March 25, 2009 Seattle, WA
description
Transcript of March 25, 2009 Seattle, WA
Crafting Integrated Strategies to Prevent and Manage Chronic Disease
Using System Dynamics
Chronic Disease Academy
March 25, 2009
Seattle, WA
Presenters • Phil Huang
– Medical Director for City of Austin Department of Health and Human Services, formerly Chronic Disease Director for TX
• Patty Mabry – Office of Behavioral and Social Sciences Research,
National Institutes of Health• Bobby Milstein
– Coordinator, Syndemics Prevention Network, Centers for Disease Control and Prevention
• Diane Orenstein – Technical Lead, Division for Heart Disease and Stroke
Prevention, Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention
• Kris Wile – Sustainability Institute, System Dynamics Facilitator
and Modeler
Workshop Agenda
Wednesday, March 25
- Why are we here?
- Introduction to System Analysis in Public Health
- Introduction to the CV/Chronic Disease Risk Model
- Testing Strategies for Reducing Preventable CVD and Chronic Disease Costs
Demonstration
Self-guided Exploration of Results
Lunch
- Building an Integrated Chronic Disease Strategy
- Conclusions from model
- Dialogue: Importance of Context
- Lessons Learned
- Dialogue: Future Opportunities
Adjourn
Office of Behavior and Social Science’s Vision at NIH
To mobilize the biomedical, behavioral, and social science research communities as partners in interdisciplinary research to solve the most pressing health challenges faced by our society.
Programmatic Directions to Achieve the Vision:
– Transdiciplinary science
– “Next generation”, basic science
– Problem-based, outcomes oriented strengthen the science of dissemination
– Systems - thinking for population impact
The Importance of Partnership for OBSSR
Adapted from Glass, McAtee (2006). Soc. Sci. Medicine, 62: 1650-1671
Health as a continuum between biological, behavioral and social factors across the lifespan and across generations
Simulation Modeling and Experimentation
• Pandemic flu
• Tobacco use
• Obesity, Diabetes
• Health inequalities
• “Non-health factors”
• Chronic disease
• Health care delivery
• Stress, mental illness, worksites, policy……….
Understanding the “Whole” System
2000 2001 2002 2003 2004 2005 2006 2007 2008
Selected Examples from CDC’s Growing Portfolio of 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
Questions Addressed by System Dynamics ModelingExploring Strategies to Redirect the Course of Change
Prevalence of Diagnosed Diabetes, US
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.
Why?
Where?
How?
Who?
What?
Markov Forecasting Model
Simulation Experiments
in Action Labs
Time Series Models
Describe trends
Multivariate Stat 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
Increasing:
• Depth of causal theory
• Robustness for longer-term projection
• Value for developing policy insights
• Degrees of uncertaintyDynamic Simulation Models
Anticipate new trends, learn about policy consequences,
and set justifiable goals
Tools for Policy Planning & Evaluation
Different Modeling Approaches For Different Purposes
Logic Models(flowcharts, maps or
diagrams)
System Dynamics(causal loop diagrams, stock-flow structures,
simulation studies, action labs)
Forecasting Models (regression models, Monte Carlo 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, better before worse)
• Test dynamic hypotheses through simulation studies
• Inspire action through visceral, game-based learning
• Make accurate forecasts of key variables
• Focus on precision of point predictions and confidence intervals
Brief Background on System Dynamics Modeling
Compartmental models resting on a general theory of how systems change (or resist change) – often in ways we don’t expect
– Developed for corporate policies in the 1950s, and applied to health 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.
What is a System? What are Dynamics?
System (Structure) = Stocks + Flows + Feedback Loops +…
• Stocks are accumulations of flows (of population, resources, changing goals, perceptions, etc.)
• Feedback loops link accumulations back to decisions that alter the flows: only 2 types (goal-seeking, self-reinforcing)
• Delays complicate things further
• As do non-linearities (need for critical mass, saturation effects)
Dynamics = Behavior over time
• Patterns in time series data (growth, fluctuation, etc.)
• Visible relationships of two or more variables (move together, move opposite, lead-lag, etc.)
StockFlow
Feedbackinfluence
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 simulation model, integrating the best information and insight available;
• Compare results from simulated “What If…” experiments to identify intervention policies that might plausibly alleviate the problem;
• Conduct sensitivity analyses 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
Simulations for Learning in Dynamic Systems
Morecroft JDW, Sterman J. Modeling for learning organizations. Portland, OR: Productivity Press, 2000.
Sterman JD. Business dynamics: systems thinking and modeling for a complex world. Boston, MA: Irwin McGraw-Hill, 2000.
Multi-stakeholder Dialogue
Dynamic Hypothesis (Causal Structure)
X Y
Plausible Futures (Policy Experiments)
Obese fraction of Adults (Ages 20-74)
0%
10%
20%
30%
40%
50%
1970 1980 1990 2000 2010 2020 2030 2040 2050
Fra
ctio
n o
f p
op
n 2
0-74
Getting Oriented
• Introduction – Name, Organization, What you do– What are you hoping to get out of today?
• Then talk with others at your tables:– What are the largest strategic issues you
see in chronic disease?
• After 10 minutes, we’ll return to large group to share highlights– Biggest strategic challenges?
CDC Diabetes System Modeling ProjectDiscovering Dynamics Through State-based Action Labs & Models
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.
