1. Syndemics Prevention Network
System Dynamics Simulation in Support of Obesity Prevention Decision-Making
Bobby Milstein and Jack Homer
For Institute of Medicine Committee on an
Evidence Framework for Obesity Prevention Decision-Making
Irvine, California
March 16, 2009
Bobby Milstein (August 26, 2004)

2. Syndemics Prevention Network
Agenda
System Dynamics Background
Obesity Life-Course Model ( , CDC)
Cardiovascular Risk Model ( , CDC & NIH)
Bobby Milstein (August 26, 2004)

3. Syndemics Prevention Network
Re-Directing the Course of Change Questions Addressed by System Dynamics Modeling
What?
Prevalence of Obese Adults, United States
Where?
Historical
Data
Regression Forecast
Simulation Experiments
Why?
How?
Who?
Regression Model Assumes
Linear growth (by age, sex)
Historical Data: NHANES
Projection: Ruhm CJ. Current and future prevalence of obesity and severe obesity in the United States Forum for Health Economics & Policy 2007;10(2):1-28.
Bobby Milstein (August 26, 2004)

4. Types of Models for Policy Planning & Evaluation
Syndemics Prevention Network
Types of Models for Policy Planning & Evaluation
Events
Time Series Models
Describe trends
Increasing:
Depth of causal theory
Robustness for longer-term projection
Value for developing policy insights
Degrees of uncertainty
Leverage for change
Multivariate Statistical Models
Identify historical trend drivers and correlates
Patterns
Dynamic Simulation Models
Anticipate new trends, learn about policy consequences, and set justifiable goals
Structure
Bobby Milstein (August 26, 2004)

5. Syndemics Prevention Network
System Dynamics Simulating Dynamic Complexity
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
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
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.
Bobby Milstein (August 26, 2004)

6. System Dynamics Health Applications 1970s to the Present
Syndemics Prevention Network
System Dynamics Health Applications 1970s 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):
Bobby Milstein (August 26, 2004)

7. An (Inter) Active Form of Policy Planning/Evaluation
Syndemics Prevention Network
An (Inter) Active Form of Policy Planning/Evaluation
System Dynamics is a methodology to…
Map the salient forces that contribute to a persistent problem;
Convert the map into a computer simulation model, integrating the best information and insight available;
Compare results from simulated “What If…” experiments to identify intervention policies that might plausibly alleviate the problem;
Conduct sensitivity analyses 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
Realistic understanding of causal dynamics (actions, reactions, accumulations, delays)
Justifiable goals (numerical targets, life-course implications, timing)
Criteria for choosing actions and impetus for implementing them (within the health sector and beyond)
Bobby Milstein (August 26, 2004)

8. Syndemics Prevention Network
An Ecological Framework for Organizing Influences on Overweight and Obesity
Energy Balance
Prevention of Overweight and Obesity Among Children, Adolescents, and Adults
Individual Factors
Behavioral Settings
Social Norms and Values
Home and Family
School
Community
Work Site
Healthcare
Genetics
Psychosocial
Other Personal Factors
Food and Beverage Industry
Agriculture
Education
Media
Government
Public Health Systems
Healthcare Industry
Business and Workers
Land Use and Transportation
Leisure and Recreation
Food and Beverage Intake
Physical Activity
Sectors of Influence
Energy Intake
Energy Expenditure
Adapted from: Koplan JP, Liverman CT, Kraak VI, editors. Preventing childhood obesity: health in the balance. Washington, DC: Institute of Medicine, National Academies Press; 2005.
Bobby Milstein (August 26, 2004)

9. The Obesity “System”: A Broad Causal Map
Syndemics Prevention Network
The Obesity “System”: A Broad Causal Map
Responses to Growth
B1 Self-improvement
B2 Medical response
B3 Improving preventive healthcare
B4 Creating better messages
B5 Creating better options in beh. settings
B6 Creating better conditions in wider environ
B7 Addressing related health conditions
Engines of Growth
R1 Spiral of poor health and habits
R2 Parents and peer transmission
R3 Media mirrors
R4 Options shape habits shape options
R5 Society shapes options shape society
Resources, Resistance, Benefits & Supports
R6 Disease care costs squeeze prevention
B8 Up-front costs undercut protection efforts
B9 Defending the status quo
B10 Potential savings build support
R7 Broader benefits build support
5/8/05
Bobby Milstein (August 26, 2004)

