Bringing Evidence to Practice in Navigating Health System Dynamics

Syndemics

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Bringing Evidence to Practice in Navigating Health System Dynamics

Public Health Association of AustraliaCanberra

September 29, 2009

Bobby MilsteinSyndemics Prevention Network

U.S. Centers for Disease Control and [email protected]

http://www.cdc.gov/syndemics

The findings and conclusions in this article are those of the authors and do not necessarily represent the official position of

the Centers for Disease Control and Prevention.

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The Scope and Quality of Evidence/Recommendations for Health Action is Expanding…Very Quickly

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Enacting Recommendations Raises Many Questions

What to do first (or together)? 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

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

System Dynamics ModelingDynamic Modeling for Complex Policy Environments

Good at Capturing

• Differences between short- and long-term consequences

• Time delays (e.g., asymptomatic periods, time to detect/respond)

• Accumulations (e.g., prevalences, resources, attitudes)

• Behavioral feedback (reactions by various actors)

• Nonlinear causal relationships (e.g., threshold effects, saturation effects)

• Differences in goals/values among stakeholders

Origins • Jay Forrester, MIT, Industrial Dynamics, 1961

(“One of the seminal books of the last 20 years.”-- NY Times)• Population health applications starting mid-1970s

Background References on System Dynamics Modeling

Forrester JW. Industrial Dynamics. Cambridge, MA: MIT Press; 1961.

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, Hirsch GB. System dynamics modeling for public health: background and opportunities. AJPH 2006;96(3):452-458.

Homer JB, Oliva R. Maps and models in system dynamics: a response to Coyle. System Dynamics Review 2001;17(4):347-355.

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, 2007. Available at <http://obssr.od.nih.gov/news_and_events/lectures_and_seminars/systems_symposia_series/system_symposium_four/systems_symposium_four.aspx>.

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>

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A Model Is…An inexact representation 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>

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Simulation and “Double-Loop Learning”

• Unknown structure • Dynamic complexity• Time delays• Impossible experiments

Real World

InformationFeedback

Decisions

MentalModels

Strategy, Structure,Decision Rules

• Selected• Missing• Delayed• Biased• Ambiguous

• Implementation• Game playing• Inconsistency• Short-term focus

• 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):291-330.

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

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2000 2001 2002 2003 2004 2005 2006 2007 2008

Selected Examples from CDC’s Growing Portfolio of Simulation Studies for Health System Change

SD Identified as a

Promising Methodology for Health System

Change Ventures

Upstream-Downstream

Dynamics

Neighborhood Transformation

Game

National Health Economics & Reform

HealthBound Policy Simulation Game

Overall Health Protection Enterprise

Diabetes Action Labs

Obesity Overthe Lifecourse

Fetal & Infant Health

Syndemics Modeling

Local Context for Chronic Diseases

(PRISM)

Selected Health Priority Areas

Centers for Disease Control and Prevention. Dynamic models. Syndemics Prevention Network, 2009. Available at http://www2.cdc.gov/syndemics/models.htm

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Re-Directing the Course of ChangeQuestions of Social Navigation

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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Time Series Models

Describe trends

Multivariate Statistical Models

Identify historical trend drivers and correlates

Patterns

Structure

Events

Increasing:

• Depth of causal theory

• Degrees of uncertainty

• Robustness for longer-term projection

• Value for developing policy insights

• Leverage for change

Increasing:

• Depth of causal theory

• Degrees of uncertainty

• Robustness for longer-term projection

• Value for developing policy insights

• Leverage for changeDynamic Simulation Models

Anticipate new trends, learn about policy consequences,

and set justifiable goals

Types of Models for Policy Planning & Evaluation

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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 (now version 2i) and it 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 Raspberry, 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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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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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

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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





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Weight lossservices for obese

JUNK FOODTax, restrict sales/mktg,

counter-marketing

Sodium in food

Trans fatIn food

HEART-HEALTHYFOOD

Access, promotionCardiovascular

events



SmokingObesity


-Diabetes


Secondhandsmoke


Physicalinactivity

PHYSICAL ACTIVITYAccess, promotion,

social support,school recs, childcare recs

Distress

Help servicesfor distress

Excesscalorie diet


Excesssodium diet

Reduce airpollution

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

Tax, restrict sales/mktg,counter-marketing,

quit services

TOBACCOBan smoking in

public places

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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Mapping Information SourcesPhysical Activity Pathway







