Syndemics Prevention Network System Dynamics and the Physics of Possibility in Health Policy Tools...

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System Dynamics and the Physics of Possibility in Health Policy

Tools for Developing a Dynamic Understanding of Access-to-Care Options During HIV Prevention Trials

Family Health InternationalOctober 25, 2004

Chapel Hill, NC

Jack HomerHomer Consulting

Voorhees, New Jersey

Bobby MilsteinCenters for Disease Control and Prevention

Atlanta, Georgia

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The Dynamic Dilemma of HIV Prevention Trials

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“When we attribute behavior to people

rather than system structure the focus of

management becomes scapegoating and

blame rather than the design of

organizations in which ordinary people

can achieve extraordinary results.”

-- John Sterman

Sterman J. System dynamics modeling: tools for learning in a complex world. California Management Review 2001;43(4):8-25.

“The tendency to blame other people instead of the system is so strong

that psychologists call it the fundamental attribution error.”

Beyond Scapegoating

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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 future trends, and find policies that maximize chances

of a desirable path

Tools for Policy Analysis

Developed by Jack Homer, Homer Consulting

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Different Modeling Approaches For Different Purposes

Logic Models(flowcharts, maps or

diagrams)

System Dynamics(causal loop diagrams and

simulation models)

Forecasting Models

• Articulate steps between actions and anticipated effects

• Improve understanding about the plausible effects of a policy over time

• Focus on patterns of change over time (e.g., long delays, worse before better)

• Make accurate forecasts of key variables

• Focus on precision of point predictions and confidence intervals

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Questions for Today

• What are system dynamics models?

• What questions guide their development?

• What the analytic steps involved?

• How can they be used to support learning and effective, transformative action?

• How can we begin thinking about the dynamic forces that affect HIV prevention trials?

Examples fromDiabetes Modeling

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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

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 System Structure

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Water Glass Model Diagram (Vensim™ software)

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

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Re-Directing the Course of ChangeQuestions from System Modeling and Social Navigation

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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Steps for Developing Dynamic Policy Models

Enact PolicyBuild power and organize actors to

establish chosen policies

Enact PolicyBuild power and organize actors to

establish chosen policies

Test Proposed Policies

Search for policies that best govern change

Test Proposed Policies

Search for policies that best govern change

Run Simulation Experiments

Compare model’s behavior to expectations and/or data to

build confidence in the model

Run Simulation Experiments

Compare model’s behavior to expectations and/or data to

build confidence in the model

Create a Dynamic Hypothesis

Identify and map the main causal forces that

create the problem

Create a Dynamic Hypothesis

Identify and map the main causal forces that

create the problem

Convert the Map Into a Simulation Model

Formally quantify the hypothesis using allavailable evidence

Convert the Map Into a Simulation Model

Formally quantify the hypothesis using allavailable evidence

Identify a Persistent Problem One that exists due to dynamic complexity

Identify a Persistent Problem One that exists due to dynamic complexity

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Diabetes System Modeling ProjectWhere is the Leverage for Health Protection?

People withUndiagnosed,Uncomplicated

Diabetes

People withDiagnosed,

UncomplicatedDiabetes

People withDiagnosed,Complicated

Diabetes

People withUndiagnosedPreDiabetes

People withDiagnosed

PreDiabetes

People withUndiagnosed,Complicated

DiabetesPeople with

NormalGlycemic

Levels

DiagnosingDiabetes

DiagnosingDiabetes

Diabetes Detection

Dying fromComplications

DevelopingComplications

Diabetes Control

PreDiabetes Detection

DiagnosingPreDiabetes

DiabetesOnset

PreDiabetes Control

PreDiabetesOnset

Recovering fromPreDiabetes


Obesity Prevention

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.

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Diabetes System Modeling ProjectWhere is the Leverage for Health Protection?

People withUndiagnosed,Uncomplicated

Diabetes

People withDiagnosed,

UncomplicatedDiabetes

People withDiagnosed,Complicated

Diabetes

DiagnosingUncomplicated

Diabetes

People withUndiagnosedPreDiabetes

People withDiagnosed

PreDiabetes

DiagnosingPreDiabetes

DevelopingComplications from

Undx diab

DevelopingDiabetes from Undx

PreD,People with

Undiagnosed,Complicated

Diabetes

DiagnosingComplicated

Diabetes

Dying from UndxComplications

People withNormal

GlycemicLevels

DiabetesDetection

Obese Fraction ofthe Population

Risk forPreDiabetes

Caloric Intake PhysicalActivity

PreDiabetesControl

DiabetesControl

PreDiabetesDetection

MedicationAffordability

Ability to SelfMonitor

ClinicalManagement of

PreDiabetes

Adoption ofHealthy Lifestyle

Clinical Managementof Diagnosed

Diabetes

LivingConditions

PersonalCapacity

PreDiabetesTesting for

Access toPreventive Health

Services Testing forDiabetes

PreDiabetesOnset


Recovering fromPreDiabetes Diabetes

Onset

Dying fromComplications

DevelopingComplications

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Selected Data Sources for Model Calibration• High Risk Population, Incidence, Prevalence, Deaths

