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Syndemics Prevention Network Overview of System Dynamics Simulation Modeling Systems Thinking and Modeling Workshop Office of Disease Prevention and Health.

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Presentation on theme: "Syndemics Prevention Network Overview of System Dynamics Simulation Modeling Systems Thinking and Modeling Workshop Office of Disease Prevention and Health."— Presentation transcript:

1 Syndemics Prevention Network Overview of System Dynamics Simulation Modeling Systems Thinking and Modeling Workshop Office of Disease Prevention and Health Promotion Bethesda, MD May 8, 2006 Bobby Milstein Syndemics Prevention Network Centers for Disease Control and Prevention Atlanta, Georgia

2 Syndemics Prevention Network Research Imperatives for Protecting Health Gerberding JL. Protecting health: the new research imperative. Journal of the American Medical Association 2005;294(11): Typical Current State Static view of problems that are studied in isolation Proposed Future State Dynamic systems and syndemic approaches "Currently, application of complex systems theories or syndemic science to health protection challenges is in its infancy.“ -- Julie Gerberding

3 Syndemics Prevention Network System Change Initiatives Encounter Limitations of Logic Models and Conventional Planning/Evaluation Methods Diabetes Action Labs* ODPHP Modelers Meeting Upstream-Downstream Investments Obesity Over the Lifecourse* Fetal & Infant Health Goal-Setting Milestones in the Recent Use of System Dynamics Modeling at CDC AJPH Systems Issue 2006 CDC Evaluation Framework Recommends Logic Models SD Emerges as a Promising Methodology Neighborhood Assistance Game Hypertension Prevention & Control * Syndemics Modeling Science Seminars and Professional Development Efforts * Dedicated multi-year budget

4 Syndemics Prevention Network System Dynamics Was Designed to Address Problems Marked By Dynamic Complexity Good at Capturing Differences between short- and long-term consequences of an action Time delays (e.g., transitions, detection, response) Accumulations (e.g., prevalence, capacity) Behavioral feedback (e.g., actions trigger reactions) Nonlinear causal relationships (e.g., effect of X on Y is not constant) Differences or inconsistencies in goals/values among stakeholders Sterman JD. Business dynamics: systems thinking and modeling for a complex world. Boston, MA: Irwin McGraw-Hill, Homer JB, Hirsch GB. System dynamics modeling for public health: background and opportunities. American Journal of Public Health 2006;96(3): Origins Jay Forrester, MIT (from late 1950s) Public policy applications starting late 1960s

5 Syndemics Prevention Network Understanding Dynamic Complexity From 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 Forrester JW. Counterintuitive behavior of social systems. Technology Review 1971;73(3): Meadows DH. Leverage points: places to intervene in a system. Sustainability Institute, Available at. Richardson GP. Feedback thought in social science and systems theory. Philadelphia, PA: University of Pennsylvania Press, Sterman JD. Business dynamics: systems thinking and modeling for a complex world. Boston, MA: Irwin McGraw-Hill, 2000.

6 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 Simulation Models Anticipate new trends, learn about policy consequences, and set justifiable goals Tools for Policy Analysis

7 Syndemics Prevention Network Wickelgren I. How the brain 'sees' borders. Science 1992;256(5063): How Many Triangles Do You See?

8 Syndemics Prevention Network Ulrich W. Reflective practice in the civil society: the contribution of critically systemic thinking. Reflective Practice 2000;1(2): Boundary Critique

9 Syndemics Prevention Network Ulrich W. Reflective practice in the civil society: the contribution of critically systemic thinking. Reflective Practice 2000;1(2): Boundary Critique

10 Syndemics Prevention Network 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; Tertiary Prevention Secondary Prevention Primary Prevention Targeted Protection Society's Health Response Demand for response Public Work Safer Healthier People Becoming vulnerable Becoming safer and healthier Vulnerable People Becoming afflicted Afflicted without Complications Developing complications Afflicted with Complications Dying from complications Health System Dynamics Adverse Living Conditions General Protection 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, Gerberding JL. CDC's futures initiative. Atlanta, GA: Public Health Training Network; April 12, Homer JB, Hirsch GB. System dynamics modeling for public health: background and opportunities. American Journal of Public Health 2006;96(3):

11 Syndemics Prevention Network Understanding Health as Public Work Safer Healthier People Vulnerable People Afflicted without Complications Afflicted with Complications Becoming vulnerable Becoming safer and healthier Becoming afflicted Developing complications Dying from complications Adverse Living Conditions Society's Health Response Demand for response General Protection Targeted Protection Primary Prevention Secondary Prevention Tertiary Prevention - Public Work - Vulnerable and Afflicted People Fraction of Adversity, Vulnerability and Affliction Borne by Disadvantaged Sub-Groups (Inequity) Public Strength - Citizen Involvement in Public Life Social Division

12 Syndemics Prevention Network Testing Dynamic Hypotheses -- How can we learn about the consequences of actions in a system of this kind? -- Could the behavior of this system be analyzed using conventional epidemoiological methods (e.g., logistic or multi-level regression)?

