Syndemics Prevention Network Maine Center for Public Health Evaluation Forum Portland, ME Friday...

Syndemics

Prevention Network

Maine Center for Public Health Evaluation ForumPortland, ME

Friday July 22, 2005

Innovations in Planning and Evaluating System Change Ventures

Bobby Milstein Centers for Disease Control and

[email protected]

Don SevilleSustainability [email protected]

Navigating Health Futures

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General Plan for the Workshop

• Dilemmas and innovations in system change ventures

• A navigational view of public health work

• Navigating diabetes dynamics in an era of rising obesity: the power of “what if...” questions

• Working lunch:Developing diabetes policy scenarios and anticipating change

• Using simulation studies to learn in and about dynamic systems

• Transforming health evaluation

• Adjourn

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Starting Premises

• Public health work has changed significantly since its formalization in the 19th Century, and even today it is poised for further transformation

• It matters how we think about the trends, dilemmas, and innovations that we experience, and it matters whether our thinking and actions match

• We are not talking about theories to explain, but conceptual and methodological orientations: the frames that shape how we think, how we act, and how we value the work

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Innovations in Public Health Work

Steps in Public Health Problem Solving Trends and Emerging Priorities

Define the problem

• Eliminate health disparities• Preparedness• Avoid activity limitation • Promote life satisfaction• Increase healthy days

Determine the cause

• Social determinants of health• Built environment• Adverse childhood experiences• Genetics

Develop and test interventions

• Comprehensive community initiatives• Ecological perspectives• Inter-sector collaboration• Health impact assessments• Simulation experiments and game-based scenarios

Implement programs and policies

• Policy interventions• Community and systems change• Adaptation to local context• Increasing health care access• Broad-based citizen organizing

And scores more….

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How have you observed public health work changing?

What types of dilemmas and innovations are driving those transformations?

Where is the field headed?

Leadership Panel

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Public health work is becoming more…

• Inter-connected (ecological, multi-causal, dynamic, systems-oriented) Concerned more with leverage than control

• Public (broad-based, partner-oriented, citizen-led, inter-sector, democratic) Concerned with many interests and mutual-accountability

• Questioning (evaluative, reflective, critical, pragmatic)Concerned with creating and protecting values like health, dignity, security, satisfaction, justice, wealth, and freedom in both means and ends

A Field in Transition

Many other orientations rely on disconnected, singular, and unthinking approaches where means and ends

have very different qualities (e.g., security by means of war)

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• Locating categorical disease programs within a broader system of health protection

• Constructing credible knowledge without comparison/control groups

• Differentiating questions that focus on attribution versus contribution

• Balancing trade-offs between short- and long-term effects

• Avoiding the pitfalls of professonalism

• Harnessing the power of citizen-led actions

• Reconciling different standards and values for judgment

• Others…

Serious Challenges for Planners and Evaluators

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“Public health is probably the most successful system of science and

technology combined, as well as social policy, that has ever been devised…It is, I

think, a paradigmatic model for how you do concerned, humane, directed science.”

-- Richard Rhodes

Rhodes R. Limiting human violence: an emerging scientific challenge. Sarewitz D, editor. Living With the Genie: Governing Science and Technology in the 21st Century; New York, NY: Center for Science, Policy, and Outcomes; 2002.

Protecting Health Through Public Work

Great Depression

End of WW II

NonsmokersRights Movement Begins

1st SurgeonGeneral’s

Report

1st Smoking-Cancer Concern

Federal CigaretteTax Doubles

BroadcastAd Ban

Source: USDA; 1986 Surgeon General's Report

Adult Per Capita Cigarette Consumption and Major Smoking-and-Health EventsUnited States, 1900-1998

0

1,000

2,000

3,000

4,000

5,000

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990

Nu

mb

er o

f Cig

aret

tes

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A Navigational View of Public Health Work

Thompson N. Reflections on voyaging and home. Polynesian Voyaging Society, 2001. Accessed July 18 at <http://leahi.kcc.hawaii.edu/org/pvs/malama/voyaginghome.html>.