Inflow
Volume
Outflow
Developing
Burden ofDiabetes
Total Prevalence(people with diabetes)
Unhealthy Days(per person with
diabetes)
Costs(per person with diabetes)
People withDiagnosedDiabetes
Diagnosis Deaths
ab
People withUndiagnosedPreDiabetes
Developing
DiabetesOnset
c
d
People withNormal
Blood SugarLevels
PreDiabetesOnset
Recovering fromPreDiabetes
e
DiabetesManagement
DiabetesDiagnosis
Obesity in theGeneral
Population
PreDiabetesDetection &
Management
People withUndiagnosed
Diabetes
Deaths
Diabetes Model: Diabetes Burden is Driven by Population Flows
Diabetes Burden is Driven by Population Flows
Inflow
Volume
Outflow
Developing
Burden ofDiabetes
Total Prevalence(people with diabetes)
Unhealthy Days(per person with
diabetes)
Costs(per person with diabetes)
People withDiagnosedDiabetes
Diagnosis Deaths
ab
People withUndiagnosedPreDiabetes
Developing
DiabetesOnset
c
d
People withNormal
Blood SugarLevels
PreDiabetesOnset
Recovering fromPreDiabetes
e
DiabetesManagement
DiabetesDiagnosis
Obesity in theGeneral
Population
PreDiabetesDetection &
Management
People withUndiagnosed
Diabetes
Deaths
Standard boundary
This larger view takes us beyond standard epidemiological models and most intervention programs
Diabetes System Dynamics Modeling ProjectConfirming Fit to Historical Trends (2 examples out of 10)
Diagnosed Diabetes % of AdultsObese % of Adults
0%
10%
20%
30%
40%
1980 1985 1990 1995 2000 2005 2010
Obese % of adults
Data (NHANES)
Simulated
0%
2%
4%
6%
8%
1980 1985 1990 1995 2000 2005 2010
Diagnosed diabetes % of adults
Data (NHIS)
Simulated
The growth of diabetes prevalence since 1980 has
been driven by growth in obesity prevalence Obese Fraction and Diabetes per Thousand
1300.7
850.35
400
1980 1990 2000 2010 2020 2030 2040 2050Time (Year)
Diabetes Prevalenc
e
Obesity Prevalenc
e
Risk multiplier on diabetes onset from
obesity = 2.6
Prevalence=92 AND RISING
Baseline Scenario: Obesity to increase little after 2006, diabetes keeps growing robustly for another 20-25 years
Obese Fraction and Diabetes per Thousand1300.7
850.35
400
1980 1990 2000 2010 2020 2030 2040 2050Time (Year)
Diabetes Prevalenc
e
Obesity Prevalenc
e
Diabetes prevalence keeps growing after obesity stops
WHY?
With high (even if flat) onset, prevalence tub
keeps filling until deaths (4-5%/yr)=onset
Onset=6.3 per thou
Estimated 2006 values
Death=3.8 per thou
Risk multiplier on diabetes onset from
obesity = 2.6
Unhealthy days impact of prevalence growth, as affected by diabetes management: Past and one possible future
Unhealthy Days per Thou and Frac ManagedObese Fraction and Diabetes per Thousand1300.7
850.35
400
1980 1990 2000 2010 2020 2030 2040 2050Time (Year)
Diabetes Prevalenc
e
Obesity Prevalenc
e
5000.65
25001980 1990 2000 2010 2020 2030 2040 2050
3750.325
Unhealthy Daysfrom Diabetes
Managed
fraction
Diabetes prevalence keeps growing after
obesity stops
If disease management gains end, the burden
grows
Reduction in unhealthy days per complicated case if
conventionally managed: 33%; if intensively managed: 67%
A Sequence of What-if Simulations
People with Diabetes per Thousand Adults150
125
100
75
501980 1990 2000 2010 2020 2030 2040 2050
Monthly Unhealthy Days from Diabetes per Thou500
450
400
350
300
250
1980 1990 2000 2010 2020 2030 2040 2050
Base
Base
Start with the base case or “status quo”: no improvements in diabetes management or prediabetes management after 2006
What if there were further Increases in Diabetes Management?
People with Diabetes per Thousand Adults150
125
100
75
501980 1990 2000 2010 2020 2030 2040 2050
Monthly Unhealthy Days from Diabetes per Thou500
450
400
350
300
250
1980 1990 2000 2010 2020 2030 2040 2050
Base
Diab mgt Base
More people living with diabetes
Keeping the burden at bay for nine years
longer
Diab mgt
Increase fraction of diagnosed diabetes getting managed from 58% to 80% by 2015. (No change in the mix of conventional and intensive.) What do you think will happen?
Diabetes mgmt does nothing to slow the growth of prevalence—in
fact, it increases it. As soon as diabetes mgmt stops improving, unhealthy days start to grow as
fast as prevalence.
What if there was a huge push for Prediabetes Management?
People with Diabetes per Thousand Adults150
125
100
75
50
1980 1990 2000 2010 2020 2030 2040 2050
Monthly Unhealthy Days from Diabetes per Thou500
450
400
350
300
250
1980 1990 2000 2010 2020 2030 2040 2050
Base
PreD mgmt
Base
PreD mgmt
The improvement is relatively modest—the growth is not stopped
Increase fraction of prediabetics getting managed from 6% to 32% by 2015. (Half of those under intensive mgmt by 2015.) No increase in diabetes mgmt. What do you think will happen?
Diabetes onset rate reduced 12% relative to base run. Not nearly
enough to offset the excess onset due to high obesity. By 2050,
diabetes prevalence reduced only 9% relative to base run.
Diabetes Model: What if Obesity is Reduced?Two Scenarios
Obese Fraction of Adult Population
0.4
0.3
0.2
0.1
01980 1990 2000 2010 2020 2030 2040 2050
Base
Obesity 25%
Obesity 18%
What if it were possible—in addition to the prediabetes mgmt intervention - to gradually lower the fraction obese from 34% (2006) to the 1994 value of 25% by 2030? Or, to the 1984 value of 18%?
