Series 2

No. 126

National Health Interview

Survey: Research for the

1995–2004 Redesign

July 1999

Vital and

Health Statistics

From the CENTERS FOR DISEASE CONTROL AND PREVENTION / National Center for Health Statistics

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES

Centers for Disease Control and Prevention

National Center for Health Statistics
Copyright information

All material appearing in this report is in the public domain and may be

reproduced or copied without permission; citation as to source, however, is

appreciated.

Suggested Citation

National Center for Health Statistics. National Health Interview Survey:

Research for the 1995–2004 redesign. Vital Health Stat 2(126). 1999.

Library of Congress Cataloging-in-Publication Data

National Health Interview Survey : research for the 1995–2004 redesign.

p. cm. — (Vital and health statistics. Series 2, Data evaluation and

methods research ; no. 126) (DHHS publication ; no. (PHS) 99-1326)

Includes bibliographical references.

ISBN 0-8406-0557-9. — ISBN 0-8406-0557-9

1. Health surveys—United States. 2. Medical statistics—United States.

3. Health status indicators—United States. I. Series. II. Series : DHHS

publication ; no. (PHS) 99-1326.

[DNLM: 1. National Health Interview Survey (U.S.) 2. Health

Surveys—United States. 3. Data Collection—methods—United States.

4. Sampling Studies—United States. 5. Statistics—United States.

W2 AN148vb no. 126 1999]

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[362.1'0723]

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for Library of Congress CIP

For sale by the U.S. Government Printing Office

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Printed on acid-free paper.
National Health Interview

Survey: Research for the

1995–2004 Redesign

Series 2:

Data Evaluation and Methods

Research

No. 126

Hyattsville, Maryland

July 1999

DHHS Publication No. (PHS) 99-1326

Vital and

Health Statistics

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES

Centers for Disease Control and Prevention

National Center for Health Statistics
National Center for Health Statistics

Edward J. Sondik, Ph.D., Director

Jack R. Anderson, Deputy Director

Jack R. Anderson, Acting Associate Director for

International Statistics

Lester R. Curtin, Ph.D., Acting Associate Director for

Research and Methodology

Jennifer H. Madans, Ph.D., Acting Associate Director for

Analysis, Epidemiology, and Health Promotion

P. Douglas Williams, Acting Associate Director for Data

Standards, Program Development, and Extramural Programs

Edward L. Hunter, Associate Director for Planning, Budget,

and Legislation

Jennifer H. Madans, Ph.D., Acting Associate Director for

Vital and Health Statistics Systems

Douglas L. Zinn, Acting Associate Director for

Management

Charles J. Rothwell, Associate Director for Data

Processing and Services

Office of Research and Methodology

Lester R. Curtin, Ph.D., Acting Associate Director

Lester R. Curtin, Ph.D., Chief, Statistical Methods Staff

Trena Ezzati-Rice, Chief, Survey Design Staff

Jimmie D. Givens, Acting Chief, Statistical Technology Staff

Kenneth W. Harris, Acting Manager, Questionnaire Design

Research Laboratory Staff

Primary contributors:

David Judkins

David Marker

Joseph Waksberg

Steven Botman

James Massey

Other contributors:

John Edmonds

Mansour Fahimi

Hüseyin Göksel

Doris Northrup (CODA)

Svetlana Ryaboy

Valerija Smith

David Wright

Prepared under Contract number 200–89–7021 by

Westat, Inc.

1650 Research Boulevard

Rockville, Maryland 20850
Preface

This report presents a detailed

description of a major portion of

the research undertaken to

redesign the National Health Interview

Survey (NHIS) for the period

1995–2004.

The NHIS is one of the major data

collection programs of the National

Center for Health Statistics (NCHS).

Through NHIS, information concerning

the health of the U.S. civilian

noninstitutionalized population is

collected in household interviews

throughout the United States. NHIS has

been in continuous operation since

1957, and its sample design has been

reevaluated and modified following each

of the last four decennial censuses of

the U.S. population.

The 1995–2004 redesign of NHIS

was a major undertaking that involved a

number of government agencies as well

as several private contractors. The

Survey Design Staff in the Office of

Research and Methodology (ORM),

NCHS, in collaboration with the

Division of Health Interview Statistics

(DHIS), NCHS, had overall

responsibility for the development and

implementation of the 1995–2004 NHIS

redesign. Monroe Sirken, formerly the

ORM Associate Director, played a major

role in the conceptualization and

planning of the research program. The

late Jim Massey, former Chief of the

Survey Design Staff, and Don Malec,

formerly of the Survey Design Staff, had

the primary responsibility for directing

the redesign research and coordinating

the research activities conducted by

NCHS, the U.S. Bureau of the Census,

and Westat, Inc. The late Steve Botman,

formerly of the Survey Design Staff, had

the lead responsibility in helping to

implement the 1995–2004 NHIS

redesign, and also carried out many

important steps leading to the

publication of this report. Van Parsons

of the Statistical Methods Staff, ORM,

contributed to the development of

subdesigns for the 1995–2004 NHIS

redesign. Keith Hoffman and Trena

Ezzati-Rice of the Survey Design Staff

participated in the research and

evaluation of the Social Security

Administration files as a potential

supplemental sampling frame for NHIS,

as described in chapter 6 of this report.

Chris Moriarity of the Survey Design

Staff carried out the final steps of Steve

Botman’s work that led to the

publication of this report. The late Owen

Thornberry, formerly the Director,

DHIS, and John Horm, formerly the

acting Chief of the Survey Planning and

Development Branch, DHIS, were the

leading DHIS participants in the

1995–2004 NHIS redesign work.

Under contract with NCHS between

1989 and 1993, Westat, Inc., conducted

research for the 1995–2004 NHIS

redesign. The primary researchers at

Westat, Inc., included David Judkins,

David Marker, and Joseph Waksberg.

Other contributors included John

Edmonds, Mansour Fahimi, Hüseyin

Göksel, Doris Northup (CODA),

Svetlana Ryaboy, Valerija Smith, and

David Wright.

Additional research was conducted

by the U.S. Bureau of the Census and

coordinated through the Task Force on

Household Survey Redesign, assembled

and directed by the late Maria Gonzalez

of the Office of Management and

Budget (OMB). The task force was

formed to coordinate and monitor the

U.S. Bureau of the Census’s

simultaneous redesign of all of the

household surveys the Bureau conducts

for other Federal government agencies.

The task force played an important role

in coordinating the technical and

funding requirements for the redesigns

of all of the surveys.

The U.S. Bureau of the Census has

been the primary data collector for the

NHIS since its inception. The Bureau

also has been involved with the research

and implementation of the NHIS

redesigns. For the 1995–2004 redesign,

the Demographic Statistical Methods

Division (DSMD) had the primary

responsibility for evaluating alternative

primary sampling unit definitions for

NHIS, and for implementing the

redesigned sample. DSMD staff also

participated in the research and

evaluation of the Social Security

Administration files as a potential

supplemental sampling frame for NHIS,

as described in chapter 6 of this report.

Persons at the U.S. Bureau of the

Census that deserve special recognition

for their contribution to the 1995–2004

NHIS redesign effort include Preston

Jay Waite, formerly Chief of DSMD;

Thomas Moore, Chief of the Health

Surveys and Supplements Branch in

DSMD; Robert Mangold, formerly Chief

of the Health Surveys Branch in the

Demographic Surveys Division; Patricia

Wilson of DSMD; and Lloyd Hicks,

formerly of DSMD.

The primary focus of the

1995–2004 NHIS redesign research was

to explore sample design options to

improve the reliability of NHIS statistics

for racial, ethnic, economic, and

geographic domains. Another objective

was to assess the integration of the

sample designs of the NCHS surveys of

health care providers and NHIS.

The decisions for the 1995–2004

NHIS redesign were based on a number

of factors including technical soundness,

competing analytical objectives,

operational feasibility, costs, and

available resources. The research results

presented in this report were the primary

basis for assessing the technical

soundness and costs for a number of

design alternatives. In particular, as part

of Westat, Inc.’s, research, a sample

design referred to as the ‘‘alpha’’ design

was developed, which assumed a

50-percent data collection budget

increase to permit oversampling of

racial and ethnic minorities. Westat, Inc.,

researchers also developed a

modification of the ‘‘alpha’’ design, the

‘‘beta’’ design, which assumed no

change in NHIS data collection budget.

The ‘‘beta’’ design, which became the

design implemented in 1995, is

described in chapter 17. The research

results in this report also led to

additional research, primarily in the area

of survey integration.

A more detailed description of the

1995–2004 NHIS design will appear in

a forthcoming NCHS Series 2 report

titled Design and Estimation for the

iii
1995–2004 National Health Interview

Survey.

As required by the contract between

NCHS and Westat, Inc., a draft

manuscript of this report was submitted

to NCHS by Westat, Inc., in early 1994.

A revised manuscript was submitted to

NCHS by Westat, Inc., in early 1996.

This report is very similar to the 1996

manuscript, although personnel from

Westat, Inc., and NCHS made additional

revisions before publication.

iv
Contents

Preface . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Abstract ................................................................................................ 1

Executive Summary....................................................................................... 1

Introduction ........................................................................................... 1

Research Objectives .................................................................................... 2

Research Methodology .................................................................................. 2

Part I. Introduction................................................................................... 7

Chapter 1. Background on the National Health Interview Survey .................................................. 8

Schedule and Content ................................................................................... 8

Sample Design for 1985–94 .............................................................................. 8

Chapter 2. New Objectives for 1995 Redesign ................................................................. 9

Statistics for Racial, Ethnic, and Economic Domains .......................................................... 9

Statistics for Geographic Domains ......................................................................... 9

Integration With Surveys of Health Care Providers ........................................................... 9

Part II. Statistics for Racial, Ethnic, and Economic Domains. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

Chapter 3. Current Precision. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Variances in Current Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

Components of Variance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Chapter 4. Potential Techniques for Increasing Sample Sizes for Selected Domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Sample Sizes With Simple Expansion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Oversampling Classes of Primary Sampling Units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Oversampling Classes of Blocks or Block Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

Screening With Subsampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Network Sampling by Nomination of Relatives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Dual-Frame Sampling With Administrative Lists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Multiyear Aggregation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Carrying Samples Over From Prior Years . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Cost Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

Chapter 5. Best Techniques for Sampling the Nonelderly Black and Hispanic Populations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Oversampling Small Decennial Census Geographic Units Without Screening . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

Screening a National Equiprobability Sample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Oversampling at the Block Level With Screening for Race, Ethnicity, Age, and Sex . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Oversampling at the Block Level With Screening for Race and Ethnicity Only. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

Chapter 6. Choosing a Sampling Technique for the Elderly Black and Hispanic Populations . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Area Sampling With Oversampling at the Block Level With Screening for Race, Ethnicity, Age, and Sex . . . . . . . . . . . . . . . 33

Dual-Frame Sampling With Social Security Administration Lists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

Chapter 7. Prospects for Statistics on Detailed Hispanic Subdomains. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Design-Based Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Model-Based Techniques. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

v
Chapter 8. Prospects for Statistics on Other Minority Groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Asians and Pacific Islanders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

American Indians, Eskimos, and Aleuts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38

Chapter 9. The Poor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

Part III. Statistics for Geographic Domains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

Chapter 10. Subnational Estimates for State Groupings and Hispanic Persons. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Subnational Domains With Current Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Subnational Domains for Hispanic Persons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Oversampling Hispanic Persons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

Chapter 11. Design-Based Approaches for State Estimation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

Estimation Based Solely on the National Health Interview Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

Dual-Frame Estimation Using Random Digit Dialing Supplementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

Chapter 12. Empirical Comparison of Model-Dependent Estimators. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

Estimators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

State-Specific Mean Square Errors. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

Empirical Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

Summary of Empirical Results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

Part IV. Coordination With Surveys of Health Care Providers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

Chapter 13. Coordination at the Primary Sampling Unit Level . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

Cost Savings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

Analysis Enrichment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

Variance Increases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

Chapter 14. Network Sampling of Providers Through the National Health Interview Survey . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76

Model for Costs and Variance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77

Comparison With List-Based Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80

Chapter 15. The National Health Interview Survey as a Tool for Linking Beneficiary and Provider Perspectives on

Health Care . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

Other Surveys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

National Health Interview Survey as a Baseline or as a Follow Up? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

Part V. Decisions on Sample Design and Contingency Options. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

Chapter 16. Decisions on Sample Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

Reconciliation of Objectives. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

Primary Sampling Unit Definition and Measure of Size. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

Primary Sampling Unit Stratification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

Primary Sampling Unit Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

Selection of Blocks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

Within-Block Selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

Screening . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

New Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

Dual-Frame Sampling for Elderly Minorities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

Panel Construction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

Summary of Design Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

Chapter 17. Contingency Plans. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

The Beta Option . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

New Construction Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

Restoration of an Equiprobability Design at Level Cost and Other Contingency Plans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

vi
Chapter 18. Recommendations for Continuing Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

Intercensal Tracking of Minority Populations Across Density Strata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

Updating Minority Density Areas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

Coverage Improvement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101

Appendixes

I. List of the National Health Interview Survey Redesign Memorandums . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102

II. National Health Interview Survey Estimates and Coefficients of Variation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

Text Figure

1. Summary of sample sizes for new design (alpha option) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92

Text Tables

A. Effective adult sample sizes by redesign option and population domain relative to the 1988 NHIS design ............ 4

B. Sample sizes for the black and Hispanic populations, aged 65 and older ....................................... 5

Detailed Tables

1. Items selected for direct variance estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2. Coefficients of variation for selected NHIS items: 1988 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3. Domain-specific required sample sizes for a 30-percent cv. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

4. Projections of the population by age, sex, race, and ethnicity to 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

5. Projections of undercoverage by age, sex, race, and ethnicity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

6. Sample sizes for equiprobability sample of 50,000 interviewed households . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

7. Sample sizes for equiprobability sample of 50,000 interviewed households with one adult per household

(corrected for within-household design effect). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

8. Distributions in 1990 of minority populations across density strata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

9. Distributions in 1990 of each density stratum that is other. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

10. Aspects of the optimal mix of screening and oversampling for various cost assumptions and racial and/or ethnic

subdomains. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

11. Distribution of the black population across density strata in 1990 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

12. Distribution of the Hispanic population across density strata in 1990 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

13. Distribution of the other population across density strata in 1990 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

14. Distribution of the total population across density strata in 1990. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

15. Distribution of the black population across density strata in 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

16. Distribution of the Hispanic population across density strata in 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

17. Distribution of the other population across density strata in 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

18. Distribution of the total population across density strata in 2000. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

19. The limits of oversampling without screening . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

20. Oversampling rates for the alpha design (relative to a design that would yield 50,000 interviews) . . . . . . . . . . . . . . . . . . 30

21. Persons in screened households for the alpha option. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

22. Percent of other households discovered during screening to be kept for full interviews under the alpha design . . . . . . . . 31

23. Effective sample sizes for alpha design when all household members are sampled . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

24. Effective sample sizes for alpha design when one adult is sampled per household. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

25. Required sample sizes by sampling method and precision target . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

26. Geographic concentration of the poor in 1990. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

27. State groupings to produce State estimates every 3 years . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

28. Oversampling rates to produce subnational estimates for Hispanics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

29. Oversampling rates when the HCFA frame is used as a sampling frame for the elderly . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

30. Summary of number screened and interviewed, assuming two different screening costs . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

31. Summary of number screened (in the Southwest) and interviewed, assuming two different screening costs. . . . . . . . . . . . 46

32. State sample sizes with and without oversampling of blacks and Hispanics; States ranked by population

size in 2000 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48

33. Number of States for which estimates would have the desired accuracy for three different sample sizes . . . . . . . . . . . . . 49

vii
34. State sample sizes (ranked by population size in 2000) for collapsed ages with and without oversampling of

blacks and Hispanics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

35. RDD supplementation sample sizes for age groups with one adult sampled per household . . . . . . . . . . . . . . . . . . . . . . . . . 52

36. Number of States for which all cells meet the desired level of accuracy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

37. Variances of an RDD sample of 1,000 and 2,000 households per year for two designs, two different percentages,

and for all adults and the rarest cell . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

38. The number of States, under the proposed redesign and p = 0.1, for which the unbiased composite estimates for

certain cells will have cv’s greater than 30 or 15 percent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

39. Number of States, by the size RDD supplement needed to derive a dual-frame NHIS estimator of accuracy similar

to stand-alone 1,000 RDD interviews. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

40. Summary of the number of States and percent reduction in bias corresponding to different size RDD supplementation

of a dual-frame estimator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

41. Differences between telephone and nontelephone households for percent of persons who smoke, and persons under

65 years old without private health insurance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

42. Comparison of 1,000 RDD interviews alone (sampling one adult per household), with dual-frame estimators

(from proposed design), and p = 30 percent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

43. Values of bj for each of the 16 age/race/sex subgroups used in estimator 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

44. Values of bj for each of the 32 central city/age/race/sex subgroups used in estimator 6. . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

45. Average number of doctor visits, by State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63

46. Percentage with perceived poor health status, by State . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

47. State-specific mean square errors for number of doctor visits, using estimators 2 and 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

48. Groupings of States by common expected biases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

49. Average number of doctor visits, with States grouped by percentages of nonblack population in central cities. . . . . . . . . 66

50. Average number of doctor visits, with States grouped by percentage of black population in central cities. . . . . . . . . . . . . 67

51. Variation in State estimates for average number of doctor visits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

52. Percentage with self-perceived poor health status, with States grouped by percentage of nonblack population in

central cities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

53. Percentage with perceived poor health status, with States grouped by percentage of black population in

central cities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

54. Robustness of the generalized synthetic estimator. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

55. State-specific mean square errors for number of doctor visits, using the synthetic estimator, with States grouped by

percent nonblack population in central cities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

56. State-specific mean square errors for percentage with perceived poor health using the generalized synthetic estimator

with 16 balanced variances, with States grouped by percentage of black population in central cities . . . . . . . . . . . . . . . . 70

57. State-specific mean square errors for percentage with perceived poor health using the synthetic estimator, with

States grouped by percentage of nonblack population in central cities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

58. Reduction in estimate of the MSE of the generalized synthetic estimator (16 subgroups) relative to

the MSE of the synthetic estimator for percent with perceived poor health . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71

59. Initial cluster sizes (in terms of placeholders) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

60. Percent of initially selected strings to be retained for actual listing (all others are put aside) . . . . . . . . . . . . . . . . . . . . . . . 86

61. Sample sizes for elderly minorities. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

62. Oversampling rates for the beta design (relative to a design that would yield 50,000 interviews) . . . . . . . . . . . . . . . . . . . 93

63. Percent of total sample reserved for alpha option to be retained for the beta option . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93

64. Desired cluster sizes for beta design (in terms of placeholders). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

65. Percent of strings reserved for the alpha option to be retained for the beta option . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

66. Percent of listing lines reserved for NHIS within strings retained for the beta option that are actually to be screened

for the beta option . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

67. Persons in screened households for the beta option. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

68. Percentage of other households discovered during screening to be kept for full interviews, beta option. . . . . . . . . . . . . . . 95

69. Effective sample sizes for beta design when all household members are sampled . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

70. Effective sample sizes for beta design when one adult is sampled per household. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97

71. Effective adult sample sizes for the year 2000 by redesign option and population domain relative to the 1988 NHIS

design when just one adult is sampled per sample household . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

72. Percent of sample originally selected that would be retained for an equiprobability design. . . . . . . . . . . . . . . . . . . . . . . . . 98

73. Percentage of strings to keep in conjunction with post-listing subsampling for equiprobability design. . . . . . . . . . . . . . . . 99

74. Percentage of listed HU’s to keep in retained strings in conjunction with string subsampling for equiprobability

design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

viii
Objectives

This report presents a major portion

of the research carried out for the

1995–2004 redesign of the National

Health Interview Survey. The primary

focus of the 1995–2004 NHIS redesign

research was to explore sample design

options to improve the reliability of

NHIS statistics for racial, ethnic,

economic, and geographic domains.

Another objective was to assess the

integration of the sample designs of the

NCHS surveys of health care providers

and NHIS.

Methods

A number of research tasks were

carried out by Westat, Inc., to explore

the feasibility and cost of various

sample design options for the

1995–2004 National Health Interview

Survey redesign. This report provides a

detailed description of the research that

was carried out.

Results

The research results presented in

this report were the primary basis for

assessing the technical soundness and

costs for a number of design

alternatives. The first option, called the

alpha option, was developed under the

assumption of a 50-percent data

collection budget increase. The second

option, called the beta option, was

developed under the assumption of no

change in the data collection budget.

The beta option was the design

implemented in 1995.

Other important research items

described in this report include

oversampling methods for minority

populations and dual-frame sampling

methods. The research results in this

report also led to additional research,

primarily in the area of survey

integration.

A more detailed description of the

1995–2004 NHIS design will appear in

a forthcoming NCHS Series 2 report

titled Design and Estimation for the

1995–2004 National Health Interview

Survey.

Keywords: integrated survey design

c model-based estimation c dualframe estimation c network sampling

National Health Interview

Survey: Research for the

1995–2004 Redesign

Executive Summary

Introduction

The sample design for the National

Health Interview Survey (NHIS) has

traditionally been revised following each

decennial census. The redesigns serve

two purposes. The first is simply to

update the sampling material through

the use of data from the recently

completed census. The second is to

redirect the sample toward new goals,

making the NHIS responsive to the

expected needs of health statistics in the

following decade.

The timing of the planning and

introduction of the revised sample is

coordinated with similar revisions the

U.S. Bureau of the Census makes for its

other demographic surveys, including

the Current Population Survey, the

American Housing Survey, the Survey

of Income and Program Participation,

and the National Crime Survey. The

coordination is necessary to ensure that

households are not part of the sample in

more than one survey; it is also an

efficient way of selecting samples for

multiple surveys. As a result, however,

changing the design is an extremely

complex task, even during the traditional

postcensus window.

The U.S. Bureau of the Census

requires a 4-year lead time to change

procedures for selecting blocks, the

basis for the area sample employed for

NHIS. A 3-year lead time is required to

change primary sampling units (PSU’s),

and an additional year is needed to list

the housing in sample blocks and select

the households that will be asked to

participate in NHIS. Cuts in sample size

can be made much more quickly,

usually in a matter of months, provided

that they follow certain guidelines, but

expansions take at least 2 years—longer

for expansions that go beyond limits set

during the redesign. This problem can

be solved by an initial selection of a

very large sample, but there are

substantial financial costs to be borne

for selecting more sample than is

actually needed. Because of the

coordination requirement, the 1995

redesign is the only opportunity that the

National Center for Health Statistics

(NCHS) will have to make fundamental

changes in the sample design of NHIS

at a reasonable cost until the year 2005.

NCHS put considerable effort into

debating and consolidating ideas about

the most critical objectives for NHIS

statistics. A number of objectives were

identified—more than it appeared likely

could be simultaneously realized, given

budget prospects. Westat, Inc., was

awarded a contract to assist in the

development of a new design that would

meet these objectives to the greatest

extent feasible, given various funding

assumptions and revised priorities. Work

to be done by Westat, Inc., consisted of

the statistical and cost aspects of sample

design options. The contract did not

include any provision for research on

the instrument content or design. Westat,

Inc.’s, full final report synthesizes 4

years of research, meetings, and 119

technical memorandums (a list of their

titles is included as appendix 1) on the

most efficient sampling methods for

achieving the objectives. From this

research a proposed design for 1995 was

developed. In case budgets did not

permit the introduction of the

Page 1
proposed design, a backup design was

also developed.

This executive summary briefly

reviews the objectives and the methods

studied. More discussion is devoted to

describing the proposed design and

discussing the extent to which it meets

each of the objectives. For more detail

the reader is referred to the full report.

Research Objectives

The highest priority requirements

for the redesigned NHIS, as specified by

NCHS, were:

+ Improved precision of health

statistics about demographically

defined domains, including

specifically those defined by the

interaction of age and sex with race,

ethnicity, and poverty status.

+ Improved precision of health

statistics for subnational geographic

areas—in particular, States.

+ Decreased bias by eliminating proxy

reporting.

+ An ability to quickly and radically

change the size of NHIS in response

to funding opportunities and

cutbacks.

+ Improved analytic potential and/or

cost reduction through linkage with

the provider surveys sponsored by

NCHS.

+ A continued ability for NHIS to

serve as a vehicle for follow-on

surveys such as the National Survey

of Family Growth (NSFG).

Research Methodology

Improved Precision for

Demographically Defined

Domains

A wide range of techniques was

studied that might be used to improve

precision for racial and ethnic minorities

and other demographically defined

domains. These techniques included

oversampling geographic areas with

strong concentrations of targeted

domains, screening a general sample to

identify members of the targeted

domains (and subsequently subsampling

others), sampling from lists such as the

Medicare list, network sampling (where

members of the targeted domains are

asked to nominate their relatives for

inclusion in the sample), and using

simple techniques such as carrying

sample cases of targeted domains over

from 1 year to the next or aggregating

statistics across years.

Important considerations in these

studies were the prevalence of the

domain, the extent of geographic

clustering of domain members, the

contribution of each stage of sampling

to the variance for a domain, coverage

of lists of domain members, the

variances and potential biases of

network sampling, and the power of

multiyear estimates, given that each

year’s sample is not independent of

other years. Another important

component of this work was the

projection of costs for alternative

designs. An attempt was made to form

an extremely general cost model based

on interviewer time sheets for NHIS

from the U. S. Bureau of the Census,

but most cost projections were actually

done in a more informal manner, based

on information supplied by persons

experienced in the management of

survey operations.

To study the prevalence and

geographic clustering of each domain,

data were examined from the NHIS

itself, the 1980 and 1990 decennial

censuses, and demographic models of

population growth from the U. S.

Bureau of the Census. To study the

contribution of each stage of sampling,

an analysis of components of variance

was conducted on the 1988 NHIS with

new methodology. The main lists

considered were the master beneficiary

lists maintained by the Social Security

Administration (SSA) and Health Care

Financing Administration (HCFA) for

administration of Social Security and

Medicare. Most of the work done on

network sampling of rare domains was

conceptual in nature with

recommendations for further research.

The power of multiyear estimates and

carryover procedures was based on the

work on components of variance and

U. S. Bureau of the Census data on the

frequency of residential moves by

minority persons.

Improved Precision for

Geographically Defined

Domains

This research started from an

analysis by NCHS staff (1) indicating

that State-level statistics from NHIS

have very poor precision for most

States. Based on U. S. Bureau of the

Census projections of State populations

in the year 2000 (the midpoint of the

period in which the redesigned sample

would be used), estimates were made of

the size of sample supplements that

would be needed to improve the

precision of State-level statistics to more

acceptable levels. These estimates were

prepared under the assumptions that the

supplemental samples would be of the

traditional area-permit type and the

samples would be obtained by means of

random digit dialing (RDD). Various

groupings of States for which acceptable

precision could be more easily obtained

were also studied. Finally, there was an

intensive study of the potential of

various small-area techniques, including

model-based statistics.

Decreased Bias Because of

Proxy Reporting

Work by NCHS staff had identified

proxy reporting as a major cause of

response bias. The size of the bias tends

to vary among items but appears to be

particularly large for the more subjective

items, such as self-perceived health

status. Obtaining response rates nearly

as good as the current response rates

while disallowing proxy response is

quite expensive. To offset the increased

cost of this requirement, the idea was

put forward to interview just one

randomly chosen adult per family.

Although the merits of this approach

were mostly outside the scope of this

research, the potential that this system

would be imposed conditioned all the

other research and required study of

design options that assumed all adults

would be interviewed in each sample

household and that just one adult would

be interviewed per family within the

household.

Page 2 [ Series 2, No. 126
Flexibility for Rapid Sample

Expansions and Contractions

Research on rapid sample

expansions consisted mostly of

discussions about the cost of preparing

and maintaining extra samples. The task

of a rapid expansion is not as easy as it

seems it should be. Given requirements

from the Office of Management and

Budget for minimization of response

burden, the complexity of the designs of

other current surveys at the U. S.

Bureau of the Census, and the NHIS

requirement for no recourse to decennial

census address registers, issues of

coordination are extremely complex.

Also, there are costs for fieldwork done

to keep a sample ready to use (even if it

is never used), for example, collecting

building permit information from

selected building permit offices. Finally,

it is difficult to select additional samples

efficiently for data collection instruments

and procedures that have not been

designed yet.

Rapid contractions are considerably

easier to design. Research in this area

focused on alternate panel construction

methodologies.

Integrated Survey Design

Integration of the NHIS design with

the design for the National Health Care

Survey (NHCS), an umbrella term for a

collection of independent surveys of

health care providers was an important

issue for the NCHS staff. There was

interest in the possibility of enhanced

analytic strength from an integrated set

of surveys and the possibility of

operational synergies that might lead to

lower overall cost for the integrated

system.

Research in this area focused most

strongly on the implications of

integration at the PSU level, where all

the surveys would be located in the

same set of PSU’s or where the NHCS

PSU’s were at least nested within the

NHIS PSU’s. Given the paucity of

county-level health and health care data,

the research that could be conducted in

this area was limited. The research that

was done focused on the compatibility

of optimal PSU-level measures of size

for the various surveys and on the

implications for the provider surveys of

two-stage versus three-stage design. The

former work was based on tabulations

of the National Master Facility

Inventory and other NCHS sources.

Similarity of the set of PSU’s to be

included with certainty for each survey

was closely examined. Also, the

correlation in the measure of size for the

noncertainty PSU’s was examined. The

latter set of work was based on National

Hospital Discharge Survey (NHDS) and

National Ambulatory Medical Care

Survey (NAMCS) data. The NHDS

work was based on a review of internal

NCHS documents. The NAMCS work

involved the calculation of components

of variance using new methodology.

Follow-on Surveys

A number of NCHS surveys use

respondents from NHIS samples from

previous years as screening samples to

find domains of special interest for

intensive interviews on topics that

cannot be included in NHIS because

of time or other constraints. The best

known example of this sort of

sampling approach is NSFG. The

flexibility to be able to use retired

NHIS samples for this purpose was

the reason for the switch in 1985 from

an NHIS sample based on a mix of

decennial census address registers,

manual block listing in areas with

poor quality registers, and new

construction building permits, to a

sample based only on manual block

listing and building permits. The cost

of this switch was far from trivial

because of the requirement for much

more listing. This extra listing cost

continues to be a factor for the 1995

redesign. To preserve the benefits of

this expense for extra listing, a factor

in all discussions about the NHIS

design was the potential impact of

design changes on these follow-on

surveys. Particular tension was noted

between the large numbers of sample

PSU’s needed for improved precision

for State estimates and the small

number of sample PSU’s usually

needed for follow-on surveys.

Major Findings and

Recommendations

The major outcome of this contract

was a new recommended design for

NHIS. This recommended design is

labeled the ‘‘alpha option.’’ The

specifications for this design flowed

directly from the research conducted by

Westat, Inc., with several iterations of

refinement in response to feedback from

NCHS and the U. S. Bureau of the

Census. The alpha option is predicated

on the assumption of an ongoing budget

for 1995 and beyond that is 50 percent

larger than would be required to sustain

the current design at a strength of

50,000 completed household interviews

per year. Because the current NHIS has

been running close to 47,500 completed

household interviews per year, this

implies a need for an increased budget

on the order of 58 percent. If an

additional option regarding elderly

minority groups is exercised, the total

budget increase becomes about

62 percent. There is also the assumption

that 1994 monies can be found for the

extra listing of blocks and recruiting and

training of interviewers required by the

alpha option.

Recognizing the uncertainties of the

Federal appropriation process, a ‘‘beta

option’’ was also identified that would

have the same long-term costs as the

current design with 47,500 annual

household interviews. NCHS set the

goals for the beta option of maintaining

current precision for statistics for black

people; and improving precision on

statistics for Hispanic persons enough to

achieve parity with those for black

persons.

Improved Precision for

Demographically Defined

Domains

The alpha option responds to all of

the goals of the 1995 design to some

extent. The primary achievement of this

design, however, is to provide

substantial improvements in the

precision of statistics about the black

and Hispanic populations. The

improvements for Hispanic persons are

particularly strong. The alpha option

provides considerable improvement in

Series 2, No. 126 [ Page 3
the precision of statistics on the black

and Hispanic populations at the cost of

precision for other demographically

defined domains. The tradeoff is sharper

for the beta option because it improves

the precision of statistics on Hispanic

persons and holds the precision of

statistics on black persons the same, all

at current budget levels. Table A

quantifies these projected precision

changes in terms of changes in effective

sample sizes. (The effective sample sizes

shown here correct the raw sample sizes

for the design effects resulting from

differential sampling rates across the

minority density strata and across adults

in households of different sizes.) The

sample sizes do not add up across the

domains because of different design

effects. The deterioration in precision for

totals and for other categories was

within a zone with which NCHS staff

felt comfortable.

Statistics on the Black and Hispanic

Populations

These improvements in the

precision of statistics for the black and

Hispanic populations were achieved by

a mix of two techniques: Stratification

on minority density with

nonproportional allocation, and

screening. Stratification on minority

density with nonproportional allocation

means that blocks are stratified by the

percentage of the local population that is

black and by the percent that is

Hispanic, and that a disproportionate

share of the initial sample is selected in

the strata with high black and/or

Hispanic density. Screening means that a

brief set of questions is first

administered to each household to

determine whether any of the residents

are black or Hispanic; if the household

is black or Hispanic, the household is

retained with certainty for a full

interview; otherwise, the household is

only retained for a full interview with a

probability less than one.

The stratification and oversampling

by minority density is a technique that

was also used for the 1985 design, albeit

to a lesser extent. For that design

enumeration districts and block groups

(higher levels of geographic units than

blocks) were stratified by percentage of

the local population in 1980 that was

black. Also, the degree of oversampling

of the strongly black areas was

constricted (by plans for the formation

of interviewer assignments) in such a

manner that there was almost no

oversampling in rural southern PSU’s

with strong black concentrations. The

new design employs finer geographic

detail and an alternate plan for building

interviewer assignments to make the

oversampling much more efficient. The

new design also focuses more on

Hispanic density than on black density,

given the fact that the Hispanic

population is currently smaller than the

black population in the United States.

Screening is a technique that has

been used at private survey research

firms for many years but has been

employed only once at the U. S. Bureau

of the Census (for the Survey of Income

and Education in the 1960’s). Given the

long length of time since the last use of

screening at the U. S. Bureau of the

Census, the Bureau decided to conduct a

field test of screening procedures. This

field test, conducted by the Bureau in

1993 with very favorable results,

focused on a particularly sensitive

aspect of the plan for screening (the

decision to contact neighbors to keep

response rates high and costs low).

The beta option also leads to

improvements for statistics on Hispanic

persons, but not as much as the alpha

option. It provides this improvement

with no change in the precision of

statistics on the black population and

with no increase in cost. As already

noted, the beta option provides for even

worse precision than the alpha option

for the population that is neither black

nor Hispanic, although the difference in

this area between the alpha and beta

options is minor.

An alternative with either the alpha

or the beta option is to supplement the

area-permit sample with a sample of

black and likely Hispanic SSA

beneficiaries. (The word ‘‘likely’’ is used

because the SSA files do not actually

have a variable that identifies Hispanic

persons. Research has shown, however,

that there is a set of surnames that is

associated strongly enough with being

Hispanic to serve as the basis for

sampling.) Table B shows the increases

in the effective sample sizes for elderly

black and Hispanic persons that could

be achieved with supplements from SSA

lists of the indicated sizes.

Other Demographically Defined

Domains

Besides the black and Hispanic

populations, the other domains of

interest that were studied include Asians

and Pacific Islander Americans,

American Indians, Eskimos, and Aleuts,

the poor, the rural population, teenagers,

the extreme elderly (85 years of age and

over), and Hispanic subgroups such as

Puerto Ricans.

A study of 1990 decennial census

results indicated that the Asian and

Pacific Islander population is too

dispersed for oversampling to be very

effective and too small for substantial

improvement via screening to be

practical. The same conclusion applies

to the Native American population. It

was noted, however, that if interest were

focused solely on the Native American

population living on reservations,

trustlands, tribal jurisdiction statistical

areas, and native villages, the relatively

few numbers of other racial and ethnic

groups living in these areas would make

screening highly feasible.

With improved precision for

estimates on the black and Hispanic

populations, there is no question that the

Table A. Effective adult sample sizes by redesign option and population domain relative to the 1988 NHIS design

Option Budget1 Total Black Hispanic Other

1988 NHIS . . . . . . . . . . . . . . . . . . . . . . . 100 35,600 4,500 2,200 30,900

Alpha option . . . . . . . . . . . . . . . . . . . . . . 158 +8% +97% +299% –4%

Beta option . . . . . . . . . . . . . . . . . . . . . . . 100 –12% +4% +113% –21%

1Budget for 1988, adjusted for inflation, equals 100.

Page 4 [ Series 2, No. 126
alpha option would also improve the

precision for statistics about the poor

black and poor Hispanic populations,

because the oversampling of strongly

black and/or Hispanic neighborhoods

means oversampling many of the

Nation’s poorest neighborhoods.

Precision for other poor Americans,

however, will be worse after the

redesign. The effect on precision for

statistics on all poor persons has not

been quantified but is probably slight.

For statistics on the rural

population, it was noted that there are a

number of competing definitions of the

term. The U.S. Bureau of the Census

definition of rural applies at the block

level. Rural areas exist within

metropolitan areas, and urban areas exist

within nonmetropolitan areas.

Population counts per block are

necessary for the classification of areas

as urban or rural under this definition.

Because such counts are only collected

in the decennial census, statistics about

rural areas become outdated fairly soon

after each census. The U.S. Department

of Agriculture has several definitions of

rural counties, but none of these seemed

to reflect NCHS needs. On consideration

of the various alternatives, the NCHS

staff decided that its analytic needs

would be met best by compiling data for

nonmetropolitan areas rather than for the

areas encompassed by one of the

definitions of a rural area.

Although precision for NHIS

nonmetropolitan statistics is already

fairly good, it was decided to further

improve the precision by increasing the

total number of PSU’s for NHIS from

198 in the 1985 full design to 358 in the

1995 full design. Also, it was decided

that metropolitan status should be a

strict stratifier in the selection of PSU’s

and that at least one nonmetropolitan

PSU would be selected from each of the

50 States (except New Jersey, which is

all metropolitan). These improvements

would be counterbalanced to some

extent; however, because the

oversampling of the black and Hispanic

populations would tend to shift sample

persons from nonmetropolitan areas to

metropolitan areas. Also, the reduction

in the total number of completed

interviews (to pay for the screener

interviews) and the design effect due to

differential sampling of black, Hispanic,

and other persons would tend to degrade

the precision of nonmetropolitan

statistics. The net effect of these changes

is that, although there would be more

nonmetropolitan PSU’s in the sample in

the 1995–2004 decade than during the

1985–1994 decade, the total number of

nonmetropolitan interviews would be

reduced.

With regard to teenagers, a plan

was considered that would have

involved screening for age and

retaining households with members in

targeted age domains at higher rates,

but this plan was rejected. At any rate

this plan was only meant to provide

for oversampling of the black and

Hispanic populations in targeted age

domains. At the planned budget level,

an important increase in the sample

size of teenagers could only be

achieved by scrapping the

oversampling of densely black and

Hispanic blocks. This goal (teenage

statistics) was essentially put aside.

Oversampling of the extremely

elderly population would be quite

efficient with Medicare or SSA lists.

However, there was no way of

accomplishing this goal while

simultaneously achieving competing

needs for precision. The main reason is

that this group is predominantly white,

and statistics on white people, in

general, will be less precise with the

1995 design than with the 1985 design.

With regard to Hispanic subgroups,

it was realized that however desirable

this goal might be in light of the

diversity of the Hispanic population, it

would simply require more funds than

NCHS foresees being available.

Model-based techniques were

investigated that might involve special

samples in only blocks with very high

concentrations of the targeted subgroups,

but the investigation was inconclusive.

Improved Precision for

Geographically Defined

Domains

The 1995 design will provide

improved flexibility for sample

supplements that would lead to

State-level statistics with acceptable

precision, but the design will not by

itself provide such statistics for more

than three States—none if the

one-adult-per-family rule is used for

interviewing. This flexibility was

achieved by stratifying the PSU sample

by State and by metropolitan status.

This stratification will make it far easier

to integrate the regular NHIS sample

with any State-specific sample. Also, at

least two PSU’s (one metropolitan and

Table B. Sample sizes for the black and Hispanic populations, aged 65 and older

Domain Desired

Effective sample sizes

Projected

under alpha

option

(one household)

Projected

under alpha

option

(all household)

With extra

2,800 cases

to be screened

from SSA

(one household)

With extra

5,800 cases

to be screened

from SSA

(one household)

Black

Male ........................... 1,000 500 1,150 1,000 1,000

Female ......................... 1,000 740 1,710 1,000 1,000

Hispanic

Male ........................... 1,000 350 910 770 1,000

Female ......................... 1,000 495 1,270 920 1,000

Series 2, No. 126 [ Page 5
one nonmetropolitan) were selected

from each State that contains

metropolitan and nonmetropolitan areas.

Having at least this marginal

representation in every State will

provide an anchor for State supplements,

whether such supplements are selected

by RDD or by area-sampling methods.

It also enhances the prospects for

model-based State-specific estimates;

intensive study of techniques for such

estimates, however, left us rather

dubious about long-term prospects in

this area.

Decreased Bias by Elimination

of Proxy Reporting

The alpha option does provide

strongly improved precision for the

black and Hispanic populations

compared with the 1985 design,

assuming that in both cases the

one-adult-per-household rule is used for

interviewing. However, there is still a

net decline in the precision of these

statistics comparing the old design with

all adults interviewed in each sample

household to the new design with only

one adult interviewed in each sample

household. The decision of how many

adults to interview in each sample

household is independent of the other

design features. The one-adult-perhousehold rule has already been used

for a number of NHIS questionnaire

supplements in the 1985 design.

Conversely, there is nothing in the

design proposed for 1995 that is

inconsistent with interviewing all adults

in every sample household. It is thus

apparent that the one-adult-perhousehold rule should be structured in a

way to at least obtain a limited set of

core data for all household members.

Flexibility for Rapid Sample

Expansions and Contractions

The new design does not really

provide improved flexibility for rapid

expansions. The cost of carrying a

reserve sample was judged by NCHS to

be too great. However, general systems

changes being made at the U. S. Bureau

of the Census for all of its current

surveys will make such expansions

easier. Nonetheless, 2–4 years will still

be needed for expansions. Rapid

contractions are easier. The new design

has a more flexible panel structure than

was used previously. It allows cuts of

whole PSU’s by sixths, in addition to

cuts by fourths. Of course, there is still

the same capability for across-the-board

cuts (where there is a small reduction in

the sample size in each PSU), although

it is well known that such cuts save

very little money.

Integrated Survey Design

The main conclusions in this area

were with respect to integration at the

PSU level. One product of the research,

however, was a fairly detailed plan for

how network sampling theory could be

used to provide unbiased estimates for

provider surveys when the provider

sample is based on those providers

identified in NHIS interviews. The

differential sampling of black, Hispanic,

and other people makes estimation with

such a network-based approach more

complicated, but it was demonstrated to

be theoretically possible. More research

on this topic is being done under a

successor contract (2).

With regard to PSU’s, one

conclusion was that the PSU’s should be

defined much as they have always been,

consisting of counties, groups of

counties, metropolitan statistical areas

(MSA’s) and New England county

metropolitan areas (NECMA’s), with the

primary consideration for grouping

counties consisting of feasible

interviewer travel. Plans were

considered for defining PSU’s in terms

of health care commuting patterns, but

these plans were rejected as increasing

within-PSU travel costs to an

unwarranted extent.

Another conclusion was that the

measure of size for each PSU should be

defined in terms of total population, not

some measure of health care provided or

consumed. Health care consumption was

found to be substantially correlated with

population. On the other hand, a type of

measure of size based on racial and

ethnic composition that would have

brought more heavily black and

Hispanic PSU’s into the sample was

rejected as being unnecessarily harmful

to the health care provider surveys that

do not, as a rule, oversample health care

providers according to the racial or

ethnic mix of their clientele.

Another conclusion was that the

health care provider surveys could most

likely be restricted to a special subset of

the NHIS PSU’s with little effect on

their precision or cost, although such a

subset would have to be defined

differently than the subsets planned for

reductions in the NHIS. This area is also

a focus of the successor contract for

more research.

Follow-on Surveys

It was believed that the policy of

oversampling the black and Hispanic

populations for the NHIS would also be

beneficial to such follow-on surveys as

the NSFG. Even though some of these

surveys have traditionally only

oversampled the black population, it was

thought that oversampling Hispanic

persons is becoming more important for

most demographic surveys. The larger

number of PSU’s and the smaller

number of interviews with households

that are neither black nor Hispanic may

reduce efficiency for some of these

surveys, but it was believed that such

losses were justified, given the

importance of improving the precision

of statistics on the black and Hispanic

populations for NHIS and of creating

the needed flexibility for State

supplemental samples.

Page 6 [ Series 2, No. 126
Part I.

Introduction
Chapter 1.

Background on the

National Health

Interview Survey

The National Health Interview

Survey (NHIS) is an ongoing

survey of the population of the

United States on health issues. The

survey has been in continuous operation

since 1957. NHIS data are obtained

through personal interviews of the

civilian, noninstitutionalized population

conducted by the U.S. Bureau of the

Census. The official goal for the sample

size traditionally has been around

50,000 interviewed households per year,

but the actual number of households is

usually smaller, sometimes much

smaller, depending on funding.

Schedule and Content

The interviews are conducted during

the year in weekly samples (each

weekly sample consists of a probability

sample of the population), so that

seasonal trends can be followed. The

core interview focuses on nonfinancial

aspects of health that household

respondents usually know, for example,

visits to doctors, visits to hospitals,

chronic ailments, and recent acute

conditions are all covered in the core

interview. Financial data, such as annual

household income and whether the

person is covered by health insurance,

are also collected. The collection of data

on diet, the cost of medical care, and

details of diagnoses and treatments is

left to other surveys conducted by the

U.S. Department of Health and Human

Services. Each year supplements to the

core interview cover topics such as

smoking behavior, knowledge about

acquired immunodeficiency syndrome,

and disabilities. Many of these

supplements require a self-response

rather than rely on a household

respondent.

Sample Design for 1985–94

The first stage of the NHIS sample

consists of 198 multicounty PSU’s.

These sample PSU’s were selected,

using a stratified probability design,

from a much larger set of PSU’s that

covers the United States. For

stratification purposes, the PSU’s were

grouped before selection to ensure that

the chosen PSU’s would be broadly

representative in terms of several

demographic and economic

characteristics. Some of these PSU’s are

so populous that they were included in

the sample with certainty. These are

called self-representing (SR) PSU’s. The

full NHIS design contains 52 SR PSU’s.

The remaining 146 PSU’s had a chance

of not being selected. These PSU’s,

which represent themselves and other

PSU’s that were not selected, are called

nonself-representing (NSR) PSU’s.

Within each sample PSU, a sample

of blocks (or small groups of blocks)

was selected as the second stage of the

sample. In PSU’s in which the black

population consisted of 5 to 50 percent

of the total in the 1980 census, blocks in

enumeration districts (ED’s) with high

black populations were selected with a

higher probability than other blocks. The

selection was made in the early 1980’s

with a planned, slow rotation through

blocks as they become exhausted. For

the third stage, within each block, a

cluster of eight housing units (HU’s) is

selected each year after fresh listing or

updating of potential residential

structures. These HU’s are spread

throughout the block as evenly as

possible.

To gain better control over the size

of the sample, HU’s constructed since

the 1980 census are selected through a

sample of building permits rather than

through area sampling. These units are

selected in clusters of four instead of

eight.

To provide continuous coverage of

the population throughout the year, the

sample of households is spread over the

52 weeks of the year; each week’s

sample is representative of the U.S.

population. Each year a totally new

sample of households is selected. Those

households tend to be neighbors of the

households interviewed the previous

year, however.

As a result of the NHIS sample

redesign of 1985, it became possible for

NCHS to transmit data on NHIS sample

households to private contractors for use

in conducting follow-on surveys. Before

1985 a significant portion of the NHIS

sample was drawn from lists created for

the previous decennial census. In 1985

all of the NHIS sample was obtained by

area and permit sampling, without any

recourse to decennial census lists. These

follow-on surveys are then said to be

linked to NHIS. The confidentiality of

the transmitted data is protected under

section 308(d) of the Public Health

Service Act. This feature of the design

will have been well utilized between

1986 and 1994 and is a feature that

NCHS wants to maintain. Thus,

decisions about NHIS design must be

made with the needs of follow-on

surveys in mind.

Page 8 [ Series 2, No. 126
Chapter 2.

New Objectives for

1995 Redesign

For the 1995 survey, it was desired

to improve the precision of basic

health statistics about various

domains defined in terms of race, ethnic

origin, poverty status, and geography.

Accompanying this desire was the

realization that it would be difficult and

expensive to improve the precision of

statistics for these domains while

maintaining the precision of statistics

about the prevalence of rare health

conditions. Accordingly, the sorts of

statistics that were identified as

important for domain analysis include

self-assessed health status, having

visited a doctor in the prior year, having

been hospitalized in the past year,

knowledge of AIDS, and smoking

behavior. Statistics about the prevalence

of specific chronic conditions such as

cancer and about the occurrence of

acute conditions such as automobile

accidents were given a lower priority.

Statistics for Racial,

Ethnic, and Economic

Domains

The goal of this part of the research

was to develop sampling methods for

NHIS that would provide data of

acceptable precision for the following:

+ National estimates for race and/or

ethnicity, age, and sex subdomains.

More specifically, the subdomains

consisted of the complete cross

classification of the following:

+ Four racial and/or ethnic groups

(i.e., black persons, Hispanic

persons, Asians and Pacific

Islanders, and all others).

+ Males and females.

+ Six age groups (i.e., under 5 years,

5–17 years, 18–24 years, 25–44

years, 45–64 years, and 65 years

and over).

+ National estimates for the three

major Spanish-origin subgroups of

Hispanic persons in the United

States (i.e., Mexican-American,

Puerto Rican, and Cuban-American).

Sex and age subdomains for these

subgroups were also desirable,

although with more limited age

detail than for the broader

race/ethnicity groups. These

subdomains consisted of under 18

years, 18–44 years, 45–64 years, and

65 years and over. As an alternative

to the production of data for the

three subgroups, sample design

implications were also examined for

data on all Hispanic persons in the

three geographic areas in which the

subgroups are concentrated (i.e., five

Southwestern States, the greater

New York City area, and Florida).

+ Statistics on the poor, where the

poor can alternately be defined as

having incomes below the poverty

level, below 125 percent of the

poverty level, below 150 percent of

the poverty level, or below some

other cutoff.

Statistics for Geographic

Domains

A second goal was to develop

methods of producing State data by sex

and three age groups: Under 18 years,

18–64 years, and 65 years and over. As

an alternative to State data, the

feasibility of producing data for State

groupings was to be explored. As

described subsequently in this report, a

conflict exists between achieving this

goal and the improvement of race and/or

ethnicity statistics: Most members of the

minority groups reside in the larger,

urbanized States, whereas the smaller

States would require sample

supplementation to provide adequately

precise State data. Another conflict

exists between this goal and the

requirements of follow-on surveys.

Improved precision for State data

requires the use of many more sample

PSU’s than would usually be desirable

for a follow-on survey.

Integration With Surveys

of Health Care Providers

The third set of issues to be

examined were those involved in

integrating NHIS with surveys of health

care providers. Some issues focused on

analytic and operational benefits of

using the same set of sample PSU’s.

Others focused on integration at the

person-event level.

Integration at the PSU level seemed

likely to have operational efficiencies

because both sets of surveys are

conducted by the U.S. Bureau of the

Census. Integration might achieve

certain synergies that could reduce

travel and other expenses. It would also

provide some analytic enhancements,

because a relationship probably exists

between the practices of health care

providers within a PSU and the

health-related characteristics of the

residents of the PSU. Integration at the

person-event level is even more

interesting because it allows direct

linking of individual practices and

outcomes. Penalties for integration also

exist, however. The optimal designs for

different surveys are not necessarily

identical. A major goal of this research

was to determine the relative advantages

and disadvantages of the different

linkage possibilities.

Reducing Response Errors

Through Greater Use of

Self-Response on the Core

Interview

Prior research (3) had shown that

proxy response has a significant impact

on a number of statistics, including

those for health status. That impact is

particularly strong for men. Apparently,

wives (the typical proxy respondents)

tend to assess their husbands’ health as

better than the men themselves do.

Although this bias is not very troubling

for tracking health statistics over time

(under the reasonable assumption that

the bias due to proxy response is fairly

stable over time), it is very worrisome

for one-time questionnaire supplements

that are designed to compare population

domains. Unfortunately, obtaining

self-response for all sample persons is

considerably more expensive than the

policy of requesting self-response but

accepting proxy response when

obtaining self-response presents

difficulties. The most common difficulty

Series 2, No. 126 [ Page 9
is failing to find all household members

home at the same time. To keep the cost

per interview under control where

self-response is necessary, plans for

rejecting proxy response involve the

interview of just one randomly chosen

adult per household. If the designated

adult cannot be interviewed by

self-response, the household is classified

as a noninterview. This procedure is

currently used for most NHIS

questionnaire supplements. It is very

likely that the number of supplements

requiring self-response will be increased

in the future. This increased number will

make it necessary to reduce the length

of the core interview to restrain costs

and to minimize response burden. With

this idea in mind, almost all of the

research on how to improve the

precision for selected domains was

repeated under two assumptions: That

data are collected on all household

members as is currently the case with

the core instrument; and that data are

collected on only one random adult per

household (and possibly on all children

in the household).

Page 10 [ Series 2, No. 126
Part II.

Statistics for Racial, Ethnic, and

Economic Domains
Chapter 3. Current

Precision

To determine the level of precision

in the 1985–94 NHIS and the

sample size necessary to achieve

satisfactory levels after the redesign, it

was necessary to examine the current

NHIS variances. Furthermore, estimates

of variance components would indicate

how any necessary increases in sample

sizes should be implemented. An early

research step therefore was to calculate

variances for the current NHIS design.

Variances in Current

Design

As a guide to the reliability that can

be achieved with the multistage sample

design and estimation procedure used in

NHIS, Westat, Inc., computed estimates

of the sampling variances in 1988 for a

group of variables collected in NHIS.

The methodology is explained in great

detail in National Health Interview

Survey: Report on Variance Estimation

(4), which is also listed as Redesign

Memorandum #40 in appendix 1. In

brief, a variation due to Fay on balanced

repeated replications was used to

estimate the variances, both total and

within PSU.

The variables covered a range of

items, and variances were calculated for

statistics relating to the total population

and to the age categories. The 15 items

selected from NHIS for direct variance

estimation are listed in table 1. They

represent several parameter sets of

characteristics in NHIS. The first set of

items is based on nonbinary person-level

data; that is, the measurement variable

at the person level can take on values

other than 1 (yes) and 0 (no). It is also

important to note that within the first set

of items, the reference period for the

respondent is the 2 weeks preceding the

interview. Because the NHIS sample is

spread out over the 52 weeks of the

year, only 1/26th of the events

experienced by a person within a year

can be reported.

The second set of items is based on

binary person-level data or recodes of

multinomial responses into simple yes

and no categories. The reference period

for these questions is either irrelevant or

it is a year.

The third set of items is composed

of counts of chronic conditions reported

by targeted respondents. Six different

flash cards of chronic conditions are

used in NHIS. The sample is divided

into (nearly) equal sixths, and each sixth

is shown a different prompting flash

card. Thus, these statistics are based on

only one-sixth of the full sample.

The fourth set of items consists of

counts of acute conditions that have

afflicted the respondents within the

previous 2 weeks. Although a few

respondents report multiple attacks of

the same acute condition within the

2-week reference period, this question is

basically binary, that is, either the

person has experienced the condition in

the last 2 weeks or has not experienced

it. The varying reference periods and

sampling fractions have important

effects on reliability, as will be seen

presently.

Variances of these items were

estimated from the 1988 NHIS for

several subdomains. The subdomains are

the two-way classifications involving

age and sex, race, census region, or

census division. Age is classified into

six categories: Under 5 years, 5–17

years, 18–24 years, 25–44 years, 45–64

years, and 65 years and over. Sex has

two classifications: Male and female.

Race is categorized twice: First, in three

classifications—Hispanic, black, but not

Hispanic, and others; and second, in two

classifications—Asian or Pacific origin,

and others. There are four census

regions and nine census divisions.

Appendix 2 shows the estimated

variances. For each item, there are three

pages of estimates and coefficients of

variation for breaks by the various

domains. (The cv, or coefficient of

variation, is the ratio of standard error to

mean, expressed as a percentage.) For

those subdomains where the estimated

mean is 0, the cv is given as 0, even

though the cv is undefined. The cv’s

vary widely. For example, the cv on the

percent of the total population reporting

at least one doctor visit in the past year

is just 0.27 percent, but the cv on the

number of moving motor vehicle

injuries to young adults between 18 and

24 years of age in the West North

Central Census Division is

117.04 percent.

Because it was evident that there

was considerable instability in the

variance estimates, the decision was

made to generalize some of them.

Table 1. Items selected for direct variance estimation

Statistics based on nonbinary person-level data about the prior 2 weeks

Average number of doctor’s visits

Average number of bed days

Average number of chronic conditions

Statistics based on binary person-level data from the full sample

Percent reporting poor health status

Percent reporting at least two hospitalizations in the past year

Percent reporting at least one doctor’s visit in the past year

Percent unable to carry on major activity

Percent with more than 5 years since last doctor’s visit

Statistics based on binary person-level data from one-sixth of the sample

Number with chronic arthritis

Number with color blindness

Number with chronic epilepsy

Statistics based on (almost) binary person-level data about the prior 2 weeks

Number of acute digestive system conditions

Number of acute bronchitis conditions

Number of acute urinary conditions

Number injured in moving motor vehicle accidents

Page 12 [ Series 2, No. 126
Variance generalization is a technique

for improving the stability of variance

estimates that is based on the fact that

the variance of the mean of a set of

independent and identically distributed

(iid) Bernoulli random variables is

functionally related to the common

mean of the variables.

In complex surveys many statistics

of interest are means of Bernoulli

random variables that are not quite

independent and not quite identically

distributed. Experience has shown that

the variance of these means can often be

well predicted by functions of the

overall mean of the variables (5). In

fact, if there are few degrees of freedom

for direct variance estimates, the mean

square error of the generalized variance

estimates can be lower than that of the

direct variance estimates (6). Various

attempts have been made over the years

to develop similar models for statistics

that are not sums of Bernoulli variables,

but these have generally been

unsuccessful (and thus, not published).

Accordingly, for this report, of the items

shown in table 1, generalized variance

estimates were only prepared for the

second, third, and fourth sets of items.

See National Health Interview Survey:

Report on Variance Estimation (4) for

details of how the generalized

variance estimates were prepared for

this study.

Table 2 contains a variety of

estimated cv’s for the selected variables

in total and by age. The table shows the

cv’s as the following:

+ Calculated from generalized

variance functions published by

NCHS.

+ Calculated from generalized

variance functions fitted by Westat,

Inc., when the domain to which a

statistic is restricted is subject to

poststratification (e.g., the

percentage of females who report

poor health).

+ Calculated from generalized

variance functions fitted by Westat,

Inc., when the domain to which a

statistic is restricted is not subject to

poststratification (e.g., percentage of

those with low income who report

poor health).

+ Estimated directly with Fay’s variant

of balanced repeated replication

(BRR).

Poststratification is an estimation

technique used to bring survey estimates

into conformity on certain dimensions

with known or more accurately

estimated population totals. NHIS

estimation routinely involves

poststratification on age, sex, and race.

The generalized variances are

somewhat lower than the ones reported

in the published NHIS reports. After

Table 2. Coefficients of variation for selected NHIS items: 1988

Estimates Coefficient of variation (%) cv’s (%) for limited age ranges direct by BRR

Published

Westat, Inc.

tabulated

From

published

curves

From new curves

Direct

by

BRR

Age

0.5

Age

6–17

Age

18–24

Age

25–44

Age

45–64

Age 65

years

and over

Controlled

margin1

Interior

cell1

Quantitative items based on 2-week data

Doctor visits per person ............... 5.400 3.863 . . . . . . . . . 0.9 1.7 1.8 2.4 1.8 2.3 2.3

Bed days per person ................. 6.300 5.344 . . . . . . . . . 2.0 3.6 2.7 3.7 2.5 3.6 4.1

Conditions per person . . . . . . . . . . . . . . . . ... 4.298 . . . . . . . . . 0.7 2.1 1.6 1.8 1.1 1.2 1.3

Rates based on full sample

Percent with poor health ............... 0.027 0.027 2.4 1.7 2.0 2.0 16.1 13.9 15.6 4.6 3.4 2.4

Percent with 2 or more hospitalizations ...... 0.016 0.016 3.2 2.3 2.6 2.7 9.6 11.5 9.4 4.7 4.5 4.1

Percent with no doctor visits ............ 0.233 . . . 1.0 . . . . . . . . .

Percent with 1 or more doctor visits . . . . . . . . ... 0.747 . . . 0.2 0.2 0.3 0.4 0.6 0.7 0.4 0.5 0.4

Percent unable major activity ............ 0.040 0.040 2.0 1.4 1.6 1.5 13.0 11.6 6.3 3.3 2.3 2.6

Percent 5+ years last doctor visit ......... 0.036 0.034 2.1 1.5 1.8 2.0 undefined 6.6 6.2 2.7 3.1 4.1

Generic full sample items

p = .01 . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 2.9 3.3

p = .1 . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 0.9 1.0

p = .2 . . . . . . . . . . . . . . . . . . . . . . . . . . 0.8 0.6 0.7

p = .5 . . . . . . . . . . . . . . . . . . . . . . . . . . 0.4 0.3 0.3

Rates based on 1/6 sample

Percent with chronic arthritis ............ 0.130 0.126 2.2 1.9 2.0 2.2 100.4 32.3 18.7 5.1 3.2 3.3

Percent with chronic color blindness ....... 0.012 0.011 6.7 6.6 7.0 6.9 undefined 17.0 27.2 10.4 12.6 17.1

Percent with chronic epilepsy ............ 0.004 0.004 11.6 11.5 12.1 12.0 40.5 33.1 44.0 21.0 21.1 28.9

Rates based on 2-week data

Percent with acute digestive condition ...... 0.063 0.063 6.9 6.0 6.0 6.9 17.4 12.7 16.9 12.1 16.2 18.0

Percent with acute bronchitis ............ 0.034 0.034 9.3 8.4 8.4 7.8 16.5 17.6 35.8 15.7 26.0 27.1

Percent with acute urinary condition ........ 0.028 0.028 10.1 9.2 9.1 10.0 44.9 38.7 30.6 14.7 19.8 18.9

Percent injured in motor vehicle accident .... 0.017 0.017 12.5 11.9 11.7 13.7 99.9 28.2 28.0 20.6 34.6 46.1

. . . Category not applicable.

1Some domains have population estimates with zero variance due to poststratification. Such a domain is referred to as a controlled margin. Other domains are referred to as interior cells.

Series 2, No. 126 [ Page 13
discussions were held with NCHS staff,

the differences were ascribed to the mix

of items that formed the basis for the

generalization process. It was agreed

that the new set of items was more

representative of the sets of items in the

published reports than the ones used in

the NCHS computations of generalized

variance functions. It was thus decided

to use the new generalized variance

functions in the decision-making process

for determining required sample sizes.

Normally in a sample survey, the cv

depends mainly on the statistic (i.e., the

value of p) and the size of the

subdomain for which the statistic is

estimated. However, in NHIS the cv’s

also differ sharply among the three

classes of statistics into which the rates

have been classified in table 2; rates

based on the full sample, those based on

the one-sixth sample used for chronic

conditions, and rates based on 2-week

data. (The 2-week data can be

considered as approximately based on a

1/26-sample of NHIS.) For the same

value of p and the same subdomain, the

cv’s for the three classes of items reflect

the different effective sample sizes

resulting from the subsampling for

condition items and 2-week data.

