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National Invitational Conference on Long-Term Care Data Bases: Conference Package

Publication Date
Apr 30, 1987

Office of Social Services Policy


This package--distributed at a national conference held at the Ritz-Carlton Hotel, Washington, D.C. on May 21-22, 1987--was prepared by the Office of Social Services Policy (now the Office of Disability, Aging and Long-Term Care Policy) with the U.S. Department of Health and Human Services. For additional information, you may visit the DALTCP home page at http://aspe.hhs.gov/daltcp/home.htm or contact the Office of Disability, Aging and Long-Term Care Policy, Room 424E, H.H Humphrey Building, 200 Independence Avenue, SW, Washington, DC 20201. The e-mail address is: webmaster.DALTCP@hhs.gov. The DALTCP Project Officer was Robert Clark.

"

AGENDA

THURSDAY, MAY 21, 1987

8:00-9:00 am (BALLROOM LOBBY)
Conference Registration and Coffee
9:00-9:30 am (BALLROOM)
Introductions
  • Mary F. Harahan, Director, Division of Disability, Aging and Long Term Care Policy, DHHS/Office of the Assistant Secretary for Planning and Evaluation
  • Welcome
  • Robert B. Helms, Ph.D., Assistant Secretary for Planning and Evaluation
  • Arnold R. Tompkins, Deputy Assistant Secretary for Social Services Policy, DHHS/Office of the Assistant Secretary for Planning and Evaluation
  • Steven A. Grossman, Deputy Assistant Secretary for Health (Planning and Evaluation), DHHS/Office of the Assistant Secretary for Health
  • 9:30-11:30 am OVERVIEW OF LTC DATA BASES: GENERAL SESSION (BALLROOM)
    Introduction
  • Mary F. Harahan, Office of the Assistant Secretary for Planning and Evaluation
  • 1982-1984 National Long Term Care Survey
  • Kenneth Manton, Ph.D., Duke University
  • National Long Term Care Channeling Demonstration
  • George Carcagno, Mathematica Policy Research
  • National Health Interview Survey 1984 Supplement on Aging
  • Gerry Hendershot, Ph.D., National Center for Health Statistics, DHHS/Office of the Assistant Secretary for Health
  • 1985 National Nursing Home Survey
  • Evelyn Mathis, National Center for Health Statistics, DHHS/Office of the Assistant Secretary for Health
  • 11:30 am-1:00 pm LUNCH BREAK (reconvene at 1:00 pm)
    1:00-3:00 pm EXAMINATION OF LONG TERM CARE DATA BASES (Breakout Session No. 1)
    1982-1984 National Long Term Care Survey (BALLROOM)
  • Kenneth Manton, Ph.D., Duke University
  • Korbin Liu, Sc.D., Urban Institute
  • National Long Term Care Channeling Demonstration (CARLTON)
  • George Carcagno, Mathematica Policy Research
  • Peter Kemper, Ph.D., National Center for Health Services Research, DHHS/Office of the Assistant Secretary for Health
  • Judith Wooldridge, Mathematica Policy Research
  • Thomas Grannemann, Ph.D., Mathematica Policy Research
  • National Health Interview Survey: 1984 Supplement on Aging (BALCONY)
  • Gerry Hendershot, Ph.D., National Center for Health Statistics
  • Susan Jack, National Center for Health Statistics, DHHS/Office of the Assistant Secretary for Health
  • Joseph Fitti, National Center for Health Statistics, DHHS/Office of the Assistant Secretary for Health
  • 1985 National Nursing Home Survey (BALCONY)
  • Evelyn Mathis, National Center for Health Statistics
  • Esther Hing, National Center for Health Statistics, DHHS/Office of the Assistant Secretary for Health
  • Genevieve Strahan, National Center for Health Statistics, DHHS/Office of the Assistant Secretary for Health
  • Edward Sekscenski, National Center for Health Statistics, DHHS/Office of the Assistant Secretary for Health
  • Jennifer Madans, Ph.D., National Center for Health Statistics, DHHS/Office of the Assistant Secretary for Health
  • William Scanlon, Ph.D., Center for Health Policy, Georgetown University
  • 3:00-3:15 pm BREAK
    3:15-5:15 pm EXAMINATION OF LONG TERM CARE DATA BASES (Breakout Session No. 2)
    1982-1984 National Long Term Care Survey (BALLROOM)
  • Kenneth Manton, Ph.D., Duke University
  • Korbin Liu, Sc.D., Urban Institute
  • National Long Term Care Channeling Demonstration (CARLTON)
  • George Carcagno, Mathematica Policy Research
  • Peter Kemper, Ph.D., National Center for Health Services Research, DHHS/Office of the Assistant Secretary for Health
  • Judith Wooldridge, Mathematica Policy Research
  • Thomas Grannemann, Ph.D., Mathematica Policy Research
  • National Health Interview Survey: 1984 Supplement on Aging (BALCONY)
  • Gerry Hendershot, Ph.D., National Center for Health Statistics
  • Susan Jack, National Center for Health Statistics, DHHS/Office of the Assistant Secretary for Health
  • Joseph Fitti, National Center for Health Statistics, DHHS/Office of the Assistant Secretary for Health
  • 1985 National Nursing Home Survey (BALCONY)
  • Evelyn Mathis, National Center for Health Statistics
  • Esther Hing, National Center for Health Statistics, DHHS/Office of the Assistant Secretary for Health
  • Genevieve Strahan, National Center for Health Statistics, DHHS/Office of the Assistant Secretary for Health
  • Edward Sekscenski, National Center for Health Statistics, DHHS/Office of the Assistant Secretary for Health
  • Jennifer Madans, Ph.D., National Center for Health Statistics, DHHS/Office of the Assistant Secretary for Health
  • William Scanlon, Ph.D., Center for Health Policy, Georgetown University
  • 5:30-7:30 pm RECEPTION (BALLROOM LOBBY)
    (Co-sponsored by the American Association of Retired Persons, the American Health Care Association, the Blue Cross and Blue Shield Association, and the Health Insurance Association of America)
    Welcome


    FRIDAY, MAY 22, 1987

    7:45-8:00 am COFFEE (BALLROOM)
    8:00-8:30 am (BALLROOM)
    NHANES I Epidemiological Followup Study (General Session)
  • Jennifer Madans, Ph.D., National Center for Health Statistics
  • 8:30-8:50 am (BALLROOM)
    Inventory of Long Term Care Places (General Session)
  • Curt Mueller, National Center for Health Services Research, DHHS/Office of the Assistant Secretary for Health
  • 8:50-9:40 am (BALLROOM)
    Overview of Survey of Income and Program Participation (General Session)
  • Daniel Kasprzyk, Population Division, U.S. Bureau of the Census
  • Robert Friedland, Ph.D., Employee Benefit Research Institute
  • 9:40-10:15 am (BALLROOM)
    Other Long Term Care Data Sources (General Session)
  • Aurora Zappolo, DHHS/Health Care Financing Administration
  • 10:15-10:30 am BREAK
    10:30 am-12:15 pm (BALLROOM)
    Long Term Care Data Base Producer Panel Applications (General Session)
  • Kenneth Manton, Ph.D., Duke University
  • Korbin Liu, Sc.D., Urban Institute
  • Judith Wooldridge, Mathematica Policy Research
  • Thomas Grannemann, Ph.D., Mathematica Policy Research
  • Joan Van Nostrand, National Center for Health Statistics, DHHS/Office of the Assistant Secretary for Health
  • Evelyn Mathis, National Center for Health Statistics
  • Gerry Hendershot, Ph.D., National Center for Health Statistics
  • Moderator
  • William Scanlon, Ph.D., Center for Health Policy
  • Participants
  • Entire Audience
  • 12:15-12:30 pm (BALLROOM)
    "In the Pipeline" (General Session)
  • Joan Van Nostrand, National Center for Health Statistics
  • 12:30-12:45 pm (BALLROOM)
    Summary and Conclusion (General Session)
  • Mary F. Harahan, Office of the Assistant Secretary for Planning and Evaluation
  • 12:45-2:00 pm LUNCH BREAK (Reconvene at 2:00 pm)
    2:00-3:35 pm EXAMINATION OF LONG TERM CARE DATA BASES (Breakout Session No. 3--Informal)
    1982-1984 National Long Term Care Survey (BALLROOM)
  • Kenneth Manton, Ph.D., Duke University
  • Korbin Liu, Sc.D., Urban Institute
  • National Long Term Care Channeling Demonstration (CARLTON)
  • George Carcagno, Mathematica Policy Research
  • Peter Kemper, Ph.D., National Center for Health Services Research, DHHS/Office of the Assistant Secretary for Health
  • Judith Wooldridge, Mathematica Policy Research
  • Thomas Grannemann, Ph.D., Mathematica Policy Research
  • National Health Interview Survey: 1984 Supplement on Aging (BALCONY)
  • Gerry Hendershot, Ph.D., National Center for Health Statistics
  • Susan Jack, National Center for Health Statistics, DHHS/Office of the Assistant Secretary for Health
  • Joseph Fitti, National Center for Health Statistics, DHHS/Office of the Assistant Secretary for Health
  • 1985 National Nursing Home Survey (BALCONY)
  • Evelyn Mathis, National Center for Health Statistics
  • Esther Hing, National Center for Health Statistics, DHHS/Office of the Assistant Secretary for Health
  • Genevieve Strahan, National Center for Health Statistics, DHHS/Office of the Assistant Secretary for Health
  • Edward Sekscenski, National Center for Health Statistics, DHHS/Office of the Assistant Secretary for Health
  • Jennifer Madans, Ph.D., National Center for Health Statistics, DHHS/Office of the Assistant Secretary for Health
  • William Scanlon, Ph.D., Center for Health Policy, Georgetown University

  • LIST OF PARTICIPANTS

    PRESENTERS/SPEAKERS

  • Mr. George Carcagno, Executive Vice President, Mathematica Policy Research, P.O. Box 2393, Princeton, New Jersey 08543-2393
  • Mr. Joseph E. Fitti, Survey Statistician, National Center for Health Statistics, Room 2-44, 3700 East-West Highway, Hyattsville, Maryland 20782
  • Dr. Robert B. Friedland, Research Associate, Employee Benefit Research Institute, Suite 860, 2121 K Street, N.W., Washington, D.C. 20037
  • Mr. Thomas Grannemann, Senior Economist, Mathematica Policy Research, P.O. Box 2393, Princeton, New Jersey 08543-2393
  • Mr. Steven A. Grossman, Deputy Assistant Secretary for Health (Planning and Evaluation), Office of the Assistant Secretary for Health, Room 717H, H.H. Humphrey Building, 200 Independence Avenue, S.W., Washington, D.C. 20201
  • Ms. Mary F. Harahan, Director, DHHS/OS/ASPE/SSP/DALTC, Room 410E, H.H. Humphrey Building, 200 Independence Avenue, S.W., Washington, D.C. 20201
  • Dr. Robert B. Helms, Assistant Secretary for Planning and Evaluation, Department of Health and Human Services, Room 415F, H.H. Humphrey Building, 200 Independence Avenue, S.W., Washington, D.C. 20201
  • Mr. Gerry E. Hendershot, Chief of Illness and Disability Statistics, National Center for Health Statistics, Room 2-44, 3700 East-West Highway, Hyattsville, Maryland 20782
  • Ms. Esther Hing, National Center for Health Statistics, Room 2-63, FCB 2, 3700 East-West Highway, Hyattsville, Maryland 20782
  • Ms. Susan S. Jack, Statistician, National Center for Health Statistics, 3700 East-West Highway, Hyattsville, Maryland 20782
  • Mr. Daniel Kasprzyk, U.S. Bureau of the Census, Population Division, Room 2025, Federal Office Building 3, Washington, D.C. 20233
  • Mr. Peter Kemper, Service Fellow, DHHS/National Center for Health Services Research, Room 18A-55, Parklawn Building, 5600 Fishers Lane, Rockville, Maryland 20857
  • Mr. Korbin Liu, Urban Institute, 2100 M Street, N.W., Washington, D.C. 20015
  • Dr. Jennifer Madans, Deputy Director, Division of Analysis, National Center for Health Statistics, Room 2-27, 3700 East-West Highway, Hyattsville, Maryland 20782
  • Mr. Kenneth Manton, Research Professor, Duke University, Demographic Studies, 2117 Campus Drive, Durham, North Carolina 27706
  • Ms. Evelyn S. Mathis, Chief, Long-Term Care Statistics Branch, National Center for Health Statistics, Room 2-43, 3700 East-West Highway, Hyattsville, Maryland 20782
  • Mr. Curt D. Mueller, Economist, DHHS/National Center for Health Services Research, Room 18A-55, Parklawn Building, 5600 Fishers Lane, Rockville, Maryland 20857
  • Mr. William Scanlon, Co-Director, Georgetown Center for Health Policy Studies, Suite 525, 2233 Wisconsin Avenue, N.W., Washington, D.C. 20007
  • Mr. Edward S. Sekscenski, Health Statistician, National Center for Health Statistics, Room 2-43, Center Building, 3700 East-West Highway, Hyattsville, Maryland 20782
  • Ms. Genevieve Strahan, National Center for Health Statistics, Room 2-63, FCB 2, 3700 East-West Highway, Hyattsville, Maryland 20782
  • Mr. Arnold R. Tompkins, Deputy Assistant Secretary, Office of Social Services Policy/ASPE/DHHS, Room 410E, H.H. Humphrey Building, 200 Independence Avenue, S.W., Washington, D.C. 20201
  • Ms. Joan F. Van Nostrand, Acting Director, Division of Health Care Statistics, National Center for Health Statistics, 3700 East-West Highway, Hyattsville, Maryland 20782
  • Ms. Judith Wooldridge, Senior Researcher, Mathematica Policy Research, P.O. Box 2393, Princeton, New Jersey 08543-2393
  • Ms. Aurora Zappola, Statistician, DHHS/Health Care Financing Administration, Room 2-A-12, Oak Meadows Building, 6340 Security Boulevard, Baltimore, Maryland 21207
  • GENERAL ATTENDEES

  • Dr. Faye G. Abdellah, Deputy Surgeon General, U.S. Public Health Service, Room 18-67, Parklawn Building, 5600 Fishers Lane, Rockville, Maryland 20857
  • Ms. Loida R. Abraham, Actuarial Fellow, F.S.A., John Hancock Mutual Life Insurance Company, 33rd Floor, 200 Clarendon Street, Boston, Massachusetts 02177
  • Mr. Michael S. Abroe, Consulting Actuary, Milliman and Robertson, Inc., 55 West Monroe, Chicago, Illinois 60603
  • Dr. Richard Adelson, Assistant Director, Planning (161A), Veterans Administration, OD, 810 Vermont Avenue, N.W., Washington, D.C. 20420
  • Mr. Gerald S. Adler, Special Assistant, Health Care Financing Administration, AAPD, Room 743, East High Rise, 6325 Security Boulevard, Baltimore, Maryland 21207
  • Dr. J.A. Alford, Executive Vice President, Brasman Research Institute, Suite 202, 814 Thayer Avenue, Silver Spring, Maryland 20910
  • Ms. Dorothy M. Amey, Principal Analyst, Congressional Budget Office, House Annex #2, Second and D Streets, S.W., Washington, D.C. 20515
  • Ms. Carol Austin, Associate Professor, Ohio State University, College of Social Work, 1947 College Road, Columbus, Ohio 43210
  • Ms. Amy Aycock, Research Coordinator, Blue Cross/Blue Shield of the National Capitol Area, Health Care Policy and Research Department, 550 12th Street, S.W., Washington, D.C. 20065
  • Mr. Philip J. Barackman, AVP and Actuary, Colonial Penn Life Insurance Company, 11 Colonial Penn Plaza, 13th Floor, 19th and Market Streets, Philadelphia, Pennsylvania 19181
  • Mr. Hal Barney, Assistant Vice President, Johnson and Higgins, 1600 Market Street, Philadelphia, Pennsylvania 19103-7216
  • Ms. Joan C. Barrett, Associate Actuary, The Travelers Companies, 9 MS, 1 Tower Square, Hartford, Connecticut 06183
  • Ms. Deborah Bass, Director, Executive Secretariat, Office of Human Development Services, Room 300E, H.H. Humphrey Building, 200 Independence Avenue, S.W., Washington, D.C. 20201
  • Dr. Anita Beckerman, Assistant Professor, College of New Rochelle, School of Nursing, Castle Place, New Rochelle, New York 10801
  • Mr. Melvin E. Beetle, Director, Evaluation Division, ACTION, Federal Domestic Volunteer Agency, Room M-601, 806 Connecticut Avenue, N.W., Washington, D.C. 20525
  • Mr. Kim Bellard, Director, Underwriting, Prudential Insurance Company, AARP Operations, P.O. Box 130, Montgomeryville, Pennsylvania 18936
  • Mr. Mark Benedict, Minority Staff Director, House Subcommittee on Health and Long-Term Care, Room 2209, Rayburn Building, Washington, D.C. 20515
  • Mr. Charles Betley, Research Assistant, Employee Benefit Research Institute, Suite 860, 2121 K Street, N.W., Washington, D.C. 20037
  • Ms. Nancy Blustein, Special Assistant to the Director, National Center for Health Services Research, HCTA, Room 18-05, Parklawn Building, 5600 Fishers Lane, Rockville, Maryland 20857
  • Dr. Rachel F. Boaz, Research Associate, Center for Social Research, Graduate Center, City University of New York, Room 623, 33 West 42nd Street, New York, New York 10036
  • Ms. Susie R. Bosstick, Chief, Long Term Care Division, Maryland Office on Aging, 301 West Preston Street, Room 1004, Baltimore, Maryland 21201
  • Mr. John Bradley, Assistant Vice President and Associate Actuary, Combined Insurance, 123 North Wacker Drive, Chicago, Illinois 60601
  • Mr. Stanley J. Brody, Professor, Physical Medicine and Rehabilitation, University of Pennsylvania Medical School, Room 22, 2 Piersol, Box 590, 3400 Spruse Street, Philadelphia, Pennsylvania 19104-4283
  • Dr. Joel H. Broida, Research Analyst, Health Care Financing Administration, ORD, Room 2-B-14, Oak Meadows Building, 6340 Security Boulevard, Baltimore, Maryland 21207
  • Mr. Richard Browdie, Deputy Secretary, Pennsylvania Department of Aging, Barto Building, 231 State Street, Harrisburg, Pennsylvania 17101
  • Mr. Floyd Brown, Research Analyst, DHHS/OS/ASPE/SSP/DALTC, Room 410E, H.H. Humphrey Building, 200 Independence Avenue, S.W., Washington, D.C. 20201
  • Dr. Joan Buchanan, Operations Research Specialist, The Rand Corporation, 1700 Main Street, Santa Monica, California 90406
  • Mr. Robert Buchanan, Assistant Professor, Cornell University, Room N132, MVR Hall, Ithaca, New York 14853
  • Dr. Terry F. Buss, Director, Center for Urban Studies, Youngstown State University, 410 Wick Avenue, Youngstown, Ohio 44555
  • Ms. Cathi M. Callahan, Research Analyst, Actuarial Research Corporation, Suite E, 6928 Little River Turnpike, Annandale, Virginia 22003
  • Mr. Edwin J. Campbell, Manager, Group Contracts, Mutual Benefit Life Insurance Company, 2323 Grand Avenue, Kansas City, Missouri 64108
  • Mr. William S. Cartwright, Chief, Demography and Economics Office, National Institute on Aging, Room 612, Federal Building, 7550 Wisconsin Avenue, Bethesda, Maryland 20892
  • Mr. Holen Chang, Actuary Associate, Actuarial Research Corporation, Suite E, 6928 Little River Turnpike, Annandale, Virginia 22003
  • Mr. Ralph Cherry, Assistant Professor, Purdue University, 408 Russell, West Lafayette, Indiana 41906
  • Dr. Robert F. Clark, Program Analyst, DHHS/OS/ASPE/SSP/DALTC, Room 410E, H.H. Humphrey Building, 200 Independence Avenue, S.W., Washington, D.C. 20201
  • Mr. William D. Clark, Social Science Research Analyst, Health Care Financing Administration/ORD/ODE, Room 2-F-6, Oak Meadows Building, 6325 Security Boulevard, Baltimore, Maryland 21207
  • Mr. Gary Claxton, Insurance Issues Analyst, American Association of Retired Persons, 1909 K Street, N.W., Washington, D.C. 20049
  • Mr. Robert M. Clinkscale, President, La Jolla Management Corporation, 11426 Rockville Pike, Rockville, Maryland 20852
  • Mr. Timothy Cole, Actuarial Assistant, Continental American Life, 300 Continental Drive, Newark, Delaware 19713
  • Ms. Terri Coughlin, Service Fellow, National Center for Health Services Research, Long-Term Care Studies Program, Room 18A-55, Parklawn Building, 5600 Fishers Lane, Rockville, Maryland 20857
  • Mr. Stephen Crystal, Chair, Division on Aging, Rutgers University, 30 College Avenue, New Brunswick, New Jersey 08903
  • Ms. Diane Davis, Data Archivist/Analyst, Institute for Health, Health Care Policy, and Aging Research, Rutgers University, 30 College Avenue, New Brunswick, New Jersey 08903
  • Mr. Raymond DePaola, Director, National Rating and Special Products, Blue Cross of Greater Philadelphia, 1333 Chestnut Street, Philadelphia, Pennsylvania 19107
  • Mr. Dennis L. DeWitt, Executive Director, HHS Task Force on Long Term Health Care Policies, Room 4406, HHS North Building, 330 Independence Avenue, S.W., Washington, D.C. 20201
  • Dr. Robert T. Deane, Chief Economist, American Health Care Association, 1200 15th Street, N.W., Washington, D.C. 20005
  • Ms. Alfreda Dempkowski, Environmental Specialist, Empire Blue Cross/Blue Shield, 622 Third Avenue, 27th Floor, New York, New York 10017
  • Mr. Arthur N. Dickerson, Vice President, Corporate Development, Provident Life and Accident Insurance Company, Fountain Square, Chattanooga, Tennessee 37402
  • Dr. Milan J. Dluhy, Associate Director and Associate Professor, Center on Aging, Florida International University, Bay Vista Campus, North Miami, Florida 33181
  • Mr. Mark G. Doherty, Director of Research, Society of Actuaries, 500 Park Boulevard, Itasco, Illinois 60143
  • Ms. Pamela Doty, Senior Policy Analyst, Health Care Financing Administration, OLP, Room 345G, H.H. Humphrey Building, 200 Independence Avenue, S.W., Washington, D.C. 20201
  • Mr. John Drabek, Economist, Bureau of Health Professions, HRSA/DHHS, Room 8-41, Parklawn Building, 5600 Fishers Lane, Rockville, Maryland 20857
  • Ms. Linda Drazga Maxfield, Senior Statistician, Maximus, Suite 400, 6723 Whittier Avenue, McLean, Virginia 22101
  • Mr. Alfred P. Duncker, Social Science Research Analyst, Administration on Aging, DHHS, Room 4272, HHS North Building, 330 Independence Avenue, S.W., Washington, D.C. 20201
  • Mr. Robert J. Dymowski, Consulting Actuary, Milliman and Robertson, Inc., Suite 300, 259 Radnor-Chester Roads, Radnor, Pennsylvania 19087
  • Mr. Paul Eggers, Chief, Program Evaluation Branch, Health Care Financing Administration, Room 2-C-14, Oak Meadows Building, 6340 Security Boulevard, Baltimore, Maryland 21207
  • Mr. Gerald Eggert, Executive Director, Monroe County Long Term Care Program, Suite 2250, 349 West Commercial Street, East Rochester, New York 14445
  • Dr. David M. Eisenberg, Director, Long-Term Care, Philadelphia Corporation for Aging, 111 North Broad Street, Philadelphia, Pennsylvania 19107
  • Mr. Paul Elstein, Program Analyst, Task Force on Long Term Health Care Policies, HCFA, Room 4406, HHS North Building, 330 Independence Avenue, S.W., Washington, D.C. 20201
  • Mr. Arthur W. Ericson, Consultant, 4448 East Camelback Road, #12 Village Drive, Phoenix, Arizona 85018
  • Mr. Lynn Etheredge, Consolidated Consulting Group, Suite 352, 1133 20th Street, N.W., Washington, D.C. 20037
  • Ms. Lydia Falconier, Research Assistant, University of Illinois at Chicago, Occupational Therapy Department, 1919 West Taylor, Chicago, Illinois 60680
  • Dr. Barbara Fallon, Social Science Research Analyst, Administration on Aging/OHDS, Room 4272, HHS North Building, 330 Independence Avenue, S.W., Washington, D.C. 20201
  • Mr. Carl R. Fenstermaker, Group Director, U.S. General Accounting Office, Room N1657, New Department of Labor Building, 200 Constitution Avenue, N.W., Washington, D.C. 20210
  • Mr. Robert Ficke, Director, Membership Services, National Association of State Units on Aging, Suite 208, 600 Maryland Avenue, S.W., Washington, D.C. 20024
  • Dr. Rhona S. Fisher, Research Associate, Intergovernmental Health Policy Project, Suite 616, 2100 Pennsylvania Avenue, N.W., Washington, D.C. 20037
  • Mr. Daniel Foley, Statistician, National Institute on Aging, Room 612, Federal Building, 7550 Wisconsin Avenue, Bethesda, Maryland 20892
  • Dr. Richard Fortinsky, Research Associate, University of Southern Maine, Human Services Development Institute, 96 Falmouth Street, Portland, Maine 04103
  • Mr. Donald G. Fowles, Statistician, DHHS/OHDS/Administration on Aging, 330 Independence Avenue, S.W., Washington, D.C. 20201
  • Dr. Glenn Fujiura, Institute for the Study of Developmental Disabilities, University of Illinois at Chicago, 1640 West Roosevelt Road, Chicago, Illinois 60608
  • Ms. Rosemary Fulcher, Vice President and Chief Actuary, United American Insurance Company, P.O. Box 810, 2909 North Buckner Boulevard, Dallas, Texas 75221-0810
  • Ms. Marie Gammon LeDuc, Account Executive, Corporate Strategic Planning, UNUM Life Insurance Company, 2211 Congress Street, Portland, Maine 04122
  • Dr. Judith Garrard, Associate Professor, University of Minnesota, Center for Health Services Research, School of Public Health, Box 729 Mayo, Minneapolis, Minnesota 55455
  • Mr. Michael Gastineau, Union Labor Life Insurance Company, 111 Massachusetts Avenue, N.W., Washington, D.C. 20001
  • Mr. Paul D. Gayer, Senior Economist, DHHS/OS/ASPE/SSP/DALTC, Room 410E, H.H. Humphrey Building, 200 Independence Avenue, S.W., Washington, D.C. 20201
  • Mr. Donald A. Gibbs, Associate Actuary, Aetna Life and Casualty, 25 Sigourney Street, Hartford, Connecticut 06156
  • Ms. Mary Jo Gibson, Policy Analyst, Public Policy Institute, American Association of Retired Persons, 1909 K Street, N.W., Washington, D.C. 20049
  • Ms. Ingrid Goldstrom, National Institute of Mental Health, Room 18C-07, Parklawn Building, 5600 Fishers Lane, Rockville, Maryland 20857
  • Dr. Evelyn W. Gordon, Assistant Director for Health Programs Research, Food and Drug Administration (HF-Z-70), 8757 Georgia Avenue, Silver Spring, Maryland 20910
  • Ms. Marian Gornick, Director, Division of Bene. Studies, Health Care Financing Administration, ORD, Room 2504, Oak Meadows Building, 6325 Security Boulevard, Baltimore, Maryland 21207
  • Mr. Leonard E. Gottesman, President, Community Services Institute, 137 North Narberth Avenue, Narberth, Pennsylvania 19072
  • Mr. George Greenberg, DHHS/OS/ASPE/HP, Room 432E, H.H. Humphrey Building, 200 Independence Avenue, S.W., Washington, D.C. 20201
  • Ms. Jewell J. Griffin, DHHS/OS/ASPE/SSP/DALTC, Room 410E, H.H. Humphrey Building, 200 Independence Avenue, S.W., Washington, D.C. 20201
  • Ms. Pamela Griffith, Clinical Studies Project Manager, Group Operations, Inc., Suite 206, 12750 Twinbrook Parkway, Rockville, Maryland 20852
  • Mr. Robert Griss, Policy Analyst, Center for Study of Social Policy, Suite 405, 236 Massachusetts Avenue, N.E., Washington, D.C. 20002
  • Ms. Marcy Gross, Senior Policy Analyst, Office of the Assistant Secretary for Health, Room 740G, H.H. Humphrey Building, 200 Independence Avenue, S.W., Washington, D.C. 20201
  • Mr. Larry Guerrero, Director, Division of Program Analysis and Evaluation, Office of Human Development Services, Room 722E, H.H. Humphrey Building, 200 Independence Avenue, S.W., Washington, D.C. 20201
  • Dr. Claire E. Gutkin, Senior Research Associate, Hebrew Rehabilitation Center for Aging, 1200 Centre Street, Boston, Massachusetts 02131
  • Mr. Mahlon N. Haines, Chief, Field Operations Unit, Bureau of Long Term Care Services, DLTCUC, Department of Public Welfare, P.O. Box 2675, Harrisburg, Pennsylvania 17105
  • Dr. Burton P. Halpert, Associate Professor of Sociology, University of Missouri, Kansas City, 2220 Holmes Street, Kansas City, Missouri 64108
  • Ms. Linda Hamm, Division of Long-Term Care, Health Care Financing Administration, Room 2-F-14, Oak Meadows Building, 6340 Security Boulevard, Baltimore, Maryland 21207
  • Mr. Raymond Hanley, Senior Research Analyst, Brookings Institution, 1775 Massachusetts Avenue, N.W., Washington, D.C. 20036
  • Mr. Glen E. Harelson, Program Analyst, DHHS/OS/ASPE/SSP/DALTC, Room 410E, H.H. Humphrey Building, 200 Independence Avenue, S.W., Washington, D.C. 20201
  • Ms. Karen Harlow, Director of Research/Associate Professor, University of Texas Health Science, Center at Dallas, Department of Gerontology and Geriatric Services, 5323 Harry Hines Boulevard, Dallas, Texas 75235
  • Dr. Mary S. Harper, Coordinator, Long Term Care Programs, National Institute of Mental Health, Room 11C-03, Parklawn Building, 5600 Fishers Lane, Rockville, Maryland 20857
  • Ms. Cynthia Harpine, Statistician (Demography), Bureau of the Census, U.S. Department of Commerce, Room 2375, Federal Building #3, Washington, D.C. 20233
  • Mr. William D. Hart, Associate Professor, Department of Nutrition and Food Sciences, Texas Woman's University, 1130 M.D. Anderson Building, Houston, Texas 77030
  • Ms. Peggy Hauser, Associate Actuary, Milliman and Robertson, Inc., 15700 Blue Mound Road, Brookfield, Wisconsin 53005
  • Mr. Rex D. Hemme, Vice President and Actuary, American General Group Insurance Company, 3988 North Central Expressway, Dallas, Texas 75204
  • Mr. Jim Heth, Program Coordinator, Division for Aging Services, CHR Building, Sixth Floor West, 275 East Main Street, Frankfort, Kentucky 40621
  • Mr. David L. Hewitt, Senior Vice President, Hay/Huggins Company, Inc., 229 South 18th Street, Philadelphia, Pennsylvania 19013
  • Ms. Jody Hoffman, Georgetown University, Center for Health Policy Studies, Suite 525, 2233 Wisconsin Avenue, N.W., Washington, D.C. 20007
  • Mr. Robert Hoyer, Research Director, National Association for Home Care, 519 C Street, N.E., Washington, D.C. 20002
  • Ms. Susan L. Hughes, Director, Programs in Gerontological Health, Center for Health Services and Policy Research, Northwestern University, Evanston, Illinois
  • Mr. K.A. Jagannathan, Chief, Program Analysis and Data Base Branch, Office of Human Development Services, OPPL, Room 726E, H.H. Humphrey Building, 200 Independence Avenue, S.W., Washington, D.C. 20201
  • Ms. Tecla Jaskulski, Director, Macro Systems, Inc., Suite 300, 8630 Fenton Street, Silver Spring, Maryland 20910
  • Mr. Richard D. Johnson, Vice President/Chief, Life Actuary, Mutual Service Life Insurance Company, P.O. Box 64035, Saint Paul, Minnesota 55164
  • Ms. Wilma G. Johnson, Program Analysis Officer, Centers for Disease Control, OPPE, Room 2060, D24, Building 1, 1600 Clifton Road, N.E., Atlanta, Georgia 30333
  • Ms. Judith D. Kasper, 1417 Park Avenue, Baltimore, Maryland 21217
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  • Ms. Catherine Kennedy, Consultant, Aetna Life and Casualty, MB65, 151 Farmington Avenue, Hartford, Connecticut 06156
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  • Mr. Deochand Persaud, Program Research Analyst, New York State Office for the Aging, Empire State Plaza, Building #2, Albany, New York 12223
  • Dr. Eric Pfeiffer, Professor of Psychiatry and Director, Suncoast Gerontology Center, University of Southern Florida, College of Medicine, MDC Box 50, 12901 North 30th Street, Tampa, Florida 33612
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  • Mr. Richard J. Price, Specialist in Health Legislation, Congressional Research Service, Education and Public Welfare Division, LM-320, Washington, D.C. 20540
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  • Ms. Rachel Pruphno, Philadelphia Geriatric Center, 5301 Old York Road, Philadelphia, Pennsylvania 19141
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  • LONG TERM CARE DATA BASE SUMMARIES

    National Long Term Care Channeling Demonstration

    Referral Person: Judith Wooldridge

    Phone Number: (609)275-2370

    Target Population: Individuals 65 years or older. Noninstitutionalized. Functional limited.

