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Navigating Health Futures

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Title: Navigating Health Futures


1
Navigating Health Futures
Innovations in Planning and Evaluating System
Change Ventures
Bobby Milstein Centers for Disease Control and
Prevention bmilstein_at_cdc.gov
Don Seville Sustainability Institute dseville_at_sust
ainer.org
Maine Center for Public Health Evaluation
Forum Portland, ME Friday July 22, 2005
2
General Plan for the Workshop
  • Dilemmas and innovations in system change
    ventures
  • A navigational view of public health work
  • Navigating diabetes dynamics in an era of rising
    obesity the power of what if... questions
  • Working lunchDeveloping diabetes policy
    scenarios and anticipating change
  • Using simulation studies to learn in and about
    dynamic systems
  • Transforming health evaluation
  • Adjourn

3
Starting Premises
  • Public health work has changed significantly
    since its formalization in the 19th Century, and
    even today it is poised for further
    transformation
  • It matters how we think about the trends,
    dilemmas, and innovations that we experience, and
    it matters whether our thinking and actions match
  • We are not talking about theories to explain, but
    conceptual and methodological orientations the
    frames that shape how we think, how we act, and
    how we value the work

4
Innovations in Public Health Work
Steps in Public Health Problem Solving Trends and Emerging Priorities
Define the problem Eliminate health disparities Preparedness Avoid activity limitation Promote life satisfaction Increase healthy days
Determine the cause Social determinants of health Built environment Adverse childhood experiences Genetics
Develop and test interventions Comprehensive community initiatives Ecological perspectives Inter-sector collaboration Health impact assessments Simulation experiments and game-based scenarios
Implement programs and policies Policy interventions Community and systems change Adaptation to local context Increasing health care access Broad-based citizen organizing
And scores more.
5
Leadership Panel
How have you observed public health work
changing? What types of dilemmas and innovations
are driving those transformations? Where is the
field headed?
6
A Field in Transition
  • Public health work is becoming more
  • Inter-connected (ecological, multi-causal,
    dynamic, systems-oriented) Concerned more with
    leverage than control
  • Public (broad-based, partner-oriented,
    citizen-led, inter-sector, democratic) Concerned
    with many interests and mutual-accountability
  • Questioning (evaluative, reflective, critical,
    pragmatic)Concerned with creating and protecting
    values like health, dignity, security,
    satisfaction, justice, wealth, and freedom in
    both means and ends

Many other orientations rely on disconnected,
singular, and unthinking approaches where means
and ends have very different qualities (e.g.,
security by means of war)
7
Serious Challenges for Planners and Evaluators
  • Locating categorical disease programs within a
    broader system of health protection
  • Constructing credible knowledge without
    comparison/control groups
  • Differentiating questions that focus on
    attribution versus contribution
  • Balancing trade-offs between short- and long-term
    effects
  • Avoiding the pitfalls of professonalism
  • Harnessing the power of citizen-led actions
  • Reconciling different standards and values for
    judgment
  • Others

