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MEPS HC-010A:
1996 Prescribed Medicines
Agency for Healthcare Research and Quality
Center for Financing, Access, and Cost Trends
540 Gaither Road
Rockville, MD 20850
(301) 427-1406
User's Notes
This release updates data previously released on MEPS HC-010A: 1996
Prescribed Medicines File which was released in Spring 2000. Differences between
this release and the Spring 2000 release are the following:
1) MEPS staff discovered an inaccuracy in the number of times a household
reported purchasing or otherwise obtaining a prescription drug in a particular
round for a small percentage of household-reported medications in the Spring
2000 release. This inaccuracy was due to an instrument design flaw which caused
interviewer error, and in isolated cases, resulted in mis- reported large
numbers of prescription refills for a medicine in a given round. For some cases,
it seems that the year that the person started taking the drug was recorded in
the field that gives the number of times that the person purchased, or otherwise
obtained the drug, during the round, as well as in the field that provides the
year the person started taking the medicine. For example in the round a drug was
first mentioned, when a person reported having first started taking the drug in
1996, a A96" was entered in the field for the year the person first started
taking the drug. For the problem cases, a A96" also appeared in the
preceding field indicating the number of times the drug was reported as being
purchased or otherwise obtained during the round. Outlier values where this
situation occurred were determined by comparing the number of days a respondent
was in the round and the number times the person reported having purchased or
otherwise obtained the drug in the round, and were determined in consultation
with an industry expert. For the cases where this error occurred, a new value
for the number of times a person reported purchasing or otherwise obtaining the
drug for that round was imputed.
2) In the Spring 2000 release, the prescribed medicine events in which a
household respondent did not know/remember the number of times a certain
prescribed medicine was purchased or otherwise obtained in a particular round
were inadvertently excluded from the file. For those events, all the data
related to that drug event, including the number of times a respondent purchased
or otherwise obtained the drug in a particular round, was imputed from a donor
drug event with similar characteristics.
3) Based on the two issues described above, as well as additional editing
rules, the variable that contains the number of times a prescription was
purchased or otherwise obtained by a person in a round was edited. Round 3
drugs, which spanned 1996 and 1997, had to be reallocated between 1996 and 1997.
Because of this reallocation of Round 3 events, it is possible that records (and
persons) included on the Spring 2000 release may not be included on this
release.
4) This release DOES include the variable SAMPLE and DOES NOT include the
variable FREEFLG. The SAMPLE variable indicates if a respondent reported
receiving a free sample of a prescription medicine in the round (0=no, 1=yes).
Therefore, SAMPLE=1 for acquisitions of a certain prescription medicine for a
person during a round that the respondent reported having received a free sample
of that prescription during the round. In the Spring 2000 release, FREEFLG
(which is NOT included on this release) indicated if a record on the file had
been designated as a free sample. After reviewing the data and questionnaire in
great detail, it was determined that it was not appropriate to make this
assumption.
5) Some of the estimation examples (see Section 4 of documentation) had to be
revised because the variable FREEFLG is not included on this release. (Please
see #4 above for details.) The variable DIABFLG was substituted for the variable
FREEFLG in the estimation examples, as necessary.
Table of Contents
A. Data Use Agreement
B. Background
1.0 Household Component
2.0 Medical Provider Component
3.0 Insurance Component
4.0 Nursing Home Component
5.0 Survey Management
C. Technical Information
1.0 General Information
2.0 Data File Information
2.1 Codebook Structure
2.2 Reserved Codes
2.3 Codebook Format
2.4 Variable Naming
2.4.1 General
2.4.2 Expenditure and Source of Payment Variables
2.5 Data Collection
2.5.1 Methodology for Collecting Household Reported Variables
2.5.2 Methodology for Collecting Pharmacy-Reported Variables
2.6 File Contents
2.6.1 Survey Administration Variables
2.6.1.1 Person Identifier Variables (DUID, PID, DUPERSID)
2.6.1.2 Record Identifier Variables (RXRECIDX, LINKIDX)
2.6.1.3 Round Variable (PURCHRD)
2.6.2 Characteristics of Prescribed Medicine Events
2.6.2.1 Date When Prescribed Medicine Was First Taken (RXBEGDD- RXBEGYR)
2.6.2.2 Prescribed Medicine Attributes (RXNAME-RXUNIT)
2.6.2.3 Type of Pharmacy (PHARTP1-PHARTP7)
2.6.2.4 Analytic Flag Variables (RXFLG-DIABFLG)
2.6.2.5 The Sample Variable
2.6.2.6 Condition Codes (RXICD1X-RXICD3X) and Clinical Classification Codes
(RXCCC1X-RXCCC3X)
2.6.2.7 Record Count Variable (NUMCOND)
2.6.3 Expenditure Variables (RXSF96X-RXXP96X)
2.6.3.1 Definition of Expenditures
2.6.3.2 Sources of Payment
2.6.4 Sample Weights and Variance Estimation Variables (WTDPER96- VARPSU96)
2.6.4.1 Overview
2.6.4.2 Details on Person Weights Construction
3.0 General Data Editing and Imputation Methodology
3.1 Rounding
3.2 Edited/Imputed Expenditure Variables (RXSF96X-RXXP96X)
4.0 Strategies for Estimation
4.1 Variables with Missing Values
4.2 Basic Estimates of Utilization, Expenditure and Sources of Payment
4.3 Estimates of the Number of Persons with Prescribed Medicine Events
4.4 Person-Based Ratio Estimates
4.4.1 Person-Based Ratio Estimates Relative to Persons with Prescribed Medicine
Events
4.4.2 Person-Based Ratio Estimates Relative to the Entire Population
4.5 Sampling Weights for Merging Previous Releases of MEPS Household Data with
this Event File
5.0 Variance Estimation
6.0 Merging/Linking MEPS Data Files
6.1 Linking a Person Level File to the Prescribed Medicines File
6.2 Linking the 1996 Conditions File and/or the Other 1996 MEPS Event Files to
the 1996 Prescribed Medicines File
6.3 Limitations/Caveats of RXLK and CLNKReferences
Attachment 1
D. Codebook (link to separate file)
E. Variable-Source Crosswalk
A. Data Use Agreement
Individual identifiers have been removed from the microdata contained in the
files on this CD- ROM. Nevertheless, under sections 308 (d) and 903 (c) of the
Public Health Service Act (42 U.S.C. 242m and 42 U.S.C. 299 a-1), data collected
by the Agency for Healthcare Research and Quality (AHRQ) and/or the National
Center for Health Statistics (NCHS) may not be used for any purpose other than
for the purpose for which they were supplied; any effort to determine the
identity of any reported cases, is prohibited by law.Therefore, in accordance with the above referenced Federal statute, it is
understood that:
- No one is to use the data in this data set in any way except for statistical reporting and
analysis.
If the identity of any person or establishment should be discovered inadvertently, then
(a) no use will be made of this knowledge, (b) the Director, Office of Management,
AHRQ will be advised of this incident, (c) the information that would identify any
individual or establishment will be safeguarded or destroyed, as requested by
AHRQ,
and (d) no one else will be informed of the discovered identity.
- No one will attempt to link this data set with individually identifiable records from any
data sets other than the Medical Expenditure Panel Survey or the National Health
Interview Survey.
By using these data you signify your agreement to comply with the
above-stated statutorily based requirements, with the knowledge that
deliberately making a false statement in any matter within the jurisdiction of
any department or agency of the Federal Government violates 18 U.S.C. 1001 and
is punishable by a fine of up to $10,000 or up to 5 years in prison.The Agency for Healthcare Research and Quality requests that users cite AHRQ
and the Medical Expenditure Panel Survey as the data source in any publications
or research based upon these data.
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B. Background
This documentation describes one in a series of public use files from the
Medical Expenditure Panel Survey (MEPS). The survey provides a new and extensive
data set on the use of health services and health care in the United States.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 (AHCPR)) and the National Center for Health
Statistics (NCHS).MEPS comprises four component surveys: the Household Component (HC), the
Medical Provider Component (MPC), the Insurance Component (IC), and the Nursing
Home Component (NHC). The HC is the core survey, and it forms the basis for the
MPC sample and part of the IC sample. The separate NHC sample supplements the
other MEPS components. Together these surveys yield comprehensive data that
provide national estimates of the level and distribution of health care use and
expenditures, support health services research, and can be used to assess health
care policy implications.MEPS is the third in a series of national probability surveys conducted by
AHRQ on the financing and use of medical care in the United States. The National
Medical Care Expenditure Survey (NMCES, also known as NMES-1) was conducted in
1977, and the National Medical Expenditure Survey (NMES-2) was conducted 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 sampling frame for the MEPS HC is drawn,
and continuous 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.
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1.0 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 5 rounds of interviews over a
2½-year period. Using computer- assisted personal interviewing (CAPI)
technology, data on medical expenditures and use for two 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.
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2.0 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.Associated with a 75-percent sample of HC households receiving care through
an HMO (health maintenance organization) or managed care plan.
Associated with a 25-percent sample of the remaining HC 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-CM (9th Revision, International
Classification of Diseases) and DSM-IV (Fourth Edition, Diagnostic and
Statistical Manual of Mental Disorders).Physician procedure codes classified by CPT-4 (Common Procedure Terminology,
Version 4).Inpatient stay codes classified by DRGs (diagnosis-related groups).Prescriptions coded by national drug code (NDC) and medication name.Charges, payments, and the reasons for any difference between charges and
payments.The MPC is conducted through telephone interviews and mailed survey
materials.
