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Prevention Network

Goal-Setting Using System Dynamics Models:An Example from Adult Diabetes

DHHS Office of Disease Prevention and Health PromotionRockville, MD

January 28, 2005

Jack HomerHomer Consulting

Voorhees, New Jersey

Bobby MilsteinCenters for Disease Control and Prevention

Atlanta, Georgia

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Dynamic Policy Modeling AddressesNavigational Questions

20202010

How?

Why?

Where?

Who?

People with Diagnosed Diabetes, US

0

5

10

15

1980 1985 1990 1995 2000

Mill

ion

peop

le

Data Source: CDC DDT and NCCDPHP. -- Change in measurement in 1996.

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Time Series Models

Describe trends

Multivariate Stat 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

Increasing:

• Depth of causal theory

• Degrees of uncertainty

• Robustness for longer-term projection

• Value for developing policy insights

Dynamic Models

Anticipate new trends, learn about policy consequences,

and set justifiable goals

Tools for Longitudinal Analysis

Developed by Jack Homer, Homer Consulting

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A Very Particular Distance“{System dynamics studies problems} from ‘a very particular distance',

not so close as to be concerned with the action of a single individual, but not so far away as to be ignorant of the internal pressures in the system.”

-- George Richardson

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

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System Dynamics is Well-Suited for Studying Population Health Problems

History• Industrial Dynamics, Jay Forrester, MIT (1961)

• SD Society (1983),

• Health Policy Special Interest Group (2003)

Major Health Studies (since 1975)

• Disease epidemiology (e.g., heart disease, diabetes, HIV/AIDS, cervical cancer, dengue fever)

• Substance abuse epidemiology (e.g., heroin, cocaine, tobacco)

• Health care patient flows (e.g., hospital, extended care)

• Health care capacity and delivery (e.g., resource planning)

• Interactions between health capacity and disease epidemiology (e.g, neighborhood- and national-level analysis)

Current CDC Projects • Syndemics

(i.e., mutually reinforcing epidemics)

• Balancing upstream/downstream effort

• Diabetes in an era of rising obesity

• Fetal and infant health

• Obesity (lifecourse view)

• Adolescent health (lifestage view)

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System Dynamics Focuses on the Connection Between Behavior and Structure

System behavior is determined by feedback structure:including accumulation, delay, and nonlinear response

Problem Situation System Structure

8

6

4

2

00 2 4 6 8 10 12 14 16 18 20

Seconds elapsed

Ou

nc

es

Water Level Over Time

System Behavior Over Time

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Water Glass Model Diagram (Vensim™ software)

Current waterlevel

Water flow

“Stock”“Flow”

Faucet openness

Water flow atfull open

Maximum faucetopenness decision

“Policy Lever”

“Constant”Desired water level

Water level gap

“Delay”Perceived water

level gap

Time to perceivewater level gap

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Diabetes Policy Model - Structure

People withUndiagnosed,Uncomplicated

Diabetes

People withDiagnosed,

UncomplicatedDiabetes

People withDiagnosed,Complicated

Diabetes

People withUndiagnosedPrediabetes

People withDiagnosed

Prediabetes

People withNormal

GlycemicLevels

DiagnosingDiabetes

DiagnosingDiabetes

Dying fromComplications

DevelopingComplications

DiagnosingPreDiabetes

DiabetesOnset

Homer J, Jones A, Seville D, Essien J, Milstein B, Murphy D. The CDC diabetes system modeling project: developing a new tool for chronic disease prevention and control. 22nd International Conference of the System Dynamics Society; Oxford, England; 2004.

PrediabetesOnset

Recovering fromPrediabetes

Recovering fromPrediabetes

DiabetesOnset Developing

Complications Dying fromComplications

People withUndiagnosed,Complicated

Diabetes

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People withUndiagnosed,Uncomplicated

Diabetes

People withDiagnosed,

UncomplicatedDiabetes

People withDiagnosed,Complicated

Diabetes

DiagnosingUncomplicated

Diabetes

People withUndiagnosedPreDiabetes

People withDiagnosedPreDiabetes

DiagnosingPreDiabetes

People with

Undiagnosed,Complicated

Diabetes

DiagnosingComplicated

Diabetes

People withNormal

GlycemicLevels

Caloric Intake PhysicalActivity

MedicationAffordability

Ability to SelfMonitor

Adoption ofHealthy Lifestyle

ClinicalManagement of

PreDiabetes

Clinical Managementof Diagnosed

Diabetes

LivingConditions

PersonalCapacity

PreDiabetesControl

DiabetesControl

DiabetesDetection

PreDiabetesDetection

Access toPreventive Health

ServicesPreDiabetesTesting for Testing for

Diabetes

PreDiabetesOnset

Recovering fromPreDiabetes

Recovering fromPreDiabetes Diabetes

Onset

Dying fromComplications

DevelopingComplications

DiabetesOnset Developing

Complications Dying fromComplications

Obese Fraction ofthe Population

Risk forPreDiabetes

Diabetes Policy Model - StructureWhere is the Leverage for Reducing Disease Burden?

