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Rehabilitation Dataset Directory: Dataset Profile


Dataset: Medicare Current Beneficiary Survey (MCBS)

Basic Information
Dataset full name: Medicare Current Beneficiary Survey
Dataset acronym MCBS
Summary The MCBS is a longitudinal survey that selects a nationally-representative sample of Medicare beneficiaries and longitudinally tracks their health status, health expenditure, and utilization of services. Currently, each survey participant is interviewed three times a year over four years, regardless of their residential settings (community or institutions). There are two types of data files in the MCBS: Access to Care, and Cost and Use. The Access to Care file contains information on beneficiaries' access to health care (including rehabilitation services), satisfaction with care, and usual source of care. The MCBS Cost and Use file links financial claims of Medicare to survey-reported events.
Key Terms Health Status, Health and Rehabilitation Services Use and Expenditures, Health Insurance Coverage, and Socioeconomic and Demographic Characteristics
Study Design Longitudinal
Data Type(s) Survey
Sponsoring Agency/Entity Department of Health and Human Services (HHS): Centers for Medicare and Medicaid Services (CMS) - Office of Strategic Planning
Health conditions/Disability measures
Health condition(s) Any/All
Disability Measures Any/All
Measures/outcomes of interest
Topics Health status, Health and rehabilitation services use and expenditures, Health insurance coverage, and Socioeconomic and Demographic characteristics
Sample
Sample Population Medicare beneficiaries
Sample Size/Notes 17,967 (2003) Medicare beneficiaries (Oversampling of individuals age 85+ and those with disabilities)
Unit of Observation Individual
Geographic Coverage National
Geographic specificity NA
Data Collection
Data Collection Mode Survey
Years Collected Access to Care: 1991-2008; Cost and Use: 1992-2006
Data Collection Frequency Annual
Strengths and limitations
Strengths Longitudinal design allows researchers to determine trajectories of health status and associated health/rehabilitation services utilization. It uses a combination of data collection strategies (administrative and survey). Can determine health expenditures for Medicare and other types of insurance coverage.
Limitations unknown
Data details
Primary Website CMS: https://www.cms.gov/Research-Statistics-Data-and-Systems/Files-for-Order/LimitedDataSets/MCBS.html
Data Access CMS: https://www.cms.gov/Research-Statistics-Data-and-Systems/Files-for-Order/LimitedDataSets/MCBS.html
Data Access Requirements Data Use agreement, $ Cost
Summary Tables/reports MCBS Data Tables: https://www.cms.gov/Research-Statistics-Data-and-Systems/Research/MCBS/Data-Tables.html
Dataset components (where applicable) MCBS Survey File
MCBS Cost Supplement File
MCBS Access to Care
MCBS Cost and Use Files
Selected papers
Technical Codebooks: https://www.cms.gov/Research-Statistics-Data-and-Systems/Research/MCBS/Codebooks.html Questionnaires: https://www.cms.gov/Research-Statistics-Data-and-Systems/Research/MCBS/Questionnaires.html
Other Papers CMS MCBS Bibliography:
https://www.cms.gov/Research-Statistics-Data-and-Systems/Research/MCBS/Bibliography.html




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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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