yyyyy zzzz {{{{{ |||| Report 7

Construction of Weights for the 1996

Medical Expenditure Panel Survey

Nursing Home Component

Methodology Methodology 

U.S. Department of Health and Human Services

Public Health Service

Agency for Health Care Policy and Research

Agency for Health Care Policy and Research
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Abstract

The Medical Expenditure Panel Survey (MEPS) is

the third in a series of nationally representative surveys

of medical care use and expenditures sponsored by the

Agency for Health Care Policy and Research (AHCPR).

MEPS comprises four component surveys. The Nursing

Home Component (NHC) produces national estimates of

insurance coverage and the use of services, expenditures,

and sources of payment for persons residing in or

admitted to nursing homes. The NHC also gathers

information on nursing home characteristics–such as

facility type, ownership, chain affiliation, certification,

facility size, and location–for a nationally representative

sample of nursing homes. Building on a previous report,

this report completes the description of the statistical

methodology used in the MEPS NHC. It completes the

reporting of response rates and describes the calculation

of weights for the remaining sample units necessary to

correct for nonresponse.

Suggested citation

Sommers J, Bethel J, Broene P. Construction of

weights for the 1996 Medical Expenditure Panel Survey

Nursing Home Component. Rockville (MD): Agency for

Health Care Policy and Research; 1999. MEPS

Methodology Report No. 7. AHCPR Pub. No. 99-0045.

http://www http://www.ahcpr.gov/
Construction of Weights for the 1996

Medical Expenditure Panel Survey

Nursing Home Component

U.S. Department of Health and Human Services

Public Health Service

Agency for Health Care Policy and Research

   yyyyy zzzz {{{{{ |||| Report 7

Methodolo Methodology 

AHCPR Pub. No. 99-0045

August 1999
The Medical Expenditure Panel

Survey (MEPS)

Background

The Medical Expenditure Panel Survey (MEPS) is

conducted to provide nationally representative estimates

of health care use, expenditures, sources of payment,

and insurance coverage for the U.S. civilian

noninstitutionalized population. MEPS also includes a

nationally representative survey of nursing homes and

their residents. MEPS is cosponsored by the Agency for

Health Care Policy and Research (AHCPR) and the

National Center for Health Statistics (NCHS).

MEPS comprises four component surveys: the

Household Component (HC), the Medical Provider

Component (MPC), the Insurance Component (IC), and

the Nursing Home Component (NHC). The HC is the

core survey, and it forms the basis for the MPC sample

and part of the IC sample. The separate NHC sample

supplements the other MEPS components. Together

these surveys yield comprehensive data that provide

national estimates of the level and distribution of health

care use and expenditures, support health services

research, and can be used to assess health care policy

implications.

MEPS is the third in a series of national probability

surveys conducted by AHCPR on the financing and use

of medical care in the United States. The National

Medical Care Expenditure Survey (NMCES) was

conducted in 1977, the National Medical Expenditure

Survey (NMES) in 1987. Beginning in 1996, MEPS

continues this series with design enhancements and

efficiencies that provide a more current data resource to

capture the changing dynamics of the health care

delivery and insurance system.

The design efficiencies incorporated into MEPS are

in accordance with the Department of Health and

Human Services (DHHS) Survey Integration Plan of

June 1995, which focused on consolidating DHHS

surveys, achieving cost efficiencies, reducing respondent

burden, and enhancing analytical capacities. To

accommodate these goals, new MEPS design features

include linkage with the National Health Interview

Survey (NHIS), from which the sample for the MEPS

HC is drawn, and enhanced longitudinal data collection

for core survey components. The MEPS HC augments

NHIS by selecting a sample of NHIS respondents,

collecting additional data on their health care

expenditures, and linking these data with additional

information collected from the respondents’ medical

providers, employers, and insurance providers.

Household Component

The MEPS HC, a nationally representative survey

of the U.S. civilian noninstitutionalized population,

collects medical expenditure data at both the person and

household levels. The HC collects detailed data on

demographic characteristics, health conditions, health

status, use of medical care services, charges and

payments, access to care, satisfaction with care, health

insurance coverage, income, and employment.

The HC uses an overlapping panel design in which

data are collected through a preliminary contact

followed by a series of five rounds of interviews over a 

21⁄

2-year period. Using computer-assisted personal

interviewing (CAPI) technology, data on medical

expenditures and use for 2 calendar years are collected

from each household. This series of data collection

rounds is launched each subsequent year on a new

sample of households to provide overlapping panels of

survey data and, when combined with other ongoing

panels, will provide continuous and current estimates of

health care expenditures.

The sampling frame for the MEPS HC is drawn

from respondents to NHIS, conducted by NCHS. NHIS

provides a nationally representative sample of the U.S.

civilian noninstitutionalized population, with

oversampling of Hispanics and blacks.

