System Dynamics Applications at the Federal Centers for Disease Control and Prevention (CDC)

1. System Dynamics Applications at the Federal Centers for Disease Control and Prevention (CDC) Dr. Jack Homer Institute on Systems Science and Health University of Michigan School of Public Health May 6, 2009

2. Agenda • Background • Diabetes Policy Model • Cardiovascular Disease Policy Model • National Health Policy Model and Game

3. CDC Mission The Centers for Disease Control and Prevention (CDC) serves as the national focus for developing and applying disease prevention and control, environmental health, and health promotion and health education activities designed to improve the health of the people of the United States. CDC administers the Preventive Health and Health Services Block Grant and specific preventive health categorical grant programs while providing program expertise and assistance in responding to Federal, State, local, and private organizations on matters related to disease prevention and control activities.

4. 1840 1880 1950 1960 1980 2000 What Accounts for Poor Population Health? Evolving Views • God’s will • Humors, miasma, ether • Poor living conditions, immorality (e.g., sanitation) • Single disease, single cause (e.g., germ theory) • Single disease, multiple causes (e.g., heart disease) • Single cause, multiple diseases (e.g., tobacco) • Multiple causes, multiple diseases (but no feedback dynamics) (e.g., multi-causality) • Dynamic interaction among afflictions, adverse conditions, and intervention capacities (e.g., syndemics) Milstein B. Hygeia's constellation: navigating health futures in a dynamic and democratic world. Atlanta, GA: Syndemics Prevention Network, Centers for Disease Control and Prevention; April 15, 2008. <http://www.cdc.gov/syndemics/monograph/index.htm Richardson GP. Feedback thought in social science and systems theory. Philadelphia, PA: University of Pennsylvania Press, 1991.

5. 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 CDC’s Simulation Studies for Health System Change 2006 2007 2008 2000 2001 2002 2003 2004 2005

6. Historical Markov Forecasting Model Data Simulation Experiments in Action Labs Re-Directing the Course of ChangeQuestions Addressed by System Dynamics Modeling Prevalence of Diagnosed Diabetes, United States 40 Where? 30 Million people 20 What? How? • Markov Model Constants • Incidence rates (%/yr) • Death rates (%/yr) • Diagnosed fractions • (Based on year 2000 data, per demographic segment) 10 Trend is not destiny! 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.

7. SD Model Uses and Audiences • Set Better Goals (Planners & Evaluators) • Identify what is likely and what is possible • Estimate intervention impact time profiles • Evaluate resource needs for meeting goals • Support Better Action (Policymakers) • Explore ways of combining policies for better results • Evaluate cost-effectiveness over extended time periods • Increase policymakers’ motivation to act differently • Develop Better Theory and Estimates (Researchers) • Integrate and reconcile diverse data sources • Identify causal mechanisms driving system behavior • Improve estimates of hard-to-measure or “hidden” variables • Identify key uncertainties to addressin intervention studies Forrester JW. Industrial Dynamics (Chapter 11: Aggregation of Variables). Cambridge, MA: MIT Press, 1961.

8. Practical Options in Causal Modeling High Detail (Disaggregation) Impractical Expansive Too hard to verify, modify, and understand Scope (Breadth) Focused Simplistic Low Low High

9. Society's Health Tertiary Targeted Primary Secondary Response Prevention Protection Prevention Prevention General Demand for Protection response Adverse Living Conditions Broad Structure of the Health Protection Enterprise “UPSTREAM” “DOWNSTREAM” Becoming safer and healthier Safer Afflicted Afflicted with Vulnerable Healthier without Complications People People Developing Becoming Becoming Complications complications vulnerable afflicted Dying from complications Milstein B, Homer J. The dynamics of upstream and downstream: why is so hard for the health system to work upstream, and what can be done about it? CDC Futures Health Systems Work Group; Atlanta, GA; December 3, 2003. Gerberding JL. CDC's futures initiative. Atlanta, GA: Public Health Training Network; April 12, 2004. Gerberding JL. FY 2008 CDC Congressional Budget Hearing. Testimony before the Committee on Appropriations, Subcommittee on Labor, Health and Human Services, Education and Related Agencies, United States House of Representatives; Washington, DC; March 9, 2007. Homer JB, Hirsch GB. System dynamics modeling for public health: background and opportunities. American Journal of Public Health 2006;96(3):452-458. Milstein B, Homer J. The dynamics of upstream and downstream: why is so hard for the health system to work upstream, and what can be done about it? CDC Futures Health Systems Workgroup; Atlanta, GA; 2003.

