yyyyy yyyyy Report 12

Outpatient Prescription Drugs:

Data Collection and Editing in the 

1996 Medical Expenditure Panel 

Survey (HC-010A)

Methodology Methodology 

U.S. Department of Health and Human Services

Public Health Service

Agency for Healthcare Research and Quality

Agency for Healthcare Research and Quality
  yyyy yyyy

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 Healthcare Research and Quality (AHRQ).

For the first time in a national expenditure survey, the

1996 MEPS included a detailed collection of information

on prescription medicines obtained from pharmacy

providers frequented by household sampled persons. The

information was collected by means of a linked survey of

pharmacy providers. This report describes the procedures

adopted to collect and edit these prescription drug data

for public release. It includes efforts made to retrieve

complete and/or partially missing pharmacy data, the

editing techniques used to fill in remaining missing data

in the pharmacy database, and the matching/imputation

procedure that linked every prescription drug mentioned

by the respondent in the MEPS Household Component

to a specific prescription drug from the Pharmacy

Component (part of the Medical Provider Component).

Suggested citation

Moeller JF, Stagnitti MN, Horan E, et al. Outpatient prescription

drugs: data collection and editing in the 1996 Medical Expenditure

Panel Survey (HC-010A). Rockville (MD):Agency for Healthcare

Research and Quality; 2001. MEPS Methodology Report No. 12.

AHRQ Pub. No. 01-0002.

Health Care Information and Electronic Ordering

Through the AHRQ Web Site

The Agency for Healthcare Research and Quality

Web site—http://www.ahrq.gov/—makes practical,

science-based health care information available in

one convenient place.

Buttons correspond to major categories of Web

site information, including funding opportunities,

research findings, quality assessments, clinical

information, consumer health, and data.

The Web site features an Electronic Catalog to the

more than 450 information products generated by

AHRQ, with information on how to obtain these

resources. Many information products have an

electronic ordering form and are mailed free of

charge from the AHRQ Clearinghouse within 5

working days.

http://www.ahrq.gov/ http://www.ahrq.gov/
  yyyyy yyyyy Report 12

Outpatient Prescription Drugs:

Data Collection and Editing in the 

1996 Medical Expenditure Panel 

Survey (HC-010A)

Methodology Methodology 

AHRQ Pub. No. 01-0002

June 2001

U.S. Department of Health and Human Services

Public Health Service

Agency for Healthcare Research and Quality
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

Healthcare Research and Quality (AHRQ), formerly the

Agency for Health Care Policy and Research, 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 AHRQ 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

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

physicians: 

• Providing care for HC respondents receiving

Medicaid.

ii
iii

• 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 private and public-sector

employers. 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 three 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.

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. Beginning in 2000, national estimates of

employer contributions to group health insurance from

the MEPS IC are being used in the computation of

Gross Domestic Product (GDP) by the Bureau of

Economic Analysis.

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,

the sample consisted of 815 responding facilities, 3,209

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

residents admitted during 1996.
iv

     yyyyy yyyyy yyyyy yyyyy yyyyy Public Health Service Act. They are edited and

Survey Management

MEPS data are collected under the authority of the

yyyyy

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 AHRQ Publications Clearinghouse. Write or

call:

AHRQ 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 AHRQ number of the

document or CD-ROM you are requesting. Selected

electronic files are available through the Internet on the

AHRQ Web site: 

http://www.ahrq.gov/

On the AHRQ Web site, under Data and Surveys,

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 Healthcare Research and Quality, 2101 East

Jefferson Street, Suite 500, Rockville, MD 20852 

(301-594-1406).
v

Table of Contents

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

Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Household Component . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

Pharmacy Component . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

Data Editing, Imputation, and Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Drug Coding and Flat Files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

Preliminary HC Event Edits. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Preliminary PC Event Edits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Matching Software . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Example of Matching. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

NDC Imputations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Other PC Imputation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Outlier Editing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

Self-Filer Matching and Imputation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

Self-Filer Payment Reconciliation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Non-Self-Filer Matching and Imputation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Unmatched PC Data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

Allocation to 1996 and 1997. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Sensitivity Testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

Consistency Edits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

The March 2001 Revision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

Reference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Tables showing:

1. Pharmacy data collection response rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2. Matching and imputation for non-self-filers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3. Impact of March 2001 file revision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

Appendixes:

A. List of Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

B. Pharmacy Component Survey Booklet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

C. Permission Form for Pharmacy Component . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

1

Introduction

This report describes the procedures adopted to

collect and edit the 1996 Medical Expenditure Panel

Survey (MEPS) prescription drug data for public release

(MEPS public use file HC-010A). For the first time in a

national expenditure survey, the 1996 MEPS included a

detailed collection of information on prescription

medicines obtained from pharmacy providers frequented

by household sampled persons. The information was

collected by means of a linked survey of pharmacy

providers. 

Because of nonresponse to the linked survey, these

prescription data were not available for every sampled

person in MEPS and were not necessarily complete for

those persons for whom the Pharmacy Component (PC)

survey data were collected. Hence, this report discusses

the data collection efforts made to retrieve complete

and/or partially missing pharmacy data, the editing

techniques used to fill in remaining missing data in the

pharmacy database, and the matching/imputation

procedure that linked every prescription drug mentioned

by the respondent in the MEPS Household Component

(HC) to a specific prescription drug from the PC. The

abbreviations used in this report are listed in Appendix

A. 

The MEPS prescription drug use and expenditure

data comprise a critical component of health

expenditures collected in the survey. Recently, there has

been considerable policy interest in developing a

prescription drug benefit for the Medicare population.

In addition, the rising cost of prescription drugs has been

singled out as a leading contributor to the escalating

premiums for plans covering the non-Medicare

population. Apart from coverage issues, it is critical to

have accurate, detailed information on drug therapies in

order to analyze alternative treatment regimens for

disease and to investigate the potential for adverse drug

interactions in treating chronic illnesses. 

Previous household health expenditure surveys have

been criticized for underestimating utilization and

expenditures on prescribed medicines because of

respondent underreporting of prescription data. The

potential for this problem is understandable when

considering the length of the recall period in the

expenditure surveys, the burden placed on the

respondent to report details of numerous medication

purchases for household members, the irregular

frequency with which prescriptions are purchased for

treating acute health conditions during a survey period,

and the complexity of reimbursement mechanisms for

prescription medicines. 

The general approach used in MEPS to address the

underreporting issue was to relieve the household of the

burden of reporting detailed financial information for

every prescription purchase during each round of the

survey. Instead, computerized printouts from

respondents’ pharmacy providers that contained such

information were used when they were available. When

computerized printouts were unavailable from a

pharmacy provider, completed written data forms were

secured, when possible. These printouts or forms also

provided detailed information about the pharmacology

of the prescription summarized by the National Drug

Code (NDC). Heretofore, this information had not been

available from household health expenditure surveys. In

addition, efforts were made to improve the reporting

accuracy of prescription utilization by asking the

household respondent about medications prescribed in

conjunction with other medical events, such as hospital

stays, emergency room visits, and doctor visits. This

information was gathered at the same time during the

survey that the respondent was queried about other

nonprescription events. 

The challenge was to match prescription mentions

by the household to the prescription purchases on their

computerized printouts. When computerized printouts

were not available for MEPS households from their

pharmacy providers, detailed information on specific

prescription purchases by participating individuals in the

PC had to be imputed to the drug mentions of the HC

nonparticipating individuals. (HC nonparticipating

utpatient Prescription Drugs: Data Collection and Editing in the 1996

Medical Expenditure Panel Survey (HC-010A)

by John F. Moeller, Ph.D., and Marie N. Stagnitti, M.P.A.,Agency for Healthcare Research and Quality; Eileen

Horan and Pat Ward, Ph.D.,Westat, Inc.; and Nancy Kieffer and Ed Hock, Social and Scientific Systems, Inc.
individuals are respondents whose pharmacy providers

were not contacted because permission forms were not

signed or whose pharmacy providers were contacted but

did not provide information about the individual’s

prescriptions.) Once the pricing and payment data from

the PC were either matched or imputed to all of the

household prescription mentions, the prescription

expenditures and payments could be “rolled up” from

the event or purchase level to the person level for

estimating national prescription expenditures and

payment sources for the entire U.S. civilian

noninstitutionalized population in 1996.

Data Collection

Household Component

Prescription drug data were collected in the MEPS

HC questionnaire and in the linked MEPS PC. During

each round of the MEPS HC, all respondents were

asked to supply the name(s) of any prescribed

medication that they or their family members purchased

or otherwise obtained during that round. 

Respondents were first given an opportunity to

mention prescribed drugs when they were surveyed

about other (nonprescription) health service events.

They were given a last opportunity to mention

prescribed medicines during the prescribed medicines

section of the HC. The order in which health care

events are described in the HC is simply an artifact of

the design of the HC. During each round of the HC,

detailed information was obtained on various types of

health care service events, including prescribed

medicines. When respondents provided information on

a health care event that was not a prescribed medicine

(e.g., an emergency room visit), they were asked to

supply information describing the health care event

itself, as well as the names of any medications that were

prescribed during that event. In addition, prescribed

medicine mentions could be added when respondents

went through the prescribed medicines section of the

household questionnaire. 

These are the ways in which a respondent “created”

(possible during Rounds 1-5) and/or “selected” (possible

during Rounds 2-5) prescribed medicine mentions for

their prescribed medicines roster. “Created” means that

the respondent had not mentioned the prescribed

medicine in any previous round of the survey, while

“selected” means that the respondent had mentioned the

prescribed medicine during a previous round. This

roster served as a “base” prescribed medicines roster for

that respondent throughout all of his or her rounds in

MEPS. In each round, respondents had a final

opportunity to add any additional medication names to

their roster of prescribed medicines in the prescription

drug section of the MEPS HC. 

