Syndemics Prevention Network Edinburgh Evaluation Summer School Edinburgh, Scotland June 6, 2006 Combining Innovations from Public Health, Systems Science,

Syndemics

Prevention Network

Edinburgh Evaluation Summer SchoolEdinburgh, Scotland

June 6, 2006

Combining Innovations from Public Health, Systems Science, and Social Navigation

Bobby Milstein Syndemics Prevention NetworkCenters for Disease Control and

[email protected]

Navigating Health System Change

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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.

How is it directed?

What concepts, methods, and moral considerations are involved?

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“Let me assure you, we will survive any

crisis that involves funding, political

support, popularity, or cyclic trends,

but we can't survive the internal crisis,

if we become provincial, focus totally

on the short term, or if we lose our

philosophy of social justice.”

-- William Foege

Foege WH. Public health: moving from debt to legacy. American Journal of Public Health 1987;77(10):1276-8.

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What forces move us to become externally focused, provincial, short-term oriented, and neglectful of social justice?

What approaches to public health work may help us to recognize and overcome these pitfalls?

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Diseases of Disarray

Hardening of the categories

Tension headache between treatment and prevention

Hypocommitment to training

Cultural incompetence

Political phobia

Input obsession

Wiesner PJ. Four disease of disarray in public health. Annals of Epidemiology. 1993;3(2):196-8.

Chambers LW. The new public health: do local public health agencies need a booster (or organizational "fix") to combat the diseases of disarray? Canadian Journal of Public Health 1992;83(5):326-8.

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New Word for a Familiar Phenomenon

Singer M, Snipes C. Generations of suffering: experiences of a treatment program for substance abuse during pregnancy. Journal of Health Care for the Poor and Underserved 1992;3(1):222-34.

Singer M. 1994. AIDS and the health crisis of the US urban poor: The perspective of critical medical anthropology. Social Science and Medicine 39(7): 931-948.

Singer M. 1996. A dose of drugs, a touch of violence, a case of AIDS: Conceptualizing the SAVA syndemic. Free Inquiry in Creative Sociology 24(2): 99-110.

Singer M, Clair S. Syndemics and public health: reconceptualizing disease in bio-social context. Medical Anthropology Quarterly 2003;17(4):423-441.

“We have introduced the term ‘syndemic’ to

refer to the set of synergistic or intertwined

and mutually enhancing health and social

problems facing the urban poor. Violence,

substance abuse, and AIDS, in this sense,

are not concurrent in that they are not

completely separable phenomena.”

-- Merrill Singer

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What was Singer doing?

What are the implications for public health work?

What concepts and methods support this perspective (scientifically, politically, morally)?

What effects do these ways of thinking and acting have on individuals and in the world at large?

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What Does it Mean to Approach Public Health Work from a Syndemic Orientation?

Centers for Disease Control and Prevention. Spotlight on syndemics. Syndemics Prevention Network, 2001. <http://www.cdc.gov/syndemics>.

Ongoing study of innovations in public health work

Network includes 427 individuals; 287 organizations; 19 countries

Learning within innovative ventures

Comprehensive Community InitiativesPhilanthropy

Legacy InitiativesState Tobacco Settlements

Efforts to Eliminate Health Inequities Government and Philanthropy

Responses to Unjust Conditions Broad-based Citizen Organizations

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Seeing Syndemics

The word syndemic signals a special concern for relationships

– Mutually reinforcing character of health problems

– Connections between health status and living conditions

– Synergy/fragmentation within the health system (e.g., by issues, sectors, organizations, professionals and other citizens)

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

-- Sufi Saying

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Placing Health in a Wider Set of Relationships

Health

LivingConditions

PublicStrength

A syndemic orientation is one of a few approaches that explicitly includes within it our power to respond.

Along with an understanding of its changingpressures, constraints, and consequences.

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A Philosophy of Means as Ends in the Making

“Social and political theory have

neglected the central question of

means, and, therefore, the

problem of inevitable conflict.”

-- Joan Bondurant

Bondurant JV. Conquest of violence: the Gandhian philosophy of conflict. New rev. ed. Princeton NJ: Princeton University Press, 1988.

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Exploring the Dynamic and Democratic Dimensions 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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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.

True innovation occurs when things are put together for the first time that had been separate.

– Arthur Koestler

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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, methodological, and moral orientations: the frames of reference that shape how we think, how we act, how we learn, and what we value

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Observing Transformations

• How do you see public health work changing?

• What types of dilemmas and innovations are driving those transformations?

• Where is the field headed?

“We make the road by walking.”

– Myles Horton & Paulo Friere

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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-sectoral, democratic) Concerned with many interests and mutual-accountability

• Questioning (evaluative, reflective, critical, ethical, pragmatic)Concerned with creating and protecting values like health, dignity, security, satisfaction, justice, wealth, and freedom as 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)

What are the implications for planning and evaluation?

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General Plan for the Workshop

• Thinking about health system change: dilemmas and innovations

• Planning/evaluating in dynamic and democratic systems

• Simulation studies and game-based learning

– Navigating diabetes futures in an era of rising obesity

– Making the most of temporary assistance

• Good discussion along the way!

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General Plan for the Workshop

• Discuss the meaning and implications of

– Dynamic complexity

– Boundary critique

– Macroscopic perspectives

– System dynamics simulation modeling

– Game-based learning

• What else would you like to cover?

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What Do These Observations Have in Common?

• Road building programs increase traffic, delays, and pollution.

• Low tar and nicotine cigarettes increase intake of carcinogens

• Antilock brakes cause some to drive more aggressively

• Forest fire suppression leads to larger, hotter, and more dangerous fires

• Flood control efforts lead to more severe floods and excess cost

• Antibiotics stimulate the evolution of drug-resistant pathogens

• Pesticides and herbicides stimulate the evolution of resistant pests and accumulate up the food chain to poison fish, birds, and humans.

• Antiretroviral treatment reduces mortality among those with HIV, but has increased risky behaviors, causing a rebound in incidence

Sterman JD. Learning from evidence in a complex world. American Journal of Public Health 2006;96(3):505-514.

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“Solutions” Can Also Create New Problems

Meadows DH, Richardson J, Bruckmann G. Groping in the dark: the first decade of global modelling. New York, NY: Wiley, 1982.

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.

Policy resistance is the tendency for interventions to be delayed, diluted, or defeated by the response of the system to the intervention itself.

-- Meadows, Richardson, Bruckman

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System Dynamics Was Designed to Address Problems Marked By Dynamic Complexity

Good at Capturing

• Differences between short- and long-term consequences of an action

• Time delays (e.g., transitions, detection, response)

• Accumulations (e.g., prevalence, capacity)

• Behavioral feedback (e.g., actions trigger reactions)

• Nonlinear causal relationships (e.g., effect of X on Y is not constant)

• Differences or inconsistencies in goals/values among stakeholders

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

Homer JB, Hirsch GB. System dynamics modeling for public health: background and opportunities. American Journal of Public Health 2006;96(3):452-458.

