Syndemics Prevention Network NCCDPHP Cross-Division Evaluation Network Atlanta, GA January 29, 2008 Roles for System Dynamics Simulation Modeling Bobby Milstein Syndemics Prevention Network Centers for Disease Control and Prevention Innovations in Planning & Evaluating System Change Ventures Diane Orenstein Division for Heart Disease and Stroke Prevention Centers for Disease Control and Prevention

Syndemics Prevention Network Left Unexamined… Singular “program” as the unit of inquiry (N=1 organizational depth) Dynamic aspects of program effectiveness (e.g., better-before-worse patterns of change) Democratic aspects of public health work (e.g., alignment among multiple actors, including those who are not professionals and who may be pursuing other goals) Evaluative aspects of planning (e.g., defining problems, setting priorities, developing options, selecting strategies) Milstein B, Wetterall S, CDC Evaluation Working Group. Framework for program evaluation in public health. MMWR Recommendations and Reports 1999;48(RR-11):1-40. Available at. Framework for Program Evaluation “Both a synthesis of existing evaluation practices and a standard for further improvement.”

Syndemics Prevention Network 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, CDC Director

Syndemics Prevention Network Rationale for Innovation Enormity of the challenges (problems of greater scale, speed, diversity, novelty) Appreciation for the effectiveness as well as the limits of narrowly-bounded approaches Potential for comprehensive changes (global, multi-sectoral, infrastructural, intergenerational, root-causes) Threat of policy resistance Mismatch with conventional methods for planning/evaluating

Syndemics Prevention Network Possible What may happen? Plausible What could happen? Probable What will likely happen? Preferable What do we want to have happen? Bezold C, Hancock T. An overview of the health futures field. Geneva: WHO Health Futures Consultation; 1983 July “Most organizations plan around what is most likely. In so doing they reinforce what is, even though they want something very different.” -- Clement Bezold Seeing Beyond the Probable

Syndemics Prevention Network Public Health Systems Science Addresses Navigational Policy Questions 17% increase Centers for Disease Control and Prevention. Health-related quality of life: prevalence data. National Center for Chronic Disease Prevention and Health Promotion, Accessed October 23, 2007 at. Milstein B. Hygeia's constellation: navigating health futures in a dynamic and democratic world. Atlanta, GA: Centers for Disease Control and Prevention; Draft, How? Why? Where? Who? What?

Syndemics Prevention Network Broad Dynamics of the Health Protection Enterprise Prevalence of Vulnerability, Risk, or Disease Time Health Protection Efforts - B Responses to Growth Resources & Resistance - B Obstacles Broader Benefits & Supporters R Reinforcers Potential Threats The concepts and methods of policy evaluation must engage the basic features of this dynamic and democratic system Size of the Safer, Healthier Population - Prevalence of Vulnerability, Risk, or Disease B Taking the Toll 0% 100% R Drivers of Growth Values for Health & Equity

Syndemics Prevention Network Locating categorical disease or risk prevention programs within a broader system of health protection Constructing credible knowledge without comparison/control groups Differentiating questions that focus on attribution vs. contribution Balancing trade-offs between short- and long-term effects Avoiding the pitfalls of professonalism (e.g., over-specialization, arrogance, reinforcement of the status quo) Harnessing the power of intersectoral and citizen-led public work Defining standards and values for judgment Others… Serious Challenges for Planners and Evaluators

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

Syndemics Prevention Network Essential Elements for System Change Ventures Selected Elements of a Sound Strategy Needed to Address… Realistic Understanding of Causal Dynamics Multiple, simultaneous lines of action and reaction 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 Navigational Goals & Framework for Charting Progress Means for Prioritizing Actions & Impetus to Implement Them