The following information was collected in the

prescribed medicines section of the questionnaire for

each medication listed on the roster in each round of

MEPS: whether any free samples of the medication

were obtained; the name(s) of any health problem(s) for

which the medication was prescribed; the number of

times the prescription drug was obtained or purchased;

the year, month, and day on which the person first used

the medication; and a list of the names, addresses, and

types of pharmacies that filled the household’s

prescriptions. In addition, all the HC respondents were

asked if they send in claim forms for their prescriptions

(this type of person is referred to as a self-filer, or SF)

or if their pharmacy providers do this automatically for

them at the point of purchase (this type of person is

designated as a non-self-filer, or NSF). Uninsured

persons were treated in the same manner as NSFs.1 The

uninsured were not asked charge and payment questions

during the HC. Only SFs were asked for charge and

payment information about their prescription purchases

on the household questionnaire. Payments by private

third parties for SF prescription purchases would not be

available from the pharmacy provider.

When diabetic supplies and equipment (such as

syringes and insulin) were mentioned in the section of

the MEPS HC on other medical expenses, the

interviewer was directed to collect information on these

items in the prescription medicines section. To the

extent that these items are purchased without

prescription, they represent a nonprescription addition to

the MEPS prescription drug expenditure and utilization

data. 

2

1 The uninsured are included in the NSF group throughout

this report.
Pharmacy Component

The PC was designed as a mail survey of the

pharmacy providers identified by household respondents

during the series of MEPS interviews covering calendar

year 1996. (See Appendix B for a facsimile of the

mailed PC survey booklet.) During the last of these

interviews, the household respondents were asked to

sign permission forms (Appendix C) authorizing the

project to contact their pharmacies and authorizing the

pharmacies to release a respondent’s pharmacy records.

Only those pharmacies for which a household

respondent signed this permission form were included

in the linked followback survey. 

The data collection protocol consisted of an initial

mailing to all of the nominated pharmacies for which

one or more permission forms had been obtained, a

second mailing to nonrespondents, and followup

telephone prompting of pharmacies that did not respond

to the mailings. The initial mailing (Appendix B) was

designed in the form of a printed booklet containing an

introductory letter from the Agency for Healthcare

Research and Quality (AHRQ) and the National Center

for Health Statistics (NCHS), a brief description of

MEPS and the PC, answers to frequently asked

questions about the study, and an explanation of the data

items being requested. A computer printout listing the

persons for whom the pharmacy was being asked to

provide information and copies of the signed permission

forms were inserted inside the back cover of the booklet.

The data request offered pharmacies two main options

for responding. If available, pharmacies were invited to

send computerized printouts of the data for the

identified patients. This was seen as a response option

imposing minimal burden on pharmacies, many of

which routinely provide such printed listings to

customers who request them. Alternatively, pharmacies

could fill in the requested information on data forms,

which were inserted in the booklet along with the

patient list and permission forms. 

The pharmacies were asked to provide information

about each prescription filled or refilled for the named

patients during calendar year 1996. For each

medication, they were asked to provide:

• The date the prescription was filled or refilled.

• The NDC.

• The medication name (generic or brand).

• The strength of the medicine.

• The quantity dispensed.

• The total charge.

• The sources of payment.

• The amount of payment made by each source.

The initial mailings were directed to the individual

retail pharmacies or other specific locations identified

by the household respondents as the places from which

household members had obtained their prescriptions.

Although it was expected that some pharmacy chains

might require corporate permission before allowing

their individual locations to participate and that some

would prefer to provide information from regional or

corporate resources, the plan was to make the first

contacts at the individual locations, working up the

corporate ladder only after being referred there by the

local pharmacies. “Chain” codes were assigned to the

individual pharmacies, creating a mechanism for

associating local establishments with a shared corporate

parent.

The final round of household interviews through

which the pharmacy sample was identified ended in

July 1997. After a period of sample preparation, the

first waves of mailings were released in September

1997. The bulk of the mailings, grouped in nine waves

defined by groups of States, was completed by the end

of October 1997; a final wave of cases that had required

problem resolution was released in January 1998. The

followup mailings began in late September 1997 and

continued into December 1997. Calls to prompt

nonresponding pharmacies began in mid-December

1997.

The response to the first wave of mailings was

promising, with replies received from as many as 40-50

percent of the pharmacies. The second mailing and the

telephone prompts, however, added only marginally to

the initial response. 

The majority of the returns received were in the

form of printouts. When reviewed for processing, many

of these proved to be incomplete or unclear in their

presentation of key data items. The identification of

third-party payers and the amounts paid by third parties

were the data items most frequently missing. Variations

in the way the printouts were formatted and the manner

in which data items were labeled frequently resulted in

ambiguity about the meaning of specific items on the

printouts, requiring some followup contact with the

pharmacy for clarification. 

3
4

To improve response at the levels of both the

pharmacy and the individual data items, the initial data

collection protocol was supplemented with a twopronged telephone data collection effort. One group of

telephone interviewers concentrated on data retrieval

calls to pharmacies that had responded. Retrieval

telephone calls to collect missing data items or to clarify

data on the printouts were needed for nearly 70 percent

of the responding pharmacies. A second group of

telephone interviewers concentrated on primary data

collection from the nonresponding pharmacies, adopting

an approach similar to that used for the MEPS Medical

Provider Component (MPC) (Cohen, Monheit,

Beauregard, et al., 1996). These interviewers contacted

pharmacies by telephone, explained the data collection

request, and faxed copies of the permission forms and

other relevant materials to the pharmacies. Within

several days of the faxing, they placed additional calls to

collect the requested data over the phone or to prompt

their pharmacy contact to send in the printed patient

printouts. Most responding pharmacies chose to mail or

fax the printouts to the study. 

The bottom-up approach adopted for dealing with

the large pharmacy chains yielded mixed results. Many

of the individual pharmacies associated with chains

responded directly to the initial mailed request for data.

However, as the telephone followup work progressed,

pharmacies for a number of chains referred interviewers

to regional or corporate offices. Corporate contacts

reacted to the data requests in several ways: some

referred the interviewers back to the individual

pharmacies, with or without corporate endorsement of

the study; some chose to consolidate the project’s

requests and provide data from a centralized location;

and several of those that undertook an effort to provide

the information were unable to provide it on a timely

basis or abandoned the effort as too burdensome. 

The permission form response rate (that is, the rate

at which household respondents who were asked to sign

permission forms actually signed them) is shown below.

Eligible person-pharmacy pairs.......................20,023

Signed permission forms.................................14,531

Permission form signing rate...............................72.6

Table 1 shows response rates for the pharmacy data

collection. Response rates are shown at both the

pharmacy level (72.2 percent) and the household

patient-pharmacy pair level (67.1 percent).

Data Editing, Imputation, and

Matching

The general approach to preparing the household

prescription data for public release was to impute

information collected from pharmacy providers to the

household drug mentions. For SFs, information on

payment sources was retained if these data were

reported in the charge and payment section of the

household questionnaire. A matching program was

developed to link drugs and drug information from the

PC to HC drug mentions. To improve the quality of

these matches, all drugs on the household files were

assigned numeric codes from a proprietary database on

the basis of the medication names provided by the

household. These codes were also assigned to the

prescriptions in the PC by using the NDC, when

available, and medication names reported by the

pharmacy providers. Considerable editing was done

prior to the matching to identify free samples among

household drug mentions, to correct data inconsistencies

in both data sets, and to fill in missing data and correct

outliers on the pharmacy file. After the matching,

household drug mentions in Round 3 of MEPS, which

spanned portions of both 1996 and 1997, had to be

allocated to each year to produce the final annual

prescription use and expenditure data for survey year

1996.

Drug Coding and Flat Files 

The initial task for the drug editing was to assign a

common set of drug codes to the household drug

mentions and to the prescription drugs reported by the

pharmacy providers. Westat (the MEPS data collection

contractor responsible for collecting the pharmacy data

from the participating pharmacies and producing the

initial HC and PC files containing each separate drug

purchase) contracted with Aspen Systems Corporation

to provide coding support services as a subcontractor for

the MEPS project. First DataBank’s proprietary 1998

Master Drug Data Base (MDDB), which contains the

Generic Product Identifier (GPI) code, was selected for

this task. The GPI is a 14-digit code that contains 7

pairs of digits. The first pair of digits represents the drug

group. Successive paired digits represent the drug class,

drug subclass, drug name, drug name extension, 
Table 1. Pharmacy data collection response rate in the 1996 Medical Expenditure

Panel Survey

Pharmacies Person-pharmacy pairs

Sample type Number Percent Number Percent

Initial sample 6,109 100.0 14,531 100.0

Out of scopea 788 12.9 2,385 16.4

Net sample 5,321 87.1 12,146 83.6

Complete 3,840 72.2 8,149 67.1

Refusal 325 6.1 1,114 9.2

Other nonresponse 1,156 21.7 2,883 23.7

a The category “out-of-scope pharmacies” included establishments reported by a household respondent that were not

pharmacies (e.g., when the medicine was given as a free sample in a physician’s office), pharmacies located outside the

United States, originally reported pharmacies that merged with another reported pharmacy during the data collection period,

and pharmacies whose associated sampled persons were out of scope. It also included pharmacies that did not fill

prescriptions for the sampled persons in 1996 but may have done so in later years. A person-pharmacy pair was treated as

out of scope if the pharmacy to which the person was linked was out of scope.

Note: The sample for the Pharmacy Component (PC) included all persons reported to have had prescriptions filled or

refilled during the first three rounds of the Household Component (HC). For pharmacies reported in the HC in Round 3,

which overlapped the end of 1996 and the start of 1997, the sample processing did not have respondents differentiate

between prescriptions obtained in 1996 and those obtained in 1997. This was not true for what the PC pharmacies were

asked and reported. PC pharmacies were asked for 1996 data only. If, during data collection for 1996 prescriptions, a

pharmacy acknowledged the person as a customer but reported having filled no prescriptions for the person in 1996, the

person was treated as out of scope for 1996 pharmacy data collection (but in scope for the 1997 data collection). 

Source: Center for Cost and Financing Studies, Agency for Healthcare Research and Quality: Medical Expenditure Panel

Survey, 1996, public use file HC-010A.

