August 2013
Agency for Healthcare Research and Quality
Center for Financing, Access, and Cost Trends
540 Gaither Road
Rockville, MD 20850
(301) 427-1406
Table of Contents
A. Data Use Agreement
B. Background
1.0 Household Component (HC)
2.0 Medical Provider Component (MPC)
3.0 Survey Management and Data Collection
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 Conventions
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, DRUGIDX)
2.6.1.3 Panel Variable (PANEL)
2.6.1.4 Round Variable (PURCHRD)
2.6.2 Characteristics of Prescribed Medicine Events
2.6.2.1 Date When Prescribed Medicine Was First Taken (RXBEGDD-RXBEGYRX)
2.6.2.2 Prescribed Medicine Attributes (RXNAME-RXDAYSUP)
2.6.2.3 Type of Pharmacy (PHARTP1-PHARTP8)
2.6.2.4 Analytic Flag Variables (RXFLG-INPCFLG)
2.6.2.5 Free Sample Variable (SAMPLE)
2.6.2.6 Condition Codes (RXICD1X-RXICD3X) and Clinical Classification Codes
(RXCCC1X-RXCCC3X)
2.6.3 Multum Lexicon Variables from Cerner Multum, Inc.
2.6.4 Expenditure Variables (RXSF11X-RXXP11X)
2.6.4.1 Definition of Expenditures
2.6.4.2 Sources of Payment
3.0 Sample Weight (PERWT11F)
3.1 Overview
3.2 Details on Person Weight Construction
3.2.1 MEPS Panel 15 Weight
3.2.2 MEPS Panel 16 Weight
3.2.3 The Final Weight for 2011
3.3 Coverage
3.4 Using MEPS Data for Trend Analysis
4.0 General Data Editing and Imputation Methodology
4.1 Rounding
4.2 Edited/Imputed Expenditure Variables (RXSF11X-RXXP11X)
5.0 Strategies for Estimation
5.1 Developing Event-Level Estimates
5.2 Person-Based Estimates for Prescribed Medicine Purchases
5.3 Variables with Missing Values
5.4 Variance Estimation (VARSTR, VARPSU)
6.0 Merging/Linking MEPS Data Files
6.1 Linking to the Person-Level File
6.2 Linking to the Medical Conditions File
6.3 Longitudinal Analysis
_._ References
D. Variable-Source Crosswalk
Appendix 1: Definitions for RXFORM, Form of Prescribed Medicines
Appendix 2: Definitions for RXFRMUNT, Unit of Measure for Form of Prescribed Medicines
Appendix 3: Definitions for RXSTRUNT, Unit of Measure for Strength of Prescribed Medicines
Appendix 4: Definitions of Therapeutic Class Code
Individual identifiers have been removed from the
micro-data contained in these files. 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; and
- 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; and
- 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 Title 18 part 1 Chapter 47 Section 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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The Medical Expenditure Panel Survey (MEPS) provides nationally representative estimates of health
care use, expenditures, sources of payment, and health insurance coverage for the U.S. civilian non-institutionalized
population. The MEPS Household Component (HC) also provides estimates of respondents’ health status, demographic
and socio-economic characteristics, employment, access to care, and satisfaction with health care. Estimates can be
produced for individuals, families, and selected population subgroups. The panel design of the survey, which includes 5
Rounds of interviews covering 2 full calendar years, provides data for examining person level changes in selected variables
such as expenditures, health insurance coverage, and health status. Using computer assisted personal interviewing (CAPI)
technology, information about each household member is collected, and the survey builds on this information from interview
to interview. All data for a sampled household are reported by a single household respondent.
The MEPS-HC was initiated in 1996. Each year a new panel of households is selected. Because the data
collected are comparable to those from earlier medical expenditure surveys conducted in 1977 and 1987, it is possible to
analyze long-term trends. Each annual MEPS-HC sample size is about 15,000 households. Data can be analyzed at either the
person or event level. Data must be weighted to produce national estimates.
The set of households selected for each panel of the MEPS HC is a subsample of households participating
in the previous year’s National Health Interview Survey (NHIS) conducted by the National Center for Health Statistics.
The NHIS sampling frame provides a nationally representative sample of the U.S. civilian non-institutionalized population
and reflects an oversample of Blacks and Hispanics. In 2006, the NHIS implemented a new sample design, which included
Asian persons in addition to households with Black and Hispanic persons in the oversampling of minority populations.
MEPS oversamples additional policy relevant sub-groups such as Asians and low income households. The linkage of the MEPS
to the previous year’s NHIS provides additional data for longitudinal analytic purposes.
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Upon completion of the household CAPI interview and obtaining permission from the household survey
respondents, a sample of medical providers are contacted by telephone to obtain information that household respondents
can not accurately provide. This part of the MEPS is called the Medical Provider Component (MPC) and information is
collected on dates of visit, diagnosis and procedure codes, charges and payments. The Pharmacy Component (PC), a
subcomponent of the MPC, does not collect charges or diagnosis and procedure codes but does collect drug detail
information, including National Drug Code (NDC) and medicine name, as well as date filled and sources and amounts of
payment. The MPC is not designed to yield national estimates. It is primarily used as an imputation source to
supplement/replace household-reported expenditure information.
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MEPS HC and MPC data are collected under the authority of the Public Health Service Act. Data are
collected under contract with Westat, Inc. (MEPS HC) and Research Triangle Institute (MEPS MPC). Data sets and summary
statistics are edited and published in accordance with the confidentiality provisions of the Public Health Service Act
and the Privacy Act. The National Center for Health statistics (NCHS) provides consultation and technical assistance.
As soon as data collection and editing are completed, the MEPS survey data are released to the
public in staged releases of summary reports, micro data files, and tables via the MEPS web site:
meps.ahrq.gov. Selected data can be analyzed through MEPSnet, an on-line
interactive tool designed to give data users the capability to statistically analyze MEPS data in a menu-driven environment.
Additional information on MEPS is available from the MEPS project manager or the MEPS public use
data manager at the Center for Financing Access and Cost Trends, Agency for Healthcare Research and Quality,
540 Gaither Road, Rockville, MD 20850 (301-427-1406).
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This documentation describes one in a series of public use event files from the 2011 Medical
Expenditure Panel Survey (MEPS) Household Component (HC) and Medical Provider Component (MPC). Released as an ASCII data
file (with related SAS, SPSS, and Stata programming statements) and SAS transport file, the 2011 Prescribed Medicines
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. Data from the Prescribed Medicines event
file can be used to make estimates of prescribed medicine utilization and expenditures for calendar year 2011. The file
contains 72 variables and has a logical record length of 539 with an additional 2-byte carriage return/line feed at the
end of each record. As illustrated below, this file consists of MEPS survey data obtained in the 2011 portion of Round 3
and Rounds 4 and 5 for Panel 15, as well as Rounds 1, 2 and the 2011 portion of Round 3 for Panel 16 (i.e., the rounds
for the MEPS panels covering calendar year 2011).
Each record on this event file represents a unique prescribed medicine event; that is, a prescribed
medicine reported as being purchased 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 2011 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 (PC) (within the MEPS-MPC, see Section B 2.0 for more details on the MPC) 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 2011 Full Year Consolidated Data 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 Weight
General Data Editing and Imputation Methodology
Strategies for Estimation
Merging/Linking MEPS Data Files
References
Variable to Source Crosswalk
For more information on MEPS HC survey design see T. Ezzati-Rice, et al. (1998-2007) 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:
meps.ahrq.gov.
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The 2011 Prescribed Medicines public use data set contains 313,747 prescribed medicine records.
Each record represents one household-reported prescribed medicine that was purchased during calendar year 2011. Of the
313,747 prescribed medicine records, 308,248 records are associated with persons having a positive person-level weight
(PERWT11F). The persons represented on this file had to meet either criterion a) or b) below:
- Be classified as a key in-scope person who responded for his or her entire period of 2011
eligibility (i.e., persons with a positive 2011 full-year person-level sampling weight (PERWT11F > 0), or
- Be an eligible member of a family all of whose key in-scope members have a positive person-level
weight (PERWT11F > 0). (Such a family consists of all persons with the same value for FAMIDYR.) That is, the person
must have a positive full-year family-level weight (FAMWT11F >0). Note that FAMIDYR and FAMWT11F are variables on
the 2011 Population Characteristics file.
Persons with no prescribed medicine use for 2011 are not included on this file (but are represented
on MEPS person-level files). A codebook for the data file is provided (in file H144acb.pdf).
This file includes prescribed medicine records for all household members who resided in eligible
responding households and for whom at least one prescribed medicine was reported. Only prescribed medicines that were
purchased in calendar year 2011 are represented on this file. This file includes prescribed medicines identified in the
Prescribed Medicines (PM) section of the HC survey instrument, as well as those prescribed medicines identified in
association with other medical events. Each record on this file represents a single acquisition of a prescribed medicine
reported by household respondents. Some household members may have multiple acquisitions of prescribed medicines and thus
will be represented in multiple records on this file. Other household members may have no reported acquisitions of
prescribed medicines and thus will have no records on this file.
When diabetic supplies, such as syringes and insulin, were mentioned in the Other Medical Expenses
(OM) section of the MEPS-HC, the interviewer was directed to collect information on these items in the Prescribed Medicines
section of the MEPS questionnaire. The respondent was also asked the questions in the Charge Payment (CP) section of the
HC. 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. Analysts are free to code and define diabetic
supply/equipment and insulin events utilizing their own coding mechanism. If desired, this would enable analysts to
subset the Prescribed Medicines file to exclude these types of events.
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 times the medicine was obtained. 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
PC, 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 HC were “unfolded” to create separate records
for each original purchase and each refill. Please note that for multiple acquisitions of the same drug, MEPS did not
collect information in the HC to distinguish 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 reported receiving 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, selected Multum Lexicon
variables [see Section 2.6.3 for more information on the Multum Lexicon variables included on this file], 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 of
which the household received a free sample 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 2011 Full Year Consolidated Data File to estimate expenditures
for persons with prescribed medicines. The Prescribed Medicines event file can also be linked to the MEPS 2011 Medical
Conditions File and additional MEPS 2011 event files. Please see the 2011 Appendix File for details on how to link MEPS
data files.
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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 for medical conditions
Clinical Classification Software codes for medical conditions
Multum Lexicon variables
Expenditure variables
Weight and variance estimation variables
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The following reserved code values are used:
Value |
Definition |
-1 INAPPLICABLE |
Question was not asked due to skip pattern |
-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 |
-14 NOT YET TAKEN/USED |
Respondent answered that the medicine has not yet been used |
Generally, values of -1, -7, -8 and -9 have not been edited on this file. However, this is not true
if the pharmacist determined a prescription drug name to be a confidentiality risk. In these instances, the corresponding
NDC was replaced with -9, and the Multum Lexicon therapeutic class replaced the drug name determined to be a confidentiality
risk. The values of -1 and -9 can be edited by analysts by following the skip patterns in the questionnaire. The value
-14 was a valid value only for the variable representing the year the household member first used the medicine (RXBEGYRX).
RXBEGYRX = -14 means that when the interviewer asked the respondent the year the household member first started using
the medicine, he/she responded that the household member had not yet started using the medicine (See section C, 2.6.2.1)
A copy of the Household Component questionnaire can be found at
meps.ahrq.gov/survey_comp/survey_questionnaires.jsp
by selecting Prescribed Medicines (PM) from the questionnaire section.
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The 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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In general, variable names reflect the content of the variable, with an eight-character limitation.
Generally, all imputed/edited variables end with an "X."
