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MEPS HC 239A: 2022 Prescribed MedicinesJuly 2024 Agency for Healthcare Research and Quality
A. Data Use Agreement Appendices
1 Definitions for RXFORM, Dosage Form A. Data Use AgreementIndividual 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:
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. B. Background1.0 Household ComponentThe 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 noninstitutionalized 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 care. Estimates can be produced for individuals, families, and selected population subgroups. The panel design of the survey includes five rounds of interviews covering 2 full calendar years. Additional rounds were added to Panel 24 in 2021 and 2022, covering the third and fourth years respectively, to compensate for the smaller number of completed interviews in later panels. These extra rounds provide data for examining person-level changes in selected variables such as expenditures, health insurance coverage, and health status. Information about each household member is collected through computer-assisted personal interviewing (CAPI) technology, 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 sample 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. Historically, each annual MEPS HC sample consists of approximately up to 15,000 households. Data can be analyzed at the person, the family, or the 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 (NCHS). The NHIS sampling frame provides a nationally representative sample of the U.S. civilian noninstitutionalized population. In 2006, the NCHS implemented a new sample design for the NHIS, to include households with Asian persons in addition to households with Black and Hispanic persons in the oversampling of minority populations. In 2016, NCHS introduced another sample design that discontinued the oversampling of these minority groups. 2.0 Medical Provider ComponentWhen the household CAPI interview is completed and permission is obtained from the sample members to contact their medical provider(s), a sample of these providers is contacted by telephone to obtain information that household respondents cannot accurately provide. This part of the MEPS is called the Medical Provider Component (MPC), and it collects information on dates of visits, diagnosis and procedure codes, and charges and payments. The Pharmacy Component (PC), a subcomponent of the MPC, does not collect data on charges or on diagnosis and procedure codes, but it does collect detailed information on drugs, including the National Drug Code (NDC) and medicine name, as well as 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. 3.0 Survey Management and Data CollectionMEPS HC and MPC data are collected under the authority of the Public Health Service Act. The MEPS HC data are collected under contract with Westat, Inc., and the MEPS MPC data are collected under contract with Research Triangle Institute. Datasets and summary statistics are edited and published in accordance with the confidentiality provisions of the Public Health Service Act and the Privacy Act. The NCHS provides consultation and technical assistance. As soon as the MEPS data are collected and edited, they are released to the public in stages of microdata files and tables via the MEPS website and datatools.ahrq.gov. 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, 5600 Fishers Lane, Rockville, MD 20857 (301-427-1406). C. Technical and Programming Information1.0 General InformationThis documentation describes one in a series of public use event files from the 2022 MEPS HC and MPC. It was released as an ASCII data file (with related SAS, SPSS, Stata, and R programming statements and data user information) and as a SAS data set, a SAS transport file, a Stata data set, and an Excel file. The 2022 Prescribed Medicines Public Use File (hereafter referred to as the PMED PUF) provides detailed information on household-reported prescribed medicines from a nationally representative sample of the U.S. civilian noninstitutionalized population. Data from the PMED PUF can be used to make estimates of retail prescribed medicine utilization and expenditures for calendar year 2022. The file contains 65 variables and has a logical record length of 587 with an additional 2-byte carriage return/line feed at the end of each record. As illustrated below, this PUF consists of MEPS data obtained in the 2022 portion of Round 7, and all of Rounds 8 and 9 for Panel 24; the 2022 portion of Round 3, and all of Rounds 4 and 5 for Panel 26; and Rounds 1 and 2, and the 2022 portion of Round 3 for Panel 27 (i.e., the rounds for MEPS panels covering calendar year 2022). Full year (FY) 2022 includes three panels of data; Panel 24 was extended to include Rounds 7, 8 and 9.
Each record in the PMED PUF represents a fill or refill of a prescribed medicine reported by the respondent as being obtained by a member of the household at any pharmacy, including mail-order or on-line. In addition to expenditures related to the prescribed medicine, each record contains household-reported characteristics. Data from this event PUF can be merged with other 2022 MEPS HC PUFs for the purpose of appending person-level data, such as demographic characteristics or health insurance coverage to each prescribed medicine record. Counts of prescribed medicine utilization are based entirely on household reports. Information from the 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.). This PUF can also be used to construct summary variables of expenditures, sources of payment, and related aspects of utilization of prescribed medicines. Aggregate annual person-level information on the use of prescribed medicines and other health services is provided in the 2022 Full Year Consolidated PUF (hereafter referred to as the Consolidated PUF), where each record represents a MEPS sampled person. This document offers a brief overview of the types and levels of data provided and the content and structure of the PUF and the codebook. It contains the following sections:
For more information on the MEPS HC sample design, see Chowdhury, et al. (2019). For information on the MEPS MPC design, see RTI (2023). A copy of the survey instrument used to collect the information in this PUF is available on the MEPS website. 2.0 Data File InformationThe 2022 PMED PUF contains 232,605 prescribed medicine records. Each record represents one household-reported fill or refill of a prescribed medicine that was obtained during calendar year 2022 at any retail pharmacy, including mail-order or on-line. Of the 232,605 prescribed medicine records, 229,956 records are associated with persons having a positive person-level weight (PERWT22F). The records in this PUF are prescribed medicine fills or refills obtained by persons who had to meet either a) or b) below:
Persons with no prescribed medicine use for 2022 are not included in this PUF (but are represented on MEPS person-level files). A codebook for the PMED PUF is provided. This PUF 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 obtained in calendar year 2022 are represented in this PUF. This PUF 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 in this PUF 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 in this PUF. Other household members may have no reported acquisitions of prescribed medicines and thus will have no records in this PUF. Prior to Panel 21 Round 5 and Panel 22 Round 3, 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 PM section of the MEPS questionnaire. To the extent that these items are purchased without a prescription, they represent a non-prescription addition to the MEPS prescription drug expenditure and utilization data. Although these items may be purchased without a prescription, a prescription purchase may be required to obtain third party payments. 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. Starting in Panel 21 Round 5 and Panel 22 Round 3, diabetic supply/equipment and insulin are no longer mentioned in the OM section but are mentioned and collected in the PM section. Therefore, diabetic supply/equipment and insulin are collected as other Prescribed Medicines. The charges and payments are no longer collected for Prescribed Medicines in the MEPS HC. It should also be noted that refills are included in this PUF. 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. Each record in this PUF 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.); when the person first used the medicine; total expenditure and sources of payments; types of pharmacies that filled the household’s prescriptions; and a full-year person-level weight. Data from this PUF can be merged with MEPS HC person-level data using the unique person identifier, DUPERSID, to append person-level data such as demographic characteristics or health insurance coverage to each record. Data from this PUF can also be merged with the Consolidated PUF to estimate expenditures for persons with prescribed medicines. The PMED PUF can also be linked to the MEPS 2022 Medical Conditions PUF. Please see Section 6.0 or the 2022 Appendix PUF, HC 239I, for details on how to link MEPS data files. 2.1 Codebook StructureFor most variables in the PMED PUF, both weighted and unweighted frequencies are provided in the accompanying codebook. The exceptions to this are weight variables and variance estimation variables. Only unweighted frequencies of these variables are included in the accompanying codebook file. See the Weights Variables list in Section D, “Variable-Source Crosswalk”. The codebook and data file list variables in the following order:
Note that the person identifier corresponds to a unique person and the prescribed medicine event identifier corresponds to a unique event. 2.2 Reserved CodesThe PMED PUF contains several reserved code values.
