sd-cdcseminar systemdynamics hircsh

7/28/2019 SD-CDCSeminar SystemDynamics Hircsh

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Syndemics

Prevention Network

ASysT Prize Seminar

Alexandria, VAJuly 25, 2008

Understanding the Dynam ic Dimensions

of Health Protect io n Pol ic ies

CDC-NIH System Dynamics Collaborative forDisease Control and Prevention (SD-CDC Team)

Joyc e Essien, Jack Hom er, Gary Hirsch , And rew Jo nes, Doc Klein,

Patty Mabry, Bobby Milstein , Diane Orens tein, Krist ina Wile

Applied Systems Thinking PrizeSeminar


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Syndemics

Prevention Network

What Are System Dynamics Models

and How Do We Use Them?


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Syndemics

Prevention Network

Basic Problem Solving Orientations

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

McGraw-Hill, 2000.

Single-Decision Open Loop View

Problem Results

Goals

Situation

Decision

SideEffects

Feedback ViewGoals

Environment

Actions

Goals of

Others

Actions ofOthers

SideEffects

Delay Delay

Delay

Delay

DelayDelay

Delay

Delay

Delay

Delay

Delay

Delay


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Syndemics

Prevention Network

Learning In and About Dynamic Systems

Unknown st ructure Dynamic complex i ty Time delays

Impossib le experiments

Real World

InformationFeedback

Decisions

Mental

Models

Strategy, Structure,Decision Rules

Selected Miss ing Delayed Biased Ambiguous

Implementat ion Game playing Inconsistency Short term

Mispercept ions

Unscient i f ic Biases Defensiveness

Inabil i ty to infer

dynamics f rommental models

Known st ructure Contro l led 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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Syndemics

Prevention Network

A Model IsAn inexact representationof the real thing

They help us understand, explain,

anticipate, and make decisions

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

Sterman J. A sketpic's guide to computer models. In: Barney GO, editor. Managing a Nation: theMicrocomputer Software Catalog. Boulder, CO: Westview Press; 1991. p. 209-229.


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Syndemics

Prevention Network

System Dynamics:Addressing Dynamic Complexity

Good at Capturing

Differences between short- and long-term consequences of an action

Time delays (e.g., incubation period, time to detect, time to respond)

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

Behavioral feedback (reactions by various actors) Nonlinear causal relationships (e.g., threshold effects, saturation effects)

Differences or inconsistencies in goals/values among stakeholders

Sterman JD. Business dynamics: systems thinking and modeling for a complex world. Boston, MA:

Irwin McGraw-Hill, 2000.

Origins

Jay Forrester, MIT, Indu str ia l Dynamics, 1961(One of the seminal books of the last 20

years.-- NY Times)

Public policy applications starting late 1960s

Population health applications starting mid-1970s


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Syndemics

Prevention Network

Brief Background on System DynamicsModeling

Compartmental models resting on a general theory of how systems

change (or resist change)often in ways we dont expect

Developed for corporate policies in the 1950s, and applied tohealth policies since the 1970s

Concerned with understanding dynamic complexity

Accumulation (stocks and flows)

Feedback (balancing and reinforcing loops)

Used primarily to craft far-sighted, but empirically based,strategies

Anticipate real-world delays and resistance

Identify high leverage interventions

Modelers engage stakeholders through interactive workshops

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.

StockFlow

Feedback

influence


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Syndemics

Prevention Network

An (Inter) Active Form of Policy Planning/Evaluation

System Dynamics is a methodology to

Map the salient forces that contribute to a

persistent problem;

Convert the map into a computer simulationmodel, integrating the best information and insightavailable;

Compare results from simulated What Ifexperiments to identify intervention policies thatmight plausibly alleviate the problem;

Conduct sensitivity analyses to assess areas ofuncertainty in the model and guide futureresearch;

Convene diverse stakeholders to participate inmodel-supported Action Labs, which allowparticipants to discov er for themselvesthe likelyconsequences of alternative policy scenarios


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Syndemics

Prevention Network

Finding the Right System Boundary: SARS in Taiwan

SARS displays the

classic S-shaped

growth pattern

associated with thediffusion of infectious

diseases

and new products,

innovations, social

norms, etc.

