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March 25, 2009 Seattle, WA

Crafting Integrated Strategies to Prevent and Manage Chronic Disease Using System Dynamics Chronic Disease Academy. March 25, 2009 Seattle, WA. Presenters. Phil Huang Medical Director for City of Austin Department of Health and Human Services, formerly Chronic Disease Director for TX

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March 25, 2009 Seattle, WA

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  1. Crafting Integrated Strategies to Prevent and Manage Chronic Disease Using System Dynamics Chronic Disease Academy March 25, 2009 Seattle, WA

  2. Presenters • Phil Huang • Medical Director for City of Austin Department of Health and Human Services, formerly Chronic Disease Director for TX • Patty Mabry • Office of Behavioral and Social Sciences Research, National Institutes of Health • Bobby Milstein • Coordinator, Syndemics Prevention Network, Centers for Disease Control and Prevention • Diane Orenstein • Technical Lead, Division for Heart Disease and Stroke Prevention, Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention • Kris Wile • Sustainability Institute, System Dynamics Facilitator and Modeler

  3. Workshop Agenda

  4. Office of Behavior and Social Science’s Vision at NIH To mobilize the biomedical, behavioral, and social science research communities as partners in interdisciplinary research to solve the most pressing health challenges faced by our society. Programmatic Directions to Achieve the Vision: • Transdiciplinary science • “Next generation”, basic science • Problem-based, outcomes oriented strengthen the science of dissemination • Systems - thinking for population impact The Importance of Partnership for OBSSR

  5. Health as a continuum between biological, behavioral and social factors across the lifespan and across generations Adapted from Glass, McAtee (2006). Soc. Sci. Medicine, 62: 1650-1671

  6. Understanding the “Whole” System • Simulation Modeling and Experimentation • Pandemic flu • Tobacco use • Obesity, Diabetes • Health inequalities • “Non-health factors” • Chronic disease • Health care delivery • Stress, mental illness, worksites, policy……….

  7. Overall Health Protection Enterprise SD Identified as a Promising Methodology for Health System Change Ventures Upstream-Downstream Dynamics Neighborhood Transformation Game Health Protection Game National Health Economics & Reform Syndemics Modeling* Diabetes Action Labs Fetal & Infant Health Obesity Overthe Lifecourse Cardiovascular Health in Context Selected Health Priority Areas Selected Examples from CDC’s Growing Portfolio of Simulation Studies for Health System Change 2006 2007 2008 2000 2001 2002 2003 2004 2005

  8. Questions Addressed by System Dynamics ModelingExploring Strategies to Redirect the Course of Change Historical Markov Forecasting Model Data Simulation Experiments in Action Labs Prevalence of Diagnosed Diabetes, US 40 Where? 30 What? Million people 20 How? • Markov Model Constants • Incidence rates (%/yr) • Death rates (%/yr) • Diagnosed fractions • (Based on year 2000 data, per demographic segment) 10 Who? Why? 0 1980 1990 2000 2010 2020 2030 2040 2050 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. Jones AP, Homer JB, Murphy DL, Essien JDK, Milstein B, Seville DA. Understanding diabetes population dynamics through simulation modeling and experimentation. American Journal of Public Health 2006;96(3):488-494.

  9. Tools for Policy Planning & Evaluation Events Time Series Models Describe trends • Increasing: • Depth of causal theory • Robustness for longer-term projection • Value for developing policy insights • Degrees of uncertainty Multivariate Stat Models Identify historical trend drivers and correlates Patterns Dynamic Simulation Models Anticipate new trends, learn about policy consequences, and set justifiable goals Structure

  10. Different Modeling Approaches For Different Purposes

  11. Brief Background on System Dynamics Modeling Compartmental models resting on a general theory of how systems change (or resist change) – often in ways we don’t expect • Developed for corporate policies in the 1950s, and applied to health policies since the 1970s • Concerned with understanding dynamic complexity • Accumulation (stocks and flows) • Feedback (balancing and reinforcing loops) • Used primarily to craft far-sighted, but empirically based, strategies • Anticipate real-world delays and resistance • Identify “high leverage” interventions • Modelers engage stakeholders through interactive workshops Forrester JW. Industrial Dynamics. Cambridge, MA: MIT Press; 1961. Sterman JD. Business Dynamics: Systems Thinking and Modeling for a Complex World. Boston, MA: Irwin/McGraw-Hill; 2000.