5

dosage form, and strength. Coders filled in as many

digits of the GPI as possible based on the medication

name (and any supplementary information appended to

the name) provided by the household, and the NDC,

medication name, and other information provided by the

pharmacy provider. Typically, 8 to 10 digits were coded

for household-reported drugs, and all 14 digits were

filled in for pharmacy-reported drugs. 

The second task for the prescription editing work

was to determine the MEPS variables to add to the

household and pharmacy event-level files for later use.

Separate household event files were constructed for

drugs reported by households classified as SFs and for

drugs reported by households classified as NSFs. The

additional data on prescription charges and payments

collected for the SFs, and the additional processing this

would entail, made this necessary. To maintain

consistency between the PC and HC databases,

household-reported drug mentions needed to be

unfolded into individual records; that is, each

prescription constituted an individual record, whether it

was a refill or an initial purchase. An individual record

on the Pharmacy Component file represented a single

prescription purchase regardless of whether the purchase

represented an initial prescription or a refill. For

matching purposes, it was therefore necessary to create

separate records for each prescription, whether initial

purchase or refill, on the household event files. 

Variables added to the household SF and NSF flat

files, as well as to the pharmacy flat file, were critical to

the matching and editing/imputation processes. (Flat

files are files in which each record represents one

prescribed medicine event, whether it be an original

prescription, a refill, or a free sample.) These variables

were: 

• Prescription event and person identifiers. 

• Beginning and ending reference period dates for each

round. 
6

• An indicator of private health insurance coverage for

prescription drugs by round. 

• Potential source-of-payment indicators by round. 

• Whether a person was in a health maintenance

organization (HMO) by type of HMO (Medicare

HMO, Medicaid or other public HMO, or private

HMO) by round. 

• Conditions associated with prescription drugs by

round.

• Geographic division and region, in addition to

metropolitan statistical area (MSA) status. 

• Various health status and demographic

characteristics. 

Additional variables added to the pharmacy flat files

included round-specific pharmacy identifiers and names

and types of pharmacy providers. 

Preliminary HC Event Edits

The three flat files contained 15,804 SF drug events

for 1,562 persons, 188,422 NSF drug events for 14,629

persons, and 84,029 PC drug events for 6,874

individuals. The individuals in the SF and NSF groups

were not mutually exclusive because 941 persons had

events in both files; however, additional analysis showed

that no single person was classified as both an SF and

NSF in the same round. Excluded from the flat files

were 899 SF drugs, 6,691 NSF drugs, and 32 PC drugs

for persons in the preliminary MEPS Round 1 and

Round 2 samples who were not in the full-year

population. These persons did not have positive-valued

full-year weights, nor were they related to anyone with

such weights. Remaining in the full-year population,

however, were some non-key persons who had zero

person weights but were members of families in which

at least one individual on the full-year file had a

positive-valued person weight. 

For SFs, a preliminary set of edits based on similar

edits applied to other nonprescription MEPS expenditure

events was run on the household-reported data for each

drug event. These edits mostly relied on household

variables created to classify actual payment source

variables or potential payment source variables. Actual

payment source variables were coded as “known payer,

known amount paid,” “known payer, unknown amount

paid,” “not known to be a payer,” or “ source not

available.” Potential payment source variables were

coded as “covered or source available in round” or “not

covered or source available to all in round.” “Source

available to all in round” was the way the potential

payment source variable for self-payments was always

coded. In addition, round-specific household variables

indicating whether the person was enrolled in a private

health insurance plan covering prescription drugs and

whether the person was enrolled in a Medicare HMO, a

Medicaid or other public HMO, or a private HMO were

used in combination with other variables from the HC

charge and payment section to edit the data and/or

correct for inconsistencies. The purposes of these edits

are summarized below. The number of events affected by

the edit is shown in parentheses.

• Make it impossible for elderly persons enrolled in a

Medicare HMO plan to claim any source other than

Medicare as a source of payment for prescriptions

(95 events).

• Equate the total charge to the sum of payments if the

two differed by $2 or less (126 events).

• Eliminate the inconsistency created from persons’

mistakenly reporting private insurance when they

actually had Medicare HMO or Medicaid HMO

insurance coverage (6 events).

• Correct inconsistencies between Medicare and

Medicaid reported as sources of payment and

coverage (0 events).

• Correct inconsistencies between reported insurance

coverage during the year and potential coverage from

private insurance, Medicaid, Medicare, and

CHAMPUS (Armed-Forces-related coverage) (68

events). 

• Assign a missing payment amount to self-pay when

no information was available to link the missing

payment to a third-party payer (2 events).

• Eliminate Medicaid as a source of payment when

sources other than out-of-pocket are present and

when there is an out-of-pocket payment greater than

$5 (118 events).

In a second set of preliminary edits on the

household drug event data for SFs, five edit rules were

imposed in the order shown below. Only one edit rule

was allowed per event. For example, if an event was

edited because of the first rule listed, that event would

not be eligible for any of the editing rules that followed

the first rule.

• Medicaid cases were processed so they would be

correctly classified for later imputation (131 events).
7

• The problem of having persons confuse “other”

source of insurance with private insurance coverage

was resolved (0 events).

• If total charge was reported to be $5 or more, no

payments were reported, and self-pay was the only

missing payment source, then self-pay was set equal

to the total charge for the drug product (24 events).

• If the payment sum exceeded $30 and no payment

sum information was reported as missing, then the

editing for the event was designated as completed

(2,639 cases).

• If the payment sum exceeded $30 and was within $5

of the total charge, and one payment source was

missing, then the missing payment amount was set

equal to zero (43 events). 

During each round, households were asked in the

prescription drug section of the HC questionnaire

whether they received any free samples of prescribed

medicines from any medical or dental provider. If they

responded in the affirmative, respondents were then

asked to name the medicines they received as free

samples in a given round. However, no information was

reported on the number of drug events for a given

medication in a given round that were free samples.

Initially, 693 prescription drug events for SFs were

deemed to be free samples. For a medicine to qualify as

a free sample for an SF, the following two conditions had

to be met: (1) all payment sources were reported as zero

and (2) the household reported receiving free samples of

the medication. After 41 free sample Round 3

prescriptions of SFs were allocated to 1997, the final

number of 1996 free samples for SFs in the MEPS HC

was 652. (See the “Allocation to 1996 and 1997”

section, below.) 

For NSFs, 4,252 drug events initially were

designated as free samples. Only one drug event per

round per drug product was allowed as a free sample if

the household reported receiving free samples of the

drug product during the round. If the person in the NSF

household was in the Pharmacy Component and the

designated “free sample” was later exactly matched to a

prescription purchase on the PC file, then the freesample designation was overridden. Of drugs originally

designated free samples for NSF households, 462 were

later matched to prescription purchases on the PC file.

After 603 Round 3 prescriptions in MEPS were

allocated to 1997, the final number of free samples for

NSFs in 1996 was 3,187. 

Preliminary PC Event Edits

Some preliminary edits were imposed on the PC

data. They were patterned after similar edits applied to

nonprescription data collected in the MEPS Medical

Provider Component. If, based on information from the

HC, a sampled person did not potentially have coverage

from a public or private insurance source, then any

missing code for the corresponding payment amount

from the PC for a specific drug event was coded as a

zero payment (4,912 events). After this, the sum of

payments for a drug event on the PC was set to missing

if any payment sources were coded as missing (11,486

events). 

Additional edit rules to the PC drug events,

patterned after similar edit rules applied to the

nonprescription MPC events, were designed to do the

following:

Rule P1: Correct information from pharmacies that

mistakenly reported a private insurance payment source

instead of a Medicare HMO (406 events) or Medicaid

HMO (211 events) payment.

Rule P2: Set the total charge equal to the sum of

payments if the two measures differed by no more than

$2 and none of the payments or the total charge was

missing (791 events).

Rule P3: Allocate the excess of total charge over a

partially reported payment sum to a specific payment

source either based on the pharmacy’s identification of a

single third-party source or based on potential thirdparty coverage of the person (95 events). 

Rule P4: Eliminate an out-of-pocket payment when

it appears that the pharmacy provider shows an unlikely

amount for a patient copayment that is equal to the

reported amount for Medicaid (18 events) or Worker’s

Compensation (1 event).

Matching Software

From the outset of the MEPS prescription drug

editing work, it was clear that the hot-deck approach to

imputing missing data was not going to be appropriate

for this task. Donor hot-deck cells defined on the basis

of a specific drug code or medication name were too

small to stratify by other variables deemed to be

correlated with the purchase of a specific drug product.

Also, because the GPI codes encompassed numerous

specific drug products with different NDC values, it was

clear that software would have to be developed that
could “read” medication names from both donor and

recipient files to improve the quality of the matches. 

To meet these needs, the data processing contractor,

Social and Scientific Systems, Inc. (SSS), developed

software that imputed PC drug data to the household

drug mentions by matching drug events from each file

based on variables with both numeric characters (e.g.,

GPI codes, potential payment sources, age, sex, health

status, and geographic location) and alpha characters

such as the medication names supplied by the household

and the pharmacy providers. The matching software

that SSS developed had the following features:

• An overall score ranging from –1 to +1 was assigned

to each donor drug that represented a potential

match to a recipient drug, with +1 representing the

highest score attainable when each match variable

received the highest score possible. Separate

weights were assigned to the match variables to

reflect their importance relative to other match

variables in determining the final overall score.

Values between –1 and +1 for final scores were

constructed as the weighted score for the match

divided by the weighted score when all match

variables receive the highest possible score. (An

example is given in the next section.) Certain match

variables could be required to match exactly or a

potential match between drugs was not deemed

possible. 

• All numeric match variables had to match exactly or

no positive contribution was made to the final score

for the specific variable. A value of +1 was assigned

in the event of any exact match. A value of –1 was

assigned if there was no exact match.

• For words (alpha characters), the best match was

found according to the following hierarchy:

The words matched exactly. 

The words sounded the same using a Soundex

function. (The Soundex system indexes names by

how they sound rather than how they are spelled).

A pair of characters was swapped in the third to

last characters.

Only one character was different in the third to last

characters.

The shorter word exactly matched the first characters

in the other word.