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A Navigational View of Public Health Work

"How do you know," I asked, "that in twenty years those

things that you consider special are still going to be

here?" At first they all raised their hands but when they

really digested the question every single one of them

put their hands down. In the end, there was not a single

hand up. No one could answer that question. It was the

most uncomfortable moment of silence that I can

remember…That was the defining moment for me. I

recognized that I have to participate in answering that

question otherwise I am not taking responsibility for the

place I love and the people I love.”

-- Nainoa Thompson

Thompson N. Reflections on voyaging and home. Polynesian Voyaging Society, 2001. Accessed July 18 at <http://leahi.kcc.hawaii.edu/org/pvs/malama/voyaginghome.html>.

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Navigating Health Futures in Maine

Adolescent Pregnancy

Mills DA, Maine Bureau of Health. Healthy Maine 2010: longer and healthier lives. Augusta, ME: Maine Department of Human Services 2002. Available at http://www.maine.gov/dhhs/boh/healthyme2k/hm2010a.htm

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Asthma

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Heart Disease

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Infant Mortality

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Scott JC. Seeing like a state: how certain schemes to improve the human condition have failed. New Haven ; London: Yale University Press, 1999.

"Certain forms of knowledge and control require a

narrowing of vision. The great advantage of such

tunnel vision is that it brings into sharp focus certain

limited aspects of an otherwise far more complex

and unwieldy reality. This very simplification, in

turn, makes the phenomenon at the center of the

field of vision more legible and hence more

susceptible to careful measurement and

calculation….making possible a high degree of

schematic knowledge, control, and manipulation."

There is Great Power in Focusing on One Problem at a Time

-- John Scott

Great Depression

End of WW II

NonsmokersRights Movement Begins

1st SurgeonGeneral’s

Report

1st Smoking-Cancer Concern

Federal CigaretteTax Doubles

BroadcastAd Ban

Source: USDA; 1986 Surgeon General's Report

0

1,000

2,000

3,000

4,000

5,000

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990

Nu

mb

er o

f Cig

aret

tes

Adult Per Capita Cigarette Consumption and Major Smoking-and-Health EventsUnited States, 1900-1998

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But “Solutions” Can Also Create New Problems

Merton RK. The unanticipated consequences of purposive social action. American Sociological Review 1936;1936:894-904.

Forrester JW. Counterintuitive behavior of social systems. Technology Review 1971;73(3):53-68.

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Side Effects of Specialization• Confusion, inefficiency, organizational

disarray

• Competition for shared resources

• Attention to “local” causes, near in time and space

• Neglected feedback (+ and -)

• Confounded evaluations

• Coercive power dynamics

• Priority on a single value, implicitly or explicitly devaluing others

• Limited mandate to address context (living conditions) or infrastructure (public strength)

• Disappointing track record for assuring the conditions for health, especially with regard to inequalities

A

C

BD

E

A B C D EIssue Organizations

Neighborhood

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Centers for Disease Control and Prevention. Behavioral risk factor surveillance system, prevalence data. Atlanta, GA: U.S. Department of Health and Human Services, 2005. Available at http://apps.nccd.cdc.gov/HRQOL/TrendV.asp?State=21&Category=1&Measure=5

0

2

4

6

8

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Adult Unhealthy Days, Maine 1993-2003

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The Ecological Perspective—Broad but Static

Health Status

Prevention of Disease, Injury, Disability

Individual Factors

Behavioral Settings

Social Norms and Values

Home and Family

School

Community

Work Site

Healthcare

Genetics

Psychosocial

Other Personal Factors

Food and Beverage Industry

Agriculture

Education

Media

Government

Public Health Systems

Healthcare Industry

Business and Workers

Land Use and Transportation

Leisure and Recreation

Sectors of Influence

Protective Behavior Risk Behavior

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Navigating Health Futures in MaineQuestions Addressed by System Dynamics Modeling

Centers for Disease Control and Prevention. Behavioral risk factor surveillance system, prevalence data. Atlanta, GA: U.S. Department of Health and Human Services, 2005. Available at http://apps.nccd.cdc.gov/HRQOL/TrendV.asp?State=21&Category=1&Measure=5

0

2

4

6

8

Adult Unhealthy Days, Maine 1993-2003

20202010

How?

Where?

Who?Why?