    Sample Size: 6,326 screens.

    Type of Survey: Longitudinal (baseline, 6, 12, 18 months); multiple data sources.

    Universe: Aged noninstitutional functionally limited persons.

    Sample Frame: Persons randomized (treatment/controls) into Channeling demonstration.

    Clustering: 10 sites.

    Stratification: Age, eligibility criteria.

    Collection Agency: Mathematica Policy Research, Inc.

    Smallest Geographical Unit Results: Demonstration site.

    Year of Data: 1982-1985.

    Final Data Tapes: January 1987.

    Cost of Survey: ASPE/HCFA/AOA--$13 million (for full Channeling evaluation).

    1982 National Long Term Care Survey

    Referral Person: Ken Manton

    Phone Number: (919)684-6126

    Target Population: Individuals 65 years or older, noninstitutionalized. Functional limited.

    Sample Size: 36,000 screened. 6,400 detailed.

    Type of Survey: Cross-sectional.

    Universe: Aged noninstitutional functionally limited persons in nation.

    Sample Frame: Medicare Health Insurance Master File.

    Clustering: 173 sampled areas.

    Stratification: Age and original reason for entitlement.

    Collection Agency: Census Bureau

    Smallest Geographical Unit Results: Census Bureau.

    Year of Data: 1982.

    Final Data Tapes: February 1984.

    Cost of Survey: ASPE--$1.1 million. HCFA--$1.0 million.

    1984 National Long Term Care Survey

    Referral Person: Ken Manton

    Phone Number: (919)684-6126

    Target Population: Individuals 65 years or older, total. Functionally limited.

    Sample Size: 20,000 screened. 11,000 detailed.

    Type of Survey: Panel with 1982 National Long-Term Care Survey, cross-sectional for 1984.

    Universe: Aged functionally limited persons in nation.

    Sample Frame: Medicare Health Insurance Master File.

    Clustering: 173 sampled areas.

    Stratification: Age and original reason for entitlement.

    Collection Agency: Census Bureau

    Smallest Geographical Unit Results: Census Bureau.

    Year of Data: 1984.

    Final Data Tapes: August 1985.

    Cost of Survey: NCHSR--$78 thousand. HCFA--$2 million.

    1985 National Nursing Home Survey

    Referral Person: Evelyn Mathis

    Phone Number: (301)436-8830

    Target Population: Persons in nursing homes. Supplemented by admissions in 1983. All ages.

    Sample Size: 2,000 facilities. 12,000 residents.

    Type of Survey: Facilities, residential cross-sectional; admissions-longitudinal.

    Universe: Facilities--nursing and related care homes. Residents--institutionalized persons. Admissions during 1983.

    Sample Frame: Master Facilities Inventory.

    Stratification: Type of ownership size, type of care.

    Collection Agency: Contract to be awarded to minority contractor.

    Smallest Geographical Unit Results: Administrative region.

    Year of Data: 1985 cross-sectional. 1984/1985 admission.

    Final Data Tapes: Early 1987.

    Cost of Survey: NCHS--not yet determined. HCFA--$1.4 million.

    1984 National Health Insurance Survey/Supplement on Aging

    Referral Person: Gerry Hendershot

    Phone Number: (301)436-7084

    Target Population: Civilian noninstitutionalized 55 years plus.

    Sample Size: 16,000 surveyed.

    Type of Survey: Cross-sectional personal.

    Universe: All households.

    Sample Frame: Area probability sample.

    Clustering: 376 primary sampling unit. 12,000 neighborhood segments.

    Stratification: Post-stratified to independent estimate of age, sex, race population subgroup.

    Collection Agency: Census Bureau

    Smallest Geographical Unit Results: 4 major geographical regions and selected SSMA's.

    Year of Data: 1984.

    Final Data Tapes: December 1986.

    Cost of Survey: NCHS--$1.5 million.

    I. 1982-1984 NATIONAL LONG TERM CARE SURVEY

    Kenneth G. Manton, Ph.D., Demographic Studies, Duke University, Durham, NC

    Korbin Liu, Sc.D., Health Policy Center, The Urban Institute, Washington, DC

    I. INTRODUCTION

    With the rapid increase in the U.S. elderly (65+) and oldest-old (85+) populations, considerable concern has emerged over the amount of future acute and long-term care (LTC) services that will be required by that population, and of the nature of the mixture of federal, state and private programs necessary to respond to that need. One of the areas of service needs with the projected greatest rate of growth is that for LTC services. The National Nursing Home Surveys (NNHS) conducted by the National Center for Health Statistics in 1963, 1969, 1973, 1977 and, most recently, 1985 (with a follow-up in 1987) have provided considerable information on the institutional component of LTC services. More recently, because of the rapid growth of the elderly and oldest-old populations, considerable interest has emerged in home LTC options, both because of concern about the economics of institutional care and because of humanitarian concern about the level of dependency and quality of life in many LTC institutions. Until the advent of the National Long Term Care Survey (NLTCS) there was no major nationally representative survey with specially designed instrumentation that dealt explicitly both with the health and functional problems of the community dwelling disabled elderly, the home LTC options (both formal and informal) available to meet those problems, and the ability to substitute, for a specific target population, home and institutional care. The 1982 NLTCS filled this gap in our knowledge and provided considerable information on which both to plan the nature of required services and to develop private insurance products to pay for such services. The 1984 NLTCS provided a basis upon which to examine changes in the home LTC populations and to examine the trajectory of service needs at the individual level.

    The 1982 and 1984 NLTCS are detailed household surveys of persons aged 65 and over who manifest some chronic (i.e., 90 days +) Activity of Daily Living (ADL) or Instrumental Activity of Daily Living (IADL) impairment. The sample for the surveys was drawn using a two-stage procedure. In 1982, 36,000 names were drawn from the Health Insurance Master file. These persons were then screened by either telephone or personal visit to see if they manifested an ADL or IADL impairment of 90 days duration (or which was anticipated to last at least 90 days). When the screen identified a person living in the community with a chronic impairment, a detailed household interview was conducted which gathered information on medical status (diag- noses), functional status (presence of ADL, IADL or other functional impairments and equipment or caregivers utilized by the person to deal with his impairments), income and assets, health service use, use of federal services, housing and living arrangements. Of particular note in the survey were detailed questions on the number and type of informal caregivers. Institutionalized persons were not interviewed in 1982.

    In 1984, a different sampling procedure was utilized. First, all persons who reported chronic disability on the screener or who were screener-noninterviewed due to institutionalization and who survived to 1984 were interviewed regardless of their 1984 functional status. Second, from the original 25,541 persons who did not report functional impairments in 1982 (and who were not institutionalized), a random sample of 47% (~12,100 persons) was drawn and subjected to the same screening procedure as in 1982. Another difference from 1982 was that 5,000 persons who became 65 between 1982 and 1984 were screened so that, in addition to having a longitudinally followed sample in 1984, the full cross-section of persons aged 65 and over in 1984 could be evaluated. In addition, persons who were in institutions in 1984 were interviewed with a specially designed instrument containing a number of questions on institutional use in the interim period and the sources of payment for those services. The interview instrument used for the community population was nearly identical in 1984 to that used in 1982. A final major difference between the 1982 and 1984 surveys was that a "next of kin" interview was conducted for persons who died between 1982 and 1984. This interview collected extensive data on the medical service use and expenditures surrounding death.

    These surveys conducted in 1982 and 1984 cannot be fully exploited without considering their linkage to another important data source--Medicare Part A bill files from 1980 to 1985 on Medicare reimbursed hospitalization, home health services and skilled nursing facility use. These files contain bills for individual service episodes and provide a continuous history of the exact date of service use and the amounts reimbursed by Medicare for those services. Each bill in this interval is linked to the corresponding sample person who participated in either the 1982 or 1984 survey.

    The dual cross-sectional and longitudinal nature of the 1982 and 1984 NLTCS and the linked Medicare service use files allow us to analyze a broad range of questions. First, they provide an impressive array of data on the community dwelling chronically disabled elderly, a population group at high risk of both extensive acute and LTC service needs. These data can help us estimate the need for LTC services, the actuarial basis of, and markets for, LTC insurance products, the role of "spend-down" for Medicaid qualification for LTC benefits, and the impact of informal caregivers on meeting the national need for LTC services.

    In addition to describing the social, economic, functional and health status characteristics of a large (~5 million persons) population group at high risk for significant Medicaid and Medicare services (and for the development of private insurance options to provide parts of those services), the data files provide considerable information on the pattern of utilization and outcome of Medicare Part A (and potentially Medicare Part B) services. That is, the continuous time Medicare service history of individuals whose detailed health and functional characteristics have been determined from the surveys is available. This linkage can allow questions to be examined such as the substitution of home health and skilled nursing facility (SNF) services for acute hospitalization after the introduction of the prospective payment system (PPS) in order to assess how the reduction of the rate of hospitalization and the shortening of hospital LOS affected the nature of the use of these other service options. This can be used to evaluate the impact of such Medicare changes and to design changes, as necessary, in the provision of hospital, home health and SNF services by Medicare.

    A third major area where these data can be important is in the study of changes, both for the individual and for the aggregate, in terms of health and functional status. Because of limitations on the availability of longitudinal data, the design of service and insurance options has been constrained. The availability of large amounts of nationally representative data on long term (two-year changes) in health, functional, economic, and social status is an important and unique feature of this data set.

    A fourth major use of this data set is to help provide national estimates of LTC service needs by combining national distributions of functional limitations from the survey with very detailed data from select populations in a wide variety of LTC demonstration projects and waivered programs. That is, detailed data on Medicaid and private payment for LTC services are available in the demonstration projects along with data on the effects of those services (and modifications of those services) on a wide variety of social and health outcomes. The problem is to extrapolate those findings from the multiple, local select populations in the demonstrations to the national population. Because the instrumentation of the NLTCS has many measures in common with many of the demonstration projects, there is a lot of information on which to base the extrapolation.

    In this introduction we have very briefly reviewed the rationale, structure, content and some potential areas of application of the 1982-1984 NLTCS and linked Medicare files. In subsequent sections we will explore specific technical issues concerning the quality of the survey data and its analysis in more detail.

    II. TEMPORAL ORGANIZATION OF THE 1982-1984 NLTCS

    We briefly described in the introduction how the samples were constructed for the cross-sectional and longitudinal components of the 1982-1984 NLTCS. A more systematic review of this can be made from Figure 1.

    FIGURE 1: unavailable at the time of HTML conversion--will be added at a later date.

    In Figure 1 we present a time line for the 1982-1984 (and a proposed 1988) NLTCS which identifies the dates of the surveys, the dates for which Medicare Part A data are to be collected and the survey instruments applied at each date. The 1988 survey is currently in a tentative planning phase. All other elements of the survey and service use data collection are in-place except for the proposed collection of death certificates for decedents over the period 1982 to 1989.

    We can see that the sample was originally "frozen" as of April 1, 1982 and contained 35,789 persons. Survey work for round 1 began in June and continued to October 1982 and produced 6,088 responses from the 6,393 persons identified as chronically disabled. In addition to the survey of disabled persons a separate survey of 1,925 caregivers (1,626 continuing caregivers and 299 caregivers who discontinued care) who were identified as having provided care to a subsample of the 6,393 persons who screened into the survey. In addition to the 6,393 community dwelling elderly disabled, 1,992 persons were found to be in institutions, either before April 1 (N=1708) or who became institutionalized between April 1 and the screening date (N=284). Thus, though no interviews in 1982 were conducted of institutionalized persons, and we cannot identify Medicaid and private pay institutionalized persons from the Medicare files, we can identify the total set (N=1992) of institutionalized persons from the screen.

    On April 1, 1984, the sample components of the 1984 survey were fixed and field work again conducted between June and October 1984. At this time three survey instruments were applied to nearly 10,000 persons. One instrument was essentially the same questionnaire as was applied to the 1982 community dwelling, disabled elderly population. A second instrument was the institutional questionnaire which allowed us to examine the retrospective reports of the institutional histories of all persons institutionalized on April 1. These reports covered all facets of institutionalization (Medicaid and private pay as well as Medicare). The third type of survey was the "next of kin" questionnaire on health services received during the terminal phase of the illness for deceased persons who were reported as disabled in 1982 and who died in the two-year intervening period. Medicare Part A data cover all service use from January 1, 1980, to currently October 1986.

    To get a better understanding of the size of the sample components and their change in sample status between 1982 and 1984, examine Figure 2.

    FIGURE 2. Component Sub-Populations of 1982 and 1984 NLTC Surveys: unavailable at the time of HTML conversion--will be added at a later date.

    We see several different types of numbers in the figure. First, above each block is a single number which represents the number of persons in that state at that time. Thus, there were 25,541 persons (of 31,934 who were not institutionalized and who responded to at least the telephone screen) who were determined to be non-disabled, community dwellers in 1982. In 1984 there were 14,130 such persons--a number much smaller than the 25,541 because only 47.4% of the 1982 non-disabled group was screened.

    Under each block is a set of four numbers. For 1982 these describe the number of persons in that state who ended up in one of the four receiving states in 1984. Thus, 9,220 persons who were non-disabled, aged 65+ and sampled (of the 47.4% of people who were non-disabled in 1982) turned out to be non-disabled in 1984. Of this group, 1,562 became disabled and were interviewed in 1984, 348 persons became institutionalized in 1984, and 970 died.

    The corresponding numbers for 1984 tell us where persons in those states come from. Thus, of the 6,182 persons receiving the detailed survey in 1984, 1,562 were not detailed in 1982, 4,114 were people who were disabled in 1982 (but who were not necessarily disabled in 1984--thus long term improvements in health and functional status can be tracked over the two-year period), 53 persons were interviewed in 1984 in the community who were institutionalized in 1982, and 453 persons became disabled and were interviewed from the sample of 4,916 persons drawn from those aged 63 or 64 in 1982. The deceased block shows that a total of 3,219 persons died from the four sample components over the two years.

    One issue that arises in evaluating the 1982-1984 NLTCS is that, in order to increase its precision, a two-stage sample capture procedure was used to identify community dwelling disabled persons. Thus, it does not provide detailed survey data on various groups. For example, though it divides the total sample in 1982 into the set of all community disabled persons, the set of all institutionalized persons, and the set of all non-disabled, non- institutionalized persons, it provides no detailed data on non-institutional, non-disabled persons. The characteristics of such persons are described in the recent 1984-1986 Supplement on Aging (SOA)-Longitudinal Supplement on Aging (LSOA) of the National Health Interview Survey. The SOA-LSOA provides far less information on the disabled extremely elderly (only 876 persons over age 85 were identified in 1984) and both smaller numbers (~1,500) and less detailed information on the functionally impaired and the formal and informal care services they receive. Thus, each of these surveys complement one another but provide very different samplings of target populations and very different (and specially tailored) instruments. Likewise, the 1985 NNHS is being followed-up in 1987. That survey gives us much larger numbers and more specialized information than the NLTCS. However, it does not contain a true admission cohort. Thus, the three surveys may be reviewed as complementary in terms of sample coverage and instrumentation. This is illustrated in the coordinated time lines of the three surveys presented in Figure 3.

    FIGURE 3. TIME LINES FOR THREE NATIONALLY REPRESENTATIVE LONGITUDINAL SURVEYS: unavailable at the time of HTML conversion--will be added at a later date.

    The three sets of longitudinal surveys provide a powerful, and cost effective base battery of surveys to monitor the health and functional status of the elderly population and its consumption of acute and LTC services.

    III. RESPONSE RATES AND PATTERNS OF PROXY RESPONSES

    Two useful measures of the quality of data in surveys are the rate of non-response and the patterns of response of proxies. This is especially true for the NLTCS because (a) it had large numbers of extreme elderly persons for whom obtaining survey responses is known to be difficult, and (b) the survey had a longitudinal dimension meaning persons have to be tracked over time.

    To evaluate these issues we provide two basic types of data. The first are the non-response rates for various sample stages in both 1982 and 1984. There are two types of non-responses to be considered. The first type are the so-called "C" type "non"-responses. Actually, this is a slight misnomer in that these people did not respond because they did not qualify for the sample. The major reasons for not qualifying were (a) death, (b) institutionalization (in 1982 only), and (c) movement out of the sample area. Thus, this type of failure to respond does not represent what we typically view as non-response. The type C non-responses are described in Table 1.

    TABLE 1. Number of Ineligible Cases (Type C) by Reason and Survey Instrument Attempled, 1982 and 1984 NLTCS's
    - '82 Screener Telephone '82 Screener Personal Visit '82 Detailed Community '84 Screener Telephone '84 Screener Personal Visit '84 Detailed Community '84 Institutional Questionnaire
    Deceased before April 1 390 340 - 537 30 - -
    Deceased on or after April 1 280 210 67 101 16 - -
    Institutionalized before April 1 1151 557 0 0 - - -
    Institutionalized on or after April 1 123 161 57 - - - -
    Moved outside country before April 1 13 21 - 13 5 - -
    Moved outside country on or after April 1 5 6 1 - 1 1 1
    Moved within country, beyond limit - 81 25 16 32 19 7
    Other Type C 72 15 14 47 6 1 -
    In correctional facility (84 only) - - - 1 - - -

    The second type of non-response was labelled "A" type non-response. These represent non- responses due to either failure to locate or contact persons, refusals, or failures of the proxy to be able to respond. The frequency of non-responses is described in Table 2.

    TABLE 2. Number of Nonrespondents (Type A) by Noninterview Reason and Survey Instrument Attempted, 1982 and 1984 NLTCS's
    - '82 Screener Personal Visit '82 Detailed Community '84 Screener Personal Visit '84 Detailed Community '84 Institutional Questionnaire '84 Deceased Questionnaire Telephone '84 Deceased Questionnaire Personal Visit
    No telephone number 250 - - - - 30 -
    No answer after repeated calls 7 - - - - 4 -
    Sample person/proxy temparily absent and proxy unavailable 15 5 21 4 3 1 2
    Refused 89 111 92 131 13 12 8
    Sample person/proxy unable to respond and proxy unavailable 4 3 2 19 - 7 3
    Other Type A 89 15 126 57 16 10 54
    Unable to locate - 1 127 5 2 - 27
    No one home - - 5 7 1 - 1
    NOTE: The '82 and '84 Screeners as well as the '84 deceased questionnaire provided for both telephone and personal visit noninterview reasons. In nonrespondent cases where a reason is given in both categories, the personal visit reason was selected for tabulation.

    We can see that the frequency of non-response was very low producing response rates that are extremely high, both for the screening and detailed interview stages, in both 1982 and 1984. The response rates average about 96%. Thus, neither the longitudinal nature of the survey nor the high proportion of the extreme elderly seems to have caused problems in the level of response to the survey.

    The second aspect of the response question is the pattern of proxy respondents. This is indicated in Table 3 where, for both 1982 and 1984, and for different levels of disability, we provide the number of responses (a) totally by sample persons, (b) totally by proxy, and (c) for combined sample person and proxy respondents.

    TABLE 3. Number of Respondents by Type, By ADL Score, and Senility Status
    - Non-Disabled IADL Only 1-2 ADL 3-4 ADL 5-6 ADL Senility
    Senile Nonsenile
    1982
    Sample Person Answered 418 1,234 1,360 498 252 --- 3,762
    Proxy Answered 39 261 318 214 533 495 870
    Sample Person and Proxy Answered 41 290 264 125 176 48 763
    1984
    Sample Person Answered 559 1,329 1,246 500 214 --- 3,848
    Proxy Answered 60 237 278 196 480 421 830
    Sample Person and Proxy Answered 52 222 226 134 172 34 772

    As would be expected the proportion of proxy responses increases as the reported disability level of the individual increase. Furthermore, we see that about 500 persons in 1982 and 1984 had a proxy respondent due to senility. Indeed, the diagnosis of senility was derived from the proxy when the person was found incapable of responding due to cognitive impairment. Of the roughly 6,000 interviews in both years, about two-thirds were totally from sample persons. The pattern of proxy response (i.e., its increase with disability), the small number of non-responses due to proxy failure (Table 2), and the large proportion of non-proxy responses provide an indication of the appropriateness of the use of proxy responses in the survey.

    IV. SAMPLE DESIGN AND ITS EFFECT ON THE ANALYSIS OF THE SURVEY

    An important factor in the analysis of any survey, but one that frequently generates confusion, is the appropriate use of sample weights in the analysis. This is because sample weights play different roles in different stages of the analysis and because there are several different methodologies for dealing with the effects of sample design in analysis. The issues become more complex in the current study because of its longitudinal nature.

    The first set of issues involves the role of weights in various stages of analysis. One stage of analysis has to do with the testing of statistical hypotheses using the survey data. The basic problem is that the samples are not simple random samples but are probability samples, i.e., different populations are drawn with a pre-specified probability to increase the precision of estimates for certain rare populations. Furthermore, in some sample designs, the samples are drawn from spatially designated clusters to reduce costs. Since persons in each cluster will tend to share certain social-economic and residential characteristics this means that their responses will tend to be correlated, i.e., each person cannot be viewed as providing an independent response.

    In the NLTCS these problems are minimized because the sample design is relatively simple. In 1982 the population was only stratified on age, sex and race. In 1984 there was the additional complication that only 47.4% of the non-disabled community dwelling persons were screened-adding an additional weighting factor.

    The problem in analysis is that stratification and sample clustering (clustering has little effect in this design) affect the estimate of error variance which is used in our test statistics to determine if a particular hypothesis should be accepted or rejected. The analytic problem is to determine how the sample design affects the variance of our parameter estimates. There are two analytic approaches to this problem. The first is to use some model of randomization to increase the error variance to provide a conservative adjustment to our test statistics. There are several analytic computer programs extant that do this for continuous variables in simple regression models. However, given the simple nature of the study design certain simple calculations can be used to adjust variance estimates. This was illustrated by the Census Bureau for the 1982 cross-sectional sample. A table of adjusted factors is provided in Table 4 below.

    TABLE 4. "a" and "b" Parameters and "f" Factors for Computing Approximate Standard Errors of Estimated Numbers and Percentages of Persons
    Characteristic Parameters "f" factor
    a b
    Black persons or persons receiving medicaid -.00008227 2094 1.4
    All other -.00004027 1025 1

    In Table 4 are the parameters for two regression equations. Both were obtained by regressing the estimate on the variance of the estimate for each of two groups, i.e., "blacks or persons receiving Medicaid" and "all others." What one does is take the number of persons having a particular characteristic in 1982 and multiply the square of that number by parameter a, and the number itself by parameter b, and add the two products. The square root of this number is the standard error of the estimate. To illustrate, in the 1982 survey there were estimated to be 1,190,764 aged persons requiring personal help in bathing. The formula described above is, symbolically

    Standard error of x = ax2 + bx * f

    If, as for the example, f = 1.0, then the calculation is

    Standard error of x = (-.00004027)(1,190,764)2 + (1025)(1,190,764) * (1.0)

    or, 34,109.

    Thus, the one standard deviation (68%) confidence interval, is ±34,109 or 1,156,655 to 1,224,873. The 95% confidence interval would be ±2*(34,109). For the confidence interval of differences one uses

    Standard error of difference = s2x + s2y - 2r * (sx * sy)

    where sx and sy are the standard errors of the two estimates to be compared and r is the correlation coefficient (which can often assumed to be zero). Alternatively, for the 1982 tables, the standard errors of both numbers and percentages were calculated. These are presented in Table 5.

    TABLE 5
    A. Standard Errors of Estimated Percentages of Persons
    Base of estimated percentage (thousands) Estimated Percentage
    2 or 98 5 or 95 10 or 90 25 or 75 50
    25 2.8 4.4 6.1 8.8 10.1
    50 2.0 3.1 4.3 6.2 7.2
    100 1.4 2.2 3.0 4.4 5.1
    250 0.9 1.4 1.9 2.8 3.2
    500 0.6 1.0 1.4 2.0 2.3
    750 0.5 0.8 1.1 1.6 1.8
    1000 0.4 0.7 1.0 1.4 1.6
    2000 0.3 0.5 0.7 1.0 1.1
    3000 0.3 1.4 0.6 0.8 0.9
    4000 0.2 0.3 0.5 0.7 0.8
    5000 0.2 0.3 0.4 0.6 0.7
    B. Standard Errors of Estimated Numbers (in thousands)
    Size of Estimate Size of Estimate Standard Error Size of Estimate Standard Error Size of Estimate
      25 5.1 1000 31.4  
      50 7.2 2000 43.5  
      100 10.1 3000 52.1  
      250 15.9 4000 58.8  
      500 22.4 5000 64.2  
      750 27.3 - -  

    The numbers in these tables need to be multiplied by the appropriate "f" values in Table 4. We do not yet have similar tables for the 1984 survey. However, knowing the size of the various sub-samples in 1982 we can present the coefficient of variation for different of the 1982 subsamples (i.e. 10,250 is the number of non-disabled persons planned to be screened in 1984; N=6,089 was approximately the number of persons interviewed in 1982; 1,712 was the number of persons institutionalized before April 1, 1982, and 856 and 428 are half and a quarter of that number). These numbers are presented in Table 6.

    TABLE 6. CV's for Variance Rates and Sample Sizes
    Sample Size Rate CV -
    n = 10,250 1% .127 1.6% gives a 10% CV
    5% .056
    10% .038
    25% .022
    50% .013
    n = 6,089 1% .165 2.7% gives a 10% CV
    5% .072
    10% .050
    25% .029
    50% .017
    n = 1,712 1% .311 8.9% gives a 10% CV
    5% .136
    10% .094
    25% .054
    50% .031
    n = 856 1% .439 16.3% gives a 10% CV
    5% .193
    10% .133
    25% .077
    50% .044
    n = 428 1% .622 28.1% gives a 10% CV
    5% .272
    10% .187
    25% .108
    50% .062

    An alternative approach to adjusting error variance estimates for sample design effects is based upon the realization that many of these design effects may be of substantive interest. Thus, an alternative approach is to explicitly model the design factors as part of one's analysis so that design effects are explicitly represented. Such an approach has the advantage of helping us better understand the mechanisms generating the phenomenon but the disadvantage of requiring that the correct model be developed. Though it may seem tedious and difficult to search for the "correct" model, rather than using a "general" model of randomization, it should be realized that only by producing the correct model can one really generalize the parameter estimates made beyond the particular sample, i.e., either to the general population or in forecasts of future needs. Thus, in many more situations than normally realized, the search for a model based adjustment for complex sample design effects is a necessity.

    A second analytic stage where sample weights are used is in "post" weighting, i.e., where one wishes to recombine parameter estimates for sub-groups to produce the parameter estimates for the total population that was sampled. This usually involves re-weighting the data to reflect the inverse of the probability of selection. This is actually a purely algebraic procedure that is independent of the methods to calculate the effects of sample weights on statistical inferences.