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3.0 Insurance Component
The MEPS IC collects data on health insurance plans obtained through
employers, unions, and other sources of private health insurance. Data obtained
in the IC include the number and types of private insurance plans offered,
benefits associated with these plans, premiums, contributions by employers and
employees, eligibility requirements, and employer characteristics.Establishments participating in the MEPS IC are selected through four
sampling frames:A list of employers or other insurance providers identified by MEPS HC
respondents who report having private health insurance at the Round 1 interview.A Bureau of the Census list frame of private-sector business establishments.The Census of Governments from Bureau of the Census.An Internal Revenue Service list of the self-employed.To provide an integrated picture of health insurance, data collected from the
first sampling frame (employers and insurance providers) are linked back to data
provided by the MEPS HC respondents. Data from the other three sampling frames
are collected to provide annual national and State estimates of the supply of
private health insurance available to American workers and to evaluate policy
issues pertaining to health insurance.The MEPS IC is an annual survey. Data are collected from the selected
organizations through a prescreening telephone interview, a mailed
questionnaire, and a telephone follow-up for nonrespondents.
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4.0 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 medicines,
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 care-giving 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 sample 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 3 rounds of data collection over a 1½-year period using
the CAPI system. Community data were collected by telephone using
computer-assisted telephone interviewing (CATI) technology. At the end of 3
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.
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5.0 Survey Management
MEPS data are collected under the authority of the U.S. Public Health Service
Act. They are edited and published in accordance with the confidentiality
provisions of this act and the Privacy Act. NCHS provides consultation and
technical assistance in this regard.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 from the Internet on the
MEPS web site: <http://www.meps.ahrq.gov/>
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.
C. Technical Information
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1.0 General Information
This documentation describes one in a series of public use event files from
the 1996 Medical Expenditure Panel Survey Household Component (MEPS HC) and
Medical Provider Component (MPC) . Released as an ASCII data file and SAS
transport file, this public use file provides detailed information on
household-reported prescribed medicines for a nationally representative sample
of the civilian noninstitutionalized population of the United States and can be
used to make estimates of prescribed medicine utilization and expenditures for
calendar year 1996. Each record on this event file represents a unique
prescribed medicine event; that is, a prescribed medicine reported as being
purchased or otherwise obtained by the household respondent. In addition to
expenditures related to the prescribed medicine, each record contains household
reported characteristics and medical conditions associated with the prescribed
medicine.Data from this event file can be merged with other 1996 MEPS HC data files,
for purposes of appending person characteristics such as demographic or health
insurance coverage to each prescribed medicine record.Counts of prescribed medicine utilization are based entirely on household
reports. Information from the Pharmacy Component (within the MEPS Medical
Provider Component) was used to provide expenditure and payment data as well as
details of the medication (e.g., strength, quantity, etc.).The file can be used to construct summary variables of expenditures, sources
of payment, and other aspects of utilization of prescribed medicines. Aggregate
annual person level information on the use of prescribed medicines and other
health services use is provided on the 1996 Population Characteristics file,
where each record represents a MEPS sampled person.The following documentation offers a brief overview of the types and levels
of data provided and the content and structure of the files and the codebook. It
contains the following sections:
Data File Information
Sample Weights and Variance Estimation Variables
Merging MEPS Data Files
References
Codebook
Variable to Source Crosswalk
For more information on MEPS HC survey design see S. Cohen, 1997; J. Cohen,
1997; and S. Cohen, 1996. For information on the MEPS MPC design, see S. Cohen,
1998. A copy of the survey instrument used to collect the information on this
file is available on the MEPS web site at the following address: <http://www.meps.ahrq.gov>
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2.0 Data File Information
This public use data set contains 147,308 prescribed medicine records. Each
record represents one household-reported prescribed medicine that was purchased
or obtained during calendar year 1996. These data were collected during rounds
1, 2, and 3 of the MEPS HC. Of the 147,308 prescribed medicine records, 145,121
records are associated with persons having a positive person level weight
(WTDPER96). The persons represented on this file had to meet either criteria a
or b below:
a) Be classified as a key in-scope person who responded for his or her entire
period of 1996 eligibility (i.e., persons with a positive 1996 full-year person
level sampling weight (WTDPER96 > 0)), or
b) Be classified as either an eligible non-key person or an eligible
out-of-scope person who responded for his or her entire period of 1996
eligibility, and belonged to a family (i.e., all persons with the same value for
a particular FAMID variable) in which all eligible family members responded for
their entire period of 1996 eligibility, and at least one family member has a
positive 1996 full-year person weight (i.e., eligible non-key or eligible out-
of-scope persons who are members of a family, all of whose members have a
positive 1996 full-year MEPS family level weight (WTFAM96 >0)).
Please refer to Attachment 1 for definitions of key, non-key, inscope and
eligible. Persons with no prescribed medicine use for 1996 are not included on
this file (but are represented on MEPS person level files). A codebook for the
data file is provided.This file includes prescribed medicine records for all household survey
respondents who resided in eligible responding households and reported at least
one prescribed medicine. Only prescribed medicines that were purchased or
otherwise obtained in calendar year 1996 are represented on this file.
This file
includes prescribed medicines identified in the Prescribed Medicines section of
the Household Component survey instrument, as well as those prescribed medicines
identified in association with medical events. Each record on this file
represents a single acquisition of a prescribed medicine reported by household
respondents. Some household respondents may have multiple acquisitions of
prescribed medicines and thus will be represented in multiple records on this
file. Other household respondents may have reported no acquisitions of
prescribed medicines and thus will have no records on this file.
It should also be noted that refills are included on this file. The HC
obtains information on the name of the prescribed medicine and the number of
refills, if any, associated with it. The data collection design for the HC does
not allow separate records to be created for multiple acquisitions of the same
prescribed medicine. However, in the Pharmacy Component, each original purchase,
as well as any refill, is considered a unique prescribed medicine event.
Therefore, for the purposes of editing, imputation and analysis, all records in
the Household Component were "unfolded" to create separate records for
each original purchase and each refill. Please note, MEPS did not collect
information in the HC to distinguish multiple acquisitions of the same drug
between the original purchase and refills. The survey only collected data on the
number of times a prescribed medicine was acquired during a round. In some
cases, all purchases may have been refills of an original purchase in a prior
round or prior to the survey year. The file also includes a variable, (SAMPLE),
which indicates whether or not the household received a free sample of that drug
in that round. (To obtain more details on free samples, please see Section
2.6.2.5)
Each record on this file includes the following: an identifier for each
unique prescribed medicine; detailed characteristics associated with the event
(e.g., National Drug Code (NDC), medicine name, etc.); conditions, if any,
associated with the medicine; the date on which the person first used the
medicine; total expenditure and sources of payments; types of pharmacies that
filled the household's prescriptions; whether the prescription is one in which
the household received a free sample of it during the round; and a full-year
person level weight.
Data from this file can be merged with previously released MEPS HC person
level data using the unique person identifier, DUPERSID, to append person
characteristics such as demographic or health insurance coverage to each record.
Data from this file can also be merged with the 1996 person level expenditure
file to estimate expenditures for persons with prescribed medicines. The
Prescribed Medicines event file can also be linked to the MEPS 1996 Medical
Conditions File and additional MEPS 1996 event files. Please see the Appendix
File for details on how to link MEPS data files.
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2.1 Codebook Structure
For each variable on the file, both weighted and unweighted frequencies are
provided. The codebook and data file sequence list variables in the following
order:
Unique person identifiers
Unique prescribed medicine identifiers
Other survey administration variables
Prescribed medicine characteristics variables
ICD-9 codes
Clinical Classification Software codes
Expenditure variables
Weight and variance estimation variables
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2.2 Reserved Codes
The following reserved code values are used:
VALUE DEFINITION
-1 INAPPLICABLE Question was not asked due to skip pattern.
-2 DETERMINED IN A Question was not asked in round because there was
PREVIOUS ROUND no change in employment status or no change in current main job
since previous round.
-3 NO DATA IN ROUND Person has no data in round.
-5 NEVER WILL KNOW Person never will know answer
.-6 INAPPLICABLE Not asked due to person being under age 5.
-7 REFUSED Question was asked and respondent refused to answer question.
-8 DK Question was asked and respondent did not know answer.
-9 NOT ASCERTAINED Interviewer did not record the data
.-13 VALUE SUPPRESSED Data suppressed.
Generally, values of -1, -7, -8 and -9 have not been edited on this file. The
values of -1 and -9 can be edited by analysts by following the skip patterns in
the questionnaire. The value of -13 was assigned when originally reported
Household Component and Pharmacy Component data were suppressed because imputed
versions of the variable are available on the Public Use File.
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2.3 Codebook Format
This codebook describes an ASCII data set (although the data are also being
provided in a SAS transport file). The following codebook items are provided for
each variable:
| IDENTIFIER |
DESCRIPTION |
| Name |
Variable name (maximum of 8
characters) |
| Description |
Variable descriptor (maximum 40
characters) |
| Format |
Number of bytes |
| Type |
Type of data: numeric (indicated by
NUM) or character (indicated by CHAR) |
| Start |
Beginning column position of
variable in record |
| End |
Ending column position of variable
in record |
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2.4 Variable Naming
In general, variable names reflect the content of the variable, with an 8
character limitation. For questions asked in a specific round, the end digit in
the variable name reflects the round in which the question was asked. Generally,
imputed/edited variables end with an "X".
