1. health and fitness
2. disease
3. diabetes

System Dynamics Applications at the
Federal Centers for Disease Control and
Prevention (CDC)
Dr. Jack Homer
Institute on Systems Science and Health
University of Michigan School of Public Health
May 6, 2009
Agenda
 Background
 Diabetes Policy Model
 Cardiovascular Disease Policy Model
 National Health Policy Model and Game
CDC Mission
The Centers for Disease Control and
Prevention (CDC) serves as the
national focus for developing and
applying disease prevention and
control, environmental health, and
health promotion and health education
activities designed to improve the
health of the people of the United
States.
CDC administers the Preventive Health
and Health Services Block Grant and
specific preventive health categorical
grant programs while providing
program expertise and assistance in
responding to Federal, State, local, and
private organizations on matters
related to disease prevention and
control activities.
What Accounts for Poor Population Health?
Evolving Views
•
God’s will
•
Humors, miasma, ether
•
Poor living conditions, immorality (e.g., sanitation)
1840
•
Single disease, single cause (e.g., germ theory)
1880
•
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)
1980
Dynamic interaction among afflictions, adverse conditions,
and intervention capacities (e.g., syndemics)
2000
•
1950
1960
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.
CDC’s Simulation Studies for
Health System Change
Overall Health Protection Enterprise
SD Identified as a
Promising
Methodology for
Health System
Change Ventures
2000
2001
Syndemics
Modeling
Neighborhood
Transformation
Game
2002
2003
Diabetes
Action Labs
UpstreamDownstream
Dynamics
2004
Fetal &
Infant Health
National Health
Economics & Reform
2005
2006
Obesity Over
the Lifecourse
Selected Health Priority Areas
Health Protection
Game
2007
Cardiovascular
Health in Context
2008
Re-Directing the Course of Change
Questions Addressed by System Dynamics Modeling
Prevalence of Diagnosed Diabetes, United States
40
Historical
Data
Million people
30
Simulation
Experiments
in
Action Labs
20
10
Markov Model Constants
• Incidence rates (%/yr)
• Death rates (%/yr)
• Diagnosed fractions
(Based on year 2000 data,
per demographic segment)
Trend is not
destiny!
0
1980
1990
2000
2010
2020
2030
2040
2050
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.
SD Model 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.
Practical Options in Causal Modeling
High
Expansive
Impractical
Too hard to verify,
modify, and understand
Scope
(Breadth)
Focused
Simplistic
Low
Low
High
Detail (Disaggregation)
Broad Structure of the
Health Protection Enterprise
Society's Health
Response
“UPSTREAM”
General
Protection
Targeted
Protection
“DOWNSTREAM”
Primary
Prevention
Demand for
response
Becoming safer
and healthier
Safer
Healthier
People
Becoming
vulnerable
Tertiary
Prevention
Secondary
Prevention
Vulnerable
People
Becoming
afflicted
Afflicted
without
Complications
Developing
complications
Afflicted with
Complications
Dying from
complications
Adverse Living
Conditions
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.
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
Gerberding JL. FY 2008 CDC Congressional Budget Hearing. Testimony before the Committee on Appropriations,
about it? CDC Futures Health Systems Workgroup; Atlanta, GA; 2003.
Subcommittee on Labor, Health and Human Services, Education and Related Agencies, United States House of
Gerberding JL. CDC's futures initiative. Atlanta, GA: Public Health Training Network; April 12, 2004.
Representatives; Washington, DC; March 9, 2007.
Homer JB, Hirsch GB. System dynamics modeling for public health: background and opportunities.
American Journal of Public Health 2006;96(3):452-458.
Diabetes Policy Model
(with Division of Diabetes Translation 2003-07)
•
Diabetes programs face tough challenges
and questions
– With rapid growth in prevalence, is
improved control good enough?
– Studies show primary prevention is
possible, but how much impact in
practice and at what cost?
– How best to balance interventions?
•
Model developed with program planners,
diabetes researchers, and
epidemiologists
•
Applied initially to U.S. overall, later
applied to 13 different states
* Done in conjunction with Sustainability Institute and the Center for
Public Health Practice at Emory University
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.
