1. Syndemics Prevention Network DRAFT: Please do not cite without permission Modeling Population Dynamics Obesity CDC Diabetes and Obesity Conference Denver, CO May 17, 2006 Syndemics Prevention Network Bobby Milstein Syndemics Prevention Network Centers for Disease Control and Prevention Atlanta, Georgia bmilstein@cdc.gov Jack Homer Homer Consulting Voorhees, NJ jhomer@comcast.net A Work in Progress Dialogue

2. Syndemics Prevention Network DRAFT: Please do not cite without permission Topics for Today Dynamic modeling for learning and action Structure of the current model –Dynamic population weight framework –Calibrating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. Syndemics Prevention Network DRAFT: Please do not cite without permission Contributors Core Design Team Dave Buchner Andy Dannenberg Bill Dietz Deb Galuska Larry Grummer-Strawn Anne Hadidx Robin Hamre Laura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)

4. Syndemics Prevention Network DRAFT: Please do not cite without permission Purposes for Modeling Obesity Dynamics Primary Uses and Users Chart Progress Toward Goals (Planners/Evaluators/Media) –Set justifiable goals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. Syndemics Prevention Network DRAFT: Please do not cite without permission Modeling Obesity Dynamics Opportunities to Integrate Diverse Policy Perspectives Lifecourse Perspective –Consider life-long impacts and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. Syndemics Prevention Network DRAFT: Please do not cite without permission Re-Directing the Course of Change Questions Addressed by System Dynamics Modeling How? Where? Prevalence of Obese Adults, United States Why? Data Source: NHANES 2020 2010 Who? What?

7. Syndemics Prevention Network DRAFT: Please do not cite without permission Modeling for Learning and Action Plausible Futures (Policy Experiments)Dynamic Hypothesis (Causal Structure) Multi-stakeholder Dialogue Model Structure Trace changes in caloric balance through to overweight and obesity prevalence 1 Trace intervention effects over the lifecourse by age and sex 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 1 Because health burden is associated with the obese tail of the BMI distribution, and cannot be accurately estimated from mean BMI alone

8. Syndemics Prevention Network DRAFT: Please do not cite without permission 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 long it 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. Syndemics Prevention Network DRAFT: Please do not cite without permission System Dynamics 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

10. Syndemics Prevention Network DRAFT: Please do not cite without permission 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. 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. Syndemics Prevention Network DRAFT: Please do not cite without permission 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

12. Syndemics Prevention Network DRAFT: Please do not cite without permission 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 IntakeEnergy 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.

13. Syndemics Prevention Network DRAFT: Please do not cite without permission A Conventional View of Causal Forces Healthiness of Diet & Activity Habits Prevalence of Overweight & Related Diseases Options Available at Home, School, Work, Community Influencing Healthy Diet & Activity Media Messages Promoting Healthy Diet & Activity Wider Environment (Economy, Technology, Laws) Influence on Healthy Diet & Activity Health Conditions Detracting from Healthy Diet & Activity Genetic Metabolic Rate Disorders Healthcare Services to Promote Healthy Diet & Activity

14. Syndemics Prevention Network DRAFT: Please do not cite without permission 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. Syndemics Prevention Network DRAFT: Please do not cite without permission The Rise and Future Fall of Obesity The Why and the How in Broad Strokes Fraction of Obese Individuals & Prevalence of Related Health Problems Time Overweight & Obesity Prevalence R Engines of Growth Health Protection Efforts - B Responses to Growth Resources & Resistance - B Obstacles Broader Benefits & Supporters R Reinforcers Drivers of Unhealthy Habits

16. Syndemics Prevention Network DRAFT: Please do not cite without permission DRAFT 5/8/05 A Closed-Loop View of Causal Forces

17. Syndemics Prevention Network DRAFT: Please do not cite without permission DRAFT 5/8/05 A Closed-Loop View of Causal Forces

18. Syndemics Prevention Network DRAFT: Please do not cite without permission A Closed-Loop View of Causal Forces DRAFT 5/8/05

19. Syndemics Prevention Network DRAFT: Please do not cite without permission 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. Syndemics Prevention Network DRAFT: Please do not cite without permission Building a Foundation for Analysis Structure of the Current Model

