Syndemics Prevention Network NCCDPHP Cross-Division Evaluation Network Atlanta, GA January 29, 2008...

Syndemics

Prevention Network

NCCDPHP Cross-Division Evaluation NetworkAtlanta, GA

January 29, 2008

Roles for System Dynamics Simulation Modeling

Bobby Milstein Syndemics Prevention NetworkCenters for Disease Control and

[email protected]

Innovations in Planning & Evaluating System Change Ventures

Diane OrensteinDivision for Heart Disease and Stroke Prevention

Centers for Disease Control and [email protected]

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Left Unexamined…

• Singular “program” as the unit of inquiry (N=1 organizational depth)

• Dynamic aspects of program effectiveness (e.g., better-before-worse patterns of change)

• Democratic aspects of public health work (e.g., alignment among multiple actors, including those who are not professionals and who may be pursuing other goals)

• Evaluative aspects of planning (e.g., defining problems, setting priorities, developing options, selecting strategies)

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

Framework for Program Evaluation“Both a synthesis of existing evaluation practices

and a standard for further improvement.”

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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 State“Static view of problems that are studied in isolation”

Proposed Future State“Dynamic systems and syndemic approaches”

“Currently, application of complex systems theories or syndemic science to health protection challenges is in its infancy.”

-- Julie Gerberding, CDC Director

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Rationale for Innovation

• Enormity of the challenges (problems of greater scale, speed, diversity, novelty)

• Appreciation for the effectiveness as well as the limits of narrowly-bounded approaches

• Potential for comprehensive changes(global, multi-sectoral, infrastructural, intergenerational, root-causes)

• Threat of policy resistance

• Mismatch with conventional methods for planning/evaluating

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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.”

-- Clement Bezold

Seeing Beyond the Probable

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Average Number of Adult Unhealthy Days per Month

4

5

6

7

1993 1995 1997 1999 2001 2003 2005

Year

Public Health Systems Science Addresses Navigational Policy Questions

17% increase

Centers for Disease Control and Prevention. Health-related quality of life: prevalence data. National Center for Chronic Disease Prevention and Health Promotion, 2007. Accessed October 23, 2007 at <http://apps.nccd.cdc.gov/HRQOL/index.asp>.

Milstein B. Hygeia's constellation: navigating health futures in a dynamic and democratic world. Atlanta, GA: Centers for Disease Control and Prevention; Draft, 2007.

How?Why?

Where?

Who?

What?

2010 2025 2050

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Broad Dynamics of the Health Protection Enterprise

Prevalence of Vulnerability, Risk, or Disease

Time

HealthProtection

Efforts

-

B

Responsesto Growth

Resources &Resistance

-B

Obstacles

Broader Benefits& Supporters

R

ReinforcersPotentialThreats

The concepts and methods of policy evaluation must engage the basic features of this

dynamic and democratic system

The concepts and methods of policy evaluation must engage the basic features of this

dynamic and democratic system

Size of the Safer, Healthier

Population-

Prevalence of Vulnerability,

Risk, or Disease

B

Taking the Toll

0%

100%

R

Drivers ofGrowth

Values for Health & Equity

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

• Constructing credible knowledge without comparison/control groups

• Differentiating questions that focus on attribution vs. contribution

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

• Avoiding the pitfalls of professonalism (e.g., over-specialization, arrogance, reinforcement of the status quo)

• Harnessing the power of intersectoral and citizen-led public work

• Defining standards and values 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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“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?

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System Dynamics Was Developed to Address Problems Marked By Dynamic Complexity

Good at Capturing• Differences between short- and long-term consequences of an

action• Time delays (e.g., developmental period, time to

detect, time to respond)• Accumulations (e.g., prevalences, resources, attitudes)• Behavioral feedback (e.g., reactions by various actors)• Nonlinear causal relationships (e.g., threshold effects, saturation

effects)• 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, Industrial Dynamics, 1961

(“One of the seminal books of the last 20 years.” -- NY Times)

• Public policy applications starting late 1960s• Population health applications starting mid-1970s

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1999 2000 2001 2002 2003 2004 2005

System Change Initiatives Encounter Limitations of Logic Models and Conventional

Planning/Evaluation Methods

Diabetes Action Labs*

Upstream-Downstream Dynamics

Obesity Overthe Lifecourse*

Fetal & Infant Health

Milestones in the Recent Use of System Dynamics Modeling at CDC

AJPH Systems

Issue

2006

CDC Evaluation Framework

Recommends Logic Models

SD Identified as a Promising Methodology

Neighborhood Grantmaking

Game

National Health Economics & Reform

Syndemics Modeling*

* Dedicated multi-year budget

CVH in Context*

2007 2008

Science Seminars and Professional Development Efforts

Hygeia’s Constellation

Health System Transformation

Game*

SDR 50th Issue

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Learning In and About Dynamic Systems