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
Civic Participation
Forces Outside the Community
• Social cohesion
• Responsibility for others
• Macroeconomy, employment
• Food supply
• Advertising, media
• National health care
• Racism
• Transportation policies
• Voluntary health orgs
• Professional assns
• University programs
• National coalitions
Health Care &
Public Health Agency Capacity
Personal Capacity
Local Living Conditions
• Understanding
• Motivation
• Social support
• Literacy
• Physio-cognitive function
• Life stages
• Availability of good/bad food
• Availability of phys activity
• Comm norms, culture
(e.g., responses to racism,
acculturation)
• Safety
• Income
• Transportation
• Housing
• Education
• Provider supply
• Provider understanding,
competence
• Provider location
• System integration
• Cost of care
• Insurance coverage
Health Care Utilization
• Ability to use care (match of
patients and providers,
language, culture)
• Openness to/fear of
screening
• Self-management,
monitoring
Metabolic Stressors
• Nutrition
• Physical activity
• Stress
Population Flows
Rehab of
High Risk
General
Population
Become
High Risk
High Risk
Not
Prediabetic
Prediabetes
onset
Rehab of
Undx PreD
Undiagnosed
Prediabetic
Diabetes onset
from Undx PreD
Diagnosis of
Prediabetes
• Baseline Flows
Rehab of
Dx PreD
Diagnosed
Prediabetic
Diabetes onset
from Dx PreD
Undiagnosed
Stage 1
Diabetics
Progression of
Undx S1 to S2
Diagnosis of
S1 diabetes
Diagnosed
Stage 1
Diabetics
Stage 2
Diabetics
Progression of
Dx S1 to S2
S2 deaths
• Percent of patients screened
• Percent of people with diabetes under control
Standard boundary
of most epi models and
intervention programs
Model Overview
The model also subdivides the diabetes and
prediabetes population stocks into stocks of
diagnosed and undiagnosed (not shown here), and
includes population inflows and non-diabetes deaths.
Unhealthy days
& costs from
diabetes
Diabetes
prevalence
People with
Normal
Glycemic
Levels
Prediabetes
onset
Unhealthy days
& costs per
person with
diabetes
Diabetes
Complications
onset People with
onset
People with
People with
Complicated
Uncomplicated
Prediabetes
Diabetes
Diabetes
Recovery
Obesity
prevalence
Black &
Hispanic
fractions
Prediabetes
detection and
management
Diabetes
detection and
management
Elderly
fraction
Death
Data Sources for Diabetes Model
Source
Topics
U.S. Census Bureau
• Population size and projected growth
• Fractions elderly, black, Hispanic
• Health insurance coverage
Vital Statistics
• Total deaths
• Diabetes-related deaths - any listed
National Health Interview Survey
(NHIS)
• Diagnosed diabetes prevalence (US)
National Health and Nutrition
Examination Survey (NHANES)
• Undiagnosed diabetes prevalence (US)
• Prediabetes prevalence (US)
• Obesity prevalence (US)
Behavioral Risk Factor Surveillance
System (BRFSS)
• Health behaviors: Taking meds, Eye exam,
Foot exam, HbA1c test, Flu shot
• Unhealthy days
• Diagnosed diabetes prevalence (US, states)
• Obesity prevalence (US, states)
Research Literature
• Effects of risk factors and management
on onset, complications, and costs
• Costs of diabetes and prediabetes care
Comparing and Combining Strategies
Monthly Unhealthy Days from
Diabetes per Thousand
U.S. Morbidity from Diabetes Simulated 1980-2050
600
Status Quo*
500
400
300
200
1980
Combination
* Status Quo assumptions for post-2005: (1) Obesity plateauing per
CDC Obesity model projection; (2) Age and ethnicity per Census
projection; (3) Health insurance and non-diabetes death rates
unchanging; (4) Disease management unchanging
1990
2000
2010
2020
2030
2040
2050
With a combination of improved control and aggressive primary
prevention (obesity, prediabetes), growth in the burden of diabetes
could be limited for the next 10 years and for decades beyond.
Cardiovascular Disease Prevention
(with Division of Heart Disease & Stroke Prevention, 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?
Quality of primary
care provision
Access to and
marketing of
primary care
Anti-smoking
social marketing
Sources of
stress
Access to and
marketing of mental
health services
Psychosocial
stress
Utilization of
quality primary
care
Tobacco taxes and
sales/marketing
Access to and marketing
regulations
of smoking quit products
and services
Smoking bans at
work and public
places
Smoking
Secondhand
smoke
Diagnosis
and control
Access to and
marketing of healthy
food options
Downward
trend in CV
event fatality
Chronic Disorders
Healthiness
of diet
High BP
High
cholesterol
Junk food taxes and
sales/marketing
regulations
Obesity
First-time CV
events and
deaths
Diabetes
Extent of
physical activity
Access to and
marketing of physical
activity options
Particulate air
pollution
Access to and
marketing of weight
loss services
Costs from CV and other risk
factor complications and
from utilization of services
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.
