System Dynamics Simulation in Support of Obesity Prevention Decision-Making Bobby Milstein and Jack...

System Dynamics Simulation in Support

of Obesity Prevention Decision-Making

Bobby Milstein and Jack Homer

For Institute of Medicine Committee on an Evidence Framework for Obesity Prevention Decision-Making

Irvine, California

March 16, 2009

http://www.cdc.gov/index.htm

Agenda

System Dynamics Background

Obesity Life-Course Model (2005-2006, CDC)

Cardiovascular Risk Model (2007-2010, CDC & NIH)


0

10

20

30

40

50

60-62 71-74 76-80 88-94 99-04 2010 2020 2030 2040

Re-Directing the Course of ChangeQuestions Addressed by System Dynamics Modeling

Where?Prevalence of Obese Adults, United States

Why?

Historical Data: NHANESProjection: Ruhm CJ. Current and future prevalence of obesity and severe obesity in the United States. Forum for Health Economics & Policy 2007;10(2):1-28.

What?

Simulation Experiments

Regression Model Assumes• Linear growth (by age, sex)

HistoricalData

Regression Forecast

How?

Who?


Time Series Models

Describe trends

Multivariate Statistical Models

Identify historical trend drivers and correlates

Patterns

Structure

Events

Increasing:

• Depth of causal theory

• Robustness for longer-term projection

• Value for developing policy insights

• Degrees of uncertainty

• Leverage for change

Increasing:

• Depth of causal theory

• Robustness for longer-term projection

• Value for developing policy insights

• Degrees of uncertainty

• Leverage for changeDynamic Simulation Models

Anticipate new trends, learn about policy consequences,

and set justifiable goals

Types of Models for Policy Planning & Evaluation


System DynamicsSimulating Dynamic Complexity

Good at Capturing

• Differences between short- and long-term consequences of an action

• Time delays (e.g., incubation period, time to detect, time to respond)

• Accumulations (e.g., prevalences, resources, attitudes)

• Behavioral feedback (reactions by various actors)

• Nonlinear causal relationships (e.g., threshold effects, saturation effects)

• Differences or inconsistencies in goals/values among stakeholders

Origins • Jay Forrester, MIT, Industrial Dynamics, 1961

(“One of the seminal books of the last 20 years.”-- NY Times)

• Public policy applications starting late 1960s• Population health applications starting mid-

1970s

Forrester JW. Industrial Dynamics. Cambridge, MA: MIT Press; 1961.

Sterman JD. Business Dynamics: Systems Thinking and Modeling for a Complex World. Boston, MA: Irwin/McGraw-Hill; 2000.


System Dynamics Health Applications1970s to the Present

• Disease epidemiology – Cardiovascular, diabetes, obesity, HIV/AIDS,

cervical cancer, chlamydia, dengue fever, drug-resistant infections

• Substance abuse epidemiology – Heroin, cocaine, tobacco

• Health care patient flows – Acute care, long-term care

• Health care capacity and delivery– Managed care, dental care, mental health care,

disaster preparedness, community health programs

• Health system economics– Interactions of providers, payers, patients, and

investors

Homer J, Hirsch G. System dynamics modeling for public health: Background and opportunities. American Journal of Public Health 2006;96(3):452-458.


http://www.ajph.org/content/vol96/issue3/cover.shtml

An (Inter) Active Form of Policy Planning/Evaluation

System Dynamics is a methodology to…

• Map the salient forces that contribute to a persistent problem;

• Convert the map into a computer simulation model, integrating the best information and insight available;

• Compare results from simulated “What If…” experiments to identify intervention policies that might plausibly alleviate the problem;

• Conduct sensitivity analyses to assess areas of uncertainty in the model and guide future research;

• Convene diverse stakeholders to participate in model-supported “Action Labs,” which allow participants to discover for themselves the likely consequences of alternative policy scenarios


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.


