Systems Science Methodologies To Protect and Improve Public Health Patricia L' Mabry, Ph'D' G' Steph

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Systems Science Methodologies To Protect and
Improve Public Health Patricia L. Mabry,
Ph.D.G. Stephane Philogene, Ph.D.Office of
Behavioral and Social Sciences Research
National Institutes of Healthhttp//obssr.od.ni
h.gov
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Presentation Overview

• Introduction
• NIH Overview
• OBSSR Overview
• OBSSRs New Vision
• Strategic Interests
• NIH Big Data
• Surveys of the Future
• Community Health Laboratories
• System Science at NIH
• What is System Science?
• Examples
• Relevant FOAs

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NIHs Mission is...
...science in pursuit of fundamental knowledge
about the nature and behavior of living systems
and the application of that knowledge to extend
healthy life and reduce the burdens of illness
and disability.
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NIHs Goals are to...
1. Foster fundamental creative discoveries,
innovative research strategies, and their
applications as a basis to advance significantly
the Nation's capacity to protect and improve
health
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NIHs Goals are to...

• Develop, maintain, and renew scientific human and
  physical resources that will assure the Nation's
  capability to prevent disease and

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NIHs Goals are to...

• Expand the knowledge base in medical and
  associated e.g. behavioral social sciences in
  order to enhance the Nation's economic well-being
  and ensure a continued high return on the public
  investment in research.

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National Institutes of Health Overview

• 27 Institutes and Centers (ICs)
• 29 billion in FY2008
• 80 goes to grants and contracts supporting
  extramural research

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Total NIH Budget Authority FY 200829 Billion
Training 3 870 Million
Research Project Grants 53 15.370 Billion
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Whats an OBSSR
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National Institutes of Health
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OBSSR Mission

• Increase scope of and support for Behavioral and
  Social Sciences Research (BSSR).
• Inform NIH leadership and community about BSSR.
• Represent NIH to BSSR community.
• Disseminate BSSR information to NIH and the
  public.

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NIH Funding for Behavioral Social SciencesFY
2006 Estimates Total 3.03 billion
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A New Vision at OBSSR

• Vision To mobilize the biomedical, behavioral,
  and social science research communities as
  partners in interdisciplinary research to solve
  the most pressing health challenges faced by our
  society.
• Necessary Steps
• Conduct interdisciplinary science
• Capitalize on new discoveries and new tools
• Informatics
• Computer technology
• Genetics
• Build partnerships to solve problems whose scope
  overwhelms single
• research paradigms

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Scope of the Science
Outside the skin
Under the skin
zz
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A New Vision at OBSSR

• Programmatic Directions to Achieve the Vision
• Trans-/inter-disciplinary science
• Next generation, basic science
• Problem-based, outcomes oriented
• strengthen the science of dissemination
• Systems science for population impact

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The S Curve of Science
Search for Unifying Theories
Rise of Partial Theories Interventions on real
complex systems
Data explosion Growing Understanding Of Subsystems
Reductionist Phase
Empirical Observations
From Qualitative to Quantitative
From Large to Small scales ( and back)
About 150 years
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NIH BIG Data Initiative

• Trans-NIH Collaboration
• Activities Information Collection on NIH and
  other Federal agency activities involving BIG
  Data
• Goal Identify opportunities for
  trans/inter-disciplinary collaborations between
  BSSR, engineers and informatics experts to
  enhance population health research efforts

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NIH BIG Data PROMIS

• Patient-Reported Outcomes Measurement Information
  System (PROMIS) - aims to revolutionize the way
  patient-reported outcome tools are selected and
  employed in clinical research and practice
  evaluation.
• It will also establish a national resource for
  accurate and efficient measurement of
  patient-reported symptoms and other health
  outcomes in clinical practice.

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NIH BIG Data GEI

• Two main components of the Gene, Environment and
  Health Initiative
• The Genetics Program is a pipeline for analyzing
  genetic variation in groups of patients with
  specific illnesses
• The Exposure Biology Program is an environmental
  technology development program to produce and
  validate new methods for monitoring environmental
  exposures that affect health

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NIH BIG Data PhenX

• PhenX a three year project to prioritize
  Phenotype and eXposure measures for Genome-wide
  Association Studies (GWAS) and thereby contribute
  to the integration of genetics and
  epidemiological research
• Consensus measures for GWAS will have a direct
  impact on biomedical research and ultimately on
  public health. The goal is to maximize the
  benefits of future research by having comparable
  measures so that studies can be integrated
• Recommended measures will be vetted with the
  research community through a Web-based PhenX
  Survey and other appropriate mechanisms

