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MEPS HC 254A: 2024 Prescribed MedicinesJuly 2026 Agency for Healthcare Research and Quality
A. Data Use Agreement 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. § 299a-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 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 previously referenced federal statute, it is understood that
By using these data you signify your agreement to comply with the previously 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 (18 U.S.C. § 1001), and is punishable by a fine of up to $10,000 or up to 5 years in prison. AHRQ requests that users cite AHRQ and the Medical Expenditure Panel Survey as the data source in any publications or research based on these data. B. Background1.0 Household ComponentThe Medical Expenditure Panel Survey (MEPS) provides nationally representative estimates of healthcare use, expenditures, payment sources, 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 survey’s panel design includes five rounds of interviews spanning 2 full calendar years. The interviews use computer-assisted personal interviewing (CAPI) technology, or computer-assisted video interviewing (CAVI) technology to collect information about each household member, which the survey builds on from interview to interview. A single household respondent reports all data for a sampled household. The MEPS HC was initiated in 1996. Each year a new panel of sampled 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 up to 15,000 households. Data can be analyzed at the person, family, or event level. Data must be weighted to produce national estimates. The set of households selected for each MEPS HC panel is a subsample of households participating in the previous year’s National Health Interview Survey (NHIS) conducted by NCHS. The NHIS sampling frame provides a nationally representative sample of the U.S. civilian noninstitutionalized population. In 2006, NCHS implemented a new NHIS sample design, that included households with Asian persons in addition to households with Black and Hispanic persons in minority group oversampling. In 2016, NCHS introduced another sample design that discontinued the oversampling of these minority groups. 2.0 Medical Provider ComponentWhen the household CAPI instrument is completed, and permission is obtained from the sampled 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 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 payment amounts. The MPC is not designed to yield national estimates; it is primarily used as an imputation source to supplement or 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, and the MEPS MPC data are collected under contract with RTI International. Datasets and summary statistics are edited and published in accordance with the confidentiality provisions of the Public Health Service Act and the Privacy Act. 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 AHRQ Data Tools site. 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 AHRQ, 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 files (PUFs) from the 2024 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 SAS dataset, SAS transport file, Stata dataset, and Excel file. The 2024 Prescribed Medicines PUF (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 estimate retail prescribed medicine utilization and expenditures for calendar year 2024. The file contains 64 variables and has a logical record length of 586 with an additional 2-byte carriage return/line feed at the end of each record. This PUF consists of MEPS data obtained in (1) the 2024 portion of Round 3 and all of Rounds 4 and 5 for Panel 28, and (2) Rounds 1 and 2 and the 2024 portion of Round 3 for Panel 29 (i.e., the rounds for MEPS panels covering calendar year 2024), as illustrated in the following figure. Figure 1 Portions of MEPS Panel 28 and Panel 29 Survey Data Included on the 2024 PMED PUF
Each record on this 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 online. In addition to prescribed medicine expenditures, each record contains household-reported characteristics. Data from this event PUF can be merged with other 2024 MEPS HC PUFs to append person-level data, such as demographic characteristics or health insurance coverage, to each prescribed medicine record. Prescribed medicine utilization counts 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 medication details (strength, quantity, etc.). This PUF can also be used to construct summary variables for 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 on the MEPS 2024 Full Year Consolidated Public Use File (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, as well as the content and structure of the PUF and 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 International (2025). A copy of the survey instrument used to collect the information on this PMED PUF is available in the Survey Questionnaires section of the MEPS website. 2.0 Data File InformationThe 2024 PMED PUF contains 204,550 prescribed medicine records. Each record represents one household-reported fill or refill of a prescribed medicine that was obtained during calendar year 2024 at any retail pharmacy, including mail order or online. Of the 204,550 prescribed medicine records, 202,902 records are associated with persons having a positive person-level weight (PERWT24F). The records on this PUF are prescribed medicine fills or refills obtained by persons who had to meet either of the following criteria:
Persons with no prescribed medicine use for 2024 are not included on this PUF but are represented on person-level 2024 Consolidated PUF. 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 2024 are represented on 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 associated with other medical events. Each record on 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 on this PUF. Other household members may have no reported acquisitions of prescribed medicines, and thus will have no records on this PUF. Before Round 5 of Panel 21 and Round 3 Panel 22 of the MEPS HC, diabetic supplies like syringes and insulin were listed in the Other Medical Expenses (OM) section, but interviewers were instructed to record them in the PM section. These items, often purchased without a prescription, were included in prescription drug expenditure and utilization data, though third-party payment may require a prescription. Analysts can define and code these events independently, allowing exclusion from the PM file if desired. Starting with Round 5 of Panel 21 and Round 3 of Panel 22, diabetic supplies and insulin are directly collected in the PM section as other prescribed medicines, but charges and payments are no longer collected. Refills are included in this PUF. The HC ascertains 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 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. For multiple acquisitions of the same drug, MEPS HC did not collect information 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 before the survey year. Each record on this PUF includes the following information: an identifier for each unique prescribed medicine, detailed characteristics associated with the event (e.g., NDC, medicine name, selected Multum Lexicon variables [see Section C.2.6.3 for more information on the Multum Lexicon variables included on this file]), 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. To append person-level information, such as demographic characteristics or health insurance coverage, to each record, data from this PUF can be merged with MEPS HC person-level data (i.e., the Consolidated PUF) using the DUPERSID person (see Section C.2.6.1 for more information on the DUPERSID identifier). 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 2024 Medical Conditions PUF (hereafter referred to as the Conditions PUF). Please see Section C.6.0, or the MEPS HC 254I: Appendix to the 2024 Event Files (hereafter referred to as the Appendix PUF), for details on how to link MEPS data files. 2.1 Codebook StructureFor most variables on the PMED PUF, both weighted and unweighted frequencies are provided in the accompanying codebook file. The exceptions to this are weight variables and variance estimation variables. Only unweighted frequencies of these variables are included on the accompanying codebook file. For more information, see the Weights table in Appendix 1: Variable-Source Crosswalk. The codebook and data file list variables in the following order:
Note: 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 (Table 1).
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 enough information is often the result of skip patterns in the data or 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 the case when a prescription drug name was determined to be a confidentiality risk. For these cases, the corresponding NDC was replaced with -15, the Multum Lexicon therapeutic class replaced the RXDRGNAM (Multum drug name) that was 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 when the household member first started using the medicine, the respondent indicated 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 website. 2.3 Codebook FormatThe PMED PUF codebook describes an ASCII dataset (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 (see Table 2).
2.4 Variable Source and Naming ConventionsIn general, variable names reflect the variable’s content. All edited/imputed variables end with “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 on this PMED PUF were derived from the MPC data collection instrument or from the Multum Lexicon database from Oracle Health The source of each variable is identified in Appendix 1: Variable-Source Crosswalk. Sources for each variable are indicated in one of five ways:
2.4.2 Expenditure and Source of Payment VariablesOnly edited or imputed versions of the expenditure variables are provided on this PUF. Expenditure variables on this PMED PUF follow a standard naming convention. The 10 sources of payment variables and 1 sum of payments variable are named consistently using the following approaches. 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 The third and fourth characters indicate the source of payment: 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 (24) indicate the year. The seventh character, “X”, indicates the variable was edited or imputed. For example, the variable RXSF24X is the edited or imputed amount paid by self or family for a prescribed medicine expenditure incurred in 2024. 2.5 Data CollectionPrescription drug data 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 online. 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 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 with 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 or could not 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 2 years, drugs mentioned in that round were allocated between the years according to 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 VariablesWhen the household member with the prescription gave written permission to release their 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, mailed, securely emailed, or provided via web portal 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 via mail, fax, secure email, or web portal, 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: 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 HC are generally consistent with those used in NHIS. The dwelling unit identifier (DUID) is a seven-digit number consisting of a two-digit panel number followed by a five-digit random number assigned after the case was sampled for MEPS. A three-digit person number (PID) uniquely identifies each person within the DU. The variable DUPERSID is the combination of the variables DUID and PID. Identifiers begin with the two-digit panel number. For detailed information on DUs and families, please refer to the documentation for the Consolidated PUF. Record Identifier Variables (RXRECIDX, LINKIDX, DRUGIDX) The variable RXRECIDX uniquely identifies each record on 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 Conditions PUF, via a link file, and is provided on this PUF as the variable LINKIDX. For more details on linking, please refer to Section C.6.1 or the 2024 Appendix PUF. The prescribed medicine person-drug-level ID generated through the HC, DRUGIDX, can be used to link 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 (2900002026002), for one person (DUPERSID = 2900002026) in two rounds. Generally, within a round, one NDC is associated with a prescribed medicine event because matching was performed at a drug level and not an acquisition level. The LINKIDX (2900002026002103) remains the same for both records in Round 1 but varies across rounds. The RXRECIDX (2900002026002103001, 2900002026002103002, 2900002026002203001) differs for all three records (see Table 3).