Diabetes: What if we Managed Prediabetes AND Reduced Obesity?
The more you reduce obesity, the sooner you
stop the growth in diabetes—and the more
you bring it down
… Same with the burden of diabetes
People with Diabetes per Thousand Adults150
125
100
75
50
1980 1990 2000 2010 2020 2030 2040 2050
Monthly Unhealthy Days from Diabetes per Thou500
450
400
350
300
250
1980 1990 2000 2010 2020 2030 2040 2050
Base
PreD mgmt
PreD & Ob 25%
PreD & Ob 18%
Base
PreD mgmt
PreD & Ob 18%
PreD & Ob 25%
What do you think will happen if, in addition to PreD mgmt, obesity is reduced moderately by 2030? What if it is reduced even more?
Why is obesity reduction so powerful? Mainly because of its strong effect on onset rate among prediabetics; but,
also, because it reduces PreD prevalence itself. However, achieving significant obesity reduction takes a
long time.
What if Intervened Effectively Upstream AND Downstream
People with Diabetes per Thousand Adults150
125
100
75
50
1980 1990 2000 2010 2020 2030 2040 2050
Monthly Unhealthy Days from Diabetes per Thou500
450
400
350
300
250
1980 1990 2000 2010 2020 2030 2040 2050
Base
PreD mgmt PreD mgmt
Base
PreD & Ob 25%
Pred & Ob 25%
All 3 --PreD & Ob 25% & Diab mgmt
All 3
With a combination of effective upstream and downstream interventions we could hold the burden of diabetes nearly flat
through 2050!
With pure upstream intervention, burden still grows for many years before turning around. What do you think will happen if we add the prior diabetes mgmt intervention on top of the PreD+Ob25 one?
Downstream improvement acts quickly against burden but cannot continue
forever. Significant upstream gains are thus
essential but will likely take 15+ years to
achieve. A flat-burden future is possible but
requires simultaneous action on both fronts.
CDC Obesity Dynamics Modeling Project Contributors
Core Design Team• Dave Buchner• Andy Dannenberg• Bill Dietz• Deb Galuska• Larry Grummer-Strawn• Anne Hadidx• Robin Hamre• Laura Kettel-Khan• Elizabeth Majestic • Jude McDivitt• Cynthia Ogden• Michael Schooley
System Dynamics Consultants• Jack Homer• Gary Hirsch
Time Series Analysts
• Danika Parchment
• Cynthia Ogden
• Margaret Carroll
• Hatice Zahran
Project Coordinator• Bobby Milstein
Workshop Participants• Atlanta, GA: May 17-18 (N=47)• Lansing, MI: July 26-27 (N=55)
Homer J, Milstein B, Dietz W, Buchner D, Majestic D. Obesity population dynamics: exploring historical growth and plausible futures in the U.S. 24th International Conference of the System Dynamics Society; Nijmegen, The Netherlands; July 26, 2006.
Cover of "The Economist", Dec. 13-19, 2003Cover of "The Economist", Dec. 13-19, 2003.
Focusing on Life-Course Dynamics
• Explore likely consequences of possible interventions affecting caloric balance (intake less expenditure) – How much impact on obesity prevalence?
– How long will it take to see?
– Should we target particular subpopulations? (age, sex, weight category; lack data for race, ethnicity)
• Consider interventions broadly but leave details (composition, coverage, efficacy, cost) outside model boundary for now– Available data inadequate
– Would require a separate research effort to estimate these details
– Not addressing feedback loops of reinforcement and resistance
– Not addressing cost-effectiveness
Obesity Dynamics Over the Decades Dynamic Population Weight Framework
Dynamic Population Weight Framework
Population by Age (0-99) and Sex
Flow-rates betweenBMI categories
Overweight andobesity prevalence
Birth Immigration
Death
CaloricBalance
Yearly aging
NotOverweight
ModeratelyOverweight
ModeratelyObese
SeverelyObese
Trends and PlannedInterventions
Changes in the Physicaland Social Environment
Weight Loss/MaintenanceServices for Individuals
Data source: National Center for Health Statistics, CDC: National Health Examination Survey (NHES) 1960-1970, National Health and Nutrition Examination Survey (NHANES) 1971-2002.
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.
Alternative Futures for Adult Obesity
Obese fraction of Adults (Ages 20-74)
0%
10%
20%
30%
40%
50%
1970 1980 1990 2000 2010 2020 2030 2040 2050
Fra
cti
on
of
po
pn
20-
74
Base SchoolYouth AllYouth
School+Parents AllAdults AllAges
AllAges+WtLoss
Results of Simulated InterventionsEnvironmental change approach
(reduce caloric balances to their 1970 values by 2015 for selected age ranges)
• Youth interventions have only small impact on overall adult obesity (assuming adult habits determined by adult environments—not by childhood1)
• Slow decline in overall adult obesity, even when program covers all ages
Targeted weight loss approach(obese lose 4 lbs per year, program terminated 2020)
• Such a program could accelerate progress and “buy time” for environmental change (but first, need to find a cost-effective program with lasting benefits—minimal relapse)
Need to assure caloric balance throughout all ages, particularly adulthood.
Contrast today’s narrow national focus on school-age youth.
Also need research on extent to which adult habits are determined by childhood.2
Need to assure caloric balance throughout all ages, particularly adulthood.
Contrast today’s narrow national focus on school-age youth.
Also need research on extent to which adult habits are determined by childhood.2
1. Christakis and Fowler. NEJM 357, 2007.
2. Bar-Or O., PCPFS Research Digest Series 2, No. 4, 1995.
Simulating the Dynamics of Cardiovascular Health and
Related Risk Factors
Work in Progress
This work was funded by the CDC’s Division for Heart Disease and Stroke Prevention and by the National Institutes of Health’s
Office of Behavioral and Social Science Research. The work was done in collaboration with the Health and Human Services
Department of Austin/Travis County, Texas, and with Integrated Care Collaboration of Central Texas. The external contractors are
Sustainability Institute and RTI International.