  12. What is a System? What are Dynamics? System (Structure) = Stocks + Flows + Feedback Loops +… • Stocks are accumulations of flows(of population, resources, changing goals, perceptions, etc.) • Feedback loops link accumulations back to decisions that alter the flows: only 2 types (goal-seeking, self-reinforcing) • Delays complicate things further • As do non-linearities (need for critical mass, saturation effects) Dynamics = Behavior over time • Patterns in time series data (growth, fluctuation, etc.) • Visible relationships of two or more variables (move together, move opposite, lead-lag, etc.)

  13. An (Inter) Active Form of Policy Planning/Evaluation System Dynamics is a methodology to… • Map the salient forces that contribute to a persistent problem; • Convert the map into a computer simulation model, integrating the best information and insight available; • Compare results from simulated “What If…” experiments to identify intervention policies that might plausibly alleviate the problem; • Conduct sensitivity analyses to assess areas of uncertainty in the model and guide future research; • Convene diverse stakeholders to participate in model-supported “Action Labs,” which allow participants to discover for themselves the likely consequences of alternative policy scenarios

  14. Simulations for Learning in Dynamic Systems Dynamic Hypothesis (Causal Structure) Plausible Futures (Policy Experiments) Obese fraction of Adults (Ages 20-74) 50% 40% 30% Fraction of popn 20-74 20% 10% 0% 1970 1980 1990 2000 2010 2020 2030 2040 2050 Multi-stakeholder Dialogue Morecroft JDW, Sterman J. Modeling for learning organizations. Portland, OR: Productivity Press, 2000. Sterman JD. Business dynamics: systems thinking and modeling for a complex world. Boston, MA: Irwin McGraw-Hill, 2000.

  15. Getting Oriented • Introduction • Name, Organization, What you do • What are you hoping to get out of today? • Then talk with others at your tables: • What are the largest strategic issues you see in chronic disease? • After 10 minutes, we’ll return to large group to share highlights • Biggest strategic challenges?

  16. CDC Diabetes System Modeling ProjectDiscovering Dynamics Through State-based Action Labs & Models Jones AP, Homer JB, Murphy DL, Essien JDK, Milstein B, Seville DA. Understanding diabetes population dynamics through simulation modeling and experimentation. American Journal of Public Health 2006;96(3):488-494.

  17. Diabetes Model: Diabetes Burden is Driven by Population Flows Burden of Diabetes Costs (per person with diabetes) Total Prevalence Unhealthy Days (people with diabetes) (per person with diabetes) Developing PreDiabetes Diabetes People with Deaths People with Diagnosis People with Onset People with Onset Normal e Undiagnosed Diagnosed Undiagnosed a b c Blood Sugar Diabetes Diabetes PreDiabetes Levels Recovering from PreDiabetes Deaths Obesity in the PreDiabetes Diabetes Diabetes General Detection & Diagnosis Management Population Management Volume Developing d Inflow Outflow

  18. Diabetes Burden is Driven by Population Flows Burden of This larger view takes us beyond standard epidemiological models and most intervention programs Diabetes Costs (per person with diabetes) Total Prevalence Unhealthy Days (people with diabetes) (per person with diabetes) Developing PreDiabetes Diabetes People with Deaths People with Diagnosis People with Onset People with Onset Normal e Undiagnosed Diagnosed Undiagnosed a b c Blood Sugar Diabetes Diabetes PreDiabetes Levels Recovering from PreDiabetes Deaths Obesity in the PreDiabetes Diabetes Diabetes General Detection & Diagnosis Management Population Management Volume Standard boundary Developing d Inflow Outflow