There can be multiple RXNDCs for a LINKIDX. All the acquisitions in the LINKIDX represent the same drug (active ingredients), but the RXNDCs may represent different manufacturers. For more details on matching, please see Section C.4.0. Panel Variable (PANEL) PANEL is a constructed variable used to specify the panel number and indicates Panel 28 or Panel 29 for each record on this PUF. Panel 28 started in 2023, and Panel 29 started in 2024. 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, or 5. Rounds 3 (partial), 4, and 5 are associated with data collected from Panel 28, and Rounds 1, 2, and 3 (partial) are associated with data collected from Panel 29. 2.6.2 Characteristics of Prescribed Medicine EventsWhen Prescribed Medicine Was First Taken (RXBEGMM-RXBEGYRX) Two variables indicate when a prescribed medicine was first taken (used), as reported by the household respondent. 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 C.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 1939. Prescribed Medicine Attributes (RXNAME-RXDAYSUP) For each prescribed medicine included on this PUF, several data items collected describe the medication obtained or purchased in detail. These data items are the following:
Many pharmacies did not provide days supplied, 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 2024 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 2, 3, and 4 for definitions of RXFORM, RXFRMUNT, and RXSTRUNT abbreviations, codes, and symbols. Please refer to Appendix 5 for therapeutic class code definitions. The NDC is an 11-digit code. The first five digits indicate the manufacturer of the prescribed medicine. The next four digits indicate the form and strength of the prescription, and the last two 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. Beginning with the 2013 data, the variable RXDRGNAM is included on the file. This drug name is the generic drug name used most 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. For more information 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 (e.g., cold remedies). For these drugs, details are generally recoded as missing, and RXNAME is recoded to whatever therapeutic class information remains. Analysts seeking access to restricted data must complete a MEPS Data Center application (see the Data Center section of the MEPS website). The variable DiabEquip (OTHER DIABETIC EQUIPMENT OR SUPPLIES) indicates the record is for diabetic supplies or 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 on this event PUF, unlike other MEPS event files, may still have missing data. This is because imputed data on 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 - PHARTP10) 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 during the survey year. All household-reported pharmacies are provided on this PUF, but there is no link in the survey or 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 PHARTP10 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) online. The value -1 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. 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. When 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 and imputation methodology, please see Section C.4.0. INPCFLG denotes whether a household member had any pharmacy-reported data, that is, at least one prescription drug purchase in the PC (0 = NO, 1 = YES). Clinical Classifications Software Refined Codes Information on household-reported medical conditions (ICD-10-CM condition codes) and aggregated clinically meaningful categories generated using Clinical Classifications Software Refined (CCSR) associated with each prescribed medicine are not provided on this PUF. For information on ICD-10-CM condition codes and associated CCSR codes, see the Conditions PUF and the Appendix PUF. 2.6.3 Multum Lexicon Variables From Oracle HealthEach record on this file contains the following Multum Lexicon variables: RXDRGNAM Generic drug name used most by prescribing physicians TCn Therapeutic classification variable - assigns a drug to one or more therapeutic or chemical categories; can have up to three categories per drug TCnSn Therapeutic subclassification variable - assigns one or more subcategories to a more general therapeutic class category given to a drug TCnSn_n Therapeutic sub-subclassification variable - assigns one or more sub-subcategories to a more general therapeutic class category and subcategory 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 MEPS. The Multum Lexicon 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 Oracle Health file. Analysts should also be aware of a problem discovered with the linking between the MEPS PMED PUFs and the Oracle Health file that resulted in some incorrect therapeutic class assignments. In particular, some diagnostic tests and medical devices were inadvertently assigned to a therapeutic class when they should not have been (see previous PMED PUFs for specific examples). Analysts should use caution when using the Oracle Health therapeutic class variables for analysis and should always check for accuracy. Researchers using the Multum Lexicon variables are requested to cite Multum Lexicon as the data source. 2.6.4 Expenditure Variables (RXSF24X-RXXP24X)Definition of Expenditures Expenditures on this PUF refer to payments for healthcare 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 National Medical Expenditure Survey (NMES) and the 1977 National Medical Care Expenditure Survey (NMCES), 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 as a result of 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. Currently, 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 associated payments. 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 Sections C.3.5 and C.6.2, respectively, for more information. Sources of Payment In addition to total expenditures, variables are provided that itemize expenditures by major source of payment category:
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 on 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, included in total expenditures, and assigned to a public source of payment or other unclassified sources based on the type of pharmacy and the person’s insurance coverage. 3.0 Survey Sample Information3.1 Discussion of Pandemic Effects on Quality of MEPS DataLike most surveys, MEPS has been substantially affected by the COVID-19 pandemic. One effect of the pandemic is significantly lower response rates (see Section C.3.2 in the Consolidated PUF document), which might differentially exclude households more likely to experience hospital inpatient stays. The demographic shifts on MEPS between 2019 and 2022 suggest a more educated, higher- income, older MEPS sample. (For more details, see Section C.3.1 of the 2020 Consolidated PUF document, Section C.3.1 of the 2021 Consolidated PUF document, and Section C.3.1.2 of the 2022 Consolidated PUF document.) MEPS sample design modifications due to the COVID-19 pandemic reverted in 2022. Thus, concerns about potential bias due to these modifications no longer apply to data collected in this PUF. To examine the quality of the MEPS full-year 2024 data, analyses compared healthcare utilization and health insurance coverage for the MEPS target population between the panels fielded. These comparisons were undertaken for the full sample and three age groups: 0-17, 18-64, and 65 or older. Analysts found no abnormal differences between the two panels. Analyses across years also suggest a rebound to pre-pandemic utilization levels for most essential event types. The development of the person-level weights for the MEPS full-year 2024 data was designed to limit the potential for response bias. However, analysts of the MEPS full-year 2024 data should continue to exercise caution when interpreting estimates and assessing analyses, especially for data collected from 2020 through 2022. This includes comparing estimates with those of other years and conducting corresponding trend analyses. 3.2 Sample Weight (PERWT24F)A single full-year person-level weight (PERWT24F) is assigned to each record for each Key in-scope person who responded to MEPS for the entire duration that the person was in scope during 2024. 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 NHIS (the latter circumstance includes newborns and those returning from military service, an institution, or residence in a foreign country). A person is in scope whenever the person is a member of the U.S. civilian noninstitutionalized population. 3.3 Details on Person Weight ConstructionThe person-level weight PERWT24F was developed in several stages. First, a person-level weight for Panel 28 was created, including an adjustment for nonresponse over time and raking. 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 to establish the initial person-level control figures include the following:
The person-level weight for Panel 29 was created similarly. A composite weight was formed by multiplying each weight from Panel 28 by the factor 0.44 and each weight from Panel 29 by the factor 0.56. The choice of factors reflects the relative effective sample sizes of the two panels, helping to limit the variance of estimates obtained from pooling both samples. Weights for the 2024 Consolidated PUF were then developed by raking the composite weight to CPS-based control totals, 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, 100% - 125% of poverty, 125% - 200% of poverty, 200% - 400% of poverty, at least 400% of poverty) in addition to age, race/ethnicity, sex, region, and MSA status are used to calibrate weights. 3.3.1 MEPS Panel 28 Weight Development ProcessThe person-level weight for Panel 28 was developed using the 2023 full-year weight as a “base” weight for survey participants present in 2024. For Key in-scope members who joined a reporting unit (RU) at some time in 2024 after being out of scope in 2023, the initially assigned person-level weight was the corresponding 2023 family weight. The weighting process also included an adjustment for person-level nonresponse over Rounds 4 and 5, as well as raking to the population control figures for December 2024 for Key responding persons in scope on December 31, 2024. These control totals were derived by scaling back the population distribution obtained from the March 2025 CPS to reflect the December 31, 2024, estimated population total (based on census projections for January 1, 2025). The six variables listed in Section C.3.3 were also used for person-level raking: education of the reference person, census region, MSA status, race/ethnicity, sex, and age. The final weight for Key responding persons who were not in scope on December 31, 2024, but were in scope earlier in the year was the nonresponse-adjusted person weight without raking. Note that the 2023 full-year weight that was used as the base weight for Panel 28 was derived using the 2023 MEPS Round 1 weight and reflected adjustment for nonresponse over the remaining data collection rounds in 2023, as well as raking to the December 2023 population control figures. 