524

26

33

36

40

4448

50

54 5558

59

636871

72

75 79

80

84 88

91101

106

114I2 GH

I

6

7810

13 19

253038

394142

4349 5152

54 55

62

64

65

7381

8283

89 92

9599

100 105108

112

114

2341 82

97


• Structure

1. Knowledge exchange partnerships

2. Learning and innovation

3. Common ground

4. Multi-sectoral approaches & healthy public policy

• Feedback Loops & delays

• Information systems

• Evidence-based primary care and population health


• Structural elements

• Built environment and food systems

• Policy instruments

• Inequity, access and exposure

• mobilization

Intervention Level Framework: Healthy Food SystemsSystems

HealthyParadigm • A healthy food supply requires consideration of the health impact of

agricultural policiesagricultural policies.Goals • Agricultural policy that maximizes positive health outcomes and

minimizes negative health impacts.

St t B d bli di i f h lth d i lt l li i l diStructure • Broad public discussion of health and agricultural policy including farmers, environmental groups, and other organizations.Agricultural practices that are ecologically sound, culturally appropriate, and socially responsible.app op a e, a d soc a y espo s b e

Feedback & Delays

• Ensure public access to information on the use of agricultural inputs such as pesticides and fertilizers.

Structural Elements

• Implement food labelling and regulate health claims.• Reduce use of pesticides.• Establish federal nutrition standards for competitive foods in schools.

Food Systems Distribution of Statements (n=353)(n 353)

180

200

140

160

180

men

ts

80

100

120

er o

f Sta

tem

40

60

80

Num

be

0

20

Paradigm Goal Structure Feedback StructuralEl tElements

Level of Framework

Conceptual Linkages

Structural elements of the food supply chain

100%

70%

80%

90%

men

ts

40%

50%

60%

ge o

f Sta

tem Production

Processing / Distribution

Retail

20%

30%

40%

Perc

enta

g

Consumption

Research

0%

10%

Healthy Green Fair Affordable

C tCategory

Take Home Messages


• We need solutions appropriate for complex problemsproblems


Acknowledgements

• Chronic Disease Systems Modeling Lab

• CollaboratorsNate Osgood U of SModeling Lab

• Tommy Merth• Luvdeep Mahli

• Nate Osgood, U of S• Allan Best, Insource• Jon Kerner, CPAC

• Carrie Matteson• Ozge Karanfil• Amanda Palmer

• Laurette Dube, McGill U• David Crouch, CAPTURE

Amanda Palmer• Molly Acheson• Holly Buhler

Chronic Disease Systems Modeling Lab

Positions Available ([email protected])

shifting the paradigm - university of saskatchewan · 2009-07-23 · shifting the paradigm: how...

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