Modeling Population Dynamics

1. Syndemics Prevention Network A Work in Progress Dialogue Modeling Population Dynamics Bobby Milstein Syndemics Prevention NetworkCenters for Disease Control and PreventionAtlanta, Georgia bmilstein@cdc.gov Jack Homer Homer Consulting Voorhees, NJ jhomer@comcast.net Obesity CDC Diabetes and Obesity Conference Denver, CO May 17, 2006

2. Topics for Today • Dynamic modeling for learning and action • Structure of the current model • Dynamic population weight framework • Calibrating the model • Behavior of the current model • A “status quo” future • Alternative futures • Conclusions, questions, and next steps

3. 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 Contributors Project Coordinator • Bobby Milstein System Dynamics Consultants • Jack Homer • Gary Hirsch Time Series Analysts • Danika Parchment • Cynthia Ogden • Margaret Carroll • Hatice Zahran Workshop Participants • Atlanta, GA: May 17-18 (N=47) • Lansing, MI: July 26-27 (N=55)

4. Purposes for Modeling Obesity DynamicsPrimary Uses and Users • Chart Progress Toward Goals (Planners/Evaluators/Media) • Set justifiable goals • Define a “status quo” future, as well as plausible alternatives based on policy scenarios • Estimate how strong interventions must be to make a difference, and how long it will take for those effects to become visible • Develop Better Measures and New Knowledge (Researchers) • Integrate diverse data sources into a single analytic environment • Infer properties of unmeasured or poorly measured parameters • Convene Multi-stakeholder Action Labs (Policymakers) • Understand how a dynamically complex obesity system functions • Discover short- and long-term consequences of alternative policies

5. Modeling Obesity DynamicsOpportunities to Integrate Diverse Policy Perspectives • Lifecourse Perspective • Consider life-long impacts and intergenerational effects • Ecological Perspective • Consider (a) weight-related behaviors, (b) behavioral settings, (c) social-cultural-economic-political forces, and (d) other health conditions, all by social position • Action Perspective • Clarify how obesity can be reduced (i.e., what kinds of actions are needed) • Clarify who is in a position to take those actions (i.e., roles for different types of organizations) • Estimate how strong new programs/policies must be to make a difference, as well as when those effects will become visible • Navigational Perspective • Set justifiable goals for the future, given existing momentum • Chart progress (annually?) by surveying actions and anticipating trajectories of change Others….

6. 2020 2010 Re-Directing the Course of ChangeQuestions Addressed by System Dynamics Modeling What? Where? Prevalence of Obese Adults, United States Why? How? Who? Data Source: NHANES

7. Modeling for Learning and Action Multi-stakeholder Dialogue Dynamic Hypothesis (Causal Structure) Plausible Futures (Policy Experiments) • Intervention Scenarios • Efforts to alter caloric balance via intensive weight loss/maintenance services and/or via broad changes in people’s food and activity environment • Focusing by age range and sex • Focusing by BMI category • Model Structure • Trace changes in caloric balance through to overweight and obesity prevalence1 • Trace intervention effects over the lifecourse by age and sex 1 Because health burden is associated with the obese tail of the BMI distribution, and cannot be accurately estimated from mean BMI alone

8. Major Project Phases • Conceptualization and Data Gathering (May 2005 – July 2005) • Convene stakeholder workshops • Collect time series data • Develop multiple iterations of a dynamic hypothesis • Formulation, Calibration, and Testing (August 2005 – November 2005) • Assure appropriate fit to history • Examine future behavior under status quo as well as policy scenarios • Policy Scenarios and Goal-setting (December 2005 – April 2006) • Study major classes of interventions, alone and in combination • Learn how strong new interventions must be to make a lasting difference, as well as how longit will take for those effects to become visible • Further Testing (May 2006 – July 2006) • Conduct sensitivity tests to see if data uncertainties affect policy conclusions • Elicit feedback from SD experts

9. System Dynamics Was Developed to Address Problems Marked By Dynamic Complexity Origins • Jay Forrester, MIT (from late 1950s) • Public policy applications starting late 1960s 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.

10. Understanding Dynamic Complexity Long—and often surprising—chains of cause and effect 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.

