System Dynamics Modeling and Applications in Public Health and Healthcare Dr. Jack Homer and Dr....

System Dynamics Modeling and Applications in Public Health and

Healthcare

Dr. Jack Homer and Dr. Bobby Milstein

Public Lecture at the College of Medicine, Ministry of Health, Singapore

13 April 2009

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

Agenda

System Dynamics Background

The Modeling Process—Example: SARS

Hospital Surge Capacity Model

Cardiovascular Disease Prevention Model

National Health Policy Model and Game


What Accounts for Poor Population Health? Evolving Views

• God’s will

• Humors, miasma, ether

• Poor living conditions, immorality (e.g., sanitation)

• Single disease, single cause (e.g., germ theory)

• Single disease, multiple causes (e.g., heart disease)

• Single cause, multiple diseases (e.g., tobacco)

• Multiple causes, multiple diseases (but no feedback dynamics) (e.g., multi-causality)

• Dynamic interaction among afflictions, adverse conditions, and intervention capacities (e.g., syndemics)

1880

1950

1960

1980

2000

1840

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

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


2000 2001 2002 2003 2004 2005 2006 2007 2008

CDC’s 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

Health ProtectionGame

Overall Health Protection Enterprise

Diabetes Action Labs

Obesity Overthe Lifecourse

Fetal & Infant Health

Syndemics Modeling*

Cardiovascular Health in Context

Selected Health Priority Areas


Re-Directing the Course of ChangeQuestions Addressed by System Dynamics Modeling

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


The Iceberg – A Metaphor for the Level at Which We Address a System

Patterns of

Behavior

Systemic Structure

We Can Be:

Creative andTransformative

Reactive and Responsive

Adaptive and Proactive

More

Leve

rag

e

ALL 3 are needed

Events


Time Series Models

Describe trends

Multivariate Statistical Models

Identify historical trend drivers and correlates

Patterns

Structure

Events

Increasing:

• Depth of causal theory

• Robustness for longer-term projection

• Value for developing policy insights

• Degrees of uncertainty

• Leverage for change

Increasing:

• Depth of causal theory

• Robustness for longer-term projection

• Value for developing policy insights

• Degrees of uncertainty

• Leverage for changeDynamic Simulation Models

Anticipate new trends, learn about policy consequences,

and set justifiable goals

Types of Models for Policy Planning & Evaluation


We Need a Broader Perspective Because Our Decisions So Often Lead To…

• Adverse side effects

• Too little effect

• Resistance

• Longer-term effects different from near-term

• Emergence of new issues

A broader, more informed view can help


Dynamic Complexity is All Around Us

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.


System DynamicsSimulating 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

Origins • Jay Forrester, MIT, Industrial 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

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.


1. Current water level = INTEG( Water flow , 0)2. Water flow = Water flow at full open * Faucet openness3. Water flow at full open = 1 ounce per second4. Faucet openness = MAX (0, MIN (Maximum faucet openness decision, Perceived water level gap / Water flow at full open ))5. Maximum faucet openness decision = 1 out of possible 16. Perceived water level gap = DELAY1I (Water level gap,Time to perceive water level gap, 0)7. Water level gap = Desired water level - Current water level8. Desired water level = 6 ounces9. Time to perceive water level gap = 1 secondFINAL TIME = 20 secondsINITIAL TIME = 0TIME STEP = 0.125 seconds

System Equations

8

6

4

2

00 2 4 6 8 10 12 14 16 18 20

Seconds elapsed

OuncesSystem Behavior

Target

A Structural Understanding of Problematic Behavior

Problem Situation System Structure

Current waterlevel

Water flow

Desired water level

Water level gap

Perceived waterlevel gap

Time to perceivewater level gap

Faucet openness

Water flow atfull open

Maximum faucetopenness decision

System Model

Perc time Max open 1 1

1 0.50.5 1

What if…?


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

• 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.


