Understanding Obesity DynamicsA Foundation for Directing Change and Charting ProgressObesity Dynamics Modeling Project May 17-18, 2005Atlanta, GA

General Plan for the WorkshopDay 1Dynamic DilemmasSystem Dynamics in ActionObesity Dynamics General Causal StructureGroup Exercise Identifying Forces of Change

Day 2

Modeling for Learning Using Simulation ExperimentsGroup Exercise Organizing Effective Health Protection EffortsDirecting Change and Charting ProgressSnapshot Evaluation

Considering Multiple Perspectives on Overweight and Obesity

Concentrating on Dynamic Dilemmas:Understanding Change, Setting Goals, Motivating Action, Charting Progress

Understanding the Dynamics of GrowthTimeHealthProtection Efforts

Re-Directing the Course of ChangeQuestions Addressed by System Dynamics ModelingHow?Where?Prevalence of Obese Adults, United StatesWhy?Data Source: NHANESWho?

Some Sources of Dynamic Complexity for ObesityMultiple GoalsImprove dietIncrease physical activityDecrease physical inactivityAssure healthful conditions in diverse behavioral settings (i.e., home, school, work, community)Harness synergies with other social values (i.e., school performance, economic productivity, environmental protection)BarriersCost of caring for weight-related diseasesCost of health protection effortsSpiral of unhealthy habits leading to poor health leading to even less healthy habitsSocial reinforcement of diet and activity based on observing parents, peers, and others behaviorDemand for unhealthy food and inactive habits stimulates supplyResistance by defenders of the status quoSimultaneous Program StrategiesDeliver healthcare servicesEnhance media messagesExpand options in behavioral settingsModify trends in the wider environment (i.e., economy, technology, laws)Address other health conditions that impede healthy diet and activity (e.g., asthma, oral health, etc.)Time Delays1-2 year lag for metabolism to stabilize after change in net caloric intake14 year lag for youth to age into adulthood58 year lag for cohorts of adultsSeveral years for programs to mature and for policies to be fully enacted/enforcedAt least several years to see policy impacts, and even longer to affect the wider environment

Dynamic Complexity is Realand ConsequentialForrester JW. Counterintuitive behavior of social systems. Technology Review 1971;73(3):53-68.Meadows DH. Leverage points: places to intervene in a system. Sustainability Institute, 1999. Available at .Sterman JD. Business dynamics: systems thinking and modeling for a complex world. Boston, MA: Irwin McGraw-Hill, 2000.

Progression of Systems Thinking & Modeling

Patterns

Events

Adapted from: Successful Systems, Inc.

Time

System Dynamics Was Designed to Address Problems Marked By Dynamic ComplexityMultiple, interrelated goalsPrograms/policies in one area can shift the burden of disease elsewhereProgress in aggregate measures conceals significant and unchanging disparitiesLong time delays Consequences/accumulations extend over multiple life stagesKnown interventions have yielded little long-term benefit or there is uncertainty about how to intervene effectivelyUnclear how to combine multiple interventions into a comprehensive strategyTrajectory of future progress is uncertain Unclear how strong interventions have to be to alter the status quoMay be a worse-before-better pattern of changeResearch agenda and information systems are not well definedSignificant drivers exist but are poorly understood and not monitored routinelySterman JD. Business dynamics: systems thinking and modeling for a complex world. Boston, MA: Irwin McGraw-Hill, 2000.Homer JB, Hirsch G. System dynamics modeling for public health: background and opportunities. American Journal of Public Health in press.

Extending a Long History of Health Policy ModelingHistoryDeveloped at MIT by Jay Forrester (1961)International 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, emergency planning)Interactions between health capacity and disease epidemiology (e.g, neighborhood- and national-level analysis)Recent CDC Projects Syndemics (i.e., mutually reinforcing epidemics)Community grantmaking strategyDiabetes in an era of rising obesityUpstream/downstream effortHealth care reform proposalsGoals for fetal and infant healthHomer JB, Hirsch G. System dynamics modeling for public health: background and opportunities. American Journal of Public Health in press.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, 2005. .

Milestones in the Growth of System Dynamics Modeling at CDC

Essential Elements for System Change Ventures

Elements of a Sound StrategyNeeded to AddressRealistic Understanding of Causal DynamicsJustifiable Goals & Framework for Charting ProgressMeans for Prioritizing Actions &Impetus to Implement Them

Essential Elements for System Change Ventures

Elements of a Sound StrategyNeeded to AddressRealistic 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 timeJustifiable Goals & Framework for Charting ProgressMeans for Prioritizing Actions &Impetus to Implement Them

Essential Elements for System Change Ventures

Elements of a Sound StrategyNeeded to AddressRealistic 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 timeJustifiable Goals & Framework for Charting Progress Plausible future targets, given existing momentum Life-course implications Timing and trajectories of change (e.g., better-before-worse, or vice versa) Leadership for choosing a particular course Clear referent for charting progressMeans for Prioritizing Actions &Impetus to Implement Them

Essential Elements for System Change Ventures

Elements of a Sound StrategyNeeded to AddressRealistic 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 timeJustifiable Goals & Framework for Charting Progress Plausible future targets, given existing momentum Life-course implications Timing and trajectories of change (e.g., better-before-worse, or vice versa) Leadership for choosing a particular course Clear referent for charting progressMeans for Prioritizing Actions &Impetus to Implement Them Experiments to test policy leverage (alone and in combination) Short and long-term consequences of actions Possible unintended effects Alignment of multiple actors Visceral and emotional learning about how dynamic systems function (i.e., better mental models)