Origins

• Jay Forrester, MIT (from late 1950s)

• Public policy applications starting late 1960s

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• Natural -- animals, ecosystem, solar system

• Conceptual -- metric system, betting system

• Designed -- computers, engines, transportation

• Social and cultural -- families, communities, networks

• Bureaucratic -- judicial, child welfare

• Causal -- forces of change governing a phenomenon

• Navigational -- endeavors to move in a valued direction

• Natural -- animals, ecosystem, solar system

• Conceptual -- metric system, betting system

• Designed -- computers, engines, transportation

• Social and cultural -- families, communities, networks

• Bureaucratic -- judicial, child welfare

• Causal -- forces of change governing a phenomenon

• Navigational -- endeavors to move in a valued direction

Kinds of Systems

Corning PA. Nature's magic: synergy in evolution and the fate of humankind. New York: Cambridge University Press, 2003.

Hastings D. Introduction to technology and policy: systems thinking. Massachusetts Institute of Technology, 2001. <http://msl1.mit.edu/ESD10/block4/4.1_-_Systems_Thinking.pdf>.

Henderson T. A systems approach to evaluation at the project level. Australasian Evaluation Society, 2004. <http://www.aes.asn.au/Qld_TMH_systems.ppt>.

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Is it Possible to Measure Movement Toward Health or Affliction?

Centers for Disease Control and Prevention. Measuring healthy days: population assessment of health-related quality of life. Atlanta, GA: U.S. Department of Health and Human Services, 2000. Available at: http://www.cdc.gov/hrqol/monograph.htm

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2

4

6

8

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Year

Observing Health DynamicsWorsening Trend in Unhealthy Days

Among Adults, United States, 1993-2004

17% increase

Centers for Disease Control and Prevention. Health-related quality of life: prevalence data. Accessed June 4, 2006. Available at: http://apps.nccd.cdc.gov/HRQOL/index.asp

Centers for Disease Control and Prevention. Measuring healthy days: population assessment of health-related quality of life. Atlanta, GA: U.S. Department of Health and Human Services, 2000. Available at: http://www.cdc.gov/hrqol/monograph.htm

Average Number of Adult Unhealthy Days per Month

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In an Era of Powerful Disease Prevention Efforts

600

500

400

200

100

501950 1960 1970 1980 1990 1995

Rate if trend continued

Peak Rate

Actual Rate

Age-a

dju

sted D

eath

Rate

per

10

0,0

00

Popula

tion

1955 1965 1975 1985

300

700

Year

Actual and Expected Death Rates for Coronary Heart Disease, 1950–1998

Marks JS. The burden of chronic disease and the future of public health. CDC Information Sharing Meeting. Atlanta, GA: National Center for Chronic Disease Prevention and Health Promotion; 2003.

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In an Era of Powerful Disease Prevention Efforts

Marks JS. The burden of chronic disease and the future of public health. CDC Information Sharing Meeting. Atlanta, GA: National Center for Chronic Disease Prevention and Health Promotion; 2003.

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

of

Cig

are

tte

sAdult Per Capita Cigarette Consumption and Major Smoking-and-Health Events United States, 1900-1998

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Public health work cannot stop with the delivery of effective disease prevention services.

Indeed, that is just the beginning….

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Summers J. Soho: a history of London's most colourful neighborhood. Bloomsbury, London, 1989. p. 117.

“No improvements at all had been

made...open cesspools are still to

be seen...we have all the materials

for a fresh epidemic...the water-

butts were in deep cellars, close to

the undrained cesspool...The

overcrowding appears to increase."

Broad Street, One Year Later

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Average Number of Adult Unhealthy Days per Month

2

4

6

8

1993 1995 1997 1999 2001 2003

Year

2005 2025 2050

Working to Redirect the Course of ChangeNavigational Inquiry with Simulation Modeling

17% increase

Centers for Disease Control and Prevention. Health-related quality of life: prevalence data. Accessed June 4, 2006. Available at: http://apps.nccd.cdc.gov/HRQOL/index.asp

Milstein B. Hygeia's constellation: navigating health futures in a dynamic and democratic world. [Dissertation]. Cincinnati, OH: Union Institute and University; April 11 draft, 2006.

How?Why?

Where?

Who?

What?

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Cultivating a Place- or Population-Based ViewThe Ke Ala Hoku Project

"Where do you want your children to live?" Without hesitation they all told me that they wanted

their children to live in Hawaii. Then I asked, "Why?“ And they told me they wanted all those things that were special about Hawaii for their future children.

"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.”

-- Nainoa Thompson

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

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Enacting a Place- or Population-Based ViewMayors’ Institute on City Design

Mayors’ Institute on City Design. About the Mayor's Institute on City Design. Mayor's Institute on City Design, 2002. http://www.archfoundation.org/micd/about/index.htm.

Siegel R. The Mayors' Institute on City Design: forum offers insight on redevelopment strategies. Washinton, DC; May 2, 2002, 2002. <http://www.npr.org/programs/atc/features/2002/may/city_design/index.html>

“When a Mayor makes a decision…

a hundred years later the citizens of your

city are going to be shaped by that. So…

the degree to which it contributes to the

public realm I think ends up being the most

important responsibility and the most lasting

action that a Mayor has.”

-- Joseph Riley

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Elliot G. Twentieth century book of the dead. New York,: C. Scribner, 1972.

“Public death was first recognized as a matter of civilized concern in the

nineteenth century, when some public health workers decided that

untimely death was a question between men and society, not between

men and God….Since then, and for that reason, millions of lives have

been saved….The pioneers of public health did not change nature, or

men, but adjusted the active relationship of men to certain aspects of

nature so that the relationship became one of watchful and healthy

respect.

Public Health Began as Public Work

-- Gil Elliot

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Epi·demic

• The term epidemic is an ancient word signifying a kind of relationship wherein something is put upon the people

• Epidemiology first appeared just over a century ago (in 1873), in the title of J.P. Parkin's book "Epidemiology, or the Remoter Causes of Epidemic Diseases“

• Ever since then, the conditions that cause health problems have increasingly become matters of public concern and public work

Martin PM, Martin-Granel E. 2,500-year evolution of the term epidemic. Emerging Infectious Diseases 2006. Available from http://www.cdc.gov/ncidod/EID/vol12no06/05-1263.htm

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Syn·demic

• The term syndemic, first used in 1992, strips away the idea that illnesses originate from extraordinary or supernatural forces and places the responsibility for affliction squarely within the public arena

• It acknowledges relationships and signals a commitment to studying health as a a fragile, dynamic state requiring continual effort to maintain and one that is imperiled when social and physical forces operate in harmful ways

Confounding

Connecting*

Synergism

Syndemic

Events

System

Co-occurring

* Includes several forms of connection or inter-connection such as synergy, intertwining, intersecting, and overlapping

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Changing (and Accumulating) Ideas in Causal Theory

What Accounts for Poor Population Health?

