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System Dynamics Modeling and Applications in Public Health and
Healthcare
Dr. Jack Homer and Dr. Bobby Milstein
Public Lecture at the College of Medicine, Ministry of Health, Singapore
13 April 2009
Agenda
System Dynamics Background
The Modeling Process—Example: SARS
Hospital Surge Capacity Model
Cardiovascular Disease Prevention Model
National Health Policy Model and Game
What Accounts for Poor Population Health? Evolving Views
• God’s will
• Humors, miasma, ether
• Poor living conditions, immorality (e.g., sanitation)
• Single disease, single cause (e.g., germ theory)
• Single disease, multiple causes (e.g., heart disease)
• Single cause, multiple diseases (e.g., tobacco)
• Multiple causes, multiple diseases (but no feedback dynamics) (e.g., multi-causality)
• Dynamic interaction among afflictions, adverse conditions, and intervention capacities (e.g., syndemics)
1880
1950
1960
1980
2000
1840
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
Richardson GP. Feedback thought in social science and systems theory. Philadelphia, PA: University of Pennsylvania Press, 1991.
2000 2001 2002 2003 2004 2005 2006 2007 2008
CDC’s Simulation Studies for Health System Change
SD Identified as a
Promising Methodology for Health System
Change Ventures
Upstream-Downstream
Dynamics
Neighborhood Transformation
Game
National Health Economics & Reform
Health ProtectionGame
Overall Health Protection Enterprise
Diabetes Action Labs
Obesity Overthe Lifecourse
Fetal & Infant Health
Syndemics Modeling*
Cardiovascular Health in Context
Selected Health Priority Areas
Re-Directing the Course of ChangeQuestions Addressed by System Dynamics Modeling
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
The Iceberg – A Metaphor for the Level at Which We Address a System
Patterns of
Behavior
Systemic Structure
We Can Be:
Creative andTransformative
Reactive and Responsive
Adaptive and Proactive
More
Leve
rag
e
ALL 3 are needed
Events
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
Types of Models for Policy Planning & Evaluation
We Need a Broader Perspective Because Our Decisions So Often Lead To…
• Adverse side effects
• Too little effect
• Resistance
• Longer-term effects different from near-term
• Emergence of new issues
A broader, more informed view can help
Dynamic Complexity is All Around Us
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.
System DynamicsSimulating 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
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
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.
1. Current water level = INTEG( Water flow , 0)2. Water flow = Water flow at full open * Faucet openness3. Water flow at full open = 1 ounce per second4. Faucet openness = MAX (0, MIN (Maximum faucet openness decision, Perceived water level gap / Water flow at full open ))5. Maximum faucet openness decision = 1 out of possible 16. Perceived water level gap = DELAY1I (Water level gap,Time to perceive water level gap, 0)7. Water level gap = Desired water level - Current water level8. Desired water level = 6 ounces9. Time to perceive water level gap = 1 secondFINAL TIME = 20 secondsINITIAL TIME = 0TIME STEP = 0.125 seconds
System Equations
8
6
4
2
00 2 4 6 8 10 12 14 16 18 20
Seconds elapsed
OuncesSystem Behavior
Target
A Structural Understanding of Problematic Behavior
Problem Situation System Structure
Current waterlevel
Water flow
Desired water level
Water level gap
Perceived waterlevel gap
Time to perceivewater level gap
Faucet openness
Water flow atfull open
Maximum faucetopenness decision
System Model
Perc time Max open 1 1
1 0.50.5 1
What if…?
Simulation and “Double-Loop Learning”
• 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.
System Dynamics Health Applications1970s to the Present
• Disease epidemiology – Cardiovascular, diabetes, obesity, HIV/AIDS,
cervical cancer, chlamydia, dengue fever, drug-resistant infections
• Substance abuse epidemiology – Heroin, cocaine, tobacco
• Health care patient flows – Acute care, long-term care
• Health care capacity and delivery– Managed care, dental care, mental health care,
disaster preparedness, community health programs
• Health system economics– Interactions of providers, payers, patients, and
investors
Homer J, Hirsch G. System dynamics modeling for public health: Background and opportunities. American Journal of Public Health 2006;96(3):452-458.