Medical Provider Component

The MEPS MPC supplements and validates

information on medical care events reported in the

MEPS HC by contacting medical providers and

pharmacies identified by household respondents. The

MPC sample includes all hospitals, hospital physicians,

home health agencies, and pharmacies reported in the

ii
iii

HC. Also included in the MPC are all office-based

physicians: 

• Providing care for HC respondents receiving

Medicaid.

• Associated with a 75-percent sample of households

receiving care through an HMO (health maintenance

organization) or managed care plan.

• Associated with a 25-percent sample of the

remaining households.

Data are collected on medical and financial

characteristics of medical and pharmacy events reported

by HC respondents, including:

• Diagnoses coded according to ICD-9 (9th Revision,

International Classification of Diseases) and DSMIV (Fourth Edition, Diagnostic and Statistical

Manual of Mental Disorders).

• Physician procedure codes classified by CPT-4

(Current Procedural Terminology, Version 4).

• Inpatient stay codes classified by DRG (diagnosisrelated group).

• Prescriptions coded by national drug code (NDC),

medication names, strength, and quantity dispensed.

• Charges, payments, and the reasons for any

difference between charges and payments.

The MPC is conducted through telephone

interviews and mailed survey materials.

Insurance Component

The MEPS IC collects data on health insurance

plans obtained through employers, unions, and other

sources of private health insurance. Data obtained in the

IC include the number and types of private insurance

plans offered, benefits associated with these plans,

premiums, contributions by employers and employees,

and employer characteristics.

Establishments participating in the MEPS IC are

selected through four sampling frames:

• A list of employers or other insurance providers

identified by MEPS HC respondents who report

having private health insurance at the Round 1

interview.

• A Bureau of the Census list frame of private-sector

business establishments.

• The Census of Governments from the Bureau of the

Census.

• An Internal Revenue Service list of the selfemployed.

To provide an integrated picture of health insurance,

data collected from the first sampling frame (employers

and other insurance providers) are linked back to data

provided by the MEPS HC respondents. Data from the

other three sampling frames are collected to provide

annual national and State estimates of the supply of

private health insurance available to American workers

and to evaluate policy issues pertaining to health

insurance.

The MEPS IC is an annual panel survey. Data are

collected from the selected organizations through a

prescreening telephone interview, a mailed

questionnaire, and a telephone followup for

nonrespondents.

Nursing Home Component

The 1996 MEPS NHC was a survey of nursing

homes and persons residing in or admitted to nursing

homes at any time during calendar year 1996. The NHC

gathered information on the demographic

characteristics, residence history, health and functional

status, use of services, use of prescription medications,

and health care expenditures of nursing home residents.

Nursing home administrators and designated staff also

provided information on facility size, ownership,

certification status, services provided, revenues and

expenses, and other facility characteristics. Data on the

income, assets, family relationships, and caregiving

services for sampled nursing home residents were

obtained from next-of-kin or other knowledgeable

persons in the community.

The 1996 MEPS NHC sample was selected using a

two-stage stratified probability design. In the first stage,

facilities were selected; in the second stage, facility

residents were sampled, selecting both persons in

residence on January 1, 1996, and those admitted during

the period January 1 through December 31.

The sampling frame for facilities was derived from

the National Health Provider Inventory, which is

updated periodically by NCHS. The MEPS NHC data

were collected in person in three rounds of data

collection over a 11⁄

2-year period using the CAPI system.

Community data were collected by telephone using

computer-assisted telephone interviewing (CATI)

technology. At the end of three rounds of data collection,
iv













































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|||| the sample consisted of 815 responding facilities, 3,209

residents in the facility on January 1, and 2,690 eligible

residents admitted during 1996.

Survey Management

MEPS data are collected under the authority of the

Public Health Service Act. They are edited and

published in accordance with the confidentiality

provisions of this Act and the Privacy Act. NCHS

provides consultation and technical assistance.

As soon as data collection and editing are

completed, the MEPS survey data are released to the

public in staged releases of summary reports and

microdata files. Summary reports are released as printed

documents and electronic files. Microdata files are

released on CD-ROM and/or as electronic files.

Printed documents and CD-ROMs are available

through the AHCPR Publications Clearinghouse. Write

or call:

AHCPR Publications Clearinghouse

Attn: (publication number)

P.O. Box 8547

Silver Spring, MD 20907

800-358-9295

410-381-3150 (callers outside the United States

only)

888-586-6340 (toll-free TDD service; hearing

impaired only)

Be sure to specify the AHCPR number of the

document or CD-ROM you are requesting. Selected

electronic files are available through the Internet on the

AHCPR Web site: 

http://www.ahcpr.gov/

On the AHCPR Web site, under Data, click the MEPS

icon.