8
Protecting Health Through Public Work
Public health is probably the most successful
system of science and technology combined, as
well as social policy, that has ever been
devisedIt is, I think, a paradigmatic model for
how you do concerned, humane, directed science.
-- Richard Rhodes
Adult Per Capita Cigarette Consumption and Major
Smoking-and-Health EventsUnited States, 1900-1998
1st Surgeon Generals Report
Broadcast Ad Ban
Federal Cigarette Tax Doubles
End of WW II
Nonsmokers Rights Movement Begins
1st Smoking- Cancer Concern
Great Depression
Source USDA 1986 Surgeon General's Report
Rhodes R. Limiting human violence an emerging
scientific challenge. Sarewitz D, editor. Living
With the Genie Governing Science and Technology
in the 21st Century New York, NY Center for
Science, Policy, and Outcomes 2002.
9
A Navigational View of Public Health Work
Thompson N. Reflections on voyaging and home.
Polynesian Voyaging Society, 2001. Accessed July
18 at lthttp//leahi.kcc.hawaii.edu/org/pvs/malama/
voyaginghome.htmlgt.
10
A Navigational View of Public Health Work
"How do you know," I asked, "that in twenty years
those things that you consider special are still
going to be here?" At first they all raised their
hands but when they really digested the question
every single one of them put their hands down. In
the end, there was not a single hand up. No one
could answer that question. It was the most
uncomfortable moment of silence that I can
rememberThat was the defining moment for me. I
recognized that I have to participate in
answering that question otherwise I am not taking
responsibility for the place I love and the
people I love.
-- Nainoa Thompson
Thompson N. Reflections on voyaging and home.
Polynesian Voyaging Society, 2001. Accessed July
18 at lthttp//leahi.kcc.hawaii.edu/org/pvs/malama/
voyaginghome.htmlgt.
11
Navigating Health Futures in Maine
Adolescent Pregnancy
Mills DA, Maine Bureau of Health. Healthy Maine
2010 longer and healthier lives. Augusta, ME
Maine Department of Human Services 2002.
Available at http//www.maine.gov/dhhs/boh/health
yme2k/hm2010a.htm
12
Navigating Health Futures in Maine
Asthma
Mills DA, Maine Bureau of Health. Healthy Maine
2010 longer and healthier lives. Augusta, ME
Maine Department of Human Services 2002.
Available at http//www.maine.gov/dhhs/boh/health
yme2k/hm2010a.htm
13
Navigating Health Futures in Maine
Heart Disease
Mills DA, Maine Bureau of Health. Healthy Maine
2010 longer and healthier lives. Augusta, ME
Maine Department of Human Services 2002.
Available at http//www.maine.gov/dhhs/boh/health
yme2k/hm2010a.htm
14
Navigating Health Futures in Maine
Infant Mortality
Mills DA, Maine Bureau of Health. Healthy Maine
2010 longer and healthier lives. Augusta, ME
Maine Department of Human Services 2002.
Available at http//www.maine.gov/dhhs/boh/health
yme2k/hm2010a.htm
15
There is Great Power in Focusing on One Problem
at a Time
"Certain forms of knowledge and control require a
narrowing of vision. The great advantage of such
tunnel vision is that it brings into sharp focus
certain limited aspects of an otherwise far more
complex and unwieldy reality. This very
simplification, in turn, makes the phenomenon at
the center of the field of vision more legible
and hence more susceptible to careful measurement
and calculation.making possible a high degree of
schematic knowledge, control, and manipulation."
-- John Scott
Scott JC. Seeing like a state how certain
schemes to improve the human condition have
failed. New Haven London Yale University
Press, 1999.
16
But Solutions Can Also Create New Problems

Merton RK. The unanticipated consequences of
purposive social action. American Sociological
Review 19361936894-904. Forrester JW.
Counterintuitive behavior of social systems.
Technology Review 197173(3)53-68.
17
Side Effects of Specialization
  • Confusion, inefficiency, organizational disarray
  • Competition for shared resources
  • Attention to local causes, near in time and
    space
  • Neglected feedback ( and -)
  • Confounded evaluations
  • Coercive power dynamics
  • Priority on a single value, implicitly or
    explicitly devaluing others
  • Limited mandate to address context (living
    conditions) or infrastructure (public strength)
  • Disappointing track record for assuring the
    conditions for health, especially with regard to
    inequalities

Neighborhood
18
Navigating Health Futures in Maine
Adult Unhealthy Days, Maine 1993-2003
Centers for Disease Control and Prevention.
Behavioral risk factor surveillance system,
prevalence data. Atlanta, GA U.S. Department
of Health and Human Services, 2005. Available at
http//apps.nccd.cdc.gov/HRQOL/TrendV.asp?State21
Category1Measure5
19
The Ecological PerspectiveBroad but Static
Social Norms and Values
  • Home and Family
  • School
  • Community
  • Work Site
  • Healthcare
  • Food and Beverage Industry
  • Agriculture
  • Education
  • Media
  • Government
  • Public Health Systems
  • Healthcare Industry
  • Business and Workers
  • Land Use and Transportation
  • Leisure and Recreation

Sectors of Influence
Behavioral Settings
  • Genetics
  • Psychosocial
  • Other Personal Factors