10. The Closed-Loop View Leads Us To Question…
Syndemics Prevention Network
The Closed-Loop View Leads Us To Question…
How can the engines of growth loops (i.e. social and economic reinforcements) be weakened?
What incentives can reward parents, school administrators, employers, and other decision-makers for expanding healthy diet and activity 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?
Bobby Milstein (August 26, 2004)

11. Syndemics Prevention Network
Mapping and Modeling
Two systems thinking tools:
Causal loop diagramming (mapping)
Mathematical simulation (modeling)
Causal diagrams provide a ‘macroscopic’ view that goes beyond ‘laundry list’ thinking by introducing circular causality.
Maps reveal possibilities, but are not testable. They have little explanatory power and cannot weigh the relative importance of factors.
A useful evidence-based systems framework includes both maps and models.
“Without modeling, we might think we are learning to think holistically when we are actually learning to jump to conclusions.”
– John Sterman
Hurricane Andrew: Aug, 23, 24, 25, 1992
Homer J and Oliva R. Maps and models in system dynamics: a response to Coyle System Dynamics Review 2001; 17:
Bobby Milstein (August 26, 2004)

12. Simulation and “Double-Loop Learning”
Syndemics Prevention Network
Simulation and “Double-Loop Learning”
Real World
Unknown structure
Dynamic complexity
Time delays
Impossible experiments
Virtual World
Known structure
Controlled experiments
Enhanced learning
Implementation
Game playing
Inconsistency
Short term
Selected
Missing
Delayed
Biased
Ambiguous
Decisions
Information
Feedback
Strategy, Structure,
Decision Rules
Inability to infer dynamics from mental models
Mental
Models
Misperceptions
Unscientific
Biases
Defensiveness
Sterman JD. Learning in and about complex systems. System Dynamics Review 1994;10(2-3):
Sterman JD. Business dynamics: systems thinking and modeling for a complex world. Boston, MA: Irwin McGraw-Hill, 2000.
Bobby Milstein (August 26, 2004)

13. What is a System? What are Dynamics?
Syndemics Prevention Network
What is a System? What are Dynamics?
System (Structure) = Stocks + Flows + Feedback Loops +…
Stocks are accumulations of flows (of population, resources, changing goals, perceptions, etc.)
Feedback loops link accumulations back to decisions that alter the flows: only 2 types (goal-seeking, self-reinforcing)
Delays complicate things further
As do non-linearities (need for critical mass, saturation effects)
Dynamics = Behavior over time
Patterns in time series data (growth, fluctuation, etc.)
Visible relationships of two or more variables (move together, move opposite, lead-lag, etc.)
Bobby Milstein (August 26, 2004)

14. Syndemics Prevention Network
System Dynamics Moves from Behavior to Structure by Identifying Accumulations and Feedback Responses
Problem Situation 8 6 4 2 10 12 14 16 18 20
Seconds elapsed
Ounces
Water Level Over Time
System Behavior
System Structure
Bobby Milstein (August 26, 2004)

15. Water Glass Model Diagram (Vensim™ software)
Syndemics Prevention Network
Water Glass Model Diagram (Vensim™ software)
Faucet openness
Water flow at
full open
Maximum faucet
openness decision
“Policy Lever”
Current
Water
Level
Water flow
“Stock”
“Flow”
“Constant”
Desired water level
Water level gap
“Delay”
Perceived water
level gap
Time to perceive
water level gap
Bobby Milstein (August 26, 2004)

16. Complete Equations for Water Glass Model
Syndemics Prevention Network
Bobby Milstein (August 26, 2004)

17. Sample Output from Water Glass Model
Syndemics Prevention Network
Sample Output from Water Glass Model
What If… 8
Ounces
Perc time Max open
Target 6 4 2 2 4 6 8 10 12 14 16 18 20
Seconds elapsed
Bobby Milstein (August 26, 2004)

18. Review: Basic Steps in SD Model Building
Syndemics Prevention Network
Review: Basic Steps in SD Model Building
Problem Situation 8 6 4 2 10 12 14 16 18 20
Seconds elapsed
Ounces
System Behavior
Target
System Structure
What if…?
Perc time Max open
Water flow at
full open
1. Current water level = INTEG( Water flow , 0)
2. Water flow = Water flow at full open * Faucet openness
3. Water flow at full open = 1 ounce per second
4. 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 1
6. Perceived water level gap = DELAY1I (Water level gap,
Time to perceive water level gap, 0)
7. Water level gap = Desired water level - Current water level
8. Desired water level = 6 ounces
9. Time to perceive water level gap = 1 second
FINAL TIME = 20 seconds
INITIAL TIME = 0
TIME STEP = seconds
System Equations
System Model
Current water
level
Water flow
Desired water level
Water level gap
Faucet openness
Perceived water
level gap
Time to perceive
water level gap
Maximum faucet
openness decision
Bobby Milstein (August 26, 2004)