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.
Cardiovascular Disease and Risks Remain Among the Leading Causes of Death
United States Texas
1. Heart Disease 26.6% 1. Heart Disease 25.7%
2. Cancer 22.8% 2. Cancer 21.9%
3. Stroke 5.9% 3. Stroke 6.0%
4. Chronic Lower Respiratory Disease
5.3% 4. Accidents 5.5%
5. Accidents 4.8%5. Chronic Lower Respiratory Disease
5.1%
6. Diabetes 3.1% 6. Diabetes 3.6%
*US: CDC/National Center for Health Statistics, Vol. 56, No.10, April 2008; TX: TX Dept. of State Health Services Preliminary Vital Statistics Table 16
Fraction of total deaths in 2005*…
Reducing Disability & Risk of
Recurrent CVD
Detecting & Treating Acute CVD Events
Controlling Increased
CVD Risk
Preserving Low
CVD Risk
From Healthy People 2010: 4 Levels of Prevention for Cardiovascular Diseases
Disability and Risk of CVD Recurrence
Acute CVD Events
Increased CVD Risk
Low CVD Risk
4 levels of prevention correspond to 4 States of Cardiovascular Health:
NUTRITION, PHYSICAL ACTIVITY & STRESS
• Salt intake• Saturated/Trans fat intake• Fruit/Vegetable intake• Net caloric intake• Physical activity• Chronic stress
CVD RISK FACTORPREVALENCE
& CONTROL
• Hypertension• High cholesterol• Diabetes• Obesity• Smoking• Secondhand smoke• Air pollution exposure
UTILIZATION OF SERVICES
• Behavioral change• Social support• Mental health• Preventive health
COSTS (CVD & NON-CVD) ATTRIBUTABLE TO
RISK FACTORS
LOCAL CONTEXT
• Eating & activity options
• Smoking policies
• Socioeconomic conditions
• Environmental policies
• Health care options
• Support service options
• Media and events
Local capacity for leadership & organizing
LOCAL ACTIONS
ESTIMATED FIRST-TIME CVD EVENTS
• CHD (MI, Angina, Cardiac Arrest)
• Stroke
• Total CVD (CHD, Stroke, CHF, PAD)
Preventing and Managing Risk Factors for CVD
Disability and Risk of CVD Recurrence
Acute CVD Events
Increased CVD Risk
Low CVD Risk
NUTRITION, PHYSICAL ACTIVITY & STRESS
• Salt intake• Saturated/Trans fat intake• Fruit/Vegetable intake• Net caloric intake• Physical activity• Chronic stress
CVD RISK FACTORPREVALENCE
& CONTROL
• Hypertension• High cholesterol• Diabetes• Obesity• Smoking• Secondhand smoke• Air pollution exposure
UTILIZATION OF SERVICES
• Behavioral change• Social support• Mental health• Preventive health
COSTS (CVD & NON-CVD) ATTRIBUTABLE TO
RISK FACTORS
LOCAL CONTEXT
• Eating & activity options
• Smoking policies
• Socioeconomic conditions
• Environmental policies
• Health care options
• Support service options
• Media and events
Local capacity for leadership & organizing
LOCAL ACTIONS
ESTIMATED FIRST-TIME CVD EVENTS
• CHD (MI, Angina, Cardiac Arrest)
• Stroke
• Total CVD (CHD, Stroke, CHF, PAD)
Interventions Through Local Context
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 (in press).
Purpose of the Cardiovascular Risk Model
• How do local conditions affect multiple risk factors for CVD, and how do those risks affect population health status and costs over time?
• How do different local interventions affect cardiovascular health and related expenditures in the short- and long-term?
• How might local health leaders better balance their policy efforts given limited resources?
The CDC has partnered 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 data of the Austin team, which has been supported by the CDC’s “STEPS” program since 2004.
The CDC has partnered 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 data of the Austin team, which has been supported by the CDC’s “STEPS” program since 2004.
