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					Overview
        Transforming Behavioral
    Medicine: Cyberinfrastructure
       in Cancer Prevention and
                Control

      Abdul R Shaikh, PhD, MHSc
      Bradford Hesse, PhD
      Health Communication and Informatics Research Branch
      Division of Cancer Control and Population Sciences
      National Cancer Institute




                                                             March 25, 2009
What is Behavioral Medicine?

 An interdisciplinary field concerned with
  the development and integration of
  behavioral, psychosocial, and biomedical
  science…and the application of this
  knowledge to prevention, diagnosis,
  treatment and rehabilitation.
  (Society of Behavioral Medicine www.sbm.org)
Theories and Frameworks (bread and butter)

 • Theory: interrelated concepts, definitions, propositions
   that present a systematic view of situations/events by
   specifying relations among variables to explain and
   predict the situations/events (Kerlinger, 1986)

   - Parsimoniously present complex information
   - Help to narrow research topics into specific questions
   - Designate variables to be operationalized
   - A priori hypotheses and parametric statistics
Theories of Behavioral Medicine

 Individual
   – Health Belief Model (Janz & Becker, 1984), Theory of Planned
     Behavior (Ajzen, 1991), Transtheoretical model (Prochaska, 1979)
 Interpersonal
   – Social Cognitive Theory (Bandura, 1978;1997), social networks
     and social support (Israel, 1982; House, 1981)
 Group/community/mass-media
   – Community building/empowerment (Minkler & Wallerstein, 2002),
     Diffusion of Innovations (Rogers, 1983)
 Ecological
   – - Ecological approach (Stokols, 1992), PRECEDE-PROCEED
     (Green & Kreuter, 1991)
Ecological Framework for Diet & Communication




          Program Announcement PA-08-239:
          Impact of Health Communication Strategies on Dietary Behaviors
MISSION: DCCPS aims to reduce the risk, incidence, and
deaths from cancer as well as enhance the quality of life for
cancer survivors.


The Division conducts and supports an integrated program
of the highest quality behavioral, epidemiologic, genetic,
social, and surveillance cancer research.
DCCPS Cancer Control Framework




          Reducing the cancer burden
     Adapted from the 1994 Advisory Committee on Cancer Control, National Cancer Institute of Canada.
Surveillance


                       Intervention
                        Research




                        Knowledge
Basic Science
                        Synthesis
                                             Surveillance




                       Application &
                     Program Delivery




                Reducing the Cancer Burden
Basic Science & Intervention


                 Intervention
                   Research



  Basic            Knowledge
                                        Surveillance
 Science           Synthesis




                  Application &
                Program Delivery




           Reducing the Cancer Burden
Application


                       Intervention
                        Research




                        Knowledge
Basic Science                                Surveillance
                        Synthesis




                     Application &
                       Program
                       Delivery




                Reducing the Cancer Burden
Synthesis                               Health Informatics
                                                  “Informatics in Action”

                  Intervention
                   Research




                  Knowledge
Basic Science                      Surveillance
                  Synthesis




                  Application &
                Program Delivery




       Reducing the Cancer Burden
Public Health Informatics

             • IT for improving cancer-related care and
             ultimately, cancer-related outcomes

             • 15+ years bench to bedside

             • Accelerate discovery & cognitive
             support

             • Bioinformatics: biology, genomics/proteomics
             • Imaging informatics: tissues and organs
             • Clinical informatics: whole organisms
             • Public       Health Informatics: populations
             Cancer Causes Control (2006) 17:861–869
Public Health Informatics &
Engineering


              PHI = the systematic application of
              information and computer science and
              technology to public health practice,
              research, and learning.
              A. Friede, H.L. Blum, and M. McDonald (1995)



              Public health informatics is primarily an
              engineering discipline, that is, a
              practical activity, undergirded by
              science, oriented to the accomplishment
              of specific tasks.
              J Public Health Management Practice, 2000, 6(6), 67–75
Behavioral Medicine and the
Information Landscape


                              Islands of
                              datasets,
                              documents,
                              analytic tools,
                              and research
                              communities