System Dynamics Health Applications1970s to the Present

• Disease epidemiology – Cardiovascular, diabetes, obesity, HIV/AIDS,

cervical cancer, chlamydia, dengue fever, drug-resistant infections

• Substance abuse epidemiology – Heroin, cocaine, tobacco

• Health care patient flows – Acute care, long-term care

• Health care capacity and delivery– Managed care, dental care, mental health care,

disaster preparedness, community health programs

• Health system economics– Interactions of providers, payers, patients, and

investors

Homer J, Hirsch G. System dynamics modeling for public health: Background and opportunities. American Journal of Public Health 2006;96(3):452-458.


http://www.ajph.org/content/vol96/issue3/cover.shtml

Moving to the Closed Loop View

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

“Side Effects”

Feedback View

Goals

Environment

Actions

Goals ofOthers

Actions ofOthers

“Side Effects”

Delay Delay

Delay

Delay

DelayDelay

Delay

Delay

Delay

Delay

Delay

Delay


The Dynamics of Population Health

Prevalence of Vulnerability, Risk, or Disease

Time

HealthProtection

Efforts

-

B

Responsesto Growth

Resources &Resistance

-B

Obstacles

Broader Benefits& Supporters

R

ReinforcersPotentialThreats

Size of the Safer, Healthier

Population-

Prevalence of Vulnerability,

Risk, or Disease

B

Taking the Toll

0%

100%

R

Drivers ofGrowth

Values for Health & Equity

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>.


Types of Loops Underlying the Dynamics

Prevalence of Vulnerability, Risk, or Disease

Time

HealthProtection

Efforts

-

B

Responsesto Growth

Resources &Resistance

-B

Obstacles

Broader Benefits& Supporters

R

ReinforcersPotentialThreats

Size of the Safer, Healthier

Population-

Prevalence of Vulnerability,

Risk, or Disease

B

Taking the Toll

0%

100%

R

Drivers ofGrowth

Values for Health & EquityDrivers of Growth

- Risky habits worse health- Families & friends- Media reinforce risky habits- Risky habits risky options- Risky conditions poor policies

Drivers of Growth- Risky habits worse health- Families & friends- Media reinforce risky habits- Risky habits risky options- Risky conditions poor policies

Responses to Growth- Personal responsibility- Urgent care- Preventive healthcare- Better media messages- Better local options- Better policies

Responses to Growth- Personal responsibility- Urgent care- Preventive healthcare- Better media messages- Better local options- Better policies

Limiting Resources & Resistance- Disease care squeezes prevention- Vested interests defend status quo

Limiting Resources & Resistance- Disease care squeezes prevention- Vested interests defend status quo

Benefits & Supports- Potential savings build support- Broader benefits build support

Benefits & Supports- Potential savings build support- Broader benefits build support


The Closed-Loop View Leads Us To Question…

• How can we weaken the engines of growth loops (i.e. social and economic reinforcements)?

• What incentives can reward parents, school administrators, employers, and other decision-makers for expanding healthier options?

• Are there resources for health protection that do not compete with disease care?

• How can industries be motivated to change the status quo rather than defend it?

• How can benefits beyond weight reduction be used to stimulate investments in expanding healthier options?


An Interactive & Scientific Modeling Process

• Map the salient forces that contribute to a persistent problem;

• Convert the map into a computer simulation model, integrating the best information and insight available, comparing the model to reality, and refining to achieve greater realism;

• Do “What If…” testing to identify intervention strategies that might alleviate the problem;

• Do sensitivity testing to assess areas of uncertainty in the model and guide future research;

• Convene diverse stakeholders to participate in model-supported “Action Labs,” which allow participants to discover for themselves the likely consequences of alternative policy scenarios

Forrester JW, Senge PM. Tests for building confidence in system dynamics models. In: Legasto A, Forrester JW, Lyneis JM, editors. System Dynamics. New York, NY: North-Holland; 1980. p. 209-228.

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


Example: SARS in Taiwan, 2003

SARS displays the classic S-shaped growth pattern associated with the diffusion 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

