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Syndemics
Prevention Network
Modeling the Population Health Dynamics of Diabetes & Obesity
Texas Public Health AssociationGalveston, TX
February 26, 2007Syndemics
Prevention Network
Bobby MilsteinSyndemics Prevention Network
Centers for Disease Control and PreventionAtlanta, Georgia
bmilstein@cdc.govhttp://www.cdc.gov/syndemics
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Mokdad AH, Bowman BA, Ford ES, Vinicor F, Marks JS, Koplan JP. The continuing epidemics of obesity and diabetes in the United States. Journal of the American Medical Association 2001;286(10):1195-200.
Kaufman FR. Diabesity: the obesity-diabetes epidemic that threatens America--and what we must do to stop it. New York, NY: Bantam Books, 2005.
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Imperatives for Protecting Health
Gerberding JL. Protecting health: the new research imperative. Journal of the American Medical Association 2005;294(11):1403-1406.
Typical Current StateStatic view of problems that are studied in isolation
Proposed Future StateDynamic systems and syndemic approaches
"Currently, application of complex systems theories or syndemic science
to health protection challenges is in its infancy.“
-- Julie Gerberding
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Wickelgren I. How the brain 'sees' borders. Science 1992;256(5063):1520-1521.
How Many Triangles Do You See?
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Boundary Judgments(System of Reference)
Observations(Facts)
Evaluations(Values)
Ulrich W. Boundary critique. In: Daellenbach HG, Flood RL, editors. The Informed Student Guide to Management Science. London: Thomson; 2002. p. 41-42. <http://www.geocities.com/csh_home/downloads/ulrich_2002a.pdf>.
Ulrich W. Reflective practice in the civil society: the contribution of critically systemic thinking. Reflective Practice 2000;1(2):247-268. http://www.geocities.com/csh_home/downloads/ulrich_2000a.pdf
Boundary CritiqueWhen it comes to the problem of boundary judgments,
experts have no natural advantage of competence over lay people.
-- Werner Ulrich
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Transforming the Future of Diabetes…
"Every new insight into Type 2 diabetes...
makes clear that it can be avoided--and
that the earlier you intervene the better.
The real question is whether we as a
society are up to the challenge...
Comprehensive prevention programs
aren't cheap, but the cost of doing
nothing is far greater..."
Gorman C. Why so many of us are getting diabetes: never have doctors known so much about how to prevent or control this disease, yet the epidemic keeps on raging. how you can protect yourself. Time 2003 December 8. Accessed at http://www.time.com/time/covers/1101031208/story.html.
…in an Era of Rising Obesity
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Prevalence of Diagnosed Diabetes, US
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.
Why?
Where?
How?
Who?
What?
Markov Forecasting Model
Simulation Experiments
in Action Labs
Questions Addressed by System Dynamics Modeling Learning to Re-Direct the Course of Change
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Time Series Models
Describe trends
Multivariate Stat Models
Identify historical trend drivers and correlates
Patterns
Structure
Events
Increasing:
• Depth of causal theory
• Data and sensitivity testing requirements
• Robustness for longer-term projection
• Value for developing policy insights
Increasing:
• Depth of causal theory
• Data and sensitivity testing requirements
• Robustness for longer-term projection
• Value for developing policy insights Dynamic Simulation Models
Anticipate new trends, learn about policy consequences,
and set justifiable goals
Tools for Policy Analysis
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A Model Is…
An inexact representation of the real thing
It helps us understand, explain, anticipate, and make decisions
“All models are wrong, some are useful.”
-- George Box
“All models are wrong, some are useful.”
-- George Box
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System Dynamics Simulation Modeling Was Developed to Address Problems Marked by Dynamic Complexity
Good at Capturing
• Differences between short- and long-term consequences of an action
• Time delays (e.g., transitions, detection, response)
• Accumulations (e.g., prevalence, capacity)
• Behavioral feedback (e.g., actions trigger reactions)
• Nonlinear causal relationships (e.g., effect of X on Y is not constant-sloped)
• Differences or inconsistencies in goals/values among stakeholders
Sterman JD. Business dynamics: systems thinking and modeling for a complex world. Boston, MA: Irwin McGraw-Hill, 2000.