0

100

200

300

400

Feb/21 Mar/27 May/1 Jun/5 Jul/10

Cumulative Reported Cases

People

0

5

10

15

20

25

Feb/21 Mar/27 May/1 Jun/5 Jul/10

New Reported Cases

People/Day


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Syndemics

Prevention Network

SusceptiblePopulation

S

Exposed

Population E

Infectious

Population IEmergence

Rate

RecoveredPopulation

RRecoveryRate

InfectionRate

Traditional Approach: SEIR Model

Most widely used paradigm in epidemiology

Compartment modelindividuals in given state aggregated

Deterministic or stochastic

Disaggregation & heterogeneity handled by adding compartments &interactions


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Syndemics

Prevention Network


S

B

Exposed

Population E

Depletion

Infectious


Rate

RemovedPopulation

RRemoval

Rate

AverageIncubation Time

-

+ +

Average Durationof Illness

Total Infectious

Contacts

ContactRates

Infectivity

+

+

+

+

R

ContagionR

Contagion

InfectionRate

+ +

-

Infection in the Standard SEIR Model


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SyndemicsPrevention Network

Expanding the Boundary: Behavioral Feedbacks


S

B

Exposed

Population E

Depletion

Infectious


Rate

RemovedPopulation

RRemoval

Rate

Average

Incubation Time

-

+ +

Average Duration

of Illness

Total Infectious

Contacts

ContactRates

Infectivity

++

+

+

R

ContagionR

Contagion

InfectionRate

++

-

SocialDistancing

Media Attention &

Public Health

Warnings

+

+

-

Safer

Practices

+

-

B

Social Distancing

B

Hygiene

DELAY

DELAY


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Model with Behavioral Feedbacks vs. Data

Cumulative Cases

400

300

200

100

0

0 14 28 42 56 70 84 98 112Time (Day)

Peop

le

Actual

Model


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How Much Detail is Best?

System dynamics studies

problems from a very particulardistance', not so close as to beconcerned with the action of asingle individual, but not so faraway as to be ignorant of the

internal pressures in the system.-- George Richardson

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

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


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Practical Options in Causal Modeling

Detail (Disaggregation)

Scope(Breadth)

Low High

Low

High

Simplistic

Impractical

Fine-

grained

Far-sighted

Too hard to verify,

modify, and understand(e.g., manysystem dynamics models)

(e.g., many

agent-based models)

But a fine-grained

model can informa far-sighted model,

and vice versa.


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Attempt to Fix Health Care Cost Problem: Lower Physician Reimbursements

Health CareCosts

Reimbursement toPhysicians

ProblemFix

B


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Unintended Consequence: Reduced Primary Care Availability Increases Costs

Health CareCosts

Reimbursement toPhysicians

ProblemFix

B

Income of PrimaryCare Physicians

Availablity of PrimaryCare in PhysiciansOffices and Clinics

Retirements andNew Entries

Patients Going toER's for Primary

Care

Availability and Quality ofDisease Management for

Chronic Conditions

Acute Events Due toChronic Conditions

HospitalAdmissions

RR

Unintended Consequences


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Expand insurance coverage Improve quality of care

Change reimbursement rates

Improve operational efficiency

Simplify administration

Offer provider incentives

Enable healthier behaviors

Build safer environments Create pathways to advantage

Ingredients for Transforming Population HealthA Short Menu of Pol icy Proposals


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Prototyp e Preview

Bobby Milstein

Centers for Disease Control and Prevention

[email protected]

http://www.cdc.gov/syndemics

CDCs

Health Protection Game

Jack Homer

Homer Consulting

[email protected]

Gary Hirsch

Independent Consultant

[email protected]

>>>> These slides are from a prototype model.


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Rules of the Health Protect ion Game

GoalNavigate the U.S. health system toward greater health and equity

Task

Prioritize intervention options across nine policy domains Decisions

Craft health protection strategies over 8 rounds (from 2010-2050),using feedback available every five years

ScoringAchieve the best results across four criteria simultaneously

Save lives (i.e., reduce the mortality rate)

Improve well-being (i.e., reduce unhealthy days)

Achieve equity (i.e., reduce unhealthy days due to Disadvantage)

Lower healthcare costs (i.e., reduce expenses per capita)

Appropriate implementation expenses (i.e., subsidy, program cost)

Game SetupA population in dynamic equilibrium, with fixed rates of birth and netimmigration, experiencing high starting levels of mortality, unhealthylife, social inequity, and healthcare costs

No changes are due to trends o r ig inat ing outs ide the heal th

sector such as aging, migrat ion, econom ic cycles, technolog y,

climate change, etc.