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Variables contained on this file were derived from the HC questionnaire itself, the MPC data
collection instrument, the CAPI, or from the Multum Lexicon database from Cerner Multum, Inc. The source of each variable
is identified in Section D, entitled “Variable-Source Crosswalk.” Sources for each variable are indicated in
one of five ways:
- Variables which are derived from CAPI or assigned in sampling are so indicated as “CAPI
derived” or “Assigned in sampling,” respectively;
- Variables which come from one or more specific questions have those numbers and the questionnaire
section indicated in the “Source” column;
- Variables constructed from multiple questions using complex algorithms are labeled
“Constructed” in the “Source” column;
- Variables which have been imputed are so indicated; and
- Variables derived from the Multum Lexicon database are so indicated.
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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 7 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
ER – emergency room visit
HH – home health event
OM – other medical equipment
OB – office-based visit
OP – outpatient visit
DV – dental visit
RX – prescribed medicine
In the case of the source of payment variables, the third and fourth characters indicate:
SF – self or family
MR – Medicare
MD – Medicaid
PV – private insurance
VA – Veterans Administration/CHAMPVA
TR – TRICARE
OF – other Federal Government
SL – State/local government
WC – Workers’ Compensation
OT – other insurance
OR – other private
OU – other public
XP – sum of payments
The fifth and sixth characters indicate the year (11). The seventh character, "X", indicates the
variable is edited/imputed.
For example, RXSF11X is the edited/imputed amount paid by self or family for the 2011 prescribed
medicine expenditure.
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Data regarding prescription drugs were obtained through the HC questionnaire and a pharmacy
follow-back component (within the Medical Provider Component).
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During each round of the MEPS-HC, 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 or if their pharmacy providers do this automatically for them at the point of purchase. For those
who said their pharmacy providers automatically send in claims for them at the point of purchase, charge and payment
information was collected in the pharmacy follow-back component (unless the purchase was an insulin or diabetic
supply/equipment event that was mentioned in the household component; see Section 4.0 for details). However, charge and
payment information was collected in the HC for those who said they send in their own prescription claim forms, because
it is thought that payments by private third-party payers for those who filed their own claim forms for prescription
purchases would not be available from pharmacies. Uninsured persons were treated in the same manner as those whose
pharmacies filed their prescription claims at the point of purchase. Persons who said they did not know if they sent in
their own prescription claim forms were treated as those who said they did send in their own prescription claim forms
In consultation with an industry expert, outlier values for the number of times a household reported
purchasing or otherwise obtaining a prescription drug in a particular round were determined by comparing the number of
days a person was in the round to the number of times the person was reported to have obtained the drug in the round.
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, for rounds in which a household respondent did not know/remember the number of times a certain
prescribed medicine was purchased or otherwise obtained, the number of fills or refills was imputed.
For those rounds that spanned two years, drugs mentioned in that round were allocated between the
years based on the number of times the respondent said the drug was purchased in the respective year, the year the person
started taking the drug, the length of the person’s round, the dates of the person’s round, and the number
of drugs for that person in the round. In addition, a “folded” version of the PC on a drug level, as opposed
to an acquisition level, was used for these types of events to assist in determining how many acquisitions of the drug
should be allocated between the years.
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If the household member with the prescription gave written permission to release his or her pharmacy
records, pharmacy providers identified by the household were contacted by telephone for the pharmacy follow-back component.
Following an initial telephone contact, the signed permission forms and materials explaining the study were faxed (or
mailed) to cooperating pharmacy providers. The materials informed the providers of all persons participating in the
survey who had prescriptions filled at their place of business and requested a computerized printout of all prescriptions
filled for each person. Pharmacies could choose to report information in computer assisted telephone interviews (CATI).
The CATI instrument was also used to enter information from printouts. 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); and payments by source. When an NDC was provided, often the drug name and other drug
characteristics were obtained from secondary proprietary data sources.
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The dwelling unit ID (DUID) is a five-digit random number assigned after the case was sampled for
MEPS. The three-digit person number (PID) uniquely identifies each person within the dwelling unit. The eight-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 for the 2011 Full
Year Population Characteristics File.
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The variable RXRECIDX uniquely identifies each record on the file. This 15-character variable comprises the following
components: prescribed medicine drug-round-level identifier generated through the HC (positions 1-12) + enumeration
number (positions 13-15). The prescribed medicine drug-round-level ID generated through the HC (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 Section 6.2 and to the 2011 Appendix
File. The prescribed medicine drug-level ID generated through the HC, DRUGIDX, can be used to link drugs across rounds.
DRUGIDX was first added to the file for 2009; for 1996 through 2008, the RXNDC linked drugs across rounds.
The following hypothetical example illustrates the structure of these ID variables. This example
illustrates a person in Rounds 1 and 2 of the household interview who reported having purchased Amoxicillin three times.
The following example shows three acquisition-level records, all having the same DRUGIDX (00002026002), for one person
(DUPERSID=00002026) in two rounds. Generally, within a round, one NDC is associated with a prescribed medicine event
because matching was performed at a drug level, as opposed to an acquisition level. The LINKIDX (000020260083) remains
the same for both records in Round 1 but varies across rounds. The RXRECIDX (000020260083001, 000020260083002,
000020260103001) differs for all three records.
DUPERSID |
PURCHRD |
RXRECIDX |
LINKIDX |
DRUGIDX |
RXNDC |
00002026 |
1 |
000020260083001 |
000020260083 |
00002026002 |
00093310905 |
00002026 |
1 |
000020260083002 |
000020260083 |
00002026002 |
00093310905 |
00002026 |
2 |
000020260103001 |
000020260103 |
00002026002 |
00003010955 |
There can be multiple RXNDCs for a LINKIDX. All the acquisitions in the LINKIDX represent the same
drug (active ingredients), but the RXNDCs may represent different manufacturers. (For more details on matching, please
see Section 4.0).
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PANEL is a constructed variable used to specify the panel number for the person. Panel will indicate
either Panel 15 or Panel 16 for each person on the file. Panel 15 is the panel that started in 2010, and Panel 16 is the
panel that started in 2011.
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The variable PURCHRD indicates the round in which the prescribed medicine was purchased and takes
on the value of 1, 2, 3, 4, or 5. Rounds 3, 4, and 5 are associated with MEPS survey data collection from Panel 15.
Similarly, Rounds 1, 2, and 3 are associated with data collected from Panel 16.
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There are three variables which indicate when a prescribed medicine was first taken (used), as reported by the household
respondent. They are the following: RXBEGDD indicates the day on which a person first started taking a medication,
RXBEGMM denotes the month in which a person first started taking a medication, and RXBEGYRX 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 respondent. These questions are not asked about refills of the prescription
in subsequent rounds. Values are carried forward from prior rounds for all medications first reported in the current
year. As a result, medications first reported in Rounds 1 or 2 in 2010 have RXBEGYRX = -1. Users should also note that
the value -14 (not yet used or taken) is not relevant for refills. The variable DRUGIDX (see Section 2.6.1.2) can be
used to determine whether a medication was reported in a prior round. For purposes of confidentiality, RXBEGYRX was
bottom-coded at 1926, consistent with top-coding of the age variables on the 2011 Full Year Population Characteristics
Public Use File (HC-141).
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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:
- Medication name – pharmacy reported (RXNAME)
- National drug code (RXNDC)
- Quantity of the prescribed medicine dispensed (RXQUANTY), e.g., number of tablets in the
prescription
- Form of the prescribed medicine (RXFORM), e.g., powder
- Unit of measurement for form of Rx/prescribed medicine (RXFRMUNT), e.g., oz
- Strength of the dose of the prescribed medicine (RXSTRENG), e.g., 10
- Unit of measurement for the strength of the dose of the prescribed medicine (RXSTRUNT),
e.g., gm
- Days supplied (RXDAYSUP)
Days supplied was first collected and released to the public on the 2010 Prescribed Medicines file.
Many pharmacies did not provide this information, and imputation was not attempted in these cases. A value of 999
indicates the medication is to be taken as needed. No edits were implemented to impose consistency between the quantity
and days supplied, and no edits were implemented for very high values.
The 2011 file contains multiple values of RXFORM and RXFRMUNT not found in Prescribed Medicines
files in prior years. There was no reconciliation of inconsistencies or duplication between RXFORM and RXFRMUNT. Please
refer to Appendices 1, 2, and 3 for definitions for RXFORM, RXFRMUNT, and RXSTRUNT abbreviations, codes and symbols.
Please refer to Appendix 4 for therapeutic class code definitions.
The national drug code (NDC) 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 PC prescriptions because the NDC reported by the pharmacy provider was not valid. These records are identified
by RXFLG=3.
For the years 1996-2004, AHRQ’s licensing agreement for the proprietary database precluded
the release of the 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 pharmacy were
provided on the file to allow users to do their own imputation. In addition, for the years 1996-2004, the imputed NDC
values for the RXFLG=3 cases could be accessed through the MEPS Data Center. For those events not falling
into the RXFLG=3 category, the reserve code (-13) was assigned to the household-reported medication name (RXHHNAME). The
household-reported name of the prescription (RXHHNAME) is no longer provided on this file; however, this variable may
be accessed through the MEPS Data Center as can the original pharmacy-reported name and NDC. For information on accessing
data through the MEPS Data Center, see the Data Center section of the MEPS Web site at:
meps.ahrq.gov/data_stats/onsite_datacenter.jsp.
Imputed data on this event file, unlike other MEPS event files, may still have missing data. This
is because imputed data on this file are imputed from the PC 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 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.
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Household respondents were asked to list the type of pharmacy from which household members purchased
their medications. A respondent could list multiple pharmacies associated with each member’s prescriptions in a
given round or over the course of all rounds combined covering the survey year. All household-reported pharmacies are
provided on this file, but there is 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 variables PHARTP1 through
PHARTP8 identify the types of pharmacy providers from which the person’s prescribed medicines were purchased. The
possible types of pharmacies include the following: (1) mail-order, (2) another store, (3) HMO/clinic/hospital, (4) drug
store, and (5) on-line. A -1 value for PHARTPn indicates that the household did not report “nth”
pharmacy.
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There are five flag variables included on this file (RXFLG, IMPFLAG, PCIMPFLG, CLMOMFLG, and INPCFLG).
RXFLG 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 PC record, RXFLG = 2. Similarly, if the imputation source was a secondary, proprietary database and not the
PC database, RXFLG = 3.
IMPFLAG indicates the method of creating the expenditure data: IMPFLAG = 1 indicates complete HC
data, IMPFLAG = 2 indicates complete PC data, IMPFLAG = 3 indicates HC and PC data, IMPFLAG = 4 indicates fully imputed
data, and IMPFLAG = 5 indicates partially imputed data
PCIMPFLG indicates the type of match between a household-reported event and a PC-reported event.
PCIMPFLG = 1 indicates an exact match for a specific event for a person between the PC and the HC. PCIMPFLG = 2 indicates
not an exact match between the PC and HC for a specific person (i.e., a person’s household-reported event did not
have a matched counterpart in the person’s corresponding PC 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. Because matching was performed at a drug level as opposed to
an acquisition level, the values for PCIMPFLG are either 1 or 2. For more details on general data editing/imputation
methodology, please see Section 4.0.
CLMOMFLG indicates if a prescription medicine event went through the Charge Payment (CP) section of
the HC. Prescription medicine events that went through the CP section of the HC include: (1) events where the person filed
their own prescription claim forms with their insurance company, (2) events for persons for whom the respondent did not
know if they filed their own prescription claim forms with their insurance company, and (3) insulin and diabetic
supply/equipment events (OMTYPE = 2 or 3) that were mentioned in the Other Medical Expenses section of the HC. For these
types of events, information on payment sources was retained to the extent that these data were reported by the household
respondent in the CP section of the HC.
INPCFLG denotes whether or not a household member had at least one prescription drug purchase in the
PC (0 = NO, 1 = YES).
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SAMPLE indicates if a respondent reported the person received a free sample of the prescription
medicine in the round (0 = NO, 1 = YES). Respondents were asked in each round whether or not the person received any
free samples of a reported prescribed medicine during the round. However, respondents were not asked to report the number
of free samples a person received, nor was it made clear that free samples were included in the count of the number of
times that the respondent reported a person purchasing or otherwise obtaining the prescribed medicine during the round.