13 Syndemics Prevention Network Learning In and About Dynamic Systems Benefits of Simulation/Game-based Learning Formal means of evaluating options Experimental control of conditions Compressed time Complete, undistorted results Actions can be stopped or reversed Visceral engagement and learning Tests for extreme conditions Early warning of unintended effects Opportunity to assemble stronger support Dynamic Complexity Hinders… Generation of evidence (by eroding the conditions for experimentation) Learning from evidence (by demanding new heuristics for interpretation) Acting upon evidence (by including the behaviors of other powerful actors) Sterman JD. Learning from evidence in a complex world. American Journal of Public Health (in press). Sterman JD. Business dynamics: systems thinking and modeling for a complex world. Boston, MA: Irwin McGraw-Hill, “In [dynamically complex] circumstances simulation becomes the only reliable way to test a hypothesis and evaluate the likely effects of policies." -- John Sterman

14 Syndemics Prevention Network System Dynamics Modeling Supports Navigational Policy Dialogues Prevalence of Diagnosed Diabetes, US Million people Historical Data Markov Model Constants Incidence rates (%/yr) Death rates (%/yr) Diagnosed fractions (Based on year 2000 data, per demographic segment) 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 Health Care Management Science 2003;6: 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): Why? Where? How? Who? What? Markov Forecasting Model Simulation Experiments in Action Labs

15 Syndemics Prevention Network Simulations for Learning in Dynamic Systems Morecroft JDW, Sterman J. Modeling for learning organizations. Portland, OR: Productivity Press, Sterman JD. Business dynamics: systems thinking and modeling for a complex world. Boston, MA: Irwin McGraw-Hill, Sterman JD. Learning from evidence in a complex world. American Journal of Public Health 2006;96(3): Sterman JD. All models are wrong: reflections on becoming a systems scientist. System Dynamics Review 2002;18(4): Multi-stakeholder Dialogue Dynamic Hypothesis (Causal Structure)Plausible Futures (Policy Experiments) Deaths per Population 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) Base Downstream Upstream Mixed “All models are wrong. Some are useful.”

16 Syndemics Prevention Network “Simulation is a third way of doing science. Like deduction, it starts with a set of explicit assumptions. But unlike deduction, it does not prove theorems. Instead, a simulation generates data that can be analyzed inductively. Unlike typical induction, however, the simulated data comes from a rigorously specified set of rules rather than direct measurement of the real world. While induction can be used to find patterns in data, and deduction can be used to find consequences of assumptions, simulation modeling can be used as an aid to intuition.” -- Robert Axelrod Axelrod R. Advancing the art of simulation in the social sciences. In: Conte R, Hegselmann R, Terna P, editors. Simulating Social Phenomena. New York, NY: Springer; p Sterman JD. Business Dynamics: Systems Thinking and Modeling for a Complex World. Boston, MA: Irwin McGraw-Hill, Simulation Experiments Open a Third Branch of Science “The complexity of our mental models vastly exceeds our ability to understand their implications without simulation." -- John Sterman How? Where? Prevalence of Obese Adults, United States Why? Data Source: NHANES Who? What?

17 Syndemics Prevention Network Questioning the Character of Public Health Work PUBLIC HEALTH WORK Innovative Health Ventures SYSTEMS THINKING & MODELING (understanding change) What causes population health problems? How are efforts to protect the public’s health organized? How and when do health systems change (or resist change)? PUBLIC HEALTH (setting direction) What are health leaders trying to accomplish? SOCIAL NAVIGATION (governing movement) Directing Change Charting Progress Who does the work? By what means? According to whose values? How are conditions changing? In which directions?

18 Syndemics Prevention Network EXTRAS

19 Syndemics Prevention Network Potential Users and Uses of Health SD Simulation Models Planners/Evaluators/Media: Chart Progress Toward Goals –Define a “status quo” future –Define alternative futures based on policy scenarios –Define types of information to be routinely collected –Track and interpret trajectories of change –Estimate how strong interventions must be to make a difference Researchers: Better Measurement and New Knowledge –Integrate diverse data sources into a single analytic environment –Infer properties of unmeasured or poorly measured parameters –Analyze historical drivers of change –Locate areas of uncertainty to be addressed in new research Policy Makers: Convene Multistakeholder Action Labs –Understand how a dynamically complex system functions –Discover short- and long-term consequences of alternative policies –Prepare for difficult patterns of change (e.g., worse-before-better) –Consider the cost effectiveness of alternative policies –Explore ways of combining and aligning policies for better results –Increase policy-makers’ motivation to act differently Others…

20 Syndemics Prevention Network Possible Roles for System Dynamics 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

21 Syndemics Prevention Network Steps for Developing Dynamic Policy Models Enact Policies Build power and organize actors to establish chosen policies Choose Among Plausible Futures Discuss values and consider trade-offs Choose Among Plausible Futures Discuss values and consider trade-offs Learn About Policy Consequences Test proposed policies, searching for ones that best govern change Learn About Policy Consequences Test proposed policies, searching for ones that best govern change Run Simulation Experiments Compare model’s behavior to expectations and/or data to build confidence in the model Convert the Map Into a Simulation Model Formally quantify the hypothesis using all available evidence Create a Dynamic Hypothesis Identify and map the main causal forces that create the problem Identify a Persistent Problem Graph its behavior over time


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