Peo

ple

0

5

10

15

20

25

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

New Reported Cases

Peo

ple

/Day


SusceptiblePopulation

S

ExposedPopulation E

InfectiousPopulation I

EmergenceRate

RecoveredPopulation

RRecoveryRate

InfectionRate

Traditional Approach: SEIR Model

• Most widely used paradigm in epidemiology

• Compartment model–individuals in given state aggregated

• Deterministic or stochastic

• Disaggregation & heterogeneity handled by adding

compartments & interactions



S

B

ExposedPopulation E

Depletion


EmergenceRate

RemovedPopulation

RRemovalRate

AverageIncubation Time

-

+ +

Average Durationof Illness

Total InfectiousContacts

ContactRates

Infectivity

++

+

+

R

Contagion R

Contagion

InfectionRate

++

-

Infection in the Standard SEIR Model


Standard SEIR Model vs. SARS Data for Taiwan

Cumulative Cases

2,500

1,875

1,250

625

0

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

Peo

ple

Model

Actual


Expanding the Boundary: Behavioral Feedbacks


S

B

ExposedPopulation E

Depletion


EmergenceRate

RemovedPopulation

RRemovalRate

AverageIncubation Time

-

+ +

Average Durationof Illness

Total InfectiousContacts

ContactRates

Infectivity

++

+

+

R

Contagion R

Contagion

InfectionRate

++

-

SocialDistancing

Media Attention &Public Health

Warnings

+

+

-

SaferPractices

+

-

B

Social Distancing

B

Hygiene

DELAY

DELAY


Model with Behavioral Feedbacks vs. Data

Cumulative Cases400

300

200

100

0

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

Pe

op

le

Actual

Model


Practical Options in Causal Modeling

Detail (Disaggregation)

Scope (Breadth)

Low High

Low

High

Simplistic

Impractical

Focused

Expansive

Too hard to verify, modify, and understand


Model Structure and Level of DetailDepends on the Intended Uses and Audiences

• Set Better Goals (Planners & Evaluators)

– Identify what is likely and what is possible

– Estimate intervention impact time profiles

– Evaluate resource needs for meeting goals

• Support Better Action (Policymakers)

– Explore ways of combining policies for better results

– Evaluate cost-effectiveness over extended time periods

– Increase policymakers’ motivation to act differently

• Develop Better Theory and Estimates (Researchers)

– Integrate and reconcile diverse data sources

– Identify causal mechanisms driving system behavior

– Improve estimates of hard-to-measure or “hidden” variables

– Identify key uncertainties to address in intervention studies

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


Tests for Building Confidence in Simulation Models

Focusing on

STRUCTURE

Focusing on BEHAVIOR

ROBUSTNESS

• Dimensional consistency• Extreme conditions• Boundary adequacy

• Parameter (in)sensitivity• Structure (in)sensitivity

REALISM

• Face validity• Parameter values

• Replication of behavior• Surprise behavior• Statistical tests

UTILITY• Appropriateness for audience and purposes

• Counterintuitive behavior• Generation of insights

Forrester 1973, Forrester & Senge 1980, Richardson and Pugh 1981


A Model Is…

An inexact representation of the real thing That helps 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>


Hospital Surge Capacity (with West Virginia University, 2003-04)

• Overcrowding due to patient surges in Emergency Dept. creates risk

– Deterioration of patients while awaiting ED admission

– Walking-out of patients who should be treated or isolated

• Hospital disaster plans are required to address surge capacity

– Flow-control methods, e.g. triage, transfer, early discharge

– Reserve resources—nurses, beds, supplies—are limited, esp. for rural hospitals

– How best to deploy limited resources?

Hoard M, Homer J, Manley W, et al. Systems modeling in support of evidence-based disaster planning for rural areas. Intl J of Hygiene and Envir Health 2005; 208:117-125.

Manley W, Homer J, et al. A dynamic model to support surge capacity planning in a rural hospital. 23rd Int’l SD Conference, Boston, MA; July 2005. <http://cgi.albany.edu/~sdsweb/sds2005.cgi?P333>

St. Joseph’s Hospital, Buckhannon, W.Va.


http://images.google.com/imgres?imgurl=http://www.lindapages.com/nurses/hosp-buck.jpg&imgrefurl=http://www.lindapages.com/nurses/nurses-schools.htm&h=405&w=665&sz=108&hl=en&start=2&tbnid=jZTcg_TWiAguyM:&tbnh=84&tbnw=138&prev=/images%3Fq%3Dhospital%2BBuckhannon%2BWest%2BVirginia%26gbv%3D2%26svnum%3D10%26hl%3Den%26sa%3DG

http://images.google.com/imgres?imgurl=http://www.pulitzer.org/year/2005/public-service/works/waitroom.jpg&imgrefurl=http://www.pulitzer.org/year/2005/public-service/works/latimes6.html&h=175&w=250&sz=46&hl=en&start=9&um=1&tbnid=Yv0-Sblz5JoCXM:&tbnh=78&tbnw=111&prev=/images%3Fq%3Dhospital%2Bwaiting%2Broom%26svnum%3D10%26um%3D1%26hl%3Den%26sa%3DN