The Obesity “System”: A Broad Causal Map

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

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

Engines of GrowthR1 Spiral of poor health and habitsR2 Parents and peer transmissionR3 Media mirrorsR4 Options shape habits shape optionsR5 Society shapes options shape society

Engines of GrowthR1 Spiral of poor health and habitsR2 Parents and peer transmissionR3 Media mirrorsR4 Options shape habits shape optionsR5 Society shapes options shape society

Responses to GrowthB1 Self-improvementB2 Medical responseB3 Improving preventive healthcareB4 Creating better messagesB5 Creating better options in beh. settingsB6 Creating better conditions in wider environB7 Addressing related health conditions

Responses to GrowthB1 Self-improvementB2 Medical responseB3 Improving preventive healthcareB4 Creating better messagesB5 Creating better options in beh. settingsB6 Creating better conditions in wider environB7 Addressing related health conditions

Resources, Resistance, Benefits & SupportsR6 Disease care costs squeeze preventionB8 Up-front costs undercut protection effortsB9 Defending the status quoB10 Potential savings build supportR7 Broader benefits build support

Resources, Resistance, Benefits & SupportsR6 Disease care costs squeeze preventionB8 Up-front costs undercut protection effortsB9 Defending the status quoB10 Potential savings build supportR7 Broader benefits build support


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?


Mapping and Modeling

Hurricane Andrew: Aug, 23, 24, 25, 1992

Homer J and Oliva R. Maps and models in system dynamics: a response to Coyle System Dynamics Review 2001; 17: 347-355.

Two systems thinking tools:

• Causal loop diagramming (mapping)

• Mathematical simulation (modeling)

Causal diagrams provide a ‘macroscopic’ view that goes beyond ‘laundry list’ thinking by introducing circular causality.

Maps reveal possibilities, but are not testable. They have little explanatory power and cannot weigh the relative importance of factors.

A useful evidence-based systems framework includes both maps and models.

“Without modeling, we might think we are learning to think holistically when we are

actually learning to jump to conclusions.”

– John Sterman

“Without modeling, we might think we are learning to think holistically when we are

actually learning to jump to conclusions.”

– John Sterman


Simulation and “Double-Loop Learning”

• Unknown structure • Dynamic complexity• Time delays• Impossible experiments

Real World

InformationFeedback

Decisions

MentalModels

Strategy, Structure,Decision Rules

• Selected• Missing• Delayed• Biased• Ambiguous

• Implementation• Game playing• Inconsistency• Short term

• Misperceptions• Unscientific• Biases• Defensiveness

• Inability to infer dynamics from

mental models

• Known structure • Controlled experiments• Enhanced learning

Virtual World

Sterman JD. Learning in and about complex systems. System Dynamics Review 1994;10(2-3):291-330.

Sterman JD. Business dynamics: systems thinking and modeling for a complex world. Boston, MA: Irwin McGraw-Hill, 2000.


What is a System? What are Dynamics?

System (Structure) = Stocks + Flows + Feedback Loops +…

• Stocks are accumulations of flows (of population, resources, changing goals, perceptions, etc.)

• Feedback loops link accumulations back to decisions that alter the flows: only 2 types (goal-seeking, self-reinforcing)

• Delays complicate things further

• As do non-linearities (need for critical mass, saturation effects)

Dynamics = Behavior over time

• Patterns in time series data (growth, fluctuation, etc.)

• Visible relationships of two or more variables (move together, move opposite, lead-lag, etc.)

StockFlow

Feedbackinfluence


System Dynamics Moves from Behavior to Structure by Identifying Accumulations and

Feedback Responses

Problem Situation

8

6

4

2

00 2 4 6 8 10 12 14 16 18 20

Seconds elapsed

Ou

nc

es

Water Level Over Time

System Behavior System Structure


Water Glass Model Diagram (Vensim™ software)

CurrentWaterLevel

Water flow

“Stock”“Flow”

Faucet openness

Water flow atfull open

Maximum faucetopenness decision

“Policy Lever”

“Constant”Desired water level

Water level gap

“Delay”Perceived water

level gap

Time to perceivewater level gap


Current water level = INTEG( Water flow , 0)

Water flow = Water flow at full open * Faucet openness

Water flow at full open = 1 ounce per second

Faucet openness = MAX ( 0, MIN ( Maximum faucet openness decision , Perceived water level gap / Water flow at full open ) )

Maximum faucet openness decision = 1 out of possible 1

Perceived water level gap = DELAY1I ( Water level gap , Time to perceive water level gap , 0)

