Syndemics
Prevention Network
Systems Science and Community Change:Frameworks, Methods, and Goals
Advancing the Science of Community InterventionChicago, IL
October 26, 2009
Robin MillerMichigan State University
The findings and conclusions in this presentation are those of the author and do not necessarily represent the official position of the Centers for Disease Control and Prevention.
Bobby MilsteinSyndemics Prevention Network
Centers for Disease Control and [email protected]
http://www.cdc.gov/syndemics
Syndemics
Prevention Network
Systems Thinking
Systems Thinking
Systems Thinking
Systems Thinking is…
Thinking
SystemsThinking
Thinking
SystemsThinking
A Typeof Thinking
Thinking About
Systems
Thinking About
Systems&
Systems Approaches
/Methods
Application of
Systems Approaches
/Methods
The thinkingthat
surroundsApproach or
Field X The thinkingthat is a
subset of Approach or
Field X
SystemsApproaches& Methods
SA 1
M1
M2
SA 2M1
M2M3
SA…
M1
ApproachOr Field X
ApproachOr Field X
Systems Thinking
Systems
partswholes
relationships
Systems
partswholes
relationships
Syndemics
Prevention Network
Prevention Impacts Simulation Model (PRISM)• Represents multiple interacting risks and interventions for heart
disease, stroke, and related chronic diseases: medical, behavioral, social, environmental
• Begun in 2007, the model has expanded in scope but remains a work-in-progress
• Engaged subject matter experts from 12 organizations (N~30), and 100s of policy officials, including a deep collaboration with local leaders in Austin, Texas
• Integrates best available information in a single testable model to support prospective planning and evaluation
• Explores the likely effects of “local interventions” (i.e., changes in local options/exposures/services that affect behavior and/or health status)
– To what extent might adverse events and costs be reduced?
– How can policymakers balance interventions for best effect with limited resources?
References: Homer J, Milstein B, Wile K, Trogdon J, Huang P, Labarthe D, Orenstein D. Simulating and evaluating local interventions to improve cardiovascular health. Preventing Chronic Disease, 2009 (in press).
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 2008;5(2). Available at <http://www.cdc.gov/pcd/issues/2008/apr/07_0230.htm
Syndemics
Prevention Network
Prevention Impacts Simulation Model (PRISM)Core Contributors
System Dynamics Modelers• Jack Homer• Kris Wile
Economists• Justin Trogdon• Amanda Honeycutt
Project Coordinators• Bobby Milstein• Diane Orenstein
CDC partnered 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.`
CDC partnered 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.`
CDC & NIH Subject Matter ExpertsBishwa Adhikari, Nicole Blair, Kristen Betts, Peter Briss, David Buchner, Susan Carlson, Michele Casper, Tom Chapel, Janet Collins, Lawton Cooper, Michael Dalmat, Alyssa Easton, Joyce Essien, Roseanne Farris, Larry Fine, Janet Fulton, Deb Galuska, Kathy Gallagher, Judy Hannon, Jan Jernigan, Darwin Labarthe, Deb Lubar, Patty Mabry, Ann Malarcher, Michele Maynard, Marilyn Metzler, Rob Merritt, Latetia Moore, Barbara Park, Terry Pechacek, Catherine Rasberry, Michael Schooley, Nancy Williams, Nancy Watkins, Howell Wechsler
External Subject Matter ExpertsCynthia Batcher, Margaret Casey, Phil Huang, Kristen Lich, Karina Loyo, David Matchar, Ella Pugo, John Robitscher, Rick Schwertfeger, Adolpho Valadez
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Prevention Network
How are Practitioners Using PRISM?
