1. Syndemics Prevention Network Goal-Setting Using System Dynamics Models: An Example from Adult Diabetes DHHS Office of Disease Prevention and Health Promotion Rockville, MD January 28, 2005 Jack Homer Homer Consulting Voorhees, New Jersey Bobby Milstein Centers for Disease Control and Prevention Atlanta, Georgia

2. Syndemics Prevention Network Dynamic Policy Modeling Addresses Navigational Questions 20202010 How? Why? Where? Who? People with Diagnosed Diabetes, US 0 5 10 15 19801985199019952000 Million people Data Source: CDC DDT and NCCDPHP. -- Change in measurement in 1996.

3. Syndemics Prevention Network Time Series Models Describe trends Multivariate Stat Models Identify historical trend drivers and correlates Patterns Structure Events Increasing: Depth of causal theory Degrees of uncertainty Robustness for longer- term projection Value for developing policy insights Increasing: Depth of causal theory Degrees of uncertainty Robustness for longer- term projection Value for developing policy insights Dynamic Models Anticipate new trends, learn about policy consequences, and set justifiable goals Tools for Longitudinal Analysis Developed by Jack Homer, Homer Consulting

4. Syndemics Prevention Network A Very Particular Distance “{System dynamics studies problems} from ‘a very particular distance', not so close as to be concerned with the action of a single individual, but not so far away as to be ignorant of the internal pressures in the system.” -- George Richardson Richardson GP. Feedback thought in social science and systems theory. Philadelphia, PA: University of Pennsylvania Press, 1991.

5. Syndemics Prevention Network System Dynamics is Well-Suited for Studying Population Health Problems History Industrial Dynamics, Jay Forrester, MIT (1961) 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) Interactions between health capacity and disease epidemiology (e.g, neighborhood- and national-level analysis) Current CDC Projects Syndemics (i.e., mutually reinforcing epidemics) Balancing upstream/downstream effort Diabetes in an era of rising obesity Fetal and infant health Obesity (lifecourse view) Adolescent health (lifestage view)

6. Syndemics Prevention Network System Dynamics Focuses on the Connection Between Behavior and Structure System behavior is determined by feedback structure: including accumulation, delay, and nonlinear response Problem Situation System Structure 8 6 4 2 0 02468101214161820 Seconds elapsed Ounces Water Level Over Time System Behavior Over Time

7. Syndemics Prevention Network Water Glass Model Diagram (Vensim ™ software) Current water level Water flow “Stock” “Flow” Faucet openness Water flow at full open Maximum faucet openness decision “Policy Lever” “Constant” Desired water level Water level gap “Delay” Perceived water level gap Time to perceive water level gap

8. Syndemics Prevention Network Diabetes Policy Model - Structure People with Undiagnosed, Uncomplicated Diabetes People with Diagnosed, Uncomplicated Diabetes People with Diagnosed, Complicated Diabetes People with Undiagnosed Prediabetes People with Diagnosed Prediabetes People with Normal Glycemic Levels Diagnosing Diabetes Diagnosing Diabetes Dying from Complications Developing Complications Diagnosing PreDiabetes Diabetes Onset Homer J, Jones A, Seville D, Essien J, Milstein B, Murphy D. The CDC diabetes system modeling project: developing a new tool for chronic disease prevention and control. 22nd International Conference of the System Dynamics Society; Oxford, England; 2004. Prediabetes Onset Recovering from Prediabetes Recovering from Prediabetes Diabetes Onset Developing Complications Dying from Complications People with Undiagnosed, Complicated Diabetes

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

10. Syndemics Prevention Network Diabetes Policy Model Integrating the Best Available Evidence Information Sources Data Topics U.S. Census Population growth and death rates Health insurance coverage National Health Interview Survey (NHIS) Diabetes prevalence Diabetes detection National Health and Nutrition Examination Survey (NHANES) Prediabetes prevalence Weight, height, and body fat Caloric intake Behavioral Risk Factor Surveillance System (BRFSS) Glucose self-monitoring Eye and foot exams Use of medications Attending diabetes self-mgmt classes Efforts to control weight Research Literature Effects of disease control and aging on onset, progression, death, and costs Physical activity trends Direct and indirect costs of diabetes

11. Syndemics Prevention Network Diabetes Policy Model - Behavior Simulating Policy Scenarios Homer J, Jones A, Seville D, Essien J, Milstein B, Murphy D. The CDC diabetes system modeling project: developing a new tool for chronic disease prevention and control. 22nd International Conference of the System Dynamics Society; Oxford, England; 2004. Historical Calibration Diagnosed Diabetes % of Adults Obese % of Adults Defining Plausible Futures 0.0035 0.003 0.0025 0.002 0.0015 19801990200020102020203020402050 Time (Year) Diabetes-related death rate per year for adult population Status Quo Disease Mgmt Reduced Obesity Partial Disease Mgmt & Obesity

12. Syndemics Prevention Network HP 2010 Diabetes Objectives Baseline HP 2010 Target Percent Change Reduce Diabetes–related Deaths Among Diagnosed (5-6) 8.8 per 1,000 7.8-11% Increase Diabetes Diagnosis (5-4) 68%80%+18% Reduce New Cases of Diabetes (5-2) 3.5 per 1,000 2.5-29% Reduce Prevalence of Diagnosed Diabetes (5-3) 40 per 1,000 25-38% U.S. Department of Health and Human Services. Healthy People 2010. Washington DC: Office of Disease Prevention and Health Promotion, U.S. Department of Health and Human Services; 2000. http://www.healthypeople.gov/Document/HTML/Volume1/05Diabetes.htm

13. Syndemics Prevention Network Connecting the Objectives: System Physics It is impossible for any policy to reduce prevalence 38% by 2010! People with Undiagnosed Diabetes People with Diagnosed Diabetes Dying from Diabetes Complications Diagnosed Onset Initial Onset People with Normal Glycemic Levels As would stepped-up detection effort Reduced death would add further to prevalence With a diagnosed onset flow of 1.1 mill/yr And a death flow of 0.5 mill/yr (4%/yr rate) The targeted 29% reduction in diagnosed onset can only slow the growth in prevalence

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

15. Syndemics Prevention Network Possible Roles for SD in Public Health SD is especially well-suited for studying… Individual diseases and risk factors Examining momentum and setting justifiable goals Life course dynamics Following health trajectories across life stages Mutually reinforcing afflictions (syndemics) Exploring interactions among related afflictions, adverse living conditions, and the public’s capacity to address them both Capacities of the health protection system Understanding how ambitious health ventures may be configured without overwhelming/depleting capacity--perhaps even strengthening it Value trade-offs Analyzing phenomena like the imbalance of upstream-downstream effort, growth of the uninsured, rising costs, declining quality, entrenched inequalities Organizational management Linking balanced scorecards to a dynamic understanding of processes Group model building and scenario planning Bringing more structure, evidence, and insight to public dialogue and judgment