The words started with the same characters.

None of the above.

To resolve ties, except when the words started with

the same characters, the longer word was used.

When the words started with the same characters,

the word that started with more of the same

characters was used, and then the longer word was

used if there were still ties.

• For words (alpha characters), once the best match

was found, it was assigned a score measuring how

closely the words matched, and it was also assigned

a weight indicating how strongly a good or poor

match should be considered. Except for an exact

match or a non-exact match where the words started

with the same characters, higher scores were

assigned to matches between longer words. For a

non-exact match where the words started with the

same characters, the larger the portion of the words

that matched, the higher the score that was assigned.

An exact match was given the highest score

regardless of the size of the word. 

• When there were ties among the final scores, a

random number between zero and one was generated

to break the ties, and the highest number assigned

determined the final match. After scrutinizing

numerous examples of these ties, it was determined

that in general the ties reflected the lack of enough

information in the database to identify a uniquely

best match. Under these circumstances, giving each

of the donors tied for first place an equal chance for

the final match seemed preferable to any further

experimentation with weights, match variables, or

scores for alpha variables to find the best match. 

• The software allowed donor records to be matched to

recipient records either with or without replacement

of the donor records in the donor pool for

subsequent matches. 

Example of Matching

Suppose a household member reported a purchase

of “AMOX” in a given round of MEPS, and an attempt

is made to match a PC record of “AMOXICILLIN” to

it. A total weight of 100 is assigned to the pharmacy

name match. The group weight of 100 is multiplied by

0.8 if the word is 4 characters long and by 1.0 if the

word is 6 or more characters long. In this example,

comparing the HC name to the PC name counts 80

toward the final score. Comparing the PC name to the

HC name has the potential to contribute 100 to the final

score, but because there is no match in this case, it

contributes –100 to the final score. In this example, if

the medication name is the only match variable, then the

final score equals 80–100, or –20, divided by 180, or 

–.111. 

8
9

In the same example, if an exact match is required

on the GPI code, then this case probably cannot even be

in the running for the best match. If an NDC were on

the PC record for the “AMOXICILLIN,” then a full 14-

digit GPI code would have been assigned. With only

“AMOX” available on the HC record, at best only 6 of

the 14 digits of the GPI could have been coded. 

In this example, weighted match variables (match

variables not requiring an exact match) for the 2-, 4-,

and 8-digit GPI code (with weights of 20, 40, and 80,

respectively) could be added to the match variable for

the medication name. The total potential score for the

match becomes 320 rather than 180 with the addition of

the weighted match GPI variables. Assuming that only

the first 4 digits of the GPI matched between the HC

and PC drug purchase, then the final score for the

attempted match becomes –20+20+40–80, or –40,

divided by 320, which equals –.125. 

NDC Imputations 

In order to identify potential outlier drug prices on

the PC database, it was necessary to add the average

wholesale unit price (AWUP) from the MDDB database

to the PC file. This was achieved by performing a

crosswalk from the PC file to the MDDB file through

the NDC to retrieve the AWUP on the MDDB file.

This, however, presented a problem for the 4,604 PC

drug events that had missing or unclassified NDC

values. In addition, another 8,266 PC drug events had

reported NDC values that did not match any NDC on

the MDDB database. For these reasons, it became

necessary to impute NDC values to these 12,870

records. 

All but 498 of the 12,870 PC drug events that could

not be linked by NDC to the MDDB data had

previously been assigned a GPI code. For the 12,372

cases with a GPI, the matching software was utilized to

find the best match to a drug product on the MDDB

based on the GPI and the medication name on the PC

file. Because a single GPI may cover multiple drug

products with differing NDC values, medication names

from the PC file were also used as match variables

against both the generic and trade/brand names of drugs

on the MDDB file. Quantity units, strength, and

strength units were also available from the MDDB file

and were used as match variables.

For 10,462 of the 12,372 PC drugs with GPI codes,

NDC values from reasonable matches to the MDDB file

were imputed to the PC file by requiring an exact match

to the full GPI and by also using the medication name

as a match variable. Another 1,894 drug events were

imputed by an exact match to the first 8 characters of

the GPI and by again using the medication name as a

match variable. The 16 PC drug events with a GPI that

did not match in either of the imputation runs above

were sent through a matching routine without requiring

an exact match on any portion of the GPI. For 9 of

these cases, the best match selected by the software was

used. Three of the remaining 7 cases were handmatched to one of the top 10 choices from the software

output. Four cases remained. For 2 records the NDC

was hand-coded to a specific NDC and for 2 others a

matching program was run to find the NDC matching

“orthonovum.”

For the 498 PC drugs lacking a GPI code, the

71,159 PC drugs with a valid reported NDC were used

as the donor base for the matching software. Match

variables included the person’s age, sex, geographic

division, MSA status, health conditions, potential

payment sources, and SF/NSF status. Most of the 498

PC drug events lacking a GPI code were also missing

the drug name, as well as specific information regarding

the quantity and strength of the drug. This explains why

these drugs were never initially assigned a GPI code.

Match donors from the PC file were restricted to those

with non-missing drug product names. The medication

names, along with the GPI and quantity and strength

information from the donor record, were merged into

the recipient drug record whenever this information, in

addition to the NDC, was missing from the recipient

record. 

The quality of the matches was not as high for the

2,408 PC drugs for which there was not an exact GPI

code match between donor and recipient. Therefore,

these PC drugs were later removed from the donor pool

for all matches between HC and PC drug events that

were run with replacement. The initial matches, as

discussed below, were run without replacement to

identify exact matches between HC and PC drug events

for the same person in the same round of the survey.

The only drug events in the donor pool for these

matches were whatever drug purchases were reported by

the person’s pharmacy provider(s) in the PC. 

Other PC Imputation

In order to screen for drug price outliers, a retail

unit price (RUP) was constructed from the Pharmacy

Component data and compared against the AWUP taken
from the MDDB file. The RUP was constructed as the

retail price for the drug product, defined as the sum of

payments for the prescription divided by the dosage

amount or quantity dispensed as reported by the

pharmacy provider. For 75 of the 84,029 PC drug

events, the amounts dispensed were missing. The

matching software was used to impute these missing

quantities. Match variables included the NDC and GPI

for the drug product and the person’s age, sex, health

conditions, and health status. Exact matches for the

first eight characters of the GPI were required and

heavier weight was placed on the NDC, followed by the

GPI. 

For 211 drug products, reported dosage amounts

contained more than one value. Values were typically

separated by a slash or dash on the computer printouts

supplied by the pharmacy providers. Pharmacy

consultants to Westat, Mediquest Associates, provided

technical assistance in establishing editing rules to

determine the appropriate value to use for the dosage

amount in these cases. 

For 206 drug products, the round in which the

prescription was purchased was missing from the PC

file. This variable was initially prepared for the PC flat

file, but because of missing month (168 events), day

(173 events), and/or year (14 events) information for

certain prescriptions on the file, the round variable was

missing. The matching software was used to impute the

round for 110 events, using the person identifier as an

exact match and the NDC and GPI as weighted match

variables. The remaining 96 events were imputed a

value for the round by a second application of the

matching software. During this second application, the

variables for the year at the beginning and the end of the

round were exact matches and the year at the beginning

of the first round was a weighted match variable. 

The AWUP variable was then merged onto the PC

file from the MDDB database by NDC value. Up to six

AWUP values, measured at different times, were

provided on the MDDB file. Prices dated the closest to

the middle of 1996 were selected, but in some cases

only prices for years prior to or after 1996 were

available. Distributions of the ratio of RUP to AWUP

(called PRATIO) for cases in which the merged AWUP

was dated before 1996, in 1996, and after 1996 were

analyzed to determine whether adjustments were needed

for the outlier analysis. Depending on the measure used

for the price variable on the PC file (described below),

AWUP prices before 1996 were inflated by either 10 or

40 percent to produce a PRATIO distribution

comparable to one using only 1996 AWUP values.

Similarly, certain AWUP prices after 1996 were deflated

by either 10 or 35 percent to produce a PRATIO

distribution comparable to one using only 1996 AWUP

values. 

For example, PRICE3 is defined below as the total

charge reported by the pharmacy provider. It serves as

the prescription price on the PC when payment

information is missing for a given prescription purchase.

The PRATIO distribution when only 1996 values of the

AWUP are used indicates that about 17 percent of the

prescriptions with the total charge as the drug price have

a retail drug price below 80 percent of the AWUP. The

PRATIO distribution when only AWUP prices before

1996 are used shows that 23 percent of the prescriptions

with the total charge as the drug price have a retail drug

price below 80 percent of the AWUP. Inflating each

pre-1996 AWUP by 10 percent for these cases is

equivalent to lowering a PRATIO value of .8 to

approximately .7. The PRATIO distribution when only

AWUP prices before 1996 are used shows that about 17

percent of the prescriptions with the total charge as the

drug price have a retail drug price below 70 percent of

the AWUP. The price deflation in this case produces a

price distribution of total charge relative to standardized

price (the AWUP) for AWUP values before 1996 that

compares favorably to a similar relative price

distribution using 1996 AWUP values.

Outlier Editing

After the preliminary editing described above was

performed on the PC file, three different measures of

the retail price of the drug product were constructed:2

PRICE1 = the sum of payments when no payments

were coded as missing (68,664 events)

PRICE2 = the sum of payments when at least one

payment is reported greater than zero and at least

one payment is reported as missing (8,030 events)

PRICE3 = the reported total charge for the drug product

when reported to be greater than zero and all

payments are reported to be either missing or zero

(2,141 events)

The remaining 5,194 drug products on the PC file

(referred to as PRICE4 cases) lacked any positivevalued payments or total charge data. 

