Linkage of NCHS Population

Health Surveys to

Administrative Records From

Social Security Administration

and Centers for Medicare &

Medicaid Services

Series 1, Number 58 September 2015
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Suggested citation

Golden C, Driscoll AK, Simon AE, et al. Linkage of NCHS population health

surveys to administrative records from Social Security Administration and

Centers for Medicare & Medicaid Services. National Center for Health Statistics.

Vital Health Stat 1(58). 2015.

Library of Congress Cataloging–in–Publication Data

Names: National Center for Health Statistics (U.S.), issuing body.

Title: Linkage of NCHS population health surveys to administrative records

from Social Security Administration and Centers for Medicare & Medicaid

Services.

Other titles: Linkage of National Center for Health Statistics population

health surveys to administrative records from Social Security Administration

and Centers for Medicare and Medicaid Services | Vital and health statistics.

Series 1, Programs and collection procedures ; no. 58. | DHHS publication ;

no. (PHS) 2015–1334. 0276–4733

Description: Hyattsville, Maryland : U.S. Department of Health and Human

Services, Centers for Disease Control and Prevention, National Center for

Health Statistics, September 2015. | Series: Vital and health statistics,

series 1, programs and collection procedure ; number 58 | Series: DHHS

publication ; no. (PHS) 2015–1334 | Includes bibliographical references.

Identifiers: LCCN 2015031985 | ISBN 0840606737 (alk. paper)

Subjects: | MESH: United States. Social Security Administration. | Health

Surveys--methods--United States. | Records as Topic--United States. |

Centers for Medicare & Medicaid Services (U.S.)--United States. | United

States Government Agencies--United States.

Classification: LCC RA407.3 | NLM W2 A N148va no.58 2015 |

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Printed on acid-free paper.
Series 1, Number 58

Linkage of NCHS Population Health

Surveys to Administrative Records

From Social Security Administration

and Centers for Medicare &

Medicaid Services

Programs and Collection Procedures

U.S. DEPARTMENT OF HEALTH AND HUMAN SERVICES

Centers for Disease Control and Prevention

National Center for Health Statistics

Hyattsville, Maryland

September 2015

DHHS Publication No. 2015–1334
National Center for Health Statistics

Charles J. Rothwell, M.S., M.B.A., Director

Nathaniel Schenker, Ph.D., Deputy Director

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

Office of Analysis and Epidemiology

Irma E. Arispe, Ph.D., Director
Contents
 

Acknowledgments ......................................................................................... v 
 

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

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

Data Sources ............................................................................................. 2 
 

NCHS Population-based and Health Care Surveys ............................................................. 2 
 

Social Security Administration Files ........................................................................ 4 
 

CMS Medicare Data Files................................................................................. 5 
 

CMS Medicaid Data Files................................................................................. 6 
 

End-stage Renal Disease Files ............................................................................. 8 
 

Linkage Eligibility......................................................................................... 8 
 

Linkage Methods.......................................................................................... 9 
 

Linkage Process: SSA.................................................................................... 9 
 

Linkage Process: Medicare and ESRD....................................................................... 10 
 

Linkage Process: Medicaid ................................................................................ 11 
 

Access to Linked Data Files................................................................................. 11 
 

Research Data Center .................................................................................... 11 
 

Public-use Feasibility Files ................................................................................ 11 
 

Analytic Guidelines and Considerations........................................................................ 11 
 

General Analytic Guidelines ............................................................................... 12 
 

Analysis Using Linked NCHS–SSA Data .................................................................... 12 
 

Analysis Using Linked NCHS–Medicare Data ................................................................ 16 
 

Analysis Using Linked NCHS–MAX Data .................................................................. 20 
 

Conclusions .............................................................................................. 24 
 

Published Research Papers Using NCHS Survey-linked SSA, CMS, and USRDS Data ................................. 24 
 

NCHS–SSA Linked Data ................................................................................. 24 
 

NCHS–CMS Medicare Linked Data ........................................................................ 24 
 

NCHS–CMS Medicaid (MAX) Linked Data .................................................................. 25 
 

NCHS–USRDS Linked ESRD Data......................................................................... 25 
 

References ............................................................................................... 26 
 

Appendix I. Social Security Administration Programs ............................................................ 35 
 

Appendix II. Centers for Medicare & Medicaid Services Programs ................................................. 36 
 

Appendix III. Adjusting Sample Weights for Linkage-eligibility Using SUDAAN...................................... 38 
 

Appendix IV. First NCHS–SSA–CMS Linkage: NSCL–I.......................................................... 43 
 

Appendix V. List of Resources for NCHS–SSA–CMS–USRDS Linked Data Files ..................................... 44 
 

Text Figures

1. Algorithm used for linking NCHS survey data to administrative data sources................................... 9 
 

2. Temporal alignment of National Health Interview Survey and Medicaid Analytic eXtract files..................... 13 
 

iii
Detailed Tables

1.	 NCHS–Social Security Administration linked file sample sizes and percentage linked, by survey and age at
 

interview .......................................................................................... 27 
 

2.	 NCHS–Medicare linked file sample sizes and percentage linked, by survey and age at interview: 1991–2007 ........ 29 
 

3.	 NCHS–Medicare linked file sample sizes and percentage linked, by survey and age at interview: 1999–2007......... 30 
 

4.	 NCHS–Medicaid linked file sample sizes and percentage linked, by survey and age at interview: 1999–2009......... 31 
 

5.	 Unweighted percentage of NCHS–Medicare linked sample of ages 65 and over at time of interview enrolled in
 

managed care (for at least 1 month during year) for surveys containing 1991–2007 Medicare enrollment and claims
 

data, by survey ..................................................................................... 33 
 

6.	 Unweighted percentage of NCHS–Medicare linked sample of ages 65 and over at time of interview enrolled in
 

managed care (for at least 1 month during year) for surveys containing 1999–2007 Medicare enrollment and claims
 

data, by survey ..................................................................................... 33 
 

7.	 Unweighted percentage of NCHS–Medicaid linked sample enrolled in Medicaid managed care programs for at least
 

1 month or full year, during 1999–2009 Medicaid Analytic eXtract data years, by survey......................... 34 
 

Appendix Figure

Plot of National Health Interview Survey annual sample weight compared with ineligibility-adjusted weight ......... 41 
 

Appendix Table

SUDAAN output for weight-adjusted marginal model...................................................... 40 
 

iv
Acknowledgments
 

The authors gratefully acknowledge

Christine S. Cox, Kimberly A. Lochner,

Gloria Wheatcroft, Stephanie Bartee,

Lisa B. Mirel, and Hannah R. Day for

their contributions to previous versions

of sections of this report. The authors

also acknowledge James Brittain,

Rashmi Tandon, Catherine R. Duran,

Eileen Call, Keith Zevallos, and Jesse

Bassich for providing programming

support related to the production of

the NCHS survey-linked data files and

tables provided in this report. The

authors gratefully acknowledge the

editor, Jane Sudol; the typesetter,

Zung T. Le; and the NCHS Office of

Information Services, Information

Design and Publishing Staff, for their

dedication and great attention to detail

during the production of this report.

v
Background

As the nation’s principal health

statistics agency, the National Center

for Health Statistics (NCHS) provides

statistical information that can be used

to guide actions and policy to improve

the health of the American people. As

part of its ongoing efforts to fulfill this

mission, NCHS conducts several

population-based and establishment

surveys that provide rich cross-sectional

information on risk factors such as

smoking, height and weight, health

status, and socioeconomic

circumstances. Although the survey

data collected provide information on a

wide range of health-related topics, they

often lack information on longitudinal

outcomes.

Methods

This report describes the second

linkage conducted between several

NCHS surveys and administrative

records from the Centers for Medicare

& Medicaid Services, the Social

Security Administration, and the United

States Renal Data Systems. A brief

overview of the data sources, the

methods used for linkage, descriptions

of the resulting linked data files, and

analytic guidance is provided.

Conclusions

Through its record linkage program,

NCHS has been able to enhance the

survey data it collects by augmenting

survey information with information from

administrative data sources. These

linkages of survey information with

administrative data provide the unique

opportunity to study changes in health

status, health care utilization, and

expenditures in specialized populations,

such as low-income families with

children, the elderly, and the disabled.

Keywords: record linkage • endstage renal disease • National

Health Interview Survey • National

Health and Nutrition Examination

Survey

Linkage of NCHS Population

Health Surveys to Administrative

Records From Social Security

Administration and Centers for

Medicare & Medicaid Services

Cordell Golden, B.A.; Anne K. Driscoll, Dr.P.H.; Alan E. Simon,

M.D.; Dean H. Judson, Ph.D.; Eric A. Miller, Ph.D.; and

Jennifer D. Parker, Ph.D.

Introduction

Federally sponsored health surveys

are a critical source of information on

public health in the United States. The

National Center for Health Statistics

(NCHS) is the nation’s principal health

statistics agency and is responsible for

collecting accurate, relevant, and timely

data. NCHS conducts several

population-based national surveys as

well as collecting vital statistics data,

which are used by a broad range of

users (researchers and policy makers,

among others) to evaluate and profile

the health of the American people.

These national health surveys provide

rich cross-sectional information on risk

factors such as smoking, height and

weight, health status, and socioeconomic

circumstances, but information on

longitudinal outcomes is often missing.

Demand is increasing to incorporate

information from additional sources in

order to enhance the availability and

quality of information on exposures and

outcomes.

NCHS’ record linkage program is

designed to maximize the value of

NCHS population-based surveys by

augmenting survey information with

information from administrative data

sources, such as vital statistics,

Medicare and Medicaid enrollment and

claims, and Social Security benefit

history. Beyond enhancing survey

information, record linkage also reduces

the cost burden of obtaining additional

information, given the expense of active

follow-up of survey respondents.

Under an interagency agreement

among NCHS, the Centers for Medicare

& Medicaid Services (CMS), the Social

Security Administration (SSA), and the

Office of the Assistant Secretary for

Planning and Evaluation of the

Department of Health and Human

Services, two linkages have been

conducted between several NCHS

population-based surveys and CMS and

SSA administrative records. The

linkages have been undertaken to

support various research initiatives of

the participating agencies. The linked

survey files provide a unique

opportunity to examine the factors that

influence health status, disability,

chronic disease, health care utilization,

and expenditures in the elderly, disabled,

and poor populations of the United

States. The linked survey files allow

examination of the administrative data

for some NCHS survey respondents

during the year the NCHS survey was

conducted, for some respondents in

years after the survey was conducted,

and for some respondents in years prior

to the NCHS survey.

The first linkage under this

interagency agreement was initiated in

July 2001. Respondents from several

NCHS surveys were matched to SSA

benefit history data covering 1962–2003

and Medicare enrollment and claims

data for 1991–2000. A detailed

Page 1
Page 2 [ Series 1, No. 58

description of the 2001 linkage of

NCHS surveys to SSA and CMS

administrative data, referred to as

NSCL–I, is provided later in this report.

(NSCL is an acronym drawn from the

federal agencies conducting the record

linkages: NCHS, SSA, and CMS.)

This report is an overview

providing guidelines for using the

NCHS survey-linked data files resulting

from the second linkage, NSCL–II,

which was initiated in 2007. The second

linkage of NCHS surveys to SSA and

CMS administrative data continues

linkages to SSA benefit history data

through 2007 and Medicare enrollment

and claims data for 1999–2007, and it

expands the linkage to Medicaid

enrollment and claims data for

1999–2009. The report describes the

data sources and linkage methods used

to create the NSCL–II data files,

followed by a description of the

resulting data files and guidance on

analytic methods. Detailed appendices

provide information on SSA (Appendix I)

and CMS (Appendix II) programs.

Appendix III provides step-by-step

instructions for adjusting sample weights

for linked data files. Appendix IV

describes the first linkage of NCHS, SSA,

and CMS data. Finally, Appendix V

includes a list of resources that may

be helpful when using the NCHS

survey-linked data.

Data Sources

NCHS Population-based

and Health Care Surveys

Although NCHS conducts several

population-based health surveys and

establishment surveys of health care

facilities, not all NCHS surveys can be

linked to administrative data because

some NCHS surveys do not collect the

information necessary for linkage. Even

within NCHS surveys that have been

linked to administrative records, not all

survey respondents are ‘‘linkageeligible,’’ meaning that they did not

supply sufficient information to enable

the linkage. Thus, linked administrative

data are available for those NCHS

respondents who were enrolled in the

Medicare, Medicaid, or SSA programs,

who provided personal identification

data to NCHS, and for whom NCHS

was able to match identifying

information with CMS or SSA

administrative records. Not all NCHS

surveys or survey years have been

linked to all CMS, SSA, and United

States Renal Data Systems (USRDS)

administrative records. For NSCL–II,

linked SSA, CMS, and USRDS

administrative data are available for the

following NCHS surveys:

+	 National Health Interview Survey

(NHIS), 1994–2005

+	 National Health and Nutrition

Examination Survey (NHANES)

C NHANES 1999–2000, 2001–2002,

and 2003–2004

C NHANES III (1988–1994)

C NHANES I Epidemiologic

Follow-up Study (NHEFS)

u 1971–1975 baseline

u 1982–1984 follow-up

u 1986 follow-up

u 1987 follow-up

u 1992 follow-up

+	 National Nursing Home Survey

(NNHS)

C 1985 (SSA only)

C 1995 (SSA only)

C 1997 (SSA only)

C 2004 (SSA, CMS, and USRDS)

+	 Second Longitudinal Study of Aging

(LSOA II)

C 1994 baseline

C 1997–1998 follow-up

C 1999–2000 follow-up

Each of the NCHS surveys linked

to SSA, CMS, and USRDS

administrative data are briefly described

below. More information about each can

be obtained from the NCHS website:

http://www.cdc.gov/nchs/index.htm.

National Health Interview

Survey

NHIS is a cross-sectional household

interview survey designed to monitor

the health of the U.S. population

through the collection and analysis of

data on a broad range of health topics. It

has been conducted continuously since

1957, and the survey content is

periodically updated. The sampling plan

follows a multistage area probability

design that permits the representative

sampling of households and

noninstitutional group quarters (e.g.,

college dormitories). The sampling plan

is redesigned after every decennial

census.

Prior to 1997, NHIS included a set

of basic health questions and

demographic items, known as the Core

questionnaire. The Core questionnaire

was used for everyone in the household,

with supplemental questionnaires used

for more specific health topics. In 1997,

NHIS underwent a sample redesign and

questionnaire revision. While the

demographic and basic health

information were still collected on

everyone in the household, one adult

and one child within the household were

randomly selected to complete a more

detailed survey on specific health topics.

Since 1997, the Core questions have

remained largely unchanged from year

to year. This allows for trends analysis

and for data from more than 1 year to

be pooled to increase sample size for

analytic purposes. The Core contains

four major components: Household,

Family, Sample Adult, and Sample

Child. The Household component

collects limited demographic

information on all of the individuals

living in a particular house. The Family

component verifies and collects

additional demographic information on

each member from each family in the

house, and collects data on topics

including health status and limitations,

injuries, health care access and

utilization, health insurance, and income

and assets.

From each family, one sample adult

and one sample child (if any children

under age 18 are present) are randomly

selected. Information is collected about

each selected person using the Sample

Adult Core and the Sample Child Core

questionnaires. The Supplements are

used to respond to new public health

data needs as they arise. Some are

fielded only once; others are repeated as

needed. For example, in 2004,

supplements were fielded on childhood

mental health and adult immunizations.

A complete list of supplements can be
Series 1, No. 58 [ Page 3

found at: http://www.cdc.gov/nchs/nhis/

supplements_cosponsors.htm.

Data are collected through a

personal household interview conducted

by interviewers employed and trained by

the U.S. Census Bureau according to

procedures specified by NCHS. A

computer-assisted personal interviewing,

or CAPI, mode using a laptop computer

has been used since 1997. More

information on NHIS is available from:

http://www.cdc.gov/nchs/nhis.htm.

National Health and Nutrition

Examination Survey

NHANES combines interviews,

laboratory tests, and physical

examinations to assess the health and

nutritional status of adults and children

in the United States. The survey

currently examines a nationally

representative sample of about 5,000

persons in 15 counties across the

country each year.

Health interviews are conducted in

respondents’ homes. Health

measurements are performed in specially

designed and equipped mobile

examination centers, which travel to

locations throughout the country. The

study team consists of a physician,

medical and health technicians, and

dietary and health interviewers. All

participants visit the physician and have

dietary interviews and body

measurements taken. All but the very

young have a blood sample taken and a

dental screening.

The NHANES interview includes

demographic, socioeconomic, dietary,

and health-related questions. The

examination component consists of

medical, dental, and physiological

measurements, as well as laboratory

tests. Survey findings are used to

determine the prevalence of major

diseases and risk factors for diseases.

NHANES findings are also the basis for

national standards for such

measurements as height, weight, and

blood pressure. Data are also collected

on chronic and previously undiagnosed

conditions.

More information on NHANES is

available from: http://www.cdc.gov/nchs/

nhanes.htm.

Third National Health and

Nutrition Examination Survey

Prior to becoming a continuous

survey in 1999, NHANES was

conducted periodically, with the last

periodic survey, NHANES III, being

conducted from 1988 to 1994.

NHANES III is a national probability

sample of 33,994 persons aged 2 months

and over. It was designed to provide

national estimates of the health and

nutritional status of the civilian

noninstitutionalized U.S. population

aged 2 months and over. NHANES III

included a standardized physical

examination, laboratory tests, and

questionnaires that covered various

health-related topics.

More information on NHANES III

is available from: http://www.cdc.gov/

nchs/nhanes/nh3data.htm.

NHANES I Epidemiologic

Follow-up Study

NHEFS is a national longitudinal

study that was jointly initiated by NCHS

and the National Institutes of Health’s

National Institute on Aging in

collaboration with other agencies of the

Public Health Service. NHEFS was

designed to investigate the relationships

among clinical, nutritional, and

behavioral factors assessed in the first

NHANES (NHANES I) and subsequent

morbidity, mortality, and hospital

utilization, as well as changes in risk

factors, functional limitation, and

institutionalization.

The NHEFS cohort includes all

persons aged 25–74 who completed a

medical examination as part of

NHANES I during 1971–1975

(n = 14,407). It comprises a series of

follow-up waves, four of which have

been conducted to date: 1982–1984,

1986, 1987, and 1992. The first

follow-up wave included an in-person

visit with brief examination. The

subsequent waves consisted of telephone

interviews.

Tracing and data collection rates in

NHEFS have been very high. Ninety-six

percent of the study population has been

successfully traced at some point

through the 1992 follow-up. Tracing

rates for each completed wave ranged

from 90% to 94%, and interview rates

ranged from 91% to 96% of those

traced. More information on NHEFS can

be obtained from: http://www.cdc.gov/

nchs/nhanes/nhefs/nhefs.htm.

National Nursing Home Survey

NNHS is part of a continuing series

of nationally representative sample

surveys of U.S. nursing homes and their

services, staff, and residents. NNHS was

first conducted during 1973–1974 and

repeated in 1977, 1985, 1995, 1997,

1999, and, most recently, 2004.

NNHS provides information on

nursing homes from two perspectives—

the provider of services and the

recipient of care. Data about the

facilities include characteristics such as

size, ownership, Medicare–Medicaid

certification, services provided and

specialty programs offered, and charges.

For recipients, data were obtained on

demographic characteristics, health

status and medications taken, services

received, and sources of payment.

Survey data were obtained through

personal interviews with facility

administrators and designated staff who

used administrative records to answer

questions about the facilities, staff,

services, and programs, and medical

records to answer questions about the

residents. More information on NNHS is

available from: http://www.cdc.gov/nchs/

nnhs.htm.

Second Longitudinal Study of

Aging

LSOA II is a nationally

representative prospective study

comprising 9,447 civilian noninstitutionalized persons aged 70 and

over at the time of their 1994 NHIS

interview, which served as the baseline

for the study. It followed this cohort of

older persons through two follow-up

interviews, conducted in 1997–1998 and

1999–2000. Thus, it follows a cohort of

the elderly who began the survey while

functioning effectively within the

community.

LSOA II provides information on

the sequence and consequences of health

events, including medical care and

services employed for assisted
Page 4 [ Series 1, No. 58

community living, changes in social

activities, living arrangements, familial

support, the use of community services,

the deployment of assisted living

strategies, use of assistive devices, and

the accessibility of technological and

environmental adaptations. It also

provides information on the causes and

correlates of changes in functioning,

including social and demographic

characteristics, pre-existing and

emerging physical illnesses, cognitive

and emotional status, and social and

environmental support, as well as new

information on individual health risks

and behaviors in the elderly including

alcohol and cigarette use, use of

hormone replacement therapy, receipt of

important health screenings such as

mammography and prostate

examinations, body mass and weight

loss, physical activity, and diet and

nutrition. More information on LSOA II

is available from: http://www.cdc.gov/

nchs/lsoa/lsoa2.htm.

Social Security

Administration Files

The NCHS surveys were linked to

five SSA administrative data files:

Master Beneficiary Record (MBR) file,

Supplemental Security Record (SSR)

file, Payment History Update System

(PHUS) file, 831 Disability Master File

(831 DMF), and a special extract of

summarized quarters of coverage (QOC)

from the Master Earnings File. Benefit

history data from 1962 through

December 2007 were provided to NCHS

by SSA. Brief descriptions of each of

the SSA administrative data files are

provided below; the data dictionary for

the linked NCHS–SSA data files is

available from: http://www.cdc.gov/nchs/

data/datalinkage/nchs_ssa_data_

codebook_2009.pdf.

NCHS surveys that have been

linked to SSA benefits history data are:

+ NHIS 1994–2005

+ NHANES 1999–2004

+ NHEFS

+ NHANES III

+ LSOA II

+ NNHS 1985, 1995, 1997, and 2004

Master Beneficiary Record File

The MBR file contains the data that

generate Social Security benefit checks

under the SSA Old Age, Survivors, and

Disability Insurance (OASDI) program.

The OASDI program provides income

support to persons who have retired or

are disabled, or to a spouse, widow(er),

or other relative of the primary

beneficiary. An MBR record is created

when a person applies for benefits,

including those whose claims were

denied. The record includes information

regarding the OASDI benefit amount,

payment status, dual entitlement (i.e.,

whether the person is entitled to benefits

based on more than one person’s work

history), and, if applicable, information

about disability entitlement, estimates

and reports of earnings, and student

entitlement.

Supplemental Security Record

File

Title XVI of the Social Security

Act, or Supplemental Security Income

(SSI), is a needs-based program that

provides cash assistance to the elderly,

the blind, and the disabled. The SSR file

maintains information on all persons

who have ever applied for Title XVI

SSI and on persons who, prior to

implementation of the federal SSI

program in 1974, received state benefits

and were converted to the SSI program.

Each record contains monthly eligibility

and payment history from the beginning

of the SSI program, providing a

retrospective look at the recipient’s

benefits amounts.

Payment History Update

System File

With the passage of the 1983

Amendments to the Social Security Act,

a portion of Social Security benefits

have been subject to federal income

taxes. Starting in 1984, the actual

amount of the check or direct deposit

that the respondent received can be

determined. The aggregate amount of

benefit payments, repayments, and

reductions for each person in each

calendar year was collected in the

PHUS file.

831 Disability Master File

The Disability Determination

Services (DDS) renders the initial

medical determination for persons

applying for disability benefits under

Title II Social Security and Title XVI

SSI. An 831 DMF record is established

as soon as DDS completes its initial

disability decision. The 831 DMF is

primarily used for research on initial

disability or continuing disability

diagnoses and is available dating to

1988.

The 831 DMF contains data from

subsequent decisions, such as those by

the SSA Office of Hearings and

Appeals. However, only minimal data

are stored regarding subsequent appeals.

Subsequent decisions, or corrections to

earlier decisions, result in additional

records with the same identifiers but

with new values and dates. When more

than one record exists for a given

application, the field AL identifies the

level of decision within a progression of

appeals (e.g., initial, reconsideration, and

others). The Date of SSA Decision, or

DODEC, variable holds the date of each

decision and can be used to identify the

most current outcome.

Quarters of Coverage File

Due to Internal Revenue Service

(IRS) regulations, NCHS was able to

extract only a small set of summarized

annual variables regarding quarters of

coverage from the Master Earnings File

(MEF). MEF is SSA’s primary

repository of earnings data for the U.S.

population. The summarized QOC

variables describe a person’s ‘‘insured

status’’ based on their earnings history

and can be found in this QOC file

developed specifically for the NCHS

linkage. Insured status is the minimum

number of credits or quarters of

coverage a worker must earn to become

eligible for his or her own Social

Security benefit under the Title II

program, or to have benefits paid to his

or her family. The QOC file contains

data regarding these credits dating to

1953.
Series 1, No. 58 [ Page 5

CMS Medicare Data Files

The Medicare data files contain

information on the enrollment status and

health care utilization and expenditures

of Medicare beneficiaries. Nearly all

Medicare beneficiaries receive Part A

hospital insurance benefits, which help

cover inpatient hospital care, skilled

nursing facility stays (not custodial or

long-term care), home health care, and

hospice care. Most beneficiaries also

subscribe to Part B medical insurance

benefits, which help to cover physician

services, outpatient care, durable

medical equipment, and some home

health care. Brief descriptions of each of

the Medicare files that were linked to

NCHS survey data are provided below.

The Medicare Standard Analytical

Files, or SAFs, are research-oriented

files containing final action claims for

Medicare beneficiaries. Each file

contains information collected by

Medicare to pay for health care services

provided to a Medicare beneficiary.