21. Syndemics Prevention Network DRAFT: Please do not cite without permission Phase 2: More Detailed Drivers of Change Obesity Prevalence Over the Decades Two Broad Phases Consequences Over Time Changing Prevalence of Four BMI Categories: 1970-2050 Dynamic Population Weight Framework (BMI Surveillance, Demography, and Nutritional Science) Policy Drivers (Trends & Interventions Affecting Caloric Balance by Age, Sex, BMI Category, etc…) Phase 1: Calculating Obesity Dynamics Policy Drivers (Trends & Interventions Affecting Caloric Balance by Age, Sex, BMI Category, etc…)

22. Syndemics Prevention Network DRAFT: Please do not cite without permission 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. Syndemics Prevention Network DRAFT: Please do not cite without permission Obesity Dynamics Over the Decades Dynamic Population Weight Framework

24. Syndemics Prevention Network DRAFT: Please do not cite without permission Obesity Dynamics Over the Decades Dynamic Population Weight Framework

25. Syndemics Prevention Network DRAFT: Please do not cite without permission 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. Syndemics Prevention Network DRAFT: Please do not cite without permission Obesity Dynamics Over the Decades Dynamic Population Weight Framework Indicates possible extensions to the existing model

27. Syndemics Prevention Network DRAFT: Please do not cite without permission Obesity Dynamics Over the Decades Dynamic Population Weight Framework Indicates possible extensions to the existing model

28. Syndemics Prevention Network DRAFT: Please do not cite without permission Obesity Prevalence Over the Decades Dynamic Population Weight Framework Not Overweight Moderately Overweight Moderately Obese Severely Obese Not Overweight Moderately Overweight Moderately Obese Severely Obese Not Overweight Moderately Overweight Moderately Obese Severely Obese 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)

29. Syndemics Prevention Network DRAFT: Please do not cite without permission Obesity Dynamics Over the Decades Drivers of Change Indicates possible extensions to the existing model

30. Syndemics Prevention Network DRAFT: Please do not cite without permission Obesity Dynamics Over the Decades Drivers of Change Indicates possible extensions to the existing model

31. Syndemics Prevention Network DRAFT: Please do not cite without permission Obesity Dynamics Over the Decades Drivers of Change Indicates possible extensions to the existing model

32. Syndemics Prevention Network DRAFT: Please do not cite without permission Obesity Dynamics Over the Decades Drivers of Change Indicates possible extensions to the existing model

33. Syndemics Prevention Network DRAFT: Please do not cite without permission Obesity Dynamics Over the Decades Drivers of Change Indicates possible extensions to the existing model

34. Syndemics Prevention Network DRAFT: Please do not cite without permission Calibrating the Model Estimating Flow-Rates and Past Changes in Caloric Balance

35. Syndemics Prevention Network DRAFT: Please do not cite without permission Information Sources Topic AreaData 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 adolescentsCDC Growth Charts Median BMI within each BMI category National Health and Nutrition Examination Survey (1971-2002) Median height Average kilocalories per kilogram of weight changeForbes 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 12 th grade assessment (2004-2005) *indicates 15-28% per year* Population Composition Population by sex and age U.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 ageFlegal, Graubard, et al. 2005.

36. Syndemics Prevention Network DRAFT: Please do not cite without permission 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. Syndemics Prevention Network DRAFT: Please do not cite without permission 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. Syndemics Prevention Network DRAFT: Please do not cite without permission 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. Syndemics Prevention Network DRAFT: Please do not cite without permission Calibration of Uncertain Parameters To 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. Syndemics Prevention Network DRAFT: Please do not cite without permission Parameters (for each age range and sex) Cut-points for BMI categories (b c ) Median BMI within each BMI category (b m ) Median height (h m ) 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 Translating Caloric Balance Changes (ΔK) into Flow Rate Changes (ΔF) 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. Syndemics Prevention Network DRAFT: Please do not cite without permission (a) Overweight fraction 0% 20% 40% 60% 80% 19701975198019851990199520002005 Fraction of women age 55-64 NHANESSimulated (b) Obese fraction 0% 10% 20% 30% 40% 50% 19701975198019851990199520002005 Fraction of women age 55-64 NHANESSimulated (c) Severely obese fraction 0% 5% 10% 15% 20% 25% 19701975198019851990199520002005 Fraction of women age 55-64 NHANESSimulated Reproducing Historical Data One of 20 {sex, age} Subgroups: Females age 55-64 Note: S-shaped curves, with inflection in the 1990s