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
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 Populationaging
Air pollutioncontrol regulations
Direct Risk Factors
Smoking
Secondhandsmoke
First-time CVevents and
deaths
Particulate airpollution
Downwardtrend in CV
event fatalityChronic Disorders
High BP
Highcholesterol
Diabetes Populationaging
Indirect Risk Factors
Smoking
Obesity
Secondhandsmoke
Healthinessof diet
Extent ofphysical activity
Psychosocialstress
Diagnosisand control
First-time CVevents and
deaths
Particulate airpollution
Utilization ofquality primary
care
Downwardtrend in CV
event fatalityChronic Disorders
High BP
Highcholesterol
Diabetes Populationaging
Tobacco and Air Quality Interventions
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
Particulate airpollution
Utilization ofquality primary
care
Tobacco taxes andsales/marketing
regulations
Downwardtrend in CV
event fatalityChronic Disorders
Anti-smokingsocial marketing
High BP
Highcholesterol
Diabetes Populationaging
Air Quality Interventions
Smoking
Obesity
Secondhandsmoke
Healthinessof diet
Extent ofphysical activity
Psychosocialstress
Diagnosis andcontrol
First-time CVevents and deaths
Access to and marketingof smoking quit products
and services
Particulate airpollution
Utilization ofquality primary
care
Tobacco taxes andsales/marketing
regulations
Smoking bans atwork and public
places
Downward trend inCV event fatality
Chronic Disorders
Anti-smokingsocial marketing
High BP
Highcholesterol
Diabetes
Air pollutioncontrol regulations
Populationaging
Health Care Interventions
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 ofprimary care
Particulate airpollution
Utilization ofquality primary
care
Tobacco taxes andsales/marketing
regulations
Downwardtrend in CV
event fatality
Quality of primarycare provision
Chronic Disorders
Anti-smokingsocial marketing
High BP
Highcholesterol
Diabetes Populationaging
Smoking bans atwork and public
placesAir pollution
control regulations
Interventions Affecting Stress
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 ofprimary care
Particulate airpollution
Utilization ofquality primary
care
Tobacco taxes andsales/marketing
regulations
Downwardtrend in CV
event fatality
Quality of primarycare provision
Chronic Disorders
Anti-smokingsocial marketing
High BP
Highcholesterol
Diabetes Populationaging
Smoking bans atwork and public
placesAir pollution
control regulations
Healthy Diet Interventions
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 ofprimary care
Particulate airpollution
Utilization ofquality primary
care
Tobacco taxes andsales/marketing
regulations
Junk food taxes andsales/marketing
regulations
Downwardtrend in CV
event fatality
Quality of primarycare provision
Chronic Disorders
Anti-smokingsocial marketing
High BP
Highcholesterol
Diabetes Populationaging
Smoking bans atwork and public
placesAir pollution
control regulations
Physical Activity & Weight Loss Interventions
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
Anti-smokingsocial marketing
High BP
Highcholesterol
Diabetes
Air pollutioncontrol regulations
Populationaging
Adding Up the Costs
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
Air pollutioncontrol regulations
Populationaging
Adding Up the Costs
Cardiovascular event costs• Medical costs (ER, inpatient, rehab)—for non-fatal & fatal events• Productivity (morbidity) losses* from non-fatal events• Productivity (premature mortality) losses* from fatal events
Non-cardiovascular complications of risk factors• Hospital costs due to non-CV complications of diabetes (e.g., kidneys,
eyes, feet), high BP, & smoking• Productivity (morbidity) losses* from non-fatal complications of diabetes,
high BP, smoking, & obesity• Productivity (mortality) losses* from fatal complications of smoking (e.g.,
cancer, COPD), diabetes, high BP, & obesity
Costs of managing risk factors• Medications & visits for diabetes, high BP, high cholesterol—by level of
care (high quality = 2 – 2.5x cost of mediocre care)• Other services: Mental health services, Weight loss services, Smoking
quit services & products
Human capital approach based on: Haddix, Teutsch, Corso, Prevention Effectiveness, 2003 (2nd ed, Tables 1.1b and 1.1c).
Relative size of included Complication Costs
CV EventsNon-CV
Complications of Diabetes
Non-CV Complications
of Hypertension
Non-CV Complications
of Smoking
Non-CV Complications
of Obesity
Direct medical costs of
complications++ ++* +* +* 0*
Indirect productivity
losses: disability
++ ++* +* +* +*
Indirect productivity
losses: premature
death
++ ++ + ++ +
* Non-CV hospitalization costs & lost workdays estimated from MEPS 2000-03 linked with NHIS. The regression analysis controlled for demographics, CVD, and unrelated diseases (e.g., HIV).
Data Sources for Modeling CVD Risk• Census
– Population, deaths, births, net immigration, health coverage
• AHA & NIH statistical reports – Cardiovascular events, deaths, and prevalence (CHD, stroke, CHF, PAD)
• National Health and Nutrition Examination Survey (NHANES) – Risk factor prevalences by age (18-29, 30-64, 65+) and sex (M, F)– Chronic disorder diagnosis and control (hypertension, high cholesterol, diabetes)
• Behavioral Risk Factor Surveillance System (BRFSS)– Diet & physical activity– Primary care utilization– Lack of needed emotional/social support Psychosocial stress
• Medical Examination Panel (MEPS) / National Health Interview (NHIS) – Medical and productivity costs attributable to smoking, obesity, and chronic disorders
• Research literature– CVD risk calculator, and relative risks from SHS, air pollution, obesity, and inactivity– Medical and productivity costs of cardiovascular events
• Questionnaires for CDC and Austin teams (expert judgment)– Potential effects of social & services marketing on utilization behavior– Effects of behavioral services on smoking, weight loss, stress reduction– Relative risks of stress for high BP, high cholesterol, smoking, and obesity
Calculating First-Time CV Events & Deaths
Based on well-established Framingham approach for calculating probability of first-time events & deaths in individuals• CVD = CHD (MI, angina, cardiac arrest) + Stroke/TIA + CHF + PAD
Modifies individual-level risk calculator for use with populations• Uses prevalences of uncontrolled chronic disorders by sex/age group
• Introduces secondhand smoke and pollution as additional risk factors
• Combines risks multiplicatively to account for overlapping conditions
• Adjustment exponents reproduce synergies seen in individual-level calculator
• Adjustment multipliers reproduce AHA event and death frequencies for 2003
- Anderson et al, Am Heart J 1991 (based on Framingham MA population N=5573, 1968-1987)
- Homer “Risk calculation in the CVD model” project document, June 19, 2007
- NHANES 1988-94 & 1999-04
- AHA Heart Disease and Stroke Statistics – 2006 Update
Interactive Model Guide
• Details key assumptions and sources of evidence for each relationship in the model
• On the CD ROM for participant today– Called “CVD interactive guide v8m.ppt”
– Functions in slide show mode
• Afterward, we will have an opportunity for remote Q&A with Jack Homer, Lead Modeler, and Justin Trogdon, Cost data expert.
Where are your efforts to manage chronic disease?
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
Air pollutioncontrol regulations
Populationaging
Individual Strategy Fly-by Exercise
Your goal is to reduce CV deaths and reduce total risk factor consequence costs
… but you have limited resources.
