Syndemics Prevention Network Modeling the Population Health Dynamics of Diabetes & Obesity Texas...

Syndemics

Prevention Network

Modeling the Population Health Dynamics of Diabetes & Obesity

Texas Public Health AssociationGalveston, TX

February 26, 2007Syndemics

Prevention Network

Bobby MilsteinSyndemics Prevention Network

Centers for Disease Control and PreventionAtlanta, Georgia

[email protected]://www.cdc.gov/syndemics

Syndemics

Prevention Network

Mokdad AH, Bowman BA, Ford ES, Vinicor F, Marks JS, Koplan JP. The continuing epidemics of obesity and diabetes in the United States. Journal of the American Medical Association 2001;286(10):1195-200.

Kaufman FR. Diabesity: the obesity-diabetes epidemic that threatens America--and what we must do to stop it. New York, NY: Bantam Books, 2005.

Syndemics

Prevention Network

Imperatives for Protecting Health

Gerberding JL. Protecting health: the new research imperative. Journal of the American Medical Association 2005;294(11):1403-1406.

Typical Current StateStatic view of problems that are studied in isolation

Proposed Future StateDynamic systems and syndemic approaches

"Currently, application of complex systems theories or syndemic science

to health protection challenges is in its infancy.“

-- Julie Gerberding

Syndemics

Prevention Network

Wickelgren I. How the brain 'sees' borders. Science 1992;256(5063):1520-1521.

How Many Triangles Do You See?

Syndemics

Prevention Network

Boundary Judgments(System of Reference)

Observations(Facts)

Evaluations(Values)

Ulrich W. Boundary critique. In: Daellenbach HG, Flood RL, editors. The Informed Student Guide to Management Science. London: Thomson; 2002. p. 41-42. <http://www.geocities.com/csh_home/downloads/ulrich_2002a.pdf>.

Ulrich W. Reflective practice in the civil society: the contribution of critically systemic thinking. Reflective Practice 2000;1(2):247-268. http://www.geocities.com/csh_home/downloads/ulrich_2000a.pdf

Boundary CritiqueWhen it comes to the problem of boundary judgments,

experts have no natural advantage of competence over lay people.

-- Werner Ulrich

Syndemics

Prevention Network

Transforming the Future of Diabetes…

"Every new insight into Type 2 diabetes...

makes clear that it can be avoided--and

that the earlier you intervene the better.

The real question is whether we as a

society are up to the challenge...

Comprehensive prevention programs

aren't cheap, but the cost of doing

nothing is far greater..."

Gorman C. Why so many of us are getting diabetes: never have doctors known so much about how to prevent or control this disease, yet the epidemic keeps on raging. how you can protect yourself. Time 2003 December 8. Accessed at http://www.time.com/time/covers/1101031208/story.html.

…in an Era of Rising Obesity

Syndemics

Prevention Network

Prevalence of Diagnosed Diabetes, US

0

10

20

30

40

1980 1990 2000 2010 2020 2030 2040 2050

Mill

ion

pe

op

le

HistoricalData

Markov Model Constants• Incidence rates (%/yr)• Death rates (%/yr)• Diagnosed fractions(Based on year 2000 data, per demographic segment)

Honeycutt A, Boyle J, Broglio K, Thompson T, Hoerger T, Geiss L, Narayan K. A dynamic markov model for forecasting diabetes prevalence in the United States through 2050. Health Care Management Science 2003;6:155-164.

Jones AP, Homer JB, Murphy DL, Essien JDK, Milstein B, Seville DA. Understanding diabetes population dynamics through simulation modeling and experimentation. American Journal of Public Health 2006;96(3):488-494.

Why?

Where?

How?

Who?

What?

Markov Forecasting Model

Simulation Experiments

in Action Labs

Questions Addressed by System Dynamics Modeling Learning to Re-Direct the Course of Change

Syndemics

Prevention Network

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

Syndemics

Prevention Network

A Model Is…

An inexact representation of the real thing

It helps us understand, explain, anticipate, and make decisions

“All models are wrong, some are useful.”

-- George Box

“All models are wrong, some are useful.”