Essential Elements for System Change VenturesLimitations of Conventional Alternatives

Elements of a Sound StrategyConventional ApproachesLimitationsRealistic Understanding of Causal DynamicsJustifiable Goals & Framework for Charting ProgressMeans for Prioritizing Actions & Impetus to Implement Them

Essential Elements for System Change VenturesLimitations of Conventional Alternatives

Elements of a Sound StrategyConventional ApproachesLimitationsRealistic 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 dataJustifiable Goals & Framework for Charting ProgressMeans for Prioritizing Actions & Impetus to Implement Them

Essential Elements for System Change VenturesLimitations of Conventional Alternatives

Elements of a Sound StrategyConventional ApproachesLimitationsRealistic 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 dataJustifiable 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 goodMeans for Prioritizing Actions & Impetus to Implement Them

Essential Elements for System Change VenturesLimitations of Conventional Alternatives

Elements of a Sound StrategyConventional ApproachesLimitationsRealistic 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 dataJustifiable 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 goodMeans 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

CDC Diabetes System Modeling ProjectDiscovering Dynamics Through Action LabsJones A, Homer J, Milstein B, Essien J, Murphy D, Sorensen S, Englegau M. Modeling the population dynamics of a chronic disease: the CDC's diabetes system model. American Journal of Public Health (in press).

Transforming the Future of Diabetes"Every new insight into Type 2 diabetes... makes clear that it can be avoided--and that the earlier you intervene the better. The real question is whether we as a society are up to the challenge... Comprehensive prevention programs aren't cheap, but the cost of doing nothing is far greater..."Gorman C. Why so many of us are getting diabetes: never have doctors known so much about how to prevent or control this disease, yet the epidemic keeps on raging. how you can protect yourself. Time 2003 December 8. Accessed at http://www.time.com/time/covers/1101031208/story.html.

Forecast of Diabetes PrevalencePrevalence of Diagnosed Diabetes, US01020304019801990200020102020203020402050Million peopleHistorical Data: CDC DDT and NCCDPHP. (Change in measurement in 1996).Model Forecast: Honeycutt et al. 2003, "A Dynamic Markov model"HistoricalDataModelForecastKey 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 2050. Health Care Management Science 2003;6:155-164.

Health Care Capacity Provider supply Provider understanding, competence Provider location System integration Cost of care Insurance coveragePopulation FlowsDiscussions Pointed to Many Interacting Factors Metabolic Stressors Nutrition Physical activity StressHealth Care Utilization Ability to use care (match of patients and providers, language, culture) Openness to/fear of screening Self-management, monitoringCivic 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 coalitionsLocal 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

Diabetes System Modeling ProjectWhere is the Leverage for Health Protection?Jones A, Homer J, Milstein B, Essien J, Murphy D, Sorensen S, Englegau M. Modeling the population dynamics of a chronic disease: the CDC's diabetes system model. American Journal of Public Health (in press).People withUndiagnosed,UncomplicatedDiabetesPeople withDiagnosed,UncomplicatedDiabetesPeople withDiagnosed,ComplicatedDiabetesPeople withUndiagnosedPreDiabetesPeople withDiagnosedPreDiabetesPeople withUndiagnosed,ComplicatedDiabetesPeople withNormalGlycemicLevels

Diabetes System Modeling ProjectWhere is the Leverage for Health Protection?People withUndiagnosed,UncomplicatedDiabetesPeople withDiagnosed,UncomplicatedDiabetesPeople withDiagnosed,ComplicatedDiabetesDiagnosingUncomplicatedDiabetesPeople withUndiagnosedPreDiabetesPeople withDiagnosedPreDiabetesDiagnosingPreDiabetesDevelopingComplications fromPeople withUndiagnosed,ComplicatedDiabetesDiagnosingComplicatedDiabetesPeople withNormalGlycemicLevelsDiabetesDetectionObese Fraction ofthe PopulationRisk forPreDiabetes & DiabetesPreDiabetesControlDiabetesControlPreDiabetesDetectionPreDiabetesOnsetRecovering fromPreDiabetesRecovering fromPreDiabetesDiabetesOnsetDying fromComplicationsDevelopingComplications

Simulations for Learning in Dynamic SystemsDiabetes Dynamics in an Era of Epidemic ObesityDynamic Hypothesis (Causal Structure)Plausible Futures (Policy Experiments)Jones A, Homer J, Milstein B, Essien J, Murphy D, Sorensen S, Englegau M. Modeling the population dynamics of a chronic disease: the CDC's diabetes system model. American Journal of Public Health (in press).Multi-stakeholder Dialogue

Using Available Data to Calibrate the Model

Information SourcesData U.S. Census Adult population and death rates Health insurance coverageNational Health Interview Survey Diabetes prevalence Diabetes detectionNational Health and Nutrition Examination Survey Prediabetes prevalence Weight, height, and body fat Caloric intakeBehavioral Risk Factor Surveillance System Glucose self-monitoring Eye and foot exams Participation in health education Use of medicationsProfessional Literature Physical activity trends Effects of control and aging on onset, progression, death, and costs Expenditures