Water level gap = Desired water level - Current water level

Desired water level = 6 ounces

Time to perceive water level gap = 1 second

FINAL TIME = 20 seconds

INITIAL TIME = 0

TIME STEP = 0.125 seconds

Complete Equations for Water Glass Model


Sample Output from Water Glass Model

Ounces8

6

4

2

00 2 4 6 8 10 12 14 16 18 20

Seconds elapsed

Perc time Max open 1 1

1 0.50.5 1

Target

What If…


1. Current water level = INTEG( Water flow , 0)2. Water flow = Water flow at full open * Faucet openness3. Water flow at full open = 1 ounce per second4. Faucet openness = MAX (0, MIN (Maximum faucet openness decision, Perceived water level gap / Water flow at full open ))5. Maximum faucet openness decision = 1 out of possible 16. Perceived water level gap = DELAY1I (Water level gap,Time to perceive water level gap, 0)7. Water level gap = Desired water level - Current water level8. Desired water level = 6 ounces9. Time to perceive water level gap = 1 secondFINAL TIME = 20 secondsINITIAL TIME = 0TIME STEP = 0.125 seconds

System Equations

8

6

4

2

00 2 4 6 8 10 12 14 16 18 20

Seconds elapsed

OuncesSystem Behavior

Target

Review: Basic Steps in SD Model Building

Problem Situation System Structure

Current waterlevel

Water flow

Desired water level

Water level gap

Perceived waterlevel gap

Time to perceivewater level gap

Faucet openness

Water flow atfull open

Maximum faucetopenness decision

System Model

Perc time Max open 1 1

1 0.50.5 1

What if…?


The Modeling Process is Iterative…

SystemConceptualization

ModelFormulation

Representation ofModel Structure

Comparison andReconcilation

Perceptions ofSystem Structure

Empirical andInferred Time

Series

Comparison andReconciliation.

Deduction OfModel Behavior

Adapted from Saeed 1992

Homer JB. Why we iterate: scientific modeling in theory and practice. System Dynamics Review 1996;12(1):1-19.


Empirical…and…Critical

EmpiricalEvidence

SystemConceptualization

ModelFormulation

Representation ofModel Structure

Comparison andReconcilation

Perceptions ofSystem Structure

Mental Models,Experience,Literature

Literature,Experience

Empirical andInferred Time

Series

Comparison andReconciliation.

Deduction OfModel Behavior

Diagramming andDescription Tools

ComputingAids

StructureValidatingProcesses

BehaviorValidatingProcesses

Forrester JW, Senge PM. Tests for building confidence in system dynamics models. In: Legasto A, Forrester JW, Lyneis JM, editors. System Dynamics. New York, NY: North-Holland; 1980. p. 209-228.

Graham AK. Parameter estimation in system dynamics modeling. In: Randers J, editor. Elements of the System Dynamics Method. Cambridge, MA: MIT Press; 1980. p. 143-161.

Sterman JD. Business dynamics: systems thinking and modeling for a complex world. Boston, MA: Irwin McGraw-Hill, 2000.


Practical Options in Causal Modeling

Detail (Disaggregation)

Scope (Breadth)

Low High

Low

High

Simplistic

Impractical

Focused

Expansive

Too hard to verify, modify, and understand


Model Structure and Level of DetailDepends on the Intended Uses and Audiences

• Set Better Goals (Planners & Evaluators)

– Identify what is likely and what is possible

– Estimate intervention impact time profiles

– Evaluate resource needs for meeting goals

• Support Better Action (Policymakers)

– Explore ways of combining policies for better results

– Evaluate cost-effectiveness over extended time periods

– Increase policymakers’ motivation to act differently

• Develop Better Theory and Estimates (Researchers)

– Integrate and reconcile diverse data sources

– Identify causal mechanisms driving system behavior

– Improve estimates of hard-to-measure or “hidden” variables

– Identify key uncertainties to address in intervention studies

Forrester JW. Industrial Dynamics (Chapter 11: Aggregation of Variables). Cambridge, MA: MIT Press, 1961.


Tests for Building Confidence in Simulation Models

Focusing on

STRUCTURE

Focusing on BEHAVIOR

ROBUSTNESS

• Dimensional consistency• Extreme conditions• Boundary adequacy

• Parameter (in)sensitivity• Structure (in)sensitivity

REALISM

• Face validity• Parameter values

• Replication of behavior• Surprise behavior• Statistical tests

UTILITY• Appropriateness for audience and purposes

• Counterintuitive behavior• Generation of insights

Forrester 1973, Forrester & Senge 1980, Richardson and Pugh 1981


The Future of Systems Simulation

• More ambitious models, addressing big strategic questions

• Supporting macro-models with more empirical data, but also with the results of focused (operational, mechanistic) micro-models

• Improved use of expert judgment; e.g., Delphi method

• More use of software tools for automated model calibration, sensitivity testing, and documentation

• Better public outreach through logical, balanced presentation that provides clear path from evidence to insight

Forrester JW. System dynamics—the next fifty years. System Dynamics Review 2007;23(2/3):345-358.