2.4.1 General
Variables contained on this file were derived from the HC questionnaire
itself, the MPC data collection instrument, or from the CAPI. The source of each
variable is identified in Section E, entitled "Variable-Source
Crosswalk." Sources for each variable are indicated in one of four ways:
(1) variables which are derived from CAPI or assigned in sampling are so
indicated; (2) variables which come from one or more specific questions have
those numbers and the questionnaire section indicated in the "Source"
column; (3) variables constructed from multiple questions using complex
algorithms are labeled "Constructed" in the "Source" column;
and (4) variables which have been imputed are so indicated.
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2.4.2 Expenditure and Source of Payment
Variables
Only imputed/edited versions of the expenditure variables are provided on the
file. Expenditure variables on this event file follow a standard naming
convention and are seven characters in length. The 12 source of payment
variables and one sum of payments variable are named consistently in the
following way:The first two characters indicate the type of event:
IP - inpatient stay
OB - office-based visit
ER - emergency room visit
OP - outpatient visit
HH - home health visit
DV - dental visit
OM - other medical equipment
RX - prescribed medicineIn the case of the source of payment variables, the third and fourth
characters indicate:
SF – self or family OF – other Federal Government
XP – sum of payments
MR – Medicare
SL – State/local government
MD – Medicaid
WC – Worker's Compensation
PV – private insurance
OT – other insurance
VA – Veterans
OR – other private
CH – CHAMPUS/CHAMPVA
OU – other publicThe fifth and sixth characters indicate the year (96). All imputed/edited
expenditure variables end with an " X".
For example, RXSF96X is the edited/imputed amount paid by self or family for
the 1996 prescribed medicine expenditure.
2.5 Data Collection
Data regarding prescription drugs were obtained through the household
questionnaire and a pharmacy follow-back component (within the Medical Provider
Component).
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2.5.1 Methodology for Collecting Household Reported Variables
During each round of the MEPS HC, all respondents were asked to supply the
name of any prescribed medicine they or their family members purchased or
otherwise obtained during that round. For each medicine in each round, the
following information was collected: whether any free samples of the medicine
were received; the name(s) of any health problems the medicine was prescribed
for; the number of times the prescription medicine was obtained or purchased;
the year, month, and day on which the person first used the medicine; and a list
of the names, addresses, and types of pharmacies that filled the household's
prescriptions. In the HC, respondents were asked if they send in claim forms for
their prescriptions (self-filers) or if their pharmacy providers do this
automatically for them at the point of purchase (non-self-filers). For
non-self-filers, charge and payment information was collected in the pharmacy
follow-back component. However, charge and payment information was collected for
self-filers in the household questionnaire, because payments by private
third-party payers for self-filers' purchases would not be available from
pharmacies.
An inaccuracy in the number of times a household reported purchasing or
otherwise obtaining a prescription drug in a particular round for a small
percentage of household-reported medications was discovered. This inaccuracy was
due to an instrument design flaw which caused interviewer error, and in isolated
cases, resulted in mis-reported large numbers of prescription refills for a
medicine in a given round. This inaccuracy was confined to only a very small
percentage of unique drugs on the original data delivered. For some cases, it
seems that the year that the person started taking the drug was recorded in the
field that gives the number of times that the person purchased, or otherwise
obtained the drug, during the round, as well as in the field that provides the
year the person started taking the medicine. For example (in the round a
specific drug was first mentioned), a person was reported to have first started
taking the drug in 1996, a A96" was entered in the field for the year the
person first started taking the drug. For a small percentage of the cases in
which persons began taking a drug in 1996, a A96" appeared in the preceding
field indicating the number of times the drug was purchased or otherwise
obtained during the round, as well. Outlier values where this situation occurred
(and similar instances) were determined by comparing the number of days a
respondent was in the round and the number times the person reported having
purchased or otherwise obtained the drug in the round, and were determined in
consultation with an industry expert. For these events, a new value for the
number of times a drug was purchased or otherwise obtained by a person in a
round was imputed.
In addition, the prescribed medicine events in which a household respondent
did not know/remember the number of times a certain prescribed medicine was
purchased or otherwise obtained in a particular round were inadvertently
excluded from the original file. For those events, all the data related to that
drug event, including the number of times a respondent purchased or otherwise
obtained the drug in a particular round, was imputed from a donor drug event
with similar characteristics.
Based on the two issues described above, as well as additional editing rules,
changes in the number of times a prescription was purchased or otherwise
obtained by a person in a round were made, and Round 3 drugs (which spanned 1996
and 1997) had to be reallocated between 1996 and 1997.
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2.5.2 Methodology for Collecting Pharmacy-Reported Variables
If the respondent with the prescription gave written permission to release
his or her pharmacy records, pharmacy providers identified by the household were
contacted by mail for the pharmacy follow-back component. The signed permission
forms were provided to the various establishments prior to making any requests
for information. Each establishment was informed of all persons participating in
the survey who had prescriptions filled at their place of business, and a
computerized printout of all prescriptions filled for each person was sought.
For each medication listed, the following information was requested: date
filled; National Drug Code (NDC); medication name; strength of medicine (amount
and unit); quantity (package size/amount dispensed); total charge; and payments
by source. For more details on the data collection procedures, as well as
response rate information, please refer to the following MEPS Methodology
Report, which will be available on the MEPS web site and from the AHRQ
Clearinghouse in the near future, "Outpatient Prescription Drugs: Data
Collection and Editing in the 1996 Medical Expenditure Panel Survey
(HC-010A)" (Moeller, Stagnitti, Horan, et al., 2001).
Return to Table of Contents
2.6 File Contents
2.6.1 Survey Administration Variables
2.6.1.1 Person Identifier Variables
(DUID, PID, DUPERSID)
The dwelling unit ID (DUID) is a 5-digit random number assigned after the
case was sampled for MEPS. The 3-digit person number (PID) uniquely identifies
each person within the dwelling unit. The 8-character variable DUPERSID uniquely
identifies each person represented on the file and is the combination of the
variables DUID and PID. For detailed information on dwelling units and families,
please refer to the documentation of a public use file containing person level
population characteristics.
2.6.1.2 Record Identifier Variables
(RXRECIDX, LINKIDX)
The variable RXRECIDX uniquely identifies each record on the file. This
17-character variable is comprised of the following components: prescribed
medicine event generated through the Household Component (positions 1-12) +
unique drug identifier (positions 13-14) + refill number (positions 15-17). The
prescribed medicine event generated through the Household Component (positions
1-12) can be used to link a prescribed medicine event to the conditions file and
to other event files, via link files, and is provided on this file as the
variable LINKIDX. (For more details on linking, please refer to the Appendix
File.) The unique drug identifier enumerates the unique drugs associated with
each household reported prescribed medicine for a person in a given round, where
a unique drug is defined as having a unique National Drug Code value (reported
in the variable RXNDC). Finally, the refill number enumerates the refills of
each unique drug. For example, a person in Round 1 of the household interview
reports having purchased Amoxicillin. This could be represented on the
prescribed medicines event file as one event or several events depending on the
number of unique NDC's and purchases of each unique NDC associated with
Amoxicillin for this person in this round.The following hypothetical example illustrates the structure of these ID
variables. These five records describe one household reported prescribed
medicine for a person in a given round, which includes three unique prescribed
medicines, with the first medicine having three purchases with the same National
Drug Code (NDC).
DUPERSID RXRECIDX
LINKIDX
RXNDC
00002026 00002026008301001
000020260083 00364021802
00002026 00002026008301002
000020260083 00364021802
00002026 00002026008301003
000020260083 00364021802
00002026 00002026008302001
000020260083 00364044201
00002026 00002026008303001
000020260083 00364046105
Return to Table of Contents
2.6.1.3 Round Variable (PURCHRD)
The variable PURCHRD indicates the round in which the prescribed medicine was
obtained/purchased and takes on the value of 1, 2, or 3.
2.6.2 Characteristics of Prescribed Medicine Events
2.6.2.1 Date When Prescribed Medicine Was First Taken
(RXBEGDD-RXBEGYR)
There are three variables which indicate when a prescribed medicine was first
taken (or obtained), as reported by the household. They are the following:
RXBEGDD indicates the day a person first started taking a medicine, RXBEGMM
denotes the month in which a person first started taking a medication, and
RXBEGYR reflects the year in which a person first started taking a medicine.
These "first taken" questions are only asked the first time a
prescription is mentioned by the household. These questions are not asked of
refills of the prescription for a person in subsequent rounds and result in a
value of -1 being assigned to those types of events for these variables.