10

2 Recall that from rule P2 (Preliminary PC Event Edits

section), the total charge was set equal to the sum of

payments when the difference between the two was no greater

than $2 and none of the payments or the total charge was

missing.
11

Based on PRICEn values (n = 1,2,3), the 78,835

drug events with a positive-valued price were sorted into

three groups for outlier analysis:

PRGRP1n = 1 if PRICEn = self-payment and PRICEn 

< $30 (32,528 events) 

= 0, otherwise

PRGRP2n = 1 if PRICEn = self-payment and PRICEn

> $30 (7,788 events)

= 0, otherwise

PRGRP3n = 1 if PRICEn does not equal self-payment

(38,519 events)

= 0, otherwise

The 78,835 drug products were next allocated to

three outlier groups based on PRATIOn, the ratio of

retail unit price (RUPn) to AWUP (using PRICEn, n =

1,2,3 and dosage amounts to construct RUPn), as

follows:

OUT1n = 1 if PRATIOn < .8 (22,002 events)

= 0, otherwise

OUT2n = 1 if .8 < PRATIOn < 20 for n = 1 and AWUP

before 1996 or if .8 < PRATIOn < 10 otherwise

(56,293 events)

= 0, else

OUT3n = 1 if PRATIOn > 20 for n = 1 and AWUP

before 1996 or if PRATIOn > 10 otherwise (540

events)

= 0, else

The thresholds for determining unit price outliers

were established after consulting with Mediquest

Associates, Inc., Westat’s pharmacy consultant experts

for the study. 

An additional 14 edit rules for the prices and

payment sources on the PC file were implemented based

on the PRICE, PRGRP, and OUT classifiers. These edit

rules, described below, are labeled Rule P5 to Rule P18.

Outlier cases were edited by setting the outlier RUP

equal to the AWUP. Tabulations of the PRATIO

distribution for non-outlier prescriptions verified that the

AWUP was the modal value for the distribution. 

Rule P5: PRICE1/PRGRP11, PRGRP21, and

PRGRP31/OUT21 (53,351 events).

This edit rule stated that when no payment sources

were missing and when the drug price was equal to a

positive-valued sum of payments and not an outlier, no

edit was required. Payment shares were constructed for

these cases for use in implementing editing rule P7. 

Rule P6: PRICE2/PRGRPm2/OUT22 

(m = 1,2,3) (1,261 events).

This edit rule stated that if at least one payment

source was missing but the others summed to a positive

amount, then no edit was required if the sum was not an

outlier price, unless the sum was less than the total

charge. In the latter case, the drug price was set equal to

the total charge. The new price was checked to make

sure it was not still an outlier price. If it was an outlier

price, then it was reclassified and edited by another

appropriate rule (P15 or P17). If it was not an outlier

price, then a single missing payment source was set

equal to the difference between the total charge and

payment sum. If more than one source was missing, a

hierarchy was established for deciding which missing

source would be allocated the difference. In the

hierarchy, Medicare was allocated as payer if the person

was 65 or older and was in a Medicare HMO; otherwise,

Medicaid was allocated as payer if the person had

Medicaid coverage; otherwise, private insurance paid

the difference if the person had private coverage; and so

on. After editing, payment shares were constructed for

each drug event for use in implementing editing rule P7.

Rule P7: PRICE3/PRGRP33/OUT23 (1,681

events).

This edit rule was applied to drug events for which

the drug price equaled a positive-valued total charge

variable. The payment sum was zero or missing because

no payment sources were positive valued. When the

drug price (total charge) was not an outlier, payment

source amounts were assigned by payment shares

merged onto the event record by matching to another

non-outlier drug event record (from P5 and P6)

containing clean payment amounts. The exact match

variables were the potential payment sources, a variable

for whether the person had private prescription drug

coverage, and the person’s sex. The weighted match

variables were the drug product’s GPI, NDC, and price,

and the person’s age, region, and MSA status. Before

matching, the potential payment source flags were set to

indicate coverage from a given source if the payment

amount for the drug product on the PC record was

greater than zero. 

Rule P8: PRICE4 (cases lacking any positivevalued payments or total charge data) (5,194 events).

In cases in which the total charge was missing or

zero and there were no positive-valued payment

amounts, the drug price and payment source amounts

were imputed to the PC drug event record by a match to

a donor drug event record. The donor records consisted

of PC drug events in which at least one payment amount

was positive valued and the drug price was not an outlier
12

(P5 and P6 events). Initially 1,447 of these cases were

matched by using exact match variables for the NDC

and GPI of the pharmaceutical, the potential payment

source flags, and an indicator of private prescription

coverage for the person. Weighted match variables

included the drug name, the pharmacy name, and the

person’s age, sex, geographic region and division, and

MSA status. Because of the high quality of these

matches, the donor record’s dosage amount was also

imputed to the P8 recipient record to avoid creating new

price outliers unnecessarily. No replacement for the

recipient dosage amount was imputed in the remaining

matches because the quality of the matches was not as

good. In a second match, the first six GPI characters

replaced the full GPI and full NDC as exact match

variables from the first match, and sex was used as an

exact match variable. This produced 2,397 more

matches. An additional 602 P8 records were matched in

a third run, in which the first two GPI characters

replaced the first six GPI characters from the second

run as an exact match variable. All but 25 of the 748

remaining unmatched P8 cases were matched in a

fourth run, in which the GPI was omitted altogether as

an exact match variable. The remaining 25 cases were

hand edited by assigning them a retail drug price equal

to the product of the AWUP and the dosage amount

from their own record. Based on the name of the

pharmacy provider, which identified it as a Veterans

Affairs (VA) or military pharmacy provider, and the

potential coverage variables, the entire imputed purchase

price of each of the 25 drug products was assigned to

either VA or CHAMPUS coverage. 

After all prices were imputed to P8 cases, 1,838

new outliers were created by combining the prescription

price from the donor record and the dosage amount

from the recipient record of the 3,747 drug events that

failed to match in the initial run of the matching

software. These 1,838 outlier events were further edited

by one of the five remaining “PRICE1” edit rules (i.e.,

Rules P9-P13).

Further analysis of the names of the pharmacy

providers for P7 and P8 drug events showed that a

substantial majority of them appeared to be VA,

CHAMPUS, or “other Federal/Indian Health Service”

providers. These pharmacy providers generally do not

know the charges and payments for specific

prescriptions. At this point, any P7 or P8 drug event in

which the words VETERAN, VETERANS, VETRANS,

VA, or VAMC appear in the name of the pharmacy

provider and the person reported VA as a potential

coverage source was assigned VA as the sole payer of

the purchase price of the drug product. Any P7 or P8

drug with ARMY, AFB, NAVY, NAVAL, NAVCARE,

MARINE, USAF, or AIR FORCE in the pharmacy

provider name and CHAMPUS as a potential coverage

source was assigned CHAMPUS as the sole payer of

the purchase price of the prescription product. If the

person indicated “other Federal,” and not CHAMPUS,

as a potential payment source, then the full amount paid

was assigned to other Federal rather than CHAMPUS.

Finally, if INDIAN or TRIBAL (other than INDIAN

RIVER or INDIAN TRAILS) appeared in the pharmacy

name of a P7 or P8 prescription and other Federal was

reported as a potential payment source, then the full

purchase price of the drug product was assigned to other

Federal. 

Rule P9: PRICE1/PRGRP11/OUT11 (8,121

events).

This edit identified cases in which the RUP was

under 80 percent of the AWUP, self-payment was the

sole positive payment amount reported, no other source

had a missing code, a corresponding potential source

was indicated, and the self-payment was $30 or less. In

the edit the RUP was increased to the AWUP. The size

of the self-payment in these cases suggested a

copayment situation. Therefore, the difference between

the new and old retail price for the drug was allocated

hierarchically to any potential third-party sources

indicated for the person. If none was indicated, then the

full retail price increase was allocated to self-payment. 

Rule P10: PRICE1/PRGRP21/OUT11 (657

events).

This drug edit identified outlier drug prices in

which the RUP of the drug was under 80 percent of the

AWUP. The RUP of the drug was edited by increasing

it to the AWUP. The resulting increase in the retail price

of the drug was allocated entirely to self-payment

because it was the only payment source indicated by the

person purchasing the drug and because the reported

amount was over $30 and not considered a potential

copayment case. 

Rule P11: PRICE1/PRGRP11 and

PRGRP21/OUT31 (231 events).

In these cases, the reported RUP of the drug

equaled or exceeded 10 times the AWUP (or 20 times

the AWUP if the AWUP was measured before 1996). In

this edit, the reported RUP was reduced to the AWUP.

The subsequent reduction in the retail price of the drug
13

was taken entirely out of the self-payment amount

because it was the sole payment source with a positive

amount reported for the individual.

Rule P12: PRICE1/PRGRP31/OUT11 (7,575

events).

For cases in which the RUP was less than 80 percent

of the AWUP, the reported RUP was increased to the

AWUP. A hierarchy was established for allocating the

increase in the retail price of the drug to a single

positive-valued payment source because there might

have been more than one payment source with a positive

amount reported. In the hierarchy, any third-party payer

had a higher priority than a self-payer. 

Rule P13: PRICE1/PRGRP31/OUT31: (567

events).

For cases in which the RUP was greater than or

equal to 10 times the AWUP (or 20 times the AWUP if

the AWUP was measured before 1996), the RUP was

decreased to the AWUP. A hierarchy was established for

allocating the decrease in the retail price of the drug to a

single payment source because multiple sources might

have been reported with positive amounts. If the entire

decline in the retail price was not used up in these cases

by one payment source, the remainder was taken from

the next source in the hierarchy. This allocation process

continued until the entire price reduction was fully

allocated. 

Rule P14: PRICE2/PRGRP12 &

PRGRP22/OUT12 (6,726 events).

In this case, at least one potential third-party

payment source was missing and the self-payment

amount, the only positive-valued source of payment

reported, initially was set equal to the retail drug price.

If the implied RUP was less than 80 percent of the

AWUP, then the RUP was inflated to the AWUP. The

difference between the new and old retail drug price was

allocated entirely to the missing third-party payer if only

one was missing. If more than one third-party payer was

missing, then a hierarchy was established to allocate the

entire difference to one of the missing payers. 

Rule P15: PRICE2/PRGRP12 &

PRGRP22/OUT32 (15 events).

