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

General Plan for the Workshop Day 1 • Dynamic Dilemmas • System Dynamics in 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 • • Syndemics Prevention Network Modeling for Learning – Using Simulation Experiments Group Exercise – Organizing Effective Health Protection Efforts Directing Change and Charting Progress Snapshot Evaluation

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

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

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

Re-Directing the Course of Change Questions Addressed by System Dynamics Modeling ? ere h Re-Directing the Course of Change Questions Addressed by System Dynamics Modeling ? ere h Prevalence of Obese Adults, United States W ? hy W Ho w? Wh o ? 2010 Data Source: NHANES Syndemics Prevention Network 2020

Some Sources of Dynamic Complexity for Obesity Multiple Goals • Improve diet • Increase 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 Syndemics Prevention Network

Dynamic Complexity is Real… and Consequential Forrester JW. Counterintuitive behavior of social systems. Technology Dynamic Complexity is Real… and Consequential Forrester JW. Counterintuitive behavior of social systems. Technology Review 1971; 73(3): 53 -68. Syndemics Prevention Network 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 Mc. Graw-Hill, 2000.

Syndemics Prevention Network Syndemics Prevention Network

System Dynamics Was Designed to Address Problems Marked By Dynamic Complexity • Multiple, interrelated 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 Mc. Graw-Hill, 2000. Syndemics Prevention Network Homer JB, Hirsch G. System dynamics modeling for public health: background and opportunities. American Journal of Public Health in press.

Extending a Long History of Health Policy Modeling History • • • Developed at 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. Syndemics Prevention Network 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. .

Milestones in the Growth of System Dynamics Modeling at CDC Science Seminar on SD Milestones in the Growth of System Dynamics Modeling at CDC Science Seminar on SD (funding from NCCDPHP, PHPPO, OPPE) CDC Evaluation Framework Recommends Logic Models 1999 Syndemics Network Identifies SD as a Promising Methodology CDC Evaluation Forum Explores Roles for SD Modeling Programs Discover Limitations of Logic Models and Other Methods for System Change Initiatives 2000 2001 2002 2003 Diabetes System Modeling Project (funding from DDT & DACH) 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 & Co. CHP) Syndemics Prevention Network AJPH Theme Issue Features SD Papers ODPHP Convenes HHS Dynamic Modelers to Discuss HP 2020 2004 2005 Infant Health Study Group Uses SD Modeling to Revise CDC Goal for 2015 (funding from OSI and Co. CHP) OSI Chooses Obesity Goal as Highest Priority for SD Modeling (initial funding from OSI)

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

Essential Elements for System Change Ventures Elements of a Sound Strategy Needed to Address… Essential Elements for System Change Ventures Elements of a Sound Strategy Needed to Address… • Multiple, simultaneous lines of action and reaction Realistic Understanding of Causal Dynamics • 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 Syndemics Prevention Network

Essential Elements for System Change Ventures Elements of a Sound Strategy Needed to Address… Essential Elements for System Change Ventures Elements of a Sound Strategy Needed to Address… • Multiple, simultaneous lines of action and reaction Realistic Understanding of Causal Dynamics • 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 • Plausible future targets, given existing momentum • Life-course implications Justifiable Goals & Framework for Charting Progress • 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 Syndemics Prevention Network

Essential Elements for System Change Ventures Elements of a Sound Strategy Needed to Address… Essential Elements for System Change Ventures Elements of a Sound Strategy Needed to Address… • Multiple, simultaneous lines of action and reaction Realistic Understanding of Causal Dynamics • 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 • Plausible future targets, given existing momentum • Life-course implications Justifiable Goals & Framework for Charting Progress • Timing and trajectories of change (e. g. , better-before-worse, or vice versa) • Leadership for choosing a particular course • Clear referent for charting progress • Experiments to test policy leverage (alone and in combination) Means for Prioritizing Actions & Impetus to Implement Them Syndemics Prevention Network • 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)