– National Diabetes Statistics: http://diabetes.niddk.nih.gov/dm/pubs/statistics/index.htm

– Prevalence of Selected Chronic Conditions: United States, 1990-1992: http://www.cdc.gov/nchs/data/series/sr_10/sr10_194.pdf

– Healthy People 2000 Review, 1997: http://www.cdc.gov/nchs/data/hp2000/hp2k97.pdf

– Deaths: Preliminary Data for 2000: http://www.cdc.gov/nchs/data/nvsr/nvsr49/nvsr49_12.pdf

– Estimated number of adults with prediabetes in the U.S. in 2000: opportunities for prevention, Benjamin SM et al (DDT/CDC), Diabetes Care 26: 645-9, 2003.

– A Dynamic Markov Model for Forecasting Diabetes Prevalence in the United States through 2050, Honeycut AA et al. (DDT/CDC), Health Care Mgmt Sci 6: 155-164, 2003.

• Complications and Benefits of Control– Model of Complications of NIDDM--1. Model Construction and Assumptions, Eastman RC et al,

Diabetes Care 20: 725-734, 1997.

– Model of Complications of NIDDM--2. Analysis of the Health Benefits and Cost-Effectiveness of Treating NIDDM with the Goal of Normoglycemia, Eastman RC et al., Diabetes Care 20: 735-744, 1997.

– The Prevention or Delay of Type 2 Diabetes, position statement from ADA and NIDDK, Diabetes Care 25: 742-749, 2002

– Effect of Improved Glycemic Control on Health Care Costs and Utilization, EH Wagner et al., JAMA 285: 182-189, 2001

– Health Economic Benefits and Quality of Life During Improved Glycemic Control in Patients with Type 2 Diabetes Mellitus: A Randomized, Controlled Double-Blind Trial, Testa MA and Simonson DC, JAMA, 280: 1490-6, 1998

One immediate benefit of the modeling process is often knowledge integration

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Diabetes System Modeling ProjectSimulating 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 Exploring Plausible FuturesDiagnosed Diabetes % of Adults

Obese % of Adults

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 Reduction

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Setting Realistic ExpectationsHP 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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The Simple Physics of Diabetes

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 2010

Pe

op

le w

ith

dia

gn

ose

d d

iab

ete

s p

er

1,0

00

Reported Simulated

Status Quo

Meet Detection Objective (5-4)

Meet Onset Objective (5-2)

HP 2010 Objective (5-3)

HP 2000 Objective

History and Futures for Diabetes PrevalenceReported Trends, HP Objectives, and Simulation Results

A

B

C

D

E

F

G

H

I

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Projecting the Community-Wide Costs and Benefits of “Pursuing Perfection” in Whatcom County, WA

Jack Homer & Gary Hirsch

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Whatcom County Pursuing Perfection (P2) Program

• A patient-centered team approach supported by:

– Electronically shared clinical information: medical record, care plan, medication list

– Idealized design of clinical office practice (IDCOP) for greater access and efficiency

– Evidence-based guidelines

– A clinical care specialist (RN) when needed

– Cost-effective screening & preventive measures

• Initial disease focus: diabetes, heart failure

• Initial community participants: family practice group, cardiology group, geriatric practice, a community health center, the hospital, and three insurers

• Two years of funding by Robert Wood Johnson Foundation

SEA MAR Clinica de la comunidad

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P2 Modeling FrameworkProgramAdoption

Healthcare Utilization

Program Personnel* & Info System

Costs

PatientDisability

Total Costs to Program, Payors,Providers, Patients, & Employers

Pre-ProgramQuality of Care

At-Risk Popn Demographics

Disease Dynamics - Incidence

- Progression- Complications

- Deaths

* Personnel include administrators and staff, process/OD consultants, and clinical care specialists.

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300

200

100

0

2001 2005 2009 2013 2017 2021Year

Deaths from Diabetes 2001-21: Four Scenarios

Status Quo

Full program*

Full program + Medicare drug coverage

Disease management only

* Full program includes community-based screening; “positives” are referred to physician for follow-up testing and counseling.

Disease-related deaths per year

A similar pattern of results is seen for diabetes-related disability losses.