3.3.2 MEPS Panel 29 Weight Development ProcessThe person-level weight for Panel 29 was developed using the 2024 Round 1 person-level weight as a base weight. The Round 1 weights incorporated the following components: the original household probability of selection for NHIS and for the NHIS subsample reserved for MEPS, an adjustment for NHIS nonresponse, the probability of selection for MEPS from NHIS, an adjustment for nonresponse at the DU level for Round 1, and raking to control figures at the person level 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 2024, as well as raking to the same population control figures for December 2024 that were used for the Panel 28 weight for Key responding persons in scope on December 31, 2024. The same six variables used for Panel 28 raking (education level of the reference person, census region, MSA status, race/ethnicity, sex, and age) were also used for Panel 29 raking. Similar to Panel 28, the Panel 29 final weight for Key responding persons who were not in scope on December 31, 2024, but were in scope earlier in the year was the nonresponse-adjusted person weight without raking. 3.3.3 The Final Weight for 2024The final raking of those in scope at the end of the year has been described previously. In addition, the composite weights of two groups of persons who were out of scope on December 31, 2024, were adjusted for expected undercoverage. Specifically, the weights of those who were out of scope on December 31, 2024, 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 Centers for Medicare & Medicaid Services (CMS). The weights of persons who died while in scope were poststratified to corresponding estimates derived using data from the Centers for Disease Control and Prevention (CDC), NCHS, and About Provisional Mortality Statistics, 2018 through Last Week on the CDC WONDER online database (released in 2025, the latest available data at the time). Separate decedent control totals were developed for the “65 or older” and “under 65” civilian noninstitutionalized populations. Overall, the weighted population estimate for the civilian noninstitutionalized population for December 31, 2024, is 336,022,966 (PERWT24F >0 and INSC1231 = 1). The sum of person-level weights across all persons assigned a positive person-level weight is 339,797,629. 3.4 CoverageThe target population associated with MEPS is the 2024 U.S. civilian noninstitutionalized population. However, the MEPS sampled households are a subsample of the NHIS households interviewed in 2022 (Panel 28) and 2023 (Panel 29). New households created after the NHIS interviews for the respective panels and consisting exclusively of persons who entered the target population after 2022 (Panel 28) or after 2023 (Panel 29) are not covered by the 2024 MEPS. Nor are previously out-of-scope persons who joined an existing household but are not related to the current household residents. Thus, persons not covered by a given MEPS panel include some members of the following groups: newborns, 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, there are uncertainties associated with 2020, 2021, and 2022 data quality, as discussed in Section C.3.1. Evaluations of important MEPS estimates suggest that the estimates are of reasonable quality. Nevertheless, analysts are advised to exercise caution when interpreting these estimates, particularly for trend analyses, because the pandemic substantially affected healthcare access and related factors (e.g., health insurance coverage, employment status). MEPS began in 1996, and the utility of the survey for analyzing healthcare trends expands with each additional year of data; however, when examining trends over time using MEPS, the duration being analyzed should be considered. In particular, large shifts in survey estimates over short periods (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 healthcare utilization from MEPS respondents; the changes were fully implemented in 2014. This effort likely improved data quality and reduced underreporting starting in the second half of 2013 and continuing throughout the 2014 full-year files. The changes have also affected analyses involving utilization trends across years. Changes in the NHIS sample design in 2016 and 2018 could also 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, and many of its subpopulations. Improved 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 involves major modifications to the MEPS instrument design and data collection process, particularly in the events sections of the instrument. These were introduced in spring 2018 and thus affected data beginning with Round 1 of Panel 23, Round 3 of Panel 22, and Round 5 of Panel 21. Because 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 reflect 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 be as consistent as possible with data collected under the previous design. The changes to the instrument were designed to make data collection more efficient and easier to administer. In addition, data on some items, such as those related to healthcare events, were expected to 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. Note: Analysts should be aware of the possible impacts of these changes on