These files are generated from raw

claims through final action algorithms

that match the original claim with

adjusted claims to resolve any

adjustments. Finalized claim files are

available from CMS approximately

9 months into the following calendar

year (for example, 2007 data would

become available in September 2008).

Medicare enrollment and claims

data covering 1991–2007 are available

for the following NCHS surveys:

+ NHIS 1994–1998

+ NHEFS

+ NHANES III

+ LSOA II

NCHS surveys that have been

linked to Medicare enrollment and

claims data covering 1999–2007 are:

+ NHIS 1999–2005

+ NHANES 1999–2004

+ 2004 NNHS

Medicare Denominator File

The Denominator File provides data

on all Medicare beneficiaries enrolled or

entitled to Medicare benefits in a given

year. Monthly information on the

enrollment status and type of enrollment

of linked Medicare beneficiaries is

provided, including third-party payer

information and Medicare Part C, also

referred to as Medicare Advantage

(MA), managed care organization

(MCO), group health organization

(GHO), or health maintenance

organization (HMO) plan enrollment.

The Denominator File contains one

record per person per year of Medicare

enrollment. The data dictionary for the

Denominator File is available from:

ftp://ftp.cdc.gov/pub/Health_Statistics/

NCHS/datalinkage/Denominator%20

(edited).pdf.

Carrier File

The Carrier File (formerly the

Physician/Supplier Part B file) contains

final action claims data submitted by

noninstitutional providers. The data are

largely made up of physician claim

records, although the file also includes

claims from other providers such as

physician assistants, clinical social

workers, nurse practitioners, independent

clinical laboratories, ambulance

providers, and stand-alone ambulatory

surgical centers. One record is in the file

for each claim, although some episodes

of care include more than one claim.

The data dictionary for the Carrier File

is available from: ftp://ftp.cdc.gov/pub/

Health_Statistics/NCHS/datalinkage/

carrier.pdf.

Durable Medical Equipment File

The Durable Medical Equipment

(DME) File contains final action claims

data submitted by DME regional

carriers. Information in the DME file

includes up to 10 ICD–9–CM diagnosis

codes, service type codes, dates of

service, and reimbursement amount. One

record is in the file for each claim,

although some episodes of care include

more than one claim. The data

dictionary for the DME file is available

from: ftp://ftp.cdc.gov/pub/

Health_Statistics/NCHS/datalinkage/

Durable%20Medical%20Equipment.pdf.

Home Health Agency File

The Home Health Agency (HHA)

File contains final action claims for

home health services. Information in this

file includes the number of visits, type

of visit (skilled nursing care, home

health aides, physical therapy, speech

therapy, occupational therapy, and

medical social services), diagnosis (10

ICD–9–CM diagnosis codes), dates of

visits, and reimbursement amount. An

HHA claim may cover services provided

over a period of time, rather than a

single day. One record is in the file for

each claim, although some episodes of

care include more than one claim. The

data dictionary for the HHA file is

available from: ftp://ftp.cdc.gov/pub/

Health_Statistics/NCHS/datalinkage/

Home%20Health%20Agency.pdf.

Hospice File

The Hospice File contains final

action claims data submitted by hospice

providers. The data in this file include

the type of hospice care received (e.g.,

routine home care or inpatient respite

care). The Hospice File contains data

fields for up to 10 ICD–9–CM diagnosis

codes and up to 6 ICD–9–CM procedure

codes, dates of service, reimbursement

amount, and some demographic

information (such as date of birth, race,

and sex). One record is in the file for

each claim although some episodes of

care include more than one claim. The

data dictionary for the Hospice File is

available from: ftp://ftp.cdc.gov/pub/

Health_Statistics/NCHS/datalinkage/

hospice.pdf.

Outpatient File

The Outpatient File contains

Medicare Part B final action claims

from institutional outpatient providers

for each calendar year. Hospital

outpatient departments, rural health

clinics, renal dialysis facilities,

outpatient rehabilitation facilities,

comprehensive outpatient rehabilitation

facilities, and community mental health

centers are examples of institutional

outpatient providers. One record is in

the file for each claim, although some

episodes of care include more than one

claim. The data dictionary for the

Outpatient File is available from:

ftp://ftp.cdc.gov/pub/Health_Statistics/

NCHS/datalinkage/outpatient.pdf.

Medicare Provider Analysis and

Review File

The Medicare Provider Analysis and

Review (MedPAR) File contains

inpatient hospitalization and skilled
Page 6 [ Series 1, No. 58

nursing facility (SNF) final action claim

records. All Medicare Part A short- and

long-stay hospitalization claims and

SNF claims for each calendar year are

included in the MedPAR file. Each

MedPAR claim record includes up to 10

ICD–9–CM diagnosis codes and up to 6

ICD–9–CM procedure codes associated

with each hospital or SNF stay.

Inclusion of hospital stay claim records

on the MedPAR file are based on year

of discharge. SNF claims are based on

year of admission into the facility.

Multiple claims records are possible per

person in the MedPAR file. The data

dictionary for the MedPAR file can be

found at: ftp://ftp.cdc.gov/pub/

Health_Statistics/NCHS/datalinkage/

MedPAR%20(edited).pdf.

Medicare Part D Denominator File

and Medicare Part D Prescription

Drug Event File

The Medicare Part D Denominator

File contains demographic and

enrollment information for each

beneficiary enrolled in Medicare during

the calendar year. In addition to the

variables available on the standard

Denominator File, the Part D

Denominator File contains a derived

race and ethnicity code, an indicator for

‘‘other credible drug coverage,’’ and

monthly indicators for Medicare

Advantage Prescription Drug Plans

(MA–PD) and prescription drug plan

(PDP) enrollment, low-income subsidy

(LIS) enrollment, retiree drug subsidy,

and state-reported dual eligibility status.

The Medicare Part D Denominator File

contains one record per person per year

of enrollment.

The Medicare Part D Prescription

Drug Event (PDE) File contains a

summary of prescription drug costs and

payment data used by CMS to

administer benefits for Medicare Part D

enrollees, including payments to the

plan providers. It does not contain

individual drug claims, but rather

summary extracts submitted to CMS by

Medicare Part D prescription drug plan

providers. The file contains one record

per event. Multiple records are possible

per person in the Part D PDE File.

The Medicare Part D Denominator

and PDE files are available for

Medicare claim years 2006 and 2007.

The data dictionary for the Part D

Denominator File can be found at:

ftp://ftp.cdc.gov/pub/Health_Statistics/

NCHS/datalinkage/nchs-cms_

medicare_part_d_denominator

_file.pdf.

The data dictionary for the

Part D PDE file can be found at:

ftp://ftp.cdc.gov/pub/Health_Statistics/

NCHS/datalinkage/nchs-cms_

medicare_part_d_prescription_drug_

event_file.pdf.

Medicare Summary Files

Chronic Condition Summary File

The Chronic Condition (CC)

Summary File includes claims,

enrollment, and assessment data for

Medicare beneficiaries who have been

diagnosed with one of the following

chronic health conditions or had one of

the following events that may indicate

the presence of a chronic health

condition:

+	 Acute myocardial infarction

+	 Alzheimer’s disease

+	 Alzheimer’s disease, related

disorders, or senile dementia

+	 Atrial fibrillation

+	 Cataract

+	 Chronic kidney disease

+	 Chronic obstructive pulmonary

disease (COPD)

+	 Depression

+	 Diabetes

+	 Glaucoma

+	 Heart failure

+	 Hip/pelvic fracture

+	 Ischemic heart disease

+	 Osteoporosis

+	 Rheumatoid arthritis/Osteoarthritis

(RA/OA)

+	 Stroke/Transient ischemic attack

(TIA)

+	 Breast cancer

+	 Colorectal cancer

+	 Prostate cancer

+	 Lung cancer

+	 Endometrial cancer

The file provides a summary of

clinical information, including date of

first diagnosis or occurrence, and yearly

and midyear flags for each of the 21

conditions. The files are available for

Medicare claim years 2005–2007.

However, the date of first diagnosis or

occurrence can be prior to 2005. The

data dictionary for the CC Summary file

can be found at: ftp://ftp.cdc.gov/pub/

Health_Statistics/NCHS/datalinkage/

nchs-cms_medicare_chronic_condition_

summary_file.pdf.

NCHS Summary Medicare

Enrollment and Claims File

NCHS created a Summary Medicare

Enrollment and Claims (SMEC) file to

assist researchers interested in analyzing

Medicare cost and claims data from

multiple Medicare service files. The

SMEC file contains data on the

beneficiary’s reason for Medicare

entitlement, total months of Medicare

entitlement per year, Medicare Part C

plan enrollment, and summarized

Medicare service charges, total

expenditures, and reimbursement

amounts. These summarized (or

summary) variables are modeled after

the Medicare Current Beneficiary

Survey cost and use files. In addition,

summary variables related to the

beneficiary’s total number of emergency

room visits and Part D prescription drug

costs have been created. Each SMEC

file has a fixed-length record format,

with one record for each survey

respondent successfully linked to

Medicare administrative record files.

The data dictionary for the SMEC file is

available from: ftp://ftp.cdc.gov/pub/

Health_Statistics/NCHS/datalinkage/

summary_medicare_enrollment_and_

claims_files.pdf.

CMS Medicaid Data Files

Medicaid Analytic eXtract Files

Medicaid data have been collected

by each state since 1999 and provided

to CMS through the Medicaid Statistical

Information System (MSIS). These data

contain enrollee eligibility information

and Medicaid claims paid in each

quarter of the federal fiscal year. Data

are extracted from MSIS to create the

Medicaid Analytic eXtract (MAX) Files

which were used to link to NCHS

surveys. Descriptions of the MSIS and

MAX files follow.

MSIS collects, manages, analyzes,

and disseminates information on
Series 1, No. 58 [ Page 7

enrollees, including utilization and

payment for services covered by state

Medicaid or the Children’s Health

Insurance Program (CHIP). CHIP

provides health coverage to low-income

uninsured children and pregnant women

in families with incomes too high to

qualify for most state Medicaid

programs. CHIP is administered by the

states but is jointly financed by the

federal and state governments. Each

state has the option of expanding

Medicaid eligibility to children who

previously had been ineligible due to

their income, or creating a program

distinct from its existing Medicaid

program. For this report, expansions of

existing Medicaid programs are referred

to as M–CHIP, and programs distinct

from existing Medicaid programs are

referred to as S–CHIP. Additional

information on the distinctions between

M–CHIP and S–CHIP can be found in

Appendix II.

In general, each state MSIS file

contains one record for each person

covered by Medicaid/CHIP for at least

1 day during the reporting quarter. The

files contain specified data elements for:

1) persons covered by Medicaid/CHIP

(enrollment files), which consist of

demographic and monthly enrollment

data; and 2) adjudicated claims (paid

claims files), which consist of

information from medical service-related

claims and capitation payments. States

submit four types of claims files

representing inpatient (IP), institutional

long-term care (LT), prescription drugs

(RX), and other noninstitutional services

(OT). Claims records contain

information on the types of services

provided, service providers, and dates,

costs, and types of reimbursement. The

files serve as the historical source of

detailed Medicaid eligibility and paid

claims data maintained by CMS.

MAX data are research extracts of

MSIS. The MAX data system was

developed to provide calendar-year

utilization and expenditure information

and serve as a research tool for

examining Medicaid/CHIP enrollment,

service utilization, and expenditures by

subgroup and over time. MAX contains

person-level information on

demographics, monthly enrollment

status, eligibility group, and use and

costs of services during the year. It also

includes claims-level records for more

detailed analysis of patterns of service

utilization, diagnoses, and cost of care

among Medicaid/CHIP enrollees.

The MAX data systems have been

modified from MSIS in a number of

ways to enhance their usefulness for

research purposes and to enable

individual enrollee-level analysis. MSIS

claims files contain separate records for

initial claims, voided claims, and

positive or negative adjustments; such

records are combined to reflect final

service event records in MAX. Changes

in program eligibility reported

retroactively are incorporated in MAX

monthly enrollment measures. MAX

data are linked to the Medicare

Enrollment Database to help identify

people dually enrolled in Medicare and

Medicaid.

MAX includes summary

information and claims data for all

Medicaid enrollees in each state and the

District of Columbia but not those in

Puerto Rico or other U.S. territories.

The MAX data system consists of a

person summary (PS) file and four

claims files: IP, LT, RX, and OT. Each

of these files is described in greater

detail below.

MAX data covering 1999–2009 are

available for the following NCHS

surveys:

+ NHIS 1994–2005

+ NHANES 1999–2004

+ NNHS 2004

+ NHEFS

+ NHANES III

+ LSOA II

Person Summary File

The PS file for each year of MAX

data contains one record for each person

included in the MAX file, including

persons enrolled in Medicaid, M–CHIP,

and some (but not all) people enrolled

in S–CHIP. In some cases, as described

further below, a beneficiary may have

more than one record on the PS file

during the same year. This might

happen, for example, if a person was

enrolled in Medicaid in more than one

state during the same year. The PS file

contains basis of eligibility, monthly

enrollment data, type of coverage,

demographic information, and summary

information regarding expenditures and

service use. The data dictionary for the

PS file is available from: ftp://

ftp.cdc.gov/pub/Health_Statistics/NCHS/

datalinkage/medicaid/MAX_2009_PS_

Dictionary.pdf.

Inpatient Hospital File

The IP hospital file contains

complete stay records for Medicaid

enrollees who used inpatient hospital

services. Data include admission and

discharge dates, diagnosis-related groups

(DRG), Medicaid payment for

fee-for-service records, third-party

payments, Medicaid-paid Medicare

copayment and deductible amounts, up

to nine ICD–9–CM diagnosis codes, and

principal and additional procedure

codes. The data dictionary for the IP file

is available from: ftp://ftp.cdc.gov/pub/

Health_Statistics/NCHS/datalinkage/

medicaid/MAX_2009_IP_Dictionary.pdf.

Long-term Care File

The LT file includes institutional

long-term care (LTC) records for

services provided by four types of

long-term care facilities: mental

hospitals for the aged, inpatient

psychiatric facilities for persons under

age 21, intermediate care facilities for

the mentally disabled, and nursing

facilities (NF). Information in the LT

file includes start and end dates of

services, patient status at discharge,

Medicaid payment amounts for

fee-for-service records, third-party

payments, Medicaid-paid Medicare

copayment and deductible amounts, and

up to five ICD–9–CM diagnosis codes.

These records do not include procedure

codes. Other community-based LTC

services (e.g., many home-based and

personal care services) are included in

the OT file. The data dictionary for

the LT file is available from:

ftp://ftp.cdc.gov/pub/Health_Statistics/

NCHS/datalinkage/medicaid/

MAX_2009_LT_Dictionary.pdf.

Prescription Drug File

The RX file contains prescribed

drugs, over-the-counter drugs, and other

items dispensed by a freestanding

pharmacy (nonhospital-based).

Information in the RX file includes
Page 8 [ Series 1, No. 58

prescription fill date, new or refill

indicator, National Drug Code, and

quantity and day supply. Also included

are payment amounts, third-party

payments, and Medicaid-paid Medicare

copayment and deductible amounts. The

data dictionary for the RX file is

available from: ftp://ftp.cdc.gov/pub/

Health_Statistics/NCHS/datalinkage/

medicaid/MAX_2009_RX_

Dictionary.pdf.

Other Services File

The OT file contains two major

types of records: 1) records for all

noninstitutional services delivered that

are not reported in other files, and

2) payment records for premiums paid to

the following types of Medicaid

managed care plans: HMOs, health

insurance organizations, prepaid health

plans (PHPs), and primary care case

management plans (PCCMs). The

service types in the OT file include

physician and professional services,

outpatient and clinic services, DME,

hospice, home health care, and

laboratory and X-ray. Information in the

OT files includes dates and types of

service, Medicaid payment for

fee-for-service enrollees, third-party

payments, Medicaid-paid Medicare

copayment and deductible amounts, a

procedure code, and up to two

ICD–9–CM diagnosis codes. The data

dictionary for the OT file is available

from: ftp://ftp.cdc.gov/pub/Health_

Statistics/NCHS/datalinkage/medicaid/

MAX_2009_OT_Dictionary.pdf.

End-stage Renal Disease

Files

NCHS surveys were linked to a

separate set of data files containing

information on patients diagnosed with

end-stage renal disease (ESRD) obtained

from USRDS. USRDS is a national data

system funded by the National Institute

of Diabetes and Digestive and Kidney

Diseases, or NIDDK, designed to

collect, analyze, and distribute

information about ESRD in the United

States. The linked ESRD data files can

be used by researchers interested in

conducting analysis specifically related

to patients with ESRD.

ESRD patient data covering

information for 1974–2008 are available

for the following NCHS surveys:

+	 NHIS 1994–2005

+	 NHANES 1999–2004

+	 NNHS 2004

+	 NHEFS

+	 NHANES III

+	 LSOA II

ESRD data are divided into four

files:

+	 Combined ESRD Patient Profile and

Death Notification (form 2746) File

+	 ESRD Medical Evidence Report

(form 2728) File

+	 ESRD Treatment History File

+	 ESRD Payment History File

Combined ESRD Patient

Profile and Death Notification

File

The Combined ESRD Patient

Profile and Death Notification (form

2746) File contains general demographic

information, primary and contributing

causes of ESRD diagnosis, date of first

dialysis, and information on kidney

transplants. It also contains death

information, if a Death Notification

form was completed. The file has one

record for each successfully linked

NCHS survey respondent entered into

USRDS.

ESRD Medical Evidence Report

File

The ESRD Medical Evidence

Report (form 2728) File contains

information obtained by USRDS from

the completed ESRD Medical Evidence

Report, Medicare Entitlement, or Patient

Registration forms. The file contains

general demographic information, health

insurance status, primary and

contributing causes of ESRD diagnosis,

values and dates of ESRD-related test

results, dialysis information (e.g., date

of first dialysis treatment), and kidney

transplant status. The file may contain

multiple records for each successfully

linked NCHS survey respondent entered

into USRDS.

ESRD Treatment History File

The ESRD Treatment History file

contains information about ESRDrelated events, such as dialysis or

transplants. It includes the treatment

start and end dates, and modality of

ESRD treatment. The file may contain

multiple records for each successfully

linked NCHS survey respondent entered

into USRDS.

ESRD Payment History File

The ESRD Payment History file

contains information on the primary

insurance payer for each ESRD

treatment period. The file also includes

information on Medicare and Medicaid

eligibility status. The ESRD Payment

History file may contain multiple

records for each successfully linked

NCHS survey respondent entered into

USRDS. The data dictionary for the

NCHS–USRDS Linked ESRD Data

Files can be found at: ftp://ftp.cdc.gov/

pub/Health_Statistics/NCHS/datalinkage/

nchs-usrds_linked_esrd_data_files_

documentation.pdf.

Linkage Eligibility

Linkage of NCHS survey

respondents to administrative records

was performed for respondents who

were linkage-eligible. Linkage eligibility

refers to the potential ability to link data

from an NCHS survey respondent to

administrative data. It is distinct from

program eligibility, which defines

whether a person meets federal and

state-specific eligibility criteria for a

specific government-administered

or-funded program.

Linkage eligibility for NCHS survey

respondents is generally determined by

whether respondents agreed to provide

personal identification data to NCHS

and for whom NCHS was able to

validate and match with administrative

records. Personal identification

information includes Social Security

number (SSN) and date of birth.

Respondents eligible for linkage

included those who had not refused to

provide their SSN or health insurance
’

NOTE: NCHS is National Center for Health Statistics; SSA is Social Security Administration; SSN is Social Security 

number; CMS is Centers for Medicare & Medicaid Services; and USRDS is U.S. Renal Data System. 

SOURCE: CDC/NCHS, NSCL-II data linkage. 

Potential matches Nonmatches 

True matches 

Linked NCHS–SSA/CMS/USRDS data files 

Medicare and Medicaid 

Analytic eXtract (MAX) files 

Centers for Medicare & Medicaid Services 

Verification by SSA 

NCHS Finder File 2 

• SSN (verified by SSA) 

• Date of birth (month, day, year) 

• Sex 

NCHS Finder File 1 

• Social Security number 

• Last name 

• First name 

• Middle initial 

• Date of birth (month, day, year) 

• Sex 

• Father s surname (women only) 

• State of birth 

• Zip code 

Eligible for linkage 

NCHS survey data 

Social Security Administration 

End-stage renal disease files 

U.S. Renal Data System 

SSA files 

Ineligible for linkage 

(e.g., no consent) 

Extract data returned to NCHS 

Nonmatches 

National Center for Health Statistics 

NCHS and SSA/CMS/USRDS file linkages 

Figure 1. Algorithm used for linking NCHS survey data to administrative data sources

claim (HIC) number (in the case of

CMS and USRDS linkages); had

sufficient personal identifier information

(PII); and had an SSN verified by the

SSA Enumeration Verification System

(EVS).

NCHS’ process for the collection of

personal identification information and

the determination of linkage eligibility

has changed over time and differs across

surveys. Note that the proportion of

NHIS respondents who provide their

SSN varies by survey year and by

respondent characteristics. The

linkage-eligible records in any given

NCHS survey comprise a subset of the

survey respondents. Of those

linkage-eligible, the actual number of

records linked depends on both program

enrollment and the linkage process.

These linkages had approval from

NCHS’ Research Ethics Review Board

(ERB) and were conducted for all

NCHS survey respondents who were

eligible for linkage. The NCHS ERB,

also known as an Institutional Review

Board or IRB, is an administrative body

of scientists and nonscientists that is

established to protect the rights and

welfare of human research subjects.

NCHS survey participants who were

under age 18 at the time of the survey

are considered linkage-eligible, if the

listed linkage eligibility criteria are met

and consent is provided by their parent

or guardian. However, in accordance

with NCHS ERB guidelines, NCHS can

provide linked data generated for

program participation, claims, and other

events only if they occurred prior to the

participant’s 18th birthday. See the data

linkage website for more information on

this NCHS ERB guidance: http://www.

cdc.gov/nchs/data/datalinkage/nchs_

survey_participants_under_age_18.pdf.

Linkage Methods

Figure 1 presents the overall process

for linking NCHS survey data to

administrative data from SSA, CMS,

and USRDS. The linkage process for the

first linkage, NSCL–I, was similar to the

process for the second, although

differences occurred (Appendix IV).

Linkage Process: SSA

This section provides an overview

of the process that linked Social

Security benefit history records with

NCHS survey data. A more detailed

description is available from:

http://www.cdc.gov/nchs/data/

datalinkage/ssa_methods_report_

2009.pdf.

The process of linking NCHS

survey data with Social Security data

Series 1, No. 58 [ Page 9

began by matching individual survey

respondents with Social Security’s

Numident file. The Numident file is a

record of applications for Social

Security cards maintained by SSA.

Persons are assigned unique, lifelong

SSNs based on these applications. A full

record of any changes to the information

(such as change of name) is maintained

within the Numident file. The data

elements on a Numident record include

name, date and place of birth, parents’
Page 10 [ Series 1, No. 58

names, and date of death (1). To carry

out this linkage, NCHS provided a

submission file to SSA with as many of

the individual identifiers listed in

Figure 1 as were available on the survey

record for all linkage-eligible survey

respondents. This file is referred to as

NCHS finder file #1. NCHS survey

participants were considered ineligible

for matching to the Numident file if

they refused to provide their SSN at the

time of the interview. Additional

ineligibility criteria included refused,

missing, or incomplete information on

last name and date of birth. The file

provided to SSA did not contain the

NCHS survey public-use ID, nor did it

contain any information that could

identify the original survey source.

The first step of the match process

consisted of verifying SSNs received

from NCHS. SSA verified whether the

SSN received from NCHS was correct

using EVS. In cases where the SSN of a

survey participant was missing or could

not be verified, SSA utilized an

enhanced EVS matching algorithm to

try to determine the correct SSN. The

enhanced EVS matching algorithm was

developed by SSA’s Office of the

Actuary to utilize additional identifying

data elements collected during the

survey interview and contained in other

administrative records held by SSA. It

features a scoring system with a

threshold score used to determine which

potential matches are acceptable and,

thus, provided the opportunity to

increase the number of successful

matches. For NCHS records determined

to be matched to the Numident file,

SSA extracted data, where available,

from the benefit history files.

Table 1 presents the total number of

survey respondents for each survey and

year, the number who were eligible for

linkage, and the number who were

linked to the Numident file, as well as

the number of respondents available in

the OASDI, SSR, and 831 Disability

Master files. Since not all survey

participants matched to the Numident

have Social Security benefit history

data, the number of records with benefit

history data is less than the number of

records matched to the Numident.

NCHS–SSA linkage rates are based

upon successful matches to the

Numident file, not to the individual SSA

administrative benefit history files. Due

to the significant variation in the

proportion of eligible survey

respondents across surveys, two linkage

rates are provided: a total survey sample

linkage rate (the percentage of all

survey respondents who were actually

linked) and an eligible sample linkage

rate (the percentage of linkage-eligible

respondents who were actually linked).

Additionally, linkage rates for each

survey were examined overall and by

two age groups—18–64 and 65 and

over. Age was defined as the survey

participant’s age at the time of the

interview.

Linkage Process: Medicare

and ESRD

This section provides an overview

of the process that links Medicare

program and ESRD data records with

NCHS survey data. A more detailed

description is available from:

http://www.cdc.gov/NCHS/data/

datalinkage/cms_medicare_methods_

report_final.pdf.