Individual Factors
Protective Behavior
Risk Behavior
Health Status
Prevention of Disease, Injury, Disability
20
Navigating Health Futures in MaineQuestions
Addressed by System Dynamics Modeling
Where?
Adult Unhealthy Days, Maine 1993-2003
How?
Who?
Why?
Centers for Disease Control and Prevention.
Behavioral risk factor surveillance system,
prevalence data. Atlanta, GA U.S. Department of
Health and Human Services, 2005. Available at
http//apps.nccd.cdc.gov/HRQOL/TrendV.asp?State21
Category1Measure5
21
Science, 256, (12 June 1992) pp. 1520-1521
22
Acknowledging Plurality
You think you understand two because you
understand one and one. But you must also
understand and. -- Sufi Saying
  • Efforts to Reduce Population Health
    ProblemsProblem, problem solver, response
  • Efforts to Organize a System that Assures
    Healthful Conditions for All Dynamic interaction
    among multiple problems, problem solvers, and
    responses

Bammer G. Integration and implementation
sciences building a new specialisation.
Cambridge, MA The Hauser Center for Nonprofit
Organizations, Harvard University 2003.
23
Solving for Pattern
"A bad solution is bad because it acts
destructively upon the larger patterns in which
it is contained...because it is formed in
ignorance or disregard of them. A bad solution
solves for a single purpose or goal, such as
increased production. And it is typical of such
solutions that they achieve stupendous increase
in production at exorbitant biological and social
costsGood solutions recognize that they are part
of a larger whole. They solve more than one
problem and don't create new problems. A good
solution should not enrich one person by the
distress or impoverishment of another."
-- Wendell Berry
Berry W. Solving for pattern. In The Gift of
Good Land. San Francisco North Point 1981. p.
134-45.
24
Seeing Beyond the Probable
Most organizations plan around what is most
likely. In so doing they reinforce what is,
even though they want something very
different. -- Ciement Bezold
  • PossibleWhat may happen?
  • PlausibleWhat could happen?
  • ProbableWhat will likely happen?
  • PreferableWhat do we want to have happen?

Bezold C, Hancock T. An overview of the health
futures field. Geneva WHO Health Futures
Consultation 1983 July 19-23.
25
Navigating Diabetes FuturesThe Power of What
if Questions
26
CDC Diabetes System Modeling ProjectDiscovering
Dynamics Through Action Labs
Jones A, Homer J, Milstein B, Essien J, Murphy D,
Sorensen S, Englegau M. Modeling the population
dynamics of a chronic disease the CDC's diabetes
system model. American Journal of Public Health
(in press).
27
Transforming the Future of Diabetes
"Every new insight into Type 2 diabetes...makes
clear that it can be avoided--and that the
earlier you intervene the better. The real
question is whether we as a society are up to the
challenge... Comprehensive prevention programs
aren't cheap, but the cost of doing nothing is
far greater..."
Gorman C. Why so many of us are getting diabetes
never have doctors known so much about how to
prevent or control this disease, yet the epidemic
keeps on raging. how you can protect yourself.
Time 2003 December 8. Accessed at
http//www.time.com/time/covers/1101031208/story.h
tml.
28
Other Models Exist, But Are Not Designed to
Explore Intervention Scenarios
Prevalence of Diagnosed Diabetes, US
40
Historical
Model
Data
Forecast
30
Million people
20
  • Key Constants
  • Incidence rates (/yr)
  • Death rates (/yr)
  • Diagnosed fractions
  • (Based on year 2000 data, per demographic segment)