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

Essential Elements for System Change Ventures Limitations of Conventional Alternatives Elements of a Sound Essential Elements for System Change Ventures Limitations of Conventional Alternatives Elements of a Sound Strategy Conventional Approaches Limitations • Processes of change in dynamic systems tend to be counterintuitive Realistic Understanding of Causal Dynamics • Logic models • Statistical models • Ad hoc research and evaluation studies • “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 Syndemics Prevention Network

Essential Elements for System Change Ventures Limitations of Conventional Alternatives Elements of a Sound Essential Elements for System Change Ventures Limitations of Conventional Alternatives Elements of a Sound Strategy Realistic Understanding of Causal Dynamics Justifiable Goals & Framework for Charting Progress Means for Prioritizing Actions & Impetus to Implement Them Syndemics Prevention Network Conventional Approaches • Logic models • Statistical models • Ad hoc research and evaluation studies • Forecasting models • Best-of-the-best • Wishful thinking Limitations • 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 • 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

Essential Elements for System Change Ventures Limitations of Conventional Alternatives Elements of a Sound Essential Elements for System Change Ventures Limitations of Conventional Alternatives Elements of a Sound Strategy Realistic Understanding of Causal Dynamics Justifiable Goals & Framework for Charting Progress Conventional Approaches • Logic models • Statistical models • Ad hoc research and evaluation studies • Forecasting models • Best-of-the-best • Wishful thinking • Ranking by burden and/or cost effectiveness Means for Prioritizing Actions & Impetus to Implement Them • Health impact assessment • Comparing importance vs. changeability • Organizational will to fund • Coalition-building Syndemics Prevention Network Limitations • 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 • 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 • 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

CDC Diabetes System Modeling Project Discovering Dynamics Through Action Labs Syndemics Prevention Network Jones CDC Diabetes System Modeling Project Discovering Dynamics Through Action Labs Syndemics Prevention Network 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).

Transforming the Future of Diabetes… 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. . . " …in an Era of Epidemic Obesity Syndemics Prevention Network 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.

Forecast of Diabetes Prevalence of Diagnosed Diabetes, US 40 Historical Data Model Forecast Million Forecast of Diabetes Prevalence of Diagnosed Diabetes, US 40 Historical Data Model Forecast Million people 30 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…" Syndemics Prevention Network Honeycutt A, Boyle J, Broglio K, Thompson T, Hoerger T, Geiss L, Narayan K. A dynamic markov model forecasting diabetes prevalence in the United States through 2050. Health Care Management Science 2003; 6: 155 -164.

Discussions Pointed to Many Interacting Factors Forces Outside the Community • Macroeconomy, employment • Discussions Pointed to Many Interacting Factors 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 Civic Participation • Social cohesion • Responsibility for others Health Care Capacity Personal Capacity • Understanding • Motivation • Social support • Literacy • Physio-cognitive function • Life stages Health Care Utilization Metabolic Stressors • Nutrition • Physical activity • Stress Syndemics Prevention Network • Provider supply • Provider understanding, competence • Provider location • System integration • Cost of care • Insurance coverage Population Flows • Ability to use care (match of patients and providers, language, culture) • Openness to/fear of screening • Self-management, monitoring

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

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

Simulations for Learning in Dynamic Systems Diabetes Dynamics in an Era of Epidemic Obesity Simulations for Learning in Dynamic Systems Diabetes Dynamics in an Era of Epidemic Obesity Multi-stakeholder Dialogue Dynamic Hypothesis (Causal Structure) Plausible Futures (Policy Experiments) Deaths per Population 0. 0035 0. 003 se Mixed Ba 0. 0025 Upstream ea str 0. 002 m n ow D 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) Syndemics Prevention Network 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).