10. Diabetes Policy Model(with Division of Diabetes Translation 2003-07) • Diabetes programs face tough challenges and questions • With rapid growth in prevalence, is improved control good enough? • Studies show primary prevention is possible, but how much impact in practice and at what cost? • How best to balance interventions? • Model developed with program planners, diabetes researchers, and epidemiologists • Applied initially to U.S. overall, later applied to 13 different states * Done in conjunction with Sustainability Institute and the Center for Public Health Practice at Emory University 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.

11. We Convened a Model-Scoping Group of 45 CDC Professionals and Epidemiologists in December 2003) to Explore the Full Range of Forces Driving Diabetes Behavior over Time 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 & Public Health Agency Capacity • Provider supply • Provider understanding, competence • Provider location • System integration • Cost of care • Insurance coverage Personal Capacity Local Living Conditions • Understanding • Motivation • Social support • Literacy • Physio-cognitive function • Life stages • 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 • Percent of patients screened • Percent of people with diabetes under control • Baseline Flows

12. Standard boundary of most epi models and intervention programs Model Overview Unhealthy days & costs from diabetes The model also subdivides the diabetes and prediabetes population stocks into stocks of diagnosed and undiagnosed (not shown here), and includes population inflows and non-diabetes deaths. Unhealthy days & costs per person with diabetes Diabetes prevalence Prediabetes onset Diabetes onset Complications onset People with People with Death People with People with Normal Complicated Uncomplicated Prediabetes Glycemic Diabetes Diabetes Levels Recovery Diabetes detection and management Obesity prevalence Prediabetes detection and management Black & Hispanic fractions Elderly fraction

13. Data Sources for Diabetes Model

14. Comparing and Combining Strategies U.S. Morbidity from Diabetes Simulated 1980-2050 600 Status Quo* 500 Control Primary prevention Monthly Unhealthy Days from Diabetes per Thousand 400 Combination 300 * Status Quo assumptions for post-2005: (1) Obesity plateauing per CDC Obesity model projection; (2) Age and ethnicity per Census projection; (3) Health insurance and non-diabetes death rates unchanging; (4) Disease management unchanging 200 1980 1990 2000 2010 2020 2030 2040 2050 With a combination of improved control and aggressive primary prevention (obesity, prediabetes), growth in the burden of diabetes could be limited for the next 10 years and for decades beyond.

15. Cardiovascular Disease Prevention(with Division of Heart Disease & Stroke Prevention, 2007-10) • What are the key pathways of CV risk, and how do these affect health outcomes and costs? • How might interventions affect the risk factors and outcomes in the short- and long-term? • How might policy efforts be better balanced given limited resources? The CDC has partnered on this project 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 local data of the Austin team. 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.

16. Risk Factors for CVD Obesity, Smoking, High BP, High Cholesterol, and Diabetes are modeled as dynamic stocks—with multiple inflows and outflows (e.g., see next slide) Data sources: NHANES, NHIS, MEPS, AHA/NIH reports, Census, Vital Statistics, Framingham risk calculators, literature on risk factors and costs

17. Obesity Stock-Flow Structure 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.

18. Tobacco and Air Quality Interventions Data sources: NHANES, NHIS, MEPS, AHA/NIH reports, Census, Vital Statistics, Framingham risk calculators, literature on risk factors and costs

19. Health Care Interventions Data sources: NHANES, NHIS, MEPS, AHA/NIH reports, Census, Vital Statistics, Framingham risk calculators, literature on risk factors and costs

20. Interventions Affecting Stress Data sources: NHANES, NHIS, MEPS, AHA/NIH reports, Census, Vital Statistics, Framingham risk calculators, literature on risk factors and costs

