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: JimG.Kahn@ucsf.edu • Janet Coffman: Janet.Coffman@ucsf.edu

/415-476-2435• Claire Will: Claire.Will@ucsf.edu/415-476-

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• http://ctsi.ucsf.edu/research/large-datasets

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