Additional information on MEPS is available from

the MEPS project manager or the MEPS public use data

manager at the Center for Cost and Financing Studies,

Agency for Health Care Policy and Research, 2101 East

Jefferson Street, Suite 500, Rockville, MD 20852 

(301-594-3075).
v

Table of Contents

Purpose . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Background. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Response Rates. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Round 1 Weights. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Final Weights. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Estimates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

Appendix. Definition of Selected Variables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1

Purpose

This is the second report from the Agency for Health

Care Policy and Research (AHCPR) describing the

statistical methodology used in the 1996 Medical

Expenditure Panel Survey Nursing Home Component

(MEPS NHC). The frame, sample design, allocation and

selection processes, and some partial response rates have

been presented in a previous publication (Bethel,

Broene, and Sommers, 1998). This publication

completes the reporting of response rates and describes

the calculation of weights for the remaining sample units

necessary to correct for nonresponse.

Background

The 1996 MEPS NHC was a survey of nursing

homes designed to collect information on nursing homes

and their residents in the United States during calendar

year 1996. The survey consisted of a sample of nursing

homes and a sample of residents within the nursing

homes. For the purpose of the survey, a nursing home

was defined as a facility or distinguishable portion of a

larger facility with at least three beds set up for nursing

care. To be eligible for the survey, the facility or portion

of a facility must have been:

• Certified by Medicare or Medicaid as a nursing

facility or

• Licensed by a State as a nursing home with a

registered nurse or licensed practical nurse onsite 24

hours a day, 7 days a week.

The sample of facilities was selected from a list

frame, the 1994 Health Provider Inventory (HPI). To

select the sample, the facilities on the list were stratified

into groups defined by facility type, the reimbursement

method used in the State, and size. A sample of

facilities was selected with probability proportional to

the number of facility beds. The sample was then

classified into four strata according to the cost of data

collection, and each of the four cost strata was

subsampled to create an optimal sample given the overall

collection budget.

Data collection was accomplished in three rounds

during 1996 and early 1997. In each round a sample of

residents was selected with equal probability of selection

within each facility. During the first round, a sample of

persons who were residents in a facility at the beginning

of the year was selected. In the remaining two rounds,

samples of persons who were admitted during the first

and second halves of the year were selected. The

average expected sample size for both current residents

and new admissions was 4 per facility. 

Because persons could reside in more than one

facility during a calendar year, special care was taken in

the process to select only persons whose stay in a

sampled facility was their first stay in any nursing home

during the calendar year. This was done to allow

accurate calculation of each person’s probability of

selection. The calculation was especially problematic for

persons who were admitted after the beginning of the

year, because they could have had a previous admission

to the same or another facility. For this reason, efforts

were made to determine whether persons selected as new

admissions had had a stay in another facility during the

year. Any new admissions selected who were found to

have had a previous stay in an eligible facility during the

year were dropped from the sample as ineligible. As a

result, the approximate number of eligible new

admissions in the sample was 3 per facility.

The primary focus of the survey was collection of

data on expenditures within the nursing homes during

calendar year 1996. Information on other characteristics

of the residents also was collected, including

demographics, health status, use of other health care, use

of prescription medications, and a complete history of

the place of residence for each sampled person. Facility

characteristics such as size, ownership, charge rates,

certification status, and existence of special care units

Construction of Weights for the 1996 Medical Expenditure Panel

Survey Nursing Home Component

by John Paul Sommers, Ph.D.,Agency for Health Care Policy and Research; James Bethel, Ph.D., and Pamela

Broene, M.S.,Westat, Inc.
also were collected (Bethel, Broene, and Sommers,

1998; Potter, 1998). 

Response Rates

Facilities

The original sample size fielded for the 1996 MEPS

NHC was 1,150 facilities. The sample size was based

on the assumption that the response rate would be 75

percent and that approximately 5 percent of the sample

would be out of scope, providing a final desired sample

size of approximately 800 facilities (Bethel, 1995).

There were 951 responding facilities with information

on 3,791 persons after the end of the first round of data

collection, so both the eligibility rate of 98 percent and

the response rate of 85 percent were higher than

expected. Thus, if a subsample of the original 1,150

facilities was kept for data collection for the last two

rounds, money could be saved and the goal of 800

cooperating facilities still could be met.

In order to allow for this possibility, the 1,139

facilities in the sample that had not been selected with

certainty (Bethel, Broene, and Sommers, 1998) had

been divided into 18 random groups. After the first

round of data collection, two of the random groups, 127

facilities, were randomly removed from further data

collection. Of these, 108 were respondents, 18 were

nonrespondents, and 1 was out of scope. This left 843

responding facilities in the remaining sample, 154

nonrespondents, and 26 out of scope—a total of 1,023

facilities in the smaller, final sample. 

After the second round of data collection, 833

facilities had responded. Finally, 815 facilities

responded for all three rounds of data collection.

Response was defined as allowing sampling of new

admissions in each round, as well as providing data for

persons sampled within the facility. Therefore, of the

997 inscope facilities in the final sample, 815, or 81.7

percent, were respondents. 

