Systems Science to Guide Implementation of Whole-of...

Systems Science to Guide Implementation of

Whole-of-community Childhood Obesity Interventions

Matthew W. Gillman, MD, SM EN Power of Programming

March 2014

Note: for non-commercial purposes only

Thanks to…

Faculty, Trainees, & Staff

Obesity Prevention Program Department of Population Medicine

Harvard Medical School/Harvard Pilgrim Health Care Institute

Questions about Obesity

• Population trends – What caused/is causing the epidemic? – How can we reverse it?

• Not necessarily the same as what caused it • Individual (between-person) variability

– Why do some people develop obesity and others not?

– How can we use that information to tailor, and evaluate, prevention and treatment

• What works, for whom, & under what circumstances


• Individual (between-person) variability – Why do some people develop obesity and

others not? – How can we use that information to tailor, and

evaluate, prevention and treatment




others not? – How can we use that—and other—information

to tailor, and evaluate, prevention and treatment




others not? – How can we use that information to tailor, and

evaluate, prevention and treatment


It’s because of what happened to them in utero and in early childhood

• Early (developmental) origins of obesity – [Motivation, Evidence] – How systems science may help

• Untangle the complex webs of etiology • Help drive design of prevention

The Childhood Obesity Epidemic

US DHHS, 2001; Hedley et al., 2004; Ogden et al., 2006, 2008

0

5

10

15

Pre

va

len

ce o

f O

ver

wei

gh

t

Year

24-71 months

0-11 months

12-23 months

1980 1985 1990 1995 2000

…in Younger Children Too Including Infants

Kim et al., Obesity 2006; ~500,000 well child visits in Mass. HMO

a

Standardized for age, race/ethnicity, and HVMA site, using the year 1999-2000 as reference

13.7

8.1

10.6

14.2

11.9

13.3 13.3 13.5

13.8

12.3

13.0

12.1

12.7 12.6

13.1 12.9

9.1

9.8 10.0

9.0

8.1

7.4

9.9 9.8 9.6 9.3

9.8 10.2

4

5

6

7

8

9

10

11

12

13

14

15

16

1980-1982

1983-1984

1985-1986

1987-1988

1989-1990

1991-1992

1993-1994

1995-1996

1997-1998

1999-2000

2001-2002

2003-2004

2005-2006

2007-2008

Year

Pre

va

len

ce

Overweight (standardized) Obesity (standardized)

Boys

X. Wen et al. Pediatrics 2012;129:823-831

Downward trend in BMI since 2004 in 0-6-year-olds

Girls

X. Wen et al. Pediatrics 2012;129:823-831

a

Standardized for age, race/ethnicity, and HVMA site, using the year 1999-2000 as reference

12.3

13.3

11.7

12.6

11.7

6.5 6.3

7.0

8.5 8.4

6.8

11.4 11.1 10.9

11.3

11.9 12.1 11.7

12.3 12.6

5.8

7.4 7.8

7.5 8.0

8.5 8.6

7.3

4

5

6

7

8

9

10

11

12

13

14

15

16

1980-1982

1983-1984

1985-1986

1987-1988

1989-1990

1991-1992

1993-1994

1995-1996

1997-1998

1999-2000

2001-2002

2003-2004

2005-2006

2007-2008

Year

Pre

va

len

ce

Overweight (standardized) Obesity (standardized)

Curious trends in SGA & LGA, U.S. 1990-2005 N = 502,716 low-risk mothers: 37-41 wk, age 25-29 y, white, >13 y educ, married,

1st trim prenatal care, non-smoker, no complications, NSVD, had U/S, GWG 26-35 lb

Donahue et al., Obstet Gynecol 2010; 115:357

Message

• The obesity epidemic has spared no age group, not even our youngest children

• Once present, obesity tenaciously resists treatment

• Prevention must start early

• Usual etiologic epidemiology – 1 determinant at a time – Independent of others

• Moving toward systems approach – More than 1 determinant

Developmental Origins of Obesity

Developmental Origins of Obesity How Important Can It Be?

Prenatal Infancy

Maternal smoking

GWG (IOM cat.)