Desired levels of precision for

black; Hispanic; Asian and

Pacific Islander; American

Indian, Eskimo, and Aleut

populations; and persons with

low income

For this research on the 1995

redesign, the required level of precision

was a 30-percent cv. Taking into

account the observed cv’s for the current

design from table 2 and known NHIS

sample sizes for 1988 (not shown),

table 3 was developed; it shows the

required sample sizes for many different

statistics to have a 30-percent cv. This

assumes the same extent of clustering as

the 1988 NHIS but without the

differential sampling by racial

composition of neighborhood. The

formula used was

nR = nD S

cvD

30 D

2

×

1

1.03

where nD is the sample size for the

domain in 1988, cvD is an estimated cv

for the statistic on the domain in 1988,

nR is the required sample size, and

division by 1.03 corrects for the design

effect because of the slight oversampling

that was done of heavily black

neighborhoods in the 1985–94 design.

As is evident from inspection of the

table, this guideline of a flat 30-percent

cv did not really provide much guidance

on the magnitude of an appropriate

sample size for each domain. For a cv

guideline to determine a sample size, a

Table 3. Domain-specific required sample sizes for a 30-percent cv

General population

Direct

BRR

Limited age domains

(Total sample size required over all domains

Size calculated using cv from: to be found by summing across domains)

Published

curves

Controlled

margin

Interior

cell1

Age

0–5

Age

6–17

Age

18–24

Age

25–44

Age

45–64

Age 65

and over

Quantitative items based on 2-week data

Doctor visits per person . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 30 86 76 126 138 83

Bed days per person . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 541 137 192 179 263 333 272

Conditions per person . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 48 66 44 48 36 26

Rates based on full sample

Percent poor health . . . . . . . . . . . . . . . . . . . . . 760 397 520 522 2,742 5,063 3,162 861 294 94

Percent with 2 or more hospitalizations ......... 1,351 701 917 967 964 3,458 1,141 892 514 276

Percent with no doctor visits . . . . . . . . . . . . . . . 132 . . . . . . . . .

Percent with 1 or more doctor visits . . . . . . . . . . . ... 4 5 9 1 8676 3

Percent unable major activity . . . . . . . . . . . . . . . 528 266 348 307 1,783 3,515 520 447 137 111

Percent 5+ years last doctor visit . . . . . . . . . . . . 582 314 411 552 (1

) 1,152 501 293 241 280

Generic full sample items

p = .01 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1,974 1,104 1,444

p = .1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 100 131

p = .2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 45 58

p = .5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 11 15

Rates based on one-sixth sample

Percent with chronic arthritis . . . . . . . . . . . . . . . 639 464 515 610 106,248 27,127 4,505 1,069 261 177

Percent with chronic color blindness .......... 5,923 5,792 6,437 6,227 (1

) 7,529 9,540 4,446 3,965 4,743

Percent with chronic epilepsy ............... 17,754 17,417 19,357 18,898 17,316 28,541 24,986 18,109 11,170 13,618

Rates based on 2-week data

Percent with acute digestive condition ......... 6,282 4,825 4,712 6,236 3,190 4,207 3,709 5,985 6,621 5,281

Percent with acute bronchitis ............... 11,412 9,420 9,201 7,955 2,855 8,116 16,554 10,080 16,953 12,000

Percent with acute urinary condition ........... 13,459 11,178 10,918 13,202 21,250 39,022 12,069 8,896 9,835 5,804

Percent injured in motor vehicle accident ....... 20,616 18,608 18,174 24,627 105,130 20,771 10,154 17,427 30,041 34,630

. . . Category not applicable.

1Because of an observed p = 0, the estimated required sample sizes to achieve the desired precision are infinite.

Page 14 [ Series 2, No. 126
single statistic for which the design

must provide the required cv must be

specified. Specifying a group of

statistics allows the rarest characteristic

to drive the sample size. Of course,

across the age domains, the rarest

health-related characteristic changes. It

did not seem to make sense to

determine a different required sample

size for each age domain. The round

figures of 1,000 as a required effective

sample size were the result of a

reasonable compromise. A sample of

1,000 persons selected with clustering

but equal probabilities provides the

desired cv of 30 percent for an estimated

proportion of 1.438 percent.

This standard of 1,000 was used for

most of the research. It is referred to

loosely as an effective sample size of

1,000, although this is a slight abuse of

standard terminology. A sample

clustered in the same manner as the

current NHIS but selected with equal

probabilities will not provide as good

precision as a simple random sample of

1,000 cases.

Current sample sizes are quite

different for estimates based on data

from the core interview than for those

based on items from most questionnaire

supplements. Most supplements are

administered to only one adult per

household to reduce the burden on the

household. Crossing the desired age

breaks by sex and by race and ethnicity

leads to the finding that sample sizes are

inadequate (i.e., fewer than 1,000) on

supplements for almost every minority

age-by-sex domain.

For the core interview, most black

domains have adequate sample sizes.

The exceptions are the domains of black

males and females under age 5 and

black males ages 65 and over. A few

Hispanic domains have adequate sample

sizes for the core, but most are

inadequate. For the other groups, no

domains have adequate sample sizes,

even for the core interview.

Components of Variance

On average, the study found that

about 6 percent of the total variance in

1988 resulted from differences among

PSU’s, with the rest resulting from

differences between and within segments

within PSU’s. Considerable variation

was found in the estimated proportion

due to between-PSU variance, however.

Some of this variation was attributable

to sampling error on the variance

estimates themselves, and some was

attributable to real differences. In

particular, because estimates pertaining

to large domains tend to have higher

average cluster size and thus greater

between-PSU variance, the size of the

estimate has some relationship to the

proportion of the total variance

contributed by the between-PSU

component.

An average between-PSU variance

of 6 percent is perhaps a little smaller

than common wisdom among sampling

statisticians would have predicted for a

survey with only 198 sample PSU’s.

This indicates that the stratification

performed by the U.S. Bureau of the

Census in the early 1980’s was quite

effective.

Between-PSU variances are even

smaller for minority groups. The study

found that between-PSU variance

accounts for between 2 and 3 percent of

total variance for black persons and

essentially 0 percent for Hispanic

persons. Components of variance were

not calculated for minorities other than

the black and Hispanic populations, but

it seems reasonable to believe that

between-PSU variance is also not

important for Asians and Pacific

Islanders. On the other hand, there is

good reason to suspect that

between-PSU variance is larger for

American Indians, Eskimos, and Aleuts

because of their large nonmetropolitan

populations. Between-PSU variance for

nonmetropolitan estimates was a notably

large 15 percent of total variance, more

than twice as large as for statistics for

the United States as a whole.

Series 2, No. 126[ Page 15
Chapter 4. Potential

Techniques for

Increasing Sample

Sizes for Selected

Domains

This chapter reviews the basic

techniques that can be used for

oversampling racial, ethnic, and

economic domains. Some of these

techniques are not discussed further in

subsequent chapters because it is clear

they do not meet NCHS requirements

for NHIS.

Sample Sizes With Simple

Expansion

The least complicated approach to

meeting the new precision requirements

for NHIS would be to simply increase

the sample size. This option would

require no changes in methodology. To

evaluate this option, it is useful to first

project sample sizes that would be

obtained for each domain given an

equiprobability design of 50,000

interviewed households, the goal that the

1985 design was built around. There are

several steps to making this projection.

The first step is to project the population

into the future. Given that the 1995

design will be used from 1995 through

2004, it seemed reasonable to evaluate

sizes as of the year 2000. Table 4 shows

such projections.

The projections for the total and

black populations were based on table 4

of Projections of the Population of the

U.S. by Age, Sex, and Race: 1988 to

2080 (7). The projections for the

Hispanic population were based on

Series 17 from tables 2 and 3 of

Projections of the Hispanic Population:

1983 to 2080 (8). The projections of

‘‘other’’ populations were computed by

subtraction, ignoring the existence of

black Hispanic persons. Because all of

the U.S. Bureau of the Census

projections were made before the 1990

decennial census, those projections were

multiplied by correction factors to

reflect the error in the projections for

1990. Thus, the numbers in table 4

(above) reflect an adjustment of

−3.2 percent for black populations,

+11.5 percent for Hispanic populations,

and −0.7 percent for ‘‘other’’

populations. Clearly, the potential for

error exists in all these projections. The

Hispanic projections are particularly

sensitive to trends in immigration. Most

critically, the projections are sensitive to

changes in ethnic identification. Studies

by the U.S. Bureau of the Census have

shown repeatedly that question wording

can have a major impact on estimates of

the Hispanic population.

The second step in projecting

sample sizes is to account for

undercoverage. Historically, population

estimates from NHIS have been lower

than those from the decennial census,

particularly for the black and Hispanic

populations. (This is true for practically

all surveys.) Table 5 shows projections

of undercoverage for typical

demographic surveys. The numbers are

from Redesign Memorandum #9 (9) and

are averaged over years. From year to

year, coverage rates can vary

substantially, depending on the size of

the survey and survey procedures.

The third step in projecting sample

sizes is to project household sizes, that

is, the number of persons per household.

Given historical trends it was decided to

predict household sizes of 2.71, 3.40,

and 2.36 for black, Hispanic, and

‘‘other’’ households, respectively. These

numbers are sensitive to assumptions

about the causes of undercoverage as

well as changes in trends for young

adults to set up separate households

from their parents.

Given these steps the sample sizes

by race, ethnicity, age, and sex can be

obtained by reducing the eligible

population by predicted undercoverage,

Table 4. Projections of the population by age, sex, race, and ethnicity to 2000

Race, Hispanic origin, and age Male Female Total

Black

Under 5 ....................... 1,358 1,301 2,659

5–17 ......................... 3,910 3,732 7,642

18–24 ........................ 1,888 1,910 3,798

25–44 ........................ 4,920 5,479 10,399

45–64 ........................ 2,933 3,538 6,470

65 years and over ................. 1,238 1,793 3,031

Total ......................... 16,247 17,753 33,999

Hispanic

Under 5 ....................... 1,685 1,612 3,297

5–17 ......................... 4,043 3,861 7,903

18–24 ........................ 1,943 1,805 3,748

25–44 ........................ 5,423 4,841 10,263

45–64 ........................ 2,405 2,524 4,929

65 years and over . . . . . . . . . . . . . . . . . 774 1,086 1,860

Total ......................... 16,271 15,729 32,000

Other

Under 5 ....................... 5,707 5,409 11,115

5–17 ......................... 17,238 16,350 33,588

18–24 ........................ 9,012 8,806 17,818

25–44 ........................ 30,445 30,236 60,681

45–64 ........................ 24,493 25,429 49,922

65 years and over ................. 12,214 17,656 29,870

Total ......................... 99,109 103,886 202,995

Total

Under 5 ....................... 8,749 8,322 17,071

5–17 ......................... 25,191 23,942 49,133

18–24 ........................ 12,844 12,520 25,364

25–44 ........................ 40,787 40,556 81,344

45–64 ........................ 29,830 31,491 61,321

65 years and over ................. 14,225 20,536 34,761

Total ......................... 131,627 137,367 268,994

NOTE: Figures may not add to totals because of rounding.

Page 16 [ Series 2, No. 126
summing across age and sex, and then

dividing by household size (specific to

race and ethnicity), thereby yielding

estimates of the counts of households

that can be subject to discovery by a

typical demographic survey in the year

2000. Yields in an equiprobability

survey of 50,000 interviewed households

are then obtained by proportionality.

Table 6 shows the results of these

calculations.

Different projections could have

been obtained by assuming interactions

between household relationships and

coverage. For example, there is some

evidence to support the proposition that

undercoverage of occupied housing is

less severe than the undercoverage of

persons within listed households because

of less severe undercoverage of

household heads than of other household

members, even after controlling for age

and sex. However, definitive studies on

the relative importance of withinhousehold undercoverage versus

undercoverage of whole households

(as a result of mistakes in the

listing/screening process such as

thinking homes unfit for habitation or

vacant) are not available. It was decided

that it would be adequate for planning

purposes to simply divide estimates of

covered persons by projected household

sizes to get estimates of covered

persons.

Not shown in the table are

projections for Asian and Pacific

Islanders nor for American Indians,

Eskimos, and Aleuts. Projections for

these racial groups are just 3,600 and

1,000, respectively, in a survey of

50,000 interviewed households. Those

numbers are for population of any age

and sex.

Given NCHS interest in

subsampling just one adult per

household, as discussed in chapter 2,

projections were also made for the

expected yield for such a procedure

given an equiprobability sample of

50,000. Such a procedure reduces the

sample size directly and also indirectly.

The sample size is directly reduced

because fewer people are interviewed. It

is indirectly reduced in the sense that

the sample is no longer an

equiprobability sample. For example,

adults in three-adult households end up

with weights three times as large as

adults in single-adult households. The

direct reductions can be obtained by

simply dividing by the projected

numbers of adults per household. For

this research it was assumed that there

would be 1.81 adults per black

household, 2.06 per Hispanic household,

and 1.82 per ‘‘other’’ household. A

study of data from the Current

Population Survey yielded projections

that the departure from equiprobability

would induce additional design effects

of 1.28, 1.25, and 1.19 for black,

Hispanic, and ‘‘other’’ adults,

respectively. Table 7 shows projections

of effective sample sizes that have been

adjusted for these design effects but not

the design effects resulting from

clustering. Because sampling only one

adult per household would reduce the

component of the design effect due to

within-household intraclass correlation,

these adjustments might be a little too

severe, but they do give a good idea of

the precision that could be expected

with such a design.

As can be readily inferred from

tables 6 and 7, simple expansion of the

sample is an extremely expensive

method for satisfying the precision

requirements discussed in chapter 3.

Because the projected sample sizes are

always larger for black domains than for

Hispanic domains, the expansion ratio

required for Hispanic goals would also

meet black goals. Thus, it is enough to

look at the expansion ratios for Hispanic

persons. To meet the precision

requirement for elderly Hispanic males

by this technique alone would require

between a threefold and fourfold

increase in the NHIS sample size, even

if NCHS were willing to take all adults

in minority households. (The required

precision is achieved with 1,000

interviews, but the equiprobability

design yields just 323.) In other

words, instead of interviewing 50,000

households per year, it would be

necessary to interview 150,000 to

200,000 households per year. A vital

category such as Hispanic males

18–24 years of age would require a

full fourfold increase if only one adult

is interviewed per household. The

numbers are even larger for elderly

Asian Americans and Native

Americans. Clearly, this is not a

feasible technique.

Table 5. Projections of undercoverage by age, sex, race, and ethnicity

Race, Hispanic origin, and age

Percent missed

Male Female

Black

Under 5 .............................. 0 0

5–17 ................................ 1 1

18–24 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 12

25–44 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 10

45–64 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 9

65 years and over ........................ 7 5

Hispanic

Under 5 .............................. 5 5

5–17 ................................ 5 5

18–24 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 22

25–44 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 18

45–64 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 18

65 years and over . . . . . . . . . . . . . . . . . . . . . . . . 15 15

Other

Under 5 .............................. 0 0

5–17 ................................ 0 0

18–24 ............................... 7 5

25–44 ............................... 6 4

45–64 ............................... 4 4

65 years and over ........................ 5 5

Series 2, No. 126 [ Page 17
Oversampling Classes of

Primary Sampling Units

It is well known that some

nonself-representing PSU’s have

disproportionate numbers of Hispanic

persons and others have disproportionate

numbers of black persons. This led to

the idea that it might be advantageous to

deliberately assign a larger measure of

size to such PSU’s so that more of them

would appear in the sample. This would

reduce between-PSU variance for

targeted minority statistics and make it

easier to oversample targeted minorities

at the block level while maintaining

reasonable PSU workloads. (The PSU

workload is the total number of

households interviewed in a PSU over

the course of a year.) With this

technique, however, the between-PSU

variance is increased for the majority

population and for untargeted other

minorities as well as for statistics for the

total population. Also, the set of sample

PSU’s becomes less suitable for the

health care provider surveys and for

follow-on surveys, such as the National

Survey of Family Growth, that

reinterview former NHIS sample

persons or housing units.

Because large majorities of the

black and Hispanic populations live in

large metropolitan areas that are not

subject to between-PSU variance (a

special tabulation of the 1988 NHIS

found 62 percent of black persons and

69 percent of Hispanic persons living in

SR PSU’s), and between-PSU variance

is already small for these domains, and

because equal workloads are not an

important consideration for NHIS with

its traveling interviewers, it was decided

not to oversample PSU’s with large

minority populations. Equal monthly

workloads are important, but they can

be controlled by the number of times

the PSU is visited in the course of the

year. More details can be found in PSU

Probabilities Given Differential

Sampling at Second Stage (10).

Oversampling Classes of

Blocks or Block Groups

Using this technique, small areas

that are known from the prior decennial

census to be rich in targeted minorities

are deliberately oversampled. Unless the

targeted minority is completely isolated

residentially, this technique naturally

gives rise to an oversample of those

nonminority persons who happen to

reside in blocks with heavy

concentrations of minorities. This

oversampling is usually undesirable as

there are no fixed precision requirements

for such nonminority persons.

Furthermore, the resulting variation in

probabilities of selection for the

untargeted demographic domains leads

to larger-than-necessary design effects

for the total estimates and for the

majority population. For these reasons,

oversampling of blocks and block

groups is most effective when combined

with screening (see the next section).

Nonetheless, this technique can and has

been used by itself and so was

considered as a possible approach in this

research.

The demographic domain is often

defined in terms of age, sex, race, or

ethnic origin. The underlying theory for

this approach is the same regardless of

the definition. Stratify the blocks in each

PSU into M strata according to the

percentage of the population within the

Table 6. Sample sizes for equiprobability sample of 50,000 interviewed households

Race, Hispanic origin, and age Male Female Total

Black

Under 5 years . . . . . . . . . . . . . . . . . . . . . 667 639 1,306

5–17 ........................... 1,900 1,813 3,713

18–24 . . . . . . . . . . . . . . . . . . . . . . . . . . 705 825 1,529

25–44 .......................... 1,908 2,420 4,372

45–64 .......................... 1,223 1,580 2,803

65 years and over . . . . . . . . . . . . . . . . . . . 565 836 1,401

Total ........................... 6,968 8,112 15,079

Total 18 years and over ............... 4,401 5,660 10,061

Households . . . . . . . . . . . . . . . . . . . . . . . ... ... 5,564

Hispanic

Under 5 years . . . . . . . . . . . . . . . . . . . . . 785 751 1,536

5–17 ........................... 1,885 1,801 3,686

18–24 . . . . . . . . . . . . . . . . . . . . . . . . . . 715 691 1,406

25–44 .......................... 1,996 1,948 3,943

45–64 . . . . . . . . . . . . . . . . . . . . . . . . . . 896 1,016 1,912

65 years and over . . . . . . . . . . . . . . . . . . . 323 453 776

Total ........................... 6,600 6,659 13,259

Total 18 years and over ............... 3,930 4,107 8,037

Households . . . . . . . . . . . . . . . . . . . . . . . ... ... 3,900

Other

Under 5 years ..................... 2,800 2,654 5,454

5–17 ........................... 8,460 8,023 16,483

18–24 .......................... 4,069 4,106 8,175

25–44 .......................... 14,045 14,245 28,290

45–64 .......................... 11,539 11,980 23,519

65 years and over ................... 5,695 8,232 13,927

Total ........................... 46,608 49,240 95,848

Total 18 years and over ............... 35,348 38,563 73,911

Households . . . . . . . . . . . . . . . . . . . . . . . ... ... 40,614

Total

Under 5 years ..................... 4,252 4,044 8,296

5–17 ........................... 12,245 11,636 23,881

18–24 .......................... 5,488 4,622 11,110

25–44 .......................... 17,948 18,612 36,561

45–64 .......................... 13,659 14,575 28,234

65 years and over ................... 6,584 9,521 16,104

Total ........................... 60,176 64,011 124,186

Total 18 years and over ............... 43,679 48,330 92,009

Households . . . . . . . . . . . . . . . . . . . . . . . ... ... 50,078

. . . Category not applicable.

NOTE: Figures may not add to totals because of rounding.

Page 18 [ Series 2, No. 126
block belonging to the targeted domain.

There does not appear to be any theory

on the optimal number of such strata or

on how to choose the cut-points that

define them. As a general rule, it would

seem that more strata are better than

fewer. The limiting constraints are the

cost of the computer runs to classify

blocks into the strata and the burden of

verifying that sampling is done for each

stratum correctly.

Let P1−PM indicate the proportion

of the total population living in each of

these strata. Let D1−DM indicate the

corresponding proportions of the

targeted domain population. Let SI1−SIM

indicate corresponding sampling

intervals for the M strata. The problem

is to determine the optimal set of

sampling intervals for producing

statistics about the targeted domain

population.

Assuming equal unit variances in all

strata, the design effects for total

statistics and for statistics about the

targeted domain will be, respectively,

DE = 3∑

i = 1

M

PiSIi

4 • 3∑

i = 1

M

Pi

SIi

4 and

(1)

DE = 3∑

i = 1

M

DiSIi

4 • 3∑

i = 1

M

Di

SIi

4

(2)

The average sampling fractions for

the overall sample and for the targeted

domain are, respectively,

O Pi

SIi

and ODi

SIi

Thus, the ratio of the sampling

fraction for the targeted domain to the

sampling fraction for the general

population (henceforth referred to as the

‘‘nominal oversampling rate’’) is

O Di

SIi

O Pi

SIi (3)

Division of this nominal

oversampling rate by the design effect

for the targeted domain gives the

effective oversampling ratio:

1

[∑ (Pi /SIi)] [∑DiSIi]

(4)

The problem is to maximize

equation 4 subject to the constraint of a

constant sample size. Let SI indicate the

sampling interval that would yield the

allowable fixed sample size with a

simple random sample. Then the optimal

sampling interval for the i-th stratum is

SIi = SIŒPi

Di ∑

j = 1

M

√ DjPj [ ] (5)

The optimal sampling intervals from

equation 5 can be substituted back into

the earlier equations to obtain the

nominal and effective oversampling

ratios and the design effects. These basic

formulas are used to discuss the

suitability of this approach compared

Table 7. Sample sizes for equiprobability sample of 50,000 interviewed households with one

adult per household (corrected for within-household design effect)

Race, Hispanic origin, and age

One adult per household

Male Female Total

Black

Under 5 . . . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

5–17 . . . . . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

18–24 . . . . . . . . . . . . . . . . . . . . . . . . . . 304 356 660

25–44 . . . . . . . . . . . . . . . . . . . . . . . . . . 823 1,044 1,867

45–64 . . . . . . . . . . . . . . . . . . . . . . . . . . 528 682 1,210

65 years and over . . . . . . . . . . . . . . . . . . . 244 361 605

Total . . . . . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

Total 18 years and over ............... 1,900 2,443 4,343

Households . . . . . . . . . . . . . . . . . . . . . . . ... ... 5.559

Hispanic

Under 5 . . . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

5–17 . . . . . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

18–24 . . . . . . . . . . . . . . . . . . . . . . . . . . 278 268 546

25–44 . . . . . . . . . . . . . . . . . . . . . . . . . . 775 756 1,531

45–64 . . . . . . . . . . . . . . . . . . . . . . . . . . 348 394 742

65 years and over . . . . . . . . . . . . . . . . . . . 126 176 302

Total . . . . . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

Total 18 years and over ............... 1,526 1,595 3,121

Households . . . . . . . . . . . . . . . . . . . . . . . ... ... 3,901

Other

Under 5 . . . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

5–17 . . . . . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

18–24 .......................... 1,879 1,896 3,775

25–44 .......................... 6,485 6,577 13,062

45–64 .......................... 5,328 5,531 10,859

65 years and over ................... 2,630 3,801 6,431

Total . . . . . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

Total 18 years and over ............... 16,321 17,805 34,127

Households . . . . . . . . . . . . . . . . . . . . . . . ... ... 40,610

Total

Under 5 . . . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

5–17 . . . . . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

18–24 .......................... 2,460 2,520 4,980

25–44 .......................... 8,083 8,378 16,461

45–64 .......................... 6,204 6,608 12,812

65 years and over ................... 2,999 4,338 7,337

Total . . . . . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

Total 18 years and over ............... 19,746 21,844 41,590

Households . . . . . . . . . . . . . . . . . . . . . . . ... ... 50,070

. . . Category not applicable.

NOTE: Figures may not add to totals because of rounding.

Series 2, No. 126 [ Page 19
with others for each of the targeted

domains in chapters 5 through 9. Some

foreshadowing of those results can be

obtained from table 8, however.

Oversampling integrated and

predominantly minority blocks can be

seen to have much more potential for

black persons than for the other groups

because the black population is much

more highly concentrated. It is

immediately apparent that Asian and

Pacific Islanders are not concentrated

enough for the technique to be effective.

This is particularly obvious when one

considers the large boost in the effective

sample size that is required for the

domain. Among American Indians,

Eskimos, and Aleuts, there are two

distinct patterns. Those who live on

reservations are extremely segregated

and thus easy to oversample; those who

live off reservations tend to be only

slightly segregated.

Table 9 shows the percentage of

each density stratum that is not black,

Hispanic, Asian, or Native American.

The table basically shows the

pervasiveness of non-Hispanic white

persons (the only racial and/or ethnic

domain with excellent precision) in all

strata. The table shows that blocks with

strong concentrations of black persons

or Native Americans are the least likely

blocks to contain non-Hispanic white

persons. Even in these blocks, however,

the numbers of non-Hispanic white

persons imply that a sample

concentrated in the densest minority

blocks will pick up substantial numbers

of non-Hispanic white persons. Because

there is no desire to improve the

precision of statistics about this group of

non-Hispanic white persons,

oversampling classes of blocks is a

wasteful technique by itself. It is far

more efficient when combined with

screening, as is discussed in the next

section.

Screening With

Subsampling

With screening, a larger sample

than actually desired is selected. A very

brief, inexpensive questionnaire is then

administered to the sample to determine

the members of the domain to be

oversampled. Those units (persons or

housing units) that are not part of that

domain are subsampled. It is possible to

set up multiple cells with different

subsampling rates. The subsampling can

be done centrally after the entire

screening operation is completed, or it

can be done by the interviewer on the

fly. Techniques have been developed

that make the subsampling process very

easy for the interviewer (11).

Interviewers do not need random

numbers. Instead, they are given

house-by-house preinterview instructions

about which domains can be interviewed

at which households. These instructions

are randomized centrally before

screening to yield the desired sampling

rates. Alternatively, with computerassisted personal interviewing (CAPI),

the sampling can be programmed and

performed automatically in the laptop

computer; the computer notifies the

interviewer which households to retain

for the full interview and which ones to

reject as a result of subsampling.

Experience at Westat, Inc., has

shown that it is possible to obtain basic

information on sex, race, and ethnicity

for the occupants of more than

99 percent of housing units. A success

rate this high requires persistent,

well-trained, door-to-door interviewers

and some recourse to proxy information

from neighbors.

The U.S. Bureau of the Census

estimates that each screening interview

that does not result in a household of

the desired type costs as much as

one-third of the cost of interviewing the

household with standard NHIS

instruments. This estimate is

parameterized in this report as R = 3,

meaning that a full standard interview

costs three times as much as a screening

interview. For example, 51,000 extra

attempted screeners should cost as much

as 17,000 completed interviews. In fact,

the Bureau made an estimate this low

only with great hesitancy. Some at the

Bureau believed that R = 2 or even

R = 1.5 would be better projections.

(The cost estimates are staff member

judgments because the Bureau has not

had any recent direct experience with

screening of this type.) The actual cost

will be sensitive to the required

response rate, the rules allowing the use

of information from neighbors, and the

Table 8. Distributions in 1990 of minority populations across density strata

Stratum (designated minority as

percent of block)

Percent of designated minority in each stratum

Black Hispanic

Asian and

Pacific

Islander

American

Indian,

Eskimo,

and Aleut

< 10 . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.5 14.8 37.0 46.8

< 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.6 19.4 34.6

5–10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8.1 17.7 12.1

10–30 .......................... 13.9 22.1 32.1 15.9

30–60 .......................... 16.2 23.3 18.0 7.7

60 and over ...................... 61.4 39.8 13.0 29.6

. . . Category not applicable.

SOURCE: 1990 STF1B file

Table 9. Distributions in 1990 of each density stratum that is other

Percent of block population constituted by

a specific racial and/or ethnic group Black Hispanic

Asian and

Pacific

Islander

American

Indian,

Eskimo,

and Aleut

< 10 . . . . . . . . . . . . . . . . . . . . . . . . . . . 86.6 83.5 77.6 76.0

<5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... 84.6 78.2 76.0

5–10 . . . . . . . . . . . . . . . . . . . . . . . . . . . ... 75.9 71.5 73.3

10–30 .......................... 63.6 62.9 59.3 70.3

30–60 .......................... 40.7 37.8 35.0 50.0

60 years and over . . . . . . . . . . . . . . . . . . . 8.1 11.8 15.4 7.7

NOTE: Other is defined as not black, Hispanic, Asian, Pacific Islander, American Indian, Eskimo, or Aleut. In most tables in this

report, other is defined as not black or Hispanic.

Page 20 [ Series 2, No. 126
complexity of the screening rules.

Because the U.S. Bureau of the Census

has not attempted screening in

household interviews since the 1960’s, a

decision was made to field test

procedures in 1993. A field test was

carried out by the Bureau with good

results (12).

Although screening can be done on

an equiprobability sample, it is often

more effective when combined with

oversampling at the block level, as is

further explored in chapter 5. Screening

can be used either at the person level or

at the household level. Given the very

low marginal cost in NHIS of collecting

data on extra members of a household,

most of the attention for this study was

on screening whole households either in

or out of the sample for full interviews.

The formulas in the previous

section for optimal sampling intervals

need to be changed when screening is

conducted in tandem with oversampling.

Those formulas assumed constant costs

per unit across the strata. When

screening is used, cost is no longer

constant across the strata. Each minority

person found in one of the less dense

stratum costs more than a similar

minority person in the most dense

stratum because more screening

interviews with untargeted domains had

to be conducted to find him or her.

Let N be the size of the total

population and ND be the size of the

targeted domain. Let c be the cost of

one screening interview. An interview

with a member of the targeted domain

in stratum i costs (R-1)c plus

NPi

ND Di

c for all the screening interviews

that did not yield households from the

targeted domain. The cost per interview

is thus proportional to

ND

N (R−1) +

Pi

Di

Under optimal allocation, assuming

equal unit variances in all strata, the

sampling interval in stratum i should be

determined by the equation

SIi α Œ Pi

Di

+

ND

N (R−1)

(6)

where the constant of proportionality k

is determined by either a cost constraint

or a sample size demand. This formula

assumes that no interviews are

conducted with anyone outside the

targeted domain. However, a subsample

of the ‘‘other’’ population can be kept

from the screened households without

affecting the optimal allocation very

much. If there are multiple targeted

domains, it appears difficult to obtain an

optimum allocation formula.

If R = 1, (i.e., screening is as

expensive as interviewing), this

proportionality gives rise to the same

relative allocation of sample as equation

5. If the cost of screening is far less

than the cost of interviewing (i.e., R is

approaching infinity) and the domain is

not extremely rare (i.e., ND/N is not

close to 0), this relationship results in

close to a flat set of sampling intervals,

which is equivalent to allocation in

proportion to total population. Table 10

illustrates this relationship well. As the

cost of interviewing increases in

proportion to the cost of screening, the

benefits of oversampling decrease. For

example, with a value of R = 40, the

percent reduction in total cost for

statistics on the black population in the

year 2000 for screening with

oversampling, compared with screening

with uniform sampling intervals, is just

1.5 percent. On the other hand, when the

population is extremely rare, as is the

case with Native Americans, the benefits

of oversampling are still substantial

(19-percent cost reduction for same

precision) even when a full interview

costs 60 times as much as a screener

interview.

Network Sampling by

Nomination of Relatives

Prior applications of network

sampling involved having an initial

probability sample of persons (obtained

by area or list sampling) report on the

number of relatives they have outside

the household and whether any of them

have certain characteristics of interest,

such as diabetes (13). A variant of this

method was studied as part of the NHIS

redesign research. It was not believed

that most persons would be able to

answer most of the questions in NHIS

accurately for relatives not living in the

household. In fact, for many NHIS

supplements, even a household

respondent is considered unsatisfactory;

interviewers are instructed to accept

nonresponse instead of proxy response if

self-response is impossible. Thus, the

scheme proposed and studied for NHIS

was to have interviewers track the

nominated relatives and administer the

NHIS instruments directly to them. Use

of the telephone for the tracking and

follow-on interviews was a possible

refinement considered.

This scheme is still under

consideration for future redesigns but

was dropped from active consideration

for 1995 for several reasons. First, the

scheme is very new. No published

descriptions came to light in which

network sampling had been used on a

large-scale survey in which the

nominees had to be tracked and

interviewed within a short time. Also,

available data on the sizes of familial

networks by race and ethnicity are

limited. The risks of implementation

without extensive pretesting would be

considerable. Because there was

insufficient time to pretest the method,

and reasonable alternatives were

identified at least for the black and

Hispanic populations, the idea was put

on the shelf until future redesigns.

Detailed Considerations

The areas carefully considered

include the following:

+ The racial and/or ethnic groups for

which network sampling might be

most advantageous.

+ Whether open or closed networks

would be more appropriate.

+ The adequacy of existing

information on multiplicities

associated with different network

definitions.

+ The biases resulting from

misreporting of multiplicities and

how to reduce those biases.

+ The use of the telephone to reduce

costs.

+ Unduplication with other current

surveys run by the U.S. Bureau of

the Census.

+ Control over sample sizes.

Series 2, No. 126 [ Page 21
Domains of Application—

Satisfactory techniques had already been

identified for the black and Hispanic

populations (see chapter 5). American

Indians, Eskimos, and Aleuts seemed

totally out of reach with any

design-unbiased sampling technique

(chapter 8). The techniques identified for

the black and Hispanic populations

would be too expensive to use for

Asians and Pacific Islanders (also

chapter 8). Network sampling, when

coupled with multiyear aggregation

(discussed later in this chapter),

however, seems to offer the possibility

of achieving the desired precision for

statistics on Asians and Pacific Islanders

at a reasonable cost. Thus, there is

reason to continue to study the potential

uses of network sampling for

oversampling demographic domains for

NHIS.

Open Versus Closed Networks—A

closed network is defined as one in

which anyone in the network can

nominate anyone else in the network.

An open network is defined as any other

type of network. An example of a closed

network is induced by the sibling rule,

and an example of an open network is

induced by the parent rule. (See

National Network Surveys of

Diabetes (13) for descriptions of both

Table 10. Aspects of the optimal mix of screening and oversampling for various cost assumptions and racial and/or ethnic subdomains

Cost assumption

Ratio of interview cost to screening cost

1:1 1.5:1 2:1 3:1 4:1 5:1 10:1 20:1 40:1 60:1

Based on 1990 distributions Optimal ratios of screening-to-interview sample

Black . . . . . . . . . . . . . . . . . . . . . . . . . 2.6 2.8 3.0 3.3 3.5 3.7 4.3 5.1 5.9 6.4

Hispanic . . . . . . . . . . . . . . . . . . . . . . . 3.7 4.0 4.2 4.5 4.8 5.0 5.7 6.6 7.6 8.2

Asian and Pacific Islander ............ 10.1 10.8 11.3 12.1 12.8 13.3 15.4 18.1 21.2 23.2

American Indian, Eskimo, Aleut ........ 15.8 18.5 20.5 23.7 26.1 28.2 35.8 45.4 57.2 65.2

Based on projections to 2000

Black . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 3.4 3.6 4.0 4.2 4.5 5.2 5.9 6.6 6.9

Hispanic . . . . . . . . . . . . . . . . . . . . . . . 3.9 4.2 4.4 4.7 4.9 5.1 5.8 6.5 7.1 7.4

Based on 1990 distributions Optimal oversampling rates for densest stratum

Black . . . . . . . . . . . . . . . . . . . . . . . . . 8.2 6.8 6.0 5.0 4.4 3.9 2.9 2.2 1.7 1.5

Hispanic . . . . . . . . . . . . . . . . . . . . . . . 9.7 8.2 7.3 6.1 5.3 4.8 3.5 2.6 2.0 1.7

Asian and Pacific Islander ............ 10.7 9.1 8.1 6.8 6.0 5.4 3.9 2.9 2.2 1.9

American Indian, Eskimo, Aleut ........ 20.4 16.2 13.9 11.2 9.7 8.6 6.1 4.4 3.2 2.6

Based on projections to 2000

Black . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 4.6 4.0 3.4 3.0 2.7 2.0 1.6 1.3 1.2

Hispanic . . . . . . . . . . . . . . . . . . . . . . . 5.6 4.7 4.1 3.4 3.0 2.8 2.1 1.6 1.4 1.3

Based on 1990 distributions Screener sample sizes for precision equivalent to simple random sample of 1,000

Black ......................... 3,900 3,900 4,000 4,100 4,200 4,300 4,700 5,300 6,000 6,400

Hispanic ....................... 5,500 5,500 5,600 5,700 5,800 5,900 6,300 7,000 7,800 8,300

Asian and Pacific Islander ............ 18,000 18,000 18,000 18,000 19,000 19,000 20,000 21,000 23,000 25,000

American Indian, Eskimo, Aleut ........ 61,000 61,000 61,000 61,000 62,000 63,000 65,000 69,000 75,000 79,000

Based on projections to 2000

Black ......................... 4,700 4,700 4,800 4,900 5,000 5,100 5,600 6,100 6,700 7,000

Hispanic ....................... 5,500 5,500 5,500 5,600 5,700 5,800 6,200 6,700 7,200 7,500

Based on 1990 distributions Interviewed sample sizes for precision equivalent to simple random sample of 1,000

Black ......................... 1,500 1,400 1,300 1,200 1,200 1,200 1,100 1,000 1,000 1,000

Hispanic ....................... 1,500 1,400 1,300 1,300 1,200 1,200 1,100 1,000 1,000 1,000

Asian and Pacific Islander ............ 1,800 1,700 1,600 1,500 1,500 1,400 1,300 1,200 1,100 1,100

American Indian, Eskimo, Aleut ........ 3,800 3,300 3,000 2,600 2,400 2,200 1,800 1,500 1,300 1,200

Based on projections to 2000

Black ......................... 1,500 1,400 1,300 1,200 1,200 1,200 1,100 1,000 1,000 1,000

Hispanic ....................... 1,400 1,300 1,300 1,200 1,200 1,100 1,100 1,000 1,000 1,000

Based on 1990 distributions Percent reduction in cost compared to screening without oversampling

Black . . . . . . . . . . . . . . . . . . . . . . . . . 53 48 43 36 31 27 16 7.6 3.1 1.7

Hispanic . . . . . . . . . . . . . . . . . . . . . . . 51 47 43 38 33 30 19 10.2 4.6 2.7

Asian and Pacific Islander . . . . . . . . . . . . 47 45 43 41 38 36 28 18.8 10.4 6.6

American Indian, Eskimo, Aleut . . . . . . . . 52 51 50 48 47 46 40 33.1 24.3 19.0

Based on projections to 2000

Black . . . . . . . . . . . . . . . . . . . . . . . . . 41 35 32 26 21 18 10 4.1 1.5 0.8

Hispanic . . . . . . . . . . . . . . . . . . . . . . . 35 31 27 22 19 16 9 3.7 1.2 0.5

Page 22 [ Series 2, No. 126
rules.) The multiplicity of a rule is the

average number of persons that someone

may nominate, including himself or

herself.

Existing data on multiplicities—

Unpublished tabulations of a special

experiment on the 1976 NHIS reveal

that the standard open network, allowing

nomination of parents and siblings,

yields a multiplicity of 5.1 and that the

standard closed network, allowing

nomination only of siblings, yields a

multiplicity of 3.6. Because of increased

variation in weights, design effects

increase by 30 to 260 percent for the

sibling rule. The networks also lead to

an increase in design effects as a result

of clustering; this effect has not been

estimated. (The ‘‘design effects’’ in

National Network Surveys of

Diabetes (13) need to be inverted and

multiplied by 3.6 to obtain the more

common design effects that indicate an

increase in variance over a simple

random sample of the same size.)

Obviously, if the multiplicity is 3.6 but

the design effect increases by

260 percent, there is no net gain in

precision. Several other caveats must be

noted here. First, it is difficult to predict

the number of households over which

nominated persons will be spread. Many

of the nominated persons will be in the

original sample household. On the other

hand, if each spouse nominates blood

relations, the multiplicity may increase

strongly. Second, these numbers apply

to the total population; results might be

very different for the Asian American

population. Third, these networks were

not geographically restricted to be in the

same PSU or in any sample PSU.

Biases—Misreporting of multiplicity

results in biased and inconsistent

estimators. Of course, all sample survey

estimators are biased and inconsistent to

some degree because of listing errors,

misclassification of the occupancy of

HU’s, incomplete household rosters,

nonresponse, and other nonsampling

errors. However, there is an additional

potential for bias in multiplicity

estimators based on network samples.

The actual level of bias depends

strongly on how the networks are

defined. If the network is defined in

terms of ambiguous relationships, the

bias will be worse. For example, if the

concept ‘‘friend’’ is used to define the

network, it is easy to see that many

people will have no real idea of how

many other people would regard them

as friends. Without that knowledge, an

individual cannot inform the survey

taker of his or her personal multiplicity.

Without knowledge of that multiplicity,

it is not possible to form an unbiased

weight for the respondent.

Even when the network is defined

in terms of less ambiguous relationships

such as those of siblings or parents,

uncertainties will remain because of step

relationships, adoptions, foster

relationships, and other special

relationships. Furthermore, it is not

sufficient to know how many persons

could have nominated the sample

person. Rather, it is necessary to know

how many persons would have

nominated the sample person had they

been selected in the basic NHIS sample.

For example, if brother Ken would

never provide to interviewers

information on brother Harry, Ken does

not count in computing the multiplicity

of Harry. Would Harry know whether or

not Ken would nominate him?

Biases attributable to poor

respondent estimation of personal

multiplicity that are common in many

network sampling applications can be

sharply reduced for a closed network if

nominated persons are also interviewed

and asked to make nominations of their

own. Depending upon the effectiveness

of procedures for resolving

discrepancies between nominations by

various members of the network, biases

could be largely eliminated. Of course,

this circular nomination and resolution

would be time consuming, in terms of

person hours and calendar time. Current

schedules for completing assignments

would have to be relaxed.

The only closed network to have

been tried in the past is the sibling rule.

Great care must be taken with

half-siblings to make even this network

closed. It might be possible to devise a

larger closed network by following only

male or only female bloodlines. For

example, if an eldest son could

nominate only his father, father’s father,

father’s father’s father, eldest son, eldest

son’s eldest son, eldest son’s eldest

son’s eldest son, the network appears to

be closed; everyone in the network can

nominate everyone else in the network.

In general, this appears to be an

extremely difficult task of dubious

utility.

Biases attributable to poor

respondent estimation of personal

multiplicity in open networks are much

more difficult to avoid. One possible

way of avoiding them is to check with

all potential nominators to see whether

they would indeed have nominated the

sample person. This would be a costly

operation because unless the potential

nominators can also be nominated

themselves, there is no other reason to

trace and contact them. Furthermore,

there is no way to determine whether

there are other people (in addition to

those mentioned by the sample person)

who might have nominated the sample

person.

Use of The Telephone—Some of the

difficulties of timing could be reduced

by using telephone interviews for

nominated households (or persons).

Also, use of the telephone would make

it possible to allow nationwide

nominations instead of only local

nominations. On the negative side,

telephoning could introduce another bias

because some households do not have

telephones and others might refuse to

provide telephone numbers.

Unduplication—It would be

extremely difficult, perhaps impossible, to

prevent duplication of network sample

cases by other census surveys. Duplication

would, of course, be very rare, but a few

cases would be nearly certain.

Control Over Sample Sizes—

Network sampling would result in poor

control over sample sizes. Controlling

the mix of ages would also be difficult.

It is likely that a network sample that

yielded enough elderly cases for one age

domain would yield far more cases from

other age domains than would optimally

be desired. Subsampling within

households is possible, but this

introduces a whole new dimension of

operational complexities. If a sibling

rule were used, it might be possible—by

restricting the ages of persons who

could make nominations—to target the

nominated sample more closely by age.

Of course, such a rule would change a

closed network to an open one.

Series 2, No. 126 [ Page 23
Further Research

The first step in extending research

forward on this idea would be to add a

supplement to some major national

survey that would fill in information

gaps about multiplicities for racial and

ethnic domains. NHIS is a possible

vehicle for such a supplement, but the

Current Population Survey would be

preferable given its larger size. The

supplement should be developed to

determine the multiplicity for various

household rules—with and without local

restrictions, and with and without

restriction to persons with listed

telephone service. The next step would

be to design an actual pretest. No such

design work was performed as part of

this project. Clearly, planning will need

to closely involve NCHS and the U.S.

Bureau of the Census.

Dual-Frame Sampling

With Administrative Lists

The only lists of persons that are

maintained by the Federal Government

and made available for sampling for

health-related research are the list of

Social Security and Railroad Retirement

Act beneficiaries maintained by SSA

and the list of Medicare beneficiaries

maintained by HCFA. Use of these lists

falls under provisions of the Privacy Act

of 1974, which permits transfer of the

data (including names and addresses)

within the Public Health Service for

health-related research. Westat, Inc., has

used the Medicare list for a number of

surveys (including the Medicare Current

Beneficiary Survey for HCFA); the list

has been easy to work with and Westat,

Inc., staff members have been able to

locate almost all of the sample persons

on it.

Commercial lists of other segments

of the population do exist, but these

were not thoroughly investigated as part

of this research.

Integration of samples from

multiple frames into a single microdata

file with a single weight requires, at a

minimum, the ability to tell which units

had more than one chance of selection.

This is enough to create unbiased

weights. Furthermore, if it is possible to

determine the sampling stratum for each

unit in each of the frames, the precision

of the estimators can be improved. If the

full joint probabilities of selection for all

units within and across all frames can

be determined, even more precise

estimators are possible. Unbiased

multiplicity-based weights can be

developed without knowing the overall

probability of selection for a unit. If this

overall probability of selection is

known, however, the Horvitz-Thompson

estimator will usually be more precise.

Multiyear Aggregation

The least costly way to improve the

precision of core statistics for rare

domains is to aggregate statistics over

multiple years. The precision thus

achieved is not quite as great as would

result from independent samples,

because each year’s sample is in the

same set of PSU’s and segments, but the

precision loss is only moderate.

Combining statistics over 2 years adds

about 10 to 15 percent to design effects.

A 3-year combination adds about 19 to

26 percent. For rare domains quite a few

years of aggregation might be necessary.

This method is clearly not suitable for

questionnaire supplements that are

carried for only a single year, as is often

the case. Still, some data on rare

domains can be collected this way. In

the absence of any other data for such

domains, NCHS should have a

tabulation program to produce these

aggregations.

Carrying Samples Over

From Prior Years

For questionnaire supplements, a

method that is closely related to

multiyear aggregation is to carry

samples over from previous years.

Clearly, the least costly source of

samples for rare domains consists of

those samples that have already been

used. The drawbacks to this method are

higher nonresponse and higher

respondent burden. High response is

very difficult to maintain in the face of

physical moves and declining interest in

participation.

This method can be made even less

expensive by not following movers, but

this reduces the available sample size

and increases design effects. Internal

U.S. Bureau of the Census’ data indicate

that when movers are not followed, a

total response rate of 70 percent can be

obtained, where all the movers are

counted as nonrespondents. If d years of

previous sample are used, this means

that the nominal sample size increases

by a factor of 1 + (.7)d. The recent

movers that are identified in the new

sample must be weighted up by a factor

of d + 1 to obtain unbiased weights.

This means that a design effect will be

incurred of

DE = d + 1

(1 + .7d)

2 [.7(d + 1) + .3

d + 1]

(7)

Thus, the effective sample size only

increases by a factor of

Effective multiyear sample size

One year sample size =

(1 + .7d)

3

.7 (d + 1)2 + .3 (8)

Adding all stationary persons of a

targeted domain from 1 prior year thus

results in an increase in the effective

sample size for the targeted domain of

about 60 percent. Two prior years yield

an increase of 110 percent. Three prior

years yield an increase of 160 percent.

Of course, if members of the targeted

domain tend to move at higher or lower

rates than the general population, these

projections will either be too optimistic

or too pessimistic. Also, there are recall

issues that could affect how unbiased

the weights are. To the extent that recent

movers make errors in reporting the fact

or timing of their moves, the weights

will suffer bias. It seems to make sense

that longer periods of recall will

increase the potential for that sort of

bias.

A solution that avoids the design

effects and possible biases of not

following movers is to retain addresses

of targeted domains rather than the

occupants themselves. Further study of

this option would require data on the

frequency with which successor

occupants of minority households are

also members of the same minority.

Such data were not readily available,

and so this option was not seriously

Page 24 [ Series 2, No. 126
studied as part of this research.

However, it is an option that may be

worthy of further study. Data could be

assembled on change in the minority

status of household members through

longitudinal address surveys such as the

American Housing Survey.

Cost Models

Making rational choices between

alternative techniques clearly requires

some understanding of the costs of the

alternatives. An effort was made to

develop a comprehensive cost model for

NHIS (14); unfortunately, the robustness

of the model was uncertain. Existing

data are thin and unlikely to be

improved upon. Instead of relying on

comprehensive cost models, the research

described in this report proceeded by

drafting fairly specific plans for intuitive

costing by survey administration experts.

A number of steps that could be

taken to improve the quality of cost data

at the U.S. Bureau of the Census were

outlined, but these steps would be rather

expensive. It is not enough to know how

much each interviewer charged. A

careful accounting of all interviewers’

actions and their associated costs would

be a useful start, but even that would

not be enough to allow major

extrapolations from current practice.

NCHS would need either to raise

budgets or reduce the collection of

substantive data enough to allow the

collection of cost data that are good

enough to support reliable cost

modeling. It is doubtful that the gains in

sample design resulting from the

improved cost models would be great

enough to compensate for the high cost

of the data collection required to create

such models.

Series 2, No. 126[ Page 25
Chapter 5. Best

Techniques for

Sampling the

Nonelderly Black and

Hispanic Populations

Oversampling Small

Decennial Census

Geographic Units Without

Screening

In the 1985–94 design, enumeration

districts (ED’s) and block groups (BG’s)

with strong concentrations of black

persons were oversampled, a random

sample of households within these ED’s

and BG’s was selected, and all residents

of selected households were retained for

full interviews regardless of race. This

procedure is not very helpful in

increasing the effective sample size for

black persons because it is coupled with

a policy of using total population as the

measure of size for the selection of

PSU’s and keeping tight constraints on

total PSU workloads. This yields a

situation in which strongly black ED’s

and BG’s in some rural southern PSU’s

are not oversampled, because to do so

would result in unacceptably large

PSU-level workloads. This concern

about total workloads is probably

mistaken; the U.S. Bureau of the Census

has the latitude to handle the large

workloads by scheduling more visits by

its traveling interviewers to such PSU’s.

Even if the workload constraints were

viewed as absolutely necessary, the large

workloads in some PSU’s could have

been prevented if the PSU size measure

had emphasized the presence of black

persons.

This strategy has another inherent

problem: It results in an oversample of

white persons, Asians, Native

Americans, and others who live in

predominantly black neighborhoods.

Although a relatively small proportion

of the population that is not black lives

in such neighborhoods, that proportion

is a substantial part of the population in

these neighborhoods. People who are

not black in these neighborhoods are not

of greater policy interest than other

people who are not black; this

oversampling, therefore, is largely a

waste of effort.

To quantify the limitations of this

approach (and to serve as the basis for

other research), new tabulations of the

1990 decennial census were made

showing how the various domains are

distributed across strata defined by the

density of minorities within blocks.

Tables 11–14 summarize key results.

Table 13 is of particular interest; it

shows that a little less than 1 percent of

the population that is neither black nor

Hispanic lives in blocks that are more

than 60-percent Hispanic and that a

comparable proportion of this population

lives in blocks that are more than

60-percent black. Although these

proportions may seem small, the

‘‘other’’ population is so large (about

80 percent of total population) that

about 14 percent of the total population

is ‘‘other’’ in the blocks where Hispanic

persons exceed 60 percent. Similarly,

about 9 percent of the total population is

‘‘other’’ in the blocks that are more than

60-percent black.

By the time the new design is

implemented, the results from the 1990

decennial census will no longer be

current. The prevalence of each group in

each stratum has been projected forward

to 2000, however, the midpoint of the

life of the new design, based upon study

of the deterioration in strata observed

between 1980 and 1988 (15).

Despite the prevalence of the

‘‘other’’ population in the blocks with

Table 11. Distribution of the black population across density strata in 1990

Percent of block that is black

Percent of block that is Hispanic

< 5 5–10 10–30 30–60 60+ Total

< 10 . . . . . . . . . . . . . . . . . . . . 4.6 1.2 1.5 0.7 0.5 8.5

10–30 . . . . . . . . . . . . . . . . . . . 6.6 2.1 2.8 1.3 1.0 13.9

30–60 . . . . . . . . . . . . . . . . . . . 8.4 1.7 2.8 2.5 0.7 16.2

60+ . . . . . . . . . . . . . . . . . . . . . 50.0 4.5 5.6 1.3 0.1 61.4

Total .................... 69.6 9.6 12.7 5.8 2.2 100.0

SOURCE: 1990 decennial census, tabulated by Westat, Inc.

NOTE: Figures may not add to totals because of rounding.

Table 12. Distribution of the Hispanic population across density strata in 1990

Percent of block that is black

Percent of block that is Hispanic

< 5 5–10 10–30 30–60 60+ Total

< 10 . . . . . . . . . . . . . . . . . . . . 5.2 6.1 15.1 14.6 32.5 73.4

10–30 . . . . . . . . . . . . . . . . . . . 0.7 1.2 3.8 4.4 5.5 15.5

30–60 . . . . . . . . . . . . . . . . . . . 0.3 0.4 1.6 3.4 1.7 7.4

60+ . . . . . . . . . . . . . . . . . . . . . 0.5 0.5 1.7 0.9 0.1 3.6

Total . . . . . . . . . . . . . . . . . . . . 6.6 8.1 22.1 23.3 39.8 100.0

SOURCE: 1990 decennial census, tabulated by Westat, Inc.

NOTE: Figures may not add to totals because of rounding.

Table 13. Distribution of the other population across density strata in 1990

Percent of block that is black

Percent of block that is Hispanic

< 5 5–10 10–30 30–60 60+ Total

< 10 . . . . . . . . . . . . . . . . . . . . 68.5 8.8 8.1 2.1 0.7 88.3

10–30 . . . . . . . . . . . . . . . . . . . 4.7 1.4 1.6 0.5 0.1 8.3

30–60 . . . . . . . . . . . . . . . . . . . 1.6 0.3 0.4 0.1 0.0 2.5

60+ . . . . . . . . . . . . . . . . . . . . . 0.8 0.1 0.1 0.0 0.0 0.9

Total .................... 75.6 10.6 10.2 2.8 0.8 100.0

0.0 Quantity more than zero but less than 0.05

SOURCE: 1990 decennial census, tabulated by Westat, Inc.

NOTE: Figures may not add to totals because of rounding.

Page 26 [ Series 2, No. 126
high concentrations of black or Hispanic

persons, oversampling without screening

can be used to achieve modest increases

in effective sample sizes for the black

and Hispanic populations, particularly if

only one of the groups is targeted.

(Table 19 illustrates this.) The table

displays the same strata and population

distributions that are displayed in

tables 15–18, but its strata are arrayed

vertically instead of in matrix form. The

oversampling rates are relative to the

sampling fraction implied by an

equiprobability sample of 50,000

households. (An oversampling rate of less

than 1 means that the sampling fraction in

that stratum is less than that for an

equiprobability sample.) The oversampling

rates were derived with the formulas

provided in chapter 4, in the section on

oversampling classes of blocks, under the

assumption of constant cost.

Examination of table 19 reveals that

oversampling without screening can

boost the nominal sample for black

persons by 157 percent. Unfortunately,

the design effect jumps up to 1.51; thus,

the increase in the effective sample size

is only 71 percent. The cost of such a

strategy is a 28-percent decline in the

effective sample size for the total

population, a 19-percent decline for the

Hispanic population, and a 35-percent

decline for the other population. The

best that can be done for Hispanic

persons is a 54-percent increase in the

effective sample size, accompanied by

declines of 28 percent, 48 percent, and

29 percent for the total population, black

persons, and others, respectively. The

last four columns of the table show

various compromise allocations of the

sample across the strata. Compromises A

and B reflect optimal allocations for an

artificial population that is part black

and part Hispanic. Compromise C takes

an oversampling rate for each minority

stratum that is the maximum of those

required for either black or Hispanic

persons and then dramatically

undersamples the first stratum that has

the lowest concentrations of the black

and Hispanic populations. Compromise

D sets oversampling rates that are

proportional to the maximums required

by the black and Hispanic populations.

None of these compromises offers the

desired level of improvement in

effective sample sizes for minorities.

Compromises A and B seem reasonable

but still fall short of goals. Compromise

D is unacceptable, and compromise C is

counterproductive even for the black

and Hispanic populations. The problem

comes from the objective of

oversampling both minorities. These

groups tend not to share the same areas

of high concentration. The blocks with

strong concentrations of black persons

have only a small proportion of the

Hispanic population, and vice versa.

Because oversampling with screening

offers much larger improvements (see

the discussion later in this chapter), the

technique of oversampling without

screening was dropped from further

Table 14. Distribution of the total population across density strata in 1990

Percent of block that is black

Percent of block that is Hispanic

< 5 5–10 10–30 30–60 60+ Total

< 10 . . . . . . . . . . . . . . . . . . . . 55.3 7.7 8.0 3.1 3.5 77.5

10–30 . . . . . . . . . . . . . . . . . . . 4.6 1.4 1.9 0.9 0.7 9.6

30–60 . . . . . . . . . . . . . . . . . . . 2.3 0.5 0.8 0.7 0.2 4.5

60+ . . . . . . . . . . . . . . . . . . . . . 6.6 0.6 0.9 0.2 0.0 8.4

Total .................... 68.9 10.3 11.5 4.9 4.4 100.0

SOURCE: 1990 decennial census, tabulated by Westat, Inc.

NOTE: Figures may not add to totals because of rounding.

Table 15. Distribution of the black population across density strata in 2000

Percent of block that is black

Percent of block that is Hispanic

< 5 5–10 10–30 30–60 60+ Total

< 10 . . . . . . . . . . . . . . . . . . . . 11.5 3.3 2.8 0.9 0.8 19.3

10–30 . . . . . . . . . . . . . . . . . . . 6.3 1.6 3.0 1.4 1.0 13.3

30–60 . . . . . . . . . . . . . . . . . . . 8.1 1.9 3.1 2.8 0.4 16.2

60+ . . . . . . . . . . . . . . . . . . . . . 44.9 2.6 3.1 0.6 0.0 51.2

Total .................... 70.8 9.3 12.0 5.7 2.2 100.0

SOURCE: Westat, Inc., projection.

NOTE: Figures may not add to totals because of rounding.

Table 16. Distribution of the Hispanic population across density strata in 2000

Percent of block that is black

Percent of block that is Hispanic

< 5 5–10 10–30 30–60 60+ Total

< 10 . . . . . . . . . . . . . . . . . . . . 15.3 10.0 15.5 13.0 23.5 77.3

10–30 . . . . . . . . . . . . . . . . . . . 1.0 1.4 3.0 3.5 5.0 13.9

30–60 . . . . . . . . . . . . . . . . . . . 0.2 0.4 2.5 3.0 0.6 6.7

60+ . . . . . . . . . . . . . . . . . . . . . 0.5 0.3 0.9 0.3 0.1 2.0

Total .................... 17.0 12.1 22.0 19.8 29.2 100.0

SOURCE: Westat, Inc., projection.

NOTE: Figures may not add to totals because of rounding.

Table 17. Distribution of the other population across density strata in 2000

Percent of block that is black

Percent of block that is Hispanic

< 5 5–10 10–30 30–60 60+ Total

< 10 . . . . . . . . . . . . . . . . . . . . 73.9 8.1 7.3 1.7 0.3 91.2

10–30 . . . . . . . . . . . . . . . . . . . 4.3 0.7 0.8 0.2 0.1 6.1

30–60 . . . . . . . . . . . . . . . . . . . 1.7 0.3 0.3 0.0 0.0 2.3

60+ . . . . . . . . . . . . . . . . . . . . . 0.4 0.1 0.0 0.0 0.0 0.5

Total .................... 80.2 9.1 8.4 1.8 0.4 100.0

0.0 Quantity more than zero but less than 0.05.

SOURCE: Westat, Inc., projection.

NOTE: Figures may not add to totals because of rounding.

Series 2, No. 126 [ Page 27
consideration for the black and Hispanic

populations.

Screening a National

Equiprobability Sample

Screening without oversampling

blocks is probably the only technique

that makes sense for oversampling

domains that are fairly evenly spread

throughout the land, such as teenagers.

For black and Hispanic persons,

however, screening by itself is much

more costly than screening in

conjunction with oversampling blocks; it

was thus dropped from further

consideration.

Oversampling at the Block

Level With Screening for

Race, Ethnicity, Age, and

Sex

Given that black and Hispanic

populations tend to be separately

segregated, that there is no particular

interest in those members of the

majority population who happen to live

in blocks where they are the minority,

and that screening interviews are

expensive, the most clearly efficient

procedure is to combine oversampling

of blocks with screening. The term

‘‘screening’’ is used here to include the

standard subsampling of untargeted

domains that immediately follows the

short interview that reveals the domain

of a household or person.

By screening households in

oversampled blocks, it is possible to

eliminate the undesired oversample of

others living there. If screening is

conducted even in the stratum with the

lowest concentration of black and

Hispanic persons (and thus the smallest

sampling fraction), it is possible to

increase the effective sample sizes

further for black and Hispanic persons.

From table 10, it is apparent that

minorities in the only-slightly-integrated

blocks should not be sampled at rates less

than 4 to 5 times those used in highly

segregated blocks. Finally, with screening,

it is possible to oversample each age

group at a separate rate. This is an

important capability, because the age

bracket of 18–24 years is critically

important for some current public health

issues (such as acquired

immunodeficiency syndrome) yet is quite

narrow; thus it is difficult to achieve the

required precision for young black adults

and Hispanic persons without

simultaneously getting larger-thannecessary samples of the more broadly

defined age brackets such as 25–44 years.

Accordingly, a detailed plan was

developed to oversample blocks and

screen sample households by race,

ethnicity, sex, and age. To do this, the

plan included screening whole households

in or out of the sample that will be asked

to go through the full interview depending

on their compositions. An alternative

would have been to screen some

household members in and others out; but

this did not make sense, given the NHIS

core instrument’s structure, which makes

the interview of additional household

members very inexpensive.

The plan was ultimately rejected for

two reasons. The first was a concern

about the complexity of the screening

process. In those cases where the

occupants of a sample housing unit

cannot be directly contacted during the

screening phase, it is often fairly easy to

get neighbors to provide occupants’ race

and sex. Ethnicity (Hispanic or not) is a

little more difficult to obtain, and age is

the most difficult.

The second reason for rejecting the

plan was a feeling that, even though

statistics about the age bracket of 18–24

years are very important, it was

sufficient just to increase the total

minority sample size. There was great

reluctance to spend money on screening

to find minority households only to

discard some of those households

because they did not contain an

occupant of the desired age and sex.

Oversampling at the Block

Level With Screening for

Race and Ethnicity Only

Once the decision had been made to

give up on the required effective sample

size of 1,000 for the age bracket of 18–24

years by sex for each minority, the

proposed design had to be altered

significantly. This relaxation of

requirements led to substantial

simplifications and cost savings. The basic

combination of oversampling blocks with

high concentrations of black and Hispanic

persons and then following with screening

is still by far the most efficient method of

increasing total minority effective sample

sizes, however. The reasons for this are

the same as discussed in the previous

section.