2.6.2.2 Prescribed Medicine Attributes
(RXNAME-RXUNIT)
For each prescribed medicine included on this file, several data items
collected describe in detail the medication obtained or purchased. These data
items are the following:
a. Medication name - pharmacy reported (RXNAME)
b. Medication name - household reported (RXHHNAME)
c. National drug code (RXNDC)
d. Quantity of the prescribed medicine dispensed, e.g., number of tablets in the
prescription (RXQUANTY)
e. Form of the prescribed medicine; e.g., tablets (RXFORM)
f. Strength of the dose of the medicine prescribed; e.g., 10 (RXSTRENG)
g. Unit of measurement for the dose of the prescribed medication; e.g., mg (RXUNIT)
The National Drug Code (NDC) generally is an 11-digit code. The first 5
digits indicate the manufacturer of the prescribed medicine. The next 4 digits
indicate the form and strength of the prescription, and the last 2 digits
indicate the package size from which the prescription was dispensed. NDC values
were imputed from a proprietary database to certain Pharmacy Component (PC)
prescriptions because the NDC reported by the pharmacy provider did not match to
the proprietary database. These records are identified by RXFLG=3. AHRQ's
licensing agreement for the proprietary database precludes the release of these
imputed NDC values to the public, so for these prescriptions, the
household-reported name of the prescription (RXHHNAME) and the original NDC (RXNDC)
and prescription name (RXNAME) reported by the pharmacist are provided to allow
users to do their own imputation. Otherwise, the imputed NDC values for the
RXFLG=3 cases may be accessed through the MEPS Data Center. For those events not
falling in the RXFLG=3 category, the reserve code (-13) is assigned to the
household reported medication name (RXHHNAME). The variable RXHHNAME was imputed
only for those records which were reallocated Round 3 drugs as well as those
events where a value of the number times the prescription was reported as having
been purchased or otherwise obtained by the respondent during a round was
missing and was imputed. For information on accessing confidential data through
the MEPS Data Center, contact the MEPS Project Director by email at: <mepspd@ahrq.gov>.
Imputed data on this event file (and not the other event files) may include
missing data. This is because imputed data on this file are imputed from the
Pharmacy Component or from a proprietary database. These sources did not always
include complete information for each variable but did include an NDC which
would typically enable an analyst to obtain any missing data items. For example,
although there are a substantial number of missing values for the form and
strength of the prescription that were not supplied by the pharmacist, these
missing values were not imputed because this information is embedded in the NDC.
Return to Table of Contents
2.6.2.3 Type of Pharmacy (PHARTP1-PHARTP7)
Household respondents were asked to list the type of pharmacy from which
their medications were purchased. A household could list multiple pharmacies
associated with their prescriptions in a given round, or over the course of all
rounds combined covering the survey year. As a result, this file contains, at
most, seven of these household reported pharmacies, but there was no link in the
survey or in the data file enabling users to know the type of pharmacy from
which a specific prescription was obtained, if multiple pharmacies are listed.
The set of variables (PHARTP1- PHARTP7) identify the types of pharmacy providers
from which the person's prescribed medicines were purchased or otherwise
obtained. The possible types of pharmacies include the following: mail-order,
another store, HMO/clinic/hospital, and drug store. A -1 value for PHARTPn
indicates that the household did not report an "nth" pharmacy. For
example, if the household only reported two pharmacies, PHARTP3-PHARTP7 = -1.
These variables were imputed only for those records which were reallocated Round
3 drugs as well as those events where a value of the number times the
prescription was reported as having been purchased or otherwise obtained by the
respondent during a round was missing and was imputed.
2.6.2.4 Analytic Flag Variables
(RXFLG-DIABFLG)
There are five flag variables included on this file (RXFLG, PCIMPFLG, SELFFLG,
INPCFLG, and DIABFLG). The variables RXFLG, INPCFLG, and DIABFLG were imputed
only for those records which were reallocated Round 3 drugs as well as those
events where a value of the number times the prescription was reported as having
been purchased or otherwise obtained by the respondent during a round was
missing and was imputed.
The variable RXFLG indicates how the NDC for a specific prescribed medicine
event was imputed. This variable indicates whether or not there was any
imputation performed on this record for the NDC variable, and if imputed, from
what source the NDC was imputed. If no imputation was performed, RXFLG=1. If the
imputation source was another pharmacy component record, RXFLG=2. Similarly, if
the imputation source was a secondary, proprietary database and not the pharmacy
component database, RXFLG=3. For these RXFLG=3 records, all the original data
reported by the pharmacy is included on the record. Including only the original
pharmacy reported data for these records was necessary in order to comply with
legal restrictions associated with using the secondary data source as an
imputation source. The imputed NDC value for the RXFLG=3 cases was used in the
data editing, but is not available for public release. However, the imputed NDCs
for the RXFLG=3 cases will be available through the MEPS Data Center.
Information on this topic can be obtained through the MEPS Project Director at
<mepspd@ahrq.gov>.
PCIMPFLG indicates the type of match between a household reported event and a
pharmacy component reported event. Possible "match-types" include: (0)
there was no matching performed; these cases were hotdecked values for the
medication prices and payment amounts, (1) an exact match for a specific event
for a person between the pharmacy component and the household survey, (2)
refills of an exact match on the pharmacy component for a person, and (3) not an
exact match, nor a refill of an exact match, between the pharmacy component and
household survey (not an exact match means that a person's household reported
event did not have a matched counterpart in their corresponding pharmacy
component records). PCIMPFLG assists analysts in determining which records have
the strongest link to data reported by a pharmacy. It should be noted that
whenever there are multiple purchases of a unique prescribed medication in a
given round, MEPS did not collect information that would enable designating any
single purchase as the "original" purchase at the time the
prescription was first filled, and then designating other purchases as
"refills". The user needs to keep this in mind when the purchases of a
medication are referred to as "refills" in the documentation.
SELFFLG indicates whether or not an event was for a self-filer (SELFFLG=1) or
a non-self-filer (SELFFLG=0). Self-filers are those respondents who reported
that they submitted their own insurance claims directly to their insurance
provider in a given round. Non-self-filers are those respondents who had their
pharmacy provider submit their health insurance claim directly to their
insurance carrier in a given round. The same person may be both a self-filer and
a non-self-filer during their period in the survey, but never in the same round.
INPCFLG denotes whether or not a household respondent had at least one
prescription drug purchase in the pharmacy component (0=no, 1=yes).
When diabetic supplies, such as syringes and insulin, were mentioned in the
Other Medical Equipment section of the MEPS HC, the interviewer was directed to
collect information on these items in the Prescription Medicines section of the
MEPS questionnaire. To the extent that these items are purchased without a
prescription, they represent a non-prescription addition to the MEPS
prescription drug expenditure and utilization data. Although these items may be
purchased without a prescription, a prescription purchase may be required to
obtain third party payments. For a majority of these types of events, third
party payments were made, therefore, they are included on this file. Diabetic
supplies can be identified in the file by using the variable, DIABFLG (0=not a
diabetic supply/equipment or insulin, 1=is a diabetic supply/equipment or
insulin). Diabetic supply/equipment and insulin events were identified by
utilizing a proprietary database which assisted in assigning codes to each
prescribed medicine event. This code assignment took into account the
characteristics of the event. However, if desired, analysts are free to code and
define diabetic supply/equipment and insulin events utilizing their own coding
mechanism. If desired, DIABFLG can also be used by analysts to exclude diabetic
supplies/equipment from their analyses.
Return to Table of Contents
2.6.2.5 The Sample Variable
SAMPLE indicates if a respondent reported receiving a free sample of the
prescription medicine in the round (0=no, 1=yes). Each household respondent was
asked in each round whether or not they received any free samples of a reported
prescribed medicine during the round. However, respondents were not asked to
report the number of free samples received, nor was it made clear that any free
samples received were included in the count of the number of times that the
respondent reported purchasing or otherwise obtaining the prescribed medicine
during the round. Therefore, SAMPLE=1 for all acquisitions for a person for a
specific prescription medicine during the round. This allows individual analysts
to determine for themselves how free samples should be handled in their
analysis.
2.6.2.6 Condition Codes (RXICD1X-RXICD3X) and Clinical Classification Codes
(RXCCC1X-RXCCC3X)
Information on household reported medical conditions
associated with each prescribed medicine event are provided on this
file. There are up to three
condition codes listed for each prescribed medicine event (99.7% of
prescribed medicine events have 0-3 condition records linked). These
variables were imputed
only for those records which were reallocated Round 3 drugs as well
as those events where a value of the number times the prescription
was reported as having
been purchased or otherwise obtained by the respondent during a round
was missing and was imputed. To obtain complete information associated
with an
event, the analyst must link to the 1996 Medical Conditions File. Details
on how to link to the MEPS 1996 Medical Conditions File are provided
in the 1996
Appendix File. The user should note that due to confidentiality restrictions,
provider reported condition information are not publicly available.
The medical conditions reported by the Household Component respondent were
recorded by the interviewer as verbatim text, which were then coded to
fully-specified 1996 ICD-9-CM codes, including medical condition and V codes
(see Health Care Financing Administration, 1980), by professional coders.
Although codes were verified and error rates did not exceed 2.5 percent for any
coder, analysts should not presume this level of precision in the data; the
ability of household respondents to report condition data that can be coded
accurately should not be assumed (see Cox and Cohen, 1985; Cox and Iachan, 1987;
Edwards, et al, 1994; and Johnson and Sanchez, 1993). For detailed information
on conditions, please refer to the documentation on the 1996 Medical Conditions
File. For frequencies of conditions by event type, please see the 1996 Appendix
File.