-- George Box

Syndemics

Prevention Network

System Dynamics Simulation Modeling 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

Syndemics

Prevention Network

Understanding Dynamic ComplexityFrom a Very Particular Distance

“{System dynamics studies problems} from ‘a very particular distance', not so close as to be concerned with the action of a single individual,

but not so far away as to be ignorant of the internal pressures in the system.”

-- George Richardson

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.


Syndemics

Prevention Network

Simulations for Learning in Dynamic Systems

Morecroft JDW, Sterman J. Modeling for learning organizations. Portland, OR: Productivity Press, 2000.


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

Multi-stakeholder Dialogue

Dynamic Hypothesis (Causal Structure)

X Y

Plausible Futures (Policy Experiments)

Syndemics

Prevention Network

CDC Diabetes System Modeling ProjectDiscovering Dynamics Through Action Labs

Jones AP, Homer JB, Murphy DL, Essien JDK, Milstein B, Seville DA. Understanding diabetes population dynamics through simulation modeling and experimentation. American Journal of Public Health 2006;96(3):488-494.

Syndemics

Prevention Network

CDC

• Dara Murphy, Bobby Milstein, Chris Benjamin, Wayne Millington, Parul Nanavati, Sharon Daves, Frank Vinicor, Mark Rivera

Contractor Team

• Drew Jones

• Jack Homer

• Joyce Essien

• Doc Klein

• Don Seville

State Diabetes Programs

• Minnesota Heather Devlin, Jay Desai

• California Gary He, Karen Black, Toshi Hayashi

• VermontRobin Edelman, Jason Roberts, Ellen Thompson

CDC Diabetes System Modeling ProjectContributors

Syndemics

Prevention Network

Project Background

• Diabetes programs face tough challenges and questions

– Pressure for results on disease burden, not just behavioral change

– The Diabetes Prevention Program indicates primary prevention is possible, but may be difficult and costly

– What is achievable on a population level?

– How should funds be allocated?

• Standard epidemiological models rarely address such policy questions

• In Fall 2003, CDC initiates System Dynamics modeling project

• In Spring 2005, some states join as collaborators in further developing and using the SD model

Syndemics

Prevention Network

Inflow

Volume

Outflow

Developing

Burden ofDiabetes

Total Prevalence(people with diabetes)

Unhealthy Days(per person with

diabetes)

Costs(per person with diabetes)

People withDiagnosedDiabetes

Diagnosis Deaths

ab

People withUndiagnosedPreDiabetes

Developing

DiabetesOnset

c

d

People withNormal

Blood SugarLevels

PreDiabetesOnset

Recovering fromPreDiabetes

e

DiabetesManagement

DiabetesDiagnosis

Obesity in theGeneral

Population

PreDiabetesDetection &

Management

People withUndiagnosed

Diabetes

Deaths

Diabetes Burden is Driven by Population Flows

Syndemics

Prevention Network

Diabetes Burden is Driven by Population Flows

Inflow

Volume

Outflow

Developing

Burden ofDiabetes

Total Prevalence(people with diabetes)

Unhealthy Days(per person with

diabetes)

Costs(per person with diabetes)

People withDiagnosedDiabetes

Diagnosis Deaths

ab

People withUndiagnosedPreDiabetes

Developing

DiabetesOnset

c

d

People withNormal

Blood SugarLevels

PreDiabetesOnset

Recovering fromPreDiabetes

e

DiabetesManagement

DiabetesDiagnosis

Obesity in theGeneral

Population

PreDiabetesDetection &

Management

People withUndiagnosed

Diabetes

Deaths

Standard boundary

This larger view takes us beyond standard epidemiological models and most intervention programs

Syndemics

Prevention Network

Using Available Data to Calibrate the Model

Information Sources Data

U.S. Census

• Population growth and death rates• Fractions elderly, black, hispanic• Health insurance coverage

National Health Interview Survey• Diabetes prevalence• Diabetes detection

National Health and Nutrition Examination Survey• Prediabetes prevalence

• Obesity prevalence

Behavioral Risk Factor Surveillance System

• Eye exam and foot exam• Taking diabetes medications• Unhealthy days (HRQOL)

Professional Literature• Effects of risk factors and mgmt on onset, complications, and costs• Direct and indirect costs of diabetes