    V. EXPLOITATION OF THE LONGITUDINAL NATURE OF THE SURVEYS AND LINKED MEDICARE SERVICE DATA

    In this section we discuss how the cross-temporal nature of the file can be exploited in several types of analyses of transition. The longitudinal nature of the file can be exploited in several ways. The first, and most basic, is simply to analyze changes in characteristics between 1982 and 1984. This is illustrated in Table 7 for disability and institutional status.

    TABLE 7. Percentage Distribution of Case Status in 1984 by Case Status in 1982
    1982 Status 1984 Status
    Nondisabled IADL Only 1-2 ADL 3-4 ADL 5-6 ADL Institutional Deceased -
    Nondisabled 79.66 4.54 3.17 1.12 1.02 1.76 8.73 100.00
    IADL Only 12.18 39.39 19.13 4.73 4.20 5.66 14.72 100.00
    1 or 2 ADLs 7.10 14.10 32.87 12.36 6.35 7.49 19.73 100.00
    3 or 4 ADLs 4.74 4.13 17.22 22.05 18.62 9.98 23.26 100.00
    5 or 6 ADLs 4.13 4.49 7.19 8.84 30.00 9.60 35.75 100.00
    Institutional 1.48 1.06 0.95 1.07 1.05 53.71 40.67 100.00
    Aged-in 89.85 3.08 2.23 1.45 1.32 0.94 1.13 100.00
    -
    All Cases in 1984 60.32 7.06 6.80 3.28 3.39 6.81 12.35 100.00
    All Cases in 1982 63.28 8.52 9.70 4.21 4.74 9.56 0.00 100.00
    Totals may not add to 100.00 due to rounding.

    Down the left hand side of the table we present the percentage distribution of persons (weighted counts) by their status in 1982. Across the table we provide the status (including death) of persons in 1984. We see that the largest proportion of persons remain in the same state that they were in 1982, or, for the most disabled, they experienced high death rates. Interestingly, there are also sizeable numbers of persons who had long term (2+ years) functional improvements. For persons with 5 to 6 ADL's in 1982 nearly a quarter improved status by 1984. Given the high mortality rate for persons with this level of impairment (~36%) and only a moderate level of institutionalization, this suggests that the functional impairment is driven by an acute morbid condition that often produce death, but also often result in improved LTC functional status.

    This table can also be stratified by other variables. For example, in Table 8, we have decomposed changes in disability by age.

    TABLE 8. Percentage Distribution of Case Status in 1984 by Case Status in 1982, By Age
    1982 Status 1984 Status
    Nondisabled IADL Only 1-2 ADLs 3-4 ADLs 5-6 ADLs Institutional Deceased
    Nondisabled
    65 to 74 years 86.38 3.56 1.91 0.88 0.63 0.58 6.06
    75 to 84 years 71.30 6.21 4.75 1.34 1.47 2.93 12.01
    85+ years 45.39 7.30 9.51 2.71 3.14 9.33 22.61
    IADL Only
    65 to 74 years 17.00 45.09 16.47 3.67 3.49 3.15 11.13
    75 to 84 years 10.32 35.93 20.85 5.53 4.40 7.14 15.84
    85+ years 1.58 30.55 22.90 5.93 5.97 9.66 23.41
    1 or 2 ADLs
    65 to 74 years 8.91 17.84 35.60 12.66 5.73 4.35 14.90
    75 to 84 years 6.95 13.92 33.22 11.05 4.97 7.72 22.18
    85+ years 4.19 7.81 27.37 14.32 10.06 12.61 23.64
    3 or 4 ADLs
    65 to 74 years 7.56 5.68 24.07 24.56 15.20 4.62 18.33
    75 to 84 years 3.85 3.60 16.37 21.47 19.35 11.93 23.43
    85+ years 1.44 2.40 6.96 18.78 23.22 15.77 31.43
    5 or 6 ADLs
    65 to 74 years 5.24 7.05 8.90 10.07 31.70 6.10 30.95
    75 to 84 years 4.87 4.39 6.54 8.80 30.24 10.96 34.20
    85+ years 1.22 0.70 5.60 6.99 26.98 12.81 45.71
    Institutional
    65 to 74 years 4.05 1.41 1.99 2.30 1.42 60.26 28.57
    75 to 84 years 1.53 1.67 1.37 0.89 1.25 55.25 38.03
    85+ years 0.35 0.35 0.13 0.71 0.70 49.50 48.26

    In Table 8 we see that there are decided shifts with age to higher levels of disability and that, at advanced ages (85+) there is a lower proportion of persons who improve and a higher proportion who become more disabled.

    The above discrete state, discrete time description of transitions can become unreliable as one attempts to stratify those transitions by more than one additional variable (e.g., stratify them by age and marital status and the cell sizes become small). To deal with this problem it is necessary to use some type of regression model where the transitions are made functions of a set of covariates. Usually, in order to conduct such modeling, some assumptions have to be made about the form of the dependency of the transitions on the covariates (e.g., in the Cox regression model it is assumed that each covariate has a proportional impact on the transitions).

    Such models simply describe the probability of a change in state, as the states are described in the survey, over the two-year period. Obviously, this does not tell us how to exploit the rich data on the use of different types of Medicare Part A services over the two-year period. These data can be exploited to different degrees by models with different degrees of sophistication.

    One approach is to simply aggregate different types of service use over specified periods of time. The problem with this approach is that it can produce severely misleading results due to the fact that events like death--or even other types of service use--represent constraints on the amounts of specific types of services that an individual consumes in a given period of time. Thus, a person who was in a nursing home for a year and used 20 days of home health care in a two-year period, used home health care twice as fast when eligible as a person who, in a two-year period used no other type of service.

    To account for these differences in exposures, one must calculate life table type measures of ex- posure for each of the types of services. In these life tables one can deal with the constraints that death, or consumption of other types of services, represents. An illustration of these types of life tables is presented in Figure 4.

    FIGURE 4. HOSPITAL EPISODES: unavailable at the time of HTML conversion--will be added at a later date.

    What we have done is to plot the proportion of persons who stay a given number of days in hospitals in 1982 and in 1984 for (a) community disabled persons, (b) persons in institutions at the time of the survey, (c) non-disabled, non-institutionalized persons, and (d) a residual group of non-respondents. To read the table note that the vertical axis describes the proportion of all hospital admissions in the specified year who were still in the hospital after X days. The horizontal axis represents the hospital LOS in days. We see that, in both 1982 and 1984, all groups experienced a decline in LOS and that non-disabled persons had the shortest stays, community disabled the next shortest stays and institutionalized persons the third longest stays, i.e., the hospital LOS was correlated with severity of the chronic conditions the person had. In calculating such tables one has to be careful to post-weight observations, and to deal with "exposure" weights based upon when the survey was conducted in the year, or misleading results may be produced.

    These life tables can be used to describe how different types of services are used in a specified period of time after adjusting for exposure differences. To combine that longitudinal information with data from the surveys on chronic functional and health problems can be done by certain multivariate procedures.

    VI. FILE STRUCTURE AND ACCESS

    The public use form of the NLTCS is found on two separate tape files. The first is a rectangular tape file which contains, on a person based record, all survey data for persons in the longitudinal two cross-sectional samples (N=25,401). A rectangular file was created in order to facilitate the processing of the tape by persons with limited programming resources. That is, the original version of the file released from the Census Bureau has six record types (sample person, caregiver/children, and household members, separate by survey year). Each of these files has different numbers of cases. In order to use the data from the caregiver file with data on the individual, one had to write a program linking across the two record types. In the current file all record types are already linked together in one large record. Clearly we have attempted to trade off between a less compact form of data storage (there will be many more blank fields in such a rectangularized file) for greater ease in processing.

    The second type of file is that containing the Medicare Part A bill files. This file is not person based since there can be up to 120 bills for a given person. Rather this file is bill-based and retains all the data (e.g., exact beginning and end dates) for each service episode. Certain standard edits have been performed on this file. Within each individual the bills are sorted by transaction type, then admission date. Thus, to use the bill data with the survey record one must perform the linkage operation using special survey identifiers that are found on both files.

    The two files are available separately in EBCDIC form on 6250 bpi tape with standard labels from NTIS. The documentation includes instructions to the interviewers, copies of all survey instruments, instructions on sample weight calculations, and detailed codebooks on the rectangular survey and bill files. In addition there is a technical write-up on the creation of the file and its logical structure.

    VII. SUMMARY

    The 1982 and 1984 NLTCS are large nationally representative surveys of a target population at high risk for high levels of both acute and LTC services. In addition the survey files are linked to Medicare Part A records. These surveys and linked files represent an extremely rich data source describing all the health and functional transitions of individuals in this population, detailed characteristics on the person at the time of the survey and detailed data on informal caregivers. These files can provide extremely valuable information on the service needs of the LTC population, changes in those needs and associated acute care needs.

    II. NATIONAL LONG TERM CARE CHANNELING DEMONSTRATION

    Judith Wooldridge, George Carcagno, Shari Miller Dunstan, and Nancy Holden, Mathematica Policy Research

    Peter Kemper, National Center for Health Services Research, U.S. Department of Health and Human Services

    I. OVERVIEW

    In September 1980 the National Long Term Care (LTC) Demonstration--known as channeling-- was initiated by three units of the United States Department of Health and Human Services--the Office of the Assistant Secretary for Planning and Evaluation (ASPE), the Administration on Aging, and the Health Care Financing Administration. It was to be a rigorous test of comprehensive case management of community care as a way to contain the rapidly increasing costs of LTC for the impaired elderly while providing adequate care to those in need.

    A. The Intervention

    Channeling was designed to use comprehensive case management to allocate community services appropriately to the frail elderly in need of LTC. The specific goal was to enable elderly persons, whenever appropriate, to stay in their own homes rather than entering nursing homes. Channeling financed direct community services, to a lesser or greater degree according to the channeling model, but always as part of a comprehensive plan for care in the community. It had no direct control over medical or nursing home expenditures.

    Channeling was implemented to work through local channeling projects. The core of the intervention (i.e., case management) consisted of seven features:

    • Outreach to identify and attract potential clients who were at high risk of entering a LTC institution.

    • Standardized eligibility screening to determine whether an applicant met the following preestablished criteria:
      • Age: had to be 65 years or older.
      • Functional disability: had to have two moderate disabilities in performing activities of daily living (ADL), or three severe impairments in ability to perform instrumental activities of daily living (IADL), or two severe IADL impairments and one severe ADL disability. Cognitive or behavioral difficulties affecting ability to perform ADL could count as one of the severe IADL impairments.
      • Unmet needs: had to have an unmet need (expected to last for at least six months) for two or more services or an informal support system in danger of collapse.
      • Residence: had to be living in the community or (if institutionalized) certified as likely to be discharged within three months.
      • Medicare coverage: for the financial control model, had to be eligible for Medicare Part A.

    • Comprehensive inperson assessment to identify individual client problems, resources, and service needs in preparation for developing a care plan.

    • Initial care planning to specify the types and amounts of care required to meet the identified needs of clients.

    • Service arrangement to implement the care plan through the provision of both formal and informal in-home and community services.

    • Ongoing monitoring to ensure that services were appropriately delivered and continued to meet client needs.

    • Periodic reassessment to adjust care plans to changing client needs.

    Two models of channeling were tested. The basic case management model relied primarily on the core features. The channeling project assumed responsibility for helping clients gain access to needed services and for coordinating the services of multiple providers. This model provided a small amount of additional funding to purchase direct services to fill in gaps in existing programs. But it relied primarily on what was already available in each community, thus testing the premise that the major difficulties in the current system were problems of information and coordination which could be solved largely by client-centered case management.

    The financial control model differed from the basic model in several ways:

    • It expanded service coverage to include a broad range of community services.

    • It established a funds pool to ensure that services could be allocated on the basis of need and appropriateness rather than on the eligibility requirements of specific categorical programs.

    • It empowered case managers to authorize the amount, duration, and scope of services paid out of the funds pool, making them accountable for the full package of community services.

    • It imposed two limits on expenditures from the funds pool. First, for the entire caseload, average estimated expenditures under care plans could not exceed 60 percent of the average nursing home rate in the area. Second, for an individual client, estimated care plan expenditures could not exceed 85 percent of that rate without special approval.

    • It required clients to share in the cost of services if their income exceeded 200 percent of the state's Supplemental Security Income (SSI) eligibility level plus the food stamp bonus amount.

    Ten sites participated in the demonstration. Their model designations were:

    Basic Case Management Model Financial Control Model
    Baltimore, Maryland Cleveland, Ohio
    Eastern Kentucky Greater Lynn, Massachusetts
    Houston, Texas Miami, Florida
    Middlesex County, New Jersey Philadelphia, Pennsylvania
    Southern Maine Rensselaer County, New York

    The ten local projects opened their doors to clients between February and June of 1982, and were fully operational through June of 1984.

    The goal of the evaluation, in addition to documenting the implementation of channeling, was to identify its effect on:

    • The use of formal health and LTC services, particularly hospital, nursing home, and community services.

    • Public and private expenditures for health services and LTC.

    • Individual outcomes, including mortality, physical functioning, unmet service needs, and social/psychological well-being.

    • Caregiving by family and friends, including the amount of care provided, the amount of financial support provided, and caregiver stress, satisfaction, and well-being.

    To compare channeling's outcomes with what would have happened in the absence of channeling, the evaluation relied on an experimental design. Applicants found eligible for channeling were randomly assigned either to a treatment group or to a control group. In all, 6,341 persons were randomly assigned.

    Several data sources were used. These included telephone and in-person surveys of the elderly members of the research sample and, for a subset, their primary informal caregivers; Medicare, Medicaid, channeling project, and provider records; and official death records obtained from state agencies. Finally, federal, state, local, and project staff were interviewed about the implementation and operation of the demonstration (these data are not included in the public use files).

    B. The Nature of the Data

    Some researchers will want to use the data to replicate the channeling results or explore certain issues in greater depth. They will simply have to master the complexity of the data base. Others will be interested in using the data to support efforts far removed from the original purposes of the evaluation. This section is written primarily for this latter group.

    The channeling sample was designed to support the evaluation, it was not designed to be a statistically representative sample of the elderly. The sample consists of frail persons who voluntarily applied to channeling and were found to meet the demonstration's eligibility criteria. Channeling sought referral sources and engaged in outreach activities to identify applicants at risk of institutionalization. Hospitals, home health agencies and social service providers were the major referral sources. A breakdown of the referral sources is presented in Table 1.

    TABLE 1. Referral Sources of Persons Screened as Eligible for Channeling (percent)
    Referral Source Basic Case Management Model Financial Control Model All Sites
    Health-Service Provider
    • Hospital
    19.4 26.0 22.7
    • Home health agency
    11.3 22.4 16.9
    • Nursing home a
    2.4 1.6 2.0
    Family/friend/self 34.8 22.1 28.4
    Social Service Agencies
    • Senior center/nutrition
    3.4 9.0 6.2
    • Casework/case management
    5.8 4.7 5.3
    • Welfare/Medicaid
    5.1 2.3 3.7
    • Information and referral
    4.5 0.8 2.6
    Channeling Outreach 1.0 2.8 1.9
    Other b 12.2 8.3 10.3
    Total 100.0 100.0 100.0
    SAMPE SIZES: Basic model 3,336; financial model 3,386.
    1. Includes referrals from nursing home preadmission screens, which accounted for 0.6 percent of total referrals, and nursing home waiting lists, which accounted for 0.3 percent of total referrals.
    2. Includes referrals from physicians, homemaker services, home-delivered meals agencies, psychiatric facilities, counseling services, legal advocacy services, adult day care, and a category simply recorded as other.

    To determine whether channeling participants were similar to the national population of the disabled elderly, we compared the baseline characteristics of the channeling sample with a nationally representative sample of the elderly. Using data from the National Long Term Care Survey (NLTCS), we simulated channeling's eligibility process to identify a subsample who were eligible for channeling. The simulation was done by selecting individuals who would have qualified according to the channeling ADL or IADL criterion. We thus ended up with a subsample from the NLTCS who, at least according to the measures of functioning, resembled the channeling sample. On the basis of that simulation, we estimated that 4.9 percent of the total noninstitutionalized population age 65 or over in 1982 qualified for channeling.

    The main differences between the channeling sample and the simulated nationally eligible sample were in living arrangements, income, and formal service use (see Table 2). Channeling clients were more likely to live alone and less likely to be married. Their use of regular informal in-home care was about the same as for the simulated national sample. The income of the channeling sample was lower than the income of the national sample. Substantial differences were found in every measure of formal service use. Before the receipt of channeling services, compared to the simulated national sample, channeling sample members were almost twice as likely to be receiving formal in-home services, more than twice as likely to have had a hospital stay in the last two months, more than six times as likely to have been in a nursing home, and almost five times as likely to have been on a nursing home waiting list. These differences provide strong support for the argument that persons often came to the attention of channeling because of some precipitating event and were probably more closely connected with the community care system as a result. The occurrence of such an event may have been a major factor that differentiated those who applied for channeling from those who did not.

    TABLE 2. Characteristics of Channeling Sample Compared with Nationally Simulated Sample That Was Functionally Eligible for Channeling
    - Channeling Simulated National Eligible Sample
    Functioning
    ADL (percent)
    • Eating
    25.0 20.6
    • Transfer
    52.7 45.2
    • Toileting
    56.3 41.3
    • Dressing
    60.6 63.9
    • Bathing
    78.8 86.2
    IADL (percent)
    • Meals
    88.0 78.9
    • Housekeeping
    97.4 68.3
    • Shopping
    95.6 92.7
    • Money management
    70.0 62.1
    • Telephone use
    54.6 46.3
    Incontinent (percent) 53.1 53.8
    Mental functioning (number incorrect 1-10) 3.5 2.3
    Informal Supports
    Pecent living alone 37.2 16.6
    Regular informal in-home care (percent) 92.0 96.0
    Percent married 32.4 46.1
    Demographics
    Mean age 79.7 78.5
    Percent female 71.3 63.0
    Monthly income (dollars) 570 644
    Formal Service Use
    Any formal in-home care (percent) 60.6 33.9
    Any hospital stays (last two months) 48.7 20.1
    Any nursing home admissions (last two months) 5.9 0.9
    Percent on nursing home wait list 6.8 1.4

    We also compared the demographic and economic characteristics of the entire aged population in the channeling sites with the characteristics of the entire aged population of the country. As a group, the demonstration sites were broadly similar to the nation as a whole. The only characteristics on which they differed markedly was the proportion who were of Hispanic origin (4.6 percent of the channeling sample were Hispanic, compared with 2.7 percent of the national elderly population). This resulted mainly from the fact that a third of the aged individuals in Miami were of Hispanic origin. Despite the general correspondence with national data, as one might expect, there was substantial variation across sites and models.

    With respect to the economic resources of the aged population in the channeling sites, monthly median family income was similar to the national data, although there were more people below the poverty threshold in the basic model than in the United States as a whole, and fewer below that level in the financial control model. The proportion of aged in the demonstration states enrolled in Medicare and monthly Medicare expenditures per aged resident were similar to the national data. For Medicaid participation, the basic states had slightly more people receiving Medicaid, and the financial control states somewhat less, than the national average. Monthly Medicaid expenditures per aged resident were somewhat less than the national average in the basic model states and somewhat greater in the financial control states because of high expenditures in New York and Massachusetts.

    To this point the focus has been on comparing the characteristics of the research sample and the aged population in the channeling sites to the national elderly population. What about the sites' service environments, were they broadly representative of the service environments throughout the country? This is a tougher issue to address because comparative data are not readily available. On the basis of an examination of nursing home bed supply data and data on waiting times to nursing home admission collected in the demonstration, we concluded that nursing home beds were probably somewhat less available in the channeling sites than in the nation, although we do not believe this had a major effect on demonstration outcomes.

    Data on the availability of community care are even more limited. We do know that there was substantial control group use of both comprehensive case management services (10-20 percent) and of community in-home services (60-69 percent). The demonstration projects applied to participate in the demonstration and were selected through a competitive process, and it could be that their case management and community care systems were more developed than those in other sites. Users of the data base should consider whether such differences could affect their research results.

    Taken together, these comparisons indicate that, even though the channeling sample is not a statistically representative sample of the frail elderly, the data can be used for applications unrelated to the evaluation as long as the differences that do exist between the channeling sample and one that would be broadly representative are not central to a particular application, and as long as careful attention is paid to the limitations of the data set.

    In using the data, the treatment and control groups can be exploited in useful ways. The control group data tell us what occurred in the sites in the absence of channeling. For example, the control group data reveal what services people who were eligible for channeling were using at the time channeling was in operation. The treatment group data indicate what services people used in response to the channeling intervention, although for this purpose, the model differences are obviously critical. These data could be used as the basis for estimating, for example, the cost of a new benefit, although such an exercise requires using a great deal of judgment in evaluating the similarities and differences between channeling and its participating population and whatever program and population are being analyzed. Furthermore, estimates of program participation must be made, a critical task which cannot be addressed using the channeling data. If possible, when using the channeling data for purposes unrelated to evaluating channeling, other data sources should be used and care taken to evaluate the effects of changing key assumptions.

    The channeling data base is very comprehensive and detailed. In exchange for that richness, one gives up representativeness. Nevertheless, it can be a very useful source of data in support of applications far removed from the evaluation of channeling.

    II. DEVELOPMENT OF THE DATA BASE

    One of the goals of the evaluation was to produce data for public use after the initial evaluation was completed. Thus, in developing the project data files and documentation, we always had the outside uses in mind. The principles followed in selecting data for public use were as follows:

    1. All data that were used in the analyses should be available so that evaluation analyses could be replicated from the files.

    2. Principle (1) should be achieved subject to maintaining the confidentiality of data.

    3. As much data as possible should be included in the public use files.

    Although the preparation of the public use files was always planned, their implementation occurred over the last six months of the project, to ensure that all variables used in analyses were available and documented for inclusion in the files.

    The public use files were prepared from the project's data base. This data base was designed in the first 18 months of the project, and implemented in March 1982 at the time that screening and sample member randomization began. As new instruments were designed, pretested, and cleared by OMB, the data base was expanded to include new files.

    Sample intake occurred over a period of 15 months, and primary data collection continued until July 1984, providing up to 18 months of followup. Secondary data associated with the followup period (claims data, death records, and provider records) continued to be acquired over the ensuing four months.

    III. SAMPLE

    A. Sample Design

    To compare the outcomes of channeling with what would have happened in the absence of channeling, the evaluation relied on an experimental design. Elderly persons who were referred to each channeling project were interviewed (most by telephone) by the project staff to determine their eligibility for channeling. If eligible, they were randomly assigned either to a treatment group, whose members could participate in channeling, or to a control group, whose members continued to rely on whatever services were otherwise available in their community. A total of 6,341 persons were randomly assigned to the two models of channeling. Given the substantial death rate among this population, as well as interview noncompletion, this yielded research samples of varying sizes, depending on the analysis. Table 3 shows the maximum sample sizes available for different subject areas for each model.

    TABLE 3. Subject Areas, Data Sources, and Maximum Sample Sizes
    - Primary Data Sources Maximum Sample Sizes
    Basic Case Management Financial Control
    6 Months 12 Months 18 Months 6 Months 12 Months 18 Months
    Formal Community Care Individual Interviews 1647 1377 520 1803 1475 546
    Nursing Home Use Medicare/Medicaid Records, Provider Records 2184 1876 741 2409 2023 774
    Hospitals and Other Medical Services Medicare/Medicaid Records, Provider Records 2712 2291 1037 2842 2406 1017
    Client Quality of Life Individual Interviews 1937 1671 647 2061 1745 668
    Mortality Death Records Searches 3124 3124 1619 3202 3202 1546
    Caregiver Quality of Life Caregiver Interviews 515 401 --- a 612 469 --- a
    Costs Medicare/Medicaid Records, Provider Records, Channeling Project Cost Records, Individual Interviews --- b --- b --- b --- b --- b --- b
    Informal Care Individual Interviews 1605 1345 510 1767 1456 534
    Caregiver Interviews 515 401 --- a 612 469 --- a
    NOTE: Maximum sample sizes are the number of observations available for analysis in each area, except for a small number of observations lost due to item nonresponse for some measures.
    1. Informal Caregiver Survey was not repeated at 18 months.
    2. The cost analysis combines estimates from the analyses of the other subject areas.

    B. Sample Characteristics

    The channeling sample was elderly and frail, with severe functional, health, social, and financial problems. This was by design; the eligibility criteria for the sample were intended to identify persons who were at risk of nursing home placement. The characteristics of the channeling clients at baseline are shown in Table 4.

    TABLE 4. Characteristics of Channeling Treatment Group at Baseline
    - Basic Case Management Model Financial Control Model All Sites
    Health and Functioning
    Any disability in ADL (percent) 83.4 84.2 83.9
    Number of ADL disabilities (maximum 5) 2.7 2.8 2.7
    Incontinent (percent) 52.5 53.6 53.1
    Any impairment in IADL (percent) 99.5 99.8 99.7
    Mental functioning (number incorrect on 10-item scale) 3.4 3.5 3.5
    Days restricted to bed in last two months 19.5 20.1 19.8
    Sociodemographic Characteristics
    Living alone (percent) 35.1 39.1 37.2
    Age (years) 79.2 80.1 79.7
    Ethnic group (percent white) 75.6 71.1 73.3
    Sex (percent female) 71.9 70.6 71.2
    Married (percent) 31.9 32.9 32.4
    Income and Assets
    Monthly income (dollars) 567 572 570
    Owns home (percent) 44.7 38.9 41.7
    No assets other than home (percent) 59.4 55.1 57.2
    Medicaid coverage (percent) 20.4 23.7 22.1
    Life Quality
    Stressful life event in past year (percent)      
    Often lonely (percent) 27.0 25.7 26.3
    No social contacts in past week (percent) 9.4 10.2 9.8
    Number of unmet needs (maximum 8) 3.3 4.0 3.7
    Not very satisfied with life (percent) 39.5 47.4 43.7
    Waitlisted or applied to nursing home (percent) 7.3 6.3 6.8
    Unwilling to go into nursing home (percent) 63.4 67.3 65.5
    Prior Service Use
    Case management received (percent) 8.8 16.9 13.1
    Regular formal in-home care (percent) 57.4 63.5 60.6
    Regular informal in-home care (percent) 92.5 92.0 92.2
    Hospital admission, past two months (percent) 47.2 49.9 48.7
    SAMPLE SIZES: Basic model 1,638; financial model 1,815.

    IV. DATA SOURCES AND DATA COLLECTION PROCEDURES

    Both primary and secondary data were collected for the channeling evaluation. These data were collected from the following sources:

    • Interviews with sample members and proxies
    • Interviews with informal caregivers
    • Provider records
    • Medicare and Medicaid claims
    • Death records
    • Client tracking records
    • Financial control system at five sites.

    This section reviews the primary data collection and provider records extracting procedures.1 The data collection through surveys, the means of collection, and the interviewer auspices were as follows:

    • Screen, telephone interview, administered by demonstration screening unit.

    • Sample member baseline, personal interview, administered by demonstration assessment staff (for clients) or MPR interviewers (for controls).

    • Sample member followup interviews (6, 12, and 18 months), personal or telephone interview, administered by MPR interviewers.

    • Caregiver baseline and followup interviews (6 and 12 months), personal or telephone interview, administered by MPR interviewers.

    • Provider record extracts, collected by trained extractors.

    Training materials and procedures for the baseline were developed by MPR in conjunction with Temple University, the demonstration's technical assistance contractor, and all trainers were trained by MPR and Temple staff. Demonstration screen and sample member (client) baseline interviewers were trained by Temple University. All MPR interviewers and extractors were trained by MPR project staff. To assess the comparability of the baselines administered by demonstration and MPR staff, a random validation sample of clients was administered a baseline by both demonstration and MPR staff.

    Training was intensive for all instruments. For example, MPR baseline interviewers received five days of training that covered the instruments, procedures, sensitivity training, practice sessions, and evaluation of interviews. The demonstration baseline interviewer staff received the same training plus instruction on collecting clinical data for case management. Sample member followup interviewers were provided with comparable training, augmented by special training on searching for respondents and determining living arrangements. For the caregiver instrument, additional special training was provided on techniques for identifying appropriate caregivers, procedures to follow if the sample member was deceased, and techniques for telephone interviewing. Provider records extractor training covered the use of a provider characteristics instrument and procedures for extracting service use, charge, and reimbursement data from provider records. Respondent payments were made to control group members for baseline interviews and all sample members for followup interviews.

    Completion rates for the interviews and provider records extraction are listed in Table 5. If deceased sample members are removed from the calculation, the response rates were uniformly high, at 76 percent or more.

    TABLE 5. Completion Rates by Instrument
    Instrument Treatment Control Total
    Sample Members Baseline 93.3 82.9 88.9
    6-Month Followup 75.2 a 73.4 a 74.5 a
    12-Month Followup 65.8 a 62.7 a 64.6 a
    18-Month Followup 56.6 a 55.0 a 55.9 a
    Caregivers Baseline 71.2 83.6 76.0
    6-Month Followup 82.4 77.7 80.4
    12-Month Followup 82.4 75.6 79.4
    Community Service Provider Records Extract --- --- 85.5
    Institutional Service Provider Records Extract --- --- 82.8
    1. The major reason for nonresponse was that the sample member had died. The percent of the sample not responding because deceased was 16.5 percent at 6 months, 26.6 percent at 12 months, and 40.9 percent at 18 months.

    V. DATA PROCESSING

    Each of the public use files was derived from one or more data base masterfiles, which, in turn, were created and maintained according to a standard set of procedures. These procedures transformed source data from data entry files into a structured data set, edited the data, and created a set of constructed variables.

    A. Interview Data Procedures

    Because these data were collected over a period ranging from one to two-and-a-half years, depending upon the data source, data were regularly added to the data base. Each cycle of data processing included the following steps: quality control checks of hard-copy interview forms; data entry; transmission of data to the research data base; quality control checks of computerized data; and the updating of both the status file and the existing masterfiles. Figure 1 summarizes these components.

    FIGURE 1. Standard Data Manipulation Procedures: unavailable at the time of HTML conversion--will be added at a later date.

    Quality Control and Data Entry. Completed instruments were manually edited and coded by trained quality control staff. This included checking the legibility of contact information, assigning codes to open-ended and "other, specify" responses, and reviewing key questions to ensure they were properly recorded. If necessary, project staff or respondents were contacted to resolve problems.