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Diabetes Policy ModelIntegrating the Best Available Evidence

Information Sources Data Topics

U.S. Census • Population growth and death rates• Health insurance coverage

National Health Interview Survey (NHIS)

• Diabetes prevalence• Diabetes detection

National Health and Nutrition Examination Survey

(NHANES)

• Prediabetes prevalence• Weight, height, and body fat• Caloric intake

Behavioral Risk FactorSurveillance System

(BRFSS)

• Glucose self-monitoring• Eye and foot exams• Use of medications• Attending diabetes self-mgmt classes• Efforts to control weight

Research Literature

• Effects of disease control and aging on onset, progression, death, and costs• Physical activity trends• Direct and indirect costs of diabetes

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Diabetes Policy Model - BehaviorSimulating Policy Scenarios

Homer J, Jones A, Seville D, Essien J, Milstein B, Murphy D. The CDC diabetes system modeling project: developing a new tool for chronic disease prevention and control. 22nd International Conference of the System Dynamics Society; Oxford, England; 2004.

0%

2%

4%

6%

8%

1980 1985 1990 1995 2000 2005 2010

Diagnosed diabetes % of adults

Data (NHIS)

Simulated

0%

2%

4%

6%

8%

1980 1985 1990 1995 2000 2005 2010

Diagnosed diabetes % of adults

Data (NHIS)

Simulated

0%

10%

20%

30%

40%

1980 1985 1990 1995 2000 2005 2010

Obese % of adults

Data (NHANES)

Simulated

0%

10%

20%

30%

40%

1980 1985 1990 1995 2000 2005 2010

Obese % of adults

Data (NHANES)

Simulated

Historical Calibration

Diagnosed Diabetes % of Adults

Obese % of Adults

Defining Plausible Futures

0.0035

0.003

0.0025

0.002

0.00151980 1990 2000 2010 2020 2030 2040 2050

Time (Year)

Diabetes-related death rate per year for adult population Status Quo

Disease Mgmt

Reduced Obesity

Partial Disease Mgmt & Obesity

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HP 2010 Diabetes Objectives

BaselineHP 2010 Target

Percent Change

Reduce Diabetes–related Deaths Among Diagnosed

(5-6)

8.8 per 1,000

7.8 -11%

Increase Diabetes Diagnosis (5-4)

68% 80% +18%

Reduce New Cases of Diabetes (5-2)

3.5per 1,000

2.5 -29%

Reduce Prevalence of Diagnosed Diabetes

(5-3)

40 per 1,000

25 -38%

U.S. Department of Health and Human Services. Healthy People 2010. Washington DC: Office of Disease Prevention and Health Promotion, U.S. Department of Health and Human Services; 2000. http://www.healthypeople.gov/Document/HTML/Volume1/05Diabetes.htm

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Connecting the Objectives: System Physics

It is impossible for any policy to reduce prevalence

38% by 2010!

People withUndiagnosed

Diabetes

People withDiagnosedDiabetes Dying from Diabetes

Complications

DiagnosedOnset

InitialOnset

People withNormal

GlycemicLevels

As would stepped-up detection effort

Reduced death wouldadd further to prevalence

With a diagnosed onset flow of

1.1 mill/yr

And a death flow of 0.5 mill/yr

(4%/yr rate)

The targeted 29% reduction in diagnosed onset can only

slow the growth in prevalence

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20

30

40

50

60

70

1980 1985 1990 1995 2000 2005 2010People

with

dia

gnosed d

iabete

s p

er

1,0

00 a

dult

popula

tion

Simulated

Status Quo

Meet Detection Objective (5-4)

Meet Onset Objective (5-2)

HP 2010 Objective (5-3)

HP 2000 Objective

Setting Realistic ExpectationsHistory, HP Objectives, and Simulated Futures

Reported

A

B

C

D

E

F

G

H

I

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Possible Roles for SD in Public Health

SD is especially well-suited for studying…

• Individual diseases and risk factorsExamining momentum and setting justifiable goals

• Life course dynamics Following health trajectories across life stages

• Mutually reinforcing afflictions (syndemics)Exploring interactions among related afflictions, adverse living conditions, and the public’s capacity to address them both

• Capacities of the health protection system Understanding how ambitious health ventures may be configured without overwhelming/depleting capacity--perhaps even strengthening it

• Value trade-offs Analyzing phenomena like the imbalance of upstream-downstream effort, growth of the uninsured, rising costs, declining quality, entrenched inequalities

• Organizational management Linking balanced scorecards to a dynamic understanding of processes

• Group model building and scenario planningBringing more structure, evidence, and insight to public dialogue and judgment