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

Syndemics Prevention Network Essential Elements for System Change Ventures Selected Elements of a Sound Strategy Needed to Address… Realistic Understanding of Causal Dynamics Multiple, simultaneous lines of action and reaction 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 Navigational Goals & Framework for Charting Progress Plausible future targets, given existing momentum Life-course and intergenerational implications Sense of timing and trajectories of change (e.g., better-before-worse, or vice versa) Leadership for choosing a particular course Clear referent(s) for charting progress Means for Prioritizing Actions & Impetus to Implement Them Experiments to test policy leverage (alone and in combination) Trade-offs between short and long-term consequences Possible unintended effects Alignment of multiple actors Visceral and emotional learning about how dynamic systems function (i.e., better mental models)

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

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

Syndemics Prevention Network “A symbolic instrument made of a number of methods and techniques borrowed from very different disciplines…The macroscope filters details and amplifies that which links things together. It is not used to make things larger or smaller but to observe what is at once too great, too slow, and too complex for our eyes.” Rosnay Jd. The macroscope: a book on the systems approach. Principia Cybernetica, < -- Joèl de Rosnay Looking Through the Macroscope Can SD simulation models provide practical macroscopes for planning and evaluating health policy?

Syndemics Prevention Network System Dynamics Was Developed to Address Problems Marked By Dynamic Complexity Good at Capturing Differences between short- and long-term consequences of an action Time delays (e.g., developmental period, time to detect, time to respond) Accumulations (e.g., prevalences, resources, attitudes) Behavioral feedback (e.g., reactions by various actors) Nonlinear causal relationships (e.g., threshold effects, saturation effects) 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, Industrial Dynamics, 1961 (“One of the seminal books of the last 20 years.” -- NY Times) Public policy applications starting late 1960s Population health applications starting mid-1970s

Syndemics Prevention Network System Change Initiatives Encounter Limitations of Logic Models and Conventional Planning/Evaluation Methods Diabetes Action Labs* Upstream-Downstream Dynamics Obesity Over the Lifecourse* Fetal & Infant Health Milestones in the Recent Use of System Dynamics Modeling at CDC AJPH Systems Issue 2006 CDC Evaluation Framework Recommends Logic Models SD Identified as a Promising Methodology Neighborhood Grantmaking Game National Health Economics & Reform Syndemics Modeling* * Dedicated multi-year budget CVH in Context* Science Seminars and Professional Development Efforts Hygeia’s Constellation Health System Transformation Game* SDR 50 th Issue

Syndemics Prevention Network Learning In and About Dynamic Systems Unknown structure Dynamic complexity Time delays Impossible experiments Real World Information Feedback Decisions Mental Models Strategy, Structure, Decision Rules Selected Missing Delayed Biased Ambiguous Implementation Game playing Inconsistency Short term Misperceptions Unscientific Biases Defensiveness Inability to infer dynamics from mental models Known structure Controlled experiments Enhanced learning Virtual World Sterman JD. Learning in and about complex systems. System Dynamics Review 1994;10(2-3): Sterman JD. Business dynamics: systems thinking and modeling for a complex world. Boston, MA: Irwin McGraw-Hill, 2000.

Syndemics Prevention Network A Model Is… An inexact representation of the real thing It helps us understand, explain, anticipate, and make decisions “All models are wrong, some are useful.” -- George Box “All models are wrong, some are useful.” -- George Box

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, Multi-stakeholder Dialogue Dynamic Hypothesis (Causal Structure)Plausible Futures (Policy Experiments) Obese fraction of Adults (Ages 20-74) 0% 10% 20% 30% 40% 50% Fraction of popn 20-74

Syndemics Prevention Network Learning In and About Dynamic Systems Benefits of Simulation Formal means of evaluating options Experimental control of conditions Compressed time Complete, undistorted results Actions can be stopped or reversed Tests for extreme conditions Early warning of unintended effects Opportunity to assemble stronger support Visceral engagement and learning 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

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 Robustness for longer- term projection Value for developing policy insights Degrees of uncertainty Increasing: Depth of causal theory Robustness for longer- term projection Value for developing policy insights Degrees of uncertainty Dynamic Simulation Models Anticipate new trends, learn about policy consequences, and set justifiable goals Tools for Policy Planning & Evaluation