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.
Risk Factors for CVD
Psychosocial
stress
Utilization of
quality primary
care
Smoking
Secondhand
smoke
Diagnosis
and control
Particulate air
pollution
Downward
trend in CV
event fatality
Chronic Disorders
Healthiness
of diet
High BP
High
cholesterol
Obesity
First-time CV
events and
deaths
Diabetes
Extent of
physical activity
Obesity, Smoking, High BP, High Cholesterol, and Diabetes are modeled as dynamic
stocks—with multiple inflows and outflows (e.g., see next slide)
Data sources: NHANES, NHIS, MEPS, AHA/NIH reports, Census, Vital Statistics,
Framingham risk calculators, literature on risk factors and costs
Obesity Stock-Flow Structure
Non-obese
teens turning 18
Non-obese
adults aging Obese adults
aging
Obese teens
turning 18
Adults becoming
obese
Non-obese
non-CVD
Non-obese
adults
adult
immigration
Non-obese
adult deaths
Adults becoming
non-obese
Non-obese
adults surviving
CV event
Obese
non-CVD
adults
Obese adult
deaths
Obese adult
immigration
Obese adults
surviving CV event
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
Tobacco taxes and
sales/marketing
Anti-smoking
Access to and marketing
regulations
social marketing
of smoking quit products
and services
Psychosocial
stress
Utilization of
quality primary
care
Smoking bans at
work and public
places
Smoking
Secondhand
smoke
Diagnosis
and control
Particulate air
pollution
Downward
trend in CV
event fatality
Chronic Disorders
Healthiness
of diet
High BP
High
cholesterol
Obesity
First-time CV
events and
deaths
Diabetes
Extent of
physical activity
Data sources: NHANES, NHIS, MEPS, AHA/NIH reports, Census, Vital Statistics,
Framingham risk calculators, literature on risk factors and costs
Health Care Interventions
Quality of primary
care provision
Psychosocial
stress
Access to and
marketing of
primary care
Tobacco taxes and
sales/marketing
Anti-smoking
Access to and marketing
regulations
social marketing
of smoking quit products
and services
Utilization of
quality primary
care
Smoking bans at
work and public
places
Smoking
Secondhand
smoke
Diagnosis
and control
Particulate air
pollution
Downward
trend in CV
event fatality
Chronic Disorders
Healthiness
of diet
High BP
High
cholesterol
Obesity
First-time CV
events and
deaths
Diabetes
Extent of
physical activity
Data sources: NHANES, NHIS, MEPS, AHA/NIH reports, Census, Vital Statistics,
Framingham risk calculators, literature on risk factors and costs
Interventions Affecting Stress
Quality of primary
care provision
Access to and
marketing of
primary care
Sources of
stress
Access to and
marketing of mental
health services
Psychosocial
stress
Tobacco taxes and
sales/marketing
Anti-smoking
Access to and marketing
regulations
social marketing
of smoking quit products
and services
Utilization of
quality primary
care
Smoking bans at
work and public
places
Smoking
Secondhand
smoke
Diagnosis
and control
Particulate air
pollution
Downward
trend in CV
event fatality
Chronic Disorders
Healthiness
of diet
High BP
High
cholesterol
Obesity
First-time CV
events and
deaths
Diabetes
Extent of
physical activity
Data sources: NHANES, NHIS, MEPS, AHA/NIH reports, Census, Vital Statistics,
Framingham risk calculators, literature on risk factors and costs
Healthy Diet Interventions
Quality of primary
care provision
Access to and
marketing of
primary care
Sources of
stress
Access to and
marketing of mental
health services
Psychosocial
stress
Tobacco taxes and
sales/marketing
Anti-smoking
Access to and marketing
regulations
social marketing
of smoking quit products
and services
Utilization of
quality primary
care
Smoking bans at
work and public
places
Smoking
Secondhand
smoke
Diagnosis
and control
Access to and
marketing of healthy
food options
Particulate air
pollution
Downward
trend in CV
event fatality
Chronic Disorders
Healthiness
of diet
High BP
High
cholesterol
Junk food taxes and
sales/marketing
regulations
Obesity
First-time CV
events and
deaths
Diabetes
Extent of
physical activity
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
Quality of primary
care provision
Access to and
marketing of