The value Cannot Be Computed (-15) is assigned to MEPS constructed variables when there was not enough information from the instrument to calculate the constructed variables. Not having enough information is often the result of skip patterns in the data or of missing information stemming from the responses Refused (-7) or Don’t Know (-8). Note that, in addition to Don’t Know, reserved code -8 also includes cases for which the information from the question was not ascertained. Generally, values of -1, -7, -8 and -15 have not been edited in this PUF. However, this is not true if a prescription drug name was determined to be a confidentiality risk. In these instances, the corresponding NDC was replaced with -15, the Multum Lexicon therapeutic class replaced the RXDRGNAM (Multum drug name) determined to be a confidentiality risk, and RXNAME (pharmacy drug name) was set to -15. When the therapeutic class, subclass, or sub-subclass was determined to be a confidentiality risk, the value was replaced with -15. 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, they responded that the household member had not yet started using the medicine (See section C, 2.6.2). Analysts who would like to recode these values can find skip patterns in the questionnaire found in the Survey Questionnaires section of the MEPS. 2.3 Codebook FormatThe PMED codebook describes an ASCII data set (although the data are also being provided in a SAS dataset, SAS transport file, Stata dataset, and Excel file) and provides the programming identifiers for each variable.
2.4 Variable Source and Naming ConventionsIn general, the variable names reflect the content of the variable. All imputed/edited variables end with an “X.” As the collection, universe, or categories of variables were altered, some variable names have been appended with “_Myy”, where “yy” indicates the collection year in which the alterations were made. Such alterations are described in detail throughout this document. 2.4.1 Variable-Source CrosswalkVariables contained in this PMED PUF were derived from the MPC data collection instrument 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:
2.4.2 Expenditure and Source of Payment VariablesOnly imputed/edited versions of the expenditure variables are provided in this PUF. Expenditure variables in this PMED PUF follow a standard naming convention. The 10 source of payment variables and one sum of payments variable are named consistently in the following way: The first two characters indicate the type of event: IP - inpatient stay OB - office-based visit ER - emergency room visit OP - outpatient visit HH - home health visit DV - dental visit OM - other medical equipment RX - prescribed medicine In the case of the source of payment variables, the third and fourth characters indicate: SF - self or family OF - other federal government MR - Medicare SL - state/local government MD - Medicaid WC - Workers’ Compensation PV - private insurance OT - other insurance VA - Veterans Administration/CHAMPVA TR - TRICARE XP - sum of payments The fifth and sixth characters indicate the year (22). The seventh character being “X” indicates the variable is edited/imputed. For example, RXSF22X is the edited/imputed amount paid by self or family for the 2022 prescribed medicine expenditure. 2.5 Data CollectionData regarding prescription drugs were obtained through the HC questionnaire and a pharmacy follow-back component within the MPC. 2.5.1 Methodology for Collecting Household-Reported VariablesDuring 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 at any pharmacy, including mail-order or on-line. For each medicine in each round, the following information was collected: 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 and month in 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 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. 2.5.2 Methodology for Collecting Pharmacy-Reported VariablesIf 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 can choose to provide printouts or data files or 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: national drug code (NDC), medication name, strength of medicine (amount and unit), quantity (package size/amount dispensed), days supplied, and payments by source. When an NDC was provided, often the drug name and other drug characteristics were obtained from secondary proprietary data sources. 2.6 File Contents2.6.1 Survey Administration VariablesPerson Identifier Variables (DUID, PID, DUPERSID) The definitions of Dwelling Units (DUs) in the MEPS Household Survey are generally consistent with the definitions employed for the NHIS. The dwelling unit ID (DUID) is a 7-digit number consisting of a 2-digit panel number followed by a 5-digit random number assigned after the case was sampled for MEPS. A 3-digit person number (PID) uniquely identifies each person within the DU. The variable DUPERSID is the combination of DUID and PID. IDs begin with the 2-digit panel number. For detailed information on dwelling units and families, please refer to the documentation for the 2022 Population Characteristics PUF. Record Identifier Variables (RXRECIDX, LINKIDX, DRUGIDX) The variable RXRECIDX uniquely identifies each record in this PUF. This 19-character variable comprises the following components: prescribed medicine person-drug-round-level identifier generated through the HC (positions 1-16) + enumeration number (positions 17-19). The prescribed medicine person-drug-round-level ID generated through the HC (positions 1-16) can be used to link a prescribed medicine event to the Medical Conditions PUF, via a link file, and is provided in this PUF as the variable LINKIDX. For more details on linking, please refer to Section 6.1: Linking to the Medical Conditions PUF and to the 2022 Appendix PUF. The prescribed medicine person-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 (2700002026002), for one person (DUPERSID=2700002026) 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 (2700002026002103) remains the same for both records in Round 1 but varies across rounds. The RXRECIDX (2700002026002103001, 2700002026002103002, 2700002026002203001) differs for all three records.