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Navigating Health FuturesGetting Ou t of a Deadly, Unhealthy , Inequ itable, and Cos tly Trap

Four Problems in the Current System: High Morbidity, Mortality, Inequity, Cost

Death rate per thousand

Unhealthy days per capita

Health inequity index

Healthcare spend per capita

10

6

0.2

6,000

0

0

0

4,000

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

How far canyou move the

system?


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S l t d E ti t f M d l C lib ti


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Parameter Proxy Initial Values (~2000) Sources

Advantaged &Disadvantaged

Prevalence

Household Income(< or $25,000)

Advantaged = 79% Disadvantaged = 21%

Census

Selected Estimates for Model Calibration

S l t d E ti t f M d l C lib ti


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Parameter Proxy Initial Values (~2000) Sources

Advantaged &Disadvantaged

Prevalence

Household Income(< or $25,000)

Advantaged = 79%Disadvantaged = 21%

Census

SymptomaticDisease/Injury

Prevalence Self-rated health isgood, fair, or poor

Overall = 27%D/A Ratio = 1.60 (= 38.5%/24%)

BRFSS JAMA

Asymptomatic ChronicDisease Prevalence

High blood pressure (HBP) High cholesterol (HC) Asymp = Tot Chron - Symp

Overall = 40%(54.5% tot chron - 14.5% Symp)

D/A Ratio (tot chronic) = 1.15 (= 61%/53%)

NHANES JAMA

No Health ProblemsPrevalence

Self-rated health isexcellent or very good

No HBP or HC

Overall = 33% Advantaged = 36%Disadvantaged = 24%

BRFSS NHANES

Mortality Deaths per 1,000 Overall = 8.4D/A Ratio = 1.80

Vital Statistics AJPH

Morbidity Unhealthy daysper month per capita

Overall = 5.25D/A Ratio = 1.78

BRFSS

Health Equity Unhealthy days (or deaths)attributable to disadvantage

Attrib. fraction (unhealthy days) = 14.1% Attrib. fraction (deaths) = 14.4%

Census BRFSS

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

Census

Sufficiency ofPrimary Care Providers

Number of PCPs per 10,000 Overall = 8.5 per 10,000D/A Ratio = 0.71

AMA Austin Study

Emergency Care forNonurgent Problems

Acute non-urgent visits in ERor outpatient department

Overall = 19%D/A Ratio = 5.5

NAMCS

Unhealthy BehaviorPrevalence

Smoking Physical inactivity

Overall = 34%D/A Ratio = 1.67

BRFSS JAMA Austin Study

Unsafe EnvironmentPrevalence

Neighborhood not safe Overall = 26%D/A Ratio = 2.5

BRFSS JAMA Austin Study

Selected Estimates for Model Calibration

Exploring Intervention Scenarios


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Exploring Intervention ScenariosCut Reimburs ements to Off ice-Based Physic ians by 20%

Scoring Criteria: Deaths, Unhealthy Days, Inequity, Cost

Death rate per 1,000Unhealthy days

Health inequity index

Healthcare spending per capita

>>>> These results are from a prototype model.


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Additional Preliminary FindingsUniversal Coverage (with Leadership)

Lowers morbidity and mortality quickly

Increases cost significantly (greater volume of mediocre services, which do little to prevent disease)

Worsens inequity (greater demand exacerbates pre-existing provider shortage for disadvantaged)

Quality of Care (with Leadership)

Lowers morbidity and mortality quickly, more so than Universal Coverage (more people benefit)

Costs rise initially, then fall (the benefits of disease prevention accrue gradually)

Worsens inequity (better quality services exacerbate pre-existing provider shortage fordisadvantaged)

Upstream Health Protection (with Leadership)

Consistent pattern of strong, sustained improvements in morbidity, mortality, cost, and equity

Takes time to generate significant effects (~10 years)

Works in three ways, all favoring the disadvantaged: (1) fewer upstream risks lower diseaseprevalence, which in turn (2) eases demand on scarce provider resources; and (3) reduces costs andimproves health care access

Average unhealthy days per capita Health care spending per capitaHealth inequity index (morbidity)6

.5

5

.5

42000 2010 2020 2050

Protection

Coverage

Quality

2030 2040

Prototype Model Output

6,000

5,500

5,000

4,500

4,0002000 2050

Protection

Coverage

Quality


2010 2020 2030 2040

0.2

0.15

0.1

0.05

02000 2050

Protection

Coverage

Quality


2010 2020 2030 2040


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Game-based Wayfinding DialoguesCombine Science and Social Change

Potential champions need more than visionary direction.They want plausible pathways and visceral preparation.

sd-cdcseminar systemdynamics hircsh

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