Syndemics Prevention Network Overview of System Dynamics Simulation Modeling Systems Thinking and Modeling Workshop Office of Disease Prevention and Health

Syndemics

Prevention Network

Overview of System Dynamics Simulation Modeling

Systems Thinking and Modeling WorkshopOffice of Disease Prevention and Health Promotion

Bethesda, MDMay 8, 2006

Bobby MilsteinSyndemics Prevention Network

Centers for Disease Control and PreventionAtlanta, Georgia

[email protected]

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Prevention Network

Research Imperatives for Protecting Health

Gerberding JL. Protecting health: the new research imperative. Journal of the American Medical Association 2005;294(11):1403-1406.

Typical Current StateStatic view of problems that are studied in isolation

Proposed Future StateDynamic systems and syndemic approaches

"Currently, application of complex systems theories or syndemic science

to health protection challenges is in its infancy.“

-- Julie Gerberding

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Prevention Network

1999 2000 2001 2002 2003 2004 2005

System Change Initiatives Encounter Limitations of Logic Models and

Conventional Planning/Evaluation Methods

Diabetes Action Labs*

ODPHP Modelers Meeting

Upstream-Downstream Investments

Obesity Overthe Lifecourse*

Fetal & Infant Health Goal-Setting

Milestones in the Recent Use of System Dynamics Modeling at CDC

AJPH Systems

Issue

2006

CDC Evaluation Framework

Recommends Logic Models

SD Emerges as a Promising Methodology

Neighborhood Assistance

Game

HypertensionPrevention &

Control *

Syndemics Modeling

Science Seminars and Professional Development Efforts

* Dedicated multi-year budget

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Prevention Network

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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Understanding Dynamic ComplexityFrom a Very Particular Distance

“{System dynamics studies problems} from ‘a very particular distance', not so close as to be concerned with the action of a single individual, but not so far away

as to be ignorant of the internal pressures in the system.”

-- George Richardson

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.


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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 new trends, learn about policy consequences,

and set justifiable goals

Tools for Policy Analysis

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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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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

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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

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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.

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

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Understanding Health as Public Work

SaferHealthierPeople

VulnerablePeople

Afflictedwithout

Complications

Afflicted withComplicationsBecoming

vulnerable


Becomingafflicted

Developingcomplications




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)?

SaferHealthierPeople

VulnerablePeople

Afflictedwithout

Complications

Afflicted withComplicationsBecoming

vulnerable


Becomingafflicted

Developingcomplications




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

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

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).


“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

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.

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.

Why?

Where?

How?

Who?

What?

Markov Forecasting Model

Simulation Experiments

in Action Labs

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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.

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

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

“All models are wrong. Some are useful.”

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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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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?

Syndemics

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EXTRAS

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Prevention Network

Potential Users and Uses of Health SD Simulation Models

• Planners/Evaluators/Media: Chart Progress Toward Goals– Define a “status quo” future– Define alternative futures based on policy scenarios– Define types of information to be routinely collected – Track and interpret trajectories of change– Estimate how strong interventions must be to make a difference

• Researchers: Better Measurement and New Knowledge– Integrate diverse data sources into a single analytic environment – Infer properties of unmeasured or poorly measured parameters– Analyze historical drivers of change– Locate areas of uncertainty to be addressed in new research

• Policy Makers: Convene Multistakeholder Action Labs– Understand how a dynamically complex system functions– Discover short- and long-term consequences of alternative policies– Prepare for difficult patterns of change (e.g., worse-before-better)– Consider the cost effectiveness of alternative policies– Explore ways of combining and aligning policies for better results– Increase policy-makers’ motivation to act differently

• Others…

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Possible Roles for System Dynamics in Public HealthSD is especially well-suited for studying…

• Individual diseases and risk factorsExamining momentum and setting justifiable goals

• Life course dynamics Following health trajectories across life stages

• Mutually reinforcing afflictions (syndemics)Exploring interactions among related afflictions, adverse living conditions, and the public’s capacity to address them both

• Capacities of the health protection system Understanding how ambitious health ventures may be configured without overwhelming/depleting capacity--perhaps even strengthening it

• Value trade-offs Analyzing phenomena like the imbalance of upstream-downstream effort, growth of the uninsured, rising costs, declining quality, entrenched inequalities

• Organizational management Linking balanced scorecards to a dynamic understanding of processes

• Group model building and scenario planningBringing more structure, evidence, and insight to public dialogue and judgment

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Steps for Developing Dynamic Policy Models

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

Documents

Syndemics Prevention Network Overview of System Dynamics Simulation Modeling Systems Thinking and Modeling Workshop Office of Disease Prevention and Health