10
0
1980
1990
2000
2010
2020
2030
2040
2050
Historical Data CDC DDT and NCCDPHP. (Change in
measurement in 1996).
Model Forecast Honeycutt et al. 2003, "A Dynamic
Markov model"
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 20036155-164.
29
Healthy People 2010 Diabetes ObjectivesWhat Can
We Accomplish?
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
30
Setting Realistic ExpectationsHistory, HP
Objectives, and Simulated Futures
Meet Detection Objective (5-4)
I
Status Quo
G
Meet Onset Objective (5-2)
H
F
D
C
HP 2000 Objective
HP 2010 Objective (5-3)
E
31
Connecting the ObjectivesPopulation Flows and
Dynamic Accounting 101
People with
People
Undiagnosed
without
Initial
Diabetes
Diabetes
Onset
With a diagnosed onset flow of 1.1 mill/yr
Diagnosed
Onset
People with
Diagnosed
Dying from Diabetes
Diabetes
Complications
It is impossible for any policy to reduce
prevalence38 by 2010!
And a death flow of 0.5 mill/yr (4/yr rate)
32
Simulations for Learning in Dynamic Systems
Multi-stakeholder Dialogue
Plausible Futures (Policy Experiments)
Dynamic Hypothesis (Causal Structure)
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.
33
Discussions Pointed to Many Interacting Factors
Civic Participation
Forces Outside the Community
  • Social cohesion
  • Responsibility for others
  • Macroeconomy, employment
  • Food supply
  • Advertising, media
  • National health care
  • Racism
  • Transportation policies
  • Voluntary health orgs
  • Professional assns
  • University programs
  • National coalitions

Health Care Capacity
  • Provider supply
  • Provider understanding, competence
  • Provider location
  • System integration
  • Cost of care
  • Insurance coverage

Local Living Conditions
  • Availability of good/bad food
  • Availability of phys activity
  • Comm norms, culture (e.g., responses to
    racism,
  • acculturation)
  • Safety
  • Income
  • Transportation
  • Housing
  • Education

Health Care Utilization
  • Ability to use care (match of patients and
    providers, language, culture)
  • Openness to/fear of screening
  • Self-management, monitoring