    After the documents had been read, they were entered into the computer. Automated skip logic, range, and consistency checks were performed as part of the key entry program. When errors were found, a trained data cleaner reviewed the instrument (if necessary, the cleaner telephoned the respondent or interviewer) and corrected the error in the instrument and on the file. Finally, once a batch of interviews had passed the skip logic, range, and consistency checks, the batch was verified by reentry.

    Data Transmission and Initial Processing. Every month, all data-entered instruments were transmitted to the mainframe computer in Extended Binary-Coded-Decimal Interchange Code (EBCDIC) format. During the initial processing of the newly transmitted data, this file was transformed into a structured intermediate SAS data set. The project status file was also updated at this time with new status information. In addition, the intermediate file picked up the data base ID and randomization information from the status file for each record. Finally, confidential variables (such as Medicare and Medicaid numbers) were added to a separate Medicare/Medicaid status monitoring file.

    Frequency distributions and other descriptive statistics were generated for each variable. In addition, range checks and further checks on consistency which were beyond the capacity of the data entry program were performed. For example, in processing each file, we printed selected variables for cases which appeared to have more than one interview (such as a complete and an incomplete interview).

    Data Entry. Potential errors identified through a review of descriptive statistics were resolved by reviewing the hard-copy questionnaire and/or consulting with the quality control staff (who recontacted the interviewer or respondent when necessary). Some "errors" (for example, some out-of-range responses) proved to be correct, and the values were retained in the data base. The frequency and nature of each type of error were documented, as was its resolution. In this way, resolution decisions could be consistent and based on precedent, where applicable.

    Masterfile Maintenance and Updating. After inconsistencies in the intermediate file were resolved, the current masterfile was updated with the new observations. For each of the new observations added to the masterfile, certain descriptive variable values were converted into standard binary codes. Once the masterfile had been updated with the new observations, frequencies of the new masterfile were produced, reviewed, and distributed periodically to the research staff.

    Once-Only Procedures. After all the completed research sample interviews had been processed and added to a masterfile via the process outlined above, a final review of the complete masterfile was undertaken. The same range and consistency checks used in initial processing were applied to the complete masterfile. In addition, descriptive statistics of all variables in the final masterfile were closely reviewed and distributed to research staff.

    Other Data. Comparable procedures were followed in processing secondary data into masterfiles.

    B. Developing Analysis Files

    One of the products generated from the data base masterfiles was a set of analysis files--files which contain only the sample and variables of interest for a particular analysis. Because analysis files are smaller than masterfiles, their contents can easily be accessed for use in statistical analysis procedures. Some of the public use files were generated from analysis files. This section describes three steps in the creation of the research data base analysis files.

    Selecting the Samples. A set of standard samples was defined in order to facilitate consistency across analyses. Once a standard sample was defined, a binary variable, or "sample flag," was created and permanently stored in the status file, facilitating the selection of the standard sample for use with any masterfile. In addition, since some analyses used subsets of the standard samples, many individual analysis files contained sample flags and data for several samples, allowing analyses of several samples.

    Specifying and Programming Constructed Variables. Initial specifications of constructed analysis variables (both dependent and independent) were prepared by the analysts. These preliminary specifications were reviewed and modified by the research data base staff, in consultation with the analysts. Constructed variables were programmed, variable labels defined, and descriptive statistics produced and reviewed for each variable.

    Extracting and Merging Data from Masterfile. Analysis files were generally "extracts" (i.e., sub- sets) of masterfiles. These extracts were based on defined samples that were selected using standard sample flags. However, some analysis files required data from more than one masterfile (for example, client tracking and status change masterfiles and caregiver and sample member masterfiles). In these cases, extracts of each file were merged together, so that a single case contained the correct information from each file.

    VI. PUBLIC USE FILES

    There are 14 separate public use files, nine based on survey instruments, four based on analysis files and the project status file. Table 6 summarizes the content of the files at a general level and describes the purposes for which the data were collected.

    TABLE 6. Summary of Public-Use File Contents
    Public-Use File Sample Description Focus of Data Reports Contributing Analytical Variables
    Screen Applicants randomized during the the caseload buildup phase. a The major purpose of the screening assessment was to determine whether an applicants was eligible to participate. It was intended to identify those at risk of institutionalization, focusing on age, place of residence, interest in participating, institutionalization status, functional impairment in performing activities of daily living (ADL) and instrumental activities of daily living (IADL), fragility of support system, and unmet needs. b
    Baseline Applicants randomized during the caseload buildup phase who completed both a screen and a baseline instrument. The major purpose of the baseline instrument was to collect data on sample members at the point of enrollment, measuring functioning, health status, recent service use, informal caregiving, financial resources, demographic factors, and unmet needs. b
    Client Tracking/Status Change All channeling clients. c The client tracking and status change forms were designed to allow client progress to be monitored through the channeling service system, as well as caseload size. The client tracking form included dates of referral to channeling, screening, randomization, care plan completion, and service initiation; the status change forms collected dates of change from active to inactive or terminated status and reasons for change of status.
    • Thornton, Will, and Davies, 1986.
    • Carcagno, et al., 1986.
    Sample Member Followup: 6-Month File, 12-Month File, and 18-Month File Applicants randomized into the research sample during the caseload buildup phase who also completed a screening interview, a baseline interview, and a followup interview. The purpose of the followup interviews was to collect outcome data at 6, 12, and 18 months after enrollment. Outcomes included insurance coverage, health status, housing conditions, expenditures, related transfers and services, in-home service use and support, formal community service use, hospital and nursing home use, social and psychological well-being, income and assets, and functioning. -
    Status File Applicants randomized during the caseload buildup phase. a The status file stores information about interview dates and completion (both complete and non-complete) status for each sample member, sample flags d, and information obtained from the death records search and Medicare and Medicaid entitlement checks.
    • Wooldridge and Schore, 1986.
    Caregiver Baseline Primary informal caregivers (to a subset of those sample members included in the screen public-use file e) who completed a caregiver baseline interview. The caregiver baseline measured the amount of various types of informal services provided to the elderly sample members, the provision of financial contributions by informal caregivers, the economic and family behavior of informal caregivers, and caregiver psychological and social well-being.
    • Christianson and Stephens, 1984.
    • Christianson, 1986.
    Caregiver Followups: 6-Month File and 12-Month File Primary informal caregivers (to a subset of those sample members included in the screen public-use file e) who completed a 6- or 12-month caregiver interview. The primary purpose was to collect data to assess the impacts of channeling on informal caregivers of elderly sample members. Questions focus on care by primary informal caregiver, as well as from other caregivers, the provision of financial contributions by informal caregivers, caregiving since institutionalization, formal services utilization prior to the death of the elderly sample member, caregiver well-being, and demographic and employment information.
    • Christianson, 1986.
    Formal Community Services (including Case Management and Housing and Transfers) Analysis File Individuals who were members of at least one of the analysis samples on which these analyses were based. The formal community services analysis file was developed from the sample member followup interviews, provider record extracts, surveys of privately contracted individuals, financial records from the sites, and Medicare and Medicaid claims. It includes information on the use of all major community services and expenditures for these services, by funding source.
    • Corson et al., 1986.
    • Thornton and Dunstan, 1986.
    • Brown and Phillips, 1986.
    Informal Care Analysis File Individuals who were members of at least one of the analysis samples on which this analysis was based. The informal care analysis file was developed from the sample member followup interviews. It includes information on the types and amounts of services provided by informal caregivers and the relationship of caregivers to sample members.
    • Christianson, 1986.
    Hospital, Nursing Home, and Other Medical Services Analysis File Individiuals who were members of at least one of the analysis samples on which this analysis was based. Specifically, this file consists of persons who completed a baseline interview and who were known to be either Medicare entitled or not Medicare entitled. This file was developed from data obtained from Medicare and Medicaid claims, provider records extracts, and sample member followup interviews. Contained in this file is information on hospital, nursing home, and other medical service use and expenditures, by funding source.
    • Wooldridge and Schore, 1986.
    Quality of Life Analysis File Individuals who were members of at least one of the analysis samples on which this analysis was based. The quality of life analysis file was developed from the sample member followup interviews. It includes information on elderly sample member satisfaction with care, social-psychological well-being, and functioning.
    • Applebaum and Harrigan, 1986.
    1. The caseload buildup phase began in March, May, or June 1982 and ended in May or June 1983, depending on the channeling project. There were applicants randomized during this phase who were not included in this sample; namely, those applicants who were members of the household of a treatment group client, fourteen cases whose screening survey instrument was lost in the mail, and one individual who was eliminated from all samples because, although assigned to the control group, the individual received channeling services.
    2. Screen and baseline standard control variables (contained in this file, as well as in most of the other files) were used in most of the reports listed below.
    3. Note that this sample includes clients who were not included in the samples used for the impact anlaysis--those individuals who enrolled before randomization began, those who were members of the household of a treatment client, and those who enrolled after the research sample size had been achieved and random assignment ceased.
    4. Sample flags are binary variables indicating membership in a survey or analysis sample.
    5. The caregiver subsample includes the caregivers named by the elderly sample members who were enrolled during an approximately six-month period beginning in November, 1982.

    Confidentiality Precautions. For confidentiality reasons, some identifying variables were excluded from the public use files. Thus, for example, names and addresses and Medicare and Medicaid ID numbers were not included. Since the user must have a method for linking information on individuals across files, unique ID numbers are used to identify individual data. The ID numbers on the public use files are different from those used by MPR for fielding and analysis.

    Additional confidentiality measures were taken. In order to avoid the possibility that one or more data items could identify, or nearly identify, an individual in a particular site, we reviewed the data and modified some fields. Variables indicating age were transferred from actual ages into age categories. In sites with very small minority populations, ethnicity was recorded by combining categories. In addition, calendar dates are not provided on the files. Instead, each data variable has been converted into a variable that indicates the number of days, screen randomization and the date of the event. Finally, we deleted information on legal guardianship.

    Public Use File Formats. We converted the SAS data sets into sequential (EBCDIC) public use files from which each variable can be accessed by its column position, thus making the file readable in virtually every mainframe computer system.

    Documentation Available. The public use file documentation consists of eleven reports, each of which documents one or more public use files. Each of the eleven reports contains three chapters:

    I. Introduction (common to all reports)

    II. Description of File

    • The development of the file
    • The variables included
    • The analysis samples included

    III. File Documentation

    • File layout: the variables included in the file, by position and field length
    • Instrument: an annotated version of the survey instrument showing variable names assigned to each question (provided for source files only)
    • Constructed variable documentation: for each constructed variable a form is provided that describes how the variable was constructed and the values for different categories
    • Descriptive statistics: frequency distributions and means for all variables included on the file.
    • Physical tape specifications: information necessary to read the tape and a dump of the contents of the first two records on the tape

    Copies of these 11 reports will be available for review at the breakout sessions of the conference.

    Physical Tape Specifications: Table 7 summarizes the physical specifications of the tapes. Note that all files are available on tape, in EBCDIC. Files are density 6250, fixed block, and no label.

    TABLE 7. Physical Specifications of Channeling Public Use files
    File Logical Record Length Block Size Number of Records Number of Variables
    Screen 471 4710 6326 182
    Sample Member Baseline 1754 1754 5626 803
    Informal Caregiver Baseline 1916 1916 1929 653
    Sample Member Followup
    • 6-month
    1857 1857 4189 782
    • 12-month
    1857 1857 3634 782
    • 18-month
    1857 1857 1409 782
    Client Tracking 312 15,600 7168 103
    Hospital, Nursing Home, Other Medical Services 1611 1611 5554 482
    Formal Community Services 2884 2884 5607 656
    Caregiver Followup
    • 6-month
    2590 25900 1667 755
    • 12-month
    2591 25910 1537 754
    Informal Care Analysis 1359 13590 5408 387
    Quality of Life 926 18520 4388 410
    Status 411 14385 6326 142
    All files are EBCDIC, fixed block, no label, 6250 BPI.

    Access. All tapes are available from NTIS. An NTIS price list will be available at the breakout session.

    VII. APPLICATIONS

    Although collected and structured for the specific purpose of evaluating the channeling demonstration, the resulting data can be used for a wide variety of other research. In this section, we briefly review completed and planned research, discuss potential applications, and caution users about some pitfalls.

    A. Completed and Planned Analyses

    Analyses conducted as part of the channeling evaluation and a followup study of targeting those at high risk of nursing home placement are now completed. A series of detailed technical reports on channeling document the results, data collection procedures, sample definitions, and methodology. Paragraph summaries of the reports and information on ordering them can be found in a summary the of Channeling Demonstration and an abstract list of reports available from the Office of Social Services Policy, Office of the Assistant Secretary for Planning and Evaluation, U.S. Department of Health and Human Services (DHHS), Room 410E, H.H. Humphrey Building, 200 Independence Avenue, S.W., Washington, D.C. 20201. The followup study of targeting is reported by Grannemann et al. (1986). It is currently not available for distribution.

    We are aware of the following analyses that are currently being undertaken with the public use files:

    • Robert Clark of the Office of the Assistant Secretary for Planning and Evaluation of DHHS: (1) estimation of the total cost of care, both public and private, with particular emphasis on out-of-pocket costs, and (2) analysis of service use by the oldest old.

    • Corbin Liu of the Urban Institute: analysis of the costs of care of older persons with cognitive impairments, both those in nursing homes and those in the community.

    • Peter Kemper of the National Center for Health Services Research of DHHS: analysis of the determinants of the use of formal and informal community care.

    • Jim Callahan and Phyllis Mutschler of Brandeis University: analysis of changes over one year in the use of all types of care by the control group.

    Other research will undoubtedly be initiated as more researchers obtain the public use files.

    B. Potential Applications

    The extensive data collected for the channeling evaluation create many research possibilities. Its special purpose--to form the basis of an evaluation--makes it better suited for some analyses than for others. Because it is a selected sample--data came from ten sites selected through competition, and the sample comprises applicants referred to a special community care program-descriptive analysis using the channeling data are less likely to be informative than analysis of the same questions using nationally representative data. The channeling data appear more appropriate for analyses that do not depend on representativeness but capitalize on the richness of the data or their original purpose.

    The channeling data appear most useful for analyses of behavioral relationships, methodological research, or re-analysis of experimental results. The extensive data on the well-being of the elderly sample, for example, is fertile ground for psychometric analysis of quality of life measures or analysis of the determinants of well-being, but would not be appropriate for an analysis of the extent of unmet need in the United States. To estimate the cost of community care and nursing home care for use in calculating premiums for LTC insurance, channeling data would have to be used in conjunction with other data (e.g., a nationally representative sample); otherwise, the estimates would pertain to the selected channeling sample rather than to likely purchasers of LTC insurance.

    C. Some Cautions

    Although the richness and comprehensiveness of the channeling data set open numerous research possibilities, researchers should be aware of their complexity. Researchers accustomed to using cross-section surveys designed to collect data on a population, rather than longitudinal data designed to evaluate a program, are likely to be surprised by the complexity of the channeling data. One researcher who was not involved in the evaluation but has begun using the channeling data remarked that this is "the most complicated data set [he had] ever seen." The complexity arises from the large number of data sources, the structure of the files, and the special evaluation sampling objectives.

    The data sources, as described above, are numerous: five interviews with the elderly sample members or their proxies (screen, baseline, and 6-, 12-, and 18-month followups), three interviews with primary informal caregivers (baseline and 6- and 12-month followups), Medicare claims, Medicaid claims, financial model channeling claims, provider billing records, client tracking data, and death records. The data set thus contains a massive amount of data. Variables can be constructed from more than one source, individually or in combination, so that researchers need to understand exactly how variables on the public use file are constructed before using them in analysis. Not all data are present in every case called for in the design--numerous data sources also provide numerous opportunities for missing data.

    Evaluation needs determined the way in which files were structured and variables were constructed. Files were organized, for example, by analytic area so that data needed for a particular analysis were all on one file. Because all analyses controlled for baseline characteristics, a standard set of baseline variables were included among constructed followup variables. Constructed variables also were defined to meet evaluation needs. For example, because some baseline data were not comparably measured for treatment and control groups, screen data were sometimes used when a baseline measure might be better for other purposes.

    Evaluation objectives also drove the hundreds of big and little decisions in the design of the data collection. Most important were the sampling decisions which optimized the usefulness of the data for evaluation purposes. Not all data were collected for all sample members. Indeed, the only data that are available for the entire sample are the screen and death records. Two examples will illustrate how sample design decisions could affect analysis possibilities. First, in order to limit the duration of the demonstration, only the first half of the sample to enroll were followed for 18 months. Longitudinal analysis must be limited, therefore, to 12 months of followup data or to the relatively small sample with 18 months of followup data. Second, in order to minimize data collection costs, provider billing records on community care costs were collected only for 20 percent of the sample for the first six months and 10 percent for the second six months. Consequently, data on the community service expenditures of private individuals and government programs other than Medicaid and Medicare are quite limited. Although these and other sample design decisions made sense in the evaluation, they may hinder the use of the data for other purposes.

    Before undertaking a project using the channeling data, it is suggested that researchers begin by assessing the implications of the complexity of the data base for their project by reading the following reports:

    • The final report (Kemper et al., 1986), to gain an overview of the evaluation design and available data

    • The report on data collection procedures (Phillips et al., 1986), to learn how the data were collected

    • The particular technical report on the relevant substantive area, to understand how analysis files were constructed, how samples were defined, what variables were constructed, and what analysis was done as part of the evaluation. (For example, researchers planning analysis of informal care should read the technical report on that subject by Christianson, 1986. In addition, those seeking to replicate the evaluation analysis should read the report on research methodology by Brown, 1986).

    Only after having assessed the complexity of the data and its implications for the contemplated research does it make sense to invest in the purchase of the public use tapes and associated documentation.

    REFERENCES

    Applebaum, Robert and Margaret Harrigan. "Channeling Effects on the Quality of Clients' Lives." Princeton, New Jersey: Mathematica Policy Research, 1986.

    Brown, Randall S. "Methodological Issues in the Evaluation of the National Long Term Care Demonstration." Princeton, New Jersey: Mathematica Policy Research, 1986. [http://aspe.hhs.gov/daltcp/reports/1986/methodes.htm]

    Brown, Randall S. and Barbara Phillips. "The Effects of Case Management and Community Services on the Impaired Elderly." Princeton, New Jersey: Mathematica Policy Research, 1986. [http://aspe.hhs.gov/daltcp/reports/1986/casmanes.htm]

    Carcagno, George, Robert Applebaum, Jon Christianson, Barbara Phillips, Craig Thornton, and Joanna Will. "The Evaluation of the National Long Term Care Demonstration: The Planning and Operational Experience of the Channeling Projects." Volume 1 and Volume 2. Princeton, New Jersey: Mathematica Policy Research, 1986. [http://aspe.hhs.gov/daltcp/reports/proceses.htm]

    Christianson, Jon B. "Channeling Effects on Informal Care." Princeton, New Jersey: Mathematica Policy Research, 1986.

    Christianson, Jon B. and Susan A. Stephens. "Informal Care to the Impaired Elderly: Report of the National Long Term Care Demonstration Survey of Informal Caregivers." Princeton, New Jersey: Mathematica Policy Research, 1984. [http://aspe.hhs.gov/daltcp/reports/impaires.htm]

    Corson, Walter, Thomas Grannemann, Nancy Holden, and Craig Thornton. "Channeling Effects on Formal Community Based Services and Housing." Princeton, New Jersey: Mathematica Policy Research, 1986. [http://aspe.hhs.gov/daltcp/reports/1986/commty.htm]

    Kemper, Peter, et al. "The Evaluation of the National Long Term Care Demonstration: Final Report." Princeton, New Jersey: Mathematica Policy Research, 1986.

    Phillips, Barbara, Susan Stephens, and Joanna Cerf. "The Evaluation of the National Long Term Care Demonstration: Survey Data Collection Design and Procedures." Princeton, New Jersey: Mathematica Policy Research, 1986. [http://aspe.hhs.gov/daltcp/reports/sydataes.htm]

    Thornton, Craig and Shari Miller Dunstan. "The Evaluation of the National Long Term Care Demonstration: Analysis of the Benefits and Costs of Channeling." Princeton, New Jersey: Mathematica Policy Research, 1986.

    Thornton, Craig, Joanna Will, and Mark Davies. "The Evaluation of the National Long Term Care Demonstration: Analysis of Channeling Project Costs." Princeton, New Jersey: Mathematica Policy Research, 1986.

    U.S. Department of Health and Human Services. Notice, "Privacy Act 1974; Report of a New System of Records." Federal Register 46, No. 234, December 7, 1981, 59640-59643.

    Wooldridge, Judith and Jennifer Schore. "Channeling Effects on Hospital, Nursing Home, and Other Medical Services." Princeton, New Jersey: Mathematica Policy Research, 1986.

    NOTES

    1. For full details on fielding procedures, see Phillips et al. (1986). Wooldridge and Schore (1986) document the collection of administrative records.

    III. NATIONAL HEALTH INTERVIEW SURVEY: 1984 SUPPLEMENT ON AGING

    NATIONAL HEALTH INTERVIEW SURVEY

    NCHS Division of Health Interview Statitistics

    I. BACKGROUND

    The National Health Interview Survey (NHIS) is the principal source of information on the health of the civilian noninstitutionalized population of the United States. The NHIS is one of the major data collection programs of the National Center for Health Statistics (NCHS). The National Health Survey Act of 1956 provided for a continuing survey and special studies to secure accurate and current statistical information on the amount, distribution, and effects of illness and disability in the U.S. and the services rendered for or because of such conditions. The survey referred to in the Act, now called the National Health Interview Survey, was initiated in July 1957. Since 1960, the survey has been conducted by NCHS, which was formed when the National Health Survey and the National Vital Statistics Division were combined.

    II. PURPOSE AND SCOPE

    The objective of the survey is to address major current health issues through the collection and analysis of data on the civilian noninstitutionalized population of the U.S. National data on the incidence of acute illness and injuries, the prevalence of chronic conditions and impairments, the extent of disability, the utilization of health care services, and other health-related topics are provided by the survey. A major strength of this survey lies in the ability to display these health characteristics by many demographic and socioeconomic characteristics.

    The NHIS data are obtained through personal interviews with household members. Interviews are conducted each week throughout the year in a probability sample of households. The interviewing is performed by a permanent staff of interviewers employed by the U.S. Bureau of the Census. Data collected over the period of a year form the basis for the development of annual estimates of the health characteristics of the population and for the analysis of trends in those characteristics.

    The survey covers the civilian noninstitutionalized population of the U.S. living at the time of the interview. Because of technical and logistical problems, several segments of the population are not included in the sample or int the estimates from the survey. Persons excluded are: patients in long-term care facilities; persons on active duty with the Armed Forces (though their dependents are included); and U.S. nationals living in foreign countries.

    III. SAMPLE DESIGN

    The NHIS is a cross-sectional household interview survey. Sampling and interviewing are continuous through each year. The sampling plan follows a multistage area probability design that permits the representative sampling of households. The first stage consists of a sample of about 200 primary sampling units (PSUs) drawn from approximately 1,900 geographically defined PSUs that cover the 50 States and the District of Columbia. A PSU consists of a county, a small group of contiguous counties, or a Metropolitan Statistical Area. Within PSUs, intermediate stage units called segments are defined in such a manner that each segment contains approximately 40 households. Within these segments, a systematic sample of eight households is selected for the NHIS sample.

    The NHIS sample implemented with the 1985 data collection year was a complete redesign from previous years. A feature that was added for the 1985 design is the formation of panels of PSUs. The total NHIS sample of PSUs is subdivided into four separate panels such that each panel is a representative sample of the U.S. population. This design feature has a number of advantages, including flexibility for the total sample size. The 1985 NHIS sample included three out of the four panels and the 1986 NHIS sample includes two panels.

    Another design feature implemented in 1985 is the over-sampling of black persons to improve the precision of estimates for that population. This resulted in an increase in the number of black persons in the NHIS sample by approximately 75 percent and an increase in the precision of most related statistics by more than 20 percent. The new sample design also facilitates followup studies of respondents and linkage with other national health-related data sets such as the National Death Index.

    The households selected for interview each week are a probability sample representative of the target population. With four sample panels, data are collected from approximately 50,000 households including about 135,000 persons in a calendar year. Participation is voluntary and confidentiality of responses is guaranteed. The annual response rate of NHIS is over 95 percent of the eligible households in the sample. The response is divided equally between refusals and households where no eligible respondent could be found at home after repeated calls.

    IV. DATA COLLECTION PROCEDURES

    Data are collected through a personal household interview conducted by interviewers employed and trained by the U.S. Bureau of the Census according to procedures specified by NCHS.

    All adult members of the household 17 years of age and over who are at home at the time of the interview are invited to participate and to respond for themselves. For children and for adults not at home during the interview, information is provided by a responsible adult family member (19 years of age and over) residing in the household. Between 65 and 70 percent of the adults 17 years of age and over are self-respondents. Generally, a random subsample of adult household members is selected to respond for themselves to questions on current health topics that are added each year.

    Nationally, there are approximately 150 interviewers, trained and directed by health survey supervisors in each of the 12 Census Bureau Regional Offices. The supervisors are career Civil Service employees whose primary responsibility is the NHIS. The interviewers are part-time employees, selected through an examination and testing process. Interviewers receive thorough training in basic interviewing procedures and in the concepts and procedures unique to the NHIS.

    Depending on the family size and the nature and extent of health conditions of family members, the length of interview ranges 20-90 minutes. On the average, the interviews require about 50 minutes in the household.

    V. CONTENT OF THE QUESTIONNAIRE

    The questionnaire consists of two basic parts: (1) a set of basic health and demographic items, and (2) one or more sets of questions on current health topics. The basic items constitute approximately 50 percent of the questionnaire and are repeated each year. These items provide continuous information on basic health variables. Questions on current health topics facilitate a response to changing needs for data and coverage of a wide variety of issues. This combination yields a unique national health data base.

    The questionnaire includes the following types of basic health and demographic questions:

    • Demographic characteristics of household members, including age, sex, race, education, and family income.

    • Disability days, including restricted-activity and bed-disability days, and work- and school-loss days occurring during the two-week period prior to the week of interview, as well as 12-month bed days.

    • Physician visits occurring during the same two-week period, interval since the last physician visit, and the number of visits in the last 12 months.

    • Acute and chronic conditions responsible for these days and visits.

    • Long-term limitation of activity resulting from chronic disease or impairment and the chronic conditions associated with the disability.

    • Short-stay hospitalization data, including the number of hospital episodes during the past year and the number of days for each stay.

    In addition, each of six representative subsamples is asked to respond to questions about one of six lists of selected chronic conditions.

    Questions on special health topics change in response to current interest and need for data. The 1983 questionnaire contained questions on alcohol, dental care, physician services, and health insurance. The 1984 current health topic questionnaire was devoted entirely to issues of aging, 1985 covered health promotion and disease prevention, and 1986 includes questions on health insurance, dental health, vitamin and mineral intake, longest job worked, and functional limitations. The 1987 NHIS includes an extensive questionnaire on cancer risk factors and questions on child adoption.

    Suggestions and requests for special health topics are solicited and received from many sources. These include university-based researchers, administrators of national organizations and programs in the private and public health sectors, and other parts of the U.S. Department of Health and Human Services such as the National Institutes of Health and the Centers for Disease Control. Topics are selected after consultation with agencies within the Public Health Service and after an assessment of priority health issues and the related need for population-based data. A lead time of at least 18 months is required to develop and pretest questions for new topics.


    NATIONAL HEALTH INTERVIEW SURVEY QUESTIONNAIRES

    NCHS Division of Health Interview Statitistics

    The objective of the National Health Interview Survey (NHIS) is to address major current health issues through the collection and analysis of data on the civilian noninstitutionalized population of the United States.

    The NHIS questionnaire consists of two basic parts: (1) a set of basic health and demographic items, and (2) one or more sets of questions on current health topics. The basic items constitute approximately 50 percent of the questionnaire and are repeated each year. This provides continuous national data on basic health topics and permits assissment of trends and changes in the health status and health-related characteristics of the population. The current health topics questionnaire generally changes each year. this facilitates a response to the need for population-based data on current or emerging health issues and coverage of a wide variety of topics. This combination yields a unique national health data base.

    I. Basic Health and Demographic Items

    The basic health and demographic questionnaire includes the following items:

    Demographics

    • Age; race; sex
    • Hispanic origin
    • Education
    • Family income
    • Martial status, family relationship
    • Region; SMSA or non-SMSA residence
    • Usual activity; industry and occupation; class of worker
    • Veteran status Respondent--self or proxy

    Health Status

    • Number of conditions--acute and chronic
    • Self-assessed health status
    • Limitation activity--degrees and duration
    • Restricted-activity days
    • Short-term disability days--bed days; work-loss days; school-loss days
    • Height and weight

    Health Care Utilization

    • Hospitalization--hospital days
    • Doctor visits--interval since last visit

    Extensive information is obtained about each condition, each hospital stay in the past 12 months, and each doctor visit in the past two months.

    II. Current Health Topics

    Suggestions and requests for special health topics are solicited and received from many sources. These include university-based researchers, administrators of national organizations and programs in the private and public sectors, and other parts of the U.S. Department of Health and Human Services such as the National Institutes of Health and the Centers for Disease Control. Topics are selected after consultation with agencies within the Public Health Service and after an assessment of priority health issues and the related need for population-based data. A lead time of at least 18 months is required to develop and pretest questions for new topics.