The ICD-9-CM condition codes were aggregated into clinically meaningful
categories. These categories, included on the file as RXCCC1X-RXCCC3X, were
generated using Clinical Classification Software (formerly known as Clinical
Classifications for Health Care Policy Research (CCHPR)), (Elixhauser, et al.,
1998), which aggregates conditions and V-codes into 260 mutually exclusive
categories, most of which are clinically homogeneous.
In order to preserve respondent confidentiality, nearly all of the condition
codes provided on this file have been collapsed from fully-specified codes to
3-digit code categories. The reported ICD- 9-CM code values were mapped to the
appropriate clinical classification category prior to being collapsed to the
3-digit categories.
The condition codes (and clinical classification codes) linked to each
prescribed medicine event are sequenced in the order in which the conditions
were reported by the household respondent, which was in chronological order of
reporting and not in order of importance or severity. Analysts who use the 1996
Medical Conditions file in conjunction with this prescribed medicines event file
should note that the conditions on this file are sorted differently than they
appear on the Medical Conditions file.
Return to Table of Contents
2.6.2.7 Record Count Variable (NUMCOND)
The variable NUMCOND indicates the total number of condition records which
can be linked from the 1996 Medical Conditions File to each prescribed medicine
event. This variable was imputed only for those records which were reallocated
Round 3 drugs as well as those events where a value of the number times the
prescription was reported as having been purchased or otherwise obtained by the
respondent during a round was missing and was imputed. For events with no
condition records linked (NUMCOND=0), the condition and clinical classification
code variables all have a value of -1 INAPPLICABLE. Similarly, for events
without a linked second or third condition record, the corresponding second or
third condition and clinical classification code variable was set to -1
INAPPLICABLE
.In order to obtain complete condition information for events with NUMCOND
greater than 3, the analyst must link to the 1996 MEPS Condition File. Please
see Section 6.0 for details on linking MEPS data files.
2.6.3 Expenditure Variables (RXSF96X-RXXP96X)
2.6.3.1 Definition of
Expenditures
Expenditures on this file refer to what is paid for health care services.
More specifically, expenditures in MEPS are defined as the sum of payments for
care received, including out of pocket payments and payments made by private
insurance, Medicaid, Medicare and other sources. The definition of expenditures
used in MEPS differs slightly from its predecessors, the 1987 NMES and 1977
NMCES surveys, where "charges" rather than "sum of payments"
were used to measure expenditures. This change was adopted because charges
became a less appropriate proxy for medical expenditures during the 1990's due
to the increasingly common practice of discounting charges. Although measuring
expenditures as the sum of payments incorporates discounts in the MEPS
expenditure estimates, the estimates do not incorporate any manufacturer or
other rebates associated with Medicaid or other purchases. Another general
change from the two prior surveys is that charges associated with uncollected
liability, bad debt, and charitable care (unless provided by a public clinic or
hospital) are not counted as expenditures, because there are no payments
associated with those classifications. For details on expenditure definitions,
please reference the following, "Informing American Health Care
Policy" (Monheit, Wilson, Arnett, 1999).
Return to Table of Contents
2.6.3.2 Sources of Payment
In addition to total expenditures, variables are provided which itemize
expenditures according to major source of payment categories. These categories
are:
1. Out of pocket by user or family
2. Medicare
3. Medicaid
4. Private Insurance
5. Veteran's Administration, excluding CHAMPVA
6. CHAMPUS or CHAMPVA
7. Other Federal sources - includes Indian Health Service, Military Treatment
Facilities, and other care by the Federal government
8. Other State and Local Source - includes community and neighborhood clinics,
State and local health departments, and State programs other than Medicaid
9. Worker's Compensation
10. Other Unclassified Sources - includes sources such as automobile,
homeowner's, liability, and other miscellaneous or unknown sourcesTwo additional source of payment variables were created to classify payments
for particular persons that appear inconsistent due to differences between
survey questions on health insurance coverage and sources of payment for medical
events. These variables include:11. Other Private - any type of private insurance payments reported for
persons not reported to have any private health insurance coverage during the
year as defined in MEPS; and
12. Other Public - Medicaid payments reported for persons who were not reported
to be enrolled in the Medicaid program at any time during the year
Though relatively small in magnitude, users should exercise caution when
interpreting the expenditures associated with these two additional sources of
payment. While these payments stem from apparent inconsistent responses to
health insurance and source of payment questions in the survey, some of these
inconsistencies may have logical explanations. For example, private insurance
coverage in MEPS is defined as having a major medical plan covering hospital and
physician services. If a MEPS sampled person did not have such coverage but had
a single service type insurance plan (e.g. dental insurance) that paid for a
particular episode of care, those payments may be classified as "other
private". Some of the "other public" payments may stem from
confusion between Medicaid and other state and local programs or may be from
persons who were not enrolled in Medicaid, but were presumed eligible by a
provider who ultimately received payments from the program.
Please note, unlike the other events, the prescribed medicine events do have
some remaining inconsistent responses between the insurance section of the
Household Component and sources of payment from the PC (more specifically,
discrepancies between Medicare only Household insurance responses and Medicaid
sources of payment provided by pharmacy providers). These inconsistencies remain
unedited because there was strong evidence from the Pharmacy Component that
these were indeed Medicaid payments. All of these types of Household Component
events were either exact matches to events in the Pharmacy Component or refills
of exact matches, and in addition, all of these types of events were purchases
by persons with positive weights.
Return to Table of Contents
2.6.4 Sample Weights and Variance Estimation Variables (WTDPER96- VARPSU96)
2.6.4.1 Overview
There is a single full year person level weight (WTDPER96) included on this
file. A person level weight was assigned to each prescribed medicine reported by
a key, in-scope person who responded to MEPS for the full period of time that he
or she was in scope during 1996. A key person either was a member of an NHIS
household at the time of the NHIS interview, or became a member of such a
household after being out-of-scope at the time of the 1995 NHIS (examples of the
latter situation include newborns and persons returning from military service,
an institution, or living outside the United States). A person is in-scope
whenever he or she is a member of the civilian noninstitutionalized portion of
the U.S. population.
Return to Table of Contents
2.6.4.2 Details on Person Weights
Construction
The person level weight WTDPER96 was developed using the MEPS Round 1 person
level weight as a base weight (for key, in scope respondents who joined an RU
after Round 1, the Round 1 RU weight served as a base weight). The weighting
process included an adjustment for nonresponse over Round 2 and the 1996 portion
of Round 3, as well as poststratification to population control figures for
December 1996 (these figures were derived by scaling the population totals
obtained from the March 1997 Current Population Survey (CPS) to reflect the
Census Bureau estimated population distribution across age and sex categories as
of December, 1996). Variables used in the establishment of person level
poststratification control figures included: poverty status (below poverty, from
100 up to 125 percent of poverty, from 125 up to 200 percent of poverty, from
200 up to 400 percent of poverty, at least 400 percent of poverty); census
region (Northeast, Midwest, South, West); MSA status (MSA, non-MSA);
race/ethnicity (Hispanic, black but non-Hispanic, and other); sex; and age.
Overall, the weighted population estimate for the civilian noninstitutionalized
population for December 31, 1996 is 265,439,511 persons. The inclusion of key,
in-scope persons who were not in scope on December 31,1996 brings the estimated
total number of persons represented by the MEPS respondents over the course of
the year up to 268,905,490 (WTDPER96 > 0). The weighting process included
poststratification to population totals obtained from the 1996 Medicare Current
Beneficiary Survey (MCBS) for the number of deaths among Medicare beneficiaries
in 1996, and poststratification to population totals obtained from the 1996 MEPS
Nursing Home Component for the number of individuals admitted to nursing homes.
The MEPS Round 1 weights incorporated the following components: the original
household probability of selection for the NHIS; ratio-adjustment to NHIS
national population estimates at the household (occupied dwelling unit) level;
adjustment for nonresponse at the dwelling unit level for Round 1; and
poststratification to figures at the family and person level obtained from the
March 1996 CPS database.
Return to Table of Contents
3.0 General Data Editing and Imputation
Methodology
The general approach to preparing the household prescription data for this
file was to utilize the pharmacy follow-back prescription data to impute
information collected from pharmacy providers to the household drug mentions.
For self-filers, information on payment sources was retained to the extent that
these data were reported by the household in the charge and payment section of
the household questionnaire. A matching program was adopted to link pharmacy
follow-back component drugs and the corresponding drug information to household
drug mentions. To improve the quality of these matches, all drugs on the
household and pharmacy files were coded using a proprietary database on the
basis of the medication names provided by the household and pharmacy, and, when
available, the NDC provided in the pharmacy follow-back component. Considerable
editing was done prior to the matching to correct data inconsistencies in both
data sets and to fill in missing data and correct outliers on the pharmacy file.
Drug price-per-unit outliers were analyzed on the pharmacy file by first
identifying the average wholesale unit price (AWUP) of the drug by linkage
through the NDC to a secondary data file. In general, prescription drug unit
prices were deemed to be outliers by comparing unit prices reported in the
pharmacy database to the AWUP reported in the secondary data file and were
edited, as necessary. Outlier thresholds were established in consultation with
industry experts.