Persons

Within each cooperating facility, almost all the

persons in the sample were respondents. A sampled

person was a respondent if he or she was sampled from

a facility that responded for all three rounds of data

collection and reported a set of key variables along with

a large enough percentage of data from each of six main

data groups: health status, expenditures, background,

income and assets, health insurance, and prescription

medications. 

Of the 3,260 current residents selected in the 815

responding facilities, 22 were ineligible, 25 had

unknown eligibility, and 4 were nonrespondents.

Current residents were ineligible if it was found during

data collection that they had not been residents of the

sampled facility as of January 1, 1996. 

Of the 1,740 new admissions selected in the second

round of data collection, 260 were ineligible, 75 had

unknown eligibility, and 24 were nonrespondents. Of

the 1,726 admissions selected in the third round of data

collection, 315 were ineligible, 88 had unknown

eligibility, and 14 were nonrespondents. Admissions

were declared ineligible if it was found during data

collection that they had had a previous stay in a nursing

home during the year before the stay for which they

were selected. This process, allowing selection for only

the first stay within a facility for the year, was used in

order to simplify the calculation of the probability of

selection for an admission. Losses were anticipated in

the sampling process.

Persons with unknown eligibilities were classified

as nonrespondents. Thus, of 3,238 current residents

considered potentially eligible, 3,209, or 99.1 percent,

were respondents conditional on their being sampled

within the responding facilities. Of the sample of 2,891

potentially eligible admissions within the responding

facilities, 2,690, or 93.0 percent, responded.

Multiplying these values by the facility response rate of

81.7 percent resulted in unconditional response rates for

the two person-level samples. The unconditional

response rate for current respondents was 81.0 percent.

The unconditional response rate for admissions was

76.1 percent.

By assuming all persons with unknown eligibility to

be nonrespondents and none to be ineligible for

response rate calculations, we used the lowest possible

value in the numerator and the highest possible value in

the denominator for our calculation of person-level

response rates. Because it is likely that some of the

2
3

persons with unknown eligibility would be ineligible

(and thus would be removed from the denominator), the

rates calculated are conservative lower bounds for the

unconditional response rates.

Round 1 Weights

Descriptive facility information was collected in the

first round of data collection (Round 1), as was

demographic and health status information for the

sample of current residents, who were persons in a

nursing home as of the beginning of calendar year 1996.

This information is ideal for creating a snapshot view of

the characteristics of nursing homes and their residents.

Because this is valuable information, the data collected

in Round 1 were used to make a preliminary set of

estimates published by AHCPR and were also released

to the public for analyses (Rhoades, Potter, and Krauss,

1998) Those estimates were preliminary, and they differ

slightly from the estimates based on data for respondents

to all three rounds of data collection given later in this

report.

Because the sample units were selected with

unequal probabilities and because of nonresponse in the

data collection, weights must be calculated for each unit

in the original sample, and the weights for

respondents—both facilities and persons within the

facilities—must be adjusted for nonresponse (Kish,

1965).

To motivate this adjustment, it is assumed that pi is

the probability of selection of the ith unit in the sample,

and wti = 1/pi

, the initial weight. Then 

where S and U are the set of sampled units and set of

units in the universe, respectively.

A method of making an unbiased estimate of a total

from the sample is to use the weighted sum from the

sample as the estimate. If there is nonresponse—if, for

example, some of the x values are missing—one would

try to adjust weights for the values of respondents in

cells for which the responding x’s have the same or very

similar expected values to those of the nonresponding

x’s. Thus, the final objective is to have the sum of

weights for respondents over cells with various

characteristics equal to the totals from the original

sample. 

Correction of weights also can be taken further

using a technique called poststratification. This method

is used when outside information is available about the

population. The sum of the weights over a particular

characteristic can be thought of as an estimate of the

population total for the particular characteristic. Ideally,

the total weight for each important subset of the

population should equal the population for that subset.

Controlling weights so that they equal population totals

is called poststratification (Madow, Olkin, and Rubin,

1983; Skinner, Holt, and Smith, 1989).

Given this motivation for weights and adjusting

these weights for nonresponse, the description of the

methods used to calculate weights for the 951 facilities

and 3,791 persons responding in the first round of data

collection follows.

Round 1 Facility-Level Weights

In the first step, the probability of selection for each

of the 1,150 facilities in the original sample was

calculated (Bethel, Broene, and Sommers, 1998). After

the 1,150 initial weights for the original sample were

produced, they were adjusted for nonresponse. This was

done using a set of 10 cells defined by 4 variables: (1)

type of ownership, (2) certification, (3) Beale code, and

(4) whether the facility was related to a hospital. These

variables, which are correlated with costs as well as

characteristics of nursing homes, are defined in the

appendix. Because of the number of cells generated by

combination of the four variables, cell collapsing was

required. Collapsing was based on the sample size in

the cell and whether the facilities in the collapsed cells

differed in characteristics and/or response rates.