    Current health topics covered by the NHIS include:

    1987

    1. Child Adoption
    2. Cancer Risk Factors
      • Cancer Control Study Questionnaire
        • * Hispanic Health and Nutrition Examination Survey Acculturation Questions
        • Usual Source of Medical Care
        • Source of Medical Information
        • Knowledge of Foods Related to Cancer
        • * Changes in Diet for Health Reasons
        • * Knowledge of Foods High in Fat and Fibre
        • Recognition of Factors which Increase Cancer Risk
        • Use of Screening Exams
        • Breast Self-Exam
        • * Have Menstral Periods
        • * Cigarette Smoking
        • * Other Tobacco Products
        • Exposure to Harmful Substances at Work
        • Smoking Restrictions at Worksite
        • Height and Weight
        • * Ever Had Cancer
      • Epidemiology Study Questionnaire
        • Quality and Frequency of Intake of Certain Foods and Beverages
        • Number of Meals and Snacks per Day
        • Vitamin and Mineral Use
        • Number of Livebirths and Stillbirths
        • Age at First Pregnancy Lasting 6 or More Months
        • Breastfeeding
        • Lumpectomies (Benign)
        • Birth Control Pill and Estrogen Use
        • Family History of Cancer
        • Occupation Held Longest
        • Number of Close Friends and Relatives
        • Participation in Group Events and Religious Activities

    * On both Cancer Risk Factor Questionnaires

    1986

    • Health Insurance
    • Dental Health
    • Vitamin and Mineral Intake
    • Longest Job Worked
    • Functional Limitations

    1985

    1. Health Promotion and Disease Prevention
      • Pregnancy and Smoking
      • General Health (Including Nutrition)
      • Injury Control
      • Maternal Health
      • High Blood Pressure
      • Stress
      • Exercise
      • Smoking
      • Alcohol Use
      • Dental Care
      • Occupational Safety and Health

    1984

    1. Health and Health Care of the Aging Population
      • Family Structure, Relationships, Support, and Living Arrangements
      • Community and Social Support
      • Occupation and Retirement
      • Conditions and Impairments
      • Activities of Daily Living
      • Nursing Home Stay, Help with Care, and Hospice
      • Health Opinions

    1983

    • Alcohol and Health Practices
    • Doctor Service
    • Dental Care
    • Health Insurance

    1982

    • Health Insurance
    • Preventive Care

    1981

    1. Child Health
      • Family Structure
      • Child Care Arrangements
      • Birth and Prenatal Events
      • Motor and Social Development
      • Infant Feeding Practices
      • Chronic Conditions
      • Lifetime Hospitalizations and Operations
      • School and Behavior Problems
      • Sleep Habits
      • Social Effects of Ill Health

    1980

    • Special Aids
    • Corrective Lenses
    • Hearing Aid
    • Health Insurance
    • Smoking
    • Home Care
    • Residential Mobility
    • Retirement Income
    • Longest Job
    • Disability
    • Medicaid
    • Aid to Families with Dependent Children

    1979

    • Special Aids
    • Corrective Lenses
    • Hearing Aid
    • Smoking
    • Child Immunization
    • Home Care
    • Eye Care
    • Residential Mobility
    • Retirement Income
    • Disability
    • Medicaid
    • Aid to Families with Dependent Children

    1978

    • Health Insurance
    • Smoking
    • Out-of-Pocket Health Expenses
    • Blood Donor
    • Usual Source of Care
    • Armed Forces Disability
    • Child Immunization
    • How Long to Get to Doctor, Hospital
    • Medicaid
    • Aid to Families with Dependent Children
    • Veterans Administration Sponsored Care

    1977

    • Special Aids
    • Corrective Lenses
    • Hearing Impairment
    • Vision Impairments
    • Barriers to Care
    • Influenza
    • Health Habits
    • Stroke
    • Disability
    • Services (for example, Physical Therapy)
    • Disability Income
    • 12-Month Work Loss
    • Medicare
    • Medicaid
    • Veterans Administration Sponsored Care
    • Aid to Families with Dependent Children

    1976

    • Health Insurance
    • Smoking
    • Out-of-Pocket Health Expenses
    • Insulin and Diabetes Pills
    • Diabetes
    • Health Habits
    • Influenza
    • Job Application Turned Down Due to Health
    • Number of Children Ever Born and Number Weighing 9 or More Pounds at Birth

    1975

    • Out-of-Pocket Health Expenses
    • Usual Source of Care
    • Accidents
    • Health Insurance
    • Health Maintenance Organizations
    • Exercise
    • Insulin and Diabetes Pills
    • Interval Between Discovery of Condition and Seeking Medical Help

    1974

    • Health Insurance
    • Smoking
    • Acute Conditions
    • Loss of Income Due to Illness
    • Hypertension
    • Usual Source of Care
    • Orthodontic Care
    • Blood Pressure
    • Barriers to Care
    • Weight Control
    • Source of Payment for Medical Bills
    • Non-physician Health Care

    1973

    • Prescribed Medicines
    • Interval Between Discovery of Condition and Seeking Medical Help
    • Blood Donor
    • Pregnancy
    • Preventive Care

    1972

    • Disability
    • Health Insurance
    • Cause of Accident
    • Source of Payment for Doctor, Hospital Visit
    • Interval Between Discovery of Condition and Seeking Medical Help

    1971

    • Disability
    • Hearing Impairment
    • Out-of-Pocket Expense
    • Corrective Lenses
    • Hearing Aid
    • Type of Dental Service
    • Edentulous Persons
    • Cause of Accident
    • Vision Impairments

    1970

    • Health Insurance
    • Smoking
    • X-Rays
    • Condition Requiring Dental Visit
    • Cause of Accident
    • Interval Between Discovery of Condition and Seeking Medical Help

    1969

    • Special Aids
    • Arthritis
    • Public Assistance
    • Condition for Seeing Dentist
    • Disability
    • Interval Between Discovery of Condition and Seeking Medical Help
    • How Long to Get to Doctor
    • Wait Time to See Doctor

    Further information about the NHIS questionnairs can be obtained from the Division of Health Interview Statistics.


    NATIONAL HEALTH INTERVIEW SURVEY PUBLIC USE DATA TAPES

    NCHS Division of Health Interview Statitistics

    Current Health Topics Questionnaires

    Data tapes on NHIS current health topics, such as the 1985 survey on health promotion and disease prevention, can be purchased directly from the Division of Health Interview Statistics.

    1973

    • Prescribed Medicine -- $160 (6250 BPI)

    1974

    • Currently Employed -- $160 (6250 BPI)
    • Hypertension -- $160 (6250 BPI)
    • Medical Care -- $160 (6250 BPI)

    1975

    • Accident -- $160 (6250 BPI)
    • HMO - All Persons -- $160* (6250 BPI)
    • Physical Fitness -- $160 (6250 BPI)
    • HMO - Sample Person -- $160 (6250 BPI)
    • Family Medical Expenses -- $160 (6250 BPI)

    1976

    • Diabetes -- $160* (6250 BPI)
    • Health Insurance -- $160* (6250 BPI)
    • Health Habits -- $160 (6250 BPI)
    • Family Medical Expenses -- $160 (6250 BPI)

    1977

    • Disability -- $160* (6250 BPI)
    • H-1 Supplement -- $160 (6250 BPI)
    • Hearing -- $160* (6250 BPI)

    1978

    • Insurance -- $160* (6250 BPI)
    • Smoking -- $160 (6250 BPI)

    1979

    • Home Care - Person Supplement -- $160* (6250 BPI)
    • Smoking -- $160 (6250 BPI)
    • Residential Mobility -- $160 (6250 BPI)
    • Eye Care -- $160 (6250 BPI)

    1980

    • Smoking -- $160 (6250 BPI)
    • Health Insurance -- $160* (6250 BPI)
    • Residential Mobility -- $160 (6250 BPI)
    • Home Care - Person Supplement -- $160* (6250 BPI)

    1981

    • Child Health Supplement -- $160 (6250 BPI)

    1982

    • Preventive Care -- $160* (6250 BPI)
    • Health Insurance -- $160* (6250 BPI)

    1983

    • Alcohol/Health Practices -- $160 (6250 BPI)
    • Bed Days and Dental Care -- $160 (6250 BPI)
    • Doctor Service Supplement -- $160 (6250 BPI)
    • Health Insurance (Quarters 3 & 4) -- $160 (6250 BPI)

    1984

    • Health Insurance -- $160** (6250 BPI)
    • Supplement on Aging -- $275** (6250 BPI)

    1985

    • Health Promotion/Disease Prevention -- $160** (6250 BPI)
    • Smoking History during Pregnancy/Child Safety and Infant Feeding -- $160** (6250 BPI)

    * Price for tape at 1600 BPI is $275.

    ** Available December 1986.

    These data tapes are available for purchase from the Division of Health Interview Statistics, National Center for Health Statistics, 3700 East-West Highway, Hyattsville, MD 20782. The order form is on the reverse side. Additional information is available upon request.

    ORDER FORM
    Data Purchase and Use Agreement. Individual identifiers have been removed from the microdata public use tapes available from the National Center for Health Statistics. Nevertheless, section 308(d) of the Public Health Service Act (42 U.S.C. 242m) specifies that data collected by the National Center for Health Statistics may not be used for any purpose other than that for which is was supplied. The information on the microdata public use tapes available for purchase was supplied to NCHS for statistical research and reporting purposes. It is necessary, therefore, that the individual ordering such tapes sign the following assurance:
    The undersigned gives assurance that individual elementary unit data on the microdata public use tapes being ordered will be used solely for statistical research or reporting purposes.
    SIGNED: DATE:
    TITLE: ORGANIZATION:
    DATA TAPES ORDERED:
    -
    -
    -
    PROPOSED USE:
    -
    -
    This form may be used for ordering data sets. Indicate the data sets you want, put your name and address below, enclose payment, and sent to: Division of Health Interview Statistics, National Center for Health Statistics, Center Building, Room 2-44, 3700 East-West Highway, Hyattsville, Maryland 20782, (301)436-7087. Make check payable to: U.S. Department of Health and Human Services (for Statistical Studies).
    SEND INDICATED DATA SETS TO:
    -
    -
    -
    -


    THE SUPPLEMENT ON AGING TO THE 1984 NATIONAL HEALTH INTERVIEW SURVEY

    Joseph E. Fitti, National Center for Health Statistics
    Mary Grace Kovar, Centers for Disease Control

    I. PROBLEM

    Concerns among a number of public health agencies and individuals about the increasing proportion of older people in the United States population led, as early as 1980, to recommendations that the National Health Interview Survey (NHIS) address this special subgroup. Issues dealing with the health and functional status of older people and the need for alternatives to institutionalization as the mode for providing care were identified at this early point by professionals in the field of aging. Information about these and related characteristics of the older population was needed.

    Statements of the need for this information were made by the Department of Health and Human Services in the 1980 National Long Term Care Plan of the Division of Long Term Care Policy, Office of the Assistant Secretary for Planning and Evaluation; by the Office of Management and Budget (OMB) in its 1980 report of the Interagency Statistical Committee on Long Term Care of the Elderly; and by the 1981 White House Conference on Aging.

    It was postulated that information about the health conditions that were most prevalent, about living arrangements, family and social support availability, retirement income and financial obligations, functional status and limitations, and attitudes and opinions about their own health and abilities would help in assessing the future needs of the elderly.

    In addition to responding to the topic recommendation of the NHIS's Technical Consultants Panel that these informational needs about the elderly could be addressed through the NHIS, a special Supplement on Aging (SOA) in 1984 was particularly timely because the National Center for Health Statistics (NCHS) planned to conduct the National Nursing Home Survey (NNHS) in 1984 (later postponed to 1985). An NHIS survey on the elderly would provide data on the noninstitutionalized population which would complement the NNHS data on residents of nursing homes and would provide, for the first time, comprehensive data on almost the total elderly population.

    II. DEVELOPMENT

    The development of a supplement to help provide some of this information from a national survey of elderly people themselves began in 1982 and resulted in the 1984 NHIS SOA. The SOA was sponsored and funded by NCHS, which was responsible for survey design, data processing, and preliminary analysis. Data collection was conducted by the Bureau of the Census under an interagency agreement.

    The first step in development was to determine the topics to be included. Topic suggestions were received from a variety of sources inside and outside NCHS. Suggestions from outside NCHS came in response to the topic solicitation from the Division of Health Interview Statistics and from notification to interested agencies and persons, including the National Institute on Aging, the Administration on Aging, the U.S. Senate Select Committee on Aging, the Social Security Administration, voluntary and nonprofit organizations, and experts in the field of aging.

    Selection of topics from those suggested was guided by three considerations: feasibility of collecting the data in a household interview survey; comparability to data collected in other NCHS surveys, especially the planned NNHS; and the creation of a baseline data source for a longitudinal study of aging. Activities undertaken in the topic selection process included literature reviews, reviews of earlier surveys, consultation with other agencies and individuals, and participation in conferences on the aging.

    During this process it was decided that the information being sought would usually be most reliably reported by the sample person himself or herself; therefore, a rule was established that the sample person would respond for himself or herself except in cases where the sample person was physically or mentally unable to respond. In these cases, an adult, preferably living in the household, would be accepted as proxy.

    The end result of this process was a pretest version of a questionnaire. Two pretests were conducted in the development of the SOA. Approval of OMB was requested in two submissions, the first covering the first pretest, and the second covering the second pretest and the full 1984 SOA. The first pretest (n=256) was conducted in June 1983 in Bradenton, Florida, which was selected because it is a popular location for retirement. The questionnaire was revised extensively on the basis of the Bradenton experience, and a second pretest was conducted in September 1983 in Wilmington, Delaware (n=234); subsequently, further revisions were made, resulting in the final form of the SOA questionnaire.

    III. SAMPLE

    The NHIS sample is designed to produce national estimates for the civilian noninstitutionalized population residing in the United States. The United States is first divided into geographically defined primary sampling units, which cover the 50 states and the District of Columbia. These units are grouped in strata with similar characteristics, and in 1984 one unit was selected from each stratum. Within each selected unit, compact clusters of housing units are then selected for the sample. Because the sample is clustered, it is not a "simple random sample."

    In 1984, 41,471 eligible households were in the NHIS sample, and interviews were conducted in 39,996 (96.4 percent), including 105,290 persons of all ages residing in the households at the time of interview. Details of the sample design for the 1984 NHIS are published in Vital and Health Statistics, Series 1, No. 18 and Series 10, No. 156.

    Because the SOA intended to obtain information on both those who were currently elderly (over age 65), and to provide baseline information for longitudinal studies of persons who would become elderly in the next ten years, it was decided to include persons 55 years of age and older. However, because there are large numbers of persons 55-64, it was not necessary to include all NHIS sample persons in that age range to achieve the desired level of statistical precision. Therefore, the sample for the SOA consisted of all NHIS sample persons 65 years of age and over, and a systematic sample of 50 percent of NHIS sample persons 55-64 years of age.

    A total of 16,347 persons were selected for the SOA, for whom 16,148 interviews were completed, for a 96.7 percent response rate; in more than 90 percent of the completed interviews the sample person was the respondent. The effective overall response rate for the NHIS-SOA (the product of the response rate on the NHIS and the SOA) was 93.2 percent.

    The NHIS is designed to make national estimates of statistics; therefore, the data must be weighted to inflate the sample numbers to national estimates. The weights indicate the estimated number of persons in the population represented by each sample person, and depend (most importantly) on the probability of selection, the response rate in the sample segment, and an adjustment to independent estimates (from the Current Population Survey) of 60 age-sex-race population groups. In addition to these NHIS adjustments, the SOA had additional adjustments for nonresponse on the SOA and for the 50 percent subsampling rate for persons 55-64 years of age. These weights are included on the public use sample tapes, and should be used in estimating population statistics from the sample.

    Because the SOA sample is large and its design is efficient, its estimates of population statistics are generally very precise. Vital and Health Statistics, Series 10, No. 156 includes instructions and examples for estimating sampling errors for estimates from the 1984 NHIS. Those procedures may be applied directly to estimates from the SOA for persons 65 and over; for persons 55-64, an additional step is necessary to account for the 50 percent subsample-multiplying variance estimates by 1.4. On average, the precision of estimates from the SOA is nearly as good as from a simple random sample of the same size: standard errors of estimates from the SOA would average less than 25 percent greater than from a single random sample of the same size.

    IV. DATA SOURCES

    Data were collected in personal interviews. Almost always the respondent was the sample person, and almost always the interviews were face-to-face; however, proxy respondents and telephone interviews were permitted in certain circumstances when necessary to complete interviews.

    V. DATA COLLECTION PROCEDURES

    Interviewing for the 1984 SOA was conducted by the Bureau of the Census, Field Division, in the standard face-to-face interviewing procedure for conducting the NHIS. The interviews period for the 1984 NHIS and SOA was January 9, 1984 through January 6, 1985, with interviewing conducted weekly throughout the year.

    NHIS interviewer training is conducted by the Bureau of the Census and consists of two types: initial training for new NHIS interviewers; and annual training for experienced interviewers on the current year's special procedures and special topics questionnaires. The initial training for NHIS interviewers takes about a full week and includes about one and one-half days for any supplement to be covered; this was modified to give additional time for training new interviewers on the SOA. Training on the SOA for experienced interviewers consisted of one and one-half days of classroom sessions in January, and portions of a later one day training session and five hours of home study materials.

    A number of measures are taken in the NHIS to control the quality of data collection. All inter- viewers are observed in the field by supervisory personnel at least twice each year, with new interviewers and interviewers with problems being observed more frequently. Interviewers are required to edit all completed interview schedules before submitting them to their supervisors in the Census Regional Offices, where additional edits are done; this editing is more intensive in the early part of a data collection year and for new interviewers. Approximately 5 percent of all interviews are designated for re-interview. The re-interview serves as a check on interviewer performance and as a measure of the reliability and accuracy of the NHIS and SOA data. For each household designated for re-interview, a subset of questions is asked by telephone by the interviewing supervisor.

    VI. DATA PROCESSING

    Quality control measures also are taken during the processing of data by NCHS staff. Quality control of the coding of questionnaire information consists of recoding 10 percent of all questionnaires by two independent coders. The quality of machine keying is maintained by a 100 percent independent key verification of all items in the questionnaires. After the data are on tape, computer edits are performed in the preparation of final data tapes. The computer edit checks for inconsistencies and invalid responses, provides algorithms for imputation, and generates recodes. The specifications for computer edits for the SOA included more than 350 decision logic tables.

    VII. PUBLIC USE FILES

    The SOA data tapes are in EBCDIC format and contain the SOA interview information with the following record structure: (1) a file of person records containing, for each person for whom an interview was completed, all items in the NHIS basic questionnaire that are on the person file, weights, all items in the SOA questionnaire (except the items used to permit matching to the National Death Index), special recodes, and selected condition and utilization information; and (2) a file of condition records, containing all conditions mentioned in the SOA interview plus any condition for the individual that is related to a "limited activities" status from the basic NHIS questions. To protect confidentiality, no items are included on the data tape which would uniquely identify an individual respondent. The detail of the content, coding, and structures of these two SOA data record types is contained in the public use tape documentation.

    VIII. PHYSICAL TAPE SPECIFICATION

    For the person tape DSN=HISY1984.AGINGPER, LRECL=1001, BLKSIZE=31031, and COUNT=16148. For the condition tape, DSN=HISY1984.AGINGCON, LRECL=380, BLKSIZE=31920 and COUNT=46320. Both tapes are standard label and are available in either 1600 BPI or 6250 BPI.

    IX. APPLICATIONS

    Preliminary reports based on data collected in the first six months of 1984 have been published in the NCHS series ADVANCE DATA, Numbers 115, 116, 121, 124, and 125. They focus on demographic and general health measures (115), persons living alone (116), urinary problems (121), community services (124), and vision and hearing impairments (125). Comparable analyses on the same topics were done using data for the full 12 months and presented in a special session at the 1986 meetings of the Gerontological Society of America. Additional ADVANCE DATA reports, based on the full year of data, which are soon to be published focus on functional limitations (activities of daily living and instrumental activities of daily living), and on ability to perform work related activities. A detailed analysis of the SOA data on functional limitations is in preparation for publication in Vital and Health Statistics, Series 10; and a full report on the SOA methodology entitled "The Supplement on Aging to the 1984 National Health Interview Survey," authored by Joseph E. Fitti and Mary Grace Kovar, will soon be published in Vital and Health Statistics, Series 1, No. 21. Additional analyses by staff of NCHS and other Federal agencies on a variety of topics are underway or planned. More than 30 public use data tapes have been sold by NCHS to researchers in universities and research centers, and the National Institute on Aging is encouraging applications for grants to conduct research using the SOA data.

    X. ACCESS

    Public use data tapes from the SOA may be purchased form NCHS for $275, which includes two data tapes (described above) and all documentation. Orders should be mailed to the Division of Health Interview Statistics, National Center for Health Statistics, Center Building, Room 2-44, 3700 East-West Highway, Hyattsville, Maryland 20782, with a check made payable to the U.S. Department of Health and Human Services for Statistical Studies. The order should include an assurance that the data will be used solely for statistical research or reporting purposes. The density desired, 1600 BPI or 6250 BPI, should be specified. Order forms are also available from the same address.


    NOTES

    1. Reproduced for the conference package from the NCHS Health Interview Statistics newsletter. Copies can be obtained from U.S. Public Health Service, National Center for Health Statistics, Division of Health Interview Statistics, Room 2-44, 3700 East-West Highway, Hyattsville, MD 20782, (301)436-7085.

    2. Excerpted or abstracted from Joseph E. Fitti and Mary Grace Kovar, "The Supplement on Aging to the 1984 National Health Interview Survey," Vital and Health Statistics, Series 1, No. 21, DHHS Publication No. (PHS)87-1323 (in preparation). Available from the U.S. Government Printing Office Beginning Summer, 1987

    IV. THE 1985 NATIONAL NURSING HOME SURVEY

    1985 NATIONAL NURSING HOME SURVEY: FACT SHEET

    National Center for Health Statistics (March 1987)

    I. BACKGROUND

    As part of its continuing program to provide information on the health of the Nation and the utilization of its health resources, the National Center for Health Statistics (NCHS) periodically conducts a nationwide survey of nursing facilities. The 1985 National Nursing Home Survey (NNHS), the third in a series, is authorized under Section 306 (42 USC 242k) of the Public Health Services Act. Facilities covered in the survey are those providing some level of nursing or personal care without regard to licensure status or to certification status under Medicare or Medicaid. Participation is voluntary.

    II. PURPOSE

    The purpose of the NNHS is to collect baseline and trend statistics about nursing facilities, their services, residents, discharges, and staff. The resulting published statistics will describe the Nation's nursing facilities and the health status of their residents. These data are used for studying the utilization of nursing facilities, for supporting research directed at finding effective means for treatment of long-term health problems, and for setting national policies and priorities.

    III. CONFIDENTIALITY

    Confidentiality is provided to all respondents in the NNHS as assured by Section 308(d) of the Public Health Services Act (42 USC 242m) which states that: "Information...which would permit identification of any individual or establishment...will be held in strict confidence, will be used only for the purposes stated for this study, and will not be disclosed or released to others without the consent of the individual or establishment."

    IV. PROCEDURES FOR DATA COLLECTION

    Data were collected from a nationally representative sample of 1,220 nursing and related-care homes using a combination of personal interview and self-enumeration techniques. Information about the facility (e.g., number of beds, certification status, number and kinds of staff) was collected through a personal interview with the administrator or designee. With the administrator's permission, a questionnaire was sent to the facility's accountant to obtain basic expense and revenue information and to a maximum of four registered nurses (RNs) to obtain information related to job retention. Through interviews with appropriate nursing staff, information was collected on maximum samples of five current residents and six recent dis- charges. In addition to basic demographic information, data were collected about the sample patients' medical conditions, impairments, functional limitations, services received and sources of payment. A family member of the patient was contacted by telephone to obtain data on socioeconomic status and prior episodes of health care -- information which generally is not available at the facility.

    V. PLANS FOR DATA RELEASE

    The results of the 1985 NNHS will be released in publications and public use computer tapes. As noted in the above section on confidentiality, no information will be released which identifies individuals or establishments. Publication plans include pamphlets presenting preliminary data, a summary volume presenting detailed tabulations, and individual analytical reports on special topics such as utilization measures and resident characteristics. Release of data will begin in 1987.


    SPEAKER COMMENTS

    Genevieve Strahan, National Center for Health Statistics
    Esther Hing, National Center for Health Statistics
    Edward S. Sekscenski, National Center for Health Statistics

    I. INTRODUCTION

    Facilities participating in the 1985 NNHS were selected from a universe of over 20,000 nursing and related-care homes. Of the 1,220 facilities selected, six were identified as having been included in the pretest phase of the survey. It was decided by NCHS not to recontact these same facilities but instead to transcribe data from the pretest instruments to the national survey instruments. During the fielding effort of the remaining 1,214 facilities, 56 were identified as out-of-scope. Of the remaining in-scope facilities 1,079 participated in the survey for a response rate of 93 percent.

    First contact to the facility was made in May 1985 prior to the beginning of the survey. A telephone prescreening procedure was performed to verify contact information for facilities selected in the sample. This prescreening was designed to update facility data concerning facility name, address, telephone number, and the administrator's name.

    The next contact made to the sample facility was in the form of an introductory information packet to the administrator. The packet contained a letter from the Director of NCHS explaining the importance of the survey and informing the administrator that an interviewer would be calling for an appointment. The packet also included letters of endorsement from professional health organizations. About a week after the packet should have been received, the interviewer contacted the administrator to set up the appointment to conduct the survey. Depending on the size of the facility, one interviewer or a team of two or three interviewers visited the facility.

    A part of the facility visit included the administration of three questionnaires: (1) Facility Questionnaire, (2) Expense Questionnaire, and (3) Nursing Staff Questionnaire.

    II. FACILITY FILE

    The Facility Questionnaire (FQ), printed in canary yellow, was completed by the interviewer in a face-to-face interview with the administrator or his/her designee. Collected on the FQ was basic information about the facility: ownership, certification status, bed size, number of admissions and inpatient days of care, services provided to residents and nonresidents, and number of nonresidents served. Staffing in several occupational categories was collected for full-time and part-time employees. Full-time equivalent employees for each category were tabulated utilizing the number of hours worked which was collected for all part-time employees. Thirty-five hours of part-time work are taken to equal that of one full-time employee. The survey collected for the first time in 1985, per diem rates for routine care set by nursing homes. These rates were collected separately for Medicare, Medicaid, and private pay patients. Per diem rates will be one of the key units of analysis from the facility file. By matching the unique facility ID number from all documents completed in a sample home, information about residents collected in two other components of the survey can be described by characteristics of the facility. For example, estimates of current residents can be tabulated by ownership of the facility.

    The administrator did not always have all the data required for the FQ at hand and needed to consult records or staff in other offices. Questions that required specific numerical data were printed on a separate sheet, referred to as the FQ work sheet. The interviewer gave this work sheet to the administrator at the end of the interview to be completed later. The interviewer picked up the work sheet at the end of the day or at a later date.

    In 1985, the typical nursing home was independently and privately owned. It had about 85 beds -- most of which had some form of certification. This typical home had 71 employees per 100 beds. The estimated 19,100 nursing homes set average rates of $61 for skilled private pay daily care and $62 for Medicare skilled care.

    These data and more are included in Advance Data report, Number 131, "Nursing Home Characteristics -- Preliminary Data from the 1985 National Nursing Home Survey." Data from the facility file along with data from four other components of the NNHS will be included in a special report to be published by the end of this year.

    III. EXPENSE DATA FILE

    Upon completion of the FQ, the Expense Questionnaire (EQ), and its accompanying Definition Booklet, printed in green, were presented to the administrator for completion. In many facilities, the administrator completed the EQ; in others, he referred the interviewer to an accountant, a bookkeeper, or a central office. This instrument was completed by a respondent at his or her convenience. A postage-paid return envelope was provided for the return of the EQ. The EQ collected data on two major topics: expenses and revenues. Expense data included payroll, health care services, insurance, taxes, food, utilities, maintenance, and drug expenses. Revenue data included sources of income from patient and nonpatient sources such as contributions. In lieu of a completed EQ, each facility was offered the option of providing the interviewer with a recent financial statement.

    IV. NURSING STAFF FILE

    After obtaining the financial statement or the name and address of the anticipated respondent for any necessary follow-up, the interviewer introduced the nursing staff component of the NNHS. These two documents (the Nursing Staff Sampling List and the Nursing Staff Questionnaire) were used to collect data on RNs working in nursing homes.

    The Nursing Staff Sampling List, printed in blue, was completed by the interviewer in collaboration with a staff member designated to help. For the preparation of this list, it was necessary to divide employment status of all facility RNs into one of three categories: (1) those who are employed on the staff of the facility, (2) those scheduled to work who were retained through a special contractual relationship, and (3) those scheduled to work who were retained through a temporary service. Three columns were provided in which to list separately persons in each category. The sampling list provided the universe of RNs separated into the three groupings. With the introduction of the Nursing Staff Sampling List came the first need to use sampling tables. Each interviewer received a pack of sampling tables. The pack consisted of ten independent sets of three different kinds of tables, which were numbered and color-coded according to the component to which they applied. Table 1 was blue and was used to select the nursing staff sample.

    In order to ensure random in-facility samples, Table 1 had ten versions, numbered 0 to 9. The fourth digit of a facility ID number determined which version of Table 1 was to be used to select the RN sample at that facility. For example, if the facility ID number were 1234-00-7, the interviewer would consult version 4 or Tables 1-4. This method of assignment assured a fairly even distribution of facilities among all the versions of the sampling tables.

    After finding the total number of RNs recorded on the Nursing Staff Sampling List, the interviewer referred to the version of Table 1 mandated by the fourth digit of the facility ID number. After locating the correct total in the "Total # Listed" column, the interviewer read across to find out which sample line numbers on the sampling list determined the individuals chosen for questionnaire completion. The selection of up to four RNs from each sampled nursing home yielded a sample of 3,349 nurses.

    The Nursing Staff Questionnaire (NSQ) also printed in blue, was, when at all possible, personally distributed by the interviewer to those RNs selected on the sampling list. The NSQ was self-administered. When personal delivery was not possible, the questionnaire was either mailed to a home address or left at the facility. A postage-paid business reply envelope was provided for return of the completed questionnaire. If a questionnaire was not received within 28 days of the facility visit, a reminder letter and duplicate instrument were sent.

    The NSQ gathered information on the work experience, hours, activities, education, training, salary, and opinions about recruitment and retention issues of RNs working in nursing homes. Basic demographics about each RN were also collected.

    Data were collected from 2,763 of the sampled RNs for a 80 percent response rate.

    The typical RN working in a nursing home was prepared to work as an RN in a diploma program and has been employed as an RN for more than ten years. She (98 percent are female) worked full-time on a nonrotating day shift. She is white, married with either no children living at home, or the children are of school age (6 to 18). She is scheduled to work an average of 32.5 hours per week and earns about $334 per week.

    An Advance Data report should be released this year, reporting characteristics of RNs in nursing homes. Future reports will provide detailed information about RNs working in nursing homes and will be published in both Series 13 and Series 14 reports. Data on RNs will also appear in the special report that combines data from several other components.

    Now Esther Hing will talk about the current resident component of the 1985 National Nursing Home Survey.

