syndemics prevention network systems science and community change: frameworks, methods, and goals...

Syndemics

Prevention Network

Systems Science and Community Change:Frameworks, Methods, and Goals

Advancing the Science of Community InterventionChicago, IL

October 26, 2009

Robin MillerMichigan State University

[email protected]

The findings and conclusions in this presentation are those of the author and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

Bobby MilsteinSyndemics Prevention Network

Centers for Disease Control and [email protected]

http://www.cdc.gov/syndemics

Syndemics

Prevention Network

Systems Thinking

Systems Thinking

Systems Thinking

Systems Thinking is…

Thinking

SystemsThinking

Thinking

SystemsThinking

A Typeof Thinking

Thinking About

Systems

Thinking About

Systems&

Systems Approaches

/Methods

Application of

Systems Approaches

/Methods

The thinkingthat

surroundsApproach or

Field X The thinkingthat is a

subset of Approach or

Field X

SystemsApproaches& Methods

SA 1

M1

M2

SA 2M1

M2M3

SA…

M1

ApproachOr Field X

ApproachOr Field X

Systems Thinking

Systems

partswholes

relationships

Systems

partswholes

relationships

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Prevention Network

Prevention Impacts Simulation Model (PRISM)• Represents multiple interacting risks and interventions for heart

disease, stroke, and related chronic diseases: medical, behavioral, social, environmental

• Begun in 2007, the model has expanded in scope but remains a work-in-progress

• Engaged subject matter experts from 12 organizations (N~30), and 100s of policy officials, including a deep collaboration with local leaders in Austin, Texas

• Integrates best available information in a single testable model to support prospective planning and evaluation

• Explores the likely effects of “local interventions” (i.e., changes in local options/exposures/services that affect behavior and/or health status)

– To what extent might adverse events and costs be reduced?

– How can policymakers balance interventions for best effect with limited resources?

References: Homer J, Milstein B, Wile K, Trogdon J, Huang P, Labarthe D, Orenstein D. Simulating and evaluating local interventions to improve cardiovascular health. Preventing Chronic Disease, 2009 (in press).

Homer J, Milstein B, Wile K, Pratibhu P, Farris R, Orenstein D. Modeling the local dynamics of cardiovascular health: risk factors, context, and capacity. Preventing Chronic Disease 2008;5(2). Available at <http://www.cdc.gov/pcd/issues/2008/apr/07_0230.htm

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Prevention Impacts Simulation Model (PRISM)Core Contributors

System Dynamics Modelers• Jack Homer• Kris Wile

Economists• Justin Trogdon• Amanda Honeycutt

Project Coordinators• Bobby Milstein• Diane Orenstein

CDC partnered 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.`

CDC partnered 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.`

CDC & NIH Subject Matter ExpertsBishwa Adhikari, Nicole Blair, Kristen Betts, Peter Briss, David Buchner, Susan Carlson, Michele Casper, Tom Chapel, Janet Collins, Lawton Cooper, Michael Dalmat, Alyssa Easton, Joyce Essien, Roseanne Farris, Larry Fine, Janet Fulton, Deb Galuska, Kathy Gallagher, Judy Hannon, Jan Jernigan, Darwin Labarthe, Deb Lubar, Patty Mabry, Ann Malarcher, Michele Maynard, Marilyn Metzler, Rob Merritt, Latetia Moore, Barbara Park, Terry Pechacek, Catherine Rasberry, Michael Schooley, Nancy Williams, Nancy Watkins, Howell Wechsler

External Subject Matter ExpertsCynthia Batcher, Margaret Casey, Phil Huang, Kristen Lich, Karina Loyo, David Matchar, Ella Pugo, John Robitscher, Rick Schwertfeger, Adolpho Valadez

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How are Practitioners Using PRISM?