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NIH BIG Data Initiative

• Trans-NIH Collaboration
• Activities Information Collection on NIH and
  other Federal agency activities involving BIG
  Data
• Goal Identify opportunities for
  trans/inter-disciplinary collaborations between
  BSSR, engineers and informatics experts to
  enhance population health research efforts
• Some examples of the kind of ventures that could
  benefit from a BSSR/engineering partnership

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NIH BIG Data Surveys of the Future

• NERD (Norms Evolving in Response to Dilemmas) a
  web-based survey instrument that is designed to
  bridge the gap between perceived and actual
  public opinion, which traditional surveys and
  focus groups are unable to capture
• Smarter Surveys (SurveyGizmo) Make surveys
  respond to user input show just the right
  questions or send an automatically triggered
  email auto-responder
• Handheld Devices/Smart Phones handheld
  computers for collecting survey data
• Computer audio-recorded interviewing (CARI) a
  laptop computer application developed for audio
  recording of field interviews. Provides the means
  for assessing the authenticity and quality of the
  field interview, including the behavior for the
  field interviewer during the interview and the
  reactions of the respondent to survey questions

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NIH BIG Data Community Health Labs

• Technological advances allows for the development
  of integrated data and application grids that
  facilitate multi-level, interdisciplinary
  research (e.g., CaBig)
• We need community health laboratories that
  would provide integrated data resources for
  research on community health
• Each community data grid would unite local data
  on social, economic, institutional and physical
  environments population characteristics,
  behaviors and health, health services (including
  medical records), and, where possible,
  genetic/genomic and other biological data
• These data, and applications developed for their
  use, would provide the infrastructure for a
  transformation of research on the interaction of
  environmental, behavioral, and biological
  processes in producing health and disease within
  local contexts and provide the basis for
  identifying new, scientifically grounded,
  strategies for improving community health.

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The New Vision Partnership
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What is Systems Science at NIH?

• Systems Science approaches appreciate the
  complexity, context, dynamic nature, and emergent
  phenomena associated with the problem under
  study
• Systems Science methodologies include
• Computational/mathematical modeling
• Agent-based modeling
• Dynamic modeling
• Network Analysis
• Related Terms
• Complexity science
• Complex adaptive systems
• Non-linear dynamics
• Chaos

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Why is NIH embracing Systems Science?

• Other approaches alone have not solved
  intractable health problems
• Health problems are embedded in dynamically
  complex systems
• Need to make best use of limited resources,
  evaluate trade offs
• Computers have the computational power to do what
  humans cannot
• keep track of large numbers of variables
• including their synergistic, cumulative and
  compounding effects, and
• delayed effects/changes over time
• System science methodologies used successfully in
  other fields tried true

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What are the benefits of systems science?

• Heuristic to better understand problems (e.g.,
  underlying dynamics)
• Hypothesis Generation new hypotheses and/or
  narrow the list of existing hypotheses prior to
  empirical studies
• Knowledge Synthesis synthesize existing
  knowledge for meaningful interpretation
• Expose Gaps in Knowlege about a problem
• Prediction to aid in preparing for the future
• Intervention Testing in a virtual environment
  saves time and money can do things that are
  impossible or unethical in the real world and
  exposes unintended consequences.

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Understanding the Whole System

• Simulation Modeling and Experimentation
• Pandemic flu
• Tobacco use
• Obesity, Diabetes
• Health inequalities
• Non-health factors
• Chronic disease
• Health care delivery
• Stress, mental illness, worksites, policy.