11. Tools for Policy Analysis Events Time Series Models Describe trends • Increasing: • Depth of causal theory • Data and sensitivity testing requirements • Robustness for longer-term projection • Value for developing policy insights Multivariate Stat Models Identify historical trend drivers and correlates Patterns Dynamic Simulation Models Anticipate new trends, learn about policy consequences, and set justifiable goals Structure

12. Social Norms and Values • Home and Family • School • Community • Work Site • Healthcare • Food and Beverage Industry • Agriculture • Education • Media • Government • Public Health Systems • Healthcare Industry • Business and Workers • Land Use and Transportation • Leisure and Recreation Sectors of Influence Behavioral Settings • Genetics • Psychosocial • Other Personal Factors Individual Factors Food and Beverage Intake Physical Activity Energy Intake Energy Expenditure Energy Balance Prevention of Overweight and Obesity Among Children, Adolescents, and Adults An Ecological Framework for Organizing Influences on Overweight and Obesity 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.

13. A Conventional View of Causal Forces Wider Environment (Economy, Health Conditions Technology, Laws) Influence Detracting from Genetic Metabolic on Healthy Diet & Activity Healthy Diet & Activity Rate Disorders Options Available at Prevalence of Home, School, Work, Healthiness of Diet Overweight & Community Influencing & Activity Habits Related Diseases Healthy Diet & Activity Media Messages Healthcare Services Promoting Healthy to Promote Healthy Diet & Activity Diet & Activity

14. A Conventional View of Causal Forces • This sort of open-loop (non-feedback) approach • Ignores intervention spill-over effects and often suggests the best strategy is a multi-pronged “fill all needs” one (even if not practical or affordable) • Ignores unintended side effects and delays that produce short-term vs. long-term differences in outcomes • Cannot fairly evaluate a phased approach; e.g. “bootstrapping” which starts more narrowly targeted but then broadens and builds upon successes over time

15. Fraction of Obese Individuals & Prevalence of Related Health Problems Health Protection Efforts B B - Responses Overweight & Obstacles to Growth Obesity R Prevalence Resources & - Resistance Engines of R Growth Reinforcers Broader Benefits & Supporters The Rise and Future Fall of ObesityThe Why and the How in Broad Strokes Drivers of Unhealthy Habits Time

16. A Closed-Loop View of Causal Forces DRAFT 5/8/05

17. A Closed-Loop View of Causal Forces DRAFT 5/8/05

18. A Closed-Loop View of Causal Forces DRAFT 5/8/05

19. 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?

20. Building a Foundation for AnalysisStructure of the Current Model

21. Phase 2: More Detailed Drivers of Change Obesity Prevalence Over the DecadesTwo Broad Phases Phase 1: Calculating Obesity Dynamics Dynamic Population Weight Framework (BMI Surveillance, Demography, and Nutritional Science) Policy Drivers (Trends & Interventions Affecting Caloric Balance by Age, Sex, BMI Category, etc…) Policy Drivers (Trends & Interventions Affecting Caloric Balance by Age, Sex, BMI Category, etc…) Consequences Over Time Changing Prevalence of Four BMI Categories: 1970-2050

22. Summary of Current Direction • Simulate overweight and obesity prevalences over the life-course • Reproduce relative stability in the 1970s and growth to the present, then extend to the future • Explore effects of new interventions affecting caloric balance • Focusing by age, sex, and/or BMI category • Treat intervention details (composition, response, coverage, efficacy, cost) as exogenous • Not yet addressing feedback loops of reinforcement and resistance • Not yet addressing cost-effectiveness

23. Obesity Dynamics Over the DecadesDynamic Population Weight Framework

24. Obesity Dynamics Over the DecadesDynamic Population Weight Framework

25. BMI Category Definitions For infants (ages 0-23 months) • Not overweight: weight-for-recumbent length (WRL)<85th percentile • Moderately overweight: WRL>85th percentile and <95th percentile • Moderately obese: WRL>95th percentile and <99th percentile; • Severely obese: WRL>99th percentile For youth (ages 2-19) • Not overweight: BMI<{85th percentile or 25} • Moderately overweight: BMI>{85th percentile and 25} and <{95th percentile or 30} • Moderately obese: BMI>{95th percentile and 30} and <{99th percentile or 35} • Severely obese: BMI>{99th percentile and 35} For adults (ages 20+) • Not overweight: BMI< 25 • Moderately overweight: BMI>25 and <30 • Moderately obese: BMI>30 and <35 • Severely obese: BMI>35 Percentiles from CDC Growth Charts based on NHANES I and II measurements.