In this case, the RUP equaled or exceeded 10 times

the AWUP. The self-payment equaled the retail price of

the drug, although at least one third-party payment was

reported as missing. The RUP was reduced to the

AWUP, and the reduction in the retail price of the drug

was taken entirely out of the out-of-pocket payment

reported by the pharmacy provider. 

Rule P16: PRICE2/PRGRP32/OUT12 (25 events).

In this case, self-payment was not the sole positivevalued payment amount, but there was at least one

missing payment amount and the RUP was less than 80

percent of the AWUP. The RUP was inflated to the

AWUP, and the retail drug price increase was allocated

to the missing payment source if only one source was

missing. A hierarchy that included self-payment was

used to allocate the drug price increase to missing

payment sources when more than one source was

missing.

Rule P17: PRICE2/PRGRP32/OUT32 (3 events). 

In this edit, the RUP equaled or exceeded 10 times

the AWUP. At least one potential payment source was

missing but at least one was positive valued. Selfpayment was not the only positive-valued source

reported. The RUP was deflated to the AWUP, and the

reduction in the retail drug price was removed from

reported positive-valued payment sources in the same

way as in editing rule P13.

Rule P18: PRICE3/PRGRP33/OUT13 and OUT33

(460 events).

In this outlier edit, the retail price was the reported

total charge when it exceeded zero and no reported

payment amount was positive valued. The RUP either

was less than 80 percent of the AWUP, or it equaled or

exceeded 10 times the AWUP. The RUP was set equal to

the AWUP, and the new price was allocated to potential

payment sources by payment shares merged into the

event record by matching to a non-outlier drug event

record containing clean payment data.

The sum of the records affected by PC edit rules P5

through P18 is 85,867, 1,838 more than the 84,029 PC

drug events. The 1,838 difference between these totals

represents the Rule P8 cases in which an imputed

prescription price, combined with the dosage amount on

the original record, produced an outlier requiring

additional editing by one of the PRICE1 rules, P9

through P13. 

After these edit rules were implemented, analysis of

the results revealed that for 530 OUT1 cases, the edited

retail price for the drug product was inflated to be

inordinately high (in excess of $200) compared to its

original retail price, and for 697 OUT3 cases, the edited

retail price for the drug product was deflated to be

inordinately low (under $2) in comparison to its original

retail price. The majority of these cases occurred when

NDC values were imputed for PC drug products and

created mismatches among retail drug prices, dosage
14

amounts, and the AWUP to create the outlier PRATIO

values. Because most of these cases were not going to

be used for imputation to household events, they were

edited by using the original retail price reported by the

pharmacy provider in combination with an edited dosage

amount for these 1,227 PC events. 

Self-Filer Matching and Imputation

After cleaning the PC database, the next step was to

match or impute PC data to the HC drug mentions of

SFs by using the matching software in a series of

applications. The first matching run was set up to

identify exact matches between prescription events

mentioned by the household respondent and reported by

the individual’s pharmacy provider. For this task, 6,356

PC potential donor events were tested for exact matches

to 8,223 HC prescription mentions (not including 361

free samples) for SF households that were in the linked

pharmacy followback survey. The exact match

variables in the first run were the person’s identification

number (PERSID), the GPI, and the round in which the

drug was purchased. The weighted match variable was

the medication name supplied by the household and by

the pharmacy provider. In the first run, 512 HC

prescription mentions were exactly matched to

prescriptions reported for the same person in the same

round in the pharmacy database. Once a match was

made, the PC donor was effectively removed from the

donor pool and not matched with any other HC drug

mentions by the individual (i.e., the matches were made

without replacement). 

In a second run, exact matches were identified for

an additional 4,495 HC prescription mentions for SFs by

requiring exact matches only for the PERSID and the

round, and by using weighted match variables for the

medicine name and the first 2 consecutive characters,

first 4 consecutive characters, first 8 consecutive

characters, and first 10 consecutive characters of the GPI

code. After being reviewed, only 3,446 of these matches

met the final criteria for an exact match (a match score

greater than zero, an exact match on the first 4

consecutive characters of the GPI code, or an exact

match on the medicine name). The quality of the 1,049

matches that did not meet these criteria was deemed too

low to be classified as an exact match. 

Another 2,028 HC drug mentions of SFs were

“refills” associated with one of the 3,958 exactly

matched HC drug mentions and were matched to the

same PC drug donor event as the exactly matched event.

(Although the term “refill” is used in this context, the

MEPS HC did not collect sufficient information to

determine which drug acquisitions were original

prescriptions and which were refills in a given round of

the survey.) Refill drug records for an individual on the

HC file were linked through a common, round-specific

event identification number (EVNTID). In case two or

more HC prescription records with the same EVNTID

were exactly matched to two or more separate PC

records, any other unmatched refills with the same

EVNTID were matched by random selection to one of

the exactly matched PC events. This left 2,237

unmatched HC prescription mentions by SFs who

participated in the MEPS PC, in addition to 6,888

prescription mentions (not including 332 free samples)

for SFs not in the PC that remained unmatched.3

The remaining 9,125 unmatched HC-reported

prescriptions were eventually imputed data from a PC

prescription drug through a series of matching runs that

included all 81,621 PC drug records in the donor base

that either did not require any NDC imputation or had an

NDC imputed with an exact GPI match. In these

matches, if a PC drug was selected for an imputation, it

went back into the donor pool and was made available

for subsequent imputations (i.e., the remainder of the SF

imputations were done with replacement). 

The 9,125 unmatched HC drugs contained 4,066

unique EVNTID values. Each EVNTID contained at

least one unmatched non-free prescription or

prescription refill. Of these EVNTIDs, 447 were

imputed PC records by using the GPI as an exact match

variable. Weighted match variables, in descending order

of the weights, included the medication name; potential

payment sources and private prescription coverage

indicators for the person; name of the person’s pharmacy

provider(s); and the person’s age, sex, condition codes,

geographic region and division, MSA status,

employment status, and self-reported health status. PC

records were imputed based on a match on the first 10

digits of the GPI code for 2,931 EVNTIDs, based on a

match on the first 8 digits of the GPI for 19 EVNTIDs,

based on a match on the first 4 digits for 323 EVNTIDs,

based on a match on the first 2 digits for 110 EVNTIDs,

and based on a match on no portion of the GPI for 236

3 A total of 15,111 HC drug mentions of SFs were imputed

PC prescription data in this part of the editing. Initially, there

were 15,804 HC drug mentions of SFs. The difference of

693 between these two totals represents SF drug acquisitions

identified as free samples.
15

EVNTIDs. All HC drug mentions with an EVNTID in

common were imputed values based on the match to a

single PC donor drug product. 

Self-Filer Payment Reconciliation

For SF households, charge and payment information

after matching or imputation was available from both the

self-reported household data and from the data merged

onto the prescription drug record from the PC

prescription donor record. The financial data were

reconciled as follows:

• If none of the household-reported payment amounts

were missing and the HC payment sum equaled the

PC payment sum, then the HC payment amounts

were used along with the HC payment sum (63

events). 

• If none of the household-reported payment amounts

were missing and the HC payment sum was greater

than zero but did not equal the PC payment sum, then

the PC payment amount was used and the payment

amounts were allocated according to the shares for

each amount based on the HC data (10,984 events). 

• If the HC payment sum was missing because at least

one payment source was missing, then the PC

payment sum/price was used along with the PC

payment amounts if all of the HC payment amounts

were missing. If at least one of the HC payment

amounts was not missing and the sum of the nonmissing HC payment amounts exceeded the PC

payment sum, then the HC payment amounts were

used after scaling by the ratio of the PC payment sum

to the “partial” HC payment sum. If at least one of

the HC payment amounts was not missing and the

sum of the non-missing HC payment amounts was

less than the PC payment sum, then the difference

was allocated to the missing HC payment source. If

more than one HC payment source was missing, then

the difference was assigned hierarchically in the

following descending order: Medicare HMO,

Medicaid, private insurance, VA, CHAMPUS, other

Federal coverage, State or local coverage, Worker’s

Compensation, other insurance, or self-payment

(3,525 events).

• If the HC payment sum was zero with no missing

payments indicated, then the PC payment sum and

amounts were used (539 events).

In general, an attempt was made to retain as much

information as possible regarding source-of-payment

shares from the household-reported data. However, the

retail drug price information, which had been edited for

price outliers on the PC database, was provided from the

PC data. Using the PC retail prices instead of the HC

payment sums avoided doing additional editing of

potential HC outlier prices and kept detailed drug

identification and pricing information from the PC intact

when it was imputed to the HC drug mentions. 

Non-Self-Filer Matching and Imputation

Data on matching and imputation for NSF

information are shown in Table 2. The procedure used to

match HC prescription mentions for NSF households to

PC prescriptions mirrors the procedure described above

for SFs. As with SFs, the first set of matches was

designed to find exact matches between the HC and PC

drug events. The recipient HC database for these

matches contained 108,353 prescription mentions, while

the donor PC database consisted of 72,615 drug product

purchases for NSFs. Drug mentions that had been

identified as free samples also were included in the HC

recipient group of drug mentions for NSF exact matches

because of the rather arbitrary way in which a specific

drug mention was identified as a free sample for the

NSF population. (See Preliminary HC Event Edits

section.) 

Even when only one prescription was reported for a

medication in a given round and the household reported

receiving a free sample of the drug, it was not clear

whether the free sample was reported as the single drug

acquisition. This was not clarified in the interview. 

In the first exact matching run, 5,950 of the HC

drug mentions for NSF households in the linked PC

followback were matched exactly and without

replacement to PC drug mentions by using the PERSID,

the complete GPI code, and the round of the drug as

exact match variables, and the medication name as the

weighted match variable. In the second run for exact

matches, 50,566 additional HC drug mentions were

matched without replacement to PC drug records; only

PERSID and the round were required to match exactly,

and the first 2-character, 4-character, 8-character, and

10-character GPI codes were used as weighted variables.