data, especially 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 C.2.5.11: Utilization, Expenditures, and Sources of Payment Variables in the Consolidated PUF (HC 256). For more details, refer to Section C.4.0 of this document when analyzing prescription drug spending over time. As always, before conducting trend analyses, analysts should review relevant documentation sections for descriptions of changes that might affect interpretation over time. To smooth or stabilize trend analyses based on the MEPS data, analysts may also wish to consider statistical approaches such as comparing pooled time periods (e.g., 1996-1997 vs. 2011-2012), working with moving averages, or using modeling techniques with several consecutive years of 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, conducting numerous statistical significance tests of trends will increase the likelihood of concluding that a change has occurred 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 use 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 datasets 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 MEPS better benchmarks to MarketScan data, overall and by patent status (Zodet et al., 2010); (2) fewer pharmacy-reported payments and quantities (e.g., number of pills) are edited; and (3) imputed prices reflect prices paid rather than AWUPs. As a result, compared with earlier years of MEPS, starting in 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 compared with 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 MEPS data, three changes in editing sources of payment data were made to improve data quality, drawing 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. There was also another change that 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. Although 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 percent of the increase in total expenditures for prescribed medicines in comparison with 2016 data. Beginning with the 2020 data, the rules used to identify outlier prices for prescription medications in the PC were improved, drawing on newer price benchmarks and analyses (Ding & 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 CMS. When the NADAC per unit is not available, the WAUC is used, and if neither are available, the AWUP is used. AWUP and WAUC are list prices and not averages, so the NADAC per unit better reflects the prices paid for drugs; 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. For 10 percent of household-reported drugs in the 2011 data, 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 Round 3 of Panel 22 and Round 5 of Panel 21, more implausibly high numbers of fills were reported than in prior years, resulting in 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, and 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 were 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 (e.g., 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 variety of supplies that 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 toward 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). 4.1 RoundingExpenditure variables on the PMED PUF have been rounded to the nearest penny. Person-level expenditure variables to be released on the Consolidated PUF will be rounded to the nearest dollar. Of note, using the MEPS event PUFs to create person-level totals will yield slightly different totals from those found on the Consolidated PUF. These differences are due to rounding only. Moreover, in some instances, the number of persons with expenditures on the event PUFs for a particular source of payment may differ from the number of persons with expenditures on the person-level Consolidated PUF for that source of payment. This difference is also an artifact of rounding only. 4.2 Edited/Imputed Expenditure Variables (RXSF24X - RXXP24X)There are 11 expenditure variables included on this event PUF. These expenditures have gone through an editing and imputation process and have been rounded to the second decimal place. For each prescribed medicine event, there is a sum of payments variable (RXXP24X) that, 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 (RXSF24X); amount paid by Medicare (RXMR24X); amount paid by Medicaid (RXMD24X); amount paid by private insurance (RXPV24X); amount paid by the Veterans Administration/CHAMPVA (RXVA24X); amount paid by TRICARE (RXTR24X); amount paid by other federal sources (RXOF24X); amount paid by state and local (non-federal) government sources (RXSL24X); amount paid by worker’s compensation (RXWC24X); and amount paid by some other source of insurance (RXOT24X). Please see Section C.2.6.4 for details on all sources of payment variables. 5.0 Strategies for Estimation5.1 Developing Event-Level EstimatesThe data on this PUF can be used to develop national 2024 event-level estimates for the U.S. civilian noninstitutionalized population for prescribed medicine purchases (events) and expenditures, as well as sources of payment for these purchases. Estimates of total number of purchases are the sum of the weight variable (PERWT24F) across relevant event records, whereas estimates of other variables must be weighted by PERWT24F to be nationally representative. Table 4 and Table 5 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 Consolidated PUF (which includes data for all MEPS sampled persons) or conversely, link person-level information from the Consolidated PUF to this event-level file (see Section C.6.0 for more details). Both this PUF and the Consolidated PUF may be used to derive estimates relative to persons with prescribed medicine purchases and annual estimates of total expenditures for these purchases. However, for estimates pertaining to those who did not have prescribed medicine purchases in addition to those who did (e.g., 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 aged 65 or older), this PUF cannot be used. Only those persons with at least one prescribed medicine purchase are represented on this PUF. The Consolidated PUF must be used for person-level analyses that include persons both with and without prescribed medicine events. 