26. Obesity Dynamics Over the DecadesDynamic Population Weight Framework Indicates possible extensions to the existing model

27. Obesity Dynamics Over the DecadesDynamic Population Weight Framework Indicates possible extensions to the existing model

28. Births Births Births Births Not Moderately Severely Moderately Age 0 Overweight Overweight Obese Obese Not Moderately Age 1 Severely Moderately Overweight Overweight Obese Obese Not Moderately Severely Moderately Age 99 Overweight Overweight Obese Obese No Change in BMI Category (maintenance flow) Increase in BMI Category (up-flow) Decline in BMI Category (down-flow) Obesity Prevalence Over the Decades Dynamic Population Weight Framework

29. Obesity Dynamics Over the DecadesDrivers of Change Indicates possible extensions to the existing model

30. Obesity Dynamics Over the DecadesDrivers of Change Indicates possible extensions to the existing model

31. Obesity Dynamics Over the DecadesDrivers of Change Indicates possible extensions to the existing model

32. Options for Affordable Recommended Foods (Work, School, Markets, Restaurants) Social Influences on Consumption & Selection Dynamic Population Weight Framework Food Price Food Activity Environment Environment Smoking Immigration Birth Yearly aging Changes in the Physical Population by Age (0-99) and Sex and Social Environment Trends and Planned Caloric Flow-rates between Moderately Moderately Severely Not Interventions Balance BMI categories Overweight Obese Obese Overweight Weight Loss/Maintenance Services for Individuals Death Overweight and obesity prevalence Obesity-attributable Obesity-attributable illness costs unhealthy days Obesity Dynamics Over the DecadesDrivers of Change Indicates possible extensions to the existing model

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

34. Calibrating the ModelEstimating Flow-Rates and Past Changes in Caloric Balance

35. Information Sources

36. Data Uncertainties & Limitations • No reliable longitudinal data on caloric intake and expenditure broken out by age, sex, BMI category • Reliable NHANES data on blacks and Mexican-Americans only since NHANES III (1988-94) • NHANES prevalence estimates are imprecise • May affect timing of inferred growth inflection point • Down-flow rate constants are imprecise • Don’t know to what extent historical caloric imbalances have led to increase in up-flows as opposed to decrease in down-flows • We have assumed entirely the former

37. Growth of Obesity for Four Age Ranges 1960-2002 Definitions Ages 2-19 (NHES): Obese BMI>=95th percentile on CDC growth chart Ages 2-19 (NHANES): Obese BMI>=30 or >=95th percentile on CDC growth chart Ages 20-74: Obese BMI>=30

38. Growth of Obesity for Four Age Ranges 1960-2002 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

39. Calibration of Uncertain ParametersTo Reproduce 60 BMI Prevalence Time Series(10 age ranges x 2 sexes x 3 high-BMI categories) • Step 1: Adjust uncertain constants and initial values to get near steady-state BMI prevalence for the early 1970s • In this step, assume no change in caloric balance after 1970 • Adjust 1970 up-rates and down-rates so that BMI prevalences settle-out at historical 1970s values • Set 1970 BMI prevalences (by annual age) to settled-out values • Repeat/adjust as necessary to minimize number of peaks and valleys (with increasing age) in assumed 1970 BMI prevalences • Step 2: Adjust uncertain time series inputs to reproduce BMI prevalence growth patterns for the 1980s and 1990s • To explain increasing overweight in infants, must assume increasing overweight/obesity at birth (3 series) • For non-infants, adjust caloric balances (54 series; by age, sex, and for Not Overwt, Mod Overwt, and Obese) to reproduce BMI growth • Calibrate from youngest age range to oldest • Within each age range calibrate first Overweight, then Obese, then Severely obese