  19. Diabetes System Dynamics Modeling ProjectConfirming Fit to Historical Trends (2 examples out of 10) Simulated Simulated Obese % of Adults Diagnosed Diabetes % of Adults 40% 8% Obese % of adults Diagnosed diabetes % of adults 30% 6% 20% 4% Data (NHIS) Data (NHANES) 10% 2% 0% 0% 1980 1985 1990 1995 2000 2005 2010 1980 1985 1990 1995 2000 2005 2010

  20. The growth of diabetes prevalence since 1980 has been driven by growth in obesity prevalence Risk multiplier on diabetes onset from obesity = 2.6 Obese Fraction and Diabetes per Thousand 130 0.7 Diabetes Prevalence 85 0.35 Obesity Prevalence 40 0 1980 1990 2000 2010 2020 2030 2040 2050 Time (Year)

  21. Baseline Scenario: Obesity to increase little after 2006, diabetes keeps growing robustly for another 20-25 years Risk multiplier on diabetes onset from obesity = 2.6 Obese Fraction and Diabetes per Thousand Onset=6.3 per thou 130 0.7 Estimated 2006 values Diabetes Prevalence Prevalence=92 AND RISING 85 0.35 Obesity Prevalence Death=3.8 per thou 40 0 1980 1990 2000 2010 2020 2030 2040 2050 Time (Year) With high (even if flat) onset, prevalence tub keeps filling until deaths (4-5%/yr)=onset Diabetes prevalence keeps growing after obesity stops WHY?

  22. Unhealthy days impact of prevalence growth, as affected by diabetes management: Past and one possible future Reduction in unhealthy days per complicated case if conventionally managed: 33%; if intensively managed: 67% Unhealthy Days per Thou and Frac Managed Obese Fraction and Diabetes per Thousand 500 Managed fraction 130 0.65 0.7 Diabetes Prevalence 375 85 0.325 0.35 Obesity Prevalence Unhealthy Days from Diabetes 40 250 0 0 1980 1990 2000 2010 2020 2030 2040 2050 1980 1990 2000 2010 2020 2030 2040 2050 Time (Year) Diabetes prevalence keeps growing after obesity stops If disease management gains end, the burden grows

  23. A Sequence of What-if Simulations Start with the base case or “status quo”: no improvements in diabetes management or prediabetes management after 2006 People with Diabetes per Thousand Adults Monthly Unhealthy Days from Diabetes per Thou 150 500 Base 450 125 Base 400 100 350 75 300 50 250 1980 1990 2000 2010 2020 2030 2040 2050 1980 1990 2000 2010 2020 2030 2040 2050

  24. What if there were further Increases in Diabetes Management? Diabetes mgmt does nothing to slow the growth of prevalence—in fact, it increases it. As soon as diabetes mgmt stops improving, unhealthy days start to grow as fast as prevalence. Increase fraction of diagnosed diabetes getting managed from 58% to 80% by 2015. (No change in the mix of conventional and intensive.) What do you think will happen? People with Diabetes per Thousand Adults Monthly Unhealthy Days from Diabetes per Thou 150 500 Base Diab mgt 450 125 Base 400 Diab mgt 100 350 75 300 50 250 1980 1990 2000 2010 2020 2030 2040 2050 1980 1990 2000 2010 2020 2030 2040 2050 Keeping the burden at bay for nine years longer More people living with diabetes

  25. What if there was a huge push for Prediabetes Management? Diabetes onset rate reduced 12% relative to base run. Not nearly enough to offset the excess onset due to high obesity. By 2050, diabetes prevalence reduced only 9% relative to base run. Increase fraction of prediabetics getting managed from 6% to 32% by 2015. (Half of those under intensive mgmt by 2015.) No increase in diabetes mgmt. What do you think will happen? People with Diabetes per Thousand Adults Monthly Unhealthy Days from Diabetes per Thou 150 500 Base Base 450 125 PreD mgmt 400 PreD mgmt 100 350 75 300 50 250 1980 1990 2000 2010 2020 2030 2040 2050 1980 1990 2000 2010 2020 2030 2040 2050 The improvement is relatively modest—the growth is not stopped