• Unknown structure • Dynamic complexity• Time delays• Impossible experiments

Real World

InformationFeedback

Decisions

MentalModels

Strategy, Structure,Decision Rules

• Selected• Missing• Delayed• Biased• Ambiguous

• Implementation• Game playing• Inconsistency• Short term

• Misperceptions• Unscientific• Biases• Defensiveness

• Inability to infer dynamics from

mental models

• Known structure • Controlled experiments• Enhanced learning

Virtual World

Sterman JD. Learning in and about complex systems. System Dynamics Review 1994;10(2-3):291-330.


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A Model Is…

An inexact representation of the real thing

It helps us understand, explain, anticipate, and make decisions

“All models are wrong, some are useful.”

-- George Box


-- George Box

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Simulations for Learning in Dynamic Systems

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


Multi-stakeholder Dialogue

Dynamic Hypothesis (Causal Structure)

X Y

Plausible Futures (Policy Experiments)

Obese fraction of Adults (Ages 20-74)

0%

10%

20%

30%

40%

50%

1970 1980 1990 2000 2010 2020 2030 2040 2050

Fra

ctio

n o

f p

op

n 2

0-74

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Learning In and About Dynamic Systems

Benefits of Simulation

• Formal means of evaluating options

• Experimental control of conditions

• Compressed time

• Complete, undistorted results

• Actions can be stopped or reversed

• Tests for extreme conditions

• Early warning of unintended effects

• Opportunity to assemble stronger support

• Visceral engagement and learning

Complexity Hinders

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

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

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

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

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

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

-- John Sterman

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Time Series Models

Describe trends

Multivariate Stat Models

Identify historical trend drivers and correlates

Patterns

Structure

Events

Increasing:

• Depth of causal theory

• Robustness for longer-term projection

• Value for developing policy insights

• Degrees of uncertainty

Increasing:

• Depth of causal theory

• Robustness for longer-term projection

• Value for developing policy insights

• Degrees of uncertaintyDynamic Simulation Models

Anticipate new trends, learn about policy consequences,

and set justifiable goals

Tools for Policy Planning & Evaluation

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Different Modeling Approaches For Different Purposes

Logic Models(flowcharts, maps or

diagrams)

System Dynamics(causal loop diagrams, stock-flow structures,

simulation studies, action labs)

Forecasting Models (regression models, Monte Carlo 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

• Inspire action through visceral, game-based learning

• Make accurate forecasts of key variables

• Focus on precision of point predictions and confidence intervals

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

Milstein B, Chapel T, Renault V, Fawcett S. Developing a logic model or theory of change. Community Tool Box, 2002. Accessed April 9, 2003 at <http://ctb.ku.edu/tools/en/section_1877.htm>.

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Model Uses and Audiences

• Set Better Goals (Planners & Evaluators)

– Identify what is likely and what is plausible– Estimate intervention impact time profiles– Evaluate resource needs for meeting goals

• Support Better Action (Policymakers)

– Explore ways of combining policies for better results– Evaluate cost-effectiveness over extended time periods– Increase policymakers’ motivation to act differently

• Develop Better Theory and Estimates (Researchers)

– Integrate and reconcile diverse data sources– Identify causal mechanisms driving system behavior– Improve estimates of hard-to-measure or “hidden” variables

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An (Inter) Active Form of Policy Planning/Evaluation

System Dynamics is a methodology to…

• Map the salient forces that contribute to a persistent problem;

• Convert the map into a computer simulation model, integrating the best information and insight available;

• Compare results from simulated “What If…” experiments to identify intervention policies that might plausibly alleviate the problem;

• Conduct sensitivity analyses to assess areas of uncertainty in the model and guide future research;

• Convene diverse stakeholders to participate in model-supported “Action Labs,” which allow participants to discover for themselves the likely consequences of alternative policy scenarios

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Syndemic Orientation

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

boundaries of what is possible.”

-- Michael Resnick

EpidemicOrientation

Problems Among

People inPlaces

Over Time

BoundaryCritique

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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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The Weight of Boundary Judgments

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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Implications for Policy Planning and Evaluation

Insights from the Overview Effect

• Maintain a particular analytic distance

• Not too close to the details, but not too far as be insensitive to internal pressures

• Potential to anticipate temporal patterns (e.g., better before worse)

• Structure determines behavior

• Potential to avoid scapegoating or lionizing

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

Richmond B. Systems thinking: critical thinking skills for the 1990s and beyond. System Dynamics Review 1993;9(2):113-134. Available at <http://www.clexchange.org/ftp/documents/whyk12sd/Y_1993-05STCriticalThinking.pdf>.