Drug matches between household drug mentions and pharmacy drug events for a
person in the pharmacy follow-back were based on drug code, medication name, and
the round in which the drug was reported. The matching of household drug
mentions to pharmacy drug mentions was performed so that the most detailed and
accurate information for each prescribed medicine event was obtained. Exact
dates of purchase were only available from the follow-back component. The
matching program assigned scores to potential matches. Numeric variables
required exact matches to receive a high score, while partial scores could be
assigned to matches between character variables, such as prescription name,
depending on the degree of similarity in the spelling and sound of the
medication names. Household drug mentions that were deemed exact matches to
pharmacy component drugs for the same person in the same round required
sufficiently high scores to reflect a high quality match. Exact matches were
used only once and were taken out of the donor pool from that point on (i.e.,
these matches were made without replacement). Any refill of a household drug
mention that had been matched to a pharmacy drug event was also matched to the
same pharmacy drug event. All remaining unmatched household drug mentions for
persons either in or out of the pharmacy follow-back were statistically matched
to the entire pharmacy donor base with replacement by medication name, drug
code, type of third party coverage, health conditions, age, sex, and other
characteristics of the individual.
For more details on the editing and imputation procedures employed to create
the prescribed medicines event file, please reference the following, forthcoming
MEPS Methods Report, which will be available soon on the MEPS web site and from
the AHRQ Clearinghouse, "Outpatient Prescription Drugs: Data Collection and
Editing in the 1996 Medical Expenditure Panel Survey (HC-010A)" (Moeller,
Stagnitti, Horan, et al., 2001).
Return to Table of Contents
3.1 Rounding
Expenditure variables on the 1996 Prescribed Medicines file have been rounded
to the nearest penny. Person level expenditure information released on the 1996
person level expenditure file were rounded to the nearest dollar. It should be
noted that using the 1996 MEPS event files to create person level totals will
yield slightly different totals than those found on the 1996 person level
expenditure file. These differences are due to rounding only. Moreover, in some
instances, the number of persons having expenditures on the 1996 event files for
a particular source of payment may differ from the number of persons with
expenditures on the 1996 person level expenditure file for that source of
payment. This difference is also an artifact of rounding only. Please see the
1996 Appendix File for details on such rounding differences.
3.2 Edited/Imputed Expenditure Variables (RXSF96X-RXXP96X)
There are 13 expenditure variables included on this event file. All of these
expenditures have gone through an editing and imputation process and have been
rounded to the second decimal place. There is a sum of payments variable
(RXXP96X) which for each prescribed medicine event sums all the expenditures
from the various sources of payment. The 12 sources of payment expenditure
variables for each prescribed medicine event are the following: amount paid by
self or family (RXSF96X), amount paid by Medicare (RXMR96X), amount paid by
Medicaid (RXMD96X), amount paid by private insurance (RXPV96X), amount paid by
the Veterans Administration (RXVA96X), amount paid by CHAMPUS/CHAMPVA (RXCH96X),
amount paid by other federal sources (RXOF96X), amount paid by state and local
(non-federal) government sources (RXSL96X), amount paid by Worker's Compensation
(RXWC96X), and amount paid by some other source of insurance (RXOT96X). As
mentioned previously, there are two additional expenditure variables called
RXOR96X and RXOU96X (other private and other public, respectively). These two
expenditure variables were created to maintain consistency between what the
household reported as their private and public insurance status for
hospitalization and physician coverage and third party prescription payments
from other private and public sources (such as a separate private prescription
policy or prescription coverage from the Veterans Administration, the Indian
Health Service, or a State assistance program other than Medicaid). Users should
exercise caution when interpreting the expenditures associated with these two
additional sources of payment. While these payments stem from apparent
inconsistent responses to health insurance and source of payment questions in
the survey, some of these inconsistencies may have logical explanations. Please
note the Prescribed Medicines file is the only file on which some of these
inconsistencies remain. Please see Section 2.6.3 for details on these and all
other source of payment variables.
Return to Table of Contents
4.0 Strategies for Estimation
This file is constructed for efficient estimation of utilization,
expenditure, and sources of payment for prescribed medicines and to allow for
estimates of number of persons with prescribed medicines for 1996.
4.1 Variables with Missing Values
It is essential that the analyst examine all variables for the presence of
negative values used to represent missing values. For example, a record with a
value of -8 for the first ICD9 condition code (RXICD1X) indicates that the
condition was reported as unknown.
For continuous or discrete variables, where means or totals may be taken, it
may be necessary to set minus values to values appropriate to the analytic
needs. That is, the analyst should either impute a value or set the value to one
that will be interpreted as missing by the computing language used. For
categorical and dichotomous variables, the analyst may want to consider whether
to recode or impute a value for cases with negative values or whether to exclude
or include such cases in the numerator and/or denominator when calculating
proportions.
Methodologies used for the editing/imputation of expenditure variables (e.g.
sources of payment, etc.) are described in Section 3.0.
Return to Table of Contents
4.2 Basic Estimates of Utilization, Expenditure and Sources of Payment
While the examples described below illustrate the use of event level data in
constructing person level total expenditures, these estimates can also be
derived from the person level expenditure file unless the characteristic of
interest is event specific.
In order to produce national estimates related to prescribed medicines
utilization, expenditure and sources of payment, the value in each record
contributing to the estimates must be multiplied by the weight (WTDPER96)
contained on that record.
Example 1
For example, the total number of prescription medicines excluding
"diabetic supply/equipment or insulin", for the civilian
non-institutionalized population of the U.S. in 1996 is estimated as the sum of
the weight (WTDPER96) across all prescription records excluding "diabetic
supply/equipment or insulin". That is,
Sum of Wj = 1,803,000,630
for all records with DIABFLGj not equal to 1 (1)
Various estimates can be produced based on specific variables and subsets of
records.
Example 2
For example, the estimate for the mean out-of-pocket payment per prescription
medicine purchase (excluding diabetic supply/equipment or insulin) should be
calculated as the weighted average of amount paid by self/family. That is,
X bar =(Sum of WjXj) / (Sum of Wj)= $15.71, (2)
where
Sum of Wj = 1,803,000,630
and
Xj = RXSF96Xj for all prescription records with RXXP96Xj > 0 and DIABFLGj not equal to 1.
This gives $15.71 as the estimated mean amount of out-of-pocket payment of
expenditures associated with prescribed medicines excluding "diabetic
supply/equipment or insulin" and 1,803,000,630 as an estimate of the total
number of prescription medicine purchases (excluding "diabetic
supply/equipment or insulin"). Both of these estimates are for the civilian
non- institutionalized population of the U.S. in 1996.
Example 3
Another example would be to estimate the average proportion of total
expenditures paid by private insurance per prescription medicine purchase
(excluding "diabetic supply/equipment or insulin"). This should be
calculated as the weighted mean of the proportion of the total prescription
purchase paid by private insurance at the prescribed medicine level
("excluding diabetic supply/equipment or insulin"). That is,
Y bar = (Sum of WjYj) / (Sum of Wj) = 0.3014 (3)
where Sum of Wj = 1,803,000,630
and
Yj = RXPV96Xj / RXXP96Xj
for all prescription records with RXXP96Xj > 0 and DIABFLGj not equal to 1.
This gives 0.3014 as the estimated mean proportion of each prescription paid
by private insurance (excluding "diabetic supply/equipment or
insulin") for the civilian non-institutionalized population of the U.S. in
1996.
Return to Table of Contents
4.3 Estimates of the Number of Persons with Prescribed Medicine Events
When calculating an estimate of the total number of persons with prescribed
medicine events, users can use a person-level file (1996 Full Year Consolidated
Data File) or the current file. However, this event file must be used when the
measure of interest is defined at the event level. For example, to estimate the
number of persons in the civilian non-institutionalized population of the U.S.
with a prescribed medicine purchase in 1996 with an RXNDC =
"00093310905" (Amoxicillin), this event file must be used. This would
be estimated as
Sum of WjXj across all unique persons i on this file, (4)
where
Wj is the sampling weight (WTDPER96) for person i
and
Xj = 1 if RXNDC = "00093310905" for any purchase of person i
or Xj = 0 otherwise.
4.4 Person-Based Ratio Estimates
4.4.1 Person-Based Ratio Estimates Relative to Persons with Prescribed
Medicine Events
This file may be used to derive person-based ratio estimates. However, when
calculating ratio estimates where the denominator is persons, care should be
taken to properly define and estimate the unit of analysis up to person level.
For example, the mean expense for persons with prescribed medicine purchases is
estimated as,
(Sum of WiZi) / (Sum of Wi) across all unique persons i on this file, (5)
where
Wi is the sampling weight (WTDPER96) for person i
and
Zi = Sum of RXXP96Xj across all prescription purchases for person i.