Planning• Engage a wider circle of stakeholders

• Situate specialties within a system

• Prioritize interventions (given tradeoffs/synergies)

• Set plausible short- and long-term goals

Evaluating

• Trace intervention effects through direct, secondary, and summary measures

• Extend the time horizon for evaluative inquiry

• Establish novel referents for comparison (self-referential counter-factuals)

Several Local Versions

• Re-calibrated to areas with different demographics, histories, and current conditions

Users (~500)Customized Versions

• East Austin, Texas

• Mississippi Delta

• New Zealand Ministry of Health

• U.S. economic stimulus health initiative

Nat’l & State Stakeholders

• CDC Staff

• National Association of Chronic Disease Directors

• Directors of Public Health Education

• National Institutes of Health (NHLBI, OBSSR)

Users (~500)Customized Versions

• East Austin, Texas

• Mississippi Delta

• New Zealand Ministry of Health

• U.S. economic stimulus health initiative

Nat’l & State Stakeholders

• CDC Staff

• National Association of Chronic Disease Directors

• Directors of Public Health Education

• National Institutes of Health (NHLBI, OBSSR)

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Prevention Network

Re-Directing the Course of ChangeQuestions for Navigating Health System Dynamics

Prevalence of Diagnosed Diabetes, United States

0

10

20

30

40

1980 1990 2000 2010 2020 2030 2040 2050

Mill

ion

pe

op

le

HistoricalData

Markov Model Constants• Incidence rates (%/yr)• Death rates (%/yr)• Diagnosed fractions(Based on year 2000 data, per demographic segment)

Honeycutt A, Boyle J, Broglio K, Thompson T, Hoerger T, Geiss L, Narayan K. A dynamic markov model for forecasting diabetes prevalence in the United States through 2050. Health Care Management Science 2003;6:155-164.

Jones AP, Homer JB, Murphy DL, Essien JDK, Milstein B, Seville DA. Understanding diabetes population dynamics through simulation modeling and experimentation. American Journal of Public Health 2006;96(3):488-494.

Markov Forecasting Model

Trend is not destiny

How?

Why?

Where?

Who?

What?

Simulation Experiments

in Action Labs

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Prevention Network

Time Series Models

Describe trends

Multivariate Statistical 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

• Leverage for change

Increasing:

• Depth of causal theory

• Robustness for longer-term projection

• Value for developing policy insights

• Degrees of uncertainty

• Leverage for changeDynamic Simulation Models

Anticipate new trends, learn about policy

consequences, and set justifiable goals

Selected Models for Policy Planning & Evaluation

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Supporting Multi-Stakeholder Planning and Action

Which interventions to prioritize? Likely consequences?

Costs? Time-frame?

How to catalyze action?

Dynamic Hypothesis (Causal Structure)

Cardiovascularevents

Air pollutionexposure(PM 2.5)

Quality of acute andrehab care for

cardiovascular events

Use of qualitypreventive care

Use of weightloss services

by obese

Use of help servicesfor distress

Bans on smokingin public places

SmokingObesity

-Hypertension-High cholesterol

-Diabetes

Uncontrolledchronic disorders

Secondhandsmoke

Junk foodinterventions

(N=4)

Physical activityinterventions

(N=6)

Heart-unhealthy diet

Physicalinactivity Distress

Efforts to promoteprovision and use of

quality preventive care

Sodiumreduction

Trans fatreduction

Excesscalorie diet

Fruit &vegetable

interventions(N=3)

CVD deaths,disability,and costs

Excesssodium diet

Air pollutionreduction

Tobaccointerventions

(N=4)

Chronic Disorders

Other deaths and costsattributable to risk factors,

and costs of risk factormanagement

Total consequencecosts

System

Plausible Futures (Policy Experiments)

Dynamics

Years of Life Lost40 M

30 M

20 M

10 M

01990 2000 2010 2020 2030 2040

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

Sterman JD. Learning from evidence in a complex world. American Journal of Public Health 2006;96(3):505-514.

Homer JB. Why we iterate: scientific modeling in theory and practice. System Dynamics Review 1996;12(1):1-19.