Definitions of the collapsed cells used are provided in

the appendix.

To adjust for nonresponse within a cell, the standard

process was used. The initial weights of all responding

units in each cell were increased by multiplying the

weight of each respondent in the cell by the ratio of the

total of the initial weights for all eligible units in the cell

over the sum of the initial weights of all responding

iεS

Σ

iεU

E[ wti

xi

]= Σ pi

wtixi [ ]xi xi iεU = Σ

iεU

pi

p = Σ

i =
4

units in the cell. Thus, in the case of the Round 1

sample for the MEPS NHC, if the value of ith unit’s

initial weight were wti

, the adjusted weight for

responding units in the response cell C, where CR is the

set of respondents in C, would be

The adjusted weights for nonrespondents were set to

zero. Weights of ineligible units were left unchanged.

At this point, the list frame of the population was

used to provide information. Because the frame is the

best description of the population and because this

information describes the potential sampling units on the

frame, the weights were poststratified so that the sum of

the weights for the responding sample and ineligible

sample for cells created by four important variables

equaled the cell population totals. Ineligible units were

included in this process because they represented the

number of ineligible units on the frame in the categories

used. Therefore, they must be accounted for in any

poststratification to the frame, because exact frame totals

for eligible units are not available.

The four variables chosen were (1) number of beds

in the facility, (2) type of ownership, (3) certification

status of the facility, and (4) Beale code (used to indicate

the urban or rural location of the facility). These

variables, which are correlated with costs as well as

characteristics of nursing homes, are defined in the

appendix.

Because the number of cells defined by the cross of

all levels of these variables was very large, the sum of

the weights for each cell created by these crossclassifications was not made equal to the population

total. Instead, the weights for each cell defined by any

one of the variables alone were made to add to the

population total for the particular cell. Thus, for each

marginal cell defined by any one of several variables, the

totals of the weights were equal to the marginal

population totals.

The method can be illustrated with an example.

Suppose one were concerned only with two variables:

(1) type of ownership, with 3 levels (for profit, not for

profit, and governments) and (2) region, with 4 levels

(Northeast, South, Midwest, and West). Rather than

making the sum of weights in the 12 cells defined by a

cross of the two variables (for instance, for profit and

South) equal the sum of the number of facilities on the

frame, one simply causes the sum of the weights for

each of the 7 cells defined by each individual variable

(for instance, for profit or South) to equal the number of

facilities on the frame. To accomplish this, one uses an

iterative technique called raking, which is explained in

the following paragraphs (Madow, Olkin, and Rubin,

1983). 

Assume that wtijk is the nonresponse-adjusted weight

for the ith sample unit in the jkth cell. Also, Nj. is the

population total defined by the jth cell of the first

variable, and N.k is the population total defined by the

kth cell of the second variable. For example, the first

variable could break the population by size of nursing

home and the second by region of the country. One

would adjust the weights to create a set of weights wt9

ijk

such that: 

To do this, one first creates values aj such that 

Then,

One continues this process through all values of j.

This makes sample weight totals sum to the proper

values for the first defining variable, but not the second.

One then operates on the values of wt9

ijk to adjust them

so that the sum of the weights for the cells defined by

the second variable equals the number of facilities on the

frame. Of course, now this third set of weights may not

jεC

Σ wtj

jεCR

Σ wtj

wti

adjwti =

i,k

Σ

i,j

wt9

ijk Nj. Σ

ik

Σ

Nj.

=

wt9

ijk wt = aj x ijk

aj =

wt9

ijk

wtijk

and = N.k
5

sum properly for cells defined by the first variable. The

cycle is repeated, readjusting each new set of weights by

continuing to iterate through the process. Usually within

several cycles the weights will converge, so that for each

cell defined by either of the two variables, the sum of the

latest set of weights equals a value very close to the

required marginal total. This completes the raking

process.

The raking process can be applied to make weights

sum to marginal cell totals for as many variables as one

likes. In this example, two variables were used to create

marginal totals, and the weights were forced to add to the

population total for the different sizes of nursing homes

and for each region, but not necessarily by size and

region. In the adjustment of the MEPS NHC weights to

the population sizes determined by the survey frame, the

four variables above defined the marginal cells and their

totals for the raking process.

As is most often the case, the raking process

converged. All the marginal totals produced for eligible

and ineligible units were very close to the control totals

from the frame used in the process. The maximum

relative difference was .00012 of the marginal total.

Most differences were on the order of .00001 of the

marginal total.