19. The Modeling Process is Iterative…
Syndemics Prevention Network
The Modeling Process is Iterative…
Adapted from Saeed 1992
Homer JB. Why we iterate: scientific modeling in theory and practice. System Dynamics Review 1996;12(1):1-19.
Bobby Milstein (August 26, 2004)

20. Empirical…and…Critical
Syndemics Prevention Network
Empirical…and…Critical
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; p
Graham AK. Parameter estimation in system dynamics modeling. In: Randers J, editor. Elements of the System Dynamics Method. Cambridge, MA: MIT Press; p
Sterman JD. Business dynamics: systems thinking and modeling for a complex world. Boston, MA: Irwin McGraw-Hill, 2000.
Bobby Milstein (August 26, 2004)

21. Practical Options in Causal Modeling
Syndemics Prevention Network
Practical Options in Causal Modeling
High
Impractical
Expansive
Too hard to verify, modify, and understand
Scope (Breadth)
Focused
Simplistic
Low
Low
High
Detail (Disaggregation)
Bobby Milstein (August 26, 2004)

22. Syndemics Prevention Network
Model Structure and Level of Detail Depends 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.
Bobby Milstein (August 26, 2004)

23. Tests for Building Confidence in Simulation Models
Syndemics Prevention Network
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
Bobby Milstein (August 26, 2004)

24. The Future of Systems Simulation
Syndemics Prevention Network
The Future of Systems Simulation
More ambitious models, addressing big strategic questions
Supporting macro-models with more empirical data, but also with the results of focused (operational, mechanistic) micro-models
Improved use of expert judgment; e.g., Delphi method
More use of software tools for automated model calibration, sensitivity testing, and documentation
Better public outreach through logical, balanced presentation that provides clear path from evidence to insight
“There seems to be a common attitude that the major difficulty is shortage of information and data. Once data are collected, people feel confident in interpreting the implications. I differ on both of these attitudes. The problem is not shortage of data but rather our inability to perceive the consequences of the information we already possess.”
– Jay Forrester
Forrester JW. System dynamics—the next fifty years. System Dynamics Review 2007;23(2/3):
Homer JB. Macro- and micro-modeling of field service dynamics. System Dynamics Review 1999;15(2):
Meadows DH, Robinson JM. The Electronic Oracle: Computer Models and Social Decisions. Wiley & Sons, 1985.
Bobby Milstein (August 26, 2004)

25. Obesity Life-Course Model (with CDC, 2005-2006)
Syndemics Prevention Network
Obesity Life-Course Model (with CDC, )
Initially considered obesity dynamics broadly
Narrowed our focus to address a fundamental question at the heart of much policy discussion:
How do changes in caloric balance affect the future BMI distribution across the life-course? What does that imply for policy?
Growth of Obesity for Four Age Ranges United States,
Obesity definitions by age
Ages 2-19: BMI>=30 or >=95th percentile on CDC growth chart
Ages 20-74: BMI>=30
Data source: National Center for Health Statistics, CDC: National Health Examination Survey (NHES) , National Health and Nutrition Examination Survey (NHANES)
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.
Bobby Milstein (August 26, 2004)

26. Focusing on Life-Course Dynamics
Syndemics Prevention Network
Focusing on Life-Course Dynamics
Explore likely consequences of possible interventions affecting caloric balance (intake less expenditure)
How much impact on obesity prevalence?
How long will it take to see?
Should we target particular subpopulations? (age, sex, weight category; lack data for race, ethnicity)
Consider interventions broadly but leave details (composition, coverage, efficacy, cost) outside model boundary for now
Available data inadequate
Would require a separate research effort to estimate these details
Not addressing feedback loops of reinforcement and resistance
Not addressing cost-effectiveness
Bobby Milstein (August 26, 2004)