primary care
Sources of
stress
Access to and
marketing of mental
health services
Psychosocial
stress
Tobacco taxes and
sales/marketing
Anti-smoking
Access to and marketing
regulations
social marketing
of smoking quit products
and services
Utilization of
quality primary
care
Smoking bans at
work and public
places
Smoking
Secondhand
smoke
Diagnosis
and control
Access to and
marketing of healthy
food options
Particulate air
pollution
Downward
trend in CV
event fatality
Chronic Disorders
Healthiness
of diet
High BP
High
cholesterol
Junk food taxes and
sales/marketing
regulations
Obesity
First-time CV
events and
deaths
Diabetes
Extent of
physical activity
Access to and
marketing of physical
activity options
Access to and
marketing of weight
loss services
Data sources: NHANES, NHIS, MEPS, AHA/NIH reports, Census, Vital Statistics,
Framingham risk calculators, literature on risk factors and costs
Adding Up the Costs
Quality of primary
care provision
Access to and
marketing of
primary care
Sources of
stress
Access to and
marketing of mental
health services
Psychosocial
stress
Tobacco taxes and
sales/marketing
Anti-smoking
Access to and marketing
regulations
social marketing
of smoking quit products
and services
Utilization of
quality primary
care
Smoking bans at
work and public
places
Smoking
Secondhand
smoke
Diagnosis
and control
Access to and
marketing of healthy
food options
Downward
trend in CV
event fatality
Chronic Disorders
Healthiness
of diet
High BP
High
cholesterol
Junk food taxes and
sales/marketing
regulations
Obesity
Extent of
physical activity
Access to and
marketing of physical
activity options
Particulate air
pollution
Access to and
marketing of weight
loss services
First-time CV
events and
deaths
Diabetes
Costs from CV and other risk
factor complications and
from utilization of services
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
Quality of primary
care provision
Access to and
marketing of
primary care
Sources of
stress
Access to and
marketing of mental
health services
Psychosocial
stress
Tobacco taxes and
sales/marketing
Anti-smoking
Access to and marketing
regulations
social marketing
of smoking quit products
and services
Utilization of
quality primary
care
Smoking bans at
work and public
places
Air pollution
control regulations
Smoking
Smoking
prevalence
Secondhand
Secondhand
smoke
smoke
Diagnosis
and control
Access to and
marketing of healthy
food options
High cholesterol
High
Obesity
Obesity
prevalence
Extent of
physical activity
pollution
<Aircontrol
pollution
regulations>
Downward
trend in CV
CV event
event
fatality
fatality
multiplier
Uncontrolled
Chronic
Disorders
Prevalences
High BP
Healthiness
of diet
Junk food taxes and
sales/marketing
regulations
Access to and
marketing of physical
activity options
exposure
Particulate air
pollution
Particulate air
PM2.5
Access to and
marketing of weight
loss services
High
cholesterol
blood pressure
Diabetes
Diabetes
First-time CV
CVD
events and
deaths
per 1000
deaths
<Populati
on aging>
Population
Age 65+
fraction
aging
of the population
Costs from CV and other risk
factor
Totalcomplications
consequence and from
utilization
of services
costs
per capita
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.
Total Consequence Costs per
Capita (2005 dollars per year)
The 15 components include:
(1) “Care” [3 interventions]
(2) “Air” (smoking/pollution) [6],
(3) “Lifestyle”: Nutrition, physical
activity, & stress reduction [6]
Deaths from CVD per 1000
Estimated Impacts of a 15-Component
Intervention, with Sensitivity Ranges
4
Reductions vs.
Base Case
CVD DEATHS
Base Case
0%
20%
(15-26%)
2
Combined 15 interventions
with sensitivity range
60%
Deaths if all risk factors = 0
0
1990
3,000
2000
2010
2020
2030
2040
DIRECT
DIRECT &
& INDIRECT
INDIRECT COSTS
COSTS
Base Case
2,000
Combined 15 interventions
with sensitivity range
1,000
0%
26%
(19-33%)
Costs if all risk factors = 0
80%
0
1990
2000
2010
2020
2030
2040
Contributions of 3 Intervention Clusters
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%
Deaths from CVD per 1000
(Clusters layered in cumulatively)
4
Reductions vs.