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Science, 256, (12 June 1992) pp. 1520-1521

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Acknowledging Plurality

• Efforts to Reduce Population Health ProblemsProblem, problem solver, response

• Efforts to Organize a System that Assures Healthful Conditions for All Dynamic interaction among multiple problems, problem solvers, and responses

Bammer G. Integration and implementation sciences: building a new specialisation. Cambridge, MA: The Hauser Center for Nonprofit Organizations, Harvard University 2003.

“You think you understand two because you understand one and one. But you must also understand ‘and’.”

-- Sufi Saying

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"A bad solution is bad because it acts destructively upon the

larger patterns in which it is contained...because it is formed

in ignorance or disregard of them. A bad solution solves for a

single purpose or goal, such as increased production. And it

is typical of such solutions that they achieve stupendous

increase in production at exorbitant biological and social

costs…Good solutions recognize that they are part of a larger

whole. They solve more than one problem and don't create

new problems. A good solution should not enrich one person

by the distress or impoverishment of another."

-- Wendell Berry

Berry W. Solving for pattern. In: The Gift of Good Land. San Francisco: North Point; 1981. p. 134-45.

Solving for Pattern

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• PossibleWhat may happen?

• PlausibleWhat could happen?

• ProbableWhat will likely happen?

• PreferableWhat do we want to have happen?

Bezold C, Hancock T. An overview of the health futures field. Geneva: WHO Health Futures Consultation; 1983 July 19-23.

“Most organizations plan around what is most likely. In so doing they reinforce what is, even though they want something very different.”

-- Ciement Bezold

Seeing Beyond the Probable

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Navigating Diabetes Futures

The Power of “What if…” Questions

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CDC Diabetes System Modeling ProjectDiscovering Dynamics Through Action Labs

Jones A, Homer J, Milstein B, Essien J, Murphy D, Sorensen S, Englegau M. Modeling the population dynamics of a chronic disease: the CDC's diabetes system model. American Journal of Public Health (in press).

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Transforming the Future of Diabetes…

"Every new insight into Type 2 diabetes...

makes clear that it can be avoided--and

that the earlier you intervene the better.

The real question is whether we as a

society are up to the challenge...

Comprehensive prevention programs

aren't cheap, but the cost of doing

nothing is far greater..."

Gorman C. Why so many of us are getting diabetes: never have doctors known so much about how to prevent or control this disease, yet the epidemic keeps on raging. how you can protect yourself. Time 2003 December 8. Accessed at http://www.time.com/time/covers/1101031208/story.html.

…in an Era of Epidemic Obesity

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Prevalence of Diagnosed Diabetes, US

0

10

20

30

40

1980 1990 2000 2010 2020 2030 2040 2050

Mill

ion

pe

op

le

Historical Data: CDC DDT and NCCDPHP. (Change in measurement in 1996).Model Forecast: Honeycutt et al. 2003, "A Dynamic Markov model…"

HistoricalData

ModelForecast

Key Constants• Incidence rates (%/yr)• Death rates (%/yr)• Diagnosed fractions(Based on year 2000 data, per demographic segment)

Honeycutt A, Boyle J, Broglio K, Thompson T, Hoerger T, Geiss L, Narayan K. A dynamic markov model for forecasting diabetes prevalence in the United States through 2050. Health Care Management Science 2003;6:155-164.

Other Models Exist, But Are Not Designed to Explore Intervention Scenarios

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Healthy People 2010 Diabetes Objectives:What Can We Accomplish?

-11%7.88.8 per 1,000

Reduce Diabetes–related Deaths Among Diagnosed

(5-6)

-38%2540 per 1,000

Reduce Prevalence of Diagnosed Diabetes

(5-3)

-29%2.53.5per 1,000

Reduce New Cases of Diabetes (5-2)

Increase Diabetes Diagnosis (5-4)

+18%80%68%

Percent Change

HP 2010 Target

Baseline

U.S. Department of Health and Human Services. Healthy People 2010. Washington DC: Office of Disease Prevention and Health Promotion, U.S. Department of Health and Human Services; 2000. http://www.healthypeople.gov/Document/HTML/Volume1/05Diabetes.htm

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20

30

40

50

60

70

1980 1985 1990 1995 2000 2005 2010People

with

dia

gnosed d

iabete

s p

er

1,0

00 a

dult

popula

tion

Simulated

Status Quo

Meet Detection Objective (5-4)

Meet Onset Objective (5-2)

HP 2010 Objective (5-3)

HP 2000 Objective

Setting Realistic ExpectationsHistory, HP Objectives, and Simulated Futures

Reported

A

B

C

D

E

F

G

H

I

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Connecting the ObjectivesPopulation Flows and Dynamic Accounting 101

It is impossible for any policy to reduce prevalence

38% by 2010!