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. Panel Variable (PANEL) PANEL is a constructed variable used to specify the panel number for the person. PANEL will indicate Panel 24, Panel 26, or Panel 27 for each person in this PUF. Panel 24 is the panel that started in 2019, Panel 26 is the panel that started in 2021, and Panel 27 is the panel that started in 2022. Round Variable (PURCHRD) The variable PURCHRD indicates the round in which the prescribed medicine was purchased and takes on the value of 1, 2, 3, 4, 5, 7, 8, or 9. Rounds 7 (partial), 8, and 9 are associated with MEPS survey data collected from Panel 24. Likewise, Rounds 3 (partial), 4, and 5 are associated with data collected from Panel 26, and Rounds 1, 2, and 3 (partial) are associated with data collected from Panel 27. 2.6.2 Characteristics of Prescribed Medicine EventsWhen Prescribed Medicine Was First Taken (RXBEGMM-RXBEGYRX) There are two variables to indicate when a prescribed medicine was first taken (used), as reported by the household respondent. They are the following: 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, including Not Yet Used or Taken (-14), are carried forward from prior rounds for all medications. The variable DRUGIDX (see Section 2.6.1) can be used to determine whether a medication was reported in a prior round. For purposes of confidentiality, RXBEGYRX was bottom-coded at 1937. Prescribed Medicine Attributes (RXNAME-RXDAYSUP) For each prescribed medicine included in this PUF, several data items collected describe in detail the medication obtained or purchased. These data items are the following:
Days supplied was first collected and released to the public on the 2010 PMED PUF. 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 2022 PMED PUF contains multiple values of RXFORM and RXFRMUNT not found in PMED PUFs 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 analysts 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 AHRQ Data Center. For those events not falling into the RXFLG = 3 category, the reserved code (-13) was assigned to the household-reported medication name (RXHHNAME). The household-reported name of the prescription (RXHHNAME) is no longer provided in this PUF; however, this variable may be accessed through the AHRQ Data Center as can the original pharmacy-reported name and NDC. For information on accessing data through the AHRQ Data Center, see the Data Center section of the MEPS website. Beginning with the 2013 data, the variable RXDRGNAM is included on the file. This drug name is the generic name of the drug most commonly used by prescribing physicians. It is supplied by the Multum Lexicon database. RXDRGNAM for earlier years can be found in the Multum Lexicon Addendum Files to MEPS Prescribed Medicines Files for 1996-2013. Additionally, the 2013 addendum file contains a version of RXDRGNAM that has corrected values for some records. See the documentation for the addendum files. Generally, orphan drugs and drugs AHRQ estimated were used by fewer than 400,000 people are masked to ensure confidentiality of the data, unless use of the drug does not reveal specific information about the condition treated (for example, cold remedies). For these drugs, details are generally recoded as missing and RXNAME is recoded to whatever therapeutic class information remains. Prospective researchers seeking access to restricted data must complete a MEPS Data Center application. See the Data Center section of the MEPS website. Starting in the 2018 PMED PUF, the variable DiabEquip (OTHER DIABETIC EQUIPMENT OR SUPPLIES) indicates the record is for diabetic supplies/equipment that were first reported in response to question PM40, which asks whether the person obtained “any other diabetic equipment or supplies, typically prescribed by a physician; for example, syringes, a blood glucose monitor machine, glucose meter, insulin pumps, lancets, alcohol swabs or control solution.” Imputed data in this event PUF, unlike other MEPS event files, may still have missing data. This is because imputed data in this PUF 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. Type of Pharmacy (PHARTP1-PHARTP11) 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 in this PUF, but there is no link in the survey or in the data file enabling analysts to know the type of pharmacy from which a specific prescription was obtained if multiple pharmacies are listed. The variables PHARTP1 through PHARTP11 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. The pharmacy types are those reportedly used by the person in the purchase round and any prior rounds. Analytic Flag Variables (RXFLG-INPCFLG) There are four flag variables included in this PUF (RXFLG, IMPFLAG, PCIMPFLG, and INPCFLG). RXFLG indicates whether 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 on this record: IMPFLAG = 2 indicates complete PC data, IMPFLAG = 4 indicates fully imputed data, and IMPFLAG = 5 indicates partially imputed data. Beginning with the 2017 data, the MEPS ceased asking households to report payments for any drugs and diabetic equipment and supplies, so the values 1 and 3 are irrelevant for prescribed medicine events. 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 drug 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 analyst 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. INPCFLG denotes whether or not a household member had any pharmacy-reported data, that is, at least one prescription drug purchase in the PC (0 = NO, 1 = YES). Clinical Classification Software Refined Codes Information on household-reported medical conditions (ICD-10-CM condition codes) and aggregated clinically meaningful categories generated using Clinical Classification Software Refined (CCSR) associated with each prescribed medicine are not provided on this file. For information on ICD-10-CM condition codes and associated CCSR codes, see the MEPS 2022 Medical Conditions PUF and the 2022 Appendix to MEPS Event PUFs. 2.6.3 Multum Lexicon Variables from Cerner Multum, Inc.Each record on this file contains the following Multum Lexicon variables: RXDRGNAM Generic name of the drug most commonly used by prescribing physicians 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 Analysts 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 PMED PUFs reflect the most recent classification available in the year the data were released. Since the release of the 1996 PMED PUF, the Multum classification has 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 PMED PUFs, 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 PMED PUFs, 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 PMED PUFs, 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 the Cerner Multum file. Analysts should also be aware of a problem discovered with the linking between the MEPS PMED PUFs 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. In 2020 and 2021, NDCs 49502019671 and 49502019675 should have RXDRGNAM=“INSULIN GLARGINE”, and TC1=358, TC1S1=99, and TC1S1_1=215. Analysts should use caution when using the Cerner Multum therapeutic class variables for analysis and should always check for accuracy. PREGCAT is no longer included in the PMED PUF. The Food and Drug Administration (2014) ceased using “the pregnancy categories because they are often viewed as confusing and overly simplistic and don’t effectively communicate the risk a drug may have during pregnancy and lactation and in females and males of reproductive potential.” Researchers using the Multum Lexicon variables are requested to cite Multum Lexicon as the data source. 2.6.4 Expenditure Variables (RXSF22X-RXXP22X)Definition of Expenditures Expenditures in this PUF refer to payments 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 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 due to the increasingly common practice of discounting. 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 paid to pharmacy benefit managers, health plans, Medicaid programs, or other purchasers. 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 refer to Monheit, et al. (1999). If examining trends in MEPS expenditures or performing longitudinal analysis on MEPS expenditures please refer to Section C, sub-sections 3.5 and 6.2 respectively for more information. Sources of Payment In addition to total expenditures, variables are provided which itemize expenditures according to major source of payment categories. These categories are:
Pharmacies rarely report discounts. Manufacturer discounts and coupons reported by pharmacies are excluded from the total expenditure and source of payment variables, because the manufacturer is paying itself. Free drugs are included in this PUF, but discounts, write-offs, and free drugs at commercial pharmacies are not counted toward the total expenditure and source of payment variables, because these reflect pharmacy pricing strategies. Discounts, write-offs, and free drugs at safety net providers and government pharmacies are paid with public sector funds, are included in total expenditures, and are assigned to a public source of payment or other unclassified sources based on the type of pharmacy and the person’s insurance coverage. Prior to 2019, for cases where reported insurance coverage and sources of payment are inconsistent, the positive amount from a source inconsistent with reported insurance coverage was moved to one or both source categories Other Private and Other Public. Beginning in 2019, this step was removed and the inconsistency between the payment sources and insurance coverage is allowed to remain - the amounts are not moved to Other Private and Other Public categories anymore. The two source of payment categories, Other Private and Other Public, are no longer available. 3.0 Survey Sample Information3.1 Discussion of Pandemic Effects on Quality of MEPS DataThe challenges associated with MEPS data collection in 2020 after the onset of the COVID-19 pandemic continued through 2021 and possibly into 2022. The major modifications to the standard MEPS study design remained in effect, permitting data to be collected safely but with accompanying concerns related to the quality of the data obtained. The suggestion made in the documentation for the FY 2020 and FY2021 MEPS Consolidated PUF data still holds. Researchers are counseled to take care in the interpretation of estimates based on data collected from these three calendar years. This includes the comparison of such estimates to those of other years and corresponding trend analyses. Section 3.1 of the documentation for the 2020 Consolidated PUF provides a general discussion of the impact of the COVID-19 pandemic on several other major in-person federal surveys as well as on MEPS. In addition, it offers a detailed look at how MEPS was modified to permit safe data collection and the development of useful estimates at a time when the way the U.S. health care system functioned underwent many transformations to meet population needs. Three sources of potential bias were identified for MEPS for FY 2020: (1) long recall period for Round 6 of Panel 23, (2) switching from in-person to telephone interviewing which likely had a larger impact on Panel 25, and (3) the impact of CPS bias on the MEPS weights. A number of statistically significant differences were found between panels for FY 2020. Those findings are discussed in MEPS HC 224. Concerns of potential bias for FY 2021 and between panel differences are discussed in Section 3.1 of the documentation for the 2021 Consolidated PUF. Additional analysis has also uncovered a concerning trend on event reporting in MEPS following the COVID-19 pandemic. While reporting of other event types has rebounded from the dip experienced in 2020, inpatient (IP) and emergency room (ER) utilization reports collected in FY 2021 did not rebound as much as key benchmarks, even though these are the most salient event types. Modifications made to the MEPS sample design discussed in the 2022 Population Characteristics PUF may have partially contributed to the concerning trend. Concerns for potential bias for FY 2022 include:
Preliminary analyses undertaken to examine the quality of the MEPS FY 2022 data compared health care utilization for the MEPS target population between the panels fielded. These comparisons were undertaken for the full sample and the three age groups of 0-17, 18-64, and 65+. These comparisons found no major differences in IP or ER visits between the three panels. Slight differences were observed in dental visits and outpatient visits. For dental visits, Panel 26 reported at a higher rate than Panel 24 or Panel 27 in the age range 18-64. For outpatient visits, Panel 24 reported at a lower rate than Panel 26 and Panel 27 in the age range 18-64. In summary, the weights developed for the MEPS FY 2022 data can be expected to produce useful estimates for initial analyses. Further analyses of MEPS estimates will be conducted as part of the production of the FY 2022 Consolidated PUF to be released later in 2024. This will help identify any additional data quality issues as well as possible improvements that could be implemented. The various actions taken in the development of the person-level weights for the MEPS FY 2022 data were designed to limit the potential for bias in the data due to changes in data collection and response bias. However, evaluations of MEPS data quality in 2021 and 2022 suggest that users of the MEPS FY 2022 PUFs should continue to exercise caution when interpreting estimates and assessing analyses based on these data, as well as in comparing 2022 estimates to those of prior years. 3.2 Sample Weight (PERWT22F)There is a single full-year person-level weight (PERWT22F) assigned to each record for each Key, in-scope person who responded to MEPS for the full period of time that they were in scope during 2022. A Key person was either a member of a responding NHIS household at the time of the 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 they are a member of the civilian noninstitutionalized portion of the U.S. population. 3.3 Details on Person Weight ConstructionThe person-level weight PERWT22F was developed in several stages. First, a person-level weight for Panel 24 was created, including an adjustment for nonresponse over time and raking. The raking involved adjusting to several sets of marginal control totals reflecting 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: educational attainment of the reference person (three categories: no degree; high school/GED only or some college; bachelor’s or a higher degree); Census region (Northeast, Midwest, South, West); MSA status (MSA, non-MSA); race/ethnicity (Hispanic; Black, non-Hispanic; Asian, non-Hispanic; and other); sex; and age (0-18, 19-25, 26-34, 35-44, 45-64, and 65 or older). (Note, however, that for confidentiality reasons, the MSA status variables are no longer released for public use.) The person-level weights for Panel 26 and Panel 27 were created similarly. Secondly, a composite weight was formed by multiplying each weight from Panel 24 by the factor .22, each weight from Panel 26 by the factor .29, and each weight from Panel 27 by the factor .49. The choice of factors reflected the relative effective sample sizes of the three panels, helping to limit the variance of estimates obtained from pooling the three samples. Weights for the 2022 Population Characteristics PUF were then developed by raking the composite weight to the same set of CPS-based control totals. The approach for establishing the 2022 Consolidated PUF weight is as follows. When poverty status information derived from MEPS income variables becomes available, a final raking is undertaken. The full sample weight appearing on the Population Characteristics PUF for a given year is re-raked, replacing educational attainment with poverty status while retaining the other five raking variables previously indicated. Specifically, control totals based on CPS estimates of 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) as well as age, race/ethnicity, sex, region, and MSA status are used to calibrate weights. 