Systems Integration:

David B. Abrams, Ph.D.Executive Director,

The Schroeder Institute for Tobacco Research and Policy Studies

American Legacy FoundationWashinton. DC.

DAbramsd@Americanlegacy.org

Theory is the net man weaves to catch the world of observation, to explain, predict, and influence it.

Theories in Social PsychologyDeutsch & Krauss, 1965

CONTEXT and CAUSAL Reductionism

It’s reallygenetics!

It’s reallysocial

context !

It’s reallybehavior !

International Network for Social Network Analysiswww.insna.org/INSNA/na_inf.html

Systems Approaches

Systems Integration: Key Concepts• Interconnectedness (networks)• Non-reductionist -- Emergent properties• Importance of system boundaries • Focus on context (embeddedness; systems within systems)• Nature of causality (non-linear, stocks, flows, - loops)• Dynamic nature over time time delayed unintended effects• Autopoesis – self-organizing nature of some systems • Progressive approximation for testing models • Inter-disciplinary approach (shared cyber-infrastructure)• Unintended consequences and policy resistant counterforces

McLeroy; Leischow & Milstein; Sterman: Am J Public Health , 2006, (March)OBSSR CONTACT: MabryP@od.nih.gov

SEE OBSSR WEBSITE ON SYSTEMS - http://obssr.od.nih.gov

• Systems dynamic modeling• Markov/stochastic modeling• Agent based modeling• Cybernetics; control systems, fuzzy networks…• Surveillance new data gathering, interoperability.

Models require data to determine output. we must build a cyber-infrastructure to capture the data at multiple levels, multiple time points, dense time units,in some cases in real-time. Use new technologySensors, Personal Devices, GIS, nanotechnology….

Systems modeling and simulation approaches

Transdisciplinary

Systems Integration

Source: Abrams (1999). Nicotine & Tobacco Research, s1.

in utero child adolesc. adult older

IndividualVariation

Bio-Behavioral

GroupVariation

Nested-Contexts

cell

sso

ciety

t1 t2 t3 t4

levels time

pathways, transitions, trajectories

Adapted from Glass, McAtee (2006). Soc. Sci. Medicine, 62: 1650-1671

Health as a continuum between biological, behavioral and social factors across the lifespan and across generations

Integrative: Causal Loop Model

The Biomedical Model:

Causes of disease lie in genes, molecules,

proteins

The Social -Ecological Environmental Model:

Causes of disease are behavioral, social

Environmental factors

INTEGRATION OF BIOMEDICAL CAUSES & SOCIO-ECOLOGICAL “CAUSES OF CAUSES”

Group Individual

Situation Behavior

Phenotype Genotype

Environment Person

Context Agent

Systems Integration: Causal Loops

Prepared,Proactive

Practice Team

Informed,Informed,ActivatedActivatedPatient & Patient &

FamilyFamily

Productive Interactions

Functional and Clinical Outcomes*E. Wagner, MD, W.A.MacColl Institute, Group Health Cooperative of Puget Sound

Health SystemOrganization of Health CareSelf-

Management Support

DecisionSupport

DeliverySystemDesign

ClinicalInformation

Systems

CommunityCommunityResources and PoliciesResources and Policies

A Model for Planned Care*

An Ecological Framework for Organizing Influences on Overweight and Obesity

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.

Energy Balance

A Broad View of Causal Forces

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

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

B5Creating Better

Options inBehavioral

Settings

-B8

Up-front CostsUndercutProtection

Efforts

Observation ofParents' andPeers' Habits

R2Parents/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

B10Potential Savings

Build Support

Genetic MetabolicRate Disorders

B7

AddressingRelated Health

Conditions

Healthcare Servicesto Promote Healthy

Diet & Activity

B2Medical Response

R1

Spiral of PoorHealth and Habits

B3

ImprovingPreventiveHealthcare

R5Society ShapesOptions Shape

Society

Broader Benefits ofHealth Protection

Efforts

R7Broader Benefits

Build Support

R3MediaMirrors

IMPACT = REACH x EFFICACY

Population Metrics for Reducing Disease Burden

EFFICIENCY = IMPACT/COST

SOURCEAbrams, Orleans et al. Stepped Care. Annals of Behav, Med,

1996

Reach

Who is intended to benefit from this

intervention? Will they participate?