Moving to the Closed Loop View
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
“Side Effects”
Feedback View
Goals
Environment
Actions
Goals ofOthers
Actions ofOthers
“Side Effects”
Delay Delay
Delay
Delay
DelayDelay
Delay
Delay
Delay
Delay
Delay
Delay
The Dynamics of Population Health
Prevalence of Vulnerability, Risk, or Disease
Time
HealthProtection
Efforts
-
B
Responsesto Growth
Resources &Resistance
-B
Obstacles
Broader Benefits& Supporters
R
ReinforcersPotentialThreats
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
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>.
Types of Loops Underlying the Dynamics
Prevalence of Vulnerability, Risk, or Disease
Time
HealthProtection
Efforts
-
B
Responsesto Growth
Resources &Resistance
-B
Obstacles
Broader Benefits& Supporters
R
ReinforcersPotentialThreats
Size of the Safer, Healthier
Population-
Prevalence of Vulnerability,
Risk, or Disease
B
Taking the Toll
0%
100%
R
Drivers ofGrowth
Values for Health & EquityDrivers of Growth
- Risky habits worse health- Families & friends- Media reinforce risky habits- Risky habits risky options- Risky conditions poor policies
Drivers of Growth- Risky habits worse health- Families & friends- Media reinforce risky habits- Risky habits risky options- Risky conditions poor policies
Responses to Growth- Personal responsibility- Urgent care- Preventive healthcare- Better media messages- Better local options- Better policies
Responses to Growth- Personal responsibility- Urgent care- Preventive healthcare- Better media messages- Better local options- Better policies
Limiting Resources & Resistance- Disease care squeezes prevention- Vested interests defend status quo
Limiting Resources & Resistance- Disease care squeezes prevention- Vested interests defend status quo
Benefits & Supports- Potential savings build support- Broader benefits build support
Benefits & Supports- Potential savings build support- Broader benefits build support
The Closed-Loop View Leads Us To Question…
• How can we weaken the engines of growth loops (i.e. social and economic reinforcements)?
• What incentives can reward parents, school administrators, employers, and other decision-makers for expanding healthier options?
• Are there resources for health protection that do not compete with disease care?
• How can industries be motivated to change the status quo rather than defend it?
• How can benefits beyond weight reduction be used to stimulate investments in expanding healthier options?
An Interactive & Scientific Modeling Process
• 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, comparing the model to reality, and refining to achieve greater realism;
• Do “What If…” testing to identify intervention strategies that might alleviate the problem;
• Do sensitivity testing 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
Forrester JW, Senge PM. Tests for building confidence in system dynamics models. In: Legasto A, Forrester JW, Lyneis JM, editors. System Dynamics. New York, NY: North-Holland; 1980. p. 209-228.
Homer JB. Why we iterate: scientific modeling in theory and practice. System Dynamics Review 1996;12(1):1-19.
Example: SARS in Taiwan, 2003
SARS displays the classic S-shaped growth pattern associated with the diffusion 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
Peo
ple
0
5
10
15
20
25
Feb/21 Mar/27 May/1 Jun/5 Jul/10
New Reported Cases
Peo
ple
/Day
SusceptiblePopulation
S
ExposedPopulation E
InfectiousPopulation I
EmergenceRate
RecoveredPopulation
RRecoveryRate
InfectionRate
Traditional Approach: SEIR Model
• Most widely used paradigm in epidemiology
• Compartment model–individuals in given state aggregated
• Deterministic or stochastic
• Disaggregation & heterogeneity handled by adding
compartments & interactions
SusceptiblePopulation
S
B
ExposedPopulation E
Depletion
InfectiousPopulation I
EmergenceRate
RemovedPopulation
RRemovalRate
AverageIncubation Time
-
+ +
Average Durationof Illness
Total InfectiousContacts
ContactRates
Infectivity
++
+
+
R
Contagion R
Contagion
InfectionRate
++
-
Infection in the Standard SEIR Model
Standard SEIR Model vs. SARS Data for Taiwan
Cumulative Cases
2,500
1,875
1,250
625
0
0 14 28 42 56 70 84 98 112Time (Day)
Peo
ple
Model
Actual
Expanding the Boundary: Behavioral Feedbacks
SusceptiblePopulation
S
B
ExposedPopulation E
Depletion
InfectiousPopulation I
EmergenceRate
RemovedPopulation
RRemovalRate
AverageIncubation Time
-
+ +
Average Durationof Illness
Total InfectiousContacts
ContactRates
Infectivity
++
+
+
R
Contagion R
Contagion
InfectionRate
++
-
SocialDistancing
Media Attention &Public Health
Warnings
+
+
-
SaferPractices
+
-
B
Social Distancing
B
Hygiene
DELAY
DELAY
Model with Behavioral Feedbacks vs. Data
Cumulative Cases400
300
200
100
0
0 14 28 42 56 70 84 98 112Time (Day)
Pe
op
le
Actual
Model
Practical Options in Causal Modeling
Detail (Disaggregation)
Scope (Breadth)
Low High
Low
High
Simplistic
Impractical
Focused
Expansive
Too hard to verify, modify, and understand
Model Structure and Level of DetailDepends on the Intended Uses and Audiences
• Set Better Goals (Planners & Evaluators)
– Identify what is likely and what is possible
– 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.
