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  • Forum overarching questions: But wait, what’s new about this? (2) This seems a little too pioneering. Is it ready for prime time? (3) How in the world are we going to convince our investigators to share data? (4) Are we the only ones out here on a ledge? Are there others struggling with the same questions? (5) Are there pressures that will make us have to solve the problem, or is this just a manufactured need? Is the impending data deluge a Katrina-like hurricane, or a merely a “tempest in a teapot”? (6) Is the culture of science ready?
  • Population sciences research --- Public population data ---Obstacles of data collaboration caBIG™ as a research platform --- Integrate data sources and analytical services ---Facilitate collaboration PopSciGrid: a grid for population sciences --- Putting tobacco data on the Grid ---Analyze and visualize datasets in PopSciGrid Knowledge discovery on the Grid
  • Population sciences research --- Public population data ---Obstacles of data collaboration caBIG™ as a research platform --- Integrate data sources and analytical services ---Facilitate collaboration PopSciGrid: a grid for population sciences --- Putting tobacco data on the Grid ---Analyze and visualize datasets in PopSciGrid Knowledge discovery on the Grid
  • Population sciences research --- Public population data ---Obstacles of data collaboration caBIG™ as a research platform --- Integrate data sources and analytical services ---Facilitate collaboration PopSciGrid: a grid for population sciences --- Putting tobacco data on the Grid ---Analyze and visualize datasets in PopSciGrid Knowledge discovery on the Grid
  • Population sciences research --- Public population data ---Obstacles of data collaboration caBIG™ as a research platform --- Integrate data sources and analytical services ---Facilitate collaboration PopSciGrid: a grid for population sciences --- Putting tobacco data on the Grid ---Analyze and visualize datasets in PopSciGrid Knowledge discovery on the Grid
  • Population sciences research --- Public population data ---Obstacles of data collaboration caBIG™ as a research platform --- Integrate data sources and analytical services ---Facilitate collaboration PopSciGrid: a grid for population sciences --- Putting tobacco data on the Grid ---Analyze and visualize datasets in PopSciGrid Knowledge discovery on the Grid
  • Population sciences research --- Public population data ---Obstacles of data collaboration caBIG™ as a research platform --- Integrate data sources and analytical services ---Facilitate collaboration PopSciGrid: a grid for population sciences --- Putting tobacco data on the Grid ---Analyze and visualize datasets in PopSciGrid Knowledge discovery on the Grid
  • Population sciences research --- Public population data ---Obstacles of data collaboration caBIG™ as a research platform --- Integrate data sources and analytical services ---Facilitate collaboration PopSciGrid: a grid for population sciences --- Putting tobacco data on the Grid ---Analyze and visualize datasets in PopSciGrid Knowledge discovery on the Grid

IEEE IEEE Presentation Transcript

  • 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
    • 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 )
    What is Behavioral Medicine?
    • 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 and Frameworks (bread and butter)
    • 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)
    Theories of Behavioral Medicine
  • Program Announcement PA-08-239: Impact of Health Communication Strategies on Dietary Behaviors Ecological Framework for Diet & Communication
  • 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 Adapted from the 1994 Advisory Committee on Cancer Control, National Cancer Institute of Canada. Reducing the cancer burden
  • Knowledge Synthesis Basic Science Application & Program Delivery Intervention Research Reducing the Cancer Burden Surveillance Surveillance
  • Knowledge Synthesis Application & Program Delivery Surveillance Reducing the Cancer Burden Basic Science & Intervention Basic Science Intervention Research
  • Knowledge Synthesis Basic Science Surveillance Intervention Research Reducing the Cancer Burden Application Application & Program Delivery Small Business Innovation Research Grants (SBIR)
  • Basic Science Application & Program Delivery Surveillance Intervention Research “ Informatics in Action” Synthesis Health Informatics Knowledge Synthesis 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 Field based data Local Interventions Public Health Surveillance National Surveillance Data (e.g., NHIS, BRFSS, NHANEs) Routine behavioral surveys (e.g., HINTS) Medical Research Settings Patient Charts Clinical trials data University Laboratories Individually published papers Locally maintained data sets
  • Behavioral Medicine and the Information Landscape - Overwhelming volume of data - Multitude of sources/levels Slide source – Peter Schad, 2008
  • “ 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 The End of Science?
  • . Enter Cyberinfrastructure caBIG Grid
  • Expand the Scope of Discovery Application Layer Users Pattern Detection Tools
    • Users
    • Epidemiologists
    • Behavioral scientists
    • Public health planners
    • Geneticists…
    Visualization Decision Support Fusion Policy Planning
  • Visualization Application Layer Users Courtesy Ben Shneiderman, 2006; NCI Speaker Series
    • Users
    • Applied/Basic
    • scientists
    • Policy makers
    • State/City public
    • health planners…
    Discovery Decision Support Fusion Policy Planning
  • Decision Support & Policy Planning Application Layer Users Courtesy Katy Börner, 2006; NCI Speaker Series
    • Users
    • Science directors
    • State health planners
    • Resource allocation
    • Clinicians
    Portfolio Analysis Tools Discovery Visualization Fusion
  • Connecting Stove-piped Data Application Layer Users Advanced Analytic Tools University Research National Systems International Systems
    • Users
    • Survey methodologists
    • Population scientists
    • Federal planners
    Discovery Visualization Decision Support Policy Planning Other Vocabularies Populomics Thesaurus Psychometric Databases
  • ‘ Populomics’ and the Grid Proteomics Nanotechnology Populomics Genomics Personalized Medicine: Pharmaco-genomics… + = Slide source – David Abrams, 2008 Personalized Health Care: … Systems Integration, from cells to society
  • .