• God’s will

• Humors, miasma, ether

• 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., syndemic)

1880

1950

1960

1980

2000

1840

Richardson GP. Feedback thought in social science and systems theory. Philadelphia, PA: University of Pennsylvania Press, 1991.

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“When X and Y affect each other, one cannot study the link between X and Y and,

independently, the link between Y and X and predict how the system will behave. Only the study of the whole system as a

feedback system will lead to correct results."

-- System Dynamics Society

The Feedback Thought

System Dynamics Society. What is system dynamics? System Dynamics Society, 2002. Available at <http://www.systemdynamics.org/>.

Richardson GP. Feedback thought in social science and systems theory. Philadelphia: University of Pennsylvania Press, 1991.

Health

LivingConditions

PublicStrength

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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 scientific and professonal views (e.g., extreme specialization, evidence before action, arrogance)

• Overcoming perceptions of zero-sum resources

• Harnessing the power of broad-based, democratic organizing

• Reconciling different values and standards for judgment

• Others…

Serious Challenges for Planners and Evaluators

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Essential Elements for System Change Ventures

Selected Elements of a Sound Strategy

Needed to Address…

Realistic Understanding of Causal Dynamics

Navigational Goals & Framework for Charting Progress

Means for Prioritizing Actions &

Impetus to Implement Them

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• Multiple, simultaneous lines of action and reaction

• Sources of dynamic complexity (e.g., accumulation, delay, non-linear response)

• Integration of relevant evidence, as well as attention to critical areas of uncertainty

• Clear roles for relevant stakeholders

• Link between system structure and behavior over time




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• Roles for relevant stakeholders



• Plausible future targets, given existing momentum

• Life-course and intergenerational implications

• Sense of timing and trajectories of change (e.g., better-before-worse, or vice versa)

• Leadership for choosing a particular course

• Clear referent(s) for charting progress



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• Roles for relevant stakeholders



• Plausible future targets, given existing momentum

• Life-course and intergenerational implications

• Sense of timing and trajectories of change (e.g., better-before-worse, or vice versa)

• Leadership for choosing a particular course

• Clear referent(s) for charting progress



• Experiments to test policy leverage (alone and in combination)

• Trade-offs between short and long-term consequences

• Possible unintended effects

• Alignment of multiple actors

• Visceral and emotional learning about how dynamic systems function (i.e., better mental models)

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Essential Elements for System Change VenturesLimitations of Conventional Alternatives


Conventional Approaches

Limitations


Navigational Goals & Framework for

Charting Progress

Means for Prioritizing Actions & Impetus to

Implement Them

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Limitations


• Logic models

• Statistical models

• Ad hoc research and evaluation studies

• Processes of change in dynamic systems tend to be counterintuitive

• “Contextual” factors have strong influences, but are not well defined

• Statistical models exclude important factors due to lack of precise measures; they also focus on correlation not causality

• Barriers to learning in dynamic systems prevent accurate interpretation of research/evaluation data


Charting Progress


Implement Them

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Limitations


• Logic models



• Processes of change in dynamic systems tend to be counterintuitive• “Contextual” factors have strong influences, but are not well defined• Statistical models exclude important factors due to lack of precise measures; they also focus on correlation, not causality• Barriers to learning in dynamic systems prevent accurate interpretation of research/evaluation data


Charting Progress

• Forecasting models

• Best-of-the-best

• Wishful thinking

• Forecasts tend to be linear extrapolations of the past

• Best-of-the-best ignores different histories and present circumstances

• Wishful targets can do more harm than good


Implement Them

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Limitations


• Logic models



• Processes of change in dynamic systems tend to be counterintuitive• “Contextual” factors have strong influences, but are not well defined• Statistical models exclude important factors due to lack of precise measures; they also focus on correlation, not causality• Barriers to learning in dynamic systems prevent accurate interpretation of research/evaluation data


Charting Progress

• Forecasting models

• Best-of-the-best

• Wishful thinking

• Forecasts tend to be linear extrapolations of the past• Best-of-the-best ignores different histories and present circumstances• Wishful targets can do more harm than good


Implement Them

• Ranking by burden and/or cost effectiveness

• Health impact assessment

• Comparing importance vs. changeability

• Organizational will to fund

• Coalition-building

• Focus on current burden obscures root causes

• Cost effectiveness often ignores dynamic complexity

• HIA lacks explicit connection between structure and behavior

• Funding drives actions, which cease after funding stops

• Coalitions are not naturally well aligned and thus avoid tough questions; they are poorly suited for implementing complex, long-term initiatives

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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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How might conventional approaches to planning and evaluation reinforce the status quo?

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Scott JC. Seeing like a state: how certain schemes to improve the human condition have failed. New Haven, CT; 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

-- James Scott

0

1,000

2,000

3,000

4,000

5,000

1900 1910 1920 1930 1940 1950 1960 1970 1980 1990

Num

ber o

f Cig

aret

tes

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SpecializationA Proven Problem Solving Approach

• Identify disease

• Determine causes

• Develop and test interventions

• Implement programs and policies

• Repeat steps 1-4, as necessary!

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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, especially with regard to inequalities

A

C

BD

E

A B C D EIssue Organizations

Neighborhood

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Dangers of Getting Too Specific

Krug EG, World Health Organization. World report on violence and health. Geneva: World Health Organization, 2002.

Conventional problem solving proliferates problems

Opens a self-reinforcing niche for professional problem solvers

Obscures patterns that transcend any specific problem (e.g., nonviolence is entirely neglected)

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Examples of Nonviolent Action

Albert Einstein Institution. Applications of nonvilolent action. Albert Einstein Institution, 2001.

Powers RS, Vogele WB, Kruegler C, McCarthy RM. Protest, power, and change: an encyclopedia of nonviolent action from ACT-UP to women's suffrage. New York: Garland Pub., 1997.

Dismantling dictatorships

Blocking coups d’état

Defending against foreign invasions and occupations

Providing alternatives to violence in extreme ethnic conflicts

Challenging unjust social and economic systems

Developing, preserving and extending democratic practices, human rights, civil liberties, and freedom of religion

Resisting genocide

“A phenomenon that cuts across ethnic, cultural, religious, geographic,

socioeconomic and other demographic lines.”

-- Albert Einstein Institution

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Systems Archetype

Fixes That Fail

Kim DH. Systems archetypes at a glance. Cambridge, MA: Pegasus Communications, Inc., 1994.