Planning• Engage a wider circle of stakeholders
• Situate specialties within a system
• Prioritize interventions (given tradeoffs/synergies)
• Set plausible short- and long-term goals
Evaluating
• Trace intervention effects through direct, secondary, and summary measures
• Extend the time horizon for evaluative inquiry
• Establish novel referents for comparison (self-referential counter-factuals)
Several Local Versions
• Re-calibrated to areas with different demographics, histories, and current conditions
Users (~500)Customized Versions
• East Austin, Texas
• Mississippi Delta
• New Zealand Ministry of Health
• U.S. economic stimulus health initiative
Nat’l & State Stakeholders
• CDC Staff
• National Association of Chronic Disease Directors
• Directors of Public Health Education
• National Institutes of Health (NHLBI, OBSSR)
Users (~500)Customized Versions
• East Austin, Texas
• Mississippi Delta
• New Zealand Ministry of Health
• U.S. economic stimulus health initiative
Nat’l & State Stakeholders
• CDC Staff
• National Association of Chronic Disease Directors
• Directors of Public Health Education
• National Institutes of Health (NHLBI, OBSSR)
Syndemics
Prevention Network
Re-Directing the Course of ChangeQuestions for Navigating Health System Dynamics
Prevalence of Diagnosed Diabetes, United States
0
10
20
30
40
1980 1990 2000 2010 2020 2030 2040 2050
Mill
ion
pe
op
le
HistoricalData
Markov Model Constants• Incidence rates (%/yr)• Death rates (%/yr)• Diagnosed fractions(Based on year 2000 data, per demographic segment)
Honeycutt A, Boyle J, Broglio K, Thompson T, Hoerger T, Geiss L, Narayan K. A dynamic markov model for forecasting diabetes prevalence in the United States through 2050. Health Care Management Science 2003;6:155-164.
Jones AP, Homer JB, Murphy DL, Essien JDK, Milstein B, Seville DA. Understanding diabetes population dynamics through simulation modeling and experimentation. American Journal of Public Health 2006;96(3):488-494.
Markov Forecasting Model
Trend is not destiny
How?
Why?
Where?
Who?
What?
Simulation Experiments
in Action Labs
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Prevention Network
Time Series Models
Describe trends
Multivariate Statistical 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
• Leverage for change
Increasing:
• Depth of causal theory
• Robustness for longer-term projection
• Value for developing policy insights
• Degrees of uncertainty
• Leverage for changeDynamic Simulation Models
Anticipate new trends, learn about policy
consequences, and set justifiable goals
Selected Models for Policy Planning & Evaluation
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Prevention Network
Supporting Multi-Stakeholder Planning and Action
Which interventions to prioritize? Likely consequences?
Costs? Time-frame?
How to catalyze action?
Dynamic Hypothesis (Causal Structure)
Cardiovascularevents
Air pollutionexposure(PM 2.5)
Quality of acute andrehab care for
cardiovascular events
Use of qualitypreventive care
Use of weightloss services
by obese
Use of help servicesfor distress
Bans on smokingin public places
SmokingObesity
-Hypertension-High cholesterol
-Diabetes
Uncontrolledchronic disorders
Secondhandsmoke
Junk foodinterventions
(N=4)
Physical activityinterventions
(N=6)
Heart-unhealthy diet
Physicalinactivity Distress
Efforts to promoteprovision and use of
quality preventive care
Sodiumreduction
Trans fatreduction
Excesscalorie diet
Fruit &vegetable
interventions(N=3)
CVD deaths,disability,and costs
Excesssodium diet
Air pollutionreduction
Tobaccointerventions
(N=4)
Chronic Disorders
Other deaths and costsattributable to risk factors,
and costs of risk factormanagement
Total consequencecosts
System
Plausible Futures (Policy Experiments)
Dynamics
Years of Life Lost40 M
30 M
20 M
10 M
01990 2000 2010 2020 2030 2040
Sterman JD. Business dynamics: systems thinking and modeling for a complex world. Boston, MA: Irwin McGraw-Hill, 2000.
Sterman JD. Learning from evidence in a complex world. American Journal of Public Health 2006;96(3):505-514.