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

Diabetes System Modeling Project Confirming the Model’s Fit to History Obese % of Adults Diabetes System Modeling Project Confirming 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). Syndemics Prevention Network

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

Connecting the Objectives Population Flows and Dynamic Accounting 101 People without Diabetes The targeted Connecting the Objectives Population Flows and Dynamic Accounting 101 People without Diabetes The targeted 29% reduction in diagnosed onset can only slow the growth in prevalence Initial Onset People with Undiagnosed Diabetes With a diagnosed onset flow of 1. 1 mill/yr Diagnosed As would stepped-up detection effort Onset People with Diagnosed Diabetes Reduced death would add further to prevalence It is impossible for any policy to reduce prevalence 38% by 2010! Syndemics Prevention Network Dying from Diabetes Complications And a death flow of 0. 5 mill/yr (4%/yr rate)

How Does Modeling Process Help DDT in Its Work with the States? • • 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 Syndemics Prevention Network

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

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

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

Syndemics Prevention Network Syndemics Prevention Network

Vermont’s Response • Very interactive meeting with partners in March 2005 (lots of ah-ha’s!) 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 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. ” Syndemics Prevention Network

Next Steps for DDT/PDB • Primary Prevention RFA with systems modeling pilot – At 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 Syndemics Prevention Network

Obesity Dynamics A General Causal Structure Syndemics Prevention Network Obesity Dynamics A General Causal Structure Syndemics Prevention Network

Re-Directing the Course of Change Questions Addressed by System Dynamics Modeling ? ere h Re-Directing the Course of Change Questions Addressed by System Dynamics Modeling ? ere h Prevalence of Obese Adults, United States W ? hy W Ho w? Wh o ? 1960 -62 1971 -74 Data Source: NHANES Syndemics Prevention Network 1976 -80 1988 -94 1999 -2000 2010 2020

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

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

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

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

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

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

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

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

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

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

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

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

Framework for Organizing Influences on Obesity Social Norms and Values § Home and Family Framework for Organizing Influences on Obesity Social Norms and Values § Home and Family § Food and Beverage Industry § School Sectors of Influence § Community § Agriculture § Education § Work Site § Media § Healthcare § Government Behavioral Settings § Public Health Systems § Healthcare Industry § Genetics § Psychosocial Individual Factors § Other Personal Factors Food and Beverage Intake § Business and Workers Physical Activity Energy Intake § Land Use and Transportation § Leisure and Recreation Energy Expenditure Energy Balance Syndemics Prevention Network Prevention of Overweight and Obesity Among Children, Adolescents, and Adults Note: Adapted from “Preventing Childhood Obesity. ” Institute of Medicine, 2005.

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

A Conventional View of Causal Forces • Syndemics Prevention Network This sort of open-loop A Conventional View of Causal Forces • Syndemics Prevention Network This sort of open-loop approach – Ignores intervention spill-over effects and often suggests the best strategy is a multipronged “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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

The Closed-Loop View Leads Us To Question… • How can the engines of growth 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? Syndemics Prevention Network

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

Identifying Forces of Change Tasks • • Make the dynamics in your assigned pathway(s) 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 Syndemics Prevention Network

Society-Behavior Pathway Syndemics Prevention Network Society-Behavior Pathway Syndemics Prevention Network

Behavior-Society Pathway Syndemics Prevention Network Behavior-Society Pathway Syndemics Prevention Network

Individual Responses to Weight Pathways Syndemics Prevention Network Individual Responses to Weight Pathways Syndemics Prevention Network

Social Transmission Pathways Syndemics Prevention Network Social Transmission Pathways Syndemics Prevention Network

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

General Plan for the Workshop Day 1 • Dynamic Dilemmas • System Dynamics in 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 • • Syndemics Prevention Network Modeling for Learning – Using Simulation Experiments Group Exercise – Organizing Effective Health Protection Efforts Directing Change and Charting Progress Snapshot Evaluation

Iterative Steps in System Dynamics Simulation Modeling Identify a Persistent Problem Graph its behavior Iterative Steps in System Dynamics Simulation Modeling Identify a Persistent Problem Graph its behavior over time Create a Dynamic Hypothesis Identify and map the main causal forces that create the problem Syndemics Prevention Network Convert the Map Into a Simulation Model Formally quantify the hypothesis using all available evidence Run Simulation Experiments Compare model’s behavior to expectations and/or data to build confidence in the model Learn About Policy Consequences Test proposed policies, searching for ones that best govern change Enact Policy Build power and organize actors to establish chosen policies 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.