Tests for Building Confidence in Simulation Models
Focusing on
STRUCTURE
Focusing on BEHAVIOR
ROBUSTNESS
• Dimensional consistency• Extreme conditions• Boundary adequacy
• Parameter (in)sensitivity• Structure (in)sensitivity
REALISM
• Face validity• Parameter values
• Replication of behavior• Surprise behavior• Statistical tests
UTILITY• Appropriateness for audience and purposes
• Counterintuitive behavior• Generation of insights
Forrester 1973, Forrester & Senge 1980, Richardson and Pugh 1981
A Model Is…
An inexact representation of the real thing That 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
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>
Hospital Surge Capacity (with West Virginia University, 2003-04)
• Overcrowding due to patient surges in Emergency Dept. creates risk
– Deterioration of patients while awaiting ED admission
– Walking-out of patients who should be treated or isolated
• Hospital disaster plans are required to address surge capacity
– Flow-control methods, e.g. triage, transfer, early discharge
– Reserve resources—nurses, beds, supplies—are limited, esp. for rural hospitals
– How best to deploy limited resources?
Hoard M, Homer J, Manley W, et al. Systems modeling in support of evidence-based disaster planning for rural areas. Intl J of Hygiene and Envir Health 2005; 208:117-125.
Manley W, Homer J, et al. A dynamic model to support surge capacity planning in a rural hospital. 23rd Int’l SD Conference, Boston, MA; July 2005. <http://cgi.albany.edu/~sdsweb/sds2005.cgi?P333>
St. Joseph’s Hospital, Buckhannon, W.Va.
Pts in EDED admits Post-ED discharges
& facility transfers
Post-ED directed tosurgery (trauma)
Post-ED directedto wards
Pts await ED
ED arrivals (byacuity, trauma, andcontagion status)
ED walkouts& deaths
Deteriorationwhile waiting
Pts await / insurgery
Pts awaitward admit
Elective surgeriesscheduled
Post-surgerydirected to wards
Post-surgerydischarges
Pts in wards
Ward admits
Direct arrivalsto wards
Ward earlydischarges & facility
transfers
Ward dischargesafter full stay
Pts await / inelective
non-surgeryElectivenon-surgeries
scheduled
Electivenon-surgeries
postponed
Post-non-surgerydischarges
Post-non-surgerydirected to wards
ED directed todiagnostic imaging
Elective surgeriespostponed
Patient Flows and Feedback Loops
ED workload
Ward workload
ED staffing
Ward staffing
ED discharges
Increased Acuity
“Boarders”
0
500
1000
1500
2000
0 2 4 6 8 10 12 14 16 18 20
Days elapsed
Pa
tien
ts
Cumulative ED Arrivals by Acuity: Baseline Scenario
Baseline, Low
Baseline, Moderate
Baseline, Severe
Baseline (no surge) scenario50 ED arrivals per day for 20 days.
Result: Volume well handled, no avoidable deaths from deterioration
Cumulative ED Arrivals by Acuity: SARS Scenario
0
500
1000
1500
2000
0 2 4 6 8 10 12 14 16 18 20
Days elapsed
Pat
ient
s
Baseline, Low
Baseline, Moderate
Baseline, Severe
SARS, Low
SARS, Moderate
SARS, Severe106 pts Day 10
13 pts Day 2
36 pts Day 14
50 pts Day 6
Singapore pattern*
* CDC. Preparedness and Response in Healthcare Facilities: Public Health Guidance for Community-Level Preparedness and Response to SARS (Supplement C). January 8, 2004.