Homer JB. Macro- and micro-modeling of field service dynamics. System Dynamics Review 1999;15(2):139-162.

Meadows DH, Robinson JM. The Electronic Oracle: Computer Models and Social Decisions. Wiley & Sons, 1985.

“There seems to be a common attitude that the major difficulty is shortage of information and data. Once data are collected, people feel confident in

interpreting the implications. I differ on both of these attitudes. The problem is not shortage of data but rather our inability to perceive the consequences

of the information we already possess.”

– Jay Forrester

“There seems to be a common attitude that the major difficulty is shortage of information and data. Once data are collected, people feel confident in

interpreting the implications. I differ on both of these attitudes. The problem is not shortage of data but rather our inability to perceive the consequences

of the information we already possess.”

– Jay Forrester


Obesity Life-Course Model(with CDC, 2005-2006)

• Initially considered obesity dynamics broadly

• Narrowed our focus to address a fundamental question at the heart of much policy discussion:

– How do changes in caloric balance affect the future BMI distribution across the life-course? What does that imply for policy?

Data source: National Center for Health Statistics, CDC: National Health Examination Survey (NHES) 1960-1970, National Health and Nutrition Examination Survey (NHANES) 1971-2002.

Homer J, Milstein B, Dietz W, et al. Obesity population dynamics: exploring historical growth and plausible futures in the U.S. Proc. 24th Int’l System Dynamics Conference; Nijmegen, The Netherlands; July 2006.

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

Obesity definitions by age

Ages 2-19: BMI>=30 or >=95th percentile on CDC growth chart

Ages 20-74: BMI>=30

Growth of Obesity for Four Age Ranges United States, 1960-2002


Focusing on Life-Course Dynamics

• Explore likely consequences of possible interventions affecting caloric balance (intake less expenditure) – How much impact on obesity prevalence?

– How long will it take to see?

– Should we target particular subpopulations? (age, sex, weight category; lack data for race, ethnicity)

• Consider interventions broadly but leave details (composition, coverage, efficacy, cost) outside model boundary for now– Available data inadequate

– Would require a separate research effort to estimate these details

– Not addressing feedback loops of reinforcement and resistance

– Not addressing cost-effectiveness


Growth of Obesity by Age Range since 1960 (Gender breakout not shown here)

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


Population Weight-Change DynamicsFrom Caloric Balance to Obesity Prevalence

Dynamic Population Weight Framework

Population by Age (0-99) and Sex

Flow-rates betweenBMI categories

Overweight andobesity prevalence

Birth Immigration

Death

CaloricBalance

Yearly aging

NotOverweight

ModeratelyOverweight

ModeratelyObese

SeverelyObese

Data sources: NHANES, CDC growth charts, Census, Vital Statistics, Arkansas K-12 assessment, Forbes 1986, Flegal 2005


Detail of Aging and BMI Change Processes

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)



Two Classes of Interventions





Birth Immigration

Death

CaloricBalance

Yearly aging

NotOverweight


ModeratelyObese

SeverelyObese

Trends and PlannedInterventions

Changes in the Physicaland Social Environment

Weight Loss/MaintenanceServices for Individuals



Is Adult Activity and Eating Much Influenced by Habits Learned in Childhood?

It is often said that activity and eating habits are shaped during early childhood, and that once poor habits are established, it is difficult to change them.1

It is true that overweight children are more likely to become obese adults.2 However, this could simply reflect adult surroundings that are in many cases similar to the childhood environment.

Adequate long-term studies to test the habit-carryover hypothesis have not been done.3

What happens when the childhood environment was healthy but the adult environment is not, or vice versa? We don’t know. But we do know there is a strong link between one’s current surroundings and social networks and one’s weight.4

1. Ritchie et al. Pediatric overweight: A review of the literature. UC Berkeley Center for Weight and Health, 2001; Byrne, J Nutrition Education and Behavior, 34(4), 2002.

2. Deckelbaum and Williams, Obesity Research 9(Suppl 4), 2001; Dietz and Gortmaker, Annual Review of Public Health 22, 2001; Goran MI, AJCN 73, 2001; Dietz WH, J Nutrition128(S), 1998.