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EXAMPLES
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DIABETESSystem Dynamics Modeling
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Diabetes Model Diabetes Burden is Driven by
Population Flows
Developing
d
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 200696(3)488-494.
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Diabetes Burden is Driven by Population Flows
Developing
d
Inflow
Outflow
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What-if Simulations
Start with the base case or status quo no
improvements in diabetes management or
prediabetes management after 2006
People with Diabetes per Thousand Adults
Monthly Unhealthy Days from Diabetes per Thou
150
500
Base
450
125
Base
400
100
350
75
300
50
250
1980
1990
2000
2010
2020
2030
2040
2050
1980
1990
2000
2010
2020
2030
2040
2050
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Diabetes What if we Managed Prediabetes AND
Reduced Obesity?
People with Diabetes per Thousand Adults
Monthly Unhealthy Days from Diabetes per Thou
150
500
Base
450
Base
PreD mgmt
125
PreD mgmt
400
PreD Ob 25
PreD Ob 25
100
350
PreD Ob 18
75
PreD Ob 18
300
50
250
1980
1990
2000
2010
2020
2030
2040
2050
1980
1990
2000
2010
2020
2030
2040
2050
The more you reduce obesity, the sooner you stop
the growth in diabetesand the more you bring it
down
Same with the burden of diabetes
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Cardiovascular DiseaseSystem Dynamics Modeling
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Simulating the Dynamics of Cardiovascular
Health and Related Risk FactorsWork in
Progress
This work was funded by the CDCs Division for
Heart Disease and Stroke Prevention and by the
National Institutes of Healths Office of
Behavioral and Social Science Research. The work
was done in collaboration with the Health and
Human Services Department of Austin/Travis
County, Texas, and with Integrated Care
Collaboration of Central Texas. The external
contractors are Sustainability Institute and RTI
International.
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Cardiovascular Disease and Risks Remain Among
the Leading Causes of Death
Fraction of total deaths in 2005
US CDC/National Center for Health Statistics,
Vol. 56, No.10, April 2008 TX TX Dept. of
State Health Services Preliminary Vital
Statistics Table 16
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Purpose of the Cardiovascular Risk Model

• How do local conditions affect multiple risk
  factors for CVD, and how do those risks affect
  population health status and costs over time?
• How do different local interventions affect
  cardiovascular health and related expenditures in
  the short- and long-term?
• How might local health leaders better balance
  their policy efforts given limited resources?

The CDC has partnered 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 data of the Austin team, which
has been supported by the CDCs STEPS program
since 2004.
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 20085(2).
Available at http//www.cdc.gov/pcd/issues/2008/ap
r/07_0230.htm
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Direct Risk Factors
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Indirect Risk Factors
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Tobacco and Air Quality Interventions
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Air Quality Interventions
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Health Care Interventions
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Interventions Affecting Stress
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Healthy Diet Interventions
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Physical Activity Weight Loss Interventions
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Adding Up the Costs
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Interventions for immediate and longer term
effects
Deaths from CVD per Capita

• Quick two interventions
• Increase Primary Care Quality from
  54 to 75
• Cut Air Pollution by half
• Long three interventions
• Increase Social Marketing Against Tobacco from 0
  to 100 of maximum.
• Increase Tobacco Tax and Sales Restrictions from
  50 to 100
• Increase Access to Physical Activity from 70 to
  100
• These five interventions provide
• 77 of cost reduction achieved by 15
  interventions, and
• 57 of mortality reduction

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Base Case
Quick two
Beneficial 15
2
Quick two Long three
If all risk factors 0
0
2040
1990
2000
2010
2020
2030
Complication Management Costs per Capita
3,000
Quick two
Base Case
Quick two Long three
1,000
Beneficial 15
Average annual cost savings of 350 per
capita For Quick 2 Long 3
0
If all risk factors 0
1990
2000
2010
2020
2030
2040
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Tobacco EpidemicMarkov Modeling
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Purpose of the Tobacco Control Model

• Are the national goals (HP 2010) goals for
  smoking prevalence realistic and
• achievable?
• What is a realistic estimate of how long would
  it take to get to the HP 2010
• goals?
• What if
• - we could increase quit attempts
• - we could improve treatment effectiveness
• - we could increase the percent of people who
  use evidence based
• treatments when they make a quit
  attempt

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One-shot Model - "What if we could increase...?"
Quit Attempts
Status Quo 17.9
HP 2010 Goal 12
60 of smokers make quit attempt 2020 prev
14.6
Levy, Abrams Mabry, 2007
80 smokers make quit attempt 2020 prev 12.3
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Two-shot Model - "What if we could increase...?"
Quit Attempts Use of Evidence Based Tx
Status Quo 17.9
HP 2010 Goal 12
2017
Double the smokers who use evidence based tx
2020 prev 16.0
QA 80 2X EB tx 2020 prev 10.7
Levy, Abrams Mabry, 2007
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3-Shot Model - "What if we could increase...?"
Quit Attempts, E-B Tx, Long-term Abstinence
Status Quo 17.9
2013
HP 2010 Goal 12
2011
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
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Obesity EpidemicMicrosimulation Modeling
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Foresight Obesity Model http//www.foresight.gov.u
k/obesity/obesity_final/14.pdf
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What is the state of the science at NIH?