SARS outbreak scenarioOver the course of 13 days, 837 cumulative SARS ED arrivals, all requiring isolation, in addition to baseline arrivals.Result: Severe bottlenecks and many avoidable deaths
ED walkouts
9
940
856809
686
0
200
400
600
800
1000
No SurgeBaseline
SARS base More EDnurses
More wardnurses
More ED &ward
nurses
Deaths due to wait for ED admit
0
109
94
52
41
0
20
40
60
80
100
120
140
No SurgeBaseline
SARS base More EDnurses
More wardnurses
More ED &ward nurses
SARS Policy Testing (20 Days Cumulative):Deaths & Walkouts Due to ED Admit Wait
Patients Patients
Reserve nurses recruited from RNs off-duty, part-time, in offices, retired
Why is the ward nurse policy so much more effective?
The build-up of boarders brings ED admission to a halt.
Why is the ward nurse policy so much more effective?
The build-up of boarders brings ED admission to a halt.
Hospital Model Findings
• Recommendations affected by particulars of the hospital and the type of surge
– St. Joseph’s → need nurses, not beds
– SARS → need ward nurses the most
(the surge creates significant need for inpatient stays, not just ED care)
• But model is broadly applicable
– Could develop optimal strategies— best practices—customized to type of hospital and type of surge
– Allows for systematic “all hazards” planning
0
20
40
60
80
100
120
0 2 4 6 8 10 12 14 16 18 20
Days elapsed
ED Patient Backlog – SARS Scenario
No additional nurses
More ED nurses
More ward nurses
More ED and ward nurses
Cardiovascular Disease Prevention(with CDC and NIH, 2007-10)
• What are the key pathways of CV risk, and how do these affect health outcomes and costs?
• How might interventions affect the risk factors and outcomes in the short- and long-term?
• How might policy efforts be better balanced given limited resources?
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
Downwardtrend in CV
event fatality
Quality of primarycare provision
Chronic Disorders
Costs from CV and other riskfactor complications and
from utilization of services
Anti-smokingsocial marketing
High BP
Highcholesterol
Diabetes
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
Homer J, Milstein B, Wile K, Trogdon J, Huang P, Labarthe D, Orenstein D. Simulating and evaluating local interventions to improve cardiovascular health. In submission to Preventing Chronic Disease.
The CDC has partnered 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 local data of the Austin team.
The CDC has partnered 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 local data of the Austin team.
Other CVD Intervention Models
Markov: Coronary Heart DiseaseWeinstein MC, Coxson PG, et al. Forecasting coronary heart disease incidence, mortality, and cost: the coronary heart disease policy model. American J Public Health 1987; 77(11):1417-1426.
System Dynamics: Heart FailureHomer J, Hirsch G, et al. Models for collaboration: how system dynamics helped a community organize cost-effective care for chronic illness. System Dynamics Review 2004; 20(3):199-222.
Micro-simulation (Archimedes): CVD Kahn R, Robertson RM, et al. The impact of prevention on reducing the burden of cardiovascular disease. Circulation 2008; 118(5):576-585.
Statistical/Monte Carlo: Coronary Heart Disease Kottke TE, Gatewood LC, et al. Preventing heart disease: is treating the high risk sufficient? J Clinical Epidemiology 1988; 41(11):1083-1093.
Our model is the most extensive to date in integrating evidence on multiple risk factor pathways, potential interventions, and outcome costs.
Our model is the most extensive to date in integrating evidence on multiple risk factor pathways, potential interventions, and outcome costs.
