syndemics prevention network draft: please do not cite without permission modeling population...
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Syndemics
Prevention Network
DRAFT: Please do not cite without permission
Modeling Population Dynamics
Obesity
CDC Diabetes and Obesity ConferenceDenver, CO
May 17, 2006Syndemics
Prevention Network
Bobby MilsteinSyndemics Prevention Network
Centers for Disease Control and PreventionAtlanta, Georgia
Jack HomerHomer Consulting
Voorhees, [email protected]
A Work in Progress Dialogue
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Topics for Today
• Dynamic modeling for learning and action
• Structure of the current model
– Dynamic population weight framework
– Calibrating the model
• Behavior of the current model
– A “status quo” future
– Alternative futures
• Conclusions, questions, and next steps
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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)
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Purposes for Modeling Obesity DynamicsPrimary Uses and Users
• Chart Progress Toward Goals (Planners/Evaluators/Media)– Set justifiable goals– Define a “status quo” future, as well as plausible alternatives based on
policy scenarios– Estimate how strong interventions must be to make a difference, and
how long it will take for those effects to become visible
• Develop Better Measures and New Knowledge (Researchers)– Integrate diverse data sources into a single analytic environment – Infer properties of unmeasured or poorly measured parameters
• Convene Multi-stakeholder Action Labs (Policymakers)– Understand how a dynamically complex obesity system functions– Discover short- and long-term consequences of alternative policies
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Modeling Obesity DynamicsOpportunities to Integrate Diverse Policy Perspectives
• Lifecourse Perspective– Consider life-long impacts and intergenerational effects
• Ecological Perspective– Consider (a) weight-related behaviors, (b) behavioral settings, (c) social-cultural-
economic-political forces, and (d) other health conditions, all by social position
• Action Perspective– Clarify how obesity can be reduced (i.e., what kinds of actions are needed)
– Clarify who is in a position to take those actions (i.e., roles for different types of organizations)
– Estimate how strong new programs/policies must be to make a difference, as well as when those effects will become visible
• Navigational Perspective– Set justifiable goals for the future, given existing momentum
– Chart progress (annually?) by surveying actions and anticipating trajectories of change
Others….
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Re-Directing the Course of ChangeQuestions Addressed by System Dynamics Modeling
How?
Where?
0
10
20
30
40
50
1960-62 1971-74 1976-80 1988-94 1999-2002
Prevalence of Obese Adults, United States
Why?
Data Source: NHANES
20202010
Who?
What?
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Modeling for Learning and Action
Plausible Futures (Policy Experiments)Dynamic Hypothesis (Causal Structure)
X Y
Multi-stakeholder Dialogue
Model Structure
• Trace changes in caloric balance through to overweight and obesity prevalence1
• Trace intervention effects over the lifecourse by age and sex
Intervention Scenarios
• Efforts to alter caloric balance via intensive weight loss/maintenance services and/or via broad changes in people’s food and activity environment
• Focusing by age range and sex
• Focusing by BMI category
1 Because health burden is associated with the obese tail of the BMI distribution, and cannot be accurately estimated from mean BMI alone
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Major Project Phases
• Conceptualization and Data Gathering (May 2005 – July 2005)– Convene stakeholder workshops– Collect time series data– Develop multiple iterations of a dynamic hypothesis
• Formulation, Calibration, and Testing (August 2005 – November 2005)– Assure appropriate fit to history– Examine future behavior under status quo as well as policy scenarios
• Policy Scenarios and Goal-setting (December 2005 – April 2006)– Study major classes of interventions, alone and in combination– Learn how strong new interventions must be to make a lasting difference, as
well as how long it will take for those effects to become visible
• Further Testing (May 2006 – July 2006)– Conduct sensitivity tests to see if data uncertainties affect policy conclusions– Elicit feedback from SD experts
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System Dynamics Was Developed to Address Problems Marked By Dynamic Complexity
Good at Capturing
• Differences between short- and long-term consequences of an action
• Time delays (e.g., transitions, detection, response)
• Accumulations (e.g., prevalence, capacity)
• Behavioral feedback (e.g., actions trigger reactions)
• Nonlinear causal relationships (e.g., effect of X on Y is not constant-sloped)
• Differences or inconsistencies in goals/values among stakeholders
Sterman JD. Business dynamics: systems thinking and modeling for a complex world. Boston, MA: Irwin McGraw-Hill, 2000.