3.3.1 MEPS Panel 24 Weight Development ProcessThe person-level weight for MEPS Panel 24 was developed using the 2021 full-year weight for an individual as a “base” weight for 2021 survey participants present in 2022. For Key, in-scope members who joined an RU some time in 2022 after being out of scope in 2021, the initially assigned person-level weight was the corresponding 2021 family weight. The weighting process included an adjustment for person-level nonresponse over Rounds 8 and 9 as well as raking to population control figures for December 2022 for Key, responding persons in scope on December 31, 2022. These control totals were derived by scaling back the population distribution obtained from the March 2023 CPS to reflect the December 31, 2022 estimated population total (estimated based on Census projections for January 1, 2023). Variables used for person-level raking included: education of the reference person (three categories: no degree; high school/GED only or some college; bachelor’s or a higher degree); Census region (Northeast, Midwest, South, West); MSA status (MSA, non-MSA); race/ethnicity (Hispanic; Black, non-Hispanic; Asian, non-Hispanic; and other); sex; and age (0-18, 19-25, 26-34, 35-44, 45-64, and 65 or older). (Note, however, that for confidentiality reasons, the MSA status variables are no longer released for public use.) The final weight for Key, responding persons who were not in scope on December 31, 2022 but were in scope earlier in the year was the nonresponse-adjusted person weight without raking. The 2021 full-year weight used as the base weight for Panel 24 was derived from the 2019 MEPS Round 1 weight and reflected adjustment for nonresponse over the remaining data collection rounds in 2019, 2020, and 2021 as well as raking to the December 2019, December 2020, and December 2021 population control figures. 3.3.2 MEPS Panel 26 Weight Development ProcessThe person-level weight for MEPS Panel 26 was developed by using the 2021 full-year weight as a “base” weight for survey participants present in 2022. For Key, in-scope members who joined an RU at some time in 2022 after being out of scope in 2021, the initially assigned person-level weight was the corresponding 2021 family weight. The weighting process also included an adjustment for person-level nonresponse over Rounds 4 and 5 as well as raking to the same population control figures for December 2022 used for the Panel 24 weight for Key, responding persons in scope on December 31, 2022. The same six variables used for Panel 24 raking (education level, Census region, MSA status, race/ethnicity, sex, and age) were also used for Panel 26 raking. Similar to Panel 24, the Panel 26 final weight for Key, responding persons not in scope on December 31, 2022 but in scope earlier in the year was the nonresponse-adjusted person weight without raking. Note that the 2021 full-year weight that was used as the base weight for Panel 26 was derived using the 2021 MEPS Round 1 weight and reflected adjustment for nonresponse over the remaining data collection rounds in 2021 as well as raking to the December 2021 population control figures. 3.3.3 MEPS Panel 27 Weight Development ProcessThe person-level weight for Panel 27 was developed using the 2022 Round 1 person-level weight as a “base” weight. The Round 1 weights incorporated the following components: the original household probability of selection for the NHIS and for the NHIS subsample reserved for the MEPS, an adjustment for NHIS nonresponse, the probability of selection for MEPS from the NHIS, an adjustment for nonresponse at the dwelling unit level for Round 1, and raking to control figures at the person level obtained from the March CPS of the corresponding year. For Key, in-scope members who joined an RU after Round 1, the Round 1 DU weight served as a “base” weight. The weighting process also included an adjustment for nonresponse over the remaining data collection rounds in 2022 as well as raking to the same population control figures for December 2022 that were used for the Panel 24 and Panel 26 weights for Key, responding persons in scope on December 31, 2022. The same six variables used for Panel 24 and Panel 26 raking (education level of the reference person, Census region, MSA status, race/ethnicity, sex, and age) were also used for Panel 27 raking. Similar to Panel 24 and Panel 26, the Panel 27 final weight for Key, responding persons who were not in scope on December 31, 2022 but were in scope earlier in the year was the nonresponse-adjusted person weight without raking. 3.3.4 The Final Weight for 2022The 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, 2022 were adjusted for expected undercoverage. Specifically, the weights of those who were out of scope on December 31, 2022, but in scope at some time during the year and were residing in a nursing home at the end of the year were poststratified to an estimate of the number of persons who were residents of Medicare- and Medicaid-certified nursing homes for part of the year (approximately 3-9 months) during 2014. This estimate was developed from data on the Minimum Data Set (MDS) of the Center for Medicare and Medicaid Services (CMS). The weights of persons who died while in scope were poststratified to corresponding estimates derived using data obtained from the Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS), Provisional Mortality Statistics, 2018 through Last Week on CDC WONDER Online Database, released in 2023, the latest available data at the time. Separate decedent control totals were developed for the “65 and older” and “under 65” civilian noninstitutionalized populations. Overall, the weighted population estimate for the civilian noninstitutionalized population for December 31, 2022 is 329,059,733 (PERWT22F >0 and INSC1231=1). The sum of person-level weights across all persons assigned a positive person-level weight is 333,053,243. 3.4 CoverageThe target population associated with MEPS is the 2022 U.S. civilian noninstitutionalized population. However, the MEPS sampled households are a subsample of the NHIS households interviewed in 2018 (Panel 24), 2020 (Panel 26), and 2021 (Panel 27). New households created after the NHIS interviews for the respective panels and consisting exclusively of persons who entered the target population after 2018 (Panel 24), after 2020 (Panel 26), or after 2021 (Panel 27) are not covered by the 2022 MEPS. Nor are previously out of scope persons who joined an existing household but are not related 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. Those not covered represent a small proportion of the MEPS target population. 3.5 Using MEPS Data for Trend AnalysisFor analysts using the MEPS data for trend analysis, we note that there are uncertainties associated with 2020, 2021, and possibly 2022 data quality for reasons discussed throughout Section 3. Preliminary evaluations of a set of MEPS estimates of particular importance suggest that they are of reasonable quality. Nevertheless, analysts are advised to exercise caution in interpreting these estimates, particularly in terms of trend analyses, since access to health care was substantially affected by the COVID-19 pandemic, as were related factors such as health insurance and employment status for many persons. The MEPS began in 1996, and the utility of the survey for analyzing health care trends expands with each additional year of data; however, when examining trends over time using the MEPS, the length of time being analyzed should 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 the MEPS methodology. With respect to methodological considerations, changes in data collection methods, such as interviewer training, were introduced in 2013 to obtain more complete information about health care utilization from MEPS respondents; the changes were fully implemented in 2014. This effort likely resulted in improved data quality and a reduction in underreporting starting in the second half of 2013 and continuing throughout 2014 full year files; the changes have also had some impact on analyses involving trends in utilization across years. The changes in the NHIS sample design in 2016 and 2018 could also potentially affect trend analyses. The new NHIS sample design is based on more up-to-date information related to the distribution of housing units across the United States. As a result, it can be expected to better cover the full civilian noninstitutionalized population, the target population for MEPS, as well as many of its subpopulations. Better coverage of the target population helps to reduce the potential for bias in both NHIS and MEPS estimates. Another change with the potential to affect trend analyses involved major modifications to the MEPS instrument design and data collection process, particularly in the events sections of the instrument. These were introduced in the spring of 2018 and thus affected data beginning with Round 1 of Panel 23, Round 3 of Panel 22, and Round 5 of Panel 21. Since the full year 2017 MEPS files were established from data collected in Rounds 1-3 of Panel 22 and Rounds 3-5 of Panel 21, they reflected two instrument designs. To mitigate the effect of