1. Syndemics Prevention Network Understanding Obesity Dynamics A Foundation for Directing Change and Charting Progress Obesity Dynamics Modeling Project May 17-18, 2005 Atlanta, GA

2. Syndemics Prevention Network General Plan for the Workshop Day 1 Dynamic Dilemmas System Dynamics in Action Obesity Dynamics – General Causal Structure Group Exercise – Identifying Forces of Change Day 2 Modeling for Learning – Using Simulation Experiments Group Exercise – Organizing Effective Health Protection Efforts Directing Change and Charting Progress Snapshot Evaluation

3. Syndemics Prevention Network Considering Multiple Perspectives on Overweight and Obesity

4. Syndemics Prevention Network Concentrating on Dynamic Dilemmas: Understanding Change, Setting Goals, Motivating Action, Charting Progress

5. Syndemics Prevention Network Understanding the Dynamics of Growth Fraction of Obese Individuals & Prevalence of Related Health Problems Time Health Protection Efforts Drivers of Unhealthy Habits R Engines Of Growth B Responses to Growth Overweight & Obesity Prevalence

6. Syndemics Prevention Network 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?

7. Syndemics Prevention Network Some Sources of Dynamic Complexity for Obesity Multiple Goals Improve diet Increase physical activity Decrease physical inactivity Assure healthful conditions in diverse behavioral settings (i.e., home, school, work, community) Harness synergies with other social values (i.e., school performance, economic productivity, environmental protection) Barriers Cost of caring for weight-related diseases Cost of health protection efforts Spiral of unhealthy habits leading to poor health leading to even less healthy habits Social reinforcement of diet and activity based on observing parents’, peers’, and others’ behavior Demand for unhealthy food and inactive habits stimulates supply Resistance by defenders of the status quo Simultaneous Program Strategies Deliver healthcare services Enhance media messages Expand options in behavioral settings Modify trends in the wider environment (i.e., economy, technology, laws) Address other health conditions that impede healthy diet and activity (e.g., asthma, oral health, etc.) Time Delays 1-2 year lag for metabolism to stabilize after change in net caloric intake 14 year lag for youth to age into adulthood 58 year lag for cohorts of adults Several years for programs to mature and for policies to be fully enacted/enforced At least several years to see policy impacts, and even longer to affect the wider environment

8. Syndemics Prevention Network Dynamic Complexity is Real… and Consequential 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. Sterman JD. Business dynamics: systems thinking and modeling for a complex world. Boston, MA: Irwin McGraw-Hill, 2000.

9. Syndemics Prevention Network

10. Syndemics Prevention Network System Dynamics Was Designed to Address Problems Marked By Dynamic Complexity Multiple, interrelated goals –Programs/policies in one area can shift the burden of disease elsewhere –Progress in aggregate measures conceals significant and unchanging disparities Long time delays –Consequences/accumulations extend over multiple life stages Known interventions have yielded little long-term benefit or there is uncertainty about how to intervene effectively –Unclear how to combine multiple interventions into a comprehensive strategy Trajectory of future progress is uncertain –Unclear how strong interventions have to be to alter the status quo –May be a worse-before-better pattern of change Research agenda and information systems are not well defined –Significant drivers exist but are poorly understood and not monitored routinely Sterman JD. Business dynamics: systems thinking and modeling for a complex world. Boston, MA: Irwin McGraw-Hill, 2000. Homer JB, Hirsch G. System dynamics modeling for public health: background and opportunities. American Journal of Public Health in press.

11. Syndemics Prevention Network Extending a Long History of Health Policy Modeling History Developed at MIT by Jay Forrester (1961) International SD Society (1983) Health Policy Special Interest Group (2003) Major Health Studies (since 1975) Disease epidemiology (e.g., heart disease, diabetes, HIV/AIDS, cervical cancer, dengue fever) Substance abuse epidemiology (e.g., heroin, cocaine, tobacco) Health care patient flows (e.g., hospital, extended care) Health care capacity and delivery (e.g., resource planning, emergency planning) Interactions between health capacity and disease epidemiology (e.g, neighborhood- and national-level analysis) Recent CDC Projects Syndemics (i.e., mutually reinforcing epidemics) Community grantmaking strategy Diabetes in an era of rising obesity Upstream/downstream effort Health care reform proposals Goals for fetal and infant health Homer JB, Hirsch G. System dynamics modeling for public health: background and opportunities. American Journal of Public Health in press. Milstein B, Homer J. Background on system dynamics simulation modeling, with a summary of major public health studies. Atlanta, GA: Syndemics Prevention Network, Centers for Disease Control and Prevention; February 1, 2005..

12. Syndemics Prevention Network 1999200020012002200320042005 CDC Evaluation Framework Recommends Logic Models Programs Discover Limitations of Logic Models and Other Methods for System Change Initiatives Syndemics Network Identifies SD as a Promising Methodology Diabetes System Modeling Project (funding from DDT & DACH) ODPHP Convenes HHS Dynamic Modelers to Discuss HP 2020 Dr. Gerberding & the Health Systems Work Group Use an SD Model to Define a Balanced System of Health Protection OSI Chooses Obesity Goal as Highest Priority for SD Modeling (initial funding from OSI) Infant Health Study Group Uses SD Modeling to Revise CDC Goal for 2015 (funding from OSI and CoCHP) OSI Kicks-Off Goal Pilot Teams with Workshop on System Dynamics (funding from OSI & CoCHP) CDC Evaluation Forum Explores Roles for SD Modeling Milestones in the Growth of System Dynamics Modeling at CDC CDC Science Seminar on SD (funding from NCCDPHP, PHPPO, OPPE) AJPH Theme Issue Features SD Papers