Syndemics Prevention Network Different Modeling Approaches For Different Purposes Logic Models (flowcharts, maps or diagrams) System Dynamics (causal loop diagrams, stock-flow structures, simulation studies, action labs) Forecasting Models (regression models, Monte Carlo models) Articulate steps between actions and anticipated effects Improve understanding about the plausible effects of a policy over time Focus on patterns of change over time (e.g., long delays, better before worse) Test dynamic hypotheses through simulation studies Inspire action through visceral, game-based learning Make accurate forecasts of key variables Focus on precision of point predictions and confidence intervals

Syndemics Prevention Network Look Reasonable, But How Much Will it Take, and What’s the Expected Benefit? When? Milstein B, Chapel T, Renault V, Fawcett S. Developing a logic model or theory of change. Community Tool Box, Accessed April 9, 2003 at.

Syndemics Prevention Network Model Uses and Audiences Set Better Goals (Planners & Evaluators) –Identify what is likely and what is plausible –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

Syndemics Prevention Network 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

Syndemics Prevention Network Syndemic Orientation Expanding Public Health Science “Public health imagination involves using science to expand the boundaries of what is possible.” -- Michael Resnick Epidemic Orientation Problems Among People in Places Over Time Boundary Critique

Syndemics Prevention Network Ulrich W. Boundary critique. In: Daellenbach HG, Flood RL, editors. The Informed Student Guide to Management Science. London: Thomson; p Ulrich W. Reflective practice in the civil society: the contribution of critically systemic thinking. Reflective Practice 2000;1(2): Boundary Critique Creating a new theory is not like destroying an old barn and erecting a skyscraper in its place. It is rather like climbing a mountain, gaining new and wider views, discovering unexpected connections between our starting point and its rich environment. -- Albert Einstein

Syndemics Prevention Network The Weight of Boundary Judgments 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.

Syndemics Prevention Network Implications for Policy Planning and Evaluation Insights from the Overview Effect Maintain a particular analytic distance Not too close to the details, but not too far as be insensitive to internal pressures Potential to anticipate temporal patterns (e.g., better before worse) Structure determines behavior Potential to avoid scapegoating or lionizing Richardson GP. Feedback thought in social science and systems theory. Philadelphia, PA: University of Pennsylvania Press, Richmond B. Systems thinking: critical thinking skills for the 1990s and beyond. System Dynamics Review 1993;9(2): Available at. White F. The overview effect: space exploration and human evolution. 2nd ed. Reston VA: American Institute of Aeronautics and Astronautics, 1998.

Syndemics Prevention Network Health Care & Public Health Agency Capacity Provider supply Provider understanding, competence Provider location System integration Cost of care Insurance coverage Population Flows 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 Personal Capacity Understanding Motivation Social support Literacy Physio-cognitive function Life stages Metabolic Stressors Nutrition Physical activity Stress Baseline Flows Health Care Utilization Ability to use care (match of patients and providers, language, culture) Openness to/fear of screening Self-management, monitoring Percent of patients screened Percent of people with diabetes under control Civic Participation Social cohesion Responsibility for others 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

Syndemics Prevention Network Developing Burden of Diabetes Total Prevalence (people with diabetes) Unhealthy Days (per person with diabetes) Costs (per person with diabetes) People with Diagnosed Diabetes Diagnosis Deaths a b People with Prediabetes Developing Diabetes Onset c d People with Normal Blood Sugar Levels Prediabetes Onset Recovering from Prediabetes e Diabetes Management Diabetes Detection Obesity in the General Population Prediabetes Detection & Management People with Undiagnosed Diabetes Deaths Diabetes Model Overview Data sources: NHIS, NHANES, BRFSS, Census, Vital statistics, Clinical studies, Cost studies