Diabetes System Modeling ProjectConfirming the Models Fit to HistoryJones A, Homer J, Milstein B, Essien J, Murphy D, Sorensen S, Englegau M. Modeling the population dynamics of a chronic disease: the CDC's diabetes system model. American Journal of Public Health (in press).Diagnosed Diabetes % of AdultsObese % of Adults

Setting Realistic ExpectationsHistory, HP Objectives, and Simulated FuturesStatus QuoMeet Detection Objective (5-4)Meet Onset Objective (5-2)HP 2010 Objective (5-3)HP 2000 ObjectiveCDEFGHI

Connecting the ObjectivesPopulation Flows and Dynamic Accounting 101It is impossible for any policy to reduce prevalence 38% by 2010!People withUndiagnosedDiabetesPeople withDiagnosedDiabetesDying from DiabetesComplicationsDiagnosedOnsetInitialOnsetPeoplewithoutDiabetesWith a diagnosed onset flow of 1.1 mill/yrAnd a death flow of 0.5 mill/yr(4%/yr rate)

How Does Modeling Process Help DDT in Its Work with the States?Builds on the Assessment ProcessModel of InfluencePartneringPlanning for Pre-Diabetes Population

Why VermontParticipated in Boston Learning SessionGovernors Panel, the Blueprint Group, charged with taking on diabetesPositive partnership experiences

No major changes status quoCare and reduction in caloric intake

Vermonts ResponseVery interactive meeting with partners in March 2005 (lots of ah-has!)State Health Commissioner presented our model results to the State Senate Appropriations Committee. Model results for per capita costs were very well received, and demonstrated need for both prevention and clinical intervention.VT Program Director: What Im learning is that what we are doing with the Blueprint Group is good and necessary, but not enough. Weve got to supplement the downstream work with enhanced primary prevention and prediabetes screening.

Next Steps for DDT/PDBPrimary Prevention RFA with systems modeling pilotAt least 2 additional sitesDeveloping PDB competency in systems thinkingIntegrate systems thinking into consultation with states

Obesity Dynamics

A General Causal Structure

Re-Directing the Course of ChangeQuestions Addressed by System Dynamics ModelingHow?Where?Who?Prevalence of Obese Adults, United States1960-621971-741976-801988-941999-2000Why?Data Source: NHANES

Decades of ChangeAdult Overweight and Obese Prevalence (NHANES)OverweightObeseSeverely Obese

Decades of ChangeAdult Obese Prevalence 2000 by Race and Sex (NHANES)

Decades of ChangeYouth Overweight and Obese Prevalence (NHANES)OverweightObese

Decades of ChangeChange in Adult Male Caloric Intake (NHANES)20-3940-5960-74Total (20-74)

Decades of ChangeChange in Adult Female Caloric Intake (NHANES)20-3940-5960-74Total (20-74)

Decades of ChangeAdult No Leisure Time Physical Activity (BRFSS)MaleFemaleCombined

Decades of ChangeHours per Week Watching TV, Internet, Video (Media Industry Report)TVTotal incl TV, Internet, VideoInternet

Decades of ChangeFraction of Meals and Caloric Intake Away From Home (USDA)CaloriesMeals

Decades of ChangeChange in Vehicle Miles Driven per Household (DOT/NPTS)

Decades of ChangeParticipation in Labor Force (BLS)MaleFemale

Decades of ChangeSmoking Prevalence (NHIS, YRBS)Adult MaleAdult FemaleHS Students

What forces have driven up obesity?

Where are the opportunities for response?

Framework for Organizing Influences on ObesityEnergy BalancePrevention of Overweight and Obesity Among Children, Adolescents, and AdultsNote: Adapted from Preventing Childhood Obesity. Institute of Medicine, 2005.Individual FactorsBehavioral SettingsSocial Norms and ValuesHome and FamilySchoolCommunityWork SiteHealthcare

GeneticsPsychosocialOther Personal FactorsFood and Beverage IndustryAgricultureEducationMediaGovernmentPublic Health SystemsHealthcare IndustryBusiness and WorkersLand Use and TransportationLeisure and RecreationFood and Beverage IntakePhysical ActivitySectors of InfluenceEnergy IntakeEnergy Expenditure

A Conventional View of Causal ForcesHealthiness of Diet& Activity HabitsPrevalence ofOverweight &Related DiseasesOptions Available atHome, School, Work,Community InfluencingHealthy Diet & ActivityMedia MessagesPromoting HealthyDiet & ActivityWider Environment (Economy,Technology, Laws) Influenceon Healthy Diet & ActivityHealth ConditionsDetracting fromHealthy Diet & ActivityGenetic MetabolicRate DisordersHealthcare Servicesto Promote HealthyDiet & Activity

A Conventional View of Causal ForcesThis sort of open-loop approachIgnores intervention spill-over effects and often suggests the best strategy is a multi-pronged fill all needs one (even if not practical or affordable)Ignores unintended side effects and delays that produce short-term vs. long-term differences in outcomesCannot fairly evaluate a phased approach; ex., bootstrapping which starts more narrowly targeted but then broadens and builds upon successes over time

A System Dynamics View of Causal Forces Direct Drivers of Diet and ActivityDRAFT 5/8/05

A System Dynamics View of Causal ForcesEngines of GrowthDRAFT 5/8/05

A System Dynamics View of Causal ForcesEngines of GrowthDRAFT 5/8/05

A System Dynamics View of Causal ForcesEngines of GrowthDRAFT 5/8/05

A System Dynamics View of Causal ForcesEngines of GrowthDRAFT 5/8/05

A System Dynamics View of Causal ForcesEngines of GrowthDRAFT 5/8/05

A System Dynamics View of Causal ForcesIndividual ResponsesDRAFT 5/8/05

A System Dynamics View of Causal ForcesTurning to Preventive HealthcareDRAFT 5/8/05