  26. Diabetes Model: What if Obesity is Reduced?Two Scenarios What if it were possible—in addition to the prediabetes mgmt intervention - to gradually lower the fraction obese from 34% (2006) to the 1994 value of 25% by 2030? Or, to the 1984 value of 18%? Obese Fraction of Adult Population 0.4 Base 0.3 Obesity 25% Obesity 18% 0.2 0.1 0 1980 1990 2000 2010 2020 2030 2040 2050

  27. Diabetes: What if we Managed Prediabetes AND Reduced Obesity? Why is obesity reduction so powerful? Mainly because of its strong effect on onset rate among prediabetics; but, also, because it reduces PreD prevalence itself. However, achieving significant obesity reduction takes a long time. What do you think will happen if, in addition to PreD mgmt, obesity is reduced moderately by 2030? What if it is reduced even more? People with Diabetes per Thousand Adults Monthly Unhealthy Days from Diabetes per Thou 150 500 Base 450 Base PreD mgmt 125 PreD mgmt 400 PreD & Ob 25% PreD & Ob 25% 100 350 PreD & Ob 18% 75 PreD & Ob 18% 300 50 250 1980 1990 2000 2010 2020 2030 2040 2050 1980 1990 2000 2010 2020 2030 2040 2050 The more you reduce obesity, the sooner you stop the growth in diabetes—and the more you bring it down … Same with the burden of diabetes

  28. What if Intervened Effectively Upstream AND Downstream Downstream improvement acts quickly against burden but cannot continue forever. Significant upstream gains are thus essential but will likely take 15+ years to achieve. A flat-burden future is possible but requires simultaneous action on both fronts. With pure upstream intervention, burden still grows for many years before turning around. What do you think will happen if we add the prior diabetes mgmt intervention on top of the PreD+Ob25 one? People with Diabetes per Thousand Adults Monthly Unhealthy Days from Diabetes per Thou 150 500 Base 450 Base 125 PreD mgmt PreD mgmt 400 All 3 100 Pred & Ob 25% PreD & Ob 25% 350 All 3 -- PreD & Ob 25% & Diab mgmt 75 300 50 250 1980 1990 2000 2010 2020 2030 2040 2050 1980 1990 2000 2010 2020 2030 2040 2050 With a combination of effective upstream and downstream interventions we could hold the burden of diabetes nearly flat through 2050!

  29. Core Design Team Dave Buchner Andy Dannenberg Bill Dietz Deb Galuska Larry Grummer-Strawn Anne Hadidx Robin Hamre Laura Kettel-Khan Elizabeth Majestic Jude McDivitt Cynthia Ogden Michael Schooley CDC Obesity Dynamics Modeling Project Contributors Project Coordinator • Bobby Milstein System Dynamics Consultants • Jack Homer • Gary Hirsch Time Series Analysts • Danika Parchment • Cynthia Ogden • Margaret Carroll • Hatice Zahran Cover of "The Economist", Dec. 13-19, 2003. Workshop Participants • Atlanta, GA: May 17-18 (N=47) • Lansing, MI: July 26-27 (N=55) Homer J, Milstein B, Dietz W, Buchner D, Majestic D. Obesity population dynamics: exploring historical growth and plausible futures in the U.S. 24th International Conference of the System Dynamics Society; Nijmegen, The Netherlands; July 26, 2006.

  30. Focusing on Life-Course Dynamics • Explore likely consequences of possible interventions affecting caloric balance (intake less expenditure) • How much impact on obesity prevalence? • How long will it take to see? • Should we target particular subpopulations? (age, sex, weight category; lack data for race, ethnicity) • Consider interventions broadly but leave details (composition, coverage, efficacy, cost) outside model boundary for now • Available data inadequate • Would require a separate research effort to estimate these details • Not addressing feedback loops of reinforcement and resistance • Not addressing cost-effectiveness