People withUndiagnosed

Diabetes

People withDiagnosedDiabetes Dying from Diabetes

Complications

DiagnosedOnset

InitialOnset

PeoplewithoutDiabetes

As would stepped-up detection effort

Reduced death wouldadd further to prevalence

With a diagnosed onset flow of

1.1 mill/yr

And a death flow of 0.5 mill/yr

(4%/yr rate)

The targeted 29% reduction in diagnosed onset can only

slow the growth in prevalence

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Simulations for Learning in Dynamic Systems

Plausible Futures (Policy Experiments)Dynamic Hypothesis (Causal Structure)

X Y

Morecroft JDW, Sterman J. Modeling for learning organizations. Portland, OR: Productivity Press, 2000.

Sterman JD. Business dynamics: systems thinking and modeling for a complex world. Boston, MA: Irwin McGraw-Hill, 2000.

Multi-stakeholder Dialogue

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Health Care Capacity

• Provider supply• Provider understanding, competence• Provider location• System integration• Cost of care• Insurance coverage

Population Flows

Discussions Pointed to Many Interacting Factors

Personal Capacity

• Understanding• Motivation• Social support• Literacy• Physio-cognitive function• Life stages

Metabolic Stressors

• Nutrition• Physical activity• Stress

Health Care Utilization

• Ability to use care (match of patients and providers, language, culture)• Openness to/fear of screening• Self-management, monitoring

Civic Participation

• Social cohesion• Responsibility for others

Forces Outside the Community

• Macroeconomy, employment• Food supply• Advertising, media• National health care• Racism• Transportation policies• Voluntary health orgs• Professional assns• University programs• National coalitions

Local Living Conditions

• Availability of good/bad food• Availability of phys activity• Comm norms, culture (e.g., responses to racism, acculturation)• Safety• Income• Transportation• Housing• Education

Undxnoncomp

popn

Dx noncomppopn

Dx complicpopn

<Noncomp diabdiagnosis>

Dx Complicdeaths

Undx PreDpopn

Dx PreDpopn

<PreDdiagnosis>

<PreD onset>

<Recovery fromDx PreD>

<Recovery fromUndx PreD>

Progression tocomplic from Dx

diab

Progression tocomplic from Undx

diab

Diabetes onsetfrom Dx PreD

Diabetes onsetfrom Undx PreD

Undx complicpopn

<Complic diabdiagnosis>

Undx Complicdeaths

Normo-glycemic

popn

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Diabetes System Modeling ProjectWhere is the Leverage for Health Protection?


People withUndiagnosed,Uncomplicated

Diabetes

People withDiagnosed,

UncomplicatedDiabetes

People withDiagnosed,Complicated

Diabetes

People withUndiagnosedPreDiabetes

People withDiagnosed

PreDiabetes

People withUndiagnosed,Complicated

DiabetesPeople with

NormalGlycemic

Levels

DiagnosingDiabetes

DiagnosingDiabetes

Diabetes Detection

Dying fromComplications

DevelopingComplications

Diabetes Control

PreDiabetes Detection

DiagnosingPreDiabetes

DiabetesOnset

PreDiabetes Control

PreDiabetesOnset

Recovering fromPreDiabetes


Obesity Prevention

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Diabetes System Modeling ProjectWhere is the Leverage for Health Protection?