Breastfeeding duration

Daily sleep

P (Ob)

at 7 y

N Inadequate/ Adequate 12+ m 12+ h 0.04

Y Excessive <12 m <12 h 0.28

Gillman , Ludwig. New Engl J Med 2013 (5 Dec); 369:2173-2175

Adjusted for maternal BMI, education; HH income; child race/ethnicity

Risk of obesity at age 7-10 y according to combinations of 4 pre/post-natal risk factors

Smoking – – – + – – + – + – + + – + + +Gest. weight gain – + – – – + + + – – – + + + – +Breastfeeding – – + – – + – – + + – + + – + +Sleep – – – – + – – + – + + – + + + +Prob. obesity 0.04 0.06 0.07 0.07 0.08 0.10 0.10 0.11 0.11 0.13 0.13 0.16 0.18 0.18 0.20 0.28

Pred. BMI-z 0.07 0.24 0.22 0.23 0.31 0.39 0.40 0.48 0.38 0.46 0.47 0.55 0.63 0.64 0.62 0.79

Pred. DXA % fat 23.2 23.0 24.5 24.1 24.4 24.4 24.0 24.2 25.4 25.7 25.3 25.3 25.5 25.2 26.6 26.5

Prevalence in this cohort6.9% 10.4% 20.3% 0.2% 5.2% 26.6% 0.2% 5.6% 1.1% 7.2% 0.1% 3.5% 9.2% 0.3% 1.5% 1.9%

Combinations of 4 risk factors

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16Combinations of 4 risk factors

Pro

bab

ility

of

ob

esi

ty

Gillman , Ludwig. New Engl J Med 2013 (5 Dec); 369:2173-2175

Smoking – – – + – – + – + – + + – + + +Gest. weight gain – + – – – + + + – – – + + + – +Breastfeeding – – + – – + – – + + – + + – + +Sleep – – – – + – – + – + + – + + + +Prob. obesity 0.04 0.06 0.07 0.07 0.08 0.10 0.10 0.11 0.11 0.13 0.13 0.16 0.18 0.18 0.20 0.28

Pred. BMI-z 0.07 0.24 0.22 0.23 0.31 0.39 0.40 0.48 0.38 0.46 0.47 0.55 0.63 0.64 0.62 0.79

Pred. DXA % fat 23.2 23.0 24.5 24.1 24.4 24.4 24.0 24.2 25.4 25.7 25.3 25.3 25.5 25.2 26.6 26.5

Prevalence in this cohort6.9% 10.4% 20.3% 0.2% 5.2% 26.6% 0.2% 5.6% 1.1% 7.2% 0.1% 3.5% 9.2% 0.3% 1.5% 1.9%

Combinations of 4 risk factors

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16Combinations of 4 risk factors

Pro

bab

ility

of

ob

esi

ty

PAR% ~ 20-50%

Risk of obesity at age 7-10 y according to combinations of 4 pre/post-natal risk factors

More risk factors • Prenatal

– Smoking, GWG, GDM • Perinatal

– C-section, leptin • Infancy

– Type of feeding, sleep duration, rapid adiposity gain, early intro solids

More risk factors • Prenatal

– Smoking, GWG, GDM • Perinatal

– C-section, leptin • Infancy

– Type of feeding, sleep duration, rapid adiposity gain, early intro solids

• Emerging – Epigenetics, toxic environment, microbiota….

• More than 1 determinant • Interacting with each other


• More than 1 determinant • Interacting with each other • Over time (age)

– Life course approach


• More than 1 determinant • Interacting with each other • Over time (age) • At multiple levels of influence

– Different influences at different developmental periods


• More than 1 determinant • Interacting with each other • Over time (age) • At multiple levels of influence *********************************************** • Dynamic

– Feedback loops


Maternal and child inter-generational vicious cycles


– Feedback loops • That may operate in different directions at different

stages of the life course


Hormone feedback loops in older

children and adults

Tend to impede weight loss

Cord blood leptin predicts slower WFL gain in 1st 6 mo, lower 3 & 7-y BMI,

but ...3-y leptin predicts higher 7-y BMI early sensitive period of leptin action, then tolerance