Syndemics Prevention Network Diabetes Model Overview Developing Burden of Diabetes Total Prevalence (people with diabetes) Unhealthy Days (per person with diabetes) Costs (per person with diabetes) People with Diagnosed Diabetes Diagnosis Deaths a b People with Prediabetes Developing Diabetes Onset c d People with Normal Blood Sugar Levels PreDiabetes Onset Recovering from PreDiabetes e Diabetes Management Diabetes Detection Obesity in the General Population Prediabetes Detection & Management People with Undiagnosed Diabetes Deaths Standard boundary This larger view takes us beyond standard epidemiological models and most intervention programs Data sources: NHIS, NHANES, BRFSS, Census, Vital statistics, Clinical studies, Cost studies

Syndemics Prevention Network Syndemic Orientation Expanding Public Health Science “Public health imagination involves using science to expand the boundaries of what is possible.” -- Michael Resnick Epidemic Orientation Problems Among People in Places Over Time Boundary Critique Governing Dynamics Causal Mapping Plausible Futures Dynamic Modeling

Syndemics Prevention Network Selected CDC Projects Featuring System Dynamics Modeling ( ) Syndemics Mutually reinforcing afflictions Diabetes In an era of rising obesity Obesity Lifecourse consequences of changes in caloric balance Infant Health Fetal and infant morbidity/mortality Heart Disease and Stroke Preventing and managing multiple risks, in context 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, Grantmaking Scenarios Timing and sequence of outside assistance Upstream-Downstream Effort Balancing disease treatment with prevention/protection Healthcare Reform Relationships among cost, quality, equity, and health status Chronic Illness Dynamics Health and economic scenarios for downstream and upstream reforms

Syndemics Prevention Network Preventing and Managing Risk Factors for Heart Disease and Stroke Modeling the Local Dynamics of Cardiovascular Health 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).

Syndemics Prevention Network What is best allocation of resources to eliminate the burden, disparity & costs of preventable CVD, recognizing the spectrum of opportunities in particular places & settings? Over what time frame? Guiding Questions

Syndemics Prevention Network Contributors Core Design Team CDC: Michele Casper, Rosanne Farris, Darwin Labarthe, Marilyn Metzler, Bobby Milstein, Diane Orenstein Austin: Cindy Batcher, Karina Loyo, Ella Pugo, Rick Schwertfeger, Adolfo Valadez, Josh Vest, NIH: David Abrams, Patty Mabry Consultants: Jack Homer, Justin Trogdon, Kristina Wile Organizational Sponsors Austin/Travis County Health and Human Services Department CDC Division for Heart Disease and Stroke Prevention CDC Division of Adult and Community Health CDC Division of Nutrition, Physical Activity, and Obesity CDC Division of Diabetes Translation CDC Office on Smoking and Health CDC NCCDPHP Office of the Director Indigent Care Collaborative (Austin, TX) NIH Office of Behavioral and Social Science Research RTI International Sustainability Institute Texas Department of Health

Syndemics Prevention Network Model Purpose and Rationale Purpose –How do multiple risk factors and social factors combine to affect cardiovascular disease (CVD) endpoints and costs? –How should we focus our policy efforts given limited resources? Rationale for systems modeling –Capturing intermediate links so that possible “confounding factors” are included explicitly rather than ignored –Non-additive effects when multiple risk factors are combined –Time delays from change in incidence to change in prevalence (accumulation or “bathtub” effects) The model described here is a work in progress funded by the CDC’s Division of Heart Disease and Stroke Prevention. We plan to finalize the model’s equations and parameter values by February The model described here is a work in progress funded by the CDC’s Division of Heart Disease and Stroke Prevention. We plan to finalize the model’s equations and parameter values by February 2008.

Syndemics Prevention Network Intervention Approaches from “Upstream” to “Downstream” Our model focuses on the prevention and control of risk factors that can lead to a first-time CVD event.