21. Healthy Diet Interventions Data sources: NHANES, NHIS, MEPS, AHA/NIH reports, Census, Vital Statistics, Framingham risk calculators, literature on risk factors and costs

22. Physical Activity & Weight Loss Interventions Data sources: NHANES, NHIS, MEPS, AHA/NIH reports, Census, Vital Statistics, Framingham risk calculators, literature on risk factors and costs

23. Adding Up the Costs Data sources: NHANES, NHIS, MEPS, AHA/NIH reports, Census, Vital Statistics, Framingham risk calculators, literature on risk factors and costs

24. A Base Case Scenario for Comparison Assumptions for Input Time Series through 2040 • Prior to 2004, model reflects historical… • Decline in fraction of workplaces allowing smoking (1990-2003) • Decline in air pollution (1990-2001) • Decline in CV event fatality (1990-2003) • Increase in diagnosis and control of high blood pressure, high cholesterol, and diabetes (1990-2002) • Rise & fall in youth smoking (1991-2003) • Rise in youth obesity (1990-2002, 2002-2020P) • After 2004, make simple yet plausible assumptions… • Assume no further changes in contextual factors affecting risk factor prevalence (aside from rise in youth obesity) • Changes in risk prevalence after 2004 are due to “bathtub” adjustment process (incidence still exceeding outflows) and population aging • Provides an easily-understood basis for comparisons

25. Base Case Trajectories 1990-2040

26. Combined 15 interventions with sensitivity range Combined 15 interventions with sensitivity range Estimated Impacts of a 15-Component Intervention, with Sensitivity Ranges Reductions vs. Base Case CVD DEATHS 4 The 15 components include: (1) “Care” [3 interventions] (2) “Air” (smoking/pollution) [6], (3) “Lifestyle”: Nutrition, physical activity, & stress reduction [6] The model contains 56 causal linkages requiring the estimation of relative risks, effect sizes, or initial values, most of which involved some level of uncertainty. The upper edge of the sensitivity range results when all uncertain parameters are set to their “lowest plausible impact” values. The lower edge results when all are set to their “greatest plausible impact” values. Base Case 0% 20% (15-26%) 2 Deaths from CVD per 1000 60% Deaths if all risk factors = 0 0 1990 2000 2010 2020 2030 2040 3,000 DIRECT & INDIRECT COSTS DIRECT & INDIRECT COSTS Base Case 0% 2,000 26% (19-33%) Total Consequence Costs per Capita (2005 dollars per year) 1,000 Costs if all risk factors = 0 80% 0 1990 2000 2010 2020 2040 2030

27. 1) Primary Care 3) + Nutrition, Physical Activity, and Stress 2) + Air Quality & Tobacco 1) Primary Care 3) + Nutrition, Physical 2) + Air Quality & Tobacco Activity, and Stress Contributions of 3 Intervention Clusters(Clusters layered in cumulatively) Reductions vs. Base Case CVD DEATHS 4 • Contributions to CVD death reduction: • Care: strong from the start; 9% • Air: good from the start (less pollution, secondhand smoke) and growing (due to smoking decline) to 6.5% • Lifestyle: small at first but growing to 5% Base Case 0% 20% Deaths from CVD per 1000 2 60% Deaths if all risk factors = 0 0 2040 1990 2000 2010 2020 2030 DIRECT & INDIRECT COSTS • Contributions to cost savings: • Air: strong from the start (pollution, SHS) and growing (due to smoking decline) to 18.5% • Lifestyle: small at first but growing to 8.5% • Care: negligible (not cost saving) 3,000 Base Case 0% 2,000 26% Total Consequence Costs per Capita (2005 dollars per year) 1,000 80% Costs if all risk factors = 0 0 2040 1990 2000 2010 2020 2030

28. National Health Policy Model & Game(with Office of the Director, 2008-09) • Americans pay the most for health care, yet suffer high rates of morbidity and premature mortality—esp. high among the poor and uneducated • About 16% of Americans have no insurance coverage • Over 75% of Americans think the current system needs fundamental change • Many health leaders realize we need a broader view of health, including health protection and health equity Nolte E, McKee CM. Measuring the health of nations: updating an earlier analysis. Health Affairs 2008; 27(1):58-71. Blendon RJ, Altman DE, Deane C, Benson JM, Brodie M, Buhr T. Health care in the 2008 presidential primaries. New England Journal of Medicine 2008;358(4):414-422. Gerberding JL. Protecting health—the new research imperative. JAMA 2005; 294(11):1403-1406. Gerberding JL. CDC: protecting people's health. Director's Update; Atlanta, GA; July, 2007.