White F. The overview effect: space exploration and human evolution. 2nd ed. Reston VA: American Institute of Aeronautics and Astronautics, 1998.

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Health Care & Public Health Agency Capacity

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

Population Flows

DiagnosedStage 1

Diabetics

Stage 2Diabetics

Progression ofDx S1 to S2

S2 deaths

High RiskNot

Prediabetic

UndiagnosedStage 1

Diabetics

Diagnosis ofS1 diabetes

Progression ofUndx S1 to S2

GeneralPopulation

BecomeHigh Risk

Rehab ofHigh Risk

UndiagnosedPrediabetic

DiagnosedPrediabetic

Diabetes onsetfrom Undx PreD

Diabetes onsetfrom Dx PreD

Diagnosis ofPrediabetes

Prediabetesonset

Rehab ofUndx PreD

Rehab ofDx PreD

We Convened a Model-Scoping Group of 45 CDC professionals and epidemiologists in December 2003 to Explore the Full Range of Forces Driving Diabetes Behavior over Time

Personal Capacity

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

Metabolic Stressors

• Nutrition• Physical activity• Stress

• Baseline Flows

Health Care Utilization

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

• Percent of patients screened• Percent of people with diabetes under control

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

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Developing

Burden ofDiabetes

Total Prevalence(people with diabetes)

Unhealthy Days(per person with

diabetes)

Costs(per person with diabetes)

People withDiagnosedDiabetes

Diagnosis Deaths

abPeople withPrediabetes

Developing

DiabetesOnset

c

d

People withNormal

Blood SugarLevels

PrediabetesOnset

Recovering fromPrediabetes

e

DiabetesManagement

DiabetesDetection

Obesity in theGeneral

Population

PrediabetesDetection &

Management

People withUndiagnosed

Diabetes

Deaths

Diabetes Model Overview

Data sources: NHIS, NHANES, BRFSS, Census, Vital statistics, Clinical studies, Cost studies

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Diabetes Model Overview

Developing

Burden ofDiabetes

Total Prevalence(people with diabetes)

Unhealthy Days(per person with

diabetes)

Costs(per person with diabetes)

People withDiagnosedDiabetes

Diagnosis Deaths

abPeople withPrediabetes

Developing

DiabetesOnset

c

d

People withNormal

Blood SugarLevels

PreDiabetesOnset

Recovering fromPreDiabetes

e

DiabetesManagement

DiabetesDetection

Obesity in theGeneral

Population

PrediabetesDetection &

Management

People withUndiagnosed

Diabetes

Deaths

Standard boundary

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

Data sources: NHIS, NHANES, BRFSS, Census, Vital statistics, Clinical studies, Cost studies

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Syndemic Orientation

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

boundaries of what is possible.”

-- Michael Resnick

EpidemicOrientation

Problems Among

People inPlaces

Over Time

BoundaryCritique

Governing Dynamics

Ca

us

al

Ma

pp

ing

Plausible Futures

DynamicModeling

Syndemics

Prevention Network

Selected CDC Projects Featuring System Dynamics Modeling (2001-2008)

• Syndemics Mutually reinforcing afflictions

• Diabetes In an era of rising obesity

• ObesityLifecourse consequences of changes in caloric balance

• Infant HealthFetal and infant morbidity/mortality

• Heart Disease and StrokePreventing and managing multiple risks, in context

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

• Chronic Illness DynamicsHealth and economic scenarios for downstream and upstream reforms

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Preventing and Managing Risk Factors for Heart Disease and Stroke

Modeling the Local Dynamics of Cardiovascular Health

Homer J, Milstein B, Wile K, Pratibhu P, Farris R, Orenstein D. Modeling the local dynamics of cardiovascular health: risk factors, context, and capacity. Preventing Chronic Disease (in press).

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What is best allocation of

resources to eliminate the

burden, disparity & costs

of preventable CVD, recognizing

the spectrum of opportunities

in particular places & settings?

Over what time frame?