Syndemics Prevention Network Crafting Effective Intervention Strategies for Upstream Prevention in Context Concentrate on “upstream” challenge of minimizing risk, rather than the better understood “downstream” task of post-event care Local conditions affect people’s health status and their responses to perceived problems Local social and physical factors may be critical when characterizing the history—and plausible futures—of cardiovascular disease in a given city or region These aspects of local context are difficult to measure and too often excluded when planning and evaluating policies or programs The CDC is partnering 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 data of the Austin team, which has been supported by the CDC’s “STEPS” program since The CDC is partnering 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 data of the Austin team, which has been supported by the CDC’s “STEPS” program since 2004.

Syndemics Prevention Network UTILIZATION OF SERVICES Behavioral change Social support Mental health Preventive health Modified Anderson Risk Calculator RISK FACTOR ONSET, PREVALENCE & CONTROL Hypertension High cholesterol Diabetes Obesity Smoking Secondhand smoke Air pollution exposure ESTIMATED FIRST-TIME FATAL AND NON-FATAL CVD EVENTS CHD (MI, Angina, Cardiac Arrest) Stroke Total CVD (CHD, Stroke, CHF, PAD) COSTS (CVD & NON-CVD) ATTRIBUTABLE TO RISK FACTORS LOCAL CONTEXT Eating & activity options Smoking policies Socioeconomic conditions Environmental policies Health care options Support service options Media and events Local capacity for leadership & organizing LOCAL ACTIONS NUTRITION, PHYSICAL ACTIVITY & STRESS Salt intake Saturated/Trans fat intake Fruit/Vegetable intake Net caloric intake Physical activity Chronic stress Preventing and Managing Risk Factors for CVD Sector Diagram DRAFT: October, 2007 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.

Syndemics Prevention Network Data Sources for CVD Risk Modeling Census –Population, deaths, births, net immigration, health coverage AHA & NIH statistical reports –CVD events, deaths, and prevalence (CHD, stroke, CHF, PAD) National Health and Nutrition Examination Survey (NHANES) –Risk factor prevalences by age (18-29, 30-64, 65+) and sex (M, F) –Risk factor diagnosis and control (hypertension, high cholesterol, diabetes) Behavioral Risk Factor Surveillance System (BRFSS) –Diet & physical activity –Primary care utilization –Lack of needed emotional/social support Research literature –CVD risk calculator, and relative risks from SHS, air pollution, obesity, and inactivity –Medical and productivity costs of CVD and risk factors Questionnaires for CDC and Austin teams (expert judgment) –Potential effects of social marketing –Potential effects of expanded access to healthy food, activity, and behavioral services –Effects of behavioral services on smoking, weight loss, stress reduction –Relative risks of stress for high BP, high cholesterol, smoking, and obesity

Syndemics Prevention Network CVD Risk Factors and Linkages

Syndemics Prevention Network Improving Primary Care

Syndemics Prevention Network Reducing Risk Factor Prevalence

Syndemics Prevention Network Adding Up the Disease Costs

Syndemics Prevention Network Developing a “Status Quo” Scenario A straightforward base case –Assume no changes after 2000 in contextual factors or in risk factor inflow/outflow rates –Any changes in risk prevalences after 2000 are due to “bathtub” adjustment and population aging Result: Past trends continue after 2000, but decelerate and level off –Increasing obesity, high BP, and diabetes –Decreasing smoking –High cholesterol mixed bag by age and sex, flat overall The model is calibrated to reproduce data from NHANES and on risk factor prevalences in the non-CVD population by age and sex. Obese % of non-CVD popn Uncontrolled hypertension % of non-CVD popn Smoking % of non-CVD popn

Syndemics Prevention Network Testing Alternative Scenarios Policy Tests –What if this intervention had been fully implemented by 1997? Sensitivity Tests –How would the effects of a particular policy change if we vary a more uncertain assumption across its plausible range? Obesity prevalence Time (Year) 1. Base Case 2. Increase access to PA Obesity prevalence Time (Year) Varying RR of Obesity w/o PA