To set the details of the design,

NCHS delineated the following new

requirements and assumptions:

+ An assumption of a budget

50-percent larger than is needed for

a sample of 50,000 households in

the current design.

+ Tolerance of a reduction in the

sample size for the population that

is neither black nor Hispanic, if

necessary, to obtain the required

effective sample sizes for black and

Hispanic populations.

+ A requirement of roughly equal

effective sample sizes for black and

Hispanic persons of 8,000 adults

each, given the random selection of

one adult per household.

+ The assumption that R = 3, meaning

that a full NHIS interview costs 3

times as much as a screening

Table 18. Distribution of the total population across density strata in 2000

Percent of block that is black

Percent of block that is Hispanic

< 5 5–10 10–30 30–60 60+ Total

< 10 . . . . . . . . . . . . . . . . . . . . 59.2 7.8 7.7 2.9 3.1 80.7

10–30 . . . . . . . . . . . . . . . . . . . 4.2 0.9 1.3 0.7 0.8 7.8

30–60 . . . . . . . . . . . . . . . . . . . 2.3 0.5 0.9 0.7 0.1 4.5

60+ . . . . . . . . . . . . . . . . . . . . . 6.0 0.4 0.5 0.1 0.0 7.0

Total .................... 71.7 9.5 10.4 4.4 4.0 100.0

0.0 Quantity more than zero but less than 0.05.

SOURCE: Westat, Inc., projection.

NOTE: Figures may not add to totals because of rounding.

Page 28 [ Series 2, No. 126
interview. See the fourth section of

chapter 2 for the rationale on

requiring only one sample adult per

household.

Designing a sample to meet these

requirements was a complex

undertaking. The theory in chapter 4

guides the allocation of sample across

minority density strata when there is

only one targeted domain, but it does

not determine an optimal allocation

when there are two targeted domains.

Also, that theory assumes that all

persons found in the screening who are

not members of the targeted domain will

be dropped from the sample. For this

design it was necessary to balance the

cost of screening and the design effects

associated with oversampling against the

gains in precision for the black and

Hispanic populations.

The basic procedure used during the

design process was to experiment with

different allocations of the sample across

the minority density strata defined

earlier in this chapter. For every trial

allocation, the nominal oversampling

rates, design effects, and effective

oversampling rates were calculated for

all three domains using equations 3, 2,

and 4, respectively. The nominal

oversampling rates were then applied to

the sample sizes that could be expected

from an equiprobability sample of

50,000 interviewed households shown in

table 6. The costs of the screening

interviews and of the full interviews for

the black and Hispanic households were

then calculated. This amount of money

was then subtracted from the total

assumed available given a 50-percent

increase in funding over the level

required for an equiprobability sample

of 50,000 interviewed households. This

then determined the number of full

interviews that could be conducted with

‘‘other’’ households. The allocation of

the sample was then chosen that yielded

the required effective sample sizes of

8,000 interviewed black adults and

8,000 interviewed Hispanic adults (given

the random selection of one adult per

household), while maximizing the

effective sample size for other adults.

The resulting oversampling rates are

shown in table 20. Recall that these

Table 19. The limits of oversampling without screening

Projections to the year 2000 Oversampling rates by stratum subject to fixed total sample size

Density

stratum

Percent

black

in 1990

Percent

Hispanic

in 1990

Percent

of total

population

Percent

of black

population

Percent

of Hispanic

population

Percent

of other

population

To minimize

variance for

blacks

To minimize

variance for

Hispanic

Compromise

A

Compromise

B

Compromise

C

Compromise

D

1 . . . . . . . . < 10 < 5 59.2 11.5 15.3 73.9 0.58 0.63 0.58 0.56 0.10 0.48

2 . . . . . . . . < 10 5–9 7.8 3.3 10.0 8.1 0.86 1.41 1.23 1.09 1.41 1.08

3 . . . . . . . . < 10 10–29 7.7 2.8 15.5 7.3 0.79 1.76 1.50 1.28 1.76 1.34

4 . . . . . . . . < 10 30–59 2.9 0.9 13.0 1.7 0.73 2.63 2.20 1.82 2.63 2.00

5 . . . . . . . . < 10 60+ 3.1 0.8 23.5 0.3 0.66 3.40 2.83 2.32 3.40 2.59

6 ........ 10–29 < 5 4.2 6.3 1.0 4.3 1.60 0.61 0.86 1.10 1.60 1.22

7 ........ 10–29 5–9 0.9 1.6 1.4 0.7 1.74 1.55 1.49 1.52 1.74 1.33

8 ........ 10–29 10–29 1.3 3.0 3.0 0.8 1.98 1.89 1.78 1.79 1.98 1.51

9 ........ 10–29 30–59 0.7 1.4 3.5 0.2 1.85 2.78 2.44 2.20 2.78 2.12

10 . . . . . . . 10–29 60+ 0.8 1.0 5.0 0.1 1.51 3.21 2.74 2.35 3.21 2.44

11 . . . . . . . . 30–59 < 5 2.3 8.1 0.2 1.7 2.45 0.37 1.11 1.58 2.45 1.87

12 . . . . . . . 30–59 5–9 0.5 1.9 0.4 0.3 2.60 1.17 1.49 1.83 2.60 1.98

13 . . . . . . . 30–59 10–29 0.9 3.1 2.5 0.3 2.40 2.07 2.01 2.07 2.40 1.83

14 . . . . . . . 30–59 30–59 0.7 2.8 3.0 0.0 2.61 2.55 2.40 2.39 2.61 1.99

15 . . . . . . . 30–59 60+ 0.1 0.4 0.6 0.0 2.58 3.04 2.76 2.62 3.04 2.32

16 . . . . . . . 60+ < 5 6.0 44.9 0.5 0.4 3.57 0.36 1.58 2.29 3.57 2.72

17 . . . . . . . 60+ 5–9 0.4 2.6 0.3 0.1 3.40 1.10 1.74 2.29 3.40 2.59

18 . . . . . . . 60+ 10–29 0.5 3.1 0.9 0.0 3.25 1.70 1.99 2.37 3.25 2.48

19 . . . . . . . 60+ 30–59 0.1 0.6 0.3 0.0 3.20 2.26 2.33 2.54 3.20 2.44

20 . . . . . . . 60+ 60+ 0.0 0.0 0.1 0.0 2.92 3.11 2.87 2.70 3.11 2.37

Totals for 2000 268,000,000 34,000,000 32,000,000 203,000,000

Resulting nominal oversampling rates across strata

Total 1.00 1.00 1.00 1.00 1.00 1.00

Black 2.57 0.87 1.47 1.85 2.64 2.05

Hispanic 1.02 2.18 1.89 1.65 2.17 1.72

Other 0.74 0.84 0.79 0.76 0.55 0.72

Design effects from unequal weights

Total 1.30 1.39 1.30 1.33 5.93 1.49

Black 1.51 1.67 1.17 1.25 3.79 1.37

Hispanic 1.26 1.41 1.33 1.27 4.01 1.37

Other 1.14 1.19 1.16 1.16 4.01 1.24

Effective oversampling rates

Total 0.72 0.72 0.77 0.75 0.17 0.67

Black 1.71 0.52 1.26 1.49 0.69 1.50

Hispanic 0.81 1.54 1.42 1.30 0.54 1.25

Other 0.65 0.71 0.68 0.65 0.14 0.58

0.0 Quantity more than zero but less than 0.05.

Series 2, No. 126 [ Page 29
ratesare relative to an equiprobability

sample that yields 50,000 completed

household interviews.

These oversampling rates result in

nominal oversampling rates for the

black, Hispanic, and ‘‘other’’

populations of 2.212, 3.195, and 1.831,

respectively (equation 3). Applying these

nominal oversampling rates to table 6

yields table 21, which shows the total

number of persons in screened

households.

Clearly, the number of ‘‘other’’

households discovered during screening

is much larger than can be afforded with

a 50-percent budget increase. At the

conclusion of the screening interview, it

is necessary to tell some of them,

‘‘Thanks for your time and have a nice

day.’’ Of course, there is no reason to

subsample at a uniform rate. Given the

differential oversampling rates shown in

table 20, it is much better to design the

retention rates in such a manner that an

‘‘other’’ household (containing only

non-Hispanic white persons, Asians, or

Native Americans) will have the same

probability of selection whether it is in a

predominantly black, Hispanic, or other

neighborhood.

The computation of retention rates

for ‘‘other’’ households depends strongly

on the relative costs of screening and

full interviews. As mentioned already, a

fundamental assumption underlying the

design was that a full interview costs 3

times as much as a screening interview

(R = 3). The differential oversampling

rates shown in table 20 are projected to

yield 123,900 addresses for screening

(before identification of vacants, other

ineligibles, and nonrespondents). With a

sample of 50,000 interviewed

households, the number of addresses to

screen is just 62,500 (allowing for a

combined loss to ineligibility and

nonresponse of 20 percent). If the

hypothetical survey of 50,000 completes

were expanded by 50 percent, that

would mean 93,750 screened addresses.

The difference of 30,150 screened

households means that the number of

full interviews must be cut by at least

10,050 households from the 75,000 that

could be done with a straight 50-percent

increase over 50,000 interviewed

households. Cutting 10,050 households

from the hypothetical 75,000 does not

Table 20. Oversampling rates for the alpha design

[Relative to a design that would yield 50,000 interviews]

Percent of block that is black

Percent of block that is Hispanic

< 5 5–10 10–30 30–60 60+

< 10 . . . . . . . . . . . . . . . . . . . . 1.6 2.00 3.20 4.00 4.00

10–30 . . . . . . . . . . . . . . . . . . . 1.6 2.00 3.50 4.00 4.00

30–60 . . . . . . . . . . . . . . . . . . . 1.7 2.00 3.50 4.00 4.00

60+ . . . . . . . . . . . . . . . . . . . . . 2.0 2.00 3.50 4.00 4.00

Table 21. Persons in screened households for the alpha option

Race, Hispanic origin, and age

All adults in each household

Male Female Total

Black

Under 5 ....................... 1,475 1,413 2,888

5–17 ......................... 4,203 4,010 8,213

18–24 ........................ 1,559 1,824 3,383

25–44 ........................ 4,220 5,352 9,572

45–64 ........................ 2,706 3,494 6,200

65 and over .................... 1,250 1,850 3,100

Total ......................... 15,412 17,943 33,355

Total 18 and over ................. 9,735 12,520 22,255

Households . . . . . . . . . . . . . . . . . . . . . ... ... 12,295

Hispanic

Under 5 ....................... 2,508 2,401 4,908

5–17 ......................... 6,023 5,753 11,776

18–24 ........................ 2,283 2,208 4,492

25–44 ........................ 6,376 6,223 12,599

45–64 ........................ 2,864 3,245 6,109

65 and over .................... 1,033 1,446 2,479

Total ......................... 21,087 21,276 42,363

Total 18 and over ................. 12,556 13,122 25,678

Households . . . . . . . . . . . . . . . . . . . . . ... ... 12,465

Other

Under 5 ....................... 5,127 4,859 9,986

5–17 ......................... 15,490 14,690 30,180

18–24 ........................ 7,450 7,518 14,968

25–44 ........................ 25,716 26,083 51,799

45–64 ........................ 21,128 21,935 43,063

65 and over .................... 10,428 15,073 25,500

Total ......................... 85,339 90,158 175,498

Total 18 and over ................. 64,722 70,609 135,331

Households . . . . . . . . . . . . . . . . . . . . . ... ... 74,358

Total

Under 5 . . . . . . . . . . . . . . . . . . . . . . . 9,110 8,673 17,783

5–17 ......................... 25,716 24,453 50,169

18–24 ........................ 11,292 11,551 22,843

25–44 ........................ 36,312 37,658 73,970

45–64 ........................ 26,698 28,674 55,372

65 and over .................... 12,711 18,369 31,080

Total ......................... 121,839 129,377 251,216

Total 18 and over ................. 87,013 96,251 183,264

Households . . . . . . . . . . . . . . . . . . . . . ... ... 99,118

. . . Category not applicable.

NOTE: Figures may not add to totals because of rounding.

Page 30 [ Series 2, No. 126
save all the cost associated with those

contacts, however. There are still the

screening costs. The actual number of

total households that can be retained for

full interviews is determined by the

equation

[50,000 + 62,500 − 50,000

3 ](1.5) =

[ x + (123,900 − x)

3 ]

Solving this equation yields the

answer that 59,900 households can be

retained for full interviews. Subtracting

out the 12,300 black households and the

12,500 Hispanic households that are

expected to receive full interviews, this

leaves room for 35,100 ‘‘other’’

households to receive full interviews.

Given that it is expected that the

screening will yield 74,300 ‘‘other’’

households, this implies that the overall

retention rate of other households

discovered during screening should be

47.3 percent.

Let ri be the retention rate for

screened ‘‘other’’ households in i-th

minority density stratum. Also, let ki be

the oversampling rate for the stratum

from table 20, and let Qi be the

proportion of the national ‘‘other’’

population that is in the i-th stratum (in

table 17). It is optimal to have the two

following relationships both be true:

OQi ri = 0.473 and ri ~ 1

ki

The first equation simply requires

that the average weighted retention rate

for other households across all the strata

be correct. The second relationship will

ensure an equiprobability sample of

‘‘other’’ households. Both relationships

can be satisfied by setting

ri = 0.473

ki∑

j

(Qj /kj)

The results of this calculation are shown

in table 22.

The differential weights associated

with the oversampling shown in table 20

leads to design effects of 1.084 and

1.135 for the black and Hispanic

populations, respectively. After

subsampling, the design effect for the

‘‘other’’ population will be 1.00 because

there will be an equiprobability sample

of the ‘‘other’’ population. Thus, the

effective sample sizes for the three

domains can be achieved by adjusting

the numbers of screened persons shown

in table 21 for the design effects and

subsampling. This means dividing by

1.084 for the black population, dividing

by 1.135 for the Hispanic population,

Table 22. Percent of other households discovered during screening to be kept for full

interviews under the alpha design

Percent of block that is black

Percent of black that is Hispanic

< 5 5–10 10–30 30–60 60+

< 10 . . . . . . . . . . . . . . . 54.1 43.3 27.0 21.6 2

10–30 .............. 54.1 43.3 24.7 21.6 2

30–60 .............. 50.9 43.3 24.7 21.6 2

60+ . . . . . . . . . . . . . . . . 44.4 43.3 24.7 21.6 2

Table 23. Effective sample sizes for alpha design when all household members are sampled

Race, Hispanic origin, and age

All adults in each household

Male Female Total

Black

Under 5 ....................... 1,361 1,304 2,664

5–17 ......................... 3,877 3,599 7,576

18–24 ........................ 1,438 1,683 3,121

25–44 ........................ 3,893 4,938 8,830

45–64 ........................ 2,496 3,223 5,720

65 and over .................... 1,153 1,706 2,860

Total ......................... 14,218 16,553 30,771

Total 18 and over ................. 8,980 11,550 20,530

Households . . . . . . . . . . . . . . . . . . . . . ... ... 11,343

Hispanic

Under 5 ....................... 2,209 2,115 4,325

5–17 ......................... 5,307 5,068 10,375

18–24 ........................ 2,012 1,946 3,957

25–44 ........................ 5,618 5,483 11,100

45–64 ........................ 2,523 2,859 5,382

65 and over . . . . . . . . . . . . . . . . . . . . 910 1,274 2,184

Total ......................... 18,579 18,745 37,324

Total 18 and over ................. 11,063 11,562 22,624

Households . . . . . . . . . . . . . . . . . . . . . ... ... 10,983

Other

Under 5 ....................... 2,425 2,299 4,724

5–17 ......................... 7,327 6,948 14,275

18–24 ........................ 3,524 3,556 7,080

25–44 ........................ 12,164 12,337 24,501

45–64 ........................ 9,994 10,375 20,369

65 and over .................... 4,932 7,129 12,062

Total ......................... 40,365 42,645 83,010

Total 18 and over ................. 30,614 33,898 64,012

Households . . . . . . . . . . . . . . . . . . . . . ... ... 35,171

Total

Under 5 ....................... 4,242 4,035 8,277

5–17 ......................... 12,216 11,609 23,826

18–24 ........................ 5,475 5,609 11,084

25–44 ........................ 17,906 18,569 36,475

45–64 ........................ 13,627 14,541 28,168

65 and over .................... 6,568 9,499 16,067

Total ......................... 60,035 63,861 123,896

Total 18 and over ................. 43,576 48,217 91,794

Households . . . . . . . . . . . . . . . . . . . . . ... ... 46,794

. . . Category not applicable.

NOTE: Figures may not add to totals because of rounding.

Series 2, No. 126 [ Page 31
and multiplying by 0.473 for the

‘‘other’’ population. The effective

sample size for the total population is

slightly more complicated to calculate

and present because the oversampling of

black and Hispanic households means

that the average household size will

increase for the sample. After

subsampling, the nominal oversampling

rate for the total population will be

1.278 with a design effect of 1.281.

Also, the nominal oversampling rate for

households will be 1.197 with the same

design effect of 1.281. The effective

sample size for the total population is

obtained by multiplying the sample sizes

shown in table 6 by either 1.278 or

1.197 and then dividing by 1.28. The

results of these adjustments are shown

in table 23.

To get effective sample sizes when

just one adult is sampled per household,

it is necessary to adjust the numbers in

table 23 in the same manner as the

numbers in table 6 were adjusted to

obtain table 7. The results are shown in

table 24.

Table 24. Effective sample sizes for alpha design when one adult is sampled per household

Race, Hispanic origin, and age

One adult per household

Male Female Total

Black

Under 5 . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

5–17 . . . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

18–24 . . . . . . . . . . . . . . . . . . . . . . . . 621 726 1,347

25–44 ........................ 1,680 2,131 3,811

45–64 ........................ 1,078 1,391 2,469

65 and over . . . . . . . . . . . . . . . . . . . . 498 737 1,234

Total . . . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

Total 18 and over ................. 3,876 4,985 8,861

Households . . . . . . . . . . . . . . . . . . . . . ... ... 11,343

Hispanic

Under 5 . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

5–17 . . . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

18–24 . . . . . . . . . . . . . . . . . . . . . . . . 781 756 1,537

25–44 ........................ 2,182 2,129 4,311

45–64 . . . . . . . . . . . . . . . . . . . . . . . . 980 1,110 2,090

65 and over . . . . . . . . . . . . . . . . . . . . 353 495 848

Total . . . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

Total 18 and over ................. 4,296 4,490 8,786

Households . . . . . . . . . . . . . . . . . . . . . ... ... 10,983

Other

Under 5 . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

5–17 . . . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

18–24 ........................ 1,627 1,642 3,269

25–44 ........................ 5,616 5,696 11,313

45–64 ........................ 4,614 4,791 9,405

65 and over .................... 2,277 3,292 5,569

Total . . . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

Total 18 and over ................. 14,135 15,421 29,556

Households . . . . . . . . . . . . . . . . . . . . . ... ... 35,171

Total

Under 5 . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

5–17 . . . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

18–24 ........................ 2,488 2,566 5,053

25–44 ........................ 7,739 8,137 15,876

45–64 ........................ 5,382 5,901 11,283

65 and over .................... 2,512 3,631 6,144

Total . . . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

Total 18 and over ................. 18,121 20,235 38,356

Households . . . . . . . . . . . . . . . . . . . . . ... ... 46,794

. . . Category not applicable.

Page 32 [ Series 2, No. 126
Chapter 6. Choosing a

Sampling Technique

for the Elderly Black

and Hispanic

Populations

Area Sampling With

Oversampling at the Block

Level With Screening for

Race, Ethnicity, Age, and

Sex

As shown in table 20, even with a

50-percent budget increase, the

oversampling of blocks with high

concentrations of black or Hispanic

populations and screening (as a result of

mortality and immigration patterns),

black and Hispanic persons constitute a

much smaller proportion of the elderly

population in this country than is the

case in other age groups. There are not

enough elderly black and Hispanic

persons to yield sample sizes from the

proposed design that meet NCHS

requirements. Although the sample sizes

for both sexes have projected

deficiencies, the problems are especially

acute for males; fewer than 500 are

projected for black males (on an

effective basis) and about 350 for

Hispanic males (also on an effective

basis). A budget increase much larger

than 50 percent would be necessary to

meet the goals by oversampling blocks

and screening alone. Other methods of

increasing the effective sample sizes for

these domains were therefore

researched.

Dual-Frame Sampling

With Social Security

Administration Lists

Because the Social Security

Administration (SSA) maintains files

with excellent coverage of the elderly

population (at least for those age 66

years and over), research was conducted

on dual-frame sampling that would

combine the traditional area-permit

sample with a supplemental list sample.

While this research was being

conducted, interest arose in

supplementing the NHIS sample for

1994 (1 year before the planned

redesign) with a list sample of disabled

SSA beneficiaries. This work is reported

separately in A Dual Frame Design for

Sampling Elderly Minorities and

Persons With Disabilities (16). The idea

of oversampling SSA disabled

beneficiaries for NHIS has been dropped

for now. A decision to use SSA lists for

oversampling either disabled

beneficiaries or elderly minority persons,

however, makes it easier, of course, to

use the list for the other type of

oversampling as well.

The task of oversampling the elderly

black population from SSA files is fairly

straightforward because race is indicated

for about 97 percent of the file.

Unfortunately, SSA files do not have an

indicator of Hispanic origin for persons

who are currently elderly. (SSA started to

collect ethnicity data in the 1980’s, too

late to be of use in classifying the

current elderly population.) Thus, an

important issue is the identification of

those who are likely to be Hispanic.

This task is possible through the use of

surname classification. The following

sections discuss this use of surnames,

effective sample sizes that can be

achieved with dual-frame sampling, and

whether to use SSA lists to oversample

either addresses or persons. Assuming

that persons are oversampled, the issue

of other household members and

estimation issues for dual-frame

sampling are discussed in the last two

sections of this chapter.

Using Surnames to Identify

Hispanic Persons

To use the SSA files for

oversampling elderly Hispanic persons

despite the lack of an indicator for

Hispanic origin, a strategy that identifies

persons likely to be Hispanic on the

basis of surname was considered. The

Hispanic surname file developed by J.

Passel and D. Word at the U. S. Bureau

of the Census (17) was used to classify

surnames by ethnic origin. The

Passel-Word file contains 12,497

surnames that tend to belong to

Hispanic persons.

The precision and cost of a

dual-frame sample based on surnames

depends strongly on the sensitivity and

specificity of the Passel-Word file. Every

false positive costs money to interview

or screen out; every false negative

increases the portion of the area-permit

sample that is not covered by the list

and that will therefore have high

sampling weights, thereby increasing

design effects because of unequal

weights. (Note, however, that neither

false positives nor false negatives make

unbiased estimation impossible. This is

discussed later in this chapter.) Passel

and Word indicated that false positives

(also called errors of commission) run at

around 15 percent and that false

negatives (also called errors of

omission) run at around 20 percent.

Given the time lapse since that original

study, it was judged prudent to repeat

the study. To that end, the surname of

every member of the 1988 NHIS was

matched against the Passel-Word file.

This allowed the comparison of

self-reported ethnicity with the ethnicity

that would be imputed on the basis of

surname using this file. At the same

time, some of the characteristics of

persons with nonconforming names

were analyzed; it was thought that this

information should be useful in making

decisions about sample allocation.

The overall false-positive rate was

12.6 percent, lower than had been

expected on the basis of previously

published research; the overall

false-negative rate was 31.6 percent,

higher than expected. Passel and Word

originally reported error rates with the

March 1976 Current Population Survey

of 15.0 percent false-positives and

20.7 percent false-negatives. Part of the

discrepancy involves persons who did

not classify their ethnicity, who had

hyphenated names, or who refused to

provide their names; Passel and Word

treated all of these differently than was

done in this research. An inspection of

nonconforming surnames of Hispanic

persons indicates, however, that even

allowing for these variations in

matching procedure, a substantial and

unexplained difference exists in the

false-negative rate. The reason for the

poorer performance could be either

lower quality recording, transcription,

Series 2, No. 126 [ Page 33
and keying of names on the 1988 NHIS

than on the March 1976 CPS

(interviewers, not respondents, record

name spellings) or real change in the

population in the relationship between

surname and self-reported ethnicity.

Both rates are slightly higher for the

elderly than for the general population:

16.6 percent false-positives and

33.6 percent false-negatives.

Both types of misclassification

based on surname are far more common

among women than among men.

Intermarriage evidently plays a strong

role in both misclassification rates.

Socioeconomic status also plays a strong

role in both rates. The general trend

seems to be that higher socioeconomic

status means a weaker association

between surname and self-reported

ethnic origin. Differences also exist by

metropolitan status, part of the country,

and country of origin (for self or

ancestors). See Using Surnames to

Oversample Hispanics From a List

Frame (18) for more detail.

Effective Sample Sizes With

Dual-Frame Sampling

Although the estimated sensitivity

of the Passel-Word Hispanic surname

list was not as great as had been hoped,

using it to create a list sample of elderly

Hispanic persons still has good

potential. The technique is far cheaper

than area sampling with screening for

the same precision. These implications

are explained more fully in this section.

Table 25 contrasts some of the numbers

that appear in the following text.

Under current plans for 1995 and

beyond, the area-permit NHIS sample

will yield nominal elderly Hispanic

sample sizes of about 500 males and

700 females. After accounting for the

design effect due to disproportionate

sampling of heavily Hispanic blocks, the

effective sample sizes (compared with a

similarly clustered sample with equal

probabilities) will be only about 350 and

500. By adding 1,000 males with

Hispanic surnames from SSA lists and

another 1,000 females in the same

manner, the nominal sample sizes can

be boosted to around 1,240 and 1,400,

and the effective sample sizes can be

boosted to around 770 and 920. (The

effective sample sizes do not increase as

much as the nominal sample sizes

because of the design effect due to the

large weights that Hispanic persons

without Hispanic surnames will bear.)

To get a comparable boost from the

area-permit sample alone would require

the screening of an additional 100,000

households.

For another contrast, suppose that

one used SSA lists as a supplemental

list sampling frame without paying

attention to surname. In that case, more

than 20,000 persons would have to be

located and screened on ethnicity to

obtain comparable boosts for elderly

Hispanic persons. Thus, although

screening a list of elderly persons is far

more efficient than screening a sample of

households (even if those households were

skewed toward heavily Hispanic blocks),

it is not as efficient as using a list in

combination with the surname list.

Nonetheless, boosting the effective

sample sizes even more sharply for

elderly Hispanic persons, to as high as

1,000 males and 1,000 females, was

desired. Here it turns out that the

false-negative rate is too high to make

that economical. About 5,000 list

persons with Hispanic surnames would

have to be added to the sample.

Interest in determining ways to

decrease the false-negative rate is

significant, even if it means some

increase in the false-positive rate as it

likely would. If, for example, the 1976

findings of Passel and Word still held,

the supplemental sample size required

for the desired effective sample sizes by

sex would be 2,400 instead of 5,000.

(Even with a perfect indicator of

Hispanic origin on the list, a

supplemental sample of 1,350 persons

would be required to achieve the desired

effective sample sizes of 1,000 by sex.)

It is suspected that adjustments in

interviewer training and in keypunching

could reduce the frequency of false

negatives. Even though the U. S. Bureau

of the Census would be sampling from

an SSA list that would probably have

Table 25. Required sample sizes by sampling method and precision target

Method Sex

Effective Hispanic

sample size1

Screener

interviews2

SSA list with match to Passel-Word file under

observed error rates ......................

M 770 1,000

F 920 1,000

Total screener interviews required .............. 2,000

SSA list without match to Passel-Word file . . . . . . . . . M 770 9,750

F 920 10,350

Total screener interviews required .............. 20,100

Area sample . . . . . . . . . . . . . . . . . . . . . . . . . . . M 770

F 920

Total screener interviews required .............. 100,000

SSA list with match to Passel-Word file under

observed error rates ......................

M 1,000 3,050

F 1,000 1,950

Total screener interviews required .............. 5,000

SSA list with match to Passel-Word file under

1976 error rates ........................

M 1,000 1,280

F 1,000 1,120

Total screener interviews required .............. 2,400

SSA list with match to Passel-Word file under

0 error rates ..........................

M 1,000 760

F 1,000 590

Total screener interviews required .............. 1,350

SSA list without match to Passel-Word file ......... M 1,000 15,100

F 1,000 12,500

Total screener interviews required .............. 27,600

Area sample ........................... M 1,000

F 1,000

Total screener interviews required .............. 180,000

1Nominal Hispanic sample sizes are larger than the effective sizes shown.

2List either persons to be tracked and screened or households from area sample to be screened in order to achieve the effective

sample sizes indicated for Hispanics. All of these screener interviews would be in addition to the 99,000 annual household

screener interviews proposed for the 1995 design.

Page 34 [ Series 2, No. 126
more accurate name spelling, it would

still be necessary to classify everyone in

the area-permit sample to determine

appropriate sampling weights for the

dual-frame estimator.

Addresses or Persons?

One of the issues that was discussed

with respect to dual-frame sampling was

whether SSA beneficiaries or their

addresses should be the sampling units.

The field costs would be lower for the

address option because tracking of

movers and persons with bad addresses

(e.g., addresses of custodians) would not

be necessary. This idea was rejected,

however, for two reasons. First, the

efficiency of the oversample would be

degraded. No data were available on

how often beneficiaries move or on the

characteristics of subsequent occupants

of their housing units. Second (and more

important), weighting would be more

difficult with the address-based

approach. Using this approach, it would

be necessary to match every address in

the regular NHIS sample against SSA

files to determine whether SSA had a

record of an elderly black person or an

elderly person with a Hispanic surname

living at the address. Given the fact that

SSA addresses have neither isolated

components nor standardized

abbreviations, the false-nonmatch rate

would be substantial.

A related approach would be to

make the person the sampling unit but

to conduct the interview only if the SSA

address was current and correct. This

would have reduced the cost of the

sample without increasing matching

difficulties. This idea was rejected

because it would result in an oversample

of a narrower domain than the domain

of interest.

Other Household Members

If persons sampled from SSA lists

are to be followed, the question of

whether to collect data about other

household members naturally arises. A

flexible approach was developed that

involves collecting data about other

household members for core items and

for some (but not all) questionnaire

supplements. When other household

members are interviewed, a multiplicity

estimator would be used to weight them.

The recommendation to administer

the core questionnaire to all household

members was based on several

considerations. First, plans have been

made to trim the core to a very short

instrument. Furthermore, the core

questionnaire structure makes it unlikely

that substantial cost savings could be

gained by restricting the core to the

SSA-designated sample person. Of

course, if those plans to trim the core

are never realized, a review of this

recommendation would be appropriate.

The decision about any particular

supplement to the core would be

influenced by several factors. First, are

other household members likely to be in

domains of interest? Second, is the

burden on the household tolerable?

Third, is the marginal cost of collecting

the data high?

Some core supplements are

administered to random samples of

household members. (The most common

rule is one random adult.) If it is

decided to administer such a supplement

in households that are in the SSA list

sample, the within-household

subsampling must be done independent

of which person or persons in the

household were selected directly from

the SSA list. The alternatives involve

sharp variations in weights.

Dual-Frame Estimation

Dual-frame sampling with SSA lists

requires somewhat more complex

weighting than surveys using a single

frame. Extra data are needed for unbiased

weighting. Specifically, it must be known

whether every person in the regular NHIS

area-permit sample has a chance of being

selected through the list. For list sampling

of elderly black and Hispanic persons,

ideally the following information would

be known about members of the

area-permit sample:

+ Whether he or she is a Social

Security beneficiary.

+ If so, how race is recorded on SSA

files for the person (black, other, or

unspecified).

+ The exact spelling of surname on

SSA files.

One possible way to obtain these

data would be to do an exact match of

all elderly persons in the area-permit

sample against SSA files. However, an

exact match would be expensive and

difficult. Several considerations indicate

that such a match is not really

necessary. First, almost all elderly

persons are SSA beneficiaries. (Some

problems might exist with persons 65

years of age in terms of the lag time

between individuals becoming

beneficiaries and files from SSA being

processed through sampling operations.

Thus, it might be best to use the list to

oversample only persons aged 66 years

and over rather than those aged 65 years

and over.) With respect to race and

surname spelling, it is reasonable to

assume that both are reported

consistently enough. (Black versus not

black is the only important racial

classification for weighting. Also, exact

consistency on surname spelling is not

required; it is enough if the misspelling

has the same status with respect to

presence on the Hispanic surname file.)

If a match were to be planned anyway

for a disabled list sample, however, it

would make sense to match the elderly

area sample at the same time to pick up

race and surname spelling from SSA

files. If no match is done, it might be

possible to harmonize spelling between

NHIS and SSA by asking to see a

Social Security card.

Details of Weighting the Core

If dual-frame sampling with SSA

lists were done, the first step each year

would be to count the number of

persons in each NHIS area-permit

household who are age 65 years or over

and either are black or have a Hispanic

surname. Then household base weights

could be developed. These base weights

would not be the traditional inverse

probabilities of selection because it

would be impossible to compute the

probability of selection for a household

across all possible area-permit and list

samples from the given joint design.

(This is because of the systematic

multistage approach being used in the

list sample.) Instead, a multiplicitybased base weight would be determined

for each household in the list sample;

Series 2, No. 126 [ Page 35
the standard base weight would be

assigned to each household in the

area-permit sample; then the two

weights would be averaged for every

sample household. The average would

most likely be a weighted average. For a

household that is only in one sample,

the base weight for the other component

would be zero. Base weights for

area-permit sample households that do

not contain any possible list sample

persons are, of course, not changed as a

result of dual-frame estimation.

As a variant, it might be preferable

to work out adjustments for nonresponse

separately for each component and then

average together the nonresponseadjusted weights.

Poststratification could be done just

as it is now, using demographic models

by age, race, and sex. Once the weights

have been calculated, the more

complicated estimation for the dual

sample would be irrelevant to users.

They would be able to make ordinary

weighted tabulations of the core in the

same way they would have for the

area-permit sample by itself.

Weighting procedures for

questionnaire supplements would depend

on the within-household sampling rules.

It is anticipated that different weights

would be needed for analysis of the core

interview than for analysis of

supplements.

Page 36 [ Series 2, No. 126
Chapter 7. Prospects

for Statistics on

Detailed Hispanic

Subdomains

Despite the progress that the

proposed design (see Part V)

will make in improving the

precision of NHIS statistics about

Hispanic persons, the diversity of the

Hispanic experience in this country is

such that the analytic potential of NHIS

would be improved by additional

breakdowns in tabulations. Accordingly,

separate statistics on Cuban Americans,

Puerto Rican Americans, Mexican

Americans (separately by whether their

ancestors lived in the United States

before 1900 or not), Central American

Americans, and other Hispanic

Americans appear to be desirable.

Design-Based Techniques

Although precise statistics on such

detailed domains would undoubtedly be

extremely interesting, limits are imposed

by a fixed budget. Given the

overwhelming importance of age and

sex for questions related to health, valid

contrasts between detailed Hispanic

subgroups would have to be supported

by adequate sample sizes for each age

and sex subgroup within each Hispanic

subgroup. Given the required precision

levels discussed in chapter 3, eight

age-by-sex subdomains for each detailed

Hispanic subgroup would require 8,000

persons. Multiplying by 6, detailed

Hispanic subgroups give a total required

sample size of 48,000 persons, about

40 percent of the total sample. Even if

some way to obtain addresses cheaply

for so many Hispanic persons existed (it

does not), devoting such a large

proportion of the total sample to

Hispanic persons would seriously erode

the utility of NHIS for addressing other

questions. The research conducted under

this contract yields the conclusion that it

is not possible to obtain design-based

statistics of the desired reliability about

detailed Hispanic subgroups with the

current budget. Even a doubling or

tripling of the budget would allow only

modest precision for each Hispanic

subgroup.

The possibility of breaking Hispanic

statistics geographically instead of

ethnically was also investigated. This

approach is related to that used in the

Hispanic Health and Nutrition

Examination Survey. The regions

investigated were the following: The

New York Consolidated Metropolitan

Statistical Area; Florida; and the five

southwestern States of California,

Arizona, New Mexico, Texas, and

Colorado. Unfortunately, this

investigation also showed the need for

budget increases far larger than NCHS

staff thought were possible. More details

can be found in chapter 11.

Model-Based Techniques

Given the impracticality of

design-based techniques, research was

conducted on model-based techniques.

In this research, the possibility was

considered of oversampling only those

Hispanic persons who live in heavily

Hispanic blocks and then extrapolating

the results to all Hispanic persons using

models. A key factor here is the

relationship between health and

segregation. If health is not related to

segregation or if the relationship can be

explained in terms of socioeconomic

variables that are available for the whole

sample, the model-based approach

would be much more precise than

design-based estimators that could be

produced at the same cost. Even if

neither of these conditions applies (and

it seems natural to believe that there is

some relationship that cannot be entirely

explained by other variables), and thus

the model-based estimates would be

biased, it is possible that model-based

estimators would still have an

acceptable mean square error.

To study the question, the

relationship between segregation and

health was studied for Hispanic persons

in the 1988 NHIS. That sample

contained too few Hispanic people to

study the detailed groups separately, so

they were simply studied as a group.

The health characteristics of Hispanic

persons were contrasted across the five

minority density strata that had been

created for the Hispanic population.

(The strata were defined as of 1980.)

Simple comparisons showed significant

differences. Logistic models were then

created for three binary health measures.

The inclusion of socioeconomic

variables reduced the nominal

relationship between segregation and

health but did not rule out the existence

of important residual relationships. (For

more detail on this study, see

Residential Segregation and Health

Characteristics of Minority

Populations (19).) The sample sizes

were not large enough to allow very

firm projections of the mean square

error for model-based estimators. More

years of NHIS samples need to be

studied to arrive at more definite

conclusions.

Series 2, No. 126 [ Page 37
Chapter 8. Prospects

for Statistics on Other

Minority Groups

Prospects are not good for

inexpensive, large samples of

American Indians, Eskimos, and

Aleuts, or of Asians and Pacific

Islanders.

Asians and Pacific

Islanders

The main obstacle to providing

precise health statistics for Asians and

Pacific Islanders is that, despite the

concentrations of population in the

Chinatowns of New York and San

Francisco, most Asians and Pacific

Islanders live in more integrated

neighborhoods. Considerable screening

at substantial cost would be necessary to

locate them.

Based on statistics from the 1990

census on the concentration within

blocks of Asians and Pacific Islanders

(tables 5 and 6), it is estimated that a

modest oversample of Asians and

Pacific Islanders could be achieved by

screening an additional 15,000

households (beyond the 99,000 already

planned) and by interviewing an

additional 2,580 households. The

resulting oversample would yield an

effective sample size of around 8,000

persons. (Effective sample size here

means the nominal sample size divided

by the design effect due to unequal

probabilities but not due to clustering.)

This sample size of 8,000 persons could

be achieved only by interviewing all

members of Asian households. (Asian

households are simply too rare to allow

subsampling within households to

reduce household burden.) Also, the

distribution across ages would not be

optimal. Narrow age bands of particular

analytic interest such as persons ages

18–24 years and children under age 6

would have smaller sample sizes than

desired. It was estimated that this

additional screening and interviewing

would cost an additional $1.4 million

per year, more than the budget NCHS

indicated would be available.

Some of the other potential

techniques discussed in chapter 4 may

have some promise for sampling Asians.

Network sampling is particularly

interesting given large family sizes for

this domain. (Among Asians and Pacific

Islanders, the number of persons per

occupied housing unit was 3.59 in 1991

compared with 2.64 for all races.) To

develop the idea further would require

the sort of research described earlier in

chapter 4. Multiyear aggregation is also

a technique to be considered.

Also, at least one or two companies

are experimenting with lists of typical

Asian surnames as a means of

identifying Asians and Pacific Islanders.

If such an approach yields a

false-negative rate below 40 percent, at

least modest oversampling would be

possible. Perhaps it could be combined

with oversampling by block and

screening and with network sampling.

Although this option has not been

pursued very vigorously, it probably is

worth further effort.

A further problem is the diversity of

the populations covered by the Office of

Management and Budget classification

of ‘‘Asian and Pacific Islander.’’ Just as

interest exists in detailed statistics on

Mexican Americans, Puerto Rican

Americans, Cuban Americans, and so

on, some of the analysts interested in

Asian statistics stress the need to

distinguish among Chinese, Japanese,

Filipinos, Polynesians, Indians, Malays,

Vietnamese, Hmong, Mongolians, and

so on. Given the relative prosperity of

some of these subgroups, a fundamental

question remains unanswered: If it is not

feasible to create precise statistics for

each subgroup of Asians and Pacific

Islanders separately (and it is surely not

feasible with any procedure short of a

special census), are statistics on the

amalgam group truly useful? It is

assumed in this report that such

statistics are indeed important, but this

question should receive more careful

attention by subject matter specialists.

In conclusion, it was believed that

the budget for NHIS could not be

increased enough to pay for obtaining

truly useful statistics about this domain

broken down by age and sex.

American Indians,

Eskimos, and Aleuts

The main obstacle to providing

precise health statistics about Native

Americans is their small numbers. About

one-half of Native Americans live on or

near reservations where they are easy to

find (although transportation can be

difficult and expensive in those areas),

but the other one-half live in mostly

well-integrated neighborhoods where

finding them is very expensive. If the

goal were only to provide reliable

statistics about Native Americans living

on reservations, it could probably be

achieved with a modest budget increase

(perhaps less than 10 percent).

Otherwise, it is estimated that a

screening sample of an additional

128,000 households (on top of the

planned 99,000) and an additional 5,860

household interviews would be required

just to produce an effective sample size

of 8,000 American Indians, Eskimos,

and Aleuts—even if all household

members were interviewed. That would

cost about $9 million per year. With the

budget that NCHS has indicated would

be available, this would require

substantial cutbacks in other domains

covered by NHIS.

Page 38 [ Series 2, No. 126
Chapter 9. The Poor

The 1985–94 NHIS has a

reasonable sample size for the

poor (roughly 16,000 persons per

year); it is sufficient for moderately

detailed analyses. Because of the

oversampling of black and Hispanic

populations, the sample of the poor will

be even larger after the redesign is

completed. The research performed did

not reveal any inexpensive method of

increasing the sample size of the poor

any further.

The main obstacle to inexpensive,

larger samples of the poor is that

persons with low income are far less

segregated than many people believe

(see table 26). (Poverty data are

available only from the decennial census

at the block-group level rather than at

the block level. The poor may be more

dramatically segregated at the block

level; but no data exist on the subject,

nor can an oversampling strategy be

built based upon data that do not exist.)

Poverty is not sufficiently concentrated

at the block-group level to make

stratification and oversampling a feasible

strategy for major increases in effective

sample sizes.

As mentioned earlier, oversampling

black and Hispanic populations will

expand the sample size of the poor a

little. Larger increases require expensive

and problematic screening. (Persuading

neighbors to speculate on the financial

condition of sample households is far

more difficult than obtaining proxy

reports on race and ethnic origin. Ethical

considerations about whether neighbors

should be asked such questions also

emerge. Even when a member of the

sample household is willing to respond,

total income tends to be inadequately

reported unless an extensive set of

screens and probes and encouragement

of record checks are done; and all of

these are incompatible with the idea that

a screening interview should be cheap

and quick to administer.)

Dual-frame samples were not

seriously considered, given the

knowledge that no national database of

beneficiaries of any of the welfare

programs has been constructed and the

fact that other surveys (such as the

Current Population Survey and the

Survey of Income and Program

Participation) have shown repeatedly

that a very large segment of the poor do

not participate in welfare programs.

Network estimators were also not

seriously considered. It thus appears that

the only feasible techniques for

oversampling the poor for NHIS are

either to retain those found in the

regular area-permit sample for extra

years of interviewing or to aggregate

statistics across years.

Table 26. Geographic concentration of the poor in 1990

Percent of block

group that is living

below poverty

Percent of

the poor

in such

block groups

Percent of

total

population

in such

block groups

Percent of

population

in such

block groups

that is

not poor

< 5 . . . . . . . . . . . . . . . . . . . . . 5.8 33.3 97.7

5 to 10 . . . . . . . . . . . . . . . . . . . 12.3 22.1 92.7

10 to 20 . . . . . . . . . . . . . . . . . . 24.8 22.8 85.7

20 to 30 . . . . . . . . . . . . . . . . . . 19.8 10.7 75.6

30 to 40 . . . . . . . . . . . . . . . . . . 14.3 5.4 65.5

40 to 50 . . . . . . . . . . . . . . . . . . 10.0 2.9 55.5

50+ . . . . . . . . . . . . . . . . . . . . . 13.0 2.8 38.2

Series 2, No. 126 [ Page 39
Part III.

Statistics for Geographic Domains
Chapter 10.

Subnational Estimates

for State Groupings

and Hispanic Persons

Subnational Domains With

Current Design

The ability of the current NHIS

design to produce accurate estimates for

States was examined. For estimates to

have the precision specified by the

NCHS technical staff, an effective

sample size of at least 1,000 individuals

is necessary (see the first section of

chapter 3). If the NHIS sample of

124,000 individuals (50,000 households)

were distributed according to a

self-weighting sample, only 33 of the

States would have the necessary 1,000

completed interviews to produce

Statewide estimates. The same number

of States would satisfy the minimum

sample size requirement if the sample

were to oversample the black and

Hispanic populations at twice the rate

applied to persons who are not black or

Hispanic.

Reasonable analyses of health

characteristics, however, require

State-level estimates by age and sex.

The four standard age breaks for

subnational statistics are 0–17 years,

18–44 years, 45–64 years, and 65 years

and over. The rarest of the eight

age-by-sex categories is males 65 years

of age and over; that category is

estimated to consist of 5.3 percent of the

U.S. population in the year 2000. Using

a self-weighting design (or one that

oversamples at a 2-to-1 rate), no State

will contain a sample large enough to

include 1,000 members of this

age-by-sex category. A fallback position

is simply to divide States by the four

age categories. (Given the strong age

patterns in most health statistics, it

appears that control by age is essential.)

The smallest category then is the age

group 65 years and over, constituting

13 percent of the population. Only three

States—California, Texas, and New

York—are sufficiently large to anticipate

including 1,000 elderly. (Florida is not

quite large enough. Given that the elderly

are a significantly larger percentage of the

population in Florida than in the average

State, it is likely that, by the year 2000,

Florida will also include 1,000 sampled

elderly.) To select a sample of 1,000

completed interviews of the elderly from

each of the 51 States (including the

District of Columbia), however, requires a

total sample of approximately 390,000

[(1,000/0.13)*51]. This is 3 times the

current sample size.

The possibility of producing State

statistics from a combination of 3 years

of NHIS data was also examined. Many

NHIS variables (e.g., chronic condition

rates) are relatively stable from year to

year. Items covered by supplements to

the core questionnaire, however, would

have to be retained for 3 years. Because

there are correlations between years

induced by remaining in the same

PSU’s and many of the same blocks, the

annual State sample size must be larger

than the 333 appropriate for combining

independent samples. Based on analysis

of intra-PSU and block correlations, and

on the current level of oversampling, it

was estimated that 397 completed

interviews would be required each year

to yield an effective sample of 1,000

after 3 years (NHIS Redesign

Memorandum #40 (4)). With a

self-weighting sample design, only 12 of

the States would receive the 397

completed interviews per year to

produce State estimates every 3 years.

NCHS suggested examining a set of

28 State groupings to ascertain whether

a sufficient sample would be available to

produce estimates for each grouping

every 3 years. Only 21 of the 28

groupings were of the necessary size.

An alternative set of 22 groupings in

which each group met this criterion was

established (see table 27). In addition to

producing estimates every 3 years,

two-thirds of these groups could

produce estimates every 2 years and

three of them every year.

Table 27. State groupings to produce State estimates every 3 years

[Roughly 6,600,000 needed per group]

State

Population

(000’s) State

Population

(000’s) State

Population

(000’s)

Washington ....... 4,991 North Dakota . . . . . . 629 Florida .......... 15,415

Alaska . . . . . . . . . . 687 South Dakota . . . . . 714

Oregon ......... 2,877 Minnesota ........ 4,490 South Carolina ..... 3,906

8,555 Nebraska ........ 1,556 Georgia ......... 7,957

Kansas ......... 2,529 11,863

California ........ 33,500 9,918

Hawaii .......... 1,345 North Carolina ..... 7,483

34,845 Iowa ........... 2,549

Missouri ......... 5,383 Virginia ......... 6,877

Idaho ........... 1,047 7,932

Neveda ......... 1,303 West Virginia ...... 1,722

Utah ........... 1,991 Wisconsin ........ 4,784 Maryland ........ 5,274

Colorado ........ 3,813 Illinois .......... 11,580 Delaware . . . . . . . . 734

Montana . . . . . . . . . 794 16,364 District of Columbia . . 610

Wyoming . . . . . . . . 489 8,349

9,437 Michigan ........ 9,250

New Jersey ....... 8,546

Arizona ......... 4,618 Indiana ......... 5,502

New Mexico ...... 1,968 Ohio ........... 10,629 Pennsylvania ...... 11,503

6,586 16,131

New York . . . . . . . . 17,986

Texas .......... 20,211 Kentucky ........ 3,733

Tennessee ....... 5,266 Maine .......... 1,271

Oklahoma ........ 3,376 8,999 New Hampshire .... 1,333

Arkansas ........ 2,529 Vermont . . . . . . . . . 591

Louisiana ........ 4,516 Mississippi ....... 2,877 Massachusetts ..... 6,087

10,421 Alabama ......... 4,410 Rhode Island ...... 1,049

7,287 Connecticut ....... 3,445

13,776

NOTE: Figures may not add to totals because of rounding.

Page 42 [ Series 2, No. 126
Subnational Domains for

Hispanic Persons

The possibility of producing

estimates for Hispanic persons living in

three different areas of the country was

also examined. The three areas are the

Southwest, Florida, and New York.

Hispanic persons in these areas are

predominantly of Mexican, Cuban, and

Puerto Rican ancestry, respectively. The

area probability sample would have to

produce an effective sample size of

1,000 completed interviews in each of

24 cells: Four ages (0–17 years, 18–44

years, 45–64 years, and 65 years and

over) by two sexes for each of the three

areas.

Hispanic growth rates in the three

areas were assumed to be the same as

for the dominant subgroup in each area.

Also, because no information is

available on undercoverage losses for

Hispanic persons by individual

geographic areas, it was assumed that

overall Hispanic-coverage rates would

apply in all areas except Florida.

Hispanic persons in Florida are

predominantly Cuban Americans, who

are older than other U.S. Hispanic

persons and have higher incomes and

educational attainment. We have

assumed the Hispanic-undercoverage

rate in Florida is one-half that of

Hispanic persons in general.

Although the results presented here

are plausible, they are based on a fair

amount of speculation. The research was

carried out before the 1990 census

update became available. These

assumptions should be updated with

1990 census data if oversampling these

subdomains is still being seriously

considered. The sampling rates needed

and screening workloads are quite

sensitive to the geographic distribution

of Hispanic persons.

The definitions of the geographic

areas were initially assumed to be

similar to those used in the Hispanic

Health and Nutrition Examination

Survey (HHANES). In HHANES the

Southwest was defined as the following

five States: California, Arizona, New

Mexico, Texas, and Colorado; Florida

was only Dade County; and the New

York area included Hartford and

Fairfield Counties, Connecticut; Essex,

Passaic, Hudson, Middlesex, and Union

Counties, New Jersey; and Nassau,

Westchester, and Suffolk Counties, and

New York City, New York. With the

rapid growth of the Hispanic population

predicted from 1980 to 2000, it is likely

that the New York and Florida areas

with high Hispanic densities will expand

beyond those used in HHANES.

State-level U. S. Bureau of the the

Census’ projections to the year 2000,

made on the basis of the 1980 census,

have been used for all three areas

(assumptions about Hispanic persons are

detailed in NHIS Redesign

Memorandums #9 and #26). For

purposes of this research, the same five

States were used for the Southwest, all

of Florida for Florida, and New York

and New Jersey (which includes most of

the suburban Hispanic population) for

New York.

Oversampling Hispanic

Persons

If 24,000 completed interviews are

allocated to these 24 cells and the total

number of interviewed households is

kept to 50,000, the base sampling rate

for persons who are not Hispanic (and

Hispanic persons in other areas of the

country) is reduced from 0.000497 to

0.000414 (see NHIS Redesign

Memorandums #16 and #31 for

derivation of base sampling rate).

Without any oversampling, only 4 of the

24 cells will attain the 1,000 interviews.

These are males and females 0–17 years

of age and 18–44 years of age in the

Southwest. To reach 1,000 in each of

the remaining 20 cells requires

oversampling Hispanic persons at a rate

of 9.3 in the Southwest, 43.5 in New

York, and 19.6 in Florida. (Although the

number of Hispanic persons will be

similar in New York and Florida,

Florida has many more elderly Hispanic

persons.) Because 21 percent of the

Hispanic population lives outside dense

Hispanic blocks, it is not possible to

adjust for such large oversampling rates

without increasing the base sampling

rate (see NHIS Redesign Memorandum

#26). It is necessary, therefore, to

increase the base sampling rate in all

three areas of the country. All

oversampling, especially increasing the

base sampling rate, will require

screening significant numbers of

households that will not be interviewed.

Even after tripling the base

sampling rate in the Southwest, there are

still three cells for which high-density

Hispanic strata must be further

oversampled: Males 65 years and over,

females 65 years and over, and males

45–64 years of age. After a sixfold

increase in Florida’s base rate, six cells

require additional oversampling. In New

York a thirteenfold increase results in

only three cells needing additional

oversampling. These 12 cells still

require oversampling of the most dense

Hispanic blocks after increasing the base

sampling rate in the area. The

differential sampling rates would range

from 7.0 to 1 in the Southwest, from 8.5

to 1 in Florida, and from 9.0 to 1 in

New York. The oversampling rates were

determined for each cell to avoid

oversampling any stratum at 10 times

the rate used for other strata in the same

geographic area. It was then necessary

for the oversampling to be sufficient for

the expected cell size to exceed 1,000

multiplied by the design effect arising

from oversampling high-density

Hispanic blocks. Table 28 summarizes

these findings.

These 24 cells will require data

collected from 30,402 persons,

corresponding to all persons in 10,134

households (Hispanic households are

assumed to average 3.0 persons,

compared with non-Hispanic

households, which average 2.5 persons).

Table 28. Oversampling rates to produce subnational estimates for Hispanics

Area

Increased

base rate

Oversampled

cells

Oversampling

some dense

stratum

Southwest ............ 3 3 7.0:1

Florida .............. 6 6 8.5:1

New York . . . . . . . . . . . . 13 3 9.0:1

Series 2, No. 126 [ Page 43
The screening from oversampling the 12

cells, however, will identify 90,339

Hispanic households; 80,205 of these

will not need to be interviewed.

To retain a person sample of the

size provided by a 50,000-household

sample requires 39,866 households

(50,000−10,134) from the remainder of

the country, including the population

that is not Hispanic of these three areas.

This population of the three areas

accounts for 32.89 percent of the

remaining population of the United

States and should therefore include

13,112.0 (0.3289 × 39,866) sampled

households. This procedure will screen

187,452 households that are not

Hispanic in these areas, so an extra

174,341 households would be screened.

The U.S. Bureau of the Census

estimates that screening costs equal

between one-third and one-half of

interviewing costs. Screening the

254,546 extra households (80,205 +

174,341) in these three areas will

therefore use considerably more money

than the current NHIS data collection

budget—without conducting a single

interview. Thus, at the current budget, it

is impossible to produce design-based

estimates for Hispanic persons living in

these three areas, using a strictly

area-sample approach.

Oversampling Hispanic Persons

When Using the Health Care

Financing Administration

Frame

Subnational estimates for Hispanic

persons, therefore, can only be produced

if an alternative more efficient

methodology is available. A possible

alternative is to select the Hispanic

elderly from a frame of Social Security

recipients maintained by the Health Care

Financing Administration (HCFA). The

selection could be carried out in the

sampled PSU’s. This would reduce the

extensive screening needed for the rarest

age-sex domains, but it would still

require the use of the area sample to

conduct 1,000 interviews in 18 cells

(sex (male or female) by age (0–17

years, 18–44 years, 45–64 years) in

three areas).

Producing a sufficient number of

interviews in each of these cells would

require increasing the sample in the

Southwest by a factor of 3.4, in New

York by 13.5, and in Florida by 12.2.

Two cells in the Southwest require this

oversampling of high-density Hispanic

strata: Males 45–64 years of age and

females 45–64 years of age. After

quadrupling the base rate in Florida, all

six cells require further oversampling.

After quadrupling the base rate in New

York, three cells require oversampling:

Males 45–64 years, females 45–64

years, and males 18–44 years. It is

interesting to note that in Florida the

most uncommon cell is females 0–17

years, not the older people.

The lower oversampling rates

shown in table 29 compared with

table 28 significantly reduce the number

of households that need to be screened.

The number of Hispanic households to

be screened dropped from 90,339 to

36,089 and non-Hispanic households

from 187,452 to 70,029.

The size of the Hispanic sample in

the three areas has hardly changed, to

30,315 (including the 6,000 coming

from the HCFA frame). This is

equivalent to 10,105 households (of

which 8,105 come from the area

probability frame). Because 36,089

Hispanic households will be screened in

the three areas, an unused 27,984

Hispanic households will be screened.

To retain a 50,000-household sample,

39,895 households (50,000−10,105) must

be sampled from the remainder of the

country and the population that is not

Hispanic of these three areas. The

non-Hispanic sample from the three areas

should therefore include 13,121 sampled

households (0.3289 × 39,895). Given that

70,029 non-Hispanic households will be

screened in these areas, an extra 56,909

households will be screened. Again, using

the U.S. Bureau of the Census’ estimate

for screening costs, the 84,893 extra

households (27,984 + 56,909) in these

three areas will reduce the number of

completed interviews, for a constant-cost

NHIS, by approximately 28,000.

It is therefore necessary to redo the

calculations, beginning with an overall

sample size of fewer than 25,000

households. This reduces the desired

number of interviews of people who are

not Hispanic, thereby increasing the

number of extra households being

screened and even further reducing the

money available. This iterative process

converges on a total sample (excluding

Hispanic persons in these three areas) of

16,771 households, 5,516 of whom are

not Hispanic persons in the Southwest,

New York, or Florida.

If retaining a cost equivalent to the

existing budget is desired, it is possible

to produce separate age and sex results

for Hispanic persons in each of the three

geographic areas from NHIS. There are,

however, two important caveats to this

statement. First, use of the HCFA frame

to sample Hispanic elderly is required.

The preceding calculations assumed that

costs are equivalent for the HCFA and

area probability frames. If, for example,

it costs 50 percent more to interview a

sample from HCFA records than from

an area sample, the number of

non-Hispanic households that can be

sampled will drop by more than 1,000.

Second, the non-Hispanic household

sample size has been reduced from the

45,985 households (50,000 −

(0.000497 × 24,236,000/3)) of a

non-oversampled design (such as is used

for the existing NHIS) to 16,771. This

will increase the standard error on

non-Hispanic estimates by 66 percent. It

will also make it impossible to provide

national estimates for black persons and

for persons who are not black or

Hispanic for all 24 age-by-sex-by-race

cells.

Table 29. Oversampling rates when the HCFA frame is used as a sampling frame for the

elderly

Area

Increase

base rate

Oversampled

cells

Oversampling

most dense

stratum

Southwest ............ 4 2 9.5:1

Florida .............. 4 6 7.0:1

New York . . . . . . . . . . . . 4 3 9.5:1

Page 44 [ Series 2, No. 126
Model-Dependent Subnational

Hispanic Estimates

Given the significant reduction in

accuracy for estimates of people who

are not Hispanic resulting from the

preceding design, the possibility of

producing model-based estimates was

explored. The levels of oversampling

that would be required were examined

for all three areas, as well as only for all

Hispanic persons in the Southwest. The

latter estimates would closely approximate

national estimates for Hispanic persons of

Mexican descent. The category of persons

65 years of age and over is again assumed

to be sampled from a frame maintained by

HCFA.

For this part of the research, it was

assumed that a composite estimator

would be used, combining a direct

sample estimator and an estimator

drawn from a model. The sample would

be drawn only from blocks with high

densities of Hispanic residents (at least

5 percent). Such an estimate will only

provide unbiased estimates of all

Hispanic persons in the geographic area

if the health characteristic has the same

expectation for the 21 percent of

Hispanic persons living outside

high-density blocks as for the 79 percent

living inside those blocks, within the

variables used in the model. (See the

second section of chapter 7 for more

detail.)

Table 30 summarizes the sample

sizes and sampling rates when screening

and subsampling are used. Data are

shown for two different ratios of the

cost of a completed household interview

to the cost of screening a household.

The findings for a 3:1 cost ratio are

described first.

Because of the design effect of

differential sampling rates in some cells,

the 24 cells (including 65 years and

over from HCFA) will require

interviews with 25,532 individuals; of

these, 19,533 must come from the area

sample (corresponding to 6,511 Hispanic

households).

To retain a 50,000-household

sample, 41,489 households (50,000−

6,511−2,000 from HCFA) must be

sampled from the remainder of the

country, as well as from the population

that is not Hispanic of these three areas

and the Hispanic population in the

nondense stratum of these three areas.

The population in nondense Hispanic

blocks in the three areas comprises

19.10 percent of the remaining

population in the United States; thus the

remaining sample from the three areas

should include 7,924 sampled

households (0.1910 × 41,489).

Identifying these sampled households in

the dense strata will require screening

an additional 12,516 Hispanic (from

nonrare cells) and 29,837 non-Hispanic

households in these three areas. Using

the U.S. Bureau of the Census’ 3:1 cost

estimate for screening the 42,353 extra

households (12,516 + 29,837) will

reduce the number of remaining

interviews that can be funded, for a

constant-cost NHIS, to 27,372.

It is therefore necessary to redo the

calculations beginning with a smaller

affordable sample size (27,372 instead

of 41,489). This reduces the desired

number of interviews of persons who

are not Hispanic, thereby increasing the

number of extra households being

screened and reducing even further the

money available. This iterative process

converges on a total sample (excluding

Hispanic persons in dense strata in these

three areas) of 26,411 households, 5,046

of whom are in the Southwest, New

York, or Florida.

With a 3:1 cost ratio, developing

design-unbiased estimates for the dense

blocks of each of the 24 cells will

require screening 56,790 households. Of

these, 45,234 are not retained for the

full interview, and 11,557 are

interviewed. Another 21,365 households

are interviewed from other than the

dense strata of the three areas, and

2,000 households are interviewed from

HCFA files. This results in a total of

34,922 households being interviewed in

the entire United States.

Table 30 also shows that, as the cost

of screening grows even larger, the

number of Hispanic interviews increases

only slightly. The reason it increases at

all is that as the sample becomes more

concentrated in the most densely

Hispanic blocks, larger design effects

are created, requiring more interviews

for the same degree of accuracy. The

maximum oversampling rates are shown

at the bottom of table 30. The number

of families that are and are not Hispanic

that are screened declines only slightly

as the cost of screening increases.

Unfortunately, the cost associated with

such screening increases much faster

than the number of screened households

decreases. As a result, the total number

of households included in NHIS drops

from 50,000 without screening to 34,922

with a 3:1 cost ratio, and to only 26,679

Table 30. Summary of number screened and interviewed, assuming two different screening

costs

Cost ratio

3 to 1

Cost ratio

2 to 1

Hispanic person sample in three areas ..................... 25,532 25,580

Hispanic non-HCFA person sample in three areas .............. 19,532 19,580

Hispanic non-HCFA household sample in three areas ............ 6,511 6,527

Remaining desired sample households ..................... 41,489 41,473

Desired nondense strata sample in three areas ............... 7,926 7,923

Final remaining sample households ....................... 26,411 18,152

Final non-Hispanic household sample in three areas ............ 5,046 3,468

Total households screened in dense strata1 .................. 56,790 56,637

Of these:

Not retained for interviewing .......................... 45,234 46,643

Hispanic households interviewed ....................... 6,511 6,527

Non-Hispanic households interviewed .................... 5,046 3,468

Total households interviewed in nondense strata ............... 21,365 14,684

Total HCFA households interviewed ....................... 2,000 2,000

Total households interviewed in entire United States ............ 34,922 26,679

Maximum oversampling rate, blocks at least 50-percent Hispanic:

Southwest — Male 45–64 years ....................... 4.93 5.03

Florida — Female 0–17 years ......................... 17.65 18.11

New York — Male 45–64 years ........................ 24.08 24.71

1Dense strata are defined as census blocks with more than 5 percent Hispanics in the most recent census.