Smoking
Obesity
Secondhandsmoke
Healthinessof diet
Extent ofphysical activity
Psychosocialstress
Diagnosisand control
First-time CVevents and
deaths
Particulate airpollution
Utilization ofquality primary
care
Downwardtrend in CV
event fatalityChronic Disorders
High BP
Highcholesterol
Diabetes
Risk Factors for CVD
Data sources: NHANES, NHIS, MEPS, AHA/NIH reports, Census, Vital Statistics, Framingham risk calculators, literature on risk factors and costs
Obesity, Smoking, High BP, High Cholesterol, and Diabetes are modeled as dynamic stocks—with multiple inflows and outflows (e.g., see next slide)
Obesity, Smoking, High BP, High Cholesterol, and Diabetes are modeled as dynamic stocks—with multiple inflows and outflows (e.g., see next slide)
Obesity Stock-Flow Structure
Obesenon-CVD
adults
Adults becomingobese
Non-obesenon-CVD
adults Adults becomingnon-obese
Obese teensturning 18
Non-obeseteens turning 18
Non-obeseadult deaths
Obese adultdeaths
Non-obeseadult
immigration
Obese adultimmigration
Non-obeseadults surviving
CV event
Obese adultssurviving CV event
Obese adultsaging
Non-obeseadults aging
Homer J, Milstein B, Dietz W, et al. Obesity population dynamics: exploring historical growth and plausible futures in the U.S. Proc. 24th Int’l System Dynamics Conference; Nijmegen, The Netherlands; July 2006.
Tobacco and Air Quality Interventions
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
Particulate airpollution
Utilization ofquality primary
care
Tobacco taxes andsales/marketing
regulations
Smoking bans atwork and public
places
Downwardtrend in CV
event fatalityChronic Disorders
Anti-smokingsocial marketing
High BP
Highcholesterol
Diabetes
Data sources: NHANES, NHIS, MEPS, AHA/NIH reports, Census, Vital Statistics, Framingham risk calculators, literature on risk factors and costs
Health Care Interventions
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 ofprimary care
Particulate airpollution
Utilization ofquality primary
care
Tobacco taxes andsales/marketing
regulations
Smoking bans atwork and public
places
Downwardtrend in CV
event fatality
Quality of primarycare provision
Chronic Disorders
Anti-smokingsocial marketing
High BP
Highcholesterol
Diabetes
Data sources: NHANES, NHIS, MEPS, AHA/NIH reports, Census, Vital Statistics, Framingham risk calculators, literature on risk factors and costs
Interventions Affecting Stress
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 ofprimary care
Particulate airpollution
Utilization ofquality primary
care
Tobacco taxes andsales/marketing
regulations
Smoking bans atwork and public
places
Downwardtrend in CV
event fatality
Quality of primarycare provision
Chronic Disorders
Anti-smokingsocial marketing
High BP
Highcholesterol
Diabetes
Data sources: NHANES, NHIS, MEPS, AHA/NIH reports, Census, Vital Statistics, Framingham risk calculators, literature on risk factors and costs
Healthy Diet Interventions
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 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
Downwardtrend in CV
event fatality
Quality of primarycare provision
Chronic Disorders
Anti-smokingsocial marketing
High BP
Highcholesterol
Diabetes
Data sources: NHANES, NHIS, MEPS, AHA/NIH reports, Census, Vital Statistics, Framingham risk calculators, literature on risk factors and costs
Physical Activity & Weight Loss Interventions
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
Downwardtrend in CV
event fatality
Quality of primarycare provision
Chronic Disorders
Anti-smokingsocial marketing
High BP
Highcholesterol
Diabetes
Data sources: NHANES, NHIS, MEPS, AHA/NIH reports, Census, Vital Statistics, Framingham risk calculators, literature on risk factors and costs
Adding Up the Costs
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
Downwardtrend in CV
event fatality
Quality of primarycare provision
Chronic Disorders
Costs from CV and other riskfactor complications and
from utilization of services
Anti-smokingsocial marketing
High BP
Highcholesterol
Diabetes
Data sources: NHANES, NHIS, MEPS, AHA/NIH reports, Census, Vital Statistics, Framingham risk calculators, literature on risk factors and costs
A Base Case Scenario for Comparison Assumptions for Input Time Series through 2040
• Prior to 2004, model reflects historical…
– Decline in fraction of workplaces allowing smoking (1990-2003)
– Decline in air pollution (1990-2001)
