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
Innovations in Planning & Evaluating System Change Ventures
Diane OrensteinDivision for Heart Disease and Stroke Prevention
Centers for Disease Control and [email protected]
Syndemics
Prevention Network
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.”
Syndemics
Prevention Network
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
Syndemics
Prevention Network
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
Syndemics
Prevention Network
• 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
Syndemics
Prevention Network
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
Syndemics
Prevention Network
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
Syndemics
Prevention Network
• 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
Syndemics
Prevention Network
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
Syndemics
Prevention Network
Essential Elements for System Change Ventures
Selected Elements of a Sound Strategy
Needed to Address…
Realistic Understanding of Causal Dynamics
• 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
Navigational Goals & Framework for Charting Progress
Means for Prioritizing Actions &
Impetus to Implement Them
Syndemics
Prevention Network
Essential Elements for System Change Ventures
Selected Elements of a Sound Strategy
Needed to Address…
Realistic Understanding of Causal Dynamics
• 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
• Roles for relevant stakeholders
• Link between system structure and behavior over time
Navigational Goals & Framework for Charting Progress
• 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
Means for Prioritizing Actions &
Impetus to Implement Them
Syndemics
Prevention Network
Essential Elements for System Change Ventures
Selected Elements of a Sound Strategy
Needed to Address…
Realistic Understanding of Causal Dynamics
• 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
• Roles for relevant stakeholders
• Link between system structure and behavior over time
Navigational Goals & Framework for Charting Progress
• 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
Means for Prioritizing Actions &
Impetus to Implement Them
• 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)
Syndemics
Prevention Network
Essential Elements for System Change VenturesLimitations of Conventional Alternatives
Selected Elements of a Sound Strategy
Conventional Approaches
Limitations
Realistic Understanding of Causal Dynamics
Navigational Goals & Framework for
Charting Progress
Means for Prioritizing Actions & Impetus to
Implement Them
Syndemics
Prevention Network
Essential Elements for System Change VenturesLimitations of Conventional Alternatives
Selected Elements of a Sound Strategy
Conventional Approaches
Limitations
Realistic Understanding of Causal Dynamics
• 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
Navigational Goals & Framework for
Charting Progress
Means for Prioritizing Actions & Impetus to
Implement Them
Syndemics
Prevention Network
Essential Elements for System Change VenturesLimitations of Conventional Alternatives
Selected Elements of a Sound Strategy
Conventional Approaches
Limitations
Realistic Understanding of Causal Dynamics
• 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
Navigational Goals & Framework for
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
Means for Prioritizing Actions & Impetus to
Implement Them
Syndemics
Prevention Network
Essential Elements for System Change VenturesLimitations of Conventional Alternatives
Selected Elements of a Sound Strategy
Conventional Approaches
Limitations
Realistic Understanding of Causal Dynamics
• 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
Navigational Goals & Framework for
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
Means for Prioritizing Actions & Impetus to
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
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
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
Syndemics
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*
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
Syndemics
Prevention Network
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.
Sterman JD. Business dynamics: systems thinking and modeling for a complex world. Boston, MA: Irwin McGraw-Hill, 2000.
Syndemics
Prevention Network
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
“All models are wrong, some are useful.”
-- George Box
Syndemics
Prevention Network
Simulations for Learning in Dynamic Systems
Morecroft JDW, Sterman J. Modeling for learning organizations. Portland, OR: Productivity Press, 2000.
Sterman JD. Business dynamics: systems thinking and modeling for a complex world. Boston, MA: Irwin McGraw-Hill, 2000.
Multi-stakeholder Dialogue
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
Syndemics
Prevention Network
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
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
• 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
Syndemics
Prevention Network
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
Syndemics
Prevention Network
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>.
Syndemics
Prevention Network
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
Syndemics
Prevention Network
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
Syndemics
Prevention Network
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
Syndemics
Prevention Network
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
Syndemics
Prevention Network
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.
Sterman JD. Business dynamics: systems thinking and modeling for a complex world. Boston, MA: Irwin McGraw-Hill, 2000.
Syndemics
Prevention Network
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.
Syndemics
Prevention Network
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
Syndemics
Prevention Network
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
Syndemics
Prevention Network
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
Syndemics
Prevention Network
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
Syndemics
Prevention Network
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).
Syndemics
Prevention Network
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
Syndemics
Prevention Network
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
Syndemics
Prevention Network
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.
Syndemics
Prevention Network
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.
Syndemics
Prevention Network
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.
Syndemics
Prevention Network
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
Prevention Network
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
Syndemics
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
SecondhandsmokeSmoker
Healthinessof diet
Extent ofphysical activity
Psychosocialstress
Diagnosis andcontrol
First-time CVevents and deaths
Access to andmarketing ofprimary care
Particulate airpollution
Utilization ofquality primary
care
Diabetes
Downward trend inCV event fatality
Quality of primarycare provision
Syndemics
Prevention Network
Reducing Risk Factor Prevalence
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
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
Particulate airpollution
Utilization ofquality primary
care
Tobacco taxes andsales/marketing
regulations Smoking bans atwork and public
places
Diabetes
Junk food taxes andsales/marketing
regulations
Downward trend inCV event fatality
Quality of primarycare provision
Syndemics
Prevention Network
Adding Up the Disease Costs
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
Costs from first-time CVand other risk factor
complications and fromutilization of services
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
Particulate airpollution
Utilization ofquality primary
care
Tobacco taxes andsales/marketing
regulations Smoking bans atwork and public
places
Diabetes
Junk food taxes andsales/marketing
regulations
Downward trend inCV event fatality
Quality of primarycare provision
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
Syndemics
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
Syndemics
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
Syndemics
Prevention Network
Simulation Framework
Smoking
Obesity
Secondhandsmoke
Healthinessof diet
Extent ofphysical activity
Psychosocialstress
Diagnosisand control
First-time CVevents and
deaths
Access to and marketingof smoking quit products
and services
Access to andmarketing of mental
health services
Sources ofstress
Access to andmarketing of healthy
food options
Access to andmarketing of physical
activity options
Access to andmarketing of weight
loss services
Access to andmarketing ofprimary care
Particulate airpollution
Utilization ofquality primary
care
Tobacco taxes andsales/marketing
regulations
Smoking bans atwork and public
places
Junk food taxes andsales/marketing
regulations
Quality of primarycare provision
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
Extent ofphysical activity
Psychosocialstress
Diagnosisand control
First-time CVevents and
deaths
Access to and marketingof smoking quit products
and services
Access to andmarketing of mental
health services
Sources ofStress
Access to andmarketing of healthy
food options
Access to andmarketing of physical
activity options
Access to andmarketing of weight
loss services
Access to andmarketing ofprimary care
Particulate airpollution
Utilization ofquality primary
care
Tobacco taxes andsales/marketing
regulations
Smoking bans atwork and public
places
Junk food taxes andsales/marketing
regulations
Quality of primarycare provision
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.
Syndemics
Prevention Network
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
Syndemics
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
Syndemics
Prevention Network
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>.
Syndemics
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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.
Syndemics
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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>.
Syndemics
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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
Syndemics
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• 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.
“All models are wrong, some are useful.”
-- 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.
Syndemics
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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.
Syndemics
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
Prevention Network
CDC Diabetes System Modeling ProjectDiscovering Dynamics Through State-based Action Labs & Models
Syndemics
Prevention Network
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>
Syndemics
Prevention Network
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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Prevention Network
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
Syndemics
Prevention Network
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
-