Will the intervention be effective in practice?

Likely adverse consequence?

Can many settings easily adopt this

intervention?

Is the intervention feasible to implement

and can it be consistently delivered?

What is the potential cost and sustainability of the intervention in

practice settings?

Source: www.RE-AIM.org

Improving Impact: RE-AIM Framework

Adoption

Estimated Efficacy and Utilization of Approaches to Smoking Cessation

16,000,000 480,000

(% abstinent)

Never Smoked

CurrentSmoker

Ex Smoker

Initiation Rate

Cessation Rate

Source:

Levy, Cummings & Hyland (2000). Am. Jnl. Public Health, 90 (8), 1311-1314

Relapse Rate

DISABILITY AND DISEASE BURDEN

Population Model of Tobacco Prevalence

Causal Map of Factors in Tobacco Prevalence

Behavioral treatment works

PHS Clinical Practice Guideline 2000

Pharmacotherapy works

PHS Clinical Practice Guideline 2000

Nathan Cobb, MDAmanda L. Graham, Ph.D.

David B Abrams, Ph.D.

Disseminating Smoking Cessation Treatment via the Internet:

Opportunities and Challenges

Opportunity: Internet Intervention

The Landscape of Online Cessation

QuitNet Stats

% U.S. zip codes with QN members……….. 90

# countries with QN members………………. 130

# referrals per month from Google…………. 15,000

# unique visitors in 2005………….……..…… 1,300,000

Quitting smoking for good……..………... priceless

QuitNet: Pay for it and they will come

020,00040,00060,00080,000

100,000120,000140,000160,000

2002 2003 2004 2005 2006

# Registered Members Per Year

• Observational study

• 7-day ppa at 3-months

• Incentives

• Total # surveyed = 1,501– Responders: 25.6% (N=385)– Bounced email: 12.3%– Non-responders: 62.1%

Initial Evaluation of QuitNet

Cobb, Graham et al. (2005). Nicotine and Tobacco Research.

Denominator, denominator wherefore art thou, o denominator ?

• Total registered users 1,501• Bounced, invalid email 185 (12.3%)• Successfully delivered 1,316• Returned completed 385 (29.3%)• no incentive 181 (47%)• $20 at 2 days 128 (33%)• $40 at 6 days 76 (19%)• Already quit at baseline 450 (30%)

• Smokers at baseline including bounced, N = 1024

Age: 37.3 ± 1.2 years

Gender: 71% female

Race: 91% Caucasian

Education: 85% some college or more

Smoking rate: 21.24 ± 9.6 cpd

Smoking status: 30% already quit at baseline

Sample Characteristics

• Adherence sample (N=223):30.0%– Respondents only

• Intention to treat (N=1,024):7.0%– Counts all non-responders as smokers

Least conservative

Most conservative

Smoking Outcomes

• Adherence sample (N=223):30.0%– Respondents only

• Intention to treat (N=1,024):7.0%– Counts all non-responders as smokers

Least conservative

Most conservative

Smoking Outcomes

• Used site >2x (N=336): 13.1%

• Used site >1x (N=488): 9.8%

• Excluding bounced (N=892): 8.0%

Relapse prevention: 7 day ppa. Among those who

had quit at baseline

• Among the 450, half had quit < 1 week before registration

• Adherence sample (N=156) 65.4%

• ITT analysis (N=450) 22.7%

• Use of any social support & smoking outcomes:

7-day pp. abstinence: OR=3.23

2-month continuous abstinence: OR=4.03

• Intensity of website use & smoking outcomes:

7-day pp. abstinence: OR=2.34

2-month continuous abstinence: OR=6.07

Social support mediated intensity: OR declined from 2.34. to 1.52

(intensity attenuated after adjusting for social support in bivariate logistic regression).

