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ASysT Prize Seminar
Alexandria, VAJuly 25, 2008
Understanding the Dynam ic Dimensions
of Health Protect io n Pol ic ies
CDC-NIH System Dynamics Collaborative forDisease Control and Prevention (SD-CDC Team)
Joyc e Essien, Jack Hom er, Gary Hirsch , And rew Jo nes, Doc Klein,
Patty Mabry, Bobby Milstein , Diane Orens tein, Krist ina Wile
Applied Systems Thinking PrizeSeminar
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What Are System Dynamics Models
and How Do We Use Them?
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Basic Problem Solving Orientations
Sterman J. Business dynamics: systems thinking and modeling for a complex world. Boston, MA: Irwin
McGraw-Hill, 2000.
Single-Decision Open Loop View
Problem Results
Goals
Situation
Decision
SideEffects
Feedback ViewGoals
Environment
Actions
Goals of
Others
Actions ofOthers
SideEffects
Delay Delay
Delay
Delay
DelayDelay
Delay
Delay
Delay
Delay
Delay
Delay
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Learning In and About Dynamic Systems
Unknown st ructure Dynamic complex i ty Time delays
Impossib le experiments
Real World
InformationFeedback
Decisions
Mental
Models
Strategy, Structure,Decision Rules
Selected Miss ing Delayed Biased Ambiguous
Implementat ion Game playing Inconsistency Short term
Mispercept ions
Unscient i f ic Biases Defensiveness
Inabil i ty to infer
dynamics f rommental models
Known st ructure Contro l led 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.
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A Model IsAn inexact representationof the real thing
They help us understand, explain,
anticipate, and make decisions
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
Sterman J. A sketpic's guide to computer models. In: Barney GO, editor. Managing a Nation: theMicrocomputer Software Catalog. Boulder, CO: Westview Press; 1991. p. 209-229.
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System Dynamics:Addressing Dynamic Complexity
Good at Capturing
Differences between short- and long-term consequences of an action
Time delays (e.g., incubation period, time to detect, time to respond)
Accumulations (e.g., prevalences, resources, attitudes)
Behavioral feedback (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.
Origins
Jay Forrester, MIT, Indu str ia l 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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Brief Background on System DynamicsModeling
Compartmental models resting on a general theory of how systems
change (or resist change)often in ways we dont expect
Developed for corporate policies in the 1950s, and applied tohealth policies since the 1970s
Concerned with understanding dynamic complexity
Accumulation (stocks and flows)
Feedback (balancing and reinforcing loops)
Used primarily to craft far-sighted, but empirically based,strategies
Anticipate real-world delays and resistance
Identify high leverage interventions
Modelers engage stakeholders through interactive workshops
Forrester JW. Industrial Dynamics. Cambridge, MA: MIT Press; 1961.
Sterman JD. Business Dynamics: Systems Thinking and Modeling for a Complex
World. Boston, MA: Irwin/McGraw-Hill; 2000.
StockFlow
Feedback
influence
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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 simulationmodel, integrating the best information and insightavailable;
Compare results from simulated What Ifexperiments to identify intervention policies thatmight plausibly alleviate the problem;
Conduct sensitivity analyses to assess areas ofuncertainty in the model and guide futureresearch;
Convene diverse stakeholders to participate inmodel-supported Action Labs, which allowparticipants to discov er for themselvesthe likelyconsequences of alternative policy scenarios
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Finding the Right System Boundary: SARS in Taiwan
SARS displays the
classic S-shaped
growth pattern
associated with thediffusion of infectious
diseases
and new products,
innovations, social
norms, etc.