Series 2, No. 126 [ Page 45
with a 2:1 cost ratio. This dropoff in

sample size is even more pronounced

when the Hispanic households in the

high-density strata are excluded. The

number then drops from 26,411 with a

3:1 cost ratio to 18,152 with a 2:1 cost

ratio. From these households (i.e.,

approximately 55,000 individual

respondents) must come estimates for

the 12 age-by-sex cells for both black

persons and persons who are neither

black nor Hispanic.

The potential of model-dependent

estimates only for Hispanic persons of

Mexican ancestry was also examined.

The majority of such Hispanic persons

reside in the Southwest and include

approximately 90 percent of all Hispanic

persons in that area. Thus, it is possible

to approximate this target population by

using model-dependent estimates for

Hispanic persons in the Southwest.

Table 31 is of the same format as

table 30, but indicates the effect of only

oversampling high-density Hispanic

strata in the Southwest.

Table 31 shows that this

oversampling would have only a minor

effect on the quality of statistics for

black persons or persons who are not

black or Hispanic. The overall sample

size would be reduced only about

10 percent (from 50,000 households to

46,000 or 44,000). The corresponding

reduction in the non-Hispanic sample

would be from 45,985 to 40,478 or

38,513.

The same caveats apply as in the

previous section, if it is desired to retain

a cost equivalent to the existing budget.

The non-Hispanic household sample size

has been reduced to between 26,411 and

18,152, depending on the cost of

screening. This will increase the

standard error on non-Hispanic estimates

by 32 to 59 percent, also depending on

the cost of screening.

Oversampling only in the Southwest

would have a much smaller effect on the

quality of non-Hispanic estimates. The

reduction in the non-Hispanic sample

would be from 45,985 to somewhere

between 40,478 and 38,513—a

reduction in accuracy of only 7 or

9 percent. The design-unbiased Hispanic

estimates would, however, be only for

those in dense strata of the Southwest.

Determining whether to implement

the foregoing procedure for

oversampling high-density Hispanic

blocks will obviously depend on a

number of factors, including the quality

of the model-dependent estimators and

the cost of screening. A major factor in

the determination is examining the

potential bias in estimating Hispanic

health characteristics from a sample in

high-density blocks. This issue was

examined and is reported in chapter 7 in

the section on model-based techniques.

Table 31. Summary of number screened (in the Southwest) and interviewed, assuming two

different screening costs

Cost ratio

3 to 1

Cost ratio

2 to 1

Hispanic person sample in the Southwest ................... 17,395 17,409

Hispanic non-HCFA person sample in the Southwest ............ 11,395 11,409

Hispanic non-HCFA household sample in the Southwest .......... 3,798 3,803

Remaining desired sample households ..................... 44,202 44,197

Desired nondense strata household sample in the Southwest ....... 5,591 5,590

Final remaining sample households ....................... 40,478 38,513

Final non-Hispanic household sample in the Southwest ........... 5,120 4,871

Total households screened in dense strata .................. 20,089 20,043

Of these:

Not retained for interviewing .......................... 11,171 11,369

Hispanic households interviewed ....................... 3,798 3,803

Non-Hispanic households interviewed .................... 5,120 4,871

Total households interviewed in nondense strata ............... 35,358 33,642

Total HCFA households interviewed ....................... 2,000 2,000

Total households interviewed in entire United States ............ 46,276 44,316

Maximum oversampling rate, blocks at least 50-percent Hispanic:

Southwest — Male 45–64 years ....................... 4.93 5.03

Florida — Female 0–17 years ......................... 1.00 1.00

New York — Male 45–64 years ........................ 1.00 1.00

Page 46 [ Series 2, No. 126
Chapter 11.

Design-Based

Approaches for State

Estimation

Estimation Based Solely on

the National Health

Interview Survey

Table 32 shows the expected sample

sizes by State for a self-weighting

sample of 50,000 households when

black and Hispanic persons are

oversampled at a rate of 2 to 1. (These

are similar to the rates in the proposed

beta option; see chapter 17 for more

details.) These sizes are shown for the

entire State and for the rarest age-by-sex

cell, males age 65 years or over. This

table assumes that only an area sample

is used, that is, there is no

supplementation of persons age 65 and

over from HCFA or SSA lists. For the

oversampled design, the effective sample

sizes are provided, adjusting for the

design effect resulting from the

differential survey weights.

In the first section of chapter 3, the

rationale was provided for the

determination that an effective sample

size of 1,000 persons or more was

required for an individual analytic

domain. It is clear from the table that no

State will have that size for all age and

sex domains; and for about one-third of

the States, it would be impossible to

produce estimates for the entire State

population. Table 33 summarizes these

findings. Even if the sample size were

doubled or quadrupled, some States

would not have the accuracy to be able

to have State totals published; and fewer

than 10 would be able to have all age

and sex domains published.

Examination of table 32 indicates

that, after the redesign, the number of

States for which the sample size will be

sufficient to produce design-based

estimates from NHIS will not change

significantly from that reported in

table 33.

Given the preceding results, all

further research into design-based

estimates assumed that HCFA lists

would be used for persons 65 years of

age or over. The rarest cell in the area

sample becomes males 45–64 years of

age. Table 34 follows a format similar to

that of table 32 but now provides

estimates for total adults and males age

45–64 years, both when using a

self-weighting sample and when using a

design with oversampling rates similar

to those proposed in the alpha option

(see chapter 5) for the redesign. (The

alpha option assumes a budget

50-percent greater than that of the

current design.) To improve the

estimates, the final columns examine the

impact of combining 3 years of data

(the effective sample sizes shown in

table 34 do not take into account the

slight reduction in accuracy resulting

from using the same PSU’s and

neighboring blocks each year). The

final column shows the coefficient of

variation (cv) for the rarest cell for a

variable that achieves a cv of

30 percent from a sample of 1,000

persons (see the first section of

chapter 3). With 3 years of data, only

three States (California, Texas, and

New York) provide the desired

accuracy for all eight age and sex

cells. Florida will provide close to the

desired level of accuracy (a cv of

32 percent for males 45–64 years of

age). For all remaining States, it will

not be possible to make State-level

age and sex estimates for all of the

eight cells.

Dual-Frame Estimation

Using Random Digit

Dialing Supplementation

States that are interested in

producing age-by-sex estimates for their

own population will have to supplement

the NHIS area sample. Given the results

of the previous section and the cost of

area sampling, it appears that random

digit dialing (RDD) supplementation is

the method most States would choose.

In this section the size of the RDD

supplement that would be needed to

provide the desired level of accuracy

(cv ≤ 30 percent) is determined. Three

composite estimators are considered:

Unbiased with a cv = 30 percent;

unbiased with an equal-sized RDD

supplement in each State; and a

minimum variance, possibly biased,

version of the second estimator.

The following assumptions were

made for this section:

+ Close geographic clustering of the

supplemental sample by PSU or

segment is not necessary because

of the use of telephone data

collection. The only clustering

would be within sets of 100

telephone numbers having the

same area code, prefix, and first

two extension digits. The

Mitofsky-Waksberg method of

sample selection would be used.

(If other RDD methods were to be

used, a different design effect

would be needed in the next

subsection.)

+ The NHIS redesign used in table 34

(see NHIS Redesign Memorandum

#50R for more details) would be the

alpha option plus subsampling of

one person per household, the

procedure generally used in NHIS

supplements.

+ The estimated RDD supplement

sizes assume that States are

interested only in age and sex cells.

For those States interested in other

decompositions by race or Hispanic

origin, it may be necessary to

enlarge the supplement further. This

will depend on whether the size of

the smallest desired cell is larger or

smaller than the smallest age and

sex cell.

+ Telephone coverage is 93 percent in

each State. (Variation among States

is small, ranging from 83 percent in

Alaska to 97 percent in Wisconsin.

Using 93 percent for each State

simplifies the calculation and the

results are subject to only minor

error.)

Design Effects

With an RDD sample, it is not

necessary to use PSU’s, and the only

clustering is the clusters of 100

telephone numbers used in the

Mitofsky-Waksberg sample selection

method. Optimizing the cluster size

generally results in a fairly small cluster

for interviewing, usually three to five or

Series 2, No. 126 [ Page 47
Table 32. State sample sizes with and without oversampling of blacks and Hispanics; States ranked by population size in 2000

Sample size with 50,000 household sample and

blacks and Hispanics oversampled at 2 to 1

Sample size with

self-weighting sample of

50,000 households2 Actual sample3 Effective sample4

Rank State

Population

year 2000

(000’s)

Percent

black or

Hispanic1

Total

adults

Males 65

years of age

and older

Total

adults

Males 65

years of age

and older

Total

adults

Males 65

years of age

and older

1 California ................. 33,500 38.7 15,444 819 17,564 931 15,701 832

2 Texas ................... 20,211 39.5 9,317 494 10,657 565 9,520 505

3 New York . . . . . . . . . . . . . . . . . 17,986 33.7 8,292 439 9,090 482 8,176 433

4 Florida ................... 15,415 33.2 7,106 377 7,761 411 6,987 370

5 Illinois ................... 11,580 27.1 5,338 283 5,563 295 5,063 268

6 Pennsylvania ............... 11,502 12.3 5,303 281 4,883 259 4,633 246

7 Ohio .................... 10,629 14.0 4,900 260 4,580 243 4,320 229

8 Michigan ................. 9,250 17.0 4,264 226 4,091 217 3,821 203

9 New Jersey ................ 8,546 27.4 3,940 209 4,116 218 2,743 198

10 Georgia .................. 7,957 28.0 3,668 194 3,850 204 3,497 185

11 North Carolina .............. 7,483 22.0 3,450 183 3,451 183 3,178 168

12 Virginia .................. 6,877 20.0 3,170 168 3,119 165 2,888 153

13 Massachusetts .............. 6,087 7.5 2,806 149 2,473 131 2,391 127

14 Indiana .................. 5,502 10.0 2,536 134 2,288 121 2,189 116

15 Missouri .................. 5,383 12.0 2,482 132 2,279 121 2,165 115

16 Maryland ................. 5,274 30.0 2,431 129 2,592 137 2,345 124

17 Tennessee ................ 5,266 17.0 2,428 129 2,329 123 2,175 115

18 Washington ................ 4,991 3.0 2,301 122 1,943 103 1,915 102

19 Wisconsin ................. 4,784 6.5 2,205 117 1,926 102 1,869 99

20 Arizona .................. 4,618 24.6 2,129 113 2,175 115 1,990 105

21 Louisiana ................. 4,516 33.0 2,082 110 2,270 120 2,044 108

22 Minnesota ................. 4,490 2.0 2,070 110 1,731 92 1,714 91

23 Alabama .................. 4,410 26.0 2,033 108 2,100 111 1,916 102

24 South Carolina .............. 3,906 30.5 1,801 95 1,927 102 1,742 92

25 Colorado ................. 3,813 15.2 1,758 93 1,660 88 1,560 83

26 Kentucky ................. 3,733 8.0 1,721 91 1,524 81 1,470 78

27 Connecticut ................ 3,445 11.5 1,588 84 1,452 77 1,382 73

28 Oklahoma ................. 3,376 7.5 1,556 82 1,372 73 1,326 70

29 Oregon .................. 2,877 2.0 1,326 70 1,109 59 1,098 58

30 Mississippi ................ 2,877 36.5 1,326 70 1,484 79 1,330 71

31 Iowa .................... 2,549 2.5 1,175 62 988 52 976 52

32 Arkansas ................. 2,529 16.0 1,166 62 1,109 59 1,039 55

33 Kansas .................. 2,529 6.5 1,166 62 1,018 54 988 52

34 Utah .................... 1,991 1.0 918 49 760 40 756 40

35 New Mexico ............... 1,968 37.7 907 48 1,024 54 917 49

36 West Virginia ............... 1,722 3.0 794 42 670 36 661 35

37 Nebraska ................. 1,556 4.0 717 38 612 32 600 32

38 Hawaii ................... 1,345 2.5 620 33 521 28 515 27

39 New Hampshire ............. 1,333 2.0 615 33 514 27 509 27

40 Nevada .................. 1,303 8.0 601 32 532 28 513 27

41 Maine ................... 1,271 1.0 586 31 485 26 483 26

42 Rhode Island ............... 1,049 6.0 484 26 420 22 409 22

43 Idaho .................... 1,047 0.0 483 26 396 21 396 21

44 Montana . . . . . . . . . . . . . . . . . . 794 0.0 366 19 300 16 300 16

45 Delaware . . . . . . . . . . . . . . . . . 734 22.0 338 18 338 18 312 17

46 South Dakota . . . . . . . . . . . . . . 714 0.5 329 17 271 14 271 14

47 Alaska . . . . . . . . . . . . . . . . . . . 687 3.5 317 17 269 14 264 14

48 North Dakota . . . . . . . . . . . . . . . 629 0.5 290 15 239 13 238 13

49 Vermont . . . . . . . . . . . . . . . . . . 591 1.0 272 14 226 12 225 12

50 Wyoming . . . . . . . . . . . . . . . . . 489 0.0 225 12 185 10 185 10

0.0 Quantity more than zero but less than 0.05.

1For total population and blacks, Series P-25 No. 1017; Hispanics from table 5.

2Total sample size = population (State)/U.S. population × 124,000; males 65+ = .053 of total sample. The overall sampling rate is (.94) × 124,000/252,668,000 = .000461. The equal-weighted sample

overall sampling rate is (.94) × 124,000/252,668,000 = .000461.

3Blacks and Hispanics will be .222 of the U.S. population in 2000. If they are oversampled at a rate of 2 to 1, the sample rate for nonblack non-Hispanics will be .00378 and the rate for blacks and

Hispanics will be .00804.

4The design effect for oversampling at a rate of 2 to 1 is (1 + p) (1 − p/2) where p is the proportion of blacks and Hispanics in the State. The effective sample size (for statistics of total population) in a

State is the actual size divided by the design effect for that State.

Page 48 [ Series 2, No. 126
six households. Intraclass correlations

for these clusters have not been

calculated for health-related variables,

but other social and demographic

surveys report very low intraclass

correlations. RDD design effects also

have a component arising from

variability in weights from

nonresponse adjustments and from

within-household clustering. It was

assumed that a design effect of 1.20

was a reasonable average for RDD

samples. The current (area sample)

design of the NHIS has a design effect

of approximately 1.25; thus the RDD

design effect relative to the current

design effect is 1.20/1.25 = 0.96. (If,

for example, list-assisted RDD

methods were used, the relative design

effect would be smaller because of a

reduction in clustering.)

In addition to the current NHIS

design effect, the proposed area sample

has a design effect that is computed in

two parts. The first component includes

the subsampling of one per household

and differential oversampling rates by

density strata computed separately for

black persons, Hispanic persons, and

‘‘others.’’ The second component

accounts for the combination of the

three groups with different average

sampling rates, computed separately by

State. The effective sample size for the

area sample is then computed by

dividing the actual sample size by the

two design effect components for that

State. The value for the first component

(see NHIS Redesign Memorandum

#56R) is close to 1.33 for all three

groups; therefore this value is used for

all three. The value of the second

component was computed separately for

each State; values vary from 1.35 to

1.93.

Variance of an Unbiased

Dual-Frame Estimator

A combined RDD and area sample

will thus have a smaller design effect

than an area sample alone and will need

a smaller actual sample size to achieve

the accuracy of an effective 1,000 cases

needed for the reliability specified by

NCHS. To derive the variance of the

dual-frame sample design, one can

proceed as follows. Let xa,t and xr be the

mean for the telephone households in the

area sample and the mean for the RDD

sample, respectively, in a particular

State. Let xa,n be the mean for

nontelephone households in the area

sample. The sample estimate of a

dual-frame sample can be expressed as

x = (0.93) [f xa,t + (1 − f)xr] + 0.07xa,n

where 0.93 is the expected proportion of

households with telephones and f is a

weight. Any value for the weight

between 0 and 1 will provide unbiased

estimates if both xa,t and xr are unbiased,

but a value should be selected that

comes close to minimizing the total

variance.

First derive the value of f that

minimizes the variance of the term in

brackets. Use the following notation:

na and nr are the sample sizes in the

area sample and RDD sample

for the domain of interest

da and dr are the design effects in the

area and RDD samples,

relative to the current design

σ2 is the population variance of

the statistic being considered.

The same σ2 applies to xa,t and

xr because the populations

are identical and no modeof-interview effects are

considered.

The variance of the term in the

brackets is

σxt

2 = f2 σxa,t2 + (1 − f)2 σxr2

The expression is minimized by

taking weights that are proportional to

the reciprocal of the variances of each

term; that is,

f = σxr

2

σxa,t

2 + σxr2

where

σxr

2 = dr

σ2

nr

σxa,t

2 = daσ2

.93na

The variance of the full estimator is

then

V(x) = S

da

na

D σ2 .93drna + .07danr

.93drna + danr

(9)

This formula understates the

variance slightly because it assumes no

covariance between the sample of

telephone households and nontelephone

households. Such a covariance will exist

for the area sample, but its effect should

be quite small.

Given the size of the area sample,

na, the goal is to determine the size of

the RDD supplemental sample, nr,

which will provide the same level of

accuracy for the composite estimator as

provided by an area sample with an

effective sample size of 1,000 persons

for the same domain. This implies that

V(x) = σ2

1,000

Substituting this into equation 9

produces

1 = S

da

na

D 1,000 S

.93drna + .07danr

.93drna + danr

D

This equation can be simplified to

.93drna + da nr =

1,000 S

da

na

D (.93drna + .07da nr)

Table 33. Number of States for which estimates would have the desired accuracy for three

different sample sizes

Sample size

Self-weighting sample Minorities oversampled 2 to 1

Total

States

Male

65 and over

Total

States

Male

65 and over

50,000 . . . . . . . . . . . . . . 33 0 32 0

100,000 . . . . . . . . . . . . . 41 1 40 2

200,000 . . . . . . . . . . . . . 50 7 48 5

Series 2, No. 126 [ Page 49
Table 34. State sample sizes (ranked by population size in 2000) for collapsed ages with and without oversampling of blacks and Hispanics

Sample size with selfweighting sample of

50,000 households2

Sample size with 50 percent budget increase, one per household and

black and Hispanics oversampled at 2.59 and 3.59 to 1

Population

year 2000

(000’s)

Actual sample3 Effective sample4

Effective sample4

using 3 years of data

Rank State

Percent

black1

Percent

Hispanic1

Total

adults

Males

45–64

Total

adults

Males

45–64

Total

adults

Males

45–64

Total

adults

Males

45–64

cv

(%)

1 California ............ 33,500 8.7 32.0 11,660 1,714 9,394 1,381 5,016 737 15,049 2,212 20

2 Texas .............. 20,211 12.1 30.0 7,731 1,136 6,234 916 3,356 493 10,067 1,480 26

3 New York . . . . . . . . . . . . 17,986 17.7 17.2 6,260 920 4,427 651 2,512 369 7,537 1,108 29

4 Florida .............. 15,415 14.8 14.3 5,365 789 3,529 519 2,050 301 6,150 904 32

5 Illinois .............. 11,580 17.5 10.9 4,030 592 2,577 379 1,525 224 4,576 673 37

6 Pennsylvania .......... 11,502 9.8 2.8 4,004 589 2,016 296 1,342 197 4,025 592 39

7 Ohio ............... 10,629 12.0 1.8 3,699 544 1,876 276 1,248 183 3,744 550 40

8 Michigan ............ 9,250 16.2 3.0 3,220 473 1,761 259 1,128 166 3,385 498 43

9 New Jersey ........... 8,546 15.8 12.1 2,974 437 1,906 280 1,123 165 3,369 495 43

10 Georgia ............. 7,957 27.0 1.9 2,769 407 1,678 247 1,040 153 3,119 459 44

11 North Carolina ......... 7,483 21.9 1.4 2,604 383 1,478 217 938 138 2,814 414 47

12 Virginia ............. 6,877 19.4 3.3 2,394 352 1,366 201 860 126 2,580 379 49

13 Massachusetts ......... 6,087 5.4 6.6 2,119 311 1,091 160 710 104 2,130 313 54

14 Indiana ............. 5,502 9.3 2.5 1,915 282 952 140 638 94 1,914 281 57

15 Missouri ............. 5,383 11.1 1.6 1,874 275 936 138 627 92 1,882 277 57

16 Maryland ............ 5,274 27.9 3.3 1,836 270 1,149 169 702 103 2,106 310 54

17 Tennessee ........... 5,266 16.8 .9 1,833 269 970 143 634 93 1,903 280 57

18 Washington ........... 4,991 2.3 6.0 1,737 255 849 125 568 84 1,704 251 60

19 Wisconsin ............ 4,784 5.7 2.7 1,665 245 792 116 543 80 1,628 239 61

20 Arizona ............. 4,618 2.7 20.8 1,607 236 1,041 153 594 87 1,782 262 59

21 Louisiana ............ 4,516 32.2 2.9 1,572 231 1,021 150 619 91 1,858 273 57

22 Minnesota ............ 4,490 1.7 1.7 1,563 230 686 101 493 72 1,479 217 64

23 Alabama ............. 4,410 25.8 .8 1,535 226 899 132 565 83 1,696 249 60

24 South Carolina ......... 3,906 30.0 1.1 1,360 200 838 123 518 76 1,554 228 63

25 Colorado ............ 3,813 4.1 15.5 1,327 195 797 117 474 70 1,421 209 66

26 Kentucky ............ 3,733 7.9 .8 1,299 191 610 90 423 62 1,270 187 69

27 Connecticut ........... 3,445 9.1 8.6 1,199 176 672 99 419 62 1,256 185 70

28 Oklahoma ............ 3,376 6.8 3.6 1,175 173 579 85 388 57 1,165 171 72

29 Oregon ............. 2,877 1.7 5.5 1,001 147 480 71 325 48 975 143 79

30 Mississippi ........... 2,877 36.0 .8 1,001 147 654 96 399 59 1,197 176 72

31 Iowa ............... 2,549 2.3 1.8 887 130 394 58 281 41 843 124 85

32 Arkansas ............ 2,529 15.7 1.1 880 129 461 68 303 45 909 134 82

33 Kansas ............. 2,529 6.2 5.2 880 129 445 65 294 43 881 129 83

34 Utah ............... 1,991 .7 5.9 693 102 330 49 224 33 672 99 95

35 New Mexico .......... 1,968 1.7 41.1 685 101 587 86 304 45 913 134 82

36 West Virginia .......... 1,722 2.6 .7 599 88 260 38 189 28 566 83 104

37 Nebraska ............ 1,556 3.7 3.3 542 80 254 37 175 26 525 77 108

38 Hawaii .............. 1,345 1.8 8.4 468 69 239 35 155 23 466 69 115

39 New Hampshire ........ 1,333 .8 1.2 464 68 198 29 145 21 435 64 119

40 Nevada ............. 1,303 7.2 13.3 454 67 271 40 163 24 488 72 112

41 Maine .............. 1,271 .3 .8 442 65 186 27 137 20 412 61 122

42 Rhode Island .......... 1,049 4.3 6.1 365 54 183 27 121 18 363 53 130

43 Idaho ............... 1,047 .6 7.1 364 54 178 26 119 17 356 52 131

44 Montana . . . . . . . . . . . . . 794 .3 2.1 276 41 120 18 87 13 260 38 153

45 Delaware . . . . . . . . . . . . 734 21.1 3.0 255 38 148 22 93 14 278 41 148

46 South Dakota . . . . . . . . . 714 .3 1.0 249 37 105 15 77 11 232 34 162

47 Alaska . . . . . . . . . . . . . . 687 3.3 3.6 293 35 112 17 77 11 232 34 162

48 District of Columbia . . . . . . 634 67.8 7.2 221 32 205 30 128 19 383 56 126

49 North Dakota . . . . . . . . . . 629 .6 1.0 219 32 93 14 68 10 205 30 173

50 Vermont . . . . . . . . . . . . . 591 .5 .9 206 30 87 13 64 9 192 28 179

51 Wyoming . . . . . . . . . . . . 489 .8 7.4 170 25 84 12 56 8 167 25 191

1For total population and blacks, Series P-25 No. 1017. For Hispanics, 1990 census figures are projected to grow 39.6 percent in each State by 2000. This percentage represents the ratio of the high

2000 projection to the 1990 figure, since the 1990 figure (P-25 No. 995) exceeded the high projection for 1990.

2Total adult sample size = population (State)/U.S. population × 124,000 × 0.755; males 45–64 are 0.147 of the total adult sample. The equal-weighted sample overall sampling rate is

(0.94) × 124,000/252,668,000 = 0.000461.

3Blacks and Hispanics will be oversampled and screened as described in Memorandum #50R, using the design resulting in 25,000 sampled ‘‘others.’’ The average oversampling rates relative to the

rate for ‘‘others’’ are 2.59 for blacks and 3.59 for Hispanics.

4There are two separate components of the design effect. Each is calculated using the standard formula found in Survey Sampling (20). The first component includes the subsampling of one per

household and differential oversampling rates by density strata, computed separately for blacks, Hispanics, and ‘‘others.’’ The second component accounts for the combination of the three groups with

different average sampling rates.This design effect will vary from State to State. The effective sample size in a State is the actual sample size divided by the two design effect components for that

State. The effective sample size using 3 years of data could be reduced by an additional 19 to 26 percent to account for the clustering of the sample in the same PSU’s each year. This would increase

the cv’s by a factor of 1:1.

Page 50 [ Series 2, No. 126
Solving this equation for the size of

the RDD supplement yields

nr =

930da dr − (.93) drna

da − 70 (da

2 /na)

=

930dr − (.93) drna

*

1 − (70/na

*

) (10)

where na

* = na /da is the State-specific

effective size of the area sample.

Substituting the RDD design effect

dr = 0.96 and the area effective sample

size into equation 10 only produces a

positive value of nr when na

* > 70. When

the area sample for a cell is less than

71, the variability in the area sample

component already exceeds the total

variability allowed in the composite

estimator.

Thus, it is impossible for RDD

supplementation to provide an unbiased

composite estimator with the desired

accuracy if the cell has an effective

sample size from the area sample of less

than 71. The reason for this is that when

the sample sizes are very small, the

component of variance arising from

nontelephone households is so large that

it exceeds the permissible variance from

the total sample.

An examination of table 35 shows

that, using a single year of data, RDD

supplementation can help for every cell

in only 24 States, but 10 of these

require more than 20,000 cases. Even in

California it would require 1,762

interviews. It can also be noted that

when interested in estimates for all

adults, it is impossible to provide an

unbiased estimator using RDD

supplementation in three of the States.

When 3 years’ data are combined,

RDD supplements can provide the

desired precision for all cells in 38

States, although 3 of them require more

than 20,000 interviews. In the 22 States

that do not have 1,000 area sample adult

cases, moderate-size RDD sample

supplementation will provide sufficient

precision.

To summarize, unbiased dual-frame

estimators with RDD supplementation of

1 year’s NHIS data are of very limited

utility. With 3 years’ data this approach

does not help in a number of States, but

it can be used in a majority of States.

Table 36 presents these findings.

Precision of Unbiased

Dual-Frame Estimator With a

Fixed-Size Random Digit

Dialing Supplement

The next question examined was, if

the same RDD resources were available

in every State, in how many (and

which) States would different conditions

provide estimates for all adults, and for

all of the eight age-by-sex cells, with

coefficients of variation below

30 percent? Using the proposed design,

the 16 sets for analysis were formed by

the complete crossing of the following

four dichotomies:

+ 1,000 versus 2,000 RDD households

interviewed per State.

+ One adult versus all adults

interviewed per RDD sample

household.

+ Characteristics with p = 0.5 versus

p = 0.1.

+ 1 year versus 3 years of data.

First, the sampling errors provided

by use of the RDD sample without the

area probability sample are examined.

Then the accuracy of the best unbiased

composite estimator is examined—

where the composite combines the RDD

sample with the NHIS area sample in

the State. Finally, the impact of limiting

the variation in the weights in the

composite estimator is examined, along

with the extent of bias that can be

expected for such biased estimators.

The accuracy of estimates made

solely from the RDD sample will be the

same for every State. Thus only the

accuracy of the estimator for 16

conditions need be examined: 1,000 or

2,000 households, one adult or all adults

in a household, p = 0.5 or 0.1, and 1

year’s or 3 years’ data.

The variance, standard error, and

coefficient of variation for each of these

16 sets, for all adults and for males

45–64 years of age, are provided in

table 37. The average number of adults

per household is assumed to be 1.85

(see NHIS Redesign Memorandum #66),

and because the average number of

adult males between ages 45–64 years

per household is assumed to be one, it is

assumed that there is only a trivial

proportion of households with more than

one male in this age range. It is

therefore necessary only to include an

intrahousehold correlation for the ‘‘all

adult’’ cell. Based on the calculations in

NHIS Redesign Memorandum #40 (4), a

conservative estimate for this

correlation, ρ = 0.1, is used.

It was also assumed that the effect

of clustering is negligible for RDD

samples (the number of completes per

cluster and the intracluster correlation

are likely to be small). The table

assumes further that an equal probability

sample of households is used for the

RDD supplement; if the modified

Waksberg method (which eliminates the

need for sequential sampling, but

introduces some differential weighting,

see Avoiding Sequential Sampling With

Random Digit Dialing (21)) is used,

then the variation in weights will yield

some increase in cv’s.

Table 37 shows that a telephone

sample of 1,000 households will provide

a cv less than 30 percent for all States

for each of the age and sex categories.

This is true for a characteristic with an

occurrence of 0.10 or 0.50, with one

adult interviewed per household or all

adults interviewed. It is not necessary to

use more than 1 year’s sample or to

double the number of households.

Unfortunately, these estimates of cv

do not take into account the bias from

sampling only telephone households.

This bias will vary from State to State

(the percentage of the 1980 population

in a State that was without telephones

varied from 3.3 percent to 16.7 percent)

and can be considerable for some health

characteristics. These biases are also

likely to be greater for black persons

and Hispanic persons than for the

general population because a higher

percentage of minorities than of white

persons lack telephones.

The minimum variance for an

unbiased dual-frame estimator was

presented in equation 9. This variance

was computed for every State for each

of the 16 models. When p = 0.5 all of

the proposed models provide unbiased

composite estimators with the desired

cv < 30 percent. In fact, with p = 0.5, all

of the models provide estimates with

cv < 15 percent. This is not the case for

the eight conditions with p = 0.1; these

Series 2, No. 126 [ Page 51
Table 35. RDD supplementation sample sizes for age groups with one adult sampled per household

Rank State

Population

year 2000

(000’s)1

Area effective sample3

using 1 year of data

RDD actual sample

using 1 year of data

Area effective sample3

using 3 years of data

RDD actual sample

using 3 years of data

Total

adults2

Males

45–64

Total

adults2

Males

45–64

Total

adults2

Males

45–64

Total

adults2

Males

45–64

1 California ............ 33,500 5,016 737 + 1,762 15,049 2,212 + +

2 Texas .............. 20,211 3,053 449 + 3,966 9,160 1,347 + +

3 New York . . . . . . . . . . . . 17,986 2,512 369 + 4,726 7,537 1,108 + +

4 Florida .............. 15,415 2,050 301 + 5,527 6,150 904 + 631

5 Illinois .............. 11,580 1,525 224 + 6,850 4,576 673 + 2,218

6 Pennsylvania .......... 11,502 1,342 197 + 7,558 4,025 592 + 2,813

7 Ohio ............... 10,629 1,248 183 + 8,020 3,744 550 + 3,129

8 Michigan ............ 9,250 1,128 166 + 8,765 3,385 498 + 3,551

9 New Jersey ........... 8,546 1,123 165 + 8,804 3,369 495 + 3,570

10 Georgia ............. 7,957 1,040 153 + 9,492 3,119 459 + 3,881

11 North Carolina ......... 7,483 938 138 60 10,635 2,814 414 + 4,286

12 Virginia ............. 6,877 860 126 136 11,887 2,580 379 + 4,623

13 Massachusetts ......... 6,087 710 104 287 16,516 2,130 313 + 5,373

14 Indiana ............. 5,502 638 94 363 21,690 1,914 281 + 5,809

15 Missouri ............. 5,383 627 92 374 22,868 1,882 277 + 5,880

16 Maryland ............ 5,274 702 103 295 16,927 2,106 310 + 5,418

17 Tennessee ........... 5,266 634 93 367 22,086 1,903 280 + 5,834

18 Washington ........... 4,991 568 84 440 34,428 1,704 251 + 6,317

19 Wisconsin ............ 4,784 543 80 469 45,597 1,628 239 + 6,530

20 Arizona ............. 4,618 594 87 411 27,949 1,782 262 + 6,117

21 Louisiana ............ 4,516 619 91 383 23,899 1,858 273 + 5,937

22 Minnesota ............ 4,490 493 72 528 165,993 1,479 217 + 7,011

23 Alabama ............. 4,410 565 83 443 35,278 1,696 249 + 6,338

24 South Carolina ......... 3,906 518 76 497 69,407 1,554 228 + 6,756

25 Colorado ............ 3,813 474 70 551 # 1,421 209 + 7,226

26 Kentucky ............ 3,733 423 62 617 # 1,270 187 + 7,903

27 Connecticut ........... 3,445 419 62 623 # 1,256 185 + 7,975

28 Oklahoma ............ 3,376 388 57 666 # 1,165 171 + 8,510

29 Oregon ............. 2,877 325 48 768 # 975 143 24 10,171

30 Mississippi ........... 2,877 399 59 651 # 1,197 176 + 8,312

31 Iowa ............... 2,549 281 41 855 # 843 124 153 12,227

32 Arkansas ............ 2,529 303 45 810 # 909 134 88 11,058

33 Kansas ............. 2,529 294 43 828 # 881 129 116 11,515

34 Utah ............... 1,991 224 33 1,008 # 672 99 327 18,784

35 New Mexico .......... 1,968 304 45 806 # 913 134 84 10,985

36 West Virginia .......... 1,722 189 28 1,151 # 566 83 442 35,042

37 Nebraska ............ 1,556 175 26 1,227 # 525 77 489 60,021

38 Hawaii .............. 1,345 155 23 1,372 # 466 69 561 #

39 New Hampshire ........ 1,333 145 21 1,477 # 435 64 601 #

40 Nevada ............. 1,303 163 24 1,314 # 488 72 534 242,780

41 Maine .............. 1,271 137 20 1,570 # 412 61 632 #

42 Rhode Island .......... 1,049 121 18 1,861 # 363 53 704 #

43 Idaho ............... 1,047 119 17 1,915 # 356 52 715 #

44 Montana . . . . . . . . . . . . . 794 87 13 4,248 # 260 38 904 #

45 Delaware . . . . . . . . . . . . 734 93 14 3,306 # 278 41 861 #

46 South Dakota . . . . . . . . . 714 77 11 8,729 # 232 34 982 #

47 Alaska . . . . . . . . . . . . . . 687 77 11 8,738 # 232 34 982 #

48 District of Columbia . . . . . . 634 128 19 1,723 # 383 56 674 #

49 North Dakota . . . . . . . . . . 629 68 10 # # 205 30 1,079 #

50 Vermont . . . . . . . . . . . . . 591 64 9 # # 192 28 1,135 #

51 Wyoming . . . . . . . . . . . . 489 56 8 # # 167 25 1,280 #

+ Cells for which the area effective sample size already exceeds 1,000 and do not require RDD supplementation.

# Cells for which the area effective sample size is less than 71 and RDD cannot help achieve the desired level of accuracy.

1For total population, Series P-25 No. 1017.

2Total adult sample size = population (State)/U.S. population x 124,000 × 0.755; males 18–24 are 0.147 of the total adult sample.

3There are two separate components of the design effect. Each is calculated using the standard formula found in Survey Sampling (20). The first component includes the subsampling of one per

household and differential oversampling rates by density strata, computed separately for blacks, Hispanics, and ‘‘others.’’ The second component accounts for the combination of the three groups with

different average sampling rates. This design effect will vary from State to State. The effective sample size in a State is the actual sample size divided by the two design effect components for that

State.

Page 52 [ Series 2, No. 126
results can be seen in table 38. This

table indicates many fewer States than

in earlier tables with the desired level of

accuracy. This is because the

1,000-interview requirement was based

on a cv of 30 percent for a characteristic

with p = 0.014, not 0.1.

Table 38 shows that the minimum

condition of a cv < 30 percent can be

achieved for most States under any of

the eight conditions. It is achieved for

all States when 3 years’ of NHIS data

are used.

The differences are much more

pronounced when the number of States

for which some cells will have estimates

with cv’s exceeding 15 percent is

examined. In general the results show

the impact of increasing the size of the

two samples. When interviewing all

adults in 1,000 households, the sample

size is quite similar to that when

interviewing 1 adult from 2,000

households, and so are the number of

States exceeding the 15-percent cutoff.

Unless all adults in 2,000 households

are interviewed by telephone and

combined with 3 years’ of NHIS data,

one can expect at least 20 of the States

to have some of the age-by-sex cells for

which the cv will exceed 15 percent.

Comparisons of Dual-Frame

Estimators With Stand-Alone

1,000 Random Digit Dialing

Estimator

Currently, the Centers for Disease

Control and Prevention funds each State

to collect health data from RDD surveys

of 1,000 individuals. RDD estimators

are subject to potential biases resulting

from differences in a health

characteristic between households with

and without telephones. Therefore the

mean square error and variance of

dual-frame estimators were compared

with the same measures of an RDD

estimator based on 1,000 interviews per

State. For these comparisons only 1 year

of NHIS data was used assuming the

proposed alpha option design. Estimates

for all adults and for the six age-by-sex

cells for adults were examined. A

95-percent response rate was assumed

for NHIS and an 80-percent response

rate for the RDD interviews.

Three different incidence rates were

examined: 0.03, 0.10, and 0.30. This

covers most of the incidence levels

found in NHIS data and provides some

measure of robustness to the results. The

comparison of accuracy compared the

relative root mean square errors

(RRMSE’s) of the two estimators. In the

case of the dual-frame estimator, this is

equal to the cv, but for the RDD

estimator, it is a function of both

variance and bias. Trends in United

States Telephone Coverage Across Time

Table 36. Number of States for which all cells meet the desired level of accuracy

Estimator

All 8 age

and sex cells

All

adults

1 year area sample only . . . . . . . . . . . . . . . . . . . . . . . 0 10

1 year RDD, less than 20,000 supplement per State . . . . . 14 48

1 year RDD, unlimited supplement . . . . . . . . . . . . . . . . 24 48

3 year area sample only . . . . . . . . . . . . . . . . . . . . . . . 3 29

3 year RDD, less than 20,000 supplement per State . . . . . 35 51

3 year RDD, unlimited supplement . . . . . . . . . . . . . . . . 38 51

Table 37. Variances of an RDD sample of 1,000 and 2,000 households per year for two designs, two different percentages, and for all adults

and the rarest cell

Variance

One adult per household1

All adults 45–64 year old males2

1 year 3 years 1 year 3 years

p = .50 p = .10 p = .50 p = .10 p = 0.50 p = 0.10 p = 0.50 p = 0.10

Variance (n = 1,000) ............... 0.00030 0.00011 0.00010 0.00004 0.00204 0.00073 0.00068 0.00024

Standard error ................... 0.01732 0.01039 0.01000 0.00600 0.04518 0.02711 0.02608 0.01565

cv ........................... 0.03464 0.10392 0.02000 0.06000 0.09035 0.27105 0.05216 0.15649

Variance (n = 2,000) ............... 0.00015 0.00005 0.00005 0.00002 0.00102 0.00037 0.00034 0.00012

Standard error ................... 0.01225 0.00735 0.00707 0.00424 0.03194 0.01917 0.01844 0.01107

cv ........................... 0.02449 0.07348 0.01414 0.04243 0.06389 0.19166 0.03689 0.11066

All adults per household

Variance (n = 1,000) ............... 0.00015 0.00005 0.00005 0.00002 0.00092 0.00033 0.00031 0.00011

Standard error ................... 0.01211 0.00727 0.00699 0.00419 0.03032 0.01819 0.01751 0.01050

cv ........................... 0.02422 0.07265 0.01398 0.04195 0.06064 0.18192 0.03501 0.10503

Variance (n = 2,000) ............... 0.00007 0.00003 0.00002 0.00001 0.00046 0.00017 0.00015 0.00006

Standard error ................... 0.00856 0.00514 0.00494 0.00297 0.02144 0.01286 0.01238 0.00743

cv ........................... 0.01712 0.05137 0.00989 0.02966 0.04288 0.12864 0.02476 0.07427

1The design effect for sampling one adult per household is assumed to be 1.2. This is based on results of 18,500 interviews from the National Household Education Survey.

2Males 45 to 64 years old are the rarest age-by-sex cell and make up 14.7 percent of all adults (see Memorandum #56R).

3From Memorandum #66 the average number of adults per household is 1.85. Therefore this design anticipates interviews with 1,850 (3,700) adults. For the 45- to 64-year-old males there is no

noticeable effect of clustering since there is only one such male in almost all households. For all adults there is an additional design effect to consider. Based on the calculations in Memorandum #40,

a conservative estimate for the intrahousehold correlation is 0.1. This value was used for the bottom left part of this table.

NOTE: These variances are independent of the area sample size in the State.Therefore these estimates apply to all States using either the current or proposed sample designs.

Series 2, No. 126 [ Page 53
and Subgroups (22) provide estimates of

the differences between telephone and

nontelephone households for selected

NHIS characteristics. The largest and

smallest percent bias shown for

(approximately) each of the three p

values previously mentioned was

determined (and the national bias was

assumed to apply to all States). (These

biases assume that no poststratification

by income is done for the RDD

estimator. To the extent that this is done,

the impact of these biases will be

reduced.) The minimum relative bias for

all three incidence levels was near zero.

The maximum bias varied by incidence

rate. The maximum bias for p = 0.03

was 3.6 percent, for p = 0.10 it was

28.9 percent, and for p = 0.30 it was

16.7 percent.

Table 39 shows, for p = 0.10 and

0.30, the size RDD supplement needed

in a State to combine with the NHIS

area-permit sample and produce

accuracy equivalent to that of a

stand-alone RDD sample of 1,000

households. The key determinant of the

size RDD supplement required by the

dual-frame estimator is the level of the

bias. For characteristics with small

biases, the size supplements for a State

are almost identical, regardless of

incidence rate and of whether all six

cells are wanted or only one. In fact,

when the bias is zero, they are identical.

For p = 0.03 the largest bias was only

3.6 percent; thus, the results closely

matched the low-bias results in table 39.

When the bias is small, a dozen

States do not require any telephone

supplementation. That is, the NHIS

sample in those States is more accurate

than an RDD sample of 1,000

households. In 36 of the 51 States

(including the District of Columbia), the

entire supplement for all age and sex

cells would be less than 1,000

households. In another 13 States, the

supplement would have to be larger than

1,000; for 1 State it would exceed 7,000

households.

For two States, Alaska and

Wyoming, no telephone sample could

reduce the variability of the estimator

sufficiently to match the accuracy of

1,000 telephone interviews. This is

because the small number of NHIS

interviews that must represent all of the

nontelephone households in the State

receive such a large weight that the

design effect from differential weights

keeps the variance of the dual-frame

estimator larger than that of the 1,000

RDD interview estimator. To a lesser

extent, it is this same variation in

weights that causes the other 13 States’

supplements to exceed 1,000

households.

For these 13 States whose

supplements exceed 1,000 households, it

is important to remember that the extra

interviews are to ensure the desired

level of accuracy, regardless of the

actual telephone or nontelephone bias in

the State. The stand-alone RDD

estimator will achieve the desired

accuracy only if its bias is no greater

than that reflected by the national

estimates. Given that this survey collects

a great number of variables, each of

which has different biases (which may

vary over time), these are likely to be

items, and States in which biases are

much greater than would be anticipated

from studies such as Trends in United

States Telephone Coverage Across Time

and Subgroups (22).

For characteristics with high biases,

the supplemental sample sizes for the

dual-frame estimator are even smaller.

This is because the larger bias inflates

the RRMSE of the stand-alone RDD

estimator. For a given incidence rate and

bias, the main determinant of the

supplement size is the telephone

coverage rate, not the NHIS sample

size. In all of the high-bias situations, at

least 45 of the States required

supplements smaller than 1,000

households, and at least 20 needed no

supplementation at all. When only one

cell per State was needed, all States

required supplements smaller than 1,000

households.

The next analysis compared the

precision (variance) of the stand-alone

RDD estimator with a minimum

Table 38. The number of States, under the proposed redesign and p = 0.1, for which the

unbiased composite estimates for certain cells will have cv’s greater than 30 or 15 percent

Sample

Number of States with

cv > 30 percent

Number of States with

cv > 15 percent

Total

adults

Males

45–64

Total

adults

Males

45–64

One adult per 1,000 households, 1 year . . . . . . . . . . 0 8 1 47

One adult per 2,000 households, 1 year . . . . . . . . . . 0 3 1 42

All adults in 1,000 households, 1 year . . . . . . . . . . . 0 3 1 42

All adults in 2,000 households, 1 year . . . . . . . . . . . 0 2 0 26

One adult per 1,000 households, 3 years . . . . . . . . . 0 0 0 34

One adult per 2,000 households, 3 years . . . . . . . . . 0 0 0 24

All adults in 1,000 households, 3 years . . . . . . . . . . . 0 0 0 23

All adults in 2,000 households, 3 years . . . . . . . . . . . 0 0 0 11

Table 39. Number of States, by the size RDD supplement needed to derive a dual-frame NHIS estimator of accuracy similar to stand-alone

1,000 RDD interviews

Size of RDD

supplemental sample

Low bias High bias

All six collapsed age/sex cells All adults one combined cell All six collapsed age/sex cells All adults one combined cell

p = 0.10

(Bias = 1.6)

p = 0.30

(Bias = 0.0%)

p = 0.10

(Bias = 1.6%)

p = 0.30

(Bias = 0.0%)

p = 0.10

(Bias = 28.9%)

p = 0.30

(Bias = 16.7%)

p = 0.10

(Bias = 28.9%)

p = 0.30

(Bias = 16.7%)

None needed . . . . . . . . . . 12 12 12 12 20 25 33 35

1–999 . . . . . . . . . . . . . . 24 24 24 24 25 22 18 16

> 1,000 . . . . . . . . . . . . . 13 13 13 13 6400

No amount can help ..... 2 2 2 2 0000

Page 54 [ Series 2, No. 126
variance (but potentially biased)

dual-frame estimator. This dual-frame

estimator is different from that

considered in earlier tables. All

interviews are given equal weights to

minimize the variance. This

underrepresents households without

telephones, however, because they are

included only in the sample of NHIS

households. Thus the estimator will only

partially reduce the bias that is usually

present in the stand-alone RDD

estimator. (The percent reduction in bias

is a function of the relative sample sizes

from NHIS and RDD, independent of

the actual bias in the RDD estimate.) By

restricting the comparison to matching

the estimators’ variance, the RDD

supplement will be between 0 and 1,000

interviews for each State. If the NHIS

area sample in the State provides an

estimate with smaller variance than that

of the stand-alone RDD estimate, no

RDD supplement is needed. If the NHIS

area sample is practically nonexistent,

the dual-frame estimator will resemble

the stand-alone estimator and require

1,000 interviews.

Table 40 summarizes the results. No

RDD supplement is required in 12

States, because the NHIS area sample in

the States provides smaller variances

than the stand-alone RDD sample. These

are the same 12 States that did not

require RDD supplementation in

table 39 under the assumption of low

nontelephone bias. When no

supplementation is used, the bias is

obviously completely eliminated because

the NHIS sample is unbiased. For

another 15 States, the supplemental

sample size would be fewer than 500

interviews. The bias of the dual-frame

estimator in these States will be from 46

to 83 percent less than that of the

stand-alone estimator. In the remaining

24 States, where the supplement would

require more than 500 interviews, the

bias reduction is from 6 to 43 percent.

For the final analysis, two variables

were chosen that are of concern as part

of the Healthy People 2000 Objectives:

Percent of persons currently smoking

cigarettes and percent of persons under

65 years of age without private health

insurance. Estimates from the 1985

NHIS are available for both of these

variables for telephone and

nontelephone households from Trends in

United States Telephone Coverage

Across Time and Subgroups (22). The

latter variable represents a worst-case

scenario for the stand-alone RDD

estimator, because it has the highest

relative bias (telephone households

relative to all households) for variables

reported with similar incidence rates.

Cigarette smoking has a more moderate

bias. For variables without any

telephone coverage bias, examination of

mean square errors would be equivalent

to the variances used in this analysis.

Information for 1985 (in percent) on

these two variables, at the national level,

is reported in table 41.

For each State, table 42 compares

the RDD stand-alone estimator with a

dual-frame estimator for the following

characteristics: Coverage rate (telephone

coverage rates are from the 1980 census,

and NHIS coverage rate is assumed to

be 100 percent); response rate (a

weighted average of RDD and NHIS

response rates weighted by the two

actual sample sizes); variance (assuming

p = 0.30, the same relative relationship

between the variances would hold if any

other incidence rate is assumed); mean

square error (MSE) for currently

smoking cigarettes; and MSE for

persons under 65 years of age without

private health insurance.

The dual-frame estimator used for

the variance comparison is the minimum

variance estimator also used in table 40.

If the characteristic to be estimated was

known not to suffer from significant

bias, a minimum variance (potentially

biased) dual-frame estimator could be

used when comparing MSE’s. If the

characteristic (such as the two

preceding) was biased, it is not obvious

whether it would be better to use the

minimum variance or unbiased versions

of the dual-frame estimator. For this

analysis the version with the best

variance or MSE (depending on the

table column) is used based on the

telephone coverage rate and NHIS

sample size in each State. In all but one

case, the unbiased dual-frame estimator

has a smaller MSE than the potentially

biased dual-frame estimator. The one

exception is for people currently

smoking cigarettes in North Dakota.

Cigarette smoking is subject to a smaller

bias than percent without private health

insurance (4.5, compared with 16.7,

percent relative bias). North Dakota also

has the third-smallest NHIS sample size

combined with a high telephonecoverage rate. Thus, the overall bias is

small, the bias in the State will be less

than average, and the sample of NHIS

households with telephones is small.

Examination of table 42 shows that

for every State, the use of a dual-frame

estimator (including NHIS data) can

improve the estimates that would be

obtained from a stand-alone RDD

survey of 1,000 households. In general,

the dual-frame estimator used in

comparing variances in table 40 is not

the same dual-frame estimator used in

Table 40. Summary of the number of States and percent reduction in bias corresponding to

different size RDD supplementation of a dual-frame estimator

Dual-frame RDD

supplemental sample size

Number

of

States

Percent of

reduction in bias

of RDD with

dual frame

0 . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 100

1–500 . . . . . . . . . . . . . . . . . . . . . . . . 15 46–83

501–1,000 . . . . . . . . . . . . . . . . . . . . . 24 6–43

Table 41. Differences between telephone and nontelephone households for percent of

persons who smoke, and persons under 65 years old without private health insurance

Characteristic

Nontelephone

households

Telephone

households

All

households

Persons who currently smoke cigarettes .......... 49.6 28.8 30.1

Persons under 65 years old without private health

insurance ............................ 64.9 19.8 23.1

Series 2, No. 126 [ Page 55
Table 42. Comparison of 1,000 RDD interviews alone (sampling one adult per household), with dual-frame estimators (from proposed

design), and p = 30 percent

Rank State

Projected

population

year 2000

(000’s)1

Actual NHIS

sample using

1 year’s data

all adults1

Coverage

rate (%)

Response

rate (%)

Variance

(× 1,000)2

Persons who currently

smoke cigarettes

MSE (× 1,000)3

Persons under 65 years old

without private health

insurance MSE (× 1,000)3

RDD

Dual

frame RDD

Dual

frame RDD

Dual

frame RDD

Dual

frame RDD

Dual

frame

1 California ............ 33,500 9,394 95 100 80 93 0.252 0.036 0.372 0.036 0.778 0.030

2 Texas .............. 20,211 5,673 91 100 80 94 0.252 0.054 0.634 0.054 2.010 0.046

3 New York . . . . . . . . . . . . 17,986 4,427 93 100 80 88 0.252 0.063 0.494 0.063 1.353 0.053

4 Florida .............. 15,415 3,529 90 100 80 92 0.252 0.073 0.669 0.074 2.174 0.062

5 Illinois .............. 11,580 2,577 95 100 80 89 0.252 0.089 0.370 0.090 0.768 0.076

6 Pennsylvania .......... 11,502 2,016 96 100 80 91 0.252 0.097 0.327 0.098 0.564 0.083

7 Ohio ............... 10,629 1,876 94 100 80 90 0.252 0.101 0.403 0.103 0.923 0.087

8 Michigan ............ 9,250 1,761 96 100 80 90 0.252 0.107 0.325 0.108 0.555 0.092

9 New Jersey ........... 8,546 1,906 95 100 80 87 0.252 0.107 0.349 0.109 0.669 0.092

10 Georgia ............. 7,957 1,678 88 100 80 88 0.252 0.112 0.853 0.117 3.037 0.099

11 North Carolina ......... 7,483 1,478 89 100 80 88 0.252 0.119 0.772 0.124 2.656 0.105

12 Virginia ............. 6,877 1,366 92 100 80 90 0.252 0.124 0.548 0.129 1.604 0.109

13 Massachusetts ......... 6,087 1,091 96 100 80 88 0.252 0.136 0.331 0.140 0.584 0.118

14 Indiana ............. 5,502 952 93 100 80 88 0.252 0.143 0.441 0.150 1.104 0.127

15 Missouri ............. 5,383 936 95 100 80 89 0.252 0.144 0.380 0.150 0.815 0.127

16 Maryland ............ 5,274 1,149 96 100 80 86 0.252 0.137 0.329 0.141 0.576 0.119

17 Tennessee ........... 5,266 970 90 100 80 88 0.252 0.143 0.674 0.154 2.198 0.130

18 Washington ........... 4,991 849 94 100 80 86 0.252 0.150 0.392 0.157 0.871 0.133

19 Wisconsin ............ 4,784 792 97 100 80 88 0.252 0.153 0.299 0.157 0.435 0.133

20 Arizona ............. 4,618 1,041 89 100 80 88 0.252 0.147 0.758 0.161 2.591 0.136

21 Louisiana ............ 4,516 1,021 89 100 80 88 0.252 0.145 0.762 0.157 2.613 0.133

22 Minnesota ............ 4,490 686 97 100 80 87 0.252 0.158 0.302 0.164 0.449 0.139

23 Alabama ............. 4,410 899 87 100 80 88 0.252 0.150 0.973 0.168 3.603 0.142

24 South Carolina ......... 3,906 838 87 100 80 86 0.252 0.155 0.935 0.176 3.425 0.149

25 Colorado ............ 3,813 797 94 100 80 84 0.252 0.161 0.422 0.172 1.013 0.146

26 Kentucky ............ 3,733 610 88 100 80 86 0.252 0.167 0.861 0.194 3.074 0.164

27 Connecticut ........... 3,445 672 97 100 80 86 0.252 0.168 0.302 0.175 0.450 0.148

28 Oklahoma ............ 3,376 579 92 100 80 85 0.252 0.172 0.524 0.192 1.490 0.163

29 Oregon ............. 2,877 480 83 100 80 85 0.252 0.181 1.449 0.240 5.839 0.203

30 Mississippi ........... 2,877 654 93 100 80 86 0.252 0.170 0.436 0.186 1.080 0.158

31 Iowa ............... 2,549 394 96 100 80 85 0.252 0.188 0.315 0.205 0.511 0.173

32 Arkansas ............ 2,529 461 87 100 80 85 0.252 0.185 0.933 0.233 3.414 0.197

33 Kansas ............. 2,529 445 95 100 80 85 0.252 0.186 0.363 0.206 0.735 0.175

34 Utah ............... 1,991 330 95 100 80 84 0.252 0.199 0.379 0.231 0.809 0.195

35 New Mexico .......... 1,968 587 86 100 80 87 0.252 0.185 1.098 0.238 4.189 0.202

36 West Virginia .......... 1,722 260 89 100 80 84 0.252 0.205 0.745 0.286 2.532 0.242

37 Nebraska ............ 1,556 254 96 100 80 83 0.252 0.208 0.314 0.240 0.507 0.203

38 Hawaii .............. 1,345 239 95 100 80 84 0.252 0.212 0.357 0.260 0.707 0.220

39 New Hampshire ........ 1,333 198 94 100 80 83 0.252 0.215 0.398 0.276 0.900 0.234

40 Nevada ............. 1,303 271 90 100 80 84 0.252 0.211 0.663 0.301 2.146 0.254

41 Maine .............. 1,271 186 93 100 80 82 0.252 0.216 0.491 0.301 1.337 0.255

42 Rhode Island .......... 1,049 183 95 100 80 83 0.252 0.220 0.350 0.283 0.672 0.240

43 Idaho ............... 1,047 178 93 100 80 83 0.252 0.221 0.471 0.318 1.242 0.269

44 Montana . . . . . . . . . . . . . 794 120 92 100 80 82 0.252 0.228 0.520 0.386 1.473 0.326

45 Delaware . . . . . . . . . . . . 734 148 95 100 80 82 0.252 0.227 0.356 0.317 0.704 0.268

46 South Dakota . . . . . . . . . 714 105 94 100 80 82 0.252 0.231 0.426 0.376 1.032 0.318

47 Alaska . . . . . . . . . . . . . . 687 112 83 100 80 81 0.252 0.231 1.459 0.616 5.887 0.521

48 District of Columbia . . . . . . 634 205 95 100 80 82 0.252 0.219 0.351 0.278 0.679 0.235

49 North Dakota . . . . . . . . . . 629 93 96 100 80 81 0.252 0.233 0.328 0.309 0.572 0.291

50 Vermont . . . . . . . . . . . . . 591 87 93 100 80 81 0.252 0.234 0.449 0.426 1.139 0.360

51 Wyoming . . . . . . . . . . . . 489 84 92 100 80 81 0.252 0.236 0.554 0.515 1.634 0.435

1The actual NHIS sample sizes come from table 1 of Memorandum #56R.

2The RDD design effect for sampling one adult per household is assumed to be 1:2. This is based on results of 18,500 interviews from the National Household Education Survey. The State-specific

design effects for the NHIS were computed in Memorandum #56R. The dual-frame estimator used for the variance comparison is the minimum variance (potentially biased) estimator also used in

table 40.

3The dual-frame estimator used in these columns is the minimum of these unbiased and minimum variance estimators.

Page 56 [ Series 2, No. 126
comparing mean square errors (except

for North Dakota). The rationale is that,

given either basis for choosing an

estimator—minimizing variance or

minimizing MSE—it is possible to

construct a dual-frame estimator with

significantly better properties than those

of the stand-alone RDD estimator.

The bias discussed in this chapter

arises from differences in the health

characteristics of households with and

without telephones. A second source of

bias is adjusting for nonresponse. An

estimator based on a higher response

rate will minimize the impact of this

second source of nonresponse. The

following summarizes the findings

comparing stand-alone RDD and

dual-frame estimation: + The coverage rate is from 3 to

17 percent higher.

+ The response rate is from 1 to

14 percent higher.

+ The dual-frame variance is always

lower; for 12 of the States, it is less

than one-half that of the RDD

estimator.

+ The dual-frame mean square error is

always lower. For cigarette smokers

in 32 States, and for those without

private health insurance in every

State, it is less than one-half that of

the RDD estimator.

Series 2, No. 126[ Page 57
Chapter 12.

Empirical Comparison

of Model-Dependent

Estimators

This chapter describes an empirical

comparison of model-dependent

estimators that could potentially

be used to produce State-level estimates

from NHIS. Five model-dependent

estimators were examined along with

the direct design-unbiased estimator.

Their point estimates for each State as

well as their estimated mean square

errors (MSE’s) were compared using the

data collected in the 1988 NHIS. The

five estimators were the synthetic

estimator originally developed for NHIS

(23), a composite estimator of the

synthetic and direct estimators (24), and

three versions of the generalized

synthetic estimator (GSE) (25).

The GSE loosens the restrictive

assumptions on which the traditional

synthetic estimator depends in order to

produce unbiased estimates. In

particular, although the synthetic

estimator requires that the mean for a

population subgroup (for example, white

females 18–44 years of age) be the

same in every State (if this assumption

approximately holds, then the synthetic

estimator will be approximately

unbiased), the GSE requires only that

they have the same expectation. The

Bayesian statistical term for this is that

the State/subgroup means for a

particular variable be exchangeable (26).

The GSE also allows one to incorporate

prior information into the estimator and

allows the subgroup means in some

States to have smaller standard

deviations than in others.

NHIS collects data on dozens of

health items. For this analysis two items

were examined: The mean of a count

variable and a proportion. The count

variable was the average number of

doctor visits in the past 12 months, and

the proportion was the percentage with a

self-perceived poor health status. The

verbatim questions on the NHIS

questionnaire were as follows:

+ During the past 12 months, how

many times did (person’s name) see

or talk to a medical doctor or

assistant? (Do not count doctors

seen while an overnight patient in a

hospital.)

+ Would you say (your/his/her) health

in general is excellent, very good,

good, fair, or poor?

Estimators

The exact formulas for the six

estimators are described next.

1. Direct, design-unbiased, inflation

estimator

y

^

1i = ∑

j

∑

k

wijk yijk /∑

j

∑

k

wijk

(11)

where

yijk = response for the kth respondent,

in subgroup j, in State i

wijk = weight for the kth respondent,

in subgroup j, in State i

i = 1, 2, . . . I

j = 1, 2, . . . J

k = 1, 2, . . . Nij

2. Synthetic estimator. Sixteen

subgroups were used, four age

categories (0–19 years, 20–44 years,

45–64 years, 65 years or over), two race

categories (black, other than black), and

two sex categories (male, female).

y

^

2 i

. = ∑

j = 1

16 Nij y.j.

Ni.

(12)

where

Nij = 1990 census total population for

subgroup j in State j

y.j. = ∑i

∑

k

wijk yijk /∑

j

∑

k

wijk

Ni. = 1990 Census total population for

State i.

The predictive version of the synthetic

estimator was also examined. This uses

the observed values of the sampled

respondents and the preceding synthetic

estimator only for the nonsampled cases.

As with almost all national

household surveys, the sampling fraction

for NHIS is quite small, resulting in

insignificant differences between the two

forms of the synthetic estimator. Thus,

only the results from the more common

version given here are reported.

3. Composite estimator. This is a

linear combination of the first two

estimators where the weights are

proportional to the mean square errors

of the two estimators.

y

^

3i. = ti y1i. + (1 − ti

) y

^

2i

(13)

where

ti = MSE (y

^

2i.)

Var (y1i.) + MSE (y

^

2i.)

Because many States contain only

one or two sampled PSU’s, direct

estimated variances would be highly

unstable. Thus, for these analyses, the

Var(y

^

1i

) were developed separately

for different types of questions (see the

first section of chapter 3). The

calculation of MSE(y

^

2i.)is described

in the second section of this chapter.

Before discussing the final three

estimators, it is necessary to discuss the

equation for the GSE. (See Small Area

Estimation: A Bayesian Perspective (25)

and Small Area Estimation for the

National Health Interview Survey (27)

for more detail.) GSE is derived based

on two assumptions. The first is that

conditional on the mean and variance

for State i and subgroup j, the Yijk are

exchangeable within their State/

subgroup and are independent between

States/subgroups. The second

assumption is that for each State/

subgroup, the mean and variance are

exchangeable across States, and they are

independent between subgroups. Under

these assumptions, GSE is the posterior

mean for State i, given the sample s

containing data y.

