The UCSF Comparative Effectiveness Large Dataset Analytic Core (CELDAC) aims to enhance the capacity for analysis of large health datasets to conduct comparative effectiveness research. It provides support in study design, dataset acquisition, and methodological issues, while also making new Medicare public use files available for free to researchers. CELDAC's initiative is funded through an NCNNR grant, with a focus on enabling timely access to health data resources and fostering collaboration among researchers.

1. UCSF’s
Comparative Effectiveness
Large Dataset Analytic Core
Janet Coffman, PhD
Philip R. Lee Institute for Health Policy Studies
University of California, San Francisco
September 7, 2011

2. Outline
• Overview of CELDAC
• New Medicare data resources
• Discussion
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3. CELDAC
CELDAC is a partnership at UCSF among the
– Philip R Lee Institute for Health Policy Studies
– Academic Research Systems
– Department of Orthopedic Surgery
– Clinical and Translational Science Institute
Funding is from an administrative supplement to
the NCRR grant for UCSF’s Clinical &
Translational Science Institute.
Seeking funding from the California HealthCare
Foundation to sustain once NCRR grant ends.
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4. CELDAC Mission
The mission of CELDAC is to enhance
UCSF's capacity for analysis of large local,
state, and national health datasets to
conduct comparative effectiveness
research and other types of health
services and health policy research.
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5. CELDAC Goals
• Accelerate access to and use of local, state, and national
health datasets, as a model for other CTSAs and health
research organizations.
• Enhance UCSF researchers’ ability to compete for
funding to use large data sets to conduct CER.
• Develop procedures and infrastructure by conducting
pilot studies.
• Support additional studies on the comparative
effectiveness of clinical interventions.
• Provide consultation to researchers currently working
with or interested in working with large data sets
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6. CELDAC Team – IHPS Members
Faculty Staff
• Jim G. Kahn • Claire Will
• Janet Coffman • Leon Traister
• Claire Brindis
• Steve Takemoto
• Adams Dudley
• Kirsten Johansen
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7. Find Large Datasets
http://ctsi.ucsf.edu/research/celdac
A guided search tool to find the best datasets for a project. Builds on previous
efforts by Andy Bindman, Nancy Adler, Claire Brindis, Charlie Irwin and others.
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8. Analyze Large Data Sets
• CELDAC has partnered with faculty in three
departments to purchase national data sets and
make them available to additional faculty at no
cost.
• These data sets include
– HCUP National Emergency Department Sample
– HCUP National Inpatient Sample
– HCUP Kids Inpatient Databases
– HCUP State Emergency Department and Inpatient
Databases (select states)
– American Hospital Association Annual Survey
– Area Resource File
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9. Provide Consultation
• Study design/conceptualization
• Identification of relevant datasets
• Assistance with data set acquisition
• Cohort selection
• Data cleaning
• Linking data sets
• Strategies to deal with common methodological
issues in analysis of observational data
• Programming support for preliminary analyses
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10. Medicare Data:
New Resources
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11. Existing Medicare Data
Resources
• Research identifiable files
– Enrollment
– Claims
– Med PAR
• Limited data sets
• Non-identifiable data sets
• Medicare statistical supplement
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12. Limitations of Existing Medicare
Data Resources
• High costs limit number of researchers
analyzing the data
• Long delays in approving requests make it
difficult for researchers to conduct
analyses in a timely manner
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13. New Medicare Public Use Files
• In June 2011, CMS released its first public use files
(PUFs)
• Files available to researchers as free downloads in CSV
format (https://www.cms.gov/BSAPUFS/)
• Derived from 5% samples de-identified claims from 2008
– Inpatient care
– Outpatient procedures
– Physician services
– Prescription drugs
– Skilled nursing care
– Home health
– Hospice
– Durable medical equipment
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14. New Medicare Public Use Files
• Strengths
– Data can be downloaded free of charge at
any time without prior approval
– Useful for conducting preliminary analyses
• Limitations
– Limited number of variables
– Data sets cannot be linked (i.e., cannot track
beneficiaries across settings)
– No information on geographic location
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15. Disclosure Treated Controlled
Use Files
• Attempt to strike a better balance between protecting
privacy and analytic utility than public use files
• Encompasses five files that can be linked to one another
(beneficiary summary data, inpatient claims, outpatient
claims, physician claims, prescription drug claims)
• Files housed in a secure environment/data enclave to
improve timeliness of access and review of analysis
output
• Uses sophisticated methods to simultaneously minimize
both disclosure risk and information loss
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16. Disclosure Treated Controlled
Use Files
• CMS and the National Opinion Research
Center will begin recruiting researchers to
pilot test the secure environment/data
enclave during the first quarter of 2012
• Applicants will be selected and given
access to the data during the second
quarter of 2012
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17. Discussion
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18. Questions for Discussion
• What services relating to large data set
analysis are likely to be most useful to you
and your mentees?
• What data sets are of greatest interest to
you and your mentees?
• How could CELDAC partner effectively
with researchers based at the SF VA?
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19. Contact CELDAC
• Janet Coffman or Claire Will
<celdac@ucsf.edu>
• http://ctsi.ucsf.edu/research/large-
datasets
• 415-476-2435
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