0
100
200
300
400
Feb/21 Mar/27 May/1 Jun/5 Jul/10
Cumulative Reported Cases
People
0
5
10
15
20
25
Feb/21 Mar/27 May/1 Jun/5 Jul/10
New Reported Cases
People/Day
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SusceptiblePopulation
S
Exposed
Population E
Infectious
Population IEmergence
Rate
RecoveredPopulation
RRecoveryRate
InfectionRate
Traditional Approach: SEIR Model
Most widely used paradigm in epidemiology
Compartment modelindividuals in given state aggregated
Deterministic or stochastic
Disaggregation & heterogeneity handled by adding compartments &interactions
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SusceptiblePopulation
S
B
Exposed
Population E
Depletion
Infectious
Population IEmergence
Rate
RemovedPopulation
RRemoval
Rate
AverageIncubation Time
-
+ +
Average Durationof Illness
Total Infectious
Contacts
ContactRates
Infectivity
+
+
+
+
R
ContagionR
Contagion
InfectionRate
+ +
-
Infection in the Standard SEIR Model
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Expanding the Boundary: Behavioral Feedbacks
SusceptiblePopulation
S
B
Exposed
Population E
Depletion
Infectious
Population IEmergence
Rate
RemovedPopulation
RRemoval
Rate
Average
Incubation Time
-
+ +
Average Duration
of Illness
Total Infectious
Contacts
ContactRates
Infectivity
++
+
+
R
ContagionR
Contagion
InfectionRate
++
-
SocialDistancing
Media Attention &
Public Health
Warnings
+
+
-
Safer
Practices
+
-
B
Social Distancing
B
Hygiene
DELAY
DELAY
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Model with Behavioral Feedbacks vs. Data
Cumulative Cases
400
300
200
100
0
0 14 28 42 56 70 84 98 112Time (Day)
Peop
le
Actual
Model
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How Much Detail is Best?
System dynamics studies
problems from a very particulardistance', not so close as to beconcerned with the action of asingle individual, but not so faraway as to be ignorant of the
internal pressures in the system.-- George Richardson
Forrester JW. Industrial Dynamics (Chapter 11: Aggregation of Variables). Cambridge, MA: MIT Press, 1961.
Richardson GP. Feedback thought in social science and systems theory. Philadelphia, PA: University of PennsylvaniaPress, 1991
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Practical Options in Causal Modeling
Detail (Disaggregation)
Scope(Breadth)
Low High
Low
High
Simplistic
Impractical
Fine-
grained
Far-sighted
Too hard to verify,
modify, and understand(e.g., manysystem dynamics models)
(e.g., many
agent-based models)
But a fine-grained
model can informa far-sighted model,
and vice versa.
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Attempt to Fix Health Care Cost Problem: Lower Physician Reimbursements
Health CareCosts
Reimbursement toPhysicians
ProblemFix
B
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Unintended Consequence: Reduced Primary Care Availability Increases Costs
Health CareCosts
Reimbursement toPhysicians
ProblemFix
B
Income of PrimaryCare Physicians
Availablity of PrimaryCare in PhysiciansOffices and Clinics
Retirements andNew Entries
Patients Going toER's for Primary
Care
Availability and Quality ofDisease Management for
Chronic Conditions
Acute Events Due toChronic Conditions
HospitalAdmissions
RR
Unintended Consequences
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Expand insurance coverage Improve quality of care
Change reimbursement rates
Improve operational efficiency
Simplify administration
Offer provider incentives
Enable healthier behaviors
Build safer environments Create pathways to advantage
Ingredients for Transforming Population HealthA Short Menu of Pol icy Proposals
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Prototyp e Preview
Bobby Milstein
Centers for Disease Control and Prevention
http://www.cdc.gov/syndemics
CDCs
Health Protection Game
Jack Homer
Homer Consulting
Gary Hirsch
Independent Consultant
>>>> These slides are from a prototype model.
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Rules of the Health Protect ion Game
GoalNavigate the U.S. health system toward greater health and equity
Task
Prioritize intervention options across nine policy domains Decisions
Craft health protection strategies over 8 rounds (from 2010-2050),using feedback available every five years
ScoringAchieve the best results across four criteria simultaneously
Save lives (i.e., reduce the mortality rate)
Improve well-being (i.e., reduce unhealthy days)
Achieve equity (i.e., reduce unhealthy days due to Disadvantage)
Lower healthcare costs (i.e., reduce expenses per capita)
Appropriate implementation expenses (i.e., subsidy, program cost)
Game SetupA population in dynamic equilibrium, with fixed rates of birth and netimmigration, experiencing high starting levels of mortality, unhealthylife, social inequity, and healthcare costs
No changes are due to trends o r ig inat ing outs ide the heal th
sector such as aging, migrat ion, econom ic cycles, technolog y,
climate change, etc.
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Navigating Health FuturesGetting Ou t of a Deadly, Unhealthy , Inequ itable, and Cos tly Trap
Four Problems in the Current System: High Morbidity, Mortality, Inequity, Cost
Death rate per thousand
Unhealthy days per capita
Health inequity index
Healthcare spend per capita
10
6
0.2
6,000
0
0
0
4,000
2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050
How far canyou move the
system?