-

FixProblemSymptom

+

-

UnintendedConsequence

+

Delay+

+

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In Public Health Vocabulary

Fixes That Fail

Kim DH. Systems archetypes at a glance. Cambridge, MA: Pegasus Communications, Inc., 1994.

+

TargetedResponse

HealthProblem -

-

Exclusions

+

Delay+

+

What issues tend to be excluded?

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Misleading Framing Assumptions

• Stepwise progress will lead to system wide improvement

• Focus on the events

• Everything that happens must have a cause

• That cause must be close in time and space– Instantaneous impacts

– Causality runs one-way

– Independence

– Impacts are linear and constant

Richmond B, Peterson S, High Performance Systems Inc. An introduction to systems thinking. Hanover NH: High Performance Systems, 1997.

These assumptions overlook non-local forces of change, such as feedback, accumulation, delay, and non-linear response

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Wickelgren I. How the brain 'sees' borders. Science 1992;256(5063):1520-1521.

How Many Triangles Do You See?

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Boundary Judgments(System of Reference)

Observations(Facts)

Evaluations(Values)

Ulrich W. Boundary critique. In: Daellenbach HG, Flood RL, editors. The Informed Student Guide to Management Science. London: Thomson; 2002. p. 41-42. <http://www.geocities.com/csh_home/downloads/ulrich_2002a.pdf>.

Ulrich W. Reflective practice in the civil society: the contribution of critically systemic thinking. Reflective Practice 2000;1(2):247-268. http://www.geocities.com/csh_home/downloads/ulrich_2000a.pdf

Boundary CritiqueCreating a new theory is not like destroying an old barn and erecting a skyscraper in its place. It is rather like climbing a mountain, gaining new and wider views, discovering unexpected connections

between our starting point and its rich environment.

-- Albert Einstein

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Boundary CritiqueEqualizing Experts and Ordinary Citizens

• “Professional expertise does not protect against the need for making boundary judgements…nor does it provide an objective basis for defining boundary judgements. It’s exactly the other way round: boundary judgements stand for the inevitable selectivity and thus partiality of our propositions.

• It follows that experts cannot justify their boundary judgements (as against those of ordinary citizens) by referring to an advantage of theoretical knowledge and expertise.

• When it comes to the problem of boundary judgements, experts have no natural advantage of competence over lay people.”

Ulrich W. Reflective practice in the civil society: the contribution of critically systemic thinking. Reflective Practice 2000;1(2):247-268.

-- Werner Ulrich

Syndemics

Prevention Network

“You Can Argue with Einstein”

Yankelovich D. Coming to public judgment: making democracy work in a complex world. 1st ed Syracuse, NY: Syracuse University Press, 1991. p. 220.

“For certain purposes, public judgment should

carry more weight than expert opinion – and not simply

because the majority may have more political power than

the individual expert but because the public’s claim to

know is actually stronger than the experts’...the judgment

of the general public can, under some conditions, be

equal or superior in quality to the judgment of experts

and elites who possess far more information, education,

and ability to articulate their views.”

-- Daniel Yankelovich

Syndemics

Prevention Network

Ulrich W. Reflective practice in the civil society: the contribution of critically systemic thinking. Reflective Practice 2000;1(2):247-268. http://www.geocities.com/csh_home/downloads/ulrich_2000a.pdf

Boundary Critique

Syndemics

Prevention Network

Core Public Health Functions Under a Syndemic Orientation

System Dynamics

SocialNavigation

POLICYDEVELOPMENT

ASSESSMENT

ASSURANCE

NetworkAnalysis

CategoricalOrientationSyndemic

Orientation

Syndemics

Prevention Network

What causes the behaviors we observe?

Syndemics

Prevention Network

System-as-Cause

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

Meadows DH. Leverage points: places to intervene in a system. Sustainability Institute, 1999. Available at <http://www.sustainabilityinstitute.org/pubs/Leverage_Points.pdf>.

Richardson GP. Feedback thought in social science and systems theory. Philadelphia, PA: University of Pennsylvania Press, 1991.


Syndemics

Prevention Network

Basic Problem Solving Orientations

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

Event Oriented View

Problem Results

Goals

Situation

Decision

“Side Effects”

Feedback View

Goals

Environment

Actions

Goals ofOthers

Actions ofOthers

“Side Effects”

Delay Delay

Delay

Delay

DelayDelay

Delay

Delay

Delay

Delay

Delay

Delay

Syndemics

Prevention Network

Time Series Models

Describe 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 Simulation Models

Anticipate future trends, and find policies that maximize chances

of a desirable path

Tools for Policy Analysis

Syndemics

Prevention Network

Different Modeling Approaches For Different Purposes

Logic Models(flowcharts, maps or

diagrams)

System Dynamics(causal loop diagrams, stock-flow

structures, 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, better before worse)

• Test dynamic hypotheses through simulation studies

• Make accurate forecasts of key variables

• Focus on precision of point predictions and confidence intervals

Syndemics

Prevention Network

Looks Reasonable, But How Much Will it Take, and What’s the Expected Benefit?

Source: Bob Goodman, University of Pittsburgh

Syndemics

Prevention Network

“A symbolic instrument made of a number of methods and techniques

borrowed from very different disciplines…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

-- Joèl de Rosnay

Looking Through the Macroscope

Can SD simulation models provide practical macroscopes for

planning and evaluating health policy?

Syndemics

Prevention Network

Milstein B, Homer J. The dynamics of upstream and downstream: why is so hard for the health system to work upstream, and what can be done about it? CDC Futures Health Systems Workgroup; Atlanta, GA; 2003.

TertiaryPrevention

SecondaryPrevention

PrimaryPrevention

TargetedProtection

Society's HealthResponse

Demand forresponse

PublicWork

SaferHealthierPeople Becoming

vulnerable

Becoming saferand healthier

VulnerablePeople Becoming

afflicted

Afflictedwithout

Complications Developingcomplications

Afflicted withComplications

Dying fromcomplications

Health System Dynamics

Adverse LivingConditions

GeneralProtection

Milstein B, Homer J. The dynamics of upstream and downstream: why is so hard for the health system to work upstream, and what can be done about it? CDC Futures Health Systems Work Group; Atlanta, GA; December 3, 2003.

Gerberding JL. CDC's futures initiative. Atlanta, GA: Public Health Training Network; April 12, 2004.


Syndemics

Prevention Network

Milstein B, Homer J. The dynamics of upstream and downstream: why is so hard for the health system to work upstream, and what can be done about it? CDC Futures Health Systems Workgroup; Atlanta, GA; 2003.