• Interest in systems science (SS) is growing
  rapidly at NIH
• Systems biology is further along than SS in the
  behavioral and social sciences.
• SS is being used to study infectious disease
  transmission (e.g., HIV, flu, smallpox, SARS).
• Less SS is being done in chronic
  disease/behavioral and social determinants of
  health these areas are ripe for SS
• Any area of health and disease is applicable for
  NIH funding
• Specific areas that NIH is targeting for SS
  include obesity, tobacco control, heart disease,
  substance abuse/addiction, demography and
  population health, and health disparities

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Examples of NIH Modeling Initiatives

• Cancer Intervention and Surveillance Modeling
  Network (CISNET) http//cisnet.cancer.gov/about/
• Interagency Modeling and Analysis Group (IMAG)
  http//www.imagwiki.org/mediawiki
  http//grants.nih.gov/grants/guide/pa-files/PAR-08
  -023.htm
• Models of Infectious Disease Agent Study (MIDAS)
  http//www.nigms.nih.gov/Initiatives/MIDAS
• NIH Guide To Grants And Contracts
  http//grants.nih.gov/grants/guide/index.html
• To Subscribe to the NIH Guide LISTSERV, send an
  e-mail to listserv_at_list.nih.gov with the
  following text in the message body (not the
  "Subject" line) subscribe NIHTOC-L your name

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Grant Funded Systems Science and BSSR at NIH

• Joshua Epstein, Directors Pioneer Award, NIGMS,
  OBSSR, 2008.  Project Title  Behavioral
  Epidemiology Applications of Agent-Based
  Modeling to Infectious Disease.
• David Lounsbury, R03, NIDA, 2008.  Project Title
   Dynamics Modeling as a Tool for Disseminating
  the PHS Tobacco Treatment Guideline
• David T. Levy, U01, NCI, 2002-2010. CISNET.
  Project Title A Simulation of Tobacco Policy,
  Smoking and Lung Cancer.
• Linda Collins Daniel Rivera, R21, 2007-2010.
   NIH Roadmap. Dynamical System /Related
  Engineering Approach /Improving Behavioral
  Intervention
• Daniel Rivera, K25, NIDA, OBSSR. Control
  Engineering Approaches to Adaptive Interventions
  in Drug Abuse Prevention.
• PAR-08-224 Awards pending.
• RFA-HD-08-023 (R01), Innovative Computational and
  Statistical Methodologies for the Design and
  Analysis of Multilevel Studies on Childhood
  Obesity (R01). Awards pending.

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What are the grant options?

• NIH has a variety of mechanisms to address most
  any stage of the scientific development cycle
• R03 small grant, in general 100K for two years
• R21 - Exploratory/Developmental 275K Direct
  cost for a two year period
• R01 up to 500K per year for up to 5 years
• Training and career development awards are also
  encouraged http//grants.nih.gov/training
• Refer to http//grants.nih.gov/grants/oer.htm for
  detailed funding info
• American Recovery and Reinvestment Act (ARRA) and
  NIH http//www.nih.gov/about/director/02252009stat
  ement_arra.htm

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Open Funding Opportunity Announcements at NIH in
Systems Science

• PAR-08-224 Using Systems Science Methodologies to
  Protect and Improve Population Health (R21).  
• PAR-08-212, -213, -214 Methodology and
  Measurement in the Behavioral and Social Sciences
  (R01, R21, R03).  
• RFA-07-079, -080  Behavioral and Social Science
  Research on Understanding and Reducing Health
  Disparities (R01, R21)  
• PAR-08-023 Predictive Multiscale Models of the
  Physiome in Health and Disease (R01). 
• To stay apprised of new Funding Opportunity
  Announcements, join the Behavioral and Social
  Science-Systems Science Listserv. Send email to
  Patty Mabry mabryp_at_od.nih.gov to join.

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Systems Science Activities

• 2007 Symposia Series on Systems Science Health
  PODCAST VIDEOCAST
• 2009 Training Institute on systems science
• Develop opportunities for cross fertilization
  - Linking AAAI attendees and other systems
  scientists with health investigators to
  collaborate on NIH applications
• Use the conference grant mechanism (R13/U13) to
  establish connections across fields
• Stay tuned to the BSSR Systems Science Listserv
  for future opportunities to connect and
  collaborate - send email to mabryp_at_od.nih.gov

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For more information

• Patty Mabry, Ph.D.mabryp_at_od.nih.gov
• Stephane Philogene, Ph.D.
• philoges_at_od.nih.gov Office of Behavioral and
  Social Sciences Research
• (OBSSR) National Institutes of
  Healthhttp//obssr.od.nih.gov