Ban smoking inpublic places

SmokingObesity


-Diabetes


Secondhandsmoke

Junk food options (N=3):Tax, restrict sales/mktg,

counter-market

Physical activity options (N=5):Access, promotion, social

support, school requirements,childcare requirements


Physicalinactivity

Distress


Sodium in food

Trans fat infood

Excesscalorie diet


Excesssodium diet

Reduce airpollution

Tobacco options (N=4):Tax, restrict sales/mktg,

counter-market, quitservices

Chronic Disorders





Heart-healthy foodoptions (N=2):

Access, promotion

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SmokingObesity


-Diabetes


Secondhandsmoke


counter-market




Physicalinactivity

Distress


Sodium in food

Trans fat infood

Excesscalorie diet


Excesssodium diet

Reduce airpollution



Chronic Disorders






Access, promotion


Physical Inactivity Prevalence52% - 65% (by age)

• NHANES, BRFSS, & YRBS • Troiano RP, et al. Med Sci Sports Ex 2008;

40(1):181-188.

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SmokingObesity


-Diabetes


Secondhandsmoke


counter-market




Physicalinactivity

Distress


Sodium in food

Trans fat infood

Excesscalorie diet


Excesssodium diet

Reduce airpollution



Chronic Disorders






Access, promotion


Effective Interventions Increase PA by 40-55%

(by age and strategy)

• Kahn EB, et al. Am J Prev Med 2002; 22:S73-102.

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SmokingObesity


-Diabetes


Secondhandsmoke


counter-market




Physicalinactivity

Distress


Sodium in food

Trans fat infood

Excesscalorie diet


Excesssodium diet

Reduce airpollution



Chronic Disorders






Access, promotion


RR for obesity onset = 2.6

• Haapanen N, et al. Intl J Obesity 1997: 21:288-296

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SmokingObesity


-Diabetes


Secondhandsmoke


counter-market




Physicalinactivity

Distress


Sodium in food

Trans fat infood

Excesscalorie diet


Excesssodium diet

Reduce airpollution



Chronic Disorders






Access, promotion


RR for distress = 1.3

• Netz Y, Wu M-J, et al. Psyh Aging 2005; 20(2):272-284. .

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SmokingObesity


-Diabetes


Secondhandsmoke


counter-market




Physicalinactivity

Distress


Sodium in food

Trans fat infood

Excesscalorie diet


Excesssodium diet

Reduce airpollution



Chronic Disorders






Access, promotion


RR of inactivity if distressed: 1.6

• Whooley MA, et al. JAMA 2008; 300(20):2379-2388.

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SmokingObesity


-Diabetes


Secondhandsmoke


counter-market




Physicalinactivity

Distress


Sodium in food

Trans fat infood

Excesscalorie diet


Excesssodium diet

Reduce airpollution



Chronic Disorders






Access, promotion


RR for High BP = 1.15RR for High Cholesterol = 1.4

RR for Diabetes = 1.4

• Ann Med 1991;23(3):319–327.• Intl J Epidemiology 1997; 26(4):739-747.

• Canadian Med Assoc J 2000;163(11):1435-1440.• Lancet 1991; 339:778-783.

• Arch Intern Med 2001; 161:1542-1548.

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SmokingObesity


-Diabetes


Secondhandsmoke


counter-market




Physicalinactivity

Distress


Sodium in food

Trans fat infood

Excesscalorie diet


Excesssodium diet

Reduce airpollution



Chronic Disorders






Access, promotion


Modification of theFramingham Risk Calculator

• Ex Rev Pharm Out Res 2006;6(4):417-24.• Am Heart J 1991;121(1 Pt 2):293-8.

• Am Heart J 2007;153(5):722-31, 31 e1-8.• JAMA 2001;286(2):180-7.

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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

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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

30

22.5

15

7.5

0

1990 2000 2010 2020 2030 2040

8

6

4

2

0

1990 2000 2010 2020 2030 2040

Draft Model Output Draft Model Output


**if all risk factors=0**

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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)

30 M

22.5 M

15 M

7.5 M

0

1990 2000 2010 2020 2030 2040

6,000

4,500

3,000

1,500

01990 2000 2010 2020 2030 2040

**if all risk factors=0**


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How are Practitioners Using PRISM?