Metabolic Stressors
  • Nutrition
  • Physical activity
  • Stress

Population Flows
34
Diabetes System Modeling ProjectWhere is the
Leverage for Health Protection?
People with
People with
People with
Undiagnosed,
Undiagnosed,
Undiagnosed
Uncomplicated
Complicated
PreDiabetes
Diabetes
Diabetes
People with
Normal
Glycemic
Levels
People with
People with
People with
Diagnosed,
Diagnosed,
Diagnosed
Uncomplicated
Complicated
PreDiabetes
Diabetes
Diabetes
Jones A, Homer J, Milstein B, Essien J, Murphy D,
Sorensen S, Englegau M. Modeling the population
dynamics of a chronic disease the CDC's diabetes
system model. American Journal of Public Health
(in press).
35
Diabetes System Modeling ProjectWhere is the
Leverage for Health Protection?
PreDiabetes
Diabetes
Detection
Detection
Developing
PreDiabetes
Complications from
People with
People with
Onset
People with
Undiagnosed,
Undiagnosed,
Undiagnosed
Uncomplicated
Complicated
PreDiabetes
Diabetes
Diabetes
Recovering from
People with
PreDiabetes
Normal
Diagnosing
Diagnosing
Diagnosing
Uncomplicated
Glycemic
Complicated
PreDiabetes
Diabetes
Diabetes
Levels
Developing
People with
People with
Dying from
Complications
People with
Diagnosed,
Diagnosed,
Complications
Recovering from
Diagnosed
Uncomplicated
Complicated
PreDiabetes
Diabetes
PreDiabetes
Diabetes
Diabetes
Onset
Risk for
PreDiabetes Diabetes
Diabetes
PreDiabetes
Control
Control
Obese Fraction of
the Population
36
Developing Diabetes Policy Scenarios and
Anticipating Change
What strategies do you see unfolding in Maine
over the next five years to address the rise of
diabetes?
How will those strategies affect the burden of
diabetes?
37
The Diabetes Simulation Model Was Developed
UsingThe Best Possible Available Data
Information Sources Data
U.S. Census Adult population and death rates Health insurance coverage
National Health Interview Survey Diabetes prevalence Diabetes detection
National Health and Nutrition Examination Survey Prediabetes prevalence Weight, height, and body fat Caloric intake
Behavioral Risk Factor Surveillance System Glucose self-monitoring Eye and foot exams Participation in health education Use of medications
Professional Literature Physical activity trends Effects of control and aging on onset, progression, death, and costs Expenditures
38
Diabetes System Modeling ProjectConfirming the
Models Fit to History
Diagnosed Diabetes of Adults
Obese of Adults
40
8
Obese of adults
Diagnosed diabetes of adults
30
6
20
4
Data (NHIS)
Data (NHANES)
10
2
0
0
1980
1985
1990
1995
2000
2005
2010
1980
1985
1990
1995
2000
2005
2010
Jones A, Homer J, Milstein B, Essien J, Murphy D,
Sorensen S, Englegau M. Modeling the population
dynamics of a chronic disease the CDC's diabetes
system model. American Journal of Public Health
(in press).
39
Explaining the PastGrowth in the Number of
People with Diabetes
Obese Fraction of Adult Population
People with Diabetes per Thousand Adults
0.4
100
Model Output
80
0.3
Model Output
60
0.2
40
Leads to rising total prevalence
More people with a primary risk factor.
0.1
20
0
0
1980
1985
1990
1995
2000
2005
1980
1985
1990
1995
2000
2005
Time (Year)
Time (Year)
(plus aging and demographics, etc)
40
Explaining the PastReducing the Burden for
People with Diabetes
Controlled Fraction of Diagnosed Population
Diagnosed Fraction of Diabetes Population
0.5
0.8
Model Output
0.4
0.7
0.3
Model Output
0.2
0.6
0.1
And helping them stay under control
We have been finding them
0
0.5
1980
1985
1990
1995
2000
2005
1980
1985
1990
1995
2000
2005
Time (Year)
Time (Year)
(although there are disparities)
41
Explaining the PastDeaths Due to Diabetes Have
Fallen
People with Diabetes per Thousand Adults
Complications Deaths per Thousand w Diabetes
100
40
Model Output
Model Output
90
30
80
20
Among people with diabetes, fewer dying every year
70
More people with diabetes
10
60
0
50
1980
1985
1990
1995
2000
2005
1980
1985
1990
1995
2000
2005
Time (Year)
Time (Year)
Deaths from Comps of Diabetes Per Thousand Adults
2.5
2
Combine to mean fewer U.S. adults dying 1980-2004
1.5
1
Model Output
0.5
0
1980
1985
1990
1995
2000
2005
Time (Year)
42
From a 30,000 Foot View and Population
Perspective, We Have Seen Two Forces Fighting to
Change the Burden of Diabetes
Stunning Progress in Reducing the Burden for the
Average Person with Diabetes
Huge Growth in Number of People with Diabetes
Overall, Total Burden per Citizen Held at Bay
43
Anticipating the Future
People with Diabetes per Thousand Adults
Obese Fraction of Adult Population
130
0.6
Model Output
Model Output
0.45
110
0.3
90
Diabetes prevalence continues to increase for
decades.
0.15
Even if obesity has topped out
70
0
50
1980
1995
2010
2025
2040
1980
1990
2000
2010
2020
2030
2040
2050
Time (Year)
Time (Year)
44
Anticipating the FutureDeaths Under Status Quo
Assumptions
Complications Deaths per Thousand w Diabetes
People with Diabetes per Thousand Adults
40
130
30
110
20
With diabetes prevalence continuing to increase...
90
And assuming no further improvement in disease
management...
10
70
0
50
1980
1990
2000
2010
2020
2030
2040
2050
1980
1990
2000
2010
2020
2030
2040
2050
Time (Year)
Time (Year)
Deaths from Complications of Diabetes Per
Thousand Adults
Assuming no change after 2004 in the 9 key
health behaviors
2.5
Diabetes-related deaths would naturally rise.
1.25
1980
1990
2000
2010
2020
2030
2040
2050
Time (Year)
45
Navigating the Future of DiabetesWhat Strategies
Would You Like To Test?
PreDiabetes
Diabetes
Detection
Detection
Developing
PreDiabetes
Complications from
People with
People with
Onset
People with
Undiagnosed,
Undiagnosed,
Undiagnosed
Uncomplicated
Complicated
PreDiabetes
Diabetes
Diabetes
Recovering from
People with
PreDiabetes