27. Syndemics Prevention Network
Growth of Obesity by Age Range since (Gender breakout not shown here)
Syndemics Prevention Network
Definitions
Ages 2-19 (NHES): Overweight BMI>=85th percentile, Obese BMI>=95th percentile on CDC growth chart
Ages 2-19 (NHANES): Overweight BMI>=25 or 85th percentile, Obese BMI>=30 or 95th percentile, Severely obese BMI>=35 or 99th percentile on CDC growth chart
Ages 20-74: Overweight BMI>=25; Obese BMI>=30; Severely obese BMI>=35
Bobby Milstein (August 26, 2004)

28. Syndemics Prevention Network
Population Weight-Change Dynamics From Caloric Balance to Obesity Prevalence
Dynamic Population Weight Framework
Birth
Immigration
Yearly aging
Population by Age (0-99) and Sex
Caloric
Flow-rates between
Not
Moderately
Moderately
Severely
Balance
BMI categories
Overweight
Overweight
Obese
Obese
Death
Overweight and
obesity prevalence
Data sources: NHANES, CDC growth charts, Census, Vital Statistics,
Arkansas K-12 assessment, Forbes 1986, Flegal 2005
Bobby Milstein (August 26, 2004)

29. Detail of Aging and BMI Change Processes
Syndemics Prevention Network
Detail of Aging and BMI Change Processes
Not
Overweight
Moderately
Obese
Severely
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)
Data sources: NHANES, CDC growth charts, Census, Vital Statistics,
Arkansas K-12 assessment, Forbes 1986, Flegal 2005
Bobby Milstein (August 26, 2004)

30. Two Classes of Interventions
Syndemics Prevention Network
Two Classes of Interventions
Dynamic Population Weight Framework
Birth
Immigration
Yearly aging
Changes in the Physical
Population by Age (0-99) and Sex
and Social Environment
Trends and Planned
Caloric
Flow-rates between
Not
Moderately
Moderately
Severely
Interventions
Balance
BMI categories
Overweight
Overweight
Obese
Obese
Weight Loss/Maintenance
Services for Individuals
Death
Overweight and
obesity prevalence
Data sources: NHANES, CDC growth charts, Census, Vital Statistics,
Arkansas K-12 assessment, Forbes 1986, Flegal 2005
Bobby Milstein (August 26, 2004)

31. Syndemics Prevention Network
Is Adult Activity and Eating Much Influenced by Habits Learned in Childhood?
It is often said that activity and eating habits are shaped during early childhood, and that once poor habits are established, it is difficult to change them.1
It is true that overweight children are more likely to become obese adults.2 However, this could simply reflect adult surroundings that are in many cases similar to the childhood environment.
Adequate long-term studies to test the habit-carryover hypothesis have not been done.3
What happens when the childhood environment was healthy but the adult environment is not, or vice versa? We don’t know. But we do know there is a strong link between one’s current surroundings and social networks and one’s weight.4
1. Ritchie et al. Pediatric overweight: A review of the literature. UC Berkeley Center for Weight and Health, 2001; Byrne, J Nutrition Education and Behavior, 34(4), 2002.
2. Deckelbaum and Williams, Obesity Research 9(Suppl 4), 2001; Dietz and Gortmaker, Annual Review of Public Health 22, 2001; Goran MI, AJCN 73, 2001; Dietz WH, J Nutrition128(S), 1998.
3. Bar-Or O., PCPFS Research Digest Series 2, No. 4, 1995.
4. Christakis and Fowler. NEJM 357, 2007.
Bobby Milstein (August 26, 2004)

32. Syndemics Prevention Network
Findings
An inflection point in the growth of overweight and obesity prevalences probably occurred during the 1990s
The daily caloric imbalance accounting for this growth has been less than 50 kcal/day (within each BMI category)
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 (assuming adult habits are determined by adult environment)
Weight loss programs for the obese could accelerate progress—but is there a realistic alternative to risky/expensive bariatric surgery?
Bobby Milstein (August 26, 2004)

33. Alternative Futures for Teenage Obesity
Syndemics Prevention Network
Alternative Futures for Teenage Obesity
Obese fraction of Teens (Ages 12-19)
50%
40%
30%
Fraction of popn 12-19
20%
10% 0%
1970
1980
1990
2000
2010
2020
2030
2040
2050
Base
SchoolYouth
AllYouth
AllAges+WtLoss
Bobby Milstein (August 26, 2004)

34. Alternative Futures for Adult Obesity
Syndemics Prevention Network
Alternative Futures for Adult Obesity
Obese fraction of Adults (Ages 20-74)
50%
40%
30%
Fraction of popn 20-74
20%
10% 0%
1970
1980
1990
2000
2010
2020
2030
2040
2050
Base
SchoolYouth
AllYouth
School+Parents
AllAdults
AllAges
AllAges+WtLoss
Bobby Milstein (August 26, 2004)