Diabetes




Diabetes

DiagnosingUncomplicated

Diabetes



PreDiabetes


DevelopingComplications from


Diabetes

DiagnosingComplicated

Diabetes

People withNormal

GlycemicLevels

DiabetesDetection

Obese Fraction ofthe Population

Risk forPreDiabetes & Diabetes

Caloric Intake PhysicalActivity

PreDiabetesControl

DiabetesControl

PreDiabetesDetection

MedicationAffordability

Ability to SelfMonitor

Adoption ofHealthy Lifestyle

ClinicalManagement of

PreDiabetes

Clinical Managementof Diagnosed

Diabetes

LivingConditions

PersonalCapacity

PreDiabetesTesting for

Access toPreventive Health

Services Testing forDiabetes

PreDiabetesOnset


Recovering fromPreDiabetes Diabetes

Onset



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Developing Diabetes Policy Scenarios and Anticipating Change

What strategies do you see unfolding in Maine over the next five years to address the rise of diabetes?

How will those strategies affect the burden of diabetes?

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The Diabetes Simulation Model Was Developed UsingThe Best Possible Available Data

Information Sources Data

U.S. Census• Adult population and death rates• Health insurance coverage

National Health Interview Survey• Diabetes prevalence• Diabetes detection

National Health and Nutrition Examination Survey

• Prediabetes prevalence

• Weight, height, and body fat

• Caloric intake

Behavioral Risk Factor Surveillance System

• Glucose self-monitoring• Eye and foot exams• Participation in health education• Use of medications

Professional Literature

• Physical activity trends• Effects of control and aging on onset, progression, death, and costs• Expenditures

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Diabetes System Modeling ProjectConfirming the Model’s Fit to History


Diagnosed Diabetes % of AdultsObese % of Adults

0%

10%

20%

30%

40%

1980 1985 1990 1995 2000 2005 2010

Obese % of adults

Data (NHANES)

Simulated

0%

2%

4%

6%

8%

1980 1985 1990 1995 2000 2005 2010

Diagnosed diabetes % of adults

Data (NHIS)

Simulated

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Explaining the PastGrowth in the Number of People with Diabetes

More people with a primary risk factor….

Leads to rising total prevalence

After adelay

(plus aging and demographics, etc…)

Obese Fraction of Adult Population

0.4

0.3

0.2

0.1

0

1980 1985 1990 1995 2000 2005Time (Year)

People with Diabetes per Thousand Adults

100

80

60

40

20

0

1980 1985 1990 1995 2000 2005Time (Year)

Model OutputModel Output

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Controlled Fraction of Diagnosed Population

0.5

0.4

0.3

0.2

0.1

0

1980 1985 1990 1995 2000 2005Time (Year)

Explaining the PastReducing the Burden for People with Diabetes

Model OutputFrom

around 5%

To above

40%

Model Output

We have been finding them…

And helping them stay under control

Diagnosed Fraction of Diabetes Population

0.8

0.7

0.6

0.5

1980 1985 1990 1995 2000 2005Time (Year)

(although there are disparities)

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Explaining the PastDeaths Due to Diabetes Have Fallen

Combine to mean fewer U.S. adults dying 1980-2004

Complications Deaths per Thousand w Diabetes40

30

20

10

0

1980 1985 1990 1995 2000 2005Time (Year)

People with Diabetes per Thousand Adults100

90

80

70

60

501980 1985 1990 1995 2000 2005

Time (Year)

More people with diabetes

Deaths from Comps of Diabetes Per Thousand Adults

2.5

2

1.5

1

0.5

0

1980 1985 1990 1995 2000 2005Time (Year)


Model Output

Among people with diabetes, fewer dying every year

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From a 30,000 Foot View and Population Perspective, We Have Seen Two Forces Fighting to Change

the Burden of Diabetes

Stunning Progress in

Reducing the Burden for the Average Person with Diabetes

Stunning Progress in

Reducing the Burden for the Average Person with Diabetes

Huge Growth in Number of People with

Diabetes

Huge Growth in Number of People with

Diabetes

Overall, Total Burden per Citizen Held at BayOverall, Total Burden

per Citizen Held at Bay

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Anticipating the Future

Obese Fraction of Adult Population

0.6

0.45

0.3

0.15

0

1980 1995 2010 2025 2040Time (Year)

Even if obesity has topped out …


110

90

70

50

1980 1990 2000 2010 2020 2030 2040 2050Time (Year)

Diabetes prevalence continues to increase

for decades.