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S l t d E ti t f M d l C lib ti
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Parameter Proxy Initial Values (~2000) Sources
Advantaged &Disadvantaged
Prevalence
Household Income(< or $25,000)
Advantaged = 79% Disadvantaged = 21%
Census
Selected Estimates for Model Calibration
S l t d E ti t f M d l C lib ti
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Parameter Proxy Initial Values (~2000) Sources
Advantaged &Disadvantaged
Prevalence
Household Income(< or $25,000)
Advantaged = 79%Disadvantaged = 21%
Census
SymptomaticDisease/Injury
Prevalence Self-rated health isgood, fair, or poor
Overall = 27%D/A Ratio = 1.60 (= 38.5%/24%)
BRFSS JAMA
Asymptomatic ChronicDisease Prevalence
High blood pressure (HBP) High cholesterol (HC) Asymp = Tot Chron - Symp
Overall = 40%(54.5% tot chron - 14.5% Symp)
D/A Ratio (tot chronic) = 1.15 (= 61%/53%)
NHANES JAMA
No Health ProblemsPrevalence
Self-rated health isexcellent or very good
No HBP or HC
Overall = 33% Advantaged = 36%Disadvantaged = 24%
BRFSS NHANES
Mortality Deaths per 1,000 Overall = 8.4D/A Ratio = 1.80
Vital Statistics AJPH
Morbidity Unhealthy daysper month per capita
Overall = 5.25D/A Ratio = 1.78
BRFSS
Health Equity Unhealthy days (or deaths)attributable to disadvantage
Attrib. fraction (unhealthy days) = 14.1% Attrib. fraction (deaths) = 14.4%
Census BRFSS
Health Insurance Lack of insurance coverage Overall = 15.6%D/A Ratio = 1.82
Census
Sufficiency ofPrimary Care Providers
Number of PCPs per 10,000 Overall = 8.5 per 10,000D/A Ratio = 0.71
AMA Austin Study
Emergency Care forNonurgent Problems
Acute non-urgent visits in ERor outpatient department
Overall = 19%D/A Ratio = 5.5
NAMCS
Unhealthy BehaviorPrevalence
Smoking Physical inactivity
Overall = 34%D/A Ratio = 1.67
BRFSS JAMA Austin Study
Unsafe EnvironmentPrevalence
Neighborhood not safe Overall = 26%D/A Ratio = 2.5
BRFSS JAMA Austin Study
Selected Estimates for Model Calibration
Exploring Intervention Scenarios
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Exploring Intervention ScenariosCut Reimburs ements to Off ice-Based Physic ians by 20%
Scoring Criteria: Deaths, Unhealthy Days, Inequity, Cost
Death rate per 1,000Unhealthy days
Health inequity index
Healthcare spending per capita
>>>> These results are from a prototype model.
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Additional Preliminary FindingsUniversal Coverage (with Leadership)
Lowers morbidity and mortality quickly
Increases cost significantly (greater volume of mediocre services, which do little to prevent disease)
Worsens inequity (greater demand exacerbates pre-existing provider shortage for disadvantaged)
Quality of Care (with Leadership)
Lowers morbidity and mortality quickly, more so than Universal Coverage (more people benefit)
Costs rise initially, then fall (the benefits of disease prevention accrue gradually)
Worsens inequity (better quality services exacerbate pre-existing provider shortage fordisadvantaged)
Upstream Health Protection (with Leadership)
Consistent pattern of strong, sustained improvements in morbidity, mortality, cost, and equity
Takes time to generate significant effects (~10 years)
Works in three ways, all favoring the disadvantaged: (1) fewer upstream risks lower diseaseprevalence, which in turn (2) eases demand on scarce provider resources; and (3) reduces costs andimproves health care access
Average unhealthy days per capita Health care spending per capitaHealth inequity index (morbidity)6
.5
5
.5
42000 2010 2020 2050
Protection
Coverage
Quality
2030 2040
Prototype Model Output
6,000
5,500
5,000
4,500
4,0002000 2050
Protection
Coverage
Quality
Prototype Model Output
2010 2020 2030 2040
0.2
0.15
0.1
0.05
02000 2050
Protection
Coverage
Quality
Prototype Model Output
2010 2020 2030 2040
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Game-based Wayfinding DialoguesCombine Science and Social Change
Potential champions need more than visionary direction.They want plausible pathways and visceral preparation.