Homer JB, Hirsch GB. System dynamics modeling for public health: background and opportunities. American Journal of Public Health 2006;96(3):452-458
Origins
• Jay Forrester, MIT (from late 1950s)
• Public policy applications starting late 1960s
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Understanding Dynamic ComplexityFrom a Very Particular Distance
“{System dynamics studies problems} from ‘a very particular distance', not so close as to be concerned with the action of a single individual,
but not so far away as to be ignorant of the internal pressures in the system.”
-- George Richardson
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.
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Simulations for Learning in Dynamic Systems
Morecroft JDW, Sterman J. Modeling for learning organizations. Portland, OR: Productivity Press, 2000.
Sterman JD. Business dynamics: systems thinking and modeling for a complex world. Boston, MA: Irwin McGraw-Hill, 2000.
Homer JB. Why we iterate: Scientific modeling in theory and practice. System Dynamics Review 1996; 12(1):1-19.
Multi-stakeholder Dialogue
Dynamic Hypothesis (Causal Structure)
X Y
Plausible Futures (Policy Experiments)
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CDC Diabetes System Modeling ProjectDiscovering Dynamics Through Action Labs
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.
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CDC
• Dara Murphy, Bobby Milstein, Chris Benjamin, Wayne Millington, Parul Nanavati, Sharon Daves, Frank Vinicor, Mark Rivera
Contractor Team
• Drew Jones
• Jack Homer
• Joyce Essien
• Doc Klein
• Don Seville
State Diabetes Programs
• Minnesota Heather Devlin, Jay Desai
• California Gary He, Karen Black, Toshi Hayashi
• VermontRobin Edelman, Jason Roberts, Ellen Thompson
CDC Diabetes System Modeling ProjectContributors
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Project Background
• Diabetes programs face tough challenges and questions
– Pressure for results on disease burden, not just behavioral change
– The Diabetes Prevention Program indicates primary prevention is possible, but may be difficult and costly
– What is achievable on a population level?
– How should funds be allocated?
• Standard epidemiological models rarely address such policy questions
• In Fall 2003, CDC initiates System Dynamics modeling project
• In Spring 2005, some states join as collaborators in further developing and using the SD model
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Inflow
Volume
Outflow
Developing
Burden ofDiabetes
Total Prevalence(people with diabetes)
Unhealthy Days(per person with
diabetes)
Costs(per person with diabetes)
People withDiagnosedDiabetes
Diagnosis Deaths
ab
People withUndiagnosedPreDiabetes
Developing
DiabetesOnset
c
d
People withNormal
Blood SugarLevels
PreDiabetesOnset
Recovering fromPreDiabetes
e
DiabetesManagement
DiabetesDiagnosis
Obesity in theGeneral
Population
PreDiabetesDetection &
Management
People withUndiagnosed
Diabetes
Deaths
Diabetes Burden is Driven by Population Flows
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Diabetes Burden is Driven by Population Flows
Inflow
Volume
Outflow
Developing
Burden ofDiabetes
Total Prevalence(people with diabetes)
Unhealthy Days(per person with
diabetes)
Costs(per person with diabetes)
People withDiagnosedDiabetes
Diagnosis Deaths
ab
People withUndiagnosedPreDiabetes
Developing
DiabetesOnset
c
d
People withNormal
Blood SugarLevels
PreDiabetesOnset
Recovering fromPreDiabetes
e
DiabetesManagement
DiabetesDiagnosis
Obesity in theGeneral
Population
PreDiabetesDetection &
Management
People withUndiagnosed
Diabetes
Deaths
Standard boundary
This larger view takes us beyond standard epidemiological models and most intervention programs
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Using Available Data to Calibrate the Model
Information Sources Data
U.S. Census
• Population growth and death rates• Fractions elderly, black, hispanic• Health insurance coverage
National Health Interview Survey• Diabetes prevalence• Diabetes detection