    V. DESCRIPTION OF CURRENT RESIDENT QUESTIONNAIRE

    Data from the Current Resident Component of the NNHS are cross sectional and are representative of nursing home residents in the United States as of the night before the survey. To draw the sample of residents, lists of residents in the facility were constructed at the time of the survey. In nearly half of the sample homes, the nursing home provided photocopied or computer generated lists of current residents. In the remaining homes, the lists had to be constructed by copying the names of residents from ledgers, or other lists of patients. A sample of five or fewer residents were selected per sample home resulting in an overall sample of 5,395 current residents.

    The Current Resident Questionnaire (CRQ) was used to collect data on the sample of residents. This questionnaire was administered by personal interview with a knowledgeable staff member who referred to the residents' medical record when necessary. The most frequent respondent to the CRQ was a nurse (55 percent), followed by the administrator or owner of the nursing home (17 percent). In about 3 percent of the cases, no staff was available and the interviewer abstracted the data from the medical records. Participation for this questionnaire was very high, the response rate was 97 percent. Item response rate in this questionnaire were also high. This result is by design, since items with low response rates in our pretest of the NNHS were not included on the final questionnaire. Item response rates from the national study were similar to those found in the pretest.

    Follow-up information on the sample of current residents was also obtained in a telephone interview with the residents' next-of-kin. The residents' next-of-kin, friends or guardian may have been contacted. Only sample residents with no next-of-kin or other known contacts were ineligible to have this telephone follow-up. The instrument used to collect the follow-up data was called the Next-of-Kin Questionnaire.

    The CRQ collects information about the demographic, medical and other utilization characteristics of the nursing home population. These items are summarized in Table 1. Demographic variables include age, sex, race, hispanic origin, and martial status. Medical data include the diagnoses at admission and currently. Up to eight diagnoses were listed for each time period. The data were coded according to the Clinical Modification of the Ninth Revision of the International Classification of Diseases. Other medical data collected include vision and hearing status and prevalence of mental disorders. Utilization data collected include the length of stay since admission and the total monthly charge last month.

    Table 1 shows items collected for the first time in the NNHS. These items have asterisks to the left. These items include marital status at admission, presence of living children, diagnoses-related group data for hospital transfers, hospital stays while a resident, history of other nursing home stays, instrumental activities of daily living (this involves the need for help in such activities as caring for personal possessions, handling money, securing personal items and using the telephone), disorientation or memory impairment and sources of payment at admission.

    TABLE 1. Summary of Current Resident Data Items
    Facility number
    Age
    Sex
    Race
    Hispanic origin
    * Marital status at admission
    Current marital status
    * Presence of living children
    Length of stay since admission
    Residence before admission
    * Diagnoses-Related Group (DRG) for persons admitted from a short-stay hospital
    * Hospital stays while a resident
    * History of nursing home stays at sample facility and other nursing homes
    Diagnoses at admission and currently
    Mental disorders
    Therapy received last month
    Vision and hearing status
    Activities of daily living characteristics
    * Instrumental activities of daily living (IADL)
    Behavioral problems
    * Disorientation or memory impairment
    Disturbance of mood
    * Sources of payment at admission
    Sources of payment last month
    Total monthly charge for care
    Resident weight
    * = Collected for first time in the NNHS.

    This table also shows that the tape for the CRQ will include the facility number and the resident weight. The facility number uniquely identifies each facility in the survey. By matching the facility number on the CRQ with the facility number on the FQ, information from the FQ such as bed size or ownership type can be moved to the CRQ for further analysis. The resident weight is used to inflate the sample data to national estimates. The weights associated with each file and how they were computed are discussed below.

    One of the principal strengths of the current resident data is that it provides national estimates of the population in nursing homes. This is useful to health planners and policy makers who need descriptive data on the utilization of nursing homes.

    In particular, several of the new items collected in the CRQ were added to shed light on long-term care policy issues. The items on sources of payment at admission and last month, for example, provides estimates of nursing home residents who had to "spend-down" before becoming eligible for medical assistance from Medicaid. A question was also added to the CRQ on the diagnoses-related group code for all persons transferred to the nursing home from short-stay hospitals. This data, along with other variables from the survey, may be used to assess the impact of the Medicare prospective payment system (PPS) on nursing home care since its implementation in 1983.

    Data from the CRQ, however, have certain limitations. Because of the resident sample is selected from patients currently residing in the facility, the length of stay for respondents is incomplete and underestimates the true length of stay that would be achieved at some point in the future. Residents with long length of stay, however, are over-represented in the current resident sample because of the short-time frame -- overnight -- of the sample. As a result, a person admitted to the nursing home for a short stay, for example, one day, has fewer chances of being included in the sample than a person with a stay of one year. Because of these limitations, the current resident data is inappropriate for examining the flow of patients in and out of nursing homes. The best data for investigating this issue would be a longitudinal study of a cohort of persons admitted to nursing homes. Longitudinal surveys, however, are expensive to conduct.

    To date one report on the use of nursing homes by the elderly has been published using the current resident data. This report discussed the utilization rate or number of residents per 1,000 population, 65 years and over by age, sex, and race. Selected health and socioeconomic characteristics were also examined. The report found that about 5 percent of the elderly resided in nursing homes on any given day during the survey period of the 1985 NNHS. Use of nursing homes increased with age for both sexes but was greater for females than males. Use of nursing homes was lower for elderly persons who were black or of other races than for white persons. For the most part, these trends have not changed since 1973-74, when the first NNHS was conducted; however, there were some exceptions. There was an increase in the use of nursing homes by elderly black persons and a decrease in use by those 85 years and over. If any one is interested in receiving a copy of this report, they can write to us.

    As Genevieve has mentioned, the next NNHS report to be published will be a summary report presenting data from most components of the 1985 NNHS. This report will include current resident tabulations covering all topics covered on the questionnaire. This report will probably be released at the end of the year.

    After the summary report, the next scheduled report using current resident data will be a study of the impact of the Medicare PPS on nursing home care.

    And now Ted will discuss the data on discharged residents.

    VI. DATA PROCESSING

    Once data were collected, a series of checks were performed to assure that all responses were accurate, consistent, logical and complete. Manual edits were performed to check the completeness, format and consistency of the data. For example, sampling lists for current residents were checked to determine that the sample was correctly selected. Following the manual edit, diagnostic data were coded according to the Clinical Modification of the Ninth Revision of the International Classification of Diseases. Range checks and checks of identifiers were also performed at the time of keying. At all steps of data preparation and data entry, quality control procedures were taken to minimize processing errors. Once the data were entered, separate files for each questionnaire were created, and extensive computer edits were performed. Computer edits performed were basically of two types: (1) data cleaning based on consistency tests and (2) data flagging for imputation. Data flagged as "missing" during the editing process were then replaced with "good data" from a randomly picked similar responding case. Once the data base was edited and missing data imputed, weights were assigned, and constructed variables such as, length of stay and age of resident were computed. At this point, national estimates may be produced from the data tapes.

    Data processing of the next-of-kin file followed a different track, since the data were basically keyed during the telephone interview. Data cleaning of the next-of-kin file was not as extensive as data obtained from the nursing home, because the computer assisted telephone interview automatically followed correct skip patterns. Quality control procedures for the next-of-kin interviews included silent monitoring of calls, review of complete and incomplete cases, and nonresponse conversion efforts. At the conclusion of interviewing for the next-of-kin, the relationship of the respondent to sampled current or discharged resident was coded. Then weights were assigned and constructed variables or recodes were computed.

    And now I will talk about how data from the NNHS are weighted to produce national estimates.

    VII. WEIGHTING

    The design of the 1985 NNHS is a complex multistage probability sample survey. For the sample data to reflect national estimates, the data needs to be inflated by a weighting factor. The weights for the 1985 NNHS estimators include three basic components:

    1. Inflation by the reciprocal of the probability of selection,
    2. Adjustment for nonresponse, and
    3. A first-stage ratio adjustment to total beds in the sampling frame.

    For facility level estimates such as the number of nursing homes, number of beds, or total cost of providing care, the probability of selection is the product of the facilities' probability of being included in the sampling frame times the probability of its being selected from the frame. Only homes from the Complement Survey had a probability of being included in the sampling frame of less than 1. For second-stage estimates of current and discharged residents, and RNs, the probability of selection is the product of the probability of facility selection times the secondstage probability of selection for these sampling units.

    The nonresponse adjustment factor brings estimates based on the responding cases up to the level that would have been achieved if all eligible cases had responded. The effect of the first-stage bed ratio adjustment is to bring the sample in closer agreement with the known universe of beds.

    All three components were used to estimate facility characteristics correlated with bed size, and estimates of current residents, discharged residents, and RNs. The first-stage bed size ratio adjustment, however, was not included in estimates of nursing homes and facility characteristics uncorrelated with bed size.

    Weighting factors used to estimate the number of residents and discharges with next-of-kin are similar to the weights for current and discharged residents with the exception of an additional nonresponse adjustment factor for nonresponse to the question requesting the names of next-of-kin and an adjustment factor for the existence of next-of-kin or other contacts for sample residents and discharges.

    As a result, estimates of residents and discharges from the next-of-kin file will be less than the overall estimates of residents and discharges.

    It should be noted that caution should be used when producing estimates by metropolitan status since the sample was not specifically designed to produce detailed estimates by this characteristic.

    VIII. DISCHARGE RESIDENTS

    My presentation deals exclusively with the discharged resident component of the 1985 NNHS. Comparisons made with other data files, including previous NNHS, are illustrative and not exhaustive; continuities do exist with many of the data items in the 1973-74 and 1977 discharged resident segments of the NNHS although some items have not been repeated and a number of new items have been added to the 1985 NNHS. This section will outline some but not all of the similarities and differences between the 1973-74, 1977 and 1985 surveys, and hope to cover all items on the 1985 survey. It is also possible to cross a number of the data items available from other components of the 1985 NNHS to yield further information on the discharged population.

    Data in the discharged resident file of the 1985 NNHS were obtained from personal interviews conducted in the sample nursing homes with employees deemed most knowledgeable of the discharge residents' health status and conditions during their stay at the sample home. In most cases the interviewee was either a nurse or medical records person who consulted with the available medical records of the discharged resident during the course of the interview. As was true in both previous NNHSs and in the current resident segment of the 1985 survey, no residents were consulted personally in the discharge component of the 1985 survey.

    Unlike the 1973-74 and 1977 surveys, the 12-month reference period from which the discharged resident's sample was drawn for the 1985 survey, ended on the date immediately preceding the survey date. Previous survey reference periods for discharges were the calendar year 1972 and 1976. The survey's reference period was changed for the 1985 survey in an attempt to obtain both more current and readily available data and to provide for information on the utilization of nursing homes by both residents and discharges over a more closely related period of time. However, data from the 1985 NNHS for the discharged resident population and current resident population continue to differ in several major areas.

    Briefly, while the discharged resident estimates represent all discharges over a 12-month period, the current resident population is estimated for a single night, that immediately prior to the survey date. The discharge sample, therefore, may underestimate those nursing home residents who tend to stay for very lengthy durations, while the current resident population may underestimate those persons with very short durations of stay. While the current resident file provides for what may be considered a "snapshot" of nursing home residents on any given day, the discharged resident file provides for some indication of the over-the-year changes in the nursing home population at least, this is, in terms of whom is being discharged from the nation's approximately 20,000 nursing and related-care homes.

    A sample of six or fewer discharged residents were selected per sample home resulting in an overall sample of 6,023 discharged residents. The Discharged Resident Questionnaire (DRQ) collected data on the discharged residents' demographic characteristics (including age, sex, race, Hispanic origin, and marital status), their discharge diagnoses, and the discharge destinations of live discharges, whether or not the resident had difficulty in controlling his/her bowel and whether he/she was bedfast or chairfast during the seven days prior to being discharged from the nursing home. Also obtained was information on the primary sources of payment for the month of discharge (although unlike in the CRQ, no charge data were obtained on the discharged residents). All of these above data items provide continuity with similar data obtained in the 1977 NNHS.

    New to the 1985 discharged resident component of the NNHS are data items on the primary diagnoses of discharged residents at admission, categorical information on prior living arrangements immediately preceding admission, and primary source of payment data for the month of admission. Also new were questions on the discharged resident's history of other stays in the sample and other nursing homes, including dates of admission and discharge, and the total number of homes in which the discharged resident had been a resident patient. These questions' data will begin to provide some evidence of patterns of nursing home utilization over a lengthier period of time than a single stay in a single nursing home.

    I have a limited number of copies of the 1985 Discharge Resident Questionnaires available for anyone who would like to peruse them after the session today. I will be open for other questions on the DRQ of the 1985 NNHS also at that time.

    Publications from the discharged resident component of the 1985 NNHS will include an Advancedata report, scheduled to be released later this summer, and a Series 13 report to be released in 1988.


    USE OF NURSING HOMES BY THE ELDERLY: PRELIMINARY DATA FROM THE 1985 NATIONAL NURSING HOME SURVEY

    Esther Hing, Division of Health Care Statistics, National Center for Health Statistics

    I. INTRODUCTION

    Most elderly people are not in nursing homes. Of an estimated 28.5 million Americans aged 65 years and over in the United States, only 5 percent were residents of nursing homes on any given day from August 1985 through January 1986. This finding from the 1985 NNHS is consistent with findings from previous NNHSs conducted in 1973-74 and 1977.2 In these surveys also it was found that about 5 percent of the elderly were residents of nursing homes.

    Differences, however, exist in the use of nursing homes by age, sex, and race subgroups. In this report, these differences in use rates are examined. Differences in the health and socio-economic characteristics examined in this report are functional dependencies in the basic activities of daily living (ADLs)--bathing, dressing, using the toilet room, transferring from a bed or chair, continence, and eating; cognitive functioning (disorientation or memory impairment and senile dementia or chronic organic brain syndrome); marital status at admission; whether residents had living children; living arrangements prior to admission to the nursing home; and primary source of payment at admission. The focus of this report will be a comparison of the characteristics of the elderly who reside in nursing homes with characteristics of those who reside in the community.

    The data presented in this report are from the 1985 NNHS, a nationwide sample survey of nursing homes, their residents, discharges, and staff conducted by NCHS. The survey, which was conducted from August 1985 through January 1986, was the third of a continuing series of nursing home surveys. The first survey was conducted from August 1973 through April 1974, and the second was conducted from May through December 1977.

    Facilities included in the 1985 NNHS were nursing and related care homes in the conterminous United States that had three or more beds set up and staffed for use by residents and that routinely provided nursing and personal care services. A facility could be free standing or could be a nursing care unit of a hospital, retirement center, or similar institution as long as the unit maintained financial and employee records separate from the parent institution. Placed providing only room and board were excluded, as were places serving only persons with specific health problems (for example, mental retardation or alcoholism).

    The sampling frame for the 1985 NNHS consisted of the following components:

    • The 1982 NMFI, a census of nursing and related care homes conducted by NCHS.
    • Homes identified in the 1982 Complement Survey of the NMFI as "missing" from the 1982 NMFI.3
    • Nursing homes opened for business from 1982 through June 1984.
    • Hospital-based nursing homes identified in records of HCFA.

    The resulting frame contained 20,749 nursing homes. In this report, the terms "nursing homes" and "nursing and related care homes" are used interchangeably.

    Estimates in this report are based on a sample of 4,646 elderly residents of the 1,079 nursing homes participating in the survey. A fixed sample of five or fewer residents per sample facility was selected. Residents included in the sample were those on the nursing home's roster the night before data collection began. Data were collected by interviewing knowledgeable nursing home staff members, who referred to the residents' medical records when necessary. Additional followup information on the sample residents was collected by telephone interview with the residents' next-of-kin. (A resident's guardian or friends were contacted if there was no next-ofkin.) Data collected from the next-of-kin focused on the circumstances and reasons for the resident's nursing home admission. In this report, only data obtained from the nursing home staff are presented. In later reports estimates from the next-of-kin component will be included.

    Data presented in this report are preliminary and may differ slightly from estimates presented in later reports because of further data editing. Another report presenting preliminary estimates of nursing homes and utilization characteristics of homes has already been published.4

    Although data on residents reported by the nursing home staff were collected in a similar manner in earlier NNHSs as in the 1985 survey, note should be taken of some differences. First, personal care and domiciliary care homes were excluded from the scope of the 1973-74 NNHS but included in the two later surveys. The effect of this difference, however, is small because only about 2 percent of all nursing homes in 1973 were personal care or domiciliary care homes and they housed only about 1 percent of the beds and residents.5 Second, certain variables presented in this and later reports were not available from the previous surveys. Data on some variables discussed in this report--marital status at admission, the presence of living children, ability to transfer in or out of a bed or chair, and primary source of payment at admission--were not collected as a single item in the 1973-74 and 1977 surveys but as separate items in 1985. This difference should be considered when comparing data by race from the 1985 NNHS and previous surveys.

    Because data in this report are national estimates based on a sample, they are subject to sampling errors. Information on sampling variability is presented in the Technical notes.

    II. UTILIZATION RATES

    In 1985 an estimated 1,491,400 residents lived in 19,100 nursing homes nationwide. Of these residents, 1,315,800, or 88 percent, were 65 years of age and over. The number of elderly residents in nursing homes increased 17 percent from 1977 to 1985. Residents aged 85 years and over comprised the largest age group (45 percent), followed by those aged 74-85 years (39 percent) and 65-74 years (16 percent). Because of the preponderance of the very old in nursing homes, those aged 85 years and over accounted for 76 percent of the increase in elderly residents from 1977 to 1985. The proportion of elderly residents who were aged 85 years and over increased from 40 percent in 1977 to 45 percent in 1985.

    Not only were nursing home residents typically very old but they also tended to be female and white. Seventy-five percent of elderly residents were female. Similarly, 93 percent of elderly residents were white. Only 6 percent were black, and less than 1 percent were other races (a category that includes Asian and Pacific Islanders, American Indians, and Alaska Natives). On the average, elderly females were older than their male counterparts (84 versus 81 years). Elderly white residents, who had an average age of 83 years, also tended to be slightly older than elderly black residents (81 years) and other residents (80 years).

    As measured by the percent of elderly residing in nursing homes, the patterns of nursing home utilization mirrored the distributions of residents by age, sex, and race. On any given day during the survey period, 5 percent of the population aged 65 years and over resided in nursing homes (table 1). The rate of nursing home use increased sharply from 1 percent of those aged 65-74 years to 22 percent of those 85 years and over. Elderly females were twice as likely as elderly males to be residents of nursing homes. Six percent of elderly females were in nursing homes, compared with 3 percent of elderly males. Although use of nursing homes increased with advancing age for both sexes, women used nursing homes at significantly higher rates than men did regardless of the age group. One in four women 85 years of age and over resided in nursing homes, compared with one in seven men of the same age (figure 1). This greater utilization by elderly women than men is a reflection of women's longer life expectancy.6 It is also a reflection of a greater tendency among persons without spouses and with poor health to enter nursing homes.

    TABLE 1. Number, Percent Distribution, and Rate of Nursing Home Residents 65 Years of Age and Over by Age, Sex, and Race: United States, 1985
    Age, Sex, and Race Number of Residents Percent Distribution Number of Residents Per 1,000 Population 65 Years and Over 1
    Total 1,315,800 100.0 46.1
    Age
    65-74 years 212,100 16.1 12.5
    75-84 years 509,000 38.7 57.7
    85 years and over 594,700 45.2 219.4
    Sex
    Male 334,000 25.4 29.0
    Female 981,900 74.6 57.7
    Race
    White 1,221,900 93.1 47.6
    Black 82,000 6.2 35.0
    Other 8,900 0.7 20.1
    1. Population data used to compute rates are from --U.S. Bureau of the Census: Estimates of the population of the United States by age, sex, and race, 1980 to 1985, Current Population Reports, Series P-25, No. 985, Washington, U.S. Government Printing Office, Apr. 1986.

    Elderly white persons are more likely to reside in nursing homes than black persons and those of other races are. In 1985, 5 percent of the elderly white population, compared with 4 and 2 percent of the population of black and other races, respectively, resided in nursing homes. The greater likelihood of elderly white people to reside in nursing homes was particularly true in the oldest age group. Of the population 85 years and over, 23 percent of white people, compared with 14 percent of black people, resided in nursing homes.

    FIGURE 1. Number of Nursing Home Residents Per 1,000 Population 65 Years of Age and Over, by Sex and Age: United States, 1985: unavailable at the time of HTML conversion--will be added at a later date.

    This lower use of elderly black people and those of other races may result from substitution of informal care at home for formal nursing home care. According to data from the 1982 National Long-Term Care Survey (NLTCS), a higher proportion of elderly black people and people of other races than elderly white people were functionally impaired and remained in the community. Overall, 29 percent of the noninstitutionalized elderly who were of black or other races were functionally impaired in ADLs or home management activities for at least three months, compared with only 19 percent of white people.7 Thus, elderly persons who were of black or other races were over represented among the noninstitutionalized most at risk of needing nursing home care. This finding suggests "the use of a more extended support system among black persons than among white persons."7 Other studies have shown that elderly black persons are more likely than elderly white persons to receive care at home.8

    The proportion of the elderly residing in nursing homes has not changed since the period 1973-74, when the first NNHS was conducted (figure 2). An exception to this trend is the increase in the proportion of elderly black persons using nursing homes. During the period 1973-74, 2 percent of the elderly black population resided in nursing homes; in 1985, the proportion was nearly 4 percent. In contrast, the proportion of the elderly in nursing homes did not change from 1973-74 to 1985 for persons who are white or of other races. About 5 percent of elderly white persons and 2 percent of elderly persons of other races were residents of nursing homes throughout this period. The percent of elderly males and females as well as the percent of the elderly aged 65-74 and 75-84 years who resided in nursing homes also remained the same. The percent of persons 85 years and over, however, decreased: 25 percent of persons aged 85 years and over resided in nursing homes in the period 1973-74, compared with 22 percent in 1985.

    FIGURE 2. Number of Nursing Home Residents Per 1,000 Population 65 Years of Age and Over, by Race: United States, 1973-74, 1977, and 1985: unavailable at the time of HTML conversion--will be added at a later date.

    III. FUNCTIONAL DEPENDENCIES

    Because of the preponderance of very old residents in nursing homes, it is not surprising that many residents required assistance in performing or did not perform the basic ADLs, which are needed for independent living. In 1985, 91 percent of elderly residents required assistance in bathing; 78 percent received assistance in dressing; 63 percent required assistance in using the toilet room; 63 percent required assistance in transferring from a bed or chair; 55 percent were incontinent (bowels, bladder, or both); and 40 percent required assistance in eating (table 2). These findings are consistent with earlier studies by Katz and Akpom, in which it was shown that loss of independence is most likely to occur in bathing and least likely to occur in eating.9

    In general, elderly residents in nursing homes were more dependent in performing the ADLs in 1985 than in 1977. A larger proportion of elderly residents required assistance or had difficulty with bathing, using the toilet room, continence, and eating in 1985 than 1977 (table 2 and table 3). The exception to this trend was for dressing. The proportion of elderly residents requiring assistance in this ADL remained the same in both years. (Information about transferring from a bed or chair is not available from the 1977 NNHS.)

    TABLE 2. Percent of Nursing Home Residents 65 Years of Age and Over, by Type of Dependency in Activities of Daily Living, Percent Distribution by Number of Dependencies, and Average Number of Dependencies, According to Age, Sex, and Race: United States, 1985
    Dependency Status Total Age Sex Race
    65-74 Years 75-84 Years 85 Years and Over Male Female White Black Other
    Type of Dependency Percent
    Requires assistance in bathing 91.2 84.8 90.3 94.1 86.9 92.6 90.9 94.2 91.5
    Requires assistance in dressing 77.7 70.2 75.9 81.9 71.5 79.7 77.3 83.7 72.9
    Requires assistance in using toilet room 63.3 56.6 60.3 68.2 56.2 65.7 62.9 68.6 61.4
    Requires assistance in transferring 1 62.7 52.1 59.7 69.0 55.3 65.2 62.2 70.2 60.9
    Continence--difficulty with bowel and/or bladder 54.5 42.9 55.0 58.1 51.9 55.3 54.1 59.9 47.6
    Requires assistance in eating 40.4 33.4 39.1 44.0 34.8 42.3 40.0 47.9 32.1
    Number of Dependencies Percent Distribution
    Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
    None 7.6 13.2 8.6 4.8 11.8 6.2 7.8 4.8 *8.5
    1 11.0 14.0 11.6 9.4 12.5 10.5 11.3 6.5 *15.8
    2 9.9 11.2 9.6 9.6 10.0 9.8 10.0 8.0 *8.8
    3 7.8 7.3 8.7 7.2 8.6 7.5 7.6 11.4 *5.5
    4 13.5 13.8 12.8 13.9 12.6 13.8 13.4 14.4 *16.6
    5 19.8 16.6 19.4 21.3 18.7 20.2 19.9 18.9 *18.6
    6 30.4 23.9 29.2 33.8 25.7 32.0 30.1 35.9 *26.3
    - Average Number
    Average number of dependencies 3.9 3.4 3.8 4.2 3.6 4.0 3.9 4.2 3.7
    1. Transferring refers to getting in or out of a bed or chair.

    A partial explanation of the increased level of functional dependency is the shift in the age dis- tribution of nursing home residents to the very old age group (85 years and over), noted earlier. However, as table 2 and table 3 show, the proportion of residents functionally dependent in each ADL was generally higher in 1985 than in 1977 even when age was held constant. For example, a larger proportion of residents aged 85 years and over were dependent in bathing, dressing, using the toilet room, continence, and eating in 1985 than in 1977. Another explanation is the impact of Medicare policy on nursing home care. Under the Medicare prospective payment system (PPS), instituted in 1983, hospitals are encouraged to reduce patient length of stay. Patients released earlier under this new system may require a higher level of care in the nursing home than they would have needed if they had stayed longer in the hospital.10

    TABLE 3. Percent of Nursing Home Residents 65 Years of Age and Over, by Age and Type of Dependency in Activities of Daily Living: United State, 1977
    Type of Dependency All Ages 65 Years and Over 65-74 Years 75-84 Years 85 Years and Over
    - Percent
    Requires assistance in bathing 88.6 81.2 88.9 91.7
    Requires assistance in dressing 77.7 61.2 72.5 75.8
    Requires assistance in using toilet room 54.8 46.9 54.3 59.0
    Continence--difficulty with bowel and/or bladder 47.3 37.6 47.1 52.2
    Requires assistance in eating 33.6 27.1 33.8 36.5

    In general, dependency in ADLs increases with age. In 1985, the percent of residents requiring assistance in bathing increased from 85 percent for residents 65-74 years to 94 percent for residents 85 years and over. Similarly, difficulty with bowel or bladder control increased from 43 percent for residents 65-74 years to 58 percent for residents 85 years and over. Because female residents were older, on the average, than male residents, they tended to require assistance in ADLs more often than males did. A greater proportion of female than male elderly residents needed assistance in bathing, dressing, using the toilet room, transferring from a bed or chair, and eating. There was no statistically significant difference in the percent incontinent by sex. Elderly black residents also needed assistance in ADLs more often than elderly white residents did. This was the case in five of the six ADLs. There was no statistically significant difference in the percent incontinent by race.

    The six ADLs may be summarized into a single measure of ADL dependency by summing the number of activities in which a resident required assistance.9 In 1985, 30 percent of elderly residents required assistance in all six ADLs, and only 8 percent were independent in all six activities. The mean number of dependencies was 3.9. The mean number of ADL dependencies increased with age from an average of 3.4 dependencies among residents 65-74 years to 4.2 dependencies among those 85 years and over. Females tended to be more functionally dependent than males. Overall, elderly females had an average of 4.0 ADL dependencies, and elderly males had an average of 3.6. Elderly black residents also tended to be more functionally dependent than elderly white residents. The average number of ADL dependencies was 4.2 among elderly black residents, compared with 3.9 among elderly white residents. Thus, the data show a greater need for care in nursing homes among female and black residents. In the case of females, this is correlated with higher use of nursing homes. This is not the case, however, for elderly black persons.

    Although it is possible that nursing home policy may preclude the resident from performing ADLs without assistance, the overwhelming need for assistance in ADLs among nursing home residents suggests that this dependency may have been a reason for entering the nursing home. (The importance of functional status as a reason for nursing home admission was also found in a study of Medicare recipients.11) In contrast, the need for such assistance is minimal among the noninstitutionalized elderly. According to data from the Supplement on Aging to the 1984 National Health Interview Survey, 6 percent of the noninstitutionalized elderly received assistance in bathing; 4 percent, in dressing; 2 percent, in using the toilet room; 3 percent, in transferring from a bed or chair; and 1 percent, in eating (table 4). Data from the 1982 NLTCS, which covered noninstitutionalized Medicare enrollees most at risk of needing long-term care (LTC) (people functionally impaired in ADLs or the instrument activities of daily living for at least three months), indicate a lower need for assistance in ADLs than was found among nursing home residents. In 1982, 42 percent of the elderly impaired living in the community required assistance in bathing, 20 percent required assistance in dressing, 21 percent required assistance in using the toilet room, 26 percent required assistance in transferring from a bed or chair, and 6 percent required assistance in eating.7 Additional insights should be provided on the reasons for admission when data from the next-of-kin component are available.

    TABLE 4. Percent of Persons 65 Years of Age and Over, by Whether Nursing Home Resident or Noninstitutionalized and Type of Dependency in Selected Activities of Daily Living: United States, 1984 and 1985
    Type of Dependency Nursing Home Residents, 1985 Noninstitutionalized Population,1 1984
    Requires Assistance in: Percent
    Bathing 91.2 6.0
    Dressing 7.77 4.3
    Using toilet room 63.3 2.2
    Transferring 2 62.7 2.8
    Eating 40.4 1.1
    1. Data are from the National Center for Health Statistics, D.Dawson, G. Hndershot, and J. Fulton: Aging in the eighties, functional limitations of individuals age 65 years and over, Advance Data From Vital and Health Statistics, No. 133, DHHS Pub. No. (PHS)87-1250, Public Health Service, Hyattsville, Md., April 30, 1987. Percent of the noninstitutionalized elderly dependent in activities of daily living is a measure of those who received help rather than those needing it.
    2. Transferring refers to getting in or out of a bed or chair.