It is important for analysts to note that SAMPLE is not a count variable of free samples; SAMPLE = 1 indicates
that a person was reported getting a free sample of the prescribed medicine during the round. This flag variable simply
allows individual analysts to determine for themselves how free samples should be handled in their analysis.
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Information on household-reported medical conditions associated with each prescribed medicine event
is provided on this file. There are up to three condition and clinical classification codes listed for each prescribed
medicine event (99.81 percent of prescribed medicine events have 0-3 condition records linked). To obtain complete
information associated with an event, the analyst must link to the 2011 Medical Conditions File. Details on how to link
to the MEPS 2011 Medical Conditions File are provided in the 2011 Appendix File. The user should note that, for
confidentiality restrictions, provider-reported condition information (for non-prescription medicines events) is not
publicly available. Provider-reported condition data for non-prescription medicines events can be accessed only through
the MEPS Data Center.
The medical conditions reported by the HC respondent were recorded by the interviewer as verbatim
text, which were then coded to fully-specified 2011 ICD-9-CM codes, including medical condition, V-codes, and a small
number of E-codes, 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. For detailed information on conditions, please
refer to the documentation on the 2011 Medical Conditions File. For frequencies of conditions by event type, please see
the 2011 Appendix File, HC-144I.
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 (CCS) (formerly known as
Clinical Classifications for Health Care Policy Research (CCHPR)), which aggregates conditions and V-codes into mutually
exclusive categories, most of which are clinically homogeneous.
In order to preserve household member 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.
Because of this collapsing, it is possible for there to be duplicate 3-digit ICD-9-CM condition codes linked to a single
prescribed medicine event when different fully-specified codes are collapsed into the same code. This would result in two
or more of the condition code variables on this file being set to the same value on a single record. For more information
on ICD-9-CM codes, see the HC-146 documentation.
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 2011 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.
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Each record on this file contains the following Multum Lexicon variables:
PREGCAT: pregnancy category variable – identifies the FDA pregnancy
category to which a particular drug has been assigned
TCn: therapeutic classification variable – assigns a drug to one or
more therapeutic/chemical categories; can have up to three categories per drug
TCnSn: therapeutic sub-classification variable – assigns one or more
sub-categories to a more general therapeutic class category given to a drug
TCnSn_n: therapeutic sub sub-classification variable – assigns one or
more sub sub-categories to a more general therapeutic class category and sub-category given to a drug
Users should carefully review the data when conducting trend analyses or pooling years or panels
because Multum’s therapeutic classification has changed across the years of the MEPS. The Multum variables on each
year of the MEPS Prescribed Medicines files reflect the most recent classification available in the year the data were
released. Since the release of the 1996 Prescribed Medicines file, the Multum classification has been changed by the
addition of new classes and subclasses, and by changes in the hierarchy of classes. Three examples follow: 1) In the
1996-2004 Prescribed Medicines files, antidiabetic drugs are a subclass of the hormone class, but in subsequent files,
the antidiabetic subclass is part of a class of metabolic drugs. 2) In the 1996-2004 files, antihyperlipidemic agents are
categorized as a class with a number of subclasses including HMG-COA reductase inhibitors (statins). In subsequent files,
antihyperlipidemic drugs are a subclass, and HMG-COA reductase inhibitors are a sub-subclass, in the metabolic class.
3) In the 1996-2004 files, the psychotherapeutic class comprises drugs from four subclasses: antidepressants,
antipsychotics, anxiolytics/sedatives/hypnotics, and CNS stimulants. In subsequent files, the psychotherapeutic class
comprises only antidepressants and antipsychotics. Changes may occur between any years. For additional information on
these and other Multum Lexicon variables, as well as the Multum Lexicon database itself, please refer to
www.multum.com/Lexicon.htm.
Users should also be aware of a problem discovered with the linking between the MEPS Prescribed
Medicines files and the Cerner Multum file that resulted in some incorrect therapeutic classes being assigned. In
particular, some diagnostic tests and medical devices were inadvertently assigned to be in a therapeutic class when they
should not have been. Specifically, from 1996-2002, some diabetic supplies were assigned to be in TC1S1=101 (sex hormone),
and from 2003 through 2010 some diabetic supplies were assigned to be in TC1S1=37 (toxoids). In addition, starting in
2006, NDC 00169750111 should have been assigned to TC1=358 and TC1S1=99. Analysts should use caution when using the
Cerner Multum therapeutic class variables for analysis and should always check for accuracy.
Researchers using the Multum Lexicon variables are requested to cite Multum Lexicon as the data source.
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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 1990s because of 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).
If examining trends in MEPS expenditures or performing longitudinal analysis on MEPS expenditures
please refer to Section C, sub-sections 3.4 and 6.3 respectively for more information.
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In addition to total expenditures, variables are provided which itemize expenditures according to
major source of payment categories. These categories are:
- Out-of-pocket by user (self) or family,
- Medicare,
- Medicaid,
- Private Insurance,
- Veterans Administration/CHAMPVA, excluding TRICARE,
- TRICARE,
- Other Federal sources – includes Indian Health Service, Military Treatment Facilities,
and other care by the Federal government,
- Other State and Local Source – includes community and neighborhood clinics, State and
local health departments, and State programs other than Medicaid,
- Workers’ Compensation, and
- Other Unclassified Sources – includes sources such as automobile, homeowner’s, and
liability insurance, and other miscellaneous or unknown sources.
Two additional source of payment variables were created to classify payments for events with
apparent inconsistencies between insurance coverage and sources of payment based on data collected in the survey.
These variables include:
- 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
- Other Public – Medicare/Medicaid payments reported for persons who were not reported to
be enrolled in the Medicare/Medicaid program at any time during the year.
Though relatively small in magnitude, data users/analysts 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 public payer.
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There is a single full year person-level weight (PERWT11F) assigned to each record for each key,
in-scope person who responded to MEPS for the full period of time that he or she was in-scope during 2011. A key person
was either a member of a responding NHIS household at the time of interview or joined a family associated with such a
household after being out-of-scope at the time of the NHIS (the latter circumstance includes newborns as well as those
returning from military service, an institution, or residence in a foreign country). A person is in-scope whenever he or
she is a member of the civilian noninstitutionalized portion of the U.S. population.
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The person-level weight PERWT11F was developed in several stages. Person-level weights for Panel
15 and Panel 16 were created separately. The weighting process for each panel included an adjustment for nonresponse over
time and calibration to independent population figures. The calibration was initially accomplished separately for each
panel by raking the corresponding sample weights for those in-scope at the end of the calendar year to Current Population
Survey (CPS) population estimates based on six variables. The six variables used in the establishment of the initial
person-level control figures were: census region (Northeast, Midwest, South, West); MSA status (MSA, non-MSA);
race/ethnicity (Hispanic; Black, non-Hispanic; Asian, non-Hispanic; and other); sex, education level; and age. A 2011
composite weight was then formed by multiplying each weight from Panel 15 by the factor .43 and each weight from Panel
16 by the factor .57. The choice of factors reflected the relative sample sizes of the two panels, helping to limit the
variance of estimates obtained from pooling the two samples. The composite weight was again raked to the same set of
CPS-based control totals. When poverty status information derived from income variables became available, a final raking
was undertaken on the previously established weight variable. Control totals were established using poverty status (five
categories: below poverty, from 100 to 125 percent of poverty, from 125 to 200 percent of poverty, from 200 to 400
percent of poverty, at least 400 percent of poverty) to replace education level as one of the six variables in the weight
calibration.
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The person-level weight for MEPS Panel 15 was developed using the 2010 full year weight as a
“base” weight for survey participants present in 2010. For key, in-scope members who joined an RU some time
in 2011 after being out-of-scope in 2010, the initially assigned person-level weight was the corresponding 2010 family
weight. The weighting process included an adjustment for person-level nonresponse over Rounds 4 and 5 as well as raking
to population control totals for December 2011 for key, responding persons in-scope on December 31, 2011. These control
totals were derived by scaling back the population distribution obtained from the March 2012 CPS to reflect the December
31, 2011 estimated population total (estimated based on Census projections for January 1, 2011). Variables used for
person-level raking included: census region (Northeast, Midwest, South, West); MSA status (MSA, non-MSA); race/ethnicity
(Hispanic, Black but non-Hispanic; Asian but non-Hispanic; and other); sex; education level; and age. (Poverty status is
not included in this version of the MEPS full year database because of the time required to process the income data
collected and then assign persons to a poverty status category). The final weight for key, responding persons who were
not in-scope on December 31, 2011 but were in-scope earlier in the year was the person weight after the nonresponse
adjustment.
It may be noted that the Panel 15 weights reflect additional adjustments not typically implemented
in MEPS weights. Additional raking dimensions were added when PERWT10F was being developed, reflecting MEPS 2008-09
estimated average annual distributions of office-based visits and care from home health agencies by age. More details
can be found in the MEPS documentation for the 2010 Full Year Consolidated data file (HC-138).
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The person-level weight for MEPS Panel 16 was developed using the 2011 MEPS Round 1 person-level
weight as a “base” weight. For key, in-scope members who joined an RU after Round 1, the Round 1 family
weight served as a “base” weight. The weighting process included an adjustment for nonresponse over the
remaining data collection rounds in 2011 as well as raking to the same population control figures for December 2011 used
for the MEPS Panel 15 weights for key, responding persons in-scope on December 31, 2011. The same six variables
employed for Panel 15 raking (census region, MSA status, race/ethnicity, sex, education level, and age) were used for
Panel 16 raking. Again, the final weight for key, responding persons who were not in-scope on December 31, 2011 but were
in-scope earlier in the year was the person weight after the nonresponse adjustment.
Note that the MEPS Round 1 weights for both panels incorporated the following components: a weight
reflecting the original household probability of selection for the NHIS and an adjustment for NHIS nonresponse; a factor
representing the proportion of the 16 NHIS panel-quarter combinations eligible for MEPS; the oversampling of certain
subgroups for MEPS among the NHIS household respondents eligible for MEPS; ratio-adjustment to NHIS-based national
population estimates at the household (occupied DU) level; adjustment for nonresponse at the DU level for Round 1; and
poststratification to U.S. civilian noninstitutionalized population estimates at the family and person level obtained
from the March CPS database.
It may be noted that there were several new features to the MEPS sample design employed for Panel
16 that were reflected in the Panel 16 weight: a sampling domain associated with those with cancer; a partitioning of
the “Other” sample domain into those who fully completed the NHIS survey and those who only partially
completed it; and a small experiment conducted in 11 PSUs, where some nonrespondents were subsampled for fielding purposes.
More detail can be found in the MEPS PUF documentation for the 2011 Full Year Population Characteristics data file
(HC-141).
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The final raking of those in-scope at the end of the year has been described above. In addition,
the composite weights of two groups of persons who were out-of-scope on December 31, 2011 were poststratified.
Specifically, the weights of those who were in-scope some time during the year, out-of-scope on December 31, and
entered a nursing home during the year were poststratified to a corresponding control total obtained from the 1996 MEPS
Nursing Home Component. The weights of persons who died while in-scope during 2011 were poststratified to corresponding
estimates derived using data obtained from the Medicare Current Beneficiary Survey (MCBS) and Vital Statistics information
provided by the National Center for Health Statistics (NCHS). Separate decedent control totals were developed for the
“65 and older” and “under 65” civilian, noninstitutionalized decedent populations.
In developing the final person-level weight for 2011 (PERWT11F), an additional raking dimension was
added that reflected the MEPS 2008-10 estimated average annual distribution of office-based visits by age (under 65, 65
and over). This additional adjustment was included to better reflect benchmark trends in office-based utilization. For
each of the two age groups, the table below shows ratios of weighted numbers of persons that resulted from including the
additional raking dimension to that of corresponding estimates without the additional raking dimensions.