Syndemics

Prevention Network

Diabetes System Modeling ProjectConfirming Fit to Historical Trends (2 examples out of 10)

Diagnosed Diabetes % of AdultsObese % of Adults

0%

10%

20%

30%

40%

1980 1985 1990 1995 2000 2005 2010

Obese % of adults

Data (NHANES)

Simulated

0%

2%

4%

6%

8%

1980 1985 1990 1995 2000 2005 2010

Diagnosed diabetes % of adults

Data (NHIS)

Simulated

Syndemics

Prevention Network

The growth of diabetes prevalence since 1980 has been driven by growth in obesity prevalence

Obese Fraction and Diabetes per Thousand1300.7

850.35

400

1980 1990 2000 2010 2020 2030 2040 2050Time (Year)

Diabetes Prevalenc

e

Obesity Prevalenc

e

Risk multiplier on diabetes onset from

obesity = 2.6

Syndemics

Prevention Network

Prevalence=92 AND RISING

Although we expect obesity to increase little after 2006, diabetes keeps growing robustly for another 20-25 years

Obese Fraction and Diabetes per Thousand1300.7

850.35

400

1980 1990 2000 2010 2020 2030 2040 2050Time (Year)

Diabetes Prevalenc

e

Obesity Prevalenc

e

Diabetes prevalence keeps growing after

obesity stopsWHY?

With high (even if flat) onset, prevalence tub

keeps filling until deaths (4-5%/yr)=onset

Onset=6.3 per thou

Estimated 2006 values

Death=3.8 per thou

Prevalence=92 / thou

Risk multiplier on diabetes onset from

obesity = 2.6

Syndemics

Prevention Network

Unhealthy days impact of prevalence growth, as affected by diabetes management: Past and one possible future

Unhealthy Days per Thou and Frac ManagedObese Fraction and Diabetes per Thousand1300.7

850.35

400

1980 1990 2000 2010 2020 2030 2040 2050Time (Year)

Diabetes Prevalenc

e

Obesity Prevalenc

e

5000.65

25001980 1990 2000 2010 2020 2030 2040 2050

3750.325

Unhealthy Daysfrom Diabetes

Managed

fraction

Diabetes prevalence keeps growing after

obesity stops

If disease management gains end, the burden

grows

Reduction in unhealthy days per complicated case if

conventionally managed: 33%;

if intensively managed: 67%

Syndemics

Prevention Network

A Sequence of What-if Simulations

People with Diabetes per Thousand Adults150

125

100

75

501980 1990 2000 2010 2020 2030 2040 2050

Monthly Unhealthy Days from Diabetes per Thou500

450

400

350

300

250

1980 1990 2000 2010 2020 2030 2040 2050

Base

Base

Start with the base case or “status quo”: no improvements in diabetes management or prediabetes management after 2006

Syndemics

Prevention Network

Further Increases in Diabetes Management


125

100

75

501980 1990 2000 2010 2020 2030 2040 2050


450

400

350

300

250

1980 1990 2000 2010 2020 2030 2040 2050

Base

Diab mgt Base

More people living with diabetes

Keeping the burden at bay for nine years

longer

Diab mgt

Increase fraction of diagnosed diabetes getting managed from 58% to 80% by 2015. (No change in the mix of conventional and intensive.) What do you think will happen?

Diabetes mgmt does nothing to slow the growth of prevalence—in fact, it

increases it. As soon as diabetes mgmt stops

improving, unhealthy days start to grow as fast as

prevalence.

Syndemics

Prevention Network

A Huge Push for Prediabetes Management


125

100

75

50

1980 1990 2000 2010 2020 2030 2040 2050


450

400

350

300

250

1980 1990 2000 2010 2020 2030 2040 2050

Base

PreD mgmt

Base

PreD mgmt

The improvement is relatively modest—the growth is not stopped

Increase fraction of prediabetics getting managed from 6% to 32% by 2015. (Half of those under intensive mgmt by 2015.) No increase in diabetes mgmt. What do you think will happen?

Diabetes onset rate reduced 12% relative to base run. Not nearly

enough to offset the excess onset due to high obesity. By 2050,

diabetes prevalence reduced only 9% relative to base run.