    • .
    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 ® Goal Clinical Research Pathology Molecular Biology Imaging Molecular Medicine caBIG ® Capabilities Enable Discovery > Clinical Research > Clinical Care
    • Track clinical trial registrations
    • Automatically capture clinical laboratory data
    • Manage reports describing adverse events during clinical trials
    • Combine proteomics, gene expression, and other basic research data
    • Submit and annotate microarray data
    • Integrate microarray data from multiple manufacturers for visualization and analysis
    • Utilize the National Cancer Imaging Archive repository for medical images including CT and MR images
    • Visualize images using DICOM-compliant tools
    • Annotate Images with distributed tools
    • Access a library of well characterized, clinically annotated biospecimens
    • Comprehensive inventory of a user’s own samples
    • Track the storage, distribution, and quality assurance of specimens
  • .
    • .
    caBIG ® Goal caGrid High-Level Architecture
  • .
    • .
    caBIG ® Goal
    • caBIG ® adoption is unfolding in:
      • 49 NCI-designated Cancer Centers
      • 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
    caBIG ® Cancer Center Deployment NCI-Designated Cancer Centers, Community Cancer Centers, and Community Oncology Programs
  • Take a Slice of the Cake Consortium in Abbas, A. (2004): Grid Computing: A Practical Guide to Technology and Applications. Hingham, MA: Charles Hingham (p. 319). Application layer GRID infrastructure CDEs, vocabularies, metadata PopSciGrid 1.0 Data Sources University Clinical Centers State & local consortia Federal Surveillance Grid Comm. Protocol Metadata, service registry Discovery Visualization Decision Support Fusion Policy Planning Grid layer, Ontology Development Application Layer Behavioral Medicine
    • 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
    • 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
    PopSciGrid 1.0 (cont.)
    • 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
    Behavioral Medicine - Getting ‘Grid-ified’
    • Prospective geo-spatial analytics
    PopSciGrid
      • 14 datasets spanning 6 years
      • Real-time access/analysis of public health and economic data
      • 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
  • Data Widgets State Cancer Profiles CISNet Decision Aids Application layer (e.g., Enhanced State Cancer Profiles; Dashboards, CDC Data Widgets) Common Vocabularies: Shared ontologies, common data elements PopSciGrid 2.0 DATA SOURCES caBIG ® BIRN, NHIN PopSciGrid GRID Middle Ware (Globus toolkit, XMi, security layer, discovery mechanisms)
    • Public Surveillance
    • NHIS
    • BRFSS
    • HINTS
    • Tax, Census,...
    • Grantees
    • CECCRS
    • TREC
    • TTURCS
    • CPHHD
    • GEI
    • Clinical/Health System
    • CRN
    • QCCC projects
    • PopSci SIG
    • Registries (SEER)
    • Mobile/Remote Sensing
    • Behavioral data
    • Environmental data
    • GIS
    • RTDC
    • Biomedical
    • Biological
    • Genomic/proteomic
    • (Grid Enabled Measures) Database
    PopSciGrid 2.0 – Priming the Pump A grid-enabled, interoperable, dynamic website for behavioral and social science theoretical constructs and measures Program Lead: Rick Moser, PhD (Behavioral Research Program, DCCPS)
    • 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
    Beyond Behavioral Medicine: GEI
    • Program Leads: Jill Reedy (NCI), Amy Subar (NCI), Catherine Loria (NHLBI)
      • .
    GEI – Genes and Environment EXPOSURE BIOLOGY PROGRAM Identify genetic variants GENETICS PROGRAM GXE Develop technology and biomarkers
    • GWA Studies
    • Data Analysis
    • Replication
    • Sequencing
    • Database
    • Function
    • Translation
    • Diet and
    • Physical Activity
    • Psychosocial Stress and
    • Addictive Substances
    • Chemical Sensors
    • Biological Response Indicators
    • Program Leads: Jill Reedy (NCI), Amy Subar (NCI), Catherine Loria (NHLBI)
    • .
    GEI - Timeline FY07 FY08 FY09 FY10 FY11 U01 U54
    • Environmental Sensors
    • Diet/Physical Activity (NCI/NHLBI)
    • Psychosocial Stress/Addictive Substances (NIDA)
    • Chemical Sensors (NIEHS)
    U01 U01 U01 DEVICES APPLICATION GWA
    • Biological Response
    • Biomarkers
    • (NIEHS)
    FINGERPRINTS U01 U54 DEVICES Centers – biomarkers/biosensors
    • .
    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, [email_address] 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, [email_address] www.cancercontrol.cancer.gov/hcirb/sbir
    • .
    Acknowledgements
    • NCI
      • Rick Moser
      • Glen Morgan
      • Erik Augustson
      • Frank White
      • Jill Reedy (GEI)
      • Amy Subar (GEI)
    • NHLBI
      • Catherine Loria (GEI)
    • BAH
      • Paul Courtney
    • Northwestern University
      • Noshir Contractor
      • Yun Huang
      • York Yao