A System Dynamics View of Causal ForcesImproving Preventive HealthcareDRAFT 5/8/05

A System Dynamics View of Causal ForcesCreating Better Media MessagesDRAFT 5/8/05

A System Dynamics View of Causal ForcesCreating Better Options in Behavioral SettingsDRAFT 5/8/05

A System Dynamics View of Causal ForcesCreating Better Conditions in the Wider EnvironmentDRAFT 5/8/05

A System Dynamics View of Causal ForcesAddressing Related Health ConditionsDRAFT 5/8/05

A System Dynamics View of Causal ForcesDisease Care Costs Undercut PreventionDRAFT 5/8/05

A System Dynamics View of Causal ForcesUp-Front Costs Undercut Protection EffortDRAFT 5/8/05

A System Dynamics View of Causal ForcesDefenders of the Status Quo Resist ChangeDRAFT 5/8/05

A System Dynamics View of Causal ForcesOther Benefits Help Make the CaseDRAFT 5/8/05

The Closed-Loop View Leads Us To QuestionHow can the engines of growth loops (i.e. social and economic reinforcements) be weakened?What incentives can reward parents, school administrators, employers, and other decision-makers for expanding healthy diet and activity options ? Are there resources for health protection that do not compete with disease care?How can industries be motivated to change the status quo rather than defend it?How can benefits beyond weight reduction be used to stimulate investments in expanding healthier options?

Group Exercise #1

Identifying Forces of Change

Identifying Forces of ChangeTasksMake the dynamics in your assigned pathway(s) more concrete Name trends/drivers that have changed significantly in recent decadesFocus on each link in the loop separately and then list the most prominent forces of change, including their timing and possible differential consequences on sub-groupsAlso indicate sources where information/documentation about each trend might be found

GroupsSociety-Behavior PathwayBehavior-Society PathwayIndividual Responses to Weight PathwaysSocial Transmission Pathways

Society-Behavior Pathway

Behavior-Society Pathway

Individual Responses to Weight Pathways

Social Transmission Pathways

Understanding Obesity DynamicsA Foundation for Directing Change and Charting ProgressObesity Dynamics Modeling Project May 17-18, 2005Atlanta, GA

General Plan for the WorkshopDay 1Dynamic DilemmasSystem Dynamics in ActionObesity Dynamics General Causal StructureGroup Exercise Identifying Forces of Change

Day 2

Modeling for Learning Using Simulation ExperimentsGroup Exercise Organizing Effective Health Protection EffortsDirecting Change and Charting ProgressSnapshot Evaluation

Iterative Steps in System Dynamics Simulation ModelingLearn About Policy ConsequencesTest proposed policies, searching for ones that best govern changeRun Simulation Experiments Compare models behavior to expectations and/or data to build confidence in the modelConvert the Map Into a Simulation Model Formally quantify the hypothesis using all available evidenceCreate a Dynamic Hypothesis Identify and map the main causal forces that create the problemIdentify a Persistent Problem Graph its behavior over timeMilstein 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, 2005.

Modeling for LearningWhy Simulate?

Why Simulate Proposed Policies?

Even the best conceptual models can only be tested and improved by relying on the learning feedback through the real worldThis feedback is very slow and often rendered ineffective by dynamic complexity, time delays, inadequate and ambiguous feedback, poor reasoning skills, defensive reactions, and the costs of experimentation. In these circumstances simulation becomes the only reliable way to test a hypothesis and evaluate the likely effects of policies."

-- John Sterman

Sterman J. Business dynamics: systems thinking and modeling for a complex world. Boston, MA: Irwin McGraw-Hill, 2000.

Syndemics Prevention Network

http://www.asph.org/aa_section.cfm/3

Bobby Milstein (August 26, 2004)

Sterman JD. Learning from evidence in a complex world. Amer J Public Health (in press), 2005.

Benefits and Challenges of Simulation

ChallengesModeling requires expertise, time, data, and upper management support for sustained effortCommunicating results persuasively to outside parties

BenefitsExperimental control of all conditionsComplete, undistorted, immediate resultsEarly warning of unintended effectsShow how things may get worse before they get betterIdentify key data needs: prioritize empirical research Powerful group learning experience

Syndemics Prevention Network

Barriers to LearningDynamic complexity Time delaysInadequate and ambiguous feedbackPoor reasoning skillsDefensive reactionsInability and costs of experimentation

Bobby Milstein (August 26, 2004)

Progression of Systems Thinking & Modeling

Patterns

Events

Adapted from: Successful Systems, Inc.

Time

A Health Care MicroworldDeveloped in mid-1990s to help providers understand implications of changeSimulates managing a health system in a difficult competitive environmentAlso deals with the dynamics of keeping a population of 100,000 people healthy with limited resourcesCan simulate the effects of a combined strategy in which the delivery system implements a health improvement strategyHirsch GB, Immediato CS. Design of simulators to enhance learning: examples from a health care microworld. International System Dynamics Conference; Quebec City; July, 1998.

Causal Map Suggests Benefits of Chronic Disease ManagementHealth StatusTotal Cost.