Ratio of Adjusted to Unadjusted Weights
Number of Visits |
Nonelderly (AGE11X < 65) |
Elderly (AGE11X >= 65) |
0 |
0.89819 |
0.80783 |
1 - 5 |
1.01544 |
0.91486 |
6 - 10 |
1.10139 |
1.03666 |
> 10 |
1.18939 |
1.15433 |
Overall, the weighted population estimate for the civilian noninstitutionalized population for
December 31, 2011 is 307,567,803 (PERWT11F>0 and INSC1231=1). The sum of the person-level weights across all persons
assigned a positive person-level weight is 311,125,758. The 2011 Full Year database is the first MEPS file to reflect
2010 census-based population estimates from the CPS.
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The target population for MEPS in this file is the 2011 U.S. civilian noninstitutionalized population.
However, the MEPS sampled households are a subsample of the NHIS households interviewed in 2009 (Panel 15) and 2010
(Panel 16). New households created after the NHIS interviews for the respective panels and consisting exclusively of
persons who entered the target population after 2009 (Panel 15) or after 2010 (Panel 16) are not covered by MEPS. Neither
are previously out-of-scope persons who join an existing household but are unrelated to the current household residents.
Persons not covered by a given MEPS panel thus include some members of the following groups: immigrants; persons leaving
the military; U.S. citizens returning from residence in another country; and persons leaving institutions. The set of
uncovered persons constitutes only a small segment of the MEPS target population.
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MEPS began in 1996, and the utility of the survey for analyzing health care trends expands with
each additional year of data; however, it is important to consider a variety of factors when examining trends over time
using MEPS. Statistical significance tests should be conducted to assess the likelihood that observed trends may be
attributable to sampling variation. The length of time being analyzed should also be considered. In particular, large
shifts in survey estimates over short periods of time (e.g. from one year to the next) that are statistically significant
should be interpreted with caution, unless they are attributable to known factors such as changes in public policy,
economic conditions, or MEPS survey methodology.
Specifically, beginning with the 2007 data, the rules MEPS uses to identify outlier prices for
prescription medications became much less stringent than in prior years. Starting with the 2007 Prescribed Medicines file,
there was: less editing of prices and quantities reported by pharmacies, more variation in prices for generics, lower
mean prices for generics, higher mean prices for brand name drugs, greater differences in prices between generic and
brand name drugs, and a somewhat lower proportion of spending on drugs by families, as opposed to third-party payers.
Starting with the 2008 Prescribed Medicines file, improvements in the data editing changed the distribution of payments
by source: (1) more spending on Medicare beneficiaries is by private insurance, rather than Medicare, and (2) less
out-of-pocket payments and more Medicaid payments among Medicaid enrollees. Starting with the 2009 data, additional
improvements increased public program amounts and reduced out-of-pocket payments and, for Medicare beneficiaries with
both Part D and Medicaid, decreased Medicare payments and increased Medicaid and other state and local government
payments. Therefore, users should be cautious in the types of comparisons they make about prescription drug spending
before and after 2007, 2008, and 2009. In addition, some therapeutic class codes have changed over time.
Looking at changes over longer periods of time can provide a more complete picture of underlying
trends. Analysts may wish to consider techniques to evaluate, smooth, or stabilize analyses of trends such as comparing
pooled time periods (e.g. 1996-97 versus 2010-11), working with moving averages, or using modeling techniques with several
consecutive years of MEPS data to test the fit of specified patterns over time. Moreover, analyses of trends in health
care utilization should be undertaken with awareness of relevant adjustments to the analytic weight (e.g., see
section 3.2.3 on the Final Person-Level Weight for 2011). Finally, researchers should be aware of the impact of multiple
comparisons on Type I error. Without making appropriate allowance for multiple comparisons, undertaking numerous statistical
significance tests of trends increases the likelihood of concluding that a change has taken place when one has not.
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The general approach to preparing the household prescription data for this file was to utilize the
PC prescription data to impute information collected from pharmacy providers to the household drug mentions. For events
that went through the Charge Payment (CP) section of the HC (events where the person filed their own prescription claim
forms with their insurance company, events for persons for whom the respondent did not know if they filed their own
prescription claim forms with their insurance company, and insulin and diabetic supply/equipment events (OMTYPE=2 or 3)
that were mentioned in the Other Medical Expenses section of the HC), information on payment sources was retained to the
extent that these data were reported by the household respondent in the CP section of the HC. A matching program was
adopted to link PC 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 respondent and pharmacy, and, when available, the NDC provided in the pharmacy
follow-back component. The matching process was done at a drug (active ingredient) level, as opposed to an acquisition
level. 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.
Beginning with the 2007 data, the rules used to identify outlier prices for prescription medications
in the PC changed. New outlier thresholds were established based on the distribution of the ratio of retail unit prices
relative to the AWUP in the 2006 MarketScan Outpatient Pharmaceutical Claims database. The new thresholds vary by patent
status, whereas in prior years they did not. These changes improve data quality in three ways: (1) the distribution of
prices in the MEPS better benchmarks to MarketScan, overall and by patent status (Zodet et al. 2010), (2) fewer
pharmacy-reported payments and quantities (for example, number of pills) are edited, and (3) imputed prices reflect prices
paid, rather than AWUPs. As a result, compared with earlier years of the MEPS, starting with 2007 there is more variation
in prices for generics, lower mean prices for generics, higher mean prices for brand name drugs, greater differences in
prices between generic and brand name drugs, and a somewhat lower proportion of spending on drugs by families, as opposed
to third-party payers. Pharmacy reports of free antibiotics were not edited as if they were outliers. Beginning with the
2010 data, some additional free drugs obtained through commercial pharmacies were not edited.
Beginning with the 2009 data, three changes in editing sources of payment data were made to improve
data quality, based on a validation study (Hill et al., 2011). Two changes were made in editing fills for which pharmacies
reported partial payment data. First, if the third party amount was missing and the third party payer was a public payer,
then pharmacy reports of zero out-of-pocket amounts were preserved rather than imputed. Second, somewhat tighter outlier
thresholds were implemented for the fills with partial payment data, and somewhat looser outlier thresholds were implemented
for fills with complete payment data. Another change affected Medicare beneficiaries with both Part D and Medicaid
coverage—reported Medicaid and other state and local program payments were no longer edited to be Medicare payments.
Beginning with the 2010 data, improvements in the payment imputation methods for pharmacy data (1)
better utilize pharmacy-reported quantities to impute missing payment amounts, and (2) preserve within-NDC variation
in the prices on the records for which third party payment amounts are imputed.
Beginning with the 2011 data, the imputation of the number of fills for a drug was improved. In the
2011 data, for 10% of household-reported drugs the respondent did not know or remember the number of times the drug was
obtained during the round. For missing and implausible values, a hot-deck procedure imputed a new number of acquisitions,
drawing from the donor pool of drugs with valid values. Prior to 2011, the imputation method gave greater weight to donors
with more acquisitions in the round. The new method conditions on insurance status, age, and geography, as well as drug.
Drug matches between household drug mentions and pharmacy drug events for a person in the PC were
based on drug code, medication name, and the round in which the drug was reported. The matching of household drug mentions
to pharmacy drugs was performed so that the most detailed and accurate information for each prescribed medicine event was
obtained. Beginning with the 2008 Prescribed Medicines file, the criteria for matching were changed to allow multiple
NDCs for the same drug reported by pharmacies (for example, different manufacturers) to match to one drug reported by the
household. Beginning with the 2010 data, the matching process was improved for diabetic supplies to better utilize pharmacy
reports of the diversity of supplies individuals purchased. 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 PC drugs for the same person in the same round required sufficiently high scores to
reflect a high quality match. Initially, 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). For remaining persons with pharmacy data from any round
and unmatched household drugs, additional matches are made with replacement across rounds. Any refill of a household drug
mention that had been matched to a pharmacy drug event was matched to the same pharmacy drug event. All remaining unmatched
household drug mentions for persons either in or out of the PC 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. PC records containing an NDC imputed without an exact match on a generic code were
omitted from the donor pool. Some matches have inconsistencies between the PC donor’s potential sources of payment
and those of the HC recipient, and these were resolved. Beginning with the 2008 data, the method used to resolve
inconsistencies in potential payers was changed to better reflect the distribution of sources of payment among the
acquisitions with consistent sources of payment. This change (1) reduced Medicare payments and increased private payments
among Medicare beneficiaries, and (2) reduced out-of-pocket payments and increased Medicaid payments among Medicaid
enrollees. In addition, Medicare, Medicaid, and private drug expenditures better benchmark totals in the National Health
Expenditure Accounts.
Also beginning with the 2011 data, many aspects of the specifications were modified so that
imputations and edits better reflect Medicare Part D donut hole rules and Medicare Part B coverage of a few medications
and diabetic supplies.
For more information on the MEPS Prescribed Medicines editing and imputation procedures, please
see J. Moeller, 2001.
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Expenditure variables on the 2011 Prescribed Medicines file have been rounded to the nearest penny.
Person-level expenditure variables released on the 2011 Full Year Consolidated Data File were rounded to the nearest dollar.
It should be noted that using the 2011 MEPS event files to create person-level totals will yield slightly different
totals than those found on the 2011 Full Year Consolidated data file. These differences are due to rounding only. Moreover,
in some instances, the number of persons having expenditures on the 2011 event files for a particular source of payment
may differ from the number of persons with expenditures on the 2011 Full Year Consolidated data file for that source of
payment. This difference is also an artifact of rounding only. Please see the 2011 Appendix File, HC-144I, for details
on such rounding differences.
Return To Table Of Contents
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 (RXXP11X) 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 (RXSF11X), amount paid by Medicare (RXMR11X), amount paid by Medicaid (RXMD11X), amount paid by
private insurance (RXPV11X), amount paid by the Veterans Administration/CHAMPVA (RXVA11X), amount paid by TRICARE
(RXTR11X), amount paid by other federal sources (RXOF11X), amount paid by state and local (non-federal) government sources
(RXSL11X), amount paid by Worker’s Compensation (RXWC11X), and amount paid by some other source of insurance
(RXOT11X). As mentioned previously, there are two additional expenditure variables called RXOR11X and RXOU11X (other
private and other public, respectively). These two expenditure variables were created to maintain consistency between
what the household respondent reported as a person’s 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 see Section 2.6.4 for details on these and all other source of payment variables.
Return To Table Of Contents
The data in this file can be used to develop national 2011 event-level estimates for the U.S.
civilian noninstitutionalized population on prescribed medicine purchases (events) as well as expenditures, and sources
of payment for these purchases. Estimates of total number of purchases are the sum of the weight variable (PERWT11F)
across relevant event records while estimates of other variables must be weighted by PERWT11F to be nationally representative.
The tables below contain event-level estimates for selected variables.