TertiaryPrevention

SecondaryPrevention

PrimaryPrevention

TargetedProtection


Demand forresponse

PublicWork

SaferHealthierPeople Becoming

vulnerable


VulnerablePeople Becoming

afflicted

Afflictedwithout

Complications Developingcomplications

Afflicted withComplications


Health System Dynamics


GeneralProtection

Milstein B, Homer J. The dynamics of upstream and downstream: why is so hard for the health system to work upstream, and what can be done about it? CDC Futures Health Systems Work Group; Atlanta, GA; December 3, 2003.

Gerberding JL. CDC's futures initiative. Atlanta, GA: Public Health Training Network; April 12, 2004.


“One major task that CDC is intending to address is balancing this portfolio of our health system so that there is much greater emphasis placed on health protection, on making sure that we invest the same kind of intense resources into keeping people

healthier or helping them return to a state of health and low vulnerability as we do to disease care and end of life care."

-- Julie Gerberding

Syndemics

Prevention Network

Balancing Two Major Areas of Emphasis

SaferHealthierPeople

VulnerablePeople

Afflictedwithout

ComplicationsAfflicted with

ComplicationsBecomingvulnerable


Becomingafflicted

Developingcomplications




Demand forresponse

GeneralProtection

TargetedProtection

PrimaryPrevention

SecondaryPrevention

TertiaryPrevention

Public Work

World of Providing…

• Education• Screening• Disease management • Pharmaceuticals• Clinical services• Physical and financial access• Etc…

Medical and Public Health Policy

DISEASE AND RISK MANAGEMENT

World of Transforming…

• Deprivation• Dependency• Violence• Disconnection• Environmental decay• Stress• Insecurity• Etc…

By Strengthening…

• Leaders and institutions• Foresight and precaution• The meaning of work• Mutual accountability• Plurality• Democracy• Freedom• Etc…

Healthy Public Policy & Public Work

DEMOCRATIC SELF-GOVERNANCE

Milstein B. Hygeia's constellation: navigating health futures in a dynamic and democratic world. Unpublished dissertation. Cincinnati, OH: Union Institute and University; October 7, 2005 (draft).

Syndemics

Prevention Network

Understanding Health as Public Work


VulnerablePeople

Afflictedwithout

Complications

Afflicted withComplicationsBecoming

vulnerable


Becomingafflicted





Demand forresponse

GeneralProtection

TargetedProtection

PrimaryPrevention

SecondaryPrevention

TertiaryPrevention

-

Public Work-

Vulnerable andAfflicted People

Fraction of Adversity,Vulnerability and AfflictionBorne by Disadvantaged

Sub-Groups (Inequity)

-

PublicStrength

Citizen Involvementin Public Life

Social Division

Syndemics

Prevention Network

Testing Dynamic Hypotheses

How can we learn about the consequences of actions in a system of this kind?

Could the behavior of this system be analyzed using conventional epidemoiological methods (e.g., logistic or multi-level regression)?


VulnerablePeople

Afflictedwithout

Complications

Afflicted withComplicationsBecoming

vulnerable


Becomingafflicted





Demand forresponse

GeneralProtection

TargetedProtection

PrimaryPrevention

SecondaryPrevention

TertiaryPrevention

-

Public Work-

Vulnerable andAfflicted People

Fraction of Adversity,Vulnerability and AfflictionBorne by Disadvantaged

Sub-Groups (Inequity)

PublicStrength

-

Citizen Involvementin Public Life

Social Division

Syndemics

Prevention Network

Even the best conceptual models can only be tested

and improved by relying on the learning feedback

through the real world…This feedback is very slow

and often rendered ineffective by dynamic

complexity, time delays, inadequate and ambiguous

feedback, poor reasoning skills, defensive reactions,

and the costs of experimentation. In these

circumstances simulation becomes the only reliable

way to test a hypothesis and evaluate the likely

effects of policies.

-- John Sterman

Why Simulate Proposed Policies?


Syndemics

Prevention Network

What Makes Learning So Difficult?


Syndemics

Prevention Network

Learning In and About Dynamic Systems

Dynamic 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.

Syndemics

Prevention Network

But We Can Create Virtual Worlds for Learning


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

-- John Sterman

Syndemics

Prevention Network

Selected CDC Projects Featuring System Dynamics Modeling

• Syndemics Mutually reinforcing afflictions

• Diabetes In an era of rising obesity

• ObesityLifecourse consequences of changes in caloric balance

• Infant HealthFetal and infant

• PolioReintroductions after eradication

• HypertensionImproving detection and control

Milstein B, Homer J. Background on system dynamics simulation modeling, with a summary of major public health studies. Atlanta, GA: Syndemics Prevention Network, Centers for Disease Control and Prevention; February 1, 2005. <http://www2.cdc.gov/syndemics/pdfs/SD_for_PH.pdf>.

• Grantmaking ScenariosTiming and sequence of outside assistance

• Upstream-Downstream EffortBalancing disease treatment with prevention/protection

• Healthcare ReformRelationships among cost, quality, equity, and health status

Syndemics

Prevention Network

Steps for Putting Maps in Motion

Enact PoliciesBuild power and organize actors to

establish chosen policies

Enact PoliciesBuild power and organize actors to

establish chosen policies

Choose AmongPlausible Futures

Discuss values and consider trade-offs

Choose AmongPlausible Futures

Discuss values and consider trade-offs

Learn About Policy Consequences

Test proposed policies, searching for ones that best

govern change

Learn About Policy Consequences

Test proposed policies, searching for ones that best

govern change

Run Simulation Experiments

Compare model’s behavior to expectations and/or data to

build confidence in the model

Run Simulation Experiments

Compare model’s behavior to expectations and/or data to

build confidence in the model

Convert the Map Into a Simulation Model

Formally quantify the hypothesis using allavailable evidence

Convert the Map Into a Simulation Model

Formally quantify the hypothesis using allavailable evidence

Create a Dynamic Hypothesis

Identify and map the main causal forces that

create the problem

Create a Dynamic Hypothesis

Identify and map the main causal forces that

create the problem

Identify a Persistent Problem

Graph its behavior over time

Identify a Persistent Problem

Graph its behavior over time

Homer JB. Why we iterate: scientific modeling in theory and practice. System Dynamics Review 1996;12(1):1-19.

Syndemics

Prevention Network

Two Examples

Syndemics

Prevention Network

Navigating Diabetes Futures

The Power of “What if…” Questions

Syndemics

Prevention Network

CDC Diabetes System Modeling ProjectDiscovering Dynamics Through Action Labs

Jones AP, Homer JB, Murphy DL, Essien JDK, Milstein B, Seville DA. Understanding diabetes population dynamics through simulation modeling and experimentation. American Journal of Public Health 2006;96(3):488-494.