13. Syndemics Prevention Network Essential Elements for System Change Ventures Elements of a Sound StrategyNeeded to Address… Realistic Understanding of Causal Dynamics Justifiable Goals & Framework for Charting Progress Means for Prioritizing Actions & Impetus to Implement Them

14. Syndemics Prevention Network Essential Elements for System Change Ventures Elements of a Sound StrategyNeeded to Address… Realistic Understanding of Causal Dynamics Multiple, simultaneous lines of action and reaction Sources of dynamic complexity (e.g., accumulation, delay, non-linear response) Integration of relevant evidence, as well as attention to critical areas of uncertainty Clear roles for relevant stakeholders Link between system structure and behavior over time Justifiable Goals & Framework for Charting Progress Means for Prioritizing Actions & Impetus to Implement Them

15. Syndemics Prevention Network Essential Elements for System Change Ventures Elements of a Sound StrategyNeeded to Address… Realistic Understanding of Causal Dynamics Multiple, simultaneous lines of action and reaction Sources of dynamic complexity (e.g., accumulation, delay, non-linear response) Integration of relevant evidence, as well as attention to critical areas of uncertainty Roles for relevant stakeholders Link between system structure and behavior over time Justifiable Goals & Framework for Charting Progress Plausible future targets, given existing momentum Life-course implications Timing and trajectories of change (e.g., better-before-worse, or vice versa) Leadership for choosing a particular course Clear referent for charting progress Means for Prioritizing Actions & Impetus to Implement Them

16. Syndemics Prevention Network Essential Elements for System Change Ventures Elements of a Sound StrategyNeeded to Address… Realistic Understanding of Causal Dynamics Multiple, simultaneous lines of action and reaction Sources of dynamic complexity (e.g., accumulation, delay, non-linear response) Integration of relevant evidence, as well as attention to critical areas of uncertainty Roles for relevant stakeholders Link between system structure and behavior over time Justifiable Goals & Framework for Charting Progress Plausible future targets, given existing momentum Life-course implications Timing and trajectories of change (e.g., better-before-worse, or vice versa) Leadership for choosing a particular course Clear referent for charting progress Means for Prioritizing Actions & Impetus to Implement Them Experiments to test policy leverage (alone and in combination) Short and long-term consequences of actions Possible unintended effects Alignment of multiple actors Visceral and emotional learning about how dynamic systems function (i.e., better mental models)

17. Syndemics Prevention Network Essential Elements for System Change Ventures Limitations of Conventional Alternatives Elements of a Sound Strategy Conventional Approaches Limitations Realistic Understanding of Causal Dynamics Justifiable Goals & Framework for Charting Progress Means for Prioritizing Actions & Impetus to Implement Them

18. Syndemics Prevention Network Essential Elements for System Change Ventures Limitations of Conventional Alternatives Elements of a Sound Strategy Conventional Approaches Limitations Realistic Understanding of Causal Dynamics Logic models Statistical models Ad hoc research and evaluation studies Processes of change in dynamic systems tend to be counterintuitive “Contextual” factors have strong influences, but are not well defined Statistical models exclude important factors due to lack of precise measures; they also focus on correlation, not causality Barriers to learning in dynamic systems prevent accurate interpretation of research/evaluation data Justifiable Goals & Framework for Charting Progress Means for Prioritizing Actions & Impetus to Implement Them

19. Syndemics Prevention Network Essential Elements for System Change Ventures Limitations of Conventional Alternatives Elements of a Sound Strategy Conventional Approaches Limitations Realistic Understanding of Causal Dynamics Logic models Statistical models Ad hoc research and evaluation studies Processes of change in dynamic systems tend to be counterintuitive “Contextual” factors have strong influences, but are not well defined Statistical models exclude important factors due to lack of precise measures; they also focus on correlation, not causality Barriers to learning in dynamic systems prevent accurate interpretation of research/evaluation data Justifiable Goals & Framework for Charting Progress Forecasting models Best-of-the-best Wishful thinking Forecasts tend to be linear extrapolations of the past Best-of-the-best ignores different histories and present circumstances Wishful targets can do more harm than good Means for Prioritizing Actions & Impetus to Implement Them

20. Syndemics Prevention Network Essential Elements for System Change Ventures Limitations of Conventional Alternatives Elements of a Sound Strategy Conventional Approaches Limitations Realistic Understanding of Causal Dynamics Logic models Statistical models Ad hoc research and evaluation studies Processes of change in dynamic systems tend to be counterintuitive “Contextual” factors have strong influences, but are not well defined Statistical models exclude important factors due to lack of precise measures; they also focus on correlation, not causality Barriers to learning in dynamic systems prevent accurate interpretation of research/evaluation data Justifiable Goals & Framework for Charting Progress Forecasting models Best-of-the-best Wishful thinking Forecasts tend to be linear extrapolations of the past Best-of-the-best ignores different histories and present circumstances Wishful targets can do more harm than good Means for Prioritizing Actions & Impetus to Implement Them Ranking by burden and/or cost effectiveness Health impact assessment Comparing importance vs. changeability Organizational will to fund Coalition-building Focus on current burden obscures root causes Cost effectiveness often ignores dynamic complexity HIA lacks explicit connection between structure and behavior Funding drives actions, which cease after funding stops Coalitions are not naturally well aligned and thus avoid tough questions; they are poorly suited for implementing complex, long-term initiatives

21. Syndemics Prevention Network CDC Diabetes System Modeling Project Discovering Dynamics Through Action Labs Jones A, Homer J, Milstein B, Essien J, Murphy D, Sorensen S, Englegau M. Modeling the population dynamics of a chronic disease: the CDC's diabetes system model. American Journal of Public Health (in press).