Base Case
CVD DEATHS
Base Case
1) Primary Care
0%
20%
3) + Nutrition, Physical
Activity, and Stress
2
2) + Air Quality & Tobacco
60%
Deaths if all risk factors = 0
0
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)
Total Consequence Costs per
Capita (2005 dollars per year)
1990
2000
2010
2020
2030
2040
DIRECT & INDIRECT COSTS
3,000
1) Primary Care
Base Case
0%
2,000
26%
2) + Air Quality & Tobacco
1,000
3) + Nutrition, Physical
Activity, and Stress
Costs if all risk factors = 0
80%
0
1990
2000
2010
2020
2030
2040
National Health Policy Model & Game
(with Office of the Director, 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
Insurance
overhead
Extent of
care
Overuse of
ERs
Healthier
behaviors
Adherence to
care guidelines
Safer
environments
Access
to care
Socioeconomic
disparities
Insurance
coverage
Out-of-pocket
costs
Overuse of
specialists
Primary
care supply
Citizen
Involvement
Reimbursement
rates
Provider
efficiency
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.
Some key concepts and measures
Concept
Proxy
Advantaged &
Disadvantaged
Prevalence
Initial Values (~2003)
Sources
Household income (< or ≥ $25,000)
Advantaged = 78.5%
Disadvantaged = 21.5%
Census
Adults: 22 specific conditions
Kids: 12 specific conditions
Overall = 38%
D/A Ratio = 1.60 (= 53.6%/33.5%)
NHIS
JAMA
High blood pressure
High cholesterol
Pre-diabetes
Overall = 51.5%
D/A Ratio = 1.15
NHANES
JAMA
Mortality
Deaths per 1,000
Overall = 7.5
D/A Ratio = 1.80
Vital Statistics
AJPH
Morbidity
Unhealthy days
per month per capita
Overall = 5.26
D/A Ratio = 1.78
BRFSS
Health Inequity
Fraction of unhealthy days
attributable to disadvantage
Attributable fraction = 14.3%
(calculated)
Health Insurance
Lack of insurance coverage
Overall = 15.6%
D/A Ratio = 1.82
Census
Sufficiency of
Primary Care Providers
Number of PCPs per 10,000
Overall = 8.5 per 10,000
D/A Ratio = 0.76
AMA
Austin Study*
Unhealthy Behavior
Prevalence
Smoking
Physical inactivity
Overall = 34%
D/A Ratio = 1.67
BRFSS
JAMA
Austin Study*
Unsafe Environment
Prevalence
Survey response: “My
neighborhood is not safe”
Overall = 26%
D/A Ratio = 2.5
BRFSS
Austin Study*
Disease & Injury
Prevalence
Asymptomatic Disorder
Prevalence
* CDC/SD study of cardiovascular risk in Austin/Travis County, TX. See Homer J, Milstein B, Wile K,
et al. Modeling the local dynamics of cardiovascular health. Preventing Chronic Disease 2008; 5(2).
Connecting the Concepts:
Start with the Outcome Measures
Morbidity &
mortality
Health
inequity
Health
care costs
Several Drivers of Health Care Costs
Insurance
complexity
Asymptomatic
disorders
Disease
& injury
Morbidity &
mortality
Health
care costs
Reimbursement
rates
Receipt of quality
health care
Health
inequity
Use of specialists
& hospitals for
non-urgent care
Quality Health Care Improves Health Outcomes
Insurance
complexity
Asymptomatic
disorders
Disease
& injury
Morbidity &
mortality
Health
care costs
Reimbursement
rates
-
-
Receipt of quality
health care
Health
inequity
Health care
access
Socioeconomic
disadvantage
-
Self-pay fraction
for the insured
-
Quality of
care delivered
Insurance
coverage
Sufficiency of
primary care
providers
-
Use of specialists
& hospitals for
non-urgent care
A Shortage of Primary Care Providers Exists for
Many Americans
Shortage of Doctors an Obstacle to Obama
Goals
By ROBERT PEAR
April 26, 2009
WASHINGTON — Obama administration officials,
alarmed at doctor shortages, are looking for ways to
increase the supply of physicians to meet the needs
of an aging population and millions of uninsured
The Robert Graham Center, with the National Association of Community
Health Centers. “Access Denied: A Look at America’s Medically
Disenfranchised”, Washington, DC, 2007.