Homer JB, Hirsch GB. System dynamics modeling for public health: background and opportunities. American Journal of Public Health 2006;96(3):452-458.
Origins
• Jay Forrester, MIT (from late 1950s)
• Public policy applications starting late 1960s
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Understanding Dynamic ComplexityLong—and often surprising—chains of
cause and effect
Forrester JW. Counterintuitive behavior of social systems. Technology Review 1971;73(3):53-68.
Meadows DH. Leverage points: places to intervene in a system. Sustainability Institute, 1999. Available at <http://www.sustainabilityinstitute.org/pubs/Leverage_Points.pdf>.
Richardson GP. Feedback thought in social science and systems theory. Philadelphia, PA: University of Pennsylvania Press, 1991.
Sterman JD. Business dynamics: systems thinking and modeling for a complex world. Boston, MA: Irwin McGraw-Hill, 2000.
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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
• Data and sensitivity testing requirements
• Robustness for longer-term projection
• Value for developing policy insights
Increasing:
• Depth of causal theory
• Data and sensitivity testing requirements
• Robustness for longer-term projection
• Value for developing policy insights Dynamic Simulation Models
Anticipate new trends, learn about policy consequences,
and set justifiable goals
Tools for Policy Analysis
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An Ecological Framework for Organizing Influences on Overweight and Obesity
Energy Balance
Prevention of Overweight and Obesity Among Children, Adolescents, and Adults
Individual Factors
Behavioral Settings
Social Norms and Values
Home and Family
School
Community
Work Site
Healthcare
Genetics
Psychosocial
Other Personal Factors
Food and Beverage Industry
Agriculture
Education
Media
Government
Public Health Systems
Healthcare Industry
Business and Workers
Land Use and Transportation
Leisure and Recreation
Food and Beverage Intake
Physical Activity
Sectors of Influence
Energy Intake Energy Expenditure
Adapted from: Koplan JP, Liverman CT, Kraak VI, editors. Preventing childhood obesity: health in the balance. Washington, DC: Institute of Medicine, National Academies Press; 2005.
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A Conventional View of Causal Forces
Healthiness of Diet& Activity Habits
Prevalence ofOverweight &
Related Diseases
Options Available atHome, School, Work,
Community InfluencingHealthy Diet & Activity
Media MessagesPromoting Healthy
Diet & Activity
Wider Environment (Economy,Technology, Laws) Influence
on Healthy Diet & Activity
Health ConditionsDetracting from
Healthy Diet & ActivityGenetic Metabolic
Rate Disorders
Healthcare Servicesto Promote Healthy
Diet & Activity
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A Conventional View of Causal Forces
• This sort of open-loop (non-feedback) approach– Ignores intervention spill-over effects and often suggests the best strategy is a multi-
pronged “fill all needs” one (even if not practical or affordable)– Ignores unintended side effects and delays that produce short-term vs. long-term
differences in outcomes– Cannot fairly evaluate a phased approach; e.g. “bootstrapping” which starts more
narrowly targeted but then broadens and builds upon successes over time
Healthiness of Diet& Activity Habits
Effective HealthProtection Efforts
Prevalence ofOverweight &
Related Diseases
Options Available atHome, School, Work,
Community InfluencingHealthy Diet & Activity
Media MessagesPromoting Healthy
Diet & Activity
Wider Environment (Economy,Technology, Laws) Influence
on Healthy Diet & Activity
Health ConditionsDetracting from
Healthy Diet & ActivityGenetic Metabolic
Rate Disorders
Healthcare Servicesto Promote Healthy
Diet & Activity
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The Rise and Future Fall of ObesityThe Why and the How in Broad Strokes
Fraction of Obese Individuals &Prevalence of Related Health Problems
Time
Overweight &Obesity
PrevalenceR
Engines ofGrowth
HealthProtection
Efforts
-
B
Responsesto Growth
Resources &Resistance
-B
Obstacles
Broader Benefits& Supporters
R
Reinforcers
Drivers of Unhealthy
Habits
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DRAFT: Please do not cite without permissionNOTE: All parameters vary by social position (e.g.,
age, sex, race/ethnicity, income, geography)