Pts in EDED admits Post-ED discharges

& facility transfers

Post-ED directed tosurgery (trauma)

Post-ED directedto wards

Pts await ED

ED arrivals (byacuity, trauma, andcontagion status)

ED walkouts& deaths

Deteriorationwhile waiting

Pts await / insurgery

Pts awaitward admit

Elective surgeriesscheduled

Post-surgerydirected to wards

Post-surgerydischarges

Pts in wards

Ward admits

Direct arrivalsto wards

Ward earlydischarges & facility

transfers

Ward dischargesafter full stay

Pts await / inelective

non-surgeryElectivenon-surgeries

scheduled

Electivenon-surgeries

postponed

Post-non-surgerydischarges

Post-non-surgerydirected to wards

ED directed todiagnostic imaging

Elective surgeriespostponed

Patient Flows and Feedback Loops

ED workload

Ward workload

ED staffing

Ward staffing

ED discharges

Increased Acuity

“Boarders”


0

500

1000

1500

2000

0 2 4 6 8 10 12 14 16 18 20

Days elapsed

Pa

tien

ts

Cumulative ED Arrivals by Acuity: Baseline Scenario

Baseline, Low

Baseline, Moderate

Baseline, Severe

Baseline (no surge) scenario50 ED arrivals per day for 20 days.

Result: Volume well handled, no avoidable deaths from deterioration


Cumulative ED Arrivals by Acuity: SARS Scenario

0

500

1000

1500

2000

0 2 4 6 8 10 12 14 16 18 20

Days elapsed

Pat

ient

s

Baseline, Low

Baseline, Moderate

Baseline, Severe

SARS, Low

SARS, Moderate

SARS, Severe106 pts Day 10

13 pts Day 2

36 pts Day 14

50 pts Day 6

Singapore pattern*

* CDC. Preparedness and Response in Healthcare Facilities: Public Health Guidance for Community-Level Preparedness and Response to SARS (Supplement C). January 8, 2004.

SARS outbreak scenarioOver the course of 13 days, 837 cumulative SARS ED arrivals, all requiring isolation, in addition to baseline arrivals.Result: Severe bottlenecks and many avoidable deaths


ED walkouts

9

940

856809

686

0

200

400

600

800

1000

No SurgeBaseline

SARS base More EDnurses

More wardnurses

More ED &ward

nurses

Deaths due to wait for ED admit

0

109

94

52

41

0

20

40

60

80

100

120

140

No SurgeBaseline

SARS base More EDnurses

More wardnurses

More ED &ward nurses

SARS Policy Testing (20 Days Cumulative):Deaths & Walkouts Due to ED Admit Wait

Patients Patients

Reserve nurses recruited from RNs off-duty, part-time, in offices, retired

Why is the ward nurse policy so much more effective?

The build-up of boarders brings ED admission to a halt.

Why is the ward nurse policy so much more effective?

The build-up of boarders brings ED admission to a halt.


Hospital Model Findings

• Recommendations affected by particulars of the hospital and the type of surge

– St. Joseph’s → need nurses, not beds

– SARS → need ward nurses the most

(the surge creates significant need for inpatient stays, not just ED care)

• But model is broadly applicable

– Could develop optimal strategies— best practices—customized to type of hospital and type of surge

– Allows for systematic “all hazards” planning

0

20

40

60

80

100

120

0 2 4 6 8 10 12 14 16 18 20

Days elapsed

ED Patient Backlog – SARS Scenario

No additional nurses

More ED nurses

More ward nurses

More ED and ward nurses


Cardiovascular Disease Prevention(with CDC and NIH, 2007-10)

• What are the key pathways of CV risk, and how do these affect health outcomes and costs?

• How might interventions affect the risk factors and outcomes in the short- and long-term?

• How might policy efforts be better balanced given limited resources?