Boeke et al, Obesity 2013;21:1430-7

Body Composition Whole Body Total Energy Expenditure Thermic Effect of Feeding Adaptive Thermogenesis Physical Activity Energy Expenditure Resting Metabolic Rate Daily Average Lipolysis Rate Ketone Oxidation Rate Daily Average Ketogenesis Rate Daily Average Ketone Excretion Rate Daily Average Glycogenolysis Rate Glycerol 3-Phosphate Production Rate Gluconeogenesis From Amino Acids De Novo Lipogenesis Rate Macronutrient Oxidation Rates Respiratory Gas Exchange Nutrient Balance Parameter Constraints Carbohydrate Perturbation Constraint Protein Perturbation Constraint Physical Inactivity Constraint

Model Parameter Values

Predicting metabolic adaptation, body weight

change, and energy intake

KD Hall


– Feedback loops • At multiple levels of influence


Health

outcomes

d

Pre- and peri-

natal factors

Micro

Macro

Time Axis

Hierarchical Axis

Weight

gain

Energy in (diet)

Energy out

(physical activity)

Health Behaviors

Genes

Appetite Metabolism

Mood HPA axis

Built environments (e.g., connectivity, walkability)

Commercial messaging (e.g., TV ads to kids)

Psychosocial hazards (e.g., crime)

Local food environment (e.g., presence of fast food)

Area deprivation (e.g., poverty)

Cultural norms (e.g., body image)

Laws, regulations, policies (e.g., farm subsidies)

Social, built, natural environment

http://kim.foresight.gov.uk/Obesity/Obesity.html

Yikes!

Complex System

• “…one whose properties are not fully explained by an understanding of its component parts”

• Whole is greater than sum of parts

Gallagher & Appenzeller, quoted in Luke and Stamatakis, Annu Rev Public Health 2012; 33:357-76

Complex System

• Elements – Large number – Heterogeneous, within and between – Interact with each other

• Interactions produce emergent properties

• Effects – Persist over time – Adapt to changing circumstances

Luke and Stamatakis, Annu Rev Public Health 2012; 33:357-76

Complex System Need to understand drivers

• Leverage for most powerful and efficient

effects on health outcomes – Seemingly unimportant elements with large

downstream effects? – Combinations of elements? – Unforeseen adverse effects (unintended

consequences)?

Luke and Stamatakis, Annu Rev Public Health 2012; 33:357-76

System Dynamics “top down”

Stocks & flows

Agent-based Modeling “bottom up”

Actors & rules

Network Analysis Nodes & ties among them

Systems Science

• Systems science approaches have the potential to – Identify the most potent early drivers of the

development of obesity and its complications

Systems Science


development of obesity and its complications – Use for developing (and evaluating) multi-

setting, mutli-component life course interventions

• Address “what works, for whom, and under what circumstances”

Implementation of Interventions How can systems science help?

• Like etiology, implementation is complex – Informed by SNPs, methylated CpG sites,

16s speciation, dopamine reward pathways, insulin resistance…

– But primarily involves stakeholders, interactions, processes

Stakeholders, Processes

But too simple—lacks interactions, feedback, etc

Implementation of Interventions How can systems science help?

• Stakeholders, interactions, processes – Whole of community interventions try to

change them all – Need to study mechanisms (same as ‘omics)

to understand “what works, for whom, and under what circumstances?”

R01 Funded by NIH (NHLBI, OBSSR) 2013-2018

Systems Science to Drive Whole-of-community Childhood Obesity Prevention Interventions

Investigator Institution Role Expertise

Gillman Harvard Univ PI Obesity etiology and prevention

Hammond Brookings Inst PI Agent-based modeling

Economos Tufts Univ Co-I CPBR, obesity whole community interventions

Hovmand Washington Univ Co-I Participatory group model building

Allender Deakin Univ Co-I Systems intervention approaches

Swinburn Deakin Univ, Univ Auckland Co-I Community/policy

approaches to obesity


Logic: •Start with 2 completed interventions •Relevant literature •Build initial computational model


Logic: •Start with 2 completed interventions •Relevant literature •Build initial computational model •Refine models with ongoing intervention