Process Tracking & Smoking Outcomes

IMPACT = REACH x EFFICACY

Population Metrics for Reducing Disease Burden

EFFICIENCY = IMPACT/COST

SOURCEAbrams, Orleans et al. Stepped Care. Annals of Behav, Med,

1996

Estimated Efficacy and Utilization of Approaches to Smoking Cessation

16,000,000 480,000

(% abstinent)

An Ecological Framework for Organizing Influences on Overweight and Obesity

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.

Energy Balance

Obesity Diabetes: Systems Integration from cells to society

A Broad View of Causal Forces

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

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

B5Creating Better

Options inBehavioral

Settings

-B8

Up-front CostsUndercutProtection

Efforts

Observation ofParents' andPeers' Habits

R2Parents/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

B10Potential Savings

Build Support

Genetic MetabolicRate Disorders

B7

AddressingRelated Health

Conditions

Healthcare Servicesto Promote Healthy

Diet & Activity

B2Medical Response

R1

Spiral of PoorHealth and Habits

B3

ImprovingPreventiveHealthcare

R5Society ShapesOptions Shape

Society

Broader Benefits ofHealth Protection

Efforts

R7Broader Benefits

Build Support

R3MediaMirrors

Source: Jones, A.P., Homer, J.B., et al., (2006). American Journal of Public Health, Vol. 96, No. 3, 488-494

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

Frac

tion

of p

opn

20-7

4

Base SchoolYouth AllYouthSchool+Parents AllAdults AllAgesAllAges+WtLoss

Never Smoked

CurrentSmoker

Ex Smoker

Initiation Rate

Cessation Rate

Source:

Levy, Cummings & Hyland (2000). Am. Jnl. Public Health, 90 (8), 1311-1314

Relapse Rate

DISABILITY AND DISEASE BURDEN

Population Model of Tobacco Prevalence

Causal Map of Factors in Tobacco Prevalence

Funding for tobacccontrol programs

Gov. incomefrom tabacco

taxes

Tobacco controlprograms

Smokers

Perceived importance tofocus on other health

programs

Public awarenessof tobaccohealth risk

Pressure on tobaccocompanies to reducemarketing activities

Tobacco marketingactivities

Taxrevenues

fromsmokers

+

+

+

-

+

People quittingsmoking

-

Fraction of peoplesmoking

Smoking as asocial norm

People startingsmoking

Tobaccorevenues

+

+ +

+

++

+

Health care costs+

Health insurerscoverage of tobacco

quitting costs

+

+

Researchersawarness of

tobacco healthrisk

Funding fortobacco health

research+

+

Govt awarenessof tobaccohealth risk

+

-

Pro-tobaccocontituencies

Anti-tobaccoconstituencies

++ +

+

Tobacco productsavailability

+

Tobaccogrowers

+

+

+

++

+

Govt willingness tolegislate tobacco

control

- + +Tobaccotaxes

Govt funding oftobacco control

--

Trend in tobaccocompany revenues

+

-

Anti-smokinglegislation

-

SimSmoke:A Simulation Model of

Tobacco Control Policies

David Levy, Ph.D.Pacific Institute

for Research and Evaluation, University of Baltimore

Basic Approach

Policy Changes

Cigarette Use

Smoking-Attributable

Deaths

Eight Modules and their interaction:Eight Modules and their interaction:•• Cigarette taxes• Clean air laws • Mass media/campaigns • Youth access policies• School education• Warning labels• Advertising Restrictions • Comprehensive cessation treatment programs

Evolves each year through:

Population model: Uses first order discrete Uses first order discrete Markov process to model births and deaths Markov process to model births and deaths over timeover time

Smoking model: Uses first order discrete Uses first order discrete Markov process to model initiation, cessation Markov process to model initiation, cessation and relapseand relapse

Policy modules- affect initiation and cessation through changes in policy

IOM Best Policies Smoking Prevalence

340000

360000

380000

400000

420000

440000

460000

480000

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025Year

Status quoIOM Best Policies

The Problem• Impact = Efficacy x Reach/cost efficiency

Not nearly as much as we

could be!

