Overview of UCSF-CTSI Comparative Effectiveness Large Dataset Analysis Core with emphasis on large, public data sets for studying the health of adults and the care they receive.

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 21, 2011

2. CELDAC Partners
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
– Administrative supplement to the NCRR grant
for UCSF’s Clinical & Translational Science
Institute
–California HealthCare Foundation
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3. CELDAC Personnel
Faculty IHPS Staff
• Jim G. Kahn • Leon Traister
• Janet Coffman • Claire Will
• Claire Brindis
ARS Staff
• Steve Takemoto
• Rob Wynden
• Adams Dudley
• Ketty Mobed
• Kirsten Johansen
• Hari Rekapalli
• Prakash Lakshminarayanan
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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. Major Types of Large Datasets
Used in Health Services Research
Type of Data Set Description Examples
Survey Collects information from • Medical Expenditure Panel
individuals, families, or Survey
organizations • National Health and
Nutrition Examination
Survey
Administrative Information from records • Medicare Research
claims of health professionals and Identifiable Files
health care facilities, • HCUP National Inpatient
usually from billing records Sample
Registries Information from datasets • California Cancer Registry
that incorporate all • San Francisco
persons with a particular Mammography Registry
condition(s)
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6. Major Types of Units of
Observation
Unit of Observation Examples
Individual • Behavioral Risk Factor Surveillance System
• National Health and Nutrition Examination Survey
Household • Medical Expenditure Panel Survey
• National Health Interview Survey
Visit or discharge • National Ambulatory Medical Care Survey
• HCUP National Inpatient Sample
Physician • American Medical Association Masterfile
• HSC Health Tracking Physician Survey
Facility (e.g., hospital, clinic) American Hospital Association Annual Survey
California OSHPD Hospital Annual Financial Data
Geographic area (e.g., county, US Census
state) Area Resource File
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7. Major Types of Designs for
Surveys
Type of Survey Description Examples
Cross-sectional Data collected from a • National Health Interview Survey
single sample at a single • National Health and Nutrition
point in time Examination Survey
• California Health Interview Survey
Panel Data collected from a • Medical Expenditure Panel Survey
single sample at multiple • Health and Retirement Survey
points in time • National Longitudinal Survey of
Youth
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8. Medical Expenditure Panel Survey
• Nationally representative sample of 22,000 to
37,000 persons
• Overlapping panel design
• 2 years of data collected through 5 rounds of
interviews
• Three major components
• Household survey
• Data on cost and utilization from providers caring for
household survey participants
• Survey of employers regarding employer-sponsored
health insurance benefits
http://www.meps.ahrq.gov/mepsweb/ 8

9. Examples of UCSF Faculty
Publications Using MEPS
• Newacheck P, Kim S. A national profile of health care
utilization and expenditures for children with special
health care need. Archives of Pediatric and Adolescent
Medicine. 2005 Jan;159(1):10-7.
• Yelin E., et al. Medical care expenditures and earnings
losses among persons with arthritis and other rheumatic
conditions in 2003, and comparisons with 1997. Arthritis
and Rheumatism. 2007 May;56(5):1397-407.
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10. National Health and Nutrition
Examination Survey
• Nationally representative sample of 5,000
persons per year
• Data collected in 15 counties per year
• Two major components
– Interviews: demographic characteristics,
socioeconomic status, diet, health behaviors
– Physical examinations: medical, dental,
physiological, lab tests
http://www.cdc.gov/nchs/nhanes.htm
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11. Examples of UCSF Faculty
Publications Using NHANES
• Seligman H.K. Food insecurity is associated with
diabetes mellitus: results from the National Health
Examination and Nutrition Examination Survey
(NHANES) 1999-2002. Journal of General Internal
Medicine. 2007 Jul;22(7):1018-23.
• Woodruff T, Zota A, Schwartz J. Environmental
chemicals in pregnant women in the United States:
NHANES 2003-2004. Environmental Health
Perspectives. 2011 Jun;119(6):878-85. 2007
Jul;22(7):1018-23.
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12. 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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13. 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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14. Search Results –
Search for administrative data on infants’ use of health care services
http://ctsi.ucsf.edu/research/celdac
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15. Analyze Large Data Sets
• CELDAC has created a repository of select large,
public data sets that are available to UCSF
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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16. 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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17. Test New Methods for Working with
Large Data Sets
• Conventional methods for managing large data
sets have important limitations, especially for
studies that draw data from multiple data sets
– Requires programmers with expertise in managing
and querying large data sets
– Source data tables continue as individual entities
– Manipulations and linkages between tables require
awareness of each table’s architecture and
customized “One-Off” programming
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18. Test New Methods for Working with
Large Data Sets
• An Integrated Data Repository (IDR) with an
i2b2 infrastructure offers an alternative
– Supports integration of diverse sources of data
– Can translate diverse coding of the same content into
standard coding
– Flexibility in data exploration
– Intuitive drag-and-drop query interface
– Query result sets can be exported for analysis and
reporting using SAS, STATA, or other software
– Reliable - backed up every 2 hours
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19. Test New Methods for Working with
Large Data Sets
• Pilot Projects
– Integrated repository of data on spine
surgery procedures and outcomes from five
data sources
– Graphical user interface for browsing
California Office of Statewide Health
Planning and Development data on hospital
discharges
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20. Questions for Discussion
• What services relating to large data set
analysis would be most useful to you?
• What data sets are of greatest interest to
you?
• How could CELDAC partner effectively
with researchers in your
school/department/division?
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21. Contact CELDAC
• Jim G. Kahn: JimG.Kahn@ucsf.edu
• Janet Coffman:
Janet.Coffman@ucsf.edu/415-476-2435
• Claire Will: Claire.Will@ucsf.edu/415-476-
6009
• http://ctsi.ucsf.edu/research/large-datasets
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