Following the SSN verification

process at SSA, a second finder file

(NCHS finder file #2) containing the

verified or assigned SSN, date of birth

(month, day, year), and sex was created

by SSA and sent directly to CMS for

linkage to Medicare and Medicaid

program data (Figure 1). After

processing by SSA, most NCHS records

in the resulting NCHS finder file #2

contained an SSN verified or assigned

by SSA, although a small subset of

records had a missing SSN (0.1%). The

survey respondents in NCHS finder file

#2 were matched to the Medicare

Denominator File for 1999–2007. To be

considered a successful match, the

NCHS survey respondent’s record

needed to match SSN exactly with only

one beneficiary in the Medicare

Denominator File. If the survey

respondent matched SSN with multiple

beneficiaries, sex and date of birth were

used to identify a single beneficiary

among all other candidates.

For the all of the surveys included

in the linkage, CMS extracted Medicare

enrollment and claims data for

1999–2007 for the records that were

determined to be a successful match to

the Medicare Denominator File for the

corresponding year. For the 1994–1998

NHIS, NHEFS, NHANES III, and

LSOA II, Medicare enrollment and

claims data for 1991–1998 were also

maintained from the previous

NCHS–CMS linkage (NSCL–I).

In some instances, NCHS

respondents with a verified SSN from

SSA had been successfully linked to

CMS records in a previous linkage but

did not match the 1999–2007 Medicare

Denominator File. The majority of these

respondents died prior to 1999 and did

not match because the Denominator File

includes only persons who are entitled

to benefits and enrolled in Medicare for

that calendar year. Other cases were

considered to be nonmatches because

their vital status could not be verified.

NCHS–Medicare linkage rates

include successful matches to the

Medicare Denominator File, which

indicate if a NCHS survey participant

was enrolled in Medicare during the

calendar years of the matching interval

(e.g., for 2004 NNHS, the matching

interval was 1999–2007), or if a

participant matched in the previous 2001

Medicare linkage (NSCL–I) but died

prior to 1999 (a small number of

participants). The NCHS–Medicare

linkage rate table is divided into two

parts. The first part includes surveys that

have Medicare data from 1991–2007;

the second includes surveys with

Medicare data from 1999–2007. For

each survey, Tables 2 and 3 show the

total survey sample size; the sample size

eligible for 1999–2007 Medicare

linkage; the number of eligible survey

respondents linked to any Medicare

Denominator File, including those who

died and had Medicare records

maintained from the previous 2001

Medicare linkage (NSCL–I); and two

linkage rates, both the percentage of all

survey respondents who were linked and

the percentage of linkage-eligible survey

respondents who were linked.

For linkage purposes in these tables,

age was defined as the survey

participant’s age at interview. Medicare

has age-based entitlement at age 65.

NCHS survey participants who died

in the period between the survey and the
Series 1, No. 58 [ Page 11

linkage interval were eligible for linkage

if they had sufficient PII and had not

refused SSN or HIC. Linkage rates for

those under age 65 were about 5%,

because Medicare entitlement for that

age group is limited to persons meeting

the SSA criteria for disability benefit

entitlement or persons diagnosed with

ESRD.

As part of the Medicare linkage,

ESRD data for the linked Medicare

beneficiaries are available to researchers.

Although nearly all NCHS respondents

linked to USRDS records are also linked

to Medicare records, a small number of

the linked USRDS records are not linked

to Medicare records (fewer than 5%).

Linkage Process: Medicaid

NCHS finder file #2 was also used

by CMS for linkage to the MAX PS

file. For NCHS records from the

verified file to link to the MAX PS file,

an exact match on SSN, month and year

of birth, and sex was required. From the

linked NCHS–PS file, CMS extracted

the associated claims from each of the

four MAX claims files. CMS linked

NCHS data separately for each state and

then combined the subsets into one

national file.

MAX records with missing SSNs

are common because many states are

unable to collect SSNs from some

enrollees. Most of those without an SSN

are children, persons who qualify only

for family planning benefits, or aliens

who qualify only for emergency

coverage. CMS removed records without

SSNs from the MAX PS file before

linking it to the NCHS finder file.

The NCHS–MAX linkage process

was conducted in four rounds. The first

round included MAX data from

1999–2004, round 2 included MAX data

from 2005–2007, round 3 included 2008

MAX data, and round 4 included 2009

data. Unfortunately, the 2009 data

contained data for only 43 states and the

District of Columbia. Seven states were

not included because their MSIS files,

the source of the MAX files, were

unavailable at the time of the linkage or

contained significant data problems. The

excluded states are Hawaii, Idaho,

Missouri, New Hampshire, Oklahoma,

Utah, and Wisconsin.

Table 4 provides the full sample

numbers for the NCHS surveys, the

numbers that are linkage-eligible in each

year, and the number actually linked

because they are in the MAX file.

Access to Linked Data

Files

Research Data Center

Due to confidentiality requirements,

the NCHS survey-linked data files are

restricted for research use, and the data

are accessible only through the Research

Data Center (RDC). RDC is a center

housed on-site at CDC facilities in

Hyattsville, Md., and Atlanta, Ga. In

addition, NCHS data can be accessed

from RDCs housed in U.S. Census

Bureau offices in several locations

across the country. Researchers need to

be on-site at one of the RDCs; no

remote access to the linked NCHS–CMS

and NCHS–SSA data files is allowed.

All researchers must submit a

research proposal to gain access to these

restricted data files. The proposal

provides a framework which allows

RDC staff to identify potential

disclosure risk. More information

regarding RDC and instructions

for the data can be found at:

http://www.cdc.gov/rdc/.

Public-use Feasibility Files

To facilitate the use of the linked

data files, NCHS has created public-use

feasibility files that can be downloaded

directly from the NCHS website. These

files provide information about survey

respondents’ eligibility for the SSA and

CMS linkages, final match status, and

the linked data files (and years, in the

case of CMS) available for each

successfully linked survey respondent.

These files can be used to determine the

maximum available sample size to

assess the feasibility of conducting

analyses using the SSA and CMS linked

data. These files make it possible to

identify demographic and health status

variables collected in the survey that

may be of interest in subdividing the

survey sample, calculating estimates of

numbers of people in categories of

interest, and determining cell sizes by

related categories. This process helps

prospective applicants for an NCHS

RDC proposal establish the likelihood of

sufficient numbers of persons and a

sufficient number of degrees of freedom

in the category of interest among those

who have been successfully linked to

SSA or CMS data for an NCHS RDC

proposal.

Note that the feasibility files do not

provide any information pertaining to

benefits or payments; instead, they

contain record status variables and

record counts to assist researchers who

are considering whether to initiate a

RDC proposal. Due to risk of

disclosure, the CMS Medicare

Feasibility Files do not contain any

variables that indicate linkage for ESRD

data.

Researchers should be aware that all

NCHS surveys linked to the SSA and

CMS administrative data have complex

survey designs. Therefore, considerations of statistical power should

account for the survey design, in

addition to the unweighted number of

observations available for a particular

project.

Documentation and instructions are

available for downloading the

NCHS–SSA (http://www.cdc.gov/nchs/

data_access/data_linkage/ssa/ssa_

feasibility.htm), NCHS–Medicare

(http://www.cdc.gov/NCHS/data_access/

data_linkage/cms/cms_medicare_

feasibility.htm), and NCHS–Medicaid

(http://www.cdc.gov/NCHS/data_access/

data_linkage/cms/cms_medicaid_

feasibility.htm) Feasibility files.

Analytic Guidelines

and Considerations

This section describes general

considerations and guidelines for

analysis using the linked data files

identified by NCHS programmers and

analysts. Potential new analytic issues

discovered in the course of analysis can

be reported to the NCHS Special

Projects Branch at datalinkage@cdc.gov.
Page 12 [ Series 1, No. 58

General Analytic Guidelines

Variables to request in RDC

applications

To create analytic files for use in

RDC, a researcher provides a file

containing the variables from the

public-use NCHS survey data to RDC

for merging with the requested restricted

variables from NCHS surveys and for

use with the CMS–SSA file variables.

The restricted variables from NCHS

surveys and the exact variables from the

CMS or SSA files that the researcher will

use also need to be specifically requested

as part of a researcher’s application to

RDC. RDC staff verify the full list of

variables (restricted and public-use) and

check for potential disclosure risk.

Although the complete list of

variables used for specific analyses

differs, the following variables from

NCHS surveys should be considered for

inclusion:

+	 Geography—Users who required

information on geography should

request data from the NCHS survey.

However, geographic information is

also available on the administrative

data for linked participants and may

differ from that in the survey.

+	 Linked mortality data for NCHS

surveys—All NCHS surveys that

have been linked to the CMS and

SSA data have also been linked to

mortality files that provide date and

cause of death for each survey

respondent who has died. These may

be of use to some researchers but

must be specifically requested as part

of the researcher’s proposal to RDC.

+	 NHANES month and year of

examination and interview—The

NHANES surveys combine at least 2

years of data. The exact year (and

month) of a respondent’s interview

and examination is not provided on

public-use files. However, many

researchers will want to know the

time elapsed between a given year

(or even month) of the CMS or SSA

data and the NHANES interview or

examination. The variables that

indicate the month and year of

NHANES interview or examination

must be requested specifically.

Researchers are advised to request

the following variables, available from

the public-use NCHS survey files, for

inclusion in analytic files:

+	 NCHS sample weights and design

variables—Sample weights are

needed to create nationally

representative estimates. Similarly,

design variables are required to

account for the survey design in

analyses. The names of the weights

and design variables differ depending

on which NCHS survey is being

used. These can be identified using

the documentation for each NCHS

survey. As discussed below, these

weights should be adjusted for

linkage eligibility.

+	 Demographic information about

survey participants from the NCHS

survey—For variables such as race

and ethnicity, NCHS demographic

information is self- or family

respondent-reported and, thus, may

be more accurate than demographic

data provided in CMS and SSA files.

Therefore, where possible, the NCHS

data should be used for demographic

variables.

Sample weights

The sample weights provided in

NCHS population health survey data

files adjust for oversampling of specific

subgroups and differential nonresponse,

and are poststratified to annual

population totals for specific population

domains to provide nationally

representative estimates. The properties

of these weights for linked data files

with incomplete linkage, due to

ineligibility for linkage and nonmatches,

are unknown. In addition, methods for

using the survey weights for some

longitudinal analyses require further

research. Because this is an important

and complex methodological topic,

ongoing work at NCHS and elsewhere

is examining the use of survey weights

for linked data in multiple ways.

Until specific recommendations are

available, preliminary guidance is to

analyze linked data files using adjusted

sample weights. The sample weights

available on NCHS population health

survey data files can be adjusted for

incomplete linkage and nonmatches

(nonresponse), using standard weighting

domains to reproduce population counts

within these domains: sex, age, and race

and ethnicity subgroups. These counts

are called ‘‘control totals’’ and are

estimated from the full survey sample.

A model-based calibration approach

developed within the SUDAAN

software package (Procedure

WTADJUST or WTADJX [SUDAAN

11]) allows auxiliary information to be

used to adjust the statistical weights for

nonresponse. This approach is

promising, and this software is

provisionally recommended for adjusting

survey weights for the linked files.

Because inferences may depend on the

approach used to develop weights,

within SUDAAN’s WTADJUST or

using a different calibration approach,

researchers should seek assistance from

a statistician for guidance on their

particular project. Other approaches or

software can be used. More detailed

information on adjusting sample weights

for linkage eligibility using SUDAAN

can be found in Appendix III.

Temporal alignment of survey

and administrative data

Each NCHS survey has been linked

to multiple years of administrative data.

Depending on the survey year,

administrative data may be available for

survey participants at the time of the

survey, as well as before or after the

survey period. Several factors may

influence the alignment of the survey

and administrative data, including: age

of the survey participant, program

eligibility, discontinuous program

coverage, and residential mobility of the

survey participant. More information on

each of these issues is provided in the

program-specific analytic considerations

sections that follow. The temporal

alignment between NHIS and MAX data

is illustrated in Figure 2.

Analysis Using Linked

NCHS–SSA Data

Social Security data in these

records, as with many administrative

data, are extracted from files designed
 

Figure 2. Temporal alignment of National Health Interview Survey and Medicaid Analytic

 

for program administration and not for

research. The following summary is a

quick overview and explanation of some

of the more complicated data usage

issues that may arise when using SSA

data.

The Rand Corporation has

published an SSA Program Data User’s

Manual (2). The majority of information

contained in this section is excerpted

from the Rand SSA User’s Manual.

MBR File

All applications to the NCHS RDC

for linked NCHS–SSA data should

include a request for the MBR file. An

MBR record is created when a person

applies for benefits, whether or not the

application was approved. Because the

MBR is quite large, researchers are

advised to request only the MBR

variables required for their project.

NCHS–MBR data files contain one

record for each survey respondent who

was linked to an MBR record from the

SSA master file. Each MBR record can

have up to three segments of OASDI

benefit history data. The first record

segment describes the benefit, if any, for

which the respondent applied through

December 2007 and payment history

data for that entitlement. Persons who

receive benefits through a dual

entitlement situation will have benefit

data on a second or third record

segment. In a dual entitlement situation,

a person is entitled to benefits as a

retired or disabled worker and is entitled

as well to benefits as the survivor of a

spouse or parent who was entitled to

benefits (2). Therefore, the second or

third record segments describes the

benefit, if any, for which the respondent

applied through December 2007 and

payment history data for that entitlement

as an auxiliary beneficiary, based on

another person’s earnings. A person may

also have benefit data on the second or

third record segments if denied benefits

on one account but entitled on another,

because denial information is also

documented. These second and third

benefit record segments have the same

file layout as the first record segment,

with the variables identified by the

extension _rp2 for the second record

segment and _rp3 for the third. In

addition, the variable OTBEN_CODE

will have a value of ‘‘2’’ if a person has

a second record segment, or ‘‘3’’ as

indication that a person has a third

benefit record segment.

The NCHS–MBR linked files

extracted from SSA retrieve the most

recent data, as well as past values for

certain data elements. Therefore, some

historic information exists on the MBR

file, which was expanded after June

2004; these files contain several

Series 1, No. 58 [ Page 13

occurrences of historical values for

certain data groups. Three main

multiple-occurrence entry fields exist in

the MBR: primary insurance history

(PIH), disability insurance benefits

(DIB), and historical payment data

(HIST) entries. As the values of the

fields change, new entries are added to

the MBR account record. The PIH entry

is part of the variable account data and

records data about the Primary

Insurance Amount (PIA) for this

account. The PIA is the benefit a person

would receive if he or she elects to

begin receiving retirement benefits at his

or her normal retirement age. At this

age, the benefit is neither reduced for

early retirement nor increased for

delayed retirement. Information related

to the disability benefits for an

individual is recorded in the DIB

entries. The HIST entry records actual

payments made on this account over

time. Multiple occurrences of entries can

be used to track events of importance to

the beneficiaries (2).

PIH entries record the earnings of

the wage earner with the PIA and the

family maximum dollar amount, or

FMAX. Changes in PIA are the result of

new earnings data or corrections to old

earnings data for active accounts. The

PIH entries occur in an MBR record up

to 50 times. If more than 50 entries are

needed, the oldest are dropped. This

information shows changes in the basis

of dollar benefit amounts over time.

Changes in PIA are the result of

cost-of-living increases, new earnings

data, or corrections to old earnings data

for active accounts. This information

shows changes in the basis of dollar

benefit amounts over time (2).

Information relating to disabilities is

recorded in the DIB entries. Specific

data elements include: Date of

entitlement to disability (DOED),

disability adjudication date (DSD), date

of disability offset (DDO), disability

award code (DAC), and drug

addiction/alcoholism code, or DAA.

Most of these fields contain date

information, but the variable DAC

identifies the type of disability. Using

the multiple occurrences of DIB, it is

possible to analyze changes in types of

disabilities over time and the duration of

entitlements (2).
Page 14 [ Series 1, No. 58

HIST entries can have up to 552

occurrences (one for each month from

January 1962 through December 2007)

for each beneficiary, and each records

the benefit amounts actually paid or

withheld for specific months. If the

benefit amount changes, the reason for

the change is given. Changes can occur

when earnings change or with a

suspension or termination. Data

elements related to historical payment

data are further described below and

include monthly benefit amount

(O_MBA), ledger account file (O_LAF),

monthly benefit paid (O_MBP), and

monthly benefit credited (O_MBC) (2).

Types of claimants

In identifying a particular type of

beneficiary or claimant (e.g., retirement,

disability, or others), users may need to

examine several fields (2). For example:

+	 Beneficiary Identification Code

(BIC)

+	 Historical Beneficiary Identification

Code (BCLM–BIC)

+	 Historical Beneficial Identification

Code—Denials (DENY–BIC)

+	 Ledger Account File (LAF)

+	 Type of Claim—retirement/disability,

primary/auxiliary (TOC)

Dates related to filing and eligibility

Date fields are critical to

determining eligibility of SSA

benefits (2). The following list includes

a few variables of particular interest:

+	 Date of birth of primary (DOBP)

+	 Date of death of primary (DODP)

+	 Date of filing—through June 2004

(DOF)

+	 Date on which the beneficiary

files—after June 2004 (BCLM–DOF)

+	 Date of filing on a denied

claim—after June 2004 (DENY–

DOF)

+	 Date of disability onset, or DDO

+	 Date of current entitlement (DOEC)

+	 Date of entitlement to disability, or

DOED

+	 Date of initial entitlement (DOEI)

+	 Date of suspension/termination

(DOST)

+	 Date of effective offset—through

June 2004 (OED)

+	 Date of offset termination—through

June 2004 (OTD)

DOBP is used to determine early

and late retirement and the amount of

outside income allowed without the loss

of benefits. DODP determines the time

at which survivor benefits are allowed

to spouses and children. BDOB and

BDOD are beneficiary date of birth and

death (2).

Disability dates are complex and

should be used carefully. To determine

the date that a person is eligible to

begin receiving benefits, or DOED, one

must determine when the person became

disabled, or DDO, as well when the

application was filed, or DOF (2).

Identified for each date is the

beneficiary identification code (BIC)

and the type of benefit (TOB), which

show why the benefit was received. For

example, DOEC_BIC=A (Primary

Claimant/ number holder) and

DOEC_TOB=1 would indicate a

retired-worker beneficiary as of the

current entitlement. DOEI_BIC=A and

DOEI_TOB=2 would indicate a

disabled-worker beneficiary as of the

initial entitlement (2).

The Dual Entitlement Data (DED)

group contains fields that are used only

in dual entitlement (2). The most

pertinent fields from the DED group

are:

+	 Dual Entitlement Status Code

(DESC)

+	 Other Beneficiary Identification

Code (OTBIC)

+	 Other date of entitlement (OTDOE)

+	 Type of dual entitlement (TOD)

Supplemental Security Record

File

SSR files’ history data can be used

to determine duration of receipt of SSI

benefits. However, take care when using

eligibility history data because it can be

overlaid by new data, making it difficult

to reconstruct specific events.

Types of claimants

To identify a particular type of

beneficiary or claimant, users may need

to examine several fields (2). Below are

some fields to be particularly aware of:

+	 Type of Action—Program category

of unit (TOA)

+	 Master File Type—Program category

of person (MFT)

+	 Payment status—Eligibility status

(PSTAT)

SSA program eligibility

SSA program eligibility is

determined based on citizenship,

income, financial resources, and age or

disability. Data elements related to

income and financial resources are listed

in the Payment History Update System

File section below; for other criteria, the

following fields describe or affect a

person’s program eligibility for SSI:

+	 Birth date (BIRTH_JD)

+	 Denial Code (DENCDE)

+	 Payment Status Code (PSTAT)

+	 State Program Category Eligibility

Code (STCONCATM)

The variable PSTAT shows eligibility

status for SSI benefits and provides

information about why a person leaves

current pay status. The variable

STCONCATM can be used to identify

state program categories for which the

SSI recipient is eligible (2).

Benefit determination

SSR contains a number of fields

related to the amount of benefits

received on a given account (2). Some

fields of interest are:

+	 Claim filed date (CLM_FIL_JD)

+	 Disability Payment Code

(DISPAYCDE)

+	 Disability onset date

(DIS_ONST_JD)

+	 Residence state and county (PDSCC)

+	 Payment Status Code, or PSTAT

Payment History Update

System File

The linked NCHS–PHUS files were

extracted from the PHUS master file by

SSA. These linked files contain two

variables that provide the information

needed to correctly compute benefits

paid: DIRECT_PAY and MEDICARE.

Each DIRECT_PAY variable is the

actual amount that a beneficiary

received in a check or direct deposit in

a specific month. Payment history is

stored as a PHUS event with up to
Series 1, No. 58 [ Page 15

264 occurrences, since 1984. The PHUS

file is strictly a payment history system

and not a transaction history file.

Data usage considerations

For months prior to January 1996,

SSA had a policy under which MBR

was not updated for single beneficiaries

going into and out of payment

suspense/termination status when the

total payment to a family was

unchanged. This process was called

Facility of Payment. For these persons,

PHUS cannot accurately determine the

benefits paid to an individual, and no

PHUS record is provided. As a result, a

small number of survey respondents will

have an MBR record with no PHUS

record. In these cases, the only available

payment data are the amounts shown in

the Payment History section of the

MBR: monthly benefit amount

(O_MBA), monthly benefit credited

(O_MBC), and monthly benefit paid

(O_MBP).

831 Disability File

Diagnosis coding

The 831 DMF primary impairment

code (PDX) and the secondary

impairment code (SDX) identify the

medical conditions considered in the

medical decision about disability or

blindness. Prior to 1985, the diagnosis

codes used were from the International

Classification of Diseases, Ninth

Revision (ICD–9). Starting in 1985,

impairment codes are assigned

SSA-developed Impairment Codes, also

known as Fussell codes. On occasion,

the PDX and SDX will still contain

ICD–9 codes set by the examining

doctor (2).

The 831 DMF also contains a

recoded primary impairment code

(RPDX) and a recoded secondary

impairment code (RSDX) that contain

only SSA impairment codes for the

medical conditions. These are derived

fields that look at the PDX and SDX

and, if necessary, convert ICD–9 codes

to their SSA equivalent. The National

Disability Determination Services

System (NDDSS) is the system used by

SSA primarily for processing Title II

disability (SI), Title XVI SSI disability,

and black lung (BL) claims. NDDSS

propagates the impairment codes to the

831 DMF data elements PDX and SDX;

the MBR data elements Diagnosis Code

(DIG) and Secondary Diagnosis Code

(SDIG); and the SSR data elements

Disability Diagnosis Code (DIBDIG)

and Secondary Disability Diagnosis

Code (DIBDIG2). Because program files

(in aforementioned variables listing

MBR and SSR files) receive the original

diagnosis codes, not the recoded values,

care must be taken when using disability

fields in the program files (2).

Linking 831 data to MBR and SSR

The 831 DMF can provide more

detailed information on the medical

conditions and severity of illness

associated with a beneficiary in the Title

II MBR or Title XVI SSR data for SSI

benefits (2).

To link 831 DMF data to MBR

records, the 831 records that apply to

MBR cases must be selected by finding

records with a RID value of ‘‘2’’ on the

831 DMF. The date values in the MBR

may not always exactly match the date

variables in the 831 DMF for the same

disability application. A tolerance of up

to plus-or-minus 3 months must be

used (2).

To match 831 DMF data with SSR

data, SSR records must be matched to

the 831 DMF using the data element

RID (value of ‘‘R’’) found on the 831

DMF. The date values in the SSR may

not always exactly match the date

variables in the 831 DMF for the same

disability application. SSR records may

require a plus-or-minus 1 month

tolerance to reconcile the difference in

dates between the linked SSR and 831

DMF (2).

QOC File

Insured status

The Master Earnings File or MEF is

SSA’s primary repository of earnings

data for the U.S. population (2). The

summarized QOC variables are

extracted from the MEF and describe

individuals’ ‘‘insured status’’ based on

their earnings history. Insured status is

the minimum number of credits or

quarters of coverage a worker must earn

to become eligible for his or her own

Social Security benefit under Title II.

The QOC File contains data regarding

these credits dating to 1953.

To determine whether a worker has

insured status, Social Security considers

the amount of the worker’s earnings

(employment or self-employment)

covered under Social Security and

assigns ‘‘credits’’ for those earnings.

These credits are called quarters of

coverage (QCs). In 2007, one QC is

credited for each $1,000 in annual

covered earnings, up to a maximum of

four QCs for the year. Earnings of

$4,000 or more in 2007 will give the

worker the maximum four QCs for the

year, regardless of when the money is

actually paid during the year (3). The

amount of earnings required for a QC is

adjusted automatically each year in

proportion to increases in the national

average wage level.

Fully insured

SSA program eligibility for most

types of benefits requires that the

worker be fully insured. To be fully

insured, a worker must have a number

of QCs at least equal to the number of

calendar years elapsing between the year

in which the worker is age 21 (or 1950,

if later) and the year in which he or she

reaches age 62, becomes disabled, or

dies—whichever occurs first. To

compute ‘‘elapsed’’ years, Social

Security does not count the year in

which the worker attains age 21 (or

1950, if later) or the year in which the

worker attains age 62, becomes

disabled, or dies. If the resulting number

of elapsed years is less than 6, the

number is raised to 6. All workers need

at least 6 QCs to be insured. Workers

who reach age 62 in 1991 or later need

40 QCs to be fully insured. Special rules

may apply if the worker had a prior

period of disability. For workers who

become disabled or die before age 62,

the number of QCs needed for fully

insured status depends on their age at

the time of disability or death.

