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Understanding Obesity Dynamics

Understanding Obesity Dynamics. A Foundation for Directing Change and Charting Progress. Obesity Dynamics Modeling Project May 17-18, 2005 Atlanta, GA. General Plan for the Workshop. Day 1 Dynamic Dilemmas System Dynamics in Action Obesity Dynamics – General Causal Structure

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Understanding Obesity Dynamics

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  1. Understanding Obesity Dynamics A Foundation for Directing Change and Charting Progress Obesity Dynamics Modeling ProjectMay 17-18, 2005 Atlanta, GA

  2. 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. Considering Multiple Perspectives on Overweight and Obesity

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

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

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

  7. Some Sources of Dynamic Complexity for Obesity 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 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) 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. 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 <http://www.sustainabilityinstitute.org/pubs/Leverage_Points.pdf>. Sterman JD. Business dynamics: systems thinking and modeling for a complex world. Boston, MA: Irwin McGraw-Hill, 2000.

  9. 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.

  10. 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. <http://www2.cdc.gov/syndemics/pdfs/SD_for_PH.pdf>.

  11. AJPH Theme Issue Features SD Papers CDC Science Seminar on SD (funding from NCCDPHP, PHPPO, OPPE) CDC Evaluation Forum Explores Roles for SD Modeling Syndemics Network Identifies SD as a Promising Methodology CDC Evaluation Framework Recommends Logic Models ODPHP Convenes HHS Dynamic Modelers to Discuss HP 2020 Programs Discover Limitations ofLogic Models and Other Methods for System Change Initiatives 1999 2000 2001 2002 2003 2004 2005 Diabetes System Modeling Project (funding from DDT & DACH) Infant Health Study Group Uses SD Modeling to Revise CDC Goal for 2015 (funding from OSI and CoCHP) Dr. Gerberding & the Health Systems Work Group Use an SD Model to Define a Balanced System of Health Protection OSI Kicks-Off Goal Pilot Teams with Workshop on System Dynamics (funding from OSI & CoCHP) OSI Chooses Obesity Goal as Highest Priority for SD Modeling (initial funding from OSI) Milestones in the Growth of System Dynamics Modeling at CDC

  12. Essential Elements for System Change Ventures

  13. Essential Elements for System Change Ventures

  14. Essential Elements for System Change Ventures

  15. Essential Elements for System Change Ventures

  16. Essential Elements for System Change VenturesLimitations of Conventional Alternatives

  17. Essential Elements for System Change VenturesLimitations of Conventional Alternatives

  18. Essential Elements for System Change VenturesLimitations of Conventional Alternatives

  19. Essential Elements for System Change VenturesLimitations of Conventional Alternatives

  20. CDC Diabetes System Modeling ProjectDiscovering 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).

  21. …in an Era of Epidemic Obesity 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.

  22. Forecast of Diabetes Prevalence Prevalence of Diagnosed Diabetes, US 40 Historical Model Data Forecast 30 Million people 20 • Key Constants • Incidence rates (%/yr) • Death rates (%/yr) • Diagnosed fractions • (Based on year 2000 data, per demographic segment) 10 0 1980 1990 2000 2010 2020 2030 2040 2050 Historical Data: CDC DDT and NCCDPHP. (Change in measurement in 1996). Model Forecast: Honeycutt et al. 2003, "A Dynamic Markov model…" 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.

  23. Personal Capacity • Understanding • Motivation • Social support • Literacy • Physio-cognitive function • Life stages Discussions Pointed to Many Interacting Factors Civic Participation Forces Outside the Community • Social cohesion • Responsibility for others • Macroeconomy, employment • Food supply • Advertising, media • National health care • Racism • Transportation policies • Voluntary health orgs • Professional assns • University programs • National coalitions Health Care Capacity • Provider supply • Provider understanding, competence • Provider location • System integration • Cost of care • Insurance coverage 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 Health Care Utilization • Ability to use care (match of patients and providers, language, culture) • Openness to/fear of screening • Self-management, monitoring Metabolic Stressors • Nutrition • Physical activity • Stress Population Flows

  24. PreDiabetes Detection Diabetes Detection PreDiabetes Onset Recovering from PreDiabetes Diagnosing Diagnosing Diagnosing Diabetes Diabetes PreDiabetes Recovering from PreDiabetes Diabetes Dying from Developing Onset Complications Complications Obesity Prevention PreDiabetes Control Diabetes Control Diabetes System Modeling ProjectWhere is the Leverage for Health Protection? People with People with People with Undiagnosed, Undiagnosed, Undiagnosed Uncomplicated Complicated PreDiabetes Diabetes Diabetes People with Normal Glycemic Levels People with People with People with Diagnosed, Diagnosed, Diagnosed Uncomplicated Complicated PreDiabetes Diabetes Diabetes 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).

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

  26. Deaths per Population 0.0035 0.003 Base Mixed 0.0025 Upstream Downstream 0.002 Striking an acceptable balance. 0.0015 1980 1990 2000 2010 2020 2030 2040 2050 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) Simulations for Learning in Dynamic SystemsDiabetes Dynamics in an Era of Epidemic Obesity Multi-stakeholder Dialogue 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).

  27. Using Available Data to Calibrate the Model

  28. Diabetes System Modeling ProjectConfirming the Model’s Fit to History Obese % of Adults Diagnosed Diabetes % of Adults 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).

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

  30. The targeted 29% reduction in diagnosed onset can only slow the growth in prevalence As would stepped-up detection effort Reduced death would add further to prevalence Connecting the ObjectivesPopulation Flows and Dynamic Accounting 101 People with People Undiagnosed without Initial Diabetes Diabetes Onset With a diagnosed onset flow of 1.1 mill/yr Diagnosed Onset People with Diagnosed Dying from Diabetes Diabetes Complications It is impossible for any policy to reduce prevalence38% by 2010! And a death flow of 0.5 mill/yr (4%/yr rate)

  31. 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

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

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

  34. No major changes – status quo Care and reduction in caloric intake

  35. 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.”

  36. 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

  37. Obesity DynamicsA General Causal Structure

  38. 2020 2010 Re-Directing the Course of ChangeQuestions Addressed by System Dynamics Modeling Where? Prevalence of Obese Adults, United States Why? How? Who? 1960-62 1971-74 1976-80 1988-94 1999-2000 Data Source: NHANES

  39. Decades of ChangeAdult Overweight and Obese Prevalence (NHANES) Overweight Obese Severely Obese

  40. Decades of ChangeAdult Obese Prevalence 2000 by Race and Sex (NHANES)

  41. Decades of ChangeYouth Overweight and Obese Prevalence (NHANES) Overweight Obese

  42. Decades of ChangeChange in Adult Male Caloric Intake (NHANES) 40-59 60-74 Total (20-74) 20-39

  43. Decades of ChangeChange in Adult Female Caloric Intake (NHANES) 20-39 Total (20-74) 60-74 40-59

  44. Decades of ChangeAdult “No Leisure Time Physical Activity” (BRFSS) Female Combined Male

  45. Decades of ChangeHours per Week Watching TV, Internet, Video (Media Industry Report) Total incl TV, Internet, Video TV Internet

  46. Decades of ChangeFraction of Meals and Caloric Intake Away From Home (USDA) Calories Meals

  47. Decades of ChangeChange in Vehicle Miles Driven per Household (DOT/NPTS)

  48. Decades of ChangeParticipation in Labor Force (BLS) Male Female

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