Syndemics

Prevention Network

Two Scenarios in which Obesity is Reduced

Obese Fraction of Adult Population

0.4

0.3

0.2

0.1

01980 1990 2000 2010 2020 2030 2040 2050

Base

Obesity 25%

Obesity 18%

What if it were possible—in addition to the prediabetes mgmt intervention—to gradually lower the fraction obese from 34% (2006) to the 1994 value of 25% by 2030? Or, to the 1984 value of 18%?

Syndemics

Prevention Network

Managing Prediabetes AND Reducing Obesity

The more you reduce obesity, the sooner you

stop the growth in diabetes—and the more

you bring it down

… Same with the burden of diabetes


125

100

75

50

1980 1990 2000 2010 2020 2030 2040 2050


450

400

350

300

250

1980 1990 2000 2010 2020 2030 2040 2050

Base

PreD mgmt

PreD & Ob 25%

PreD & Ob 18%

Base

PreD mgmt

PreD & Ob 18%

PreD & Ob 25%

What do you think will happen if, in addition to PreD mgmt, obesity is reduced moderately by 2030? What if it is reduced even more?

Why is obesity reduction so powerful? Mainly because of its strong effect on onset rate among prediabetics; but,

also, because it reduces PreD prevalence itself. However, achieving significant obesity reduction takes a

long time.

Syndemics

Prevention Network

Intervening Effectively Upstream AND Downstream


125

100

75

50

1980 1990 2000 2010 2020 2030 2040 2050


450

400

350

300

250

1980 1990 2000 2010 2020 2030 2040 2050

Base

PreD mgmt PreD mgmt

Base

PreD & Ob 25%

Pred & Ob 25%

All 3 --PreD & Ob 25% & Diab mgmt

All 3

With a combination of effective upstream and downstream interventions we could hold the burden of diabetes nearly flat

through 2050!

With pure upstream intervention, burden still grows for many years before turning around. What do you think will happen if we add the prior diabetes mgmt intervention on top of the PreD+Ob25 one?

Downstream improvement acts quickly against burden but cannot continue

forever. Significant upstream gains are thus

essential but will likely take 15+ years to

achieve. A flat-burden future is possible but

requires simultaneous action on both fronts.

Syndemics

Prevention Network

Cover of "The Economist", Dec. 13-19, 2003Cover of "The Economist", Dec. 13-19, 2003.

Syndemics

Prevention Network

CDC Obesity Dynamics Modeling Project Contributors

Core Design Team• Dave Buchner• Andy Dannenberg• Bill Dietz• Deb Galuska• Larry Grummer-Strawn• Anne Hadidx• Robin Hamre• Laura Kettel-Khan• Elizabeth Majestic • Jude McDivitt• Cynthia Ogden• Michael Schooley

System Dynamics Consultants• Jack Homer• Gary Hirsch

Time Series Analysts

• Danika Parchment

• Cynthia Ogden

• Margaret Carroll

• Hatice Zahran

Project Coordinator• Bobby Milstein

Workshop Participants• Atlanta, GA: May 17-18 (N=47)• Lansing, MI: July 26-27 (N=55)

Homer J, Milstein B, Dietz W, Buchner D, Majestic D. Obesity population dynamics: exploring historical growth and plausible futures in the U.S. 24th International Conference of the System Dynamics Society; Nijmegen, The Netherlands; July 26, 2006.

Syndemics

Prevention Network

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

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

Syndemics

Prevention Network

Focus of Our Simulation Model• Explore effects of new interventions affecting caloric balance

(intake less expenditure) – U.S. policy discourse is primarily focused on:

• prevention among school-aged youth • medical treatment for the severely obese

– What are the likely consequences?• How much impact on adult obesity?• How long will it take to see?• Should we target other subpopulations?

(age, sex, weight category)

• Consider two classes of interventions– Changes in food & activity environments – Weight loss/maintenance services for individuals

• Additional intervention details (composition, coverage, efficacy, cost) left outside model boundary for now– Available data are inadequate to quantify impacts and cost-effectiveness – Could stakeholder Delphi help?