Normal
Diagnosing
Diagnosing
Diagnosing
Uncomplicated
Glycemic
Complicated
PreDiabetes
Diabetes
Diabetes
Levels
Developing
People with
People with
Dying from
Complications
People with
Diagnosed,
Diagnosed,
Complications
Recovering from
Diagnosed
Uncomplicated
Complicated
PreDiabetes
Diabetes
PreDiabetes
Diabetes
Diabetes
Onset
Risk for
PreDiabetes Diabetes
Diabetes
PreDiabetes
Control
Control
Obese Fraction of
the Population
46
Navigating the Future of Diabetes
What strategies would you like to test in the
simulated environment to better address diabetes?
47
Developing a Scenario-based Research Design
Scenario Effect of Public Health Effort on Effect of Public Health Effort on
Scenario Clinical Management of Diagnosed Diabetes ( under control) Caloric Intake (Kcal/day)
Base Run (no changes after 2000)
Enhanced Disease Control (Downstream)
Enhanced Obesity Prevention (Upstream)
Combined Disease Control and Obesity Prevention(Up and Down)
48
Diabetes System Modeling ProjectWhere Flow
Drivers are Involved in Each Strategy?
49
Developing a Scenario-based Research Design
Scenario Effect of Public Health Effort on Effect of Public Health Effort on
Scenario Clinical Management of Diagnosed Diabetes ( under control) Caloric Intake (Kcal/day)
Base Run (no changes after 2000) 66 2465
Enhanced Disease Control (Downstream)
Enhanced Obesity Prevention (Upstream)
Combined Disease Control and Obesity Prevention(Up and Down)
50
Downstream-Only Intervention
Deaths per Population
0.0035
0.003
0.0025
0.002
0.0015
1980
1990
2000
2010
2020
2030
2040
2050
Time (Year)
Blue Base run
51
Developing a Scenario-based Research Design
Scenario Effect of Public Health Effort on Effect of Public Health Effort on
Scenario Clinical Management of Diagnosed Diabetes ( under control) Caloric Intake (Kcal/day)
Base Run (no changes after 2000) 66 2465
Enhanced Disease Control (Downstream)
Enhanced Obesity Prevention (Upstream)
Combined Disease Control and Obesity Prevention(Up and Down)
52
Developing a Scenario-based Research Design
Scenario Effect of Public Health Effort on Effect of Public Health Effort on
Scenario Clinical Management of Diagnosed Diabetes ( under control) Caloric Intake (Kcal/day)
Base Run (no changes after 2000) 66 2465
Enhanced Disease Control (Downstream) 24 (90 under control) No change
Enhanced Obesity Prevention (Upstream)
Combined Disease Control and Obesity Prevention(Up and Down)
53
Downstream-Only Intervention
Deaths per Population
0.0035
Disease control acts quickly but does not slow
the growth in deaths.
0.003
0.0025
0.002
0.0015
1980
1990
2000
2010
2020
2030
2040
2050
Time (Year)
Blue Base run Red Clinical mgmt of diagnosed
up from 66 to 90
54
Developing a Scenario-based Research Design
Scenario Effect of Public Health Effort on Effect of Public Health Effort on
Scenario Clinical Management of Diagnosed Diabetes ( under control) Caloric Intake (Kcal/day)
Base Run (no changes after 2000) 66 2465
Enhanced Disease Control (Downstream) 24 (90 under control) No change
Enhanced Obesity Prevention (Upstream)
Combined Disease Control and Obesity Prevention(Up and Down)
55
Developing a Scenario-based Research Design
Scenario Effect of Public Health Effort on Effect of Public Health Effort on
Scenario Clinical Management of Diagnosed Diabetes ( under control) Caloric Intake (Kcal/day)
Base Run (no changes after 2000) 66 2465
Enhanced Disease Control (Downstream) 24 (90 under control) No change
Enhanced Obesity Prevention (Upstream) No Change -4 (99 fewer Kcal/day)
Combined Disease Control and Obesity Prevention(Up and Down)
56
Upstream-Only Intervention
Deaths per Population
0.0035
Obesity prevention slows the growth but takes a
long time to do so.
0.003
Base
0.0025
Downstream
0.002
0.0015
1980
1990
2000
2010
2020
2030
2040
2050
Time (Year)
Blue Base run Red Clinical mgmt up from 66
to 90 Green Caloric intake down 4 (99
Kcal/day)
57
Developing a Scenario-based Research Design
Scenario Effect of Public Health Effort on Effect of Public Health Effort on
Scenario Clinical Management of Diagnosed Diabetes ( under control) Caloric Intake (Kcal/day)
Base Run (no changes after 2000) 66 2465
Enhanced Disease Control (Downstream) 24 (90 under control) No change
Enhanced Obesity Prevention (Upstream) No Change -4 (99 fewer Kcal/day)
Combined Disease Control and Obesity Prevention(Up and Down)
58
Developing a Scenario-based Research Design
Scenario Effect of Public Health Effort on Effect of Public Health Effort on
Scenario Clinical Management of Diagnosed Diabetes ( under control) Caloric Intake (Kcal/day)
Base Run (no changes after 2000) 66 2465
Enhanced Disease Control (Downstream) 24 (90 under control) No change
Enhanced Obesity Prevention (Upstream) No Change -4 (99 fewer Kcal/day)
Combined Disease Control and Obesity Prevention(Up and Down) 14 (80 under control) -2.5 (62 fewer Kcal/day)
59
Mixed Intervention
Deaths per Population
0.0035
Striking an acceptable balance.
0.003
Base
0.0025
Upstream
Downstream
0.002
0.0015
1980
1990
2000
2010
2020
2030
2040
2050
Time (Year)
Blue Base run Red Clinical mgmt up from 66
to 90 Green Caloric intake down 4 (99
Kcal/day) Black Clin mgmt up to 80 Intake
down 2.5 (62 Kcal/day)
60
The Modeling Process is Having an Impact
  • Budget for primary prevention was doubled
  • from meager to modest
  • HP2010 prevalence goal has been modified
  • from a large reduction to no change (but still
    not an increase)
  • Research, program, and policy staff are working
    more closely
  • but truly cross-functional teams still forming
  • State health departments and their partners are
    now engaged
  • initial engagement in VT, with two additional
    states being considered