4.4.2 Person-Based Ratio Estimates Relative to the Entire Population
If the ratio relates to the entire population, this file cannot be used to
calculate the denominator, as only those persons with at least one prescribed
medicine event are represented on this data file. In this case the 1996 Full
Year Consolidated Data File, which has data for all sampled persons, must be
used to estimate the total number of persons (i.e. those with use and those
without use). For example, to estimate the proportion of civilian
non-institutionalized population of the U.S. with at least one prescribed
medicine event with RXNDC = "00093310905" (Amoxicillin), the numerator
would be derived from data on this event file, and the denominator would be
derived from data on the 1996 Full Year Consolidated Data File. That is,
(Sum of WiZi) / (Sum of Wi) across all unique persons i
on the 1996 Full Year Consolidated
Data File, (6)
where
Wi is the sampling weight (WTDPER96) for person i
and
Zi = 1 if RXNDCj = "00093310905" for any event of person i on the event-level file
or Zi = 0 othewise for all remaining persons on the 1996 full year Consolidated Data File
4.5 Sampling Weights for Merging Previous
Releases of MEPS Household Data
with this Event File
There have been several previous releases of MEPS Household Survey public use
data. Unless a variable name common to several tapes is provided, the sampling
weights contained on these data files are file-specific. The file-specific
weights reflect minor adjustments to eligibility and response indicators due to
birth, death, or institutionalization among respondents.In general, for estimates from a MEPS data file that do not require merging
with variables from other MEPS data files, the sampling weight(s) provided on
that data file are the appropriate weight(s). When merging a MEPS Household data
file to another, the major analytical variable (i.e. the dependent variable)
determines the correct sampling weight to use.
Return to Table of Contents
5.0 Variance Estimation
To obtain estimates of variability (such as the standard error of sample
estimates or corresponding confidence intervals) for estimates based on MEPS
survey data, one needs to take into account the complex sample design of MEPS.
Various approaches can be used to develop such estimates of variance including
use of the Taylor series or various replication methodologies. Replicate weights
have not been developed for the MEPS 1996 data. Variables needed to implement a
Taylor series estimation approach are described in the paragraph below.
Using a Taylor Series approach, variance estimation strata and the variance
estimation PSUs within these strata must be specified. The corresponding
variables on the MEPS full year utilization database are VARSTR96 and VARPSU96,
respectively. Specifying a "with replacement" design in a computer
software package such as SUDAAN (Shah, 1996) should provide standard errors
appropriate for assessing the variability of MEPS survey estimates. It should be
noted that the number of degrees of freedom associated with estimates of
variability indicated by such a package may not appropriately reflect the actual
number available. For MEPS sample estimates for characteristics generally
distributed throughout the country (and thus the sample PSUs), there are over
100 degrees of freedom associated with the corresponding estimates of variance.
The following illustrates these concepts using two examples from Section 4.2.
Example 2 from Section 4.2
Using a Taylor Series approach, specifying VARSTR96 and VARPSU96 as the
variance estimation strata and PSUs (within these strata) respectively and
specifying a "with replacement" design in a computer software package
SUDAAN will yield the standard error estimate of $0.3132 for the estimated mean
of out-of-pocket payment.
Example 3 from Section 4.2
Using a Taylor Series approach, specifying VARSTR96 and VARPSU96 as the
variance estimation strata and PSUs (within these strata) respectively and
specifying a "with replacement" design in a computer software package
SUDAAN will yield the standard error estimate of 0.0067 for the weighted mean
proportion of total expenditures paid by private insurance.
Return to Table of Contents
6.0 Merging/Linking MEPS Data Files
Data from this event file can be used alone or in conjunction with other
files. This section provides instructions for linking the 1996 prescribed
medicines file with other 1996 MEPS public use files, including a 1996 person
level file, the 1996 conditions file, and the other 1996 event files.
6.1 Linking a Person Level File to the Prescribed Medicines File
Merging characteristics of interest from other 1996 MEPS files (e.g., the
1996 Full Year Consolidated File or the 1996 Office Based Provider File) expands
the scope of potential estimates. For example, to estimate the total number of
prescribed medicines purchased or otherwise obtained by persons with specific
characteristics (e.g., age, race, and sex), population characteristics from a
person level file need to be merged onto the prescribed medicines file. This
procedure is illustrated below. The 1996 Appendix File provides additional
details on how to merge 1996 MEPS data files.
1. Create data set PERSX by sorting a Full Year Population Characteristics
File (file XXX), by the person identifier, DUPERSID. Keep only variables to be
merged on to the prescribed medicines file and DUPERSID.
2. Create data set PMEDS by sorting the prescribed medicines file by person
identifier, DUPERSID.
3. Create final data set NEWPMEDS by merging these two files by DUPERSID,
keeping only records on the prescribed medicines file.The following is an example of SAS code which completes these steps:
PROC SORT DATA=HCXXX(KEEP=DUPERSID AGE SEX EDUC) OUT=PERSX;
BY DUPERSID;
RUN;
PROC SORT DATA=PMEDS;
BY DUPERSID;
RUN;
DATA NEWPMEDS;
MERGE PMEDS (IN=A) PERSX(IN=B);
BY DUPERSID;
IF A;
RUN;
Return to Table of Contents
6.2 Linking the 1996 Conditions File and/or the Other 1996 MEPS Event
Files
to the 1996 Prescribed Medicines File
Due to survey design issues, there are limitations/caveats that an analyst
must keep in mind when linking the different files. Those limitations/caveats
are listed below. For detailed linking examples, including SAS code, analysts
should refer to the 1996 Appendix File.
6.3 Limitations/Caveats of RXLK and CLNK
The RXLK file provides a link from the 1996 prescribed medicine records to
the other 1996 MEPS event files. When using RXLK, analysts should keep in mind
that a prescribed medicine event may link to more than one medical event. When
this occurs, it is up to the analyst to determine how the prescribed medicine
expenditures should be allocated among those events. In order to obtain complete
information about those other event files, the analyst must link to the other
public use event files.The CLNK provides a link from the 1996 Conditions File to the 1996 Prescribed
Medicines file. When using the CLNK, analysts should keep in mind that (1)
conditions are self reported and (2) there may be multiple conditions associated
with a drug purchase. Analysts need to verify that a particular medication is
indeed an appropriate medication in treating the condition. Moreover, there may
be some drugs that were purchased to treat a specific health condition for which
there is no such link to the condition file.
Return to Table of Contents
References
Cohen, S.B. (1998). Sample Design of the 1996 Medical Expenditure Panel
Survey Medical Provider Component. Journal of Economic and Social Measurement.
Vol 24, 25-53.
Cohen, S.B. (1997). Sample Design of the 1996 Medical Expenditure Panel
Survey Household Component. Rockville (MD): Agency for Health Care Policy and
Research; 1997. MEPS Methodology Report, No. 2. AHCPR Pub. No. 97-0027.
Cohen, J.W. (1997). Design and Methods of the Medical Expenditure Panel
Survey Household Component. Rockville (MD): Agency for Health Care Policy and
Research; 1997. MEPS Methodology Report, No. 1. AHCPR Pub. No. 97-0026.
Cohen, S.B. (1996). The Redesign of the Medical Expenditure Panel Survey: A
Component of the DHHS Survey Integration Plan. Proceedings of the COPAFS Seminar
on Statistical Methodology in the Public Service.
Cox, B.G. and Cohen, S.B. (1985). Chapter 6: A Comparison of Household and
Provider Reports of Medical Conditions. In Methodological Issues for Health Care
Surveys. Marcel Dekker, New York.
Cox, B.G. and Cohen, S.B. (1985). Chapter 8: Imputation Procedures to
Compensate for Missing Responses to Data Items. In Methodological Issues for
Health Care Surveys. Marcel Dekker, New York.
Cox, B. and Iachan, R. (1987). A Comparison of Household and Provider Reports
of Medical Conditions. Journal of the American Statistical Association
82(400):1013-18.
Edwards, W.S., Winn, D.M., Kurlantzick V., et al. (1994). Evaluation of
National Health Interview Survey Diagnostic Reporting. National Center for
Health Statistics, Vital Health2(120).
Elixhauser A., Steiner C.A., Whittington C.A., and McCarthy E. Clinical
Classifications for Health Policy Research: Hospital Inpatient Statistics, 1995.
Healthcare Cost and Utilization Project, HCUP-3 Research Note. Rockville, MD:
Agency for Health Care Policy and Research; 1998. AHCPR Pub. No. 98-0049.
Health Care Financing Administration (1980). International Classification of
Diseases, 9thRevision, Clinical Modification (ICD-CM). Vol. 1. (DHHS Pub. No.
(PHS) 80-1260). DHHS: U.S. Public Health Services.
Johnson, A.E. and Sanchez, M.E. (1993). Household and Medical Provider
Reports on Medical Conditions: National Medical Expenditure Survey, 1987.
Journal of Economic and Social Measurement. Vol. 19, 199-233.
Moeller J.F., Stagnitti, M., Horan, E., et al. Data Collection and Editing
Procedures for Prescribed Medicines in the 1996 Medical Expenditure Panel Survey
Household Component. Rockville (MD): Agency for Healthcare Research and Quality;
2000. MEPS Methodology Report (forthcoming).
Monheit, A.C., Wilson, R., and Arnett, III, R.H. (Editors). Informing
American Health Care Policy. (1999). Jossey-Bass Inc, San Francisco.
Shah, B.V., Barnwell, B.G., Bieler, G.S., Boyle, K.E., Folsom, R.E., Lavange,
L., Wheeless, S.C., and Williams, R. (1996). Technical Manual: Statistical
Methods and Algorithms Used in SUDAAN Release 7.0, Research Triangle Park, NC:
Research Triangle Institute.