– Decline in CV event fatality (1990-2003)
– Increase in diagnosis and control of high blood pressure, high cholesterol, and diabetes (1990-2002)
– Rise & fall in youth smoking (1991-2003)
– Rise in youth obesity (1990-2002, 2002-2020P)
• After 2004, make simple yet plausible assumptions…
– Assume no further changes in contextual factors affecting risk factor prevalence (aside from rise in youth obesity)
– Changes in risk prevalence after 2004 are due to “bathtub” adjustment process (incidence still exceeding outflows) and population aging
– Provides an easily-understood basis for comparisons
Base Case Trajectories 1990-2040
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
Downwardtrend in CV
event fatality
Quality of primarycare provision
Costs from CV and other riskfactor complications and from
utilization of services
Anti-smokingsocial marketing
High BP
Highcholesterol
Diabetes
<Air pollutioncontrolregulations>
Air pollutioncontrol regulations
<Population aging>
Populationaging
Chronic Disorders
Smoking prevalence
Secondhand smoke
exposure
Particulate air pollution
PM2.5
Total consequence costs per capita
CVD deaths per 1000
Age 65+ fraction of the population
CV event fatality multiplier
Obesity prevalence
Diabetes
High blood pressure
Uncontrolled Prevalences
High cholesterol
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
Downwardtrend in CV
event fatality
Quality of primarycare provision
Costs from CV and other riskfactor complications and from
utilization of services
Anti-smokingsocial marketing
High BP
Highcholesterol
Diabetes
<Air pollutioncontrolregulations>
Air pollutioncontrol regulations
<Population aging>
Populationaging
Chronic Disorders
Smoking prevalence
Secondhand smoke
exposure
Particulate air pollution
PM2.5
Total consequence costs per capita
CVD deaths per 1000
Age 65+ fraction of the population
CV event fatality multiplier
Obesity prevalence
Diabetes
High blood pressure
Uncontrolled Prevalences
High cholesterol
Estimated Impacts of a 15-Component Intervention, with Sensitivity Ranges
CVD DEATHS
DIRECT & INDIRECT COSTS
The 15 components include: (1) “Care” [3 interventions](2) “Air” (smoking/pollution) [6],(3) “Lifestyle”: Nutrition, physical activity, & stress reduction [6]
The model contains 56 causal linkages requiring the estimation of relative risks, effect sizes, or initial values, most of which involved some level of uncertainty.
The upper edge of the sensitivity range results when all uncertain parameters are set to their “lowest plausible impact” values. The lower edge results when all are set to their “greatest plausible impact” values.
The 15 components include: (1) “Care” [3 interventions](2) “Air” (smoking/pollution) [6],(3) “Lifestyle”: Nutrition, physical activity, & stress reduction [6]
The model contains 56 causal linkages requiring the estimation of relative risks, effect sizes, or initial values, most of which involved some level of uncertainty.
The upper edge of the sensitivity range results when all uncertain parameters are set to their “lowest plausible impact” values. The lower edge results when all are set to their “greatest plausible impact” values.
60%
20%(15-26%)
80%
26%(19-33%)
Reductions vs. Base Case
0%
0%
1990 2000 2010 2020 2030 2040D
eath
s fr
om
CV
D p
er 1
000 4
2
0
Combined 15 interventionswith sensitivity range
Base Case
Deaths if all risk factors = 0
1990 2000 2010 2020 2030 2040To
tal
Co
ns
eq
ue
nc
e C
os
ts p
er
Ca
pit
a (
20
05
do
lla
rs p
er
ye
ar)
3,000
2,000
0
1,000
Combined 15 interventionswith sensitivity range
Base Case
Costs if all risk factors = 0
DIRECT & INDIRECT COSTS
Contributions of 3 Intervention Clusters(Clusters layered in cumulatively)
Contributions to CVD death reduction:
(1) Care: strong from the start; 9%
(2) Air: good from the start (less pollution, secondhand smoke) and
growing (due to smoking decline) to 6.5%
(3) Lifestyle: small at first but growing to 5%
Contributions to CVD death reduction:
(1) Care: strong from the start; 9%
(2) Air: good from the start (less pollution, secondhand smoke) and
growing (due to smoking decline) to 6.5%
(3) Lifestyle: small at first but growing to 5%
CVD DEATHS
DIRECT & INDIRECT COSTSContributions to cost savings:
(1) Air: strong from the start (pollution, SHS) and growing (due to smoking decline) to 18.5%
(2) Lifestyle: small at first but growing to 8.5%