Currently insured

Generally, if a worker dies before

meeting fully insured status, benefits can

still be paid to certain survivors if the

worker was ‘‘currently insured’’ at the
Page 16 [ Series 1, No. 58

time of death. Survivors’ benefits are

potentially payable to a worker’s

children and to a widow(er) who takes

care of the deceased’s child who is

under age 16 or disabled and receiving

Social Security benefits. To be currently

insured, the worker must have earned 6

QCs in the 13 quarters ending with the

quarter of death.

Disability insured

To qualify for disability benefits, a

nonblind worker must have recent work

activity in addition to being fully

insured. Under the requirement

involving recent work, a nonblind

worker who is age 31 or over must have

earned at least 20 QCs during the 40

calendar-quarter period ending with the

quarter in which the disability began. In

general, workers disabled at ages 24–30

must have earned QCs in one-half of the

calendar quarters, beginning with the

quarter following the quarter in which

age 21 is attained and ending with the

calendar quarter in which the disability

began. In this case, the quarters counted

will date to before the quarter in which

the worker turned age 21. Workers

under age 24 need 6 QCs in the

12-quarter period ending with the

quarter in which the disability began.

Workers who qualify for benefits based

on blindness need only be fully insured.

Special rules may apply if the worker

had a prior period of disability. More

information on the basic provisions

concerning insured status can be found

at: http://www.ssa.gov/regulations/

index.htm.

Analysis Using Linked

NCHS–Medicare Data

All applications to NCHS RDC for

linked NCHS–Medicare data should

include a request for the Denominator

File for the years that the researcher is

examining data. The Denominator File

contains basic demographic and

enrollment information about each

beneficiary entitled to Medicare during

each calendar year and is needed to

construct an analytic data file, particularly

to identify Medicare beneficiaries enrolled

in a Medicare Part C plan.

Medicare Part C or Medicare

Advantage plans

Medicare Part C plans are also

referred to as Medicare Advantage or

MA and include HMOs, preferred

provider organizations (PPOs), private

fee-for-service (PFFS) plans, special

needs plans, and Medicare Medical

Savings Account Plans. CMS generally

does not receive claims data for

Medicare beneficiaries who enroll in

Medicare Part C plans (including PFFS

plans paid on a capitation basis). There

are exceptions; for example, all hospice

claims are processed as Medicare claims

regardless of whether the beneficiary is

in a fee-for-service (FFS) or a Medicare

Part C plan (4,5). During the time

covered by the linked database,

enrollment in Medicare Part C plans

increased from approximately 6% of

beneficiaries in 1991 to 20% in 2007.

In general, studies based on analysis

of claims data should exclude Medicare

Part C enrollees from their beneficiary

samples. For health outcome or

epidemiologic studies (as opposed to

utilization or cost studies), an alternative

approach for dealing with Medicare Part

C enrollees is to include them for the

time period prior to entering a Medicare

Part C plan and then censor them at the

time they enter a Medicare Part C plan.

A summary of the percentage of

NCHS survey respondents who were

enrolled in a Medicare Part C plan by

year and survey can be found in Tables 5

and 6. Researchers should consider the

percentage of respondents enrolled in a

Medicare Part C program when

determining the feasibility and sample

sizes of their proposed research projects.

The following documents and

citations provide detailed information

about Medicare Part C enrollees and

Medicare Utilization Files, and how to

address them in analyses:

+	 Medicare Managed Care Enrollees

and Medicare Utilization Files,

available from: http://www.resdac.

org/resconnect/articles/114.

+	 Virnig BA, Ash A, Kind S, Mesler

DE. Survival analysis using

Medicare data: Example and

methods. Health Serv Res 35(5 Pt

3):86–101. 2000.

Services not covered

(1991–2007)

Although Medicare provides

coverage for a wide range of services,

there are health care services not

covered by Medicare. Examples include

routine physical examinations, long-term

care, and some cancer screening

procedures. Therefore, no claims records

exist for these services or for certain

time periods.

In addition, Medicare data contain

little information on prescription drugs

for years prior to 2006. In 2006,

prescription drug coverage for Medicare

beneficiaries became available through

the Medicare Part D program.

Prescription drug information paid by

Medicare for 2006–2007 is available in

the Part D Prescription Drug Event

(PDE) File. Prescription drug

information for data years 1991–2005

includes:

+	 Medication given in an inpatient/

hospice/SNF setting—although

specific medicines dispensed are

rarely coded, if at all.

+	 Chemotherapy administered

intravenously (IV), chemotherapy

administered orally as a substitute

for a medication that could be

administered IV, and oral

chemotherapeutic agents that break

down to a compound comparable to

a chemotherapeutic agent

administered IV.

Medicare does not pay for

chemotherapeutic agents that are

administered exclusively in an oral form

(e.g., Tamoxifen) and, prior to 2006,

most outpatient prescription drugs were

not covered by Medicare.

Cost sharing

Medicare beneficiaries often have a

number of cost-sharing requirements

(i.e., deductibles and coinsurance).

Although claims are generated for

services where beneficiary cost sharing

is involved, the Medicare payment

amount does not necessarily represent

the full cost to the beneficiary for the

service. It is not possible to determine

whether the beneficiary paid the

cost-sharing amount out of pocket
Series 1, No. 58 [ Page 17

or whether the cost-sharing was paid

by a third party, such as Medigap

insurance.

Gaps and discrepancies in

coverage periods

Medicare enrollment and claims

data linked to NCHS data are available

for 1991–2007. Several of the surveys

linked to Medicare data, such as NHEFS

(1971–1992) and NHANES III

(1988–1994), have gaps of several years

between the end of the study period and

the beginning of the Medicare data.

Note that Medicare data may differ by

age depending on the survey year(s).

For example, an NHIS respondent who

is 80 years old when interviewed in

1999 and matched at some point to the

1999–2007 Medicare Denominator File

would have Medicare data for only

1999–2007, when that NHIS respondent

was aged 80–88. A similar NHIS

respondent, who was 80 years old when

interviewed in 1994 and linked to the

1999–2007 Medicare Denominator File

as well as linked in the previous 2001

Medicare linkage (NSCL–I) would

hypothetically have Medicare data for

1991–2007, when that NHIS respondent

was aged 77–93. This issue is

particularly important when combining

data across survey and Medicare

coverage years, and researchers must

determine how to address these

discrepancies in coverage periods in

their analyses.

Records with only 1991–1998

Medicare claims data

Several NCHS surveys (1994–1998

NHIS, NHEFS, NHANES III, and

LSOA II) were included in the 2001

NCHS–SSA–CMS linkage (NSCL–I). In

some instances, survey respondents with

a verified SSN from SSA were

successfully linked to CMS records in

the 2001 linkage, but did not match the

1999–2007 Medicare Denominator File.

The majority of these respondents died

prior to 1999 and, thus, did not match to

the 1999–2007 Denominator File. The

other cases remained as nonmatches and

were dropped because their vital status

could not be verified.

Refer to the NCHS–CMS Medicare

Linkage Methodology Report for more

detailed information about linkage

eligibility for both the first and second

Medicare linkages, available from:

http://www.cdc.gov/nchs/data/

datalinkage/cms_medicare_methods_

report_final.pdf.

Instances may occur in which an

NCHS survey respondent is on the

Denominator File but no claims data

exist. It is possible to be enrolled in

Medicare but not utilize Medicare

services during the coverage period. In

addition, some recordkeeping

inconsistencies may occur because CMS

data are collected for administrative, not

research, purposes.

Medicare entitlement variables

The Denominator File includes

three variables indicating Medicare

entitlement: original reason for

entitlement (ORIG_REASON_FOR_

ENTITLEMENT), current reason for

entitlement (CURR_REASON_FOR_

ENTITLEMENT), and Medicare status

code (MEDICARE_STATUS_CODE),

which specifies the most recent status

of the beneficiary’s entitlement to

Medicare benefits. The values for these

three codes follow.

ORIG_REASON_FOR_

ENTITLEMENT and CURR_REASON_

FOR_ENTITLEMENT values include:

+	 OASI (Old Age and Survivors

Insurance)

+	 DIB (Disability Insurance Benefits)

+	 ESRD, or end-stage renal disease

MEDICARE_STATUS_CODE

values include:

+	 Aged without ESRD

+	 Aged with ESRD

+	 Disabled without ESRD

+	 Disabled with ESRD

Medicare’s Prospective

Payment System

Medicare’s Prospective Payment

System (PPS) is a method of

reimbursement in which the Medicare

payment is made based on a

predetermined, fixed amount. Medicare

uses a separate PPS for several services,

in which the particular payment amount

is based on the classification system for

that particular service. The PPS for

outpatient and HHA reimbursable

claims was implemented in July 2000,

so claims submitted for reimbursement

before this date will differ from those

after July 2000. More information on

PPS is available from: http://cms.hhs.

gov/Medicare/Medicare-Fee-for-ServicePayment/ProspMedicareFeeSvcPmtGen/

index.html.

Employer-sponsored insurance

While almost all adults are enrolled

in Medicare as they turn age 65, some

working adults may still have

employer-sponsored insurance, which is

primary to Medicare. Although Medicare

data do not contain an enrollment

variable to identify Medicare

beneficiaries who have primary

employer-sponsored insurance, the

Medicare data do show that

beneficiaries with only Part A

enrollment (and not parts B, C, or D)

have lower health care utilization rates.

This may be due to claims being

submitted to only primary insurers, not

Medicare. Therefore, for particular

analyses, researchers may want to

consider excluding beneficiaries with

only Medicare Part A.

Additional analytic issues

specific to each of the Medicare

Administrative Files

Denominator File

Date of death information obtained

by CMS is available in the Denominator

File. CMS updates the Denominator File

with death information collected through

the first 3 months of the following

calendar year. Deaths to Medicareeligible beneficiaries occurring in the

first quarter of the year will be recorded

on that year’s Denominator File but may

also be recorded on the previous year’s

Denominator File.

Death information is occasionally

misreported to CMS and included on the

yearly Denominator File. This erroneous

information is not corrected by CMS;

however, these cases can be identified

because they continue to be eligible for

Medicare benefits in later years, or they
Page 18 [ Series 1, No. 58

have new death information recorded in

a later Denominator File. Analysts

should use extra caution in analyzing

Medicare death information to ensure

that deaths are not overcounted. In

addition, the actual date of death

information is occasionally misreported

to CMS. Cases can be identified by

examining the variable, ‘‘Valid Date of

Death Switch,’’ in which a value of

‘‘V’’ indicates that CMS has validated

the actual date the beneficiary died,

whereas a blank indicates that it was not

validated. If the date of death is not

validated, CMS assigns the date of death

as the last day of the month.

Mortality information also is

available from the NCHS Linked

Mortality Files (http://www.cdc.gov/

nchs/data_access/data_linkage/

mortality.htm), which ascertain deaths

from probabilistic matches to the

National Death Index, death certificates,

or longitudinal survey recontacts for the

1985–2009 NHIS, NHEFS, NHANES

II, NHANES III, NHANES 1999–2010,

LSOA II, Supplement on Aging (SOA),

2007 National Home and Hospice Care

Survey (NHHCS), and 1985, 1995,

1997, and 2004 NNHS. No attempt has

been made to reconcile inconsistent

death information from CMS and these

other sources. RDC research proposals

that intend to analyze mortality

outcomes should utilize death

information from both the Medicare data

and the NCHS Linked Mortality Files.

Carrier File

The claims on the Carrier File are

processed by private carriers working

under contract to CMS. Each carrier

claim includes a Health Care Procedure

Classification Code (HCPCS) to

describe the nature of the billed service.

The HCPCS are composed primarily of

Level I HCPCS or CPT–4 codes

developed by the American Medical

Association, with additional codes

specific to CMS called Level II HCPCS.

Level II HCPCS are used to identify

products, supplies, and services that are

not included in CPT codes. These may

include ambulance services, durable

medical equipment or DME, prosthetics,

and orthotics. Each HCPCS code on the

carrier claim must be accompanied by

an ICD–9–CM diagnosis code,

providing a reason for the service. In

addition, each record includes the date

of service and reimbursement amount.

The Carrier File includes records

for noninstitutional claims; however, this

does not mean that they are outpatient

claims. Providers, such as physicians,

can bill for services provided in the

office, hospital, or other sites.

PLCSRVC ‘‘Line Place of Service

Code’’ indicates where the service was

provided, but it is not required for

payment purposes, is not a validated

code, and may contain inaccuracies.

The Carrier File contains DME

claims processed by carriers who also

process physician claims. The DME line

items on the Carrier File can be

identified by Claim Type Code

(CLM_TYPE) equal to 72. DME claims

processed through DME regional

carriers are found on the DME Files, not

on the Carrier File. DME claims on the

Carrier File are for separate services.

For additional information on DME

regional carrier claims, see the DME

File description below.

The Carrier File has two pairs of

date fields. FROM_DT ‘‘Claim From

Date’’ and THRU_DT ‘‘Claim Through

Date’’ generally cover a period of

service (but not always a single date of

service), while EXPNSDT1 ‘‘Line First

Expense Date’’ and EXPNSDT2 ‘‘Line

Last Expense Date’’ represent the

specific day of service.

For every billed procedure (using an

HCPCS code), a corresponding

ICD–9–CM diagnosis code

(LINEDGNS) should appear providing

the reason for the billed service. In the

case of laboratory tests, the diagnosis

will often be XX000, because the

outside laboratory has no information

from the physician about the reason for

the test. In addition, the Carrier File

contains space for up to four diagnoses,

DGNS_CD1 through DGNS_CD4.

These are not necessarily linked with

any of the billed procedures and may

reflect coexisting health conditions.

Some services may not appear in

the Carrier claims, although they may

have been received by the beneficiary.

For example, CMS pays physicians a

fixed amount for surgeries; this practice

is called bundling. As part of bundling,

CMS expects that certain care will be

included in the payment amount, such as

the first one or two office visits

following surgery, or a biopsy just

before surgery. Bundled services will

not appear in the physician data.

Interpretation of the rules on bundling

varies by carrier (physician).

DME File

Durable medical equipment or DME

can be billed through either a) the

carriers who also process physician

claims, or b) DME Regional Carriers

(DMERCs), who process only DME

claims. Each year, CMS distributes a

jurisdiction list, available from the CMS

website, which specifies whether a

carrier or a DMERC can process a

claim for a particular service. Often,

both carriers and DMERCs are allowed

to process and pay a DME claims

service, depending on whether the DME

was provided as ‘‘incident to the

physician’s service.’’

As noted previously, DME claims

processed by suppliers who also process

physician claims are included only on

the Carrier File. These claims can be

identified by Claim Type Code

(CLM_TYPE) equal to 72 on the

Carrier File. DME claims processed by

regional carriers are included only on

the DME File. Researchers should

examine both files for 1993–2000,

because approximately 90% of DME

claims data are found on the DMERC

File. However, for 1991 and 1992,

nearly 100% of DME claims data are

found on the Carrier File.

Hospice File

All beneficiaries in the Hospice File

have a primary diagnosis, but most have

no secondary diagnosis (90%). Although

data fields exist for procedure codes,

such information generally is not found

in the Hospice File. Physician claims are

for services provided by physicians

employed or receiving payment from the

hospice facility. All hospice claims are

processed as Medicare claims regardless

of whether the beneficiary is in a feefor-service (FFS) or managed care plan.

Outpatient File

Same-day surgeries performed in a

hospital are included in the Outpatient

File. However, claims for surgeries
Series 1, No. 58 [ Page 19

performed in freestanding surgical

centers appear in the Carrier File, not in

the Outpatient File. Information in the

Outpatient File includes diagnosis and

procedure codes, dates of service,

reimbursement amounts, revenue center

codes, and some demographic

information (such as date of birth, race,

and sex). The Outpatient File contains

data fields for 10 ICD–9–CM diagnosis

and 6 procedure codes, but the reporting

of these codes is sporadic. Services

provided can be obtained from the

Health Care Procedure Classification

Codes or HCPCS (HCPSCD01 through

HCPSCD45), which can occur 10 times

for a total of 450 occurrences.

Additional information can be found in

the revenue center codes (REV_CNTR).

Definitions for revenue center codes can

be found in the file documentation for

the Outpatient File.

Medicare Provider Analysis and

Review File

The MedPAR file includes all

hospitalizations that had a discharge

date during the calendar year and all

SNF stays with an admission date

during the calendar year. Hospital stays

starting in one calendar year and

continuing past the end of the calendar

year are not provided on the MedPAR

file until the year of discharge. To

determine if a record is for a long- or

short-stay hospitalization, use the short

stay/long stay/SNF indicator variable

MEDPAR_SS_LS_SNF_IND_CD. It is

coded S for short stay or L for long

stay.

Each MedPAR record represents a

stay in an inpatient ‘‘acute stay’’ or

‘‘long stay’’ hospital. An inpatient stay

record summarizes all services rendered

to a beneficiary from the time of

admission to a facility through

discharge. Each MedPAR record may

represent one claim or multiple claims,

depending on the length of a

beneficiary’s stay and the amount of

inpatient services used throughout the

stay.

The following fields on MedPAR

files are not used for payment purposes

and should be used with caution:

+	 Source of admission

(MEDPAR_SRC_IP_ADMSN_CD)

+	 Group health organization payment

code (MEDPAR_GHO_PD_CD)

In addition, MedPAR files include a

mortality variable. However, if the

outcome of interest is mortality, users

should use the mortality indicator,

DATE OF DEATH, on the Denominator

File, or mortality status from the NCHS

Linked Mortality Files.

Medicare Part D Denominator File

and Medicare Part D Prescription

Drug Event File

General Dynamics Information

Technology, or GDIT (formerly

Buccaneer, Inc.) has published a manual

on understanding and analyzing

Medicare Part D data. The majority of

the text in this section was excerpted

from the Chronic Condition Data

Warehouse Part D Data User Manual,

Version 3.1 (6).

Information is available on the

Medicare Part D Denominator File for

both Medicare beneficiaries who did not

obtain Part D coverage and those who

did obtain Part D coverage. In a small

number of cases, the enrollment

information in the standard Medicare

Denominator File and the Part D

Denominator File may differ. This is

due to updates made by CMS after the

standard Medicare Denominator File

was delivered.

Generally, Part D coverage is

provided under prescription drug plans

(PDPs), which offer only prescription

drug coverage, or through Medicare

Advantage Prescription Drug Plans

(MA–PD) plans, which offer

prescription drug coverage that is

integrated with the health care coverage

provided to Medicare beneficiaries

under Part C. The PDE file includes

data from beneficiaries in both types of

plans. The PDE file contains summary

extracts submitted to CMS by Medicare

Part D prescription drug plan providers.

All Medicare Part D prescription drug

benefits are provided through private

plans (plan sponsors) (6).

Claims for prescription drugs are

submitted by pharmacies to the Part D

health plans for beneficiaries enrolled in

Medicare Part D. PDE data are created

from point-of-service transactional data

at the time a prescription is filled. Data

for prescriptions that are ordered but not

filled do not exist in this database (i.e.,

data are not ‘‘final action’’), because

they represent the final status of a drug

claim at the time of CMS’ payment

reconciliation process (i.e., the records

account for post-transaction

adjustments). Not all Medicare-enrolled

beneficiaries elect to purchase Part D

coverage. Note that PDE data are not

submitted by plans that receive retiree

drug subsidies (RDS), or for other types

of plans that are considered to be Part D

creditable coverage (e.g., Veterans

Administration [VA] or TRICARE) (6).

PDE differs from a pharmacy claim

in several ways. Each PDE record is a

summary record containing the final

status of a drug claim sent by a

pharmacy to Part D sponsors,

accounting for any subsequent

adjustments. Pharmacy claims rejected

by the sponsor are not included in PDE

data. For example, if a pharmacy

submits an original claim to a plan

sponsor that is rejected due to a prior

authorization requirement, and later,

when the prior authorization criteria are

met, resubmits the claim which is then

accepted by the sponsor, the sponsor

would then submit only one PDE record

to CMS reflecting the final status of the

accepted claim. Similarly, if a pharmacy

submits a claim to a plan sponsor and

then soon after reverses (cancels) the

claim, the sponsor would not submit a

PDE record to CMS. Additionally, since

the PDE data represent ‘‘final action,’’

all PDE adjustments received by CMS

through the PDE submission deadline

for payment reconciliation is accounted

for in the data, including PDE

adjustments, resubmissions, and

deletions (6).

Not all drugs used by Part D

enrolled beneficiaries are included in the

PDE files. Data generally do not include

Part D-excluded prescription drugs

(unless the plan covers excluded drugs

as a supplemental benefit). Prescriptions

obtained through a third party (e.g., VA)

or those for which a claim is not

submitted (e.g., if a beneficiary pays

cash out of pocket) are not available. In

addition, over-the-counter (OTC) drugs

are excluded from Part D and typically

are not included in the PDE files, unless
Page 20 [ Series 1, No. 58

they are part of an approved step

therapy protocol (6).

Several situations occur in which

Benefit Phase (i.e., pre-initial coverage

limit, coverage gap, and catastrophic

[post-out-of-pocket threshold]) and

Utilization Management (UM) (i.e., tier,

step therapy, quantity limits, and prior

authorization) values cannot be

determined:

+	 The PDE is for a noncovered drug.

In this situation the Benefit Phase

value will be blank. If the drug is

found in the plan’s formulary, then

the UM variables will be assigned

based on the formulary values.

Otherwise, the UM variables will be

assigned ‘‘NA’’ if the drug is not

found in the formulary.

+	 Due to special waivers, some

organization types are not required to

submit details of their drug benefit

package (e.g., employer direct and

Programs of All-inclusive Care for

the Elderly [PACE] plans). The

Benefit Phase value for PDEs

associated with these plans is ‘‘NA.’’

+	 Some plans may not utilize or be

required to submit a formulary to

CMS as part of the plan/formulary

approval process (e.g., PACE plans).

PDEs occurring for beneficiaries

enrolled in these types of plans will

have the least restrictive values for

each of the four utilization

management variables.

+	 Plans waived from submitting plan

benefit information that did submit

formulary information (e.g.,

employer direct) have ‘‘NA’’

assigned for the Benefit Phase. UM

variables are assigned according to

the plans’ submitted formulary.

+	 If the drug on the PDE is not found

on the plan’s formulary, then all UM

variables are assigned values of

‘‘NA.’’

+	 If the plan information on the PDE

cannot be linked to the Health Plan

Management System (HMPS) plan

information, the value of ‘‘XX’’ is

applied for all five of the event

characteristics variables (6).

Chronic Condition Summary File

GDIT has also published a technical

guidance manual for researchers using

data from CMS’ Chronic Condition

Data Warehouse (CCW) for calculating

population statistics. This manual is a

useful resource for information on the

CC Summary file, as well other CMS

data containing information on Medicare

beneficiaries. The majority of the text in

this section was excerpted from the

CMS Chronic Condition Data

Warehouse, ‘‘Technical Guidance for

Researchers Calculating Population

Statistics’’ (7).

The CC Summary file is

constructed each year, based on the

specified reference period for each

condition. The three CC variables for

each of the 21 CCs have values that

signify whether the pattern of utilization

(i.e., FFS claims) indicated the presence

of the condition for the beneficiary

during the surveillance period ending

with the last month of the reference

period (December for the yearly

indicators, June for the midyear

indicators). Note that claims prior to the

reference year may have been examined

to make this determination if the CC

definition was a 2- or 3-year condition

(e.g., diabetes, congestive heart failure,

or Alzheimer’s disease) (7). Refer to the

CC definitions document for more

details regarding reference periods and

clinical specifications for individual CC

definitions: http://www.ccwdata.org/cs/

groups/public/documents/document/

ccw_conditioncategories.pdf.

Analysis Using Linked

NCHS–MAX Data

All applications to NCHS RDC for

linked Medicaid (MAX) data should

include a request for the Person

Summary or PS file for the years that

the researcher is examining data. For

each NCHS survey and survey year, a

file has been created that includes MAX

PS file observations for every MAX

data year (1999–2009) that link to

observations in that NCHS survey file.

For example, for NHIS 2000, a PS file

exists that has PS file observations from

1999–2009 that link to NHIS 2000.

Similar IP, LT, RX, and OT files are

available for each NCHS survey and

survey year.

PS File

Each Medicaid enrollee is classified

by two eligibility groups, a maintenance

of assistance status (MAS) group and a

basis of eligibility (BOE) group. MAS

describes the financial criteria that allow

an enrollee to be eligible for Medicaid:

receiving cash assistance, being a parent

with income below the 1996 Aid to

Families with Dependent Children

(AFDC) income thresholds, being part

of a demonstration project, or other.

BOE describes the group to which the

enrollee belongs that is categorically

eligible for Medicaid (children, elderly,

disabled, and other). These measures are

combined into the variable

EL_MAX_ELGBLTY_CD_LTST, which

concatenates MAS and BOE. MAS is in

the first position; BOE is in the second

position.

In general, across the years of

linked MAX files, variables have

changed little, and variable names have

been largely standardized by CMS and

NCHS. However, occasional additions

or changes in variables have been made,

so careful examination of the data

dictionaries prior to analyses is

suggested.

Multiple PS records

Many NCHS survey respondents

will be linked to multiple MAX file PS

records. Most often, this is because a

respondent is linked to several years of

MAX data. However, less frequently, a

survey respondent may be linked to

multiple PS records within the same

year. For example, for NHIS 1999–

2005, 7.9% of NHIS survey respondents

who were linked to the MAX files had

linkages to more than one PS record in

at least 1 year. For NHANES

1999–2004, 9.5% of observations that

linked to MAX files had linkages to

more than one PS record in at least 1

year. There are multiple explanations for

this situation:

+	 Medicaid enrollees moving between

states in a given year

+	 Eligibility changes resulting in

survey respondents disenrolling and

re-enrolling in Medicaid within the

same year
Series 1, No. 58 [ Page 21

+	 Administrative changes or errors

with Medicaid reporting

C Some administrative changes and

errors can be state- and

year-specific. Certain record

anomalies in each state have been

identified and are provided by

CMS (https://www.cms.gov/

MedicaidDataSourcesGenInfo/

downloads/anomalies1.pdf).