LEGEND: Blue arrows indicate same directionlinks; Green arrows indicate opposite directionlinks; R loops indicate reinforcing processes;
B loops indicate balancing processes
NOTE: All parameters vary by social position (e.g.,age, sex, race/ethnicity, income, geography)
LEGEND: Blue arrows indicate same directionlinks; Green arrows indicate opposite directionlinks; R loops indicate reinforcing processes;
B loops indicate balancing processes
DRAFT 5/8/05
Healthiness of Diet& Activity Habits
Prevalence ofOverweight &
Related Diseases
-Options Available atHome, School, Work,
Community InfluencingHealthy Diet & Activity
Media MessagesPromoting Healthy
Diet & Activity
Wider Environment(Economy, Technology,
Laws) Influence on Options
Health ConditionsDetracting from
Healthy Diet & Activity
-Genetic Metabolic
Rate Disorders
Healthcare Servicesto Promote Healthy
Diet & Activity
A Closed-Loop View of Causal Forces
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age, sex, race/ethnicity, income, geography)
LEGEND: Blue arrows indicate same directionlinks; Green arrows indicate opposite directionlinks; R loops indicate reinforcing processes;
B loops indicate balancing processes
NOTE: All parameters vary by social position (e.g.,age, sex, race/ethnicity, income, geography)
LEGEND: Blue arrows indicate same directionlinks; Green arrows indicate opposite directionlinks; R loops indicate reinforcing processes;
B loops indicate balancing processes
DRAFT 5/8/05
Healthiness of Diet& Activity Habits
R4Options ShapeHabits Shape
OptionsPrevalence ofOverweight &
Related Diseases
-Options Available atHome, School, Work,
Community InfluencingHealthy Diet & Activity
Observation ofParents' andPeers' Habits
R2
Parents/PeersTransmission
Media MessagesPromoting Healthy
Diet & Activity
Wider Environment(Economy, Technology,
Laws) Influence on Options
B1
Self-Improvement
Health ConditionsDetracting from
Healthy Diet & Activity
-Genetic Metabolic
Rate Disorders
Healthcare Servicesto Promote Healthy
Diet & Activity
B2
Medical Response
R1
Spiral of PoorHealth and Habits
R5
Society ShapesOptions Shape
Society
R3
MediaMirrors
A Closed-Loop View of Causal Forces
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A Closed-Loop View of Causal Forces
NOTE: All parameters vary by social position (e.g.,age, sex, race/ethnicity, income, geography)
LEGEND: Blue arrows indicate same directionlinks; Green arrows indicate opposite directionlinks; R loops indicate reinforcing processes;
B loops indicate balancing processes
NOTE: All parameters vary by social position (e.g.,age, sex, race/ethnicity, income, geography)
LEGEND: Blue arrows indicate same directionlinks; Green arrows indicate opposite directionlinks; R loops indicate reinforcing processes;
B loops indicate balancing processes
DRAFT 5/8/05
Healthiness of Diet& Activity Habits
Effective HealthProtection Efforts
R6
Disease CareCosts Squeeze
PreventionB4
Creating BetterMessages
R4Options ShapeHabits Shape
OptionsPrevalence ofOverweight &
Related Diseases
-
Costs of Caringfor Overweight-
Related Diseases
-
Options Available atHome, School, Work,
Community InfluencingHealthy Diet & Activity
Costs of Developing &Maintaining HealthProtection Efforts
B5
Creating BetterOptions inBehavioral
Settings
-B8
Up-front CostsUndercut
ProtectionEfforts
Observation ofParents' andPeers' Habits
R2
Parents/PeersTransmission
Media MessagesPromoting Healthy
Diet & Activity
Wider Environment(Economy, Technology,
Laws) Influence on Options
B1
Self-Improvement
B6
Creating BetterConditions in the
Wider Environment
Health ConditionsDetracting from
Healthy Diet & Activity
-
Perceived ProgramBenefits Beyond Weight
Reduction
Resistance andCountervailing Effortsby Opposed Interests
-
B9
DefendingStatus Quo
Cost Implicationsof Overweight inOther Spheres
B10
Potential SavingsBuild Support
Genetic MetabolicRate Disorders
B7
AddressingRelated Health
Conditions
Healthcare Servicesto Promote Healthy
Diet & Activity
B2
Medical Response
R1
Spiral of PoorHealth and Habits
B3
ImprovingPreventiveHealthcare
R5
Society ShapesOptions Shape
Society
Broader Benefits ofHealth Protection
Efforts
R7
Broader BenefitsBuild Support
R3
MediaMirrors
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The Closed-Loop View Leads Us To Question…
• How can the engines of growth loops (i.e. social and economic reinforcements) be weakened?