Selected Event (Purchase) Level Estimates
All Prescribed Medicine Purchases
Estimate of Interest |
Variable Name |
Estimate (SE) |
Number of purchases (in millions) |
PERWT11F |
3304.3 (81.05) |
Mean total payments per purchase |
RXXP11X |
$90 (4.5) |
Mean out-of-pocket payment per purchase |
RXSF11X |
$18 (0.4) |
Mean proportion of expenditures paid by
private insurance per purchase |
RXPV11X /RXXP11X |
0.169 (0.0047) |
Example by Drug Type: Statins (TC1S1_1 = 173 or TC1S1_2 = 173 or TC1S2_1 = 173 or TC1S3_1 = 173 or TC2S1_1 = 173 or TC2S1_2 = 173)
Estimate of Interest |
Variable Name |
Estimate (SE) |
Number of purchases (in millions) |
PERWT11F |
230.1 (7.21) |
Mean total payments per purchase |
RXXP11X |
$82 (2.4) |
Mean annual total payments per person |
RXXP11X (aggregated across purchases within person) |
$467 (14.1) |
Example by Associated Condition: Hypertension (RXICD1X = "401" or RXICD2X = "401" or RXICD3X = "401")
Estimate of Interest |
Variable Name |
Estimate (SE) |
Number of purchases (in millions) |
PERWT11F |
513.9 (16.44) |
Mean total payments per purchase |
RXXP11X |
$37 (1.1) |
Mean annual total payments per person |
RXXP11X (aggregated across purchases within person) |
$333 (12.2) |
Return To Table Of Contents
To enhance analyses of prescribed medicine purchases, analysts may link information about prescribed
medicine purchases to the annual full year consolidated file (which has data for all MEPS sample persons), or conversely,
link person-level information from the full year consolidated file to this event-level file (see Section 6 below for more
details). Both this file and the full year consolidated file may be used to derive estimates for persons with prescribed
medicine purchases and annual estimates of total expenditures for these purchases; however, if the estimate relates to
the entire population, this file cannot be used to calculate the denominator, as only those persons with at least one
prescribed medicine purchase are represented on this data file. Therefore, the full year consolidated file must be used
for person-level analyses that include both persons with and without prescribed medicine events.
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It is essential that the analyst examine all variables for the presence of negative values used to
represent missing values. For continuous or discrete variables, whose means or totals may be calculated, the analyst
should either impute a value or set the value such that it 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., total expenditures
and sources of payment) are described in Section 4.2.
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The MEPS is based on a complex sample design. To obtain estimates of variability (such as the
standard error of sample estimates or corresponding confidence intervals) for MEPS estimates, analysts need to take into
account the complex sample design of MEPS for both person-level and family-level analyses. Several methodologies have been
developed for estimating standard errors for surveys with a complex sample design, including the Taylor-series
linearization method, balanced repeated replication, and jackknife replication. Various software packages provide analysts
with the capability of implementing these methodologies. Replicate weights have not been developed for the MEPS data.
Instead, the variables needed to calculate appropriate standard errors based on the Taylor-series linearization method
are included on this file (as well as all other MEPS public use files). Software packages that permit the use of the
Taylor-series linearization method include SUDAAN, Stata, SAS (version 8.2 and higher), and SPSS (version 12.0 and higher).
For complete information on the capabilities of each package, analysts should refer to the corresponding software user
documentation.
Using the Taylor-series linearization method, variance estimation strata and the variance estimation
PSUs within these strata must be specified. The variance strata variable is named VARSTR, while the variance PSU variable
is named VARPSU. Specifying a “with replacement” design in one of the previously mentioned computer software
packages will provide estimated 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 number available. For variables of interest distributed throughout the country
(and thus the MEPS sample PSUs), one can generally expect to have at least 100 degrees of freedom associated with the
estimated standard errors for national estimates based on this MEPS database.
Prior to 2002, MEPS variance strata and PSUs were developed independently from year to year, and
the last two characters of the strata and PSU variable names denoted the year. However, beginning with the 2002
Point-in-Time PUF, the variance strata and PSUs were developed to be compatible with MEPS data associated with the NHIS
sample design used through 2006. Such data can be pooled and the variance strata and PSU variables provided can be used
without modification for variance estimation purposes for estimates covering multiple years of data.
As a result of the change in the NHIS sample design in 2006, a new set of variance strata and PSUs
have been established for variance estimation purposes for use with MEPS Panel 12 and subsequent MEPS panels. There were
165 variance strata associated with both MEPS Panel 15 and Panel 16, providing a substantial number of degrees of freedom
for subgroups as well as the nation as a whole. Each variance stratum contains either two or three variance estimation
PSUs.
Return To Table Of Contents
Data from this file can be used alone or in conjunction with other files for different analytic
purposes. This section summarizes various scenarios for merging/linking MEPS files. Each MEPS panel can also be linked
back to the previous year’s National Health Interview Survey public use data files. For information on obtaining
MEPS/NHIS link files please see
meps.ahrq.gov/data_stats/more_info_download_data_files.jsp.
Return To Table Of Contents
Merging characteristics of interest from the person-level file (e.g., MEPS 2011 Full Year Consolidated
File) expands the scope of potential estimates. For example, to estimate the total number of prescribed medicine purchases
of persons with specific demographic characteristics (such as age, race, sex, and education), population characteristics
from a person-level file need to be merged onto the prescribed medicines file. This procedure is illustrated below. The
MEPS 2011 Appendix File, HC-144I, provides additional detail on how to merge MEPS data files.
- Create data set PERSX by sorting the 2011 Full Year Consolidated File by the person identifier,
DUPERSID. Keep only variables to be merged onto the prescribed medicines file and DUPERSID
- Create data set PMEDS by sorting the 2011 Prescribed Medicines File by person identifier,
DUPERSID
- 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 AGE31X AGE42X
AGE53X SEX RACEX EDUCYR EDUYRDEG EDRECODE) OUT=PERSX;
BY DUPERSID;
RUN;
PROC SORT DATA=IN.HCXXXA
OUT=PMEDS;
BY DUPERSID;
RUN;
DATA NEWPMEDS;
MERGE PMEDS (IN=A) PERSX(IN=B);
BY DUPERSID;
IF A;
RUN;
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The condition-event link file (CLNK) provides a link from MEPS event files to the 2011 Medical
Conditions File. When using the CLNK, data users/analysts should keep in mind that (1) conditions are self-reported,
(2) there may be multiple conditions associated with a prescribed medicine purchase, and (3) a condition may link to
more than one prescribed medicine purchase or any other type of purchase. Users should also note that not all prescribed
medicine purchases link to the condition file.
Return To Table Of Contents
For Panels 1 through 8, panel-specific files (called Longitudinal Weight Files) containing
estimation variables to facilitate longitudinal analysis are available for downloading in the data section of the MEPS
Web site. To create longitudinal files for these panels, it is necessary to link data from two subsequent annual files
that contain data for the first and second years of the panel, respectively. Starting with Panel 9, it is not necessary
to link files for longitudinal analysis because Longitudinal Data Files have been constructed and are available for
downloading on the Web.
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,24, 25-53.
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). Imputation Procedures to Compensate for Missing Responses to
Data Items. In D.B. Owen and R.G.Cornell (Eds.), Methodological Issues for Health Care Surveys (pp. 214-234).
New York, NY: Marcel Dekker.
Ezzati-Rice, T.M., Rohde, F., Greenblatt, J. (2008). Sample Design of the Medical Expenditure
Panel Survey Household Component, 1998–2007 (Methodology Report No. 22). Rockville, MD: Agency for
Healthcare Research and Quality.
Hill, S.C., Zuvekas, S.H., and Zodet, M.W. (2011). Implications of the Accuracy of MEPS
Prescription Drug Data for Health Services Research. Inquiry 48(3). Forthcoming 2011.
Moeller J.F., Stagnitti, M., Horan, E., et al. (2001). Outpatient Prescription Drugs: Data
Collection and Editing in the 1996 Medical Expenditure Panel Survey (HC-010A) (MEPS Methodology Report No. 12,
AHRQ Pub. No. 01-0002). Rockville, MD: Agency for Healthcare Research and Quality.
Monheit, A.C., Wilson, R., and Arnett, III, R.H. (Eds.). (1999) Informing American Health
Care Policy. San Francisco, CA: Jossey-Bass Inc.
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.
Zodet, M.W., Hill, S.C., and Miller, E. Comparison of Retail Drug Prices in the MEPS and
MarketScan: Implications for MEPS Editing Rules. Agency for Healthcare Research and Quality Working Paper No. 10001,
February 2010.
Return To Table Of Contents
VARIABLE-SOURCE CROSSWALK
FOR MEPS HC-144A: 2011 Prescribed Medicines 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 |
DRUGIDX |
Link to drugs across rounds |
CAPI derived |
PANEL |
Panel indicator |
Assigned in sampling |
PURCHRD |
