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Rehabilitation Dataset Directory: Dataset Profile


Dataset: National Nursing Home Survey (NNHS)

Basic Information
Dataset full name: National Nursing Home Survey
Dataset acronym NNHS
Summary The NNHS is a nationwide sample survey of nursing homes, staff, and residents. It collects data at the provider as well ). The provider-level characteristics include the number of beds, ownership status, certification status, and provision of services and program to residents. Resident-level information is collected on a random sample of 12 residents based primarily from administrative records and includes resident demographic characteristics, health-related factors, services received, and source of payment used by residents. The survey uses Computer-Assisted Personal Interviews (CAPI) for data collection from nursing home administrators, staff, and residents.
Key Terms Aging, Activities of Daily Living, Disability, Medicare and Medicaid, Nursing Home, Long Term Care, Computer Assisted Personal Interviews, Nursing Home Staffing, Nursing Home Characteristics, Resident Health Status, Medication Status, Source of Payment
Study Design Longitudinal
Data Type(s) Survey
Sponsoring Agency/Entity Centers for Disease Control and Prevention (CDC): National Center for Health Statistics (NCHS)
Health conditions/Disability measures
Health condition(s) ICD-9 diagnostic codes
Disability Measures ADL and IADL measures of functional limitations, Alzheimers/dementia, Children with disabilities- (mentally retarded, DD)
Measures/outcomes of interest
Topics Aging, Activities of Daily Living, Disability, Medicare, Medicaid, Nursing home long term care, Nursing home staffing, Nursing home characteristics, Resident health status, Medication status, Source of payment, Facility characteristics, Facility services, Facility staffing, Resident characteristics, Medical diagnosis, Length of stay, Functional status, Disability, Hospitalization, Emergency department use, Pressure ulcers, Pain, Falls
Sample
Sample Population Random sample of 12 residents in each Nursing Home
Staff nested in Nursing Homes
Sample Size/Notes 2004: 1,174 (Nursing Home) and 13,507 (Residents)
Unit of Observation Facility & Individual (Resident)
Geographic Coverage National
Geographic specificity Metropolitan Statistical Area (MSA)
Data Collection
Data Collection Mode Survey (CAPI)
Years Collected 1973-1974, 1977, 1985, 1995,1997, 1999, and 2004
Data Collection Frequency Variable
Strengths and limitations
Strengths Data collection is done at two levels: Facility (including staffing) and resident level. Can be used for reporting longitudinal trends and time series analysis, sampling methods allow for national estimation of results.
Limitations Most recent data available from 2004, Data collection inconsistency across the period.
Data details
Primary Website http://www.cdc.gov/nchs/nnhs.htm
Data Access http://www.cdc.gov/nchs/nnhs/nnhs_questionnaires.htm
Data Access Requirements Public Use Dataset
Summary Tables/reports http://www.cdc.gov/nchs/nnhs/nnhs_products.htm
Dataset components (where applicable) Facility questionnaire
Current resident questionnaire
Staffing questionnaire
Nursing assistant questionnaire
Selected papers
Technical The National Nursing Home Survey: 2004 Overview:
http://www.cdc.gov/nchs/data/series/sr_13/sr13_167.pdf
The National Nursing Home Survey: 1999 Summary:
http://www.cdc.gov/nchs/data/series/sr_13/sr13_152.pdf
Other Papers



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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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