Guiding Questions

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ContributorsCore Design Team• CDC: Michele Casper, Rosanne Farris, Darwin Labarthe,

Marilyn Metzler, Bobby Milstein, Diane Orenstein• Austin: Cindy Batcher, Karina Loyo, Ella Pugo, Rick

Schwertfeger, Adolfo Valadez, Josh Vest, • NIH: David Abrams, Patty Mabry• Consultants: Jack Homer, Justin Trogdon, Kristina Wile

Organizational Sponsors• Austin/Travis County Health and Human Services Department• CDC Division for Heart Disease and Stroke Prevention• CDC Division of Adult and Community Health• CDC Division of Nutrition, Physical Activity, and Obesity• CDC Division of Diabetes Translation • CDC Office on Smoking and Health• CDC NCCDPHP Office of the Director• Indigent Care Collaborative (Austin, TX)• NIH Office of Behavioral and Social Science Research• RTI International• Sustainability Institute• Texas Department of Health

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Model Purpose and Rationale

• Purpose

– How do multiple risk factors and social factors combine to affect cardiovascular disease (CVD) endpoints and costs?

– How should we focus our policy efforts given limited resources?

• Rationale for systems modeling

– Capturing intermediate links so that possible “confounding factors” are included explicitly rather than ignored

– Non-additive effects when multiple risk factors are combined

– Time delays from change in incidence to change in prevalence (accumulation or “bathtub” effects)

The model described here is a work in progress funded by the CDC’s Division of Heart Disease and Stroke Prevention. We plan to finalize the

model’s equations and parameter values by February 2008.

The model described here is a work in progress funded by the CDC’s Division of Heart Disease and Stroke Prevention. We plan to finalize the

model’s equations and parameter values by February 2008.

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Intervention Approaches from “Upstream” to “Downstream”

Our model focuses on the prevention and control of risk factors that can lead to a first-time CVD event.

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Crafting Effective Intervention Strategies for Upstream Prevention in Context

• Concentrate on “upstream” challenge of minimizing risk, rather than the better understood “downstream” task of post-event care

• Local conditions affect people’s health status and their responses to perceived problems

• Local social and physical factors may be critical when characterizing the history—and plausible futures—of cardiovascular disease in a given city or region

• These aspects of local context are difficult to measure and too often excluded when planning and evaluating policies or programs

The CDC is partnering on this project with the Austin (Travis County), Texas, Dept. of Health and Human Services. The model is calibrated to represent the

overall US, but is informed by the experience and data of the Austin team, which has been supported by the CDC’s “STEPS” program since 2004.

The CDC is partnering on this project with the Austin (Travis County), Texas, Dept. of Health and Human Services. The model is calibrated to represent the

overall US, but is informed by the experience and data of the Austin team, which has been supported by the CDC’s “STEPS” program since 2004.

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UTILIZATION OF SERVICES• Behavioral change

• Social support

• Mental health

• Preventive health

Modified AndersonRisk Calculator

RISK FACTOR ONSET,PREVALENCE & CONTROL

• Hypertension

• High cholesterol

• Diabetes

• Obesity

• Smoking

• Secondhand smoke

• Air pollution exposure

ESTIMATED FIRST-TIME FATALAND NON-FATAL CVD EVENTS

• CHD (MI, Angina, Cardiac Arrest)

• Stroke

• Total CVD (CHD, Stroke, CHF, PAD)

COSTS (CVD & NON-CVD)ATTRIBUTABLE TO RISK FACTORS

LOCAL CONTEXT• Eating & activity options

• Smoking policies

• Socioeconomic conditions

• Environmental policies

• Health care options

• Support service options

• Media and events

Local capacity for leadership & organizing

LOCAL ACTIONS

NUTRITION, PHYSICALACTIVITY & STRESS

• Salt intake• Saturated/Trans fat intake• Fruit/Vegetable intake• Net caloric intake• Physical activity• Chronic stress

Preventing and Managing Risk Factors for CVDSector Diagram

DRAFT: October, 2007

Homer J, Milstein B, Wile K, Pratibhu P, Farris R, Orenstein D. Modeling the local dynamics of cardiovascular health: risk factors, context, and capacity. Preventing Chronic Disease in press.