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Prevention Network

Models are…Inexact representations of the real thing They help 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

Sterman JD. All models are wrong: reflections on becoming a systems scientist. System Dynamics Review 2002;18(4):501-531. Available at <http://web.mit.edu/jsterman/www/All_Models.html>

Sterman J. A sketpic's guide to computer models. In: Barney GO, editor. Managing a Nation: the Microcomputer Software Catalog. Boulder, CO: Westview Press; 1991. p. 209-229. <http://web.mit.edu/jsterman/www/Skeptic%27s_Guide.html>

Over-relianceOver-reliance Under-relianceUnder-reliance

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Tobacco

Air Pollution

Stress

Healthy Food

Sodium

Trans fat

PhysicalActivity

WeightLoss

MentalHealthServices

PrimaryCare

Emergency & Rehab Care

BloodPressure

Cholesterol

ObesityHeart Disease & Stroke

Cancer

Health CareCost

Diabetes

The Popular (and Professional) View of Chronic Disease Challenges is Largely One Headline after Another

Alcohol

Sleep Arthritis

JunkFood

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Prevention Network

PRISM Situates Multiple Medical, Behavioral, and Environmental Factors into a Single Set of Causal Pathways




SmokingObesity


-Diabetes


Secondhandsmoke


Physicalinactivity

Distress

Excesscalorie diet


Excesssodium diet

Chronic Disorders

Trans fatconsumption

Syndemics

Prevention Network

PRISM Situates Multiple Medical, Behavioral, and Environmental Factors into a Single Set of Causal Pathways




SmokingObesity


-Diabetes


Secondhandsmoke


Physicalinactivity

Distress

Excesscalorie diet


Excesssodium diet

Chronic Disorders





Syndemics

Prevention Network




SmokingObesity


-Diabetes


Secondhandsmoke

unhealthy diet

Physicalinactivity

PHYSICAL ACTIVITYAccess, promotion,

school and childcare requirements

Distress

Help servicesfor distress

Excesscalorie diet


JUNK FOODTaxes, counter-mkting

Sodium in food

Trans fatin food

HEART-HEALTHYFOOD

Access, promotion

Heart-

Excesssodium diet

Air pollution

Chronic Disorders




Quality of acuteand rehab care

Quality and use ofpreventive care


Local Context for TobaccoLocal Context for DietLocal Context for Physical ActivityLocal Context for Air PollutionLocal Context for Health Care ServicesLocal Context for Weight Loss ServicesLocal Context for Mental Health Services

PRISM Also Includes Frontiers for Social Action

Taxes, counter-mkting,quit services

TOBACCO

public placesSmoking in

services for obeseWeight loss

Primary Information Sources• Census

– Population, deaths, births, net immigration

• American Heart Association & NIH statistical reports

– Cardiovascular events, deaths, and prevalence

• National Health and Nutrition Examination Survey (NHANES)

– Risk factor prevalence by age and sex

– Diagnosis and control of hypertension, high cholesterol, and diabetes

• Medical Examination Panel (MEPS), National Health Interview (NHIS), Behavioral Risk Factor Surveillance System (BRFSS), Youth Risk Behavior Survey (YRBS)

– Medical and productivity costs attributable to risk factors

– Prevalence of distress in non-CVD and post-CVD populations

– Primary care utilization

– Extent of physical activity

• Research literature

– CVD risk calculator (Framingham)

– Relative risks from secondhand smoke, air pollution, obesity, poor diet, inactivity, distress

– Quality of diet (USDA Healthy Eating Index)

– Medical and productivity costs of cardiovascular events

– Effect sizes of behavioral interventions

• Expert judgment

– Effect sizes of behavioral interventions

Uncertainties are assessed through sensitivity testing

Uncertainties are assessed through sensitivity testing

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PRISM Simulates 24 Intervention Strategies

Health Domains• Air pollution

• Diet

• Distress

• Physical activity

• Primary care

• Tobacco

• Weight loss services

Intervention Types• Availability

• Price (tax/subsidy)