such differences within the same full-year file, the Panel 22 Round 3 data and the Panel 21 Round 5 data were transformed to make them as consistent as possible with data collected under the previous design. The changes in the instrument were designed to make the data collection effort more efficient and easier to administer. In addition, expectations were that data on some items, such as those related to health care events, would be more complete with the potential of identifying more events. Increases in service use reported since the implementation of these changes are consistent with these expectations. Analysts should be aware of the possible impacts of these changes on the data and especially on trend analyses that include the year 2018 because of the design transition. Process changes, such as data editing and imputation, may also affect trend analyses. For example, analysts should refer to Section 2.5.11: Utilization, Expenditures, and Sources of Payment Variables in the Consolidated PUF (HC 243) and, for more detail, to Section 4.0 of this document when analyzing prescription drug spending over time. As always, it is recommended that, before conducting trend analyses, analysts should review relevant sections of the documentation for descriptions of these types of changes that might affect the interpretation of changes over time. To smooth or stabilize trend analyses based on the MEPS data, analysts may also wish to consider using statistical techniques such as comparing pooled time periods (e.g. 1996-1997 versus 2011-2012), working with moving averages, or using modeling techniques with several consecutive years of the data. Advice about adjusting prices for inflation is available on the MEPS website. Finally, statistical significance tests should be conducted to assess the likelihood that observed trends are not attributable to sampling variation. In addition, researchers should be aware of the impact of multiple comparisons on Type I error. Without making appropriate allowance for multiple comparisons, the use of numerous statistical significance tests of trends will increase the likelihood of concluding that a change has taken place when one has not. 4.0 General Data Editing and Imputation MethodologyThe general approach to preparing the household prescription data for this PUF was to utilize the PC prescription data to impute information collected from pharmacy providers to the household drug mentions. 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 based on 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 national average drug acquisition cost (NADAC) per unit, wholesale acquisition unit cost (WAUC), and average wholesale unit price (AWUP) of the drug by linkage through the NDC to secondary data files. In general, prescription drug unit prices were deemed to be outliers by comparing unit prices reported in the pharmacy database to the NADAC per unit reported in the secondary data files and were edited, as necessary. Prior to 2020, AWUP was the benchmark used to identify outlier prices for prescription medications in the PC. Beginning with the 2007 data, the rules used to identify outlier prices 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 2017 data, higher imputed prices were allowed. Imputed prices are capped to prevent the creation of unreasonable prices in cases with unreasonable quantity data. For the 2017 data, the cap was raised to account for the rising prices of specialty drugs. While there are relatively few cases for which the cap is relevant, these are expensive drugs, and this change in editing procedures accounts for more than 95% of the increase in total expenditures for prescribed medicines relative to 2016. Beginning with the 2020 data, the rules used to identify outlier prices for prescription medications in the PC were improved based on newer price benchmarks and analyses (Ding and Hill, 2022). New outlier thresholds were established based on the distribution of the ratio of retail unit prices relative to the NADAC per unit, collected for the Centers for Medicare and Medicaid Services. When the NADAC per unit is not available, then the WAUC is used, and if neither are available, the AWUP is used. AWUP and WAUC are list prices, not averages, so the NADAC per unit better reflects the prices paid for drugs, and as a result the prices paid for generics are lower in the 2020 data, compared with the 2019 data, and fewer generic fills have third party payments. 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. In the 2017 data for Panel 22 Round 3 and Panel 21 Round 5, more implausibly high numbers of fills were reported than in prior years, and so there was more extensive imputation of number of fills. 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. 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. Beginning with the 2008 PMED PUF, 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. 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. Discounts on brand name drugs in the donut hole do not count towards total expenditures and are not included in source of payment variables. For more information on the MEPS Prescribed Medicines editing and imputation procedures, please see Abdus et al., 2024, Methodology Report. 4.1 RoundingExpenditure variables on the 2022 PMED PUF have been rounded to the nearest penny. Person-level expenditure variables released on the 2022 Consolidated PUF were rounded to the nearest dollar. It should be noted that using the 2022 MEPS event PUFs to create person-level totals will yield slightly different totals than those found on the 2022 Consolidated PUF. These differences are due to rounding only. Moreover, in some instances, the number of persons having expenditures on the 2022 event PUFs for a particular source of payment may differ from the number of persons with expenditures on the 2022 Consolidated PUF for that source of payment. This difference is also an artifact of rounding only. 4.2 Edited/Imputed Expenditure Variables (RXSF22X-RXXP22X)There are 11 expenditure variables included in this event PUF. 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 (RXXP22X) which, for each prescribed medicine event, sums all the expenditures from the various sources of payment. The 10 sources of payment expenditure variables for each prescribed medicine event are the following: amount paid by self or family (RXSF22X), amount paid by Medicare (RXMR22X), amount paid by Medicaid (RXMD22X), amount paid by private insurance (RXPV22X), amount paid by the Veterans Administration/CHAMPVA (RXVA22X), amount paid by TRICARE (RXTR22X), amount paid by other federal sources (RXOF22X), amount paid by state and local (non-federal) government sources (RXSL22X), amount paid by Worker’s Compensation (RXWC22X), and amount paid by some other source of insurance (RXOT22X). Please see Section 2.6.4 for details on all sources of payment variables. 5.0 Strategies for Estimation5.1 Developing Event-Level EstimatesThe data in this PUF can be used to develop national 2022 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 (PERWT22F) across relevant event records while estimates of other variables must be weighted by PERWT22F to be nationally representative. The tables below contain event-level estimates for selected variables.