Smoking

Obesity

Secondhandsmoke

Healthinessof diet

Extent ofphysical activity

Psychosocialstress

Diagnosisand control

First-time CVevents and

deaths

Access to and marketingof smoking quit products

and services

Access to andmarketing of mental

health services

Sources ofstress

Access to andmarketing of healthy

food options

Access to andmarketing of physical

activity options

Access to andmarketing of weight

loss services

Access to andmarketing ofprimary care

Particulate airpollution

Utilization ofquality primary

care

Tobacco taxes andsales/marketing

regulations

Smoking bans atwork and public

places

Junk food taxes andsales/marketing

regulations

Downwardtrend in CV

event fatality

Quality of primarycare provision

Chronic Disorders

Costs from CV and other riskfactor complications and

from utilization of services

Anti-smokingsocial marketing

High BP

Highcholesterol

Diabetes

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

Homer J, Milstein B, Wile K, Trogdon J, Huang P, Labarthe D, Orenstein D. Simulating and evaluating local interventions to improve cardiovascular health. In submission to Preventing Chronic Disease.

The CDC has partnered on this project 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 local data of the Austin team.

The CDC has partnered on this project 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 local data of the Austin team.


Other CVD Intervention Models

Markov: Coronary Heart DiseaseWeinstein MC, Coxson PG, et al. Forecasting coronary heart disease incidence, mortality, and cost: the coronary heart disease policy model. American J Public Health 1987; 77(11):1417-1426.

System Dynamics: Heart FailureHomer J, Hirsch G, et al. Models for collaboration: how system dynamics helped a community organize cost-effective care for chronic illness. System Dynamics Review 2004; 20(3):199-222.

Micro-simulation (Archimedes): CVD Kahn R, Robertson RM, et al. The impact of prevention on reducing the burden of cardiovascular disease. Circulation 2008; 118(5):576-585.

Statistical/Monte Carlo: Coronary Heart Disease Kottke TE, Gatewood LC, et al. Preventing heart disease: is treating the high risk sufficient? J Clinical Epidemiology 1988; 41(11):1083-1093.

Our model is the most extensive to date in integrating evidence on multiple risk factor pathways, potential interventions, and outcome costs.

Our model is the most extensive to date in integrating evidence on multiple risk factor pathways, potential interventions, and outcome costs.


Smoking

Obesity

Secondhandsmoke

Healthinessof diet


Psychosocialstress



deaths



care

Downwardtrend in CV

event fatalityChronic Disorders

High BP

Highcholesterol

Diabetes

Risk Factors for CVD

Data sources: NHANES, NHIS, MEPS, AHA/NIH reports, Census, Vital Statistics, Framingham risk calculators, literature on risk factors and costs

Obesity, Smoking, High BP, High Cholesterol, and Diabetes are modeled as dynamic stocks—with multiple inflows and outflows (e.g., see next slide)

Obesity, Smoking, High BP, High Cholesterol, and Diabetes are modeled as dynamic stocks—with multiple inflows and outflows (e.g., see next slide)


Obesity Stock-Flow Structure

Obesenon-CVD

adults

Adults becomingobese

Non-obesenon-CVD

adults Adults becomingnon-obese

Obese teensturning 18

Non-obeseteens turning 18

Non-obeseadult deaths

Obese adultdeaths

Non-obeseadult

immigration

Obese adultimmigration

Non-obeseadults surviving

CV event

Obese adultssurviving CV event

Obese adultsaging

Non-obeseadults aging

Homer J, Milstein B, Dietz W, et al. Obesity population dynamics: exploring historical growth and plausible futures in the U.S. Proc. 24th Int’l System Dynamics Conference; Nijmegen, The Netherlands; July 2006.


