syndemics prevention network finding the foresight and strength to transform health systems...

Syndemics

Prevention Network

Finding the Foresight and Strength to Transform Health Systems

Queensland GovernmentBrisbane

October 7, 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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Wickelgren I. How the brain 'sees' borders. Science 1992;256(5063):1520-1521.

How Many Triangles Do You See?

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The Weight of Boundary Judgments

Forrester 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 <http://www.sustainabilityinstitute.org/pubs/Leverage_Points.pdf>.

Richardson GP. Feedback thought in social science and systems theory. Philadelphia, PA: University of Pennsylvania Press, 1991.

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

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Implications for Policy Planning and Evaluation

Insights from the Overview Effect

• Maintain a particular analytic distance

• Not too close to the details, but not too far as be insensitive to internal pressures

• Potential to anticipate temporal patterns (e.g., better before worse)

• Structure determines behavior

• Potential to avoid scapegoating or lionizing

Richardson GP. Feedback thought in social science and systems theory. Philadelphia, PA: University of Pennsylvania Press, 1991.

Richmond B. Systems thinking: critical thinking skills for the 1990s and beyond. System Dynamics Review 1993;9(2):113-134. Available at <http://www.clexchange.org/ftp/documents/whyk12sd/Y_1993-05STCriticalThinking.pdf>.

White F. The overview effect: space exploration and human evolution. 2nd ed. Reston VA: American Institute of Aeronautics and Astronautics, 1998.

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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 review ofpublic health literature, 1970-2005

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“Public health [work] is what we, as a society, do collectively to assure the conditions in which

[all] people can be healthy.”

-- Institute of Medicine

Institute of Medicine. The future of public health. Washington, D.C.: National Academy Press, 1988.

Institute of Medicine. The future of the public's health in the 21th century. Washington D.C.: National Academy Press, 2002.

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What does it mean to organize science and society around the goal of assuring more healthful and equitable conditions?

• Constantly in flux

• Politically contested

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Crafting a Place-based View

Thompson N. Reflections on voyaging and home. Polynesian Voyaging Society, 2001. Accessed July 18 at <http://leahi.kcc.hawaii.edu/org/pvs/malama/voyaginghome.html>.

Milstein B. Hygeia's constellation: navigating health futures in a dynamic and democratic world. Doctoral dissertation. Cincinnati, OH: Union Institute and University. November, 2006.

Where we want to go?

How do we prepare to get there?

Where do you want your children to live?

Where you do want to live?

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Cultivating a Place-based View

"How do you know," I asked, "that in twenty years

those things that you consider special are still going to

be here?" At first they all raised their hands but when

they really digested the question every single one of

them put their hands down. In the end, there was not a

single hand up. No one could answer that question.

It was the most uncomfortable moment of silence that I

can remember…That was the defining moment for me.

I recognized that I have to participate in answering that

question otherwise I am not taking responsibility for

the place I love and the people I love.”

-- Nainoa Thompson

Thompson N. Reflections on voyaging and home. Polynesian Voyaging Society, 2001. Accessed July 18 at <http://leahi.kcc.hawaii.edu/org/pvs/malama/voyaginghome.html>.

Milstein B. Hygeia's constellation: navigating health futures in a dynamic and democratic world. Doctoral dissertation. Cincinnati, OH: Union Institute and University. November, 2006.

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Summers J. Soho: a history of London's most colourful neighborhood. Bloomsbury, London, 1989. p. 117.

“No improvements at all had been

made...open cesspools are still to

be seen...we have all the materials

for a fresh epidemic...the water-

butts were in deep cellars, close to

the undrained cesspool...The

overcrowding appears to increase."

-- The Builder Magazine

Broad Street, One Year Later

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A Complementary Science of Relationships

• Efforts to Reduce Population Health ProblemsProblem, problem solver, response

• Efforts to Organize a System that Assures Healthful Conditions for All Dynamic interaction among multiple problems, problem solvers, and responses

Institute of Medicine. The future of public health. Washington, DC: National Academy Press, 1988.