Simulating the Microworld to Address a Strategic QuestionCan chronic disease management improve system performance and subscribers health?Simulation indicates that if CDM is implemented at the same time as the system improvements, both will likely failWhy? Additional workload created by CDM drives up waiting times and provider workloads, puts entire system into a tailspin of increasing cost and declining revenueAnother simulation demonstrates that phasing-in CDM after system improvements have time to increase capacity can produce better system performance and improve subscribers healthThese results are not obvious without simulation

Causal Map Suggests Benefits of Chronic Disease ManagementHealth StatusTotal CostWaiting TimesRevenues.

All formal modelsincluding simulationsare wrong: incomplete and imprecise But some are better than others and capture more important aspects of the real worlds dynamic complexityA valuable model is one that can help us understand and anticipate better than we do with the unaided mindor with a causal map alone

How Should We Value Models? Sterman JD. All models are wrong: reflections on becoming a systems scientist. System Dynamics Review 2002;18(4):501-31.

Obesity DynamicsAn Illustrative Simulation Model

Decades of ChangeAdult Overweight and Obese Prevalence (NHANES)OverweightObeseSeverely Obese

Decades of ChangeYouth Overweight and Obese Prevalence (NHANES)OverweightObese

How the BMI Distribution Has Shifted and StretchedCurves here generated from Gamma functions, not from actual data. Gamma parameters [a, b] shown on left. Corresponding overweight (BMI 25), obese (BMI 30), and severely obese (BMI 40) fractions shown on right. These approximations do a reasonably good job of matching prevalence data shown on previous slide, but are slightly low for overweight and severely obese, and slighly high for obese. Analysis shows that standard (unskewed) Normal distribution cannot come as close to matching prevalence data, and in particular predicts much lower prevalence of severely obese.

Preliminary Dynamic Hypothesis forUnderstanding and Reversing the Growth of ObesityHealthiness of Diet& Activity HabitsEffective HealthProtection EffortsR6Disease CareCosts SqueezePreventionB4Creating BetterMessagesR4Options ShapeHabits ShapeOptionsPrevalence ofOverweight &Related Diseases-Costs of Caringfor Overweight-Related Diseases-Options Available atHome, School, Work,Community InfluencingHealthy Diet & ActivityCosts of Developing &Maintaining HealthProtection EffortsB5Creating BetterOptions inBehavioralSettings-B8Up-front CostsUndercutProtectionEffortsObservation ofParents' andPeers' HabitsR2Parents/PeersTransmissionMedia MessagesPromoting HealthyDiet & ActivityWider Environment(Economy, Technology,Laws) Influence on OptionsB1Self-ImprovementB6Creating BetterConditions in theWider EnvironmentHealth ConditionsDetracting fromHealthy Diet & Activity-Perceived ProgramBenefits Beyond WeightReductionResistance andCountervailing Effortsby Opposed Interests-B9DefendingStatus QuoCost Implicationsof Overweight inOther SpheresB10Potential SavingsBuild SupportGenetic MetabolicRate DisordersB7AddressingRelated HealthConditionsHealthcare Servicesto Promote HealthyDiet & ActivityB2Medical ResponseR1Spiral of PoorHealth and HabitsB3ImprovingPreventiveHealthcareR5Society ShapesOptions ShapeSocietyBroader Benefits ofHealth ProtectionEffortsR7Broader BenefitsBuild SupportR3MediaMirrors

Demonstration Model StructureCore Pieces of the Larger TheoryHealthiness of Diet& Activity HabitsEffective HealthProtection EffortsPrevalence ofOverweight &Related Diseases-Options Available atHome, School, Work,Community InfluencingHealthy Diet & ActivityObservation ofParents' andPeers' HabitsR2Parents/PeersTransmissionWider Environment(Economy, Technology,Laws) Influence on OptionsHealth ConditionsDetracting fromHealthy Diet & Activity-R1Spiral of PoorHealth and Habits

Demonstration Model Structure

Demonstration Model Structure

Demonstration Model Structure

Demonstration Model Structure

Demonstration Model Structure

Demo Model Input AssumptionsTime constantsYears of childhood and adolescence (14 yrs.)Years of adulthood (58 yrs.)Metabolic adjustment time (1 yr.)Youth (3 yrs.) and adult (3 yrs.) options adjustment timesOther constantsMinimum (0.01) and maximum (0.5) youth overweight fractionsMinimum (0.3) and maximum (0.9) adult overweight fractionsFraction of youth habits imitating adult habits (0.33)Fraction of adult habits established in childhood (0.33)X-Y functionsEffect of overweight on healthiness of youth habits (f(1) = 0.6)Effect of overweight on healthiness of adult habits (f(1) = 0.6) Obese % of overweight youth, as a fcn of overwt youth % (history/Gamma)Obese % of overweight adults, as a fcn of overwt adult % (hist/Gamma)Severely obese % of obese adults, as a fcn of overwt adult % (hist/Gamma)Time Series InputsHealthiness of broader environment (0-1)Interventions to improve options in behavioral settings

Demo Model Base Run Results vs NHANES:Adult Overweight Fraction*Adult overweight fraction0.80.60.40.2019601970198019902000201020202030204020502060Time (year)Adult overwt frac : Base2dNHANES adult overwt frac : Base2d* Includes all BMI>25. Data available for NHANES surveys from 60-62, 71-74, 76-80, 88-94, and 99-02. Shown as data points for 1961, 1973, 1978, 1991, and 2000.