Homer JB. Why we iterate: scientific modeling in theory and practice. System Dynamics Review 1996;12(1):1-19.
Syndemics
Prevention Network
Models are…Inexact representations of the real thing They help 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
Sterman JD. All models are wrong: reflections on becoming a systems scientist. System Dynamics Review 2002;18(4):501-531. Available at <http://web.mit.edu/jsterman/www/All_Models.html>
Sterman J. A sketpic'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>
Over-relianceOver-reliance Under-relianceUnder-reliance
Syndemics
Prevention Network
Tobacco
Air Pollution
Stress
Healthy Food
Sodium
Trans fat
PhysicalActivity
WeightLoss
MentalHealthServices
PrimaryCare
Emergency & Rehab Care
BloodPressure
Cholesterol
ObesityHeart Disease & Stroke
Cancer
Health CareCost
Diabetes
The Popular (and Professional) View of Chronic Disease Challenges is Largely One Headline after Another
Alcohol
Sleep Arthritis
JunkFood
Syndemics
Prevention Network
PRISM Situates Multiple Medical, Behavioral, and Environmental Factors into a Single Set of Causal Pathways
Cardiovascularevents
Air pollutionexposure(PM 2.5)
Use of qualitypreventive care
SmokingObesity
-Hypertension-High cholesterol
-Diabetes
Uncontrolledchronic disorders
Secondhandsmoke
Heart-unhealthy diet
Physicalinactivity
Distress
Excesscalorie diet
CVD deaths,disability,and costs
Excesssodium diet
Chronic Disorders
Trans fatconsumption
Syndemics
Prevention Network
PRISM Situates Multiple Medical, Behavioral, and Environmental Factors into a Single Set of Causal Pathways
Cardiovascularevents
Air pollutionexposure(PM 2.5)
Use of qualitypreventive care
SmokingObesity
-Hypertension-High cholesterol
-Diabetes
Uncontrolledchronic disorders
Secondhandsmoke
Heart-unhealthy diet
Physicalinactivity
Distress
Excesscalorie diet
CVD deaths,disability,and costs
Excesssodium diet
Chronic Disorders
Other deaths and costsattributable to risk factors,
and costs of risk factormanagement
Total consequencecosts
Trans fatconsumption
Syndemics
Prevention Network
Cardiovascularevents
Air pollutionexposure(PM 2.5)
Use of qualitypreventive care
SmokingObesity
-Hypertension-High cholesterol
-Diabetes
Uncontrolledchronic disorders
Secondhandsmoke
unhealthy diet
Physicalinactivity
PHYSICAL ACTIVITYAccess, promotion,
school and childcare requirements
Distress
Help servicesfor distress
Excesscalorie diet
CVD deaths,disability,and costs
JUNK FOODTaxes, counter-mkting
Sodium in food
Trans fatin food
HEART-HEALTHYFOOD
Access, promotion
Heart-
Excesssodium diet
Air pollution
Chronic Disorders
Other deaths and costsattributable to risk factors,
and costs of risk factormanagement
Total consequencecosts
Quality of acuteand rehab care
Quality and use ofpreventive care
Trans fatconsumption
Local Context for TobaccoLocal Context for DietLocal Context for Physical ActivityLocal Context for Air PollutionLocal Context for Health Care ServicesLocal Context for Weight Loss ServicesLocal Context for Mental Health Services
PRISM Also Includes Frontiers for Social Action
Taxes, counter-mkting,quit services
TOBACCO
public placesSmoking in
services for obeseWeight loss
Primary Information Sources• Census
– Population, deaths, births, net immigration
• American Heart Association & NIH statistical reports
– Cardiovascular events, deaths, and prevalence
• National Health and Nutrition Examination Survey (NHANES)
– Risk factor prevalence by age and sex
– Diagnosis and control of hypertension, high cholesterol, and diabetes
• Medical Examination Panel (MEPS), National Health Interview (NHIS), Behavioral Risk Factor Surveillance System (BRFSS), Youth Risk Behavior Survey (YRBS)