    IV. COGNITIVE IMPAIRMENT

    Another reason for nursing home placement that is cited in the literature is deteriorating cognitive functioning.12 In 1985, 63 percent of elderly residents were disoriented or memory impaired to such a degree that performance of the basic ADLs, mobility, and other tasks were impaired nearly every day. Disorientation or memory impairment was defined as being unable to remember dates or time, unable to identify familiar locations or people, unable to recall important aspects of recent events, or unable to make straightforward judgments. Major causes of disorientation or memory impairment in the elderly are senile dementia and chronic organic brain syndrome. In 1985, 47 percent of elderly residents were reported to have at least one of these conditions (table 5). Sixty-six percent of elderly residents who were disoriented or memory impaired were also reported to have senile dementia or chronic organic brain syndrome.

    In general, disorientation or memory impairment increased with age: 56 percent of residents 65- 74 years of age had memory impairment or disorientation, compared with 67 percent of those 85 years and over. Elderly female residents were memory impaired or disoriented more often than elderly male residents were--64 percent and 59 percent, respectively. This finding may be related to females' greater longevity. Although it appears that elderly black residents were memory impaired more often than elderly white residents were (70 percent, compared with 62 percent of elderly white residents), the difference was not statistically significant. Similar patterns were also found for residents with senile dementia or chronic organic brain syndrome when examined by age, sex, and race.

    TABLE 5. Percent of Nursing Home Residents 65 Years of Age and Over, by Whether They Had Disorientation or Memory Impairment and Senile Dementia or Chronic Organic Brain Syndrome, Age, Sex, and Race: United States, 1985
    Age, Sex, and Race Disorientation or Memory Impairment Senile Dementia or Chronic Organic Brain Syndrome
    - Percent
    Total 62.6 47.0
    Age
    65-74 years 55.7 34.0
    75-84 years 60.8 45.4
    85 years and over 66.6 52.9
    Sex
    Male 58.8 42.1
    Female 63.9 48.6
    Race
    White 62.2 46.8
    Black 69.5 51.4
    Other 56.2 *35.2

    V. MARTIAL STATUS AT ADMISSION

    The marital status of residents may have influenced the decision to enter the nursing home because persons without spouses may not have anyone living with them to provide personal care services that would allow them to stay in the community longer. In 1985 the majority of elderly residents were without spouses at the time of admission to the nursing home: 65 percent were widowed, 6 percent were divorced or separated, and 14 percent had never married (table 6). In contrast, only 16 percent of elderly residents were married at the time of admission. The likelihood of being widowed increased with age, and the proportion who were married decreased with age. In the group 65-74 years, 36 percent of residents were widowed; 77 percent of residents 85 years and over were widowed. Elderly female residents were more likely to be widowed (74 percent) than elderly male residents (37 percent). Elderly males were more likely to be married (33 percent) than elderly female residents (11 percent).

    The tendency of persons without spouses to enter nursing homes is highlighted by comparing the marital status of the functionally impaired elderly living in the community with that of elderly nursing home residents. The proportion married was larger among the functionally impaired elderly living in the community (44 percent) than among elderly nursing home residents (16 percent). Thus, 84 percent of the elderly in nursing homes were without spouses, compared with 56 percent of the functionally impaired living in the community.7

    TABLE 6. Percent Distribution of Nursing Home Residents 65 Years of Age and Over by Marital Status at Admission and Percent with Living Children, According to Age, Sex, and Race: United States, 1985
    Age, Sex, and Race Total Marital Status at Admission Proportion with Living Children
    Married Widowed 1 Divorced or Separated Never Married
    - Percent Distribution Percent
    Total 100.0 16.4 64.2 5.9 13.5 63.1
    Age
    65-74 years 100.0 22.8 35.9 14.2 27.2 50.1
    75-84 years 100.0 19.2 60.9 6.5 13.4 62.2
    85 years and over 100.0 11.8 77.2 2.3 8.6 68.6
    Sex
    Male 100.0 32.5 36.7 10.1 20.6 55.7
    Female 100.0 11.0 73.6 4.4 11.0 65.7
    Race
    White 100.0 16.6 64.4 5.6 13.3 64.5
    Black 100.0 13.8 61.9 9.8 14.5 41.8
    Other 100.0 *14.9 64.9 - *20.2 68.1
    1. A small number of persons of unknown marital status are included.

    VI. PRESENCE OF LIVING CHILDREN

    Data on whether nursing home residents had living children were collected for the first time in the 1985 NNHS. Among elderly nursing home residents, the majority (63 percent) had living children. The proportion of residents with children increased with age and was greater for female residents (66 percent) than male residents (56 percent). The trends among residents with children mirror the increasing utilization rates by age and the greater nursing home use by elderly women. Additionally, these trends appear to contradict the notion that the lack of children, which is a proxy measure for the lack of a social support network, is a risk factor for nursing home institutionalization. The finding that most elderly residents had children does not explain by itself why people enter nursing homes because this variable is confounded by several factors. First, it is not known whether the residents' children lived close enough to provide care and, if they did, whether they were physically able to provide care. Although 69 percent of residents 85 years and over had children, their children were probably in their sixties and may not have been physically able to care for their aging parents. Furthermore, for many residents, admission to the current nursing home was not from the community but from another health institution. As will be discussed in the next section, more than one-half of elderly residents were transferred to the nursing home from another health facility. For these residents, obtaining appropriate continuing care was a deciding factor in entering the current nursing home. Their children may not have been able to provide adequate informal care in the home. Further insights on this issue should be gained when the next-of-kin data on the sample residents are available.

    There was one exception to this trend. Only 42 percent of elderly black residents had children, compared with 65 percent of elderly white residents. In the 1982 NLTCS it was found that noninstitutionalized elderly black persons who were functionally impaired were more likely to live with children than functionally impaired elderly white persons were.7

    VII. LIVING ARRANGEMENTS PRIOR TO NURSING HOME ADMISSION

    The living arrangements of residents prior to admission reflect both the amount of support given in the environment in which they previously lived and their health. A majority of the residents (57 percent) were transferred from another health facility (table 7). The most common type of health facility transferred from was a short-stay hospital (39 percent). Only 12 percent of residents were transferred from another nursing home, and 3 percent were transferred from some type of mental facility (mental hospital, facility for the mentally retarded, psychiatric unit of a short-stay hospital, or mental health center). The proportion of elderly residents admitted from a short-stay hospital in 1985 (39 percent) was a significant increase from the proportion in 1977 (34 percent). This finding may also be related to the introduction of the Medicare PPS, under which hospitals have a strong incentive for early discharge of patients needing LTC services.10 Further analysis of this issue will be presented in a later report.

    TABLE 7. Percent Distribution of Nursing Home Residents 65 Years of Age and Over by Living Arrangement Prior to Admission, According to Age, Sex, and Race: United States, 1985
    Living Arrangement Prior to Admission Total Age Sex Race
    65-74 Years 75-84 Years 85 Years and Over Male Female White Black Other
    - Percent Distribution
    All living arrangements 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
    Private or semiprivate residence 40.0 29.2 40.5 43.3 36.3 41.2 40.5 31.9 35.6
    • Alone
    14.7 8.2 14.7 17.0 11.6 15.8 15.2 6.9 *15.5
    • With family members
    18.9 16.0 19.8 19.2 19.3 18.8 18.9 19.0 *15.5
    • With nonfamily members
    3.4 *3.1 3.3 3.5 3.2 3.4 3.3 3.9 *2.0
    • Unknown if with others
    3.0 1.8 2.7 3.7 2.2 3.3 3.1 *2.1 *2.5
    Another health facility 57.0 67.7 56.5 53.6 60.4 55.9 56.5 65.2 59.0
    • Another nursing home
    12.2 12.9 12.6 11.5 13.1 11.8 12.4 9.2 *9.7
    • General or short-stay hospital 1
    38.7 39.5 38.2 38.9 35.2 40.0 37.9 49.5 49.4
    • Mental facility 2
    3.0 7.6 3.2 1.1 5.0 2.3 3.1 *1.8 -
    • Veterans hopsital
    1.4 4.6 0.9 0.7 5.4 *0.0 1.5 *1.9 -
    • Other health facility or unknown
    1.9 3.3 1.9 1.4 1.9 1.9 1.8 *3.8 -
    Unknown or other arrangement 2.9 2.9 2.7 3.0 3.1 2.8 2.9 *2.9 *5.4
    1. Psychiatric units of hospitals are excluded.
    2. Mental hospitals, facilities for the mentally retarded, general or short-stay hospital psychiatric units, and mental health centers are included.

    The increasing proportion of residents transferred from short-stay hospitals to nursing homes also reflects the increasing role hospitals play in the provision of care to the elderly. For example, in the 1985 NNHS it was found that 22 percent of elderly residents were hospitalized for acute episodes of illness while still a resident of the nursing home. Thirteen percent of elderly residents had only one hospitalization, and 9 percent had two or more hospital stays (table 8). Hospitalizations of elderly residents were less likely among those who were admitted to the nursing home from a short-stay hospital, only 14 percent of whom had a subsequent hospitalization while a resident. In contrast, 27 percent of elderly residents not admitted from a short-stay hospital were hospitalized while a resident of the home.

    When examined by age, the proportion of elderly residents transferred from a short-stay hospital or nursing home did not vary. Elderly female residents, however, were more likely to be admitted from a short-stay hospital (40 percent) than elderly male residents were (35 percent). In addition, a higher proportion of elderly black residents (50 percent) than elderly white residents (38 percent) were transferred from a short-stay hospital. These findings appear to be correlated with the generally more dependent functional status of elderly women and black residents.

    TABLE 8. Percent Distribution of Nursing Home Residents 65 Years of Age and Over by Number of Hospital Admissions While a Resident, According to Whether Admitted From a Short-Stay Hospital: United States, 1985
    Number of Hospital Stays While a Resident Total Admitted from Short-Stay Hospital Not Admitted from Short-Stay Hospital
    - Percent Distribution
    Total 100.0 100.0 100.0
    None 1 77.7 85.5 72.8
    1 13.1 9.2 15.6
    2 or more 9.1 5.2 11.6
    1. A small number of persons with unknown number of hospital stays are included.

    Forty percent of elderly residents were admitted from a private or semiprivate residence; 15 percent had lived alone prior to the nursing home admission, 19 percent lived with family members, and 3 percent lived with persons who were not family members. Residents 75 years and over were more likely than those 65-74 years to have lived alone prior to being admitted to the nursing home. Elderly female residents were more likely to have lived alone (16 percent) than elderly male residents (12 percent). Elderly black residents were less likely (7 percent) than elderly white residents (15 percent) to have lived alone prior to admission. This may result from the tendency of functionally impaired elderly black people to "draw on a more extended range of relationships in their living arrangement than white persons."7

    VIII. PRIMARY SOURCE OF PAYMENT AT ADMISSION

    Data on sources of funds used to pay for nursing home care provide a rough measure of residents' income sources because public funds for nursing home care under certain government programs are available only to those who cannot afford to pay for such care. The Medicaid program, for example, is a joint Federal-State program providing medical benefits to persons who qualify for welfare and to some of the "medically needy" (those who would be on welfare if their incomes were a little lower). The State-set criteria for Medicaid eligibility vary from State to State but cover most poor people in the United States.13

    Information on the payment sources used during the month of admission was collected for the first time in the 1985 NNHS. Table 9 shows the primary payment source used by elderly residents in the home one month or more. One-half of elderly residents relied primarily on their own income or family support to pay for the first month in the nursing home, and 40 percent relied primarily on the Medicaid program to pay for care. Medicaid finances both skilled nursing and intermediate care services in nursing homes. At the time of admission, 26 percent of elderly residents received intermediate care and 14 percent received skilled nursing care through the Medicaid program. Only 5 percent of elderly residents relied on Medicare. Extended care benefits under Medicare are limited to 100 days following a hospital stay of at least three days. Three percent of elderly residents relied on other government assistance or welfare, and another 3 percent relied on other payment sources. Overall, 48 percent of elderly residents relied on some form of public funds to pay for their stay at the time of admission.

    TABLE 9. Percent Distribution of Nursing Home Residents 65 Years of Age and Over by Primary Source of Payment at Admission, According to Age, Sex, and Race: United States, 1985
    Age, Sex, and Race Primary Source of Payment at Admission
    All Sources Own Income or Family Support Medicare Medicare Payment for: Other Government Assistance or Welfare All Other Sources
    Skilled Nursing Intermediate Care
    - Percent Distribution
    Total 100.0 49.8 4.9 13.9 26.2 2.7 2.5
    Age
    65-74 years 100.0 39.0 4.7 13.9 31.5 5.5 5.4
    75-84 years 100.0 51.2 5.2 13.5 25.3 2.6 2.3
    85 years and over 100.0 52.4 4.6 14.3 25.1 1.9 1.7
    Sex
    Male 100.0 50.9 4.8 11.9 23.7 4.0 4.8
    Female 100.0 49.5 4.9 14.6 27.0 2.3 1.7
    Race
    White 100.0 52.2 4.9 13.2 24.6 2.6 2.5
    Black 100.0 17.1 *5.0 21.1 49.3 *5.3 *2.2
    Other 100.0 *32.0 - 39.3 *28.7 - -
    NOTE: Data cover only persons who were residents for 1 month or more.

    There were differences in primary payment source by age. Residents 75 years of age and over were more likely to use their own income or family support for primary payment than were residents aged 65-74 years. Of residents 75-84 years and 85 years and over, 51 and 52 percent, respectively, relied on their own income or family support to pay for care, compared with 39 percent of residents 65-74 years. Medicaid was the primary payment source for a larger proportion of residents 65-74 years (45 percent) than residents aged 75-84 years (39 percent) or 85 years and over (39 percent). The primary payment source also varied by sex. A larger proportion of elderly females (42 percent) than elderly males (36 percent) relied on Medicaid for payment.

    There were major differences in the patterns of payment at admission by race. Elderly black residents were almost twice as likely to use Medicaid as the primary source of payment (70 percent) as elderly white residents were (38 percent). Conversely, elderly white residents were more likely to use their own income or family support as their primary payment source (52 percent) than elderly black residents were (17 percent). The differences in payment source by sex and race reflect the generally low income of elderly women and elderly black people in the noninstitutionalized population,14 and in particular among the functionally impaired elderly living in the community. In 1982, 46 percent of elderly females who were functionally impaired and living in the community had family incomes of less than $7,000, compared with 31 percent of their male counterparts. Similarly, 61 percent of functionally impaired elderly black persons had family incomes of less than $7,000, compared with 37 percent of functionally impaired elderly white persons. (Family income included income of the functionally impaired individual and all members living with him or her.)7

    IX. CONCLUSIONS

    On any given day during the survey period for the 1985 NNHS, about 5 percent of the elderly were residents of nursing homes. Use of nursing homes increased with age for both sexes but was greater for females than for males, especially in the older age groups. Use of nursing homes was lower for elderly persons who were black or of other races than for white persons. These trends have remained constant since the period 1973-74, when the first NNHS was conducted, with the exception of an increase in the use of nursing homes by elderly black persons and a decrease in use by those aged 85 years and over.

    Examination of some health and social characteristics revealed that dependency in ADLs was widely prevalent among elderly nursing home residents but much rarer among the noninstitutionalized elderly. The lack of available caregivers may have been a confounding factor for the preponderance of persons without spouses in nursing homes. The role of the residents' children or their living arrangements prior to admission in the decision to enter a nursing home is not clear from the data examined. The need for continuing care in a nursing home and the availability and willingness of the residents' children to provide informal home care are issues that need further examination before conclusions can be drawn. There issues will be examined in future reports in which data from the next-of-kin component are presented. The lower use of nursing homes by elderly black persons appears to be related to a greater substitution of informal care at home for formal nursing home care.

    In this report, data on the primary source of payment for care used by residents during the month of admission were also presented. The data show that in 1985 one-half of elderly residents relied primarily on their own income or family support to pay for the first month in the nursing home, and 40 percent relied primarily on the Medicaid program to pay for care. Residents 75 years of age and over were more likely to use their own income or family support to pay for care at admission than residents aged 65-74 years were. Residents aged 65-74 years were more likely to use Medicaid. Elderly black residents were almost twice as likely as elderly white residents to use Medicaid as the primary payment source at admission. Overall, 48 percent of elderly residents relied on some form of public funds (Medicaid, Medicare, other government assistance, or welfare) to pay for their stay at the time of admission.

    TECHNICAL NOTES

    Because the statistics presented in this report are based on a sample, they will differ somewhat from figures that would have been obtained if a complete census had been taken using the same schedules, instructions, and procedures. The standard error is primarily a measure of the variability that occurs by chance because only a sample, rather than the entire universe, is surveyed. The standard error also reflects part of the measurement error, but it does not measure any systematic biases in the data. The chances are 95 out of 100 that an estimate from the sample differs from the value that would be obtained from a complete census by less than twice the standard error.

    The standard errors used in this report were approximated using the balanced repeated-replication procedure. This method yields overall variability through observation of variability among random subsamples of the total sample. A description of the development and evaluation of the replication technique for error estimation has been published.15, 16

    Although exact standard error estiamtes were used in tests of significance, it is impractical to present exact standard error estimates for all statistics used in this report. Thus, a generalized variance function was produced for aggregated resident estimates by fitting the data presented in this report into a curve using the empirically determined relationship between the size of an estimate X and its relative variance (rel var X). This relationship is expressed as:

    rel var X = S2x/X2 = a + b/X

    where a and b are regression estimates determined by an iterative procedure. Preliminary estimates of standard errors for the percents of the estimated number of residents are presented in table 10.

    The Z-test with a 0.05 level of significance was used to test all comparisons mentioned in this report. Not all observed differences were tested, so lack of comment in the text does not mean that the difference was not statistically significant.

    TABLE 10. Standard Errors of Percents for Residents
    Base of Percent (Residents) Estimated Percent
    1 or 99 5 or 95 10 or 90 20 or 80 40 or 60 50
    - Standard Errors in Percentage Points
    5,000 2.84 6.22 8.56 11.41 13.97 14.26
    10,000 2.01 4.40 6.05 8.07 9.88 10.09
    30,000 1.16 2.54 3.49 4.66 5.71 5.82
    50,000 0.90 1.97 2.71 3.61 4.42 4.51
    100,000 0.63 1.39 1.91 2.55 3.12 3.19
    200,000 0.45 0.98 1.35 1.80 2.21 2.26
    400,000 0.32 0.70 0.96 1.28 1.56 1.59
    800,000 0.22 0.49 0.68 0.90 1.10 1.13
    1,000,000 0.20 0.44 0.61 0.81 0.99 1.01
    1,491,000 0.16 0.36 0.50 0.66 0.81 0.83


    Symbols
    --- Data not available
    ... Category not applicable
    - Quantity zero
    0.0 Quantity more than zero but less than 0.05
    Z Quantity more than zero but less than 500 where number are rounded to thousands
    * Figure does not meet standard of reliability or precision
    # Figure suppressed to comply with confidentiality requirements


    DISCHARGES FROM NURSING HOMES: PRELIMINARY DATA FROM THE 1985 NATIONAL NURSING HOME SURVEY

    Edward S. Sekscenski, M.P.H., Division of Health Care Statistics

    This report presents information on discharged residents of nursing and related-care homes based on preliminary estimates from the 1985 National Nursing Home Survey (NNHS). The 1985 NNHS is the third in an ongoing series of sample surveys designed to provide a variety of data on nursing homes in the conterminous United States and is conducted periodically by the National Center for Health Statistics (NCHS). Previous surveys were conducted in 1973-74 (NCHS, 1977) and 1977 (NCHS, 1979).

    The data presented in this report were collected between August 1985 and January 1986 and deal specifically with demographic, health, and other characteristics of persons formally discharged from nursing homes during the 12-month period immediately prior to the survey date. Other reports already published present information on nursing home residents (NCHS, 1987a) and facilities (NCHS, 1987b) based on national estimates from the same survey. Two other reports resulting from the 1985 NNHS will provide information on registered nurses employed at nursing homes and on current and discharged nursing home residents. The latter report will be based on a followup survey of the next of kin of the sample population. A summary report presenting data from all five components of the survey also will be prepared by NCHS. Because data in this report are preliminary, they may differ slightly from those published later after further edits are conducted.

    Facilities included in the 1985 NNHS were nursing and related-care homes in the conterminous United States that had three beds or more set up and staffed for use by residents and that routinely provided nursing and personal care services. A facility could be freestanding or could be a nursing care unit of a hospital, retirement center, or similar institution as long as the unit maintained financial and employee records separate from the parent institution. Facilities providing only room and board were excluded, as were those serving only persons with specific health problems (for example, mental retardation or alcoholism).

    The sampling frame for the 1985 NNHS consisted of the following components:

    • The 1982 National Master Facility Inventory (NMFI) (NCHS, 1986), a census of nursing and related-care homes conducted by NCHS.
    • Homes identified in the 1982 Complement Survey of the NMFI as "missing" from the 1982 NMFI.
    • Nursing homes opened for business from 1982 through June 1984 and identified by the NCHS Agency Reporting System (NCHS, 1968).
    • Hospital-based nursing homes identified in records of the Health Care Financing Administration.

    The resulting frame contained about 20,500 nursing homes, and a sample of 1,220 homes was selected. In this report, the terms "nursing homes" and "nursing and related-care homes" are used interchangeably.

    Estimates in this report are based on a sample of 6,023 discharges from the 1,079 nursing homes participating in the survey. A more detailed description of the survey design, data collection methodology, and estimation procedures for the NNHS has been published elsewhere (Shimizu, 1987). A brief discussion of the standard errors associated with these data is presented in the Technical notes to this report. For convenience, this report uses the terms "discharges" and "discharged residents" interchangeably.

    I. BACKGROUND AND TYPE OF DATA

    Data in this report were obtained from personal interviews conducted in the sample nursing homes with the employees deemed most knowledgeable of the medical records of the discharged residents. In most cases the interviewee was either a nurse or medical records person who consulted with the available medical records of the discharged resident during the interview. As was true in the NNHS of previous years, no discharges were consulted personally in this component of the survey. The full sample consisted of six or fewer discharges from each nursing home whose discharge dates fell within the 12 months prior to the survey date.

    The 12-month reference period from which the discharge residents' sample was drawn for the 1985 survey ended on the date immediately preceding the survey date. Previous survey reference periods for discharges were the calendar years 1972 and 1976. The reference period of the 1985 survey was changed in an attempt to obtain more current and readily available data and to provide information on the utilization of nursing homes by both residents and discharges over a more closely related period of time. However, data from the 1985 NNHS for the discharged resident population and current resident population differ in several major areas. These differences are discussed in more detail in other NCHS publications (NCHS, 1978). Briefly, while the discharged resident estimates represent all discharges over a 12-month period, the current resident population is estimated for a single night, that immediately prior to the survey date. The discharge sample, therefore, may underestimate those nursing home residents who tend to stay for very lengthy periods, while the current resident population may underestimate those persons with very short durations of stay. While the current resident file provides for what may be considered a "snapshot" of nursing home residents on any given day, the discharged resident file provides for some indication of the over-the-year changes in the nursing home population.

    Because the methodology for counting discharged residents from the 1973-74 NNHS differed from that of the 1977 (NCHS, 1981) and 1985 surveys, no comparisons will be made in this report between estimates from the 1973-74 survey and those derived from the 1985 NNHS. The 1973-74 NNHS estimated the total number of discharges from each nursing home in the sample from one question in the facility component of the survey. The 1985 NNHS obtained a complete listing of all discharges from the sample nursing home. Comparisons will be presented of estimates from teh 1977 and 1985 discharged resident components of the NNHS where appropriate.

    II. DEMOGRAPHIC CHARACTERISTICS, DEPENDENCY, AND DURATION OF STAY

    The 1985 NNHS found that an estimated 1,223,500 persons were discharged from an estiamted 19,100 nursing and related-care homes during the 12 months prior to the survey date. Because the survey was conducted between August 1985 and January 1986, the 12-month reference period could have fallen anywhere beginning August 1984 and ending January 1986. The preliminary 1985 estimate represents about a 9.5 percent increase over the 1,117,500 discharges estimated by the 1977 NNHS. Of the recent total, about 37 percent were men while 63 percent were women, roughly the same as was found in the 1977 survey (see table 1). In contrast to the discharge population of 8 years earlier, however, the distribution of discharges in the 1985 survey was more heavily weighted with persons aged 85 years and over and by persons more dependent on the nursing home staff in terms of performance of selected activities of daily living.

    Although nearly 9 of every 10 discharges in both surveys were aged 65 years and over, the proportion aged 85 years or over rose from 30 to 38 percent between 1976 and 1984-85. Partly as a result of the aging of the discharge population, the proportion of all discharges who were not dependent in either mobility or continence decreased during the 8-year period from 40 to 31 percent while the proportion who were dependent in both of these functions increased drom 35 to 45 percent. The proportion of all discharges who were totally bedfast also rose between survey from about 21 to 35 percent and the proportion who were chairfast remained about 25 percent. Although in both the 1977 and 1985 surveys older discharges tended to be more dependent than were younger discharges (NCHS, 1981), increased dependencies were evident in all major age groups between surveys (see table 2 and table 3).

    In the 1977 and 1985 surveys, persons who were discharged at older ages were more likely to have had lengthier durations of stay in the nursing home than persons discharged at younger ages. This was as true for men as it was for women. The median duration of stay for all discharges was 82 days according to the 1985 survey; for persons aged 85 years and over, however, it was 145 days (see table 4). Women discharges, who tend to be older than discharged men (overall median ages, 83 and 79 years, respectively), also had a longer median duration of stay, 93 as compared with 66 days, according to the 1985 survey. Older women, however, also tended to stay longer in nursing homes than older men. At least half of all women over 84 years of age had been confined to the sample nursing home for more than 4 months according to the 1985 survey, while comparable older men had a median duration of stay of little over 3 months.

    Although the estimated overall median durations of stay for all discharges, as well as those for all men and all women in the 1985 survey show observable increases over comparable estimates from the 1977 survey, none of these increases is statistically significant (according to a Z test with 0.05 level of significance). Similarly, none of the differences between surveys in the proportional distribution of discharges by similar duration-of-stay categories was significant. Nearly two-thirds of all discharges in either survey had stays of less than 6 months. About 31 percent in the 1985 survey had been discharged after stays of 1 month to less than 6 months. The remaining 37 percent of discharges in the 1985 survey had been confined to the nursing home for 6 months or more (see table 4).

    Because these data represent durations of stay in a nursing home identified with a single discharge, they tend to underestimate the overall duration of stay for persons who may have had a series of admissions and discharges to the same or multiple nursing homes over one episode or more of illness. Definitions of nursing home stays used in this report coincide with those used in the 1977 NNHS. The 1985 NNHS also attempted collection of information on multiple stays in nursing homes of the discharged residents with histories of other nursing home stays. These data will be presented in forthcoming publications on the 1985 NNHS.

    The 1985 NNHS was the first in the series to obtain race and Hispanic origin information on discharged residents. According to the 1985 survey, about 92.8 percent of all discharged residents were white persons, while only 6.7 percent were black persons. Another half percent were of other racial groups including Asian and Pacific Islander, American Indian, and Alaskan native. About 3 percent of the total were known to have been of Hispanic origin, an ethnicity designation distinct from race (see table 1). These distributions are similar to the distributions by race and Hispanic origin of current nursing home residents in the 1985 survey (NCHS, 1987a). Although differences in overall durations of stay are suggested in the median estimates of white and black discharged residents, these differences are not statistically significant at the 0.05 level of significance. Similarly, no statistically significant difference exists between the median duration of stay of Hispanic persons and that for all discharges in the 1985 survey. Discharged residents of Hispanic origin, however, had a male-to-female ratio nearly the reverse of that of the overall discharged population, 66 to 34 percent.

    The distribution of discharged residents by marital status did not change appreciably between the 1977 and 1985 surveys. It appears, however, that factors associated with a person being married at the time of discharge impact favorably on shorter durations of stay in a nursing home. Other studies have also found that the availability of a spouse as home caregiver is one factor in decreasing the likelihood of admission to a nursing home (for example, Butler and Newacheck, 1981), and previous NNHS's have found similar favorable impacts on short durations of stay for nursing home discharges.

    Widowed persons constituted the majority of all discharges, 55 percent in the 1985 survey. their median duration of stay was 107 days (see table 4). By contrast, the median duration of stay of married discharges, who constituted the next largest marital group, 22 percent, was only 41 days. Discharges who were never married, however, as well as divorced or separated discharges also had relatively lengthy median stays (see table 4).

    Not surprisingly, widowed discharged residents, noted above as having relatively long stays, were also the oldest of the marital group, with an overall median age of 85 years. However, married discharged residents, who as a group has relatively short durations of stay, had an older median age, 78 years, than discharges who were divorced or separated, 70 years.

    The effects of age do appear to explain many of the differences in the abilities of discharged residents to perform selected activities of daily living during their final week in the nursing home. While about 40 percent of persons who were aged 65-74 years at discharge had been dependent in both mobility and continence, about half of all discharges older than 84 years were dependent in both categories. In terms of specific dependencies, about one-third of discharges between ages 65 and 84 years were bedfast in their last week in the nursing home, while about 4 in 10 aged 85 years or over were bedfast (see table 2).

    Bladder and bowel incontinence was also related to age at discharge. About half of all discharges aged 75-84 years were incontinent of bladder in their last 7 days in the nursing home. Among persons aged 85 years and over, this proportion rises to about 59 percent. Similarly, while about 39 percent of discharges aged 65-74 years were incontinent of bowel in their last week in the nursing home, the comparable proportion rises to 52 percent for persons aged 85 years or over. As might be expected, median duration of stay was longer for discharges who were dependent in both continence and mobility, 108 days, than for those not dependent in either of these daily activities, 64 days.

    Differences in functional statuses in selected activities of daily living for discharges in the 1977 and 1985 NNHS are summarized in table 3. As is noted above, discharges in the 1985 survey were generally less mobile and more likely to have been incontinent of bowel, bladder, or both in their last 7 days in the nursing home than were discharges in the 1977 survey. These general increases in dependencies are partially a function of the increased proportion of discharges aged 85 years and over, who as a group are more dependent in these activities than are younger discharges. However, there were also increases in the proportions of discharges who were dependent in both mobility and continence among those under 65 years, 65-74 years, and 75-84 years, as well as those aged 85 years and over (see table 3).