Syndemics

Prevention Network

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

Syndemics

Prevention Network




Flow-rates betweenBMI categories

Overweight andobesity prevalence

Birth Immigration

Death

Yearly aging

NotOverweight


ModeratelyObese

SeverelyObese

Syndemics

Prevention Network

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)

Syndemics

Prevention Network






Birth Immigration

Death

CaloricBalance

Yearly aging

NotOverweight


ModeratelyObese

SeverelyObese

Syndemics

Prevention Network

Obesity Dynamics Over the DecadesTwo 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

Syndemics

Prevention Network

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.

Syndemics

Prevention Network

Calibration of Uncertain ParametersTo Reproduce 60 BMI Prevalence Time Series(10 age ranges x 2 sexes x 3 high-BMI categories)

• Step 1: Iteratively adjust up-rate and down-rate constants

and initial BMI prevalences to reproduce steady-state BMI

prevalence for the early 1970s

• Step 2: Adjust 57 caloric balance time series (by age, sex,

and BMI category, 1975-2000) to reproduce BMI

prevalence growth for the 1980s and 1990s

Syndemics

Prevention Network

(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 Trends One of 20 {sex, age} Subgroups: Females age 55-64

Note: S-shaped curves, with inflection in the 1990s

Syndemics

Prevention Network

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

Syndemics

Prevention Network

Assumptions for Future Scenarios

Base Case• Caloric balances stay at 2000 values through 2050

Altering Food and Activity Environments

• 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

– ‘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 calories/day (translates to 1.8 kg weight loss per year)

• Fully effective by 2010 and terminated by 2020

Syndemics

Prevention Network

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

Findings• Inflection point in obesity probably occurred during the 1990s

– Simple extrapolations based on 1990s growth likely exaggerate future prevalences

• Caloric imbalance vs. 1970 only 1-2% (less than 50 cal./day) within any given age, sex, and BMI category

– Most of observed 9-13% cal./day increase in intake (USDA 1977-1996) has been natural consequence of weight gain (via metabolic adjustment), not its cause

• 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

– Assumes (1) adult habits determined by adult environment, and (2) childhood overweight causes no irreversible metabolic changes

• Weight-loss for the obese could accelerate progress--but, first, an effective program that minimizes recidivism must be found

Syndemics

Prevention Network

Conclusions & Limitations• 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

Learning In and About Dynamic Systems

Benefits of Simulation/Game-based Learning

• Formal means of evaluating options

• Experimental control of conditions

• Compressed time

• Complete, undistorted results

• Actions can be stopped or reversed

• Visceral engagement and learning

• Tests for extreme conditions

• Early warning of unintended effects

• Opportunity to assemble stronger support

Dynamic Complexity Hinders…

• Generation of evidence (by eroding the conditions for experimentation)

• Learning from evidence (by demanding new heuristics for interpretation)

• Acting upon evidence (by including the behaviors of other powerful actors)

Sterman JD. Learning from evidence in a complex world. American Journal of Public Health (in press).


“In [dynamically complex] circumstances simulation becomes the only reliable way to test a hypothesis and evaluate the likely effects of policies."

-- John Sterman

Syndemics

Prevention Network

“Simulation is a third way of doing science.

Like deduction, it starts with a set of explicit

assumptions. But unlike deduction, it does not

prove theorems. Instead, a simulation generates

data that can be analyzed inductively. Unlike

typical induction, however, the simulated data

comes from a rigorously specified set of rules

rather than direct measurement of the real world.

While induction can be used to find patterns in

data, and deduction can be used to find

consequences of assumptions, simulation

modeling can be used as an aid to intuition.”

-- Robert Axelrod

Axelrod R. Advancing the art of simulation in the social sciences. In: Conte R, Hegselmann R, Terna P, editors. Simulating Social Phenomena. New York, NY: Springer; 1997. p. 21-40. <http://www.pscs.umich.edu/pub/papers/AdvancingArtofSim.pdf>.

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

Simulation ExperimentsOpen a Third Branch of Science

“The complexity of our mental models vastly exceeds our ability to understand their implications without simulation."

-- John Sterman

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?

Syndemics

Prevention Network

For Additional Informationhttp://www.cdc.gov/syndemics

Syndemics Prevention Network Modeling the Population Health Dynamics of Diabetes & Obesity Texas...

Documents

Transcript of Syndemics Prevention Network Modeling the Population Health Dynamics of Diabetes & Obesity Texas...