3. Bar-Or O., PCPFS Research Digest Series 2, No. 4, 1995.

4. Christakis and Fowler. NEJM 357, 2007.


Findings

• An inflection point in the growth of overweight and obesity prevalences probably occurred during the 1990s

• The daily caloric imbalance accounting for this growth has been less than 50 kcal/day (within each BMI category)

• Impacts of changing environments on adult obesity take decades to play out fully: “Carryover effect”

• Youth interventions have only small impact on overall adult obesity (assuming adult habits are determined by adult environment)

• Weight loss programs for the obese could accelerate progress—but is there a realistic alternative to risky/expensive bariatric surgery?


Alternative Futures for Teenage Obesity

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


Alternative Futures for Adult Obesity

Obese fraction of Adults (Ages 20-74)

0%

10%

20%

30%

40%

50%

1970 1980 1990 2000 2010 2020 2030 2040 2050

Fra

cti

on

of

po

pn

20-

74

Base SchoolYouth AllYouth

School+Parents AllAdults AllAges

AllAges+WtLoss


Possible Extensions to Model Details of Activity & Food Environments

Indicates possible extensions to the existing model





Obesity-attributableunhealthy days

Obesity-attributableillness costs

Birth Immigration

Death

CaloricBalance

Yearly aging

NotOverweight


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


Cardiovascular Disease Intervention Strategy(with CDC and NIH, 2007-10)

• What are the key pathways of CV risk, and how do these affect health outcomes and costs?

• How might interventions affect the risk factors and outcomes in the short- and long-term?

• How might policy efforts be better balanced given limited resources?

Smoking

Obesity

Secondhandsmoke

Healthinessof diet

Extent ofphysical activity

Psychosocialstress

Diagnosisand control

First-time CVevents and

deaths

Access to and marketingof smoking quit products

and services

Access to andmarketing of mental

health services

Sources ofstress

Access to andmarketing of healthy

food options

Access to andmarketing of physical

activity options

Access to andmarketing of weight

loss services

Access to andmarketing ofprimary care

Particulate airpollution

Utilization ofquality primary

care

Tobacco taxes andsales/marketing

regulations

Smoking bans atwork and public

places

Junk food taxes andsales/marketing

regulations

Downwardtrend in CV

event fatality

Quality of primarycare provision

Chronic Disorders

Costs from CV and other riskfactor complications and

from utilization of services

Anti-smokingsocial marketing

High BP

Highcholesterol

Diabetes

Homer J, Milstein B, Wile K, Pratibhu P, Farris R, Orenstein D. Modeling the local dynamics of cardiovascular health: risk factors, context, and capacity. Preventing Chronic Disease 2008;5(2). Available at http://www.cdc.gov/pcd/issues/2008/apr/07_0230.htm

Homer J, Milstein B, Wile K, Trogdon J, Huang P, Labarthe D, Orenstein D. Simulating and evaluating local interventions to improve cardiovascular health. In submission to Preventing Chronic Disease.

The CDC has partnered on this project with the Austin (Travis County), Texas, Dept. of Health and Human Services. The model is calibrated to represent the overall US, but is informed by the experience and local data of the Austin team.

The CDC has partnered on this project with the Austin (Travis County), Texas, Dept. of Health and Human Services. The model is calibrated to represent the overall US, but is informed by the experience and local data of the Austin team.


Smoking

Obesity

Secondhandsmoke

Healthinessof diet


Psychosocialstress



deaths



care

Downwardtrend in CV

event fatalityChronic Disorders

High BP

Highcholesterol

Diabetes

Risk Factors for CVD

Data sources: NHANES, NHIS, MEPS, AHA/NIH reports, Census, Vital Statistics, Framingham risk calculators, literature on risk factors and costs


