Data Sharing - The National Academies

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Transcript Data Sharing - The National Academies

Data Sharing:
Biomedical Research Data
versus Personal Health Data
Belinda Seto, Ph.D.
Deputy Director
National Institute of Biomedical Imaging
and Bioengineering
President Obama’s Health IT Vision
• A secure, nationwide, interoperable health
information infrastructure that will connect
providers, consumers, and others involved in
supporting health and healthcare.
Nationwide Health
Information Network
• Improve coordination of care across providers
• Ensure that consumers’ health information is
secure and confidential
• Enable consumers control and decision
• Reduce risks from medical errors
• Enable evidence-based decision support
• Lower health care costs
Healthcare is the Largest Sector
of the Economy
Medicare alone is currently
3.2 % of GDP and
increasing rapidly!
BCBS Medical Cost Reference Guide 2008
National Health Expenditure
(NHE), 2003-2015
The NHE is projected to increase by more than 70 percent between 2007 and
2015, with healthcare expected to account for almost 20 percent of GDP.
BCBS Medical Cost Reference Guide 2008
Projected Growth in Imaging
Procedures, US Market 1998-2008
The use of diagnostic imaging is increasing rapidly.
BCBS Medical Cost Reference Guide 2006
Aggregate Imaging Growth Was the Fastest
for Physician Services: 1999-2004
Image Data Sharing:
NIBIB Activities
• Contract awarded to Radiological Society of
North America: Image Sharing Network, PI:
Dr. David Mendelson
• Grant awarded to Wake Forest University
• Grant awarded to University of Alabama
Wake Forest Project
• Develop a patient controlled platform for
medical image sharing
• Test a model using electronic keys to access
• Integrate image data with electronic health
• Include imaging facilities in rural and urban
southeast U.S.
Alabama Project
• Establish regional health image exchange
system among hospitals in Alabama
• Design a web accessible point for physicians
and patients to view images
• Adopt standards of National Health
Information networks, 2007
• Initial targeting trauma patients
• Scalable
Health Information Technology (HIT):
The Means toward Better Care
• “HIT is not the end itself but a means to
improving quality of health care”, Dr. David
• Data is the fuel that drives HIT
Data Sharing to
Support Better Decisions
Decision Support in Health
Information Systems
• Patient data need to be integrated and
assessed to provide real-time, point-of-care
information regarding the right care
• Improves clinical decision support with
enriched data
• Develop algorithms to use comparative
effectiveness findings to optimize outcomes
Comparative Effectiveness
• Purpose: to improve health outcomes by
providing evidenced-based information to
patients and providers.
• Mandate: to conduct study of outcomes and
to derive conclusions to inform medical
Cost Savings from Clinical Decision
Support System
Sharing Research Data
• Open access: no personal health data, no
• Tiered access: data use agreements
PHS Grants Policy Statement
April 1994
“Restricted availability of unique resources upon
which further studies are dependent can impede
the advancement of research and the delivery of
medical care. Therefore, when these resources are
developed with PHS funds and the associated
research findings have been published or after they
have been provided to the agencies under
contract, it is important that they be made readily
available for research purposes to qualified
individuals within the scientific community. This
policy applies to grants, cooperative agreements,
and contracts.”
NIH Data Sharing Policy
Effective with October 1, 2003 receipt
date for NIH applications
• NIH expects timely release and sharing of final
research data for use by other researchers.
• NIH expects grant applicants to include a plan for
data sharing or to state why data sharing is not
possible, especially if $500K or more of direct cost is
requested in any single year
• NIH expects contract offerors to address data sharing
regardless of cost
Data Sharing Models
• NIH serves as central data repository
• A federated model with grantee institutions
provide data repositories
NIH Central Data Repositories
Genome-wide association study
Protein Cluster
Many others at:
Alzheimer Diseases and
Neuroimaging Initiative
Goals of the ADNI: Longitudinal
Multi-Site Observational Study
• Major goal is collection of data and samples to establish a
brain imaging, biomarker, and clinical database in order to
identify the best markers for following disease progression
and monitoring treatment response
• Determine the optimum methods for acquiring, processing,
and distributing images and biomarkers in conjunction with
clinical and neuropsychological data in a multi-site context
• “Validate” imaging and biomarker data by correlating with
neuropsychological and clinical data.