22. Syndemics Prevention Network 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. http://www.time.com/time/covers/1101031208/story.html …in an Era of Epidemic Obesity

23. Syndemics Prevention Network Prevalence of Diagnosed Diabetes, US 0 10 20 30 40 19801990200020102020203020402050 Million people Historical Data: CDC DDT and NCCDPHP. (Change in measurement in 1996). Model Forecast: Honeycutt et al. 2003, "A Dynamic Markov model…" Historical Data Model Forecast Forecast of Diabetes Prevalence Key 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.

24. Syndemics Prevention Network Health Care Capacity Provider supply Provider understanding, competence Provider location System integration Cost of care Insurance coverage Population Flows Discussions Pointed to Many Interacting Factors Personal Capacity Understanding Motivation Social support Literacy Physio-cognitive function Life stages Metabolic Stressors Nutrition Physical activity Stress Health Care Utilization Ability to use care (match of patients and providers, language, culture) Openness to/fear of screening Self-management, monitoring Civic Participation Social cohesion Responsibility for others Forces Outside the Community Macroeconomy, employment Food supply Advertising, media National health care Racism Transportation policies Voluntary health orgs Professional assns University programs National coalitions Local Living Conditions Availability of good/bad food Availability of phys activity Comm norms, culture (e.g., responses to racism, acculturation) Safety Income Transportation Housing Education

25. Syndemics Prevention Network Diabetes System Modeling Project Where is the Leverage for Health Protection? Jones A, Homer J, Milstein B, Essien J, Murphy D, Sorensen S, Englegau M. Modeling the population dynamics of a chronic disease: the CDC's diabetes system model. American Journal of Public Health (in press). People with Undiagnosed, Uncomplicated Diabetes People with Diagnosed, Uncomplicated Diabetes People with Diagnosed, Complicated Diabetes People with Undiagnosed PreDiabetes People with Diagnosed PreDiabetes People with Undiagnosed, Complicated Diabetes People with Normal Glycemic Levels Diagnosing Diabetes Diagnosing Diabetes Diabetes Detection Dying from Complications Developing Complications Diabetes Control PreDiabetes Detection Diagnosing PreDiabetes Diabetes Onset PreDiabetes Control PreDiabetes Onset Recovering from PreDiabetes Recovering from PreDiabetes Obesity Prevention

26. Syndemics Prevention Network Diabetes System Modeling Project Where is the Leverage for Health Protection? People with Undiagnosed, Uncomplicated Diabetes People with Diagnosed, Uncomplicated Diabetes People with Diagnosed, Complicated Diabetes Diagnosing Uncomplicated Diabetes People with Undiagnosed PreDiabetes People with Diagnosed PreDiabetes Diagnosing PreDiabetes Developing Complications from People with Undiagnosed, Complicated Diabetes Diagnosing Complicated Diabetes People with Normal Glycemic Levels Diabetes Detection Obese Fraction of the Population Risk for PreDiabetes & Diabetes Caloric Intake Physical Activity PreDiabetes Control Diabetes Control PreDiabetes Detection Medication Affordability Ability to Self Monitor Adoption of Healthy Lifestyle Clinical Management of PreDiabetes Clinical Management of Diagnosed Diabetes Living Conditions Personal Capacity PreDiabetes Testing for Access to Preventive Health Services Testing for Diabetes PreDiabete s Onset Recovering from PreDiabetes Recovering from PreDiabetes Diabetes Onset Dying from Complications Developing Complications

27. Syndemics Prevention Network Simulations for Learning in Dynamic Systems Diabetes Dynamics in an Era of Epidemic Obesity Dynamic Hypothesis (Causal Structure) Plausible Futures (Policy Experiments) Jones A, Homer J, Milstein B, Essien J, Murphy D, Sorensen S, Englegau M. Modeling the population dynamics of a chronic disease: the CDC's diabetes system model. American Journal of Public Health (in press). Deaths per Population 0.0035 0.003 0.0025 0.002 0.0015 19801990200020102020203020402050 Time (Year) Blue: Base run; Red: Clinical mgmt up from 66% to 90%; Green: Caloric intake down 4% (99 Kcal/day); Black: Clin mgmt up to 80% & Intake down 2.5% (62 Kcal/day) Base Downstream Upstream Mixed Striking an acceptable balance. Multi-stakeholder Dialogue

28. Syndemics Prevention Network Using Available Data to Calibrate the Model Information SourcesData U.S. Census Adult population and death rates Health insurance coverage National Health Interview Survey Diabetes prevalence Diabetes detection National Health and Nutrition Examination Survey Prediabetes prevalence Weight, height, and body fat Caloric intake Behavioral Risk Factor Surveillance System Glucose self-monitoring Eye and foot exams Participation in health education Use of medications Professional Literature Physical activity trends Effects of control and aging on onset, progression, death, and costs Expenditures

29. Syndemics Prevention Network Diabetes System Modeling Project Confirming the Model’s Fit to History Jones A, Homer J, Milstein B, Essien J, Murphy D, Sorensen S, Englegau M. Modeling the population dynamics of a chronic disease: the CDC's diabetes system model. American Journal of Public Health (in press). Diagnosed Diabetes % of AdultsObese % of Adults

30. Syndemics Prevention Network Simulated Status Quo Meet Detection Objective (5-4) Meet Onset Objective (5-2) HP 2010 Objective (5-3) HP 2000 Objective Setting Realistic Expectations History, HP Objectives, and Simulated Futures Reported A B C D E F G H I