29. The U.S. Health Policy Arena is Dense with Diverse Issues Insurance overhead Extent of care Overuse of ERs Healthier behaviors Reimbursement rates Adherence to care guidelines Safer environments Access to care Out-of-pocket costs Socioeconomic disparities Overuse of specialists Insurance coverage Provider efficiency Primary care supply CitizenInvolvement

30. Simulating the Health System Integrating prior findings and estimates • On costs, prevalence, risk factors, health disparities, health care utilization, insurance, quality of care, etc. • Our own previous health system modeling* Simplifying as appropriate • Three states of health: Disease/injury, Asymptomatic disorder, No significant health problem • Two SES categories: Advantaged, Disadvantaged (allowing study of disparities and equity) • Start in equilibrium (all variables unchanging), approximating the U.S. in 2003 • Some complicating trends not included for simplicity: aging, migration, technology, economy, etc. * E.g., Homer, Hirsch, Milstein. Chronic illness in a complex health economy: the perils and promises of downstream and upstream reforms. System Dynamics Review 2007; 23:313-343.

31. Some key concepts and measures * CDC/SD study of cardiovascular risk in Austin/Travis County, TX. See Homer J, Milstein B, Wile K, et al. Modeling the local dynamics of cardiovascular health. Preventing Chronic Disease 2008; 5(2).

32. Connecting the Concepts:Start with the Outcome Measures

33. Several Drivers of Health Care Costs

34. Quality Health Care Improves Health Outcomes

35. A Shortage of Primary Care Providers Exists for Many Americans Shortage of Doctors an Obstacle to Obama Goals By ROBERT PEAR April 26, 2009 WASHINGTON — Obama administration officials, alarmed at doctor shortages, are looking for ways to increase the supply of physicians to meet the needs of an aging population and millions of uninsured The Robert Graham Center, with the National Association of Community Health Centers. “Access Denied: A Look at America’s Medically Disenfranchised”, Washington, DC, 2007.

36. PCP Sufficiency: Supply vs. Demand

37. Upstream Determinants of Disease & Injury

38. From Model to an Interactive Game HealthBound • Experiential learning for health leaders • Four simultaneous goals: save lives, improve health, achieve health equity, and lower health care cost • Intervene without expense, risk, or delay • Not a prediction, but a way for multiple stakeholders to explore how the health system can change Milstein B, Homer J, Hirsch G. The "HealthBound" policy simulation game: an adventure in US health reform. International System Dynamics Conference; Albuquerque, NM; July 26-30, 2009.

39. Improve primary care efficiency Expand insurance coverage Coordinate care Improve quality of care Enable healthier behaviors Expand primary care supply Simplify insurance Build safer environments Change self pay fraction Create pathways to advantage Change reimbursement rates Strengthen civic muscle Options for Intervening in the Health SystemA Short Menu of Major Policy Proposals

40. Scorecard Results in Context CompareRuns ProgressReport “Winning” Involves Not Just Posting High Scores, But Understanding How and Why You Got Them HealthBound HealthBound HealthBound HealthBound

41. Some Policy Conclusions • Expanded coverage and improved quality would improve health but, if done alone, would raise costs and worsen equity • Expanding primary care capacity to eliminate shortages (esp. for the poor) would reduce costs and improve equity • Cutting reimbursement rates would reduce costs but worsen health outcomes • Upstream protection (behavioral and environmental remedies) would—increasingly over time—reduce costs, improve health, and improve equity Milstein B, Homer J, Hirsch G. Are coverage and quality enough? A dynamic systems approach to health policy. Draft paper currently in CDC clearance.