Round in which the Rx/prescribed medicine was obtained/purchased |
CAPI derived |
Return To Table Of Contents
Prescribed Medicines Events Variables
Variable |
Description |
Source |
RXBEGDD |
Day person first used medicine |
PM11OV2 |
RXBEGMM |
Month person first used medicine |
PM11OV1 |
RXBEGYRX |
Year person first used medicine |
PM11 |
RXNAME |
Medication name (Imputed) |
Imputed |
RXNDC |
National drug code (Imputed) |
Imputed |
RXQUANTY |
Quantity of Rx/prescribed medicine (Imputed) |
Imputed |
RXFORM |
Form of Rx/prescribed medicine (Imputed) |
Imputed |
RXFRMUNT |
Unit of measurement for form of Rx/prescribed medicine (Imputed) |
Imputed |
RXSTRENG |
Strength of Rx/prescribed medicine dose (Imputed) |
Imputed |
RXSTRUNT |
Unit of measurement for strength of Rx/prescribed medicine dose (Imputed) |
Imputed |
RXDAYSUP |
Days supplied of prescribed med(Imputed) |
Imputed |
PHARTP1-PHARTP8 |
Type of pharmacy provider – (1st-7th) |
PM16 |
RXFLG |
Flag variable indicating imputation source for NDC on pharmacy donor record |
Constructed |
IMPFLAG |
Method of expenditure data creation |
Constructed |
PCIMPFLG |
Flag indicating type of household to pharmacy prescription match |
Constructed |
CLMOMFLG |
Charge/payment, Rx claim filing, and OMTYPE =2 or =3
(insulin and diabetic supply equipment events) status |
CP01/Constructed |
INPCFLG |
Flag indicating if the person has at least one record in the pharmacy component |
Constructed |
SAMPLE |
Flag indicating if a person received a free sample of this drug in the round |
CAPI derived |
RXICD1X |
3 digit ICD-9 condition code |
PM09 |
RXICD2X |
3 digit ICD-9 condition code |
PM09 |
RXICD3X |
3 digit ICD-9 condition code |
PM09 |
RXCCC1X |
Modified clinical classification code |
Constructed/Edited |
RXCCC2X |
Modified clinical classification code |
Constructed/Edited |
RXCCC3X |
Modified clinical classification code |
Constructed/Edited |
PREGCAT |
Multum pregnancy category |
Cerner Multum, Inc. |
TC1 |
Multum therapeutic class #1 |
Cerner Multum, Inc. |
TC1S1 |
Multum therapeutic sub-class #1 for TC1 |
Cerner Multum, Inc. |
TC1S1_1 |
Multum therapeutic sub-sub-class for TC1S1 |
Cerner Multum, Inc. |
TC1S1_2 |
Multum therapeutic sub-sub-class for TC1S1 |
Cerner Multum, Inc. |
TC1S2 |
Multum therapeutic sub-class #2 for TC1 |
Cerner Multum, Inc. |
TC1S2_1 |
Multum therapeutic sub-sub-class for TC1S2 |
Cerner Multum, Inc. |
TC1S3 |
Multum therapeutic sub-class #3 for TC1 |
Cerner Multum, Inc. |
TC1S3_1 |
Multum therapeutic sub-sub-class for TC1S3 |
Cerner Multum, Inc. |
TC2 |
Multum therapeutic class #2 |
Cerner Multum, Inc. |
TC2S1 |
Multum therapeutic sub-class #1 for TC2 |
Cerner Multum, Inc. |
TC2S1_1 |
Multum therapeutic sub-sub-class for TC2S1 |
Cerner Multum, Inc. |
TC2S1_2 |
Multum therapeutic sub-sub-class for TC2S1 |
Cerner Multum, Inc. |
TC2S2 |
Multum therapeutic sub-class #2 for TC2 |
Cerner Multum, Inc. |
TC3 |
Multum therapeutic class #3 |
Cerner Multum, Inc. |
TC3S1 |
Multum therapeutic sub-class #1 for TC3 |
Cerner Multum, Inc. |
TC3S1_1 |
Multum therapeutic sub-sub-class for TC3S1 |
Cerner Multum, Inc. |
RXSF11X |
Amount paid, self or family (Imputed) |
CP11/Edited/Imputed |
RXMR11X |
Amount paid, Medicare (Imputed) |
CP12/CP13/Edited/Imputed |
RXMD11X |
Amount paid, Medicaid (Imputed) |
CP12/CP13/Edited/Imputed |
RXPV11X |
Amount paid, private insurance (Imputed) |
CP12/CP13/Edited/Imputed |
RXVA11X |
Amount paid, Veteran’s Administration/CHAMPVA (Imputed) |
CP12/CP13/Edited/Imputed |
RXTR11X |
Amount paid, TRICARE (Imputed) |
CP12/CP13/Edited/Imputed |
RXOF11X |
Amount paid, other Federal (Imputed) |
CP12/CP13/Edited/Imputed |
RXSL11X |
Amount paid, state and local government (Imputed) |
CP12/CP13/Edited/Imputed |
RXWC11X |
Amount paid, Worker’s Compensation (Imputed) |
CP12/CP13/Edited/Imputed |
RXOT11X |
Amount paid, other insurance (Imputed) |
CP12/CP13/Edited/Imputed |
RXOR11X |
Amount paid, other private (Imputed) |
Constructed/Imputed |
RXOU11X |
Amount paid, other public (Imputed) |
Constructed/Imputed |
RXXP11X |
Sum of payments RXSF11X – RXOU11X (Imputed) |
CP12/CP13/Edited/Imputed |
Return To Table Of Contents
Weights
Variable |
Description |
Source |
PERWT11F |
Final person-level weight |
Constructed |
VARSTR |
Variance estimation stratum, 2011 |
Constructed |
VARPSU |
Variance estimation PSU, 2011 |
Constructed |
Return To Table Of Contents
Definitions for RXFORM, Form of Prescribed Medicines
Dosage Form |
Definition |
-7 |
refused |
-8 |
don’t know |
-9 |
not ascertained |
ACC |
accessory |
ACETONIDE |
|
ACT |
actuation |
ADR |
acetic acid drop |
AE |
aerosol |
AEPB |
aerosol powder, breath activated |
AER |
aerosol |
AER SPRAY |
aerosol spray |
AERA |
aerosol with adapter |
AERB |
aerosol, breath activated |
AERO |
aerosol |
AEROP |
aerosol powder |
AEROSOL |
|
AERS |
aerosol, solution |
ALM |
|
AMI |
|
AMO |
|
AMP |
ampule |
ARA |
aerosol liquid w/adapter (inhaler) |
ARD |
aerosol solid w/adapter |
ARO |
aerosol solid |
ASS |
|
AUTO INJ |
auto-injection |
BACK SUPPORT BELT |
|
BAG |
|
BAL |
balm |
BALM |
|
BAN |
bandage |
BANDAGE |
|
BAR |
|
BATTERY |
|
BENCH |
|
BOT |
bottle |
BOTTLE |
|
BOX |
|
BOXES |
|
BRACE |
|
BRIEF |
|
BUT |
butterfly |
C |
capsules, or cream (varies) |
C12 |
12 hour extended-release capsule |
C24 |
24 hour extended-release capsule |
CA |
capsule |
CANE |
|
CAP |
capsule, caplets |
CAP DR |
delayed-release capsule |
CAP ER |
extended-release capsule |
CAP SA |
slow-acting capsule |
CAPLET |
|
CAPLT |
caplet |
CAPS |
capsules |
CAPSULE |
|
CAPSULE SA |
slow-acting capsule |
CATHETER |
|
CC |
cubic centimeter |
CER |
capsule, extended-release tablet,
extended-release |
CHAMBER |
|
CHEW |
chewable tablet |
CHEW TAB |
chewable tablet |
CHEW TABS |
chewable tablets |
CHEWABLE |
|
CHW |
chewable tablets |
CLEANSER |
|
COLLAR |
|
COMBO |
|
COMPOUND |
|
CON |
condom |
CONC |
concentrate |
CONDOM |
|
CONTAINER |
|
COS |
|
COTTON |
|
CP12 |
capsule, extended-release, 12 hour |
CP24 |
capsule, extended-release, 24 hour |
CPCR |
capsule, extended-release |
CPDR |
capsule, delayed release |
CPEP |
capsule, delayed release particles |
CPSP |
capsule sprinkle |
CPSR |
slow-release capsule |
CR |
cream |
CRE |
cream |
CREA |
cream |
CREAM |
|
CRM |
cream |
CRY |
crystal |
CRYS |
crystals |
CRYSTAL |
|
CTB |
chewable tablets |
CTG |
cartridge |
CURVE |
|
CUTTER |
|
DEV |
device |
DEVI |
device |
DEVICE |
|
DIA |
diaper |
DIAPER |
|
DIAPHRAM |
|
DIHYDROCHLOR |
|
DIPROPION |
|
DIS |
disk, or dermal infusion system |
DISK |
|
DISKUS |
|
DISPOSABLE |
|
DOS PAK |
dose pack |
DR |
drop |
DRC |
delayed-release capsule |
DRE |
dressing |
DRESSING |
|
DRO |
drop |
DROP |
|
DROPS |
|
DROPS OPTH OTI |
ophthalmic/otic drops |
DROPS SUSP |
drops suspension |
DRP |
drop |
DRPS |
drops |
DSK |
disk |
DSPK |
tablets in a dose pack |
DSPT |
tablet, dispersible |
DT |
tablet, disintegrating |
EAM |
|
EAR DROP |
|
EAR DROPS |
|
EAR DRP |
ear drop |
EAR SUSP |
ear suspension |
EC TABS |
enteric coated tablets |
ECC |
enteric coated capsules |
ECO |
|
ECT |
enteric coated tablets |
ELI |
elixir |
ELIX |
elixir |
ELIXER |
|
ELIXIR |
|
ELX |
elixir |
EMERGENCY KIT |
|
EMO |
emollient |
EMU |
emulsion |
EMUL |
emulsion |
EMULSION |
|
ENE |
enema |
ENEM |
enema |
ENEMA |
|
ER |
|
ERC |
capsule, extended-release |
ERSUS |
suspension, extended-release |
ERT |
tablet, extended-release |
ERTA |
extended-release-tablets |
ERTC |
tablet, chewable, extended-release |
ESI |
|
EST |
|
ETA |
|
EXTN CAP |
extended-release capsule |
EXTRACT |
|
EYE DRO |
eye drop |
EYE DROP |
|
EYE DROPS |
|
EYE DRP |
eye drop |
EYE EMU |
|
EYE OIN |
|
EYE SO |
eye solution |
EYEDRO |
|
FIL |
film |
FILM |
film |
FILM ER |
film, extended-release |
FILMTAB |
|
FILMTABS |
|
FLOWMETER |
|
FOA |
foam |
FOAM |
|
GAU |
gauze |
GAUZE |
|
GEF |
effervescent granules |
GEL |
|
GELC |
|
GEL CAP |
gel capsule |
GELS |
gel-forming solution |
GER |
granule, extended-release |
GFS |
gel-forming solution |
GLOVE |
|
GRA |
granules |
GRAN |
granules |
GRANULES |
|
GRAR |
granules for reconstitution |
GRR |
grams |
GTT |
drops |
GUL |
|
GUM |
|
HFA |
|
HOSE |
medical hosiery |
HU |
capsule |
HYDROBROMIDE |
|
ICR |
control-release insert |
IMPL |
implant |
IMPLANT |
|
IN |
injectible |
INH |
inhalant, inhaler |
INHA |
inhaler |
INH AER |
inhalant aerosol |
INHAL |
inhalant |
INHAL SOL |
inhalant solution |
INHALER |
|
INHL |
inhalant |
INJ |
injectible |
INJECTION (S) |
|
INSERT |
|
INSULIN |
|
IPA |
|
IUD |
intrauterine devise |
IV |
intravenous |
JEL |
jelly |
JELLY |
|
KI |
|
KIT |
|
L |
lotion |
LAN |
|
LANCET |
|
LANCET (S) |
|
LI |
liquid |
LINIMENT |
|
LIP |
|
LIQ |
liquid |
LIQD |
liquid |
LIQUID |
|
LO |
|
LOLLIPOP |
|
LOT |
Lotion |
LOTION |
|
LOTN |
Lotion |
LOZ |
Lozenge |
LOZENGE |
|
LOZG |
lozenge |
LPOP |
lollipop |
LQCR |
liquid, extended-release |
MALEATE |
|
MASK |
|
MCG |
microgram |
MEQ |
milliequivalent |
METER |
|
MG |
milligram |
MIS |
miscellaneous |
MISC |
miscellaneous |
MIST |
|
MONITOR |
|
MONOH |
|
MOUTHWASH |
|
NAS |
nasal spray |
NASAL |
|
NASAL INHALER |
|
NASAL POCKET HL |
nasal inhaler, pocket |
NASAL SOLN |
nasal solution |
NASAL SPR |
nasal spray |
NASAL SPRAY |
|
NDL |
needle |
NE |
nebulizer |
NEB |
nebulizer |
NEBU |
nebulization solution |
NEBULIZER |
|
NEEDLE |
|
NEEDLES |
|
NHL |
|
NMA |
enema |
NMO |
nanomole, millimicromole |
NOP |
|
NOS |
|
NOSE DROPS |
|
ODR |
ophthalmic drop (ointment) |
ODT |
oral disintegrating tablet |
OIL |
|
OIN |
ointment |
OINT |
ointment |
OINT TOP |
topical ointment |
OINTA |
ointment with applicator |
OINTMENT |
|
OLN |
|
OMB |
|
ONT |
ointment |
OP |
ophthalmic solution |
OP DROPS |
ophthalmic drops |
OP SOL |
ophthalmic solution |
OPA |
|
OPH |
ophthalmic |
OPH S |
ophthalmic solution or suspension |
OPH SOL |
ophthalmic solution |
OPH SOLN |
ophthalmic solution |
OPHT SOL |
ophthalmic solution |
OPHTH DROP (S) |
ophthalmic drops |
OPHTH OINT |
ophthalmic ointment |
OPHTH SOLN |
ophthalmic solution |
OPT SLN |
ophthalmic solution |
OPT SOL |
ophthalmic solution |