61
Transforming Health Evaluation
62
Simulation is a third way of doing science.
Like deduction, it starts with a set of explicit
assumptions. But unlike deduction, it does not
prove theorems. Instead, a simulation generates
data that can be analyzed inductively. Unlike
typical induction, however, the simulated data
comes from a rigorously specified set of rules
rather than direct measurement of the real world.
While induction can be used to find patterns in
data, and deduction can be used to find
consequences of assumptions, simulation modeling
can be used as an aid to intuition.
Simulation ExperimentsOpen a Third Branch of
Science
The complexity of our mental models vastly
exceeds our ability to understand their
implications without simulation." -- John
Sterman
-- Robert Axelrod
Axelrod R. Advancing the art of simulation in the
social sciences. In Conte R, Hegselmann R, Terna
P, editors. Simulating Social Phenomena. New
York, NY Springer 1997. p. 21-40.
lthttp//www.pscs.umich.edu/pub/papers/AdvancingArt
ofSim.pdfgt. Sterman JD. Business Dynamics
Systems Thinking and Modeling for a Complex
World. Boston, MA Irwin McGraw-Hill, 2000.
63
Learning In and About Dynamic Systems
In dynamically complex circumstances
simulation becomes the only reliable way to test
a hypothesis and evaluate the likely effects of
policies." -- John Sterman
  • Benefits of Simulation/Game-based Learning
  • Formal means of evaluating options
  • Experimental control of conditions
  • Compressed time
  • Complete, undistorted results
  • Actions can be stopped or reversed
  • Visceral engagement and learning
  • Tests for extreme conditions
  • Early warning of unintended effects
  • Opportunity to assemble stronger support
  • Complexity Hinders
  • Generation of evidence (by eroding the
    conditions for experimentation)
  • Learning from evidence (by demanding new
    heuristics for interpretation)
  • Acting upon evidence (by including the behaviors
    of other powerful actors)