• What incentives can reward parents, school administrators, employers, and other decision-makers for expanding healthy diet and activity options ?
• Are there resources for health protection that do not compete with disease care?
• How can industries be motivated to change the status quo rather than defend it?
• How can benefits beyond weight reduction be used to stimulate investments in expanding healthier options?
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Building a Foundation for Analysis
Structure of the Current Model
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Phase 2:
More Detailed Drivers of Change
Obesity Prevalence Over the Decades Two Broad Phases
Consequences Over TimeChanging Prevalence of
Four BMI Categories: 1970-2050
Dynamic Population Weight Framework(BMI Surveillance, Demography, and
Nutritional Science)
Policy Drivers(Trends & Interventions
Affecting Caloric Balance by Age, Sex, BMI Category, etc…)
Phase 1: Calculating Obesity Dynamics
Policy Drivers(Trends & Interventions
Affecting Caloric Balance by Age, Sex, BMI Category, etc…)
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Summary of Current Direction
• Simulate overweight and obesity prevalences over the life-course
– Reproduce relative stability in the 1970s and growth to the present, then extend to the future
• Explore effects of new interventions affecting caloric balance
– Focusing by age, sex, and/or BMI category
• Treat intervention details (composition, response, coverage, efficacy, cost) as exogenous
– Not yet addressing feedback loops of reinforcement and resistance
– Not yet addressing cost-effectiveness
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Obesity Dynamics Over the DecadesDynamic Population Weight Framework
Dynamic Population Weight Framework
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Obesity Dynamics Over the Decades Dynamic Population Weight Framework
Dynamic Population Weight Framework
Population by Age (0-99) and Sex
Birth Immigration
Death
Yearly aging
NotOverweight
ModeratelyOverweight
ModeratelyObese
SeverelyObese
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BMI Category Definitions
For infants (ages 0-23 months)• Not overweight: weight-for-recumbent length (WRL)<85th percentile• Moderately overweight: WRL>85th percentile and <95th percentile• Moderately obese: WRL>95th percentile and <99th percentile; • Severely obese: WRL>99th percentile
For youth (ages 2-19)• Not overweight: BMI<{85th percentile or 25}• Moderately overweight: BMI>{85th percentile and 25} and <{95th percentile or 30} • Moderately obese: BMI>{95th percentile and 30} and <{99th percentile or 35}• Severely obese: BMI>{99th percentile and 35}
For adults (ages 20+)• Not overweight: BMI< 25• Moderately overweight: BMI>25 and <30• Moderately obese: BMI>30 and <35• Severely obese: BMI>35
Percentiles from CDC Growth Charts based on NHANES I and II measurements.