National Health and Nutrition Examination Survey• Prediabetes prevalence
• Obesity prevalence
Behavioral Risk Factor Surveillance System
• Eye exam and foot exam• Taking diabetes medications• Unhealthy days (HRQOL)
Professional Literature• Effects of risk factors and mgmt on onset, complications, and costs• Direct and indirect costs of diabetes
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Diabetes System Modeling ProjectConfirming Fit to Historical Trends (2 examples out of 10)
Diagnosed Diabetes % of AdultsObese % of Adults
0%
10%
20%
30%
40%
1980 1985 1990 1995 2000 2005 2010
Obese % of adults
Data (NHANES)
Simulated
0%
2%
4%
6%
8%
1980 1985 1990 1995 2000 2005 2010
Diagnosed diabetes % of adults
Data (NHIS)
Simulated
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The growth of diabetes prevalence since 1980 has been driven by growth in obesity prevalence
Obese Fraction and Diabetes per Thousand1300.7
850.35
400
1980 1990 2000 2010 2020 2030 2040 2050Time (Year)
Diabetes Prevalenc
e
Obesity Prevalenc
e
Risk multiplier on diabetes onset from
obesity = 2.6
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Prevalence=92 AND RISING
Although we expect obesity to increase little after 2006, diabetes keeps growing robustly for another 20-25 years
Obese Fraction and Diabetes per Thousand1300.7
850.35
400
1980 1990 2000 2010 2020 2030 2040 2050Time (Year)
Diabetes Prevalenc
e
Obesity Prevalenc
e
Diabetes prevalence keeps growing after
obesity stopsWHY?
With high (even if flat) onset, prevalence tub
keeps filling until deaths (4-5%/yr)=onset
Onset=6.3 per thou
Estimated 2006 values
Death=3.8 per thou
Prevalence=92 / thou
Risk multiplier on diabetes onset from
obesity = 2.6
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Unhealthy days impact of prevalence growth, as affected by diabetes management: Past and one possible future
Unhealthy Days per Thou and Frac ManagedObese Fraction and Diabetes per Thousand1300.7
850.35
400
1980 1990 2000 2010 2020 2030 2040 2050Time (Year)
Diabetes Prevalenc
e
Obesity Prevalenc
e
5000.65
25001980 1990 2000 2010 2020 2030 2040 2050
3750.325
Unhealthy Daysfrom Diabetes
Managed
fraction
Diabetes prevalence keeps growing after
obesity stops
If disease management gains end, the burden
grows
Reduction in unhealthy days per complicated case if
conventionally managed: 33%;
if intensively managed: 67%
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A Sequence of What-if Simulations
People with Diabetes per Thousand Adults150
125
100
75
501980 1990 2000 2010 2020 2030 2040 2050
Monthly Unhealthy Days from Diabetes per Thou500
450
400
350
300
250
1980 1990 2000 2010 2020 2030 2040 2050
Base
Base
Start with the base case or “status quo”: no improvements in diabetes management or prediabetes management after 2006
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Further Increases in Diabetes Management
People with Diabetes per Thousand Adults150
125
100
75
501980 1990 2000 2010 2020 2030 2040 2050
Monthly Unhealthy Days from Diabetes per Thou500
450
400
350
300
250
1980 1990 2000 2010 2020 2030 2040 2050
Base
Diab mgt Base
More people living with diabetes
Keeping the burden at bay for nine years
longer
Diab mgt
Increase fraction of diagnosed diabetes getting managed from 58% to 80% by 2015. (No change in the mix of conventional and intensive.) What do you think will happen?
Diabetes mgmt does nothing to slow the growth of prevalence—in fact, it
increases it. As soon as diabetes mgmt stops
improving, unhealthy days start to grow as fast as
prevalence.
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A Huge Push for Prediabetes Management
People with Diabetes per Thousand Adults150
125
100
75
50
1980 1990 2000 2010 2020 2030 2040 2050
Monthly Unhealthy Days from Diabetes per Thou500
450
400
350
300
250
1980 1990 2000 2010 2020 2030 2040 2050
Base
PreD mgmt
Base
PreD mgmt
The improvement is relatively modest—the growth is not stopped
Increase fraction of prediabetics getting managed from 6% to 32% by 2015. (Half of those under intensive mgmt by 2015.) No increase in diabetes mgmt. What do you think will happen?