    III. LIVING ARRANGEMENTS BEFORE ADMISSION AND AFTER DISCHARGE

    The 1985 NNHS collected information on the living arrangements of all discharged residents for the periods immediately prior to admission and, for live discharges, immediately after discharge. The 1977 survey obtained comparable data only for the living arrangements after discharge. Information on both prestay and poststay living arrangements of discharged nursing home residents provides for a more comprehensive understanding from a wider perspective of the population that utilizes nursing homes.

    A minority, about 28 percent of all discharged residents, had been admitted to the nursing home from a private or semi-private residence (see table 5). Slightly over half of these discharged residents had been living with family members at the time of their admission.

    About 69 percent of all nursing home discharges had been admitted directly from another health facility, with 8 of every 10 of them representing transfers from general or short-stay hospitals. A slightly higher proportion of female discharges had been admitted from general or short-stay hospitals than had men, 57 versus 51 percent. However, another 7 percent of the male discharges had been admitted directly from a veterans hospital. About 1 in every 10 discharges who had been admitted from another health facility came from another nursing home. The porportions were about the same for both men and women.

    The median duration of stay in the sample nursing home was far longer for those discharges who had been admitted from a private or semiprivate residence, 118 days, than for those admitted from a hospital, 57 days. This was partially dur to the differences in ages of those in either group. Among those discharges admitted from a residence, about 42 percent were over age 84 years at their discharges. About 37 percent of those admitted from a hospital were aged 85 years or over.

    Discharges who had originally been admitted from another nursing home also tended to have long durations of stay. According to the 1985 survey, their median duration of stay was 263 days. The proportion of those discharged over 84 was comparable to that of persons admitted from private or semiprivate residences, 43 percent.

    The proportion of live discharges going to private or semiprivate residences immediately following their nursing home stay decreased between the 1977 and 1985 surveys from 37 to 30 percent (see table 6). As a corollary, the proportion of live discharges who were discharged to another health facility increased from 59 to 68 percent. The latter was almost entirely the result of an increase in the proportion of live discharges going to general or short-stay hospitals, from 41 to 49 percent. (Unknown living arrangements following discharge remained about 2-4 percent of the total.)

    The increase in live discharges to hospitals, although partially a result of the increased proportion of older persons among all discharged residents, is not fully explained by this shift in demographics. While the proportion of discharges aged 85 years or over going directly to hospitals is slightly larger than is the comparable proportion for discharges aged 65-84 years in both the 1977 and 1985 surveys, the increase in either proportion between surveys is greater among the younger age group. Among live discharges aged 85 years or over, the proportion discharged to hospitals did not rise significantly between the 1977 and 1985 surveys. In 1977 it was 52 percent and in 1985 it was 54 percent. Among live discharges aged 65-84 years old, however, the proportion discharged directly to hospitals increased from 39 to 50 percent over the same period.

    The median duration of stay was longer for those persons discharged to another health facility, 113 days, than for those discharged to a private or semiprivate residence, 36 days. Among the former, those who were discharged to a general or short-stay hospital had a median duration of stay of 130 days. In contrast, among those discharged to a private or semiprivate residence, those who went to live with family members had a median duration of stay of 34 days.

    IV. PRIMARY SOURCE OF PAYMENT AT ADMISSION AND DISCHARGE

    For the first time, the 1985 NNHS collected information on the primary sources of payment for all discharges for the month in which they were admitted to the sample nursing home as well as for the month in which they were discharged. The 1977 NNHS obtained primary source of payment data only for the month of discharge from the nursing home. As might be expected, primary payment sources often differed depending on whether the payment was for the admissino or the discharge month. These differences generally are greater the longer the duration of stay. When observation is made of the total discharge population as a whole, much less shifting among various payment sources is evident, partially due to the large proportion of persons with relatively short durations of stay. However, patterns are evident in shifts of primary payment sources, especially among discharges who shift to medicaid at some time during their stay.

    For the month of admission, own income or family support was the primary source of payment for the largest proportion of discharges regardless of their eventual duration of stay. About 4 of every 10 discharges relied primarily on this source to pay for nursing home care in the month of admission, a ratio that was the same whether the completed stay was of short, medium, or lengthy duration (see table 7). The median duration of stay for persons whose primary source of payment for their admission month was own income or family support, 77 days, was similar to that of the overall discharge population. Their distribution by duration of stay was also similar to that for all discharges.

    According to the 1985 surveys, the proportion of all discharges who relied on medicaid as the primary payment source in their month of admission totaled about 35 percent. Medicaid coverage for nursing home care is dividedinto two categories, skilled and intermediate, depending on the certification status of the nursing home. While about 15.5 percent of all discharges relied on medicaid skilled funds in their admission month, another 19.6 percent relied on medicaid intermediate care funds. Unlike the proportion of discharges relying on own income to pay for care in their admission month, the proportion of discharges relying primarily on medicaid differed by the eventual durations of stay. Discharges whose completed stays were relatively lengthy were more likely to have relied on a type of medicaid in their admission month than were those whose stays were relatively short (see table 7).

    For example, while 12 percent of all discharges whose stays were less than 1 month in duration relied primarily on medicaid skilled care funds to pay for their nursing home care, 19 percent of those whose stays were 6 months or longer relied primarily on this source in their admission months. Comparable proportions for discharges who relied on medicaid intermediate care funds were 11 percent among those whose stays were less than 1 month and 27 percent for those whose completed stays were 6 months or more.

    The median durations of stay of discharges who relied on either medicaid skilled or medicaid intermediate funds to pay for nursing home care in their admission months were 145 and 187 days, respectively, each of which is significantly above the median for the discharge population as a whole.

    Medicare accounted for a smaller proportion of all discharges' primary sources of payment in their admission months than either their own income or family support, or the combined total of medicaid. Medicare, however, varied quite widely as a primary admission month payment source according to eventual completed duration of stay. Unlike similar differences outlined above for those relying on medicaid, the proportion of all discharges relying on medicare as their primary source of payment in their admission month was greater among discharges with relatively short durations of stay and smaller for those with longer completed stays. About 18 percent of all discharges relied primarily on medicare for payment for nursing home care in their admission months. But, while the proportion was 30 percent among discharges whose stays were less than 1 month, for discharges whose completed stays were 6 months or more, only 6 percent had relied primarily on medicare in their admission month. The median duration of stay was 29 days for all discharges whose primary source of payment in the month of admission was medicare, significantly below the median for all discharges.

    All other sources of payment, including other government assistance or welfare, religious organizations, volunteer agencies, Veterans Administration contracts, initial payment-for-life care funds, and others accounted for about 5 percent of all discharges' primary sources of payment for month of admission. This proportion did not vary significantly by completed duration of stay. Discharges relying on these other sources, however, tended to be younger than those whose primary payment sources were medicare, medicaid, or own income. Only about 22 percent were over age 84 years at their discharges, which is significantly below the comparable proportion for all discharges.

    For the month of discharge, own income or family support was also the primary source of payment for about 4 of every 10 discharges. Although some variability exists in this ratio by duration of stay, as many as 38 percent of all discharges whose stays were 6 months or more relied primarily on this source for payment of nursing home care in their discharge month as opposed to 45 percent among discharges whose stays were 1 month to less than 6 months in length.

    Medicaid, skilled and intermediate care funds combined, accounted for another 40 percent of all discharges' primary payment sources in their discharge months. The overall proportion who relied primarily on medicaid, however, was larger for those with longer stays than for those with relatively short stays. For example, while a total of 22 percent of discharges with stays of less than 1 month relied on some form of medicaid as their primary payment source, among discharges whose stays were 6 months or longer, a total of 56 percent relied on medicaid in their discharge months. About 25 percent of those who stayed 6 months or longer relied on medicaid skilled care funds, and another 31 percent relied on medicaid intermediate care funds as the primary payment sources in their discharge months.

    The proportion of discharges who relied on medicare as the primary payment source in their discharge month is a reflection of the limitations of coverage for nursing home care imposed by this Federal health care program. Medicare is limited to the first 100 days of nursing home care for residents who had been admitted directly from a general, short-stay hospital. The resident must also require specific medical assistance according to criteria established by the Federal Health Care Financing Administration (Health Care Financing Administration, 1986). Reliance on medicare as the primary source of payment for the discharge month, therefore, is restricted to discharges with relatively short durations of stay.

    Among all dischrages, about 12 percent used medicare as their primary source of payment in their discharge months. Among those whose stays were less than 1 month, however, about 29 percent relied primarily on medicare, as opposed to about 9 percent with stays of from 1 month to less than 6 months in length.

    Changes in primary sources of payment between admission and discharge months are summarized in table 8 for all discharges with a duration of stay of 1 month or more. The percent distributions show that except for those entering with medicare as their primary payment source, more than 8 of every 10 discharges relied on the same primary source of payment in their discharge month as they had utilized in their admission month. For example, among persons using primarily their own income or family support in their admission month, 85 percent relied primarily on this source also in their discharge month. The comparable proportion for medicaid (skilled and intermediate combined) is about 90 percent, while about 87 percent who primarily used other sources in their admission month also relied on those other sources in their discharge month.

    Among all persons with durations of stay of 1 month or more who utilized medicare as their primary payment source in their admission month, however, only about 37 percent relied primarily on medicare in their discharge montn as well. This was largely the result of the 100-day limitation for medicare coverage of nursing home care. About 32 percent of discharges who used primarily medicare in their admission month shifted to their own income or family support as primary payment source in their discharge month, while another 28 percent shifted to some form of medicaid.

    As noted above, while the overall proportion of discharges relying primarily on medicare decreased between admission and discharge months, the proportion using some form of medicare rose. Shifts to medicaid as the primary source of payment varied by both duration of stay and primary payment source in admission month (see table 9). About 11 percent of persons who entered with other than medicaid as their primary payment source shifted to medicaid by the month of their discharge. The proportions of discharges shifting in this manner varied from 10 percent for those with stays of 1 month to less than 6 months to about 22 percent for those with stays of 6 months or more in duration.

    Persons entering with medicare as the primary payment source in their admission month were more likely to shift to medicaid than persons entering with own income or family support. This was especially true for discharges whose durations of stay were beyond the 100-day limit imposed by the medicare program. About 10 percent of discharges who had used their own income in their admission month shifted to medicaid by their discharge month, while 15 percent of those relying primarily on medicare in the admission month converted to medicaid. About 8 percent of persons who entered using primarily their own income or family support and had stays of from 1 month to less than 6 months shifted to medicaid, as opposed to 19 percent of those with equal durations of stay who relied primarily on medicare in their admission month. Among discharges entering with medicare whose durations of stay were 6 months or longer, 52 percent shifted to some form of medicaid by their discharge months.

    It is not possible from the discharged resident data to pinpoint, however, when during a discharged resident's stay a shift from one payment source to another may have occurred. Differences in primary sources of payment in admission and discharge months are indicative only of a change between two point in time. While a pattern is suggested in the differential proportions of discharges shifting from one primary payment source to another, especially for discharges shifting to medicaid, it is not discernible from the data when these shifts occurred. Although the disaggregation of discharges who shift to medicaid by various duration of stay categories provides some evidence of a "spend down" to medicaid, more detailed data are required to determine when during a resident's stay this shift actually occurs and, for those with multiple stays, in which stay it occurred. Data on the latter issue are available from the next-of-kin component of the survey. Data from the next-of-kin component of the 1985 NNHS will be published in a forthcoming report from NCHS.

    V. SUMMARY AND HIGHLIGHTS OF DATA

    The 1.22 million nursing home discharges in the 1985 NNHS represent about a 9.5-percent increase from the 1977 survey. Dependencies in both mobility and continence were more prevalent among all age groups in the most recent survey while there was also an increase of from about 30 to 38 percent in the proportion of discharges aged 85 years and over. While the overall median duration of stay, as well as those of men and women, showed observed increases between the 1977 and 1985 surveys, none of these increases is statistically significant. The rise from 41 to 49 percent in the proportion of live discharges going to a hospital, however, is statistically significant. The increase is largely the result of increased hopsitalization of live nursing home discharges aged 65 to 84 years, although the proportion of discharges to a hospital remains larger among those aged 85 years and over.

    About 4 of every 10 discharges used own income or family support as primary payment source in both admission and discharge months. The proportion using medicaid, however, generally rose with duration of stay, while only discharges with relatively short stays relied primarily on medicare, due to the limitations on coverage for nursing home care by the medicare program.

    More detailed information from the 1985 NNHS, especially on sources of payment, diagnoses at admission and discharge, and duration of stay by admission and discharge characteristics, will be forthcoming in subsequent publication from NCHS.

    VIII. REFERENCES

    TECHNICAL NOTES

    Because the statistics presented in this report are based on a sample, they will differ somewhat from figures that would have been obtained if a complete census had been taken using the same schedules, instructions, and procedures. The standard error is primarily a measure of the variability that occurs by chance because only a sample, rather than the entire universe, is surveyed. The standard error also reflects part of the measurement error, but it does not measure any systematic biases in the data. The chances are 95 out of 100 that an estimate from the sample differs from the value that would be obtained from a complete census by less than twice the standard error.

    The standard errors used in this report were approximated using the balanced repeated-replication procedure. This method yields overall variability through observation of variability among random subsamples of the total sample. A description of the development and evaluation of the replication technique for error estimation has been published.15, 16

    Although exact standard error estiamtes were used in tests of significance, it is impractical to present exact standard error estimates for all statistics used in this report. Thus, a generalized variance function was produced for aggregated resident estimates by fitting the data presented in this report into a curve using the empirically determined relationship between the size of an estimate X and its relative variance (rel var X). This relationship is expressed as:

    rel var X = S2x/X2 = a + b/X

    where a and b are regression estimates determined by an iterative procedure. Preliminary estimates of standard errors for the percents of the estimated number of residents are presented in table 10.

    The Z-test with a 0.05 level of significance was used to test all comparisons mentioned in this report. Not all observed differences were tested, so lack of comment in the text does not mean that the difference was not statistically significant.


    Symbols
    --- Data not available
    ... Category not applicable
    - Quantity zero
    0.0 Quantity more than zero but less than 0.05
    Z Quantity more than zero but less than 500 where number are rounded to thousands
    * Figure does not meet standard of reliability or precision (more than 30 percent relative standard error)
    # Figure suppressed to comply with confidentiality requirements


    TABLE 1. Number and Percent Distribution of Nursing Home Discharges by Selected Characteristics: United States, 1984-85 and 1976
    Characteristic 1984-85 Discharges 1976 Discharges
    Number 1, 2 Percent Distribution Number 1, 2 Percent Distribution
    Discharge Status
    All discharges 1,223,500 100.0 2 1,117,500 100.0 2
    Live discharges 877,400 71.7 825,500 73.9
    Dead discharges 343,800 28.1 289,800 25.9
    Sex
    Male 455,500 37.2 407,700 36.5
    Female 768,000 62.8 709,800 63.5
    Age at Discharge
    Under 65 years 129,400 10.6 136,200 12.1
    • Under 45 years
    33,400 2.7 33,900 3.0
    • 45-54 years
    29,200 2.4 33,500 3.0
    • 55-64 years
    66,800 5.5 68,800 6.2
    65 years and over 1,094,100 89.4 981,300 87.8
    • 65-69 years
    63,500 5.2 81,300 7.3
    • 70-74 years
    119,400 9.8 122,300 10.9
    • 75-79 years
    196,500 16.1 204,600 18.3
    • 80-84 years
    255,700 20.9 241,200 21.6
    • 85-89 years
    233,900 19.1 210,100 18.8
    • 90-94 years
    155,900 12.7 90,500 8.1
    • 95 years and over
    69,200 5.7 31,100 2.8
    Marital Status at Discharge
    Married 273,200 22.3 255,900 22.9
    Widowed 669,200 54.7 628,400 56.2
    Divorced or separated 84,800 6.9 75,200 6.7
    Never married 151,800 12.4 127,200 11.4
    Unknown 44,600 3.6 30,800 2.8
    Race
    White 1,135,900 92.8 --- ---
    Black 82,000 6.7 --- ---
    Other 5,600 0.5 --- ---
    Hispanic Origin
    Hispanic 35,500 2.9 --- ---
    Non-Hispanic 1,130,700 92.4 --- ---
    Unknown 57,400 4.7 --- ---
    1. Figures may not add to totals dur to rounding.
    2. Total includes small number of unknowns.


    TABLE 2. Number of Nursing Home Discharges by Sex and Age at Discharge, and Percent Distribution by Type of Dependency During Last 7 Days in Nursing Home, According to Sex and Age at Discharge: United States, 1984-85
    Sex and Age Discharges Type of Dependency
    Total Bedfast Chairfast Incontinent of Bladder Incontinent of Bowel
    Sex
    Both sexes 1,223,500 100.0 34.8 25.4 52.8 45.2
    Male 455,500 100.0 33.2 26.9 54.8 46.3
    Female 768,000 100.0 35.8 24.6 51.6 44.6
    Age at Discharge
    Under 65 years 129,400 100.0 23.9 22.6 40.4 30.2
    65 years and over 1,094,100 100.0 36.1 25.8 54.2 47.0
    • 65-74 years
    182,900 100.0 32.8 24.5 45.5 39.1
    • 75-84 years
    452,300 100.0 32.9 27.7 52.8 44.7
    • 85 years and over
    458,900 100.0 40.6 24.3 59.1 52.4


    TABLE 3. Number and Percent of Distribution of Nursing Home Discharges by Partial Index of Dependency, According to Age at Discharge: United States, 1984-85 and 1976
    Age Total Partial Index of Dependency
    Total Not Dependent in Mobility or Continence Dependent in Mobility Only Dependent in Continence Only Dependent in Mobility and Continence
    Number Percent Distribution
    DISCHARGES IN 1984-85
    All discharges 1,223,500 100.0 31.0 14.8 8.8 45.4
    Under 65 years 129,400 100.0 44.9 13.6 8.5 33.0
    65 years and over 1,094,100 100.0 29.3 15.0 8.8 46.9
    • 65-74 years
    182,900 100.0 35.5 17.2 7.2 40.1
    • 75-84 years
    452,300 100.0 30.3 15.7 9.2 44.9
    • 85 years and over
    458,900 100.0 25.9 13.5 9.1 51.5
    DISCHARGES IN 1976
    All discharges 1,117,500 100.0 40.1 12.6 12.7 34.5
    Under 65 years 136,300 100.0 52.4 13.5 9.7 24.3
    65 years and over 981,200 100.0 38.4 12.5 13.1 35.9
    • 65-74 years
    203,600 100.0 43.2 11.6 13.5 31.7
    • 75-84 years
    445,800 100.0 40.9 12.7 13.5 32.9
    • 85 years and over
    331,800 100.0 32.3 12.8 12.3 42.6


    TABLE 4. Percent Distribution of Nursing Home Discharges by Duration of Stay, According to Selected Demographic Characteristics, with Median Duration of Stay: United States, 1984-85
    Characteristic Duration of State
    Number 1, 2 Percent Distribution Number 1, 2 Percent Distribution
    Discharge Status
    All discharges
    Live discharges
    Dead discharges
    Sex
    Male
    Female
    Age at Discharge
    Under 65 years
    • Under 45 years
    • 45-54 years
    • 55-64 years
    65 years and over
    • 65-69 years
    • 70-74 years
    • 75-79 years
    • 80-84 years
    • 85-89 years
    • 90-94 years
    • 95 years and over
    Marital Status at Discharge
    Married
    Widowed
    Divorced or separated
    Never married
    Unknown
    Race
    White
    Black
    Other
    Hispanic Origin
    Hispanic
    Non-Hispanic
    Unknown


    TABLE 7. Number of Nursing Home Discharges by Sex and Age at Discharge, and Percent Distribution by Type of Dependency During Last 7 Days in Nursing Home, According to Sex and Age at Discharge: United States, 1984-85
    Sex and Age Discharges Type of Dependency
    Total Bedfast Chairfast Incontinent of Bladder Incontinent of Bowel
    Discharge Status
    All discharges
    Live discharges
    Dead discharges
    Sex
    Male
    Female
    Age at Discharge
    Under 65 years
    • Under 45 years
    • 45-54 years
    • 55-64 years
    65 years and over
    • 65-69 years
    • 70-74 years
    • 75-79 years
    • 80-84 years
    • 85-89 years
    • 90-94 years
    • 95 years and over
    Marital Status at Discharge
    Married
    Widowed
    Divorced or separated
    Never married
    Unknown
    Race
    White
    Black
    Other
    Hispanic Origin
    Hispanic
    Non-Hispanic
    Unknown


    TABLE 1. Number and Percent Distribution of Nursing Home Discharges by Selected Characteristics: United States, 1984-85 and 1976
    Characteristic 1984-85 Discharges 1976 Discharges
    Number 1, 2 Percent Distribution Number 1, 2 Percent Distribution
    Discharge Status
    All discharges
    Live discharges
    Dead discharges
    Sex
    Male
    Female
    Age at Discharge
    Under 65 years
    • Under 45 years
    • 45-54 years
    • 55-64 years
    65 years and over
    • 65-69 years
    • 70-74 years
    • 75-79 years
    • 80-84 years
    • 85-89 years
    • 90-94 years
    • 95 years and over
    Marital Status at Discharge
    Married
    Widowed
    Divorced or separated
    Never married
    Unknown
    Race
    White
    Black
    Other
    Hispanic Origin
    Hispanic
    Non-Hispanic
    Unknown


    TABLE 1. Number and Percent Distribution of Nursing Home Discharges by Selected Characteristics: United States, 1984-85 and 1976
    Characteristic 1984-85 Discharges 1976 Discharges
    Number 1, 2 Percent Distribution Number 1, 2 Percent Distribution
    Discharge Status
    All discharges
    Live discharges
    Dead discharges
    Sex
    Male
    Female
    Age at Discharge
    Under 65 years
    • Under 45 years
    • 45-54 years
    • 55-64 years
    65 years and over
    • 65-69 years
    • 70-74 years
    • 75-79 years
    • 80-84 years
    • 85-89 years
    • 90-94 years
    • 95 years and over
    Marital Status at Discharge
    Married
    Widowed
    Divorced or separated
    Never married
    Unknown
    Race
    White
    Black
    Other
    Hispanic Origin
    Hispanic
    Non-Hispanic
    Unknown


    TABLE 1. Number and Percent Distribution of Nursing Home Discharges by Selected Characteristics: United States, 1984-85 and 1976
    Characteristic 1984-85 Discharges 1976 Discharges
    Number 1, 2 Percent Distribution Number 1, 2 Percent Distribution
    Discharge Status
    All discharges
    Live discharges
    Dead discharges
    Sex
    Male
    Female
    Age at Discharge
    Under 65 years
    • Under 45 years
    • 45-54 years
    • 55-64 years
    65 years and over
    • 65-69 years
    • 70-74 years
    • 75-79 years
    • 80-84 years
    • 85-89 years
    • 90-94 years
    • 95 years and over
    Marital Status at Discharge
    Married
    Widowed
    Divorced or separated
    Never married
    Unknown
    Race
    White
    Black
    Other
    Hispanic Origin
    Hispanic
    Non-Hispanic
    Unknown


    TABLE 1. Number and Percent Distribution of Nursing Home Discharges by Selected Characteristics: United States, 1984-85 and 1976
    Characteristic 1984-85 Discharges 1976 Discharges
    Number 1, 2 Percent Distribution Number 1, 2 Percent Distribution
    Discharge Status
    All discharges
    Live discharges
    Dead discharges
    Sex
    Male
    Female
    Age at Discharge
    Under 65 years
    • Under 45 years
    • 45-54 years
    • 55-64 years
    65 years and over
    • 65-69 years
    • 70-74 years
    • 75-79 years
    • 80-84 years
    • 85-89 years
    • 90-94 years
    • 95 years and over
    Marital Status at Discharge
    Married
    Widowed
    Divorced or separated
    Never married
    Unknown
    Race
    White
    Black
    Other
    Hispanic Origin
    Hispanic
    Non-Hispanic
    Unknown


    TABLE 1. Number and Percent Distribution of Nursing Home Discharges by Selected Characteristics: United States, 1984-85 and 1976
    Characteristic 1984-85 Discharges 1976 Discharges
    Number 1, 2 Percent Distribution Number 1, 2 Percent Distribution
    Discharge Status
    All discharges
    Live discharges
    Dead discharges
    Sex
    Male
    Female
    Age at Discharge
    Under 65 years
    • Under 45 years
    • 45-54 years
    • 55-64 years
    65 years and over
    • 65-69 years
    • 70-74 years
    • 75-79 years
    • 80-84 years
    • 85-89 years
    • 90-94 years
    • 95 years and over
    Marital Status at Discharge
    Married
    Widowed
    Divorced or separated
    Never married
    Unknown
    Race
    White
    Black
    Other
    Hispanic Origin
    Hispanic
    Non-Hispanic
    Unknown


    TABLE 1. Number and Percent Distribution of Nursing Home Discharges by Selected Characteristics: United States, 1984-85 and 1976
    Characteristic 1984-85 Discharges 1976 Discharges
    Number 1, 2 Percent Distribution Number 1, 2 Percent Distribution
    Discharge Status
    All discharges
    Live discharges
    Dead discharges
    Sex
    Male
    Female
    Age at Discharge
    Under 65 years
    • Under 45 years
    • 45-54 years
    • 55-64 years
    65 years and over
    • 65-69 years
    • 70-74 years
    • 75-79 years
    • 80-84 years
    • 85-89 years
    • 90-94 years
    • 95 years and over
    Marital Status at Discharge
    Married
    Widowed
    Divorced or separated
    Never married
    Unknown
    Race
    White
    Black
    Other
    Hispanic Origin
    Hispanic
    Non-Hispanic
    Unknown


    TABLE 1. Number and Percent Distribution of Nursing Home Discharges by Selected Characteristics: United States, 1984-85 and 1976
    Characteristic 1984-85 Discharges 1976 Discharges
    Number 1, 2 Percent Distribution Number 1, 2 Percent Distribution
    Discharge Status
    All discharges
    Live discharges
    Dead discharges
    Sex
    Male
    Female
    Age at Discharge
    Under 65 years
    • Under 45 years
    • 45-54 years
    • 55-64 years
    65 years and over
    • 65-69 years
    • 70-74 years
    • 75-79 years
    • 80-84 years
    • 85-89 years
    • 90-94 years
    • 95 years and over
    Marital Status at Discharge
    Married
    Widowed
    Divorced or separated
    Never married
    Unknown
    Race
    White
    Black
    Other
    Hispanic Origin
    Hispanic
    Non-Hispanic
    Unknown


    NOTES

    1. Reproduced for the conference package from the NCHS Advancedata, Number 135, May 14, 1987, DHHS Pub.No.(PHS)87-1250. Copies can be obtained from U.S. Public Health Service, National Center for Health Statistics, 3700 East-West Highway, Hyattsville, MD 20782, (301)436-8500.

    2. National Center for Health Statistics, E. Hing: Characteristics of nursing home residents, health status, and care received, National Nursing Home Survey, United States, May-December 1977. Vital and Health Statistics, Series 13, No.51, DHHS Pub.No.(PHS)81-1712. Public Health Services, Washington, U.S. Government Printing Office, Apr. 1981.

    3. National Center for Health Statistics, D. Roper: Nursing and related care homes as reported from the 1982 National Master Facility Inventory Survey. Vital and Health Statistics, Series 14, No.32, DHHS Pub.No.(PHS)86-1827. Public Health Service, Washington, U.S. Government Printing Office, Sept. 1986.

    4. National Center for Health Statistics, G. Strahan: Nursing home characteristics, preliminary data from the 1985 National Nursing Home Survey. Advance Data From Vital and Health Statistics, No.131, DHHS Pub.No.(PHS)87-1250. Hyattsville, Md., Mar. 27, 1987.

    5. National Center for Health Statistics, A. Sirrocco: Inpatient health facilities as reported from the 1973 MFI Survey. Vital and Health Statistics, Series 14, No.16, DHEW Pub.No.(HRA)76-1811. Health Resources Administration, Washington, U.S. Government Printing Office, May 1976.

    6. National Center for Health Statistics: Advance report, final mortality statistics, 1984. Monthly Vital Statistics Report, Vol.35, No.6, Supp.2, DHHS Pub.No.(PHS)82-1120. Public Health Service, Hyattsville, Md., Sept. 26, 1986.

    7. C. Macken: A profile of functionally impaired elderly persons living in the community. Health Care Financing Review, Vol.7, No.4, HCFA Pub.No.03223. Office of Research and Demonstrations, Health Care Financing Administration, Washington, U.S. Government Printing Office, Summer 1986.

    8. Institute of Medicine: Racial Differences in Use of Nursing Homes, in Health Care in a Context of Civil Rights. Washington, D.C., National Academy Press, 1981.

    9. S. Katz and C.A. Akpom: Measure of primary sociobiological functions. Int. J. Health Serv. 6(3):493-508, 1976.

    10. M. Meiners and R. Coffey: Hospital DRGs and the need for long-term care services, an empirical analysis. Health Serv. Res. 20(3):359-384, Aug. 1985.

    11. M. Cohen, E. Tell, and S. Wallack: Client-related risk factors of nursing home entry among elderly adults. J. Gerontol. 20(6):785-792, Nov. 1986.

    12. R. Kane, R. Matthias, and S. Sampson: The risk of placement in a nursing home after acute hospitalization. Med. Care 21(11): 1055-1061, Nov. 1983.

    13. K. Davis and C. Schoen: Health and the War on Poverty, A Ten-Year Appraisal. Washington, D.C. The Brookings Institution, 1978.

    14. U.S. Bureau of the Census: Characteristics of the population below the poverty level, 1982. Current Population Reports, Series P-60, No.144. Washington, U.S. Government Printing Office, 1984.

    15. National Center for Health Statistics, P.J. McCarthy: Replication, an approach to the analysis of data from complex surveys. Vital and Health Statistics, Series 2, No.14, PHS Pub.No.1000. Public Health Service, Washington, U.S. Government Printing Office, Apr. 1966.

    16. National Center for Health Statistics, P.J. McCarthy: Pseudoreplication, further evaluation and application of the balance half-sample technique. Vital and Health Statistics, Series 2, No.31, DHEW Pub.No.(HSM)73-1270. Health Services and Mental Health Administration, Washington, U.S. Government Printing Office, Jan. 1969.

    17. Reproduced for the conference package from the NCHS Advancedata, Number 142, September 30, 1987, DHHS Pub.No.(PHS)87-1250. Copies can be obtained from U.S. Public Health Service, National Center for Health Statistics, 3700 East-West Highway, Hyattsville, MD 20782, (301)436-8500.