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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
Obesity-attributableunhealthy days
Obesity-attributableillness costs
Birth Immigration
Death
Yearly aging
NotOverweight
ModeratelyOverweight
ModeratelyObese
SeverelyObese
Indicates possible extensions to the existing model
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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
Obesity-attributableunhealthy days
Obesity-attributableillness costs
Birth Immigration
Death
CaloricBalance
Yearly aging
NotOverweight
ModeratelyOverweight
ModeratelyObese
SeverelyObese
Indicates possible extensions to the existing model
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Obesity Prevalence Over the DecadesDynamic Population Weight Framework
NotOverweight
ModeratelyOverweight
ModeratelyObese
SeverelyObese
NotOverweight
ModeratelyOverweight
ModeratelyObese
SeverelyObese
NotOverweight
ModeratelyOverweight
ModeratelyObese
SeverelyObese
Births Births Births Births
Age 0
Age 1
Age 99
No Change in BMI Category (maintenance flow)
Increase in BMI Category (up-flow)
Decline in BMI Category (down-flow)
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Obesity Dynamics Over the DecadesDrivers of Change
Dynamic Population Weight Framework
Population by Age (0-99) and Sex
Flow-rates betweenBMI categories
Overweight andobesity prevalence
Obesity-attributableunhealthy days
Obesity-attributableillness costs
Birth Immigration
Death
CaloricBalance
Yearly aging
NotOverweight
ModeratelyOverweight
ModeratelyObese
SeverelyObese
Trends and PlannedInterventions
Indicates possible extensions to the existing model
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Obesity Dynamics Over the DecadesDrivers of Change
Dynamic Population Weight Framework
Population by Age (0-99) and Sex
Flow-rates betweenBMI categories
Overweight andobesity prevalence
Obesity-attributableunhealthy days
Obesity-attributableillness costs
Birth Immigration
Death
CaloricBalance
Yearly aging
NotOverweight
ModeratelyOverweight
ModeratelyObese
SeverelyObese
Trends and PlannedInterventions
Changes in the Physicaland Social Environment
Weight Loss/MaintenanceServices for Individuals
Indicates possible extensions to the existing model
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Obesity Dynamics Over the DecadesDrivers of Change
Dynamic Population Weight Framework
Population by Age (0-99) and Sex
Flow-rates betweenBMI categories
Overweight andobesity prevalence
Obesity-attributableunhealthy days
Obesity-attributableillness costs
Birth Immigration
Death
CaloricBalance
Yearly aging
NotOverweight
ModeratelyOverweight
ModeratelyObese
SeverelyObese
Trends and PlannedInterventions
Changes in the Physicaland Social Environment
Weight Loss/MaintenanceServices for Individuals
ActivityEnvironment
FoodEnvironment
Indicates possible extensions to the existing model
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Obesity Dynamics Over the DecadesDrivers of Change
Indicates possible extensions to the existing model
Dynamic Population Weight Framework
Population by Age (0-99) and Sex
Flow-rates betweenBMI categories
Overweight andobesity prevalence
Obesity-attributableunhealthy days
Obesity-attributableillness costs
Birth Immigration
Death
CaloricBalance
Yearly aging
NotOverweight
ModeratelyOverweight
ModeratelyObese
SeverelyObese
Trends and PlannedInterventions
Changes in the Physicaland Social Environment
Weight Loss/MaintenanceServices for Individuals
Food Price
Smoking
Social Influences onConsumption &
Selection
Options for AffordableRecommended Foods (Work,School, Markets, Restaurants)
ActivityEnvironment
FoodEnvironment
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Obesity Dynamics Over the DecadesDrivers of Change
Indicates possible extensions to the existing model
Dynamic Population Weight Framework
Population by Age (0-99) and Sex
Flow-rates betweenBMI categories
Overweight andobesity prevalence
Obesity-attributableunhealthy days
Obesity-attributableillness costs
Birth Immigration
Death
CaloricBalance
Yearly aging
NotOverweight
ModeratelyOverweight
ModeratelyObese
SeverelyObese
Trends and PlannedInterventions
Changes in the Physicaland Social Environment
Weight Loss/MaintenanceServices for Individuals
Food Price
Smoking
Social Influences onConsumption &
Selection
Options for AffordableRecommended Foods (Work,
School, Markets, Restaurants)
Activity LimitingConditions
Options for Safe, AccessiblePhysical Activity (Work,School, Neighborhoods)
Distance from Home toWork, School, Errands
Electronic Mediain the Home
Social Influences onActive/Inactive
Options
ActivityEnvironment
FoodEnvironment
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Calibrating the Model
Estimating Flow-Rates and Past Changes in Caloric Balance
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Information SourcesTopic Area Data Source
Prevalence of Overweight and Obesity
BMI prevalence by sex and age (10 age ranges)National Health and Nutrition Examination Survey (1971-2002)
Translating Caloric Balances into BMI Flow-Rates
BMI category cut-points for children and adolescents
CDC Growth Charts
Median BMI within each BMI category National Health and Nutrition Examination Survey (1971-2002)Median height
Average kilocalories per kilogram of weight change Forbes 1986
Estimating BMI Category Down-Flow Rates
In adults: Self-reported 1-year weight change by sex and age
NHANES (2001-2002) *indicates 7-12% per year*
In children: BMI category changes by grade and starting BMI
Arkansas pre-K through 12th grade assessment (2004-2005) *indicates 15-28% per year*
Population Composition
Population by sex and ageU.S. Census and Vital Statistics (1970-2000 and projected)
Death rates by sex and age
Birth and immigration rates
Influence of BMI on Mortality
Impact of BMI category on death rates by age Flegal, Graubard, et al. 2005.