PCP Sufficiency: Supply vs. Demand
Insurance
complexity
Asymptomatic
disorders
Disease
& injury
Morbidity &
mortality
Health
care costs
Reimbursement
rates
-
-
Receipt of quality
health care
Health
inequity
-
-
Self-pay fraction
for the insured
Quality of
care delivered
Insurance
coverage
Health care
access
Socioeconomic
disadvantage
-
-
Use of specialists
& hospitals for
non-urgent care
Gatekeeper
requirement
-
-
-
Sufficiency of primary care
providers
PCP net
income
Number of
primary care
providers
Primary care
efficiency
PCP training
& placement
programs
Upstream Determinants of Disease & Injury
Environmental
hazards
Insurance
complexity
Asymptomatic
disorders
Disease
& injury
Morbidity &
mortality
Reimbursement
rates
-
-
Behavioral
risks
Health
care costs
Receipt of quality
health care
Health
inequity
Health care
access
-
-
Self-pay fraction
for the insured
Quality of
care delivered
Insurance
coverage
-
Socioeconomic
disadvantage
-
-
Use of specialists
& hospitals for
non-urgent care
Gatekeeper
requirement
-
-
-
Sufficiency of primary care
providers
PCP net
income
Number of
primary care
providers
Primary care
efficiency
PCP training
& placement
programs
From Model to an Interactive Game
HealthBound
•
•
•
•
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 "HealthBound" 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 System
A Short Menu of Major Policy Proposals
Expand insurance coverage
Improve primary care efficiency
Improve quality of care
Coordinate care
Expand primary care supply
Enable healthier behaviors
Simplify insurance
Change self pay fraction
Change reimbursement rates
Build safer environments
Create pathways to advantage
Strengthen civic muscle
“Winning” Involves Not Just Posting High Scores,
But Understanding How and Why You Got Them
Scorecard
HealthBound
Results in Context
HealthBound
HealthBound
HealthBound
Progress
Report
Compare
Runs
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.
Secrets of Successful Modeling Projects
• Learn the client’s perspective—think in practical terms and
understand how much transformation is really possible
• Keep discussing scope, level of detail, terminology, intervention
levers, validation, and documentation as project proceeds—be
prepared to change course, even midway through project
• Quickly develop a meaningful initial (prototype) model
• Keep your eyes open for ways to improve the model—don’t depend
on the client for that
• Seek the most reliable data, but be open to data of many types
• “Freeze” the model at an appropriate time to allow for full policy
testing, reporting, and documentation
Roberts EB (1978). “Strategies for Effective Implementation of Complex Corporate Models.” In Managerial Applications
of System Dynamics, ed. EB Roberts (pp. 77-85). Pegasus Communications; www.pegasuscom.com.
Robinson JM (1980). “Managerial Sketches of the Steps of Modeling.” In Elements of the System Dynamics Method,
ed. J Randers (pp. 250-270). Pegasus Communications; www.pegasuscom.com.
Homer JB (1996). “Why We Iterate: Scientific Modeling in Theory and Practice.” System Dynamics Review 12(1):1-19.
EXTRAS
Chronic Illness as a Cascade of Bathtubs
Low risk
Risk onset
Risk
prevention
High risk
Illness onset
Risk
mgmt
Mildly ill
Complications onset
Bathtub = Stock
Water from faucet = Inflow
Drain = Outflow
Hand = Intervention affecting flow
Disease
mgmt
Severely ill
Urgent &
long-term care
Death
System Dynamics Health Applications
1970s to the Present
• Disease epidemiology
– Cardiovascular, diabetes, obesity, HIV/AIDS,
cervical cancer, chlamydia, dengue fever, drugresistant 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.
Broad Dynamics of the
Health Protection Enterprise
Prevalence of
Vulnerability, Risk, or Disease
100%
Values for
Health & Equity
Size of the
Safer, Healthier
Population
B
Taking the Toll
R
Potential
Threats
Drivers of
Growth
B
Prevalence of
Vulnerability,
Risk, or Disease
Responses
to Growth
Health
Protection
Efforts
B
-
Obstacles
Resources &
Resistance
R
Reinforcers
Broader Benefits
& Supporters
0%
Time
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>.
Planned Extensions – Next 2 Years
Potential value (better health, lower cost) from
•
Treating borderline conditions (pre-hypertension, borderline cholesterol,
pre-diabetes)
•
Focusing on prevention of recurrent events
•
Focusing on acute and rehab care
•
Further reducing control targets for blood pressure, cholesterol, blood
glucose
•
Targeting other risk factors; e.g., excess salt, low vitamin D, periodontal
disease, C-reactive protein
•
Targeting particular age-gender subgroups
•
Targeting African American or Hispanic populations
Our NIH sponsors feel this analysis will help them set research
priorities and extrapolate from clinical trials—more effectively
leveraging the value of their (very expensive!) sponsored research.
Other CVD Intervention Models
System Dynamics: Heart Failure
Homer 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.
Markov: Coronary Heart Disease
Weinstein 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.
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.