Syndemics Prevention Network CVD RISK FACTORS DIRECTLY AFFECTED INTERVENTION TARGET High BPHigh cholesterolDiabetes Smoking 1, SHS & Air pollution Obesity Access to primary care services 2 √√√ Effectiveness of primary care services 2 √√√ Sources of stress (poverty, crime, discrimination) √ √√ 3√ 3 Access to mental health services 4 √ √√ 3√ 3 Access to good diet √√ √ Access to physical activity √√√ √ Access to weight loss services √ Access to smoking quit services √ Smoking in workplaces & in public places 5 √ Air pollution √ Marketing of healthy behaviors 6 √√√ √ √ Marketing of health & social services 7 √√√√√ 1 Reductions in smoking may lead, in turn, to some increase in eating and obesity; 2 Primary care improves diagnosis and control of affected conditions, not their prevalence; 3 Due to stress-eating; 4 Affects chronic stress; 5 Affects secondhand smoke; 6 Affects nutrition, PA, smoking; 7 Affects use of available services

Syndemics Prevention Network Broader Categories of Policy Change Policies that decrease socioeconomic gaps –Educational policies –Fiscal policies –Skills training policies Policies that mitigate adverse conditions –Policies affecting the environment –Polices affecting the workplace –Policies enabling healthier behaviors –Policies affecting the medical system Adapted from: Adler N, Stewart J. Reaching for a healthier life: facts on socioeconomic status and health in the USA. San Francisco, CA: John D. and Catherine T. MacArthur Research Network on Socioeconomic Status and Health 2007

Syndemics Prevention Network Simulation Framework

Syndemics Prevention Network POLICIES ENABLING HEALTHIER BEHAVIORS Simulation Framework and Policy Space EDUCATION POLICIES FISCAL POLICIES SKILLS TRAINING POLICIES POLICIES AFFECTING THE ENVIRONMENT POLICIES AFFECTING THE WORKPLACE POLICIES AFFECTING THE MEDICAL SYSTEM

Syndemics Prevention Network Simulation Control Panel

Syndemics Prevention Network Three Illustrative Policies Expand Access and Social Support –Provide full access for all to healthy food, safe physical activity, primary care, and behavioral services –Provide social supports to mitigate stress, reducing it 50% Strengthen Primary Care and Promote Healthy Living –Transform primary care to meet highest standards for prevention and control activities and referrals –Strongly promote healthy eating, activity, no smoking, and use of primary care and behavioral services Fight Tobacco and Air Pollution –Tobacco control package: Raise taxes, police sales to minors, and ban smoking in workplaces and public places –Reduce particulate (PM 2.5) air pollution by 50% The interventions are tested retroactively with implementation starting in 1995 and ramping up to full effectiveness by 1997, continuing unabated through The interventions are tested retroactively with implementation starting in 1995 and ramping up to full effectiveness by 1997, continuing unabated through 2040.

Syndemics Prevention Network Annual Disease Costs in 5 Illustrative Scenarios Total Annual Risk Factor Complication Costs per Capita Among the Never-CVD Population  Base  Access & social support  Strengthen primary care & promote healthy living  Fight tobacco & air pollution  All of the above  Base  Access & social support  Strengthen primary care & promote healthy living  Fight tobacco & air pollution  All of the above 2,000 1,750 1,500 1,250 1, dollars/(Year*person) Work in progress - for illustration only

Syndemics Prevention Network Obesity, Uncontrolled Hypertension, and Smoking Five Illustrative Scenarios Obese % of non-CVD popn  Base  Access & social support  Strengthen primary care & promote healthy living  Fight tobacco & air pollution  All of the above  Base  Access & social support  Strengthen primary care & promote healthy living  Fight tobacco & air pollution  All of the above Smoking % of non-CVD popn Uncontrolled hypertension % of non-CVD popn

Syndemics Prevention Network What are We Learning? Literature on risk factors and social determinants poses a challenge for modeling –Many studies skip causal links or don’t quantify effect sizes BRFSS offers reasonable proxies for tricky variables like stress and access Health departments are practically oriented and can help refine concepts and estimate effect sizes Policy analysts want us to model broadly despite the numerical uncertainties –Give more attention to how effectiveness of social interventions may change over time (erosion, bandwagon effects) Take audience background into account when presenting concepts and intervention approaches