Demo Model Base Run Results vs NHANES:Youth Overweight Fraction** Overweight here refers to combined NHANES At risk and Overweight, and represents average of children and adolescents. NHANES data exist for both children and adolescents for 71-74, 76-80, 88-94, and 99-02 surveys. Data points shown for 1973, 1978, 1991, 2000. Also, data available for children in 63-65 and adolescents in 66-70; these are averaged for the first data point in 1968. Youth overweight fraction0.40.30.20.1019601970198019902000201020202030204020502060Time (year)Youth overwt frac : Base2dNHANES youth overwt frac : Base2d

Demo Model Base Run Results Healthiness of Habits and the EnvironmentAdult habits worsen more gradually than youth habits do, because of the lingering carryover effect of adult habits established in childhood. Both ultimately (2010 or later) worsen to 25%. This value is lower than the 30% healthiness of the broader environment, because the overweight, who are increasing in prevalence, find it harder than the non-overweight do to maintain healthy habits in any environment. Healthiness of Habits and Environment0.80.60.40.2019601970198019902000201020202030204020502060Time (year)Healthiness of adult habits : Base2dHealthiness of youth habits : Base2dHealthiness of broader environment : Base2d

Demo Model Base Run Results vs NHANES:Adult Obese Fraction*Adult obese fraction0.80.60.40.2019601970198019902000201020202030204020502060Time (year)Adult obese frac : Base2dNHANES adult obese frac : Base2d* Includes all BMI>30. Data available for NHANES surveys from 60-62, 71-74, 76-80, 88-94, and 99-02. Shown as data points for 1961, 1973, 1978, 1991, and 2000.

Demo Model Base Run Results vs NHANES:Adult Severely Obese Fraction*Adult severely obese fraction0.10.080.060.040.02019601970198019902000201020202030204020502060Time (year)Adult sev obese frac : Base2dNHANES adult sev obese frac : Base2d* Based on BMI>40. Data available for NHANES surveys from 60-62, 71-74, 76-80, 88-94, and 99-02. Shown as data points for 1961, 1973, 1978, 1991, and 2000.

Demo Model Base Run Results vs NHANES:Youth Obese Fraction** Obese here refers to NHANES Overweight and represents average of children and adolescents. NHANES data exist for both children and adolescents for 71-74, 76-80, 88-94, and 99-02 surveys. Data points shown for 1973, 1978, 1991, 2000. Also, data available for for children in 63-65 and adolescents in 66-70; these are averaged for the first data point in 1968. Youth obese fraction0.40.30.20.1019601970198019902000201020202030204020502060Time (year)Youth obese frac : Base2dNHANES youth obese frac : Base2d

X-Y Function Obese Fraction of Overweight AdultsOVERWEIGHT FRACTION OF ADULTSOBESE FRACTION OF OVERWEIGHT ADULTS||HistoricalrangeBased on a family of Gamma functions closely approximating actuals during the historical period.

Dynamic Effects of Interventions Illustrative Policy Tests BaseAll time series inputs flat after 2005Healthiness of youth and adult options decline to 0.3 by 2010 (having started at .75 in 1960) and remain at that level thereafterYouthOpt50 (Improve youth options)Efforts to improve youth options starting in 2005 increase healthiness of youth options to 0.65 (where they were in 1980) by 2015AdultOpt50 (Improve adult options)Efforts to improve adult options starting in 2005 increase healthiness of adult options to 0.65 (where they were in 1980) by 2015 AllOpt50 (Improve options for youth and adults)Efforts to improve both youth and adult options starting in 2005 increase healthiness of both to 0.65 (where they were in 1980) by 2015

Policy Testing OutputAdult Obese FractionAdult obese fraction0.60.40.2019601970198019902000201020202030204020502060Time (year)Adult obese frac : Base2dAdult obese frac : Youthopt50Adult obese frac : Adultopt50Adult obese frac : Allopt50The improvement in adult options by itself initially reduces adult obesity by 2015 to where it was in early 1990s. But continued poor youth habits cause some gradual erosion of the interventions benefit as the children become adults. However, if youth options are improved as well, virtually no erosion occurs in the short term and there is actually some further improvement in the longer term.

Policy Testing OutputYouth Obese FractionYouth obese fraction0.30.20.1019601970198019902000201020202030204020502060Time (year)Youth obese frac : Base2dYouth obese frac : Youthopt50Youth obese frac : Adultopt50Youth obese frac : Allopt50The improvement in youth options by itself reduces youth obesity by 2015 to where it was in the early 1990s. Continued poor adult options causes a slight amount of rebound due to the imitation effect. But if adult options are improved as well, this can further reduce youth obesity due to the imitation effect, reducing it by 2025 to where it was in the mid-1980s.

Group Exercise #2

Organizing Health Protection Efforts

Organizing Health Protection EffortsTasksMake the dynamics in your assigned pathways more concrete by identifying specific types of program/policy efforts that have beenor might beenacted in response to the rise of obesityNote the key features of each action, for example, how long it takes to organize, where the cost burden lies, what kinds of resistance might arise, and what benefits might accrue regarding weight as well as other areas (e.g., economic productivity, school performance, environmental quality, crime reduction, social capital, or other health issues)GroupsImproving Preventive Healthcare & Addressing Problems Beyond WeightCrafting Better MessagesCreating Better Options in Behavioral SettingsCreating Better Conditions in the Wider Environment

Improving Healthcare & Addressing Problems Beyond Weight

Crafting Better Messages

Creating Better Options in Behavioral Settings

Creating Better Conditions in the Wider Environment

Transforming Essential Ways of ThinkingKarash 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, 2005..Richmond B. Systems thinking: critical thinking skills for the 1990s and beyond. System Dynamics Review 1993;9(2):113-134.Richmond B. The "thinking" in systems thinking: seven essential skills. Waltham, MA: Pegasus Communications, 2000.