Diabetes onset rate reduced 12% relative to base run. Not nearly
enough to offset the excess onset due to high obesity. By 2050,
diabetes prevalence reduced only 9% relative to base run.
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Two Scenarios in which Obesity is Reduced
Obese Fraction of Adult Population
0.4
0.3
0.2
0.1
01980 1990 2000 2010 2020 2030 2040 2050
Base
Obesity 25%
Obesity 18%
What if it were possible—in addition to the prediabetes mgmt intervention—to gradually lower the fraction obese from 34% (2006) to the 1994 value of 25% by 2030? Or, to the 1984 value of 18%?
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Managing Prediabetes AND Reducing Obesity
The more you reduce obesity, the sooner you
stop the growth in diabetes—and the more
you bring it down
… Same with the burden of diabetes
People with Diabetes per Thousand Adults150
125
100
75
50
1980 1990 2000 2010 2020 2030 2040 2050
Monthly Unhealthy Days from Diabetes per Thou500
450
400
350
300
250
1980 1990 2000 2010 2020 2030 2040 2050
Base
PreD mgmt
PreD & Ob 25%
PreD & Ob 18%
Base
PreD mgmt
PreD & Ob 18%
PreD & Ob 25%
What do you think will happen if, in addition to PreD mgmt, obesity is reduced moderately by 2030? What if it is reduced even more?
Why is obesity reduction so powerful? Mainly because of its strong effect on onset rate among prediabetics; but,
also, because it reduces PreD prevalence itself. However, achieving significant obesity reduction takes a
long time.
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Intervening Effectively Upstream AND Downstream
People with Diabetes per Thousand Adults150
125
100
75
50
1980 1990 2000 2010 2020 2030 2040 2050
Monthly Unhealthy Days from Diabetes per Thou500
450
400
350
300
250
1980 1990 2000 2010 2020 2030 2040 2050
Base
PreD mgmt PreD mgmt
Base
PreD & Ob 25%
Pred & Ob 25%
All 3 --PreD & Ob 25% & Diab mgmt
All 3
With a combination of effective upstream and downstream interventions we could hold the burden of diabetes nearly flat
through 2050!
With pure upstream intervention, burden still grows for many years before turning around. What do you think will happen if we add the prior diabetes mgmt intervention on top of the PreD+Ob25 one?
Downstream improvement acts quickly against burden but cannot continue
forever. Significant upstream gains are thus
essential but will likely take 15+ years to
achieve. A flat-burden future is possible but
requires simultaneous action on both fronts.
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Cover of "The Economist", Dec. 13-19, 2003Cover of "The Economist", Dec. 13-19, 2003.
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CDC Obesity Dynamics Modeling Project Contributors
Core Design Team• Dave Buchner• Andy Dannenberg• Bill Dietz• Deb Galuska• Larry Grummer-Strawn• Anne Hadidx• Robin Hamre• Laura Kettel-Khan• Elizabeth Majestic • Jude McDivitt• Cynthia Ogden• Michael Schooley
System Dynamics Consultants• Jack Homer• Gary Hirsch
Time Series Analysts
• Danika Parchment
• Cynthia Ogden
• Margaret Carroll
• Hatice Zahran
Project Coordinator• Bobby Milstein
Workshop Participants• Atlanta, GA: May 17-18 (N=47)• Lansing, MI: July 26-27 (N=55)
Homer J, Milstein B, Dietz W, Buchner D, Majestic D. Obesity population dynamics: exploring historical growth and plausible futures in the U.S. 24th International Conference of the System Dynamics Society; Nijmegen, The Netherlands; July 26, 2006.