Most NCHS survey respondents

with multiple PS records per year had

records in multiple states. Among

observations in NHIS 1999–2005 linked

to multiple PS records in the same year,

86.6% came from different states. For

NHANES 1999–2004, 81.7% came from

different states.

Another source of multiple PS

records in the same year is false

matches due to misreporting of PII or

issues with linkage methodology. The

validity of multiple records in the same

year can be difficult to ascertain. While

some records show eligibility in

different states in nonoverlapping

months, others show eligibility in

different states in the same months

of the same year. A researcher may

choose to exclude these records,

depending on the research question

being explored.

The existence of multiple PS

records within 1 year with overlapping

months of Medicaid enrollment data

between the PS records can complicate

analyses. In considering how to assess

Medicaid enrollment in the presence of

multiple PS records within a year,

researchers may consider the use of

variables that indicate enrollment by

month in each record. By determining

whether a person was enrolled in each

month across the multiple records within

a year, the number of total months of

enrollment across records can be

obtained. The variables

MAX_ELG_CD_MO_1 through

MAX_ELG_CD_MO_12 indicate

whether an enrollee was eligible for

Medicaid in a given month and, if so,

under what criteria.

To help identify enrollees with

multiple PS records within the same

year, NCHS has added a set of flag

variables to the PS file to identify these

observations. FLG_MULT_RECS

identifies whether enrollees have

multiple records in any year. Additional

variables identify enrollees who had any

multiple PS records in a year and

whether they occurred in the same or

different state overall and in a given

year. FLG_PRSN_MULT_RECS

identifies enrollees with multiple records

in any year; FLG_YEAR_MULT_RECS

identifies enrollees with multiple records

in a given year. The response categories

for both variables are:

+	 0 = No multiple records

+	 1 = Multiple records in the same

state

+	 2 = Multiple records in different

states

+	 3 = Multiple records in the same and

different states

If multiple PS files within a year

link to an NCHS survey observation,

and both a PS and an IP file, for

example, were linked to the NCHS

survey, which PS observation is related

to a specific IP observation would be

unclear. Thus, to merge data from both

the PS file and another file to the NCHS

survey data, the following steps must be

taken to identify which PS observations

are related to a specific claims file

observation:

1.Merge NCHS survey observations

for an individual year to the PS file

from that year using the

identification variable on the NCHS

survey and the PS file.

2.Merge the desired claims file (IP,

LT, RX, or OT) to the already

merged NCHS–PS file using all

three of the following variables:

•	 FILE_YEAR4 on the claims file

(IP, LT, RX, or OT) and the

already merged PS file

•	 MSIS_SEQN on the claims file

and the already merged PS file

•	 Identification variable on the

claims file and the already

merged PS–NCHS survey file.

Identifying Medicaid enrollees

The best method for identification

of NCHS survey respondents who were

Medicaid enrollees depends on the exact

research question. In general, however,

MSNG_ELG_DATA on the PS file

provides information on enrollment

status. The values for

MSNG_ELG_DATA include:

+	 c = Enrolled in Medicaid during the

year

+	 2 = Enrolled in S–CHIP

+	 1 = Enrolled in neither Medicaid nor

S–CHIP

MSNG_ELG_DATA exists on each

of the MAX files (PS, RX, IP, LT, OT)

and at times, different values are

assigned in the different files for the

same person (in the same year).

However, the value on the PS file is the

most valid of these and should be used

for all data for that observation.

Non-Medicaid/Non-S–CHIP

observations

A small number of observations in

the MAX files are coded as having been

enrolled in neither Medicaid nor

S–CHIP. It is not possible to determine

why an individual is in the MAX files if

he or she was not enrolled in either

Medicaid or S–CHIP for any months of

the year. Therefore, it is advisable to

eliminate these observations from data

analyses. These observations represent a

small portion of the total data. They

cluster during the 1999–2002 period;

however, they remain less than 1.5% of

records that link to the NHIS files in

every year. Additionally, these

observations cluster by state; 7 states

had rates greater than 2%.

For MAX PS files between

1999–2002, some observations for

which the enrollee was coded as having

neither Medicaid nor S–CHIP during the

year have contradictory data when

examined at the monthly enrollment

level. Specifically, some of these

observations are coded as having had

S–CHIP during some individual months,

despite the overall yearly variable

(MSNG_ELG_DATA) indicating they

had neither S–CHIP nor Medicaid

during the year. These data have not

been changed, as it is not clear which

data are correct. Treatment of these

records is up to the researcher.
Page 22 [ Series 1, No. 58

Days in the month

In a small number of observations,

the Monthly Days of Eligibility exceed

the total number of days in the month.

For example, these variables may

suggest that the enrollee was eligible for

31 days in September. These values are

caused by administrative errors by the

state and should be corrected if an

analysis includes these values. An

individual can enroll and disenroll in

Medicaid at any time during a month.

As a result, enrollees may have fewer

days of Medicaid eligibility in a given

month than exist in the month. The

number days of Medicaid eligibility per

month are captured in EL_DAYS_EL_

CNT_1 through EL_DAY_EL_CNT_12.

The values range from 0 to 31.

Dual eligible

Interest exists among researchers

and policy makers in the group of

people who receive benefits from both

Medicaid and Medicare, often referred

to as ‘‘dual eligible.’’ In the NCHS–

MAX linked files, this group can be

identified several ways. On the PS file,

EL_MDCR_DUAL_ANN identifies dual

eligibles. Values 50–59 signify that the

enrollee was found in the Medicare

database. Values 01–09 signify that the

enrollee was not found in the Medicare

database but was believed to be

Medicare-eligible by the state. Values

50–59 should be used to identify dual

eligibles because the Medicare

enrollment database is the preferred

indicator of dual enrollment.

To assess whether sample size will

be adequate for a particular analysis, as

discussed above, using the feasibility

files is recommended. While there is no

flag for dual eligibles on the feasibility

files, researchers can use the feasibility

files for the Medicare linked data files

and the feasibility files for the MAX

linked data files to create their own dual

eligible flag. This method approximates

the number identified as dual eligible on

the MAX file.

For analyses of dual eligibles, some

researchers will choose to use NCHS

surveys linked to both Medicare data

and the MAX files. Researchers should

note that the methodology used for

linking the NCHS surveys to MAX data

differs from the methodology used to

link Medicare data to the NCHS

surveys. The Medicare linkage required

that the records match exactly on SSN,

sex, and day, month, and year of birth,

whereas the MAX linkage required

matching exactly on SSN, sex, and

month and year of birth, but not

necessarily on day of birth. A flag in the

MAX files (EXACT_DOB_FLAG)

indicates records that did match exactly

on month, day and year of birth. Using

this flag, researchers may choose to

select records from the MAX file that

used the same linkage methodology as

the Medicare linkage and exclude the

other records.

CHIP enrollment

Two issues related to S–CHIP

should be considered when using MAX

data. First, states have the option of not

reporting information on S–CHIP

enrollees to MSIS. Therefore, whereas

the data on persons with Medicaid or

M–CHIP can be considered universal,

the MAX files do not include all

S–CHIP enrollees. Variables provide

monthly information on CHIP eligibility,

as well as whether a person was

enrolled in M–CHIP or S–CHIP.

For S–CHIP enrollees in the files,

some data elements contain no

information. Therefore, variables that

are counts of months may not be

accurate for persons enrolled in S–CHIP

for one or more months, because those

months are not counted in the total

counts. Although S–CHIP enrollees may

be a group of particular interest for

some researchers, note that they account

for a small percentage of NCHS survey

respondents linked to the MAX files.

For example, among those linked to the

NHIS in 2005, less than 1.5% of

enrollees in the MAX files are in

S–CHIP in any given month. The

variables EL_CHIP_FLAG_1 through

EL_CHIP_FLAG_12 document CHIP

eligibility monthly, and whether an

enrollee was in M–CHIP or S–CHIP.

The values include:

+	 0 = Not eligible for Medicaid or

CHIP during this month

+	 1 = Enrolled in Medicaid during this

month

+ 2 = M–CHIP during this month

+ 3 = S–CHIP during this month

For enrollees with a value of ‘‘3’’

(S–CHIP) for EL_CHIP_FLAG_1–

through EL_CHIP_FLAG_12, no

information is recorded for other

monthly variables for that month.

Diagnosis and procedure coding

Diagnoses are uniformly provided

in the MAX files for inpatient (IP),

outpatient (OT), and long-term care (LT)

using International Classification of

Diseases, Ninth Revision, Clinical

Modification (ICD–9–CM) codes.

However, the coding systems used in

the IP and OT files for procedures vary

(no procedure codes are provided in the

LT files). In the IP files, PRCDR_CD_

SYS_1 through PRCDR_CD_SYS_6

describe the type of codes used for

procedure codes in PRCDR_CD_1

through PRCDR_CD_6, respectively.

In the OT file, the variable PRCDR_

CD_SYS describes the type of codes

used in the procedure code variable

PRCDR_CD. For each procedure code

variable, a separate variable is provided

that identifies the coding system used

for that code. For the IP file, the

majority of procedure codes are

ICD–9–CM. However, for the OT file,

the majority of codes are either in

CPT–4 or HCPCS codes.

State differences in Medicaid

Although Medicaid is administered

under general federal guidelines,

Medicaid varies substantially at the state

level. Medicaid program eligibility,

services offered, provider

reimbursement, and other factors vary

greatly from state to state. Consideration

of these differences by state may be

necessary for many analyses. State

identifiers for each NCHS survey need

to be specifically requested in these

circumstances.
Series 1, No. 58 [ Page 23

State-specific data issues

Because the data for the MAX files

are obtained from each state, data

quality differs between states. Prior to

conducting analyses, researchers should

consult the CMS website on the MAX

files at: https://www.cms.gov/

Research-Statistics-Data-and-Systems/

Computer-Data-and-Systems/

MedicaidDataSourcesGenInfo/

MAXGeneralInformation.html. This

website provides data dictionaries, data

anomalies for the whole MAX file and

by state within the MAX files, and

summary information by state from the

MAX files. For any given analysis,

there may be states or variables that

present problematic data, and careful

examination of the resources on the

CMS website may reveal these issues

before attempting data analysis.

Managed care, fee for service

Many Medicaid and CHIP enrollees

are enrolled in managed care plans, and

enrollment in these programs has grown

over time. Rates of managed care

enrollment also vary markedly across

states. The PS file contains variables

that identify beneficiaries enrolled in

any type of managed care plan and

the number of months that they

were enrolled in the plan.

EL_PHP_TYPE_1_1 through

EL_PHP_TYPE_4_12 identify up to

four types of plans for each month.

Types of managed care plans identified

in EL_PHP_TYPE_1_1 through

EL_PHP_TYPE_4_12 are:

+	 Medical or comprehensive managed

care plan

+	 Dental managed care plan

+	 Behavioral managed care plan

+	 Prenatal/delivery managed care plan

+	 Long-term care managed care plan

+	 All-inclusive care for the elderly or

PACE plan

+	 Primary care case management or

PCCM plan

+	 Other managed care plan

Waivers

Section 1115 of the Social Security

Act provides the Secretary of Health

and Human Services broad authority to

authorize experimental, pilot, or

demonstration projects requested by the

states that are likely to assist in

promoting the objectives of the

Medicaid statute. These projects are

intended to test and evaluate a policy or

approach that has not been widely used.

Some states expand eligibility to persons

not otherwise eligible under the

Medicaid program, provide services that

are not typically covered, or use

innovative service delivery systems.

Examples include expanding care for

children in foster care, providing

specialty mental health care, and

expanding Medicaid eligibility for

family planning services to women of

childbearing age not otherwise eligible

for Medicaid. General information

about Medicaid waivers can be

found at: https://www.cms.gov/

MedicaidStWaivProgDemoPGI/

03_Research&DemonstrationProjectsSection1115.asp.

Waivers for specific groups make

up one of the Maintenance Assistance

Status or MAS categories. The variables

MAX_ELG_CD_MO_1 through

MAX_ELG_CD_MO_12 can be used to

identify MAS and BOE monthly

enrollment information, although the

specific type of waiver cannot be

identified before 2005. Starting in 2005,

MAX files include three elements for

each month (MAX_WAIVER_TYPE_1_

MO_1 through MAX_WAIVER_

TYPE_3_MO_12) that give detailed

information on the type of waivers

under which enrollees are eligible for

Medicaid.

Prescription Drug File

The RX file does not include drugs

provided during an inpatient hospital

stay. Injectable drugs administered by a

health professional are included in the

Other Services or OT file described

below. Beginning in 2006, full-benefit

dual eligibles receive the Medicare Part

D drug benefit, and their utilization for

Part D-covered drugs is in the Medicare

Part D Event data. Drugs provided to

patients in LTC facilities are included in

the Part D file for most states. However,

a small number of states bundle these

drugs in their LTC facility payment rate;

in these cases, no details on drugs are

available.

As with the other files, occasional

additions or changes in variables have

been made to the RX files over time;

therefore, careful examination of the

data dictionaries prior to analyses is

suggested.

Other Services File

Because of how certain types of

services are billed, multiple claims may

exist for the same person and service

dates in the OT file. These are not

errors or data anomalies. What appear to

be duplicate records are not true

duplicates, but instead distinct services

or portions of a service provided. For

example, a patient who has a visit to an

outpatient physician and then is sent to

an outside laboratory to have a blood

test on the same day would have a

separate record for each of these

services, but both would have the same

date of service.

General limitations of MAX

There are some limitations to the

information contained in the MAX files.

Because these files contain only

Medicaid-paid services, they do not

capture service use or expenditures

during periods of nonenrollment,

services paid by other payers, or

services provided at no charge. Because

MAX files consist only of enrollee-level

information, they do not include

prescription drug rebates received by

Medicaid; Medicaid payments made to

disproportionate share hospitals

(DSH)—hospitals that serve a

disproportionate share of low-income

patients with special needs; payments

made through upper payment limit

(UPL) programs; and payments to states

to cover administrative costs.

In addition, service information in

MAX may be missing or incomplete for

certain groups of enrollees. This is

particularly important for individuals

enrolled in both Medicaid and Medicare

(dual eligibles), persons enrolled in

Medicaid managed care plans (either

comprehensive or partial plans), and

children with S–CHIP coverage.
Page 24 [ Series 1, No. 58

Because Medicare is the first payer for

services used by dual enrollees covered

by both Medicare and Medicaid, MAX

captures such service use only if

additional Medicaid payments are made

on behalf of the enrollee for Medicare

cost sharing or for shared services.

Medicare premiums paid by Medicaid

on behalf of duals are not included in

MAX.

For enrollees in Medicaid managed

care plans, information in MAX is

restricted to premium payments and

some service-specific utilization

information. While records for services

delivered (including diagnoses and

procedures) are uniformly provided for

recipients with FFS coverage, encounter

records for those with comprehensive

managed care plans are not provided by

all states. In some states, only a portion

of managed care recipients have

encounter data recorded. When included

in the files, managed care encounter

data list $0 as the amount paid for the

services provided, even when the

services are covered by the managed

care plan.

A summary of the percentage of

NCHS survey respondents who were

enrolled in a Medicaid managed care

plan by year and survey can be found in

Table 7. Researchers should consider the

percentage of respondents enrolled in a

Medicaid managed care plan when

determining the feasibility and sample

sizes of their proposed research projects.

Conclusions

The SSA and CMS programs are

complex, as are the resulting

administrative data. Although this

complexity can complicate analyses of

linked data files, the wide range of

information added to the NCHS

population health surveys through

linkage to SSA and CMS records

provides a unique opportunity to study

changes in health status, health care

utilization, and expenditures in

specialized populations, such as

low-income families with children, the

elderly, and the disabled.

Published Research

Papers Using NCHS

Survey-linked SSA,

CMS, and USRDS

Data

NCHS–SSA Linked Data

Gindi R, Cohen RA. Assessing

measurement error in Medicare coverage

from the National Health Interview

Survey. Medicare Medicaid Res Rev

2(2):E1–E15. 2012.

Livermore GA, Stapleton DC, Claypool

H. Health care when workers need it

most: Before and after entry into the

Social Security Disability Insurance

Program. Inquiry 47(2):135–49. 2010.

Livermore G, Stapleton D, Claypool H.

Costs and benefits of eliminating the

Medicare waiting period for SSDI

beneficiaries. Mathematica Policy

Research Inc., Disability Policy

Research Brief, no 09–02. 2009.

Available from: http://www.

mathematica-mpr.com/our-publicationsand-findings/publications/costs-andbenefits-of-eliminating-the-medicarewaiting-period-for-ssdi-beneficiaries.

Livermore G, Stapleton D,

Claypool H. Health insurance and health

care access before and

after SSDI entry. The Commonwealth

Fund. 2009. Available from: http://

www.commonwealthfund.org/

publications/fund-reports/2009/may/

health-insurance-and-health-care-accessbefore-and-after.

Rasch EK, Huynh M, Ho PS, Heuser A,

Houtenville A, Chan L. First in line:

Prioritizing receipt of Social Security

disability benefits based on likelihood of

death during adjudication. Med Care

52(11):944–50. 2014.

Riley GF. Health insurance and access

to care among Social Security Disability

Insurance beneficiaries during the

Medicare waiting period. Inquiry

43(3):222–30. 2006.

NCHS–CMS Medicare

Linked Data

Cai L, Lubitz J, Flegal KM, Pamuk ER.

The predicted effects of chronic obesity

in middle age on Medicare costs and

mortality. Med Care 48(6):510–7. 2010.

Day HR, Parker JD. Self-report of

diabetes and claims-based identification

of diabetes among Medicare

beneficiaries. National health statistics

reports; no 69. Hyattsville, MD:

National Center for Health Statistics.

2013. Available from: http://www.cdc.

gov/nchs/data/nhsr/nhsr069.pdf.

Decker SL, Doshi JA, Knaup AE,

Polsky D. Health service use among the

previously uninsured: Is subsidized

health insurance enough? Health Econ

21(10):1155–68. 2012.

Gorina Y, Kramarow EA. Identifying

chronic conditions in Medicare claims

data: Evaluating the Chronic Condition

Data Warehouse algorithm. Health Serv

Res 46(5):1610–27. 2011. Available

from: http://onlinelibrary.wiley.com/doi/

10.1111/j.1475-6773.2011.01277.x/pdf.

Gorina Y, Pratt L, Kramarow E,

Elgaddal N. Hospitalization,

readmission, and death experience of

noninstitutionalized Medicare

fee-for-service beneficiaries aged 65 and

over. National health statistics reports;

no 84. Hyattsville, MD: National Center

for Health Statistics. 2015

[Forthcoming].

Honeycutt AA, Segel JE, Zhuo X,

Hoerger TJ, Imai K, Williams D.

Medical costs of CKD in the Medicare

population. J Am Soc Nephrol

24(9):1478–83. 2013.

Livermore GA, Stapleton DC, Claypool

H. Health care when workers need it

most: Before and after entry into the

Social Security Disability Insurance

Program. Inquiry 47(2):135–49. 2010.

Livermore G, Stapleton D, Claypool H.

Costs and benefits of eliminating the

Medicare waiting period for SSDI

beneficiaries. Mathematica Policy

Research Inc., Disability Policy
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Research Brief, no 09–02. 2009.

Available from: http://www.

mathematica-mpr.com/our-publicationsand-findings/publications/costs-andbenefits-of-eliminating-the-medicarewaiting-period-for-ssdi-beneficiaries.

Livermore G, Stapleton D, Claypool H.

Health insurance and health care access

before and after SSDI entry. The

Commonwealth Fund. 2009. Available

from: http://www.commonwealthfund.

org/publications/fund-reports/2009/may/

health-insurance-and-health-careaccess-before-and-after.

Lloyd JT, Blackwell SA, Wei II, Howell

BL, Shrank WH. Validity of a claimsbased diagnosis of obesity among

Medicare beneficiaries. Eval Health Prof

2014.

Looker AC, Dawson-Hughes B,

Tosteson AN, Johansson H, Kanis JA,

Melton LJ 3rd. Hip fracture risk in older

US adults by treatment eligibility status

based on new National Osteoporosis

Foundation guidance. Osteoporos Int

22(2):541–9. 2011.

Looker AC, Eberhardt MS, Saydah SH.

Diabetes and fracture risk in older U.S.

adults. Bone 2015.

Looker AC. Femur neck bone mineral

density and fracture risk by age, sex,

and race or Hispanic origin in older US

adults from NHANES III. Arch

Osteoporos 8(141):141. 2013.

Looker AC. Hemoglobin and hip

fracture risk in older non-Hispanic

white adults. Osteoporos Int 25(10):

2389–98. 2014. Available from:

http://link.springer.com/article/10.1007/

s00198-014-2769-3.

Looker AC, Mussolino ME. Serum

25-hydroxyvitamin D and hip fracture

risk in older U.S. white adults. J Bone

Miner Res 23(1):143–50. 2008.

Looker AC. Serum 25-hydroxyvitamin

D and risk of major osteoporotic

fractures in older U.S. adults. J Bone

Miner Res 28(5):997–1006. 2013.

Mainous AG 3rd, Diaz VA, Knoll ME,

Hulihan MM, Grant AM, Wright RU.

Transferrin saturation and hospital

length of stay and mortality in Medicare

beneficiaries. J Am Geriatr Soc

61(1):132–6. 2013.

Miller EA, Decker SL, Parker JD.

Characteristics of those who choose

Medicare advantage over fee-for-service

upon Medicare enrollment at age 65. J

Ambul Care Manage. In press 2015.

Mirel LB, Wheatcroft G, Parker JD,

Makuc DM. Health characteristics of

Medicare traditional fee-for-service and

Medicare Advantage enrollees:

1999–2004 National Health and

Nutrition Examination Survey linked to

2007 Medicare data. National health

statistics reports; no 53. Hyattsville,

MD: National Center for Health

Statistics. 2012. Available from:

http://www.cdc.gov/nchs/data/nhsr/

nhsr053.pdf.

Polsky D. How the newly insured use

health services: A lesson for the U.S.

from Medicare. LDI Issue Brief

17(4):1–4. 2011.

Riley GF. Health insurance and access

to care among Social Security Disability

Insurance beneficiaries during the

Medicare waiting period. Inquiry

43(3):222–30. 2006.

NCHS–CMS Medicaid

(MAX) Linked Data

Capo-Ramos DE, Duran C, Simon AE,

Akinbami LJ, Schoendorf KC.

Preventive asthma medication

discontinuation among children enrolled

in fee-for-service Medicaid. J Asthma

51(6):618–26. 2014.

Hedley Dodd A, Gleason PM. Using the

MAX–NHANES merged data to

evaluate the association of obesity and

Medicaid costs. MAX Medicaid Policy

Brief #16. Washington, DC:

Mathematica Policy Research. 2013.

Available from: https://www.cms.gov/

Research-Statistics-Data-and-Systems/

Computer-Data-and-Systems/

MedicaidDataSourcesGenInfo/

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NHANES.PDF.

Lee JS. Food insecurity and healthcare

costs: Research strategies using local,

state, and national data sources for older

adults. Adv Nutr 4(1):42–50. 2013.

Lloyd PC, Simon AE, Parker JD.

Characteristics of children in Medicaid

managed care and Medicaid fee-forservice, 2003–2005. National health

statistics reports; no 80. Hyattsville,

MD: National Center for Health

Statistics. 2015. Available from:

http://www.cdc.gov/nchs/data/nhsr/

nhsr080.pdf.

Mirel LB, Golden C, Wheatcroft G,

Simon AE, Schoendorf KC. Linking

children from the National Health and

Nutrition Examination Survey to

Medicaid enrollment and claims data.

In: 2012 Proceedings of the Federal

Committee on Statistical Methodology

Research Conference. Washington, DC:

Federal Committee on Statistical

Methodology. 2012.

Mirel LB, Simon AE, Golden C., et al.

Concordance between survey report of

Medicaid enrollment and linked

Medicaid administrative records in two

national studies. National health

statistics reports; no 72. Hyattsville,

MD: National Center for Health

Statistics. 2014. Available from:

http://www.cdc.gov/nchs/data/nhsr/

nhsr072.pdf.

Simon AE, Driscoll A, Gorina Y, Parker

JD, Schoendorf KC. A longitudinal view

of child enrollment in Medicaid.

Pediatrics 132(4):656–62. 2013.

Available from: http://pediatrics.

aappublications.org/content/132/4/

656.full.html.

Simon AE, Schoendorf KC. Medicaid

enrollment gap length and number of

Medicaid enrollment periods among US

children. Am J Public Health

104(9):e55–61. 2014. Available from:

http://ajph.aphapublications.org/doi/pdf/

10.2105/AJPH.2014.301976.

Tao G, Hua J, Chen JL. Understanding

sexual activity and Chlamydia testing

rate based on linked national survey and

Medicaid claims data. PLoS One 10(4).

2015.

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ESRD Data

Muzaale AD, Massie AB, Wang MC,

Montgomery RA, McBride MA,

Wainright JL, Segev DL. Risk of

end-stage renal disease following live

kidney donation. JAMA 311(6):579–86.