Syndemics

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Data Sources for CVD Risk Modeling • Census

– Population, deaths, births, net immigration, health coverage

• AHA & NIH statistical reports – CVD events, deaths, and prevalence (CHD, stroke, CHF, PAD)

• National Health and Nutrition Examination Survey (NHANES) – Risk factor prevalences by age (18-29, 30-64, 65+) and sex (M, F)– Risk factor diagnosis and control (hypertension, high cholesterol, diabetes)

• Behavioral Risk Factor Surveillance System (BRFSS)– Diet & physical activity– Primary care utilization– Lack of needed emotional/social support

• Research literature– CVD risk calculator, and relative risks from SHS, air pollution, obesity, and inactivity– Medical and productivity costs of CVD and risk factors

• Questionnaires for CDC and Austin teams (expert judgment)– Potential effects of social marketing– Potential effects of expanded access to healthy food, activity, and behavioral services– Effects of behavioral services on smoking, weight loss, stress reduction– Relative risks of stress for high BP, high cholesterol, smoking, and obesity

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Prevention Network

CVD Risk Factors and Linkages

High cholesterol

Hypertension

Smoking

Obesity

Notobese

Obese

Not highcholest

Highcholest

Nondiab

Diabetic

Nonsmoker

Nonhypt

Hypert

SecondhandsmokeSmoker

Healthinessof diet

Extent ofphysical activity

Psychosocialstress

Diagnosis andcontrol

First-time CVevents and deaths

Particulate airpollution

Diabetes

Downward trend inCV event fatality

Syndemics

Prevention Network

Improving Primary Care

High cholesterol

Hypertension

Smoking

Obesity

Notobese

Obese

Not highcholest

Highcholest

Nondiab

Diabetic

Nonsmoker

Nonhypt

Hypert


Healthinessof diet


Psychosocialstress



Access to andmarketing ofprimary care


Utilization ofquality primary

care

Diabetes


Quality of primarycare provision

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Prevention Network

Reducing Risk Factor Prevalence

High cholesterol

Hypertension

Smoking

Obesity

Notobese

Obese

Not highcholest

Highcholest

Nondiab

Diabetic

Nonsmoker

Nonhypt

Hypert


Healthinessof diet


Psychosocialstress



Access to and marketingof smoking quit products

and services

Access to andmarketing of mental

health services

Adverse living andworking conditions

Access to andmarketing of healthy

food options

Access to andmarketing of physical

activity optionsAccess to and marketing of

weight loss services andmedical interventions Access to and

marketing ofprimary care



care

Tobacco taxes andsales/marketing

regulations Smoking bans atwork and public

places

Diabetes

Junk food taxes andsales/marketing

regulations



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Prevention Network

Adding Up the Disease Costs

High cholesterol

Hypertension

Smoking

Obesity

Notobese

Obese

Not highcholest

Highcholest

Nondiab

Diabetic

Nonsmoker

Nonhypt

Hypert


Healthinessof diet


Psychosocialstress



Costs from first-time CVand other risk factor

complications and fromutilization of services


and services


health services

Adverse living andworking conditions


food options


activity optionsAccess to and marketing of

weight loss services andmedical interventions Access to and

marketing ofprimary care



care


regulations Smoking bans atwork and public

places

Diabetes


regulations



Syndemics

Prevention Network

Developing a “Status Quo” Scenario

• A straightforward base case

– Assume no changes after 2000 in contextual factors or in risk factor inflow/outflow rates

– Any changes in risk prevalences after 2000 are due to “bathtub” adjustment and population aging

• Result: Past trends continue after 2000, but decelerate and level off

– Increasing obesity, high BP, and diabetes

– Decreasing smoking

– High cholesterol mixed bag by age and sex, flat overall

Obesity prevalence

0.4

0.3

0.2

0.1

0

1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040Time (Year)

Uncontrolled high BP prevalence

0.3

0.2

0.1

0

1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040Time (Year)

Smoking prevalence

0.3

0.2

0.1

0

1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040Time (Year)

The model is calibrated to reproduce data from NHANES 1988-94 and 1999-2004 on risk factor prevalences in the non-CVD population by age and sex.

The model is calibrated to reproduce data from NHANES 1988-94 and 1999-2004 on risk factor prevalences in the non-CVD population by age and sex.

Obese % of non-CVD popn

Uncontrolled hypertension %of non-CVD popn

Smoking % of non-CVD popn

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Prevention Network

Testing Alternative Scenarios

• Policy Tests– What if this intervention had

been fully implemented by 1997?

• Sensitivity Tests– How would the effects of a

particular policy change if we vary a more uncertain assumption across its plausible range?