42. Syndemics Prevention Network DRAFT: Please do not cite without permission 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% 19701975198019851990199520002005 Fraction of women by age group Age 55-64Age 45-54 Obese fractions of middle-aged women 0% 10% 20% 30% 40% 50% 19701975198019851990199520002005 Fraction of women by age group Age 55-64Age 45-54 Severely obese fractions of middle-aged women 0% 5% 10% 15% 20% 25% 19701975198019851990199520002005 Fraction of women by age group Age 55-64Age 45-54 Estimated caloric imbalances for women age 55-64 0 5 10 15 20 19701975198019851990199520002005 Kcal per day Not overwtMod overwtObese

43. Syndemics Prevention Network DRAFT: Please do not cite without permission Estimated Caloric Balances in 1990 and 2000 For Every Age Range & BMI Category (vs. 1970)

44. Syndemics Prevention Network DRAFT: Please do not cite without permission Behavior of the Current Model

45. Syndemics Prevention Network DRAFT: Please do not cite without permission Assumptions for Future Scenarios Base Case Caloric balances stay at 2000 values through 2050 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 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. Syndemics Prevention Network DRAFT: Please do not cite without permission Intervention Scenario Changing Food & Activity Environments Focused On… Weight Loss Programs for Obese Individuals Selected Results Pre- School School-age Youth Adult Parents of School- aged Youth All Other Adults All Ages Obese Fraction Among Teens (12-19) Obese Fraction Among Adults (20-74) 2020205020202050 Base or Status Quo -- School Youth All Youth School + Parents All Adults All Ages All Ages + Wt Loss Exploring Future Scenarios Through Simulation Experiments

47. Syndemics Prevention Network DRAFT: Please do not cite without permission Alternative Futures Obesity in Teens (12-19) Obese fraction of Teens (Ages 12-19) 0% 10% 20% 30% 40% 50% 197019801990200020102020203020402050 Fraction of popn 12-19 BaseSchoolYouthAllYouthAllAges+WtLoss

48. Syndemics Prevention Network DRAFT: Please do not cite without permission Alternative Futures Obesity in Adults (20-74) Obese fraction of Adults (Ages 20-74) 0% 10% 20% 30% 40% 50% 197019801990200020102020203020402050 Fraction of popn 20-74 BaseSchoolYouthAllYouth School+ParentsAllAdultsAllAges AllAges+WtLoss

49. Syndemics Prevention Network DRAFT: Please do not cite without permission Intervention Scenario Changing Food & Activity Environments Focused On… Weight Loss Programs for Obese Individuals Selected Results Pre- School School-age Youth Adult Parents of School- aged Youth All Other Adults All Ages Obese Fraction Among Teens (12-19) Obese Fraction Among Adults (20-74) 2020205020202050 Base or Status Quo -- 20.1%20.0%37.9%39.1% School Youth 11.5%10.1%37.3%36.6% All Youth 9.7%6.1%37.3%36.1% School + Parents 11.5%10.1%33.1%29.3% All Adults 20.1%20.0%25.3%18.7% All Ages 9.7%6.1%24.7%15.5% All Ages + Wt Loss 5.3%6.1%14.7%15.1% Exploring Future Scenarios Through Simulation Experiments

50. Syndemics Prevention Network DRAFT: Please do not cite without permission 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

51. Syndemics Prevention Network DRAFT: Please do not cite without permission Reconciling the CSFII Data with Our Estimates of Caloric Balance A Dynamic Hypothesis Model Scope Caloric balance (up 1-2%) Caloric expenditure (up with greater BMI) Weight-neutral intake (natural appetite up with greater expenditure) Mean caloric intake (up 9-13%) Mean BMI (up 9-12%)

52. Syndemics Prevention Network DRAFT: Please do not cite without permission Simulation-based Findings (2) Current focus on interventions during childhood will have only small impact on overall adult obesity (~6% relative to status quo) –Unless effectively linked to the rest of the population Impacts on adult obesity of changing food and activity environments (by 2015) take decades to play out fully –Due to age-to-age carryover effect Effective weight-loss programs—if any exist—could accelerate progress through subsidies for obese individuals –But the cost could be high (even if subsidies terminated by 2020) –And may be undermined by diet failure and recidivism

53. Syndemics Prevention Network DRAFT: Please do not cite without permission Conclusions 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