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The Rise and Future Fall of ObesityThe Why and the How in Broad Strokes
Fraction of Obese Individuals &Prevalence of Related Health Problems
Time
Overweight &Obesity
PrevalenceR
Engines ofGrowth
HealthProtection
Efforts
-
B
Responsesto Growth
Resources &Resistance
-B
Obstacles
Broader Benefits& Supporters
R
Reinforcers
Drivers of Unhealthy
Habits
Engines of GrowthR1 Spiral of poor health and habitsR2 Parents and peer transmissionR3 Media mirrorsR4 Options shape habits shape optionsR5 Society shapes options shape society
Engines of GrowthR1 Spiral of poor health and habitsR2 Parents and peer transmissionR3 Media mirrorsR4 Options shape habits shape optionsR5 Society shapes options shape society
Responses to GrowthB1 Self-improvementB2 Medical responseB3 Improving preventive healthcareB4 Creating better messagesB5 Creating better options in beh. settingsB6 Creating better conditions in wider environB7 Addressing related health conditions
Responses to GrowthB1 Self-improvementB2 Medical responseB3 Improving preventive healthcareB4 Creating better messagesB5 Creating better options in beh. settingsB6 Creating better conditions in wider environB7 Addressing related health conditions
Resources, Resistance, Benefits & SupportsR6 Disease care costs squeeze preventionB8 Up-front costs undercut protection effortsB9 Defending the status quoB10 Potential savings build supportR7 Broader benefits build support
Resources, Resistance, Benefits & SupportsR6 Disease care costs squeeze preventionB8 Up-front costs undercut protection effortsB9 Defending the status quoB10 Potential savings build supportR7 Broader benefits build support
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Focus of Our Simulation Model• Explore effects of new interventions affecting caloric balance
(intake less expenditure) – U.S. policy discourse is primarily focused on:
• prevention among school-aged youth • medical treatment for the severely obese
– What are the likely consequences?• How much impact on adult obesity?• How long will it take to see?• Should we target other subpopulations?
(age, sex, weight category)
• Consider two classes of interventions– Changes in food & activity environments – Weight loss/maintenance services for individuals
• Additional intervention details (composition, coverage, efficacy, cost) left outside model boundary for now– Available data are inadequate to quantify impacts and cost-effectiveness – Could stakeholder Delphi help?
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Obesity Dynamics Over the Decades Dynamic Population Weight Framework
Dynamic Population Weight Framework
Population by Age (0-99) and Sex
Birth Immigration
Death
Yearly aging
NotOverweight
ModeratelyOverweight
ModeratelyObese
SeverelyObese
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Obesity Dynamics Over the Decades Dynamic Population Weight Framework
Dynamic Population Weight Framework
Population by Age (0-99) and Sex
Flow-rates betweenBMI categories
Overweight andobesity prevalence
Birth Immigration
Death
Yearly aging
NotOverweight
ModeratelyOverweight
ModeratelyObese
SeverelyObese
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Obesity Prevalence Over the DecadesDynamic Population Weight Framework
NotOverweight
ModeratelyOverweight
ModeratelyObese
SeverelyObese
NotOverweight
ModeratelyOverweight
ModeratelyObese
SeverelyObese
NotOverweight
ModeratelyOverweight
ModeratelyObese
SeverelyObese
Births Births Births Births
Age 0
Age 1
Age 99
No Change in BMI Category (maintenance flow)
Increase in BMI Category (up-flow)
Decline in BMI Category (down-flow)
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Obesity Dynamics Over the Decades Dynamic Population Weight Framework
Dynamic Population Weight Framework
Population by Age (0-99) and Sex
Flow-rates betweenBMI categories
Overweight andobesity prevalence
Birth Immigration
Death
CaloricBalance
Yearly aging
NotOverweight
ModeratelyOverweight
ModeratelyObese
SeverelyObese
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Obesity Dynamics Over the DecadesTwo Classes of Interventions
Dynamic Population Weight Framework
Population by Age (0-99) and Sex
Flow-rates betweenBMI categories
Overweight andobesity prevalence
Birth Immigration
Death
CaloricBalance
Yearly aging
NotOverweight
ModeratelyOverweight
ModeratelyObese
SeverelyObese
Trends and PlannedInterventions
Changes in the Physicaland Social Environment
Weight Loss/MaintenanceServices for Individuals
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Information SourcesTopic Area Data Source
Prevalence of Overweight and Obesity
BMI prevalence by sex and age (10 age ranges)National Health and Nutrition Examination Survey (1971-2002)
Translating Caloric Balances into BMI Flow-Rates
BMI category cut-points for children and adolescents
CDC Growth Charts
Median BMI within each BMI category National Health and Nutrition Examination Survey (1971-2002)Median height
Average kilocalories per kilogram of weight change Forbes 1986
Estimating BMI Category Down-Flow Rates
In adults: Self-reported 1-year weight change by sex and age
NHANES (2001-2002) *indicates 7-12% per year*
In children: BMI category changes by grade and starting BMI
Arkansas pre-K through 12th grade assessment (2004-2005) *indicates 15-28% per year*
Population Composition
Population by sex and ageU.S. Census and Vital Statistics (1970-2000 and projected)
Death rates by sex and age
Birth and immigration rates
Influence of BMI on Mortality
Impact of BMI category on death rates by age Flegal, Graubard, et al. 2005.