OPTH |
ophthalmic solution or suspension or ointment |
OPTH S |
ophthalmic solution or suspension |
OPTH SLN |
ophthalmic solution |
OPTH SOL |
ophthalmic solution |
OPTH SUSP |
ophthalmic suspension |
OPTIC |
|
ORA |
|
ORAL |
|
ORAL INHL |
oral inhalant |
ORAL INHALER |
|
ORAL PWD |
oral powder |
ORAL RINSE |
|
ORAL SOL |
oral solution |
ORAL SUS |
oral suspension |
ORAL SUSP |
oral suspension |
ORM |
|
OSE |
|
OTHER |
|
OTI |
otic solution |
OTIC |
|
OTIC SOL |
otic solution |
OTIC SOLN |
otic solution |
OTIC SUS |
otic suspension |
OTIC SUSP |
otic suspension |
PA |
tablet pack, pad or patch (varies) |
PAC |
pack |
PACK |
|
PAD |
|
PADS |
|
PAK |
pack |
PAS |
paste |
PASTE |
|
PAT |
patch |
PATCH |
|
PATCHES |
|
PCH |
patch |
PDI |
powder for injection |
PDR |
powder |
PDS |
powder for reconstitution |
PEDIATRIC DROPS |
|
PEL |
pellets |
PEN |
|
PI1 |
powder for injection, 1 month |
PI3 |
powder for injection, 3 months |
PIH |
powder for inhalation |
PKG |
package |
PKT |
packet |
PLASTER |
|
PLEDGETS |
|
PLLT |
pellet |
PO-SYRUP |
syrup by mouth (oral syrup) |
POPSICLE |
|
POUCH |
|
POW |
powder |
POWD |
powder |
POWDER |
|
POWDER/SUSPENS |
powder/suspension |
PRO |
prophylactic |
PST |
paste |
PSTE |
paste |
PT24 |
patch, 24 hour |
PT72 |
patch, 72 hour |
PTCH |
patch |
PTTW |
patch, biweekly |
PTWK |
patch, weekly |
PULVULE |
|
PWD |
powder |
PWD F/SOL |
powder for solution |
PWDI |
powder for injection |
PWDIE |
powder for injection, extended-release |
PWDR |
powder for reconstitution |
PWDRD |
powder for reconstitution, delayed-release |
RAL |
|
RCTL SUPP |
rectal suppository |
RECTAL CREAM |
|
REDITABS |
|
REF |
|
RIN |
rinse |
RING |
|
RINSE |
|
RMO |
|
ROLL |
|
RTL |
|
S |
syrup, suspension, solution (varies) |
SA CAPS |
slow-acting capsules |
SA TAB |
slow-acting tablet |
SA TABLETS |
slow-acting tablets |
SA TABS |
slow-acting tablets |
SAL |
salve |
SALIC |
|
SCRUB |
|
SE |
|
SER |
extended-release suspension |
SET |
|
SGL |
soft b23gel cap |
SHA |
shampoo |
SHAM |
shampoo |
SHAMPOO |
shampoo |
SHMP |
shampoo |
SHOE |
|
SLT |
sublingual tablet |
SL TAB |
sublingual tablet |
SO |
solution |
SOA |
soap |
SOL |
solution |
SOLG |
gel forming solution |
SOLN |
solution |
SOLR |
solution, reconstituted |
SOLUTION |
|
SOLU |
solution |
SP |
spray |
SPG |
sponge |
SPN |
|
SPONGE |
|
SPR |
spray |
SPRAY |
|
SQU |
|
SRN |
syringe |
ST |
|
STA |
|
STAT |
immediately |
STK |
stick |
STOCKING |
|
STP |
strip |
STR |
strip |
STRIP |
|
STRIPS |
|
STRP |
strip |
SU |
suspension, solution, suppository, powder,
or granules for reconstitution (varies) |
SUB |
sublingual |
SUBL |
tablet, sublingual |
SUBLINGUAL |
|
SUP |
suppository |
SUPP |
suppository |
SUPPOSITORIES |
|
SUPPOSITORY |
|
SUS |
suspension |
SUS/LIQ |
suspension/liquid |
SUSP |
suspension |
SUSPEN |
suspension |
SUSPENDED RELEASE CAPLET |
|
SUSPENSION |
|
SUSR |
suspension, reconstituted |
SWA |
swab |
SWAB |
|
SWABS |
|
SYG |
|
SYP |
syrup |
SYR |
syrup |
SYRG |
syringe |
SYRINGE |
|
SYRP |
syrup |
SYRUP |
|
T |
tablet |
T12 |
12 hour extended-release tablet |
T24 |
24 hour extended-release tablet |
TA |
tablet |
TAB |
tablet |
TAB CHEW |
chewable tablet |
TAB DR |
delayed-release tablet |
TAB EC |
enteric coated tablet |
TAB SL |
Slow-acting tablet |
TAB SUBL |
sublingual tablet |
TABL |
tablet |
TABLET |
|
TABLET CUTTER |
|
TABLET SPLITTER |
|
TABLETS |
|
TABS |
tablets |
TAM |
tampon |
TAP |
tape |
TAPE |
|
TB |
tablet |
TB12 |
tablet, extended-release 12 hour |
TB24 |
tablet, extended-release 24 hour |
TBCH |
chewable tablet |
TBCR |
tablet, extended-release |
TBDP |
tablet, dispersible |
TBEC |
tablet, delayed-release |
TBS |
tablets |
TBSL |
sublingual tablet |
TBSO |
tablet, soluble |
TBSR |
slow-release tablet |
TC |
tablet, chewable |
TCP |
tablet, coated particles |
TDM |
extended-release film |
TDR |
orally disintegrating tablets |
TDS |
transdermal system |
TEF |
effervescent tablet |
TER |
extended-release tablet |
TERF |
film, extended-release |
TES |
test |
TEST |
|
TEST STRIP |
|
TEST STRIPS |
|
TIN |
tincture |
TINC |
tincture |
TOP CREAM |
topical cream |
TOP OINT |
topical ointment |
TOP SOL |
topical solution |
TOP SOLN |
topical solution |
TOPICAL |
|
TOPICAL CREAM |
|
TOPICAL GEL |
|
TOPICAL OINTMENT |
|
TOPICAL SOLUTION |
|
TRO |
troche |
TROC |
troche |
TROCHE |
|
TTB |
time release tablet |
TUB |
tube |
TUBE |
|
UNDERWEAR |
|
UNIT DOSE |
|
UNT |
unit |
VAGINAL CREAM |
|
VAPORIZER |
|
VIA |
vial |
VIAL |
|
VIAL(S) |
|
VIL |
vial |
WAB |
|
WAF |
wafer |
WAFR |
wafer |
WALKER |
|
WASH |
|
WIPES |
|
Z-PAK |
|
Return To Table Of Contents
Definitions for RXFRMUNT, Unit of Measure for Form of Prescribed Medicines
Code |
Description |
-7 |
refused |
-8 |
don’t know |
-9 |
not ascertained |
CAPLT |
caplet |
CAPS |
capsule |
CC |
cubic centimeter |
G |
gram |
GM |
gram |
GR |
gram |
L |
liter |
MCL |
microliter |
MCM |
micrometer |
MCN |
|
MG |
milligram |
ML |
milliliter |
OTHER |
other |
OZ |
ounce |
QT |
quarter |
TAB |
tablet |
Return To Table Of Contents
Definitions for RXSTRUNT, Unit of Measure for Strength of Prescribed Medicines
Abbreviations, Codes and Symbols |
Definition |
-7 |
refused |
-8 |
don’t know |
-9 |
not ascertained |
% |
percent |
09 |
compound |
9HR |
|
24HR |
|
91 |
other specify |
ACT |
actuation |
ACTIVATION |
activation |
ACTUATION |
actuation |
BLIST |
blister |
CC |
cubic centimeters |
CM2 |
square centimeter |
DOSE |
dose |
DROP |
drop |
DRP |
drop |
EL |
ELISA (enzyme linked immunosorbent assay) |
G |
gram |
GM |
gram |
GR |
grain |
HR or HRS |
hour, hours |
INH |
inhalation |
IU |
international unit |
MCG |
microgram |
MEQ |
milliequivalent |
MG |
milligram |
ML |
milliliter |
MM |
millimeter |
MMU |
millimass units |
MU |
|
OTHER |
other |
OZ |
ounce |
PACKET |
packet |
PFU |
plaque forming units |
SPRAY |
spray |
SQ CM |
square centimeter |
U or UNIT |
units |
UNT |
|
Return To Table Of Contents
Appendix 4
Definitions of Therapeutic Class Code
Therapeutic Class Code |
Definition |
-9 |
not ascertained |
-1 |
inapplicable |
1 |
anti-infectives |
2 |
amebicides |
3 |
anthelmintics |
4 |
antifungals |
5 |
antimalarial agents |
6 |
antituberculosis agents |
7 |
antiviral agents |
8 |
carbapenems |
9 |
cephalosporins |
10 |
leprostatics |
11 |
macrolide derivatives |
12 |
miscellaneous antibiotics |
13 |
penicillins |
14 |
quinolones |
15 |
sulfonamides |
16 |
tetracyclines |
17 |
urinary anti-infectives |
18 |
aminoglycosides |
19 |
antihyperlipidemic agents |
20 |
antineoplastics |
21 |
alkylating agents |
22 |
antineoplastic antibiotics |
23 |
antimetabolites |
24 |
antineoplastic hormones |
25 |
miscellaneous antineoplastics |
26 |
mitotic inhibitors |
27 |
radiopharmaceuticals |
28 |
biologicals |
30 |
antitoxins and antivenins |
31 |
bacterial vaccines |
32 |
colony stimulating factors |
33 |
immune globulins |
34 |
in vivo diagnostic biologicals |
36 |
recombinant human erythropoietins |
37 |
toxoids |
38 |
viral vaccines |
39 |
miscellaneous biologicals |
40 |
cardiovascular agents |
41 |
agents for hypertensive emergencies |
42 |
angiotensin converting enzyme inhibitors |
43 |
antiadrenergic agents, peripherally acting |
44 |
antiadrenergic agents, centrally acting |
45 |
antianginal agents |
46 |
antiarrhythmic agents |
47 |
beta-adrenergic blocking agents |
48 |
calcium channel blocking agents |
49 |
diuretics |
50 |
inotropic agents |
51 |
miscellaneous cardiovascular agents |
52 |
peripheral vasodilators |
53 |
vasodilators |
54 |
vasopressors |
55 |
antihypertensive combinations |
56 |
angiotensin II inhibitors |
57 |
central nervous system agents |
58 |
analgesics |
59 |
miscellaneous analgesics |
60 |
narcotic analgesics |
61 |
nonsteroidal anti-inflammatory agents |
62 |
salicylates |
63 |
analgesic combinations |
64 |
anticonvulsants |
65 |
antiemetic/antivertigo agents |
66 |
antiparkinson agents |
67 |
anxiolytics, sedatives, and hypnotics |
68 |
barbiturates |
69 |
benzodiazepines |
70 |
miscellaneous anxiolytics, sedatives and hypnotics |
71 |
CNS stimulants |
72 |
general anesthetics |
73 |
muscle relaxants |
74 |
neuromuscular blocking agents |
76 |
miscellaneous antidepressants |
77 |
miscellaneous antipsychotic agents |
79 |
psychotherapeutic combinations |
80 |
miscellaneous central nervous system agents |
81 |
coagulation modifiers |
82 |
anticoagulants |
83 |
antiplatelet agents |
84 |
heparin antagonists |
85 |
miscellaneous coagulation modifiers |
86 |
thrombolytics |
87 |
gastrointestinal agents |
88 |
antacids |
89 |
anticholinergics/antispasmodics |
90 |
antidiarrheals |
91 |
digestive enzymes |
92 |
gallstone solubilizing agents |
93 |
GI stimulants |
94 |
H2 antagonists |
95 |
laxatives |
96 |
miscellaneous GI agents |
97 |
hormones/hormone modifiers |
98 |
adrenal cortical steroids |
99 |
antidiabetic agents |
100 |
miscellaneous hormones |
101 |
sex hormones |
102 |
contraceptives |
103 |
thyroid hormones |
104 |
immunosuppressive agents |
105 |
miscellaneous agents |
106 |
antidotes |
107 |
chelating agents |
108 |
cholinergic muscle stimulants |
109 |
local injectable anesthetics |
110 |
miscellaneous uncategorized agents |
111 |
psoralens |
112 |
radiocontrast agents |
113 |
genitourinary tract agents |
114 |
illicit (street) drugs |
115 |
nutritional products |
116 |
iron products |
117 |
minerals and electrolytes |
118 |
oral nutritional supplements |
119 |
vitamins |
120 |
vitamin and mineral combinations |
121 |
intravenous nutritional products |
122 |
respiratory agents |
123 |
antihistamines |
124 |
antitussives |
125 |
bronchodilators |
126 |
methylxanthines |
127 |
decongestants |
128 |
expectorants |
129 |
miscellaneous respiratory agents |
130 |
respiratory inhalant products |
131 |
antiasthmatic combinations |
132 |
upper respiratory combinations |
133 |
topical agents |
134 |
anorectal preparations |
135 |
antiseptic and germicides |
136 |
dermatological agents |
137 |
topical anti-infectives |
138 |
topical steroids |
139 |
topical anesthetics |
140 |
miscellaneous topical agents |
141 |