5.2 Person-Based Estimates for Prescribed Medicine PurchasesTo enhance analyses of prescribed medicine purchases, analysts may link information about prescribed medicine purchases to the annual Full Year Consolidated PUF (which has data for all MEPS sample persons), or conversely, link person-level information from the Full Year Consolidated PUF to this event-level file (see Section 6 below for more details). Both this file and the Full Year Consolidated PUF may be used to derive estimates for persons with prescribed medicine purchases and annual estimates of total expenditures for these purchases. However, for estimates that pertain to those who did not have prescribed medicine purchases as well as those who did (for example, the percentage of adults with at least one prescribed medicine purchase during the past year or the mean number of prescribed medicine purchases in the past year among those 65 or older), this PUF cannot be used. Only those persons with at least one prescribed medicine purchase are represented in this PUF. The Full Year Consolidated PUF must be used for person-level analyses that include both persons with and without prescribed medicine events. 5.3 Variables with Missing ValuesIt is essential that the analyst examine all variables for the presence of negative values used to represent missing values. For continuous or discrete variables, where means or totals may be estimated, it may be necessary to set negative values to values appropriate to the analytic needs. That is, the analyst should either impute a value or set the value to one that will be interpreted as missing by the software package 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. 5.4 Variance Estimation (VARSTR, VARPSU)To obtain estimates of variability in the MEPS estimates (such as the standard error of sample estimates or corresponding confidence intervals), analysts should take into account the complex sample design of the 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 (BRR), and jackknife replication. Various software packages provide analysts with the capability of implementing these methodologies. MEPS analysts most commonly use the Taylor series approach. Although this PUF does not contain replicate weights, analysts can use the BRR methodology to construct replicate weights to develop variances for more complex estimators (see Section 5.4.2). 5.4.1 Taylor-series Linearization MethodThe variables needed to calculate appropriate standard errors based on the Taylor-series linearization method are included on this and all other MEPS PUFs. Software packages that permit the use of the Taylor-series linearization method include SUDAAN, R, Stata, SAS (version 8.2 and higher), and SPSS (version 12.0 and higher). For complete information on the capabilities of a package, analysts should refer to the user documentation for the software. With the Taylor-series linearization method, variance estimation strata and the variance estimation PSUs within these strata must be specified. The variables VARSTR and VARPSU on this PMED PUF identify the sampling strata and primary sampling units required by the variance estimation programs. Specifying a “with replacement” design in one of the previously mentioned software packages will provide estimated standard errors appropriate for assessing the variability of the MEPS 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 see at least 100 degrees of freedom associated with the estimated standard errors for national estimates based on this MEPS database. Before 2002, the 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. Beginning with the 2002 point-in-time PUF, the approach changed with the intention that variance strata and PSUs would be developed to be compatible with all future PUFs until the NHIS design changed. Thus, when pooling data across years 2002 through Panel 11 of the 2007 files, analysts can use the variance strata and PSU variables provided without modifying them for variance estimation purposes for estimates covering multiple years of data. There are 203 variance estimation strata, each stratum with either two or three variance estimation PSUs. Beginning in Panel 12 of the 2007 files, a new set of variance strata and PSUs was developed because of the introduction of a new NHIS design. There are 165 variance strata with either two or three variance estimation PSUs per stratum. Therefore, there are a total of 368 (203+165) variance strata in the 2007 Population Characteristics PUF, as it consisted of two panels that were selected under two independent NHIS sample designs. Since both MEPS panels in the full-year files from 2008 through 2016 are based on the same NHIS design, there are only 165 variance strata. These strata (VARSTR values) have been numbered from 1001 to 1165 so that they can be readily distinguished from those developed under the former NHIS sample design if data are pooled for several years. The NHIS sample design was changed again in 2016, effectively changing the MEPS design beginning with calendar year 2017. Beginning in Panel 22 of the 2017 files, a new set of variance strata and PSUs were developed. There are 117 variance strata with either two or three variance estimation PSUs per stratum. Therefore, there are a total of 282 (165+117) variance strata in the 2017 Population Characteristics PUF, as it consisted of two panels that were selected under two independent NHIS sample designs. To make the pooling of data across multiple years of the MEPS more straightforward, the numbering system for the variance strata was changed. The strata associated with the new design are numbered from 2001 to 2117. The NHIS sample design was further modified in 2018, so the MEPS variance structure for the 2019 Population Characteristics PUF was also modified, reducing the number of variance strata to 105. Consistency was maintained with the prior structure in that the 2019 variance strata were also numbered within the range of values from 2001-2117, although there are now gaps in the values assigned within this range. Because of the modification, each stratum could contain up to 5 variance estimation PSUs. For Panel 26 in the 2021 and 2022 Population Characteristics PUF, an additional NHIS sample was used for the MEPS to account for increasing nonresponse during the pandemic (as discussed in Section 3.1). The additional sample was assigned to the existing variance strata, so the Population Characteristics PUF continues to have 105 variance strata, numbered 2001-2117, with a few gaps in the values in that range. In many cases, the additional sample was assigned to new variance estimation PSUs; thus, in the Population Characteristics PUF, each stratum contains up to eight variance estimation PSUs. Some analysts may be interested in pooling data across multiple years of MEPS data. When doing so, analysts should note that, to obtain appropriate standard errors, it is necessary to specify a common variance structure. Before 2002, each annual PUF was released with a variance structure unique to the particular MEPS sample in that year. Starting in 2002, the annual PUFs were released with a common variance structure that allowed analysts to pool data from 2002 through 2018. However, analysts can no longer do this routinely because the variance structure had to be modified beginning with 2019. To ensure that variance strata are identified appropriately for variance estimation purposes when pooling MEPS data across several years, analysts can proceed as follows:
5.4.2 Balanced Repeated Replication MethodBRR replicate weights are not provided on this MEPS PUF for the purposes of variance estimation. However, a file containing a BRR replication structure is made available so that analysts can form replicate weights, if desired, from the final MEPS weight to compute variances of MEPS estimates using either BRR or Fay’s modified BRR (Fay, 1989) methods. The replicate weights are useful for computing variances of complex nonlinear estimators for which a Taylor linear form is neither easy to derive nor available in commonly used software. For instance, it is not possible to calculate the variances of a median or the ratio of two medians by using the Taylor linearization method. For these types of estimators, analysts can calculate a variance using BRR or Fay’s modified BRR methods. However, it should be noted that the replicate weights have been derived from the final weight through a shortcut approach. Specifically, the replicate weights are not computed starting with the base weight, and all adjustments made in different stages of weighting are not applied independently in each replicate. Thus, the variances computed by using this one-step BRR do not capture the effects of all weighting adjustments that would be captured in a set of fully developed BRR replicate weights. The Taylor series approach does not fully capture the effects of the different weighting adjustments either. The dataset HC-036BRR, MEPS 1996-2021 Replicates for Variance Estimation File, contains the information necessary to construct the BRR replicates. It includes a set of 128 flags (BRR1-BRR128) in the form of half sample indicators, each of which is coded 0 or 1 to indicate whether the person should or should not be included in that particular replicate. These flags can be used in conjunction with the full-year weight to construct the BRR replicate weights. For an analysis of MEPS data pooled across years, the BRR replicates can be formed in the same way by using the HC-036, MEPS 1996-2021 Pooled Linkage Variance Estimation File. For more information about creating BRR replicates, analysts can refer to the documentation for the HC-036BRR pooled linkage file on the AHRQ website. 