– Medical and productivity costs attributable to risk factors
– Prevalence of distress in non-CVD and post-CVD populations
– Primary care utilization
– Extent of physical activity
• Research literature
– CVD risk calculator (Framingham)
– Relative risks from secondhand smoke, air pollution, obesity, poor diet, inactivity, distress
– Quality of diet (USDA Healthy Eating Index)
– Medical and productivity costs of cardiovascular events
– Effect sizes of behavioral interventions
• Expert judgment
– Effect sizes of behavioral interventions
Uncertainties are assessed through sensitivity testing
Uncertainties are assessed through sensitivity testing
Syndemics
Prevention Network
PRISM Simulates 24 Intervention Strategies
Health Domains• Air pollution
• Diet
• Distress
• Physical activity
• Primary care
• Tobacco
• Weight loss services
Intervention Types• Availability
• Price (tax/subsidy)
• Promotion, counter-marketing
• Bans, regulations, restrictions
• Social supports
• Individual services
Custom Settings for Each Intervention
• Ramp-in time
• Duration
• Direct effect size (within a plausible range)
Custom Settings for Each Intervention
• Ramp-in time
• Duration
• Direct effect size (within a plausible range)
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Prevention Network
Base Case & Illustrative Intervention Scenarios
Base Case (a simple scenario for comparison)
• Assume no further changes in the contextual factors that affect risk factor prevalences
• Any changes in prevalences after 2004 are due to “inflow/outflow” adjustment process and population aging
• Result: Past trends level off after 2004, after which results reflect only slow adjustments in risk factors
– Increasing obesity, high BP, and diabetes
– Decreasing smoking
– Increases in risk factors and population aging lead to eventual rebound in attributable deaths
Example Intervention Scenarios (max plausible effects, sustained)
• Four clusters of interventions layered to show their partial contribution and combined effects
• Services (health care, weight loss, smoking quit, distress)+ Diet & Physical Activity+ Tobacco + Air Pollution & Sodium & Trans fat
Syndemics
Prevention Network 17
Illustrative Intervention Scenarios: Maximum Plausible StrengthIndividual Services + Diet & PA + Tobacco + Air Pollution & Sodium & Trans
fat
Work in Progress, Please do no cite or distribute.
Smoking Prevalence (Adults) Obesity Prevalence (Adults)
Cardiovascular Events per 1000(CHD, Stroke, CHF, PAD)
Deaths from All Risk Factors per 1,000
0.4
0.3
0.2
0.1
0
1990 2000 2010 2020 2030 2040
0.4
0.3
0.2
0.1
0
1990 2000 2010 2020 2030 2040
40
30
20
10
0
1990 2000 2010 2020 2030 2040
8
6
4
2
0
1990 2000 2010 2020 2030 2040
**if all risk factors=0**
Syndemics
Prevention Network 18
Illustrative Intervention Scenarios: Maximum Plausible StrengthIndividual Services + Diet & PA + Tobacco + Air Pollution & Sodium & Trans
fat
Work in Progress, Please do no cite or distribute.
Years of Life Lost from Attributable Deaths
Consequence Costs per Capita(medical costs + productivity)
**if all risk factors=0**
200 M
175 M
150 M
125 M
100 M
1990 2000 2010 2020 2030 2040
6,000
4,500
3,000
1,500
01990 2000 2010 2020 2030 2040
Interactive ModelingBuilds Foresight, Experience, and Motivation to Act
Experiential Learning“Wayfinding”
Expert Recommendations
Milstein B. Hygeia's constellation: navigating health futures in a dynamic and democratic world. Atlanta, GA: Syndemics Prevention Network, Centers for Disease Control and Prevention; April 15, 2008. <http://www.cdc.gov/syndemics/monograph/index.htm>.