• Pick 6 interventions for your strategy• Record your selected interventions on
your sheet under Section #1• Questions?• You have 10 minutes.
A Base Case Scenario for Comparison Assumptions for Input Time Series through 2040
• A plausible and straightforward scenario– Assume no further changes in
contextual factors affecting risk factor prevalences
– Any changes in prevalences after 2004 are due to “bathtub” adjustment process and population aging
– Provides an easily-understood basis for comparisons
• Prior to 2004, model reflects declining …– Fraction workplaces allowing
smoking (1990-2003)
– Air pollution (1990-2001)
– Youth smoking (rise 1991-99, decline 1999-2003)
– CV event fatality (1990-2003)
Total RF Complication Costs per Capita
2,000
1,000
0
1990 2000 2010 2020 2030 2040
Complication Costs per 1000 if all risk factors = 0
Also note: Cost minimum if all proximal risk factor prevalences were zero.
Consequence costs would decrease 80%CV death rate would be 60% below the base case.
3,000
No Further Changes in Drivers
No Further Changes in Drivers
Base run behaviors
CVD & Risk Factor Complication Costs and CVD Mortality
0.6
0.3
0
1990 2000 2010 2020 2030 2040
Smoking Prevalence
Air Pollution PM2.5
Diabetes Prevalence
High BP Prevalence
High Cholesterol Prevalence
CV Risk Factor Prevalences30
15
0
Obese Adults
Newly obeseadults
Becoming non-obese or
dying
2040
0
0.4% Obese
1990
Result: Past trends level off after 2004, after which results reflect only slow “bathtub” adjustments in risk factors
• Increasing obesity, high BP, and diabetes
• Decreasing smoking and air pollution
• Increases in risk factors and population aging lead to eventual rebound in deaths
(Air
pollu
tion
only
)
1990 2000 2010 2020 2030 2040
4
3
2
1
0
Deaths from CVD per 1000 if all risk factors = 0
Deaths from CVD per 1000
Complication Costs per 1000
Complication Costs per 1000 if all risk factors = 0
3,000
2,250
1,500
750
0
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 Populationaging
Base case behavior for 1990-2040
1
0Use of Primary Care Services
0.3
0
Stress Prevalence
0.8
0
Poor DietFraction
0.8
0
Inadequate Physical Activity
4
0
0.3
0Smoking
Prevalence
0.4
0
Obesity Prevalence
0.6
0
Secondhand Smoke
Exposure
0.6
0Diabetes
High BP
High cholesterol
30
0
Particulate Air Pollution
PM2.5
3,000
0
CVD & Risk factorcosts per capita
Uncontrolled
CVD Deaths per 1000
Prevalences
mcg per m3
Age 65+ fraction of the population
CV event fatality multiplier
0.3
0
1.5
0
New quality ofprimary care
PRIMARY CARE INTERVENTIONS
PHYSICAL ACTIVITY INTERVENTIONS
AIR QUALITY INTERVENTIONS
TOBACCO INTERVENTIONS
NUTRITIONAL INTERVENTIONS
INTERVENTIONS AFFECTING STRESS
New PC servicesmarketing
New access toprimary care svcs
New multiplier onair pollution
New multiplier onworkplaces allowing
smoking
New social marketingfor healthy diet
New access tohealthy diet
New junk food taxand sales restrict
New socialmarketing for PA New access to PA
New WL servicesmarketing
New access toweight loss svcs
New socialmarketing
against smoking
New tobacco taxand sales restrict
New SQ servicesmarketing
New access tosmoking quit svcs and
products
New multiplier onsources of stress
New MH servicesmarketing
New access tomental health svcs
INDIVIDUAL INTERVENTIONS SELECTOR
WEIGHT LOSS INTERVENTIONS
Area of effect
and type of intervention
– Increasing access – Marketing of
services– Social marketing – Taxes and/or
sales restriction– Others
Intervention Options
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
Air pollutioncontrol regulations
Populationaging
Tracing interventions through the system- Increasing access to physical activity options
Interpreting Cost Results
• Complication costs are for CV and non-CV related complications, both direct and indirect
• Management costs include
– Annual costs for services provided
– Medication costs
• When these costs are less than baseline, the difference is the per capita health cost savings per year – the maximum economically justifiable spending for the intervention
Complication & Management Costs per Capita
3,000
2,000
1990 2000 2010 2020 2030 2040
*
Average annual savings of *$ 49 per capita from interventions to
increase access to physical activity options from 2010 - 2040.
Base Case
Increased Access to Physical Activity options
Develop and Test Your Team Strategies
• Form groups of 4 or 5 people• Same goals –
Reduce CV deaths and total risk factor consequence costs with limited resources.
• Choose 6 interventions for your strategy• Prepare a flipchart to present your strategy.• You have 15 minutes.
• We will test some team strategies with the simulator.
Interactive Results Exploration
Purpose: To develop conclusions about which strategies are most effective to achieve our goals
1. Work in groups of 2-3 with a laptop.
2. As you explore, fill out Section #2 of worksheet.– Which interventions were more or less
powerful than you expected? – Your conclusions? Ideas?
• Please note your questions so we can explore them in the larger group.
• You have 25 minutes.
Break for Lunch
We will begin again promptly at 1:00 pm.
This afternoon, we will:– Revise and test team strategies.
– Share conclusions from this systemic analysis.