As in the SF matching, not all second-run matches were

retained. Only 37,405 of these NSF matches in the

second run were deemed to be exact matches because
Table 2. Matching and imputation for non-self-filers in the 1996

Medical Expenditure Panel Survey

Number Description

188,422 HC drug mentions by NSFs

80,069 HC drug mentions by NSFs without PC data

108,353 HC drug mentions by NSFs with PC data

43,355 Exact matches of HC drug mentions by NSFs to PC data (includes 462 free samples that were 

exactly matched to PC drugs)

23,838 “Refills” (additional acquisitions) of exact matches of HC drug mentions by NSFs

41,160 HC drug mentions by NSFs with PC data still unmatched

121,229 HC drug mentions by NSFs with and without PC data still unmatched (includes 3,790 free 

samples that remain unmatched)

117,439 HC drug mentions by NSFs that were imputed a PC drug in matches “with replacement”

(excludes 3,790 unmatched free samples)

Note: HC is the Household Component of the Medical Expenditure Panel Survey (MEPS). NSF is non-self-filer (someone

whose pharmacy automatically sends the insurance company claim form for prescriptions or handles third-party payments

electronically at the point of sale). PC is the Pharmacy Component of MEPS. 

Source: Center for Cost and Financing Studies, Agency for Healthcare Research and Quality: Medical Expenditure Panel

Survey, 1996, public use file HC-010A.

16

they either had a match score greater than zero, matched

exactly on the first four characters of the GPI, or

matched exactly on the medication name. Of the 2,279

HC drug mentions designated as free samples for NSF

households in the PC followback, 462 were selected as

exact matches and are included in the count of the

43,355 exact matches. 

Next, an additional 23,838 unmatched drug refill

mentions by NSF household respondents in the PC were

matched to one of the 43,355 PC drugs that had been

exactly matched to a purchase of the same drug by the

same person in the same round. This produced 67,193

matches to the original 188,422 drug mentions by NSF

household respondents. Of the remaining 121,229

unmatched NSF drug mentions, 3,790 were set aside as

unmatched free samples, leaving 117,439 HC drug

mentions unmatched for all NSF households (39,343 for

households in the PC and 78,096 for households not in

the PC), representing 47,705 unique EVNTID values.

These 47,705 EVNTIDs were eventually imputed values

from PC prescription drugs through the same general

series of matching runs without replacement as for SFs,

described above. The donor pool for these matches

consisted of 81,621 PC prescriptions, the full 84,029

sample minus the 2,408 drugs with an imputed NDC

that had no exact match to the GPI code. 

In the first NSF imputation without replacement,

which required an exact match with the full GPI, 5,624

of the 47,725 EVNTIDs were matched. In the

subsequent imputations, 32,066 of these EVNTIDs

matched on the first 10 digits of the GPI; 274 matched

on the first 8 digits; 4,746 matched on the first 4 digits;

1,854 matched on the first 2 digits; and 3,161 did not

match on any portion of the GPI. After the matches

were linked to every unmatched refill with the same

EVNTID, all 117,439 previously unmatched HC drug

mentions of the NSF population were imputed a PC

prescription record. Because no charge and payment

data were collected in the HC for the NSF population,

there was no need to reconcile charge and payment data

from the two sources after the matching and imputation.

Unmatched PC Data

In the PC donor database of 81,621 drugs (all

84,029 PC drugs less the 2,408 excluded from the

matches with replacement), over one-third, or 29,871

drugs, were never imputed or matched exactly to an HC
17

drug mention. Because this number is close to the

25,666 refills of household drug mentions that were

exactly matched to PC prescriptions, an exercise was

undertaken to determine how many of the unimputed,

unmatched PC drug purchases could be matched to the

imputed, matched PC drug purchases by NDC, GPI, and

medication name. 

Results of this exercise confirmed the hypothesis

that the vast majority of the unmatched PC drug

purchases were duplicates, or refills, of PC drug

purchases that were matched to HC drug mentions.

Using NDC and GPI as exact match variables, and the

medication name, prescription price, and potential

payment sources (in descending order) as weighted

match variables, 25,908 of the 29,871 unmatched PC

drug purchases matched to one of the 51,750 PC donor

drug purchases. Of the remaining 3,963 unmatched PC

drug purchases, 3,699 matched to a PC donor by using

only the GPI as an exact match variable and the same

partial match variables as before. All but 2 of the

remaining 264 unmatched PC drug purchases were

ultimately matched to one of the PC donor purchases by

successively reducing from 8 to 2 the number of digits in

the GPI that were required to match exactly. The final

two PC drug purchases were matched to a PC donor by

not requiring any exact match variables and using only

partial match variables for the drug name, prescription

price, and potential payment sources. 

The average prescription price for PC drug

purchases that were not matched or imputed to any HC

drug mentions was $32.87. This was close to $5.00 less

than the average price of PC drug purchases that were

matched or imputed to HC drug mentions one or more

times ($36.53). Of the latter 51,750 PC drug purchases,

2,794 were matched or imputed five times or more to

HC drug mentions. These drugs had an average

prescription price of $31.51. The 48,956 PC drugs that

were matched or imputed to HC drug mentions from one

to four times had an average prescription price of

$36.82. The PC drug purchases that were not matched

or imputed to HC drugs had slightly lower average outof-pocket and private insurance payment shares (38.3

and 39.9 percent, respectively) than those of PC drug

purchases that were matched or imputed at least once

(40.6 and 41.7 percent, respectively). PC drug purchases

that were not matched or imputed to HC drug mentions

also had slightly higher Medicaid and other payment

shares (14.8 and 7.0 percent, respectively) than those of

PC donor drug purchases (12.0 and 5.7 percent,

respectively). This suggests that if all of the PC

prescription drugs had been matched or imputed to HC

drug mentions, the average HC prescription price and

aggregate prescription expenditures, out-of-pocket

payments, and private insurance payments would have

been slightly lower, and aggregate Medicaid and other

public prescription expenditures would have been

slightly higher. 

Allocation to 1996 and 1997

After all SF and NSF prescription mentions in the

MEPS HC were matched to PC prescriptions, Round 3

drug mentions that were not exactly matched to PC drug

products were allocated to either 1996 or 1997. As

mentioned earlier, the Round 3 survey spanned both

years for respondents, and reported prescription

purchases in the HC were not dated within the round

unless they were matched exactly to a PC drug record.

Each Round 3 drug mention that was not exactly

matched was allocated to 1996 or 1997 by using the

beginning and ending dates of Round 3 for each person

and the percentage of Round 3 covering 1996 and

covering 1997, in combination with a random draw

between 0 and 1.

For the HC population, 12,923 of the 77,110 Round

3 prescriptions were exactly matched to PC drugs and

classified as 1996 drug purchases. Of the remaining

64,187 prescriptions, 52.0 percent (33,394 prescriptions)

were classified as 1996 drug purchases using the random

allocation method, while the other 48.0 percent (30,793

prescriptions) were classified as 1997 drug purchases. 

Sensitivity Testing

The edit rules imposed on the PC donor database

described above, on balance, were likely to increase the

average retail price of prescriptions because substantially

more PC drugs were outliers at the low end of the

distribution (22,002 PRATIO values below .8) than at the

high end (540 PRATIO values above 10 or 20). Of

particular concern were the outlier cases at the low end

of the distribution that were classified as not missing any

payment sources (PRICE1 cases). Furthermore, there

was some evidence to suggest that, in certain situations,

the outlier PRATIO value may have been caused by a

misreported dosage amount rather than a misreported

price in the PC. To analyze the sensitivity of national

estimates of prescription expenditures to the outlier edits,
18

alternative estimates were made by successively undoing

various outlier unit price edits that had been performed

on the PC data. 

The national estimate of prescription expenditures in

1996 for the MEPS population before any changes were

made to the edit rules described above was $75.4 billion.

The edits reversed were all edits to drug prices reported

without any missing payment sources (PRICE1 cases).

A hierarchy was established for undoing the edits such

that the most likely candidates for a quantity edit rather

than a price edit had their price edits reversed first.

Reversing the 7,575 lower end edits under rule P12, in

which no payments were missing and the original retail

price/payment sum did not equal the out-of-pocket

payment, lowered the national prescription expenditure

estimate by $3.7 billion to $71.7 billion. Reversing the

657 lower end edits under rule P10, in which no

payments were missing and the original retail

price/payment sum equaled the self-payment but was

greater than $30, lowered national prescription

expenditures by another half billion dollars to $71.2

billion. 

The final lower end edit that was reversed when no

payment sources were reported missing involved the

8,121 rule P9 cases in which the retail price/payment

sum equaled the self-payment amount and was less than

or equal to $30. These are more likely to be outlier

retail price cases because the low price and 100 percent

self-payment suggest a copayment coupled with a unit

retail price less than 80 percent of the AWUP. Undoing

this edit reduced the national prescription expenditure

estimate by another $4.6 billion to $66.6 billion. 

Finally, undoing the two higher end price edits in

which no payment sources were missing for the 798

events edited under rules P11 and P13 increased the

national prescription expenditure estimate by $.1 billion

to $66.7 billion. 

As suspected, the PC pricing edit rules on the linked

pharmacy followback data for cases in which no

payment source amounts were reported missing had an

impact on the national prescription expenditure estimate

from the MEPS data. For the public use data (the 1996

Prescribed Medicines File, HC-010A), the middle

estimate of $71.2 billion was selected. For this estimate,

pricing edit rules P9, P11, and P13 were left intact, but

pricing edits P10 and P12 were replaced by

quantity/dosage amount edits. New dosage amounts

were imputed by dividing the original retail price of the

drug by the AWUP. Price changes for PC prescriptions

that were matched to SFs required that the SF HCreported charge and payment data be reconciled to the

new PC price and payment data before finalizing

expenditure and payment amounts for the public use file. 

Consistency Edits

As discussed above, imputations of PC data to HC

drug mentions that were not exactly matched to PC drug

purchases, or refills (additional acquisitions) thereof,

were primarily based on match variables for the GPI

code and the medication name. Additional weighted

match variables included potential third-party payment

sources, but these were not required to be exact-match

variables in the imputations because of small cell sizes.