After adelay

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110

90

70

50

1980 1990 2000 2010 2020 2030 2040 2050Time (Year)

Complications Deaths per Thousand w Diabetes40

30

20

10

0

1980 1990 2000 2010 2020 2030 2040 2050Time (Year)

Deaths from Complications of Diabetes Per Thousand Adults2.5

1.25

1980 1990 2000 2010 2020 2030 2040 2050Time (Year)

Diabetes-relateddeaths would naturally rise.

Anticipating the FutureDeaths Under ‘Status Quo’ Assumptions*

And assuming no further improvement in disease management...

With diabetes prevalence continuing to increase...

* Assuming no change after 2004 in the 9 key health behaviors

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Navigating the Future of DiabetesWhat Strategies Would You Like To Test?


Diabetes




Diabetes


Diabetes



PreDiabetes




Diabetes


Diabetes

People withNormal

GlycemicLevels

DiabetesDetection




PreDiabetesControl

DiabetesControl






PreDiabetes


Diabetes




PreDiabetesOnset



Onset



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Navigating the Future of Diabetes

What strategies would you like to test in the simulated environment to better address diabetes?

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Scenario

Effect of Public Health Effort on…

Clinical Management of Diagnosed Diabetes

(% under control)

Caloric Intake(Kcal/day)

Base Run(no changes after 2000)

Enhanced Disease Control (Downstream)

Enhanced Obesity Prevention (Upstream)

Combined Disease Control and Obesity Prevention(Up and Down)

Developing a Scenario-based Research Design

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Diabetes System Modeling ProjectWhere Flow Drivers are Involved in Each Strategy?


Diabetes




Diabetes


Diabetes



PreDiabetes




Diabetes


Diabetes

People withNormal

GlycemicLevels

DiabetesDetection




PreDiabetesControl

DiabetesControl






PreDiabetes


Diabetes




PreDiabetesOnset



Onset



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Scenario



(% under control)


Base Run(no changes after 2000) 66% 2465





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Deaths per Population0.0035

0.003

0.0025

0.002

0.0015

1980 1990 2000 2010 2020 2030 2040 2050Time (Year)

Downstream-Only Intervention

Blue: Base run

Base

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Scenario



(% under control)







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Scenario



(% under control)




+24%

(90% under control)No change




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0.003

0.0025

0.002

0.0015

1980 1990 2000 2010 2020 2030 2040 2050Time (Year)

Downstream-Only Intervention

Blue: Base run; Red: Clinical mgmt of diagnosed up from 66% to 90%

Base

Downstream

Disease control acts quickly but does not slow the growth in deaths.

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Scenario



(% under control)




+24%





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Scenario



(% under control)




+24%



No Change-4%

(99 fewer Kcal/day)



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Deaths per Population

0.0035

0.003

0.0025

0.002

0.00151980 1990 2000 2010 2020 2030 2040 2050

Time (Year)

Upstream-Only Intervention

Blue: Base run; Red: Clinical mgmt up from 66% to 90%;Green: Caloric intake down 4% (99 Kcal/day)

Downstream

UpstreamBase

Obesity prevention slows the growth but takes a long time to do so.

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Scenario



(% under control)




+24%



No Change-4%

(99 fewer Kcal/day)



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Scenario



(% under control)




+24%



No Change-4%

(99 fewer Kcal/day)


+14%

(80% under control)

-2.5%

(62 fewer Kcal/day)


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0.003

0.0025

0.002

0.00151980 1990 2000 2010 2020 2030 2040 2050

Time (Year)

Mixed Intervention

Blue: Base run; Red: Clinical mgmt up from 66% to 90%;Green: Caloric intake down 4% (99 Kcal/day);Black: Clin mgmt up to 80% & Intake down 2.5% (62 Kcal/day)

Base

Downstream

Upstream

Mixed

Striking an acceptable balance.

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The Modeling Process is Having an Impact

• Budget for primary prevention was doubled– from meager to modest

• HP2010 prevalence goal has been modified– from a large reduction to no change (but still not an increase)

• Research, program, and policy staff are working more closely– but truly cross-functional teams still forming

• State health departments and their partners are now engaged– initial engagement in VT, with two additional states being

considered

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Transforming Health Evaluation

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“Simulation is a third way of doing science.