Modeling the Effects of Policies and Cessation Interventions

on Adult Smoking Prevalence

David Levy, Ph.D.David Abrams, Ph.D.

Tracy Orleans& Patty Mabry, Ph.D.

Systems Modeling: What is it good for?

• Modeling is a great heuristic tool

• Modeling helps reveal relationships by making mental models explicit and by organizing assumptions coherently

• The formalization of assumptions through modeling usually proves more robust than the informal approach of relying on intuition alone.

• Models are useful for evaluating alternative futures

Outline of Models

• 3 Traditional Policies Model (data driven)

• 5 Cessation Treatment Policies Model (data-driven)

• 3-shot Model ( “what if” …. 3 scenarios)

• Full Throttle Model (combination data-driven + what if). Everything - integrated systems model.

5 Cessation Policies Model (adults)

• Based on data from:

• Complete financial access to evidence based treatment (pharmacotherapy/behavioral tx - e.g. free NRT in NYC)

• Proactive telephone quit lines – free to all

• Web-based treatment – free to all

• Brief interventions – ask, advise, assist, arrange for every patient in every health care setting

• Combine, in stepwise fashion, all of the above

12%

14%

16%

18%

20%

2006 2008 2010 2012 2014 2016 2018 2020

Smok

ing

Prev

alen

ce5 Cessation Policies Model (adult)

Status Quo17.9%

HP 2010 Goal: 12%

Free proactive quitlines: 2020 prev = 17.7%Complete financial access to EB tx: prev 2020 16.9%

Free P-QL + free NRT: 2020 prev = 17.2% Complete financial access + P-QL + Free NRT: 2020 prev = 16.5%

Free web-based tx: 2020 prev = 17.6%Complete financial access + P-QL + NRT + free web tx: 2020 prev = 16.1%

Brief intervention at every health care visit: 2020 prev = 17.4%All 5 cessation policies combined: 2020 prev = 15.8%

3-Shot Model

• This model presents a series of “what if” scenarios.

• What if we could…

1. increase the number of smokers making quit attempts?

2. increase the number of smokers who use evidence-based treatments?

3. Increase long term abstinence across all forms of treatment?

5%7%9%

11%13%15%17%19%21%

2006 2008 2010 2012 2014 2016 2018 2020

Smok

ing

Prev

alen

ce3-Shot Model - "What if we could increase...?"Quit Attempts, E-B Tx, Long-term Abstinence

50% of quitters achieve long term abstinence: 2020 prev = 14.6%100% of quitters achieve LTA: 2020 prev = 12.3%

3-shot - QA 80% + 2X EB tx + LTA 50%: 2020 prev = 8.1% 3-shot - QA 80% + 2X EB tx + LTA 100%: 2020 prev = 6.6%

Status

Quo17.9%

14.6%

12.3%

8.1%

6.6%

HP 2010 Goal: 12%

2011

2013

Full Throttle Comprehensive Model:Data-driven + what if…

• Traditional tobacco control policies (all 3)

*AND*

• Cessation Policies (adult, all 5)

*AND*

• “What if we could…”: Increase Long-Term Abstinence Rates by 50% through all 3 “what if” assumptions

5%7%9%

11%13%15%17%19%21%

2006 2008 2010 2012 2014 2016 2018 2020

Smok

ing

Prev

alen

ceFull Throttle Model:

Traditional Policies, Adult Cessation Policies, Increase LTA

Status Quo17.9%

15.8%15.0%

9.7%

7.5%

HP 2010 Goal: 12%

$2 Taxes + Clean Indoor Air laws + Media: 15.0%All 5 cessation policies: 15.8%All 3 traditional policies + all 5 cessation policies: 13.0%All 8 policies + increase LTA by 50%: prev 2020 9.7%All 8 policies + increase LTA by 100%: 7.5%

2012

2015