Institute of Medicine. The future of the public's health in the 21th century. Washington, DC: National Academy Press, 2002.

Bammer G. Integration and implementation sciences: building a new specialisation. Cambridge, MA: The Hauser Center for Nonprofit Organizations, Harvard University 2003.

True innovation occurs when things are put together for the first time that had been separate.

– Arthur Koestler

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Syndemic Orientation

Expanding the Scope of Public Health Work“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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Developing Foresight and Public Strength

How shall we respond to health challenges? 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. 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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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.


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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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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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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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Exploratory Insight Goal SettingLeadership Development

Selected CDC Models of Health System DynamicsAcross a Continuum of Purposes



Upstream-Downstream

Dynamics




Game


Syndemics

Local Context for Chronic Disease

(PRISM)

HealthBoundGame

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HealthBound Get in the Game to Re-direct the U.S. Health System

…In support of Healthiest Nation

Bobby Milstein, PhD, MPHCenters for Disease Control

and [email protected]

Jack Homer, PhDHomer Consulting

[email protected]

Gary Hirsch, MSIndependent [email protected]

The name “HealthBound” is used courtesy of Associates & Wilson, Inc.

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Poised for Transformation…

• America has a national health shortage: we pay the most for health care, yet suffer comparatively poor health, especially among the disadvantaged

• High cost of poor health drives personal bankruptcy and business failure

• Over 75% think the current system needs fundamental change

• Analyses that focus narrowly on parts of the system, without examining connections, often miss the potential for policy resistance

Commission to Build a Healthier America. America is not getting good value for its health dollar. Princeton, NJ: Robert Wood Johnson Foundation 2008. Nolte E, McKee CM. Measuring the health of nations: updating an earlier analysis. Health Affairs 2008; 27(1):58-71.Blendon RJ, Altman DE, Deane C, Benson JM, Brodie M, Buhr T. Health care in the 2008 presidential primaries. NEJM 2008;358(4):414-422. White House. Americans speak on health reform: report on health care community discussions. Washington, DC: HealthReform.gov; March, 2009. <http://www.healthreform.gov/reports/hccd/>Altman DE, Levitt L. The sad history of health care cost containment as told in one chart. Health Affairs 2002;Web Exclusive:hlthaff.w2.83.

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Exploratory Insight Goal SettingLeadership Development

Selected CDC Models of Health System DynamicsAcross a Continuum of Purposes


Homer J, Hirsch G, Milstein B. Chronic illness in a complex health economy: the perils and promises of downstream and upstream reforms. System Dynamics Review 2007;23(2/3):313–343.

Causal diagrams with practical definitions of states, rates, and

interventions

Inflationary trends and self-sustaining tendencies of the

downstream healthcare industry


Upstream-Downstream

Dynamics




Game


Syndemics

Local Context of Chronic Disease Prevention and

Control

HealthBoundGame

Important Structures

EmpiricalData

Creative policies for moving out of an entrenched and unhealthy state

Experiential learning to devise strategies, interpret dynamics, and weigh tradeoffs

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• Cognitive and experiential learning for health leaders• Four simultaneous goals: save lives, improve health,

achieve health equity, and lower health care cost• Intervene without expense, risk, or delay• Not a prediction, but a way for diverse stakeholders

to explore how the health system can change

HealthBound

HealthBound is a Simplified Health System to be Explored Through Game-based Learning

Milstein B, Homer J, Hirsch G. The "HealthBound" policy simulation game: an adventure in US health reform. International System Dynamics Conference; Albuquerque, NM; July 26-30, 2009.

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HealthBound Presents a Navigational ChallengeGet Out of a Deadly, Unhealthy, Inequitable, and Costly Predicament

Starting Values for Mortality, Morbidity, Inequity, Cost (~2003)

Death rate per thousand

Unhealthy days per capitaHealth inequity indexHealthcare spend per capita

8 6

0.2 7,000

4 3

0.1 5,000

0 0 0

3,000

-5 0 5 10 15 20 25

How far can you move

the system?