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$(6,000,000)

$(4,000,000)

$(2,000,000)

$0

$2,000,000

$4,000,000

2001 2005 2009 2013 2017 2021

Year

Even during 2003-08 period of increased spending, cost per dollar of disability loss avoided averages only $0.37, and cost per life-year saved only $22,000.

Impact of Full Program on Spending for Diabetes and Heart Failure 2001-21

Including Infrastructure Costs

Excluding Infrastructure Costs(Health care spending only*)

Constant (2001) dollars per year

* Health care spending by insurers and patients pays for physicians, hospital, ancillary services, hospice, home care, skilled nursing facility, exercise rehab, drugs, and implanted devices.

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Financial Impacts 2003-08: Winners and Losers

(in Year 2001 dollars)

Impact on Payor Payments

Private plans +$1,840,415

Medicaid +$3,612,247

Medicare & Medigap -$15,467,915

Patient self payments +$12,020,045

Impact on Provider Finances Revenue Net Income

Primary care practices +$2,220,068 -$411,864

per physician +$16,344 -$3,032

Specialist practices -$1,664,642 -$1,817,969

per physician -$10,515 -$11,484

Hospital -$22,464,098 -$8,830,881

Impact on Other Revenues

Ancillary & other services -$766,832

Pharmaceuticals +$28,315,101

Implanted devices -$3,634,803

Impact on Employer Disability Losses -$8,257,290

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Commitment to a Comprehensive Strategy(based on model sensitivity testing)

• Disease management quickly starts improving health outcomes, but does not by itself reduce total spending

• Preventive measures produce increasing savings over time

• Solid program execution that delivers expected health benefits is necessary to achieve savings

• Clinical care specialists must be sufficient to meet referral demand

• Full drug coverage for the elderly would further improve health outcomes and program cost-effectiveness

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Toward a Dynamic View of HIV Prevention Trials

UninfectedPeople

People HIV+no Symptoms

People withAIDS

HIV infection AIDS onset AIDS deathsand illness

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UninfectedPeople


People withAIDS

HIV infection AIDS onset AIDS deathsand illness

1Disease burden

motivates preventioninterventions

Effective primaryprevention

interventions

-

Perceived need forprevention

interventions


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-

UninfectedPeople


People withAIDS

HIV infection AIDS onset

Demonstratedeffectiveness of

preventioninterventions

AIDS deathsand illness

Perceived need forprevention research

New and continuingprevention trials Willingness to

participate in trials

1Disease burden


2HIV prevalence and need

for evidence motivateprevention research


interventions

-


interventionsHIV prevalence


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-

UninfectedPeople


People withAIDS


Advocacy anddemand for care




Guaranteedaccess to care fortrial participants




1Disease burden




3

Guaranteed access tocare motivates trial

participation


interventions

-


interventions

IRB concerns aboutfinancial coercion

-

HIV prevalence

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-

UninfectedPeople


People withAIDS










Costs of caring fortrial participants

Funds available fortrial research

activities -

1Disease burden




Total budget fortrials

Trialparticipants

4

Cost of guaranteedcare limits research

3


participation


interventions

-


interventions


-

HIV prevalence

HIV prevalence intrial participants

Researcher concernsabout potential costs of

guaranteed care

-

<Demonstratedeffectiveness of prevention

interventions>

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-

UninfectedPeople


People withAIDS












activities -

1Disease burden





Trialparticipants

4


3


participation


interventions

-


interventions

Perceived healthbenefits vs risks of

participation


-

HIV prevalence



guaranteed care

-

5

Mounting evidence ofbenefit encourages further

trial participation


interventions>

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-

UninfectedPeople


People withAIDS










Access to careoutside of trials



activities -

-

1Disease burden





Trialparticipants

4


3


participation


interventions

-


interventions

6

Universal accesseliminates need for

guarantee but therebyundercuts participation


participation


-

HIV prevalence



guaranteed care

-

5


trial participation


interventions>

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-

UninfectedPeople


People withAIDS










Access to careoutside of trials



activities -

-

1Disease burden





Trialparticipants

4


3


participation


interventions

-


interventions

6

Universal accesseliminates need for

guarantee but therebyundercuts participation

-

7

Universal access delaysAIDS onset, prolongs life,

and reduces disease burden


participation


-

HIV prevalence



guaranteed care

-

5


trial participation


interventions>

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Widespread Interest in the Promise of a Systems Orientation

See: http://www.cdc.gov/syndemics/ajph-systems.htm

Submission Deadline: February 1, 2005

Syndemics Prevention Network System Dynamics and the Physics of Possibility in Health Policy Tools...

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