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Table 1. NCHS–Social Security Administration linked file sample sizes and percentage linked, by survey and age at interview

Sample size Percent linked Respondents with benefit history data

Eligible Linked to OASDI2 SSI4 831

Total for Numident Total Eligible from payments disability

Survey and age group (years)1 sample linkage File sample sample MBR3 from SSR5 File6

National Health Interview Survey

NHIS 1994 ................. 83,719 67,916 63,835 76.2 94.0 32,304 8,078 6,977

18–64 .................. 69,148 55,844 52,121 75.4 93.3 20,869 6,473 6,797

65 and over ............... 14,571 12,072 11,714 80.4 97.0 11,435 1,605 180

NHIS 1995 ................. 72,756 57,719 53,390 73.4 92.5 26,165 7,228 6,205

18–64 .................. 60,801 47,994 43,988 72.3 91.7 16,984 5,806 5,959

65 and over ............... 11,955 9,725 9,402 78.6 96.7 9,181 1,422 246

NHIS 1996 ................. 63,402 47,262 30,926 48.8 65.4 15,015 4,196 3,691

18–64 .................. 38,140 28,244 25,523 66.9 90.4 9,718 3,402 3,564

65 and over ............... 7,134 5,591 5,403 75.7 96.6 5,297 794 127

NHIS 1997 ................. 73,685 50,848 46,207 62.7 90.9 22,059 6,378 5,564

18–64 .................. 61,794 42,067 37,749 61.1 89.7 13,797 5,091 5,266

65 and over ............... 11,891 8,781 8,458 71.1 96.3 8,262 1,287 298

NHIS 1998 ................. 70,663 44,072 39,451 55.8 89.5 18,378 5,306 4,723

18–64 .................. 59,243 36,696 32,447 54.8 88.4 11,507 4,220 4,413

65 and over ............... 11,420 7,376 7,004 61.3 95.0 6,871 1,086 310

NHIS 1999 ................. 69,788 41,847 36,669 52.5 87.6 16,478 4,903 4,390

18–64 .................. 58,788 34,891 30,236 51.4 86.7 10,182 3,886 4,038

65 and over ............... 11,000 6,956 6,433 58.5 92.5 6,296 1,017 352

NHIS 2000 ................. 72,123 41,890 36,141 50.1 86.3 15,560 4,855 4,314

18–64 .................. 60,917 35,198 30,000 49.2 85.2 9,554 3,854 3,985

65 and over ............... 11,206 6,692 6,141 54.8 91.8 6,006 1,001 329

NHIS 2001 ................. 72,188 39,873 34,912 48.4 87.6 14,791 4,605 4,230

18–64 .................. 61,171 33,492 28,993 47.4 86.6 9,015 3,633 3,798

65 and over ............... 11,017 6,381 5,919 53.7 92.8 5,776 972 432

NHIS 2002 ................. 67,195 46,278 36,531 54.4 78.9 14,967 4,522 4,162

18–64 .................. 56,610 39,025 30,417 53.7 77.9 8,978 3,620 3,739

65 and over ............... 10,585 7,253 6,114 57.8 84.3 5,989 902 423

NHIS 2003 ................. 66,611 43,435 33,645 50.5 77.5 13,408 4,139 3,832

18–64 .................. 56,338 36,775 28,057 49.8 76.3 7,914 3,299 3,366

65 and over ............... 10,273 6,660 5,588 54.4 83.9 5,494 840 466

NHIS 2004 ................. 68,299 41,410 32,611 47.7 78.8 12,712 4,021 3,721

18–64 .................. 57,541 34,920 27,058 47.0 77.5 7,265 3,181 3,195

65 and over ............... 10,758 6,490 5,553 51.6 85.6 5,447 840 525

NHIS 2005 ................. 71,835 41,683 32,243 44.9 77.4 12,136 3,871 3,605

18–64 .................. 60,605 35,364 26,942 44.5 76.2 6,928 3,086 3,114

65 and over ............... 11,230 6,319 5,301 47.2 83.9 5,208 785 491

National Health and

Nutrition Examination Survey

NHEFS7 ................... 14,407 13,540 12,974 90.1 95.8 11,434 2,382 1,033

18–64 .................. 10,551 9,932 9,577 90.8 96.4 8,134 1,625 1,033

65 and over ............... 3,856 3,608 3,397 88.1 94.2 3,300 757 –

NHANES III................. 19,618 19,183 18,586 94.7 96.9 11,339 3,977 2,481

18–64 .................. 14,366 14,047 13,510 94.0 96.2 6,385 2,768 2,443

65 and over ............... 5,252 5,136 5,076 96.6 98.8 4,954 1,209 38

NHANES 1999–2000 ........... 5,448 4,610 4,413 81.0 95.7 2,300 878 619

18–64 .................. 4,056 3,423 3,252 80.2 95.0 1,173 565 531

65 and over ............... 1,392 1,187 1,161 83.4 97.8 1,127 313 88

NHANES 2001–2002 ........... 5,993 5,393 5,081 84.8 94.2 2,457 823 663

18–64 ................... 4,530 4,101 3,805 84.0 92.8 1,205 569 560

65 and over ............... 1,463 1,292 1,276 87.2 98.8 1,252 254 103

NHANES 2003–2004 ........... 5,620 5,101 4,891 87.0 95.9 2,476 838 697

18–64 ................... 4,126 3,747 3,551 86.1 94.8 1,169 576 561

65 and over ............... 1,494 1,354 1,340 89.7 99.0 1,307 262 136

See footnotes at end of table.
Page 28 [ Series 1, No. 58

Table 1. NCHS–Social Security Administration linked file sample sizes and percentage linked, by survey and age at interview—Con.

Sample size Percent linked Respondents with benefit history data

Eligible Linked to OASDI2 SSI4 831

Total for Numident Total Eligible from payments disability

Survey and age group (years)1 sample linkage File sample sample MBR3 from SSR5 File6

National Nursing Home Survey

NNHS 1985................. 11,166 10,542 9,945 89.1 94.3 9,544 3,051 49

18–64 ................... 1,203 1,147 1,059 88.0 92.3 905 735 49

65 and over ............... 9,963 9,395 8,886 89.2 94.6 8,639 2,316 –

NNHS 19958 ................ 8,045 7,342 6,524 81.1 88.9 6,299 1,933 249

18–64 ................... 674 583 522 77.4 89.5 448 391 176

65 and over ............... 7,371 6,759 6,002 81.4 88.8 5,851 1,542 73

NNHS 19978 ................ 14,739 13,663 12,554 85.2 91.9 12,113 3,582 772

18–64 ................... 1,377 1,282 1,193 86.6 93.1 1,019 879 579

65 and over ............... 13,362 12,381 11,361 85.0 91.8 11,094 2,703 193

NNHS 20048 ................ 13,492 13,372 13,165 97.6 98.5 12,737 4,001 1,572

18–64 ................... 1,553 1,532 1,504 96.8 98.2 1,299 1,158 921

65 and over ............... 11,939 11,840 11,661 97.7 98.5 11,437 2,843 651

Longitudinal Study of Aging

LSOA II9................... 9,447 7,635 7,499 79.4 98.2 7,304 1,042 26

– Quantity zero.
 

1Excludes information for participants under age 18 at time of survey. For this age group, in accordance with NCHS Ethics Review Board guidelines, NCHS provides linked administrative data
 

generated only for program participation, claims, and other events occurring prior to the participant’s 18th birthday. NCHS no longer releases linked administrative data generated on or after such
 

participants’ 18th birthday. Availability of information in Social Security Administration (SSA) Files for these participants depends on the variables requested.
 

2Old Age, Survivors, and Disability Insurance.
 

3Master Beneficiary Record.
 

4Supplemental Security Income.
 

5Supplemental Security Record.
 

6Containing data on decisions rendered by the SSA Disability Determination Services and on subsequent decisions by the SSA Office of Hearings and Appeals for persons applying for disability
 

benefits under Title II (Social Security) and Title XVI (SSI).
 

7NHANES I Epidemiologic Followup Study.
 

8Age at interview missing (1995: n = 2; 1997: n = 57; 2004: n = 1). Total survey population count is less than reported by health survey due to lack of age data for match rate tables.
 

9All participants were aged 70 and over at time of interview.
 

NOTES: NHIS is National Health Interview Survey; NHANES is National Health and Nutrition Examination Survey; NNHS is National Nursing Home Survey; and LSOA is Longitudinal Study of Aging.

NHANES III was conducted in 1988–1994, and NHEFS in 1971–1975, with continued follow-up of the NHEFS population in 1982–1984, 1986, 1987, and 1992. LSOA II was conducted in 1994, with

continued follow-up of the LSOA II population in 1997–1998 and 1999–2000.
Series 1, No. 58 [ Page 29

Table 2. NCHS–Medicare linked file sample sizes and percentage linked, by survey and age at interview: 1991–2007

Sample size Percent linked

Linked to

Total Eligible Medicare Total Eligible

Survey and age group (years) sample for linkage Denominator File sample sample

National Health Interview Survey

NHIS 1994 ........................ 116,179 87,079 23,662 20.4 27.2

Under 65 ........................ 101,608 75,387 12,235 12.0 16.2

65 and over ...................... 14,571 11,692 11,427 78.4 97.7

NHIS 1995 ........................ 102,467 73,809 18,816 18.4 25.5

Under 65 ........................ 90,512 64,410 9,576 10.6 14.9

65 and over ...................... 11,955 9,399 9,240 77.3 98.3

NHIS 1996 ........................ 63,402 42,407 10,482 16.5 24.7

Under 65 ........................ 56,268 37,036 5,202 9.2 14.0

65 and over ...................... 7,134 5,371 5,280 74.0 98.3

NHIS 1997 ........................ 103,477 62,689 14,972 14.5 23.9

Under 65 ........................ 91,586 54,675 7,102 7.8 13.0

65 and over ...................... 11,891 8,014 7,870 66.2 98.2

NHIS 1998 ........................ 98,785 53,535 12,080 12.2 22.6

Under 65 ........................ 87,365 47,028 5,673 6.5 12.1

65 and over ...................... 11,420 6,507 6,407 56.1 98.5

National Health and

Nutrition Examination Survey

NHEFS1 .......................... 14,407 12,879 7,258 50.4 56.4

Under 65 ........................ 10,551 9,491 6,254 59.3 65.9

65 and over ...................... 3,856 3,388 1,004 26.0 29.6

NHANES III ........................ 33,994 31,607 8,900 26.2 28.2

Under 65 ........................ 28,742 26,531 4,026 14.0 15.2

65 and over ...................... 5,252 5,076 4,874 92.8 96.0

Longitudinal Study of Aging

LSOA II2 .......................... 9,447 7,390 7,235 76.6 97.9

1NHANES I Epidemiologic Followup Study.
 

2All participants were aged 70 and over at time of interview.
 

NOTES: NHIS is National Health Interview Survey; NHANES is National Health and Nutrition Examination Survey; and LSOA is Longitudinal Study of Aging. NHANES III was conducted in 1988–1994,
 

and NHEFS in 1971–1975, with continued follow-up of the NHEFS population in 1982–1984, 1986, 1987, and 1992. LSOA II was conducted in 1994, with continued follow-up of the LSOA II population
 

in 1997–1998 and 1999–2000.
 
Page 30 [ Series 1, No. 58

Table 3. NCHS–Medicare linked file sample sizes and percentage linked, by survey and age at interview: 1999–2007

Sample size Percent linked

Linked to

Eligible Medicare

Total for Denominator Total Eligible

Survey and age group (years) sample linkage File sample sample

National Health Interview Survey

NHIS 19991 ........................ 97,059 49,467 10,577 10.9 21.4

Under 65 ........................ 86,059 43,663 4,879 5.7 11.2

65 and over ...................... 11,000 5,804 5,698 51.8 98.2

NHIS 2000 ........................ 100,618 49,127 9,750 9.7 19.8

Under 65 ........................ 89,412 43,510 4,237 4.7 9.7

65 and over ...................... 11,206 5,617 5,513 49.2 98.1

NHIS 2001 ........................ 100,760 47,452 9,238 9.2 19.5

Under 65 ........................ 89,743 42,027 3,910 4.4 9.3

65 and over ...................... 11,017 5,425 5,328 48.4 98.2

NHIS 2002 ........................ 93,386 53,074 9,336 10.0 17.6

Under 65 ........................ 82,801 47,325 3,683 4.4 7.8

65 and over ...................... 10,585 5,749 5,653 53.4 98.3

NHIS 2003 ........................ 92,148 49,095 8,052 8.7 16.4

Under 65 ........................ 81,875 43,881 2,915 3.6 6.6

65 and over ...................... 10,273 5,214 5,137 50.0 98.5

NHIS 2004 ........................ 94,460 45,805 7,617 8.1 16.6

Under 65 ........................ 83,702 40,537 2,443 2.9 6.0

65 and over ...................... 10,758 5,268 5,174 48.1 98.2

NHIS 2005 ........................ 98,649 44,835 6,926 7.0 15.4

Under 65 ........................ 87,419 39,972 2,142 2.5 5.4

65 and over ...................... 11,230 4,863 4,784 42.6 98.4

National Health and

Nutrition Examination Survey

NHANES 1999–2000 .................. 9,965 7,852 1,715 17.2 21.8

Under 65 ........................ 8,573 6,737 615 7.2 9.1

65 and over ...................... 1,392 1,115 1,100 79.0 98.7

NHANES 2001–2002 .................. 11,039 9,274 1,790 16.2 19.3

Under 65 ........................ 9,576 8,033 571 6.0 7.1

65 and over ...................... 1,463 1,241 1,219 83.3 98.2

NHANES 2003–2004 .................. 10,122 8,624 1,737 17.2 20.1

Under 65 ........................ 8,628 7,328 463 5.4 6.3

65 and over ...................... 1,494 1,296 1,274 85.3 98.3

National Nursing Home Survey

NNHS 20041 ....................... 13,506 13,180 12,518 92.7 95.0

Under 65 ........................ 1,567 1,518 1,012 64.6 66.7

65 and over ...................... 11,939 11,661 11,505 96.4 98.7

1Age at interview missing (n = 1). Total survey population count is less than reported by health survey due to lack of age data for match rate tables.

NOTE: NHIS is National Health Interview Survey; NHANES is National Health and Nutrition Examination Survey; and NNHS is National Nursing Home Survey.
Series 1, No. 58 [ Page 31

Table 4. NCHS–Medicaid linked file sample sizes and percentage linked, by survey and age at interview: 1999–2009

Sample size Percent linked

Linked to MAX PS1 File Total sample Eligible sample

Eligible

Total for In survey In any In survey In any In survey In any

Survey and age group (years) sample linkage year year year year year year

National Health Interview Survey

NHIS 1994 ................. 116,179 83,738 . . . 16,187 . . . 13.9 . . . 19.3

Under 18 ................ 32,460 19,939 . . . 5,636 . . . 17.4 . . . 28.3

18–64 .................. 69,148 52,107 . . . 7,986 . . . 11.5 . . . 15.3

65 and over ............... 14,571 11,692 . . . 2,565 . . . 17.6 . . . 21.9

NHIS 1995 ................. 102,467 70,922 . . . 15,524 . . . 15.2 . . . 21.9

Under 18 ................ 29,711 17,540 . . . 5,846 . . . 19.7 . . . 33.3

18–64 .................. 60,801 43,983 . . . 7,555 . . . 12.4 . . . 17.2

65 and over ............... 11,955 9,399 . . . 2,123 . . . 17.8 . . . 22.6

NHIS 1996 ................. 63,402 40,973 . . . 9,452 . . . 14.9 . . . 23.1

Under 18 ................ 18,128 10,086 . . . 3,539 . . . 19.5 . . . 35.1

18–64 .................. 38,140 25,516 . . . 4,616 . . . 12.1 . . . 18.1

65 and over ............... 7,134 5,371 . . . 1,297 . . . 18.2 . . . 24.1

NHIS 1997 ................. 103,477 60,862 . . . 15,058 . . . 14.6 . . . 24.7

Under 18 ................ 29,792 15,172 . . . 5,952 . . . 20.0 . . . 39.2

18–64 .................. 61,794 37,676 . . . 7,121 . . . 11.5 . . . 18.9

65 and over ............... 11,891 8,014 . . . 1,985 . . . 16.7 . . . 24.8

NHIS 1998 ................. 98,785 52,296 . . . 13,435 . . . 13.6 . . . 25.7

Under 18 ................ 28,122 13,424 . . . 5,585 . . . 19.9 . . . 41.6

18–64 .................. 59,243 32,365 . . . 6,187 . . . 10.4 . . . 19.1

65 and over ............... 11,420 6,507 . . . 1,663 . . . 14.6 . . . 25.6

NHIS 1999 ................. 97,059 48,568 7,173 13,157 7.4 13.6 14.8 27.1

Under 18 ................ 27,271 12,584 3,762 5,559 13.8 20.4 29.9 44.2

18–64 .................. 58,788 30,180 2,721 6,139 4.6 10.4 9.0 20.3

65 and over ............... 11,000 5,804 690 1,459 6.3 13.3 11.9 25.1

NHIS 2000 ................. 100,618 48,427 7,699 13,913 7.7 13.8 15.9 28.7

Under 18 ................ 28,495 12,884 4,040 6,108 14.2 21.4 31.4 47.4

18–64 .................. 60,917 29,926 2,893 6,359 4.7 10.4 9.7 21.2

65 and over ............... 11,206 5,617 766 1,446 6.8 12.9 13.6 25.7

NHIS 2001 ................. 100,760 46,939 7,931 14,036 7.9 13.9 16.9 29.9

Under 18 ................ 28,572 12,599 4,239 6,358 14.8 22.3 33.6 50.5

18–64 .................. 61,171 28,915 2,979 6,326 4.9 10.3 10.3 21.9

65 and over ............... 11,017 5,425 713 1,352 6.5 12.3 13.1 24.9

NHIS 2002 ................. 93,386 52,498 9,267 15,367 9.9 16.5 17.7 29.3

Under 18 ................ 26,191 16,388 5,256 7,723 20.1 29.5 32.1 47.1

18–64 .................. 56,610 30,361 3,250 6,389 5.7 11.3 10.7 21.0

65 and over ............... 10,585 5,749 761 1,255 7.2 11.9 13.2 21.8

NHIS 2003 ................. 92,148 48,665 9,115 14,576 9.9 15.8 18.7 30.0

Under 18 ................ 25,537 15,451 5,270 7,375 20.6 28.9 34.1 47.7

18–64 .................. 56,338 28,000 3,143 6,086 5.6 10.8 11.2 21.7

65 and over ............... 10,273 5,214 702 1,115 6.8 10.9 13.5 21.4

NHIS 2004 ................. 94,460 45,556 8,772 14,101 9.3 14.9 19.3 31.0

Under 18 ................ 26,161 13,291 4,863 6,811 18.6 26.0 36.6 51.2

18–64 .................. 57,541 26,997 3,144 6,109 5.5 10.6 11.6 22.6

65 and over ............... 10,758 5,268 765 1,181 7.1 11.0 14.5 22.4

NHIS 2005 ................. 98,649 44,689 8,765 13,792 8.9 14.0 19.6 30.9

Under 18 ................ 26,814 12,939 4,810 6,669 17.9 24.9 37.2 51.5

18–64 .................. 60,605 26,887 3,214 6,083 5.3 10.0 12.0 22.6

65 and over ............... 11,230 4,863 741 1,040 6.6 9.3 15.2 21.4

See footnotes at end of table.
Page 32 [ Series 1, No. 58

Table 4. NCHS–Medicaid linked file sample sizes and percentage linked, by survey and age at interview: 1999–2009—Con.

Sample size Percent linked

Linked to MAX PS1 File Total sample Eligible sample

Eligible

Total for In survey In any In survey In any In survey In any

Survey and age group (years) sample linkage year year year year year year

National Health and

Nutrition Examination Survey

NHEFS2 ................... 14,407 12,879 . . . 1,570 . . . 10.9 . . . 12.2

Under 65 ................ 10,551 9,491 . . . 1,422 . . . 13.5 . . . 15.0

65 and over ............... 3,856 3,388 . . . 148 . . . 3.8 . . . 4.4

NHANES III................. 33,994 29,300 . . . 8,614 . . . 25.3 . . . 29.4

Under 18 ................ 14,376 10,714 . . . 4,128 . . . 28.7 . . . 38.5

18–64 .................. 14,366 13,510 . . . 3,323 . . . 23.1 . . . 24.6

65 and over ............... 5,252 5,076 . . . 1,163 . . . 22.1 . . . 22.9

NHANES 1999–2000 ........... 9,965 7,594 . . . 3,431 . . . 34.4 . . . 45.2

Under 18 ................ 4,517 3,232 . . . 1,989 . . . 44.0 . . . 61.5

18–64 .................. 4,056 3,247 . . . 1022 . . . 25.2 . . . 31.5

65 and over ............... 1,392 1,115 . . . 420 . . . 30.2 . . . 37.7

NHANES 2001–2002 ........... 11,039 9,100 . . . 3,908 . . . 35.4 . . . 42.9

Under 18 ................ 5,046 4,059 . . . 2,419 . . . 47.9 . . . 59.6

18–64 .................. 4,530 3,800 . . . 1,136 . . . 25.1 . . . 29.9

65 and over ............... 1,463 1,241 . . . 353 . . . 24.1 . . . 28.4

NHANES 2003–2004 ........... 10,122 8,522 . . . 4,020 . . . 39.7 . . . 47.2

Under 18 ................ 4,502 3,677 . . . 2,451 . . . 54.4 . . . 66.7

18–64 .................. 4,126 3,549 . . . 1,200 . . . 29.1 . . . 33.8

65 and over ............... 1,494 1,296 . . . 369 . . . 24.7 . . . 28.5

National Nursing Home Survey

NNHS 20043 ................ 13,506 13,179 9,029 9,827 66.9 72.8 68.5 74.6

Under 65 ................ 1,567 1,518 1,280 1,343 81.7 85.7 84.3 88.5

65 and over ............... 11,939 11,661 7,749 8,484 64.9 71.1 66.5 72.8

Longitudinal Study of Aging

LSOA II4................... 9,447 7,390 . . . 1,782 . . . 18.9 . . . 24.1

. . . Category not applicable.
 

1Medicaid Analytic eXtract Person Summary.
 

2NHANES I Epidemiologic Followup Study.
 

3Missing age at interview (n = 1). Total survey population count is less than reported by health survey due to lack of age data for match rate tables.
 

4All participants were aged 70 and over at time of interview.
 

NOTES: NHIS is National Health Interview Survey; NHANES is National Health and Nutrition Examination Survey; NNHS is National Nursing Home Survey; and LSOA is Longitudinal Study of Aging.
 

NHANES III was conducted in 1988–1994, and NHEFS in 1971–1975, with continued follow-up of the NHEFS population in 1982–1984, 1986, 1987, and 1992. LSOA II was conducted in 1994, with
 

continued follow-up of the LSOA II population in 1997–1998 and 1999–2000.
 
Series 1, No. 58 [ Page 33

Table 5. Unweighted percentage of NCHS–Medicare linked sample of ages 65 and over at time of interview enrolled in managed care (for at

least 1 month during year) for surveys containing 1991–2007 Medicare enrollment and claims data, by survey

Medicare enrollment and claims year

Survey 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

NHIS 1994 .......... 7.1 7.8 8.5 9.8 11.9 14.6 17.6 19.9 20.6 20.6 19.2 18.0 17.1 17.1 18.3 20.7 22.5
 

NHIS 1995 .......... 6.9 7.6 8.4 9.8 12.1 15.0 17.8 19.9 20.6 20.2 18.7 17.3 16.2 16.5 17.4 20.4 22.5
 

NHIS 1996 .......... 8.0 8.5 9.1 10.3 12.7 15.3 18.2 20.5 21.3 20.8 19.1 17.4 16.0 16.6 17.7 20.7 22.8
 

NHIS 1997 .......... 6.4 7.1 7.5 8.6 11.0 14.0 17.0 19.4 19.8 19.6 17.8 16.3 15.4 15.8 17.0 20.5 23.3
 

NHIS 1998 .......... 7.6 8.4 8.8 10.0 12.4 14.9 18.1 20.8 21.5 21.1 19.3 16.8 15.9 16.4 17.8 21.1 24.5
 

NHANES III ......... 6.3 7.0 8.2 9.2 11.9 14.3 16.2 19.2 21.2 20.8 19.7 17.8 17.4 17.9 18.5 21.7 24.5
 

LSOA II ............ 7.3 8.0 8.5 9.8 11.4 13.8 16.4 18.7 19.5 19.5 18.3 17.1 15.9 16.2 17.4 19.8 21.2
 

NOTES: NHIS is National Health Interview Survey; NHANES is National Health and Nutrition Examination Survey; and LSOA is Longitudinal Study of Aging. NHANES III was conducted in 1988–1994;

LSOA II was conducted in 1994, with continued follow-up of the LSOA II population in 1997–1998 and 1999–2000.