Tobacco and Air Quality Interventions

Smoking

Obesity

Secondhandsmoke

Healthinessof diet


Psychosocialstress



deaths


and services



care


regulations


places

Downwardtrend in CV

event fatalityChronic Disorders


High BP

Highcholesterol

Diabetes



Health Care Interventions

Smoking

Obesity

Secondhandsmoke

Healthinessof diet


Psychosocialstress



deaths


and services




care


regulations


places

Downwardtrend in CV

event fatality


Chronic Disorders


High BP

Highcholesterol

Diabetes



Interventions Affecting Stress

Smoking

Obesity

Secondhandsmoke

Healthinessof diet


Psychosocialstress



deaths


and services


health services

Sources ofstress




care


regulations


places

Downwardtrend in CV

event fatality


Chronic Disorders


High BP

Highcholesterol

Diabetes



Healthy Diet Interventions

Smoking

Obesity

Secondhandsmoke

Healthinessof diet


Psychosocialstress



deaths


and services


health services

Sources ofstress


food options




care


regulations


places


regulations

Downwardtrend in CV

event fatality


Chronic Disorders


High BP

Highcholesterol

Diabetes



Physical Activity & Weight Loss Interventions

Smoking

Obesity

Secondhandsmoke

Healthinessof diet


Psychosocialstress



deaths


and services


health services

Sources ofstress


food options


activity options


loss services




care


regulations


places


regulations

Downwardtrend in CV

event fatality


Chronic Disorders


High BP

Highcholesterol

Diabetes



Adding Up the Costs

Smoking

Obesity

Secondhandsmoke

Healthinessof diet


Psychosocialstress



deaths


and services


health services

Sources ofstress


food options


activity options


loss services




care


regulations


places


regulations

Downwardtrend in CV

event fatality


Chronic Disorders

Costs from CV and other riskfactor complications and

from utilization of services


High BP

Highcholesterol

Diabetes



A Base Case Scenario for Comparison Assumptions for Input Time Series through 2040

• Prior to 2004, model reflects historical…

– Decline in fraction of workplaces allowing smoking (1990-2003)

– Decline in air pollution (1990-2001)

– Decline in CV event fatality (1990-2003)

– Increase in diagnosis and control of high blood pressure, high cholesterol, and diabetes (1990-2002)

– Rise & fall in youth smoking (1991-2003)

– Rise in youth obesity (1990-2002, 2002-2020P)

• After 2004, make simple yet plausible assumptions…

– Assume no further changes in contextual factors affecting risk factor prevalence (aside from rise in youth obesity)

– Changes in risk prevalence after 2004 are due to “bathtub” adjustment process (incidence still exceeding outflows) and population aging

– Provides an easily-understood basis for comparisons


Base Case Trajectories 1990-2040

Smoking

Obesity

Secondhandsmoke

Healthinessof diet


Psychosocialstress



deaths


and services


health services

Sources ofstress


food options


activity options


loss services




care


regulations


places


regulations

Downwardtrend in CV

event fatality


Costs from CV and other riskfactor complications and from

utilization of services


High BP

Highcholesterol

Diabetes

<Air pollutioncontrolregulations>

Air pollutioncontrol regulations

<Population aging>

Populationaging

Chronic Disorders

Smoking prevalence

Secondhand smoke

exposure

Particulate air pollution

PM2.5

Total consequence costs per capita

CVD deaths per 1000

Age 65+ fraction of the population

CV event fatality multiplier

Obesity prevalence

Diabetes

High blood pressure

Uncontrolled Prevalences

High cholesterol

Smoking

Obesity

Secondhandsmoke

Healthinessof diet


Psychosocialstress



deaths


and services


health services

Sources ofstress


food options


activity options


loss services




care


regulations


places


regulations

Downwardtrend in CV

event fatality


Costs from CV and other riskfactor complications and from

utilization of services


High BP

Highcholesterol

Diabetes

<Air pollutioncontrolregulations>

Air pollutioncontrol regulations

<Population aging>

Populationaging

Chronic Disorders

Smoking prevalence

Secondhand smoke

exposure

Particulate air pollution

PM2.5

Total consequence costs per capita

CVD deaths per 1000

Age 65+ fraction of the population

CV event fatality multiplier

Obesity prevalence

Diabetes

High blood pressure

Uncontrolled Prevalences

High cholesterol


Estimated Impacts of a 15-Component Intervention, with Sensitivity Ranges

CVD DEATHS

DIRECT & INDIRECT COSTS

The 15 components include: (1) “Care” [3 interventions](2) “Air” (smoking/pollution) [6],(3) “Lifestyle”: Nutrition, physical activity, & stress reduction [6]

The model contains 56 causal linkages requiring the estimation of relative risks, effect sizes, or initial values, most of which involved some level of uncertainty.

The upper edge of the sensitivity range results when all uncertain parameters are set to their “lowest plausible impact” values. The lower edge results when all are set to their “greatest plausible impact” values.

The 15 components include: (1) “Care” [3 interventions](2) “Air” (smoking/pollution) [6],(3) “Lifestyle”: Nutrition, physical activity, & stress reduction [6]

The model contains 56 causal linkages requiring the estimation of relative risks, effect sizes, or initial values, most of which involved some level of uncertainty.