When this adjustment is made, the sum of the

facility weights for responding units equals the sum of

the number of units on the original frame less the sum of

the raked weights of ineligible units. The relationship to

the frame is maintained in this method by using the sum

of the raked weights of the ineligible units in the sample

as an estimate of the ineligible units on the frame. The

difference between the total number of units on the

frame in a cell and this value would be an estimate of

eligible facilities in a cell. After raking, the ineligible

units are no longer of use because one is concerned only

with eligible nursing homes. Thus, their final raked

weight is set to zero.

Round 1 Person-Level Weights

Weights also were calculated for current residents

selected in the 951 facilities that responded in the first

round of data collection. The following basic weighting

techniques were used:

• A base weight is equal to 1 divided by the probability

of selection of a unit; the probability of selection

would be 1 divided by the weight. 

• The sample selection for the MEPS NHC was done

in two stages: first, the facilities were selected, and

then persons were selected within the selected

facilities.

• The weight of a person within a facility is equal to 1

divided by the probability of selection within the

facility. For the MEPS NHC, the selection

probability is simply the number of current residents

on the list for the facility divided by the number of

persons selected in the facility. (Note that these

numbers include the totals on the list and the totals

selected even if some persons in each group were

later found to be ineligible, because the ineligibles

were used in the selection process.)

Using these concepts, the probability of selection of

a person would be the probability of selecting a person

within a selected facility multiplied by the probability of

selecting the facility. In the case of the first set of

responding facilities in the MEPS NHC, an adjusted

facility weight had been calculated. This was used as the

inverse of the probability of selection of the facility,

assuming that subsampling was done to select the

respondent nursing home from the original sample.

Therefore, the basic weight for a person in the sample

before nonresponse at the person level was calculated as

the adjusted facility weight multiplied by the number of

persons on the selection list within the facility and

divided by the number of persons selected from the list

within the facility.

After the original probabilities of selection were

calculated, adjustments were made for nonresponding

persons. A simple two-step adjustment process was

chosen because there were so few nonresponding

persons. The steps were as follows:

• For any facility with a nonresponding person, if there

were 3 sampled persons remaining within the facility

(the fixed sample size was 4 unless there were fewer

than 4 residents in the facility), the weights for the

remaining persons were adjusted by multiplying by

4/3.

• After this adjustment, the persons were broken into

two groups: (1) those from hospital-based facilities in
6

States with facility-specific, non-case-mix Medicaid

reimbursement methods (those States that do not

consider the mix of patients in determining the

Medicaid reimbursement that a facility receives) and

(2) those from all other facilities. These cells were

chosen because the former was the only cell defined

by any variables related to nursing home costs or

characteristics with any nonresponse after the first

correction within facilities. The weights for

respondents in the first group were adjusted upward

so that their sum equaled the weighted total for the

entire group. The second group had no

nonrespondents, and no adjustment was made.

Final Weights

Final Facility-Level Weights

After the third round of data collection, facility-level

and person-level weights were again calculated. Because

the final sample was based on the sample of 1,023

nursing homes in the subsample selected in the first

round of data collection, the probability of selection of

each of the 1,023 nursing homes was used to produce the

basic weights for these facilities. Thus, the process

ignored the corrections and weighting done for the first

round alone. The process was started again from the

beginning sample.

The method used followed the same pattern as for

the set of weights produced earlier for data from the first

round of data collection. Starting with probabilities of

selection, (1) basic weights were produced, (2) weights

of respondents were adjusted within cells to account for

nonrespondents, and (3) respondent weights were raked

to frame totals. The first two steps were performed as

before. Basic weights were calculated as the inverse of

the probability of selection, and nonresponse

adjustments were made using the same set of 10 cells

used to produce weights for the first round.

The variables used to provide controls for raking

differed somewhat from the calculation of the weights

used after the first round of data collection. This change

was made because new information existed about

variables that were correlated with nonresponse for both

facilities and persons. To properly adjust the weights,

members of this set of variables needed to be considered

if they also were likely to correlate with a respondent’s

data.

To accomplish this adjustment, raking to frame

values was performed using eight sets of marginal totals

defined by the following variables: ownership type,

certification status combined with whether a facility was

hospital based, Beale code, size class, Census region

combined with type of State reimbursement system, cost

stratum, whether the survey was endorsed by the State

nursing home association, and number of nursing home

beds per population age 75 and over in the county. All

these variables were available for facilities on the frame

except cost stratum, for which estimates from the

original sample, before subsampling within each cost

stratum, were used as control totals.

As in the previous raking process, this second raking

used the set of eligible facility respondents and the

ineligible facilities. The nonresponse-adjusted weights

were used for responding facilities, and initial weights

were used for ineligible units. Again, this was done

because the two sets together should produce totals of

units equal to the entire frame. These were raked over

the eight sets of marginal values. Like the first raking

process, this process converged to produce a set of raked

facility weights that met the marginal total constraints.

The weights for the ineligible units were set to zero, and

the weights for responding facilities again represented

the eligible units from the frame.