A Few Local Versions

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

Planning• Engage a wider circle of stakeholders

• Situate silos 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)

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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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

• Narrower boundaries tend to be more empirically grounded

• Wider boundaries may legitimize “invisible” processes

• 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

Interactive ModelingBuilds Foresight, Experience, and Motivation to Act

Experiential Learning“Wayfinding”

Expert Recommendations

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

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Imperatives for Protecting Health

Gerberding JL. Protecting health: the new research imperative. Journal of the American Medical Association 2005;294(11):1403-1406.

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

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Epi·demic

• The term epidemic is an ancient word signifying a kind of relationship wherein something is put upon the people

• Epidemiology first appeared just over a century ago (in 1873), in the title of J.P. Parkin's book "Epidemiology, or the Remote Cause of Epidemic Diseases“

• Ever since then, the conditions that cause health problems have increasingly become matters of public concern and public work

Elliot G. Twentieth century book of the dead. New York,: C. Scribner, 1972.

Martin PM, Martin-Granel E. 2,500-year evolution of the term epidemic. Emerging Infectious Diseases 2006. Available from http://www.cdc.gov/ncidod/EID/vol12no06/05-1263.htm

National Institutes of Health. A Short History of the National Institutes of Health. Bethesda, MD: 2006. Available from http://history.nih.gov/exhibits/history/

Parkin J. Epidemiology; or the remote cause of epidemic diseases in the animal and the vegetable creation. London: J and A Churchill, 1873.

A representation of the cholera epidemic of the nineteenth century.Source: NIH

“The pioneers of public health did not change nature, or men, but adjusted the active relationship of men to certain aspects of nature so that the relationship became one of watchful and healthy respect.”

-- Gil Elliot

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Syn·demic

• The term syndemic, first used in 1992, strips away the idea that illnesses originate from extraordinary or supernatural forces and places the responsibility for affliction squarely within the public arena

• It acknowledges the importance of relationships and signals a commitment to understanding population health as a fragile, dynamic state requiring continual effort to maintain and one that is imperiled when social and physical forces operate in harmful ways

Confounding

Connecting*

Synergism

Syndemic

Events

System

Co-occurring

* Includes several forms of connection or inter-connection such as synergy, intertwining, intersecting, and overlapping

Each member of society is a system citizen in the literal sense of

being a (potential) agent of change in the systems of which he or she is a part

Each member of society is a system citizen in the literal sense of

being a (potential) agent of change in the systems of which he or she is a part

Findings from a Bibliometric Analysis of Epidemiology & Public Health Literature

1970-2005

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Modern health policy—and evaluation—are becoming more…

• Inter-connected (ecological, multi-causal, dynamic, systems-oriented) Concerned more with leverage than control

• Public (broad-based, partner-oriented, citizen-led, inter-sector, democratic) Concerned with many interests and mutual-accountability

• Questioning (evaluative, reflexive, critical, practical)Concerned with creating and protecting values like health, equity,dignity, security, satisfaction, justice, wealth, and freedom in both means and ends

A Field in Transition

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Syndemic Orientation

Expanding Public Health Science“Public health imagination involves using science to expand the

boundaries of what is possible.”

-- Michael Resnick

EpidemicOrientation

People and Problemsin Places Over Time

BoundaryCritique

Governing Dynamics

Ca

us

al

Ma

pp

ing

Plausible Futures

DynamicModeling

Navigational Freedoms

De

mo

cra

tic

Pu

bli

c W

ork

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>.

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Evidence and Action Both Emerge fromBroader Philosophies of Science and Society

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• Begins with a response to a perplexity or injustice

in the world• Learning through action and reflection

(even simulated action can be illuminating)• Asks, “How does this make a difference?”

Positivism • Begins with a theory about the world• Learning through observation and falsification• Asks, “Is this theory true?”

These are conceptual, methodological, and moral orientations, which shape how we think, how we act, how we learn, and what we value

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For Further Informationhttp://www.cdc.gov/syndemics

Bringing Evidence to Practice in Navigating Health System Dynamics

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