Syndemics Prevention Network Conceptual and Methodological Features of System Dynamics Modeling Thinking dynamically Move from events and decisions to patterns of continuous behavior over time and policy structure Thinking in circular causal /feedback patterns Self-reinforcing and self- balancing processes Compensating feedback structures and policy resistance Communicating complex nonlinear system structure Thinking in stocks and flows Accumulations are the resources and the pressures on policy Policies influence flows Modeling and simulation Accumulating (and remembering) complexity Quantification (distinct from measurement) Rigorous (daunting) model evaluation processes Controlled experiments Reflection Richardson GP, Homer JB. System dynamics modeling: population flows, feedback loops, and health. NIH/CDC Symposia on System Science and Health; Bethesda, MD: August 30, Available at.

Syndemics Prevention Network A Specific Set of Thinking Skills Conventional ThinkingSystems 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. Microscopic Thinking: Focusing on the details in order to “know.” Macroscopic Thinking: Seeing beyond the details to the context of relationships in which they are embedded. Engaging in active boundary critique. 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, Richmond B. Systems thinking: critical thinking skills for the 1990s and beyond. System Dynamics Review 1993;9(2): Richmond B. The "thinking" in systems thinking: seven essential skills. Waltham, MA: Pegasus Communications, 2000.

Syndemics Prevention Network Revisiting the Framework Simulation Modeling Offers Support for multi-stakeholder dialogue A larger conception of the “program” context Another avenue for experimentation and visceral learning, with the need for comparison or control groups Ability to track interrelated indicators (both states and rates) An emphasis on pragmatism (learning through action) “Steps in the framework are starting points for tailoring an evaluation to a particular public health effort at a particular time.” Milstein B, Wetterall S, CDC Evaluation Working Group. Framework for program evaluation in public health. MMWR Recommendations and Reports 1999;48(RR-11):1-40. Available at.

Syndemics Prevention Network An Alternative Philosophical Tradition Shook J. The pragmatism cybrary Available at. Addams J. Democracy and social ethics. Urbana, IL: University of Illinois Press, West C. The American evasion of philosophy: a genealogy of pragmatism. Madison, WI: University of Wisconsin Press, "Grant an idea or belief to be true…what concrete difference will its being true make in anyone's actual life? -- William James Pragmatism Begins with a response to a perplexity or injustice in the world Learning through action and reflection Asks, “How does this work make a difference?” Positivism Begins with a theory about the world Learning through observation and falsification Asks, “Is this theory true?” We are not talking about theories to explain, but conceptual, methodological, and moral orientations: the frames of reference that shape how we think, how we act, how we learn, and what we value

Syndemics Prevention Network All models, including simulations, are 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 and anticipate better than we do with the unaided mind How Should We Value Simulation Studies? Artist: Rene Magritte Sterman JD. All models are wrong: reflections on becoming a systems scientist. System Dynamics Review 2002;18(4): Meadows DH, Richardson J, Bruckmann G. Groping in the dark: the first decade of global modelling. New York, NY: Wiley, Forrester JW. Counterintuitive behavior of social systems. Technology Review 1971;73(3): “All models are wrong, some are useful.” -- George Box

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?

Syndemics Prevention Network 2007 Extramural funding for methodology and technology (NIH Roadmap) Symposia series on system science and health (NIH/OBSSR and CDC/SPN; ~6,000 participants) Conference on complexity approaches to population health (Univ of Michigan; ~250 participants) NIH monograph, “Greater Than the Sum” CDC monograph, “Hygeia’s Constellation” CDC to hire directors for preparedness modeling and public health systems research Concept mapping of public health policy resistance (NIH/OBSSR and CDC/SPN) Historical examples of health system transformation (CDC Public Health Practice Council) Methodology to support CDC’s focus on “health protection…health equity” (PriceWaterhouseCoopers) 2008 Summer training institute for system science and health (NIH/OBSSR and CDC/SPN) 2009 Extramural funding for “Health System Change” (NIH and CDC?) What’s on the Horizon for System Science & Health?