31. Syndemics Prevention Network Connecting the Objectives Population Flows and Dynamic Accounting 101 It is impossible for any policy to reduce prevalence 38% by 2010! People with Undiagnosed Diabetes People with Diagnosed Diabetes Dying from Diabetes Complications Diagnosed Onset Initial Onset People without Diabetes As would stepped-up detection effort Reduced death would add further to prevalence With a diagnosed onset flow of 1.1 mill/yr And a death flow of 0.5 mill/yr (4%/yr rate) The targeted 29% reduction in diagnosed onset can only slow the growth in prevalence

32. Syndemics Prevention Network How Does Modeling Process Help DDT in Its Work with the States? Builds on the Assessment Process Model of Influence Partnering Planning for Pre-Diabetes Population

33. Syndemics Prevention Network Why Vermont Participated in Boston Learning Session Governor’s Panel, the Blueprint Group, charged with taking on diabetes Positive partnership experiences

34. Syndemics Prevention Network Where is the Greatest Leverage for Reducing the Burden of Diabetes? People with Uncomplicated Diabetes Progre- ssion People with Pre-diabetes OnsetPeople with Complicated Diabetes Deaths Prediabetes onset Recovery People with Normal Glycemic Levels Should we diagnose and treat Pre-diabetes? Controlled fraction Should we focus on disease management? Should we focus on detection? Obese fraction Should we prevent obesity? Total burden

35. Syndemics Prevention Network No major changes – status quo Care and reduction in caloric intake

36. Syndemics Prevention Network

37. Syndemics Prevention Network Vermont’s Response Very interactive meeting with partners in March 2005 (lots of ah-ha’s!) State Health Commissioner presented our model results to the State Senate Appropriations Committee. Model results for per capita costs were “very well received,” and demonstrated need for both prevention and clinical intervention. VT Program Director: “What I’m learning is that what we are doing with the Blueprint Group is good and necessary, but not enough. We’ve got to supplement the downstream work with enhanced primary prevention and prediabetes screening.”

38. Syndemics Prevention Network Next Steps for DDT/PDB Primary Prevention RFA with systems modeling pilot –At least 2 additional sites Developing PDB competency in systems thinking Integrate systems thinking into consultation with states

39. Syndemics Prevention Network Obesity Dynamics A General Causal Structure

40. Syndemics Prevention Network Re-Directing the Course of Change Questions Addressed by System Dynamics Modeling How? Where? Who? Prevalence of Obese Adults, United States 1960-621971-74 1976-801988-941999-2000 Why? Data Source: NHANES 2020 2010

41. Syndemics Prevention Network Decades of Change Adult Overweight and Obese Prevalence (NHANES) Overweight Obese Severely Obese

42. Syndemics Prevention Network Decades of Change Adult Obese Prevalence 2000 by Race and Sex (NHANES)

43. Syndemics Prevention Network Decades of Change Youth Overweight and Obese Prevalence (NHANES) Overweight Obese

44. Syndemics Prevention Network Decades of Change Change in Adult Male Caloric Intake (NHANES) 20-39 40-59 60-74 Total (20-74)

45. Syndemics Prevention Network Decades of Change Change in Adult Female Caloric Intake (NHANES) 20-39 40-59 60-74 Total (20-74)

46. Syndemics Prevention Network Decades of Change Adult “No Leisure Time Physical Activity” (BRFSS) Male Female Combined

47. Syndemics Prevention Network Decades of Change Hours per Week Watching TV, Internet, Video (Media Industry Report) TV Total incl TV, Internet, Video Internet

48. Syndemics Prevention Network Decades of Change Fraction of Meals and Caloric Intake Away From Home (USDA) Calories Meals

49. Syndemics Prevention Network Decades of Change Change in Vehicle Miles Driven per Household (DOT/NPTS)

50. Syndemics Prevention Network Decades of Change Participation in Labor Force (BLS) Male Female

51. Syndemics Prevention Network Decades of Change Smoking Prevalence (NHIS, YRBS) Adult Male Adult Female HS Students

52. Syndemics Prevention Network What forces have driven up obesity? Where are the opportunities for response?

53. Syndemics Prevention Network Framework for Organizing Influences on Obesity Energy Balance Prevention of Overweight and Obesity Among Children, Adolescents, and Adults Note: Adapted from “Preventing Childhood Obesity.” Institute of Medicine, 2005. 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

54. Syndemics Prevention Network 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

55. Syndemics Prevention Network A Conventional View of Causal Forces This sort of open-loop 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; ex., “bootstrapping” which starts more narrowly targeted but then broadens and builds upon successes over time

56. Syndemics Prevention Network A System Dynamics View of Causal Forces Direct Drivers of Diet and Activity DRAFT 5/8/05

57. Syndemics Prevention Network A System Dynamics View of Causal Forces Engines of Growth DRAFT 5/8/05

58. Syndemics Prevention Network A System Dynamics View of Causal Forces Engines of Growth DRAFT 5/8/05

59. Syndemics Prevention Network A System Dynamics View of Causal Forces Engines of Growth DRAFT 5/8/05

60. Syndemics Prevention Network A System Dynamics View of Causal Forces Engines of Growth DRAFT 5/8/05

61. Syndemics Prevention Network A System Dynamics View of Causal Forces Engines of Growth DRAFT 5/8/05

62. Syndemics Prevention Network A System Dynamics View of Causal Forces Individual Responses DRAFT 5/8/05

63. Syndemics Prevention Network A System Dynamics View of Causal Forces Turning to Preventive Healthcare DRAFT 5/8/05

64. Syndemics Prevention Network A System Dynamics View of Causal Forces Improving Preventive Healthcare DRAFT 5/8/05

65. Syndemics Prevention Network A System Dynamics View of Causal Forces Creating Better Media Messages DRAFT 5/8/05

66. Syndemics Prevention Network A System Dynamics View of Causal Forces Creating Better Options in Behavioral Settings DRAFT 5/8/05