Table 6. Unweighted percentage of NCHS–Medicare linked sample of ages 65 and over at time of interview enrolled in managed care (for at

least 1 month during year) for surveys containing 1999–2007 Medicare enrollment and claims data, by survey

Medicare enrollment and claims year

Survey 1999 2000 2001 2002 2003 2004 2005 2006 2007

HIS 1999 ................. 21.2 21.1 19.3 16.8 15.8 16.1 17.2 20.7 23.2

HIS 2000 ................. 21.9 21.8 19.5 17.7 16.9 17.5 19.1 22.4 25.3

HIS 2001 ................. 21.1 20.6 18.4 16.2 15.6 15.6 17.0 20.5 23.4

HIS 2002 ................. 22.3 21.6 19.5 17.4 16.7 16.5 18.3 21.9 24.5

HIS 2003 ................. 21.8 20.9 18.4 16.2 15.2 15.8 17.5 21.1 23.2

HIS 2004 ................. 21.2 20.4 18.7 16.0 15.3 15.6 17.4 21.8 25.0

HIS 2005 ................. 20.4 19.9 17.9 15.6 14.8 14.8 16.5 20.4 23.8

HANES 1999–2000 ........... 23.3 23.8 21.8 20.5 19.8 19.0 20.1 26.5 30.1

HANES 2001–2002 ........... 23.7 23.0 20.2 19.0 19.1 19.7 20.8 23.7 26.9

HANES 2003–2004 ........... 29.5 28.5 26.0 23.4 22.4 22.4 22.3 27.2 29.2

NHS 2004................. 12.0 11.3 10.0 8.6 7.8 7.2 6.7 9.2 9.9

N

N

N

N

N

N

N

N

N

N

N

NOTE: NHIS is National Health Interview Survey; NHANES is National Health and Nutrition Examination Survey; and NNHS is National Nursing Home Survey.
NCHS–Medicaid linked sample enrolled in Medicaid managed care programs for at least 1 month or full year, during 1999–2009 Medicaid

ey

MAX data (months)

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

1 1 1 1 1 1 1 1 1 1

month month month month month month month month month month

or 12 or 12 or 12 or 12 or 12 or 12 or 12 or 12 or 12 or 12

more months more months more months more months more months more months more months more months more months more months

35.4 18.6 34.4 19.3 35.3 16.5 30.7 16.7 29.8 16.2 30.2 16.2 31.4 16.1 32.6 16.1 32.1 16.8 33.3 18.8

37.1 19.1 36.7 20.0 36.8 17.8 33.6 17.9 33.0 17.6 33.6 18.4 33.9 17.2 34.6 16.7 33.4 17.5 35.4 19.2

37.3 19.8 36.9 20.0 37.7 17.6 34.8 19.1 34.6 18.7 34.8 20.0 35.6 18.8 35.9 17.8 36.2 19.0 38.7 21.5

39.1 20.0 37.8 20.9 38.9 17.8 35.5 18.7 35.0 18.2 35.1 19.0 35.8 18.1 37.2 18.3 36.5 18.9 39.0 20.5

40.7 21.7 39.1 22.2 40.2 19.3 37.7 20.3 36.8 19.0 36.9 19.3 36.7 18.3 38.2 19.1 38.5 20.4 40.8 22.1

40.4 19.3 39.4 21.1 41.0 19.1 37.2 20.0 37.2 19.4 37.5 20.2 38.4 18.8 40.6 19.9 39.9 21.3 42.2 23.2

42.6 20.2 41.8 22.0 43.4 20.1 40.4 21.4 40.2 20.8 39.8 21.4 41.2 20.7 41.3 21.5 42.6 21.3 45.0 23.9

42.1 20.6 43.4 21.7 44.0 20.7 40.3 21.3 39.3 20.5 38.7 20.2 40.3 19.7 42.6 20.1 43.7 22.1 46.2 24.9

44.1 20.6 43.7 21.7 44.8 20.5 42.3 21.9 41.3 21.6 40.8 22.3 42.8 21.2 44.4 20.5 44.1 22.8 46.2 24.3

42.4 19.0 41.8 21.5 44.1 19.8 42.7 21.5 42.2 22.4 42.1 22.6 45.1 22.0 46.1 22.8 45.8 22.2 48.0 25.2

41.9 20.3 42.5 20.9 43.9 19.3 42.0 21.3 41.5 21.2 42.4 21.8 43.8 20.9 44.3 20.8 44.9 22.1 46.8 25.2

44.1 20.9 42.7 20.9 45.4 18.8 42.0 21.2 41.5 21.3 42.3 21.8 45.1 21.6 46.0 21.7 45.6 22.9 48.1 26.3

16.3 12.4 16.5 11.7 15.6 4.1 7.0 4.1 7.0 4.2 8.1 5.1 8.6 4.7 10.4 4.9 11.1 6.7 15.1 9.2

35.3 19.3 33.7 18.4 33.8 16.9 31.6 17.8 30.8 16.7 29.3 17.2 30.5 14.0 29.1 14.6 28.2 14.4 28.1 15.6

37.3 17.5 37.2 18.0 39.5 19.2 40.1 21.7 41.4 23.4 41.8 25.1 43.5 22.8 42.4 21.2 43.1 21.9 45.5 24.3

46.2 22.9 49.4 25.5 49.6 22.7 46.6 23.8 44.9 23.5 43.9 23.6 49.3 22.7 54.1 23.5 53.9 30.0 56.0 31.9

53.6 28.6 52.9 29.6 55.1 27.5 53.0 28.9 50.9 27.6 50.0 29.5 50.0 27.6 54.1 28.0 54.1 30.3 58.1 33.1

9.1 6.2 8.4 5.9 7.6 3.0 4.4 2.7 4.2 2.7 4.2 2.5 4.2 2.7 5.0 2.6 6.4 3.4 7.8 6.0

8.3 4.9 7.8 4.7 6.9 2.2 3.4 2.1 4.4 2.9 5.4 3.4 5.0 3.0 6.1 3.6 7.2 4.9 8.4 5.1

Medicaid comprehensive managed care plan (Medicaid Analytic eXtract [MAX] element EL_PPH_PLN_MO_CNT_CMCP) was used to compute data shown. Data represent persons having only one MAX

Survey; NHANES is National Health and Nutrition Examination Survey; NNHS is National Nursing Home Survey; and LSOA is Longitudinal Study of Aging. NHANES III was conducted in 1988–1994, and

he NHEFS population in 1982–1984, 1986, 1987, and 1992. LSOA II was conducted in 1994, with continued follow-up of the LSOA II population in 1997–1998 and 1999–2000.

Page 34[ Series 1, No. 58
Series 1, No. 58 [ Page 35

Appendix I. Social Security Administration Programs

Old Age, Survivors, and

Disability Insurance

The Old Age, Survivors, and

Disability Insurance (OASDI)

program—Social Security—is the largest

income-maintenance program in the

United States. The program provides

monthly benefits designed to replace, in

part, the loss of income resulting from

retirement, disability, or death. OASDI

has two programs: the Old Age and

Survivors Insurance (OASI) program

and the Social Security Disability

Insurance (DI) program. Coverage is

nearly universal, with the two programs

covering about 91% of U.S. jobs.

Workers finance most of the program

through a payroll tax levied under the

Federal Insurance Contribution Act or

FICA and the Self-Employment

Contribution Act or SECA.

Nearly all work performed by

citizens and noncitizens is covered if

performed within the United States. In

addition, the program covers work

performed outside the United States by

American citizens or residents employed

by an American employer.

To qualify for Social Security, a

person must be insured for benefits by

acquiring a certain number of credits

from earnings in covered employment.

The number of credits needed depends

on the worker’s age and type of benefit.

For DI entitlement, disability is defined

as ‘‘the inability to engage in any

substantial gainful activity (SGA) by

reason of any medically determinable

physical or mental impairment that can

be expected to result in death or that has

lasted or can be expected to last for a

continuous period of not less than 12

months.’’ A person’s age, education, and

work experience are considered along

with medical evidence in making a

determination of disability. A less strict

rule is provided for blind workers aged

55 and over. Such workers are

considered disabled if, because of their

blindness, they are unable to engage in

SGA that requires skills and abilities

comparable with those required in their

past occupations.

The impairment must be of a degree

of severity that renders the person

unable to engage in any kind of

substantial gainful work in the national

economy, regardless of whether such

work exists in the immediate area in

which the person lives, whether a

specific job vacancy exists for that

person, or whether that person would be

hired upon application for the work.

Unlike the OASI program, which is

entirely federally administered, the law

mandates federal–state cooperation in

carrying out the DI program. Each

state’s Disability Determination Services

(DDS) evaluates the medical evidence

and makes an initial determination of

disability based on federal criteria, after

the Social Security Administration

(SSA) determines that the applicant is

insured for benefits. The federal

government reimburses states for DDS

costs.

Monthly retirement benefits are

payable at age 62 but are permanently

reduced if claimed before the normal

retirement age (NRA). Before 2000,

NRA was age 65; it will gradually

increase to age 67. Benefits may also be

payable to the spouse and children of

retired-worker beneficiaries. A spouse

receives benefits at age 62, or at any

age if he or she is caring for a child

under age 16 or a disabled child. A

divorced spouse aged 62 or over

married to the worker for at least 10

years is also entitled to benefits.

Benefits are payable to unmarried

children under age 18, or aged 18 or 19

if they attend school full time.

Monthly benefits are payable to

disabled workers after a 5-month waiting

period, because DI is not intended to

cover short-term disabilities. Benefits

terminate if the beneficiary medically

improves or returns to work (at an SGA

level) despite the impairment. Upon

reaching NRA, beneficiaries are

transferred to the retirement program.

Benefits for family members of a disabled

worker are payable under the same

conditions as those for retired workers.

Monthly benefits are payable to survivors

of a deceased worker.

Supplemental Security

Income

The Supplemental Security Income

(SSI) program consists of two parts: the

federal program, and state

supplementation of the federal payment.

Eligibility requirements and benefit

payments for the federal program are

uniform across states. Almost 90% of

SSI payments are federal. State

supplementation can be federally or

state administered. States with

state-administered programs establish

their own eligibility criteria and issue

their own checks, but these payments

are not captured on the Supplemental

Security Record file.

Basic requirements for SSI

eligibility involve citizenship, age,

disability, income, and financial

resources. SSI eligibility is generally

restricted to U.S. citizens; however,

eligibility is still possible for noncitizen

members of certain groups of refugees,

military personnel and their families,

and some permanent residents. Persons

must be at least 65 years old. Qualifying

standards for SSI payments based on

disability are almost the same as those

used for the DI program. Persons do not

have to be entirely without income to be

eligible for SSI benefits. The maximum

SSI payment is reduced dollar-for-dollar

to reflect other income and in-kind

support and maintenance. In most cases,

ownership of financial assets is limited

to $2,000 for an individual or $3,000 for

a couple.

More information about SSA

programs can be found at:

http://www.ssa.gov.
Page 36 [ Series 1, No. 58

Appendix II. Centers for Medicare & Medicaid Services Programs

Medicare

Medicare was created in 1965 under

Title XCVIII of the Social Security Act

to provide health insurance for people

aged 65 and over. In 1972, Medicare

eligibility was expanded to younger

people with permanent disabilities who

receive Social Security Disability

Insurance and people of all ages with

end-stage renal disease (ESRD),

permanent kidney failure requiring

dialysis, or a kidney transplant.

Medicare consists of three parts:

hospital, medical, and prescription drug

insurance. Medicare Part A (hospital

insurance) helps cover inpatient care in

hospitals, including critical access

hospitals, and skilled nursing facilities

(not including custodial or long-term

care). It also helps cover hospice care

and some home health care.

Beneficiaries must meet certain

conditions to get these benefits. Most

people do not pay a premium for Part A

because they or a spouse paid for it

through their payroll taxes while

working. However, they generally pay

deductibles and coinsurance and may

pay copayments.

Medicare Part B (medical

insurance) helps cover doctors’ services

and outpatient care. It also covers some

medical services that Part A does not

cover, such as certain physical and

occupational therapy and home health

care when considered medically

necessary. Most people pay a monthly

premium for Part B as well as

deductibles, coinsurance, and

copayments.

Medicare Part C (Medicare

Advantage), created in 1997, is a type

of Medicare health plan offered by a

private company that contracts with

Medicare to provide Part A and Part B

benefits. Medicare Advantage plans

include health maintenance

organizations, preferred provider

organizations, private fee-for-service

plans, special needs plans, and Medicare

medical savings account plans. The

services provided through Medicare

Advantage are covered through the plan

and are not paid for under original

Medicare. Most Medicare Advantage

plans offer prescription drug coverage.

Medicare Part D (prescription drug

insurance) became available January 1,

2006, to all Medicare enrollees. Private

companies provide various drug

insurance plans, and beneficiaries pay a

monthly premium for the plan they

choose. Beneficiaries who do not enroll

in a drug plan when they are first

eligible may pay a penalty if they

choose to join later.

Medicare funds the vast majority of

medical residency training in the United

States. Medicare financing covers

residents’ salaries and benefits through

Direct Medical Education payments.

Medicare also funds Indirect Medical

Education, a subsidy paid to teaching

hospitals in exchange for training

resident physicians.

Some Medicare beneficiaries,

known as ‘‘dual eligibles,’’ are also

eligible for Medicaid benefits or for a

Medicare Savings Program. These

beneficiaries have limited income and

resources, meeting financial criteria for

Medicaid coverage. More than one-third

of dual eligibles are people under age

65 with disabilities. Dual eligibles

qualify for Qualified Medicare

Beneficiary benefits in which Medicare

Part A and B premiums are covered by

Medicaid, as are deductibles,

coinsurance, and copayments.

Medicaid

Medicaid is a means-tested

entitlement program that provides health

care coverage to some vulnerable U.S.

populations, including low-income

children and the aged or disabled poor.

The program was enacted in 1965 by

Title XIX of the Social Security Act and

is the third-largest source of health care

spending in the United States after

Medicare and employer-provided health

insurance. Medicaid accounts for almost

one-sixth of national spending on

personal health care. The number of

Medicaid enrollees is greater than the

number enrolled in Medicare.

Medicaid is the main payer of

nursing home care and long-term care

services overall; it is also the largest

source of public funding for mental

health care. Health centers and

safety-net hospitals that serve

low-income and uninsured people rely

heavily on Medicaid. Seniors and people

with disabilities make up approximately

25% of all Medicaid enrollees but

account for two-thirds of Medicaid

benefit expenditures.

Medicaid is administered by states

under guidelines established by the

federal government and is financed

jointly by federal and state funds. The

Federal Medical Assistance Percentage

(FMAP), also called the federal match

rate, represents the percentage of

Medicaid financed by the federal

government in each state. FMAP differs

by state and takes into account the

average per capita income in a state

relative to the national average. The

2014 FMAP for individual states can be

found at: http://aspe.hhs.gov/health/

reports/2014/FMAP2014/fmap2014.cfm.

FMAPs for earlier years can be found

at: http://aspe.hhs.gov/health/fmap.cfm.

State Medicaid programs must

cover mandatory services specified in

federal law to receive federal matching

funds. Beneficiaries are entitled to the

following mandatory services:

+	 Physicians’ services

+	 Hospital services (inpatient and

outpatient)

+	 Laboratory and X-ray services

+	 Early and periodic screening,

diagnostic, and treatment services for

persons under age 21

+	 Federally qualified health center and

rural health clinic services

+	 Family planning services and

supplies

+	 Pediatric and family nurse

practitioner services

+	 Nurse midwife services

+	 Nursing facility services for persons

aged 21 and over

+	 Home health care for persons

eligible for nursing facility services

+	 Transportation services

+	 Medicaid long-term care services
Series 1, No. 58 [ Page 37

States are permitted to cover

services that federal law designates as

optional, including dental services,

prescription drugs, case management,

and hospice services. State variation in

Medicaid coverage, with regard to both

program eligibility and covered services,

results in state differences in enrollment

rates and expenditures. Other factors,

including the age distribution, poverty

rate, and Medicaid provider

reimbursement rates, also contribute to

variation among states in enrollment,

service use, and costs. As a result,

Medicaid operates as more than 50

distinct programs—one in each state, the

District of Columbia, and each of the

territories.

To qualify for Medicaid, a person

must belong to one of the mandatory

eligibility groups based on age and

poverty level as well as, in some

groups, eligibility for other programs.

Federal law requires states to cover

certain mandatory eligibility groups, but

the states have broad flexibility to

determine their own methods for

assessing income and may impose asset

tests. Prior to 2014, states were required

to cover the following mandatory

eligibility groups:

+	 Pregnant women with family income

below 133% of the federal poverty

level (FPL)

+	 Infants (up to age 1 year) born to

Medicaid-eligible pregnant women

+	 Children under age 6 years with

family income below 133% FPL

+	 Children aged 6–18 with family

income below 100% FPL

+	 Limited-income families with

dependent children who meet states’

July 1996 welfare eligibility levels

(often below 50% FPL)

+	 Supplemental Security Income (SSI)

recipients

FPL cutoffs for children and parents

vary across states and over time. Most

elderly persons and persons with

disabilities who receive SSI, and most

low-income elderly or disabled

Medicare beneficiaries, are eligible for

Medicaid. Medicare beneficiaries must

meet state income and assets criteria.

Under the terms of the Patient

Protection and Affordable Care Act of

2010, Medicaid was to be expanded in

2014 to include nearly everyone under

age 65 with income up to 138% of FPL

(income cutoff of 133% plus a 5%

disregard). However, in 2012, the U.S.

Supreme Court ruled that state

participation in expanded coverage of

Medicaid was optional. As of this

report, 27 states and the District of

Columbia had expanded Medicaid.

Some Medicaid enrollees may be

enrolled in both Medicaid and Medicare

and are referred to as dual eligible

beneficiaries, or ‘‘dual eligibles.’’

More information about dual eligibles

can be found through Centers for

Medicare & Medicaid Services (CMS)

at: https://www.cms.gov/MedicareMedicaid-Coordination/MedicareMedicaidCoordination.html.

Health care through Medicaid is

delivered through fee-for-service and

managed care programs. Medicaid

managed care programs are insurance

plans in which a health care

organization provides a defined bundle

of health services for a fixed monthly

fee paid by the state’s Medicaid

program. States use an array of different

types of managed care arrangements in

Medicaid. Medicaid managed care plans

include comprehensive plans that cover

most (but not necessarily all) enrollee

health services. Other plans provide

more limited services; coverage varies

greatly by plan. Primary care case

management plans are the least

comprehensive managed care type.

Since the 1990s, state Medicaid

programs have increasingly relied on

managed care to organize and deliver

services. Between 1999 and 2009,

managed care enrollment rose from 56%

to 75% of the Medicaid population.

Children’s Health

Insurance Program

Implemented in 1997 via Title XXI

of the Social Security Act, Children’s

Health Insurance Program (CHIP) is

jointly financed by the federal and state

governments and is administered by the

states. It targets low-income uninsured

children and pregnant women in

families with incomes too high to

qualify for most state Medicaid

programs. Within broad federal

guidelines, each state determines its

program’s design, eligibility groups,

benefits, payment levels, and

administrative and operating procedures.

The federal government provides

funding to states for CHIP but caps the

amount of funds provided on a matching

basis.

Each state has the option of

expanding Medicaid eligibility to

children who previously had been

ineligible due to their income

(M–CHIP), or creating a program

distinct from its existing Medicaid

program (S–CHIP). The federal

government matches state spending for

CHIP at a higher rate than that for

Medicaid. As of 2009, more than 30

states had an S–CHIP program, either

by itself or in combination with an

M–CHIP program in the same state.

S–CHIP programs are allowed to tailor

their programs to a greater extent than

M–CHIP programs. Data availability is

different for M–CHIP and S–CHIP

recipients. CHIP was reauthorized in

2009 and has been extended since that

time, but authorization for CHIP is set

to expire in 2015.

More information about

CMS programs can be found at:

http://www.cms.gov.
Page 38 [ Series 1, No. 58

Appendix III. Adjusting Sample Weights for Linkage-eligibility Using

SUDAAN

This appendix provides an example

of adjusting sample weights to correct

for linkage ineligibility. For National

Center for Health Statistics (NCHS) data

files linked with Centers for Medicare &

Medicaid Services (CMS) and Social

Security Administration data files,

linkage ineligibility refers to a survey

respondent who, via action or inaction,

excludes himself or herself from the

population of persons whose data can be

linked to administrative records; linkage

eligibility is described in the main

report. More detailed information about

adjustment methods can be found in an

earlier version of this appendix (8),

which describes alternative models for

adjustment.

For its population health surveys,

NCHS creates sample weights based on

probabilities of selection into the survey,

with adjustments for nonresponse and

census poststratification. Poststratification involves adjusting the

sample weights within specific domains

to match corresponding population

totals. Users of NCHS surveys are

encouraged to use these sample weights

in their calculations. However, only

linkage-eligible survey respondents can

be used when analyzing NCHS linked

data, not the full sample. Survey

respondents who provide sufficient

personally identifiable information for

linkage are not a random sample of

respondents (9). As with other forms of

nonresponse, biased analyses of linked

data may result when respondents who

are linkage eligible differ systematically

from respondents who are not linkage

eligible for some characteristics (10).

Of critical importance is whether

linkage ineligibility varies by sample

subsets and whether responses to

outcomes of interest vary across these

groups (11). Nonresponse weighting

adjustments can be effective for

handling linkage ineligibility if the

nonresponse weighting adjustment is

related to the variables that influence the

probability of linkage ineligibility and to

the variables being studied.

For developing and testing

reweighting models, PROC WTADJUST

in SUDAAN software (12) is useful.

This SUDAAN procedure is designed to

work with complex surveys and is

flexible enough to implement different

approaches to reweighting. As shown

below, multiple approaches can also be

easily applied and compared. NCHS

uses a SAS-callable version of

SUDAAN, but a stand-alone version is

also available.

This appendix has four sections:

The first section begins with a brief

overview of SUDAAN’s PROC

WTADJUST; next, the data files used in

the example are described; then, an

example using PROC WTADJUST is

presented; and in the final section, some

suggestions are provided for examining

the adjusted sample weights, to enhance

confidence that the weight adjustment

model chosen will lead to valid

inferences.

User familiarity with standard SAS

program language conventions (e.g.,

libnames and filenames) is assumed for

the example that follows. Users are

advised to read the SUDAAN

documentation (12) for a detailed

understanding of the WTADJUST

procedure and its implementation.

Although an example of its use is

shown below, the best approach for a

particular project may require different

applications of the procedure.

Furthermore, other software can be used

to reweight survey data (13), and other

methods can be used to address the

issue of linkage eligibility (14).

PROC WTADJUST

SUDAAN is a statistical package

designed to correctly handle data

analysis for data from complex sample

designs. Within SUDAAN, PROC

WTADJUST is a module designed

specifically for nonresponse and

poststratification adjustments. Weight

adjustments are created using a

model-based calibration approach. There

are two ways to use PROC

WTADJUST: 1) to correct for

nonresponse using a model-based

approach (nonresponse option), and 2) to

directly poststratify to external control

totals (poststratify option). Control totals

are population estimates calculated for

specific cross-categories of subgroups,

often defined by race and ethnicity, age,

and sex categories. If the variables that

were used to define control totals for the

original sample weights are used in

PROC WTADJUST, then applying the

nonresponse option and fitting a model

using all cross-categories of the

variables defining the control totals will

produce the same adjusted sample

weights as using the poststratify option

with those control totals.

Data Files Used in

Example

Feasibility files

Publicly available feasibility data

files for the linkages of NCHS survey

data to CMS Medicare claims can be

downloaded directly from the NCHS

website. The NCHS–CMS Medicare

feasibility files provide a limited set of

variables that can be used to determine

the maximum available sample size for

each linked file and assess the potential

impact of linkage-ineligible records.

These files are especially useful to

researchers considering whether to

initiate a Research Data Center proposal

to analyze the restricted-use linked

NCHS–CMS Medicare files. Each

feasibility file is NCHS survey- and

survey year-specific.

The following information is

included in each feasibility study file:

NCHS public-use data file identifier (for

the National Health Interview Survey

[NHIS], this is called PUBLICID),

survey respondent eligibility and final

match status (CMS_MEDICARE_

MATCH), and variables specifying

which CMS Medicare data files contain

information on the successfully linked

survey respondent. The feasibility study
Series 1, No. 58 [ Page 39

files do not contain any specific

information about CMS Medicare

benefits. Data users need to use

information from the public-use survey

or external information to approximate

the number of respondents with a

specific condition.

The NCHS–CMS Medicare

feasibility file can be found at:

http://www.cdc.gov/nchs/data_access/

data_linkage/cms/cms_medicare_

feasibility.htm. For this example,

the feasibility file for the 2005 NHIS

is placed into SAS library NHIS05

and referred to as NHIS05.FEAS.

The linkage eligibility flag,

CMS_MEDICARE_MATCH, takes on

the following values: one for linkage

eligible and linked; two for linkage

eligible and not linked; three for linkage

eligible but reached age 18 during the

administrative follow-up period; and

nine for linkage ineligible. The first

three values are considered linkage

eligible as a group.

2005 NHIS public-use person

file

The public-use 2005 NHIS

person file can be found at:

http://www.cdc.gov/nchs/nhis/

nhis_2005_data_release.htm.

From the 2005 NHIS person file,

the following public-use variables are

used: PUBLICID, AGE_P (respondent’s

age at survey interview), HISCODI2

(race and Hispanic origin, which is

renamed ‘‘RACEETH’’ to be more

descriptive), SEX, EDUC1 (education),

PHSTAT (which is recoded into a logical

variable FAIRPOORHEALTH for

assessment), REGION (West, Northeast,

South, Midwest), WTFA (the original

design-based sample weight), STRATUM,

and PSU (primary sampling unit).

In this example, the variable

PUBLICID, which is on both the

feasibility file and the 2005 NHIS

person file, is used. Because public-use

NHIS files have had varying variable

names in different years, PUBLICID

may need to be created in a yearspecific way. More information is

available from: http://www.cdc.gov/nchs/

data/datalinkage/important_information_

on_merging_nchs_restricted_and_

public_use_survey_data.pdf.

To create the data file, sort and

merge the files and code the variables.

For this example, the input data files

and created data files were kept in SAS

library NHIS05. The public-use 2005

NHIS person file is called

NHIS05.PERSONSX.