E (Y..|(s,y)) =

3ni. yi.. +∑

j = 1

J

(Nij − nij)z

μ

^

.j vp + mj

*

vs

vp + vs 4/Niz

(14)

Page 58 [ Series 2, No. 126
where

ni = sample size in State i

yi.. = ∑i

∑

j

wijk yijk /∑i

∑

j

wijk

nij = sample size in subgroup j and

State j

μˆ.j = best linear unbiased estimate

(BLUE) for the mean of

subgroup j

vp = a priori variance on the prior

mean for subgroup j

mj

* = prior mean for subgroup j

vs = expected sampling variance of the

sample mean for subgroup j

As with any Bayesian prediction of

finite populations, the GSE uses the

observed data and estimates for only the

unobserved values. The right-hand side

of equation 14 is also seen to contain a

weighted average of a sample estimate

and a prior mean separately for each

subgroup. If the a priori variance, vp, is

much larger than the expected sampling

error, vs, GSE reduces to

E(Yizz |(s,y))=3niz yizz+∑

j = 1

J

(Nij − nij) μ^

.j4/Niz

(15)

In general, the BLUE for the mean of

subgroup j is a weighted average of

every State average for subgroup

j, yij., where the weights are inversely

proportional to the sampling variance

for that State/subgroup. If for any

subgroup the elemental variances

(σij

2

) are assumed to be equal in all States,

the BLUE is simply equal to y.j.. In this

case the GSE is equal to (the predictive

version of) the synthetic estimator.

The remaining three estimators, all

forms of the GSE, can now be described.

4. GSE with heteroscedastic

variances. It is desired to examine the

robustness of the traditional synthetic

estimator in view of the assumption that

the variances are equal across small

areas. It can be shown that for

proportions the State/subgroup variances

must be homoscedastic if their means

are assumed equal. Thus, this estimator

applies only to the doctor visit variable.

Assume the mean response for a given

subgroup is the same for all small areas

(to reflect the assumption used by the

synthetic estimator) and that the prior

variance is diffuse (at least relative

to vs). Assume that within a subgroup,

there are differences in the distributions

of doctor visits between those in central

cities and those who live elsewhere.

Estimates for subgroups in States that

have either almost all of their population

in central cities or not in central cities

would be subject to less variation than

for subgroups in States with a mixture.

In particular, assume the mean and

variance of each small area/subgroup is

[μij , σij

2

zij (1 − zij)]

where

zij = the proportion of the State’s black

(or nonblack) population living in

central cities (capped at 0.95)

Thus,

y

^

4i. = 3ni. yi. +∑

j =1

J

(Nij − nij) μˆj4 / Niz

(16)

where

μˆ.j = 3∑i = 1

I nij yij

zij (1 − zij)4

/3∑i = 1

I nij

zij (1 − zij)4

5. GSE with balanced prior and

sampling variances (16 subgroups). A

basic requirement for all Bayes

estimators is that the weights associated

with the BLUE and the prior mean must

be nonnegative. Thus, vp and vs must be

nonnegative. Further, when their exact

formulas are examined, the relative

magnitudes of these two terms are also

constrained (24, 26), partially as a

function of how evenly the population is

spread across all States. Define bj as the

midpoint in the allowable range in the

relative sizes of the prior and sampling

variances for each subgroup j. It is

interesting to note that when the

midpoint in the possible range of

relationships for the two variances is

chosen, the resulting estimator gives

greater weight to the prior mean. This

would be most appropriate when the

prior is based on more data (or greater

knowledge) than the current survey. In

this empirical examination, subgroups

are spread across 51 States (the

following formula is a function of I-1

and (I-1)2

, where I = 51), and the prior

mean is simply estimated by the mean

of the previous year’s NHIS.

y

^

5i = { ni

yi +∑

j = 1

J

(Nij − nij)z

3

μˆ.j (51/50bj− 1/50) + mj

*

(51/50bj)

(51/25bj− 1/50) 4} / Niz

(17)

Table 43. Values of bj for each of the 16 age/race/sex subgroups used in estimator 5

Age Race Sex bj

0–19 .................... Black Male 21.46

0–19 .................... Black Female 21.43

0–19 .................... Nonblack Male 22.26

0–19 .................... Nonblack Female 22.37

20–44 ................... Black Male 20.56

20–44 ................... Black Female 20.61

20–44 ................... Nonblack Male 21.26

20–44 ................... Nonblack Female 22.00

45–64 ................... Black Male 20.21

45–64 ................... Black Female 19.85

45–64 ................... Nonblack Male 23.50

45–64 ................... Nonblack Female 23.43

65 and over ............... Black Male 21.86

65 and over ............... Black Female 21.33

65 and over ............... Nonblack Male 23.09

65 and over ............... Nonblack Female 23.51

Series 2, No. 126 [ Page 59
Where for these analyses

μˆ.j = y.j. from the 1988 NHIS

mj

* = y.j. from the 1987 NHIS

Table 43 provides the values of bj for

each of the 16 subgroups. A value near

1 indicates that the subgroup is

concentrated in one State, and a value

near 51 implies that the subgroup is

evenly spread throughout the States.

6. GSE with balanced prior and

sampling variances (32 subgroups).

This estimator is of the same form as

estimator 5. To examine the impact on

the synthetic estimator when it is subject

to greater variability, however, this

estimator uses 32 subgroups. This

increases the sampling errors associated

with the subgroup means. The 32

subgroups are each of the 16 subgroups

used previously, subdivided into central

city and noncentral city. This examines

the possibility of significant differences

between members of a subgroup who

live in a central city versus outside the

central city. (It was not possible to

separate the 1987 NHIS data by central

city and noncentral city; thus the same

prior means were used for estimator 6

as for estimator 5.) Table 44 provides

the values of bj for each of the 32

subgroups. It is worth noting that

although the bj values used for estimator

5 only vary from 19.85 to 23.51, the bj

values used for estimator 6 vary from

14.30 to 25.92.

State-Specific Mean Square

Errors

Before discussing the empirical

results, it is necessary to describe briefly

a procedure for comparing the accuracy

of the estimators. Estimation of The

Error of Synthetic Estimates (28)

introduced the average mean square

error (across small areas) as a measure

of accuracy for model-based small area

estimates. Let y1i be the design-unbiased

inflation estimator for small area i;

y

^

fi be any model-based estimator

f for small area i; and Yi. be the true mean

for small area i.

1

I ∑

i = 1

I

(y1i − y

^

f i)2 =

1

I ∑

i = 1

I

[(y1i − yi.) − (y

^

f i − yi.)]2

= 1

I∑

i = 1

I

(y1i − yi.)

2 +

1

I∑

i = 1

I

(y

^

f i − yi.)

2

− 2

I ∑

i = 1

I

(y1i − yi.) (y

^

f i − yi.)

(18)

By taking the expectation of both sides

of equation 18

E 3

1

I∑

i = 1

I

(y1i − y

^

f i)2

4

= aveMSE (y1) + aveMSE (y

^

f

)

− E3

2

I∑

i = 1

I

(y1i − yi.) (y

^

f i − yi.)4

If the number of areas I is relatively

large, the covariance-like last term on

the righthand side will be quite small,

because E (y1i − yiz

) = 0 and as I → ∞, y1i

and y

^

fi become approximately

independent. Thus, for large I, this

equation is approximately

E 3

1

I∑

i = 1

I

(y1i − y

^

fi)2

4 ≈

aveVAR (y1) + aveMSE (y

^

f

)

and the approximate (for relatively large

I) average MSE may be written as

aveMSE (y

^

f

) ≈ E 3

1

I ∑

i = 1

I

(y1i − y

^

f i)2

4

− aveVAR (y1) (19)

Table 44. Values of bj for each of the 32 central city/age/race/sex subgroups used in

estimator 6

Central city Age Race Sex bj

Yes . . . . . . . . . . . . . . . . 0–19 Black Male 18.20

Yes . . . . . . . . . . . . . . . . 0–19 Black Female 18.16

Yes . . . . . . . . . . . . . . . . 0–19 Nonblack Male 14.83

Yes . . . . . . . . . . . . . . . . 0–19 Nonblack Female 14.92

Yes . . . . . . . . . . . . . . . . 20–44 Black Male 17.28

Yes . . . . . . . . . . . . . . . . 20–44 Black Female 17.12

Yes . . . . . . . . . . . . . . . . 20–44 Nonblack Male 14.30

Yes . . . . . . . . . . . . . . . . 20–44 Nonblack Female 14.85

Yes . . . . . . . . . . . . . . . . 45–64 Black Male 16.43

Yes . . . . . . . . . . . . . . . . 45–64 Black Female 15.72

Yes . . . . . . . . . . . . . . . . 45–64 Nonblack Male 16.17

Yes . . . . . . . . . . . . . . . . 45–64 Nonblack Female 16.18

Yes . . . . . . . . . . . . . . . . 65 and over Black Male 18.37

Yes . . . . . . . . . . . . . . . . 65 and over Black Female 17.58

Yes . . . . . . . . . . . . . . . . 65 and over Nonblack Male 16.81

Yes . . . . . . . . . . . . . . . . 65 and over Nonblack Female 17.05

No . . . . . . . . . . . . . . . . 0–19 Black Male 18.34

No . . . . . . . . . . . . . . . . 0–19 Black Female 18.26

No . . . . . . . . . . . . . . . . 0–19 Nonblack Male 25.17

No . . . . . . . . . . . . . . . . 0–19 Nonblack Female 25.28

No . . . . . . . . . . . . . . . . 20–44 Black Male 18.28

No . . . . . . . . . . . . . . . . 20–44 Black Female 18.17

No . . . . . . . . . . . . . . . . 20–44 Nonblack Male 24.01

No . . . . . . . . . . . . . . . . 20–44 Nonblack Female 24.70

No . . . . . . . . . . . . . . . . 45–64 Black Male 18.62

No . . . . . . . . . . . . . . . . 45–64 Black Female 18.68

No . . . . . . . . . . . . . . . . 45–64 Nonblack Male 25.92

No . . . . . . . . . . . . . . . . 45–64 Nonblack Female 25.79

No . . . . . . . . . . . . . . . . 65 and over Black Male 18.77

No . . . . . . . . . . . . . . . . 65 and over Black Female 18.41

No . . . . . . . . . . . . . . . . 65 and over Nonblack Male 24.85

No . . . . . . . . . . . . . . . . 65 and over Nonblack Female 25.29

Page 60 [ Series 2, No. 126
and an approximately unbiased estimate

of the average MSE is

1

I ∑

i = 1

I

(y1i − y

^

fi)2 − aveVAR (y1)

(20)

To estimate the average MSE, the first

term of equation 20 is computed by

taking the average squared difference

between the two estimators across all

small areas. The second term can be

computed for any item by calculating

the variances of the direct estimator in

each small area and taking their average.

With many sample designs, however,

there will be very few PSU’s in each

small area; therefore, the estimated

variances for direct estimators for such

small areas will be very unstable.

Averaging the estimated variances

across all small areas will provide a

stable estimate of average variance.

The approximately unbiased estimate of

the average MSE is simple to compute

and provides a design-based overall

measure of accuracy. Unfortunately, by

averaging across all small areas, it

overstates the error associated with areas

where the model fits well or for which

the sampling error is small (compared

with an average small area). Similarly, it

understates the error associated with

areas where the model fails or for which

the sampling error is large.

It would be far preferable to produce

small area-specific mean square errors

for model-based estimators. This would

provide smaller MSE’s in areas where

the model fits well or for which the

sampling error is small. It would also

provide larger MSE’s in small areas

where the model fails or for which the

sampling error is large. The following

develops a new procedure for estimating

State-specific MSE’s. The definition of

MSE for small area i of model-based

extimator y

^

f is

MSE(y

^

f i) = Var(y

^

f i) + Bias2

(y

^

f i)

(21)

Var(y

^

fi) can be calculated using

replicated variances (jackknife or

balanced repeated replication).

Replicated variances are frequently used

for survey estimates based on complex

survey designs and/or when weights

vary within a stratum. The changes in

the estimated statistic are examined as

different PSU’s are dropped from the

analysis, with other PSU’s

simultaneously given additional weight.

These procedures incorporate the

survey’s complex sample design into the

estimated variance and produce

estimates of precision for each small

area. Replicated variances of

model-based statistics are computed

using the same procedure as for direct

estimates of means or ratios.

Unfortunately, the lack of an

estimate of the ‘‘truth’’ for potentially

biased model-based estimators requires

the use of an average bias, in

conjunction with a State-specific

variance. (If the true value were known

for each small area, MSE’s could be

computed directly.)

aveBias2

(y

^

f

) = aveMSE(y

^

f

)

− aveVar(y

^

f

)

(22)

Combining equations 19 and 22 gives

aveBias2

(y

^

f

) = E3

1

I ∑

i =1

I

(y1i − y

^

f i)2

4

−aveVAR(y1) − aveVAR(y

^

f

) (23)

Using this average bias (equation

23), we can produce small area-specific

mean square errors by replacing the

definition of MSE (equation 21) with

MSE(y

^

f

) = Var(y

^

fi) + aveBias2 (y

^

f

).

(24)

To estimate equation 24, the

computations for the design-based small

area-specific MSE’s involve the

following six-step process:

Estimate

E 3

1

I ∑

i = 1

I

(y1 i − y

^

f i)2

4

with

1

I ∑

i = 1

I

(y1i − y

^

f i)2

(25)

and aveVar(y1) with 1

I ∑

i = 1

I

var(y1i)

(26)

Var(y

^

f i) are estimated using replicated

variances, call these estimates var(y

^

f i)

(27)

Estimate aveVar(y

^

f

) with 1

I ∑

i = 1

I

var(y

^

f i)

(28)

Then

aveBias2

(y

^

f

) = (eq. 25) − (eq. 26)

− (eq. 28) (29)

MSE(y

^

f i) = (eq. 27) + (eq. 29)

(30)

An examination of equation 24 reveals

that the six-step procedure will produce

improvements (that is, differentiation in

MSE’s for small areas) only when the

variance is a significant proportion of

MSE. This can occur under either of

two conditions. First, if the model used

to produce the model-based estimates is

a close approximation of reality, the bias

term will be small. Second, if the

model-based estimates depend heavily

on the sample in that small area and the

sample size of the survey in the small

area is small, the variances will be

large. It is important to note, however,

that these model-based estimators can

depend on factors other than the sample;

thus, a small sample size does not

necessarily imply a large variance. For

example, if a form of the generalized

synthetic estimator (equation 14) is used

that relies heavily on the prior

distribution, even when the sample size

for each subgroup is small, the estimator

will have little variance. Alternatively, if

the form of the GSE weights the sample

data heavily, the variance of the

estimator will increase and will increase

even more if the number of subgroups

into which the data are split increases.

In the ideal situation for producing

small area-specific mean square errors,

the variance term will dominate the bias

term. In such situations the root mean

square error can be used to provide

approximate confidence intervals for the

model-based estimates.

Series 2, No. 126 [ Page 61
Empirical Results

National Model

Tables 45 and 46 provide the

estimates derived for each State for each

of the estimators for number of doctor

visits and perceived poor health status,

respectively. The States are sorted in

descending order of the estimates using

estimator 1. Note that for two of the

States, North Dakota and Nebraska, no

sample was collected; and thus, no

direct design-unbiased estimate exists.

The largest and smallest estimates are

shaded for each estimator. Although the

model-based estimators are not

consistent with the design-unbiased

estimator, it is striking to note the

similarities between the different

model-based estimators. In particular for

doctor visits, the standard synthetic

estimator and all three generalized

versions found Alaska to have the

smallest estimated numbers. All but

estimator 6 found Florida to have the

largest estimated number (Florida was

second largest for estimator 6). The

different versions of the synthetic

estimator had very little difference,

generally less than 1 percent. This

robustness of the synthetic estimator to

heterogeneous variances and inclusion

of data from the prior year would be

comforting if this estimator were to be

used to produce State-level estimates,

and it is probably a result of the large

sample sizes from each subgroup.

Similar results are observed for

self-perceived poor health status. The

three versions of the synthetic estimator

each found Alaska to have the lowest

incidence rate and the District of

Columbia, the highest. It is intriguing to

note that these two States had direct

estimates that were very similar. Again,

the robustness of the synthetic estimator

for NHIS is a striking finding.

Many estimators based on

exchangeable models produce estimates

similar to those of composite estimators,

that is, a weighted average of the direct

estimator and the estimator developed

conditional on the truth of a particular

model (for example, the synthetic

estimator). The GSE assumes

exchangeability of State/subgroup means

across States for a given subgroup. It

therefore produces estimates that, in the

absence of strong prior information, are

more similar to the synthetic than to the

composite estimator.

One other finding from these tables

is the strong shrinkage toward the mean

in all of the model-dependent estimators.

Although the direct estimator spans a

range of more than 3.0 doctor visits per

year (2.685−6.036), each of the

model-dependent estimators has a span

of only 0.4 doctor visits. Similar results

are found for self-perceived poor health.

Table 47 shows the results of the

six-step process for computing MSE’s

for estimators 2 and 5 for average

number of doctor visits. The third row

(the Gonzalez-Waksberg average MSE)

is calculated by subtracting the second

from the first. The fourth row is the

average of the replicated variances for

the 51 States. The average bias (row 6,

step 5) is found by subtracting row 4

from row 3. Unfortunately, the average

variance across States is quite small

compared with the average bias (less

than 2 percent). As a result the largest

and smallest State-specific MSE’s are

very similar to the average MSE. For

the synthetic estimator, the

aveMSE = 0.1703, while the smallest

State-specific MSE is 0.1701 (Georgia)

and the largest is 0.1732 (the District of

Columbia). MSE’s of similar magnitude

with small variances were found for the

other estimators and for perceived poor

health. These results are not very

surprising as numerous examples have

shown that the magnitude of the bias of

the synthetic estimator is bigger than its

variability. This is compounded by the

fact that the sample size of NHIS is so

large (125,000 respondents) that the

sampling variation is quite small. For

most surveys where the sample size is

likely to be much smaller, the sampling

variation can be expected to play a more

important role, increasing the utility of

State-specific MSE’s.

Subnational Models

Given the large sample size of

NHIS, it is possible to divide the Nation

into groups of States having anticipated

similar biases with respect to the

variable of interest. When this is true,

the differences in MSE’s between these

groups can be large; and, for States in

groupings where the model fits well, the

average bias will be small and the

State-specific MSE’s will vary. This

allows for significantly smaller

approximate confidence intervals for

some States compared with other States.

The range of estimated mean values

may also increase when the

computations are based on data from a

restricted subset of States.

Three potential groupings of States

are examined. They are based on the

percent of the State’s population living

below the poverty level, the percent of

the population that is not black living in

central cities, and the percent of the

black population living in central cities.

Table 48 identifies which States were

classified in the high, medium, and low

categories in each of these groupings.

Although each State still only has

16 (or 32) subgroups, the national data

set is being split into 48 (or 96)

subgroups under each of these

subnational models. Thus, the subgroup

means will be more variable, and one

would expect the State-specific MSE’s

to be more variable.

One limitation when using the

average MSE of M. Gonzalez and J.

Waksberg (28) is that its computation

depends on taking the difference of two

estimates. It is quite possible that,

because of the errors associated with

these two estimates, the estimated

average MSE may be negative. Because

MSE’s are by definition nonnegative, it

may be impossible to produce estimated

MSE’s for some data sets. The same

holds for the six-step procedure

described earlier for computing

State-specific MSE’s. Unfortunately,

this problem is exacerbated when the

Nation is split into multiple groupings.

When the Nation is split into three

groups of States (as is the case for

each of the three groupings already

mentioned), there is a much greater

chance of producing negative

estimated MSE’s than when all States

are combined into one estimate. Every

time State-specific MSE’s were

calculated for each estimator for both

variables using the poverty-based

groupings, at least one of the three

groups of States produced a negative

estimated average MSE. Thus, the

Page 62 [ Series 2, No. 126
analyses are restricted to the latter two

State groupings.

Tables 49 and 50 provide the

estimates derived for each State for each

of the estimators for number of doctor

visits, based only on data from within

that State’s group of States, grouped by

percent of other-than-black and black

populations in central cities,

respectively. As with table 46, the States

are sorted in descending order of the

estimates for estimator 1, with the

largest and smallest estimate shaded for

each estimator. Again, it is striking to

note the similarities between the

different model-based estimators. With

States grouped by percentage of the

population that is not black in central

Table 45. Average number of doctor visits, by State

State

Designunbiased

inflation

estimator

(1)

Synthetic

estimator

(2)

Composite

estimator

(3)

Heteroscedastic

variances

(4)

GSE with

(16)

balanced

variances

(5)

GSE with

(32)

balanced

variances

(6)

Vermont .................. 6.036 3.889 4.600 3.912 3.904 3.893

Delaware ................. 5.648 3.856 4.178 3.898 3.895 3.873

Colorado ................. 4.868 3.831 4.477 3.858 3.851 3.859

District of Columbia ........... 4.706 3.852 4.121 3.967 3.987 4.084

Michigan ................. 4.594 3.852 4.461 3.890 3.888 3.890

Connecticut ................ 4.589 3.926 4.343 3.957 3.948 3.956

Arizona .................. 4.562 3.889 4.350 3.915 3.906 3.927

Nevada .................. 4.530 3.841 4.026 3.871 3.862 3.865

Massachusetts .............. 4.481 3.933 4.362 3.960 3.951 3.959

Rhode Island ............... 4.424 3.959 4.098 3.984 3.974 3.977

Montana .................. 4.394 3.911 4.159 3.933 3.925 3.920

Kentucky ................. 4.333 3.895 4.179 3.925 3.921 3.913

Pennsylvania ............... 4.204 3.967 4.175 4.000 3.993 3.994

Ohio .................... 4.201 3.896 4.162 3.930 3.926 3.930

Wyoming ................. 4.156 3.827 3.912 3.849 3.844 3.842

California . . . . . . . . . . . . . . . . . 4.111 3.817 4.093 3.848 3.841 3.850

Oklahoma ................. 4.104 3.898 4.041 3.927 3.923 3.928

New Mexico ............... 4.084 3.842 3.898 3.865 3.860 3.866

Maine ................... 4.075 3.928 3.981 3.949 3.941 3.933

Maryland ................. 4.045 3.811 3.970 3.863 3.861 3.841

Florida ................... 3.981 4.003 3.985 4.041 4.030 4.025

Tennessee ................ 3.950 3.876 3.931 3.916 3.915 3.924

Washington ................ 3.869 3.871 3.870 3.896 3.888 3.890

Arkansas ................. 3.840 3.908 3.867 3.949 3.949 3.937

New York . . . . . . . . . . . . . . . . . 3.828 3.902 3.836 3.943 3.937 3.958

Iowa .................... 3.821 3.957 3.878 3.980 3.971 3.973

New Jersey ................ 3.806 3.913 3.828 3.950 3.943 3.930

West Virginia ............... 3.734 3.975 3.888 3.999 3.992 3.981

Oregon .................. 3.704 3.928 3.800 3.951 3.941 3.943

Utah .................... 3.704 3.740 3.721 3.761 3.761 3.760

Wisconsin ................. 3.692 3.894 3.732 3.920 3.912 3.920

South Carolina .............. 3.677 3.780 3.730 3.838 3.843 3.801

Virginia .................. 3.671 3.819 3.704 3.864 3.864 3.859

Alabama .................. 3.638 3.846 3.698 3.899 3.903 3.893

Hawaii ................... 3.575 3.850 3.796 3.875 3.864 3.869

Kansas .................. 3.573 3.897 3.690 3.924 3.917 3.922

Missouri .................. 3.549 3.913 3.655 3.947 3.942 3.940

Mississippi ................ 3.489 3.763 3.674 3.829 3.840 3.793

Texas ................... 3.450 3.789 3.482 3.824 3.823 3.838

Illinois ................... 3.420 3.862 3.483 3.901 3.897 3.908

Louisiana ................. 3.410 3.754 3.502 3.813 3.819 3.810

Indiana .................. 3.380 3.885 3.516 3.915 3.910 3.920

South Dakota .............. 3.334 3.921 3.702 3.924 3.933 3.932

New Hampshire ............. 3.236 3.879 3.798 3.901 3.893 3.893

Idaho .................... 3.162 3.857 3.616 3.877 3.871 3.864

Alaska ................... 3.093 3.629 3.542 3.656 3.653 3.665

Minnesota ................. 3.034 3.885 3.258 3.908 3.900 3.899

Georgia .................. 3.000 3.755 3.190 3.809 3.812 3.787

North Carolina .............. 2.685 3.842 2.949 3.890 3.891 3.874

North Dakota . . . . . . . . . . . . . . . ... 3.908 3.908 3.931 3.920 3.923

Nebraska . . . . . . . . . . . . . . . . . ... 3.913 3.913 3.938 3.931 3.938

. . . Category not applicable.

NOTE: Shading indicates the largest and smallest estimates in the column.

Series 2, No. 126 [ Page 63
cities, the standard synthetic estimator

and all three generalized versions find

Alaska to have the smallest estimated

number of doctor visits and West

Virginia to have the largest estimated

number. The four model-based

estimators again agree on the States

with the largest and smallest number of

doctor visits when States are grouped by

percentage of black population in central

cities, but the extreme States are not the

same as with the first grouping; instead,

the District of Columbia is always the

highest and Mississippi, the lowest.

A major difference between the

estimates in tables 49 and 50 compared

with those in table 45 is that the

State-to-State range of model-dependent

Table 46. Percentage with perceived poor health status, by State

State

Designunbiased

inflation

estimator

(1)

Synthetic

estimator

(2)

Composite

estimator

(3)

GSE with

(16)

balanced

variances

(5)

GSE with

(32)

balanced

variances

(6)

West Virginia ............... 7.17 2.87 5.28 2.86 2.87

Mississippi ................ 6.28 3.19 5.28 3.18 3.23

Alabama .................. 5.42 3.13 4.94 3.13 3.14

Maine ................... 5.19 2.60 4.01 2.59 2.59

Arkansas ................. 5.10 3.07 4.49 3.07 3.08

Kentucky ................. 5.06 2.70 4.56 2.69 2.70

North Carolina .............. 4.68 2.99 4.48 2.99 3.01

Tennessee ................ 4.46 2.91 4.23 2.91 2.90

Oklahoma ................. 3.81 2.75 3.61 2.74 2.74

Louisiana ................. 3.52 2.96 3.44 2.96 2.97

Georgia .................. 3.44 2.81 3.38 2.81 2.83

South Carolina .............. 3.42 3.03 3.36 3.03 3.07

Indiana .................. 3.40 2.69 3.33 2.68 2.68

Arizona .................. 3.29 2.57 3.18 2.56 2.55

Virginia .................. 3.29 2.80 3.25 2.80 2.80

Texas ................... 3.02 2.49 3.00 2.48 2.47

Florida ................... 2.96 3.29 2.97 3.30 3.30

District of Columbia ........... 2.95 4.34 3.62 4.35 4.25

Alaska ................... 2.87 1.76 2.13 1.74 1.74

Idaho .................... 2.80 2.42 2.66 2.40 2.41

Ohio .................... 2.79 2.81 2.79 2.80 2.80

Michigan ................. 2.78 2.76 2.78 2.75 2.75

Missouri .................. 2.64 2.88 2.66 2.87 2.87

Delaware ................. 2.61 2.85 2.72 2.85 2.87

Oregon .................. 2.50 2.65 2.53 2.64 2.64

Wyoming ................. 2.41 2.32 2.36 2.30 2.30

Illinois ................... 2.40 2.82 2.42 2.82 2.81

California ................. 2.31 2.45 2.31 2.45 2.44

New York . . . . . . . . . . . . . . . . . 2.31 2.96 2.33 2.96 2.94

Kansas .................. 2.21 2.69 2.28 2.68 2.68

Massachusetts .............. 2.21 2.71 2.24 2.70 2.69

Montana .................. 2.10 2.59 2.27 2.58 2.58

Vermont .................. 2.09 2.45 2.24 2.44 2.44

New Jersey ................ 2.03 2.94 2.07 2.94 2.95

South Dakota .............. 2.00 2.63 2.23 2.62 2.62

Rhode Island ............... 1.97 2.78 2.19 2.77 2.77

Maryland ................. 1.84 2.91 1.91 2.92 2.93

Utah .................... 1.75 1.99 1.79 1.97 1.97

Nevada .................. 1.74 2.56 1.92 2.56 2.56

Pennsylvania ............... 1.69 3.02 1.72 3.01 3.01

New Mexico ............... 1.67 2.36 1.79 2.35 2.35

Iowa .................... 1.65 2.77 1.76 2.76 2.76

Washington ................ 1.44 2.50 1.50 2.49 2.49

Wisconsin ................. 1.38 2.64 1.45 2.63 2.62

Connecticut ................ 1.27 2.82 1.38 2.82 2.81

Colorado ................. 1.20 2.36 1.27 2.35 2.35

Minnesota ................. 1.16 2.51 1.23 2.49 2.49

Hawaii ................... 0.87 2.44 1.08 2.44 2.43

New Hampshire ............. 0.68 2.42 0.86 2.40 2.40

North Dakota . . . . . . . . . . . . . . . ... 2.59 2.59 2.58 2.58

Nebraska . . . . . . . . . . . . . . . . . ... 2.67 2.67 2.66 2.66

. . . Category not applicable.

NOTE: Shading indicates the largest and smallest estimates in the column.

Page 64 [ Series 2, No. 126
point estimates is much larger when the

States are grouped than when all States

used national subgroup estimates.

Table 51 demonstrates that for number

of doctor visits, the range of

model-dependent estimates across the

States doubles when the States are

grouped. For example, when data from

all States are combined, the GSE with

32 subgroups has a range of 0.41 (from

3.67 (Arkansas) to 4.08 (the District of

Columbia). When data only from within

a State’s grouping are used, this range is

increased to 0.76 for groupings that are

not black (3.42 (Arkansas) to 4.18 (West

Virginia)) and to 0.97 for black

groupings (3.48 (Mississippi) to 4.45

(the District of Columbia)). This is

important because one of the concerns

about the synthetic estimator is that

many analysts believe it ‘‘overshrinks’’

the estimates toward the national

average, underrepresenting the true

variability from small area to small area.

Similar results are observed for

self-perceived poor health status. With

States grouped by populations that are

not black in central cities (table 52), the

three versions of the synthetic estimator

each find Alaska to have the lowest

incidence rate and the District of

Columbia the highest. It is again

intriguing to note that these two States

had direct estimates that were very

similar. When grouped by black

populations in central cities (table 53),

Alaska still has the lowest rate, but

Mississippi has the highest rate.

Table 54 examines the robustness of

the GSE by comparing the variation among

the different model-dependent estimators

for each State. Small variation (as is found

when national subgroup estimates are

used) would indicate that the synthetic

estimator is robust to its assumptions that

are loosened in the various forms of the

GSE that are examined. For doctor visits,

the variation among the model-based

estimators is similar when States are

grouped by population other than black as

when all States are combined. When

grouped by percentage of the black

population in central cities, there is

significant variation among the

model-dependent estimates for the 11

States with more rural black populations.

Grouping the States also increases

the variation among model-dependent

estimates for percent with poor health in

each State. This variation is particularly

pronounced for States with large

percentages of its population that are not

black, or small percentages of its black

population, in central cities.

Thus, using subnational groupings

of States results in model-dependent

estimates that vary more from State to

State and are, in general, still consistent

across the form of estimator that is used.

In some States, however, the form of the

GSE can have a significant impact on

the estimate. The remainder of this

chapter examines the impact on MSE’s

of using the subnational groupings.

The six-step process for computing

MSE’s is recalculated averaging only

across those States in the same grouping.

The subgroup means are calculated

separately for each grouping. Tables 55–57

are demonstrative of results for the two

Table 47. State-specific mean square errors for number of doctor visits, using estimators 2

and 5

Synthetic

estimator

GSE with (16)

balanced

variances

(1) E (y1i. − y

^

fi.)

2 ............... .3777 .3754

(2) aveVar (y1) ................ .2074 .2074

aveMSE (yf

) ............... .1703 .1680

(4) aveVar (yf

) ................ .0020 .0003

aveVar (yf

) . . . . . . . . . . . . . . . 1.2 percent 0.1 percent

aveMSE

(5) aveBias2 (yf

) ............... .1684 .1678

(6) MSE (yf

)

Smallest ................ .1701 (Georgia) .1680 (many)

Largest ................. .1732 (District of Columbia) .1684 (District of Columbia)

RMSE (yf

)................. .412–.416 .410

Table 48. Groupings of States by common expected biases

Percentage of population below poverty in 1988:

0.0–8.7 .................... Connecticut, New Jersey, New Hampshire, Wisconsin,

Kansas, Vermont, Massachusetts, Delaware, Nevada,

Washington

9.4–17.6 ................... Iowa, Wyoming, Maryland, Rhode Island, Utah, Indiana,

Nebraska, Pennsylvania, Oregon, Virginia, Alaska, Hawaii,

Minnesota, North Dakota, Michigan, Ohio, Colorado, Idaho,

North Carolina, Illinois, Michigan, California, Maine, New York,

Florida, Georgia, Arizona, South Dakota, Montana, District of

Columbia, South Carolina, Oklahoma, Kentucky

17.9–27.2 . . . . . . . . . . . . . . . . . . West Virginia, Tennessee, Texas, Alabama, Arkansas,

Louisiana, New Mexico, Mississippi

Percentage of 1990 nonblack population in central city:

0–15 ...................... Delaware, Vermont, South Carolina, Mississippi, West Virginia,

Georgia, New Jersey, Maryland, Idaho, Maine, Kentucky,

Michigan

16–37 . . . . . . . . . . . . . . . . . . . . . All 34 others

38–100 .................... Alaska, New York, Texas, Arizona, District of Columbia

Percentage of 1990 black population in central city:

0–35 ...................... Mississippi, South Carolina, Hawaii, Vermont, North Dakota,

Idaho, West Virginia, Maine, Georgia, Delaware, North

Carolina

36–77 ..................... Arkansas, Maryland, Montana, Utah, Florida, Nevada, New

Mexico, New Jersey, South Dakota, Alabama, Kentucky,

Louisiana, Virginia, New Hampshire, Wyoming, Washington,

California, Missouri, Oklahoma, Colorado, Alaska, Texas,

Rhode Island, Kansas, Ohio, Arizona, Tennessee, Minnesota,

Pennsylvania, Connecticut, Iowa

78–100 .................... Illinois, Michigan, Oregon, Massachusetts, New York,

Nebraska, Indiana, Wisconsin, District of Columbia

Series 2, No. 126 [ Page 65
different groupings of States examined for

each of the two variables.

Table 55 demonstrates that grouping

States with similar expected biases can

result in State-specific MSE’s where the

variance is a nontrivial component of

the MSE. For States with a high

percentage of their population that is

not black residing in central cities, the

variance of the synthetic estimator is

on average 21 percent of the MSE’s.

When all States are taken together, the

root mean square error (RMSE) varies

only from 0.412 to 0.416. When States

are grouped by the distribution of

population that is not black, RMSE’s

vary from 0.190 in Utah to 0.717 in

West Virginia, reflecting the fact that

Table 49. Average number of doctor visits, with States grouped by percentage of nonblack population in central cities

State

Designunbiased

inflation

estimator

(1)

Synthetic

estimator

(2)

Composite

estimator

(3)

Heteroscedastic

variances

(4)

GSE with

(16)

balanced

variances

(5)

GSE with

(32)

balanced

variances

(6)

Vermont .................. 6.036 4.055 5.241 4.079 4.099 4.097

Delaware ................. 5.648 3.999 4.653 4.041 4.044 4.042

Colorado ................. 4.868 3.831 4.100 3.822 3.861 3.876

District of Columbia ........... 4.706 3.807 3.928 3.971 4.051 4.053

Michigan ................. 4.594 3.997 4.554 4.035 4.052 4.090

Connecticut ................ 4.589 3.923 4.099 3.919 3.958 3.969

Arizona .................. 4.562 3.767 4.074 3.799 3.713 3.703

Nevada .................. 4.530 3.839 3.888 3.832 3.872 3.881

Massachusetts .............. 4.481 3.931 4.170 3.923 3.961 3.973

Rhode Island ............... 4.424 3.956 3.995 3.947 3.982 3.986

Montana .................. 4.394 3.908 3.997 3.895 3.930 3.921

Kentucky ................. 4.333 4.050 4.290 4.080 4.101 4.110

Pennsylvania ............... 4.204 3.961 4.107 3.959 3.997 3.995

Ohio .................... 4.201 3.892 4.074 3.891 3.932 3.935

Wyoming ................. 4.156 3.826 3.849 3.813 3.850 3.847

California . . . . . . . . . . . . . . . . . 4.111 3.816 4.042 3.812 3.850 3.867

Oklahoma ................. 4.104 3.894 3.963 3.890 3.929 3.938

New Mexico ............... 4.084 3.840 3.855 3.829 3.866 3.879

Maine ................... 4.075 4.091 4.081 4.115 4.132 4.134

Maryland ................. 4.045 3.940 4.031 3.990 3.991 3.997

Florida ................... 3.981 3.994 3.987 3.997 4.034 4.030

Tennessee ................ 3.950 3.871 3.901 3.878 3.922 3.931

Washington ................ 3.869 3.870 3.870 3.860 3.897 3.899

Arkansas ................. 3.840 3.900 3.885 3.907 3.950 3.937

New York . . . . . . . . . . . . . . . . . 3.828 3.801 3.820 3.857 3.779 3.781

Iowa .................... 3.821 3.954 3.923 3.942 3.976 3.980

New Jersey ................ 3.806 4.057 3.826 4.096 4.093 4.101

West Virginia ............... 3.734 4.131 3.881 4.159 4.177 4.177

Oregon .................. 3.704 3.925 3.876 3.912 3.947 3.951

Utah .................... 3.704 3.741 3.734 3.729 3.766 3.763

Wisconsin ................. 3.692 3.891 3.798 3.883 3.920 3.931

South Carolina .............. 3.677 3.910 3.739 3.964 3.968 3.936

Virginia .................. 3.671 3.817 3.754 3.827 3.872 3.868

Alabama .................. 3.638 3.837 3.768 3.856 3.904 3.894

Hawaii ................... 3.575 3.848 3.835 3.837 3.872 3.884

Kansas .................. 3.573 3.894 3.806 3.887 3.924 3.930

Missouri .................. 3.549 3.908 3.785 3.908 3.948 3.942

Mississippi ................ 3.489 3.886 3.651 3.946 3.958 3.921

Texas ................... 3.450 3.665 3.508 3.713 3.634 3.630

Illinois ................... 3.420 3.858 3.613 3.863 3.904 3.917

Louisiana ................. 3.410 3.747 3.624 3.772 3.824 3.816

Indiana .................. 3.380 3.881 3.698 3.877 3.917 3.929

South Dakota .............. 3.334 3.918 3.852 3.904 3.938 3.935

New Hampshire ............. 3.236 3.879 3.860 3.865 3.902 3.901

Idaho .................... 3.162 4.017 3.491 4.043 4.087 4.089

Alaska ................... 3.093 3.456 3.437 3.495 3.416 3.417

Minnesota ................. 3.034 3.884 3.567 3.872 3.908 3.905

Georgia .................. 3.000 3.888 3.090 3.936 3.947 3.944

North Carolina .............. 2.685 3.837 3.356 3.851 3.897 3.881

Nebraska . . . . . . . . . . . . . . . . . ... 3.910 3.910 3.901 3.937 3.949

North Dakota . . . . . . . . . . . . . . . ... 3.906 3.906 3.893 3.925 3.930

. . . Category not applicable.

NOTE: Shading indicates the largest and smallest estimates in the column.

Page 66 [ Series 2, No. 126
the model-dependent estimators are

able to produce estimates for some

States with much greater accuracy

than for others.

Table 56 demonstrates results that

do not differentiate quite as strongly

between States. Using the generalized

synthetic estimator with 16 balanced

variances to estimate percent with

perceived poor health without

subgrouping the States, the variance of

the model-based estimator is only

0.4 percent of its MSE, and the RMSE

varies only from 1.16 to 1.17 in any

State. When the States are grouped

according to the percent of the black

population in central cities, however,

the State-specific MSE’s are different.

Table 50. Average number of doctor visits, with States grouped by percentage of black population in central cities

State

Designunbiased

inflation

estimator

(1)

Synthetic

estimator

(2)

Composite

estimator

(3)

Heteroscedastic

variances

(4)

GSE with

(16)

balanced

variances

(5)

GSE with

(32)

balanced

variances

(6)

Vermont .................. 6.036 3.471 5.369 3.703 3.856 3.859

Delaware ................. 5.648 3.336 4.622 3.559 3.699 3.709

Colorado ................. 4.868 3.897 3.897 3.898 3.893 3.900

District of Columbia ........... 4.706 4.230 4.342 4.282 4.440 4.449

Michigan ................. 4.594 3.877 4.410 3.934 3.878 3.873

Connecticut ................ 4.589 3.992 3.992 3.991 3.991 3.998

Arizona .................. 4.562 3.954 3.945 3.953 3.948 3.969

Nevada .................. 4.530 3.904 3.904 3.903 3.903 3.907

Massachusetts .............. 4.481 3.891 4.303 3.953 3.870 3.875

Rhode Island ............... 4.424 4.028 4.028 4.026 4.020 4.023

Montana .................. 4.394 3.978 3.978 3.976 3.968 3.963

Kentucky ................. 4.333 3.961 3.961 3.961 3.961 3.953

Pennsylvania ............... 4.204 4.033 4.033 4.032 4.033 4.033

Ohio .................... 4.201 3.960 3.960 3.961 3.964 3.966

Wyoming ................. 4.156 3.892 3.892 3.892 3.884 3.882

California . . . . . . . . . . . . . . . . . 4.111 3.880 3.880 3.881 3.880 3.889

Oklahoma ................. 4.104 3.962 3.962 3.962 3.964 3.968

New Mexico ............... 4.084 3.907 3.907 3.906 3.900 3.906

Maine ................... 4.075 3.506 3.940 3.740 3.887 3.882

Maryland ................. 4.045 3.866 3.866 3.873 3.893 3.878

Florida ................... 3.981 4.063 4.063 4.061 4.071 4.068

Tennessee ................ 3.950 3.937 3.937 3.939 3.950 3.956

Washington ................ 3.869 3.937 3.937 3.936 3.931 3.932

Arkansas ................. 3.840 3.967 3.967 3.969 3.982 3.974

New York . . . . . . . . . . . . . . . . . 3.828 3.934 3.845 3.993 3.937 3.948

Iowa .................... 3.821 4.026 4.026 4.023 4.017 4.019

New Jersey ................ 3.806 3.976 3.976 3.977 3.982 3.971

West Virginia ............... 3.734 3.520 3.683 3.754 3.908 3.907

Oregon .................. 3.704 3.873 3.795 3.934 3.845 3.845

Utah .................... 3.704 3.801 3.801 3.804 3.797 3.795

Wisconsin ................. 3.692 3.859 3.738 3.918 3.834 3.837

South Carolina .............. 3.677 3.184 3.597 3.391 3.532 3.532

Virginia .................. 3.671 3.878 3.878 3.882 3.896 3.893

Alabama .................. 3.638 3.900 3.900 3.905 3.928 3.922

Hawaii ................... 3.575 3.455 3.526 3.699 3.805 3.771

Kansas .................. 3.573 3.961 3.961 3.960 3.960 3.963

Missouri .................. 3.549 3.977 3.977 3.977 3.981 3.978

Mississippi ................ 3.489 3.135 3.395 3.335 3.477 3.476

Texas ................... 3.450 3.848 3.848 3.852 3.858 3.872

Illinois ................... 3.420 3.890 3.518 3.947 3.891 3.894

Louisiana ................. 3.410 3.801 3.801 3.810 3.841 3.836

Indiana .................. 3.380 3.871 3.559 3.929 3.858 3.861

South Dakota .............. 3.334 3.987 3.987 3.984 3.977 3.976

New Hampshire ............. 3.236 3.949 3.949 3.947 3.937 3.937

Idaho .................... 3.162 3.447 3.232 3.679 3.826 3.822

Alaska ................... 3.093 3.668 3.688 3.692 3.689 3.702

Minnesota ................. 3.034 3.952 3.952 3.950 3.944 3.942

Georgia .................. 3.000 3.182 3.010 3.386 3.534 3.541

North Carolina .............. 2.685 3.285 2.715 3.499 3.642 3.637

Nebraska . . . . . . . . . . . . . . . . . ... 3.872 3.872 3.930 3.848 3.853

North Dakota . . . . . . . . . . . . . . . ... 3.505 3.505 3.747 3.862 3.836

. . . Category not applicable.

NOTE: Shading indicates the largest and smallest estimates in the column.

Series 2, No. 126 [ Page 67
Table 51. Variation in State estimates for average number of doctor visits

Direct

inflation Synthetic Composite

Heteroscedatic

variances

GSE

with 16

groupings

GSE

with 32

groupings

All States ................. 2.69–6.04 3.63–4.00 2.95–4.60 3.66–4.04 3.65–4.03 3.67–4.08

Percent nonblack ............ 2.69–6.04 3.46–4.13 3.09–5.24 3.50–4.16 3.42–4.18 3.42–4.18

Percent black .............. 2.69–6.04 3.14–4.23 2.72–5.37 3.34–4.28 3.48–4.44 3.48–4.45

Table 52. Percentage with self-perceived poor health status, with States grouped by percentage of nonblack population in central cities

State

Designunbiased

inflation

estimator

Synthetic

estimator

Composite

estimator

GSE with

(16)

balanced

variances

GSE with

(32)

balanced

variances

West Virginia .................... 7.17 3.34 6.02 3.44 3.43

Mississippi ..................... 6.28 3.37 5.67 3.56 3.56

Alabama ....................... 5.42 3.10 4.71 3.11 3.13

Maine ........................ 5.19 3.06 4.52 3.13 3.13

Arkansas ...................... 5.10 3.00 4.23 3.02 3.04

Kentucky ...................... 5.06 3.10 4.81 3.19 3.19

North Carolina ................... 4.68 2.95 4.36 2.97 3.00

Tennessee ..................... 4.46 2.84 4.09 2.86 2.85

Oklahoma ...................... 3.81 2.65 3.48 2.67 2.67

Louisiana ...................... 3.52 2.95 3.40 2.97 2.98

Georgia ....................... 3.44 3.04 3.42 3.18 3.17

South Carolina ................... 3.42 3.25 3.40 3.40 3.41

Indiana ....................... 3.40 2.59 3.27 2.62 2.61

Arizona ....................... 3.29 2.72 2.81 2.47 2.45

Virginia ....................... 3.29 2.75 3.21 2.77 2.78

Texas ........................ 3.02 2.59 2.78 2.34 2.34

Florida ........................ 2.96 3.19 2.98 3.23 3.24

District of Columbia ................ 2.95 4.07 3.99 3.82 3.81

Alaska ........................ 2.87 1.88 1.91 1.68 1.68

Idaho ......................... 2.80 2.83 2.81 2.90 2.90

Ohio ......................... 2.79 2.72 2.78 2.75 2.74

Michigan ...................... 2.78 3.09 2.79 3.20 3.20

Missouri ....................... 2.64 2.79 2.66 2.82 2.82

Delaware ...................... 2.61 3.18 2.78 3.30 3.29

Oregon ....................... 2.50 2.53 2.51 2.56 2.56

Wyoming ...................... 2.41 2.20 2.28 2.22 2.22

Illinois ........................ 2.40 2.75 2.42 2.78 2.77

California ...................... 2.31 2.36 2.31 2.39 2.39

New York . . . . . . . . . . . . . . . . . . . . . . 2.31 3.03 2.63 2.77 2.78

Kansas ....................... 2.21 2.59 2.29 2.61 2.61

Massachusetts ................... 2.21 2.60 2.25 2.62 2.62

Montana ....................... 2.10 2.46 2.27 2.49 2.49

Vermont ....................... 2.09 2.89 2.32 2.94 2.93

New Jersey ..................... 2.03 3.31 2.07 3.43 3.43

South Dakota ................... 2.00 2.51 2.25 2.53 2.54

Rhode Island .................... 1.97 2.67 2.24 2.69 2.69

Maryland ...................... 1.84 3.16 1.89 3.30 3.30

Utah ......................... 1.75 1.89 1.78 1.91 1.91

Nevada ....................... 1.74 2.46 1.97 2.49 2.49

Pennsylvania .................... 1.69 2.92 1.74 2.95 2.94

New Mexico .................... 1.67 2.25 1.82 2.28 2.28

Iowa ......................... 1.65 2.65 1.81 2.67 2.67

Washington ..................... 1.44 2.38 1.52 2.41 2.41

Wisconsin ...................... 1.38 2.52 1.48 2.55 2.55

Connecticut ..................... 1.27 2.72 1.43 2.75 2.74

Colorado ...................... 1.20 2.26 1.31 2.28 2.28

Minnesota ...................... 1.16 2.39 1.26 2.41 2.41

Hawaii ........................ 0.87 2.32 1.16 2.36 2.36

New Hampshire .................. 0.68 2.30 0.95 2.32 2.32

Nebraska . . . . . . . . . . . . . . . . . . . . . . ... 2.56 2.56 2.58 2.58

North Dakota . . . . . . . . . . . . . . . . . . . . ... 2.47 2.47 2.50 2.49

. . . Category not applicable.

NOTE: Shading indicates the largest and smallest estimates in the column.

Page 68 [ Series 2, No. 126
For States with a high percentage in

central cities, the average variance is

12.8 percent of the MSE. Among these

States, RMSE’s vary from 0.411 in

Michigan to 0.475 in the District of

Columbia, and the State-specific RMSE

for Mississippi (with a low percent of the

black population in central cities) is 1.46,

three and one-half times the RMSE for

Michigan.

This is the greatest differentiation

found between State RMSE’s using this

estimator. In general, the traditional

synthetic estimator has a larger variance

than do forms of GSE that rely on prior

information. By incorporating prior

information, the GSE subgroup means

are no longer completely a function of

the sampled data and therefore are less

variable than the subgroup means of the

synthetic estimator.

Table 53. Percentage with perceived poor health status, with States grouped by percentage of black population in central cities

State

Designunbiased

inflation

estimator

Synthetic

estimator

Composite

estimator

GSE with

(16)

balanced

variances

GSE with

(32)

balanced

variances

West Virginia ............... 7.17 3.94 6.09 4.40 4.39

Mississippi ................ 6.28 4.32 5.82 4.75 4.75

Alabama .................. 5.42 3.09 4.79 3.06 3.06

Maine ................... 5.19 3.57 4.62 3.96 3.95

Arkansas ................. 5.10 3.07 4.34 3.03 3.04

Kentucky ................. 5.06 2.72 4.42 2.69 2.70

North Carolina .............. 4.68 4.06 4.63 4.47 4.40

Tennessee ................ 4.46 2.91 4.15 2.87 2.87

Oklahoma ................. 3.81 2.77 3.55 2.74 2.74

Louisiana ................. 3.52 2.91 3.41 2.88 2.89

Georgia .................. 3.44 3.77 3.46 4.16 4.12

South Carolina .............. 3.42 4.09 3.49 4.50 4.50

Indiana .................. 3.40 2.28 2.85 2.21 2.21

Arizona .................. 3.29 2.60 3.15 2.58 2.56

Virginia .................. 3.29 2.78 3.23 2.75 2.76

Texas ................... 3.02 2.49 2.99 2.46 2.45

Florida ................... 2.96 3.30 2.98 3.28 3.28

District of Columbia ........... 2.95 4.04 3.87 3.90 3.95

Alaska ................... 2.87 1.77 2.22 1.74 1.74

Idaho .................... 2.80 3.32 2.93 3.67 3.66

Ohio .................... 2.79 2.82 2.79 2.79 2.79

Michigan ................. 2.78 2.38 2.65 2.30 2.28

Missouri .................. 2.64 2.89 2.67 2.86 2.86

Delaware ................. 2.61 3.87 3.03 4.28 4.27

Oregon .................. 2.50 2.21 2.33 2.13 2.13

Wyoming ................. 2.41 2.35 2.38 2.32 2.32

Illinois ................... 2.40 2.44 2.41 2.36 2.36

California ................. 2.31 2.47 2.31 2.44 2.44

New York . . . . . . . . . . . . . . . . . 2.31 2.56 2.36 2.48 2.50

Kansas .................. 2.21 2.72 2.31 2.69 2.69

Massachusetts .............. 2.21 2.28 2.23 2.20 2.21

Montana .................. 2.10 2.63 2.32 2.60 2.60

Vermont .................. 2.09 3.35 2.49 3.70 3.71

New Jersey ................ 2.03 2.94 2.09 2.92 2.92

South Dakota .............. 2.00 2.68 2.30 2.64 2.65

Rhode Island ............... 1.97 2.82 2.26 2.79 2.79

Maryland ................. 1.84 2.87 1.94 2.86 2.87

Utah .................... 1.75 2.03 1.81 1.99 1.99

Nevada .................. 1.74 2.58 1.98 2.56 2.56

Pennsylvania ............... 1.69 3.04 1.74 3.00 3.00

New Mexico ............... 1.67 2.40 1.84 2.37 2.36

Iowa .................... 1.65 2.82 1.81 2.78 2.78

Washington ................ 1.44 2.53 1.52 2.50 2.50

Wisconsin ................. 1.38 2.22 1.63 2.14 2.15

Connecticut ................ 1.27 2.84 1.42 2.81 2.81

Colorado ................. 1.20 2.39 1.30 2.36 2.35

Minnesota ................. 1.16 2.54 1.26 2.51 2.51

Hawaii ................... 0.87 3.37 1.09 3.70 3.61

New Hampshire ............. 0.68 2.46 0.93 2.42 2.42

North Dakota . . . . . . . . . . . . . . . ... 3.59 3.59 3.96 3.89

Nebraska . . . . . . . . . . . . . . . . . ... 2.23 2.23 2.16 2.17

. . . Category not applicable.

NOTE: Shading indicates the largest and smallest estimates in the column.

Series 2, No. 126 [ Page 69
Table 54. Robustness of the generalized synthetic estimator

Variable State grouping

Range among estimators

2, 4, 5, and 6

Doctor visits .................. None 6 percent District of Columbia

0–2 percent All others

6 percent District of Columbia

Nonblack 3 percent Arizona

0–2 percent All others

Black ....................... 10–11 percent All 11 States with < 36 percent

4 percent District of Columbia

0–2 percent All others

Poor health ................... None 0–2 percent All states

Nonblack 7–12 percent All 5 States with > 37 percent

3–6 percent 9 of 12 States with < 16 percent

0–2 percent All others

Black 10–12 percent All 11 States with < 36 percent

3–4 percent 8 of 9 States with > 77 percent

0–2 percent All others

Table 55. State-specific mean square errors for number of doctor visits, using the synthetic estimator, with States grouped by percent

nonblack population in central cities

Nonblack population in central cities1

All States Low Moderate High

(1) E (y1i. − y

^

2i.)

2 ............... .3777 .7536 .2203 .3238

(2) aveVar (y1) ................ .2074 .2417 .1842 .2736

aveMSE (y2) ............... .1703 .5119 .0361 .0502

(4) aveVar (y2) ................ .0020 .0119 .0029 .0106

aveVar (y2) . . . . . . . . . . . . . . . 1.2 percent 2.3 percent 8.0 percent 21.1 percent

aveMSE

(5) aveBias2 (y2)............... .1684 .5001 .0333 .0396

(6) MSE (y2)

Smallest ................ .1701 (Georgia) .5097 (Georgia) .0360 (Utah) .0474 (Texas)

Largest ................. .1732 (District of Columbia) .5141 (West Virginia) .0364 (Iowa) .0586 (District of Columbia)

RMSE (y2)

Smallest ................ .412 .714 .190 .218

Largest ................. .416 .717 .191 .242

1See table 48 for State groupings.

Table 56. State-specific mean square errors for percentage with perceived poor health using the generalized synthetic estimator with 16

balanced variances, with States grouped by percentage of black population in central cities

Black population in central cities1

All States Low Moderate High

(1) E (y1i. − y

^

6i.)

2 ............... 1.727 2.752 1.276 .431

(2) aveVar (y1) ................ 0.369 0.641 0.312 0.254

aveMSE (y6) ............... 1.358 2.111 0.964 0.177

(4) aveVar (y6) ................ .0006 .0898 .0073 .0227

aveVar (y6) . . . . . . . . . . . . . . . 0.4 percent 4.3 percent 0.8 percent 12.8 percent

aveMSE

(5) aveBias2 (y6)............... 1.357 2.022 0.957 0.154

(6) MSE (y6)

Smallest ................ 1.357 (Alaska, Utah) 2.102 (Georgia) 0.962 (Alaska) 0.169 (Michigan)

Largest ................. 1.360 (District of Columbia) 2.125 (Mississippi) 0.967 (Florida) 0.226 (District of Columbia)

RMSE (y6)

Smallest ................ 1.16 1.45 0.981 0.411

Largest ................. 1.17 1.46 0.983 0.475

1See table 48 for State groupings.

NOTE: All values are times 0.0001, except RMSE, which is times 0.01.

Page 70 [ Series 2, No. 126
Table 57 shows much more

promising results. Using the synthetic

estimator to estimate percent with

perceived poor health without

subgrouping the States yields results that

are almost identical to those in the

previous table. When the States are

grouped according to the percent of the

population that is not black in central

cities, the State-specific MSE’s are quite

different. For States with a high

percentage in central cities, the average

variance is 78 percent of MSE. Among

these States, the RMSE’s vary from

0.179 in Alaska to 0.311 in the District

of Columbia; thus, a confidence interval

on the estimate for the District of

Columbia would be almost twice as

wide as one for Alaska. The

State-specific RMSE for West Virginia is

1.57, almost 9 times the RMSE for

Alaska.

In addition to examining the

variation of MSE’s from State to State,

it is also of interest to examine when

estimates produced by the synthetic

estimator or GSE have smaller MSE’s.

By incorporating prior information, the

GSE is based on more information and

can therefore be expected to have

smaller variance (unless the variance on

the prior estimate is large). If the prior

estimates are not consistent with the

sample means for certain subgroups,

however, the GSE will introduce an

extra source of bias. Thus, it is possible

for either estimator to produce smaller

MSE’s. The data used in this empirical

analysis produced MSE’s that were

sometimes smaller for the GSE and at

other times, for the synthetic estimator.

Table 58 summarizes these findings for

self-perceived poor health.

Summary of Empirical

Results

With a national model, where the

subgroup averages are subject to little

sampling error, the generalized synthetic

estimator was quite robust to alternative

model assumptions that were examined.

That is, the point estimates of the

traditional synthetic estimator were

hardly changed when the small area

and/or subgroup variances were allowed

to vary across small areas or when prior

information (in the form of the previous

year’s survey estimate) was included. The

large sample sizes in the national model

resulted in MSE’s that were dominated by

the bias term and thus showed little

difference from State to State.

Doubling the number of subgroups

in the national model from 16 to 32 did

not have much impact on the estimates.

The empirical findings were quite

different when the sample size per

subgroup was reduced by using

subnational models. The form of the

GSE was important in that the point

estimates for a given State could differ

by approximately 10 percent, depending

on the form of the estimator. All of the

model-dependent estimators had more

State-to-State variation using the

subnational models, reducing the

amount of shrinkage toward the national

mean.

By grouping the States on the basis

of similar expected biases, it was

possible to produce State-specific MSE’s

that varied markedly from group to

group. For groupings where the average

bias was a small component of MSE,

the individual State MSE’s also varied

within the group. For example, in those

States where a high percentage of the

population that is not black lives in

Table 57. State-specific mean square errors for percentage with perceived poor health using the synthetic estimator, with States grouped by

percentage of nonblack population in central cities

Nonblack population in central cities1

All States Low Moderate High

(1) E (y1i. − y

^

2i.)

2 ............... 1.724 3.011 1.080 .0651

(2) aveVar (y1) ................ 0.369 0.552 0.265 0.600

aveMSE (y2) ............... 1.355 2.459 0.815 0.051

(4) aveVar (y2) ................ .0005 .0055 .0007 .0040

aveVar (y2) . . . . . . . . . . . . . . . 0.4 percent 2.2 percent 0.9 percent 78.4 percent

aveMSE

(5) aveBias2 (y2)............... 1.350 2.404 0.809 0.011

(6) MSE (y2)

Smallest ................ 1.353 (Alaska) 2.446 (Georgia) 0.813 (Utah) 0.032 (Alaska)

Largest ................. 1.374 (District of Columbia) 2.475 (West Virginia) 0.819 (Iowa) 0.097 (District of Columbia)

RMSE (y2)

Smallest ................ 1.16 1.56 0.902 0.179

Largest ................. 1.17 1.57 0.905 0.311

1See table 48 for State groupings.

NOTE: All values are times 0.0001, except RMSE, which is times 0.01.

Table 58. Reduction in estimate of the MSE of the generalized synthetic estimator (16

subgroups) relative to the MSE of the synthetic estimator for percent with perceived poor

health

Percent in

central city

Grouped by

percentage black

Grouped by

percentage nonblack

Low . . . . . . . . . . . . . . . . . . . . . . . . . < 1 5–6

Medium . . . . . . . . . . . . . . . . . . . . . . . <1 (1–2)

High–District of Columbia . . . . . . . . . . . . 26 (14)

High–other States . . . . . . . . . . . . . . . . . 5–7 (128–226)

NOTE: When the MSE of the GSE is larger, it is shown in parentheses.

Series 2, No. 126 [ Page 71
central cities, the variance averaged to

be almost 80 percent of MSE. This

resulted in RMSE’s for some of these

States that were almost twice as large as

those for other States in the same group.

Across all States RMSE’s varied by a

factor of 9.

When the GSE incorporated prior

information, the State-to-State variation

in MSE’s was not as significant. This

results from the fact that, by basing the

estimates only partially on the sample

data, the variance is reduced. Because

only the variance component of the

MSE is State-specific, this minimizes

the differences between States.

No consistent results were found for

whether the GSE would produce smaller

MSE’s than the traditional synthetic

estimator. When States were grouped by

percent of black (or other-than-black)

population in central cities, the GSE

always had a smaller MSE for

self-perceived poor health. When they

were grouped by percent of black (or

other-than-black) population in central

cities, however, the reverse was true for

two groups of States. This analysis was

limited by the many occasions when the

MSE procedure produced negative

estimates for one or more groups of

States. Although this limitation also

arises when using average MSE’s, it is

more pronounced the greater the number

of groups of small areas.

The GSE has the potential to improve

significantly on the traditional synthetic

estimator when the sampling error of the

subgroup mean is not small—for example,

when the number of subgroups is large. In

such cases the synthetic estimator is

sensitive to the assumptions that are

generalized in the GSE. When this

situation arises, the GSE can be used

either to produce estimates based on

assumptions with which the analyst feels

more comfortable or to test the sensitivity

of the traditional estimator to its

assumptions.

The GSE appears to have potential

for producing State-level estimates from

NHIS for at least some variables. The

procedure for producing State-specific

MSE’s may be useful for detecting

differences between States. In either

situation more research is needed on the

covariates for the model-dependent

estimators and the effects on the

estimates of using different prior

distributions.

Page 72 [ Series 2, No. 126
Part IV.

Coordination With Surveys of

Health Care Providers
Chapter 13.

Coordination at the

Primary Sampling

Unit Level

Locating all the surveys of health

care providers (known

collectively as the National

Health Care Survey, or NHCS) in a

single set of PSU’s that is a subset of

the NHIS PSU’s was a possibility in

which National Center for Health

Statistics was extremely interested, as

discussed in chapter 2. Two potential

benefits of such coordination were

identified and researched: One is cost

savings; the other is enrichments to

analysis. They are discussed separately

in the following sections. A major

potential drawback—variance

increases—was identified and

researched. After advantages and

disadvantages of this approach are

discussed, a conclusion is presented.

Cost Savings

Placing all the component surveys

of the NHCS into a single subset of

NHIS PSU’s could lead to certain

synergies that could translate into cost

savings. To achieve the synergies,

however, it would be necessary to limit

the flexibility of the combined design.