Return to Table of Contents
Attachment 1
Definitions
Dwelling Units, Reporting Units, Families, and Persons – The definitions of
Dwelling Units (DUs) and Group Quarters in the MEPS Household Survey are
generally consistent with the definitions employed for the National Health
Interview Survey. The dwelling unit ID (DUID) is a five-digit random ID number
assigned after the case was sampled for MEPS. The person number (PID) uniquely
identifies all persons within the dwelling unit. The variable DUPERSID is the
combination of the variables DUID and PID.A Reporting Unit (RU) is a person or group of persons in the sampled dwelling
unit who are related by blood, marriage, adoption or other family association,
and who are to be interviewed as a group in MEPS. Thus, the RU serves chiefly as
a family-based "survey operations" unit rather than an analytic unit.
Regardless of the legal status of their association, two persons living together
as a "family" unit were treated as a single reporting unit if they
chose to be so identified.Unmarried college students under 24 years of age who usually live in the
sampled household, but were living away from home and going to school at the
time of the Round 1 MEPS interview, were treated as a Reporting Unit separate
from that of their parents for the purpose of data collection. These variables
can be found on MEPS person level files.In-Scope - A person was classified as in-scope (INSCOPE) if he or she was a
member of the U.S. civilian, non-institutionalized population at some time
during the Round 1 interview. This variable can be found on MEPS person level
files.
Keyness - The term "keyness" is related to an individual's chance
of being included in MEPS. A person is key if that person is appropriately
linked to the set of 1995 NHIS sampled households designated for inclusion in
MEPS. Specifically, a key person either was a member of an NHIS household at the
time of the NHIS interview, or became a member of such a household after being
out-of-scope prior to joining that household (examples of the latter situation
include newborns and persons returning from military service, an institution, or
living outside the United States).A non-key person is one whose chance of selection for the NHIS (and MEPS) was
associated with a household eligible but not sampled for the NHIS, who happened
to have become a member of a MEPS reporting unit by the time of the MEPS Round 1
interview. MEPS data, (e.g., utilization and income) were collected for the
period of time a non-key person was part of the sampled unit to permit family
level analyses. However, non-key persons who leave a sample household would not
be recontacted for subsequent interviews. Non-key individuals are not part of
the target sample used to obtain person level national estimates.It should be pointed out that a person may be key even though not part of the
civilian, non- institutionalized portion of the U.S population. For example, a
person in the military may be living with his or her civilian spouse and
children in a household sampled for the 1995 NHIS. The person in the military
would be considered a key person for MEPS. However, such a person would not
receive a person-level sample weight so long as he or she was in the military.
All key persons who participated in the first round of the 1996 MEPS received a
person level sample weight except those who were in the military. The variable
indicating "keyness" is KEYNESS. This variable can be found on MEPS
person level files.Eligibility - The eligibility of a person for MEPS pertains to whether or not
data were to be collected for that person. All key, in-scope persons of a
sampled RU were eligible for data collection. The only non-key persons eligible
for data collection were those who happened to be living in the same RU as one
or more key persons, and their eligibility continued only for the time that they
were living with a key person. The only out-of-scope persons eligible for data
collection were those who were living with key in-scope persons, again only for
the time they were living with a key person. Only military persons meet this
description. A person was considered eligible if they were eligible at any time
during Round 1. The variable indicating "eligibility" is ELIGRND1,
where 1 is coded for persons eligible for data collection for at least a portion
of the Round 1 reference period, and 2 is coded for persons not eligible for
data collection at any time during the first round reference period. This
variable can be found on MEPS person level files.
Pre-imputed - This means that only a series of logical edits were applied to the
HC data to correct for several problems including outliers, copayments or
charges reported as total payments, and reimbursed amounts counted as out of
pocket payments. Missing data remains.Unimputed - This means that only a series of logical edits were applied to
the MPC data to correct for several problems including outliers, copayments or
charges reported as total payments, and reimbursed amounts counted as out of
pocket payments. This data was used as the imputation source to account for
missing HC data.Imputation - Imputation is more often used for item missing data adjustment
through the use of predictive models for the missing data, based on data
available on the same (or similar) cases. Hot-deck imputation creates a data set
with complete data for all nonrespondent cases, often by substituting the data
from a respondent case that resembles the nonrespondent on certain known
variables.
Household Reported Drug (mention) - A household reported drug is a unique
prescribed medication reported by a household respondent. A household reported
drug is checked on the prescribed medicines roster as being created during that
round or selected from a roster from a previous round. Associated with each
household reported drug mention in a given round may be multiple acquisitions of
the medication during that round. Due to the editing and imputation procedures
for these data, cases with multiple purchases of the same medication may be
assigned more than one variant of the medication based on its form, strength,
manufacturer, or package size (i.e., its NDC). Thus, what originally was
reported as a single medication in the Household Component may appear as
multiple unique medications on the prescribed medicines event file.
Return to Table of Contents
D. Codebook (link to separate file)
Return to Table of Contents
E. Variable-Source Crosswalk
MEPS HC010A: 1996 Prescribed Medicine Events
Survey Administration Variables
| Variable |
Description |
Source |
DUID |
Dwelling unit ID |
Assigned in sampling |
PID |
Person number |
Assigned in sampling |
DUPERSID |
Sample person ID (DUID + PID) |
Assigned in sampling |
RXRECIDX |
Record ID – Unique Prescribed Medicine Identifier |
Constructed |
LINKIDX |
Link to condition and other event files |
CAPI derived |
PURCHRD |
Round in which the Rx/prescribed medicine was obtained/purchased |
Constructed |
Return to Table of Contents
Prescribed Medicines Events Variables
| Variable |
Description |
Source |
RXBEGDD |
Day person first used medicine |
PM11OV1 |
RXBEGMM |
Month person first used medicine |
PM11OV2 |
RXBEGYR |
Year person first used medicine |
PM11 |
RXNAME |
Medication name (Imputed) |
Imputed |
RXHHNAME |
Household reported medication name |
PM05/Imputed |
RXNDC |
National drug code (Imputed) |
Imputed |
RXQUANTY |
Quantity of Rx/prescribed medicine (Imputed) |
Imputed |
RXFORM |
Form of Rx/prescribed medicine (Imputed) |
Imputed |
RXSTRENG |
Strength of Rx/prescribed medicine dose (Imputed) |
Imputed |
RXUNIT |
Unit of measurement for Rx/prescribed medicine dose (Imputed) |
Imputed |
PHARTP1-PHARTP7 |
Type of pharmacy provider – (1st-7th) |
PM16/Imputed |
RXFLG |
Flag variable indicating imputation source for NDC on pharmacy donor record |
Constructed/Imputed |
PCIMPFLG |
Flag indicating type of household to pharmacy prescription match |
Constructed |
SELFFLG |
Flag indicating whether or not the event is a self-filer event |
CP01/Constructed |
INPCFLG |
Flag indicating if the person has at least one record in the pharmacy component |
Constructed/Imputed |
DIABFLG |
Flag indicating whether or not prescribed medicine was classified as insulin or diabetic supply/equipment |
Constructed/Imputed |
SAMPLE |
Indicates if respondent received a free sample of this drug in the round |
Constructed |
RXICD1X |
3 digit ICD-9 condition code |
PM09/Imputed |
RXICD2X |
3 digit ICD-9 condition code |
PM09/Imputed |
RXICD3X |
3 digit ICD-9 condition code |
PM09/Imputed |
RXCCC1X |
Modified Clinical Classification Code |
Constructed/Edited/Imputed |
RXCCC2X |
Modified Clinical Classification Code |
Constructed/Edited/Imputed |
RXCCC3X |
Modified Clinical Classification Code |
Constructed/Edited/Imputed |
NUMCOND |
Total number of conditions associated with a prescribed medicine event |
Constructed/Imputed |
RXSF96X |
Amount paid, self or family (Imputed) |
CP11/Edited/Imputed |
RXMR96X |
Amount paid, Medicare (Imputed) |
CP12/CP13/Edited/Imputed |
RXMD96X |
Amount paid, Medicaid (Imputed) |
CP12/CP13/Edited/Imputed |
RXPV96X |
Amount paid, private insurance (Imputed) |
CP12/CP13/Edited/Imputed |
RXVA96X |
Amount paid, Veterans (Imputed) |
CP12/CP13/Edited/Imputed |
RXCH96X |
Amount paid, CHAMPUS/CHAMPVA (Imputed) |
CP12/CP13/Edited/Imputed |
RXOF96X |
Amount paid, other Federal (Imputed) |
CP12/CP13/Edited/Imputed |
RXSL96X |
Amount paid, state and local gov’t (Imputed) |
CP12/CP13/Edited/Imputed |
RXWC96X |
Amount paid, Worker’s Compensation (Imputed) |
CP12/CP13/Edited/Imputed |
RXOT96X |
Amount paid, other insurance (Imputed) |
CP12/CP13/Edited/Imputed |
RXOR96X |
Amount paid, other private (Imputed) |
Constructed/Imputed |
RXOU96X |
Amount paid, other public (Imputed) |
Constructed/Imputed |
RXXP96X |
Sum of payments RXSF96X – RXOU96X (Imputed) |
CP12/CP13/Edited/Imputed |
Return to Table of Contents
Weights
| Variable |
Description |
Source |
WTDPER96 |
Poverty/mortality adjusted person level weight |
Constructed |
VARSTR96 |
Variance estimation stratum, 1996 |
Constructed |
VARPSU96 |
Variance estimation PSU,1996 |
Constructed |
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