67. Syndemics Prevention Network A System Dynamics View of Causal Forces Creating Better Conditions in the Wider Environment DRAFT 5/8/05

68. Syndemics Prevention Network A System Dynamics View of Causal Forces Addressing Related Health Conditions DRAFT 5/8/05

69. Syndemics Prevention Network A System Dynamics View of Causal Forces Disease Care Costs Undercut Prevention DRAFT 5/8/05

70. Syndemics Prevention Network A System Dynamics View of Causal Forces Up-Front Costs Undercut Protection Effort DRAFT 5/8/05

71. Syndemics Prevention Network A System Dynamics View of Causal Forces Defenders of the Status Quo Resist Change DRAFT 5/8/05

72. Syndemics Prevention Network A System Dynamics View of Causal Forces Other Benefits Help Make the Case DRAFT 5/8/05

73. 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?

74. Syndemics Prevention Network Group Exercise #1 Identifying Forces of Change

75. Syndemics Prevention Network Identifying Forces of Change Tasks Make the dynamics in your assigned pathway(s) more concrete Name trends/drivers that have changed significantly in recent decades Focus on each link in the loop separately and then list the most prominent forces of change, including their timing and possible differential consequences on sub-groups Also indicate sources where information/documentation about each trend might be found Groups Society-Behavior Pathway Behavior-Society Pathway Individual Responses to Weight Pathways Social Transmission Pathways

76. Syndemics Prevention Network Society-Behavior Pathway

77. Syndemics Prevention Network Behavior-Society Pathway

78. Syndemics Prevention Network Individual Responses to Weight Pathways

79. Syndemics Prevention Network Social Transmission Pathways

80. Syndemics Prevention Network Understanding Obesity Dynamics A Foundation for Directing Change and Charting Progress Obesity Dynamics Modeling Project May 17-18, 2005 Atlanta, GA

81. Syndemics Prevention Network General Plan for the Workshop Day 1 Dynamic Dilemmas System Dynamics in Action Obesity Dynamics – General Causal Structure Group Exercise – Identifying Forces of Change Day 2 Modeling for Learning – Using Simulation Experiments Group Exercise – Organizing Effective Health Protection Efforts Directing Change and Charting Progress Snapshot Evaluation

82. Syndemics Prevention Network Iterative Steps in System Dynamics Simulation Modeling Enact Policy Build power and organize actors to establish chosen policies Learn About Policy Consequences Test proposed policies, searching for ones that best govern change Learn About Policy Consequences Test proposed policies, searching for ones that best govern change Run Simulation Experiments Compare model’s behavior to expectations and/or data to build confidence in the model Convert the Map Into a Simulation Model Formally quantify the hypothesis using all available evidence Create a Dynamic Hypothesis Identify and map the main causal forces that create the problem Identify a Persistent Problem Graph its behavior over time Milstein B, Homer J. Background on system dynamics simulation modeling, with a summary of major public health studies. Atlanta, GA: Syndemics Prevention Network, Centers for Disease Control and Prevention; February 1, 2005.

83. Syndemics Prevention Network Modeling for Learning Why Simulate?

84. Syndemics Prevention Network

85. Syndemics Prevention Network Sterman JD. Learning from evidence in a complex world. Amer J Public Health (in press), 2005.

86. Syndemics Prevention Network

87. Syndemics Prevention Network A Health Care “Microworld” Developed in mid-1990s to help providers understand implications of change Simulates managing a health system in a difficult competitive environment Also deals with the dynamics of keeping a population of 100,000 people healthy with limited resources Can simulate the effects of a combined strategy in which the delivery system implements a health improvement strategy Hirsch GB, Immediato CS. Design of simulators to enhance learning: examples from a health care microworld. International System Dynamics Conference; Quebec City; July, 1998.

88. Syndemics Prevention Network Causal Map Suggests Benefits of Chronic Disease Management Medical Management of Chronic Illness Health Status Utilization Acute Episodes Activity Days Lost Total Cost. Time Delays

89. Syndemics Prevention Network Simulating the Microworld to Address a Strategic Question Can chronic disease management improve system performance and subscribers’ health? –Simulation indicates that if CDM is implemented at the same time as the system improvements, both will likely fail –Why? Additional workload created by CDM drives up waiting times and provider workloads, puts entire system into a tailspin of increasing cost and declining revenue –Another simulation demonstrates that phasing-in CDM after system improvements have time to increase capacity can produce better system performance and improve subscribers’ health –These results are not obvious without simulation

90. Syndemics Prevention Network Causal Map Suggests Benefits of Chronic Disease Management Medical Management of Chronic Illness Health Status Utilization Acute Episodes Activity Days Lost Total Cost Funds Available for Medical and Risk Management Waiting Times Provider Capacity Network Population Revenues Total Available Funds Delivery System Investments. Time Delays

91. Syndemics Prevention Network All formal models—including simulations—are wrong: incomplete and imprecise But some are better than others and capture more important aspects of the real world’s dynamic complexity A valuable model is one that can help us understand and anticipate better than we do with the unaided mind…or with a causal map alone How Should We Value Models? Sterman JD. All models are wrong: reflections on becoming a systems scientist. System Dynamics Review 2002;18(4):501-31.