6.0 Merging/Linking MEPS Data FilesData from this PUF can be used alone or in conjunction with other PUFs for different analytic purposes. Merging characteristics of interest from other MEPS PUFs expands the scope of potential estimates. For example, the medical event PUFs can be merged with the person-level Consolidated PUF to calculate event-level estimates for persons with specific characteristics (e.g., age, race, sex, and education). Most of the event PUFs can also be linked to the Medical Conditions PUF by using the condition-event link (CLNK) PUF. When using the CLNK PUF, analysts should keep in mind that (1) conditions are household reported, (2) there may be multiple conditions associated with a medical event, (3) one condition may link to more than one event and (4) not all medical events link to the Medical Conditions PUF. In addition to linking to other MEPS PUFs, each MEPS panel can also be linked back to the previous year’s NHIS public use data files. For information on obtaining MEPS/NHIS link files please see the data files section of the MEPS website. 6.1 Linking to the Medical Conditions PUFThe condition-event link PUF (CLNK) provides a link from MEPS event PUFs to the 2022 Medical Conditions PUF. When using the CLNK PUF, 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. Analysts should also note that not all prescribed medicine purchases link to the Medical Conditions PUF. 6.2 Longitudinal AnalysisPanel-specific longitudinal files can be downloaded from the data section of the MEPS website. For all three panels (Panel 24, Panel 26, and Panel 27), the longitudinal file comprises MEPS data obtained in all rounds of the panel and can be used to analyze changes over the entire length of the panel. Variables in the file pertaining to survey administration, demographics, employment, health status, disability days, quality of care, patient satisfaction, health insurance, and medical care use and expenditures were obtained from the MEPS Consolidated PUFs from the years covered by that panel. For more details or to download the data files, please see Longitudinal Weight Files on the MEPS website. ReferencesAbdus, S., Hill, S., and Ahrnsbrak, R. (2024). Outpatient prescription drugs: collection and editing in the 2021 Medical Expenditure Panel Survey. Methodology Report #37. Agency for Healthcare Research and Quality, Rockville, MD. Bramlett, M.D., Dahlhamer, J.M., & Bose, J. (2021, September). Weighting Procedures and Bias Assessment for the 2020 National Health Interview Survey. Centers for Disease Control and Prevention. Chowdhury, S.R., Machlin, S.R., & Gwet, K.L. Sample designs of the Medical Expenditure Panel Survey Household Component, 1996-2006 and 2007-2016. Methodology Report #33. January 2019. Agency for Healthcare Research and Quality, Rockville, MD. Cohen, S.B. (1996). The redesign of the Medical Expenditure Panel Survey: A component of the DHHS survey integration plan. Proceedings of the Council of Professional Associations on Federal Statistics 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. Dahlhamer, J.M., Bramlett, M.D., Maitland, A., & Blumberg, S.J. (2021). Preliminary evaluation of nonresponse bias due to the COVID-19 pandemic on National Health Interview Survey estimates, April-June 2020. Hyattsville, MD: National Center for Health Statistics. Ding, Y. and Hill, S.C. (2022). Evaluating alternative benchmarks to improve identification of outlier drug prices for MEPS Prescribed Medicines (PMED) data editing. (Working Paper No. 22001). Agency for Healthcare Research and Quality, Rockville, MD. Fay, R.E. (1989). Theory and application of replicate weighting for variance calculations. Proceedings of the Survey Research Methods Sections of the American Statistical Association, 212-217. Food and Drug Administration (2014). Questions and Answers on the Pregnancy and Lactation Labeling Rule. 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), 242-259. Lau, D.T., Sosa, P., Dasgupta, N., & He, H. (2021). Impact of the COVID-19 pandemic on public health surveillance and survey data collections in the United States. American Journal of Public Health, 111 (12), 2118-2121. Monheit, A.C., Wilson, R., and Arnett, III, R.H. (Eds.). (1999) Informing American health care policy. Jossey-Bass Inc. Rothbaum, J. & Bee, A. (2021, May 3). Coronavirus infects surveys, too: Survey nonresponse bias and the coronavirus pandemic. Washington, DC: U.S. Census Bureau. Rothbaum, J. & Bee, A. (2022, September 13). How has the pandemic continued to affect survey response? Using administrative data to evaluate nonresponse in the 2022 Current Population Survey Annual Social and Economic Supplement. Washington, DC: U.S. Census Bureau. RTI International (2023). Medical Provider Component (MEPS-MPC) Methodology Report 2021 Data Collection. Rockville, MD. Agency for Healthcare Research and Quality. 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 Institute. U.S. Census Bureau. Current Population Survey: 2021 Annual Social and Economic (ASEC) Supplement. (2021). Washington, DC: Author. Zodet, M.W., Hill, S.C., and Miller, E. (2010). Comparison of retail drug prices in the MEPS and MarketScan: Implications for MEPS editing rules. (Working Paper No. 10001). Agency for Healthcare Research and Quality, Rockville, MD. Zuvekas, S.H. & Kashihara, D. (2021). The impacts of the COVID-19 pandemic on the Medical Expenditure Panel Survey. American Journal of Public Health, 111 (12), 2157-2166. D. Variable-Source CrosswalkFOR MEPS HC 239A: 2022 Prescribed Medicines Events
Appendix 1 Definitions for RXFORM, Dosage Form
* No definition for the dosage form. Appendix 2 Definitions for RXFRMUNT, Quantity Unit of Medication
* No description for the code. Appendix 3 Definitions for RXSTRUNT, Unit of Medication
* No definition for the abbreviations, codes and symbols. Appendix 4
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| Therapeutic Class Code | Definition |
|---|---|
| -15 | cannot be computed |
| -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 |
| 460 | phosphate binders |
| 461 | topical anti-rosacea agents |
| 462 | allergenics |
| 463 | protease-activated receptor-1 antagonists |
| 464 | miscellaneous diagnostic dyes |
| 465 | diarylquinolines |
| 466 | bone morphogenetic proteins |
| 467 | ace inhibitors with thiazides |
| 468 | antiadrenergic agents (central) with thiazides |
| 469 | antiadrenergic agents (peripheral) with thiazides |
| 470 | miscellaneous antihypertensive combinations |
| 472 | beta blockers with thiazides |
| 473 | angiotensin II inhibitors with thiazides |
| 474 | beta blockers with calcium channel blockers |
| 475 | potassium sparing diuretics with thiazides |
| 476 | ace inhibitors with calcium channel blocking agents |
| 479 | angiotensin II inhibitors with calcium channel blockers |
| 480 | antiviral boosters |
| 481 | NK1 receptor antagonists |
| 482 | angiotensin receptor blockers and neprilysin inhibitors |
| 483 | neprilysin inhibitors |
| 484 | PCSK9 inhibitors |
| 485 | NS5A inhibitors |
| 486 | oxazolidinone antibiotics |
| 487 | cftr combinations |
| 488 | anticoagulant reversal agents |
| 489 | CD38 monoclonal antibodies |
| 490 | peripheral opioid receptor mixed agonists/antagonists |
| 491 | local injectable anesthetics with corticosteroids |
| 493 | anti-PD-1 monoclonal antibodies |
| 494 | PARP inhibitors |
| 495 | Calcimimetics |
| 496 | VMAT2 inhibitors |
| 497 | cation exchange resins |
| 498 | antineoplastic combinations |
| 499 | carbapenems/beta-lactamase inhibitors |
| 500 | PI3K inhibitors |
| 501 | CDK 4/6 inhibitors |
| 502 | CGRP inhibitors |
| 503 | streptogramins |
| 504 | antimanic agents |
| 505 | transthyretin stabilizers |
| 506 | topical allergy diagnostic agents |
| 507 | malignancy photosensitizers |
| 508 | NHE3 inhibitors |
| 509 | BTK inhibitor |
| 510 | miscellaneous erythropoiesis agents |
| 511 | renal replacement solutions |
| 512 | melanocortin receptor agonists |
| 513 | investigational drugs |
| 514 | hereditary angiodema agents |
| 515 | peripheral opioid receptor agonists |
| 516 | noradrenergic uptake inhibitors for ADHD |
| 517 | CD19 monoclonal antibodies |
| 518 | other cephalosporins |
| 519 | alpha-adrenoreceptor antagonists |
|
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