Modeling for Learning Why Simulate? Syndemics Prevention Network Modeling for Learning Why Simulate? Syndemics Prevention Network

Syndemics Prevention Network Syndemics Prevention Network

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

Syndemics Prevention Network Syndemics Prevention Network

A Health Care “Microworld” • Developed in mid-1990 s to help providers understand implications A Health Care “Microworld” • Developed in mid-1990 s 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 Syndemics Prevention Network Hirsch GB, Immediato CS. Design of simulators to enhance learning: examples from a health care microworld. International System Dynamics Conference; Quebec City; July, 1998.

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

Simulating the Microworld to Address a Strategic Question • Can chronic disease management improve 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 Syndemics Prevention Network

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

How Should We Value Models? • All formal models—including simulations—are wrong: incomplete and imprecise How Should We Value Models? • 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 anticipate better than we do with the unaided mind…or with a causal map alone Syndemics Prevention Network Sterman JD. All models are wrong: reflections on becoming a systems scientist. System Dynamics Review 2002; 18(4): 501 -31.

Obesity Dynamics An Illustrative Simulation Model Syndemics Prevention Network Obesity Dynamics An Illustrative Simulation Model Syndemics Prevention Network

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

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

How the BMI Distribution Has Shifted and Stretched Curves here generated from Gamma functions, 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. Syndemics Prevention Network

Preliminary Dynamic Hypothesis for Understanding and Reversing the Growth of Obesity Wider Environment (Economy, Preliminary Dynamic Hypothesis for Understanding and Reversing the Growth of Obesity Wider Environment (Economy, Technology, Laws) Influence on Options B 7 Options Available at Home, School, Work, Community Influencing Healthy Diet & Activity Spiral of Poor Health and Habits Observation of Parents' and Peers' Habits - R 5 Society Shapes Options Shape Society R 1 Health Conditions Detracting from Healthy Diet & Activity Parents/Peers Transmission R 4 Options Shape Habits Shape Options - Healthiness of Diet & Activity Habits R 3 Addressing Related Health Conditions B 1 B 5 Creating Better Conditions in the Wider Environment B 2 Media Messages Promoting Healthy Diet & Activity Creating Better Options in Behavioral Settings Healthcare Services to Promote Healthy Diet & Activity Creating Better Messages Improving Preventive Healthcare - Up-front Costs Undercut Protection Efforts Costs of Developing & Maintaining Health Protection Efforts R 6 Disease Care Costs Squeeze Prevention B 3 Costs of Caring for Overweight. Related Diseases B 9 Defending Status Quo Resistance and Countervailing Efforts by Opposed Interests Cost Implications of Overweight in Other Spheres B 10 Potential Savings Build Support - B 8 Syndemics Medical Response B 4 Effective Health Protection Efforts Prevention Network Prevalence of Overweight & Related Diseases Self-Improvement Media Mirrors B 6 Genetic Metabolic Rate Disorders R 2 R 7 Broader Benefits Build Support Perceived Program Benefits Beyond Weight Reduction Broader Benefits of Health Protection Efforts

Demonstration Model Structure Core Pieces of the Larger Theory Wider Environment (Economy, Technology, Laws) Demonstration Model Structure Core Pieces of the Larger Theory Wider Environment (Economy, Technology, Laws) Influence on Options R 1 Health Conditions Detracting from Healthy Diet & Activity Spiral of Poor Health and Habits Observation of Parents' and Peers' Habits R 2 Options Available at Home, School, Work, Community Influencing Healthy Diet & Activity Parents/Peers Transmission Healthiness of Diet & Activity Habits Effective Health Protection Efforts Syndemics Prevention Network - Prevalence of Overweight & Related Diseases

Demonstration Model Structure Syndemics Prevention Network Demonstration Model Structure Syndemics Prevention Network

Demonstration Model Structure Syndemics Prevention Network Demonstration Model Structure Syndemics Prevention Network

Demonstration Model Structure Syndemics Prevention Network Demonstration Model Structure Syndemics Prevention Network

Demonstration Model Structure Syndemics Prevention Network Demonstration Model Structure Syndemics Prevention Network