Tobacco and Air Quality Interventions

Smoking

Obesity

Secondhandsmoke

Healthinessof diet


Psychosocialstress



deaths


and services



care


regulations


places

Downwardtrend in CV

event fatalityChronic Disorders


High BP

Highcholesterol

Diabetes



Health Care Interventions

Smoking

Obesity

Secondhandsmoke

Healthinessof diet


Psychosocialstress



deaths


and services




care


regulations


places

Downwardtrend in CV

event fatality


Chronic Disorders


High BP

Highcholesterol

Diabetes



Interventions Affecting Stress

Smoking

Obesity

Secondhandsmoke

Healthinessof diet


Psychosocialstress



deaths


and services


health services

Sources ofstress




care


regulations


places

Downwardtrend in CV

event fatality


Chronic Disorders


High BP

Highcholesterol

Diabetes



Healthy Diet Interventions

Smoking

Obesity

Secondhandsmoke

Healthinessof diet


Psychosocialstress



deaths


and services


health services

Sources ofstress


food options




care


regulations


places


regulations

Downwardtrend in CV

event fatality


Chronic Disorders


High BP

Highcholesterol

Diabetes



Physical Activity & Weight Loss Interventions

Smoking

Obesity

Secondhandsmoke

Healthinessof diet


Psychosocialstress



deaths


and services


health services

Sources ofstress


food options


activity options


loss services




care


regulations


places


regulations

Downwardtrend in CV

event fatality


Chronic Disorders


High BP

Highcholesterol

Diabetes



Adding Up the Costs

Smoking

Obesity

Secondhandsmoke

Healthinessof diet


Psychosocialstress



deaths


and services


health services

Sources ofstress


food options


activity options


loss services




care


regulations


places


regulations

Downwardtrend in CV

event fatality


Chronic Disorders

Costs from CV and other riskfactor complications and

from utilization of services


High BP

Highcholesterol

Diabetes



Graphs of assumptionsObese fraction of age 18

0.2

0.15

0.1

0.05

0

1990 2000 2010 2020 2030 2040Total obese frac of age 18 : basee DmnlTotal obese frac of age 18 : teen_dietPA Dmnl

Poor Diet Fraction

0.8

0.6

0.4

0.2

0

1990 2000 2010 2020 2030 2040Poor diet frac : basee Poor diet frac : teen_dietPA

Inadequate PA Fraction

0.8

0.6

0.4

0.2

0

1990 2000 2010 2020 2030 2040Inadequate PA frac of total popn : basee Inadequate PA frac of total popn : teen_dietPA

Use of Weight Loss Svcs by non-CVD Obese

0.4

0.3

0.2

0.1

0

1990 2000 2010 2020 2030 2040Use of WL svcs by obese nonCVD : basee Use of WL svcs by obese nonCVD : teen_dietPA_WL

#1: Age 18 obesity reduced 70% by 2030

#2a: Poor diet reduced from 64% to 41%

#2b: Inadequate PA reduced from 51% to 22%

#3: Use of weight loss services among obese increased from 10% to 24%


Obesity prevalence in adults (all subgroups combined)

Obesity Prevalence

0.4

0.3

0.2

0.1

0

1990 2000 2010 2020 2030 2040Obese frac of total popn : basee Obese frac of total popn : teenobdown70pct Obese frac of total popn : teen_dietPA Obese frac of total popn : teen_dietPA_WL

As in the life-course model, lower childhood obesity accounts for only 12% of the adult prevalence reduction

projected in the full-population approach of “Teen_DietPA”

-3.5%

-30.5%-37.2%


Further Consequences

With the three policies combined, cumulative deaths through 2040 can be reduced by 2.36M and cumulative consequence costs by $1.08Tr

Teen obesity down 70%

plus better adult diet and physical activity

plus more use of weight loss services

Obese fraction of adults (base2040=.339)

3.5% 30.5% 37.2%

High blood pressure fraction of adults (base2040=.449)

0.4% 8.9% 10.4%

High cholesterol fraction of adults (base2040=.435)

0.3% 8.9% 9.6%

Diabetic fraction of adults (base2040=.163)

0.4% 16.8% 19.5%

Heart attacks (base2040=3.077M/yr.)

0.1% 3.2% 3.7%

Strokes (base2040=1.447M/yr.)

0.1% 4.1% 4.9%

Combined CV events (base2040=7.424M/yr.)

0.1% 3.3% 3.8%

Deaths from CV events (base2040=1.603M/yr.)

0.1% 5.5% 6.4%

Other deaths from CV risk factors (base2040=1.217M/yr.)

0.2% 3.9% 4.5%

Total consequence costs (base2040=1.418Tr/yr.)

0.1% 5.0% 5.4%

PERCENTAGE REDUCTION FROM BASE CASE VALUE IN 2040, BY SCENARIO


System Dynamics Simulation in Support of Obesity Prevention Decision-Making Bobby Milstein and Jack...

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