As a result, 16,829 HC drug purchases for 1996 had at

least one inconsistent third-party payment source. These

were defined as imputed payments from a given thirdparty source that was not indicated by the individual in

the HC as a potential payment source. For SFs only, this

source also had to be coded as “not a known payer” in a

second set of variables to be classified as “inconsistent.” 

For the 16,829 drug purchases with inconsistent

imputed payment sources, a hot deck was run in which

the 148,644 purchases in 1996 with consistent payments

were used as the donor group for the hot deck. The

class variables selected for the hot deck were the

primary payer and the price category for the drug

purchase. The primary payer was determined for the

recipient group according to potential payment sources

in the following hierarchical order: Medicare; private

insurance; CHAMPUS; Medicaid; VA; other Federal;

State and local; other; Worker’s Compensation; out of

pocket or self-payment. The same order was applied to

donors, although in their case, the order referred to

actual payments from a source rather than variables

indicating potential payment sources. Then six

categories of the primary payer variable were

constructed as the class variable for the hot deck:

private; Medicaid; Medicare; other public (CHAMPUS,

VA, other Federal, State and local, and Worker’s

Compensation); other; and self or family. The price

category was divided into four categories: greater than

zero and up to $15; $15.01 up to $30.00; $30.01 up to

$100.00; and over $100.00. Sort variables for the hot

deck consisted of six variables indicating whether each
19

of the six categories of the primary payer variable was a

consistent payment source. 

The hot deck imputed percentage shares to each

recipient drug purchase. The percentage shares came

from each of the six payment source categories from the

donor record. They were used to allocate the drug price

of the recipient drug to each source. If the “other

public” share from a donor was non-zero and only one

of the five sources in this group was a consistent source,

then the full share was allocated to that source. If more

than one of the five sources in the “other public”

category was a consistent source, then the full share was

allocated according to the hierarchy above for choosing

among the five sources in this category. 

The March 2001 Revision

In the late fall of 2000, records not included on the

original public use version of file HC-010A were

identified with missing values of the variable

MEDCYCLE, which indicates the number of times a

drug was purchased during a round. In addition, it was

discovered that the value of MEDCYCLE had been

misreported in the survey for certain medications on the

original HC-010A file. A revised public use version of

the 1996 prescription drug event file, which corrected

for the missing and misreported MEDCYCLE values,

was released in March 2001. This section of the report

briefly describes the modifications made to the

previously released public use version of the HC-010A

file in May 2000 and the consequent impacts on the size

of the file and on national estimates of prescription drug

utilization and expenditures. 

To identify misreported MEDCYCLE values, a

variable called MEDRATE was constructed as the days

in a round divided by MEDCYCLE. MEDRATE

indicates the maximum value of the average number of

days between refills of a prescription drug within a

round for an individual. This estimate is considered to

be a maximum value because it assumes that the person

purchased the drug on the first day of the round.

Identifying values of MEDRATE of 3 or less enabled

AHRQ staff, in consultation with a pharmacy expert, to

eliminate all of the implausible MEDCYCLE values.

Certain values of MEDCYCLE with values of

MEDRATE at or below this threshold were left intact

because a low MEDCYCLE value was combined with a

small number of days in the MEPS round; these were

plausible cases, according to the pharmacy expert.

Cases identified as misreported MEDCYCLE values

were imputed new values drawn at random from the

distribution of valid MEDCYCLE values for drugs with

the same GPI code. 

Moreover, as part of the revision to the HC-010A

file, all previously identified free samples were

reclassified as purchases because respondents were not

specifically instructed to include counts of free samples

in reporting the survey data that produced the

MEDCYCLE variable. Also, a revision was made to the

allocation of Round 3 HC prescription mentions

between 1996 and 1997 to incorporate information

regarding the year in which a sampled individual first

started taking the prescription drug (RXBEGYR). With

the exception of exactly matched HC drug mentions, all

Round 3 HC drug mentions with MEDCYCLE = 1 and

RXBEGYR = 1996 were allocated to 1996, and all

Round 3 HC drug mentions with RXBEGYR = 1997

were allocated to 1997 regardless of the MEDCYCLE

value. All other Round 3 HC drug acquisitions that

were not exact matches but had been assigned new

MEDCYCLE values were allocated to 1996 and 1997

based on the proportion of time the sampled person was

in Round 3 in each year. 

Any 1996 HC drug acquisitions added to the revised

1996 HC-010A file that had been omitted from the

original file were imputed prescription drug information

from the PC by hot decking to HC drug purchases on

both the revised and original files that had been

previously matched to PC drugs. Class variables for the

hot deck included the GPI code for the drug, filer status

(SF or NSF), age, sex, and Medicaid and private drug

insurance status. The sort variables included the

remaining insurance status variables, HMO status, and

region. 

The results of the March 2001 revisions to the 1996

file are reported in Table 3. Of the original 167,784 drug

purchases on the file, 26,368 acquisitions (15.7 percent)

were deleted because of misreported MEDCYCLE

values. In addition, 4,021 acquisitions with valid

MEDCYCLE values (2.4 percent) were reassigned to

1997 because of the revised allocation rules. Another

6,712 acquisitions (4.0 percent) were added to the

original file from the newly identified missing

MEDCYCLE cases or were reassigned to 1996 from

1997 because of the new allocation rules. Finally,

another 3,201 previous free samples on the original file

(1.9 percent) became drug purchases in 1996 on the

revised file. The March 2001 HC-010A file contains
147,308 drug purchases, representing a net decline of

20,476 purchases, or 12.2 percent, from the original

drug file.

National estimates of drug purchases declined from

2.116 billion purchases to 1.865 billion purchases,

representing a net decline of .251 billion purchases, or

an 11.9-percent reduction. National estimates of

prescription drug expenditures for the civilian

noninstitutionalized population in the 1996 MEPS HC

declined from $71.208 billion to $65.291 billion,

representing a net decline of $5.917 billion, or an 8.3-

percent decline in national expenditures because of the

modifications made to the file.

Reference

Cohen JW, Monheit AC, Beauregard KM, et al. The

Medical Expenditure Panel Survey: a national health

information resource. Inquiry 1996; 33:379-89.

20

Acquisitions Weighted acquisitions Expenditures

Category (unweighted) (billions) (billions of dollars)

Original HC-010A file 167,784 2.116 $71.208

Changes:

Misreported refills –26,368 –.328 –8.874

Reallocated to 1997 –4,021 –.049 –1.433

Missing acquisitions +5,565 +.069 +2.381

Reallocated to 1996 +1,147 +.014 +.350

Free samples +3,201 +.042 +1.659

Total net change –20,476 –.251 –5.971

Revised HC-010-A file 147,308 1.865 65.291

Note: Any differences between components and totals are a result of rounding.

Source: Center for Cost and Financing Studies, Agency for Healthcare Research and Quality: Medical Expenditure Panel

Survey, 1996, public use files HC-010A.

Table 3. Impact of March 2001 file revision on prescription utilization and

expenditures in the 1996 Medical Expenditure Panel Survey
Appendix A. List of

Abbreviations

AHRQ Agency for Healthcare Research and

Quality

AWUP Average wholesale unit price

CCFS Center for Cost and Financing Studies

CHAMPUS Civilian Health and Medical Program

for the Uniformed Services

EVNTID Event identification number

GPI Generic Product Identifier

HC Household Component (of MEPS)

HCFA Health Care Financing Administration

HMO Health maintenance organization

MDDB Master Drug Data Base

MEDCYCLE The number of times a prescription drug

was purchased during a round

MEDRATE The days in a round divided by 

MEDCYCLE

MEPS Medical Expenditure Panel Survey

MPC Medical Provider Component (of 

MEPS)

MSA Metropolitan statistical area

NCHS National Center for Health Statistics

NDC National Drug Code

NSF Non-self-filer

OUT1n Outlier group based on PRATIOn 

(= 1 if PRATIOn < .8; = 0 otherwise).

OUT2n Outlier group based on PRATIOn (= 1 if 

.8 < PRATIOn < 20 for n = 1 and 

AWUP before 1996 or if .8 <

PRATIOn < 10 otherwise; = 0 else).

OUT3n Outlier group based on PRATIOn (= 1 if

PRATIOn > 20 for n = 1 and AWUP 

before 1996 or if PRATIOn > 10 

otherwise; = 0 else).

PC Pharmacy Component (of MEPS)

PERSID Person identification number

PRATIOn The ratio of RUPn to AWUP, using 

PRICEn, n = 1,2,3 and dosage amounts

to construct RUPn.

PRGRP1 One of three groups used to sort drug

events with a positive-valued price for

outlier analysis (= 1 if PRICEn = selfpayment and PRICEn < $30; = 0

otherwise).

PRGRP2 One of three groups used to sort drug

events with a positive-valued price for

outlier analysis (= 1 if PRICEn = selfpayment and PRICEn > $30; = 0

otherwise).

PRGRP3 One of three groups used to sort drug

events with a positive-valued price for

outlier analysis (= 1 if PRICEn does not

equal self-payment; = 0 otherwise).

PRICE1 Measure of the retail price of the drug

product (= the sum of payments when 

no payments were coded as missing) 

PRICE2 Measure of the retail price of the drug

product (= the sum of payments when at

least one payment is reported greater 

than zero and at least one payment is 

reported as missing) 

PRICE3 Measure of the retail price of the drug

product (= the reported total charge for

the drug product when reported to be

greater than zero and all payments are

reported to be either missing or zero)

PRICE4 Measure of the retail price of the drug

product (lacked any positive-valued

payments or total charge data) 

RU Reporting unit

RUP Retail unit price

SF Self-filer

SSS Social and Scientific Systems, Inc.

VA Veterans Affairs

21
22

Appendix B. Pharmacy Component Survey Booklet
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24
25
26
27
28
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Appendix C. Permission Form for Pharmacy Component

30
31
U.S. Department of Health and Human Services

Public Health Service

Agency for Healthcare Research and Quality

2101 East Jefferson Street, Suite 501

Rockville, MD 20852

Official Business

Penalty for Private Use $300

AHRQ Pub. No. 01-0002

June 2001

ISBN 1-58763-024-9

ISSN 1531-5673

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