• Rapid public access of all data and access to samples
Study Design
MCI (n= 400): 0, 6, 12, 18, 24, 36 months
AD (n= 200): 0, 6, 12, 24 months
Controls (n= 200): 0, 6, 12, 24, 36 months
Clinical/neuropsychological evaluations, MRI (1.5 T) at all
time points
FDG PET at all time points in 50%
3 T MRI at all time points in 25%
PIB sub-study on 120 subjects
Blood and urine at all time points from all subjects; CSF
from 50% of subjects 0, 1 yr, 2 yr (subset); DNA and
immortalized cell lines from all subjects
GWAS study
Data and Sample Sharing
• Goal is rapid public access of all raw and processed data
• Central repository for all QA’d MRI and PET [Laboratory of
Neuroimaging, UCLA (LONI)]
• Clinical data base at UCSD is linked to LONI
• Databases- in the public domain, available to all qualified
• Sample sharing-Resource Allocation Review Committee
• No special access
• Data Sharing & Publication Committee (DPC)
– -ADNI Data Use Agreement
Genome-wide Association Studies
(GWAS): Purpose, Goals
• To identify common genetic factors that influence
health and disease
• To study genetic variations, across the entire
human genome, that are associated with
observable traits
• To combine genomic information with clinical and
phenotypic data to understand disease
mechanism and prediction of disease
• To develop the knowledge base for personalized
GWAS Data Sharing Policy
All GWAS-funded investigators are expected to
submit to the NIH data repository descriptive
information, curated and coded phenotype,
exposure, genotype, and pedigree data as
soon as quality control procedures are
completed at the grantee institutions.
Database of Genotype and Phenotype
• Serves as a single point of access to GWAS
• To archive and distribute results from studies
of the interaction of genotype and phenotype
• Provides pre-competitive data, no IP
• Encourages use of primary data from dbGP to
develop commercial products or tests
Protection of Research Participants:
• NIH does not possess direct identifiers of
research participants; does not have access to
link between data keycode and identifiable
information; such information resides with the
grantee institutions
• Research institutions submitting dataset must
certify that an IRB and/or Privacy Board has
considered and approved the submission
• Investigators must stripped the data of all
identifiers before data submission
• Optional: Certificate of Confidentiality
Protection of Research Participants:
Informed Consent
• NIH expects specific discussion and
documentation that participants’ genotype
and phenotype data will be shared for
research purposes through dbGP
• If participants withdraw consent for sharing
individual-level genotype and phenotype data,
the submitting institution will be responsible
for requesting the dbGP to remove the data
involved from future data distributions.
Data Access
• Requesters are expected to meet data security
measures: physical security, information
technology security and user training
• Requires signed data use certification:
– Proposed research use of data
– Follows local laws
– Not sell data elements
– Not share with individuals not listed in proposal
– Provide annual progress reports
dbGP Access: Two Levels
• Open-access data includes:
– Summaries of studies
– Study documents, reports
– Measured variables, e.g., phenotypes
– Genotype-phenotype analyses
dbGP: Controlled-Access
• Requires varying levels of authorization
• Provides data on a per-study basis
• Controlled-access data includes:
– De-identified phenotypes and genotypes for
individual study subjects
– Pedigrees
– Pre-computed univariate association between
genotype and phenotype
Controlled-Access Data Requests
• Requester must submit a Data Use
• Access is granted by an NIH Data Access
• Approval of proposed research use will be
consistent with patient consent and data
provider’s institutional terms and conditions
Intellectual Properties?
• Discourages premature claims on pre-competitive
information that may impede research
• Encourages patenting of technology for downstream
product development, e.g.,
Markers for assays
Drug targets
• Up to one year of exclusivity is allowed for the primary
investigators to submit GWAS data analyses for publication
• Clock begins when the GWAS datasets is first made
available to the NIH data repository
NIH Viewpoint
“Data should be made as widely and freely
available as possible while safeguarding the
privacy of participants, and protecting
confidential and proprietary data.”
-- NIH Statement on Sharing Research Data
February 26, 2003