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Rehabilitation Dataset Directory: Dataset Profile

Dataset: Basic Stand Alone Skilled Nursing Facility Beneficiary Public Use Files (BSA SNF Beneficiary PUF)

Basic Information
Dataset full name: Basic Stand Alone Skilled Nursing Facility Beneficiary Public Use Files
Dataset acronym BSA SNF Beneficiary PUF
Summary The BSA SNF Beneficiary PUF was released from CMS for 2008 and 2010. Contains information for a random 5% sample of Medicare beneficiaries that had at least one Skilled Nursing Facility (SNF) admission in the reference year. Note that the sample included in this file is deliberately disjoint (no overlap) from the samples in the CMS research file and other Public Use Files (PUF) and cannot be linked.
Key Terms Skilled nursing facilities utilization, cost
Study Design Longitudinal
Data Type(s) Administrative
Sponsoring Agency/Entity Department of Health and Human Services (HHS): Centers for Medicare and Medicaid Services (CMS)
Health conditions/Disability measures
Health condition(s) NA
Disability Measures NA
Measures/outcomes of interest
Topics Rehabilitation services use and expenditures, Gender and Age differences in SNF utilization, Trends in SNF utilization and cost
Sample
Sample Population Medicare beneficiaries
Sample Size/Notes 59,141 (2008) Medicare beneficiaries 65,138 (2010) Medicare Beneficiaries
Unit of Observation Patient
Geographic Coverage National
Geographic specificity National
Data Collection
Data Collection Mode Administrative claims data
Years Collected 2008 and 2010
Data Collection Frequency NA
Strengths and limitations
Strengths Large sample size - adequate to detect age or gender differences in SNF utilization and costs. Comparisons can be made between the 2 waves of data (2008 and 2010). Well documented measures and data. SAS program code available to create dataset.
Limitations Data cannot be linked with other CMS datasets No regional or state identifiers available No patient diagnosis or reason for SNF admission available Some variables are rounded or categorized to preserve confidentiality
Data details
Primary Website https://www.cms.gov/Research-Statistics-Data-and-Systems/Downloadable-Public-Use-Files/BSAPUFS/SNF_Bene_PUF.html
Data Access https://www.cms.gov/Research-Statistics-Data-and-Systems/Downloadable-Public-Use-Files/BSAPUFS/SNF_Bene_PUF.html
Data Access Requirements Public Use Dataset
Summary Tables/reports 2008 Data documentation and frequency tables https://www.cms.gov/Research-Statistics-Data-and-Systems/Downloadable-Public-Use-Files/BSAPUFS/Downloads/2008_BSA_SNF_Bene_PUF_DataDic_CB.pdf 2010 Data documentation and frequency tables Selected papers
Technical Data dictionary, codebooks, SAS data user guides, etc. https://www.cms.gov/Research-Statistics-Data-and-Systems/Downloadable-Public-Use-Files/BSAPUFS/SNF_Bene_PUF.html
Other Papers CMS 2008 Enrollment and User Rates for Basic Stand Alone Public Use Files: https://www.cms.gov/Research-Statistics-Data-and-Systems/Downloadable-Public-Use-Files/BSAPUFS/Downloads/2008_Enrollment_and_User_Rates.pdf CMS 2010 BSA SNF Beneficiary PUF General Documentation: https://www.cms.gov/Research-Statistics-Data-and-Systems/Downloadable-Public-Use-Files/BSAPUFS/Downloads/2010_BSA_SNF_Bene_PUF_GenDoc.pdf

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The Rehabilitation Research Cross-dataset Variable Catalog has been developed through the Center for Large Data Research & Data Sharing in Rehabilitation (CLDR). The Center for Large Data Research and Data Sharing in Rehabilitation involves a consortium of investigators from the University of Texas Medical Branch, Cornell University's Yang Tan Institute (YTI), and the University of Michigan. The CLDR is funded by NIH - National Institute of Child Health and Human Development, through the National Center for Medical Rehabilitation Research, the National Institute for Neurological Disorders and Stroke, and the National Institute of Biomedical Imaging and Bioengineering. (P2CHD065702).

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Acknowledgements: This tool was developed through the efforts of William Erickson and Arun Karpur, and web designers Jason Criss and Jeff Trondsen at Cornell University. Many thanks to graduate students Kyoung Jo Oh and Yeong Joon Yoon who developed much of the content used in this tool.

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