Syndemics

Prevention Network

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

Syndemics

Prevention Network

System Dynamics Modeling SupportsNavigational Policy Dialogues

Prevalence of Diagnosed Diabetes, US

0

10

20

30

40

1980 1990 2000 2010 2020 2030 2040 2050

Mill

ion

pe

op

le

HistoricalData

Markov Model 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.


Markov Forecasting Model

Simulation Experiments

in Action Labs

Trend is not destiny!

Syndemics

Prevention Network

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

Syndemics

Prevention Network

20

30

40

50

60

70

1980 1985 1990 1995 2000 2005 2010

Pe

op

le w

ith

dia

gn

ose

d d

iab

ete

s p

er

1,0

00

Reported Simulated

Status Quo

Meet Detection Objective (5-4)

Meet Onset Objective (5-2)

HP 2010 Objective (5-3)

HP 2000 Objective

History and Futures for Diabetes PrevalenceReported Trends, HP Objectives, and Simulation Results

A

B

C

D

E

F

G

H

I

Syndemics

Prevention Network

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

Syndemics

Prevention Network

Simulations for Learning in Dynamic Systems

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


Sterman JD. Learning from evidence in a complex world. American Journal of Public Health 2006;96(3):505-514.

Multi-stakeholder Dialogue

Dynamic Hypothesis (Causal Structure) Plausible Futures (Policy Experiments)Deaths per Population

0.0035

0.003

0.0025

0.002

0.0015

1980 1990 2000 2010 2020 2030 2040 2050

Time (Year)

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

Syndemics

Prevention Network

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

Syndemics

Prevention Network

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


Syndemics

Prevention Network

Diabetes System Modeling ProjectWhere is the Leverage for Health Protection?

People withUndiagnosed,Uncomplicated

Diabetes

People withDiagnosed,

UncomplicatedDiabetes

People withDiagnosed,Complicated

Diabetes

DiagnosingUncomplicated

Diabetes

People withUndiagnosedPreDiabetes

People withDiagnosed

PreDiabetes

DiagnosingPreDiabetes

DevelopingComplications from

People withUndiagnosed,Complicated

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

PreDiabetesTesting for

Access toPreventive Health

Services Testing forDiabetes

PreDiabetesOnset


Recovering fromPreDiabetes Diabetes

Onset

Dying fromComplications

DevelopingComplications

This larger view takes us beyond standard epidemiological models and most intervention programs

Conventional Model Boundary

Syndemics

Prevention Network

Integrating the Best of Diverse Information Sources

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

Syndemics

Prevention Network

Diabetes System Modeling ProjectConfirming the Model’s Fit to History

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).

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

Syndemics

Prevention Network

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

Syndemics

Prevention Network

The Growth of Diabetes Prevalence Since 1980 has been Driven by Growth in Obesity Prevalence

Obese Fraction and Diabetes per Thousand1300.7

850.35

400

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

Diabetes Prevalenc

e

Obesity Prevalenc

e

Risk multiplier on diabetes onset from obesity = 2.6

CDC Diabetes System Modeling Project

Syndemics

Prevention Network

Prevalence=92 AND RISING

Although Obesity May Increase Little After 2006, Diabetes Keeps Growing Robustly for Another 20-25 Years

Obese Fraction and Diabetes per Thousand1300.7

850.35

400

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

Diabetes Prevalenc

e

Obesity Prevalenc

e

Diabetes prevalence keeps growing after obesity stops

WHY?

With high (even if flat) onset, prevalence tub keeps filling

until deaths (4-5%/yr)=onset

Onset=6.3 per thou Estimated 2006

values

Death=3.8 per thou

Prevalence=92 / thou

Risk multiplier on diabetes onset from obesity = 2.6


Syndemics

Prevention Network

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 significant disparities

Syndemics

Prevention Network

Impact of Prevalence Growth on Unhealthy DaysDiabetes Management: Past and One Plausible Future

Unhealthy Days per Thou and Frac ManagedObese Fraction and Diabetes per Thousand1300.7

850.35

400

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

Diabetes Prevalence

Obesity Prevalence

5000.65

25001980 1990 2000 2010 2020 2030 2040 2050

3750.325

Unhealthy Daysfrom Diabetes

Managed fraction

Diabetes prevalence keeps growing after obesity stops

If disease management gains end, the burden grows

Reduction in unhealthy days per complicated case if

conventionally managed: 33%; if intensively managed: 67%


Syndemics

Prevention Network

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 OutputModel Output

Model Output

Fewer dying every year

Syndemics

Prevention Network

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

the Burden of Diabetes

Great Progress in Reducing the Burden

for the Average Person with Diabetes

Great 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 person Held at BayOverall, Total Burden per person Held at Bay

Syndemics

Prevention Network


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

Syndemics

Prevention Network

A Sequence of What-if Simulations


125

100

75

501980 1990 2000 2010 2020 2030 2040 2050

Monthly Unhealthy Days from Diabetes per Thou500

450

400

350

300

250

1980 1990 2000 2010 2020 2030 2040 2050

Base

Base

Start with the base case or “status quo”: no improvements in diabetes management or prediabetes management after 2006


Syndemics

Prevention Network

Further Increases in Diabetes Management


125

100

75

501980 1990 2000 2010 2020 2030 2040 2050


450

400

350

300

250

1980 1990 2000 2010 2020 2030 2040 2050

Base

Diab mgt Base

More people living with diabetes

Keeping the burden at bay for nine years longer

Diab mgt

Increase fraction of diagnosed diabetes getting managed from 58% to 80% by 2015. (No change in the mix of conventional and intensive.)

What do you think will happen?

Diabetes mgmt does nothing to slow the growth of prevalence—in

fact, it increases it. As soon as diabetes mgmt stops improving, unhealthy days start to grow as

fast as prevalence.


Syndemics

Prevention Network

A Huge Push for Prediabetes Management


125

100

75

50

1980 1990 2000 2010 2020 2030 2040 2050


450

400

350

300

250

1980 1990 2000 2010 2020 2030 2040 2050

Base

PreD mgmt

Base

PreD mgmt

The improvement is relatively modest—the growth is not stopped

Increase fraction of prediabetics getting managed from 6% to 32% by 2015. (Half of those under intensive mgmt by 2015.)

No increase in diabetes mgmt. What do you think will happen?

Diabetes onset rate reduced 12% relative to base run. Not nearly enough to offset the

excess onset due to high obesity. By 2050, diabetes prevalence reduced only 9%

relative to base run.


Syndemics

Prevention Network

Managing Prediabetes and Reducing Obesity

The more you reduce obesity, the sooner you stop the growth in diabetes—and the

more you bring it down

… Same with the burden of diabetes


125

100

75

50

1980 1990 2000 2010 2020 2030 2040 2050


450

400

350

300

250

1980 1990 2000 2010 2020 2030 2040 2050

Base

PreD mgmt

PreD & Ob 25%

PreD & Ob 18%

Base

PreD mgmt

PreD & Ob 18%

PreD & Ob 25%

What do you think will happen if, in addition to PreD mgmt, obesity is reduced moderately by 2030? What if it is reduced even more?