        Peter Schad, 2008
Behavioral Data
                                                      Local Interventions
Public Health Surveillance

                              National Surveillance
                              Data (e.g., NHIS,
                              BRFSS, NHANEs)



                              Routine behavioral
                              surveys (e.g., HINTS)   Field based data




 University Laboratories                              Medical Research Settings

                             Individually              Patient Charts
                             published papers


                             Locally maintained
                             data sets


                                                        Clinical trials data
Behavioral Medicine and the
Information Landscape
                                           - Overwhelming
                                             volume of data

                                           - Multitude of
                                             sources/levels




        Slide source – Peter Schad, 2008
The End of Science?
                      “The Petabyte Age: Sensors
                      everywhere. Infinite storage.
                      Clouds of processors. Our ability to
                      capture, warehouse, and
                      understand massive amounts of
                      data is changing science, medicine,
                      business, and technology. As our
                      collection of facts and figures
                      grows, so will the opportunity to find
                      answers to fundamental questions.
                      Because in the era of big
                      data, more isn't just
                      more. More is different. ”
                            - Chris Anderson
                      06.23.08
Enter Cyberinfrastructure   .




  caBIG
   Grid
Expand the Scope of Discovery
Pattern Detection Tools                                    Application
                                                 Users
                                                             Layer
                       Users
                      • Epidemiologists
                      • Behavioral scientists             Discovery
                      • Public health planners
                      • Geneticists…                     Visualization

                                                           Decision
                                                           Support

                                                            Fusion

                                                           Policy
                                                          Planning
Visualization

                                                                         Application
                                                               Users
                                                                           Layer




                                                                        Discovery


                                                                       Visualization

                                                                         Decision
 Users                                                                   Support
• Applied/Basic
  scientists                                                              Fusion

• Policy makers                                                           Policy
• State/City public                                                      Planning
  health planners…

          Courtesy Ben Shneiderman, 2006; NCI Speaker Series
Decision Support & Policy Planning

 Portfolio Analysis Tools                                       Users     Application
                                                                            Layer




                                                                         Discovery


                                                                        Visualization

                                                                          Decision
                                                                          Support

                                                                           Fusion

 Users                                                                    Policy
                                                                         Planning
• Science directors
• State health planners
• Resource allocation
• Clinicians

               Courtesy Katy Börner, 2006; NCI Speaker Series
  Connecting Stove-piped Data
 Users
• Survey methodologists                                Users     Application
• Population scientists                                            Layer

• Federal planners                      Psychometric
              Advanced Analytic Tools    Databases
                                                                Discovery


                                                               Visualization
                                        Populomics
                                         Thesaurus
                                                                 Decision
            University                                           Support
            Research                       Other
                                        Vocabularies
                                                                  Fusion

                                                                 Policy
                                                                Planning



                 International              National
                   Systems                  Systems
„Populomics‟ and the Grid
                                                         Personalized Health
Personalized Medicine:             Populomics            Care: … Systems
Pharmaco-genomics…
                             +                       =   Integration, from
                                                         cells to society




                                                             Proteomics




Nanotechnology
                                                                   Genomics


                 Slide source – David Abrams, 2008
                                      .



caBIG® cancer Biomedical Informatics Grid
                                                • .
  caBIG® Goal
  A virtual web of interconnected data, individuals, and organizations that
  redefines how research is conducted, care is provided, and
  patients/participants interact with the biomedical enterprise.




   caBIG® Vision
   • Connect the cancer research community through a shareable,
     interoperable infrastructure
   • Deploy and extend standard rules and a common language to more
     easily share information
   • Build or adapt tools for collecting, analyzing, integrating and
     disseminating information associated with cancer research and care
                                               .


 caBIG® Capabilities Enable
         Discovery > Clinical Research > Clinical Care

• Track clinical trial            Molecular Medicine
                                                 • .           • Utilize the National
                                                                 Cancer Imaging Archive
  registrations
        caBIG® Goal
                                                                 repository for medical
• Automatically capture                                          images including CT
  clinical laboratory                                            and MR images
  data
                                                               • Visualize images using
• Manage reports                                                 DICOM-compliant tools
  describing adverse
  events during clinical                                       • Annotate Images with
  trials                                                         distributed tools