Syndemics Prevention Network For Further Information CDC Syndemics Prevention Network NIH/CDC Symposia on System Science and Health Recommended Reading –AJPH theme issue on systems thinking and modeling (March, 2006) Sterman JD. Learning from evidence in a complex world. AJPH 2006;96(3): Midgley G. Systemic intervention for public health. AJPH 2006;96(3): Homer JB, Hirsch GB. System dynamics modeling for public health: background and opportunities. AJPH 2006;96(3): –Sterman JD. A skeptic's guide to computer models. In: Barney GO, editor. Managing a Nation: the Microcomputer Software Catalog. Boulder, CO: Westview Press; p –Meadows DH. Leverage points: places to intervene in a system. Sustainability Institute, –Meadows DH, Robinson JM. The electronic oracle: computer models and social decisions. System Dynamics Review 2002;18(2):

Syndemics Prevention Network Forthcoming Report Milstein B. Hygeia's constellation: navigating health futures in a dynamic and democratic world. Atlanta, GA: Centers for Disease Control and Prevention 2008.

Syndemics Prevention Network EXTRAS

Syndemics Prevention Network CDC Diabetes System Modeling Project Charting Plausible Futures for HP 2010 Milstein B, Jones A, Homer J, Murphy D, Essien J, Seville D. Charting plausible futures for diabetes prevalence: a role for system dynamics simulation modeling. Preventing Chronic Disease 2007;4(3):1-8. Available at

Syndemics Prevention Network CDC Diabetes System Modeling Project Understanding Population Dynamics 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):

Syndemics Prevention Network CDC Diabetes System Modeling Project Discovering Dynamics Through State-based Action Labs & Models

Syndemics Prevention Network CDC Obesity Dynamics Modeling Project Exploring Historical Growth and Plausible Futures 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, Centers for Disease Control and Prevention. The state of the CDC, fiscal year Atlanta, GA: CDC 2007.

Syndemics Prevention Network Homer J, Milstein B. Optimal decision making in a dynamic model of poor community health. 37th Hawaii International Conference on System Science; Big Island, HI; January 5-8, < Homer J, Milstein B. Syndemic simulation. Forio Business Simulations, CDC Syndemics Modeling Neighborhood Transformation Game

Syndemics Prevention Network Homer J, Hirsch G, Milstein B. Chronic illness in a complex health economy: the perils and promises of downstream and upstream reforms. System Dynamics Review 2007 (in press). SD Society Health Policy Dynamics Modeling Upstream and Downstream Reforms

Syndemics Prevention Network Time 100: the people who shape our world. Time Magazine 2004 April 26. Gerberding JL. CDC: protecting people's health. Director's Update; Atlanta, GA; July, Gerberding JL. Health protectionomics: a new science of people, policy, and politics. Public Health Grand Rounds; Washington, DC: George Washington University School of Public Health and Health Services; September 19, Available at Centers for Disease Control and Prevention. Health system transformation: Office of Strategy and Innovation; September 28, CDC Leadership on Health System Transformation

Syndemics Prevention Network Mapping the Dynamics of Upstream and Downstream: Why is So Hard for the Health System to Work Upstream? 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, Jackson DJ, Valdesseri R, CDC Health Systems Work Group. Health systems work group report. Atlanta, GA: Centers for Disease Control and Prevention, Office of Strategy and Innovation; January 6, Milstein B, Homer J. Health system dynamics: mapping the drivers of population health, vulnerability, and affliction. Atlanta, GA: Syndemics Prevention Network; June 27 (work in progress), Milstein B. Hygeia's constellation: navigating health futures in a dynamic and democratic world. Atlanta, GA: Centers for Disease Control and Prevention 2008.