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Calibration of Uncertain ParametersTo Reproduce 60 BMI Prevalence Time Series(10 age ranges x 2 sexes x 3 high-BMI categories)
• Step 1: Iteratively adjust up-rate and down-rate constants
and initial BMI prevalences to reproduce steady-state BMI
prevalence for the early 1970s
• Step 2: Adjust 57 caloric balance time series (by age, sex,
and BMI category, 1975-2000) to reproduce BMI
prevalence growth for the 1980s and 1990s
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(a) Overweight fraction
0%
20%
40%
60%
80%
1970 1975 1980 1985 1990 1995 2000 2005
Fra
ctio
n o
f w
om
en a
ge
55-6
4
NHANES Simulated
(b) Obese fraction
0%
10%
20%
30%
40%
50%
1970 1975 1980 1985 1990 1995 2000 2005
Fra
ctio
n o
f w
om
en a
ge
55-6
4
NHANES Simulated
(c) Severely obese fraction
0%
5%
10%
15%
20%
25%
1970 1975 1980 1985 1990 1995 2000 2005
Fra
ctio
n o
f w
om
en a
ge
55-6
4
NHANES Simulated
Reproducing Historical Trends One of 20 {sex, age} Subgroups: Females age 55-64
Note: S-shaped curves, with inflection in the 1990s
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Explaining BMI Prevalence Growth: Age-to-Age Carryover + Caloric Imbalance
Example: Females Age 55-64
Overweight fractions of middle-aged women
0%
20%
40%
60%
80%
1970 1975 1980 1985 1990 1995 2000 2005
Fra
ctio
n o
f w
om
en b
y ag
e g
rou
p
Age 55-64 Age 45-54
Obese fractions of middle-aged women
0%
10%
20%
30%
40%
50%
1970 1975 1980 1985 1990 1995 2000 2005
Fra
ctio
n o
f w
om
en b
y ag
e g
rou
p
Age 55-64 Age 45-54
Severely obese fractions of middle-aged women
0%
5%
10%
15%
20%
25%
1970 1975 1980 1985 1990 1995 2000 2005
Fra
ctio
n o
f w
om
en b
y ag
e g
rou
p
Age 55-64 Age 45-54
Estimated caloric imbalances for women age 55-64
0
5
10
15
20
1970 1975 1980 1985 1990 1995 2000 2005
Kca
l p
er d
ay
Not overwt Mod overwt Obese
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Assumptions for Future Scenarios
Base Case• Caloric balances stay at 2000 values through 2050
Altering Food and Activity Environments
• Reduce caloric balances to their 1970 values by 2015
• Focused on
– ‘School Youth’: youth ages 6-19
– ‘All Youth’: all youth ages 0-19
– ‘School+Parents’: school youth plus their parents
– ‘All Adults’: all adults ages 20+
– ‘All Ages’: all youth and adults
Subsidized Weight Loss Programs for Obese Individuals
• Net daily caloric reduction of program is 40 calories/day (translates to 1.8 kg weight loss per year)
• Fully effective by 2010 and terminated by 2020
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Alternative FuturesObesity in Adults (20-74)
Obese fraction of Adults (Ages 20-74)
0%
10%
20%
30%
40%
50%
1970 1980 1990 2000 2010 2020 2030 2040 2050
Fra
cti
on
of
po
pn
20-
74
Base SchoolYouth AllYouth
School+Parents AllAdults AllAges
AllAges+WtLoss
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Findings• Inflection point in obesity probably occurred during the 1990s
– Simple extrapolations based on 1990s growth likely exaggerate future prevalences
• Caloric imbalance vs. 1970 only 1-2% (less than 50 cal./day) within any given age, sex, and BMI category
– Most of observed 9-13% cal./day increase in intake (USDA 1977-1996) has been natural consequence of weight gain (via metabolic adjustment), not its cause
• Impacts of changing environments on adult obesity take decades to play out fully: “Carryover effect”
• Youth interventions have only small impact on overall adult obesity
– Assumes (1) adult habits determined by adult environment, and (2) childhood overweight causes no irreversible metabolic changes