• Promotion, counter-marketing

• Bans, regulations, restrictions

• Social supports

• Individual services

Custom Settings for Each Intervention

• Ramp-in time

• Duration

• Direct effect size (within a plausible range)

Custom Settings for Each Intervention

• Ramp-in time

• Duration

• Direct effect size (within a plausible range)

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Base Case & Illustrative Intervention Scenarios

Base Case (a simple scenario for comparison)

• Assume no further changes in the contextual factors that affect risk factor prevalences

• Any changes in prevalences after 2004 are due to “inflow/outflow” adjustment process and population aging

• Result: Past trends level off after 2004, after which results reflect only slow adjustments in risk factors

– Increasing obesity, high BP, and diabetes

– Decreasing smoking

– Increases in risk factors and population aging lead to eventual rebound in attributable deaths

Example Intervention Scenarios (max plausible effects, sustained)

• Four clusters of interventions layered to show their partial contribution and combined effects

• Services (health care, weight loss, smoking quit, distress)+ Diet & Physical Activity+ Tobacco + Air Pollution & Sodium & Trans fat

Syndemics

Prevention Network 17

Illustrative Intervention Scenarios: Maximum Plausible StrengthIndividual Services + Diet & PA + Tobacco + Air Pollution & Sodium & Trans

fat

Work in Progress, Please do no cite or distribute.

Smoking Prevalence (Adults) Obesity Prevalence (Adults)

Cardiovascular Events per 1000(CHD, Stroke, CHF, PAD)

Deaths from All Risk Factors per 1,000

0.4

0.3

0.2

0.1

0

1990 2000 2010 2020 2030 2040

0.4

0.3

0.2

0.1

0

1990 2000 2010 2020 2030 2040

40

30

20

10

0

1990 2000 2010 2020 2030 2040

8

6

4

2

0

1990 2000 2010 2020 2030 2040

**if all risk factors=0**

Syndemics

Prevention Network 18

Illustrative Intervention Scenarios: Maximum Plausible StrengthIndividual Services + Diet & PA + Tobacco + Air Pollution & Sodium & Trans

fat

Work in Progress, Please do no cite or distribute.

Years of Life Lost from Attributable Deaths

Consequence Costs per Capita(medical costs + productivity)

**if all risk factors=0**

200 M

175 M

150 M

125 M

100 M

1990 2000 2010 2020 2030 2040

6,000

4,500

3,000

1,500

01990 2000 2010 2020 2030 2040

Interactive ModelingBuilds Foresight, Experience, and Motivation to Act

Experiential Learning“Wayfinding”

Expert Recommendations

Milstein B. Hygeia's constellation: navigating health futures in a dynamic and democratic world. Atlanta, GA: Syndemics Prevention Network, Centers for Disease Control and Prevention; April 15, 2008. <http://www.cdc.gov/syndemics/monograph/index.htm>.

http://www.cdc.gov/index.htm

Syndemics

Prevention Network

Conversations Around the Model

Other health

priorities

Available information

Health inequities

Local interventionopportunities and costs

Communitythemes and strengths

Political willStakeholder

relationships

• What’s in the model does not define what’s in the room

• Simulations intentionally raise questions to spark broader thinking and judgment

• Boundary judgments follow from the intended purpose and users

SYSTEMDYNAMICS MODEL

STRATEGICPRIORITIES



Quality of acute andrehab care for

cardiovascular events


Use of weightloss services

by obese

Use of help servicesfor distress

Bans on smokingin public places

SmokingObesity


-Diabetes


Secondhandsmoke

Junk foodinterventions

(N=4)

Physical activityinterventions

(N=6)


Physicalinactivity Distress

Efforts to promoteprovision and use of

quality preventive care

Sodiumreduction

Trans fatreduction

Excesscalorie diet

Fruit &vegetable

interventions(N=3)


Excesssodium diet

Air pollutionreduction

Tobaccointerventions

(N=4)

Chronic Disorders




Researchagenda

Boundary CritiqueEqualizing Experts and Ordinary Citizens

• “Professional expertise does not protect against the need for making boundary judgements…nor does it provide an objective basis for defining boundary judgements. It’s exactly the other way round: boundary judgements stand for the inevitable selectivity and thus partiality of our propositions.