  31. Obesity Dynamics Over the DecadesDynamic Population Weight Framework Dynamic Population Weight Framework Immigration Birth Yearly aging Changes in the Physical Population by Age (0-99) and Sex and Social Environment Trends and Planned Caloric Flow-rates between Moderately Moderately Severely Not Interventions Balance BMI categories Overweight Obese Obese Overweight Weight Loss/Maintenance Services for Individuals Death Overweight and obesity prevalence Data source: National Center for Health Statistics, CDC: National Health Examination Survey (NHES) 1960-1970, National Health and Nutrition Examination Survey (NHANES) 1971-2002. Homer J, Milstein B, Dietz W, et al. Obesity population dynamics: exploring historical growth and plausible futures in the U.S. Proc. 24th Int’l System Dynamics Conference; Nijmegen, The Netherlands; July 2006.

  32. Alternative Futures for Adult Obesity Obese fraction of Adults (Ages 20-74) 50% 40% 30% Fraction of popn 20-74 20% 10% 0% 1970 1980 1990 2000 2010 2020 2030 2040 2050 Base SchoolYouth AllYouth School+Parents AllAdults AllAges AllAges+WtLoss

  33. Environmental change approach (reduce caloric balances to their 1970 values by 2015 for selected age ranges) • Youth interventions have only small impact on overall adult obesity (assuming adult habits determined by adult environments—not by childhood1) • Slow decline in overall adult obesity, even when program covers all ages • Targeted weight loss approach • (obese lose 4 lbs per year, program terminated 2020) • Such a program could accelerate progress and “buy time” for environmental change (but first, need to find a cost-effective program with lasting benefits—minimal relapse) Results of Simulated Interventions Need to assure caloric balance throughout all ages, particularly adulthood. Contrast today’s narrow national focus on school-age youth. Also need research on extent to which adult habits are determined by childhood.2 1. Christakis and Fowler. NEJM 357, 2007. 2. Bar-Or O., PCPFS Research Digest Series 2, No. 4, 1995.

  34. Simulating the Dynamics of Cardiovascular Health and Related Risk FactorsWork in Progress This work was funded by the CDC’s Division for Heart Disease and Stroke Prevention and by the National Institutes of Health’s Office of Behavioral and Social Science Research. The work was done in collaboration with the Health and Human Services Department of Austin/Travis County, Texas, and with Integrated Care Collaboration of Central Texas. The external contractors are Sustainability Institute and RTI International. Homer J, Milstein B, Wile K, Pratibhu P, Farris R, Orenstein D. Modeling the local dynamics of cardiovascular health: risk factors, context, and capacity. Preventing Chronic Disease 2008;5(2). Available at http://www.cdc.gov/pcd/issues/2008/apr/07_0230.htm Homer J, Milstein B, Wile K, Trogdon J, Huang P, Labarthe D, Orenstein D. Simulating and evaluating local interventions to improve cardiovascular health. In submission to Preventing Chronic Disease.

  35. Cardiovascular Disease and Risks Remain Among the Leading Causes of Death Fraction of total deaths in 2005*… *US: CDC/National Center for Health Statistics, Vol. 56, No.10, April 2008; TX: TX Dept. of State Health Services PreliminaryVital Statistics Table 16

  36. Preserving Low CVD Risk Controlling Increased CVD Risk Reducing Disability & Risk of Recurrent CVD Detecting & Treating Acute CVD Events From Healthy People 2010: 4 Levels of Prevention for Cardiovascular Diseases

  37. Disability and Risk of CVD Recurrence Low CVD Risk Increased CVD Risk Acute CVD Events 4 levels of prevention correspond to 4 States of Cardiovascular Health:

