investigating the health of adults: leveraging large data sets for your study, report or program
DESCRIPTION
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.TRANSCRIPT
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
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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CELDAC Personnel
Faculty
• Jim G. Kahn• Janet Coffman• Claire Brindis• Steve Takemoto• Adams Dudley• Kirsten Johansen
IHPS Staff
• Leon Traister• Claire Will
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ARS Staff• Rob Wynden• Ketty Mobed• Hari Rekapalli• Prakash Lakshminarayanan
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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Major Types of Large Datasets Used in Health Services ResearchType of Data Set Description ExamplesSurvey Collects information from
individuals, families, or organizations
• Medical Expenditure Panel Survey
• National Health and Nutrition Examination Survey
Administrative claims
Information from records of health professionals and health care facilities, usually from billing records
• Medicare Research Identifiable Files
• HCUP National Inpatient Sample
Registries Information from datasets that incorporate all persons with a particular condition(s)
• California Cancer Registry• San Francisco
Mammography Registry
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Major Types of Units of Observation
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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 SurveyCalifornia OSHPD Hospital Annual Financial Data
Geographic area (e.g., county, state)
US CensusArea Resource File
Major Types of Designs for Surveys
Type of Survey Description Examples
Cross-sectional Data collected from a single sample at a single point in time
• National Health Interview Survey• National Health and Nutrition
Examination Survey• California Health Interview Survey
Panel Data collected from a single sample at multiple points in time
• Medical Expenditure Panel Survey• Health and Retirement Survey• National Longitudinal Survey of
Youth
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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
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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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.htm10
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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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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Find Large Datasetshttp://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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Search Results –Search for administrative data on infants’ use of health care services
http://ctsi.ucsf.edu/research/celdac
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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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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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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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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
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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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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Contact CELDAC
• Jim G. Kahn: [email protected] • Janet Coffman: [email protected]
/415-476-2435• Claire Will: [email protected]/415-476-
6009
• http://ctsi.ucsf.edu/research/large-datasets
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