syndemics prevention network draft: please do not cite without permission modeling population...

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DRAFT: Please do not cite without permission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

[email protected]

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


Effective HealthProtection Efforts


Related Diseases




Diet & Activity

Wider Environment (Economy,Technology, Laws) Influence

on Healthy Diet & Activity


Healthy Diet & ActivityGenetic Metabolic

Rate Disorders


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)



DRAFT 5/8/05



Related Diseases

-Options Available atHome, School, Work,



Diet & Activity

Wider Environment(Economy, Technology,

Laws) Influence on Options


Healthy Diet & Activity

-Genetic Metabolic

Rate Disorders


Diet & Activity

A Closed-Loop View of Causal Forces

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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)






DRAFT 5/8/05


R4Options ShapeHabits Shape

OptionsPrevalence ofOverweight &

Related Diseases

-Options Available atHome, School, Work,


Observation ofParents' andPeers' Habits

R2

Parents/PeersTransmission


Diet & Activity



B1

Self-Improvement



-Genetic Metabolic

Rate Disorders


Diet & Activity

B2

Medical Response

R1

Spiral of PoorHealth and Habits

R5

Society ShapesOptions Shape

Society

R3

MediaMirrors


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DRAFT 5/8/05


Effective HealthProtection Efforts

R6

Disease CareCosts Squeeze

PreventionB4

Creating BetterMessages

R4Options ShapeHabits Shape

OptionsPrevalence ofOverweight &

Related Diseases

-

Costs of Caringfor Overweight-

Related Diseases

-



Costs of Developing &Maintaining HealthProtection Efforts

B5

Creating BetterOptions inBehavioral

Settings

-B8

Up-front CostsUndercut

ProtectionEfforts

Observation ofParents' andPeers' Habits

R2

Parents/PeersTransmission


Diet & Activity



B1

Self-Improvement

B6

Creating BetterConditions in the

Wider Environment



-

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


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


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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Flow-rates betweenBMI categories

Overweight andobesity prevalence

Obesity-attributableunhealthy days

Obesity-attributableillness costs

Birth Immigration

Death

Yearly aging

NotOverweight


ModeratelyObese

SeverelyObese

Indicates possible extensions to the existing model

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Birth Immigration

Death

CaloricBalance

Yearly aging

NotOverweight


ModeratelyObese

SeverelyObese


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Obesity Prevalence Over the DecadesDynamic Population Weight Framework

NotOverweight


ModeratelyObese

SeverelyObese

NotOverweight


ModeratelyObese

SeverelyObese

NotOverweight


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







Birth Immigration

Death

CaloricBalance

Yearly aging

NotOverweight


ModeratelyObese

SeverelyObese

Trends and PlannedInterventions


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Birth Immigration

Death

CaloricBalance

Yearly aging

NotOverweight


ModeratelyObese

SeverelyObese


Changes in the Physicaland Social Environment

Weight Loss/MaintenanceServices for Individuals


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Birth Immigration

Death

CaloricBalance

Yearly aging

NotOverweight


ModeratelyObese

SeverelyObese




ActivityEnvironment

FoodEnvironment


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Birth Immigration

Death

CaloricBalance

Yearly aging

NotOverweight


ModeratelyObese

SeverelyObese




Food Price

Smoking

Social Influences onConsumption &

Selection

Options for AffordableRecommended Foods (Work,School, Markets, Restaurants)

ActivityEnvironment

FoodEnvironment

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Birth Immigration

Death

CaloricBalance

Yearly aging

NotOverweight


ModeratelyObese

SeverelyObese




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

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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

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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

Syndemics

Prevention Network


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%)

Syndemics

Prevention Network


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

Syndemics

Prevention Network


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

syndemics prevention network draft: please do not cite without permission modeling population...

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