Why is obesity reduction so powerful? Mainly because of its strong effect on onset

rate among prediabetics; but, also, because it reduces PreD prevalence itself. However,

achieving significant obesity reduction takes a long time.


Syndemics

Prevention Network

Intervening Effectively Upstream and Downstream


125

100

75

50

1980 1990 2000 2010 2020 2030 2040 2050


450

400

350

300

250

1980 1990 2000 2010 2020 2030 2040 2050

Base

PreD mgmt PreD mgmt

Base

PreD & Ob 25%

Pred & Ob 25%

All 3 --PreD & Ob 25% & Diab mgmt

All 3

With a combination of effective upstream and downstream interventions we could hold the burden

of diabetes nearly flat through 2050!

With pure upstream intervention, burden still grows for many years before turning around. What do you think will happen if we add the prior diabetes

mgmt intervention on top of the PreD+Ob25 one?

Downstream improvement acts quickly against burden but cannot continue forever.

Significant upstream gains are thus essential but will likely take 15+ years to achieve. A flat-

burden future is possible but requires simultaneous action on both fronts.


Syndemics

Prevention Network

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– Many new leaders emerging, but truly cross-functional

teams are still forming

• State health departments and their partners are now engaged– initial engagements in three states (Vermont, Minnesota,

California)

Syndemics

Prevention Network

That was an example focused on one health issue driven by another. Can we also use this technique to support

broader, syndemic thinking?

Syndemics

Prevention Network

Neighborhood Assistance Gamehttp://broadcast.forio.com/sims/syndemic2003/

Homer J, Milstein B. Syndemic simulation. Forio Business Simulations, 2003. Available at <http://broadcast.forio.com/sims/syndemic2003/>.

Syndemics

Prevention Network

Neighborhood Assistance Game

See also: Homer J, Milstein B. Syndemic simulation. Forio Business Simulations, 2003. Available at <http://broadcast.forio.com/sims/syndemic2003/>.

Syndemics

Prevention Network

Picture a Neighborhood Where…

• Conditions are not supportive of healthy living

• People are either afflicted by or at risk for mutually reinforcing health problems

• Citizen leaders are making an effort to alleviate afflictions and improve living conditions, but their power is limited

• More could be done with effective assistance from outside allies (e.g., philanthropy, government)

Syndemics

Prevention Network

Your Mission

Assure the conditions in all which people can be healthy

Health

LivingConditions

PublicStrength

• Improve health

• Enhance living conditions

• Build strength

Syndemics

Prevention Network

KeyRectangle: Stock/state variableBlue arrow: same-direction linkGreen arrow: opposite-direction linkCircled “B”: balancing causal loopCircled “R”: reinforcing causal loop

Dynamic Hypothesis Under What Conditions Do Syndemics Emerge?

How Can they be Controlled?

Adapted from: Homer J, Milstein B. Optimal decision making in a dynamic model of poor community health. Proceedings of the 37th Hawaii International Conference on System Science; Big Island, HI; January 5-8, 2004. Available at <http://csdl.computer.org/comp/proceedings/hicss/2004/2056/03/205630085a.pdf>.

Afflictionprevalence& burden

Adverseliving

conditions

Publicstrength

R1

At-risk fraction

Afflictioncross-impacts

Effort to alleviate andprevent affliction

B1a

Effort to improveliving conditions

B1b

Effort to build public strength

B2

Social disparityR2c

R2b

R2a

R3a

Public work fraction

United efforts

Divided efforts

R3b

Outside assistance toalleviate and prevent

affliction

Outside assistanceto improve living

conditions

Outside assistanceto build public strength

Magnitude ofameliorative efforts

R4a

R4b B3b

B3a

Syndemics

Prevention Network

About the Feedback Loops• Syndemic: Each affliction increases vulnerability to other afflictions, thereby amplifying the effect

of increases or decreases in the prevalence of individual afflictions.

• Citizen Response: Area residents make efforts to fight affliction and adverse living conditions in response to their prevalence, and to build greater public strength when it is perceived as low. Outside assistance may bolster such efforts.

• Social Disparity and Public Strength: Response efforts, especially those to improve adverse living conditions, are greater in magnitude when citizens are strong and unified through democratic public institutions. But public strength is hindered by social disparity, which, in turn, is made worse by the very afflictions and adverse living conditions the citizen efforts are trying to fight.

• Public Strength and Public Work: Public strength is also affected by the character of the response efforts themselves. When problems spread in an area with strong democratic institutions, the response tends to be more multi-faceted and elicit greater contributions from ordinary citizens in the form of "public work", a united process that reinforces public strength. Conversely, when problems spread in an area with weaker democratic institutions, problem-fighting efforts tend to be taken over by small groups of professionals who specialize in those problems, a divided process that ends up reinforcing the public’s weakness.

Present Strategy and Future Strength: Strategies for fighting afflictions or improving living conditions today may also affect people’s ability to mount similar efforts in the future. Outside assistance given to a weaker community for problem fighting may amplify the divided response and undermine the citizen’s internal response capability. Outside assistance to build public strength, however, may revitalize democratic institutions and prepare citizens to make a more united response.

R1

R3

R2

R4 B3

B1 B2

Syndemics

Prevention Network

Moving from the Map to a Model

• The model contains about two dozen parameters that may vary from case to case. These are constants describing

– the neighborhood’s baseline rates of affliction incidence and recovery, baseline strength and living conditions, and linkages among these variables

– effectiveness of programs (benefit per unit program effort)

– cost-effectiveness of assistance (program effort per unit of outside assistance)

Syndemics

Prevention Network

The neighborhood is relatively disadvantaged and divided, with significant afflictions, adverse living conditions, and low public strength:

• Affliction Prevalence baseline: 33%

• Adverse living conditions ‘Baseline’: 29%

• Public strength Baseline’: 25%

AssumptionsNeighborhood Characteristics

Homer J, Milstein B. Optimal decision making in a dynamic model of poor community health. 37th Hawaii International Conference on System Science; Big Island, HI; January 5-8, 2004. Available at <http://csdl.computer.org/comp/proceedings/hicss/2004/2056/03/205630085a.pdf>.