92. Syndemics Prevention Network Obesity Dynamics An Illustrative Simulation Model

93. Syndemics Prevention Network Decades of Change Adult Overweight and Obese Prevalence (NHANES) Overweight Obese Severely Obese

94. Syndemics Prevention Network Decades of Change Youth Overweight and Obese Prevalence (NHANES) Overweight Obese

95. Syndemics Prevention Network How the BMI Distribution Has Shifted and Stretched Curves here generated from Gamma functions, not from actual data. Gamma parameters [a, b] shown on left. Corresponding overweight (BMI 25), obese (BMI 30), and severely obese (BMI 40) fractions shown on right. These approximations do a reasonably good job of matching prevalence data shown on previous slide, but are slightly low for overweight and severely obese, and slighly high for obese. Analysis shows that standard (unskewed) Normal distribution cannot come as close to matching prevalence data, and in particular predicts much lower prevalence of severely obese.

96. Syndemics Prevention Network Preliminary Dynamic Hypothesis for Understanding and Reversing the Growth of Obesity Healthiness of Diet & Activity Habits Effective Health Protection Efforts R6 Disease Care Costs Squeeze Prevention B4 Creating Better Messages R4 Options Shape Habits Shape Options Prevalence of Overweight & Related Diseases - Costs of Caring for Overweight- Related Diseases - Options Available at Home, School, Work, Community Influencing Healthy Diet & Activity Costs of Developing & Maintaining Health Protection Efforts B5 Creating Better Options in Behavioral Settings - B8 Up-front Costs Undercut Protection Efforts Observation of Parents' and Peers' Habits R2 Parents/Peers Transmission Media Messages Promoting Healthy Diet & Activity Wider Environment (Economy, Technology, Laws) Influence on Options B1 Self-Improvement B6 Creating Better Conditions in the Wider Environment Health Conditions Detracting from Healthy Diet & Activity - Perceived Program Benefits Beyond Weight Reduction Resistance and Countervailing Efforts by Opposed Interests - B9 Defending Status Quo Cost Implications of Overweight in Other Spheres B10 Potential Savings Build Support Genetic Metabolic Rate Disorders B7 Addressing Related Health Conditions Healthcare Services to Promote Healthy Diet & Activity B2 Medical Response R1 Spiral of Poor Health and Habits B3 Improving Preventive Healthcare R5 Society Shapes Options Shape Society Broader Benefits of Health Protection Efforts R7 Broader Benefits Build Support R3 Media Mirrors

97. Syndemics Prevention Network Demonstration Model Structure Core Pieces of the Larger Theory Healthiness of Diet & Activity Habits Effective Health Protection Efforts Prevalence of Overweight & Related Diseases - Options Available at Home, School, Work, Community Influencing Healthy Diet & Activity Observation of Parents' and Peers' Habits R2 Parents/Peers Transmission Wider Environment (Economy, Technology, Laws) Influence on Options Health Conditions Detracting from Healthy Diet & Activity - R1 Spiral of Poor Health and Habits

98. Syndemics Prevention Network Demonstration Model Structure

99. Syndemics Prevention Network Demonstration Model Structure

100. Syndemics Prevention Network Demonstration Model Structure

101. Syndemics Prevention Network Demonstration Model Structure

102. Syndemics Prevention Network Demonstration Model Structure

103. Syndemics Prevention Network Demo Model Input Assumptions Time constants –Years of childhood and adolescence (14 yrs.) –Years of adulthood (58 yrs.) –Metabolic adjustment time (1 yr.) –Youth (3 yrs.) and adult (3 yrs.) options adjustment times Other constants –Minimum (0.01) and maximum (0.5) youth overweight fractions –Minimum (0.3) and maximum (0.9) adult overweight fractions –Fraction of youth habits imitating adult habits (0.33) –Fraction of adult habits established in childhood (0.33) X-Y functions –Effect of overweight on healthiness of youth habits (f(1) = 0.6) –Effect of overweight on healthiness of adult habits (f(1) = 0.6) –Obese % of overweight youth, as a fcn of overwt youth % (history/Gamma) –Obese % of overweight adults, as a fcn of overwt adult % (hist/Gamma) –Severely obese % of obese adults, as a fcn of overwt adult % (hist/Gamma) Time Series Inputs –Healthiness of broader environment (0-1) –Interventions to improve options in behavioral settings

104. Syndemics Prevention Network Adult overweight fraction 0.8 0.6 0.4 0.2 0 19601970198019902000201020202030204020502060 Time (year) Adult overwt frac : Base2d NHANES adult overwt frac : Base2d Demo Model Base Run Results vs NHANES: Adult Overweight Fraction* * Includes all BMI>25. Data available for NHANES surveys from ‘60-’62, ‘71-’74, ‘76- ’80, ‘88-’94, and ‘99-’02. Shown as data points for 1961, 1973, 1978, 1991, and 2000. Simulated Data

105. Syndemics Prevention Network Demo Model Base Run Results vs NHANES: Youth Overweight Fraction* * “Overweight” here refers to combined NHANES “At risk” and “Overweight”, and represents average of children and adolescents. NHANES data exist for both children and adolescents for ‘71-’74, ‘76-’80, ‘88-’94, and ‘99-’02 surveys. Data points shown for 1973, 1978, 1991, 2000. Also, data available for children in ‘63-’65 and adolescents in ‘66-’70; these are averaged for the first data point in 1968. Youth overweight fraction 0.4 0.3 0.2 0.1 0 19601970198019902000201020202030204020502060 Time (year) Youth overwt frac : Base2d NHANES youth overwt frac : Base2d Simulated Data