Sterman JD. Learning from evidence in a complex
world. American Journal of Public Health (in
press). Sterman JD. Business Dynamics Systems
Thinking and Modeling for a Complex World.
Boston, MA Irwin McGraw-Hill, 2000.
64
Analysis Process for Developing Policy
Events
Patterns
Time
Adapted from Successful Systems, Inc.
65
Tools for Policy Analysis
66
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
67
Transforming Essential Ways of Thinking
Conventional Thinking Systems Thinking
Static Thinking Focusing on particular events. Dynamic Thinking Framing a problem in terms of a pattern of behavior over time.
System-as-Effect Thinking Focus on individuals as the sources of behavior. Hold individuals responsible or blame outside forces. System-as-Cause Thinking Seeing the structures and pressures that drive behavior. Examine the conditions in which decisions are made, as well as their consequences for oneself and others.
Tree-by-Tree Thinking Focusing on the details in order to know. Forest Thinking Seeing beyond the details to the context of relationships in which they are embedded.
Factors Thinking Listing factors that influence, or are correlated with, a behavior. To forecast milk production, consider economic elasticities. Operational Thinking Understanding how a behavior is actually generated. To forecast milk production, you must consider cows.
Straight-Line Thinking Viewing causality as running one way, treating causes as independent and instantaneous. Root-Cause thinking. Closed-Loop Thinking Viewing causality as an ongoing process, not a one-time event, with effects feeding back to influence causes, and causes affecting each other, sometimes after long delays.
Measurement Thinking Focusing on the things we can measure seeking precision. Quantitative Thinking Knowing how to quantify, even though you cannot always measure.
Proving-Truth Thinking Seeking to prove our models true by validating them with historical data. Scientific Thinking Knowing how to define testable hypotheses (everyday, not just for research).
Karash R. The essentials of systems thinking and
how they pertain to healthcare and colorectal
cancer screening. Dialogue for Action in
Colorectal Cancer Baltimore, MD March 23,
2005.. Richmond B. Systems thinking critical
thinking skills for the 1990s and beyond. System
Dynamics Review 19939(2)113-134. Richmond B.
The "thinking" in systems thinking seven
essential skills. Waltham, MA Pegasus
Communications, 2000.
68
Looking Through the Macroscope
The macroscope filters details and amplifies
that which links things together. It is not used
to make things larger or smaller but to observe
what is at once too great, too slow, and too
complex for our eyes.
-- Joèel de Rosnay
Rosnay Jd. The macroscope a book on the systems
approach. Principia Cybernetica, 1997.
lthttp//pespmc1.vub.ac.be/MACRBOOK.html
69
Are We Posing Questions About Attribution or
Contribution?
if a programs activities are aligned with
those of other programs operating in the same
setting, certain effects (e.g., the creation of
new laws or policies) cannot be attributed solely
to one program or another. In such situations,
the goal for evaluation is to gather credible
evidence that describes each programs
contribution in the combined change effort.
Establishing accountability for program results
is predicated on an ability to conduct
evaluations that assess both of these kinds of
effects. p.11-12
Calls into question the conditions in which one
focuses on a program as the unit of analysis
Milstein B, Wetterall S, CDC Evaluation Working
Group. Framework for program evaluation in public
health. MMWR Recommendations and Reports
199948(RR-11)1-40. Available at
lthttp//www.cdc.gov/mmwr/PDF/RR/RR4811.pdfgt.
70
Questioning the Character of Public Health Work
PUBLIC HEALTH WORK
InnovativeHealth Ventures
71
Changing (and Accumulating) Ideas About
CausationWhat accounts for poor population
health?
  • Gods will
  • Humors, miasma, ether (e.g., epidemic
    constitution)
  • Poor living conditions, immorality (e.g.,
    sanitation)
  • Single disease, single cause (e.g., germ theory)
  • Single disease, multiple causes (e.g., heart
    disease)
  • Single cause, multiple diseases (e.g., tobacco)
  • Multiple causes, multiple diseases (but no
    feedback dynamics) (e.g., social epidemiology)
  • Dynamic feedback among afflictions, living
    conditions, and public strength (e.g., syndemics)

1840
1880
1950
1960
1980
2000
72
Expanding Prevention Science
Public health imagination involves using science
to expand the boundaries of what is
possible. -- Michael Resnick
EpidemicOrientation
73
For Additional Information http//www.cdc.gov/synd
emics
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