/*DEFINE FORMATS FOR LATER USE/*

PROC FORMAT ;
 

VALUE EDUCF
 

1 = ‘‘< High school’’
 

2 = ‘‘High school or GED or some
 

college’’
 

3 = ‘‘College degree’’ ;
 

VALUE AGECATF
 

1 = ‘‘UNDER 18 YEARS’’
 

2 = ‘‘18–44 YEARS’’
 

3 = ‘‘45–64 YEARS’’
 

4 = ‘‘65 YEARS OR OLDER’’ ;
 

VALUE REGIONF
 

1 = ‘‘NORTHEAST’’
 

2 = ‘‘MIDWEST’’
 

3 = ‘‘SOUTH’’
 

4 = ‘‘WEST’’ ;
 

VALUE RACEETHF
 

1 = ‘‘HISPANIC’’
 

2 = ‘‘NON-HISPANIC WHITE’’
 

3 = ‘‘NON-HISPANIC BLACK’’
 

4 = ‘‘ALL OTHER RACES AND
 

ETHNICITIES’’ ;
 

VALUE FAIRPOORF
 

1 = ‘‘FAIR OR POOR HEALTH’’
 

0 = ‘‘GOOD, VERY GOOD,
 

EXCELLENT HEALTH’’
 

. = ‘‘MISSING’’ ;
 

/*STEP 1: MERGE THE FEASIBILITY

AND PUBLIC USE DATA FILES.*/

PROC SORT DATA=NHIS05.FEAS ; BY

PUBLICID ;

PROC SORT DATA=NHIS05.PERSONSX ;

BY PUBLICID ;

DATA NHIS05.MERGED NOTMERGED1

NOTMERGED2 ;

MERGE

NHIS05.FEAS (IN=A KEEP=PUBLICID

CMS_MEDICARE_MATCH)

NHIS05.PERSONSX (IN=B

KEEP=PUBLICID AGE_P HISCODI2

PHSTAT SEX REGION EDUC1 WTFA

STRATUM PSU) ;

BY PUBLICID ;
 

IF A=1 AND B=1 THEN OUTPUT
 

NHIS05.MERGED ;
 

IF A=0 AND B=1 THEN OUTPUT
 

NOTMERGED1 ;
 

IF A=1 AND B=0 THEN OUTPUT
 

NOTMERGED2 ;
 

/*CHECK THE LOG FILE. NO RECORDS

SHOULD BE OUTPUT TO NOTMERGED1

OR NOTMERGED2. */

/*STEP 2: DEFINE THE LINKAGEELIGIBILITY (MISSING DATA)

INDICATOR, LINKABLE. RECODE THE

VARIABLES. CREATE VARIABLE

FORMATS. */

DATA NHIS05.MERGED ; SET

NHIS05.MERGED ;

LINKABLE=CMS_MEDICARE_MATCH

IN (1,2,3) ; /*DEFINES LINKAGEELIGIBILTY /*

FAIRPOORHEALTH=(PHSTAT IN (4,5))

;

IF PHSTAT=7 OR PHSTAT=8 OR

PHSTAT=9 THEN FAIRPOORHEALTH=.

;

/*DEFINE THE FAIR OR POOR

HEALTH VARIABLE */ ;

AGE_CAT=. ;
 

IF 0 LE AGE_P LE 17 THEN
 

AGE_CAT=1 ;
 

IF 18 LE AGE_P LE 44 THEN
 

AGE_CAT=2 ;
 

IF 45 LE AGE_P LE 64 THEN
 

AGE_CAT=3 ;
 

IF AGE_P > 64 THEN AGE_CAT=4 ;
 

EDUC=. ;
 

IF EDUC1 IN (0,1,2,3,4,5,6,7,8,9,
 

10,11) THEN EDUC=1 ; < HIGH
 

SCHOOL ;
 

ELSE IF EDUC1 IN (12,13,14,15,16,17)

THEN EDUC=2 ; HIGH SCHOOL OR

GED OR SOME COLLEGE ;

ELSE IF EDUC1 IN (18,19,20,21) THEN

EDUC=3 ; COLLEGE DEGREE ;

/*CONVERT PUBLICID TO NUMERIC

FOR USE IN SUDAAN /*

ID=PUBLICID*1 ;

RACEETH=HISCODI2; * CREATES A

MORE DESCRIPTIVE VARIABLE

NAME ;

FORMAT AGE_CAT AGECATF. RACEETH

RACEETHF. SEX SEXF. REGION

REGIONF. EDUC EDUCF.

FAIRPOORHEALTH FAIRPOORF. ;

Example

Marginal model

The marginal, or main effects,

model is fitted using AGE_CAT,

RACEETH, SEX, REGION, and EDUC.

By definition of a marginal model, in

this model no interaction terms are

specified. The factors REGION and

EDUC (education, in three categories)
Page 40 [ Series 1, No. 58

are often related to the propensity to

agree to linkage as well as the

underlying survey design, where strata

are formed based on geographic and

socioeconomic characteristics of

locations within the United States.

After fitting the model, using the output

statement, the adjusted weights produced

by the model are saved in a new

temporary SAS dataset called MATCH1.

Next, MATCH1 is merged into the

original files, NHIS05.MERGED, so the

adjusted weights can be evaluated and

used in an analysis.

/*MARGINAL MODEL: THIS MODEL

INCLUDES MAIN EFFECTS ONLY/*

PROC WTADJUST DATA=NHIS05.

MERGED DESIGN=WR

ADJUST=NONRESPONSE NOTSORTED ;

NEST STRATUM PSU ;

WEIGHT WTFA ;

CLASS AGE_CAT HISCODI2 SEX

REGION EDUC / INCLUDE=MISSING;

REFLEVEL AGE_CAT=2 RACEETH=2

SEX=1 REGION=1 EDUC=2 ;

MODEL LINKABLE=AGE_CAT

RACEETH SEX REGION EDUC ;

IDVAR LINKABLE AGE_CAT

RACEETH SEX REGION EDUC ID ;

PRINT BETA SEBETA P_BETA

MARGADJ / BETAFMT=F10.4

SEBETAFMT=F10.4 ;

OUTPUT /PREDICTED=ALL

FILENAME=MATCH1 FILETYPE=SAS

REPLACE ;

RUN ;

PROC SORT DATA=MATCH1 ; BY ID ;

RUN ;

PROC SORT DATA=MERGED ; BY ID ;

RUN ;

/*MERGE THE WEIGHT FILE TO THE

MERGED FILE. RENAME ADJFACTOR

AND WTFINAL, WHICH ARE SUDAAN

INTERNAL VARIABLES, TO

ADJFACT_MARGINAL AND

WTFIN_MARGINAL, SO THAT WE CAN

COMPARE THE ADJUSTMENTS./*

DATA NHIS05.MERGED_WEIGHTED ;

MERGE NHIS05.MERGED MATCH1

(KEEP=ID ADJFACTOR WTFINAL) ;

BY ID ;

ADJFACT_MARGINAL=ADJFACTOR ;

WTFIN_MARGINAL=WTFINAL ;

RUN;

Assessment of Adjusted	 

Weights

Examine the adjusted weights

before using them in analysis. Some

approaches are illustrated here, although

others exist. The basic SUDAAN model

results from the marginal model are

shown in the Table.

The default under SAS/SUDAAN is

to define the last category as the

baseline category and set the coefficient

on baseline category to zero. The other

coefficients are interpreted as deviations

from the baseline category. The baseline

can be changed from its default using

the REFLEVEL statement, as illustrated

above and subsequently (12). The

/INCLUDE=MISSING option in the

model code instructs SUDAAN to treat

missing values as a separate class; thus,

EDUC=MISSING gets its own

parameter estimate. Although this

approach is not generally recommended

for statistical analysis, applying other

methods (e.g., multiple imputation) or

identifying the best way to include

nonresponse in the models was beyond

the scope of this project.

WTADJUST provides a marginal

weight adjustment column, which

indicates the average adjustment to the

initial weights for records in each

category. Examine these values to

determine if any one category has undue

influence. Because about 50% of

respondents were ineligible for linkage

in 2005, these weight adjustments

generally are expected to be around two.

Large differences in the marginal weight

Table. SUDAAN output for weight-adjusted marginal model

Marginal

β SE weight

Independent variables and effects coefficient β adjustment

Intercept .......................... –0.1693 0.0365 2.1289

AGE_CAT (years)

Under 18 ......................... –0.6819 0.0381 1.9661

18–44 (ref) ........................ . . . . . . 2.1607

45–64 ........................... 0.0563 0.0219 2.1891

65 and over ........................ 0.1389 0.0350 2.2897

RACEETH

Hispanic .......................... 0.5940 0.0391 2.7480

Non-Hispanic white (ref) ................ . . . . . . 1.9927

Non-Hispanic black ................... 0.2767 0.0386 2.1864

Non-Hispanic other ................... 0.4917 0.0617 2.6872

Sex

Male (ref) ......................... . . . . . . 2.0730

Female........................... 0.1169 0.0152 2.1854

Region

Northeast (ref) ...................... . . . . . . 2.1764

Midwest .......................... –0.2345 0.0429 1.9180

South............................ –0.2183 0.0423 2.0258

West ............................ 0.2873 0.0479 2.6268

EDUC

Missing response .................... 1.0530 0.0595 2.5872

Less than high school diploma ............ 0.2888 0.0319 2.0303

High school or GED1

, or some college (ref) .... . . . . . . 2.0715

College degree ...................... 0.1539 0.0279 2.2230

. . . Category not applicable.
 

1General Educational Development high school equivalency diploma.
 

NOTES: β coefficient is the estimated effect on adjustment; SE β is the standard error of the estimated effect; marginal weight

adjustment is the effect of the category on the original annual weights; and (ref) is the reference group used when comparing each

cofactor.

SOURCES: CDC/NCHS, public-use National Health Interview Survey Person File merged with NCHS-Medicare linked Feasibility

Data, 2005.
adjustment across groups are to be

avoided, because they can increase

variance estimates.

After inspecting the results provided

by SUDAAN, additional examinations

can be performed. For example, the

adjustment cell sizes should be

examined; a cell size less than 30 is not

generally recommended (11). In

addition, correlations and scatter plots of

adjusted and unadjusted weights should

be examined, to qualitatively identify

outliers or other anomalies in the

adjustment process.

When assessing the results from the

WTADJUST procedure, records not

eligible for record linkage will have a

positive WTFA and a zero for

WTFIN_MARGINAL as well as

ADJFACT_MARGINAL. These zeroes

for the adjusted weights will distort

summary statistics, correlations, and

graphics, so the zeroes should be

removed before performing these steps.

The following code implements some of

these checks:

DATA NHIS05.MERGED_WEIGHTED ;

SET NHIS05.MERGED_WEIGHTED ;

IF WTFIN_MARGINAL=0 THEN

WTFIN_MARGINAL=. ;

IF ADJFACT_MARGINAL=0 THEN

ADJFACT_MARGINAL=. ;

RUN ;
 

TITLE ‘‘ANY ZERO WEIGHT HAS BEEN
 

CONVERTED TO MISSING’’ ;
 

PROC MEANS DATA=NHIS05.MERGED_
 

WEIGHTED N SUM MIN MAX VAR
 

SKEW KURT;
 

VAR WTFA WTFIN_MARGINAL

ADJFACT_MARGINAL;

RUN;

Based on the results of the PROC

MEANS statement above, the original

annual weight, WTFA, sums to

291,143,602 persons, and adjusted

weights do the same. WTFA has a

maximum value of 19,434, and the

adjusted weights have a higher

maximum (46,481 to 51,772), or about

twice as much, reflecting the 50%

linkage ineligibility. Variance terms for

adjusted weights are larger than the

original weight, also reflecting this fact.

The kurtosis measures the peakedness

and heavy tailedness of a distribution;

these results show that the adjusted

weights are more peaked and

heavier-tailed than the original weight.

All of the adjusted weights are relatively

close to the same value, but in the

analytic phase, some use of various

influence statistics would be advisable.

Additional code implements other

checks:

PROC CORR DATA=NHIS05.MERGED_

WEIGHTED NOSIMPLE ;

VAR ADJFACT_MARGINAL;

WHERE WTFIN_MARGINAL> > 0;

RUN ;
 

PROC CORR
 

DATA=NHIS05.MERGED_WEIGHTED ;
 

VAR ADJFACT_MARGINAL ;

WHERE WTFIN_MARGINAL> > 0;

RUN ;

Ideally, the Pearson correlation

coefficient between the original and

adjusted weights would be high; using

the SAS code above, in this case it takes

on the value 0.88. However, the Pearson

correlation measures linear association

and can be misleading if nonlinearities

are present, which is why examining

plots is desirable as well. A scatterplot

of the original weight against the

Figure. Plot of National Health Interview Survey annual sample weight compared with

ineligibility-adjusted weight

Series 1, No. 58 [ Page 41

adjusted weight should be examined,

as in:

PROC PLOT DATA=NHIS05.MERGED_

WEIGHTED ;

PLOT WTFA*WTFIN_MARGINAL

WHERE WTFIN_MARGINAL > 0;

RUN ;

As shown in the Figure, many

values clump in the range of zero to

about 15,000, with individual values

reaching about 50,000 (thus, these cases

may have greater influence in

subsequent analytic inference). For these

plots, records with high weights are

generally singletons, and many more

records with lower values are in the

lower left corner.

To check for cell sizes, construct

unweighted tables that reflect the

properties of the chosen reweighting

model. For the marginal model, only the

margins need to be checked. The

following code implements these

checks:

TITLE ‘‘FREQUENCY TABULATION TO

CHECK CELL SIZES CELL SIZES FOR

MODELS’’ ;

PROC FREQ DATA=NHIS05.MERGED_

WEIGHTED ;
Page 42 [ Series 1, No. 58

WHERE WTFIN_MARGINAL> > 0;

TABLES AGE_CAT RACEETH SEX

REGION EDUC /NOPERCENT LIST ;

RUN ;

Using the SAS code above, no

margin (or category) was close to the

‘‘rule of thumb’’ cutoff of 30 cases (not

shown).

Finally, the variable

FAIRPOORHEALTH is tabulated,

which takes on the value 1 if the

respondent reported ‘‘poor’’ or ‘‘fair’’

health; 0 if the respondent reported

‘‘good,’’ ‘‘very good,’’ or ‘‘excellent’’

health; and missing if no response was

provided. These results indicate effects

of linkage ineligibility. Using the

original NHIS 2005 sample and the

original sample weight, 9.30% of

respondents had fair or poor health. The

weighted percentages were elevated for

the linkage-eligible subset at 10.43%,

using the weights from the MARGINAL

model. For comparison, unweighted

percentages for the original NHIS 2005

sample were 9.79%, and unweighted

percentages for the linkage-eligible

subset were 10.49%, respectively.
Series 1, No. 58 [ Page 43

Appendix IV. First NCHS–SSA–CMS Linkage: NSCL–I

The first linkage of National Center

for Health Statistics (NCHS) survey

respondents to their Social Security

benefit history records and Medicare

enrollment and claims records was

initiated with NSCL–I in 2001 upon

approval of the NCHS Research Ethics

Review Board (ERB). NCHS ERB, also

known as an institutional review board

or IRB, is an administrative body of

scientists and nonscientists established

to protect the rights and welfare of

human research subjects.

The process of linking NCHS

survey data with Social Security data

began at the Social Security

Administration (SSA), where records for

individual NCHS survey respondents

were matched with SSA’s Numident file.

The Numident file is a numerically

ordered master file for each Social

Security number (SSN) ever issued and

contains records for approximately 400

million SSNs, including personal

identifying information.

To link NCHS survey respondents

with their Social Security benefit

histories, NCHS provided SSA with as

many of the following individual

identifiers that were available on the

survey record for all eligible survey

respondents:

+ SSN

+ Last name

+ First name

+ Middle initial

+ Date of birth (month, day, year)

+ Sex

+ Father’s surname (women only)

+ State of birth

+ Zip code

NCHS survey participants were

considered ineligible for matching to the

Numident file if they refused to provide

their SSN at the time of the interview.

Additional ineligibility criteria included

refused, missing, or incomplete

information on last name or date of birth.

The match process consisted of two

steps. First, SSA verified whether the

SSN received from NCHS was correct

using the Enumeration Verification

System (EVS). In cases where the SSN

of a survey participant was missing or

could not be verified, SSA used an

enhanced EVS matching algorithm to

try to determine the correct SSN. The

enhanced EVS matching algorithm was

developed by the SSA Office of the

Actuary to apply additional identifying

data elements collected during the

survey interview and contained in other

administrative records held by SSA. It

features a scoring system with a

threshold score used to determine which

potential matches are acceptable, with

the potential to increase the number of

successful matches. For NCHS records

determined to be a match with the

Numident file, SSA extracted data,

where available, from the benefit history

files. Because not all survey participants

matched to the Numident have Social

Security benefit history data, the records

with available benefit history data was

less than the number matched to the

Numident.

The next part of the process—

linking NCHS survey data with

Medicare enrollment and claims

data—was performed by the Centers for

Medicare & Medicaid Services (CMS).

NCHS survey respondents were matched

with Medicare’s Enrollment Database

(EDB). EDB is a master enrollment file

of all people ever entitled to Medicare.

EDB records are comprehensive and

updated daily.

To link NCHS survey respondents

with their Medicare data, NCHS

provided CMS as many of the following

individual identifiers as available on the

survey record for all eligible survey

respondents:

+ SSN

+ Health insurance claim (HIC) number

+ Last name

+ First name

+ Middle initial

+ Date of birth (month, day, year)

+ Sex

+ Father’s surname (women only)

+ State of birth

+ Zip code

NCHS survey participants were

considered ineligible for matching to

EDB if they refused to provide their

SSN or HIC number at the time of the

interview. Additional ineligibility criteria

included refused, missing, or incomplete

information on last name and date of

birth.

CMS identified potential matches

between NCHS survey participants and

EDB records based on on 1) HIC

number, 2) SSN, or 3) name and date of

birth. For these potential matches,

NCHS employed a deterministic

matching algorithm to verify which

matches were correct. All potential

matches were assigned a score based on

whether the identifying information

provided matched NCHS and CMS

records. For example, if NCHS provided

a participant’s SSN and it matched the

SSN in a potential EDB match record,

then a predetermined point value was

added. If an SSN was submitted and the

potential EDB match record for that

participant did not match the SSN

provided, no points were assigned for that

identifying data element. This process was

done for each of the identifying data

elements submitted. Using this process, a

total match score was established for all

potential EDB matches.

Next, the scored matches were

classified according to which identifying

data elements matched, reflecting the

fact that concordance between some

identifying data elements (e.g., SSN) is

more important than others. Within each

class category, matches above a

threshold score were considered

acceptable, while matches below a

threshold were considered nonmatches.

Matches falling between these

thresholds were manually reviewed to

determine the match status. For those

NCHS records determined to be

matched to the Medicare EDB, CMS

extracted data, where available, from

each of the Medicare claims files for

those records for 1991–2000. Because

not all survey participants matched to

the EDB had claims information for

1991–2000, the number of records with

available Medicare claims data was less

than the number matched to EDB.
Page 44 [ Series 1, No. 58

Appendix V. List of Resources for NCHS–SSA–CMS–USRDS Linked

Data Files

National Center for Health

Statistics

National Center for Health Statistics

(NCHS) website:

http://www.cdc.gov/nchs/index.htm

NCHS Data Linkage Activities:

http://www.cdc.gov/nchs/data_access/

data_linkage_activities.htm

NHANES–CMS Linked Data Tutorial:

http://www.cdc.gov/nchs/tutorials/

NHANES-CMS/index.htm

NCHS Research Data Center (RDC):

http://www.cdc.gov/rdc/

Social Security

Administration

Social Security Administration (SSA)

website:

http://www.ssa.gov/

Social Security Program Rules:

www.ssa.gov/regulations/index.htm

Online Social Security Handbook:

http://ssa.gov/OP_Home/handbook/

handbook.html

Centers for Medicare &

Medicaid Services

Centers for Medicare & Medicaid

Services (CMS) website:

http://www.cms.gov/

Research Data Assistance Center

(ResDAC) at the University of

Minnesota:

http://www.resdac.org/

Medicare

Medicare and You Handbook:

https://www.medicare.gov/medicaareand-you/medicare-and-you.html

Medicare Program—General

Information:

http://www.cms.gov/Medicare/MedicareGeneral-Information/MedicareGenInfo/

index.html

Medicare Prospective Payment

Systems—General Information:

http://www.cms.gov/

ProspMedicareFeeSvcPmtGen/

American Medical Association:

http://www.ama-assn.org/ama

Medicaid

Medicaid–Children’s Health Insurance
 

Program:
 

http://www.medicaid.gov/medicaid-chipprogram-information/by-population/
 

children/children.html
 

The Henry J. Kaiser Family Foundation:
 

State Health Facts
 

http://www.statehealthfacts.org/
 

Medicaid Benefits Data Collection
 

http://medicaidbenefits.kff.org/
 

National Pharmaceutical Council—
 

Medicaid Pharmaceutical Plan
 

Resources:
 

http://www.npcnow.org/Public/Issues/
 

i_rel_research/Medicaid_
 

Pharmaceutical_Plan_Resources.aspx
 

NCHS–Medicaid Analytic eXtract
 

(MAX) linkage reports:
 

MAX and NCHS Survey Linkage
 

Design Report (2010)
 

https://www.cms.gov/Research-StatisticsData-and-Systems/Computer-Data-andSystems/MedicaidDataSourcesGenInfo/
 

Downloads/MAX_
 

NCHSLinkageReport.pdf
 

MAX and NCHS Survey Linkage,
 

1999–2009 (2012)
 

https://www.cms.gov/Research-StatisticsData-and-Systems/Computer-Data-andSystems/MedicaidDataSourcesGenInfo/
 

Downloads/MAX_NCHS_
 

Linkage_Report1999_2009.pdf
 

United States Renal Data

System

United States Renal Data System

(USRDS) website:

http://www.usrds.org/
Vital and Health Statistics

Series Descriptions

ACTIVE SERIES

Series 1.	 Programs and Collection Procedures—This type of report

describes the data collection programs of the National Center

for Health Statistics. Series 1 includes 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—This type of

report concerns statistical methods and includes analytical

techniques, objective evaluations of reliability of collected

data, and contributions to statistical theory. Also included are

experimental tests of new survey methods, comparisons of

U.S. methodologies with those of other countries, and as of

2009, studies of cognition and survey measurement, and final

reports of major committees concerning vital and health

statistics measurement and methods.

Series 3.	 Analytical and Epidemiological Studies—This type of

report presents analytical or interpretive studies based on vital

and health statistics. As of 2009, Series 3 also includes

studies based on surveys that are not part of continuing data

systems of the National Center for Health Statistics and

international vital and health statistics reports.

Series 10.	 Data From the National Health Interview Survey—This

type of report contains 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. Series 10 is based on data collected in

this 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—

In this type of report, 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 13.	 Data From the National Health Care Survey—This type of

report contains 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 20.	 Data on Mortality—This type of report contains 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—This type of

report contains 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 23.	 Data From the National Survey of Family Growth—These

reports contain statistics on factors that affect birth rates,

including contraception and infertility; factors affecting the

formation and dissolution of families, including cohabitation,

marriage, divorce, and remarriage; and behavior related to

the risk of HIV and other sexually transmitted diseases.

These statistics are based on national surveys of women and

men of childbearing age.

DISCONTINUED SERIES

Series 4.	 Documents and Committee Reports—These are final

reports of major committees concerned with vital and health

statistics and documents. The last Series 4 report was

published in 2002. As of 2009, this type of report is included

in Series 2 or another appropriate series, depending on the

report topic.

Series 5.	 International Vital and Health Statistics Reports—This

type of report compares U.S. vital and health statistics with

those of other countries or presents other international data of

relevance to the health statistics system of the United States.

The last Series 5 report was published in 2003. As of 2009,

this type of report is included in Series 3 or another series,

depending on the report topic.

Series 6.	 Cognition and Survey Measurement—This type of report

uses methods of cognitive science to design, evaluate, and

test survey instruments. The last Series 6 report was

published in 1999. As of 2009, this type of report is included

in Series 2.

Series 12.	 Data From the Institutionalized Population Surveys—

The last Series 12 report was published in 1974. Reports

from these surveys are included in Series 13.

Series 14.	 Data on Health Resources: Manpower and Facilities—

The last Series 14 report was published in 1989. Reports on

health resources are included in Series 13.

Series 15.	 Data From Special Surveys—This type of report contains

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. The last

Series 15 report was published in 2002. As of 2009, reports

based on these surveys are included in Series 3.

Series 16.	 Compilations of Advance Data From Vital and Health

Statistics—The last Series 16 report was published in 1996.

All reports are available online, and so compilations of

Advance Data reports are no longer needed.

Series 22.	 Data From the National Mortality and Natality Surveys—

The last Series 22 report was published in 1973. Reports

from these sample surveys, based on vital records, are

published in Series 20 or 21.

Series 24.	 Compilations of Data on Natality, Mortality, Marriage, and

Divorce—The last Series 24 report was published in 1996.

All reports are available online, and so compilations of reports

are no longer needed.

For answers to questions about this report or for a list of reports published

in these series, contact:

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Centers for Disease Control and Prevention

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Tel: 1–800–CDC–INFO (1–800–232–4636)

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Online request form: http://www.cdc.gov/info

For e-mail updates on NCHS publication releases, subscribe

online at: http://www.cdc.gov/nchs/govdelivery.htm.
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