– Explore opportunities
Revising Team Strategies
1. Teams choose their goals. What are you trying to achieve? What is your timeframe?
2. Put together a strategy, choosing six different interventions that you think will work.
3. Record on flipchart.
4. We will try them out with the simulator.
5. You have 10 minutes.
Conclusions: Comparing intervention groups
Care• Primary Care Quality = 75%• PC Marketing = 100%• PC Access = 100%
Lifestyle• Physical Activity Access =
100%• Physical Activity Social
Marketing = 100%• Access to Healthy Nutrition =
100%• Healthy Nutrition Social
Marketing = 100%• Stress Multiplier = ½
Air• Tobacco Tax = 100% • Marketing Against Smoking =
100%• Air Pollution Multiplier = ½ • Smoking Bans = 100%
New quality ofprimary care
PRIMARY CARE INTERVENTIONS
PHYSICAL ACTIVITY INTERVENTIONS
AIR QUALITY INTERVENTIONS
TOBACCO INTERVENTIONS
NUTRITIONAL INTERVENTIONS
INTERVENTIONS AFFECTING STRESS
New PC servicesmarketing
New access toprimary care svcs
New multiplier onair pollution
New multiplier onworkplaces allowing
smoking
New social marketingfor healthy diet
New access tohealthy diet
New junk food taxand sales restrict
New socialmarketing for PA New access to PA
New WL servicesmarketing
New access toweight loss svcs
New socialmarketing
against smoking
New tobacco taxand sales restrict
New SQ servicesmarketing
New access tosmoking quit svcs and
products
New multiplier onsources of stress
New MH servicesmarketing
New access tomental health svcs
INDIVIDUAL INTERVENTIONS SELECTOR
WEIGHT LOSS INTERVENTIONS
Comparing Care, Air & Lifestyle Interventions
• Care provides – quick and sustained
reduction in CV events, – but little cost savings.
• Air provides – quick and growing
reduction in CV events, – and major cost savings.
• Lifestyle provides– Growing CV event
reductions over time, but little immediately
– Substantially increasing cost savings over time
Deaths from CVD per 10004
2
0
1990 2000 2010 2020 2030 2040
Base CaseCare
Care + Air
Care + Air + Lifestyle
If all risk factors = 0
Complication & Mgmt Costs per Capita3,000
0
1990 2000 2010 2020 2030 2040
Base Case
Care
Care + Air
Care + Air + Lifestyle
If all risk factors = 0
Cost Conclusions• AIR – Smoking and air quality interventions can save
lives quickly and can justify intervention spending up to $300 per capita for 30 years ($355 in ET).
• CARE – Improving utilization and quality of primary care services can save lives quickly, but should not be expected to save much on total costs. Justified intervention spending could be up to $25 per capita for 30 years ($35 in ET).
• LIFESTYLE – Improving nutrition and physical activity, and reducing sources of stress take longer to affect CV events though obesity and chronic conditions. However their contribution grows over time and intervention spending of up to $100 per capita could be justified ($177 in ET).
Comparing E. Travis to US: More Effective Individual Interventions
After 10 years (2015) After 35 years (2040)
CVD Death Rate
Compl + Mgmt Costs
CVD Death Rate
Compl + Mgmt Costs
ET US ET US ET US ET US
Social Marketing Against Smoking 4 4 1 1 4 3 1 1
Quality of Primary Care 1 1 3 1 1
Tobacco Tax and Sales Restrictions 5 2 2 2 2
Air Pollution 3 2 4* 3 5 2 4
Access to Primary Care 2 3 4 4 2 4
Access to Physical Activity 5 2 4 3 3
Access to Healthy Diet 4 4
Stress Reduction 4 5
*Duplicates ranks indicate ties.
Overall Conclusions• CV death rate has declined due to improvements
in acute care, and also reductions in smoking, second hand smoke, and air pollution.
• Risk factor consequence costs have decreased as a consequence, but also because of reductions in smoking related deaths.
• Smoking will probably continue to decline in growing elderly population, helping to lower costs.
• Of 19 interventions, at least 15 have the potential to reduce CVD events without increasing costs.
Revise Individual Strategies
With this additional information, would you adjust your individual strategy? Why?
• Please record selected interventions on your sheet under Section #3
• You have 10 minutes.
Integrating your contextual knowledge for integrated public health policy
1. Are there gaps in specific policy areas? Where?
2. Where are the opportunities?
3. What new partnerships can be identified now?
How we can make it happen
1. What partnerships or processes need to be created or strengthened?
2. Make a personal commitment
To making those connections and to collaborating
Indicate your commitment in Section 5 of your worksheet
Model Boundaries / Limitations
• More contextual information must come into play. • Decision support tool to inform multi-stakeholder
dialogue.• Local experts provide crucial link to relevant data
and implementation.
Multi-stakeholder Dialogue
Dynamic Hypothesis (Causal Structure)
X Y
Dynamic Hypothesis (Causal Structure)
X YX Y
Plausible Futures (Policy Experiments)
Obese fraction of Adults (Ages 20-74)
0%
10%
20%
30%
40%
50%
1970 1980 1990 2000 2010 2020 2030 2040 2050
Fra
ctio
n o
f p
op
n20
-74
Plausible Futures (Policy Experiments)
Obese fraction of Adults (Ages 20-74)
0%
10%
20%
30%
40%
50%
1970 1980 1990 2000 2010 2020 2030 2040 2050
Fra
ctio
n o
f p
op
n20
-74
Societal Dialogue Incorporates Model Omissions
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
Air pollutioncontrol regulations
Populationaging
Other chronic disease
endpoints
Downstreaminterventions
and costs
Local implementationopportunitiesLocal
implementationstrengths
and success
Political will
• What measures of improvement ought to be included?
• What else is missing?
• What would be helpful to you?