Deaths

Unhealthy Days

Health Inequity

Healthcare costs

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The U.S. health system is dense

with diverse issues and opportunities

Healthier behaviorsHealthier behaviors

Adherence to care guidelines Adherence to

care guidelines

Insurance coverageInsurance coverage

Insurance overheadInsurance overhead

Socioeconomic disadvantage

Socioeconomic disadvantage

Provider capacityProvider capacity

Reimbursement rates

Reimbursement rates

Extent of care

Extent of care

Provider income

Provider income

Provider efficiencyProvider efficiency

Access to careAccess to care

ER useER use

Safer environments

Safer environments

CitizenInvolvement

CitizenInvolvement

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Major Causal Pathways

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Intervention OptionsA Short Menu of Major Policy Proposals

Improve quality of care

Expand primary care supply

Simplify insurance

Change self pay fraction

Change reimbursement ratesExpand insurance coverage

Enable healthier behaviors

Build safer environments

Create pathways to advantage

Strengthen civic muscle

Improve primary care efficiency

Coordinate care

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Science Behind the Game

Integrating prior findings and estimates• On costs, prevalence, risk factors, inequity,

utilization, insurance, quality of care, etc. (8 databases and large professional literatures)

Using sound methodology• Reflecting real-world accumulations, resource

constraints, delays, behavioral feedback

Simplifying as appropriate• Three states of health:

Healthy, Asymptomatic disorder, Disease/injury

• Two SES categories: Advantaged, Disadvantaged (allowing study of disparities and equity)

• Some complicating trends not included in simplified game (e.g., aging, technology, economy); an extended model incorporates such factors

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Concept Proxy Initial Values (~2003) Sources

Advantaged & Disadvantaged

Prevalence Household income (< or ≥ $25,000)

Advantaged = 78.5% Disadvantaged = 21.5%

Census

Some key concepts and measures

• CDC/SD study of cardiovascular risk in Austin/Travis County, TX. See Homer J, Milstein B, Wile K, et al. Modeling the local dynamics of cardiovascular health. Preventing Chronic Disease 2008;5(2).

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Concept Proxy Initial Values (~2003) Sources

Advantaged & Disadvantaged

Prevalence Household income (< or ≥ $25,000)

Advantaged = 78.5% Disadvantaged = 21.5%

Census

Disease & InjuryPrevalence

Adults: 22 specific conditions Kids: 12 specific conditions

Overall = 38% D/A Ratio = 1.60 (= 53.6%/33.5%)

NHIS JAMA

Asymptomatic Disorder Prevalence

High blood pressure High cholesterol Pre-diabetes

Overall = 51.5% D/A Ratio = 1.15

NHANES JAMA

Mortality Deaths per 1,000 Overall = 7.5 D/A Ratio = 1.80

Vital Statistics AJPH

Morbidity Unhealthy days per month per capita

Overall = 5.26 D/A Ratio = 1.78

BRFSS

Health Inequity Fraction of unhealthy days attributable to disadvantage

Attributable fraction = 14.3% (calculated)

Health Insurance Lack of insurance coverage Overall = 15.6% D/A Ratio = 1.82

Census

Sufficiency of Primary Care Providers

Number of PCPs per 10,000 Overall = 8.5 per 10,000 D/A Ratio = 0.76

AMA PCD

Unhealthy Behavior Prevalence

Smoking Physical inactivity

Overall = 34% D/A Ratio = 1.67

BRFSS JAMA PCD

Unsafe Environment Prevalence

Survey response: “My neighborhood is not safe”

Overall = 26% D/A Ratio = 2.5

BRFSS PCD

Some key concepts and measures

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Three Intervention ScenariosExpand Insurance CoverageReduces the uninsured fraction by 90%