At a broad level, the coordination would

save money if a single small set of

interviewers could work on all the

surveys. Cost savings would be largest

if the interviewers could work only in

their home PSU’s, thereby eliminating

the cost of long-distance travel. For the

U.S. Bureau of the Census to be able to

retain such a cadre of local interviewers,

it would be necessary to schedule the

provider surveys well in advance in a

pattern that allowed optimal staff

utilization. Any breaks in the pattern

could lead to loss of that staff, thereby

requiring costly travel by backup

interviewers. Overlaps in the pattern

where local staff were stretched beyond

their capabilities would also require

expensive travel by backups. Given the

uncertain nature of multiyear funding

for these surveys, this sort of

predictability seems unlikely; therefore,

major cost savings also appear unlikely.

Analysis Enrichment

Most health care policy research

is done at the individual level.

However, some research is done at the

community level, some at the State

level, and some at the national level.

Examples of research at the individual

level include studies linking smoking

to heart disease, evaluating different

treatments for breast cancer, and

testing drugs. Examples at the broader

levels consist mostly of trying to link

different policies, such as per capita

spending, with average health

measures. The idea behind this

research was that it might make sense

to study linkages at the PSU level. For

PSU’s to be an interesting level to

study, they should be defined as

interesting entities in and of

themselves.

It would be a promising start

if PSU’s could be defined as clearly

distinct units with respect to patterns

of health care delivery. Toward this

aim, NCHS staff (29) defined a set of

health care service areas (HCSA’s)

that were fairly well self-contained in

terms of health care delivery; that is,

the HCSA’s were defined in such a

way that for most of the areas,

residents obtained their health care

services from providers located in the

same HCSA’s.

If a sample of HCSA’s was

used as sample PSU’s for NHIS and for

the full set of health care provider

surveys (NHCS), an analysis file could

be set up in which each record

corresponded to a different HCSA and

each record contained average health

statistics gathered from NHIS and

average provider traits collected from

NHCS.

It turned out, however, that HCSA’s

had to be defined as quite large areas

for them to be self-contained. This

resulted in a substantial reduction in the

number of PSU’s in the United States

(about 800 rather than the 1,900 PSU’s

defined for NHIS in 1985). Even at this

level, persons seeking health care

services still participated in considerable

crossing of HCSA borders. Furthermore,

a clear and convincingly urgent set of

hypotheses about linkages at the HCSA

level was never set forth.

Variance Increases

Confining NHIS and all the surveys

of health care providers to a single set

of PSU’s will inevitably lead to

increases in variance for analyses

restricted to just one of the surveys.

This is true because the optimal

definition, stratification, and selection

procedure of PSU’s is different for each

survey.

Because there are far fewer

providers than households and far more

work to be done by the interviewer at

each sample provider than at each

sample household, the optimal PSU size

for several provider surveys is far larger

than for NHIS. For example, it might be

reasonable to define a substantial part of

South Dakota as a single PSU for a

survey of hospitals; for NHIS, however,

the travel costs would be extremely high

if more than a few rural counties were

collapsed together to form a PSU.

Stratification is quite different, too,

because no thought was devoted to

trying to produce provider statistics at

the State level. Trying to keep NHIS

flexible for State expansions means that

strata must respect State boundaries. The

provider surveys have no parallel need.

For example, Cheyenne, Wyoming;

Boise, Idaho; Helena, Montana; and

Billings, Montana, might make a

reasonable stratum for one of the

provider surveys.

In particular, the optimal number of

sample PSU’s is very different for

various surveys. To provide flexibility

for State expansions, the optimal

number of PSU’s for NHIS is quite

large—at least 300. The optimal number

of sample PSU’s for health care

provider surveys is closer to 100.

Even given the same PSU

definitions and stratification, the optimal

probabilities of selection are different

across surveys. For each survey, the

optimal probabilities of selection are

proportional to a different measure of

size. For NHIS, the best measure of size

would be some weighted combination of

population by domain, as discussed in

Page 74 [ Series 2, No. 126
chapters 4 and 15. For the National

Hospital Discharge Survey, the optimal

measure of size would be a recent count

of hospital discharges. For the National

Ambulatory Medical Care Survey, the

optimal measure of size would be a

recent count of ambulatory visits to

doctors’ offices. Similar statements can

be made about the other provider

surveys.

Unfortunately, it is impossible using

currently available data to quantify

accurately the extent of the variance

increase for one survey to use the

sample PSU’s designed as optimal for a

different survey. To solve this problem,

it would be necessary to have accurate

data on the characteristics of interest at

the county level. Other than a question

on disability, the decennial census does

not contain health-related data of any

sort, much less health care data. The

health-related information that is

available on a county basis covers such

items as numbers of physicians, hospital

beds, nursing homes, mortality from

various causes, and so forth. Past study

has shown that these do not correlate

well with each other, making it unlikely

that they would be efficient stratifiers or

measures of size for different provider

surveys. Consequently, research on

alternate PSU definitions, stratification,

and selection methodology is not very

productive. The only other alternative is

to conduct experiments at the national

level where two independent designs are

used with independent field staffs; to do

so, of course, would be extremely

expensive.

Nonetheless, a set of PSU’s that

would be reasonable for NHIS, with a

measure of size that would also be

reasonable for NHIS, was examined to

see how satisfactory this set of PSU’s

would be for the provider surveys. The

set consisted of those defined for the

Current Population Survey outside New

England in 1984 and those defined in

New England for the Survey of Income

and Program Participation in 1985. For

this set of PSU’s, the degree of overlap

across the surveys was examined with

respect to the subset of PSU’s that

should be self-representing; that is,

selected with certainty. The correlations

between the optimal measures of size at

the PSU level were also examined

among a common set of nonselfrepresenting PSU’s.

A finding of this study was that all

the PSU’s that should be selfrepresenting for one provider survey or

more (given 112 sample PSU’s) would

be contained within the set of NHIS

self-representing PSU’s (assuming 358

sample NHIS PSU’s). In addition, high

correlations were found between the

alternate measures of size. Although it is

true that the correlations became rather

low for the smallest PSU’s (in terms of

population), only a small part of the

national population lives in such PSU’s.

From these findings, it was concluded

that the penalties to the provider surveys

of being confined to a subset of NHIS

sample PSU’s would probably be

modest.

Conclusion

With respect to NHIS itself, the

potential for enriching analysis and

improving operational efficiencies for

the provider surveys does not appear

concrete enough to justify picking a set

of PSU’s that is less than optimal for

NHIS. Furthermore, investigations of

correlations of measure of size indicate

that the provider surveys can probably

fit within the NHIS sample PSU’s with

little precision loss, if that is desired.

Series 2, No. 126 [ Page 75
Chapter 14.

Network Sampling of

Providers Through the

National Health

Interview Survey

Introduction

In its report, the Panel on the

National Health Care Survey (30) made

two recommendations related to network

sampling. The first (recommendation

4–1) was that providers other than

short-term hospitals, office-based

physicians, and nursing homes be

surveyed by network sampling based on

nominations from patients of these

providers who happened to be included

in NHIS. The second (recommendation

4–2) was that NCHS study the utility of

sampling even the short-term hospitals

and office-based physicians in this

manner. This chapter examines the

circumstances under which network

sampling based on NHIS could be more

efficient than list-based sampling.

It is important to note that

recommendations 4–1 and 4–2 by the

panel concern only the types of data

traditionally collected in NCHS provider

surveys and not dual patient-provider

data on individual health care events

such as were obtained through the

National Medical Expenditure Survey

(NMES) of 1987 with its component

Medical Provider Survey (MPS). Other

recommendations in the panel’s report

deal with the analytic potential of those

types of data and problems involved in

collecting such data. These other issues

are briefly discussed in chapter 15.

List Quality

For an arbitrary class of health care

providers, it is useful to define three

grades of list quality. In the first case, it

is possible that no list exists at all. This

seems doubtful because the sorts of

health care providers listed in the

panel’s report (psychologists, dentists,

physical and occupational therapists,

pharmacists, podiatrists, chiropractors,

nurses, nurse practitioners, physician

assistants, nurse midwives, and

optometrists, among others) are typically

State licensed or are associated with

licensed practitioners or facilities. If a

licensing procedure exists, a list can be

constructed. Of course, it may be

necessary to negotiate individually with

each State to obtain a list of licensed

providers within that State, and merging

the State lists together into a national

list would require some effort. (Also,

classification and unduplication issues

are likely to be severe, but that is true

of any sampling approach.) The only

types of entities that are sometimes

viewed as health care providers that are

probably not susceptible to list-based

sampling are those that consider

religious practices a form of health care.

The panel did not recommend that

coverage be extended to this class of

caregivers, however.

At the next level of list quality, a

list exists but contains no indication of

practice size. This is frequently the case

with membership lists of national

voluntary associations of health care

providers. This may be the most

common situation for the types of health

care providers under consideration. At

the highest level of quality, a list exists

with a quantification of the number of

persons served by each provider.

Another dimension of list quality

concerns coverage of the intended

population. Lists of health care

providers are likely to age fairly quickly.

Providers move, retire, enter the

business, form new partnerships,

dissolve partnerships, and so on. If the

State licensing procedure does not

require annual renewals, the information

may become extremely outdated.

Furthermore, a small class of unlicensed

providers may exist. Coverage gaps due

to out-of-date lists and unlicensed

providers do cause bias. Undercoverage

is also a problem with network samples,

however, as is explored below.

Network Sample

Undercoverage

A number of possible sources of

undercoverage exist. The first source is

undercoverage in NHIS itself. Coverage

rates generally run between 60 and

100 percent depending on age, sex, race,

and ethnic origin, with an overall figure

around 90 percent (relative to the

unadjusted decennial census). The

second source is unit nonresponse in

NHIS. For various reasons about

5 percent of eligible sample households

are not interviewed. The third source is

underreporting of health care events.

Little empirical evidence currently exists

on how often this occurs, but research at

HCFA (matching self-reported events

from the Medicare Current Beneficiary

Survey against administrative records)

may soon shed additional light in this

area. This source of undercoverage can

clearly be reduced by keeping the

reference period short for qualifying

events. (There could also be

overreporting of events if steps are not

taken to prevent telescoping.)

The fourth source of undercoverage

is failure to name the health care

provider and to provide enough

information so that the provider can be

located. Experience with the provider

followup (MPS) to NMES is not

directly relevant to projecting the rate at

which this might occur but does contain

some clues. NMES respondents were

asked to sign consent forms in addition

to naming the health care provider and

supplying address information. Although

just 81 percent of the NMES sample

provided the signed consent forms and

provider information, it could be that a

network sample based on NHIS would

encounter much more favorable rates. A

reasonable projection of 95 percent

overall coverage can be made on the

assumption that consent forms would

not be required because a random

sample of patient records would be

drawn at the provider’s office and

information would presumably be

recorded anonymously; it is possible

that the nominator would not even be in

the sample. Coverage can be anticipated

to be far worse for some providers,

however. In particular, separately billing

doctors associated with hospital visits

are likely to be poorly covered because

many patients do not remember their

physicians’ names or even know that

they were in attendance.

All in all, it seems reasonable to

project provider coverage of between 75

and 85 percent before provider

nonresponse. (Implications for response

Page 76 [ Series 2, No. 126
rates are discussed in the section entitled

‘‘Other considerations.’’) Depending on

State licensing procedures and/or the

frequency of membership updates for

professional societies, this range of

coverage could be better or worse than

what could be expected from a list.

Within a provider type, coverage of

those providers who serve

predominantly black and Hispanic

populations can be expected to be less

well covered because of the

undercoverage of these groups in NHIS

itself. The decision of which approach

would lead to higher coverage would

probably have to be based on a

thorough review of possible list sources

for the specific provider to be surveyed.

Of course, coverage alone will not

determine the decision; there are still

questions of variance and cost.

Provider or Event

Characteristics?

The variance structure of a sample

survey of health care providers depends

strongly on the nature of the survey’s

focus. If primary interest is in personal

and/or professional characteristics of the

provider (e.g., office costs, technical

equipment owned, fee for service,

income), network sampling will almost

always lead to very large variances. This

is attributable to the highly variable

probabilities of selection (arising from

the fact that the probability will depend

on the number of visits to the facility).

The obstacles to list formation would

have to be severe indeed for network

sampling with such a substantive focus

to be considered. Only if the primary

interest is on volumes of various types

of services provided (e.g., numbers of

appendectomies, tranquilizer

prescriptions, root canals, babies

delivered at home, thin lenses ground),

does network sampling have any appeal.

This is because network sampling is

similar to probability proportionate-tosize (PPS) sampling.

The range of provider sizes is an

important consideration in whether PPS

sampling or some other method of

sampling is efficient. PPS sampling is

more likely to be important in a survey

of institutional providers than in a

survey of individual professionals

because of the much greater room for

variation in workload. Because good

lists are also more likely to be available

for institutional providers, however, this

criterion will not usually facilitate the

decision between list-based sampling

and network sampling.

Areas With Rich Potential for

Network Sampling

Taking these factors together, the

chief potential for NHIS-based network

sampling of providers appears to be in

surveys where the focus is on event

characteristics; the only available lists of

providers have no adequate information

on measures of practice size; and the

providers are not associated with

institutions for which good lists are

available. We will assume that dentists

are a potential domain of interest in

order to simplify language in creating a

model for costs and variances of

network sampling. A structure is

developed below for comparing

variances and costs for network and

list-based samples of dental visits.

Analyses for other domains would

follow similar lines.

Model for Costs and

Variance

A Network Sample of Dentists

Suppose that a survey of dentists

has been commissioned in which the

main focus is on volumes of services

such as prophylaxis, x rays, fillings,

crowns, root canals, orthodontia, and

gum surgery. Note that this sort of data

would probably be most efficiently

obtained from a dentist by abstracting

individual patient records rather than by

asking the dentist to keep count.

Although gross revenue, expenses, and

net income are examples of variables

related to service volume where no

abstraction is required, at least some

abstraction is probably typical of

provider surveys interested in service

volumes and will be an assumed feature

of the design.

Let

N be the annual number of dental

visits throughout the United States

t be the length of the reference

period in weeks preceding the

NHIS sample week

Niv be the number of appointments

kept by the i-th dentist within the

t-week reference period

immediately prior to the vth NHIS

sample week (It is explicitly

assumed here that appointments by

the same person cannot reasonably

be unduplicated and will be

counted separately.)

n be the number of dental visits

reported in the NHIS sample

during the t-week period preceding

the vth sample week (assume

constant across sample weeks for

simplicity)

m be the number of networks that

are established for the t-week

period preceding the vth sample

week (assume constant across

sample weeks for simplicity)

δ be the intraclass correlation for

events at the same dentist and

λ be the number of events sampled

per network (may not include the

event that brought the practice into

sample)

The planned average sampling

interval for persons who are neither

black nor Hispanic in 1995 and beyond

is about 1,900. Only 1 part in 52 of the

sample can report on any t-week period,

however. Also, only t parts in 52 of

annual visits occur during a t-week

period. Thus, the expected number of

dental visits to be detected in a t-week

period is n = Nt/[(522

)1,900]. For

example, suppose that N = 500,000,000

and t = 1. Then n = 97; that is, it is

possible to form 97 network samples

around 97 dental visits reported in 1

week’s sample. For ease of exposition,

each of these network samples is

referred to as a cluster. With 97 dental

visits identified each week, a maximum

of 5,044 clusters can be established over

the course of a year. That would

Series 2, No. 126 [ Page 77
probably be more than necessary to

achieve adequate precision.

Note that some dentists will be hit

(nominated) more than once. Most

often, this will occur when a patient has

two visits during the reference period

for related services. Siblings may often

receive routine dental care in the same

week. To reduce the number of multiple

hits, it may be advantageous to identify

a specific subset of NHIS that is

allowed to make nominations. The

objective of that subsample design

would be to minimize the number of

households with multiple potential

nominators without increasing the

variation in selection probabilities. This

can be done with the same sort of

subsampling that was used for Cycle IV

of the National Survey of Family

Growth. There, single-eligible

households were selected at lower rates

than multieligible households, and only

one eligible was selected per household.

(This would not be an option if all visits

detected in a year had to be retained as

cluster nuclei, as might be the case with

a rarer domain such as endodontists.)

It is reasonable to assume that

probabilities of selection for providers

will be too difficult to determine, and

that, therefore, it will not be possible to

use the Horvitz-Thompson estimator.

(The problem is made particularly

complex by the systematic selection of

blocks for NHIS. To establish the

probability of selection for the provider,

it would be necessary to geocode all

patients who could have nominated the

provider and then establish their relative

positions in the systematic block sort

(and within-block sort).) Instead, a

multiplicity estimator will be required,

and one is suggested in the following:

Let i be the index dentist and j the

index appointments within the t-week

reference period immediately before the

vth NHIS sample week. If a person who

made the jth appointment at the ith

dentist happens to be interviewed for

NHIS within the vth week of the year,

let Wivj be the NHIS noninterviewadjusted weight for the person;

otherwise, let Wivj = 0. Assume that

nonresponse to NHIS is ignorable given

the adjustment cells employed by NHIS.

Then an unbiased weight for vth week

for the dental provider is

WDiv = 52

t ∑

j

Wivj Niv

This weight can be averaged over

all 52 weeks to obtain an unbiased

weight for the dental provider for the

year. (Note that it is not necessary to

know Niv for weeks where there are no

hits because Σ

j

Wivj Niv = 0 for such

periods.)

Let miv be the number of networks

that are established at the ith practice

based on the v-th interview week. Then

an unbiased weight for all the λmiv

sample events is

WEiv = WDiv

Niv

λmiv

Note that if the number of networks

established at a practice were equal to

the number of times that it was hit for

NHIS, and if NHIS were self-weighting,

we would have

WEiv = (52/t) BW

λ

where BW was the common NHIS

weight. Note, however, that clients of

dentists with racially or ethnically

integrated practices will have different

probabilities of selection than those at

mostly white or mostly minority

practices.

The actual variance associated with

using WEiv to create a weighted estimate

is extremely complicated, depending on

patterns of multiple visits within NHIS

segments. Nonetheless, a reasonable

approximation can be obtained by

treating the sample as a self-weighting

sample of 52m clusters of λ events each.

The relative variance of such a sample

is well known to be

vw

2 =

[1 + δ (λ − 1)] Vw

2

52mλ

where m = Σi miv, the number of

networks per week, is assumed not to

vary by week; where 52mλ is the

total number of sample events; and

where Vw

2 is the relative variance within

the population of events of the

characteristic of interest. For example, if

the interest lies in the proportion of

dental visits that are for root canals, and

the true proportion is 0.05, then

Vw

2 = 0.95/0.05 = 19.

To allow for multiple hits and the

variation in the NHIS sample, an

adjustment factor ξ is introduced:

vw

2 =

[1 + δ(λ − 1)] ξVw

2

52mλ

Adding on the between-PSU relative

variance, the final approximation is

obtained:

v

2 = vw

2 + vb2

=

[1 + δ(λ − 1)] ξVw

2

(52mλ) + vb

2

where vb

2 is the between-PSU relative

variance associated with the selected

number of PSU’s.

Of course, the most critical

parameter to fix for the subsampling is

the number of clusters to retain. Given

either a fixed-cost or a fixed-precision

requirement, that number is fixed by the

cluster size. To identify the optimal

cluster size, it is necessary to quantify

some cost estimates for the various

phases of data collection. Let

c0 be the cost of developing sampling

and data collection procedures and

training interviewers on the new

procedures in the desired number

of PSU’s

c1 be the extra cost per NHIS

household interview of screening

for recent dental visits (equal to 0

if NHIS already asks about visits

to this type of provider)

c2 be the cost associated with each

network, such as obtaining from

the NHIS respondent the name

and address of the dentist and a

consent form to release records,

tracing and recruiting the

nominated dentist, and sampling

from his or her records

c3 be the cost per event of

abstraction including data

processing and editing

Then the total costs for the

hypothetical survey of dentists would be

C = c0 + c1 (55,000) + 52c2m + 52c3mλ

Page 78 [ Series 2, No. 126
With these assumptions, for a given

fixed cost C, the optimal cluster size is

λ = Œ 1 − δ

δ Œc2c3

and the optimal number of clusters is

m =

C−c0 − 55,000c1

52(c2 + c3λ)

For a fixed-precision v2

, the optimal

cluster size is unchanged and the

optimal number of clusters is

m =

[1 + δ(λ − 1)] ξVw

2

(v

2 − vb

2

)52λ

Some plausible values for the

parameters are shown next. Obviously,

however, each parameter would have to

be carefully reconsidered for a specific

survey. For illustrative purposes, then,

assume the following cost structure,

variance structure, and precision

requirement:

c0 = $300,000

c1 = $3

c2 = $300

c3 = $15

Vw

2 = 99

vb

2 = 0.0005

ξ = 1.1

δ = 0.15

v2 = 0.01

(Note that Vw

2 is a population-relative

variance, whereas vb

2 is the relative

between-PSU variance on a sample

estimator—hence the difference in

magnitude between Vw

2 and vb2

.) Then

λ = 15 and m = 46. The total annual

sample size would then be 35,880

abstracted events and the total cost

would be $1.7 million.

Comparison With

List-Based Sampling

A Comparable List Sample of

Dentists

Cost implications are discussed first,

followed by variance implications, and

last by some operational implications

that may affect response rates. The effort

to patch together State lists would

increase c0, perhaps by as much as

$200,000. On the other hand, cost c1

does not apply. Furthermore, cost c2

might be lower because State lists might

have better name and address

information than what NHIS

respondents can supply. State lists will

probably contain enough dirty and

incomplete records, however, to offset

most of these savings. Thus, it seems

reasonable to speculate that c2 might

decrease from $300 to $290. It is also

possible that c3 would increase

somewhat because of variation in the

number of cases to be abstracted per

dental practice. (Note: It is being

assumed here that the provider class of

interest is one for which lists cannot be

expected to contain useful measures of

size.)

Assuming that the list will not

contain any information on the number

of patients served by the dentist, the

relative variance from a list sample

would be

v

2 =

[1 + δ(λ − 1)] ξVw

2

(52mλ)

+

VN

2

(52m)

+ vb

2

where VN

2 is the relative variation in

practice size, λ is now the average

cluster size, and ξ is now the extra

design effect attributable to variation in

practice size that persists even when the

characteristic of interest is a ratio in

which the numerator and the

denominator are highly correlated with

practice size. If poststratification is

possible (unlikely for a dental survey),

or the main interest is in ratios such as

the percent of patients who receive root

canals, then the VN

2

/(52m) term will

disappear. (The term cancels out by

subtraction after linearization because it

affects both numerator and

denominator.) However, if

poststratification is not possible and the

main focus is on totals such as the total

number of root canals performed in the

country or on ratios where the

denominator is not correlated with

practice size, such as the average

number of root canals per dentist, then

the VN

2

/(52m) term does apply. In either

case, the ξ factor is likely to persist.

Also note that ξ does not approach 1 as

m becomes large. (This factor is

discussed in Sample Survey Methods

and Theory volumes I and II (31). It is

written there as the ratio of V2 with a

caret to V2 without a caret.)

For fixed precision, the optimal

number of clusters is now

m =

[1 + δ(λ − 1)] ξVw

2 + λVN2

(v

2 − vb

2

) 52λ

To illustrate, suppose the following

distribution of weekly practice sizes:

Practices Visits

1–10 10,000 55,000

11–20 20,000 310,000

21–50 60,000 2,130,000

51–100 60,000 4,530,000

101–200 10,000 1,515,000

201–500 100 35,100

501–1,000 2 1,500

Total 160,102 8,576,600

Then the average practice size would be

53 and the population variance of

practice size would be 1,326 for a

population-relative variance of 0.47.

This variation is probably a bit on the

high side, assuming that, in

multiple-dentist practices, the sample

would be taken of all patients seen by

any of the partners. Whether the sample

could be stratified by individual dentists

would depend on how lists are

maintained in the office.

Assume then the following cost

structure, variance structure, and

precision requirement:

c0 = $500,000

c1 = $0

c2 = $290

c3 = $18

Vw

2 = 99

vb

2 = 0.0005

ξ = 0.47

δ = 1.2

v2 = 0.01

Then λ = 10 and m = 57. The total

annual sample size would then be

29,640 abstracted events, and the total

cost would be $1.9 million.

Series 2, No. 126 [ Page 79
Other Considerations

Besides coverage, cost, and variance

implications, other possible implications

exist for response rates, other

nonsampling errors, and elapsed time

from survey authorization to release of

data. With network sampling, it is more

difficult to plan the work in advance. It

is fairly likely that routine dental visits

will be scheduled on the same day for

some family members. Also possible,

but somewhat less likely—neighbors

may be scheduled for the same week.

Multiple hits cause operational

nuisances by requiring twice as much

abstracting to keep the sample

self-weighting (or k times as much if

there are k hits in the same practice). It

is also possible for the same practice to

be hit multiple times over the year,

thereby adding to variable and

unpredictable response burden and

resulting in irritation in some dentists

toward the survey taker. The number of

multiple hits will be difficult to predict

and probably will vary considerably

from one type of provider to another.

For example, multiple hits seem

unlikely to be much of a problem for

chiropractors but do seem a likely

problem for emergency rooms. If this

unpredictability is a problem with

providers, one solution would be to wait

until an entire year of NHIS had made

nominations before following up any of

the nominations. This, of course, implies

an extra time lag between the time the

survey is approved and when data

collection may begin. Another problem

with such an approach would be the

destruction or transfer to long-term

storage of service records.

With list sampling, it is possible to

plan the visits in advance, possibly

leading to cost savings and

improvements in response rates. On the

other hand, if the list does not have a

good measure of size, the number of

records to be abstracted per practice

becomes even more unpredictable than

with network sampling. This might have

unfavorable implications for costs and

response rates. In the example of

dentists just given, the number could

vary from 1 to more than 100.

One problem inherent in both

sampling approaches is the existence of

joint practices. If the provider named by

the list or nominated by the patient has

a joint practice with one provider or

more, establishing the exact definition of

the sampling unit is an important and

possibly difficult task. If separate

appointment logs are kept, there is no

real problem. If no separate logs are

kept and each patient is simply seen by

the next available provider, the entire

practice may have to be viewed as the

sampling unit. Alternatively, it may be

possible to select an oversample of

patient records and subsample them

after inspection to just those of patients

seen by the provider of interest. This

problem’s existence does not seem to

favor either network or list-based

sampling.

Last is the consideration of multiple

provider surveys. Such surveys would

undoubtedly involve some economies of

scale. The cost c0 for the network

sample and the list sample would be

lower per survey if multiple surveys

were done. The reduction might be

sharper for network sampling, however,

than for list sampling.

Conclusion

If a reasonable list with a

reasonable measure of size can be

found, list-based sampling is probably

preferable; otherwise, it appears that

network sampling should be considered.

Factors that would tilt the balance in

favor of network sampling include the

following:

+ Primary interest is on event

characteristics, not provider

characteristics.

+ Considerable uncertainty exists

about the quality of the lists,

particularly with respect to coverage

and currentness.

+ Available lists do not have

reasonable measures of size; and

either:

– The numbers of events per

provider varies widely, control

totals for poststratification are not

available, and only a small

number of providers can be

sampled or

– The numbers of events per

provider varies widely, and

variation in the number of records

to be abstracted per provider

increases abstracting costs or

nonresponse rates.

+ The cost of forming a list is

projected to be greater than the cost

of collecting nominations from

NHIS.

+ The difficulties in tracing nominated

providers are thought to be less

onerous than finding providers

mentioned on State licensing lists

(or lists obtained through other

sources).

+ Flexibility in scheduling visits to

providers (including multiple visits)

is not problematic, or an extra delay

in the survey schedule can be

accommodated.

Formulas have been provided in this

report that will facilitate deciding which

approach is less expensive for fixed

precision. Some of the parameters will

be extremely difficult to project;

however, considerable room for

judgment will remain.

Page 80 [ Series 2, No. 126
Chapter 15.

The National Health

Interview Survey as a

Tool for Linking

Beneficiary and

Provider Perspectives

on Health Care

Agreat deal of attention has

recently been focused on the

possibility of fundamentally

redesigning NHIS in such a manner that

it provides a more complete picture of

the health care process, incorporating

data from the beneficiaries and

providers. Ideally, health economists

would like to be able to use NHIS as a

tool for assessing the beneficiaries’

satisfaction with different treatment

modalities for various conditions and the

cost of those treatment modalities.

Other Surveys

Several of the recommendations of

the Panel on the National Health Care

Survey, if implemented, would make

NHIS resemble other Federal surveys

more closely. This brings up the

question of the proper goals for NHIS

vis-a-vis those other surveys.

Specifically, the panel’s call for detailed

medical cost and utilization data to be

collected through longitudinal follow

ups of the health care providers to NHIS

respondents would make NHIS resemble

the National Medical Expenditure

Survey (NMES) rather closely. Along

with its predecessors, the National

Medical Care and Expenditure Survey

and the National Medical Care

Utilization and Expenditure Survey, and

the Medicare Current Beneficiary

Survey conducted by HCFA, NMES

allows the linkage of health care costs

with the outcomes of health care.

National Health Interview

Survey as a Baseline or as

a Follow Up?

If NHIS is to be used to obtain

dual-perspective data on health care

(data about the same events from both

the provider and the beneficiary), the

question arises of whether it is better to

track patients from a sample of provider

records or to track providers from a

sample of patient nominations. A

different research effort (2) funded by

NCHS has been underway to study the

feasibility of the first approach. NMES

stands as an example of how the second

approach can work. Both approaches

obviously involve problems. The Panel

of the National Research Council

favored the NMES approach. This report

takes no position.

Series 2, No. 126 [ Page 81
Part V.

Decisions on Sample Design and

Contingency Options
Chapter 16. Decisions

on Sample Design

The recommended design is based

on the assumption that the field

budget for NHIS can be boosted

(starting in 1995) by 50 percent (over

the level needed to support 50,000

household interviews with the 1985–94

design) to pay for improved precision

on health statistics about black and

Hispanic persons. Starting in 1991, the

U. S. Bureau of the Census undertook

all necessary preparations for the

recommended design. As discussed in

chapter 17, there was not a budget

increase in 1995. Also, the preparation

costs in 1994 for the recommended

design are substantial. Thus, an

alternative was also designed that would

lead to some improvement in the

precision of Hispanic statistics under a

level 1995 budget (equal to the 1988

budget after adjustment for inflation).

The recommended design is referred to

as the alpha option; the alternative is

known as the beta option.

Reconciliation of

Objectives

It was recognized early in the

research that there were some

fundamental tensions between the

objectives for the redesign. The desire

for improved statistics on the black and

Hispanic populations implied a need to

increase the sample in the major

metropolitan areas of the large States.

This desire also implied a need to

stratify PSU’s by ethnic composition,

with a lower priority for respect of State

boundaries. Furthermore, for this

objective, nonself-representing PSU’s

with significant numbers of minority

persons should be oversampled. Last,

for this objective, all household

members should be interviewed in black

and Hispanic households. Pulling in a

second direction, the desire for

improved State statistics implied a need

to allocate a disproportionate share of

the sample to small States where

relatively few minority persons live, to

define and stratify nonself-representing

PSU’s in such a manner that State

boundaries were strictly respected, and

to select nonself-representing PSU’s

solely in proportion to population within

States. Pulling in a third direction, the

desire to integrate the provider surveys

with NHIS implied a need to define

large PSU’s as sometimes crossing State

lines (so that most inhabitants of a PSU

would receive their medical services

within the boundaries of the PSU) and

to oversample PSU’s that tend to

provide disproportionate amounts of

medical services to nonresidents. Pulling

in a fourth direction, the desire for

reduced response bias through

self-response implied a need to

interview just one randomly selected

adult per household.

These inherent conflicts were

resolved by placing the highest priority

on improving NHIS statistics about the

black and Hispanic populations despite

interviewing just one random adult per

household. Deviations from optimal

design features for this objective were

tolerated where the effect on the primary

objective was not severe.

Primary Sampling Unit

Definition and Measure of

Size

The definition of PSU’s and the

measure of size used to select sample

PSU’s have important impacts on

between-PSU variances. For PSU’s with

cost functions such as NHIS, it is

important that there be more

heterogeneity within PSU’s than across

PSU’s. Thus, for example, it would be

inefficient to define the suburbs of a

metropolitan area as a PSU separate

from the central city. With respect to the

measure of size, the between-PSU

variance on a particular statistic is

closely related to the correlation

between the measure of size and the

PSU totals for the characteristic of

interest. If the correlation is perfect, the

between-PSU variance will be zero.

Unfortunately, it is extremely

difficult to project the between-PSU

variances that would result from

alternate definitions and/or measures of

size. The current survey can provide

information only about between-PSU

variance for the current definition and

measure of size. To answer this question

objectively, it is necessary to have

precise county-level summary

information on the characteristic of

interest for every county in the Nation.

Such information exists for

characteristics tabulated in the decennial

census, the Census of Agriculture, the

Economic Census, Internal Revenue

Service studies of payrolls, and other

sources. Unfortunately, none of these

sources has much information that is

relevant to NHIS. NCHS also has the

U.S. Department of Health and Human

Services Area Reference File that

contains county-level information on

hospitals and doctors as well as other

health providers. These, however, still

have very little to do with the aspects of

health about which the population is

queried in NHIS. Some conclusions can

be reached using basic demographic

data from the census, but much of the

work in this area is highly speculative

and driven by expert opinion.

Primary Sampling Unit

Definition

A study conducted by the U. S.

Bureau of the Census showed that using

the Health Care Service Areas as PSU’s

would decrease survey precision,

assuming that the budget was held

constant (32). This is because the

HCSA’s tend to be much larger in terms

of land area than traditional PSU’s. The

larger land area would mean more

within-PSU travel expense. To offset

that increase in cost, it would be

necessary to reduce the number of

sample PSU’s. The decrease in the

number of sample PSU’s would

decrease survey precision. Although

there is some question about just how

large a budget-neutral cut in the number

of sample PSU’s would have to be and

some question about the impact of the

cut on between-PSU variance, Westat,

Inc., agreed with the overall thrust of

this research.

Having ruled out HCSA’s as PSU’s,

the next question was whether the PSU

definitions used for any of the other

current surveys at the U. S. Bureau of

the Census might be suitable for NHIS.

The PSU’s for the Current Population

Page 84 [ Series 2, No. 126
Survey respect State and metropolitan

statistical area (MSA) boundaries (as

defined in 1990). Given the desire to

make NHIS flexible for State

expansions, the NHIS PSU’s need to

respect State boundaries. So from that

perspective, the CPS PSU’s were

attractive. Administrative data that might

be useful in small area estimation

projects, however, are generally

available only at the county level, even

in New England, where MSA’s violate

county boundaries. Thus, the CPS

PSU’s in New England were unsuitable

for NHIS. The PSU’s for the Survey of

Income and Program Participation

(SIPP) do not respect State boundaries

outside New England and are thus

unsuitable for NHIS, but the SIPP

PSU’s in New England respect State

and NECMA (New England county

metropolitan area) boundaries, which

means they also respect county

boundaries.

The final NHIS PSU’s were

therefore defined to be coincident with

CPS PSU’s outside New England and

SIPP PSU’s within New England. This

resulted in 1,955 PSU’s.

Primary Sampling Unit

Measure of Size

Based on the research described in

chapters 4 and 13, the recommendation

was made to use total population (all

ages) as the measure of size. This is the

same measure of size that was used in

the 1985 redesign.

Primary Sampling Unit

Stratification

The stratification performed for the

1985 redesign was extremely efficient

for regional and national statistics.

Despite a drop in the number of sample

PSU’s from 376 in 1984 to 198 in 1987

and beyond (because of budget

problems, not all of the 198 sample

PSU’s were used in 1985 and 1986), the

level of between-PSU variance stayed

about the same. (Estimates of

between-PSU variances are quite

unstable from both surveys; this

instability may be masking some level

of change.) The 1985 stratification was

based on multivariate clustering

techniques and used more than a dozen

dimensions to define similarity between

PSU’s. This efficiency is particularly

impressive given the fact that health

characteristics were not among the

dimensions used for stratification. The

clustering program appears to have

identified natural clusters that made for

efficient strata for a wide range of

statistics.

One type of statistic was not well

served by the stratification, however.

Statistics for individual States are

subject to very high between-PSU

variances in the 1985–94 design. This is

because the strata do not respect State

boundaries. (The strata for the 1973–84

design did not respect State boundaries

either, leading to similar high

between-PSU variances for State

statistics.) PSU’s with similar

demographic, social, and economic

characteristics were grouped together

from different States. The cv on many

State estimates is 100 percent or worse.

Because the flexibility to

supplement NHIS for specific States was

a major goal of the 1995 redesign, it

was decided that the strata should

strictly respect State boundaries. To

counteract the increase in between-PSU

variances that would affect regional and

national statistics, it was also decided to

force the strata to respect metropolitan

status and to increase the number of

sample PSU’s to a level comparable to

that of the 1973–84 design.

Primary Sampling Unit

Selection

The methodology used for selecting

PSU’s was much the same for 1995 as

for 1985. In strata where two PSU’s

were selected, the U.S. Bureau of the

Census selected two PSU’s without

replacement using the Brewer-Durbin

methodology. The measure of size was

total population (all ages). No attempt

was made to maximize or minimize

overlap with the old NHIS design or

with the new design for the CPS or any

other current survey at the U.S. Bureau

of the Census. The only difference from

1985 was that some strata were quite

small as a result of respecting State

boundaries and metropolitan status. In

these small strata, just one sample PSU

was selected with probability

proportional to total population.

Selection of Blocks

As discussed in chapter 1, NHIS

utilizes a mix of area and permit

sampling rather than the list sampling

used by most of the other demographic

surveys conducted by the U.S. Bureau

of the Census. To facilitate coordination

across surveys and the preparation of

field materials, dummy entities are set

up to represent all the housing units

(HU’s) and group-quarter equivalents

(GQE’s) reported in the 1990 census to

exist in a PSU. (A GQE is a collection

of usually three persons in

noninstitutional group quarters. This

collection is counted as equivalent to a

household for sampling purposes.)

Census Bureau headquarters personnel

select placeholders rather than HU’s and

GQE’s directly for NHIS. Census

Bureau field personnel then list the

blocks that contain the sample

placeholders. Census Bureau

headquarters personnel then draw a

correspondence between the sample

placeholders and the physically listed

HU’s and GQE’s to identify the final

sample of NHIS.

The selection of placeholders

involves two stages. First, blocks must

be selected. As the first step in block

selection, these placeholders were sorted

by the characteristics of their parent

blocks and, within that sort, by their

block labels. One of the block

characteristics used in the sort was

minority density stratum, as described

later.

Blocks with fewer than four known

HU’s and GQE’s were combined with

neighboring blocks and given a

combined block label. A larger

minimum combined block size was

considered but rejected by U.S. Bureau

of the Census staff because of concerns

about coordination with other current

surveys.

Each block was classified into one

of the 24 minority density strata defined

in table 59. A different optimal cluster

size was identified for each minority

density stratum. The possibilities were

Series 2, No. 126 [ Page 85
8, 12, and 16. Although intermediate

sizes, not multiples of 4, might have been

even better for NHIS, coordination with

the Bureau’s other current surveys

required the clusters be multiples of 4.

The formula used to determine the

optimal cluster size for the i-th stratum

was

λi = 4 F 8

1 − Oi(1 − ri)

×

1

4 + 0.5G

where Oi is the proportion of the

stratum that is neither black nor

Hispanic (derived from tables 17 and

18), and ri is the planned retention rate

for such other households (table 22).

The idea of the formula is to inflate the

cluster size enough so that eight

households are still designated for full

interviews after screening and

subsampling. Although it would have

been easier to equalize the number of

placeholders per cluster, it was thought

that field operations would be more

efficient with a more uniform number of

full interviews per cluster.

The placeholders were strung

together into initial clusters of

corresponding lengths. Each such cluster

is also known as a segment. (The word

‘‘segment’’ has two common usages at

the U.S. Bureau of the Census. It is also

used to refer to the block or collection

of blocks from which the HU’s and

GQE’s are drawn corresponding to the

placeholders. For example, a block

might have 35 HU’s, with 8 scheduled

for interview in NHIS in a particular

year. The 8 and the 35 are referred to as

a ‘‘segment.’’) Consecutive groups of 10

initial clusters were strung together to

provide enough sample for 10 years of

interviewing. Thus, each string consisted

of 80, 120, or 160 placeholders.

A systematic sample was then

drawn of the strings from each PSU

with a single sampling interval of

40 × Pr{PSU} and a single random start.

(PSU’s with a probability of selection

less than 1/40 were grouped into

rotating clusters.) This sampling plan

will yield an overall sample of 1/40th of

all U.S. housing. The number 40 was

chosen so that the sample would yield

10 samples of 200,000 interviewed

households each. The number 200,000

was chosen because it corresponds to 4

times 50,000, and the largest

oversampling ratio specified in table 20

is 4.0. No measure of size was used in

the selection, so long strings had the

same probability of selection as short

strings. (Because string length is

inversely related to frequency, the

probability of selection for an

individual placeholder is 1/40 whether

it is in a long or short string.) This

resulted in a random clustered

equiprobability sample comprising

2.5 percent of the universe. The

placeholders in this sample are

reserved for the sole use of NHIS

even though NHIS will use only a

subsample of them. The reason for not

sharing the unused portion with other

current surveys at the U.S. Bureau of

the Census is that the unused NHIS

subsample will not represent the entire

U.S. population.

As is discussed in chapter 5, the

best plan identified for nonelderly black

persons and Hispanic persons involves a

combination of oversampling and

screening. The strata and oversampling

rates desired for the oversampling are

those shown in table 20. The initial

string selection previously described

resulted in an oversampling rate of 4 in

all the strata. Thus, some strings had to

be dropped in some strata. The string

retention rates are shown in table 60.

These rates were calculated by dividing

the desired oversampling rates in

table 20 by 4.

Within-Block Selection

At the end of block selection, the

U.S. Bureau of the Census had a list of

sample blocks and, for each block, a

count of how many placeholders were to

be allocated to NHIS. They have

procedures in place to list physically the

residential structures in the block and to

line them up with the placeholders.

These procedures guarantee that the

HU’s and GQE’s assigned to each

survey will be nonoverlapping even if

several current surveys have selected the

block. To make this possible, all blocks

that were selected by NHIS had to be

converted to area-type sampling

procedures for all the current surveys,

even though good address registers exist

for many of the blocks, and the other

Table 59. Initial cluster sizes (in terms of placeholders)

Percent of block that is black

Percent of block that is Hispanic

< 5 5–10 10–30 30–60 60+

< 10 . . . . . . . . . . . . . . . . . . . . . . . . . 16 16 16 12 8

10–30 . . . . . . . . . . . . . . . . . . . . . . . . 12 12 12 8 8

30–60 . . . . . . . . . . . . . . . . . . . . . . . . 12 12 12 8 8

60+ . . . . . . . . . . . . . . . . . . . . . . . . . . 8 8 8 8 8

Table 60. Percent of initially selected strings to be retained for actual listing (all others are put aside)

Percent of block that is black

Percent of block that is Hispanic

< 5 5–10 10–30 30–60 60+

< 10 . . . . . . . . . . . . . . . . . . . . . . . . . 40.0 50.0 80.0 100.0 100.0

10–30 ........................ 40.0 50.0 87.5 100.0 100.0

30–60 ........................ 42.5 50.0 87.5 100.0 100.0

60+ . . . . . . . . . . . . . . . . . . . . . . . . . . 48.8 50.0 87.5 100.0 100.0

Page 86 [ Series 2, No. 126
surveys could have used them to select

samples more cheaply. The listing began

sometime in 1994.

Westat, Inc.’s, projection is that the

listing operation for the year 2000 will

yield 123,900 HU’s and GQE’s to be

screened. Of course, there is considerable

uncertainty when projecting the results

even a few years into the future. Factors

that can affect the accuracy of the

projection include new housing

construction rates, housing demolition

rates, residential/nonresidential conversion

rates, and the growth rate of the

population living in group quarters such as

dormitories. The details of the listing

procedures can also affect the accuracy of

the projections. It is important to note that

the number of units listed each year is

likely to increase from 1995 through 2004

and that as a result, some adjustment of

the string retention rates might be required

over time.

Screening

The new sample was introduced in

January 1995. Screening of the sample

housing units began immediately. All

households with black or Hispanic

occupants will be retained for full

interviews. Other households will be

retained at the rates shown in table 22.

Of the 123,900 HU’s and GQE’s

that are projected to be designated for

screening in the year 2000, not all will

have cooperative occupants, some will

have no occupants at all, and some will

not even be inhabitable. The U.S.

Bureau of the Census made a projection

that in the average year about 80 percent

of the listed units will have occupants

who will cooperate with the screening

effort. These 99,100 successfully

screened HU’s are expected to consist

of 12,300 black households, 12,500

Hispanic households, and 74,400

‘‘other’’ households.

Turning to the mechanics of

screening, the exact procedures that are

used to cope with HU’s that are difficult

to screen will depend on whether laptop

computers are used by the interviewers

as they screen. The screening procedures

are described separately for CAPI and

paper-based screening.

(The next paragraph describes a

proposed procedure for CAPI screening.

However, when CAPI interviewing was

implemented in 1997, this procedure

was not implemented; the paper-based

screening process described later was

retained.)

With CAPI screening, the

interviewer will approach the door with

the computer to enter the race and

ethnicity of the occupants. The computer

will have preloaded instructions to guide

the interviewer through the regular

NHIS interview or to terminate the

interview after ascertaining the race and

ethnicity of all occupants. If any of the

occupants are black or Hispanic, the

decision will always be for a regular

interview. If none of the occupants are

either black or Hispanic, the computer

will draw a random number to decide

whether to proceed with a full interview

or to terminate. The random decision

will give the correct retention

probability for these other households,

but there will be some variability from

expected yields. Consideration was

given to coordinating the random draws

across households, but this will probably

not be feasible across interviewers, and

it is not clear that it is worth the effort

to coordinate across households

screened by a single interviewer.

With paper-based procedures,

interviewers will have an instruction for

each assigned HU on whether to

conduct a regular interview regardless of

the race or ethnicity of the household

occupants (an ‘‘I’’ instruction for

interview) or whether to only conduct

the regular interview if at least one of

the occupants is either black or Hispanic

(an ‘‘S’’ instruction for screen). These

instructions will be made up in the

central office after the listing phase for

sample blocks and will ensure the

desired retention probabilities.

Furthermore, it will be possible to

coordinate the sampling instructions

across interviewers. However, there will

still be some variance about expected

yields as can be seen from the

possibility that just by chance, the

households that are neither black nor

Hispanic could happen to fall into the

‘‘I’’ category at disproportionate rates.

With either procedure, allowance

needs to be made for housing units that

are difficult or impossible to interview,

even just to get the race and ethnicity of

the occupants. To keep response rates

high without raising costs exorbitantly

through many repeat visits, interviewers

will be trained to use information from

neighbors under some circumstances.

(Screening using neighbors was

discontinued in 1997.) After two

unsuccessful attempts on different days

to find anyone at home, interviewers

will call on neighbors. With CAPI,

interviewers will be required to obtain

consistent information from two

neighbors before accepting their reports

on the race and ethnicity of the targeted

household. Upon obtaining consistent

information, the computer will decide

whether additional attempts are needed

to conduct a regular interview or

whether the HU can be closed as

successfully screened and subsampled

out. In the latter case, considerable

expense will have been saved. In the

first case, the interviewer is not much

worse off than prior to contacting the

neighbors, and valuable information has

been gathered to inform the weighting

procedures for nonresponse adjustment

even if the household is never

interviewed. Note that the base weights

are formed based on the neighbor

information and are unbiased even if the

neighbor information turns out to be

wrong.

With paper-based procedures, the

interviewer will never bother to contact

neighbors for an HU that is marked ‘‘I.’’

For an HU marked ‘‘S,’’ the interviewer

will start to contact neighbors after two

unsuccessful attempts at direct contact

on different days. If the first neighbor

says that no one in the targeted

household is black or Hispanic, the

interviewer will close the case as

successfully screened out. Otherwise,

the interviewer will try a second

neighbor. If both neighbors say that the

household has some members who are

black or Hispanic, the interviewer will

resume attempts to contact the

household directly. If the interviewer

eventually succeeds and it turns out that

the neighbor’s information was wrong

(there are not any members who are

black or Hispanic), the interviewer will

close the case as successfully screened

out. Note that this procedure results in a

slight bias in the baseweights, but this

bias should be quite small. To make the

Series 2, No. 126 [ Page 87
paper-based procedure unbiased, it

would be necessary to seek neighbor

information on all the ‘‘I’’ households

that are difficult to contact. It was the

U.S. Bureau of the Census’ decision that

using neighbors for ‘‘I’’ households

would be expensive and

incomprehensible to interviewers.

Because there was some initial

skepticism about using neighbors at all,

the U.S. Bureau of the Census Bureau

conducted a field test of screening

procedures in the summer of 1993. They

focused in particular on the accuracy of

neighbor information on race and ethnic

origin. The results were strongly

supportive of the utility of neighbor

contacts during screening. Information

about race from neighbors was shown to

be extremely accurate, at least for the

dichotomy of black versus not black.

Information about Hispanic origin was

less reliable but still useful.

New Construction

A plan was considered where new

construction would be treated the same

as the stratum for old construction

least dense with minorities (under

10 percent black and under 5 percent

Hispanic). This would have required

taking a considerably larger sample of

new construction and then screening it

for minorities. This recommendation

was rejected out of concern that the

new construction sample would be

more expensive to screen because it is

difficult to cluster effectively. There

was also a feeling that the new

construction universe would contain

even fewer minorities than the least

minority-dense old construction

stratum. (This supposition may be

tested with the 1995 design after

implementation. Such a study would

provide useful input to the 2005

redesign.) Current plans are not

specific on the sampling interval to be

used in new construction, but it is

likely to be larger than that used in

the 1985–94 design.

Dual-Frame Sampling for

Elderly Minorities

The oversampling and screening

plans described earlier will not yield

satisfactory precision for elderly black

persons or elderly Hispanic persons

unless NCHS allows all elderly minority

adults found in a household to be

interviewed, in addition to a randomly

selected adult between the ages of 18

and 64. These groups are simply too

rare to allow within-household

subsampling. As discussed in chapter 6,

there are attractive dual-frame sampling

systems employing SSA lists that could

improve the precision for elderly black

persons and Hispanic persons at

reasonable cost, while keeping the

restriction that only one adult be

interviewed per household. Such a

dual-frame system is not part of the

formal plan for the 1995 design at this

time. As of this writing, a dual-frame

system may be implemented in 1996.

Table 61 shows the projected effective

sample sizes for elderly black persons

and Hispanic persons from the planned

design (the alpha option). It also

shows the desired effective sample

sizes, the projected effective sample

sizes that could be attained with

dual-frame sampling, and the numbers

of additional screening interviews that

would be necessary to obtain those

effective sample sizes with dual-frame

sampling.

Panel Construction

This section begins with a review of

panel formation in 1985 and continues

with a review of the objectives for

panels, a discussion of the additional

complexities for 1995, a description of

some adjustments to the methods used

in 1985, and a couple of brief notes on

implications for weighting.

Panel Design for 1985

Four panels were defined in the

1985 design. To support panel definition,

self-representing PSU’s were divided

into three classes: Large, medium, and

small. The panels were defined at the

PSU level for NSR PSU’s and small SR

PSU’s. They were defined at the

assignment level for large- and

medium-sized SR PSU’s. (This may not

have been done in 1985 itself but was in

place for 1986 and beyond.) An

assignment was a collection of segments

within a PSU that were scheduled to be

interviewed in the same week. A PSU

was defined as the first-stage unit in

sample selection, not as the unit for

interviewer assignment as used for field

management and control. The panels

were defined by first forming pairs of

strata and then randomly assigning panel

designations 1 and 2 to the two PSU’s

in one stratum and 3 and 4 to the two

PSU’s in the other stratum within the

Table 61. Sample sizes for elderly minorities

Domain Desired

Effective sample sizes

Projected

under alpha

option

(1 household)

Projected

under alpha

option

(all households)

With extra

screened

2,800 cases

from SSA

(1 household)

With extra

screened

5,800 cases

from SSA

(1 household)

Black

Male ......................... 1,000 600 1,150 1,000 1,000

Female ....................... 1,000 740 1,710 1,000 1,000

Hispanic

Male ......................... 1,000 350 910 770 1,000

Female ....................... 1,000 495 1,270 920 1,000

Page 88 [ Series 2, No. 126
superstratum. The random assignment was

not independent across superstrata.

Dependence was created to better equalize

panel sizes. For example, if a PSU with a

large workload was assigned to panel 1 in

one superstratum, a PSU with a small

workload was chosen for panel 1 in the

next superstratum. The panels were

exhaustive and mutually exclusive. Each

panel was an equally valid national

sample. (See Design and Estimation for

the National Health Interview Survey,

1985–94 (33) for more details on 1985

procedures.)

Objectives

Panels had several uses in the 1985

design. The first use was sample

reduction. When adequate funds were not

available in 1985 and 1986 for the full

sample design, the panels were a quick

and efficient way to cut back the sample

because whole NSR PSU’s and complete

assignments in SR PSU’s were dropped.

The second use was advance

selection of a conveniently sized PSU

sample for occasional surveys such as

the National Hospital Discharge Survey.

Selecting general purpose designs in

advance saves some time and effort if a

survey comes along that needs

approximately that number of PSU’s.

There are also some operational

efficiencies in terms of knowing where

the U.S. Bureau of the Census should

maintain an interviewing potential.

Subsamples could obviously be drawn

when necessary, but it seemed

convenient to have plans in place for

such subsampling.

The third use of the panels was

splitting retired NHIS samples up

among competing followup surveys such

as the National Survey of Family

Growth. By stating that survey A could

have panels 1 and 3, and survey B could

have panels 2 and 4, none of the sample

was wasted or used twice. It is not clear

whether this capability was actually

used. Whether this would have been the

best way to split the sample between the

two surveys is also unclear. For some

pairs of surveys, it would probably have

been better to randomize segments or

even to split each segment between the

competing surveys.

The fourth use of the panels was

conducting randomized experiments

with the NHIS sample. This potential

does not appear to have been used. The

panels would not necessarily have

yielded a good experimental design

anyway, because randomization would

have been at a high level in the

sampling, allowing between-PSU and

between-interviewer variances to

obscure the results. A better design for

many purposes would be obtained by

splitting each work assignment, or even

each segment, between treatments.

Given the utilization patterns of the

panel structure in the 1985–94 decade, it

seems clear that the highest priority in

designing panels for the next decade

should be on the potential for orderly

cuts in the sample. (Cutting PSU’s

makes sense if a desire exists to reduce

costs while maintaining the same

relative sampling rates for minorities as

proposed for the full design. If, instead,

the decision is made to drop back to an

equiprobability sample, dropping PSU’s

is not the best way to cut the sample.

See chapter 17 for more details.) Based

on the cuts experienced in 1985 and

1986 (25 percent and 50 percent,

respectively) and the fact that the

number of PSU’s in the 1995 design

was chosen based on the assumption of

a 50-percent budget increase, it is

convenient to have panels that can make

cuts of the following magnitudes easy:

25 percent, 33 percent, 50 percent,

67 percent. The second priority should

be to have conveniently sized, first-stage

designs available for the provider surveys

and other new or occasional surveys; or at

least there should be a clear mechanism

for generating appropriately sized

subsamples of the NHIS first-stage design

with short lead times. The uses for

splitting the sample for either follow-on

surveys or experiments seem less

important and should probably not be

considered at all. It also seems reasonable

to abandon all support for State estimates

in subdesigns.

Problems for 1995

The NSR strata in the 1985 design

were roughly equal in size. Two PSU’s

were selected from each NSR stratum.

Neither of these two statements applies

to the 1995 design. To provide the

flexibility in the 1995 design for State

expansions, the NSR strata were defined

by mostly State and metropolitan status.

In slightly fewer than one-half of the

State-by-metro-status domains, it was

possible to stratify more deeply than

that. The most strata that were defined

within a State-by-metro-status domain

was five. In States where either the NSR

metropolitan population or the

nonmetropolitan population is small,

only one PSU was selected from the

stratum instead of two. In larger strata,

two PSU’s were selected. Some smaller

metropolitan areas were selected with

certainty because they made up a large

portion of the State metropolitan

population. The total number of PSU’s

in the 1995 design is 358. (Consolidated

metropolitan areas are just counted once,

regardless of how many administrative

PSU’s they might be divided into. A

number of smaller metropolitan areas

are counted twice, however, because

they cross State lines and multiple

fragments were selected.)

To minimize the variation in

baseweights for a reduced NHIS, it is

important to form the superstrata in such

a manner that the number of full-design

sample PSU’s per superstratum is

constant and the original strata within a

superstratum are nearly constant in size.

Also, the full-design sample PSU’s

should be subsampled with equal

probability within a superstratum. It

does not matter for this objective

whether the superstrata vary in size.

For the second objective of having

the subdesigns serve as appropriate

first-stage samples for provider surveys,

however, the superstrata for subsampling

PSU’s should be nearly equal in size

(population, health care providers, health

care events, etc.). This means collapsing

unequal numbers of original strata of

various sizes together to form

superstrata. For this objective, the

full-design sample PSU’s should be

subsampled with probability

proportionate to stratum size. If one

superstratum contains seven sample

PSU’s and another contains three, there

is no simple way of coming up with

exhaustive and mutually exclusive sets

of NSR PSU’s.

Series 2, No. 126 [ Page 89
Panel Construction for 1995

(This section describes panel

structures created by Westat, Inc., and/or

the U.S. Bureau of the Census.

Subsequently, NCHS created a panel

structure consisting of four subsets; that

is the panel structure that has been used

in the 1995–2004 redesign.)

It was decided to construct two

distinct panel structures. For the first of

these, to be constructed immediately, it

was decided that the same general

procedures would be followed as in

1985, forming superstrata with a fixed

number of sample PSU’s per

superstratum and then subsampling with

equal probabilities. For the second

structure, to be constructed when

needed, it was decided to form

superstrata with equal populations and

then to subsample with probability

proportionate to stratum population. The

plans for each of these structures is

discussed in more detail later. The first

panel structure will serve the first, third,

and fourth objectives just discussed. The

second panel structure will efficiently

satisfy the second objective. At the

conclusion of this section, the penalties

that would be suffered if the decision

were reached to use the first panel

structure to serve the second objective

are discussed.

Reduction panels—The general

procedures used in 1985 need only

minor adjustments. The most significant

of these adjustments is to provide the

capability of cuts of 16.7, 25.0, 33.3,

50.0, 66.7, 75.0, and 83.3 percent. To

provide this capability, two panel

structures will actually be created for

reduction purposes—one with four

panels and one with six. Both will be

created using the same methodology.

Only the four-PSU case is discussed in

detail here, but the generalization to six

PSU’s is straightforward.

The first adjustment to the old

four-PSU procedure is that, rather than

pairing only two 2-ps strata (strata in

which two PSU’s are selected per

stratum), it will be necessary to group

together either two 2-ps strata, one 2-ps

stratum and two 1-ps strata, or four 1-ps

strata. (If one 2-ps stratum is paired

with two 1-ps strata, it is important that

the 1-ps strata be roughly equal in size

and half the size of the 2-ps stratum.)

The second adjustment is that it may be

more difficult to pair strata that are

equal in size while still respecting

regional and metropolitan category

boundaries. On the one hand, original

strata vary much more in size, and on

the other hand, there are more original

strata to work with. Although adequate

data were not available at the time of

this writing to project the numbers of

SR PSU’s by size class that the

procedure will yield, this will be a

routine task for staff at the U.S. Bureau

of the Census. It is currently anticipated

that the resulting set of panels could

yield nested subdesigns with roughly

110 PSU’s, 200 PSU’s, 280 PSU’s, and

358 PSU’s. (Of course, the set of six

panels will provide additional options

beyond these.)

Within the large and medium SR

PSU’s, the reduction panels can be set

up along the same lines as in

1985—that is, by systematically

subsampling assignments. Current plans

call for assignments to be all roughly

equal in size, so there will be no need to

incorporate a measure of size into the

sampling. The sort for the subsampling

should probably include region, PSU,

administrative PSU, and an indicator of

whether the assignment is predominantly

central city or suburb. The systematic

subsampling should probably be done

across PSU’s with alternating sorts on

the indicators for central city status. The

subsampling interval should be four so

that the populations in SR and NSR

PSU’s have the same probabilities of

selection.

An additional feature of the

procedure for 1985 for assigning panel

numbers to the four subsets of the

PSU’s will be retained: Either expected

or actual NHIS sample sizes (in terms

of segment counts) by sample PSU will

be used to try to equalize total panel

sample sizes.

Subdesigns for other surveys—For a

number of reasons, it was decided to

delay forming panel structures for other

surveys. Although this work could be

done in advance, the panel structures

will be more efficient if designed after

the actual requirements of other surveys

are known. That will allow different

sorts and specialized measures of size.

For example, for a survey of hospices,

data on local numbers and sizes of

hospices could be used for stratification

and also be built into the measure of

size of each PSU to create a more

efficient design.

As was discussed in chapter 3,

further research is planned on the

benefits of concentrating all the provider

surveys into a single set of PSU’s.

Based on current research, however, and

assuming that U.S. Bureau of the

Census interviewers will be visiting all

358 NHIS PSU’s (i.e., that there are no

sample cuts), it is unclear whether there

are substantial additional operational

efficiencies from concentrating all the

provider surveys into a single subset of

NHIS. This suggests that it might be

best to custom-select a different subset

of the NHIS PSU’s for each of the

provider surveys.

It may be determined later that

there are important benefits in having

the provider surveys nested within each

other. The rest of this section indicates a

reasonable method for creating such a

set of subdesigns.

Two surveys that are likely to share

the NHIS design are NHDS and

NAMCS. These have recently (the early

1990’s) been carried out in the 112 PSU

two-panel subdesign of the 1985 NHIS.

Another recurring survey is the National

Nursing Home Survey (NNHS). It has

traditionally not been clustered. To ease

into clustering so as to avoid large risks

in the changeover process, it might

make sense to have a design with as

many as 200 PSU’s for it. (With more

than 1,200 sample facilities, even 200

PSU’s should generate enough

clustering to yield substantial

operational efficiencies.) After having

been fielded once with 200 PSU’s, it

would be possible to study the

components of variance to see whether

a smaller number of PSU’s would

serve survey objectives adequately.

Other surveys of providers are planned

but are brand new, so there is no basis

for choosing an optimal number of

PSU’s for them. For demographic

surveys, past (not publicly

documented) experience has shown

that between-PSU variance on

minority estimates is important at 100

PSU’s and small at 200 PSU’s.

Page 90 [ Series 2, No. 126
Interpolation between those extremes

is uncertain and not attempted here.

The 52 largest SR PSU’s in the full

1995 design (358 PSU’s) were identified

with the thought that they would be

appropriate for a national survey of

approximately 200 PSU’s. Additional

SR PSU’s were identified for State

purposes, but there is no guarantee that

all the PSU’s that should be SR for a

national survey of more than 200 PSU’s

are in the sample. Thus, it is

recommended that the largest subdesign

contain 200 sample PSU’s. These 200

should be selected in such a manner that

each sample PSU represents an

approximately equal number of persons.

Once that subdesign has been selected,

it will then be possible to split this set

of 200 sample PSU’s into panels in the

same manner as was done for the 198

PSU’s in the full 1985 design. In this

manner a set of nested subdesigns can

be created with 62 PSU’s, 113 PSU’s,

155 PSU’s, and 200 PSU’s.

The 306 full-design sample PSU’s

outside of the 52 largest consolidated

metropolitan areas should be grouped

into about 74 superstrata. This means an

average of 4.2 sample PSU’s per

superstratum. The superstrata should be

formed by applying the stratification

software developed at the U.S. Bureau

of the Census directly to the 306 PSU’s.

The measure of size for a PSU should

be defined as the stratum population

represented by the PSU (one-half of

stratum population in the case of PSU’s

from 2-ps strata). Substantive

characteristics of the sample PSU itself

should be used to define similarity

rather than stratum characteristics. (It is

permissible and indeed most efficient to

use the characteristics of the sample

PSU itself rather than just those of the

stratum that it represents. However,

unbiased estimates of variance are

then not possible, and reasonable

estimates of variance components are

likely to be particularly problematic. See

‘‘Using Sample Information for

Stratification’’ (34).) The superstrata

should be constrained to respect region

and metro status and to be roughly

equal in size. Respecting region and

metro status is important because these

are likely variables in NCHS reports.