Syndemics

Prevention Network

• Affliction

Baseline at risk fraction 20%

Max additional at risk fraction from affliction cross impact 40%

Max additional at risk fraction from living conditions 75%

Baseline non-contagious incidence rate (% of at-risk) 10%/year

Baseline contagious incidence rate (% of at-risk contacted) 60%/year

Baseline affliction recovery rate 10%/year

Baseline affliction mortality rate 0.5%

Effect of max programs on affliction incidence 60%

Effect of max programs on affliction recovery 200%

Affliction prevalence causing full program demand 20%

Internal capacity for affliction pgms if no public strength 33%

Max boost in affliction programs from assistance 30%

Other Assumptions


Syndemics

Prevention Network

• Adverse living conditions

Living conditions improvement time 4 years

Living conditions erosion time 8 years

Effect of max programs on adverse living conditions 50%

Adverse living conditions causing full program demand 20%

Internal capacity for LC programs if no public strength 0%

Max boost in LC programs from assistance 50%

• Social Disparity

Affliction prevalence indicating 100% social disparity 50%

Adverse conditions prevalence indicating 100% disparity 50%

Weight on affliction (vs. living conditions) for social disparity 40%

Other Assumptions


Syndemics

Prevention Network

• Public Strength

Public strength development time 4 years

Public strength erosion time 8 years

Effect of max social disparity on public strength 50%

Effect of max public work on public strength 200%

Effect of max professional work on public strength 50%

Max public work fraction (when strength=100%) 80%

Weight on affliction (vs. conditions) programs for strength 50%

Max boost in public strength from assistance 30%

Other Assumptions


Syndemics

Prevention Network


Simulating the Development of a SyndemicOnset and Plausible Futures

12

10.5

9

7.5

6

4.5

0-20 -13.3 -6.7 0 6.7 13.3 20

YearGrowthNo AssistAll Afflict

Affliction Burden (Average Unhealthy Days per person/month)

2004 National Average = 6.1

Syndemic Onset

All Affliction Assistance

No Assistance

What Other Futures are Plausible?

Disguising a skewed distribution: 85% = 4.3 days/month

15% = 15.7 days/month

New Average = 10.14Even deeper disparity: 49% = 4.3 days/month51% = 15.7 days/month

Syndemics

Prevention Network

Planning Effective ResponsesEvaluating Policy Scenarios

Focus assistance on…

• Fighting affliction

• Improving adverse living conditions

• Building public strength

Different proportions

Different combinations

Different sequences

Syndemics

Prevention Network

Small Group Tasks

* Total must be 1.0

Scenario Name

Fraction of Assistance to…*

Affliction Living Conditions Public Strength

T0 T4 T8 T0 T4 T8 T0 T4 T8

No Assist 0 0 0 0 0 0 0 0 0

All Afflict 1 1 1 0 0 0 0 0 0

Affliction BurdenAverage Unhealthy Days per person/month (0-30)

12

10

8

6

40 4 8 12 16 20

Year

Adverse Living Conditions PrevalenceFraction of Living Conditions that Threaten Health (0-1)

0.4

0.3

0.2

0.1

00 4 8 12 16 20

Year

0.4

0.3

0.2

0.1

00 4 8 12 16 20

Year

Public StrengthPower of Citizens to Act Effectively (0-1)

Step 1: Define Intervention Scenarios

Step 2: Sketch the Consequences Over Time*

* Draw multiple lines on the same graph by labeling each

Affliction burden : All AfflictAffliction burden : No Assist



Syndemics

Prevention Network

Exploring the Consequences of Assistance Scenarios

Scenario Name

Fraction of Assistance to…*

Average Affliction Burden (T4-T20)

Improvement Over

Baseline (%)

Affliction Living Conditions Public Strength

T0 T4 T8 T0 T4 T8 T0 T4 T8

No Assist 0 0 0 0 0 0 0 0 0 10.20 --

All Afflict 1 1 1 0 0 0 0 0 0 8.52 16.0

All ALC 0 0 0 1 1 1 0 0 0

Holistic .4 .25 .15 .4 .25 .25 .2 .5 .6 8.61

Building .3 .3 .2 .3 .3 .5 .4 .4 .3

Syndemics

Prevention Network

• All formal models, including simulations, are wrong: incomplete and imprecise

• But some are better than others and capture more important aspects of the real world’s dynamic complexity

• A valuable model is one that can help us understand and anticipate better than we do with the unaided mind

How Should We Value Simulation Models?

Sterman JD. All models are wrong: reflections on becoming a systems scientist. System Dynamics Review 2002;18(4):501-531.

Meadows DH, Richardson J, Bruckmann G. Groping in the dark: the first decade of global modelling. New York, NY: Wiley, 1982.

Syndemics

Prevention NetworkSterman JD. Learning from evidence in a complex world. Amer J Public Health (in press), 2005.

Benefits and Challenges of Simulation

Benefits

• Formal means of evaluating options

• Experimental control of conditions

• Compressed time

• Complete, undistorted results

• Actions can be stopped or reversed

• Chance to rehearse plans, prepare for worse-before-better (vice-versa) patterns

• Set plausible objectives for the future

• Identify data needs and prioritize research

• Tests for extreme conditions

• Early warning of unintended effects

• Opportunity to assemble stronger support

• Visceral enagement and powerful group learning

Challenges

• Requires skilled facilitation, technical support, time, data, and upper management engagement for sustained effort

• Difficult to convey results persuasively to outside parties

• Often yields counterintuitive results that provoke defensive reactions

Syndemics

Prevention Network

“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?

Syndemics

Prevention Network

Serious GamesGame-based Learning for Social Change

Gudmundsen J. Movement aims to get serious about games. USA Today 2006 May 19. <http://www.usatoday.com/tech/gaming/2006-05-19-serious-games_x.htm>

Foresight and Governance Project. Serious games: improving public policy through game-based learning and simulation. Washington, DC: Woodrow Wilson International Center for Scholars 2002. <http://wwics.si.edu/subsites/game/index.htm>.

As the Serious Games Movement has

gained credability, funding is starting

to become available….Even

universities are supporting

development of serious games by

permitting students to produce these

games for academic credit.

-- USA Today

Syndemics

Prevention Network

A Specific Set of Thinking SkillsConventional Thinking Systems Thinking

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.

Syndemics

Prevention Network


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




Syndemics

Prevention Network



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.




Syndemics

Prevention Network





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. Engaging in active boundary critique.




Syndemics

Prevention Network





Microscopic Thinking: Focusing on the details in order to “know.”

Macroscopic Thinking: Seeing beyond the details to the context of relationships in which they are embedded. Engaging in active boundary critique.

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.




Syndemics

Prevention Network









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.




Syndemics

Prevention Network











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

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




Syndemics

Prevention Network











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).




Syndemics

Prevention Network

“We are as confused as ever, but on a higher level

and about more important things.”

Humor Consultants, Inc.

To Sum Up

Syndemics

Prevention Network

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

Documents

Syndemics Prevention Network Edinburgh Evaluation Summer School Edinburgh, Scotland June 6, 2006 Combining Innovations from Public Health, Systems Science,