title: Leveraging Administrative Data for Program Evaluations: A Method for Linking Data Sets Without Unique Identifiers.
abstract: In community-based wellness programs, Social Security Numbers (SSNs) are rarely collected to encourage participation and protect participant privacy. One measure of program effectiveness includes changes in health care utilization. For the 65 and over population, health care utilization is captured in Medicare administrative claims data. Therefore, methods as described in this article for linking participant information to administrative data are useful for program evaluations where unique identifiers such as SSN are not available. Following fuzzy matching methodologies, participant information from the National Study of the Chronic Disease Self-Management Program was linked to Medicare administrative data. Linking variables included participant name, date of birth, gender, address, and ZIP code. Seventy-eight percent of participants were linked to their Medicare claims data. Linking program participant information to Medicare administrative data where unique identifiers are not available provides researchers with the ability to leverage claims data to better understand program effects.
authors.affiliation: Department of Health Policy and Management, School of Public Health, Texas A&M Health Science Center, College Station, TX, USA lorden@sph.tamhsc.edu.
authors.forename: Andrea L
authors.initials: AL
authors.lastname: Lorden
authors.affiliation: Department of Health Policy and Management, School of Public Health, Texas A&M Health Science Center, College Station, TX, USA.
authors.forename: Tiffany A
authors.initials: TA
authors.lastname: Radcliff
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Texas A&M Health Science Center, College Station, TX, USA.
authors.forename: Luohua
authors.initials: L
authors.lastname: Jiang
authors.affiliation: Department of Epidemiology and Biostatistics, School of Public Health, Texas A&M Health Science Center, College Station, TX, USA.
authors.forename: Scott A
authors.initials: SA
authors.lastname: Horel
authors.affiliation: Department of Health Promotion and Behavior, College of Public Health, University of Georgia, Athens, GA, USA.
authors.forename: Matthew L
authors.initials: ML
authors.lastname: Smith
authors.affiliation: Department of Medicine/Immunology & Rheumatology, Stanford University, Stanford, CA, USA.
authors.forename: Kate
authors.initials: K
authors.lastname: Lorig
authors.affiliation: Centers for Medicare and Medicaid Innovation and Strategic Planning, Centers for Medicare and Medicaid Services, Baltimore, MD, USA.
authors.forename: Benjamin L
authors.initials: BL
authors.lastname: Howell
authors.affiliation: Center for Healthy Aging, National Council on Aging, Washington, DC, USA.
authors.forename: Nancy
authors.initials: N
authors.lastname: Whitelaw
authors.affiliation: Department of Health Promotion and Community Health Sciences, School of Public Health, Texas A&M Health Science Center, College Station, TX, USA.
authors.forename: Marcia
authors.initials: M
authors.lastname: Ory
mesh_terms: D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D002908:Chronic Disease / Q000628:therapy*; D003625:Data Collection / Q000379:methods*; D005260:Female; D017143:Fuzzy Logic; D006296:Health Services / Q000706:statistics & numerical data*; D006801:Humans; D007345:Insurance Claim Review; D008137:Longitudinal Studies; D008297:Male; D006278:Medicare / Q000706:statistics & numerical data; D010349:Patient Compliance; D010353:Patient Education as Topic / Q000379:methods*; D015397:Program Evaluation / Q000379:methods*; D012107:Research Design; D000073278:Self-Management / Q000379:methods*; D012737:Sex Factors; D014481:United States
keywords: Medicare; administrative data; fuzzy matching; linkage; program evaluation