SYSTEMDYNAMICS MODEL
STRATEGICDIALOGUE
Implementationactions and costs
Health inequities
Local leadershipcapacity
Ability to engage all
stakeholders
Borderline conditions
What we have learned
• The simulator and surrounding dialogue can be used to:– Create alignment among stakeholders– Initiate systemic thinking, increasing
leadership capacity– Spur people to action– Identify opportunities and build commitment
to address them– Inform the development of business cases
for investment in interventions
Sample Austin Implementation Worksheet
InterventionArea
Schools Community Worksites Healthcare
Physical Activity
Nutrition
Tobacco
CVD
Diabetes
Cancer
Integrative Modeling for Strategy Building
• Integrating large and varied sources of evidence in simulations is fundamentally useful.
• Complex problems, those with many causal pathways and significant time delays, are suitable.
• All models are limited, so they are useful as decision-support tools.
• Local context affects appropriate strategy. • Translucent box – making assumptions visible
can develop trust and leadership capacity. • Don’t forget the “ask”. Give people an
opportunity to take action.
What opportunities do you see for integrated policy making?
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
Air pollutioncontrol regulations
Populationaging
Other chronic disease
endpoints
Implementationactions and costs
Downstreaminterventions
and costs
Health inequities
Local implementationopportunitiesLocal
implementationstrengths
and success
Local leadershipcapacity
Political will
Ability to engage all
stakeholders
• We plan to extend this model– Borderline conditions, ex-
smokers– Downstream
interventions and costs
• Investigate transferability of this model to other locales
• Tools allowing wider dissemination
• What do need for this to be useful to you?
• Needs for other systemic analyses?
SYSTEMDYNAMICS MODEL
STRATEGICDIALOGUE
Borderline conditions
OBSSR at NIH
Vision: To mobilize the biomedical, behavioral, and social science research communities as partners in interdisciplinary research to solve the most pressing health challenges faced by our society.
27 NIH Institutes and Centers and the extramural community.
Programmatic Directions to Achieve the Vision:
– Trans-/inter-disciplinary science
– “Next generation”, basic science
– Problem-based, outcomes oriented strengthen the science of dissemination
– Systems science for population impact
What is the challenge?
• Other approaches alone have not solved intractable health problems
• Health problems are embedded in dynamically complex systems
• Policies, programs, interventions have limited resources and involve trade offs
• Could try “kitchen sink” approach, but resources are limited
• Could try a “thought experiment” but the human mind cannot execute beyond simple
Systems Science Activities at NIH
• 2007 Symposia Series on Systems Science & Health
• Institute on Systems Science and Health (annually)
• BSSR-Systems Science listserv - send email to [email protected]
• CDC SD Modeling with OBSSR and NHLBI
Examples of NIH Modeling Initiatives
Cancer Intervention and Surveillance Modeling Network (CISNET): http://cisnet.cancer.gov/about/
Interagency Modeling and Analysis Group (IMAG): http://www.imagwiki.org/mediawiki
http://grants.nih.gov/grants/guide/pa-files/PAR-08-023.htm
Models of Infectious Disease Agent Study (MIDAS): http://www.nigms.nih.gov/Initiatives/MIDAS
NIH Guide To Grants And Contracts http://grants.nih.gov/grants/guide/index.htmlTo Subscribe to the NIH Guide LISTSERV, send an e-mail to
[email protected] with the following text in the message body (not the "Subject" line): subscribe NIHTOC-L your name
Active NIH FOA’s in Systems Science and BSSR
• PAR-08-224 Using Systems Science Methodologies to Protect and Improve Population Health (R21). Expires Sept 2011. 3 receipt dates per year.
Contact Patty Mabry, OBSSR.
• PAR-08-212, -213, -214 Methodology and Measurement in the Behavioral and Social Sciences (R01, R21, R03). Expires September 2011. 3 receipt dates per year.
Contact Deb Olster, OBSSR.
• RFA-07-079, -080 Behavioral and Social Science Research on Understanding and Reducing Health Disparities (R01, R21) Expires September 2009. One receipt date per year Sept.
Contact: Ron Abeles, OBSSR.
• PAR-08-023 Predictive Multiscale Models of the Physiome in Health and Disease (R01). Expires September 2010. 3 receipt dates per year.
Contact: Grace Peng, NIBIB.
Grant Funded Systems Science and BSSR at NIH
• Joshua Epstein, Director’s Pioneer Award, NIGMS, OBSSR, 2008. Project Title: Behavioral Epidemiology: Applications of Agent-Based Modeling to Infectious Disease.
• David Lounsbury, R03, NIDA, 2008. Project Title: Dynamics Modeling as a Tool for Disseminating the PHS Tobacco Treatment Guideline
• David T. Levy, U01, NCI, 2002-2010. CISNET. Project Title: A Simulation of Tobacco Policy, Smoking and Lung Cancer.
• Linda Collins & Daniel Rivera, R21, 2007-2010. NIH Roadmap. Dynamical System /Related Engineering Approach /Improving Behavioral Intervention
• Daniel Rivera, K25, NIDA, OBSSR. Control Engineering Approaches to Adaptive Interventions in Drug Abuse Prevention.
• PAR-08-224 – Awards pending. • RFA-HD-08-023 (R01), Innovative Computational and Statistical
Methodologies for the Design and Analysis of Multilevel Studies on Childhood Obesity (R01). Awards pending.
For more information:
Patty Mabry, [email protected]
Office of Behavioral and Social Sciences Research
(OBSSR) National Institutes of Health
http://obssr.od.nih.gov
Check-out and evaluation
Please fill out Sections 4 and 5 of your worksheet.
Do you have any thoughts you would like to offer? Questions? Reactions? Plans? Feedback?
Chronic Disease Leaders
THANK YOU!
Small favors:
• Please leave your Strategy Worksheets with us.
• Take a CD-ROM for your own use. We encourage you to share.