Keeping the superstrata sizes roughly

equal is important because it reduces

design effects and gives greater control

over workloads. (Workload control is

not that important for NHIS with its

itinerant interviewers but may be for the

provider surveys.) Of course, it is very

likely that it will be difficult to find a

stratification that respects all these

constraints and has internally

homogeneous strata. The phrase

‘‘roughly equal’’ already provides some

slack, but it may be necessary to relax

the constraint on strictly metro and

nonmetro superstrata. This would not be

catastrophic because the definitions of

metropolitan areas change over time

anyway.

Within each superstratum, two

PSU’s should be selected by the

Brewer-Durbin method with probability

proportionate to the measure of size

defined earlier. Then the stratification

program should be applied to the

superstrata to form hyper-superstrata.

The hyper-superstrata should also

respect regional and metropolitan

boundaries to the greatest extent

compatible with roughly equal sizes for

the hyper-superstrata. One superstratum

should then be selected from each

hyper-superstratum with probability

proportionate to superstratum size. With

four sample PSU’s per hypersuperstrata, the four subpanels can then

easily be identified.

An alternative that is less efficient

but more elegant to describe is to form

just 37 superstrata to begin with and

then select four PSU’s from each

superstratum by either the

Durbin-Sampford method or the

Rao-Hartley-Cochran method with

probability proportionate to the measure

of size defined earlier. The DurbinSampford method is generally more

efficient than the Rao-Hartley-Cochran

method but is substantially more

difficult to implement for n = 4.

Why not a unified set of panels?

Two distinct panel structures have been

recommended in this report because

each will be more efficient for a

particular objective and because no

serious disadvantages are foreseen. To

play the devil’s advocate, however,

suppose there were a cut in the NHIS

sample size to 280 PSU’s and there

were a survey of dentists in 200 PSU’s

for which the U.S. Bureau of the Census

could promise substantial budget savings

if the sample were concentrated within

NHIS PSU’s. Because some of the 200

PSU’s in the national subdesign for

provider surveys would probably have

been among the 78 PSU’s dropped (in

this example) by NHIS, some efficiency

would have been lost. If the cost savings

are great enough, however, it would be

possible to pick a new national design

of 200 PSU’s from the 280 still in the

NHIS.

If the smaller design effects and

more stable workloads associated with

uniform superstratum sizes are not

important for a particular provider

survey or for some other piggyback

survey, however, the subdesigns

prepared for NHIS cuts could also be

used for such a survey. To recapitulate

the sizes that will be available under the

plans recommended here, it will be easy

to reduce the NHIS to (roughly) either

85, 110, 140, 200, 250, 280, or 300

PSU’s from the planned 358 PSU’s; and

subdesigns readily available for surveys

with a requirement for more equal

workloads will have 62, 113, 155, or

200 PSU’s.

Note that the two 200-PSU

subdesigns will have quite different

characteristics. The NHIS will be

self-weighting (within block strata)

across the set of 200 PSU’s designed for

a reduced NHIS, but NHIS workloads

will vary widely across those 200

PSU’s. NHIS will not be

self-weighting across the set of 200

PSU’s designed for a piggyback

survey nor will it have equal

workloads across those PSU’s

(because of oversampling minority

density strata). However, a uniform

survey of total population could be

made self-weighting with equal

workloads across the set of 200 PSU’s

designed for a piggyback survey.

Weighting Implications

If it is necessary to breach

metropolitan boundaries when

assembling superstrata, it would

probably make sense to incorporate a

first-stage adjustment with metro and

nonmetro cells into the weighting. Also,

if a piggyback survey uses one of the

Series 2, No. 126 [ Page 91
fixed subpanels designed for

multipurpose use even though the ideal

measure of size for the survey is very

different from total population, a

first-stage adjustment based on the ideal

measure of size should be incorporated

into the weighting for that survey.

Summary of Design

Characteristics

Figure 1 shows the projected sample

sizes for the new design. It shows the

total number of listing lines designated

for screening, total number of screened

households, number of interviewed

households, and effective person-sample

sizes for both options of interviewing all

adults and for interviewing just one

adult per household.

Primary Sampling Units

PSU’s were selected for the 1995

design. The PSU’s are clusters of whole

adjacent counties. In nonmetropolitan

areas, the clusters were formed subject

to minimum constraints on population

and maximum constraints on land area.

In metropolitan areas, the PSU’s are

complete 1990 MSA’s except where

MSA’s cross State lines and in New

England. Multistate MSA’s were split

into State components. PSU’s in New

England were based on NECMA’s.

After the PSU’s had been defined,

they were stratified by State and

metropolitan status. Large MSA’s were

designated SR and placed into separate

strata. In States with large numbers of

NSR MSA PSU’s or large numbers of

nonmetropolitan PSU’s, the PSU’s were

further stratified by income with the

constraint that the strata within a State

and metropolitan category be

approximately equal in size.

After stratification, one or two

PSU’s were selected from each stratum

with probability proportionate to size.

The measure of size was the 1995

estimated total resident population (all

ages). Where two PSU’s were selected,

the selection was done without

replacement using the Brewer-Durbin

method. A total of 358 PSU’s were

selected.

Household Clusters

Placeholders were created for all

HU’s reported in the 1990 census in a

PSU. These placeholders were sorted by

the characteristics of their parent blocks

and, within that sort, by their block

labels. (Blocks with fewer than four

known housing units were collapsed

with other blocks and given a combined

block label.) One of the block

characteristics used in the sort was

whether there were substantial additional

operational efficiencies from

concentrating all the provider surveys

into a single subset of NHIS. This

suggests that it might be best to

custom-select a different subset of the

NHIS PSU’s for each of the provider

surveys.

Figure 1. Summary of sample sizes for new design (alpha option)

Page 92 [ Series 2, No. 126
Chapter 17.

Contingency Plans

At the time the research described

in this report was performed, it

was not clear whether the

50-percent budget increase needed to

implement the recommended design

would be available. Consequently, a

number of contingency plans were

developed. After intense study and

discussion, one of these options was

chosen as the fallback position. It is

referred to as the beta option. (The

intended redesign described in chapter

16 is referred to as the alpha option.)

In fact, in late 1994, a decision was

made to implement the beta option

rather than the alpha option. The beta

option assumes the same level of

funding as in 1988 (after adjustment for

inflation). It calls for the continued

oversampling of black persons at the

current modest rate and for new

oversampling of Hispanic persons at a

rate high enough so that the precision

for Hispanic estimates is comparable to

that for black estimates.

Several other alternatives that were

seriously considered are briefly

described later for documentary

purposes.

The Beta Option

The beta option is similar to the

recommended design but has a much

lower level of screening. Instead of

designating 123,900 HU’s and GQE’s

for screening, only 71,500 will be

designated. Also, the number of ‘‘other’’

households discovered during screening

to be retained for full interviews will be

reduced from 35,100 to 28,900. These

two steps will reduce the cost from a

level 50 percent over a survey with

50,000 regular interviews, to a level

5 percent below such a survey. Because

the 1988 NHIS had about 47,500 regular

interviews, these reductions will make

the beta option close to budget-neutral

relative to 1988, after correcting for

inflation.

This cut was crafted in such a way

that the precision for black statistics will

be about the same as in 1988, the

precision for Hispanic statistics will be

sharply improved over 1988 levels to

about the same level as the precision of

black statistics, and the precision for all

other statistics will drop fairly sharply

relative to 1988. These decisions were

guided by NCHS perceptions of

precision needs for the domains. The

basic directive was to hold the precision

for black statistics steady, improve the

precision for Hispanic statistics to bring

them into parity with black statistics,

and let the precision for other statistics

fall into place as dictated by the budget.

Table 62 shows the oversampling

rates that are projected to generate the

required numbers of black and Hispanic

households with an approximately

optimal tradeoff between cost for

screening and higher design effects for

black and Hispanic statistics. These rates

are relative to the sampling rate for a

hypothetical equiprobability survey that

would yield 50,000 completed

interviews. These rates can be compared

with those for the alpha option in

table 20. Note that under the beta

option, blocks with heavy concentrations

of Hispanic persons and light

concentrations of black persons are

actually sampled more heavily than

blocks with heavy concentrations of

both minorities. This was arranged

deliberately to gain parity between black

and Hispanic effective sample sizes.

From a systems engineering

standpoint, it was important that all

sample clusters for the beta design be a

subset of those selected for the alpha

design. The U.S. Bureau of the Census

had done a tremendous amount of work

by the fall of 1994 to select and prepare

the clusters for the alpha option. Any

use of different clusters by the beta

design would have resulted in at least a

2-year delay in the 1995 NHIS. Table 63

shows the percentages of the sample

selected for the alpha option that will be

needed for the beta option. These were

calculated by the formula

Table 62. Oversampling rates for the beta design (relative to a design that would yield 50,000 interviews)

Percent of block that is black

Percent of block that is Hispanic

< 5 5–10 10–30 30–60 60+

< 10 . . . . . . . . . . . . . . . . . . . . . . . . . 1.0 1.5 1.5 1.6 2.3

10–30 . . . . . . . . . . . . . . . . . . . . . . . . 1.0 1.5 1.5 1.5 2.0

30–60 . . . . . . . . . . . . . . . . . . . . . . . . 1.0 1.0 1.0 1.5 2.0

60+ . . . . . . . . . . . . . . . . . . . . . . . . . . 1.0 1.0 1.2 1.5 1.5

NOTE: Compare with table 20 for the alpha design.

Table 63. Percent of total sample reserved for alpha option to be retained for the beta option

Percent of block that is black

Percent of block that is Hispanic

< 5 5–10 10–30 30–60 60+

< 10 . . . . . . . . . . . . . . . . . . . . . . . . . 62.5 75.0 46.9 40.0 57.5

10–30 ........................ 62.5 75.0 42.9 37.5 50.0

30–60 ........................ 58.8 50.0 28.6 37.5 50.0

60+ . . . . . . . . . . . . . . . . . . . . . . . . . . 51.3 50.0 34.3 37.5 37.5

Series 2, No. 126 [ Page 93
keepi = ki

'

ki

where the numerator is the oversampling

rate for the beta design from table 62

and the denominator is the oversampling

rate for the alpha design from table 20.

The cut in the designated alpha

sample will be achieved by a mixture of

dropping whole clusters before listing

and subsampling listed HU’s in other

clusters. Although it would have been

possible to achieve the cut either by

only dropping whole clusters or by only

subsampling listed HU’s within the

whole set of clusters selected for the

alpha option, the mixture approach will

result in the smallest variation in the

number of interviewed households per

cluster. Table 64 shows the optimal

cluster sizes for the beta design by

stratum. These may be compared with

those in table 59 and were calculated by

the formulas given in chapter 16.

Let yi be the proportion of clusters

selected for the alpha design that will be

kept for the beta design. Let zi be the

proportion of listed HU’s (and GQE’s)

that would be used for the alpha design

that still need to be used for the beta

design. Let si and si

' be the optimal

cluster sizes for the alpha and beta

designs, respectively. Then y and z are

obtained by solving the set of equations

ki

' = yiziki and

si

' = zisi

The results of solving this system of

equations are shown in tables 65 and 66.

Tables 67–70 parallel tables 21–24

showing persons in screened households

by domain, retention rates for ‘‘other’’

households by stratum, effective sample

sizes by domain when all household

members are sampled, and effective

sample sizes by domain when just one

adult is sampled per household,

respectively. These tables were derived

using the same formulas and methods as

discussed in chapter 5. As in chapter 5,

the effective sample sizes reflect the

reduced power of the sample (relative to

a simple random sample of the same

size) due to differential weights but not

the reduced power due to clustering.

The effective sample sizes are best

compared with an equiprobability design

that is subject to the same clustering as

the 1985–94 design of NHIS.

The total number of screened

households with cooperative occupants

is projected to be about 57,200 in the

year 2000 with a total of 144,000

occupants of all ages, of whom 106,000

will be 18 years and over. The

distribution of these screened

households across the domains of

interest is shown in table 67. The total

of 57,200 screened households is too

many to retain for full interviews given

an inflation-adjusted 1988 NHIS budget.

According to the projection of a

screening cost equal to one-third of a

full interview cost, the total number of

households that can be kept for full

interviews is 41,400. This means that

only 64.7 percent of the ‘‘other’’

households discovered during screening

can be retained. The retention rates in

table 68 restore the sample for ‘‘other’’

Table 64. Desired cluster sizes for beta design (in terms of placeholders)

Percent of block that is black

Percent of block that is Hispanic

< 5 5–10 10–30 30–60 60+

< 10 . . . . . . . . . . . . . . . . . . . . . . . . . 12 12 12 12 8

10–30 . . . . . . . . . . . . . . . . . . . . . . . . 12 12 12 8 8

30–60 ........................ 8 8 8 8 8

60+ . . . . . . . . . . . . . . . . . . . . . . . . . . 8 8 8 8 8

NOTE: Compare with table 59.

Table 65. Percent of strings reserved for the alpha option to be retained for the beta option

Percent of block that is black

Percent of block that is Hispanic

< 5 5–10 10–30 30–60 60+

< 10 . . . . . . . . . . . . . . . . . . . . . . . . . 83.3 100.0 62.5 40.0 57.5

10–30 ........................ 62.5 75.0 42.9 37.5 50.0

30–60 ........................ 88.2 75.0 42.9 37.5 50.0

60+ . . . . . . . . . . . . . . . . . . . . . . . . . . 51.3 50.0 34.3 37.5 37.5

Table 66. Percent of listing lines reserved for NHIS within strings retained for the beta option that are actually to be screened for the beta

option

Percent of block that is black

Percent of block that is Hispanic

< 5 5–10 10–30 30–60 60+

< 10 . . . . . . . . . . . . . . . . . . . . . . . . . 75 75 75 100 100

10–30 . . . . . . . . . . . . . . . . . . . . . . . . 100 100 100 100 100

30–60 . . . . . . . . . . . . . . . . . . . . . . . . 67 67 67 100 100

60+ . . . . . . . . . . . . . . . . . . . . . . . . . . 100 100 100 100 100

Page 94 [ Series 2, No. 126
Table 67. Persons in screened households for the beta option

Race, Hispanic origin, and age

All adults in each household

Male Female Total

Black

Under 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 743 712 1,454

5–17 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2,117 2,019 4,136

18–24 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 785 919 1,704

25–44 ............................... 2,125 2,695 4,821

45–64 ............................... 1,363 1,760 3,122

65 and over . . . . . . . . . . . . . . . . . . . . . . . . . . . 630 932 1,561

Total ................................ 7,762 9,036 16,798

Total 18 and over ........................ 4,903 6,305 11,208

Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... ... 6,192

Hispanic

Under 5 .............................. 1,275 1,221 2,496

5–17 ................................ 3,064 2,926 5,989

18–24 ............................... 1,161 1,123 2,284

25–44 ............................... 3,243 3,165 6,408

45–64 ............................... 1,457 1,650 3,107

65 and over . . . . . . . . . . . . . . . . . . . . . . . . . . . 525 736 1,261

Total ................................ 10,725 10,821 21,546

Total 18 and over ........................ 6,386 6,674 13,060

Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... ... 6,340

Other

Under 5 .............................. 3,080 2,919 5,999

5–17 ................................ 9,306 8,825 18,131

18–24 ............................... 4,476 4,517 8,993

25–44 ............................... 15,450 15,670 31,119

45–64 ............................... 12,693 13,178 25,871

65 and over ........................... 6,265 9,055 15,320

Total ................................ 51,269 54,164 105,433

Total 18 and over ........................ 38,883 42,419 81,302

Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... ... 44,671

Total

Under 5 .............................. 5,098 4,852 9,950

5–17 ................................ 14,486 13,771 28,257

18–24 ............................... 6,422 6,558 12,981

25–44 ............................... 20,817 21,530 42,347

45–64 ............................... 15,512 16,588 32,100

65 and over ........................... 7,420 10,722 18,142

Total ................................ 69,756 74,021 143,777

Total 18 and over ........................ 50,171 55,399 105,570

Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... ... 57,204

. . . Category not applicable.

NOTE: Figures may not add to totals because of rounding.

Table 68. Percentage of other households discovered during screening to be kept for full interviews, beta option

Percent of block that is black

Percent of block that is Hispanic

< 5 5–10 10–30 30–60 60+

< 10 . . . . . . . . . . . . . . . . . . . . . . . . . 71.2 47.4 47.4 44.5 30.9

10–30 ........................ 71.2 47.4 47.4 47.4 35.6

30–60 ........................ 71.2 71.2 71.2 47.4 35.6

60+ . . . . . . . . . . . . . . . . . . . . . . . . . . 71.2 71.2 59.3 47.4 47.4

Series 2, No. 126 [ Page 95
households to an equiprobability basis.

The design effects implied by the

oversampling rates in table 62 are 1.033,

1.082, 1.000, and 1.095 for the black,

Hispanic, ‘‘other,’’ and total populations,

respectively. Application of these design

effects to the raw sample sizes in

table 67 and allowing for the random

subsampling of ‘‘other’’ households

leads to the effective sample sizes

shown in table 69. These are the sample

sizes that should be used to predict

precision for the core questionnaire

items that are asked of all household

members. For questionnaire items that

are administered to just one random

selected adult per household, table 70

shows the effective sample sizes that

can be used to predict precision.

Table 71 contrasts the alpha and

beta options with both the old design

(1988) and equiprobability samples.

Note that under either option, the

precision for black and Hispanic

estimates will be comparable to each

other. With the beta option, the effective

sample size for ‘‘others’’ drops by about

20 percent, much steeper than the

4-percent drop under the alpha option.

The effective sample size for total

population declines by about 10 percent

under the beta option, compared with

about a 10-percent improvement under

the alpha option. There are two

equiprobability designs shown in the

table. One consists of 47,000

interviewed households (the same size

as the 1988 NHIS) and the other

consists of 50,000 interviewed

households (the design that was the

basis of projections in chapter 4). The

effective sample sizes for the black

Table 69. Effective sample sizes for beta design when all household members are sampled

Race, Hispanic origin, and age

All adults per household

Male Female Total

Black

Under 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 719 689 1,408

5–17 ................................ 2,049 1,955 4,004

18–24 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 760 889 1,649

25–44 ............................... 2,057 2,609 4,667

45–64 ............................... 1,319 1,703 3,023

65 and over . . . . . . . . . . . . . . . . . . . . . . . . . . . 610 902 1,511

Total ................................ 7,514 8,748 16,262

Total 18 and over ........................ 4,746 6,104 10,850

Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... ... 5,994

Hispanic

Under 5 .............................. 1,179 1,129 2,307

5–17 ................................ 2,831 2,704 5,535

18–24 ............................... 1,073 1,038 2,111

25–44 ............................... 2,997 2,925 5,922

45–64 ............................... 1,346 1,525 2,872

65 and over . . . . . . . . . . . . . . . . . . . . . . . . . . . 486 680 1,165

Total ................................ 9,912 10,001 19,913

Total 18 and over ........................ 5,902 6,168 12,070

Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... ... 5,859

Other

Under 5 .............................. 1,993 1,889 3,882

5–17 ................................ 6,021 5,710 11,731

18–24 ............................... 2,896 2,922 5,818

25–44 ............................... 9,996 10,138 20,134

45–64 ............................... 8,212 8,526 16,738

65 and over ........................... 4,053 5,859 9,912

Total ................................ 33,171 35,044 68,215

Total 18 and over ........................ 25,157 27,445 52,602

Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... ... 28,902

Total

Under 5 .............................. 3,335 3,173 6,508

5–17 ................................ 9,606 9,128 18,734

18–24 ............................... 4,305 4,410 8,716

25–44 ............................... 14,080 14,601 28,681

45–64 ............................... 10,715 11,434 22,149

65 and over ........................... 5,165 7,469 12,634

Total ................................ 47,206 50,215 97,421

Total 18 and over ........................ 34,265 37,914 72,179

Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... ... 37,821

. . . Category not applicable.

NOTE: Figures may not add to totals because of rounding.

Page 96 [ Series 2, No. 126
population declines under either

equiprobability design relative to the

1988 NHIS because of the oversampling

of the black population that was done in

the 1985–94 NHIS design. Because that

oversampling was not very effective, the

declines in the black effective sample

size associated with a switch to an

equiprobability design would be modest.

The oversampling in the 1985–94 design

also explains the increases in the

effective sample sizes for the ‘‘other’’

and total populations under the

equiprobability designs relative to the

1988 NHIS. The strong increases in the

Hispanic effective sample sizes under

the equiprobability design are mostly

due to the projected growth of the

American Hispanic population between

1988 and 2000. Finally, it is important

to remember the caveats about the

projections given in chapter 4. All the

projections are very sensitive to

undercoverage rates for the black and

Hispanic populations, and it is well

established that these rates fluctuate

considerably from year to year for no

known reason. Also, the projections of

the growth of the Hispanic population

are quite sensitive to immigration

policies and economic conditions abroad

and in the United States.

New Construction

Sampling

Sampling of permits for new

residential buildings is being done about

the same for the 1995 design as for the

1985 design. The permits carry no

information on race or ethnicity of

future occupants. Furthermore, it is

extremely difficult to map individual

permits into blocks defined for the 1990

decennial census. The main reason for

Table 70. Effective sample sizes for beta design when one adult is sampled per household

Race, Hispanic origin, and age

One adult per household

Male Female Total

Black

Under 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

5–17 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

18–24 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 328 384 712

25–44 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 888 1,126 2,014

45–64 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 569 735 1,305

65 and over . . . . . . . . . . . . . . . . . . . . . . . . . . . 263 389 652

Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

Total 18 and over ........................ 2,048 2,635 4,683

Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... ... 5,994

Hispanic

Under 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

5–17 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

18–24 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 417 403 820

25–44 ............................... 1,164 1,136 2,300

45–64 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523 592 1,115

65 and over . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 264 453

Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

Total 18 and over ........................ 2,292 2,395 4,688

Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... ... 5,859

Other

Under 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

5–17 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

18–24 ............................... 1,337 1,349 2,686

25–44 ............................... 4,615 4,681 9,296

45–64 ............................... 3,792 3,937 7,729

65 and over ........................... 1,871 2,705 4,577

Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

Total 18 and over ........................ 11,616 12,672 24,288

Households . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... ... 28,902

Total

Under 5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

5–17 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

18–24 ............................... 1,942 1,993 3,935

25–44 ............................... 6,203 6,460 12,663

45–64 ............................... 4,517 4,874 9,391

65 and over ........................... 2,144 3,098 5,242

Total . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ... ... ...

Total 18 and over ........................ 14,805 16,425 31,230

. . . Category not applicable.

NOTE: Figures may not add to totals because of rounding.

Series 2, No. 126 [ Page 97
this difficulty is that much new

construction takes place along new

roads that did not exist at the last

census. Also, sometimes the permits are

identified in terms of legal language

(e.g., plots, land grants) rather than by

street addresses. Given that the

occupants of newly constructed housing

units tend to be disproportionately white

and not Hispanic, and given the higher

cost of screening permits, the decision

was reached to sample permits at the

same rate as ‘‘other’’ housing is sampled

after screening. This means that new

construction permits will be sampled at

rather low rates and that any black or

Hispanic households that happen to be

found there will have rather large

weights compared with other black and

Hispanic households in the sample.

Restoration of an

Equiprobability Design at

Level Cost and Other

Contingency Plans

Another option seriously considered

as a contingency plan was to drop back

to an equiprobability sample that would

cost the same as the 1988 survey after

adjustment for inflation. (The idea here

was an all-or-nothing approach on

minority statistics.) One idea was to

bring back dropped segments and then

do a uniform cut. Theoretically, this

would work because (as discussed in the

fifth section of chapter 16) the U.S.

Bureau of the Census initially selected a

random clustered equiprobability

5-percent sample of the universe. Much

of this sample in the lower minority

density strata was set aside as not to be

used. These ‘‘dropped’’ clusters are

being reserved for NHIS use, but

nothing is being done to make them

ready for quick use. During the course

of the redesign research, the U.S.

Bureau of the Census estimated that

2 years or more of lead time would be

required to restore any dropped clusters.

This is a longer lead time than is

compatible with quick response to

funding contingencies. Thus, in

designing a contingency plan to restore

an equiprobability design, it was

important to think of a way to do it

without using the dropped clusters.

This is indeed possible. The key to

the feasibility of restoring an

equiprobability sample without using the

dropped clusters is the fact that the

proposed redesign includes large

oversamples (relative to a design with

50,000 completes) in all strata for

screening even after dropping initial

clusters. In fact, after dropping clusters

according to the alpha option, the

sample is still 60 percent higher even in

the lowest minority density stratum than

under the old design. The only

drawback to not being able to bring

back dropped clusters on short notice is

that the expected segment sizes will be

larger than desired in several strata. The

retention rates for an equiprobability

sample (designed to yield about 48,500

completed household interviews) are

shown in table 72.

Note that if the subsampling is done

on the household level after listing in

the low-minority-density strata rather

than subsampling whole segments, the

variation in segment size can be reduced

considerably. The drawbacks of such a

procedure would be decreased savings

in listing cost and increased complexity

in sampling systems. Tables 73 and 74

show retention rates at the segment and

within-segment levels that would

minimize the variation in segment size.

Projections of effective sample sizes

for this plan are given in table 71. Note

Table 71. Effective adult sample sizes for the year 2000 by redesign option and population domain relative to the 1988 NHIS design when

just one adult is sampled per sample household

Budget Total Black Hispanic Other

1988 NHIS 95 35,600 4,500 2,200 30,900

Projections for the year 2000

Alpha option . . . . . . . . . . . . . . . . . . . . . . . . . . . 150 38,400 8,900 8,800 29,600

Beta option . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 31,230 4,700 4,700 24,300

Equiprobability . . . . . . . . . . . . . . . . . . . . . . . . . . 95 39,500 4,100 3,000 32,400

Equiprobability . . . . . . . . . . . . . . . . . . . . . . . . . . 100 41,600 4,300 3,100 34,100

Percent change from 1988

Alpha option . . . . . . . . . . . . . . . . . . . . . . . . . . . 150 +8% +97% +299% −4%

Beta option . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 −12% +4% +113% −21%

Equiprobability . . . . . . . . . . . . . . . . . . . . . . . . . . 95 +11% −8% +35% +5%

Equiprobability . . . . . . . . . . . . . . . . . . . . . . . . . . 100 +17% −3% +42% +10%

NOTE: Budget for 1988, adjusted for inflation, equals 95.

Table 72. Percent of sample originally selected that would be retained for an equiprobability design

Percent of block that is black

Percent of block that is Hispanic

< 5 5–10 10–30 30–60 60+

< 10 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.5 50.0 31.3 25.0 25.0

10–30 ............................... 62.5 50.0 28.6 25.0 25.0

30–60 ............................... 58.8 50.0 28.6 25.0 25.0

60+ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51.3 50.0 28.6 25.0 25.0

Page 98 [ Series 2, No. 126
that the effective sample size for black

persons would drop relative to the 1988

design because they would not be

oversampled. Effective sample sizes for

Hispanic persons would rise simply

because of their projected population

growth.

By using the reduction panels

described in chapter 16 in the section on

1995 panel construction, it is also

possible to shrink the whole design

proportionately.

Table 73. Percentage of strings to keep in conjunction with post-listing subsampling for equiprobability design

Percent of block that is black

Percent of block that is Hispanic

< 5 5–10 10–30 30–60 60+

< 10 . . . . . . . . . . . . . . . . . . . . . . . . . 100.0 100.0 62.5 37.5 25.0

10–30 ........................ 93.8 75.0 42.9 25.0 25.0

30–60 ........................ 88.2 75.0 42.9 25.0 25.0

60+ . . . . . . . . . . . . . . . . . . . . . . . . . . 51.3 50.0 28.6 25.0 25.0

Table 74. Percentage of listed HU’s to keep in retained strings in conjunction with string subsampling for equiprobability design

Percent of block that is black

Percent of block that is Hispanic

< 5 5–10 10–30 30–60 60+

< 10 . . . . . . . . . . . . . . . . . . . . . . . . . 62.5 50.0 50.0 66.7 100.0

10–30 ........................ 66.7 66.7 66.7 100.0 100.0

30–60 ........................ 66.7 66.7 66.7 100.0 100.0

60+ . . . . . . . . . . . . . . . . . . . . . . . . . . 100.0 100.0 100.0 100.0 100.0

Series 2, No. 126 [ Page 99
Chapter 18.

Recommendations for

Continuing Research

Intercensal Tracking of

Minority Populations

Across Density Strata

As the decade progresses, it will be

possible to use the NHIS to estimate the

proportion of each domain living in

each minority density stratum by using

the technique used to estimate the 1988

distributions (as described in ‘‘Cost

Functions for NHIS and Implications for

Survey Design’’ (15)). Such tracking

might indicate that adjustments should

be made in the allocation of the sample

across the strata. These adjustments can

be made by adding and/or dropping

segments (within the 2.5 percent

reserved for NHIS) and by adjusting

postscreening retention rates. A lag will

occur, probably of several years, in

implementing such adjustments. It is

recommended that estimates of the

proportion of minorities in the density

strata be made every year. Furthermore,

these should not be viewed as done only

for research purposes but that

consideration of modification of

subsampling rates be performed at

regular intervals.

Updating Minority Density

Areas

By the year 1998, the information

on the distribution of minorities by

density strata was 8 years old. Data

from the 2000 census will not be

available at the block level for another 3

years. Even when available, it will

probably take several years to update

the 1990 data about the blocks. Work

comparing 1980 and 1988 showed

significant movement of black persons

out of areas that had strong black

concentrations in 1980. Many of these

migrants’ new neighborhoods were also

predominantly black by 1988, although

they were mostly white in 1980. Similar

trends may be expected for the 1990’s.

Black persons are predicted to continue

to migrate out of heavily segregated

inner-city neighborhoods into less

segregated city and suburban

neighborhoods. Some white persons are

likely to leave these neighborhoods; if

they do, new predominantly black

neighborhoods will have formed. As this

process continues, the oversample in the

old black neighborhoods will yield

smaller and smaller black samples, and

the black persons who are found in the

new, formerly white, areas will have

large weights, reducing precision for

minorities.

A middecade census with a

crosswalk to 1990 blocks would largely

solve this problem, but given the low

probability of a mid-decade census,

alternatives were sought to update the

minority density of blocks, block

groups, or even just tracts.

Unfortunately, no viable alternatives

are currently apparent. A study

performed in a number of local

jurisdictions found almost all of them

do not maintain data bases that would

be helpful (35). The sole exception

was a school district involved in

court-ordered desegregation.

It would be useful to repeat this

study in 1998–99. It is possible that

local area administrative requirements

will make such data bases more

common. They would permit updating

of the density strata. The cost of this

kind of research is quite modest.

Coverage Improvement

As the research was underway, an

aspect of NHIS emerged that had not

been previously identified as a

high-priority concern. NHIS coverage

rates for adult black males (where

coverage is defined as the proportion of

the census ‘‘adjusted’’ total population

that is covered in the survey) appears to

be in the 60- to 75-percent range for

most age groups. Coverage rates for

Hispanic persons are not computed on a

regular basis, but the U.S. Bureau of the

Census’ experience with Current

Population Survey indicates that black

and Hispanic male coverage rates are

roughly comparable. Black female

coverage is not as low as the male rates,

but Current Population Survey evidence

indicates that Hispanic female coverage

is probably not much different than that

for Hispanic males.

It is fairly obvious that the health

and social characteristics of the

uncovered population are quite different

from those of the covered population.

Furthermore, it is unlikely that the

weighting procedures fully compensate

for the differences. The resulting bias in

the NHIS estimates could be quite

serious, possibly more troubling than the

bias due to accepting proxy response.

NHIS research may have been focused

too tightly on sampling error at the

expense of bias. Unfortunately, this

problem is extremely difficult to

alleviate. At a meeting of staff members

of NCHS, the U.S. Bureau of the

Census, and Westat, Inc., a number of

suggestions were made for additional

research on the causes of the

undercoverage. Westat, Inc., staff felt

strongly that enough basic research had

been done, however, and that it was

time to start testing methods of

changing NHIS procedures, using some

of the ideas that had been put forward

previously. The U.S. Bureau of the

Census subsequently started to

implement such research. For

preliminary results, see ‘‘Coverage

Improvement from Experimental

Residence Questions’’ (36).

Page 100 [ Series 2, No. 126
References

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estimates for the National Health

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854–9. 1989.

2. Alternate survey design for the

National Health Care Survey. Contract

number 200–93-7043.

3. Botman SB. Gauging the effects of the

NHIS respondent rule and

within-household sampling strategy. In:

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research methods. American Statistical

Association. 196–201. 1991.

4. NHIS redesign memorandum number

40 from D Judkins to D Malec. Draft

report on variance estimation.

September 26, 1990.

5. Wolter K. Chapter 5. In: Introduction to

variance estimation. Springer-Verlag:

New York. 1985.

6. Valliant R. Generalized variance

functions in stratified two-stage

sampling. Journal of the American

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7. U.S. Bureau of the Census. Projections

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by age, sex, and race: 1988 to 2080.

Current population reports; series P-25,

no 1018. 1989.

8. U.S. Bureau of the Census. Projections

of the Hispanic Population: 1983 to

2080. Current population reports; series

P-25, no 995. 1986.

9. NHIS redesign memorandum number 9

from J Waksberg to D Malec.

Preliminary analysis of revisions in

NHIS sample design required for

subdomain statistics. December 13,

1989.

10. Fahimi M, Judkins D. PSU

probabilities given differential sampling

at second stage. In: Proceedings of the

section on survey research methods.

American Statistical Association.

538–43. 1991.

11. Waksberg J, Mohadjer L. Automation

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research methods. American Statistical

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Survey. In: Proceedings of the section

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Statistical Association. 210–5. 1995.

13. Sirken MG, Graubard BI, McDaniel

MJ. National network surveys of

diabetes. Proceedings of the section on

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14. Judkins D, Waksberg J, Northrup D.

Cost functions for NHIS and

implications for survey design. In:

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research methods. American Statistical

Association. 34–43. 1990.

15. Judkins D, Massey J, Waksberg J.

Patterns of residential concentrations by

race and Hispanic origin. In:

Proceedings of the section on social

statistics. American Statistical

Association. 51–60. 1992.

16. Ezzati TM, Hoffman K, Judkins DR, et

al. A dual-frame design for sampling

elderly minorities and persons with

disabilities. Statistics in Medicine.

14:571–83. 1995.

17. Passel JS, Word DL. Constructing the

list of Spanish surnames for the 1980

census: An application of Bayes’

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18. Judkins D, Massey J, Smith V. Using

surnames to oversample Hispanics from

a list frame. Proceedings of the section

on survey research methods. American

Statistical Association. 847–52. 1992.

19. Wright D, Judkins D, Ryaboy S.

Residential segregation and health

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Association. 198–203. 1992.

20. Kish L. Survey sampling. New York:

Wiley. 1965.

21. Brick JM, Waksberg J. Avoiding

sequential sampling with random digit

dialing. Survey Methodology.

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22. Thornberry OT, Massey JT. Trends in

United States telephone coverage across

time and subgroups. In: Groves RM,

Biemer PP, Lyberg LE, et al., eds.

Telephone survey methodology. New

York: Wiley. 1988.

23. National Center for Health Statistics.

Synthetic state estimates of disability.

Public Health Service Publication No.

1759. Washington: Government

Printing Office. 1968.

24. Schaible WL. A composite estimator

for small area statistics. National

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Monograph. 24:36–62. 1978.

25. Marker DA. Small area estimation: A

Bayesian perspective (Unpublished

doctoral dissertation). Ann Arbor,

Michigan: University of Michigan.

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26. Ericson W. Subjective Bayesian models

in sampling finite populations. Journal

of the Royal Statistical Society B.

31:195–233. 1969.

27. Marker DA. Small area estimation for

the National Health Interview Survey.

Proceedings of the section on survey

research methods. American Statistical

Association. 11–20. 1993.

28. Gonzalez M, Waksberg J. Estimation of

the error of synthetic estimates. Paper

presented at the first meeting of the

International Association of Survey

Statisticians. Vienna, Austria:

International Association of Survey

Statisticians. August 18–25, 1973.

29. Makuc DM, Haglund B, Ingram DD, et

al. Use of cluster analysis to identify

health care service areas. In:

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Association. 260–5. 1990.

30. Panel on the National Health Care

Survey. Toward a national health care

survey: A data system for the 21st

century. Wunderlich GS, ed.

Washington, DC: National Academy

Press. 1992.

31. Hansen MH, Hurwitz WN, Madow

WG. Sample survey methods and

theory. Volume 1, chapter 6, sections 1,

8, and 11. Volume 2, chapter 8, section

4. New York: Wiley. 1953.

32. Hicks LW, Keathley DH, Moore TF.

Alternate PSU definitions for the

redesigned National Health Interview

Survey. In: Proceedings of the section

on survey research methods. American

Statistical Association. 732–4. 1992.

33. Massey JT, Moore TF, Parsons VL,

Tadros W. Design and estimation for

the National Health Interview Survey,

1985–94. National Center for Health

Statistics. Vital Health Statistics 2(110).

1989.

34. Judkins DR, Huang H, Shapiro GM.

Using sample information for

stratification. In: Proceedings of the

section on survey research methods.

American Statistical Association.

248–53. 1984.

35. NHIS redesign memorandum number

98 from D Malec to J Waksberg.

Feasibility of updating geographic

distribution of minorities in postcensal

years. August 20, 1992.

36. Kearny AT, Tourangeau R, Shapiro

GM, Ernst LR. Coverage improvement

from experimental residence questions.

In: Proceedings of the section on

survey research methods. American

Statistical Association. 162–7. 1993.

Series 2, No. 126 [ Page 101
Appendix I

List of the National Health Interview Survey Redesign Memorandums

No. Date To1 From Letter/Memo (if memo, subject)

1 October 3, 1989 Malec Judkins (Letter) Draft Specifications for NHIS Extract for Westat

2 October 10 Malec Waksberg (Letter) Specifications for NHIS Extract for Westat

3 October 17 Malec Westat Research Plan

4 October 20 Malec Waksberg Notes on October 12 Meeting Regarding NHIS Redesign

5 October 24 Malec Judkins Letter (Draft NHIS Variance Estimation)

6 November 19 Malec Judkins Letter (NHIS Variance Estimation)

7 November 29 Malec Waksberg Letter

8 December 13 Malec Judkins Letter (Merge of NHIS and Payroll Files)

9 December 13 Malec Waksberg Preliminary Analysis of Revisions in NHIS Sample Design Required for Subdomain Statistics

10 January 3, 1990 Malec Waksberg Preliminary Analysis of Revisions in NHIS Sample Design Required for State Statistics

11 January 11 Malec Waksberg Numbering of Memoranda and Reports

12 January 12 Malec Judkins Preliminary Report on Choice of First-Stage Probabilities

13 January 16 Malec Waksberg Condition Items in NHIS

14 January 16 Malec Judkins Preliminary Report on Network Sampling Application

15 January 17 Malec Judkins Corrected Variance Specifications

16 January 24 Malec Waksberg Further Analysis of Revision in NHIS Sample Design Required for Subdomain Statistics for

Blacks and Hispanics

17 March 26 Malec Waksberg Households as Sampling Units for Subdomain Statistics in the NHIS

18 March 28 Malec Waksberg Interim Report on Oversampling Techniques

19 March 30 Malec Judkins Preliminary Report on Dual Frame Sampling with Medicare Lists

20 April 6 Malec Waksberg NHIS Sample Size at Constant Cost When Screening is Necessary for Oversampling of

Subdomains

21 April 30 Malec Waksberg Current Status of Westat’s NHIS Design Research

22 June 7 Malec Judkins The limits of oversampling without screening

23 June 12 Malec Judkins Research on Minimum Sample Size for Desired Reliability

24 June 13 Malec Judkins Supplemental Report on Minimum Sample Size for Desired Reliability

25 June 14 Malec Judkins Variance Decomposition and Multi-Year Estimation

26 June 18 Malec Waksberg Oversampling of Hispanic Spanish Origin Subgroups

27 June 18 Malec Judkins State Grouping

28 June 21 Malec Judkins Required Sample Sizes by Age

29 June 26 Malec Judkins NHIS Redesign Research — Minutes and Feedback to Requests from Meeting of May 8, 1990

30 June 26 Massey Waksberg Census Bureau’s Cost Estimates for Using Designated Respondents within Households in

NHIS

31 July 3 Malec Marker Hispanic Estimates by Geographic Area

32 July 9 Malec Judkins NHIS Redesign Research — Comparison of Oversampling with Screening versus

Oversampling by Itself

33 July 12 Malec Waksberg First Draft of Executive Summary of NHIS Redesign Research Report

34 July 19 Malec Waksberg Comparison of Sampling Plans that Produce Acceptable Race/Ethnicity Data for NHIS

35 July 26 Malec Judkins More Thoughts on Oversampling Procedures

36 August 6 Malec Judkins Revisions to Table in Memoranda 22, 32, and 33

37 August 23 Malec Judkins Revised Report on Network Sampling Applications

38 September 25 Malec Judkins Variation in Segment Size

39 September 25 Malec Judkins Comparison of Oversampling Strategies for Several Cost Assumptions

40 September 26 Malec Judkins Draft report on variance estimation attached

41 October 19 Malec Waksberg Robustness of Race/Ethnic Stratification Based on Decennial Census Data

42 November 7 Malec Marker Model-Based Hispanic Estimates by Geographic Area

43 October 30 Malec Judkins Draft Report on Subdomain and Design-Based Small Area Estimates

44 November 29 Malec Judkins Oversampling Implications Tables

45 December 12 Malec Waksberg Unlocatable Building Permit Records

46 December 19 Malec Judkins Costs of Using HCFA Lists

46A December 19 Malec Marker Subnational Research Plan for FY 91

47 December 26 Malec Wright (David) Master Area Reference File

48 January 11, 1991 Massey Waksberg Sample Sizes, Screening Levels and Relative Costs of Alternative Sample Designs in Your

December 28 Memo

48R January 4, 1991 Massey Waksberg Sample Sizes, Screening Levels and Relative Costs of Alternative Sample Designs in Your

December 28 Memo

49 January 11, 1991 Massey Waksberg Information Requested in Your December 28 Memo that Was Not Covered in Our Memo

Number 48

49R January 8, 1991 Massey Waksberg Information Requested in Your December 28 Memo that Was Not Covered in Our Memo

Number 48

1

The normal distribution list is: Westat, Inc.: Joe Waksberg, Mansour Fahimi, David Judkins, David Marker, and David Wright; CODA: Doris Northrup; NCHS: Dr. James

Massey (2 copies), Dr. Owen Thornberry, Dr. Monroe Sirken, and Tommy McLemore; Census Bureau: Thomas Moore; and one copy for the chronological file. (Listing goes

only to J.M., J.W., and chronological File.)

Page 102 [ Series 2, No. 126
No. Date To1 From Letter/Memo (if memo, subject)

50 February 14, 1991 Malec Judkins Design for a 50% Increase in Budget Authority

50R February 14, 1991 Malec Judkins NHIS Redesign Research Design for 50% Increase in Budget Authority

50A February 15, 1991 Massey Judkins Brief Description of Current Projects

51 February 19, 1991 Massey Waksberg Use of Nonhousehold Respondent in NHANES III

52 February 19, 1991 Malec Marker Comparison of Model-Based Estimators for States

53 March 12, 1991 Malec Fahimi/Judkins Simulation Study on First-Stage Probabilities

54 March 27, 1991 Massey Judkins Components of Variance for Minority and Nonmetropolitan Statistics

55 April 20, 1991 Massey Judkins Integrated Survey Design

56 May 1, 1991 Massey Marker Area Sample-Based and RDD Supplemented State Level Estimates

56R June 5, 1991 Malec Marker, Waksberg Area Sample-Based and RDD Supplemented, State-Level Estimates

57 May 14, 1991 Massey Waksberg Effect of Oversampling PSU’s with High Proportions of Blacks or on Rural Statistics

58 May 30, 1991 Massey Waksberg, Wright,

Judkins

Robustness of Distribution of the Population by Minority Density and Effect on Sample Designs

Which Oversample Strata with High Minority Density

59 June 3, 1991 Massey Waksberg Additional Tasks on NHIS Research

60 June 12, 1991 Massey Waksberg Current Status of Tasks Listed in the Contract

61 June 12, 1991 Massey Judkins/Searls Univariate Exploration of Health Characteristics by Minority Density Stratum

62 June 14, 1991 Massey Waksberg Cost Estimates for Random Digit Dialing Surveys to Supplement NHIS

63 June 19, 1991 Massey Judkins Allocation of Strata and PSU’s given State Stratification

64 July 17, 1991 Massey Judkins Use of Phone Number for Medicare Supplement

65 July 17, 1991 Massey Judkins Sensitivity and Specificity of SSA Indicators for Institutionalization

66 August 2, 1991 Massey Judkins NHIS Redesign Research — Revised Sampling Parameters

67 October 8, 1991 Massey Marker Precision of RDD Supplementation for State Level Age/Sex Estimates

68 October 9, 1991 Massey Judkins NHIS Redesign Research — Further Results of PL-94 Tabulations

69 October 14, 1991 Massey Judkins Decomposition of NHDS Variances

70 October 17, 1991 Massey Judkins NHIS Redesign — Census Screening System

71 November 12, 1991 Massey Marker Comparison of Stand-Alone RDD with Unbiased Dual Frame Estimators

72 November 14, 1991 Massey Waksberg Oversampling Density Strata for Statistics on Asian and American Indian Subdomains

73 December 9, 1991 Massey Marker Comparison of stand-alone RDD with biased dual frame estimators

74 December 20, 1991 Moore Judkins NHIS Redesign — Sample Selection Specifications for the Medicare Pilot Study

75 January 10, 1992 Massey Judkins/Edmonds Documentation of Variance Estimation Software

76 January 15, 1992 Massey Göksel Specifications for the Data File to Analyze Measures of Size for the NCHS

77 February 3, 1992 Massey Judkins Restoration of an Equi-Probability Sample

78 February 4, 1992 Massey Judkins Within Household Sampling Rules

78R March 4, 1993 Massey Judkins Within Household Sampling Rules (Revised)

79 February 19, 1992 Massey Marker Comparison of Stand-alone RDD with Dual Frame Estimators

80 February 21, 1992 Massey Judkins New Construction Sampling

81 February 25, 1992 Massey Wright/Judkins Logistic Regressions on Health Characteristics of Blacks and Hispanics by Minority 82 Density

Strata

82 March 6, 1992 Massey Judkins Questionnaire Implications of SSA Sampling

83 April 20, 1992 Malec Marker Detailed plan for empirical comparison of small area estimators

84 April 27, 1992 Thornberry Marker Comments on the Interviewer Assignment Period for the 1995 NHIS

85 May 29, 1992 Massey Göksel The correlations among the health care service based and population based measures of size

for NHIS

86 June 2, 1992 Massey Judkins NHIS Redesign Research—Interim Report on the Use of List of Surnames to Oversample

Hispanics

87 June 11, 1992 Massey Judkins NHIS Redesign: Variance Implications of a Macro-level Incorporation of the SSA Disabled List

Sample

88 June 11, 1992 Massey Judkins Comments on Third Working Draft of UCF Specifications

89 June 24, 1992 Massey Waksberg Current Status of Westat’s Research on ISD Research Contract

90 July 7, 1992 Massey Judkins NHIS Redesign: Utility of Hispanic Surname List for Elderly

91 July 31, 1992 Massey Waksberg Interim Report on Oversampling Persons Whose Income Is Below the Poverty Level

92 July 8, 1992 Malec Edmonds Gibbs Sampler Software Research

93 July 15, 1992 Massey Judkins Interviewing Other Household Members in the 1992 SSA List Pilot for NHIS

94 July 15, 1992 Massey Judkins Dual-Frame Estimation for 1992 Pilot

95 July 22, 1992 Massey Judkins Construction of Panels for the 1995 NHIS Design

96 July 23, 1992 Massey Waksberg NHIS Coverage Research

97 July 31, 1992 Massey Göksel Variance Estimation for NAMCS

98 August 20, 1992 Malec Waksberg Feasibility of Updating Geographic Distribution of Minorities in Postcensal Years

99 August 28, 1992 Sirken Judkins Decisions from Meeting of July 30, 1992, on NHIS Panel Formation

100 August 31, 1992 Malec Marker Update on Empirical Comparison of Model-Based Estimators

101 September 8, 1992 Malec Judkins Assistance on Preparation of Sample Size Tables for Questionnaire Design

102 September 23, 1992 Shimizu Judkins Network Sampling of Health Care Events Based upon the National Health Interview Survey

103 October 5, 1992 Hoffman Judkins Comments on Draft Analytic Plan for SSA List Sample Pilot

104 October 6, 1992 Malec Judkins Possible Benefits of a Mid-Design Update to the Minority Density Strata

105 October 12, 1992 Sirken Judkins NHIS Redesign: Improvement Due to Screening and Equivalency to Multi-Year Reuse

1

The normal distribution list is: Westat, Inc.: Joe Waksberg, Mansour Fahimi, David Judkins, David Marker, and David Wright; CODA: Doris Northrup; NCHS: Dr. James

Massey (2 copies), Dr. Owen Thornberry, Dr. Monroe Sirken, and Tommy McLemore; Census Bureau: Thomas Moore; and one copy for the chronological file. (Listing goes

only to J.M., J.W., and chronological File.)

Series 2, No. 126 [ Page 103
No. Date To1 From Letter/Memo (if memo, subject)

106 October 20, 1992 Sirken Judkins NHIS Redesign: Oversampling/Screening Designs for a 15-Percent Increase in Budget

Authority

107 October 27, 1992 Massey Waksberg Updating Minority Density Areas

108 November 12, 1992 Sirken Judkins NHIS Redesign: Changes in Effective Sample Sizes for Several Designs Options relative to the

1985–94 Design

109 January 25, 1993 Massey Judkins County-Level Clustering for Surveys of Health Care Facilities

109R February 16, 1993 Massey Judkins County-Level Clustering for Surveys of Health Care Facilities

110 December 3, 1992 Massey Judkins NHIS Redesign: Changes in Effective Sample Sizes for Several More Design Options Relative

to the 1985–94 Design

111 December 7, 1992 Massey Waksberg Undercoverage in Sample Household Surveys

112 February 8, 1993 Massey Judkins NHIS Redesign: Draft Outline for Final Report

113 February 16, 1993 Massey Judkins Comments by Daniel Horvitz on Memorandum #102

114 February 26, 1993 Massey Judkins Tables on Benefits of SSA Sample of Disabled Beneficiaries

102R March 1, 1993 Massey Judkins Network Sampling of Health Care Events Based upon the National Health Interview

115 August 5, 1993 Massey Göksel Variance Estimation for NAMCS Report, cover letter

116 August 17, 1993 Massey Waksberg Distribution of Poverty in Census Block Groups and Implications for Sample Design

117 December 6, 1993 Milliken Judkins Draft NHIS Neighbor Screening Plan

118 September 20, 1994 Moore Judkins Corrections to Specifications for the 1995 NHIS

119 September 27, 1994 Moore Judkins Revised Retention Rates for the 1995 NHIS

1The normal distribution list is: Westat, Inc.: Joe Waksberg, Mansour Fahimi, David Judkins, David Marker, and David Wright; CODA: Doris Northrup; NCHS: Dr. James Massey (2 copies), Dr. Owen

Thornberry, Dr. Monroe Sirken, and Tommy McLemore; Census Bureau: Thomas Moore; and one copy for the chronological file. (Listing goes only to J.M., J.W., and chronological File.)

Page 104 [ Series 2, No. 126
Appendix II

National Health Interview Survey Estimates and Coefficients of Variation

Age Category Male Female

Subdomain

Black not

Hispanic

Other not

Hispanic Hispanic Asian Not Asian

Substantive variable: Number of chronic arthritis conditions

Aged under 5 years ...... Estimate 10,834.15 0.00 0.00 10,834.15 0.00 0.00 10,834.15

cv (%) 100.42 0.00 0.00 100.42 0.00 0.00 100.42

Aged 5 to 17 years ...... Estimate 20,936.29 102,472.95 2,989.95 88,779.63 31,639.65 0.00 123,409.24

cv (%) 72.06 36.06 110.05 32.99 85.01 0.00 32.25

Aged 18 to 24 years ..... Estimate 185,408.77 312,945.07 57,570.42 428,290.51 12,492.91 0.00 498,353.84

cv (%) 28.64 20.29 33.17 19.97 99.51 0.00 18.67

Aged 25 to 44 years ..... Estimate 1,704,849.19 2,992,162.51 462,040.58 3,843,956.66 391,014.45 24,536.29 4,672,475.41

cv (%) 9.19 5.78 13.80 5.35 17.75 71.47 5.11

Aged 45 to 64 years ..... Estimate 4,183,376.07 7,471,621.61 1,114,563.03 10,078,130.83 462,303.82 122,063.96 11,532,933.72

cv (%) 4.99 3.85 8.86 3.42 15.44 31.80 3.31

Aged 65 years or older .... Estimate 4,467,372.24 8,933,255.70 1,067,112.78 12,030,460.38 303,054.77 74,758.73 13,325,869.20

cv (%) 4.72 3.76 8.75 3.52 23.39 48.58 3.31

Total ............... Estimate 10,572,776.71 19,812,457.84 2,704,276.77 26,480,452.17 1,200,505.61 221,358.98 30,163,875.56

cv (%) 2.87 2.54 5.24 2.30 10.90 24.08 2.19

Age . . . . . . . . . . . . . . . . Category Northeast Midwest South West New England Mid-Atlantic East North Central

Aged under 5 years ...... Estimate 0.00 0.00 10,834.15 0.00 0.00 0.00 0.00

cv (%) 0.00 0.00 100.42 0.00 0.00 0.00 0.00

Aged 5 to 17 years ...... Estimate 32,556.67 24,084.00 45,325.06 21,443.51 21,342.66 11,214.01 2,989.95

cv (%) 55.18 103.70 45.66 70.41 71.02 87.10 110.05

Aged 18 to 24 years ..... Estimate 90,312.81 117,621.35 153,598.07 136,821.61 12,749.08 77,563.72 93,995.48

cv (%) 30.80 33.42 29.18 40.28 93.92 35.07 38.30

Aged 25 to 44 years ..... Estimate 942,920.08 1,211,535.27 1,614,182.24 928,374.10 336,063.52 606,856.56 900,800.67

cv (%) 12.38 9.10 9.00 13.36 26.65 15.06 11.08

Aged 45 to 64 years ..... Estimate 2,241,755.71 3,160,059.56 4,285,532.46 1,967,649.95 463,956.43 1,777,799.28 2,402,975.30

cv (%) 7.57 7.24 6.55 7.61 21.64 9.45 9.48

Aged 65 years or older .... Estimate 2,742,113.80 3,636,458.13 4,917,022.86 2,105,033.14 709,838.86 2,032,274.94 2,696,136.12

cv (%) 6.93 6.38 6.26 8.22 14.83 8.18 7.98

Total ............... Estimate 6,049,659.07 8,149,758.31 11,026,494.85 5,159,322.31 1,543,950.55 4,505,708.52 6,096,897.52

cv (%) 4.75 3.85 3.97 5.33 14.69 6.17 5.30

Age . . . . . . . . . . . . . . . . Category West North Central South Atlantic East South Central West South Central Mountain Pacific Total

Aged under 5 years ...... Estimate 0.00 0.00 0.00 10,834.15 0.00 0.00 10,834.15

cv (%) 0.00 0.00 0.00 100.42 0.00 0.00 100.42

Aged 5 to 17 years ...... Estimate 21,094.04 22,659.13 0.00 22,665.93 21,443.51 0.00 123,409.24

cv (%) 116.94 72.33 0.00 56.20 70.41 0.00 32.25

Aged 18 to 24 years ..... Estimate 23,625.87 101,341.64 13,338.67 38,917.76 58,444.31 78,377.30 498,353.84

cv (%) 66.51 35.79 101.16 59.05 53.65 49.02 18.67

Aged 25 to 44 years ..... Estimate 310,734.60 560,452.09 306,884.03 746,846.13 236,257.40 692,116.70 4,697,011.70

cv (%) 21.90 15.87 22.71 14.57 25.39 14.87 5.11

Aged 45 to 64 years ..... Estimate 757,084.26 2,013,710.77 922,738.94 1,349,082.75 509,677.94 1,457,972.01 11,654,997.68

cv (%) 16.23 10.23 21.37 14.03 19.66 10.86 3.22

Aged 65 years or older .... Estimate 940,322.01 2,303,120.77 1,059,233.05 1,554,669.04 646,225.28 1,458,807.86 13,400,627.93

cv (%) 13.04 12.19 14.31 12.95 20.29 9.46 3.29

Total ............... Estimate 2,052,860.79 5,001,284.41 2,302,194.69 3,723,015.76 1,472,048.44 3,687,273.87 30,385,234.54

cv (%) 12.09 8.89 12.69 11.02 14.03 7.39 2.15

Series 2, No. 126 [ Page 105
Age Category Male Female

Subdomain

Black not

Hispanic

Other not

Hispanic Hispanic Asian Not Asian

Substantive variable: Number of acute digestive system conditions

Aged under 5 years ...... Estimate 1,516,847.23 540,828.34 308,811.96 1,247,309.73 501,553.88 0.00 2,057,675.57

cv (%) 22.01 27.00 44.33 19.84 46.42 0.00 17.40

Aged 5 to 1 years ....... Estimate 1,920,859.80 1,830,977.12 568,326.74 2,810,454.68 373,055.50 183,761.45 3,568,075.47

cv (%) 15.11 19.63 28.24 14.95 57.91 61.36 12.30

Aged 18 to 24 years ...... Estimate 865,943.43 835,393.36 453,038.64 1,085,033.40 163,264.75 54,640.27 1,646,696.51

cv (%) 26.72 22.64 30.41 21.64 53.71 99.10 17.34

Aged 25 to 44 years ...... Estimate 1,367,065.02 2,675,392.92 955,278.74 2,882,052.71 205,126.50 0.00 4,042,457.94

cv (%) 20.76 15.25 23.59 15.09 50.84 0.00 12.09

Aged 45 to 64 years ...... Estimate 552,387.05 1,318,405.62 174,859.12 1,587,611.76 108,321.79 103,691.29 1,767,101.39

cv (%) 31.39 18.72 50.71 18.40 70.59 71.34 16.61

Aged 65 years or older .... Estimate 750,600.42 1,115,956.27 224,480.32 1,345,371.22 296,705.15 0.00 1,866,556.69

cv (%) 28.53 23.45 52.74 20.76 52.73 0.00 17.99

Total ............... Estimate 6,973,702.97 8,316,953.63 2,684,795.51 10,957,833.50 1,648,027.58 342,093.01 14,948,563.58

cv (%) 10.00 8.44 11.91 7.74 29.29 42.64 6.78

Age . . . . . . . . . . . . . . . . Category Northeast Midwest South West New England Mid-Atlantic East North Central

Aged under 5 years ...... Estimate 174,007.40 660,931.80 762,888.36 459,848.02 49,837.06 124,170.34 567,035.82

cv (%) 52.10 37.50 28.41 36.44 99.77 60.55 40.53

Aged 5 to 17 years ...... Estimate 508,359.98 913,315.35 1,458,410.70 871,750.89 93,690.31 414,669.67 626,789.38

cv (%) 31.30 29.43 16.58 28.79 64.51 35.49 36.98

Aged 18 to 24 years ...... Estimate 452,404.79 569,220.92 396,622.16 283,088.91 163,168.59 289,236.20 397,032.36

cv (%) 31.89 30.55 36.32 44.57 38.62 44.76 38.38

Aged 25 to 44 years ...... Estimate 774,154.66 600,205.98 1,893,077.96 775,019.35 407,873.05 366,281.60 428,158.27

cv (%) 25.25 29.86 16.71 32.45 34.55 37.13 34.73

Aged 45 to 64 years ...... Estimate 304,545.80 527,534.64 716,149.61 322,562.62 100,013.74 204,532.06 270,751.80

cv (%) 40.88 31.23 26.46 36.36 65.91 51.58 43.01

Aged 65 years or older .... Estimate 411,102.33 214,092.39 891,701.57 349,660.41 0.00 411,102.33 64,588.35

cv (%) 48.04 46.51 24.95 27.98 0.00 48.04 71.87

Total ............... Estimate 2,624,574.96 3,485,301.08 6,118,850.36 3,061,930.19 814,582.75 1,809,992.21 2,354,355.97

cv (%) 14.01 16.00 10.23 15.71 23.35 17.85 20.55

Age . . . . . . . . . . . . . . . . Category West North Central South Atlantic East South Central West South Central Mountain Pacific Total

Aged under 5 years ...... Estimate 93,895.98 198,849.70 133,896.93 430,141.73 99,251.95 360,596.07 2,057,675.57

cv (%) 99.42 50.43 55.59 41.35 70.40 42.88 17.40

Aged 5 to 17 years ...... Estimate 286,525.97 452,778.38 374,560.87 631,071.45 137,701.16 734,049.73 3,751,836.92

cv (%) 41.11 35.04 56.55 34.02 56.94 31.64 12.70

Aged 18 to 24 years ...... Estimate 172,188.56 87,461.85 191,202.69 117,957.62 110,378.83 172,710.08 1,701,336.79

cv (%) 53.00 63.08 56.30 64.06 71.11 57.47 16.94

Aged 25 to 44 years ...... Estimate 172,047.72 602,939.16 385,299.81 904,838.98 253,513.58 521,505.77 4,042,457.94

cv (%) 58.20 32.93 37.61 24.80 45.28 40.47 12.09

Aged 45 to 64 years ...... Estimate 256,782.85 322,385.23 188,064.42 205,699.96 153,669.10 168,893.52 1,870,792.67

cv (%) 45.37 40.08 49.83 59.82 49.25 53.27 16.23

Aged 65 years or older .... Estimate 149,504.04 536,324.46 106,945.02 248,432.09 183,360.34 166,300.07 1,866,556.69

cv (%) 58.91 30.98 70.13 51.93 49.92 61.82 17.99

Total ............... Estimate 1,130,945.11 2,200,738.79 1,379,969.75 2,538,141.83 937,874.96 2,124,055.23 15,290,656.60

cv (%) 21.58 19.04 31.24 18.68 24.56 19.56 6.87

Page 106 [ Series 2, No. 126
Age Category Male Female

Subdomain

Black not

Hispanic

Other not

Hispanic Hispanic Asian Not Asian

Substantive variable: Number of acute bronchitis conditions

Aged under 5 years ...... Estimate 1,132,721.10 932,992.47 174,278.96 1,681,713.59 209,721.03 0.00 2,065,713.57

cv (%) 22.35 23.35 50.24 20.16 41.04 0.00 16.46

Aged 5 to 17 years ...... Estimate 863,924.92 939,198.84 230,337.38 1,480,137.09 92,649.29 0.00 1,803,123.76

cv (%) 23.77 24.94 46.15 19.16 62.65 0.00 17.64

Aged 18 to 24 years ...... Estimate 111,977.36 342,820.50 62,544.24 328,439.51 63,814.11 0.00 454,797.86

cv (%) 74.34 40.60 98.78 41.82 96.60 0.00 35.79

Aged 25 to 44 years ...... Estimate 726,719.19 1,313,878.85 198,576.82 1,842,021.22 0.00 0.00 2,040,598.04

cv (%) 29.77 21.24 48.82 16.91 0.00 0.00 15.69

Aged 45 to 64 years ...... Estimate 283,565.40 479,666.93 0.00 737,641.85 25,590.48 0.00 763,232.33

cv (%) 41.33 31.07 0.00 26.74 99.85 0.00 25.97

Aged 65 years or older .... Estimate 604,458.85 349,479.40 0.00 953,938.25 0.00 0.00 953,938.25

cv (%) 35.16 36.86 0.00 27.12 0.00 0.00 27.12

Total ............... Estimate 3,723,366.82 4,358,036.99 665,737.40 7,023,891.50 391,774.91 0.00 8,081,403.81

cv (%) 11.82 10.42 29.54 8.24 33.03 0.00 7.77

Age . . . . . . . . . . . . . . . . Category Northeast Midwest South West New England Mid-Atlantic East North Central