Final Person-Level Weights

In order to calculate a weight for each person in a

facility, a basic weight for each person was calculated in

the same fashion used to calculate basic weights for

current residents in weighting done for the Round 1 data.

A conditional within-facility weight for each selected

person was produced, equal to 1 divided by the

probability of selecting the person from a facility, given

that the facility was selected. This probability was equal

to the number of persons selected within the facility

divided by the number of persons on the sampling list

for the particular round. In the first round, residents

were selected as of the beginning of the year. In the later

two rounds, persons were selected from a list of
7

admissions. The conditional within-facility weight was

then multiplied by the facility weight to obtain the basic

person-level weight for each person selected. 

At this point the data were ready to be adjusted for

person-level nonresponse. Because of obvious

differences in length of stay, expenditures, and

characteristics, persons selected in each round were

adjusted in separate although identical steps. The first

step was to adjust for nonresponse due to unknown

eligibility in each of the three groups: current residents,

second-round admissions, and third-round admissions.

The sum of the weights for persons with unknown

eligibility in each of the groups of sampled persons was

distributed proportionally across the remaining sample

in the group for both eligible and ineligible members.

As an example, suppose that, in a particular round

of sampled persons, 60 percent of the total weight

belonged to eligible persons, 30 percent to ineligible

persons, and 10 percent to persons with unknown

eligibility. In this case, for those persons with known

eligibility, two-thirds (60/(60 + 30)) were eligible, and

thus two-thirds of the unknown 10 percent of the

weights were assigned to the eligible persons. This gave

them 66.7 percent of the total weight, and the other onethird of the 10 percent unknown weight was assigned to

those persons known to be ineligible. The weights for

persons with unknown eligibility were set to zero. The

increase for all persons with known eligibility status was

the same. The basic weight for each person with known

eligibility remained essentially the same percentage of

those with known eligibility as before the adjustment.

To make this adjustment, the weight for each person

with known eligibility is multiplied by 1 divided by the

percentage of the weight represented by persons with

known eligibility. In the example, this factor would be

1/.9.

The second step for each of the three groups was to

adjust the weights for respondents who were eligible so

that they would add to the total weights for all eligible

persons in the group, both respondents and

nonrespondents. The process used for current residents,

those selected in the first round, was the same as that

used for the set of weights previously discussed for

first-round respondents. Adjustments were made for

any person from a facility with only one nonrespondent

by adjusting the weights for the remaining persons

selected in the facility. After this adjustment, the

persons were divided into the two groups described

previously (those from hospital-based facilities in States

with facility-specific, non-case-mix Medicaid

reimbursement methods and those from all other

facilities), and the weights for respondents in each

group were increased to adjust for the nonrespondents.

Because the nonresponse was so small, adjustment

factors were not very different from 1.

For the two sets of persons selected as new

admissions during Rounds 2 and 3, the adjustment after

that for unknown eligibility was different. For these two

groups, nonresponse, although represented by a small

fraction of the eligible persons, was correlated with the

length of a person’s stay in the nursing home, which was

obtained during sampling. The eligible persons in each

of the two groups were broken into three cells based on

length of stay:

0 ≤ length of stay ≤ 20 days

21 ≤ length of stay ≤ 60 days

61 days ≤ length of stay

Within each group, the respondents’ weights were

adjusted upward to account for the weight of the

nonrespondents.

Trimmed Weights for New Admissions

After completion of the weighting process described

above, the weights were reviewed. During this review,

the weights for new admissions were determined to be

highly skewed because a number of smaller hospitalbased nursing homes had extremely large numbers of

admissions relative to the numbers of beds within the

facilities. Because the sample size within each facility

was limited to control the data collection burden within

the facility (Bethel, Broene, and Sommers, 1998), the

conditional probability of selection for an admission

within each facility was quite small; as a result, the

inverse of this value—the within-facility weight—was

very large, and the overall person weight was very large.

However, less than 1 percent of the persons in the

sample of eligible admissions accounted for about 18

percent of the total population of admissions

represented in the MEPS NHC, and the variances for

the estimates of total admissions were therefore very

high. The relative standard error for the estimate of the

national total of admissions was more than 8 percent.
8

To lower these variances, the weights for the new

admissions in smaller hospital-based nursing homes

were trimmed. Trimming is a process that lowers the

weights for the persons with excess weights by

distributing some of those weights to the remaining

sample. 

A simple example illustrates the process. Suppose

that one has a sample that can be broken into two sets.

The sample in the first set contains 80 percent of the

total of all weights and the second contains 20 percent.

However, suppose that the second set has only a small

number of sample units. Suppose further that one

decides to restrict the total weight of the second group to

10 percent of the total. To do this, one proportionally

redistributes the 10 percent of weights from the second

set to the first set of sample units by multiplying the

weights for each unit in the first set by the new total they

are to represent, divided by the original total weight for

the first set. In the example, the factor would be .9/.8.