Demonstration Model Structure Syndemics Prevention Network Demonstration Model Structure Syndemics Prevention Network

Demo Model Input Assumptions • Time constants – – Years of childhood and adolescence 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 Syndemics Prevention Network

Demo Model Base Run Results vs NHANES: Adult Overweight Fraction* Adult overweight fraction 0. Demo Model Base Run Results vs NHANES: Adult Overweight Fraction* Adult overweight fraction 0. 8 Simulated 0. 6 Data 0. 4 0. 2 0 1960 1970 1980 1990 2000 2010 2020 Time (year) 2030 2040 2050 2060 Adult overwt frac : Base 2 d NHANES adult overwt frac : Base 2 d * 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. Syndemics Prevention Network

Demo Model Base Run Results vs NHANES: Youth Overweight Fraction* Youth overweight fraction 0. Demo Model Base Run Results vs NHANES: Youth Overweight Fraction* Youth overweight fraction 0. 4 Simulated 0. 3 0. 2 Data 0. 1 0 1960 1970 1980 1990 2000 2010 2020 Time (year) 2030 2040 2050 2060 Youth overwt frac : Base 2 d NHANES youth overwt frac : Base 2 d * “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. Syndemics Prevention Network

Demo Model Base Run Results Healthiness of Habits and the Environment Healthiness of Habits Demo Model Base Run Results Healthiness of Habits and the Environment Healthiness of Habits and Environment 0. 8 0. 6 0. 4 Adult Environment Youth 0. 2 0 1960 1970 1980 1990 2000 2010 2020 Time (year) 2030 2040 2050 2060 Healthiness of adult habits : Base 2 d Healthiness of youth habits : Base 2 d Healthiness of broader environment : Base 2 d 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. Syndemics Prevention Network

Demo Model Base Run Results vs NHANES: Adult Obese Fraction* Adult obese fraction 0. Demo Model Base Run Results vs NHANES: Adult Obese Fraction* Adult obese fraction 0. 8 0. 6 Simulated 0. 4 0. 2 Data 0 1960 1970 1980 1990 2000 2010 2020 Time (year) 2030 2040 2050 2060 Adult obese frac : Base 2 d NHANES adult obese frac : Base 2 d * 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. Syndemics Prevention Network

Demo Model Base Run Results vs NHANES: Adult Severely Obese Fraction* Adult severely obese Demo Model Base Run Results vs NHANES: Adult Severely Obese Fraction* Adult severely obese fraction 0. 1 0. 08 Simulated 0. 06 0. 04 0. 02 Data 0 1960 1970 1980 1990 2000 2010 2020 Time (year) 2030 2040 2050 Adult sev obese frac : Base 2 d NHANES adult sev obese frac : Base 2 d * 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. Syndemics Prevention Network 2060

Demo Model Base Run Results vs NHANES: Youth Obese Fraction* Youth obese fraction 0. Demo Model Base Run Results vs NHANES: Youth Obese Fraction* Youth obese fraction 0. 4 0. 3 Simulated 0. 2 0. 1 Data 0 1960 1970 1980 1990 2000 2010 2020 Time (year) 2030 2040 2050 2060 Youth obese frac : Base 2 d NHANES youth obese frac : Base 2 d * “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 children in ‘ 63 -’ 65 and adolescents in ‘ 66 -’ 70; these are Syndemics Prevention Network averaged for the first data point in 1968.

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

Dynamic Effects of Interventions Illustrative Policy Tests • Base – All time series inputs 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 • ‘Youth. Opt 50’ (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 • ‘Adult. Opt 50’ (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 • ‘All. Opt 50’ (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 Syndemics Prevention Network

Policy Testing Output Adult Obese Fraction Adult obese fraction 0. 6 Base 0. 4 Policy Testing Output Adult Obese Fraction Adult obese fraction 0. 6 Base 0. 4 Youth options Adult options 0. 2 0 1960 Both 1970 1980 1990 2000 2010 2020 Time (year) 2030 2040 2050 2060 Adult obese frac : Base 2 d Adult obese frac : Youthopt 50 Adult obese frac : Adultopt 50 Adult obese frac : Allopt 50 Syndemics Prevention Network The improvement in adult options by itself initially reduces adult obesity by 2015 to where it was in early 1990 s. 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.