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Data Uncertainties & Limitations
• No reliable longitudinal data on caloric intake and expenditure broken out by age, sex, BMI category
• Reliable NHANES data on blacks and Mexican-Americans only since NHANES III (1988-94)
• NHANES prevalence estimates are imprecise
– May affect timing of inferred growth inflection point
• Down-flow rate constants are imprecise
• Don’t know to what extent historical caloric imbalances have led to increase in up-flows as opposed to decrease in down-flows
– We have assumed entirely the former
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0%
10%
20%
30%
40%
1960-62 1963-65 1966-70 1971-74 1976-80 1988-94 1999-2002
Per
cent
obe
se
Age 2-5 Age 6-11 Age 12-19 Age 20-74
Growth of Obesity for Four Age Ranges 1960-2002
Definitions
Ages 2-19 (NHES): Obese BMI>=95th percentile on CDC growth chart
Ages 2-19 (NHANES): Obese BMI>=30 or >=95th percentile on CDC growth chart
Ages 20-74: Obese BMI>=30
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Growth of Obesity for Four Age Ranges 1960-2002
Definitions
Ages 2-19 (NHES): Overweight BMI>=85th percentile, Obese BMI>=95th percentile on CDC growth chart
Ages 2-19 (NHANES): Overweight BMI>=25 or 85th percentile, Obese BMI>=30 or 95th percentile, Severely obese BMI>=35 or 99th percentile on CDC growth chart
Ages 20-74: Overweight BMI>=25; Obese BMI>=30; Severely obese BMI>=35
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Calibration of Uncertain ParametersTo Reproduce 60 BMI Prevalence Time Series(10 age ranges x 2 sexes x 3 high-BMI categories)
• Step 1: Adjust uncertain constants and initial values to get near steady-state BMI prevalence for the early 1970s
– In this step, assume no change in caloric balance after 1970– Adjust 1970 up-rates and down-rates so that BMI prevalences
settle-out at historical 1970s values– Set 1970 BMI prevalences (by annual age) to settled-out values– Repeat/adjust as necessary to minimize number of peaks and valleys
(with increasing age) in assumed 1970 BMI prevalences
• Step 2: Adjust uncertain time series inputs to reproduce BMI prevalence growth patterns for the 1980s and 1990s
– To explain increasing overweight in infants, must assume increasing overweight/obesity at birth (3 series)
– For non-infants, adjust caloric balances (54 series; by age, sex, and for Not Overwt, Mod Overwt, and Obese) to reproduce BMI growth
• Calibrate from youngest age range to oldest• Within each age range calibrate first Overweight, then Obese, then
Severely obese
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Parameters (for each age range and sex)
• Cut-points for BMI categories (bc)
• Median BMI within each BMI category (bm)
• Median height (hm)
• Assumption for the average number of kilocalories per kilogram of weight change (k)
– Forbes’ empirical estimate of 8,050 kcal./kg
– Implicitly takes into account the efficiency of weight deposition reflecting metabolic and other regulatory adjustments.
– Glosses over known differences among individuals: starting weight, composition of diet, efficiency of weight deposition
Translating Caloric Balance Changes (ΔK) into Flow Rate Changes (ΔF)
]}365)()[(K 0.5 MAX{0, F 2 khbb mmc
Forbes GB. Human body composition: growth, aging, nutrition, and activity. Springer: Berlin, Heidelberg; 1987.