(3) Care: negligible (not cost saving)
Contributions to cost savings:
(1) Air: strong from the start (pollution, SHS) and growing (due to smoking decline) to 18.5%
(2) Lifestyle: small at first but growing to 8.5%
(3) Care: negligible (not cost saving)
60%
20%
80%
26%
Reductions vs. Base Case
0%
0%
Dea
ths
fro
m C
VD
per
100
0 4
2
0
1990 2000 2010 2020 2030 2040
Base Case
3) + Nutrition, Physical Activity, and Stress
Deaths if all risk factors = 0
1) Primary Care
2) + Air Quality & Tobacco
3,000
0
To
tal
Co
nse
qu
ence
Co
sts
per
Cap
ita
(200
5 d
oll
ars
pe
r ye
ar)
1990 2000 2010 2020 2030 2040
Costs if all risk factors = 0
Base Case
3) + Nutrition, Physical Activity, and Stress
1) Primary Care
2) + Air Quality & Tobacco
2,000
1,000
National Health Policy Model & Game(with CDC, 2008-09)
• Americans pay the most for health care, yet suffer high rates of morbidity and premature mortality—esp. high among the poor and uneducated
• About 16% of Americans have no insurance coverage
• Over 75% of Americans think the current system needs fundamental change
• Many health leaders realize we need a broader view of health, including health protection and health equity
Nolte E, McKee CM. Measuring the health of nations: updating an earlier analysis. Health Affairs 2008; 27(1):58-71.Blendon RJ, Altman DE, Deane C, Benson JM, Brodie M, Buhr T. Health care in the 2008 presidential primaries. New England Journal of Medicine 2008;358(4):414-422. Gerberding JL. Protecting health—the new research imperative. JAMA 2005; 294(11):1403-1406.Gerberding JL. CDC: protecting people's health. Director's Update; Atlanta, GA; July, 2007.
The U.S. Health Policy Arena is
Dense with Diverse Issues
Healthier behaviorsHealthier behaviors
Adherence to care guidelines Adherence to
care guidelines
Insurance coverageInsurance coverage
Insurance overheadInsurance overhead
Socioeconomic disparities
Socioeconomic disparities
Primary care supply
Primary care supply
Reimbursement rates
Reimbursement rates
Out-of-pocket costs
Out-of-pocket costs
Provider efficiencyProvider efficiency
Access to careAccess to care
Overuse of ERs
Overuse of ERs
Safer environments
Safer environments
Overuse of specialists Overuse of specialists
CitizenInvolvement
CitizenInvolvement
Extent of care
Extent of care
Simulating the Health System
Integrating prior findings and estimates
• On costs, prevalence, risk factors, health disparities, health care utilization, insurance, quality of care, etc.
• Our own previous health system modeling*
Simplifying as appropriate
• Three states of health: Disease/injury, Asymptomatic disorder, No significant health problem
• Two SES categories: Advantaged, Disadvantaged (allowing study of disparities and equity)
• Start in equilibrium (all variables unchanging), approximating the U.S. in 2003
• Some complicating trends not included for simplicity: aging, migration, technology, economy, etc.
* E.g., Homer, Hirsch, Milstein. Chronic illness in a complex health economy: the perils and promises of downstream and upstream reforms. System Dynamics Review 2007; 23:313-343.
Connecting the Concepts:Start with the Outcome Measures
Healthcare costs
Morbidity &mortality
Healthinequity
Healthcare costs
Reimbursementrates
Disease& injury
Morbidity &mortality
Receipt of qualityhealth care
Healthinequity
Insurancecomplexity
Use of specialists& hospitals for
non-urgent care
Asymptomaticdisorders
Several Drivers of Health Care Costs
Quality Health Care Improves Health Outcomes
Healthcare costs
Sufficiency ofprimary care
providers
Reimbursementrates
Disease& injury
Morbidity &mortality
Receipt of qualityhealth care
- -
Health careaccess
Insurancecoverage
-Health
inequity
Quality ofcare delivered
Socioeconomicdisadvantage
-
Insurancecomplexity
Use of specialists& hospitals for
non-urgent care-
Self-pay fractionfor the insured
-
Asymptomaticdisorders
The “Medically Disenfranchised” Live in Areas Where PCPs are in Short Supply
PCPs per 10,000 population in Travis County, Texas(GP/FP/IM/Ped+ObGyn+Geriat; Texas DSHS 2004-06)
0
5
10
15
20
East Travis West Travis
PCPs per 10,000 population in Travis County, Texas(GP/FP/IM/Ped+ObGyn+Geriat; Texas DSHS 2004-06)
0
5
10
15
20
East Travis West Travis
The Robert Graham Center, with the National Association of Community Health Centers. “Access Denied: A Look at America’s Medically Disenfranchised”, Washington, DC, 2007.