Upon review of a selected set of estimates made

with (1) the original untrimmed weights, (2) a test set of

weights created by allocating the trimmed values to all

facilities, and (3) the weights created by allocating the

trimmed weights only to hospital-based facilities, the

estimates made with the last set of weights and those

with the first were found to be similar. However, the

second set of weights changed some estimates

significantly. So, because of the decreased errors, the

third set was chosen as the final set of weights for the

persons in the sample who were selected as new

admissions. 

The method was implemented in two steps. First,

the 99th percentile of the weights was determined. This

value was approximately 6,909. The weight for any unit

that had a larger weight (all were hospital-based units)

was reduced to this value. The excess was allocated

proportionately to the remaining hospital-based units as

described in the example. Upon review of this process, it

was discovered that the weights for some units that

originally were smaller than 6,909 were now larger than

6,909 because of the portion of the excess weight that

was allocated to them during the trimming process. 

To handle this problem, the set of weights obtained

from the first trimming process was trimmed again using

the same process. On the second iteration, no weights

higher than 6,908 remained. The variances were lowered

with these weights. The relative standard error for the

national total of admissions was reduced to

approximately 6 percent. The revised estimates did not

show any significant bias, and the objective of the

trimming process was met.

Estimates

With the weighting complete, it was possible to

produce final estimates for national totals of nursing

homes, their current residents on January 1, 1996, and

their new admissions during 1996. The estimates are as

follows:

• Nursing homes: 16,760.

• Current residents: 1,560,003.

• Persons with admissions who were not residents of a

nursing home as of January 1, 1996: 1,536,525.

The relative standard errors of these estimates are 2.5

percent, 1.5 percent, and 6.4 percent, respectively.

References

Bethel JB. Response and eligibility rate assumptions

for NNHES. Personal correspondence with John

Sommers; 1995. 

Bethel J, Broene P, Sommers JP. Sample design of

the 1996 Medical Expenditure Panel Survey Nursing

Home Component. Rockville (MD): Agency for Health

Care Policy and Research; 1998. MEPS Methodology

Report No. 4. AHCPR Pub. No. 98-0042.

Kish L. Survey sampling. New York: John Wiley

and Sons; 1965.

Madow WG, Olkin I, Rubin DR. Incomplete data in

sample surveys, volume 2: theory and bibliographies.

New York: Academic Press; 1983. 

Potter DEB. Design and methods of the 1996

Medical Expenditure Panel Survey Nursing Home

Component. Rockville (MD): Agency for Health Care

Policy and Research; 1998. MEPS Methodology Report

No. 3. AHCPR Pub. No. 98-0041.

Rhoades J, Potter DEB, Krauss N. Nursing

homes––structure and selected characteristics, 1996.

Rockville (MD): Agency for Health Care Policy and

Research; 1998. MEPS Research Findings No. 4.

AHCPR Pub. No. 98-0006.

Skinner CJ, Holt D, Smith TMF. Analysis of

complex surveys. New York: John Wiley and Sons;

1989.
9

Bed category =

1 if 2 < beds < 88

2 if 87 < beds < 120

3 if 119 < beds < 170

4 if 169 < beds

Type of ownership =

1 if for profit

2 if nonprofit

3 if government

Certification/hospital status =

1 if hospital based

2 if certified but not hospital based

3 if not certified or hospital based

Beale code =

1 if large metro core

2 if large metro fringe

3 if medium metro

4 if lesser metro

5 if adjacent to metropolitan statistical area (MSA)

6 if not adjacent to MSA

Survey endorsed by State association =

1 if yes

2 if no

Region/reimbursement system = 

1 if Northeast and prospective system

2 if Northeast and case-mix system

3 if North Central and prospective system

4 if North Central and case-mix system

5 if South and prospective system

6 if South and case-mix system

7 if West and prospective system

8 if West and case-mix system

Cost stratum =

1 if full workload in a single geographic area

2 if partial workload in single area requiring 

considerable travel

3 if single facility requiring considerable travel but 

within range of other facilities

4 if single facility requiring air travel

Ratio of nursing home beds to population age 75 and

over in county =

1 if first quarter of distribution

2 if second quarter of distribution

3 if third quarter of distribution 

4 if fourth quarter of distribution

Cell definitions used for nonresponse adjustments

for facility weights =

1 if hospital based

2 if certified, not hospital based, for profit in a large 

metro core

3 if certified, not hospital based, for profit on fringe of 

a large metro area

4 if certified, not hospital based, for profit in a 

medium metro area

5 if certified, not hospital based, for profit in a small 

metro area

6 if certified, not hospital based, for profit adjacent to 

a metro area

7 if certified, not hospital based, for profit not adjacent 

to a metro area

8 if certified, not hospital based, not for profit

9 if not certified, not hospital based, for profit

10 if not certified, not hospital based, not for profit

Appendix. Definition of Selected Variables
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