Obesity prevalence0.4

0.3

0.2

0.1

01990 2005 2015 2030 2040

Time (Year)

1. Base Case2. Increase access to PA

Obesity prevalence0.4

0.325

0.25

0.175

0.11990 2003 2015 2028 2040

Time (Year)

Varying RR of Obesity w/o PA

Syndemics

Prevention Network

CVD RISK FACTORS DIRECTLY AFFECTED

INTERVENTION TARGETHigh BP High cholesterol Diabetes

Smoking1, SHS & Air pollution

Obesity

Access to primary care services2 √ √ √

Effectiveness of primary care services2 √ √ √

Sources of stress (poverty, crime, discrimination)

√ √ √ 3

Access to mental health services4 √ √ √ 3

Access to good diet √ √ √

Access to physical activity √ √ √ √

Access to weight loss services √

Access to smoking quit services √

Smoking in workplaces & in public places5 √

Air pollution √

Marketing of healthy behaviors6 √ √ √ √ √

Marketing of health & social services7 √ √ √ √ √

1 Reductions in smoking may lead, in turn, to some increase in eating and obesity; 2 Primary care improves diagnosis and control of affected conditions, not their prevalence; 3 Due to stress-eating; 4 Affects chronic stress; 5 Affects secondhand smoke; 6 Affects nutrition, PA, smoking; 7 Affects use of available services

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Prevention Network

Broader Categories of Policy Change

• Policies that decrease socioeconomic gaps– Educational policies– Fiscal policies– Skills training policies

• Policies that mitigate adverse conditions– Policies affecting the environment– Polices affecting the workplace– Policies enabling healthier behaviors– Policies affecting the medical system

Adapted from: Adler N, Stewart J. Reaching for a healthier life: facts on socioeconomic status and health in the USA.San Francisco, CA: John D. and Catherine T. MacArthur Research Network on Socioeconomic Status and Health 2007

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Prevention Network

Simulation Framework

Smoking

Obesity

Secondhandsmoke

Healthinessof diet


Psychosocialstress

Diagnosisand control

First-time CVevents and

deaths


and services


health services

Sources ofstress


food options


activity options

Access to andmarketing of weight

loss services




care


regulations

Smoking bans atwork and public

places


regulations


Hypertension, Highcholesterol, and

Diabetes

Syndemics

Prevention Network

POLICIES ENABLING HEALTHIER BEHAVIORS

Simulation Framework and Policy Space

EDUCATION POLICIES

FISCAL POLICIES

SKILLS TRAINING POLICIES

POLICIES AFFECTING THE ENVIRONMENT POLICIES AFFECTING

THE WORKPLACE

POLICIES AFFECTING THE MEDICAL SYSTEM

Smoking

Obesity

Secondhandsmoke

Healthinessof diet


Psychosocialstress

Diagnosisand control

First-time CVevents and

deaths


and services


health services

Sources ofStress


food options


activity options

Access to andmarketing of weight

loss services




care


regulations

Smoking bans atwork and public

places


regulations


Hypertension,High Cholesterol,

Diabetes

Syndemics

Prevention Network

Simulation Control Panel

Syndemics

Prevention Network

Three Illustrative Policies • Expand Access and Social Support

– Provide full access for all to healthy food, safe physical activity, primary care, and behavioral services

– Provide social supports to mitigate stress, reducing it 50% • Strengthen Primary Care and Promote Healthy Living

– Transform primary care to meet highest standards for prevention and control activities and referrals

– Strongly promote healthy eating, activity, no smoking, and use of primary care and behavioral services

• Fight Tobacco and Air Pollution– Tobacco control package: Raise taxes, police sales to minors, and

ban smoking in workplaces and public places

– Reduce particulate (PM 2.5) air pollution by 50%

The interventions are tested retroactively with implementation starting in 1995 and ramping up to full effectiveness by 1997, continuing unabated through 2040.The interventions are tested retroactively with implementation starting in 1995

and ramping up to full effectiveness by 1997, continuing unabated through 2040.

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Annual Disease Costs in 5 Illustrative ScenariosTotal Annual Risk Factor Complication Costs per Capita

Among the Never-CVD Population

Base Access & social support Strengthen primary care & promote healthy living

Fight tobacco & air pollution All of the above

Base Access & social support Strengthen primary care & promote healthy living

Fight tobacco & air pollution All of the above

2,000

1,750

1,500

1,250

1,0001990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040

do

llars

/(Y

ea

r*p

ers

on

)

Work in progress - for illustration only Work in progress - for illustration only

Syndemics

Prevention Network

Obesity, Uncontrolled Hypertension, and Smoking Five Illustrative Scenarios

Obese % of non-CVD popn0.4

0.3

0.2

0.1

01990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040

Base Access & social support Strengthen primary care &

promote healthy living Fight tobacco & air pollution All of the above

Base Access & social support Strengthen primary care &

promote healthy living Fight tobacco & air pollution All of the above

0.3

0.2

0.1

01990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040

Smoking % of non-CVD popn

0.3

0.2

0.1

0

1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040

Uncontrolled hypertension % of non-CVD popn

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Prevention Network

What are We Learning?