Syndemics
Prevention Network
Conversations Around the Model
Other health
priorities
Available information
Health inequities
Local interventionopportunities and costs
Communitythemes and strengths
Political willStakeholder
relationships
• What’s in the model does not define what’s in the room
• Simulations intentionally raise questions to spark broader thinking and judgment
• Boundary judgments follow from the intended purpose and users
SYSTEMDYNAMICS MODEL
STRATEGICPRIORITIES
Cardiovascularevents
Air pollutionexposure(PM 2.5)
Quality of acute andrehab care for
cardiovascular events
Use of qualitypreventive care
Use of weightloss services
by obese
Use of help servicesfor distress
Bans on smokingin public places
SmokingObesity
-Hypertension-High cholesterol
-Diabetes
Uncontrolledchronic disorders
Secondhandsmoke
Junk foodinterventions
(N=4)
Physical activityinterventions
(N=6)
Heart-unhealthy diet
Physicalinactivity Distress
Efforts to promoteprovision and use of
quality preventive care
Sodiumreduction
Trans fatreduction
Excesscalorie diet
Fruit &vegetable
interventions(N=3)
CVD deaths,disability,and costs
Excesssodium diet
Air pollutionreduction
Tobaccointerventions
(N=4)
Chronic Disorders
Other deaths and costsattributable to risk factors,
and costs of risk factormanagement
Total consequencecosts
Researchagenda
Boundary CritiqueEqualizing Experts and Ordinary Citizens
• “Professional expertise does not protect against the need for making boundary judgements…nor does it provide an objective basis for defining boundary judgements. It’s exactly the other way round: boundary judgements stand for the inevitable selectivity and thus partiality of our propositions.
• It follows that experts cannot justify their boundary judgements (as against those of ordinary citizens) by referring to an advantage of theoretical knowledge and expertise.
• When it comes to the problem of boundary judgements, experts have no natural advantage of competence over lay people.”
Ulrich W. Reflective practice in the civil society: the contribution of critically systemic thinking. Reflective Practice 2000;1(2):247-268.
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
Midgley G. The sacred and profane in critical systems thinking. Systems Practice 1992;5:5-16.
-- Werner Ulrich
Alternative Scientific Traditions
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.
Pragmatism• Presumes engagement (insider consciousness)• Begins with a response to a perplexity or injustice in the world• Learning through action and reflection (even simulated action)• Asks, “Under what conditions can we make a difference?”• Produces working relationships that shape public life
Positivism • Presumes objectivity (onlooker consciousness)• Begins with a theory about the world• Learning through observation and falsification• Asks, “Is this theory true?”• Produces knowledge to serve those in need
These are not theories. They are different orientations, which shape how we think, how we act, and what we value.
These are not theories. They are different orientations, which shape how we think, how we act, and what we value.
Practical Options in Causal Modeling
Detail (Disaggregation)
Scope (Breadth)
Low High
Low
High
Simplistic
Impractical
Focused
Expansive
Too hard to verify, modify, and understand
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Prevention Network
Modeling 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
– Identify key uncertainties to address in intervention studies
Forrester JW. Industrial Dynamics (Chapter 11: Aggregation of Variables). Cambridge, MA: MIT Press, 1961.
“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
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?
“In [dynamically complex] circumstances simulation becomes the only reliable way to test a hypothesis and evaluate the likely effects of policies."
-- John Sterman
Discussion• What can be done to move beyond the loose
appreciation of system concepts to the use of formal methods in real-world, high-stakes situations for improving the success and sustainability of interventions?
• In what ways might a systems approach alter…
– How problems/opportunities are defined?
– How potential solutions are identified and selected?
– How intervention options are evaluated?
– How applied science and policy action are understood?
– Who makes these decisions?
• Others…?
Discussion
For Further Information
CDC Syndemics Prevention Networkhttp://www.cdc.gov/syndemics
NIH Office of Behavioral and Social Sciences Research http://obssr.od.nih.gov/scientific_areas/methodology/systems_science/index.aspx
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