• Weight-loss for the obese could accelerate progress--but, first, an effective program that minimizes recidivism must be found
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Conclusions & Limitations• This model improves our understanding of population dynamics of weight
change and supports pragmatic planning/evaluation
– No other analytical model plays out effects of changes in caloric balance on BMI prevalences over the life-course
– Traces plausible impacts of population-level and individual-level interventions
• And addresses questions of whom to target, by how much, and by when
• But it has limitations—some addressable, some due to lack of data
– Does not indicate exact nature of interventions
• Does not address cost-effectiveness of interventions, nor political reinforcement and resistance
– Does not address racial/ethnic sub-groups
– Does not trace individual life histories (compartmental structure)
– Assumes habits determined by current environment, not by childhood learning
– Assumes no irreversible metabolic changes sustained as a result of childhood overweight/obesity
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Learning In and About Dynamic Systems
Benefits of Simulation/Game-based Learning
• Formal means of evaluating options
• Experimental control of conditions
• Compressed time
• Complete, undistorted results
• Actions can be stopped or reversed
• Visceral engagement and learning
• Tests for extreme conditions
• Early warning of unintended effects
• Opportunity to assemble stronger support
Dynamic Complexity Hinders…
• Generation of evidence (by eroding the conditions for experimentation)
• Learning from evidence (by demanding new heuristics for interpretation)
• Acting upon evidence (by including the behaviors of other powerful actors)
Sterman JD. Learning from evidence in a complex world. American Journal of Public Health (in press).
Sterman JD. Business dynamics: systems thinking and modeling for a complex world. Boston, MA: Irwin McGraw-Hill, 2000.
“In [dynamically complex] circumstances simulation becomes the only reliable way to test a hypothesis and evaluate the likely effects of policies."
-- John Sterman
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“Simulation is a third way of doing science.
Like deduction, it starts with a set of explicit
assumptions. But unlike deduction, it does not
prove theorems. Instead, a simulation generates
data that can be analyzed inductively. Unlike
typical induction, however, the simulated data
comes from a rigorously specified set of rules
rather than direct measurement of the real world.
While induction can be used to find patterns in
data, and deduction can be used to find
consequences of assumptions, simulation
modeling can be used as an aid to intuition.”
-- Robert Axelrod
Axelrod R. Advancing the art of simulation in the social sciences. In: Conte R, Hegselmann R, Terna P, editors. Simulating Social Phenomena. New York, NY: Springer; 1997. p. 21-40. <http://www.pscs.umich.edu/pub/papers/AdvancingArtofSim.pdf>.
Sterman JD. Business Dynamics: Systems Thinking and Modeling for a Complex World. Boston, MA: Irwin McGraw-Hill, 2000.
Simulation ExperimentsOpen a Third Branch of Science
“The complexity of our mental models vastly exceeds our ability to understand their implications without simulation."
-- John Sterman
How?
Where?
0
10
20
30
40
50
1960-62 1971-74 1976-80 1988-94 1999-2002
Prevalence of Obese Adults, United States
Why?
Data Source: NHANES 20202010
Who?
What?
Syndemics
Prevention Network
For Additional Informationhttp://www.cdc.gov/syndemics
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