• Literature on risk factors and social determinants poses a challenge for modeling– Many studies skip causal links or don’t quantify effect sizes

• BRFSS offers reasonable proxies for tricky variables like stress and access

• Health departments are practically oriented and can help refine concepts and estimate effect sizes

• Policy analysts want us to model broadly despite the numerical uncertainties– Give more attention to how effectiveness of social interventions

may change over time (erosion, bandwagon effects)

• Take audience background into account when presenting concepts and intervention approaches

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Conceptual and Methodological Features of System Dynamics Modeling

Thinking dynamically• Move from events and

decisions to patterns of continuous behavior over time and policy structure

Thinking in circular causal /feedback patterns

• Self-reinforcing and self-balancing processes

• Compensating feedback structures and policy resistance

• Communicating complex nonlinear system structure

Thinking in stocks and flows• Accumulations are the

resources and the pressures on policy

• Policies influence flows

Modeling and simulation• Accumulating (and

remembering) complexity• Quantification (distinct from

measurement)• Rigorous (daunting) model

evaluation processes • Controlled experiments• Reflection

Richardson GP, Homer JB. System dynamics modeling: population flows, feedback loops, and health. NIH/CDC Symposia on System Science and Health; Bethesda, MD: August 30, 2007. Available at <http://obssr.od.nih.gov/Content/Lectures+and+Seminars/Systems_Symposia_Series/Systems_Symposium_Four/SEMINARS.htm>.

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A Specific Set of Thinking SkillsConventional Thinking Systems Thinking

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

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

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

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.

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

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

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

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

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

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

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

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

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

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Revisiting the Framework

Simulation Modeling Offers

• Support for multi-stakeholder dialogue

• A larger conception of the “program” context

• Another avenue for experimentation and visceral learning, with the need for comparison or control groups

• Ability to track interrelated indicators (both states and rates)

• An emphasis on pragmatism (learning through action)

“Steps in the framework are starting points for tailoring an evaluation to a particular public health effort at a particular time.”

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

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An Alternative Philosophical Tradition

Shook J. The pragmatism cybrary. 2006. Available at <http://www.pragmatism.org/>.

Addams J. Democracy and social ethics. Urbana, IL: University of Illinois Press, 2002.

West C. The American evasion of philosophy: a genealogy of pragmatism. Madison, WI: University of Wisconsin Press, 1989.

"Grant an idea or belief to be true…what concrete difference will its being true make in anyone's actual life?

-- William James

Pragmatism• Begins with a response to a perplexity or injustice

in the world• Learning through action and reflection• Asks, “How does this work make a difference?”

Positivism • Begins with a theory about the world• Learning through observation and falsification• Asks, “Is this theory true?”

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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Prevention Network

• All models, including simulations, are 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 Studies?

Artist: Rene Magritte

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.

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


-- George Box

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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?

Syndemics

Prevention Network

2007

Extramural funding for methodology and technology (NIH Roadmap)

Symposia series on system science and health (NIH/OBSSR and CDC/SPN; ~6,000 participants)

Conference on complexity approaches to population health (Univ of Michigan; ~250 participants)

NIH monograph, “Greater Than the Sum”

• CDC monograph, “Hygeia’s Constellation”

• CDC to hire directors for preparedness modeling and public health systems research

• Concept mapping of public health policy resistance (NIH/OBSSR and CDC/SPN)

• Historical examples of health system transformation (CDC Public Health Practice Council)

• Methodology to support CDC’s focus on “health protection…health equity” (PriceWaterhouseCoopers)

2008

• Summer training institute for system science and health (NIH/OBSSR and CDC/SPN)

2009

• Extramural funding for “Health System Change” (NIH and CDC?)

What’s on the Horizon for System Science & Health?

Syndemics

Prevention Network

For Further Information

• CDC Syndemics Prevention Network http://www.cdc.gov/syndemics

• NIH/CDC Symposia on System Science and Healthhttp://obssr.od.nih.gov/Content/Lectures+and+Seminars/Systems_Symposia_Series/SEMINARS.htm

• Recommended Reading

– AJPH theme issue on systems thinking and modeling (March, 2006)http://www.ajph.org/content/vol96/issue3/

• Sterman JD. Learning from evidence in a complex world. AJPH 2006;96(3):505-514.

• Midgley G. Systemic intervention for public health. AJPH 2006;96(3):466-472.

• Homer JB, Hirsch GB. System dynamics modeling for public health: background and opportunities. AJPH 2006;96(3):452-458.