Conventional ThinkingSystems ThinkingStatic 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.Tree-by-Tree Thinking: Focusing on the details in order to know.Forest Thinking: Seeing beyond the details to the context of relationships in which they are embedded.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).

At this workshop I learned.

As a result of this workshop I intend toSnapshot Evaluation

We make the roadby walking-- Myles Horton & Paulo FreireHorton M, Freire P. We make the road by walking: conversations on education and social change. Philadelphia: Temple University Press, 1990.

EXTRAS

Dynamic effects of behavioral assumptions: Illustrative sensitivity testsBase: Fraction of youth habits imitating adult habits = .33Fraction of adult habits established in childhood = .33Effect of being overweight on healthiness of youth habits = 0.6Effect of being overweight on healthiness of adult habits = 0.6YouthImitate0: Youth habits are not influenced by parents or other adultsAdultCarryover0: Childhood habits do not carry over to adulthoodYouthEffOverwt0: Being overweight does not make it harder for youths to maintain healthy habits AdultEffOverwt0: Being overweight does not make it harder for adults to maintain healthy habits

Sensitivity Testing OutputYouth Obese Fraction- Youth imitation and adult carryover both buffer the impact of a changing environment on youths; without them, youth obesity would have climbed sooner and faster than it has actually done. - The reinforcing effect of overweight on unhealthy habits (both youth and adult) causes youth obesity to climb further than it would without this effect. Youth obese fraction0.30.20.1019601970198019902000201020202030204020502060Time (year)Youth obese frac : Base2dYouth obese frac : YouthImitate0Youth obese frac : AdultCarryover0Youth obese frac : YouthEffOverwt0Youth obese frac : AdultEffOverwt0

Sensitivity Testing OutputAdult Obese Fraction- Adult carryover buffers the impact of a changing environment on adults; without it, adult obesity would have climbed sooner and faster than it has actually done. - The reinforcing effect of overweight on unhealthy adult habits causes adult obesity to climb further than it would without this effect.Adult obese fraction0.60.40.2019601970198019902000201020202030204020502060Time (year)Adult obese frac : Base2dAdult obese frac : YouthImitate0Adult obese frac : AdultCarryover0Adult obese frac : YouthEffOverwt0Adult obese frac : AdultEffOverwt0

Demo Model Base Run Results Healthiness of Youth and Adult HabitsAdult habits worsen more gradually than youth habits do, because of the lingering carryover effect of adult habits established in childhood. Both ultimately (2010 or later) worsen to 25%. This value is lower than the 30% healthiness of the broader environment, because the overweight, who are increasing in prevalence, find it harder than the non-overweight do to maintain healthy habits in any environment. Healthiness of habits0.80.60.40.2019601970198019902000201020202030204020502060Time (year)Healthiness of youth habits : Base2dHealthiness of adult habits : Base2d

The Modeling Process is Having an Impact Budget for primary prevention was doubledfrom meager to modestHP2010 prevalence goal has been modifiedfrom a large reduction to no change (but still not an increase)Research, program, and policy staff are working more closelybut truly cross-functional teams still formingState health departments and their partners are now engagedinitial engagement in VT, with two additional states being considered

Tools for Policy AnalysisTime Series ModelsDescribe trendsMultivariate Stat ModelsIdentify historical trend drivers and correlatesPatternsStructureEventsIncreasing:Depth of causal theoryDegrees of uncertaintyRobustness for longer-term projectionValue for developing policy insightsDynamic Simulation Models Anticipate future trends, and find policies that maximize chances of a desirable path

Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkRealistic understanding of causal dynamics (actions, reactions, accumulations, delays)Justifiable goals (numerical targets, life-course implications, timing)Criteria for choosing actions and impetus for implementing them (within the health sector and beyond)

Bobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkMultiple, interrelated goalsPrograms/policies in one area can shift the burden of disease elsewhere, as is the case with reductions in FMR causing increases in NMRProgress in aggregate measures conceals significant and unchanging disparities, as is the case with the black/white gapLong time delays Connections/accumulations that extend over multiple life stages, as is the case with infant survivors having impaired health as children and adults, or with maternal health affecting that of her fetus and childKnown interventions have yielded little long-term benefit or there is uncertainty about how to intervene effectivelyNot clear how to combine multiple interventions effectively into a comprehensive strategyTrajectory of future progress is uncertain Not clear how strong interventions have to be to alter the status quoMay be a worse-before-better pattern of changeResearch agenda and information systems are not well definedSignificant drivers are known to exist but are poorly understood and not monitored routinely

Bobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkMultiple, interrelated goalsPrograms/policies in one area can shift the burden of disease elsewhere, as is the case with reductions in FMR causing increases in NMRProgress in aggregate measures conceals significant and unchanging disparities, as is the case with the black/white gapLong time delays Connections/accumulations that extend over multiple life stages, as is the case with infant survivors having impaired health as children and adults, or with maternal health affecting that of her fetus and childKnown interventions have yielded little long-term benefit or there is uncertainty about how to intervene effectivelyNot clear how to combine multiple interventions effectively into a comprehensive strategyTrajectory of future progress is uncertain Not clear how strong interventions have to be to alter the status quoMay be a worse-before-better pattern of changeResearch agenda and information systems are not well definedSignificant drivers are known to exist but are poorly understood and not monitored routinely

Bobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkIn its current form, the diabetes system model can test over 10 classes of intervention policies. Here is a simplified picture of what those are and where they fit in the overall system. Our analyses are attempting to learn what combination of strategies has the greatest leverage for delivering both short- and long-term health effects. Bobby Milstein (August 26, 2004)Syndemics Prevention NetworkIn its current form, the diabetes system model can test over 10 classes of intervention policies. Here is a simplified picture of what those are and where they fit in the overall system. Our analyses are attempting to learn what combination of strategies has the greatest leverage for delivering both short- and long-term health effects. Bobby Milstein (August 26, 2004)Syndemics Prevention NetworkIn its current form, the diabetes system model can test over 10 classes of intervention policies. Here is a simplified picture of what those are and where they fit in the overall system. Our analyses are attempting to learn what combination of strategies has the greatest leverage for delivering both short- and long-term health effects. Bobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkIn its current form, the diabetes system model can test over 10 classes of intervention policies. Here is a simplified picture of what those are and where they fit in the overall system. Our analyses are attempting to learn what combination of strategies has the greatest leverage for delivering both short- and long-term health effects. Bobby Milstein (August 26, 2004)Syndemics Prevention NetworkThey asked us to use our model to analyze the internal consistency (and, therefore, effectiveness) of the CDCs long term (10 year goals), which are created by a committee of public health leaders. These goals drive tens of millions of dollars of investment in diabetes all federal $ is tied to meeting them. We focused on the goals for the diagnosed prevalence fraction which is the number of people with diagnosed diabetes per thousand adults (2 bottom right stocks in the diagram Jack showed). This is an indexed graph 1 is the 2000 value. *** In 1990, public health leaders aimed for an 11% decrease by 2000. In the interim, they succeeded with short term, proximal results with many of their programs.*** They got a 33% increase. *** Then, in 2000, the committee created new goals for 2010, and aimed for a 38% reduction. (Note that these people are under great pressure to anticipate gains.).*** Like you saw earlier, if there is no continued progress, we anticipate a 24% increase. This supports how incredibly ambitious the 2010 goal was. The momentum in the system is driving the prevalence up THAT much. We ran policy tests for 7 other programmatic goals they have detection, care, health care access, education but here have focused on the one goal that has the most possibility of reducing prevalence: primary prevention that reduces onset of diabetes. So what if this decade brought a revolution in identifying people with Prediabetes and on the cusp of getting prediabetes and leading them to diet, exercise, and, in some cases, take drugs so that the rate of onset drops an outrageous amount -- 29%??*** Prevalence grows and levels, with a net 10% decrease.

The Healthy People 2000 team set a goal to make an 11% reduction in diagnosed diabetes prevalence between 1990 and 2000.[1] During the 90s, public health and clinical professionals succeeded at increasing rates of diagnosis and improving quality of care. At the same time, there was a large influx of new diabetes cases. Between 1990 and 2000 the prevalence of those diagnosed with diabetes per thousand adults increased 33%.[2] Healthy People 2000 Final Review reported that diagnosed prevalence moved away from target by 367% (Healthy People 2000). [1] Healthy People 2000 Final Review. Baseline was 28 per 1000 in 1986-1988 (not per adult pop, but total pop). 1990 value was 26. Target for 2000 was 25%. (28-25/28= 11% decrease).[2] If we look at NHIS Dx diab % of 18+ popn, 1990=3.6% and 2000=5.7%, so that is a 58% increase. However, much of that increase is due to the change in the survey in 1997. The adjusted number for 1990 is 4.3%.So the adjustedincrease in Dx diab 1990-2000 is from 4.3% to 5.7%, a 33% increase. Note that we are using NHIS data here. Different from the HP data that was used, but ought to be comparable at percentage change level.Bobby Milstein (August 26, 2004)Syndemics Prevention Network

Bobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkRealistic understanding of causal dynamics (actions, reactions, accumulations, delays)Justifiable goals (numerical targets, life-course implications, timing)Criteria for choosing actions and impetus for implementing them (within the health sector and beyond)

Bobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkRealistic understanding of causal dynamics (actions, reactions, accumulations, delays)Justifiable goals (numerical targets, life-course implications, timing)Criteria for choosing actions and impetus for implementing them (within the health sector and beyond)

Bobby Milstein (August 26, 2004)Syndemics Prevention NetworkRelated References: Homer J, Hirsch G. System dynamics modeling for public health: background and opportunities. American Journal of Public Health under review.Homer JB. Why we iterate: scientific modeling in theory and practice. System Dynamics Review 1996;12(1):1-19.Sterman JD. Business dynamics: systems thinking and modeling for a complex world. Boston, MA: Irwin McGraw-Hill, 2000.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, 2005.

Bobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkRealistic understanding of causal dynamics (actions, reactions, accumulations, delays)Justifiable goals (numerical targets, life-course implications, timing)Criteria for choosing actions and impetus for implementing them (within the health sector and beyond)

Bobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)Syndemics Prevention NetworkBobby Milstein (August 26, 2004)