  38. Disability and Risk of CVD Recurrence Low CVD Risk Increased CVD Risk Acute CVD Events COSTS (CVD & NON-CVD) ATTRIBUTABLE TO RISK FACTORS NUTRITION, PHYSICAL ACTIVITY & STRESS LOCAL CONTEXT • Salt intake • Saturated/Trans fat intake • Fruit/Vegetable intake • Net caloric intake • Physical activity • Chronic stress • Eating & activity options • Smoking policies • Socioeconomic conditions • Environmental policies • Health care options • Support service options • Media and events CVD RISK FACTOR PREVALENCE & CONTROL ESTIMATED FIRST-TIME CVD EVENTS • Hypertension • High cholesterol • Diabetes • Obesity • Smoking • Secondhand smoke • Air pollution exposure • CHD (MI, Angina, Cardiac Arrest) • Stroke • Total CVD (CHD, Stroke, CHF, PAD) UTILIZATION OF SERVICES • Behavioral change • Social support • Mental health • Preventive health Preventing and Managing Risk Factors for CVD Local capacity for leadership & organizing LOCAL ACTIONS

  39. Local capacity for leadership & organizing LOCAL ACTIONS COSTS (CVD & NON-CVD) ATTRIBUTABLE TO RISK FACTORS NUTRITION, PHYSICAL ACTIVITY & STRESS LOCAL CONTEXT • Salt intake • Saturated/Trans fat intake • Fruit/Vegetable intake • Net caloric intake • Physical activity • Chronic stress • Eating & activity options • Smoking policies • Socioeconomic conditions • Environmental policies • Health care options • Support service options • Media and events CVD RISK FACTOR PREVALENCE & CONTROL ESTIMATED FIRST-TIME CVD EVENTS • Hypertension • High cholesterol • Diabetes • Obesity • Smoking • Secondhand smoke • Air pollution exposure • CHD (MI, Angina, Cardiac Arrest) • Stroke • Total CVD (CHD, Stroke, CHF, PAD) UTILIZATION OF SERVICES • Behavioral change • Social support • Mental health • Preventive health Interventions Through Local Context Homer J, Milstein B, Wile K, Pratibhu P, Farris R, Orenstein D. Modeling the local dynamics of cardiovascular health: risk factors, context, and capacity. Preventing Chronic Disease (in press).

  40. Purpose of the Cardiovascular Risk Model • How do local conditions affect multiple risk factors for CVD, and how do those risks affect population health status and costs over time? • How do different local interventions affect cardiovascular health and related expenditures in the short- and long-term? • How might local health leaders better balance their policy efforts given limited resources? The CDC has partnered with the Austin (Travis County), Texas, Dept. of Health and Human Services. The model is calibrated to represent the overall US, but is informed by the experience and data of the Austin team, which has been supported by the CDC’s “STEPS” program since 2004. Homer J, Milstein B, Wile K, Pratibhu P, Farris R, Orenstein D. Modeling the local dynamics of cardiovascular health: risk factors, context, and capacity. Preventing Chronic Disease 2008;5(2). Available at http://www.cdc.gov/pcd/issues/2008/apr/07_0230.htm

  41. Direct Risk Factors

  42. Indirect Risk Factors

  43. Tobacco and Air Quality Interventions

  44. Air Quality Interventions

  45. Health Care Interventions

  46. Interventions Affecting Stress

  47. Healthy Diet Interventions

  48. Physical Activity & Weight Loss Interventions

  49. Adding Up the Costs

  50. Adding Up the Costs Cardiovascular event costs Medical costs (ER, inpatient, rehab)—for non-fatal & fatal events Productivity (morbidity) losses* from non-fatal events Productivity (premature mortality) losses* from fatal events Non-cardiovascular complications of risk factors Hospital costs due to non-CV complications of diabetes (e.g., kidneys, eyes, feet), high BP, & smoking Productivity (morbidity) losses* from non-fatal complications of diabetes, high BP, smoking, & obesity Productivity (mortality) losses* from fatal complications of smoking (e.g., cancer, COPD), diabetes, high BP, & obesity Costs of managing risk factors Medications & visits for diabetes, high BP, high cholesterol—by level of care (high quality = 2 – 2.5x cost of mediocre care) Other services: Mental health services, Weight loss services, Smoking quit services & products Human capital approach based on: Haddix, Teutsch, Corso, Prevention Effectiveness, 2003 (2nd ed, Tables 1.1b and 1.1c).

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