topical steroids with anti-infectives |
143 |
topical acne agents |
144 |
topical antipsoriatics |
146 |
mouth and throat products |
147 |
ophthalmic preparations |
148 |
otic preparations |
149 |
spermicides |
150 |
sterile irrigating solutions |
151 |
vaginal preparations |
153 |
plasma expanders |
154 |
loop diuretics |
155 |
potassium-sparing diuretics |
156 |
thiazide diuretics |
157 |
carbonic anhydrase inhibitors |
158 |
miscellaneous diuretics |
159 |
first generation cephalosporins |
160 |
second generation cephalosporins |
161 |
third generation cephalosporins |
162 |
fourth generation cephalosporins |
163 |
ophthalmic anti-infectives |
164 |
ophthalmic glaucoma agents |
165 |
ophthalmic steroids |
166 |
ophthalmic steroids with anti-infectives |
167 |
ophthalmic anti-inflammatory agents |
168 |
ophthalmic lubricants and irrigations |
169 |
miscellaneous ophthalmic agents |
170 |
otic anti-infectives |
171 |
otic steroids with anti-infectives |
172 |
miscellaneous otic agents |
173 |
HMG-CoA reductase inhibitors |
174 |
miscellaneous antihyperlipidemic agents |
175 |
protease inhibitors |
176 |
NRTIs |
177 |
miscellaneous antivirals |
178 |
skeletal muscle relaxants |
179 |
skeletal muscle relaxant combinations |
180 |
adrenergic bronchodilators |
181 |
bronchodilator combinations |
182 |
androgens and anabolic steroids |
183 |
estrogens |
184 |
gonadotropins |
185 |
progestins |
186 |
sex hormone combinations |
187 |
miscellaneous sex hormones |
191 |
narcotic analgesic combinations |
192 |
antirheumatics |
193 |
antimigraine agents |
194 |
antigout agents |
195 |
5HT3 receptor antagonists |
196 |
phenothiazine antiemetics |
197 |
anticholinergic antiemetics |
198 |
miscellaneous antiemetics |
199 |
hydantoin anticonvulsants |
200 |
succinimide anticonvulsants |
201 |
barbiturate anticonvulsants |
202 |
oxazolidinedione anticonvulsants |
203 |
benzodiazepine anticonvulsants |
204 |
miscellaneous anticonvulsants |
205 |
anticholinergic antiparkinson agents |
206 |
miscellaneous antiparkinson agents |
208 |
SSRI antidepressants |
209 |
tricyclic antidepressants |
210 |
phenothiazine antipsychotics |
211 |
platelet aggregation inhibitors |
212 |
glycoprotein platelet inhibitors |
213 |
sulfonylureas |
214 |
biguanides |
215 |
insulin |
216 |
alpha-glucosidase inhibitors |
217 |
bisphosphonates |
218 |
alternative medicines |
219 |
nutraceutical products |
220 |
herbal products |
222 |
penicillinase resistant penicillins |
223 |
antipseudomonal penicillins |
224 |
aminopenicillins |
225 |
beta-lactamase inhibitors |
226 |
natural penicillins |
227 |
NNRTIs |
228 |
adamantane antivirals |
229 |
purine nucleosides |
230 |
aminosalicylates |
231 |
nicotinic acid derivatives |
232 |
rifamycin derivatives |
233 |
streptomyces derivatives |
234 |
miscellaneous antituberculosis agents |
235 |
polyenes |
236 |
azole antifungals |
237 |
miscellaneous antifungals |
238 |
antimalarial quinolines |
239 |
miscellaneous antimalarials |
240 |
lincomycin derivatives |
241 |
fibric acid derivatives |
242 |
psychotherapeutic agents |
243 |
leukotriene modifiers |
244 |
nasal lubricants and irrigations |
245 |
nasal steroids |
246 |
nasal antihistamines and decongestants |
247 |
nasal preparations |
248 |
topical emollients |
249 |
antidepressants |
250 |
monoamine oxidase inhibitors |
251 |
antipsychotics |
252 |
bile acid sequestrants |
253 |
anorexiants |
254 |
immunologic agents |
256 |
interferons |
257 |
immunosuppressive monoclonal antibodies |
261 |
heparins |
262 |
coumarins and indandiones |
263 |
impotence agents |
264 |
urinary antispasmodics |
265 |
urinary pH modifiers |
266 |
miscellaneous genitourinary tract agents |
267 |
ophthalmic antihistamines and decongestants |
268 |
vaginal anti-infectives |
269 |
miscellaneous vaginal agents |
270 |
antipsoriatics |
271 |
thiazolidinediones |
272 |
proton pump inhibitors |
273 |
lung surfactants |
274 |
cardioselective beta blockers |
275 |
non-cardioselective beta blockers |
276 |
dopaminergic antiparkinsonism agents |
277 |
5-aminosalicylates |
278 |
cox-2 inhibitors |
279 |
gonadotropin-releasing hormone and analogs |
280 |
thioxanthenes |
281 |
neuraminidase inhibitors |
282 |
meglitinides |
283 |
thrombin inhibitors |
284 |
viscosupplementation agents |
285 |
factor Xa inhibitors |
286 |
mydriatics |
287 |
ophthalmic anesthetics |
288 |
5-alpha-reductase inhibitors |
289 |
antihyperuricemic agents |
290 |
topical antibiotics |
291 |
topical antivirals |
292 |
topical antifungals |
293 |
glucose elevating agents |
295 |
growth hormones |
296 |
inhaled corticosteroids |
297 |
mucolytics |
298 |
mast cell stabilizers |
299 |
anticholinergic bronchodilators |
300 |
corticotropin |
301 |
glucocorticoids |
302 |
mineralocorticoids |
303 |
agents for pulmonary hypertension |
304 |
macrolides |
305 |
ketolides |
306 |
phenylpiperazine antidepressants |
307 |
tetracyclic antidepressants |
308 |
SSNRI antidepressants |
309 |
miscellaneous antidiabetic agents |
310 |
echinocandins |
311 |
dibenzazepine anticonvulsants |
312 |
cholinergic agonists |
313 |
cholinesterase inhibitors |
314 |
antidiabetic combinations |
315 |
glycylcyclines |
316 |
cholesterol absorption inhibitors |
317 |
antihyperlipidemic combinations |
318 |
insulin-like growth factor |
319 |
vasopressin antagonists |
320 |
smoking cessation agents |
321 |
ophthalmic diagnostic agents |
322 |
ophthalmic surgical agents |
323 |
antineoplastic monoclonal antibodies |
324 |
antineoplastic interferons |
325 |
sclerosing agents |
327 |
antiviral combinations |
328 |
antimalarial combinations |
329 |
antituberculosis combinations |
330 |
antiviral interferons |
331 |
radiologic agents |
332 |
radiologic adjuncts |
333 |
miscellaneous iodinated contrast media |
334 |
lymphatic staining agents |
335 |
magnetic resonance imaging contrast media |
336 |
non-iodinated contrast media |
337 |
ultrasound contrast media |
338 |
diagnostic radiopharmaceuticals |
339 |
therapeutic radiopharmaceuticals |
340 |
aldosterone receptor antagonists |
341 |
atypical antipsychotics |
342 |
renin inhibitors |
343 |
tyrosine kinase inhibitors |
344 |
nasal anti-infectives |
345 |
fatty acid derivative anticonvulsants |
346 |
gamma-aminobutyric acid reuptake inhibitors |
347 |
gamma-aminobutyric acid analogs |
348 |
triazine anticonvulsants |
349 |
carbamate anticonvulsants |
350 |
pyrrolidine anticonvulsants |
351 |
carbonic anhydrase inhibitor anticonvulsants |
352 |
urea anticonvulsants |
353 |
anti-angiogenic ophthalmic agents |
354 |
H. pylori eradication agents |
355 |
functional bowel disorder agents |
356 |
serotoninergic neuroenteric modulators |
357 |
growth hormone receptor blockers |
358 |
metabolic agents |
359 |
peripherally acting antiobesity agents |
360 |
lysosomal enzymes |
361 |
miscellaneous metabolic agents |
362 |
chloride channel activators |
363 |
probiotics |
364 |
antiviral chemokine receptor antagonist |
365 |
medical gas |
366 |
integrase strand transfer inhibitor |
368 |
non-ionic iodinated contrast media |
369 |
ionic iodinated contrast media |
370 |
otic steroids |
371 |
dipeptidyl peptidase 4 inhibitors |
372 |
amylin analogs |
373 |
incretin mimetics |
374 |
cardiac stressing agents |
375 |
peripheral opioid receptor antagonists |
376 |
radiologic conjugating agents |
377 |
prolactin inhibitors |
378 |
drugs used in alcohol dependence |
379 |
next generation cephalosporins |
380 |
topical debriding agents |
381 |
topical depigmenting agents |
382 |
topical antihistamines |
383 |
antineoplastic detoxifying agents |
384 |
platelet-stimulating agents |
385 |
group I antiarrhythmics |
386 |
group II antiarrhythmics |
387 |
group III antiarrhythmics |
388 |
group IV antiarrhythmics |
389 |
group V antiarrhythmics |
390 |
hematopoietic stem cell mobilizer |
391 |
mTOR kinase inhibitors |
392 |
otic anesthetics |
393 |
cerumenolytics |
394 |
topical astringents |
395 |
topical keratolytics |
396 |
prostaglandin D2 antagonists |
397 |
multikinase inhibitors |
398 |
BCR-ABL tyrosine kinase inhibitors |
399 |
CD52 monoclonal antibodies |
400 |
CD33 monoclonal antibodies |
401 |
CD20 monoclonal antibodies |
402 |
VEGF/VEGFR inhibitors |
403 |
mTOR inhibitors |
404 |
EGFR inhibitors |
405 |
HER2 inhibitors |
406 |
glycopeptide antibiotics |
407 |
inhaled anti-infectives |
408 |
histone deacetylase inhibitors |
409 |
bone resorption inhibitors |
410 |
adrenal corticosteroid inhibitors |
411 |
calcitonin |
412 |
uterotonic agents |
413 |
antigonadotropic agents |
414 |
antidiuretic hormones |
415 |
miscellaneous bone resorption inhibitors |
416 |
somatostatin and somatostatin analogs |
417 |
selective estrogen receptor modulators |
418 |
parathyroid hormone and analogs |
419 |
gonadotropin-releasing hormone antagonists |
420 |
antiandrogens |
422 |
antithyroid agents |
423 |
aromatase inhibitors |
424 |
estrogen receptor antagonists |
426 |
synthetic ovulation stimulants |
427 |
tocolytic agents |
428 |
progesterone receptor modulators |
429 |
trifunctional monoclonal antibodies |
430 |
anticholinergic chronotropic agents |
431 |
anti-CTLA-4 monoclonal antibodies |
432 |
vaccine combinations |
433 |
Catecholamines |
435 |
selective phosphodiesterase-4 inhibitors |
437 |
Immunostimulants |
438 |
Interleukins |
439 |
other immunostimulants |
440 |
therapeutic vaccines |
441 |
calcineurin inhibitors |
442 |
TNF alfa inhibitors |
443 |
interleukin inhibitors |
444 |
selective immunosuppressants |
445 |
other immunosuppressants |
446 |
neuronal potassium channel openers |
447 |
CD30 monoclonal antibodies |
448 |
topical non-steroidal anti-inflammatories |
449 |
hedgehog pathway inhibitors |
450 |
topical antineoplastics |
451 |
topical photochemotherapeutics |
452 |
CFTR potentiators |
453 |
topical rubefacient |
454 |
proteasome inhibitors |
455 |
guanylate cyclase-c agonists |
456 |
ampa receptor antagonists |
457 |
hydrazide derivatives |
458 |
sglt-2 inhibitors |
459 |
urea cycle disorder agents |
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