Data Sharing: Biomedical Research Data versus Personal Health Data

1. Data Sharing: Biomedical Research Data versus Personal Health Data Belinda Seto, Ph.D. Deputy Director National Institute of Biomedical Imaging and Bioengineering

2. 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.

3. Nationwide Health Information Network • Improve coordination of care across providers • Ensure that consumers’ health information is secure and confidential • Enable consumers control and decision making • Reduce risks from medical errors • Enable evidence-based decision support systems • Lower health care costs

4. 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

5. 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

6. Projected Growth in Imaging Procedures, US Market 1998-2008 The use of diagnostic imaging is increasing rapidly. BCBS Medical Cost Reference Guide 2006

7. Aggregate Imaging Growth Was the Fastest for Physician Services: 1999-2004

8. 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

9. Wake Forest Project • Develop a patient controlled platform for medical image sharing • Test a model using electronic keys to access data • Integrate image data with electronic health records • Include imaging facilities in rural and urban southeast U.S.

10. 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

11. 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 Blumenthal • Data is the fuel that drives HIT

12. Data Sharing to Support Better Decisions

13. 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

14. Comparative Effectiveness Research • 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 choices/decisions.

15. Cost Savings from Clinical Decision Support System Health Care Savings Health Information Exchange Clinical Decision Support EHR

16. Sharing Research Data • Open access: no personal health data, no identifiers • Tiered access: data use agreements

17. PHS Grants Policy StatementApril 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.”

18. 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

19. Data Sharing Models • NIH serves as central data repository • A federated model with grantee institutions provide data repositories

20. NIH Central Data Repositories • Genome-wide association study • GenBank • Protein Cluster • PubChem • Many others at: http://www.nlm.nih.gov/databases/

21. Alzheimer Diseases and Neuroimaging Initiative

22. 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

23. 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

24. 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 investigators • Sample sharing-Resource Allocation Review Committee • No special access • Data Sharing & Publication Committee (DPC) • -ADNI Data Use Agreement

25. 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 medicine

26. 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.

27. Database of Genotype and Phenotype (dbGP) • Serves as a single point of access to GWAS data • To archive and distribute results from studies of the interaction of genotype and phenotype • Provides pre-competitive data, no IP protection • Encourages use of primary data from dbGP to develop commercial products or tests

28. Protection of Research Participants: De-Identification • 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

29. 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.

30. 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

31. dbGP Access: Two Levels • Open-access data includes: • Summaries of studies • Study documents, reports • Measured variables, e.g., phenotypes • Genotype-phenotype analyses

32. 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

33. Controlled-Access Data Requests • Requester must submit a Data Use Certification • Access is granted by an NIH Data Access Committee • Approval of proposed research use will be consistent with patient consent and data provider’s institutional terms and conditions

34. 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 • Therapeutics • Diagnostics • 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

35. 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