42. Secrets of Successful Modeling Projects • Learn the client’s perspective—think in practical terms and understand how much transformation is really possible • Keep discussing scope, level of detail, terminology, intervention levers, validation, and documentation as project proceeds—be prepared to change course, even midway through project • Quickly develop a meaningful initial (prototype) model • Keep your eyes open for ways to improve the model—don’t depend on the client for that • Seek the most reliable data, but be open to data of many types • “Freeze” the model at an appropriate time to allow for full policy testing, reporting, and documentation Roberts EB (1978). “Strategies for Effective Implementation of Complex Corporate Models.” In Managerial Applications of System Dynamics, ed. EB Roberts (pp. 77-85). Pegasus Communications; www.pegasuscom.com. Robinson JM (1980). “Managerial Sketches of the Steps of Modeling.” In Elements of the System Dynamics Method, ed. J Randers (pp. 250-270). Pegasus Communications; www.pegasuscom.com. Homer JB (1996). “Why We Iterate: Scientific Modeling in Theory and Practice.” System Dynamics Review 12(1):1-19.

43. EXTRAS

44. Risk prevention Risk mgmt Disease mgmt Urgent & long-term care Chronic Illness as a Cascade of Bathtubs Low risk Risk onset High risk Illness onset Mildly ill Complications onset Bathtub = Stock Water from faucet = Inflow Drain = Outflow Hand = Intervention affecting flow Severely ill Death

45. System Dynamics Health Applications1970s to the Present • Disease epidemiology • Cardiovascular, diabetes, obesity, HIV/AIDS, cervical cancer, chlamydia, dengue fever, drug-resistant infections • Substance abuse epidemiology • Heroin, cocaine, tobacco • Health care patient flows • Acute care, long-term care • Health care capacity and delivery • Managed care, dental care, mental health care, disaster preparedness, community health programs • Health system economics • Interactions of providers, payers, patients, and investors Homer J, Hirsch G. System dynamics modeling for public health: Background and opportunities. American Journal of Public Health 2006;96(3):452-458.

46. - Health B Protection Taking the Toll Efforts B B - Responses Prevalence of Vulnerability, Risk, or Disease Obstacles to Growth R Resources & - Resistance Drivers of R Growth Reinforcers Broader Benefits & Supporters Broad Dynamics of the Health Protection Enterprise Prevalence of Vulnerability, Risk, or Disease 100% Values for Health & Equity Size of the Safer, Healthier Population PotentialThreats 0% Time Milstein B. Hygeia's constellation: navigating health futures in a dynamic and democratic world. Atlanta, GA: Syndemics Prevention Network, Centers for Disease Control and Prevention; April 15, 2008. <http://www.cdc.gov/syndemics/monograph/index.htm>.

47. Planned Extensions – Next 2 Years Potential value (better health, lower cost) from • Treating borderline conditions (pre-hypertension, borderline cholesterol, pre-diabetes) • Focusing on prevention of recurrent events • Focusing on acute and rehab care • Further reducing control targets for blood pressure, cholesterol, blood glucose • Targeting other risk factors; e.g., excess salt, low vitamin D, periodontal disease, C-reactive protein • Targeting particular age-gender subgroups • Targeting African American or Hispanic populations Our NIH sponsors feel this analysis will help them set research priorities and extrapolate from clinical trials—more effectively leveraging the value of their (very expensive!) sponsored research.

48. Other CVD Intervention Models System Dynamics: Heart Failure Homer J, Hirsch G, et al. Models for collaboration: how system dynamics helped a community organize cost-effective care for chronic illness. System Dynamics Review 2004; 20(3):199-222. Markov: Coronary Heart Disease Weinstein MC, Coxson PG, et al. Forecasting coronary heart disease incidence, mortality, and cost: the coronary heart disease policy model. American J Public Health 1987; 77(11):1417-1426. Micro-simulation (Archimedes): CVD Kahn R, Robertson RM, et al. The impact of prevention on reducing the burden of cardiovascular disease. Circulation 2008; 118(5):576-585. Statistical/Monte Carlo: Coronary Heart Disease Kottke TE, Gatewood LC, et al. Preventing heart disease: is treating the high risk sufficient? J Clinical Epidemiology 1988; 41(11):1083-1093. Our model is the most extensive to date in integrating evidence on multiple risk factor pathways, potential interventions, and outcome costs.