Policy Testing Output Youth Obese Fraction Youth obese fraction 0. 3 Base 0. 2 Policy Testing Output Youth Obese Fraction Youth obese fraction 0. 3 Base 0. 2 Adult options Youth options 0. 1 Both 0 1960 1970 1980 1990 2000 2010 2020 Time (year) 2030 2040 2050 2060 Youth obese frac : Base 2 d Youth obese frac : Youthopt 50 Youth obese frac : Adultopt 50 Youth obese frac : Allopt 50 Syndemics Prevention Network The improvement in youth options by itself reduces youth obesity by 2015 to where it was in the early 1990 s. 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-1980 s.

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

Organizing Health Protection Efforts Tasks • Make the dynamics in your assigned pathways more 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 Syndemics Prevention Network

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

Crafting Better Messages Syndemics Prevention Network Crafting Better Messages Syndemics Prevention Network

Creating Better Options in Behavioral Settings Syndemics Prevention Network Creating Better Options in Behavioral Settings Syndemics Prevention Network

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

Transforming Essential Ways of Thinking Conventional Thinking Systems Thinking Static Thinking: Focusing on particular Transforming Essential Ways of Thinking Conventional Thinking Systems 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. . Syndemics Prevention Network Richmond B. Systems thinking: critical thinking skills for the 1990 s 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.

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

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

EXTRAS Syndemics Prevention Network EXTRAS Syndemics Prevention Network

Dynamic effects of behavioral assumptions: Illustrative sensitivity tests • Base: – – Fraction of 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 • Youth. Imitate 0: Youth habits are not influenced by parents or other adults • Adult. Carryover 0: Childhood habits do not carry over to adulthood • Youth. Eff. Overwt 0: Being overweight does not make it harder for youths to maintain healthy habits • Adult. Eff. Overwt 0: Being overweight does not make it harder for adults to maintain healthy habits Syndemics Prevention Network

Sensitivity Testing Output Youth Obese Fraction Youth obese fraction 0. 3 0. 2 0. Sensitivity Testing Output Youth Obese Fraction Youth obese fraction 0. 3 0. 2 0. 1 0 1960 1970 1980 1990 2000 2010 2020 Time (year) 2030 2040 2050 2060 Youth obese frac : Base 2 d Youth obese frac : Youth. Imitate 0 Youth obese frac : Adult. Carryover 0 Youth obese frac : Youth. Eff. Overwt 0 Youth obese frac : Adult. Eff. Overwt 0 Syndemics Prevention Network - 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.

Sensitivity Testing Output Adult Obese Fraction Adult obese fraction 0. 6 0. 4 0. Sensitivity Testing Output Adult Obese Fraction Adult obese fraction 0. 6 0. 4 0. 2 0 1960 1970 1980 1990 2000 2010 2020 Time (year) 2030 2040 2050 2060 Adult obese frac : Base 2 d Adult obese frac : Youth. Imitate 0 Adult obese frac : Adult. Carryover 0 Adult obese frac : Youth. Eff. Overwt 0 Adult obese frac : Adult. Eff. Overwt 0 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.

Demo Model Base Run Results Healthiness of Youth and Adult Habits Healthiness of habits Demo Model Base Run Results Healthiness of Youth and Adult Habits Healthiness of habits 0. 8 0. 6 Adult 0. 4 Youth 0. 2 0 1960 1970 1980 1990 2000 2010 2020 Time (year) 2030 2040 2050 2060 Healthiness of youth habits : Base 2 d Healthiness of adult habits : Base 2 d 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 nonoverweight do to maintain healthy habits in any environment. Syndemics Prevention Network

The Modeling Process is Having an Impact • Budget for primary prevention was doubled The Modeling Process is Having an Impact • Budget for primary prevention was doubled – from meager to modest • HP 2010 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 Syndemics Prevention Network

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