• It follows that experts cannot justify their boundary judgements (as against those of ordinary citizens) by referring to an advantage of theoretical knowledge and expertise.

• When it comes to the problem of boundary judgements, experts have no natural advantage of competence over lay people.”

Ulrich W. Reflective practice in the civil society: the contribution of critically systemic thinking. Reflective Practice 2000;1(2):247-268.

Ulrich W. Boundary critique. In: Daellenbach HG, Flood RL, editors. The Informed Student Guide to Management Science. London: Thomson; 2002. p. 41-42. <http://www.geocities.com/csh_home/downloads/ulrich_2002a.pdf>.

Ulrich W. Reflective practice in the civil society: the contribution of critically systemic thinking. Reflective Practice 2000;1(2):247-268. http://www.geocities.com/csh_home/downloads/ulrich_2000a.pdf

Midgley G. The sacred and profane in critical systems thinking. Systems Practice 1992;5:5-16.

-- Werner Ulrich


Alternative Scientific Traditions

Shook J. The pragmatism cybrary. 2006. Available at <http://www.pragmatism.org/>.

Addams J. Democracy and social ethics. Urbana, IL: University of Illinois Press, 2002.

West C. The American evasion of philosophy: a genealogy of pragmatism. Madison, WI: University of Wisconsin Press, 1989.

Pragmatism• Presumes engagement (insider consciousness)• Begins with a response to a perplexity or injustice in the world• Learning through action and reflection (even simulated action)• Asks, “Under what conditions can we make a difference?”• Produces working relationships that shape public life

Positivism • Presumes objectivity (onlooker consciousness)• Begins with a theory about the world• Learning through observation and falsification• Asks, “Is this theory true?”• Produces knowledge to serve those in need

These are not theories. They are different orientations, which shape how we think, how we act, and what we value.

These are not theories. They are different orientations, which shape how we think, how we act, and what we value.


Practical Options in Causal Modeling

Detail (Disaggregation)

Scope (Breadth)

Low High

Low

High

Simplistic

Impractical

Focused

Expansive

Too hard to verify, modify, and understand


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Prevention Network

Modeling 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

– Identify key uncertainties to address in intervention studies

Forrester JW. Industrial Dynamics (Chapter 11: Aggregation of Variables). Cambridge, MA: MIT Press, 1961.

“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; 1997. p. 21-40. <http://www.pscs.umich.edu/pub/papers/AdvancingArtofSim.pdf>.

Sterman JD. Business Dynamics: Systems Thinking and Modeling for a Complex World. Boston, MA: Irwin McGraw-Hill, 2000.

Simulation ExperimentsOpen a Third Branch of Science

How?

Where?

0

10

20

30

40

50

1960-62 1971-74 1976-80 1988-94 1999-2002

Prevalence of Obese Adults, United States

Why?

Data Source: NHANES 20202010

Who?

What?

“In [dynamically complex] circumstances simulation becomes the only reliable way to test a hypothesis and evaluate the likely effects of policies."

-- John Sterman


Discussion• What can be done to move beyond the loose

appreciation of system concepts to the use of formal methods in real-world, high-stakes situations for improving the success and sustainability of interventions?

• In what ways might a systems approach alter…

– How problems/opportunities are defined?

– How potential solutions are identified and selected?

– How intervention options are evaluated?

– How applied science and policy action are understood?

– Who makes these decisions?

• Others…?


Discussion

For Further Information

CDC Syndemics Prevention Networkhttp://www.cdc.gov/syndemics

NIH Office of Behavioral and Social Sciences Research http://obssr.od.nih.gov/scientific_areas/methodology/systems_science/index.aspx


syndemics prevention network systems science and community change: frameworks, methods, and goals...

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