106. Syndemics Prevention Network Demo Model Base Run Results Healthiness of Habits and the Environment Adult habits worsen more gradually than youth habits do, because of the lingering “carryover” effect of adult habits established in childhood. Both ultimately (2010 or later) worsen to 25%. This value is lower than the 30% healthiness of the broader environment, because the overweight, who are increasing in prevalence, find it harder than the non-overweight do to maintain healthy habits in any environment. Healthiness of Habits and Environment 0.8 0.6 0.4 0.2 0 19601970198019902000201020202030204020502060 Time (year) Healthiness of adult habits : Base2d Healthiness of youth habits : Base2d Healthiness of broader environment : Base2d Adult Youth Environment

107. Syndemics Prevention Network Adult obese fraction 0.8 0.6 0.4 0.2 0 19601970198019902000201020202030204020502060 Time (year) Adult obese frac : Base2d NHANES adult obese frac : Base2d Demo Model Base Run Results vs NHANES: Adult Obese Fraction* * Includes all BMI>30. Data available for NHANES surveys from ‘60-’62, ‘71-’74, ‘76- ’80, ‘88-’94, and ‘99-’02. Shown as data points for 1961, 1973, 1978, 1991, and 2000. Simulated Data

108. Syndemics Prevention Network Adult severely obese fraction 0.1 0.08 0.06 0.04 0.02 0 19601970198019902000201020202030204020502060 Time (year) Adult sev obese frac : Base2d NHANES adult sev obese frac : Base2d Demo Model Base Run Results vs NHANES: Adult Severely Obese Fraction* * Based on BMI>40. Data available for NHANES surveys from ‘60-’62, ‘71-’74, ‘76-’80, ‘88-’94, and ‘99-’02. Shown as data points for 1961, 1973, 1978, 1991, and 2000. Simulated Data

109. Syndemics Prevention Network Demo Model Base Run Results vs NHANES: Youth Obese Fraction* * “Obese” here refers to NHANES “Overweight” and represents average of children and adolescents. NHANES data exist for both children and adolescents for ‘71-’74, ‘76-’80, ‘88-’94, and ‘99-’02 surveys. Data points shown for 1973, 1978, 1991, 2000. Also, data available for for children in ‘63-’65 and adolescents in ‘66-’70; these are averaged for the first data point in 1968. Youth obese fraction 0.4 0.3 0.2 0.1 0 19601970198019902000201020202030204020502060 Time (year) Youth obese frac : Base2d NHANES youth obese frac : Base2d Simulated Data

110. Syndemics Prevention Network X-Y Function Obese Fraction of Overweight Adults OVERWEIGHT FRACTION OF ADULTS OBESE FRACTION OF OVERWEIGHT ADULTS | Historical range Based on a family of Gamma functions closely approximating actuals during the historical period.

111. Syndemics Prevention Network Dynamic Effects of Interventions Illustrative Policy Tests Base –All time series inputs flat after 2005 –Healthiness of youth and adult options decline to 0.3 by 2010 (having started at.75 in 1960) and remain at that level thereafter ‘YouthOpt50’ (Improve youth options) –Efforts to improve youth options starting in 2005 increase healthiness of youth options to 0.65 (where they were in 1980) by 2015 ‘AdultOpt50’ (Improve adult options) –Efforts to improve adult options starting in 2005 increase healthiness of adult options to 0.65 (where they were in 1980) by 2015 ‘AllOpt50’ (Improve options for youth and adults) –Efforts to improve both youth and adult options starting in 2005 increase healthiness of both to 0.65 (where they were in 1980) by 2015

112. Syndemics Prevention Network Adult obese fraction 0.6 0.4 0.2 0 19601970198019902000201020202030204020502060 Time (year) Adult obese frac : Base2d Adult obese frac : Youthopt50 Adult obese frac : Adultopt50 Adult obese frac : Allopt50 Policy Testing Output Adult Obese Fraction The improvement in adult options by itself initially reduces adult obesity by 2015 to where it was in early 1990s. But continued poor youth habits cause some gradual erosion of the intervention’s benefit as the children become adults. However, if youth options are improved as well, virtually no erosion occurs in the short term and there is actually some further improvement in the longer term. Base Youth options Adult options Both

113. Syndemics Prevention Network Youth obese fraction 0.3 0.2 0.1 0 19601970198019902000201020202030204020502060 Time (year) Youth obese frac : Base2d Youth obese frac : Youthopt50 Youth obese frac : Adultopt50 Youth obese frac : Allopt50 Policy Testing Output Youth Obese Fraction The improvement in youth options by itself reduces youth obesity by 2015 to where it was in the early 1990s. Continued poor adult options causes a slight amount of rebound due to the imitation effect. But if adult options are improved as well, this can further reduce youth obesity due to the imitation effect, reducing it by 2025 to where it was in the mid-1980s. Base Youth options Adult options Both

114. Syndemics Prevention Network Group Exercise #2 Organizing Health Protection Efforts

115. Syndemics Prevention Network Organizing Health Protection Efforts Tasks Make the dynamics in your assigned pathways more concrete by identifying specific types of program/policy efforts that have been—or might be—enacted in response to the rise of obesity Note the key features of each action, for example, how long it takes to organize, where the cost burden lies, what kinds of resistance might arise, and what benefits might accrue regarding weight as well as other areas (e.g., economic productivity, school performance, environmental quality, crime reduction, social capital, or other health issues) Groups Improving Preventive Healthcare & Addressing Problems Beyond Weight Crafting Better Messages Creating Better Options in Behavioral Settings Creating Better Conditions in the Wider Environment