Victorian Trial

• Cluster RCT • 12 intervention, 12 control communities • Funded by Victoria state and Australia

federal government ($160m) • Consortium of state government,

academia, NGO evaluation unit


Logic: •Start with 2 completed interventions •Relevant literature •Build initial computational model •Refine models with ongoing intervention •Use to design new intervention

Departure from:

Linear thinking

Multiple causation

Independent levels of influence

Systems Approach to Obesity Prevention: “Whole of community”

Courtesy Christina Economos

Creating a Causal Loop Map: The Shape Up Somerville Experience

• Develop understanding of whole system – Describes key dynamics of social change

within Somerville over time (10 y) – Based on CBPR – Informed by qualitative individual and group

interviews with key SUS stakeholders and researchers

– Illustrated through integration of complex, reciprocal, interdependent, and interactive relationships among individuals and their environments

• Highlights importance of context

X

Economos et al., submitted

SUS Model

• Based on deep qualitative knowledge • But

– Retrospective – Not quantitative – School age, not younger – Replicable? Refinable? Predictive?

ABM = Agent-based modeling GMB = Participatory group model building


Participatory Group Model Building

• Involves participants and other stakeholders in iterative process of developing system dynamics (and, now, agent-based) models – Problem conceptualization – Formulation – Policy analysis – Implementation

• Reasons for using GMB

– Sharing of insights – Developing consensus – Design for implementation

Hovmand

Levels of System Insights

There is a system

The components of a system

How the components are related through feedback

How people might think about a system

Where one could intervene

What is transformation

What is the generic structure

What are the implications of accumulations and nonlinear relationships

What systems can generate the dynamic behavior

Where are the leverage points

When do boundary conditions determine behavior

Why do things happen Dee

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syst

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ts

Surf

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in

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Graphical models or

maps

Simulation models

Dep

th

Informal Formal Modeling

System pictures or diagrams

Hovmand


There is a system












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Graphical models or

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Simulation models

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October 5, 2012

There is a system












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Graphical models or

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Simulation models

Dep

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There is a system












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Hovmand

Conclusions


development of obesity and its complications – Use for developing multi-setting, mutli-

component life course interventions • Address what works, for whom, and under what

circumstances

Questions

• In general…What do system science approaches offer over and above …

• More specifically – Initial steps often qualitative – Inputs are often quantitative – Pros and cons of ABM, SD for different

circumstances. Other approaches?

Questions


• More specifically – Initial steps often qualitative – Inputs are often quantitative – Pros and cons of ABM, SD – When do we have enough data?

• Right balance of too little/too much • Face, construct, criterion validity?

Questions


• More specifically – Initial steps often qualitative – Inputs are often quantitative – Pros and cons of ABM, SD – When do we have enough data? – Generalizability (as usual)

Agent-based Model

Finucane et al., Lancet 2011; 377: 557–67

Worldwide increases in adult obesity 1980-2008

Global Burden of Disease Study Lancet 12/21/12

…much of it due to decrease in early

childhood mortality

…and resulting increase in life

expectancy

Developmental Origins of Health and Disease

• DOHaD emphasizes prenatal period and early childhood as important periods for development of chronic disease throughout life


• Together pre- and post-natal risk factors predict a substantial fraction of childhood obesity

• Prevention interventions starting in pregnancy and infancy have potential to – Reduce these risk factors – Thus obesity-related disorders – And interrupt intergenerational vicious cycles

Developmental Origins Research

• In animal models, perinatal programming of adult health outcomes well known

• Programming – Perturbation at a critical period of

development causes alterations with lifelong, sometimes irreversible consequences

Zygote

Adult

Neonate 45 Divisions

55 Divisions

Godfrey et al., Trends Endocrinol Metab 2010 ; 21:199-205

Why Early Intervention Makes Sense

Conclusions

• Together pre- and post-natal risk factors predict a substantial fraction of childhood obesity

• Prevention interventions starting in pregnancy and infancy have potential to – Reduce these risk factors – Thus obesity-related disorders – And interrupt intergenerational vicious cycles

Luke and Stamatakis, Annu Rev Public Health 2012; 33:357-76.

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