Forbes GB. Deliberate overfeeding in women and men: Energy costs and composition of the weight gain. British Journal of Nutrition 56:1-9; 1986.
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(a) Overweight fraction
0%
20%
40%
60%
80%
1970 1975 1980 1985 1990 1995 2000 2005
Fra
ctio
n o
f w
om
en a
ge
55-6
4
NHANES Simulated
(b) Obese fraction
0%
10%
20%
30%
40%
50%
1970 1975 1980 1985 1990 1995 2000 2005
Fra
ctio
n o
f w
om
en a
ge
55-6
4
NHANES Simulated
(c) Severely obese fraction
0%
5%
10%
15%
20%
25%
1970 1975 1980 1985 1990 1995 2000 2005
Fra
ctio
n o
f w
om
en a
ge
55-6
4
NHANES Simulated
Reproducing Historical Data One of 20 {sex, age} Subgroups: Females age 55-64
Note: S-shaped curves, with inflection in the 1990s
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Explaining BMI Prevalence Growth: Age-to-Age Carryover + Caloric Imbalance
Example: Females Age 55-64
Overweight fractions of middle-aged women
0%
20%
40%
60%
80%
1970 1975 1980 1985 1990 1995 2000 2005
Fra
ctio
n o
f w
om
en b
y ag
e g
rou
p
Age 55-64 Age 45-54
Obese fractions of middle-aged women
0%
10%
20%
30%
40%
50%
1970 1975 1980 1985 1990 1995 2000 2005
Fra
ctio
n o
f w
om
en b
y ag
e g
rou
p
Age 55-64 Age 45-54
Severely obese fractions of middle-aged women
0%
5%
10%
15%
20%
25%
1970 1975 1980 1985 1990 1995 2000 2005
Fra
ctio
n o
f w
om
en b
y ag
e g
rou
p
Age 55-64 Age 45-54
Estimated caloric imbalances for women age 55-64
0
5
10
15
20
1970 1975 1980 1985 1990 1995 2000 2005
Kca
l p
er d
ay
Not overwt Mod overwt Obese
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Estimated Caloric Balances in 1990 and 2000 For Every Age Range & BMI Category (vs. 1970)
2 to 5 6 to 11 12 to 19 20 to 34 35 to 44 45 to 54 55 to 64 65 to 74 75+MALENot overweight 0 12 5 9 22 10 31 27 18Moderately overwt. 3 29 0 19 24 19 37 21 19Obese 1 10 0 24 41 15 24 19 11FEMALENot overweight 3 7 12 17 12 25 18 3 7Moderately overwt. 7 13 0 38 14 43 6 16 0Obese 4 4 0 19 19 26 11 12 5
2 to 5 6 to 11 12 to 19 20 to 34 35 to 44 45 to 54 55 to 64 65 to 74 75+MALENot overweight 3 14 5 24 12 33 28 30 27Moderately overwt. 11 23 0 24 19 13 43 6 14Obese 3 9 3 27 30 29 10 16 11FEMALENot overweight 4 10 17 41 10 19 10 18 8Moderately overwt. 10 12 19 37 0 38 0 34 0Obese 5 3 6 15 11 14 6 18 13
Estd. caloric balances for 1990 (vs. 1970) by age group & BMI category
Estd. caloric balances for 2000 (vs. 1970) by age group & BMI category
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Behavior of the Current Model
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Assumptions for Future ScenariosBase Case• Caloric balances stay at 2000 values through 2050
Altering Food and Activity Environments
• Efforts to reduce caloric balances to their 1970 values by 2015
• Focused on– ‘School Youth’: youth ages 6-19– ‘All Youth’: all youth ages 0-19– ‘School+Parents’: school youth plus their parents
• Used 2000 Census birth data by age of mother to estimate % of each adult age range that are parents of 6-19 year olds
– ‘All Adults’: all adults ages 20+ – ‘All Ages’: all youth and adults
Subsidized Weight Loss Programs for Obese Individuals
• Net daily caloric reduction of program is 40 kcal/day (i.e., 14,600 kcal/year or 1.8kg weight loss per year)
• Fully effective by 2010 and terminated by 2020
• ‘All Ages+WtLoss’: program applies to all obese youth and adults, and occurs on top of the ‘All Ages’ environmental improvement scenario
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Intervention Scenario
Changing Food & Activity Environments
Focused On…
Weight Loss Programs for
Obese Individuals
Selected Results
Pre-School
School-age Youth
Adult Parents of School-aged Youth
All Other Adults
All
Ages
Obese Fraction Among Teens
(12-19)
Obese Fraction Among Adults
(20-74)
2020 2050 2020 2050
Base or Status Quo
-- -- -- -- --
School Youth
All Youth School + Parents
All Adults