116. Syndemics Prevention Network Improving Healthcare & Addressing Problems Beyond Weight

117. Syndemics Prevention Network Crafting Better Messages

118. Syndemics Prevention Network Creating Better Options in Behavioral Settings

119. Syndemics Prevention Network Creating Better Conditions in the Wider Environment

120. Syndemics Prevention Network Transforming Essential Ways of Thinking Conventional ThinkingSystems Thinking Static Thinking: Focusing on particular events.Dynamic Thinking: Framing a problem in terms of a pattern of behavior over time. System-as-Effect Thinking: Focus on individuals as the sources of behavior. Hold individuals responsible or blame outside forces. System-as-Cause Thinking: Seeing the structures and pressures that drive behavior. Examine the conditions in which decisions are made, as well as their consequences for oneself and others. Tree-by-Tree Thinking: Focusing on the details in order to “know.” Forest Thinking: Seeing beyond the details to the context of relationships in which they are embedded. Factors Thinking: Listing factors that influence, or are correlated with, a behavior. To forecast milk production, consider economic elasticities. Operational Thinking: Understanding how a behavior is actually generated. To forecast milk production, you must consider cows. Straight-Line Thinking: Viewing causality as running one way, treating causes as independent and instantaneous. Root-Cause thinking. Closed-Loop Thinking: Viewing causality as an ongoing process, not a one-time event, with effects feeding back to influence causes, and causes affecting each other, sometimes after long delays. Measurement Thinking: Focusing on the things we can measure; seeking precision. Quantitative Thinking: Knowing how to quantify, even though you cannot always measure. Proving-Truth Thinking: Seeking to prove our models true by validating them with historical data. Scientific Thinking: Knowing how to define testable hypotheses (everyday, not just for research). Karash R. The essentials of systems thinking and how they pertain to healthcare and colorectal cancer screening. Dialogue for Action in Colorectal Cancer; Baltimore, MD; March 23, 2005.. Richmond B. Systems thinking: critical thinking skills for the 1990s and beyond. System Dynamics Review 1993;9(2):113-134. Richmond B. The "thinking" in systems thinking: seven essential skills. Waltham, MA: Pegasus Communications, 2000.

121. Syndemics Prevention Network At this workshop I learned…. As a result of this workshop I intend to… Snapshot Evaluation

122. Syndemics Prevention Network “We make the road by walking” -- Myles Horton & Paulo Freire Horton M, Freire P. We make the road by walking: conversations on education and social change. Philadelphia: Temple University Press, 1990.

123. Syndemics Prevention Network EXTRAS

124. Syndemics Prevention Network Dynamic effects of behavioral assumptions: Illustrative sensitivity tests Base: –Fraction of youth habits imitating adult habits =.33 –Fraction of adult habits established in childhood =.33 –Effect of being overweight on healthiness of youth habits = 0.6 –Effect of being overweight on healthiness of adult habits = 0.6 YouthImitate0: Youth habits are not influenced by parents or other adults AdultCarryover0: Childhood habits do not carry over to adulthood YouthEffOverwt0: Being overweight does not make it harder for youths to maintain healthy habits AdultEffOverwt0: Being overweight does not make it harder for adults to maintain healthy habits

125. Syndemics Prevention Network Sensitivity Testing Output Youth Obese Fraction - Youth imitation and adult carryover both buffer the impact of a changing environment on youths; without them, youth obesity would have climbed sooner and faster than it has actually done. - The reinforcing effect of overweight on unhealthy habits (both youth and adult) causes youth obesity to climb further than it would without this effect. Youth obese fraction 0.3 0.2 0.1 0 19601970198019902000201020202030204020502060 Time (year) Youth obese frac : Base2d Youth obese frac : YouthImitate0 Youth obese frac : AdultCarryover0 Youth obese frac : YouthEffOverwt0 Youth obese frac : AdultEffOverwt0

126. Syndemics Prevention Network - Adult carryover buffers the impact of a changing environment on adults; without it, adult obesity would have climbed sooner and faster than it has actually done. - The reinforcing effect of overweight on unhealthy adult habits causes adult obesity to climb further than it would without this effect. Sensitivity Testing Output Adult Obese Fraction Adult obese fraction 0.6 0.4 0.2 0 19601970198019902000201020202030204020502060 Time (year) Adult obese frac : Base2d Adult obese frac : YouthImitate0 Adult obese frac : AdultCarryover0 Adult obese frac : YouthEffOverwt0 Adult obese frac : AdultEffOverwt0

127. Syndemics Prevention Network Demo Model Base Run Results Healthiness of Youth and Adult Habits Adult habits worsen more gradually than youth habits do, because of the lingering “carryover” effect of adult habits established in childhood. Both ultimately (2010 or later) worsen to 25%. This value is lower than the 30% healthiness of the broader environment, because the overweight, who are increasing in prevalence, find it harder than the non- overweight do to maintain healthy habits in any environment. Healthiness of habits 0.8 0.6 0.4 0.2 0 19601970198019902000201020202030204020502060 Time (year) Healthiness of youth habits : Base2d Healthiness of adult habits : Base2d Youth Adult

128. Syndemics Prevention Network The Modeling Process is Having an Impact Budget for primary prevention was doubled –from meager to modest HP2010 prevalence goal has been modified –from a large reduction to no change (but still not an increase) Research, program, and policy staff are working more closely –but truly cross-functional teams still forming State health departments and their partners are now engaged –initial engagement in VT, with two additional states being considered

129. Syndemics Prevention Network Time Series Models Describe trends Multivariate Stat Models Identify historical trend drivers and correlates Patterns Structure Events Increasing: Depth of causal theory Degrees of uncertainty Robustness for longer- term projection Value for developing policy insights Increasing: Depth of causal theory Degrees of uncertainty Robustness for longer- term projection Value for developing policy insights Dynamic Simulation Models Anticipate future trends, and find policies that maximize chances of a desirable path Tools for Policy Analysis