The upper edge of the sensitivity range results when all uncertain parameters are set to their “lowest plausible impact” values. The lower edge results when all are set to their “greatest plausible impact” values.

60%

20%(15-26%)

80%

26%(19-33%)

Reductions vs. Base Case

0%

0%

1990 2000 2010 2020 2030 2040D

eath

s fr

om

CV

D p

er 1

000 4

2

0

Combined 15 interventionswith sensitivity range

Base Case

Deaths if all risk factors = 0

1990 2000 2010 2020 2030 2040To

tal

Co

ns

eq

ue

nc

e C

os

ts p

er

Ca

pit

a (

20

05

do

lla

rs p

er

ye

ar)

3,000

2,000

0

1,000

Combined 15 interventionswith sensitivity range

Base Case

Costs if all risk factors = 0

DIRECT & INDIRECT COSTS


Contributions of 3 Intervention Clusters(Clusters layered in cumulatively)

Contributions to CVD death reduction:

(1) Care: strong from the start; 9%

(2) Air: good from the start (less pollution, secondhand smoke) and

growing (due to smoking decline) to 6.5%

(3) Lifestyle: small at first but growing to 5%

Contributions to CVD death reduction:

(1) Care: strong from the start; 9%

(2) Air: good from the start (less pollution, secondhand smoke) and

growing (due to smoking decline) to 6.5%

(3) Lifestyle: small at first but growing to 5%

CVD DEATHS

DIRECT & INDIRECT COSTSContributions to cost savings:

(1) Air: strong from the start (pollution, SHS) and growing (due to smoking decline) to 18.5%

(2) Lifestyle: small at first but growing to 8.5%

(3) Care: negligible (not cost saving)

Contributions to cost savings:

(1) Air: strong from the start (pollution, SHS) and growing (due to smoking decline) to 18.5%

(2) Lifestyle: small at first but growing to 8.5%

(3) Care: negligible (not cost saving)

60%

20%

80%

26%

Reductions vs. Base Case

0%

0%

Dea

ths

fro

m C

VD

per

100

0 4

2

0

1990 2000 2010 2020 2030 2040

Base Case

3) + Nutrition, Physical Activity, and Stress

Deaths if all risk factors = 0

1) Primary Care

2) + Air Quality & Tobacco

3,000

0

To

tal

Co

nse

qu

ence

Co

sts

per

Cap

ita

(200

5 d

oll

ars

pe

r ye

ar)

1990 2000 2010 2020 2030 2040

Costs if all risk factors = 0

Base Case

3) + Nutrition, Physical Activity, and Stress

1) Primary Care

2) + Air Quality & Tobacco

2,000

1,000


National Health Policy Model & Game(with CDC, 2008-09)

• Americans pay the most for health care, yet suffer high rates of morbidity and premature mortality—esp. high among the poor and uneducated

• About 16% of Americans have no insurance coverage

• Over 75% of Americans think the current system needs fundamental change

• Many health leaders realize we need a broader view of health, including health protection and health equity

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. New England Journal of Medicine 2008;358(4):414-422. Gerberding JL. Protecting health—the new research imperative. JAMA 2005; 294(11):1403-1406.Gerberding JL. CDC: protecting people's health. Director's Update; Atlanta, GA; July, 2007.


The U.S. Health Policy Arena is

Dense with Diverse Issues

Healthier behaviorsHealthier behaviors

Adherence to care guidelines Adherence to

care guidelines

Insurance coverageInsurance coverage

Insurance overheadInsurance overhead

Socioeconomic disparities

Socioeconomic disparities

Primary care supply

Primary care supply

Reimbursement rates

Reimbursement rates

Out-of-pocket costs

Out-of-pocket costs

Provider efficiencyProvider efficiency

Access to careAccess to care

Overuse of ERs

Overuse of ERs

Safer environments

Safer environments

Overuse of specialists Overuse of specialists

CitizenInvolvement

CitizenInvolvement

Extent of care

Extent of care


Simulating the Health System

Integrating prior findings and estimates

• On costs, prevalence, risk factors, health disparities, health care utilization, insurance, quality of care, etc.

• Our own previous health system modeling*

Simplifying as appropriate

• Three states of health: Disease/injury, Asymptomatic disorder, No significant health problem

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

• Start in equilibrium (all variables unchanging), approximating the U.S. in 2003

• Some complicating trends not included for simplicity: aging, migration, technology, economy, etc.