                           Clinical Research       Imaging
• Combine proteomics,
  gene expression, and                                         • Access a library of well
  other basic research                                           characterized, clinically
  data                                                           annotated biospecimens
• Submit and annotate                                          • Comprehensive
  microarray data                                                inventory of a user‟s
                                                                 own samples
• Integrate microarray
  data from multiple                                           • Track the storage,
  manufacturers for                                              distribution, and quality
  visualization and                                              assurance of specimens
  analysis
                           Molecular Biology       Pathology
                  .


caGrid High-Level Architecture
                      • .
  caBIG®   Goal
                                     .



 caBIG® Cancer Center Deployment
• caBIG® adoption is unfolding in:             • .
         ®
      caBIG Goal
                                         NCI-Designated Cancer Centers,
    – 49 NCI-designated
                                         Community Cancer Centers, and
       Cancer Centers
                                         Community Oncology Programs
    – 16 NCI Community
       Cancer Centers

• caBIG® being integrated into
  federal health architecture to
  connect National Health
  Information Network

• Global Expansion
   – UK, China, India,
      Latin America
   Take a Slice of the Cake

            Application    Grid layer, Ontology                                   Data         PopSciGrid 1.0
              Layer           Development
                             Metadata, service registry                          Sources


                                                                                 University
          Discovery                                       Grid Comm. Protocol
                                                                                               Application layer
        Visualization
                                                                                  Clinical
                                                                                  Centers
          Decision
          Support
                                                                                               GRID infrastructure
                                                                                  State &
            Fusion                                                                 local
                                                                                 consortia
           Policy
          Planning
                                                                                               CDEs, vocabularies, metadata
                                                                                  Federal
                                                                                Surveillance




Consortium in Abbas, A. (2004): Grid Computing: A Practical Guide to Technology and Applications.
                                                                                                                     Behavioral
    Hingham, MA: Charles Hingham (p. 319).                                                                           Medicine
PopSciGrid 1.0

• Proof of concept for CI in population health and cancer
  control
• Use state-of-the-science technology to link data,
  researchers, and resources:
      - Expert Panel Workshop (March ‟08); caBIG® Annual
        Mtg (June‟08); DCCPS Fall Forum (November „08);
        HICSS, SBM

Noshir Contractor, PhD, Yun Huang, PhD, York Yao, MS
  Science of Networks in Communities (SONIC)
  Laboratory, Northwestern University
PopSciGrid 1.0 (cont.)
 Challenges…Not just technology &
  infrastructure
 • Collaboration
   – Within and across disciplines
 • Privacy, de-identification, and data ownership
 • Data Harmonization
   –   Standardize data collection
   –   Different national surveys, codebooks, and datasets
   –   Different measures/instruments for same phenomena
   –   Legacy datasets
Behavioral Medicine - Getting „Grid-ified‟

       Implement services on the Grid
         HINTS, NHIS, and tobacco tax data
         Basic statistics, categorical analysis, and
          prevalence analysis
         Visualization by region
       Demonstrate the power of the Grid
         Publish data
         Analyze data from multiple sources
         Visualize data on the Grid
PopSciGrid
                                                    • 14 datasets spanning 6 years
                                                    • Real-time access/analysis of
                                                      public health and economic
                                                      data




• Prospective geo-
  spatial analytics
• Potential links to GEM database
•   http://129.105.36.86/GridServer/c/index.html#
Web 2.0 / Science 2.0

                   • Architectures for
                     Participation
                   • Collective Intelligence
                   • Data as the new “Intel
                     Inside”




                Volume 22: No. 2, February 2008
                Psychology and the Grid
                by Steven Breckler, Executive Director
Virtual Organizations & Interdisciplinary
Science
PopSciGrid 2.0
                                  State Cancer        CISNet Decision
                                                           Aids
                                    Profiles                                         Data Widgets




     Application layer (e.g., Enhanced State Cancer                 PopSciGrid
     Profiles; Dashboards, CDC Data Widgets)