Improve Quality of Care Raises provider adherence to guidelines for preventive, chronic and urgent care (eliminating non-adherence by 50%)Implementation Cost = $10k/MD/yr.; $500k/hospital/yr. Expand Primary Care SupplyRaises the number of primary care providers per capita to the Disadvantaged by 60% over 15 yearsImplementation Cost = $300k/additional MD Improve Primary Care EfficiencyRaises the fraction of primary care offices that run efficiently (eliminating inefficiency by 90%)Implementation Cost = $10k/MD/yr. Enable Healthier BehaviorsIncreases the fraction with healthier behavior (eliminating unhealthy behavior by 40% over 15 years)Implementation Cost = $2,000 per person helped Build Safer EnvironmentsIncreases the fraction living in safer environments(eliminating unsafe environments by 50% over 15 years)Implementation Cost = $500 per person helped

Capacity

Protection

Coverage & Quality

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Some Policy InsightsValue Tradeoffs Come to the Foreground

• Expanded coverage and higher quality of care may improve health but, if done alone, would likely raise costs and worsen equity

• Additional primary care supply and greater efficiency could eliminate current shortages (esp. for the poor), reducing costs and improving equity

• Upstream health protection (behavioral + environmental remedies) could reduce costs, elevate health, and improve equity, with an initial investment and a time delay, but the benefits would grow over time

Milstein B, Homer J, Hirsch G. Are coverage and quality enough? A dynamic systems approach to health policy. AJPH (under review).

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“Winning” Involves Not Just Posting High Scores, But Understanding How and Why You Got Them

Scorecard

ProgressReport

Results in Context

CompareScenarios

HealthBound

HealthBound

HealthBound

HealthBound

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Why a Game?To Build Foresight, Experience, and Motivation to Act

Experiential Learning“Wayfinding”

Expert Recommendations

Who Has Been Playing? (N~500)

• Federal, state, local health officials

• Public health leadership institutes

• Citizen organizations

• Labor unions

• University faculty and students

• Think tanks

• Philanthropists

Who Has Been Playing? (N~500)

• Federal, state, local health officials

• Public health leadership institutes

• Citizen organizations

• Labor unions

• University faculty and students

• Think tanks

• Philanthropists

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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

Researchagenda

Healthcare costs

Sufficiency ofprimary care

providers

PCP netincome

Reimbursementrates

Disease& injury

Morbidity &mortality

Receipt of qualityhealth care

- -

Health careaccess

Primary careefficiency

Insurancecoverage

-Health

inequity

Behavioralrisks

Quality ofcare delivered

- -

Number ofprimary care

providers

-

Socioeconomicdisadvantage

-

Environmentalhazards

PCP training& placement

programs

Insurancecomplexity

Use of specialists& hospitals for

non-urgent care-

-

-

-

Self-pay fractionfor the insured

-

Asymptomaticdisorders

Carecoordination

-

Health careprice inflation

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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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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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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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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

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

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

Syndemics

Prevention Network

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

Syndemics

Prevention Network

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

Syndemics

Prevention Network








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.

Syndemics

Prevention Network








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.

Syndemics

Prevention Network








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

Syndemics

Prevention Network








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. .

Syndemics

Prevention Network








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.

Syndemics

Prevention Network








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.

Syndemics

Prevention Network








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.

Syndemics

Prevention Network

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

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**

Syndemics

Prevention Network

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**


Syndemics

Prevention Network

Example of Sensitivity TestingEstimated impacts of a 15-component intervention,

with uncertainty ranges

1990 2000 2010 2020 2030 2040

Total Consequence Costs per capita (2005 dollars per year)

3,000

2,000

0

1,000

Combined 15 interventionswith range of uncertainty

Base Case

Costs if all risk factors = 0

1990 2000 2010 2020 2030 2040

Deaths from CVD per 1000

4

2

0

Combined 15 interventionswith range of uncertainty

Base Case

Deaths if all risk factors = 0

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).

Model Output (v2008)Model Output (v2008)

Syndemics

Prevention Network

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)

Syndemics

Prevention Network

Discussion

For Further Information

CDC Syndemics Prevention Network http://www.cdc.gov/syndemics

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

• Examples

– HealthBound

– PRISM

• Try your own scenarios

– HealthBound

– PRISM

• System Dynamics 101

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