– Sterman JD. A skeptic's guide to computer models. In: Barney GO, editor. Managing a Nation: the Microcomputer Software Catalog. Boulder, CO: Westview Press; 1991. p. 209-229. http://web.mit.edu/jsterman/www/Skeptic%27s_Guide.html

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

– Meadows DH, Robinson JM. The electronic oracle: computer models and social decisions. System Dynamics Review 2002;18(2):271-308.

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Forthcoming Report

Milstein B. Hygeia's constellation: navigating health futures in a dynamic and democratic world. Atlanta, GA: Centers for Disease Control and Prevention 2008.

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Prevention Network

EXTRAS

Syndemics

Prevention Network

CDC Diabetes System Modeling ProjectCharting Plausible Futures for HP 2010

Milstein B, Jones A, Homer J, Murphy D, Essien J, Seville D. Charting plausible futures for diabetes prevalence: a role for system dynamics simulation modeling. Preventing Chronic Disease 2007;4(3):1-8. Available at <http://www.cdc.gov/pcd/issues/2007/jul/06_0070.htm>

Syndemics

Prevention Network

CDC Diabetes System Modeling ProjectUnderstanding Population Dynamics

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

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CDC Diabetes System Modeling ProjectDiscovering Dynamics Through State-based Action Labs & Models

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CDC Obesity Dynamics Modeling ProjectExploring Historical Growth and Plausible Futures

Homer J, Milstein B, Dietz W, Buchner D, Majestic D. Obesity population dynamics: exploring historical growth and plausible futures in the U.S. 24th International Conference of the System Dynamics Society; Nijmegen, The Netherlands; July 26, 2006.

Centers for Disease Control and Prevention. The state of the CDC, fiscal year 2006. Atlanta, GA: CDC 2007. <http://www.cdc.gov/about/stateofcdc/index.htm>

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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. <http://csdl.computer.org/comp/proceedings/hicss/2004/2056/03/205630085a.pdf

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

CDC Syndemics ModelingNeighborhood Transformation Game

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Homer J, Hirsch G, Milstein B. Chronic illness in a complex health economy: the perils and promises of downstream and upstream reforms. System Dynamics Review 2007 (in press).

SD Society Health Policy Dynamics ModelingUpstream and Downstream Reforms

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Time 100: the people who shape our world. Time Magazine 2004 April 26.

Gerberding JL. CDC: protecting people's health. Director's Update; Atlanta, GA; July, 2007.

Gerberding JL. Health protectionomics: a new science of people, policy, and politics. Public Health Grand Rounds; Washington, DC: George Washington University School of Public Health and Health Services; September 19, 2007. Available at <http://www.kaisernetwork.org/health_cast/hcast_index.cfm?display=detail&hc=2349>

Centers for Disease Control and Prevention. Health system transformation: Office of Strategy and Innovation; September 28, 2007. <http://intradev.cdc.gov/od/osi/policy/healthSystems_overview.htm>.

CDC Leadership on Health System Transformation

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Mapping the Dynamics of Upstream and Downstream: Why is So Hard for the Health System to Work Upstream?

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.

Jackson DJ, Valdesseri R, CDC Health Systems Work Group. Health systems work group report. Atlanta, GA: Centers for Disease Control and Prevention, Office of Strategy and Innovation; January 6, 2004. <http://intranet.cdc.gov/od/futures/wrkgroup/stage_i/hswg.htm>

Milstein B, Homer J. Health system dynamics: mapping the drivers of population health, vulnerability, and affliction. Atlanta, GA: Syndemics Prevention Network; June 27 (work in progress), 2006.

Milstein B. Hygeia's constellation: navigating health futures in a dynamic and democratic world. Atlanta, GA: Centers for Disease Control and Prevention 2008.

Safer,Healthier

Population

VulnerablePopulation

Becomingvulnerable

Becoming nolonger vulnerable

Afflictedwithout

ComplicationsBecomingafflicted

Afflicted withComplications

Developingcomplications

Dying fromComplications

Effect onincidence

-

Effect onprogression

-

Effect oncomplications

-

Effect on livingconditions

Effect onvulnerabilityreduction

GeneralProtection

TargetedProtection

TertiaryPrevention

SecondaryPrevention

PrimaryPrevention

Vulnerable andAfflicted Population

Upstreamwork

Downstreamwork

Professionalconcern

Publicconcern

AdverseLiving

Conditions

-

PublicStrength

SocialDisparity

-

Citizen Involvementand Organizing

SocialDivision

-

Publicwork

Institutional/organizationalemphasis on disease rather

than vulnerability

-

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