     GRID Middle Ware (Globus toolkit, XMi, security                    caBIG®
                                                                        BIRN, NHIN
     layer, discovery mechanisms)

     Common Vocabularies: Shared ontologies,
     common data elements


  DATA SOURCES
                                                                                           Biomedical
Public Surveillance
                                                                                           • Biological
• NHIS
                                                                                           • Genomic/proteomic
• BRFSS               Grantees
• HINTS               • CECCRS
                                   Clinical/Health System           Mobile/Remote Sensing
• Tax, Census,...     • TREC
                                   • CRN                            • Behavioral data
                      • TTURCS                                      • Environmental data
                                   • QCCC projects
                      • CPHHD                                       • GIS
                                   • PopSci SIG
                      • GEI                                         • RTDC
                                   • Registries (SEER)
PopSciGrid 2.0 – Priming the Pump
            (Grid Enabled Measures) Database
 A grid-enabled, interoperable, dynamic website for behavioral
 and social science theoretical constructs and measures




             Program Lead: Rick Moser, PhD (Behavioral Research Program, DCCPS)
Beyond Behavioral Medicine: GEI
GEI: Genes, Environment, and Health Initiative
• NIH-wide, led by NHGRI and NIEHS
• Goal: to accelerate understanding of the
  genetic and environmental contributions to
  health and disease
• 2007-2011, $46.5 million
  – 30 environmental technology projects
  – 8 genome-wide association studies
  – 2 genotyping centers and coordinating center

        Program Leads: Jill Reedy (NCI), Amy Subar (NCI), Catherine Loria (NHLBI)
GEI – Genes and Environment
  –.   EXPOSURE BIOLOGY
           PROGRAM                               GENETICS PROGRAM




            Develop
                                                       Identify genetic
        technology and
          biomarkers
                                     GXE                    variants
   •   Diet and
       Physical Activity                     •   GWA
   •   Psychosocial Stress and                   Studies        •   Database
       Addictive Substances                  •   Data           •   Function
   •   Chemical Sensors                          Analysis       •   Translation
   •   Biological Response                   •   Replication
       Indicators
                                             •   Sequencing

         Program Leads: Jill Reedy (NCI), Amy Subar (NCI), Catherine Loria (NHLBI)
GEI - Timeline
FY07              .
                  FY08                 FY09          FY10             FY11

Environmental Sensors
                                          U01
• Diet/Physical Activity (NCI/NHLBI)
• Psychosocial Stress/Addictive                      DEVICES
    Substances (NIDA)                     U01
                                          U01
•   Chemical Sensors (NIEHS)


                                                APPLICATION
                                                                      GWA

Biological Response
               U01
• Biomarkers
                                       FINGERPRINTS U54
    Centers – biomarkers/biosensors                         DEVICES
       (NIEHS)
GEI – Technology (cont.)
 Innovative technologies to measure diet, PA, stress, addictive
              .
 substances, chemical sensors, biological response indicators
  5 use cell phones to capture and/or transmit data
  3 combine accelerometers with physiologic sensors (e.g.,
   heart rate) to improve estimates of energy expenditure
  3 pair camera/video/audio components with automated
   processing (e.g., image detection, voice recognition)
  2 use GPS coordinates to track location of activities
  1 uses web-based multimedia software as a tool for
   reporting diet among children




         Program Leads: Jill Reedy (NCI), Amy Subar (NCI), Catherine Loria (NHLBI)
GEI – PALMS (Physical Activity Location Measurement System)




           PI: Kevin Patrick, University of California San Diego
 Looking Ahead: Institute for the Future
Mike Liebhold (2008); www.IFTF.org
                   .
 Talk is cheap…
American Recovery and Reinvestment Act 2009:
http://www.cancer.gov/recovery
NIH Challenge Grants in Health and Science Research (RC1)
http://grants.nih.gov/grants/guide/rfa-files/RFA-OD-09-003.html