* E.g., Homer, Hirsch, Milstein. Chronic illness in a complex health economy: the perils and promises of downstream and upstream reforms. System Dynamics Review 2007; 23:313-343.


Connecting the Concepts:Start with the Outcome Measures

Healthcare costs

Morbidity &mortality

Healthinequity


Healthcare costs

Reimbursementrates

Disease& injury


Receipt of qualityhealth care

Healthinequity

Insurancecomplexity

Use of specialists& hospitals for

non-urgent care

Asymptomaticdisorders

Several Drivers of Health Care Costs


Quality Health Care Improves Health Outcomes

Healthcare costs

Sufficiency ofprimary care

providers

Reimbursementrates

Disease& injury



- -

Health careaccess

Insurancecoverage

-Health

inequity

Quality ofcare delivered

Socioeconomicdisadvantage

-

Insurancecomplexity


non-urgent care-

Self-pay fractionfor the insured

-



The “Medically Disenfranchised” Live in Areas Where PCPs are in Short Supply

PCPs per 10,000 population in Travis County, Texas(GP/FP/IM/Ped+ObGyn+Geriat; Texas DSHS 2004-06)

0

5

10

15

20

East Travis West Travis

PCPs per 10,000 population in Travis County, Texas(GP/FP/IM/Ped+ObGyn+Geriat; Texas DSHS 2004-06)

0

5

10

15

20

East Travis West Travis

The Robert Graham Center, with the National Association of Community Health Centers. “Access Denied: A Look at America’s Medically Disenfranchised”, Washington, DC, 2007.


Healthcare costs


providers

PCP netincome

Reimbursementrates

Disease& injury



- -

Health careaccess

Primary careefficiency

Insurancecoverage

-Health

inequity


- -

Number ofprimary care

providers

-


-

PCP training& placement

programs

Insurancecomplexity


non-urgent care-

-

-


-


Gatekeeperrequirement

-

-

PCP Sufficiency: Supply vs. Demand


Upstream Determinants of Disease & Injury

Healthcare costs


providers

PCP netincome

Reimbursementrates

Disease& injury



- -

Health careaccess

Primary careefficiency

Insurancecoverage

-Health

inequity

Behavioralrisks


- -

Number ofprimary care

providers

-


-

Environmentalhazards

PCP training& placement

programs

Insurancecomplexity


non-urgent care-

-

-

-


-


Gatekeeperrequirement

-

-


From Model to an Interactive Game

• 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 multiple stakeholders to explore

how the health system can change

• 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 multiple stakeholders to explore

how the health system can change

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


Options for Intervening in the Health SystemA Short Menu of Major Policy Proposals

Options for Intervening in the Health SystemA Short Menu of Major Policy Proposals

Improve primary care efficiency

Improve quality of care

Expand primary care supply

Simplify insurance

Change self pay fraction

Change reimbursement rates

Expand insurance coverage

Enable healthier behaviors

Build safer environments

Create pathways to advantage

Strengthen civic muscle

Coordinate care


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

Scorecard

ProgressReport

Results in Context

CompareRuns


Some Policy Conclusions

• Expanded coverage and improved quality would improve health but, if done alone, would raise costs and worsen equity

• Expanding primary care capacity to eliminate shortages (esp. for the poor) would reduce costs and improve equity

• Cutting reimbursement rates would reduce costs but worsen health outcomes

• Upstream protection (behavioral and environmental remedies) would—increasingly over time—reduce costs, improve health, and improve equity

Milstein B, Homer J, Hirsch G. Are coverage and quality enough? A dynamic systems approach to health policy. Draft paper currently in CDC clearance.


System Dynamics: Looking Further for the Key

The world is complex, and many important things are not well-measured.

(The key is not always under the light.)

SD allows for broader causal structures and types of data.

Such models often lead to novel conclusions—and firm ones despite the uncertainties.

This is why SD is a powerful approach to support planning and policymaking.

The world is complex, and many important things are not well-measured.

(The key is not always under the light.)

SD allows for broader causal structures and types of data.

Such models often lead to novel conclusions—and firm ones despite the uncertainties.

This is why SD is a powerful approach to support planning and policymaking.


System Dynamics Modeling and Applications in Public Health and Healthcare Dr. Jack Homer and Dr....

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