Like deduction, it starts with a set of explicit

assumptions. But unlike deduction, it does not prove

theorems. Instead, a simulation generates data that

can be analyzed inductively. Unlike typical induction,

however, the simulated data comes from a rigorously

specified set of rules rather than direct measurement

of the real world. While induction can be used to find

patterns in data, and deduction can be used to find

consequences of assumptions, simulation modeling

can be used as an aid to intuition.”

-- Robert Axelrod

Axelrod R. Advancing the art of simulation in the social sciences. In: Conte R, Hegselmann R, Terna P, editors. Simulating Social Phenomena. New York, NY: Springer; 1997. p. 21-40. <http://www.pscs.umich.edu/pub/papers/AdvancingArtofSim.pdf>.

Sterman JD. Business Dynamics: Systems Thinking and Modeling for a Complex World. Boston, MA: Irwin McGraw-Hill, 2000.

Simulation ExperimentsOpen a Third Branch of Science

“The complexity of our mental models vastly exceeds our ability to understand their implications without simulation."

-- John Sterman

How?

Where?

0

10

20

30

40

50

1960-62 1971-74 1976-80 1988-94 1999-2002

Prevalence of Obese Adults, United States

Why?

Data Source: NHANES 20202010

Who?

What?

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Learning In and About Dynamic Systems

Benefits of Simulation/Game-based Learning

• Formal means of evaluating options

• Experimental control of conditions

• Compressed time

• Complete, undistorted results

• Actions can be stopped or reversed

• Visceral engagement and learning

• Tests for extreme conditions

• Early warning of unintended effects

• Opportunity to assemble stronger support

Complexity Hinders

• Generation of evidence (by eroding the conditions for experimentation)

• Learning from evidence (by demanding new heuristics for interpretation)

• Acting upon evidence (by including the behaviors of other powerful actors)

Sterman JD. Learning from evidence in a complex world. American Journal of Public Health (in press).

Sterman JD. Business Dynamics: Systems Thinking and Modeling for a Complex World. Boston, MA: Irwin McGraw-Hill, 2000.

“In [dynamically complex] circumstances simulation becomes the only reliable way to test a hypothesis and evaluate the likely effects of policies."

-- John Sterman

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Patterns

Events

Analysis Process for Developing Policy

Adapted from: Successful Systems, Inc.

IssueIdentification

Variable & Behavior Analysis

Time

IssueIdentification

Variable & Behavior Analysis

Causal Mapping

Understanding Strategy &Policy Implications

Implementing Action Plan

StructureCausal Mapping

SimulationModeling

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Time Series ModelsDescribe trends

Multivariate Stat Models

Identify historical trend drivers and correlates

Patterns

Structure

Events

Increasing:

• Depth of causal theory

• Degrees of uncertainty

• Robustness for longer-term projection

• Value for developing policy insights

Increasing:

• Depth of causal theory

• Degrees of uncertainty

• Robustness for longer-term projection

• Value for developing policy insights

Dynamic Models

Anticipate future trends, and find policies that maximize chances

of a desirable path

Tools for Policy Analysis

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Different Modeling Approaches For Different Purposes

Logic Models(flowcharts, maps or

diagrams)

System Dynamics(causal loop diagrams and

simulation models)

Forecasting Models

• Articulate steps between actions and anticipated effects

• Improve understanding about the plausible effects of a policy over time

• Focus on patterns of change over time (e.g., long delays, worse before better)

• Make accurate forecasts of key variables

• Focus on precision of point predictions and confidence intervals

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Transforming Essential Ways of ThinkingConventional Thinking Systems Thinking

Static Thinking: Focusing on particular events. Dynamic Thinking: Framing a problem in terms of a pattern of behavior over time.

System-as-Effect Thinking: Focus on individuals as the sources of behavior. Hold individuals responsible or blame outside forces.

System-as-Cause Thinking: Seeing the structures and pressures that drive behavior. Examine the conditions in which decisions are made, as well as their consequences for oneself and others.