Application Due Date: 4/27/2009
       Peer Review Date: 6-7/2009
             Council Review Date: 8/2009
                    Anticipated Start Date: 9/30/2009
Funding – Challenge Grants
(10) Information Technology for Processing Health Care Data

10-CA-101* Cyber-Infrastructure for Health: Building Technologies to
Support Data Coordination and Computational Thinking. The National Science
Foundation has identified research based on “cyberinfrastructure” as the single most important challenge
confronting the nation’s science laboratories (http://www.nsf.gov/news/special_reports/cyber/index.jsp).
The challenge is based on a “grand convergence” of three trends: (a) maturation of the Internet as
connective data technology; (b) ubiquity of microchips in computers, appliances, and sensors; and (c) an
explosion of data from the research enterprise. The NIH, for example, has invested millions within its
Genes, Environment, and Health Initiative (GEI) to develop new technologies for measuring environmental
exposure to accompany the millions already spent on data from Genome Wide Association studies. The
DHHS is spending millions to catalyze the deployment of interoperable electronic health records as a
springboard for research (i.e., in the “learning health system”). Relatively little has been spent on
accommodating the petabytes (i.e., 10 15 bytes of data) of data expected from these investments. What is
needed is a focused concentration of resources to stimulate the creation of new technologies to
accommodate these data and accelerate knowledge discovery through computational means. Such a
stimulus should help bootstrap a new sector of the knowledge economy, one that is dedicated to
accelerating the pace by which data are turned into population health. Contact:              Dr. Bradford
Hesse, 301-594-9904, hesseb@mail.nih.gov

                           NIH Challenge Grants in Health and Science Research (RC1)
                           http://grants.nih.gov/grants/guide/rfa-files/RFA-OD-09-003.html
Funding – Challenge Grants (cont.)
(10) Information Technology for Processing Health Care Data

10-EB-101 Engineering improved quality of health care at a reduced cost.

10-HL-101 Develop data sharing and analytic approaches to obtain from
large-scale observational data, especially those derived from electronic
health records, reliable estimates of comparative treatment effects and
outcomes of cardiovascular, lung, and blood diseases .

10-LM-101 Informatics for post-marketing surveillance.

10-LM-102 Advanced decision support for complex clinical decisions.

10-OD-101 Adapt existing genetic and clinical databases to make them
interoperable for pharmacogenomics studies.

10-RR-101 Information Technology Demonstration Projects Facilitating
Secondary Use of Healthcare Data for Research
                    NIH Challenge Grants in Health and Science Research (RC1)
                    http://grants.nih.gov/grants/guide/rfa-files/RFA-OD-09-003.html
Funding – SBIR / STTR
SBIR (Small Business Innovation Research): PI from small
company (51%) with academic consultants

STTR (Small Business Technology Transfer Research): PI
from non-profit org. working with small businesses
Established to promote collaborations between small businesses and non-profit
organizations for the purpose of developing science-based commercially viable
products that help meet the goals of different federal agencies.

    Phase I: $100k, 6-12 months; feasibility, pilot/prototype

    Phase II: $750k 2-4 years; implementation & evaluation
            (moving to Phase I: $300,000; Phase II: up to $2.2 million)
Funding – SBIR / STTR
Products Examples: cancer-related PC software;
interactive DVDs; wireless devices; web/TV/radio programs;
videos or PSAs; EHR apps.

Target Audience: cancer survivors and their families,
decision making tools; educational and/or training tools;
devices that improve health behaviors or change lifestyle
habits; and screening, assessment, or management
programs.

                    NCI DCCPS SBIR/STTR Program Advisor
                    Connie Dresser, RDPH, LN
                    HCIRB, BRP, DCCPS, NCI
                    301-435-2846, cd34b@nih.gov
                    www.cancercontrol.cancer.gov/hcirb/sbir
Acknowledgements
NCI               .             BAH
      – Rick Moser                 – Paul Courtney
      – Glen Morgan
      – Erik Augustson          Northwestern University
      – Frank White                – Noshir Contractor
      – Jill Reedy (GEI)           – Yun Huang
      – Amy Subar (GEI)            – York Yao


NHLBI
      – Catherine Loria (GEI)

				
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