35. Possible Extensions to Model Details of Activity & Food Environments
Dynamic Population Weight Framework
Population by Age (0-99) and Sex
Flow-rates between
BMI categories
Overweight and
obesity prevalence
Obesity-attributable
unhealthy days
illness costs
Birth
Immigration
Death
Caloric
Balance
Yearly aging
Not
Overweight
Moderately
Obese
Severely
Trends and Planned
Interventions
Changes in the Physical
and Social Environment
Weight Loss/Maintenance
Services for Individuals
Food Price
Smoking
Social Influences on
Consumption &
Selection
Options for Affordable
Recommended Foods (Work,
School, Markets, Restaurants)
Activity Limiting
Conditions
Options for Safe, Accessible
Physical Activity (Work,
School, Neighborhoods)
Distance from Home to
Work, School, Errands
Electronic Media
in the Home
Active/Inactive
Options
Activity
Environment
Food
Syndemics Prevention Network
Possible Extensions to Model Details of Activity & Food Environments
Indicates possible extensions to the existing model
Bobby Milstein (August 26, 2004)

36. Syndemics Prevention Network
Cardiovascular Disease Intervention Strategy (with CDC and NIH, )
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?
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
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.
Bobby Milstein (August 26, 2004)

37. Syndemics Prevention Network
Risk Factors for CVD
Data sources: NHANES, NHIS, MEPS, AHA/NIH reports, Census, Vital Statistics,
Framingham risk calculators, literature on risk factors and costs
Bobby Milstein (August 26, 2004)

38. Tobacco and Air Quality Interventions
Syndemics Prevention Network
Tobacco and Air Quality Interventions
Data sources: NHANES, NHIS, MEPS, AHA/NIH reports, Census, Vital Statistics,
Framingham risk calculators, literature on risk factors and costs
Bobby Milstein (August 26, 2004)

39. Health Care Interventions
Syndemics Prevention Network
Health Care Interventions
Data sources: NHANES, NHIS, MEPS, AHA/NIH reports, Census, Vital Statistics,
Framingham risk calculators, literature on risk factors and costs
Bobby Milstein (August 26, 2004)

40. Interventions Affecting Stress
Syndemics Prevention Network
Interventions Affecting Stress
Data sources: NHANES, NHIS, MEPS, AHA/NIH reports, Census, Vital Statistics,
Framingham risk calculators, literature on risk factors and costs
Bobby Milstein (August 26, 2004)

41. Healthy Diet Interventions
Syndemics Prevention Network
Healthy Diet Interventions
Data sources: NHANES, NHIS, MEPS, AHA/NIH reports, Census, Vital Statistics,
Framingham risk calculators, literature on risk factors and costs
Bobby Milstein (August 26, 2004)

42. Physical Activity & Weight Loss Interventions
Syndemics Prevention Network
Physical Activity & Weight Loss Interventions
Data sources: NHANES, NHIS, MEPS, AHA/NIH reports, Census, Vital Statistics,
Framingham risk calculators, literature on risk factors and costs
Bobby Milstein (August 26, 2004)

43. Syndemics Prevention Network
Adding Up the Costs
Data sources: NHANES, NHIS, MEPS, AHA/NIH reports, Census, Vital Statistics,
Framingham risk calculators, literature on risk factors and costs
Bobby Milstein (August 26, 2004)

44. Syndemics Prevention Network
Graphs of assumptions
#1: Age 18 obesity reduced 70% by 2030
#2a: Poor diet reduced from 64% to 41%
#3: Use of weight loss services among obese increased from 10% to 24%
#2b: Inadequate PA reduced from 51% to 22%
Bobby Milstein (August 26, 2004)

45. Obesity prevalence in adults (all subgroups combined)
Syndemics Prevention Network
Obesity prevalence in adults (all subgroups combined)
-3.5%
-30.5%
-37.2%
As in the life-course model, lower childhood obesity
accounts for only 12% of the adult prevalence reduction
projected in the full-population approach of “Teen_DietPA”
Bobby Milstein (August 26, 2004)

46. Syndemics Prevention Network
Further Consequences
With the three policies combined, cumulative deaths through 2040 can be reduced by 2.36M and cumulative consequence costs by $1.08Tr
Bobby Milstein (August 26, 2004)