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Syndemics
Prevention Network
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
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
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 HIV+no Symptoms
People withAIDS
HIV infection AIDS onset AIDS deathsand illness
1Disease burden
motivates preventioninterventions
Effective primaryprevention
interventions
-
Perceived need forprevention
interventions
Toward a Dynamic View of HIV Prevention Trials
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-
UninfectedPeople
People HIV+no Symptoms
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
motivates preventioninterventions
2HIV prevalence and need
for evidence motivateprevention research
Effective primaryprevention
interventions
-
Perceived need forprevention
interventionsHIV prevalence
Toward a Dynamic View of HIV Prevention Trials
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Toward a Dynamic View of HIV Prevention Trials
-
UninfectedPeople
People HIV+no Symptoms
People withAIDS
HIV infection AIDS onset
Advocacy anddemand for care
Demonstratedeffectiveness of
preventioninterventions
AIDS deathsand illness
Guaranteedaccess to care fortrial participants
Perceived need forprevention research
New and continuingprevention trials Willingness to
participate in trials
1Disease burden
motivates preventioninterventions
2HIV prevalence and need
for evidence motivateprevention research
3
Guaranteed access tocare motivates trial
participation
Effective primaryprevention
interventions
-
Perceived need forprevention
interventions
IRB concerns aboutfinancial coercion
-
HIV prevalence
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Toward a Dynamic View of HIV Prevention Trials
-
UninfectedPeople
People HIV+no Symptoms
People withAIDS
HIV infection AIDS onset
Advocacy anddemand for care
Demonstratedeffectiveness of
preventioninterventions
AIDS deathsand illness
Guaranteedaccess to care fortrial participants
Perceived need forprevention research
New and continuingprevention trials Willingness to
participate in trials
Costs of caring fortrial participants
Funds available fortrial research
activities -
1Disease burden
motivates preventioninterventions
2HIV prevalence and need
for evidence motivateprevention research
Total budget fortrials
Trialparticipants
4
Cost of guaranteedcare limits research
3
Guaranteed access tocare motivates trial
participation
Effective primaryprevention
interventions
-
Perceived need forprevention
interventions
IRB concerns aboutfinancial coercion
-
HIV prevalence
HIV prevalence intrial participants
Researcher concernsabout potential costs of
guaranteed care
-
<Demonstratedeffectiveness of prevention
interventions>
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Toward a Dynamic View of HIV Prevention Trials
-
UninfectedPeople
People HIV+no Symptoms
People withAIDS
HIV infection AIDS onset
Advocacy anddemand for care
Demonstratedeffectiveness of
preventioninterventions
AIDS deathsand illness
Guaranteedaccess to care fortrial participants
Perceived need forprevention research
New and continuingprevention trials Willingness to
participate in trials
Costs of caring fortrial participants
Funds available fortrial research
activities -
1Disease burden
motivates preventioninterventions
2HIV prevalence and need
for evidence motivateprevention research
Total budget fortrials
Trialparticipants
4
Cost of guaranteedcare limits research
3
Guaranteed access tocare motivates trial
participation
Effective primaryprevention
interventions
-
Perceived need forprevention
interventions
Perceived healthbenefits vs risks of
participation
IRB concerns aboutfinancial coercion
-
HIV prevalence
HIV prevalence intrial participants
Researcher concernsabout potential costs of
guaranteed care
-
5
Mounting evidence ofbenefit encourages further
trial participation
<Demonstratedeffectiveness of prevention
interventions>
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Toward a Dynamic View of HIV Prevention Trials
-
UninfectedPeople
People HIV+no Symptoms
People withAIDS
HIV infection AIDS onset
Advocacy anddemand for care
Demonstratedeffectiveness of
preventioninterventions
AIDS deathsand illness
Guaranteedaccess to care fortrial participants
Perceived need forprevention research
New and continuingprevention trials Willingness to
participate in trials
Access to careoutside of trials
Costs of caring fortrial participants
Funds available fortrial research
activities -
-
1Disease burden
motivates preventioninterventions
2HIV prevalence and need
for evidence motivateprevention research
Total budget fortrials
Trialparticipants
4
Cost of guaranteedcare limits research
3
Guaranteed access tocare motivates trial
participation
Effective primaryprevention
interventions
-
Perceived need forprevention
interventions
6
Universal accesseliminates need for
guarantee but therebyundercuts participation
Perceived healthbenefits vs risks of
participation
IRB concerns aboutfinancial coercion
-
HIV prevalence
HIV prevalence intrial participants
Researcher concernsabout potential costs of
guaranteed care
-
5
Mounting evidence ofbenefit encourages further
trial participation
<Demonstratedeffectiveness of prevention
interventions>
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Prevention Network
Toward a Dynamic View of HIV Prevention Trials
-
UninfectedPeople
People HIV+no Symptoms
People withAIDS
HIV infection AIDS onset
Advocacy anddemand for care
Demonstratedeffectiveness of
preventioninterventions
AIDS deathsand illness
Guaranteedaccess to care fortrial participants
Perceived need forprevention research
New and continuingprevention trials Willingness to
participate in trials
Access to careoutside of trials
Costs of caring fortrial participants
Funds available fortrial research
activities -
-
1Disease burden
motivates preventioninterventions
2HIV prevalence and need
for evidence motivateprevention research
Total budget fortrials
Trialparticipants
4
Cost of guaranteedcare limits research
3
Guaranteed access tocare motivates trial
participation
Effective primaryprevention
interventions
-
Perceived need forprevention
interventions
6
Universal accesseliminates need for
guarantee but therebyundercuts participation
-
7
Universal access delaysAIDS onset, prolongs life,
and reduces disease burden
Perceived healthbenefits vs risks of
participation
IRB concerns aboutfinancial coercion
-
HIV prevalence
HIV prevalence intrial participants
Researcher concernsabout potential costs of
guaranteed care
-
5
Mounting evidence ofbenefit encourages further
trial participation
<Demonstratedeffectiveness of prevention
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