Tree-by-Tree Thinking: Focusing on the details in order to “know.”

Forest Thinking: Seeing beyond the details to the context of relationships in which they are embedded.

Factors Thinking: Listing factors that influence, or are correlated with, a behavior. To forecast milk production, consider economic elasticities.

Operational Thinking: Understanding how a behavior is actually generated. To forecast milk production, you must consider cows.

Straight-Line Thinking: Viewing causality as running one way, treating causes as independent and instantaneous. Root-Cause thinking.

Closed-Loop Thinking: Viewing causality as an ongoing process, not a one-time event, with effects feeding back to influence causes, and causes affecting each other, sometimes after long delays.

Measurement Thinking: Focusing on the things we can measure; seeking precision.

Quantitative Thinking: Knowing how to quantify, even though you cannot always measure.

Proving-Truth Thinking: Seeking to prove our models true by validating them with historical data.

Scientific Thinking: Knowing how to define testable hypotheses (everyday, not just for research).

Karash R. The essentials of systems thinking and how they pertain to healthcare and colorectal cancer screening. Dialogue for Action in Colorectal Cancer; Baltimore, MD; March 23, 2005..

Richmond B. Systems thinking: critical thinking skills for the 1990s and beyond. System Dynamics Review 1993;9(2):113-134.

Richmond B. The "thinking" in systems thinking: seven essential skills. Waltham, MA: Pegasus Communications, 2000.

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“The macroscope filters details and amplifies that which links things

together. It is not used to make things larger or smaller but to observe what is at once too great, too slow, and too

complex for our eyes.”

Rosnay Jd. The macroscope: a book on the systems approach. Principia Cybernetica, 1997. <http://pespmc1.vub.ac.be/MACRBOOK.html

-- Joèel de Rosnay

Looking Through the Macroscope

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Are We Posing Questions About Attribution or Contribution?

“…if a program’s activities are aligned with those of other programs operating in the same setting, certain effects (e.g., the creation of new laws or policies) cannot be attributed solely to one program or another. In such situations, the goal for evaluation is to gather credible evidence that describes each program’s contribution in the combined change effort. Establishing accountability for program results is predicated on an ability to conduct evaluations that assess both of these kinds of effects.” p.11-12

Calls into question the conditions in which one focuses on a “program” as the unit of analysis

Milstein B, Wetterall S, CDC Evaluation Working Group. Framework for program evaluation in public health. MMWR Recommendations and Reports 1999;48(RR-11):1-40. Available at <http://www.cdc.gov/mmwr/PDF/RR/RR4811.pdf>.

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Questioning the Character of Public Health Work

PUBLIC HEALTH WORK

InnovativeHealth

Ventures

SYSTEMS THINKING & MODELING (understanding change)

• What causes population health problems?

• How are efforts to protect the public’s health organized?

• How and when do health systems change (or resist change)?

PUBLIC HEALTH(setting direction)

What are health leaderstrying to accomplish?

SOCIAL NAVIGATION(governing movement)

Directing Change

Charting Progress

• Who does the work?• By what means?• According to whose values?

• How are conditions changing?• In which directions?

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Changing (and Accumulating) Ideas About Causation

What accounts for poor population health?

• God’s will

• Humors, miasma, ether (e.g., epidemic constitution)

• Poor living conditions, immorality (e.g., sanitation)

• Single disease, single cause (e.g., germ theory)

• Single disease, multiple causes (e.g., heart disease)

• Single cause, multiple diseases (e.g., tobacco)

• Multiple causes, multiple diseases (but no feedback dynamics) (e.g., social epidemiology)

• Dynamic feedback among afflictions, living conditions, and public strength (e.g., syndemics)

1880

1950

1960

1980

2000

1840

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Syndemic Orientation

Expanding Prevention Science“Public health imagination involves using science to expand the

boundaries of what is possible.”

-- Michael Resnick

EpidemicOrientation

People inPlaces

EcologicalThinking

Governing Dynamics

Ca

us

al

Ma

pp

ing

Plausible Futures

DynamicModeling

Navigational Freedoms

De

mo

cra

tic

Pu

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ork

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For Additional Informationhttp://www.cdc.gov/syndemics

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