All Ages
All Ages + Wt Loss
Exploring Future Scenarios Through Simulation Experiments
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Alternative FuturesObesity in Teens (12-19)
Obese fraction of Teens (Ages 12-19)
0%
10%
20%
30%
40%
50%
1970 1980 1990 2000 2010 2020 2030 2040 2050
Fra
ctio
n o
f p
op
n 1
2-19
Base SchoolYouth AllYouth AllAges+WtLoss
Syndemics
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Alternative FuturesObesity in Adults (20-74)
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
Syndemics
Prevention Network
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Intervention Scenario
Changing Food & Activity Environments
Focused On…
Weight Loss Programs for
Obese Individuals
Selected Results
Pre-School
School-age Youth
Adult Parents of School-aged Youth
All Other Adults
All
Ages
Obese Fraction Among Teens
(12-19)
Obese Fraction Among Adults
(20-74)
2020 2050 2020 2050
Base or Status Quo
-- -- -- -- -- 20.1% 20.0% 37.9% 39.1%
School Youth 11.5% 10.1% 37.3% 36.6%
All Youth 9.7% 6.1% 37.3% 36.1%
School + Parents 11.5% 10.1% 33.1% 29.3%
All Adults 20.1% 20.0% 25.3% 18.7%
All Ages 9.7% 6.1% 24.7% 15.5%
All Ages + Wt Loss 5.3% 6.1% 14.7% 15.1%
Exploring Future Scenarios Through Simulation Experiments
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Simulation-based Findings (1)
• An inflection point in the growth of overweight and obesity prevalences probably occurred during the 1990s
– Extrapolations assuming linear growth may therefore exaggerate future prevalences
• The caloric imbalance relative to 1970 accounting for this growth has been only in the range of 1-3% of daily caloric intake
– Less than 50 kcal/day…per age, sex, and BMI category
– Most of the overall observed increase in caloric intake (USDA CSFII ’77-’96: 9% F, 13% M) has been the natural consequence of weight gain, not its cause
• Both expenditure and intake naturally increase with greater weight
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Reconciling the CSFII Data with Our Estimates of Caloric Balance
A Dynamic Hypothesis
Model Scope
Caloric balance(up 1-2%)
Caloric expenditure(up with greater BMI)
Weight-neutral intake(natural appetite up with
greater expenditure)
Mean caloricintake (up 9-13%)
Mean BMI(up 9-12%)
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Simulation-based Findings (2)
• Current focus on interventions during childhood will have only small impact on overall adult obesity (~6% relative to status quo)
– Unless effectively linked to the rest of the population
• Impacts on adult obesity of changing food and activity environments (by 2015) take decades to play out fully
– Due to age-to-age carryover effect
• Effective weight-loss programs—if any exist—could accelerate progress through subsidies for obese individuals
– But the cost could be high (even if subsidies terminated by 2020)
– And may be undermined by diet failure and recidivism
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Conclusions• This model improves our understanding of population dynamics of
weight change and supports pragmatic planning/evaluation– No other analytical model plays out effects of changes in caloric balance on
BMI prevalences over the life-course
– Traces plausible impacts of population-level and individual-level interventions• And addresses questions of whom to target, by how much, and by when
• But it has limitations—some addressable, some due to lack of data – Does not indicate exact nature of interventions
• Does not address cost-effectiveness of interventions, nor political reinforcement and resistance
– Does not address racial/ethnic sub-groups – Does not trace individual life histories (compartmental structure)
– Assumes habits determined by current environment, not by childhood learning
– Assumes no irreversible metabolic changes sustained as a result of childhood overweight/obesity