Healthcare costs
Sufficiency ofprimary care
providers
PCP netincome
Reimbursementrates
Disease& injury
Morbidity &mortality
Receipt of qualityhealth care
- -
Health careaccess
Primary careefficiency
Insurancecoverage
-Health
inequity
Quality ofcare delivered
- -
Number ofprimary care
providers
-
Socioeconomicdisadvantage
-
PCP training& placement
programs
Insurancecomplexity
Use of specialists& hospitals for
non-urgent care-
-
-
Self-pay fractionfor the insured
-
Asymptomaticdisorders
Gatekeeperrequirement
-
-
PCP Sufficiency: Supply vs. Demand
Upstream Determinants of Disease & Injury
Healthcare costs
Sufficiency ofprimary care
providers
PCP netincome
Reimbursementrates
Disease& injury
Morbidity &mortality
Receipt of qualityhealth care
- -
Health careaccess
Primary careefficiency
Insurancecoverage
-Health
inequity
Behavioralrisks
Quality ofcare delivered
- -
Number ofprimary care
providers
-
Socioeconomicdisadvantage
-
Environmentalhazards
PCP training& placement
programs
Insurancecomplexity
Use of specialists& hospitals for
non-urgent care-
-
-
-
Self-pay fractionfor the insured
-
Asymptomaticdisorders
Gatekeeperrequirement
-
-
From Model to an Interactive Game
• Experiential learning for health leaders• Four simultaneous goals: save lives, improve health, achieve
health equity, and lower health care cost• Intervene without expense, risk, or delay• Not a prediction, but a way for multiple stakeholders to explore
how the health system can change
• Experiential learning for health leaders• Four simultaneous goals: save lives, improve health, achieve
health equity, and lower health care cost• Intervene without expense, risk, or delay• Not a prediction, but a way for multiple stakeholders to explore
how the health system can change
Milstein B, Homer J, Hirsch G. The "Health Run" policy simulation game: an adventure in US health reform. International System Dynamics Conference; Albuquerque, NM; July 26-30, 2009.
Options for Intervening in the Health SystemA Short Menu of Major Policy Proposals
Options for Intervening in the Health SystemA Short Menu of Major Policy Proposals
Improve primary care efficiency
Improve quality of care
Expand primary care supply
Simplify insurance
Change self pay fraction
Change reimbursement rates
Expand insurance coverage
Enable healthier behaviors
Build safer environments
Create pathways to advantage
Strengthen civic muscle
Coordinate care
“Winning” Involves Not Just Posting High Scores, But Understanding How and Why You Got Them
Scorecard
ProgressReport
Results in Context
CompareRuns
Some Policy Conclusions
• Expanded coverage and improved quality would improve health but, if done alone, would raise costs and worsen equity
• Expanding primary care capacity to eliminate shortages (esp. for the poor) would reduce costs and improve equity
• Cutting reimbursement rates would reduce costs but worsen health outcomes
• Upstream protection (behavioral and environmental remedies) would—increasingly over time—reduce costs, improve health, and improve equity
Milstein B, Homer J, Hirsch G. Are coverage and quality enough? A dynamic systems approach to health policy. Draft paper currently in CDC clearance.
System Dynamics: Looking Further for the Key
The world is complex, and many important things are not well-measured.
(The key is not always under the light.)
SD allows for broader causal structures and types of data.
Such models often lead to novel conclusions—and firm ones despite the uncertainties.
This is why SD is a powerful approach to support planning and policymaking.
The world is complex, and many important things are not well-measured.
(The key is not always under the light.)
SD allows for broader causal structures and types of data.
Such models often lead to novel conclusions—and firm ones despite the uncertainties.
This is why SD is a powerful approach to support planning and policymaking.