Aged under 5 years ...... Estimate 307,542.36 598,984.08 717,203.55 441,983.59 49,837.06 257,705.30 458,693.50

cv (%) 38.13 32.18 27.15 27.27 102.98 40.64 39.54

Aged 5 to 17 years ...... Estimate 180,212.05 421,801.37 564,983.09 636,127.24 48,594.05 131,618.01 243,749.11

cv (%) 48.53 36.05 29.23 30.06 83.39 59.20 45.28

Aged 18 to 24 years ...... Estimate 51,563.12 113,522.21 233,592.39 56,120.14 51,563.12 0.00 113,522.21

cv (%) 99.76 70.60 50.94 99.80 99.76 0.00 70.60

Aged 25 to 44 years ...... Estimate 347,465.61 1,040,638.57 343,083.46 309,410.40 51,555.03 295,910.58 586,994.94

cv (%) 41.49 22.84 39.53 33.61 99.11 45.45 27.21

Aged 45 to 64 years ...... Estimate 202,146.32 260,811.67 49,327.30 250,947.04 0.00 202,146.32 163,370.52

cv (%) 51.08 44.90 101.54 44.78 0.00 51.08 57.67

Aged 65 years or older .... Estimate 99,848.26 206,471.86 358,928.78 288,689.35 0.00 99,848.26 206,471.86

cv (%) 71.44 50.50 37.96 56.76 0.00 71.44 50.50

Total ............... Estimate 1,188,777.72 2,642,229.77 2,267,118.57 1,983,277.76 201,549.25 987,228.47 1,772,802.14

cv (%) 20.70 14.76 17.43 17.96 48.50 22.96 17.87

Age . . . . . . . . . . . . . . . . Category West North Central South Atlantic East South Central West South Central Mountain Pacific Total

Aged under 5 years ...... Estimate 140,290.58 140,473.88 242,401.58 334,328.09 91,121.73 350,861.85 2,065,713.57

cv (%) 70.85 58.70 35.88 47.01 69.85 29.01 16.46

Aged 5 to 17 years ...... Estimate 178,052.27 149,421.31 101,987.37 313,574.41 241,046.73 395,080.52 1,803,123.76

cv (%) 53.04 75.27 68.48 37.10 48.93 38.18 17.64

Aged 18 to 24 years ...... Estimate 0.00 55,451.51 0.00 178,140.88 0.00 56,120.14 454,797.86

cv (%) 0.00 105.88 0.00 58.10 0.00 99.80 35.79

Aged 25 to 44 years ...... Estimate 453,643.63 233,637.93 0.00 109,445.53 96,819.49 212,590.91 2,040,598.04

cv (%) 44.82 46.61 0.00 73.03 70.79 50.34 15.69

Aged 45 to 64 years ...... Estimate 97,441.14 0.00 0.00 49,327.30 48,694.13 202,252.91 763,232.33

cv (%) 71.30 0.00 0.00 101.54 101.42 50.00 25.97

Aged 65 years or older .... Estimate 0.00 157,907.61 0.00 201,021.17 47,540.24 241,149.11 953,938.25

cv (%) 0.00 57.71 0.00 50.44 101.31 65.02 27.12

Total ............... Estimate 869,427.62 736,892.24 344,388.95 1,185,837.37 525,222.32 1,458,055.44 8,081,403.81

cv (%) 35.44 35.64 38.90 23.40 33.18 20.97 7.77

Series 2, No. 126 [ Page 107
Age Category Male Female

Subdomain

Black not

Hispanic

Other not

Hispanic Hispanic Asian Not Asian

Substantive variable: Number of acute urinary conditions

Aged under 5 years ...... Estimate 49,837.06 201,295.03 0.00 251,132.09 0.00 0.00 251,132.09

cv (%) 100.69 49.98 0.00 44.91 0.00 0.00 44.91

Aged 5 to 17 years ...... Estimate 0.00 319,296.08 88,479.72 230,816.36 0.00 0.00 319,296.08

cv (%) 0.00 38.68 73.83 44.73 0.00 0.00 38.68

Aged 18 to 24 years ...... Estimate 83,394.94 799,442.55 81,427.18 688,390.59 113,019.73 0.00 882,837.49

cv (%) 77.78 31.83 75.66 35.09 69.76 0.00 30.56

Aged 25 to 44 years ...... Estimate 587,183.97 2,134,905.33 377,161.26 2,081,547.32 263,380.73 49,899.27 2,672,190.03

cv (%) 37.06 15.80 34.88 17.20 44.48 99.71 14.91

Aged 45 to 64 years ...... Estimate 444,449.11 974,334.29 72,700.28 1,346,083.12 0.00 0.00 1,418,783.40

cv (%) 33.31 23.99 74.84 20.44 0.00 0.00 19.78

Aged 65 years or older .... Estimate 444,834.40 807,739.79 138,542.87 1,051,811.77 62,219.54 0.00 1,252,574.19

cv (%) 35.96 23.26 48.54 22.00 102.89 0.00 18.86

Total ............... Estimate 1,609,699.47 5,237,013.08 758,311.31 5,649,781.24 438,620.00 49,899.27 6,796,813.28

cv (%) 21.15 10.84 23.24 10.65 38.96 99.71 10.02

Age . . . . . . . . . . . . . . . . Category Northeast Midwest South West New England Mid-Atlantic East North Central

Aged under 5 years ...... Estimate 58,172.05 96,937.60 46,185.39 49,837.06 0.00 58,172.05 51,115.62

cv (%) 99.37 69.27 101.34 100.69 0.00 99.37 99.21

Aged 5 to 17 years ...... Estimate 88,479.72 0.00 135,191.30 95,625.05 0.00 88,479.72 0.00

cv (%) 73.83 0.00 57.64 71.04 0.00 73.83 0.00

Aged 18 to 24 years ...... Estimate 118,529.80 344,193.21 309,007.16 111,107.31 53,585.91 64,943.90 281,350.14

cv (%) 71.10 58.95 41.85 70.99 100.19 100.52 53.44

Aged 25 to 44 years ...... Estimate 238,666.14 692,642.11 1,270,746.34 520,034.72 154,706.16 83,959.98 431,523.91

cv (%) 42.14 27.31 25.08 26.71 48.65 79.83 33.93

Aged 45 to 64 years ...... Estimate 365,603.96 509,122.92 494,821.01 49,235.51 0.00 365,603.96 301,864.91

cv (%) 40.93 31.64 32.16 100.27 0.00 40.93 40.73

Aged 65 years or older .... Estimate 0.00 298,918.98 612,170.27 341,484.94 0.00 0.00 157,433.20

cv (%) 0.00 47.06 24.62 37.48 0.00 0.00 59.42

Total ............... Estimate 869,451.67 1,941,814.83 2,868,121.47 1,167,324.59 208,292.07 661,159.60 1,223,287.78

cv (%) 24.97 17.39 14.41 20.08 44.58 32.12 20.27

Age . . . . . . . . . . . . . . . . Category West North Central South Atlantic East South Central West South Central Mountain Pacific Total

Aged under 5 years ...... Estimate 45,821.98 0.00 46,185.39 0.00 0.00 49,837.06 251,132.09

cv (%) 96.52 0.00 101.34 0.00 0.00 100.69 44.91

Aged 5 to 17 years ...... Estimate 0.00 89,982.98 0.00 45,208.32 51,177.33 44,447.73 319,296.08

cv (%) 0.00 70.56 0.00 99.14 100.36 100.32 38.68

Aged 18 to 24 years ...... Estimate 62,843.07 103,181.26 58,890.15 146,935.75 0.00 111,107.31 882,837.49

cv (%) 114.54 69.39 99.10 61.19 0.00 70.99 30.56

Aged 25 to 44 years ...... Estimate 261,118.20 576,214.70 222,177.88 472,353.76 100,789.16 419,245.56 2,722,089.30

cv (%) 46.63 32.91 77.76 36.15 69.13 32.56 14.74

Aged 45 to 64 years ...... Estimate 207,258.01 236,053.19 0.00 258,767.82 49,235.51 0.00 1,418,783.40

cv (%) 49.67 47.35 0.00 43.82 100.27 0.00 19.78

Aged 65 years or older .... Estimate 141,485.79 204,100.43 144,310.85 263,758.98 139,487.22 201,997.72 1,252,574.19

cv (%) 74.36 45.11 39.81 40.26 57.76 49.35 18.86

Total ............... Estimate 718,527.05 1,209,532.57 471,564.28 1,187,024.62 340,689.22 826,635.38 6,846,712.55

cv (%) 28.48 22.96 39.15 24.64 37.67 26.01 10.00

Page 108 [ Series 2, No. 126
Age Category Male Female

Subdomain

Black not

Hispanic

Other not

Hispanic Hispanic Asian Not Asian

Substantive variable: Number of chronic color blindness

Aged under 5 years ...... Estimate 0.00 0.00 0.00 0.00 0.00 0.00 0.00

cv (%) 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Aged 5 to 17 years ...... Estimate 360,797.82 11,117.47 0.00 347,494.82 24,420.47 0.00 371,915.28

cv (%) 17.34 99.21 0.00 17.58 70.63 0.00 16.99

Aged 18 to 24 years ...... Estimate 217,425.86 11,899.18 0.00 215,055.95 14,269.09 0.00 229,325.04

cv (%) 28.12 100.14 0.00 28.28 100.29 0.00 27.17

Aged 25 to 44 years ...... Estimate 1,011,047.67 94,985.16 42,279.77 978,638.48 85,114.58 26,619.79 1,079,413.04

cv (%) 10.65 33.58 55.55 10.79 42.11 70.74 10.47

Aged 45 to 64 years ...... Estimate 631,363.19 51,355.20 37,329.58 633,092.92 12,295.89 12,295.89 670,422.51

cv (%) 13.07 49.73 48.93 13.45 100.58 100.58 12.93

Aged 65 years or older .... Estimate 309,106.73 52,550.96 14,983.71 346,673.97 0.00 0.00 361,657.69

cv (%) 19.06 45.98 70.01 17.52 0.00 0.00 17.05

Total ............... Estimate 2,529,741.27 221,907.97 94,593.07 2,520,956.15 136,100.02 38,915.68 2,712,733.56

cv (%) 7.08 23.34 33.12 7.17 29.70 57.68 6.97

Age . . . . . . . . . . . . . . . . Category Northeast Midwest South West New England Mid-Atlantic East North Central

Aged under 5 years ...... Estimate 0.00 0.00 0.00 0.00 0.00 0.00 0.00

cv (%) 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Aged 5 to 17 years ...... Estimate 80,121.74 158,797.64 22,949.32 110,046.58 11,171.90 68,949.85 112,792.10

cv (%) 37.90 27.65 70.34 36.34 99.44 40.99 33.08

Aged 18 to 24 years ...... Estimate 0.00 130,077.01 49,724.29 49,523.74 0.00 0.00 67,149.61

cv (%) 0.00 40.00 46.17 49.46 0.00 0.00 42.56

Aged 25 to 44 years ...... Estimate 241,773.09 398,900.33 262,240.34 203,119.08 57,600.08 184,173.01 290,119.27

cv (%) 19.63 17.38 24.80 25.41 43.19 22.73 21.27

Aged 45 to 64 years ...... Estimate 210,933.25 161,375.08 203,393.67 107,016.40 57,416.53 153,516.72 127,973.21

cv (%) 25.46 26.77 23.04 31.88 33.82 30.88 29.54

Aged 65 years or older .... Estimate 131,820.55 115,353.27 45,505.75 68,978.12 35,697.90 96,122.65 93,819.43

cv (%) 30.64 31.41 50.01 40.51 57.08 36.19 34.93

Total ............... Estimate 664,648.63 964,503.32 583,813.38 538,683.91 161,886.40 502,762.23 691,853.62

cv (%) 12.03 11.41 17.06 15.10 27.37 14.37 12.91

Age . . . . . . . . . . . . . . . . Category West North Central South Atlantic East South Central West South Central Mountain Pacific Total

Aged under 5 years ...... Estimate 0.00 0.00 0.00 0.00 0.00 0.00 0.00

cv (%) 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Aged 5 to 17 years ...... Estimate 46,005.54 22,949.32 0.00 0.00 56,502.03 53,544.54 371,915.28

cv (%) 50.48 70.34 0.00 0.00 44.41 58.94 16.99

Aged 18 to 24 years ...... Estimate 62,927.40 11,849.17 0.00 37,875.13 25,665.47 23,858.27 229,325.04

cv (%) 62.06 100.45 0.00 52.18 71.24 68.22 27.17

Aged 25 to 44 years ...... Estimate 108,781.05 118,806.37 92,391.81 51,042.17 103,037.96 100,081.12 1,106,032.84

cv (%) 36.06 36.90 43.17 43.94 36.93 31.57 10.42

Aged 45 to 64 years ...... Estimate 33,401.86 93,450.63 28,806.17 81,136.88 35,220.62 71,795.77 682,718.39

cv (%) 61.51 38.29 60.53 36.84 49.79 40.67 12.56

Aged 65 years or older .... Estimate 21,533.84 34,466.02 0.00 11,039.73 33,202.75 35,775.36 361,657.69

cv (%) 72.01 57.22 0.00 101.13 58.40 56.96 17.05

Total ............... Estimate 272,649.69 281,521.51 121,197.98 181,093.90 253,628.84 285,055.07 2,751,649.24

cv (%) 27.85 27.01 33.11 25.32 23.77 20.82 6.87

Series 2, No. 126 [ Page 109
Age Category Male Female

Subdomain

Black not

Hispanic

Other not

Hispanic Hispanic Asian Not Asian

Substantive variable: Number of chronic epilepsy conditions

Aged under 5 years ...... Estimate 54,167.69 10,658.17 8,326.56 33,322.19 23,177.11 0.00 64,825.86

cv (%) 44.47 100.01 104.12 56.63 70.05 0.00 40.54

Aged 5 to 17 years ...... Estimate 16,635.28 69,835.91 16,635.28 57,747.66 12,088.25 0.00 86,471.19

cv (%) 77.13 39.51 77.13 42.90 100.78 0.00 33.08

Aged 18 to 24 years ...... Estimate 55,308.01 24,206.85 17,707.41 48,242.72 13,564.73 0.00 79,514.85

cv (%) 44.57 69.99 71.36 49.36 99.89 0.00 43.97

Aged 25 to 44 years ...... Estimate 144,131.23 183,932.50 57,489.96 240,461.87 30,111.90 12,845.05 315,218.68

cv (%) 35.51 25.20 47.67 25.15 59.54 99.72 21.50

Aged 45 to 64 years ...... Estimate 113,211.68 133,631.76 24,478.67 210,732.62 11,632.16 0.00 246,843.45

cv (%) 31.71 29.69 48.89 23.09 100.34 0.00 21.08

Aged 65 years or older .... Estimate 56,905.22 59,396.44 22,686.32 58,557.18 35,058.16 0.00 116,301.66

cv (%) 35.34 44.73 58.03 39.34 58.11 0.00 28.89

Total ............... Estimate 440,359.11 481,661.63 147,324.20 649,064.24 125,632.30 12,845.05 909,175.69

cv (%) 16.50 17.20 24.56 14.16 30.41 99.72 11.90

Age . . . . . . . . . . . . . . . . Category Northeast Midwest South West New England Mid-Atlantic East North Central

Aged under 5 years ...... Estimate 23,177.11 13,084.48 28,564.27 0.00 0.00 23,177.11 13,084.48

cv (%) 70.05 99.66 55.81 0.00 0.00 70.05 99.66

Aged 5 to 17 years ...... Estimate 0.00 11,961.48 63,848.35 10,661.35 0.00 0.00 11,961.48

cv (%) 0.00 100.59 36.81 102.01 0.00 0.00 100.59

Aged 18 to 24 years ...... Estimate 36,733.13 30,836.97 0.00 11,944.75 0.00 36,733.13 20,194.85

cv (%) 74.51 60.24 0.00 97.13 0.00 74.51 75.13

Aged 25 to 44 years ...... Estimate 66,198.97 76,137.71 116,095.06 69,632.00 11,138.18 55,060.79 52,692.82

cv (%) 59.58 38.90 33.55 40.74 83.50 69.77 45.76

Aged 45 to 64 years ...... Estimate 47,773.59 61,273.37 88,991.71 48,804.77 23,162.50 24,611.10 37,552.77

cv (%) 49.25 44.49 33.95 46.08 68.91 70.18 59.05

Aged 65 years or older .... Estimate 12,183.05 23,077.76 58,629.13 22,411.73 0.00 12,183.05 23,077.76

cv (%) 100.71 70.90 41.03 54.19 0.00 100.71 70.90

Total ............... Estimate 186,065.84 216,371.77 356,128.52 163,454.61 34,300.68 151,765.16 158,564.17

cv (%) 31.32 23.09 20.78 25.33 53.82 36.40 27.10

Age . . . . . . . . . . . . . . . . Category West North Central South Atlantic East South Central West South Central Mountain Pacific Total

Aged under 5 years ...... Estimate 0.00 8,326.56 13,448.12 6,789.59 0.00 0.00 64,825.86

cv (%) 0.00 104.12 89.17 92.43 0.00 0.00 40.54

Aged 5 to 17 years ...... Estimate 0.00 11,214.93 23,013.69 29,619.73 0.00 10,661.35 86,471.19

cv (%) 0.00 104.51 70.71 55.24 0.00 102.01 33.08

Aged 18 to 24 years ...... Estimate 10,642.12 0.00 0.00 0.00 11,944.75 0.00 79,514.85

cv (%) 98.62 0.00 0.00 0.00 97.13 0.00 43.97

Aged 25 to 44 years ...... Estimate 23,444.89 66,752.50 17,835.18 31,507.38 34,640.88 34,991.11 328,063.73

cv (%) 71.66 46.49 74.33 61.60 57.85 57.16 21.03

Aged 45 to 64 years ...... Estimate 23,720.60 30,218.27 32,359.11 26,414.33 11,712.11 37,092.66 246,843.45

cv (%) 66.44 61.00 58.61 56.98 100.17 51.50 21.08

Aged 65 years or older .... Estimate 0.00 19,042.88 27,415.07 12,171.18 11,125.20 11,286.53 116,301.66

cv (%) 0.00 73.19 59.97 80.84 42.73 99.48 28.89

Total ............... Estimate 57,807.61 135,555.15 114,071.16 106,502.21 69,422.94 94,031.67 922,020.74

cv (%) 44.28 33.62 40.41 37.19 38.11 33.94 11.97

Page 110 [ Series 2, No. 126
Age Category Male Female

Subdomain

Black not

Hispanic

Other not

Hispanic Hispanic Asian Not Asian

Substantive variable: Number of moving motor vehicle injuries

Aged under 5 years ...... Estimate 0.00 91,643.96 0.00 91,643.96 0.00 0.00 91,643.96

cv (%) 0.00 99.89 0.00 99.89 0.00 0.00 99.89

Aged 5 to 17 years ...... Estimate 172,150.80 435,241.14 53,453.18 499,541.00 54,397.76 0.00 607,391.94

cv (%) 48.70 35.24 70.09 32.68 99.64 0.00 28.22

Aged 18 to 24 years ...... Estimate 737,646.81 432,467.37 91,416.57 853,135.17 225,562.44 0.00 1,170,114.18

cv (%) 37.76 34.30 67.82 34.40 50.45 0.00 28.03

Aged 25 to 44 years ...... Estimate 769,740.68 774,975.18 224,596.54 1,158,288.58 161,830.73 101,395.75 1,443,320.10

cv (%) 31.41 26.45 50.96 24.27 61.67 100.30 20.83

Aged 45 to 64 years ...... Estimate 172,682.31 260,628.76 50,214.68 328,234.13 54,862.26 48,694.13 384,616.93

cv (%) 58.90 42.60 71.03 41.51 98.85 100.60 36.85

Aged 65 years or older .... Estimate 203,736.01 109,345.86 48,149.92 208,638.38 56,293.58 0.00 313,081.87

cv (%) 46.82 70.54 62.72 50.23 99.49 0.00 46.07

Total ............... Estimate 2,055,956.60 2,104,302.26 467,830.89 3,139,481.21 552,946.77 150,089.88 4,010,168.98

cv (%) 20.37 16.62 32.59 16.16 32.18 75.26 13.91

Age . . . . . . . . . . . . . . . . Category Northeast Midwest South West New England Mid-Atlantic East North Central

Aged under 5 years ...... Estimate 0.00 0.00 0.00 91,643.96 0.00 0.00 0.00

cv (%) 0.00 0.00 0.00 99.89 0.00 0.00 0.00

Aged 5 to 17 years ...... Estimate 97,230.71 46,289.09 366,611.44 97,260.70 45,547.58 51,683.12 0.00

cv (%) 65.72 100.48 33.73 70.86 83.53 99.87 0.00

Aged 18 to 24 years ...... Estimate 109,813.31 233,824.37 391,893.97 434,582.53 58,743.07 51,070.24 176,929.60

cv (%) 69.12 53.49 49.98 38.01 99.93 97.84 58.87

Aged 25 to 44 years ...... Estimate 440,352.42 149,464.55 546,029.86 408,869.02 201,656.79 238,695.63 149,464.55

cv (%) 35.38 56.77 38.04 39.90 48.15 51.20 56.77

Aged 45 to 64 years ...... Estimate 5,0703.93 130,989.73 148,061.01 103,556.39 50,703.93 0.00 130,989.73

cv (%) 100.00 61.77 61.18 70.40 100.00 0.00 61.77

Aged 65 years or older .... Estimate 108,160.19 0.00 101,202.21 103,719.48 0.00 108,160.19 0.00

cv (%) 99.77 0.00 59.39 71.70 0.00 99.77 0.00

Total ............... Estimate 806,260.56 560,567.74 1,553,798.49 1,239,632.08 356,651.38 449,609.18 457,383.87

cv (%) 30.90 31.94 22.18 23.29 44.47 39.64 34.62

Age . . . . . . . . . . . . . . . . Category West North Central South Atlantic East South Central West South Central Mountain Pacific Total

Aged under 5 years ...... Estimate 0.00 0.00 0.00 0.00 91,643.96 0.00 91,643.96

cv (%) 0.00 0.00 0.00 0.00 99.89 0.00 99.89

Aged 5 to 17 years ...... Estimate 46,289.09 47,891.53 103,080.35 215,639.55 50,575.91 46,684.79 607,391.94

cv (%) 100.48 87.17 70.71 45.62 100.57 100.39 28.22

Aged 18 to 24 years ...... Estimate 56,894.78 289,788.91 0.00 102,105.05 164,983.75 269,598.78 1,170,114.18

cv (%) 117.04 62.89 0.00 70.47 58.48 53.39 28.03

Aged 25 to 44 years ...... Estimate 0.00 59,956.38 123,477.46 362,596.02 150,941.13 257,927.89 1,544,715.85

cv (%) 0.00 99.97 99.81 42.94 57.88 52.84 20.63

Aged 45 to 64 years ...... Estimate 0.00 0.00 66,351.30 81,709.72 54,862.26 48,694.13 433,311.06

cv (%) 0.00 0.00 99.98 75.82 98.85 100.60 34.57

Aged 65 years or older .... Estimate 0.00 0.00 53,052.29 48,149.92 0.00 103,719.48 313,081.87

cv (%) 0.00 0.00 99.56 62.72 0.00 71.70 46.07

Total ............... Estimate 103,183.87 397,636.83 345,961.39 810,200.27 513,007.00 726,625.07 4,160,258.86

cv (%) 78.71 49.56 48.08 28.20 37.41 30.30 13.66

Series 2, No. 126 [ Page 111
Age Category Male Female

Subdomain

Black not

Hispanic

Other not

Hispanic Hispanic Asian Not Asian

Substantive variable: Percentage unable to carry out major activity

Aged under 5 years ...... Estimate 0.49 0.59 1.08 0.35 1.10 0.00 0.56

cv (%) 17.20 21.70 25.01 19.08 40.12 0.00 13.03

Aged 5 to 17 years ...... Estimate 0.31 0.41 0.48 0.33 0.37 0.00 0.37

cv (%) 17.18 15.22 23.77 12.94 30.69 0.00 11.66

Aged 18 to 24 years ...... Estimate 1.86 1.41 2.46 1.41 2.17 1.91 1.62

cv (%) 9.57 9.44 15.11 9.05 19.80 63.53 6.61

Aged 25 to 44 years ...... Estimate 2.99 2.42 4.80 2.36 3.18 1.40 2.74

cv (%) 4.11 5.16 7.08 3.92 11.25 29.27 3.32

Aged 45 to 64 years ...... Estimate 10.05 7.18 14.40 7.82 9.09 3.87 8.65

cv (%) 3.09 3.85 4.44 2.67 11.36 25.76 2.38

Aged 65 years or older .... Estimate 11.79 9.71 19.67 9.67 11.91 10.26 10.57

cv (%) 4.02 3.29 6.05 2.91 13.30 28.15 2.68

Total ............... Estimate 4.35 3.72 5.89 3.83 3.21 1.88 4.08

cv (%) 1.90 2.34 3.08 1.82 7.10 17.81 1.56

Age . . . . . . . . . . . . . . . . Category Northeast Midwest South West New England Mid-Atlantic East North Central

Aged under 5 years ...... Estimate 0.51 0.37 0.60 0.69 0.30 0.58 0.46

cv (%) 34.48 31.18 23.15 34.21 80.38 37.86 32.26

Aged 5 to 17 years ...... Estimate 0.18 0.35 0.47 0.33 0.26 0.16 0.36

cv (%) 37.57 23.45 17.22 22.44 73.88 42.15 25.30

Aged 18 to 24 years ...... Estimate 1.24 1.72 1.47 2.20 1.68 1.07 1.75

cv (%) 19.72 12.90 13.36 13.33 30.47 27.83 13.74

Aged 25 to 44 years ...... Estimate 2.69 2.46 2.82 2.80 2.75 2.67 2.61

cv (%) 7.64 7.31 5.53 6.07 15.42 7.63 7.68

Aged 45 to 64 years ...... Estimate 7.74 7.98 9.85 7.91 7.63 7.77 8.16

cv (%) 5.57 4.47 4.08 6.48 11.70 6.32 5.65

Aged 65 years or older .... Estimate 9.15 9.59 12.35 10.31 8.67 9.33 10.09

cv (%) 5.72 5.68 4.14 6.91 9.16 7.10 6.36

Total ............... Estimate 3.84 3.68 4.51 3.83 3.78 3.85 3.83

cv (%) 3.86 3.22 2.83 4.40 8.15 4.41 3.81

Age . . . . . . . . . . . . . . . . Category West North Central South Atlantic East South Central West South Central Mountain Pacific Total

Aged under 5 years ...... Estimate 0.13 0.50 0.36 0.78 1.46 0.34 0.54

cv (%) 101.48 38.37 86.87 27.54 41.34 47.58 13.01

Aged 5 to 17 years ...... Estimate 0.33 0.63 0.13 0.46 0.53 0.25 0.36

cv (%) 40.58 24.13 67.35 26.68 33.99 32.69 11.61

Aged 18 to 24 years ...... Estimate 1.64 1.19 1.86 1.64 1.73 2.38 1.63

cv (%) 32.36 22.93 25.23 19.77 29.59 15.05 6.34

Aged 25 to 44 years ...... Estimate 2.08 2.37 4.25 2.74 3.27 2.62 2.70

cv (%) 15.44 8.44 10.94 9.82 8.65 8.18 3.30

Aged 45 to 64 years ...... Estimate 7.47 8.53 12.86 9.96 7.89 7.92 8.55

cv (%) 7.66 6.60 8.64 8.53 15.20 5.96 2.33

Aged 65 years or older .... Estimate 8.25 10.62 15.83 13.09 9.78 10.50 10.57

cv (%) 10.32 5.96 8.83 6.59 15.21 7.48 2.61

Total ............... Estimate 3.26 4.05 6.00 4.38 3.87 3.81 4.03

cv (%) 5.34 3.86 5.60 6.56 9.66 4.51 1.53

Page 112 [ Series 2, No. 126
Age Category Male Female

Subdomain

Black not

Hispanic

Other not

Hispanic Hispanic Asian Not Asian

Substantive variable: Percentage with interval since last doctor visit more than 5 years

Aged under 5 years ...... Estimate 0.00 0.00 0.00 0.00 0.00 0.00 0.00

cv (%) 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Aged 5 to 17 years ...... Estimate 1.83 1.84 2.33 1.50 3.30 3.27 1.79

cv (%) 7.79 9.12 16.02 8.89 11.98 36.33 6.68

Aged 18 to 24 years ...... Estimate 4.64 1.41 3.59 2.47 6.02 6.61 2.88

cv (%) 6.97 12.13 12.25 7.65 13.62 23.38 6.65

Aged 25 to 44 years ...... Estimate 6.58 2.05 4.11 4.06 6.63 8.90 4.14

cv (%) 3.19 5.13 9.02 3.20 9.30 14.02 2.86

Aged 45 to 64 years ...... Estimate 6.17 4.11 4.12 5.17 5.83 6.37 5.07

cv (%) 3.84 4.34 9.50 3.52 9.71 16.77 3.20

Aged 65 years or older .... Estimate 4.10 3.26 3.35 3.55 5.89 4.78 3.60

cv (%) 6.78 5.11 15.22 4.66 17.77 62.55 4.13

Total ............... Estimate 4.57 2.36 3.18 3.33 4.80 6.07 3.36

cv (%) 2.29 2.95 6.46 2.35 5.82 11.62 2.09

Age . . . . . . . . . . . . . . . . Category Northeast Midwest South West New England Mid-Atlantic East North Central

Aged under 5 years ...... Estimate 0.00 0.00 0.00 0.00 0.00 0.00 0.00

cv (%) 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Aged 5 to 17 years ...... Estimate 0.65 1.05 2.61 2.51 0.78 0.60 0.81

cv (%) 24.07 16.72 8.86 11.19 58.31 23.11 18.10

Aged 18 to 24 years ...... Estimate 2.47 2.11 3.57 3.57 0.71 3.15 1.87

cv (%) 12.87 13.20 10.54 13.57 20.84 13.53 15.03

Aged 25 to 44 years ...... Estimate 3.99 4.23 4.16 4.78 3.85 4.05 4.24

cv (%) 6.08 5.56 5.31 5.84 10.69 7.41 6.14

Aged 45 to 64 years ...... Estimate 5.33 5.23 4.94 4.95 6.30 5.01 5.33

cv (%) 7.14 6.38 6.23 6.87 10.62 8.49 7.13

Aged 65 years or older .... Estimate 3.53 4.01 3.39 3.58 3.72 3.45 4.19

cv (%) 9.40 8.20 8.44 8.85 18.56 11.21 8.82

Total ............... Estimate 3.19 3.22 3.53 3.74 3.16 3.20 3.19

cv (%) 4.17 4.08 4.12 4.65 7.51 5.11 4.15

Age . . . . . . . . . . . . . . . . Category West North Central South Atlantic East South Central West South Central Mountain Pacific Total

Aged under 5 years ...... Estimate 0.00 0.00 0.00 0.00 0.00 0.00 0.00

cv (%) 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Aged 5 to 17 years ...... Estimate 1.66 1.58 3.36 3.34 3.17 2.24 1.83

cv (%) 30.33 17.83 17.92 13.84 19.64 13.67 6.64

Aged 18 to 24 years ...... Estimate 2.86 2.66 2.50 5.23 1.96 4.23 2.98

cv (%) 23.10 15.42 30.62 13.60 28.08 15.20 6.22

Aged 25 to 44 years ...... Estimate 4.20 3.44 3.89 5.10 4.41 4.92 4.27

cv (%) 11.51 7.02 10.39 8.64 8.30 7.58 2.67

Aged 45 to 64 years ...... Estimate 4.94 4.15 6.15 5.33 5.86 4.63 5.10

cv (%) 11.64 9.78 13.15 10.57 15.05 7.77 3.09

Aged 65 years or older .... Estimate 3.52 3.04 3.67 3.74 3.71 3.54 3.61

cv (%) 19.48 13.10 24.51 12.33 20.52 11.88 4.14

Total ............... Estimate 3.29 2.86 3.79 4.21 3.69 3.76 3.43

cv (%) 9.02 5.98 8.82 5.92 8.71 5.75 2.05

Series 2, No. 126 [ Page 113
Age Category Male Female

Subdomain

Black not

Hispanic

Other not

Hispanic Hispanic Asian Not Asian

Substantive variable: Percentage reporting poor health

Aged under 5 years ...... Estimate 0.55 0.59 0.43 0.52 1.08 0.00 0.59

cv (%) 19.83 24.32 37.53 19.61 37.14 0.00 16.09

Aged 5 to 17 years ...... Estimate 0.22 0.37 0.49 0.23 0.43 0.00 0.30

cv (%) 25.15 14.87 35.42 16.75 31.29 0.00 13.93

Aged 18 to 24 years ...... Estimate 0.31 0.55 0.54 0.37 0.83 0.00 0.45

cv (%) 25.58 17.67 33.78 18.81 32.16 0.00 15.62

Aged 25 to 44 years ...... Estimate 1.29 1.29 2.44 1.10 1.61 0.76 1.30

cv (%) 6.65 5.87 8.87 4.75 14.32 42.31 4.60

Aged 45 to 64 years ...... Estimate 5.87 5.05 9.64 4.94 5.51 3.18 5.49

cv (%) 4.95 4.38 6.37 4.12 14.92 33.64 3.44

Aged 65 years or older .... Estimate 9.71 9.43 19.24 8.64 9.67 4.07 9.60

cv (%) 3.88 3.07 5.12 2.65 13.01 34.38 2.42

Total ............... Estimate 2.63 2.82 4.09 2.60 1.99 0.99 2.77

cv (%) 2.45 2.58 4.10 2.36 8.09 24.81 2.00

Age . . . . . . . . . . . . . . . . Category Northeast Midwest South West New England Mid-Atlantic East North Central

Aged under 5 years ...... Estimate 0.27 0.35 0.92 0.52 0.00 0.36 0.44

cv (%) 45.75 35.98 20.45 42.16 0.00 45.45 38.58

Aged 5 to 17 years ...... Estimate 0.16 0.25 0.41 0.27 0.17 0.16 0.25

cv (%) 28.20 27.54 22.66 28.47 66.78 40.01 32.54

Aged 18 to 24 years ...... Estimate 0.22 0.50 0.52 0.44 0.13 0.26 0.39

cv (%) 39.50 28.61 23.92 32.22 95.90 43.04 34.50

Aged 25 to 44 years ...... Estimate 1.08 1.14 1.69 1.03 0.98 1.12 1.26

cv (%) 10.52 10.40 6.65 9.95 24.00 12.84 11.93

Aged 45 to 64 years ...... Estimate 3.54 4.96 7.45 4.66 4.11 3.36 5.37

cv (%) 7.74 6.67 5.76 8.49 18.18 9.57 7.39

Aged 65 years or older .... Estimate 7.04 7.97 12.95 8.35 7.51 6.87 8.46

cv (%) 6.91 6.19 4.27 7.05 14.92 8.30 6.48

Total ............... Estimate 2.07 2.35 3.68 2.22 2.17 2.03 2.53

cv (%) 5.21 4.35 2.90 5.64 13.86 6.25 4.88

Age . . . . . . . . . . . . . . . . Category West North Central South Atlantic East South Central West South Central Mountain Pacific Total

Aged under 5 years ...... Estimate 0.13 0.65 1.14 1.11 1.17 0.23 0.57

cv (%) 102.85 41.73 42.25 27.81 50.58 62.30 16.13

Aged 5 to 17 years ...... Estimate 0.24 0.37 0.57 0.37 0.14 0.33 0.29

cv (%) 55.23 42.85 34.87 33.91 61.83 30.58 13.93

Aged 18 to 24 years ...... Estimate 0.81 0.38 0.51 0.71 0.39 0.46 0.44

cv (%) 43.10 41.68 43.66 36.19 61.34 38.23 15.64

Aged 25 to 44 years ...... Estimate 0.81 1.21 2.79 1.78 1.02 1.03 1.29

cv (%) 21.81 13.29 14.41 8.54 22.74 11.46 4.59

Aged 45 to 64 years ...... Estimate 3.83 6.76 9.99 6.99 4.58 4.69 5.44

cv (%) 12.06 9.15 12.31 9.57 14.87 9.52 3.42

Aged 65 years or older .... Estimate 6.67 12.07 16.39 12.52 8.97 8.12 9.55

cv (%) 13.44 5.79 9.89 8.54 11.62 8.37 2.40

Total ............... Estimate 1.90 3.40 5.04 3.40 2.22 2.23 2.73

cv (%) 7.98 5.35 4.36 7.04 13.42 5.13 1.99

Page 114 [ Series 2, No. 126
Age Category Male Female

Subdomain

Black not

Hispanic

Other not

Hispanic Hispanic Asian Not Asian

Substantive variable: Percentage reporting at least 2 hospitalizations in the past year

Aged under 5 years ...... Estimate 1.47 1.15 1.26 1.36 1.05 0.00 1.35

cv (%) 14.02 14.10 23.41 11.10 24.87 0.00 9.56

Aged 5 to 17 years ...... Estimate 0.32 0.35 0.21 0.39 0.13 0.00 0.35

cv (%) 16.36 16.34 35.06 12.12 49.27 0.00 11.49

Aged 18 to 24 years ...... Estimate 0.50 1.65 1.24 1.05 1.18 0.58 1.11

cv (%) 17.97 10.77 19.18 10.82 25.45 71.12 9.35

Aged 25 to 44 years ...... Estimate 0.79 1.40 1.51 1.05 0.99 0.09 1.13

cv (%) 8.57 5.81 12.14 5.47 18.27 100.07 4.68

Aged 45 to 64 years ...... Estimate 2.32 1.94 2.75 2.08 1.60 0.21 2.16

cv (%) 6.74 7.20 9.95 5.06 23.71 98.96 4.55

Aged 65 years or older .... Estimate 4.75 4.26 4.71 4.44 4.44 3.18 4.48

cv (%) 6.65 5.57 11.58 4.35 21.31 45.49 4.12

Total ............... Estimate 1.41 1.71 1.61 1.61 1.03 0.29 1.60

cv (%) 3.55 3.94 6.16 2.97 9.97 33.41 2.71

Age . . . . . . . . . . . . . . . . Category Northeast Midwest South West New England Mid-Atlantic East North Central

Aged under 5 years ...... Estimate 0.95 1.64 1.67 0.66 1.01 0.93 1.80

cv (%) 23.17 19.28 14.99 26.99 42.01 27.77 18.37

Aged 5 to 17 years ...... Estimate 0.15 0.40 0.41 0.28 0.18 0.14 0.40

cv (%) 30.94 19.00 20.39 26.60 71.78 32.12 22.54

Aged 18 to 24 years ...... Estimate 0.77 1.25 1.19 1.05 0.58 0.85 1.13

cv (%) 20.81 15.94 17.86 19.40 46.80 24.11 21.03

Aged 25 to 44 years ...... Estimate 1.06 1.23 1.16 0.89 1.00 1.09 1.31

cv (%) 10.23 10.28 7.14 10.89 17.73 12.41 12.13

Aged 45 to 64 years ...... Estimate 2.06 2.31 2.23 1.78 2.11 2.04 2.49

cv (%) 9.27 9.06 7.18 13.11 16.39 11.19 9.35

Aged 65 years or older .... Estimate 3.55 4.41 5.27 4.15 3.26 3.66 4.32

cv (%) 8.46 9.09 6.85 10.14 15.53 10.58 9.49

Total ............... Estimate 1.40 1.68 1.75 1.28 1.34 1.43 1.72

cv (%) 5.03 5.97 4.85 7.05 9.34 6.02 6.49

Age . . . . . . . . . . . . . . . . Category West North Central South Atlantic East South Central West South Central Mountain Pacific Total

Aged under 5 years ...... Estimate 1.25 1.74 3.04 1.07 1.03 0.49 1.31

cv (%) 56.07 16.51 27.56 37.20 29.57 37.47 9.57

Aged 5 to 17 years ...... Estimate 0.41 0.24 0.86 0.40 0.40 0.23 0.34

cv (%) 36.48 39.90 33.09 34.66 32.30 34.39 11.51

Aged 18 to 24 years ...... Estimate 1.62 0.53 1.43 1.91 1.09 1.04 1.09

cv (%) 20.46 27.49 40.42 19.51 33.67 24.24 9.40

Aged 25 to 44 years ...... Estimate 1.02 0.91 2.10 1.06 0.98 0.86 1.10

cv (%) 17.52 15.08 10.72 13.67 21.05 13.38 4.67

Aged 45 to 64 years ...... Estimate 1.80 2.00 2.24 2.52 2.45 1.53 2.12

cv (%) 23.32 12.04 16.01 11.09 26.16 12.79 4.52

Aged 65 years or older .... Estimate 4.66 4.34 6.25 6.10 5.57 3.63 4.46

cv (%) 21.67 10.47 15.00 9.21 16.72 11.50 4.11

Total ............... Estimate 1.55 1.46 2.36 1.82 1.60 1.15 1.57

cv (%) 11.97 6.38 8.98 8.51 10.79 7.52 2.71

Series 2, No. 126 [ Page 115
Age Category Male Female

Subdomain

Black not

Hispanic

Other not

Hispanic Hispanic Asian Not Asian

Substantive variable: Percentage reporting at least 1 doctor visit in the past year

Aged under 5 years ...... Estimate 91.08 90.47 87.68 91.89 87.63 90.55 90.79

cv (%) 0.50 0.48 1.23 0.43 1.39 2.34 0.36

Aged 5 to 17 years ...... Estimate 72.97 74.54 66.95 76.21 66.80 68.40 73.88

cv (%) 0.69 0.74 1.77 0.65 1.54 3.99 0.57

Aged 18 to 24 years ...... Estimate 59.37 80.23 65.50 71.97 61.73 59.53 70.36

cv (%) 1.14 0.78 2.16 0.71 2.79 5.97 0.67

Aged 25 to 44 years ...... Estimate 60.55 79.10 69.24 70.69 64.14 60.36 70.28

cv (%) 0.68 0.44 1.22 0.45 1.35 2.87 0.39

Aged 45 to 64 years ...... Estimate 69.22 78.62 75.41 74.21 70.47 68.56 74.25

cv (%) 0.68 0.49 1.26 0.54 1.84 3.18 0.47

Aged 65 years or older .... Estimate 81.69 85.27 82.29 84.00 81.38 76.37 83.85

cv (%) 0.68 0.45 1.17 0.44 2.61 5.40 0.44

Total ............... Estimate 69.12 79.97 72.02 75.72 68.70 66.65 74.92

cv (%) 0.35 0.29 0.86 0.29 0.98 2.05 0.26

Age . . . . . . . . . . . . . . . . Category Northeast Midwest South West New England Mid-Atlantic East North Central

Aged under 5 years ...... Estimate 93.80 91.62 89.05 89.98 96.67 92.78 91.66

cv (%) 0.82 0.53 0.68 0.92 0.97 1.02 0.64

Aged 5 to 17 years ...... Estimate 81.47 75.89 69.52 71.53 85.15 80.21 76.71

cv (%) 0.98 1.16 1.00 1.12 2.45 1.13 1.26

Aged 18 to 24 years ...... Estimate 69.80 72.12 68.92 69.76 76.58 67.19 71.23

cv (%) 1.59 1.14 1.33 1.69 2.09 1.86 1.23

Aged 25 to 44 years ...... Estimate 69.27 71.65 69.20 70.10 72.23 68.15 71.73

cv (%) 0.92 0.78 0.83 0.83 1.96 1.05 0.91

Aged 45 to 64 years ...... Estimate 74.32 74.19 73.26 75.32 74.91 74.13 74.78

cv (%) 0.85 0.91 0.77 0.88 1.52 1.07 1.10

Aged 65 years or older .... Estimate 85.58 83.68 83.29 82.61 86.47 85.25 83.91

cv (%) 0.80 0.84 0.77 0.81 1.48 0.95 1.02

Total ............... Estimate 76.28 75.96 73.20 74.20 78.98 75.31 76.17

cv (%) 0.53 0.50 0.49 0.61 0.92 0.61 0.56

Age . . . . . . . . . . . . . . . . Category West North Central South Atlantic East South Central West South Central Mountain Pacific Total

Aged under 5 years ...... Estimate 91.53 88.83 90.37 88.76 87.27 91.19 90.78

cv (%) 1.15 1.11 1.42 1.00 1.79 1.06 0.37

Aged 5 to 17 years ...... Estimate 73.69 71.23 69.07 67.94 69.62 72.32 73.73

cv (%) 2.78 1.91 2.20 1.53 2.29 1.32 0.57

Aged 18 to 24 years ...... Estimate 74.84 69.63 70.15 67.44 70.48 69.47 70.06

cv (%) 2.36 1.92 3.20 1.82 2.73 2.17 0.67

Aged 25 to 44 years ...... Estimate 71.43 68.99 71.54 68.44 69.74 70.24 70.01

cv (%) 1.32 1.32 1.41 1.27 1.55 1.02 0.40

Aged 45 to 64 years ...... Estimate 72.58 73.73 74.06 72.22 73.74 75.88 74.13

cv (%) 1.84 1.12 1.89 1.57 2.36 0.96 0.48

Aged 65 years or older .... Estimate 83.06 82.62 83.77 84.00 81.75 82.93 83.78

cv (%) 1.59 1.17 1.49 1.24 1.97 0.96 0.44

Total ............... Estimate 75.42 73.57 74.19 72.32 73.26 74.57 74.72

cv (%) 1.05 0.82 0.97 0.66 1.20 0.72 0.27

Page 116 [ Series 2, No. 126
Age Category Male Female

Subdomain

Black not

Hispanic

Other not

Hispanic Hispanic Asian Not Asian

Substantive variable: Average number of doctor visits per capita

Aged under 5 years ...... Estimate 4.49 4.35 3.25 4.69 4.15 3.56 4.45

cv (%) 2.28 2.65 3.80 2.12 6.68 8.34 1.71

Aged 5 to 17 years ...... Estimate 2.43 2.52 1.81 2.71 1.88 1.61 2.50

cv (%) 2.14 2.57 4.85 2.12 3.96 10.28 1.83

Aged 18 to 24 years ...... Estimate 1.93 4.32 2.59 3.34 2.51 1.67 3.20

cv (%) 4.21 3.03 5.88 2.77 5.32 13.72 2.47

Aged 25 to 44 years ...... Estimate 2.76 4.69 3.33 3.85 3.23 2.65 3.78

cv (%) 2.98 1.83 3.72 1.98 5.26 7.41 1.77

Aged 45 to 64 years ...... Estimate 3.94 4.88 5.37 4.28 4.99 2.92 4.46

cv (%) 3.67 2.63 6.25 2.46 13.72 16.61 2.35

Aged 65 years or older .... Estimate 5.60 5.84 6.47 5.61 7.73 5.59 5.74

cv (%) 3.42 2.54 5.94 2.17 21.01 20.16 2.26

Total ............... Estimate 3.26 4.43 3.45 3.98 3.31 2.58 3.89

cv (%) 1.43 1.08 2.41 1.11 4.20 6.50 0.94

Age . . . . . . . . . . . . . . . . Category Northeast Midwest South West New England Mid-Atlantic East North Central

Aged under 5 years ...... Estimate 5.04 4.42 4.14 4.34 4.95 5.08 4.51

cv (%) 6.10 3.74 2.85 3.14 7.60 7.32 5.04

Aged 5 to 17 years ...... Estimate 2.63 2.66 2.25 2.49 2.76 2.59 2.72

cv (%) 3.10 3.69 3.09 3.85 6.16 3.85 4.04

Aged 18 to 24 years ...... Estimate 2.91 3.33 2.80 3.80 3.41 2.71 3.41

cv (%) 4.30 3.97 3.89 7.08 5.48 6.03 5.06

Aged 25 to 44 years ...... Estimate 3.79 3.64 3.54 4.14 4.28 3.60 3.80

cv (%) 2.86 2.84 3.26 3.31 7.06 3.73 2.96

Aged 45 to 64 years ...... Estimate 4.70 4.16 4.29 4.70 5.19 4.54 4.36

cv (%) 5.48 3.93 4.50 5.03 16.02 4.46 4.58

Aged 65 years or older .... Estimate 6.38 5.31 5.42 6.13 7.23 6.06 5.49

cv (%) 5.99 4.12 3.36 5.28 20.08 4.43 4.92

Total ............... Estimate 4.12 3.77 3.63 4.11 4.56 3.96 3.90

cv (%) 2.24 1.69 1.68 1.99 5.73 2.17 1.96

Age . . . . . . . . . . . . . . . . Category West North Central South Atlantic East South Central West South Central Mountain Pacific Total

Aged under 5 years ...... Estimate 4.19 4.08 4.25 4.16 4.59 4.22 4.42

cv (%) 3.08 3.90 7.00 4.64 5.15 3.63 1.68

Aged 5 to 17 years ...... Estimate 2.50 2.10 2.89 2.13 2.66 2.42 2.48

cv (%) 9.30 3.91 6.66 5.70 8.23 5.05 1.82

Aged 18 to 24 years ...... Estimate 3.10 2.92 2.48 2.82 4.63 3.46 3.16

cv (%) 7.01 6.17 6.71 4.72 17.27 4.54 2.42

Aged 25 to 44 years ...... Estimate 3.23 3.52 3.75 3.47 4.39 4.05 3.74

cv (%) 6.23 4.71 5.02 4.78 7.85 3.10 1.75

Aged 45 to 64 years ...... Estimate 3.64 4.04 4.77 4.36 5.02 4.58 4.43

cv (%) 6.01 6.40 6.92 8.47 12.73 4.79 2.35

Aged 65 years or older .... Estimate 4.85 5.28 5.68 5.50 5.97 6.18 5.74

cv (%) 7.86 4.22 4.25 5.43 13.38 4.82 2.26

Total ............... Estimate 3.42 3.57 3.91 3.56 4.36 4.02 3.86

cv (%) 3.10 2.48 3.13 2.48 4.79 2.03 0.94

Series 2, No. 126 [ Page 117
Age Category Male Female

Subdomain

Black not

Hispanic

Other not

Hispanic Hispanic Asian Not Asian

Substantive variable: Average number of bed days per capita

Aged under 5 years ...... Estimate 3.23 3.33 2.61 3.40 3.33 2.26 3.31

cv (%) 4.15 5.65 9.54 4.16 7.87 15.48 3.59

Aged 5 to 17 years ...... Estimate 2.54 3.14 1.99 3.07 2.42 1.21 2.88

cv (%) 3.65 3.41 7.75 2.92 9.02 12.53 2.73

Aged 18 to 24 years ...... Estimate 2.36 4.14 3.35 3.35 2.58 1.57 3.32

cv (%) 5.24 4.78 14.35 3.75 9.05 16.08 3.76

Aged 25 to 44 years ...... Estimate 3.66 5.25 5.90 4.33 3.92 2.16 4.54

cv (%) 4.11 2.98 5.60 2.96 8.41 15.14 2.54

Aged 45 to 64 years ...... Estimate 7.16 7.95 1.16 7.22 6.49 3.71 7.65

cv (%) 4.84 4.34 8.35 4.03 17.47 28.93 3.64

Aged 65 years or older .... Estimate 0.76 1.67 9.59 0.55 0.26 8.47 1.32

cv (%) 5.43 5.15 10.19 4.15 15.72 33.60 4.12

Total ............... Estimate 4.64 6.00 6.32 5.35 3.89 2.45 5.42

cv (%) 2.43 2.35 4.20 2.23 6.10 10.78 2.03

Age . . . . . . . . . . . . . . . . Category Northeast Midwest South West New England Mid-Atlantic East North Central

Aged under 5 years ...... Estimate 3.52 2.95 3.39 3.27 3.48 3.54 3.08

cv (%) 8.74 7.41 6.31 7.16 8.84 11.15 9.28

Aged 5 to 17 years ...... Estimate 3.01 2.79 2.69 2.97 3.39 2.88 2.90

cv (%) 7.45 4.69 4.99 4.30 7.57 9.97 5.68

Aged 18 to 24 years ...... Estimate 3.13 3.34 3.16 3.52 3.32 3.05 3.45

cv (%) 7.61 7.44 7.58 5.63 15.46 10.31 9.07

Aged 25 to 44 years ...... Estimate 4.58 3.84 4.84 4.52 4.91 4.46 4.11

cv (%) 6.28 5.10 4.58 4.49 8.18 8.32 6.17

Aged 45 to 64 years ...... Estimate 7.69 7.12 8.05 7.19 9.00 7.26 7.80

cv (%) 7.57 7.85 6.41 7.75 14.76 8.55 9.16

Aged 65 years or older .... Estimate 0.30 8.68 3.67 1.53 8.83 0.87 9.39

cv (%) 7.75 8.63 6.84 8.77 17.04 8.96 9.55

Total ............... Estimate 5.47 4.70 5.79 5.25 5.68 5.40 5.03

cv (%) 3.47 4.67 3.31 3.70 6.63 4.62 5.54

Age . . . . . . . . . . . . . . . . Category West North Central South Atlantic East South Central West South Central Mountain Pacific Total

Aged under 5 years ...... Estimate 2.62 3.03 4.04 3.50 3.64 3.11 3.28

cv (%) 9.40 7.70 12.91 12.22 9.90 9.25 3.60

Aged 5 to 17 years ...... Estimate 2.51 2.42 2.97 2.83 3.46 2.77 2.83

cv (%) 9.26 8.59 7.14 7.36 8.63 4.85 2.71

Aged 18 to 24 years ...... Estimate 3.02 2.63 4.34 3.24 3.48 3.54 3.27

cv (%) 10.80 9.45 24.17 8.20 10.28 6.85 3.73

Aged 25 to 44 years ...... Estimate 3.13 4.01 7.42 4.74 4.59 4.49 4.47

cv (%) 7.26 7.21 9.49 5.75 7.36 5.62 2.54

Aged 45 to 64 years ...... Estimate 5.27 6.11 0.71 9.17 6.51 7.44 7.57

cv (%) 12.01 8.49 11.05 11.58 10.95 9.41 3.64

Aged 65 years or older .... Estimate 6.83 1.00 8.87 4.90 9.18 2.40 1.29

cv (%) 16.34 10.00 15.11 8.79 17.68 9.96 4.08

Total ............... Estimate 3.79 4.81 7.97 5.97 4.96 5.36 5.34

cv (%) 6.55 5.05 6.74 5.49 5.51 4.63 2.02

Page 118 [ Series 2, No. 126
Age Category Male Female

Subdomain

Black not

Hispanic

Other not

Hispanic Hispanic Asian Not Asian

Substantive variable: Average number of conditions per capita

Aged under 5 years ...... Estimate 2.17 1.90 1.64 2.15 1.84 1.24 2.06

cv (%) 2.76 3.00 6.10 2.63 6.75 15.81 2.18

Aged 5 to 17 years ...... Estimate 2.19 2.12 1.64 2.32 1.79 0.84 2.19

cv (%) 2.02 2.22 4.07 1.69 5.45 18.72 1.57

Aged 18 to 24 years ...... Estimate 2.06 3.00 1.97 2.73 1.87 1.35 2.57

cv (%) 2.81 2.45 5.26 2.05 6.95 23.30 1.80

Aged 25 to 44 years ...... Estimate 3.06 4.08 3.40 3.68 2.84 1.89 3.63

cv (%) 1.66 1.18 2.61 1.25 4.15 8.43 1.08

Aged 45 to 64 years ...... Estimate 5.68 6.62 7.05 6.12 5.37 3.58 6.23

cv (%) 1.57 1.41 2.95 1.29 5.27 8.88 1.21

Aged 65 years or older .... Estimate 8.80 0.25 1.11 9.52 9.36 4.95 9.69

cv (%) 1.71 1.32 3.56 1.25 5.68 11.21 1.28

Total ............... Estimate 3.78 4.78 3.86 4.50 2.93 1.97 4.36

cv (%) 0.85 0.72 1.92 0.78 2.83 6.02 0.66

Age . . . . . . . . . . . . . . . . Category Northeast Midwest South West New England Mid-Atlantic East North Central

Aged under 5 years ...... Estimate 1.84 2.14 2.03 2.10 2.00 1.78 2.09

cv (%) 6.09 4.51 3.80 4.99 11.04 7.08 5.04

Aged 5 to 17 years ...... Estimate 2.02 2.34 2.01 2.27 2.24 1.94 2.38

cv (%) 3.85 2.98 3.28 3.18 6.82 4.53 3.61

Aged 18 to 24 years ...... Estimate 2.09 2.70 2.52 2.84 2.31 2.00 2.71

cv (%) 3.81 3.67 2.99 5.00 8.03 4.61 4.21

Aged 25 to 44 years ...... Estimate 3.19 3.78 3.58 3.74 3.40 3.11 3.82

cv (%) 2.34 2.11 2.33 2.06 4.44 3.15 2.37

Aged 45 to 64 years ...... Estimate 5.19 6.33 6.74 6.07 5.64 5.05 6.54

cv (%) 2.55 2.30 2.50 2.30 4.80 3.00 2.68

Aged 65 years or older .... Estimate 8.38 9.73 0.57 9.38 9.27 8.04 9.88

cv (%) 2.12 2.18 2.64 2.90 3.27 2.78 2.88

Total ............... Estimate 3.87 4.43 4.47 4.26 4.22 3.75 4.51

cv (%) 1.39 1.39 1.50 1.51 2.87 1.74 1.74

Age . . . . . . . . . . . . . . . . Category West North Central South Atlantic East South Central West South Central Mountain Pacific Total

Aged under 5 years ...... Estimate 2.27 1.75 2.70 2.06 2.10 2.10 2.04

cv (%) 7.81 6.45 9.45 5.19 7.35 6.32 2.14

Aged 5 to 17 years ...... Estimate 2.26 1.78 2.53 2.02 2.57 2.15 2.15

cv (%) 4.77 4.67 6.96 5.05 5.88 3.73 1.59

Aged 18 to 24 years ...... Estimate 2.68 2.35 2.79 2.61 3.10 2.74 2.54

cv (%) 8.41 5.08 8.16 5.08 6.62 6.39 1.85

Aged 25 to 44 years ...... Estimate 3.65 3.13 4.74 3.61 4.04 3.63 3.58

cv (%) 5.02 2.80 6.73 2.88 4.36 2.66 1.09

Aged 45 to 64 years ...... Estimate 5.78 6.12 8.48 6.61 6.54 5.90 6.17

cv (%) 3.64 3.18 5.36 3.16 4.44 2.51 1.20

Aged 65 years or older .... Estimate 9.32 9.54 3.72 0.50 0.37 9.02 9.65

cv (%) 3.26 3.75 6.39 3.04 6.01 3.20 1.26

Total ............... Estimate 4.24 4.11 5.75 4.32 4.55 4.14 4.30

cv (%) 3.21 2.03 3.80 2.11 3.43 1.71 0.67

Series 2, No. 126 [ Page 119
Vital and Health Statistics

series descriptions

SERIES 1. Programs and Collection Procedures—These reports

describe the data collection programs of the National Center

for Health Statistics. They include descriptions of the methods

used to collect and process the data, definitions, and other

material necessary for understanding the data.

SERIES 2. Data Evaluation and Methods Research—These reports

are studies of new statistical methods and include analytical

techniques, objective evaluations of reliability of collected

data, and contributions to statistical theory. These studies also

include experimental tests of new survey methods and

comparisons of U.S. methodology with those of other

countries.

SERIES 3. Analytical and Epidemiological Studies—These reports

present analytical or interpretive studies based on vital and

health statistics. These reports carry the analyses further than

the expository types of reports in the other series.

SERIES 4. Documents and Committee Reports—These are final

reports of major committees concerned with vital and health

statistics and documents such as recommended model vital

registration laws and revised birth and death certificates.

SERIES 5. International Vital and Health Statistics Reports—These

reports are analytical or descriptive reports that compare U.S.

vital and health statistics with those of other countries or

present other international data of relevance to the health

statistics system of the United States.

SERIES 6. Cognition and Survey Measurement—These reports are

from the National Laboratory for Collaborative Research in

Cognition and Survey Measurement. They use methods of

cognitive science to design, evaluate, and test survey

instruments.

SERIES 10. Data From the National Health Interview Survey—These

reports contain statistics on illness; unintentional injuries;

disability; use of hospital, medical, and other health services;

and a wide range of special current health topics covering

many aspects of health behaviors, health status, and health

care utilization. They are based on data collected in a

continuing national household interview survey.

SERIES 11. Data From the National Health Examination Survey, the

National Health and Nutrition Examination Surveys, and

the Hispanic Health and Nutrition Examination Survey—

Data from direct examination, testing, and measurement on

representative samples of the civilian noninstitutionalized

population provide the basis for (1) medically defined total

prevalence of specific diseases or conditions in the United

States and the distributions of the population with respect to

physical, physiological, and psychological characteristics, and

(2) analyses of trends and relationships among various

measurements and between survey periods.

SERIES 12. Data From the Institutionalized Population Surveys—

Discontinued in 1975. Reports from these surveys are

included in Series 13.

SERIES 13. Data From the National Health Care Survey—These

reports contain statistics on health resources and the public’s

use of health care resources including ambulatory, hospital,

and long-term care services based on data collected directly

from health care providers and provider records.

SERIES 14. Data on Health Resources: Manpower and Facilities—

Discontinued in 1990. Reports on the numbers, geographic

distribution, and characteristics of health resources are now

included in Series 13.

SERIES 15. Data From Special Surveys—These reports contain

statistics on health and health-related topics collected in

special surveys that are not part of the continuing data

systems of the National Center for Health Statistics.

SERIES 16. Compilations of Advance Data From Vital and Health

Statistics—Advance Data Reports provide early release of

information from the National Center for Health Statistics’

health and demographic surveys. They are compiled in the

order in which they are published. Some of these releases

may be followed by detailed reports in Series 10–13.

SERIES 20. Data on Mortality—These reports contain statistics on

mortality that are not included in regular, annual, or monthly

reports. Special analyses by cause of death, age, other

demographic variables, and geographic and trend analyses

are included.

SERIES 21. Data on Natality, Marriage, and Divorce—These reports

contain statistics on natality, marriage, and divorce that are

not included in regular, annual, or monthly reports. Special

analyses by health and demographic variables and

geographic and trend analyses are included.

SERIES 22. Data From the National Mortality and Natality Surveys—

Discontinued in 1975. Reports from these sample surveys,

based on vital records, are now published in Series 20 or 21.

SERIES 23. Data From the National Survey of Family Growth—

These reports contain statistics on factors that affect birth

rates, including contraception, infertility, cohabitation,

marriage, divorce, and remarriage; adoption; use of medical

care for family planning and infertility; and related maternal

and infant health topics. These statistics are based on

national surveys of women of childbearing age.

SERIES 24. Compilations of Data on Natality, Mortality, Marriage,

Divorce, and Induced Terminations of Pregnancy—

These include advance reports of births, deaths, marriages,

and divorces based on final data from the National Vital

Statistics System that were published as supplements to the

Monthly Vital Statistics Report (MVSR). These reports provide

highlights and summaries of detailed data subsequently

published in Vital Statistics of the United States. Other

supplements to the MVSR published here provide selected

findings based on final data from the National Vital Statistics

System and may be followed by detailed reports in Series 20

or 21.

For answers to questions about this report or for a list of reports published

in these series, contact:

Data Dissemination Branch

National Center for Health Statistics

Centers for Disease Control and Prevention

6525 Belcrest Road, Room 1064

Hyattsville, MD 20782-2003

(301) 436–8500

E-mail: nchsquery@cdc.gov

Internet: www.cdc.gov/nchswww/
DEPARTMENT OF

HEALTH & HUMAN SERVICES

Centers for Disease Control and Prevention

National Center for Health Statistics

6525 Belcrest Road

Hyattsville, Maryland 20782-2003

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