40. Translating Caloric Balance Changes (ΔK)into Flow Rate Changes (ΔF) Parameters (for each age range and sex) • Cut-points for BMI categories (bc) • Median BMI within each BMI category (bm) • Median height (hm) • Assumption for the average number of kilocalories per kilogram of weight change (k) • Forbes’ empirical estimate of 8,050 kcal./kg • Implicitly takes into account the efficiency of weight deposition reflecting metabolic and other regulatory adjustments. • Glosses over known differences among individuals: starting weight, composition of diet, efficiency of weight deposition Forbes GB. Human body composition: growth, aging, nutrition, and activity. Springer: Berlin, Heidelberg; 1987. Forbes GB. Deliberate overfeeding in women and men: Energy costs and composition of the weight gain. British Journal of Nutrition 56:1-9; 1986.

41. Reproducing Historical Data One of 20 {sex, age} Subgroups: Females age 55-64 (a) Overweight fraction (b) Obese fraction 80% 50% 40% 60% 30% Fraction of women age 55-64 Fraction of women age 55-64 40% 20% 20% 10% 0% 0% 1970 1975 1980 1985 1990 1995 2000 2005 1970 1975 1980 1985 1990 1995 2000 2005 NHANES Simulated NHANES Simulated (c) Severely obese fraction 25% 20% 15% Fraction of women age 55-64 10% 5% 0% 1970 1975 1980 1985 1990 1995 2000 2005 NHANES Simulated Note: S-shaped curves, with inflection in the 1990s

42. Explaining BMI Prevalence Growth: Age-to-Age Carryover + Caloric ImbalanceExample: Females Age 55-64 Severely obese fractions of middle-aged women Overweight fractions of middle-aged women Obese fractions of middle-aged women 25% 50% 80% 20% 40% 60% 15% 30% Fraction of women by age group Fraction of women by age group Fraction of women by age group 40% 10% 20% 20% 5% 10% 0% 0% 0% 1970 1975 1980 1985 1990 1995 2000 2005 1970 1975 1980 1985 1990 1995 2000 2005 1970 1975 1980 1985 1990 1995 2000 2005 Age 55-64 Age 45-54 Age 55-64 Age 45-54 Age 55-64 Age 45-54 Estimated caloric imbalances for women age 55-64 20 15 Kcal per day 10 5 0 1970 1975 1980 1985 1990 1995 2000 2005 Not overwt Mod overwt Obese

43. Estimated Caloric Balances in 1990 and 2000 For Every Age Range & BMI Category (vs. 1970)

44. Behavior of the Current Model

45. Assumptions for Future Scenarios Altering Food and Activity Environments • Efforts to 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 • Used 2000 Census birth data by age of mother to estimate % of each adult age range that are parents of 6-19 year olds • ‘All Adults’: all adults ages 20+ • ‘All Ages’: all youth and adults Base Case • Caloric balances stay at 2000 values through 2050 Subsidized Weight Loss Programs for Obese Individuals • Net daily caloric reduction of program is 40 kcal/day (i.e., 14,600 kcal/year or 1.8kg weight loss per year) • Fully effective by 2010 and terminated by 2020 • ‘All Ages+WtLoss’: program applies to all obese youth and adults, and occurs on top of the ‘All Ages’ environmental improvement scenario

46. Exploring Future Scenarios Through Simulation Experiments

47. Alternative FuturesObesity in Teens (12-19) 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

48. Alternative FuturesObesity in Adults (20-74) 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

49. Exploring Future Scenarios Through Simulation Experiments

50. Simulation-based Findings (1) • An inflection point in the growth of overweight and obesity prevalences probably occurred during the 1990s • Extrapolations assuming linear growth may therefore exaggerate future prevalences • The caloric imbalance relative to 1970 accounting for this growth has been only in the range of 1-3% of daily caloric intake • Less than 50 kcal/day…per age, sex, and BMI category • Most of the overall observed increase in caloric intake (USDA CSFII ’77-’96: 9% F, 13% M) has been the natural consequence of weight gain, not its cause • Both expenditure and intake naturally increase with greater weight