5.3 Variables With Missing ValuesAnalysts must 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 calculated, it may be necessary to set negative values to values appropriate to analytic needs. That is, analysts should either impute a value or set the negative value to a value that will be interpreted as missing by the software package. For categorical and dichotomous variables, analysts can consider whether to recode or impute a value for cases with negative values or whether to include or exclude such cases in the numerator, denominator, or both when calculating proportions. Section C.4.2 describes the methodologies used for editing or imputation of expenditure variables (e.g., total expenditures, sources of payment). 5.4 Variance Estimation (VARSTR, VARPSU)To obtain estimates of variability in MEPS estimates (e.g., the standard error of sample estimates or corresponding confidence intervals), analysts should consider MEPS’s complex sample design for both person-level and family-level analyses. Several methods have been developed to estimate standard errors for surveys with complex sample designs, including the Taylor series linearization method, balanced repeated replication (BRR), and jackknife replication; various software packages can implement these methods. MEPS analysts most commonly use the Taylor series approach. Although this PUF does not contain replicate weights, analysts can use the BRR method to construct replicate weights to develop variances for more complex estimators (see Section C.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 file, as well as all other MEPS PUFs. Software packages that support the Taylor series linearization method include SUDAAN, R, Stata, SAS (version 8.2 or higher), and SPSS (version 12.0 or higher). For complete information on a package’s capabilities, analysts should refer to the software’s user documentation. With the Taylor series linearization method, variance estimation strata and the variance estimation primary sampling units (PSUs) within these strata must be specified. The variables VARSTR and VARPSU on this PMED PUF identify the sampling strata and PSUs 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 MEPS estimates. Note that the number of degrees of freedom associated with estimates of variability indicated by a package may not appropriately reflect the number available. For variables of interest distributed throughout the country (and thus across 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 from 2002 through Panel 11 in 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 has either two or three variance estimation PSUs. Beginning with Panel 12 in 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 Consolidated PUF because it consists of two panels selected under two independent NHIS sample designs. Because both MEPS panels in the full-year files from 2008 to 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 they can be readily distinguished from those developed under the former NHIS sample design when pooling data across multiple years. The NHIS sample design was changed again in 2016, effectively changing the MEPS design beginning with calendar year 2017. Beginning with Panel 22 in the 2017 files, a new set of variance strata and PSUs was 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 on the 2017 Consolidated PUF because it consists of two panels selected under two independent NHIS sample designs. To simplify data pooling across multiple years of MEPS, the variance strata numbering system 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 Consolidated PUF was also modified, reducing the number of variance strata to 105. The new variance structure maintained consistency with the prior structure by assigning the 2019 variance strata to values within the same 2001 - 2117 range, though there are now some gaps in the sequence of assigned values. Because of the modification, each stratum could contain up to five variance estimation PSUs. For Panel 26 in the 2021 and 2022 Consolidated PUFs, an additional NHIS sample was used for MEPS to account for increasing nonresponse during the pandemic (as discussed in Section C.3.1). The additional sample was assigned to the existing variance strata, so the 2021 and 2022 Consolidated PUFs continued 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 2021 and 2022 Consolidated PUFs, each stratum contained up to eight variance estimation PSUs. Additional NHIS samples were no longer needed beginning in 2023, leading to fewer variance estimation PSUs than in the 2021 and 2022 Consolidated PUFs. The Consolidated PUF continues to have 105 variance strata, numbered 2001-2117, with a few gaps in the values in that range. Each stratum contains up to seven variance estimation PSUs. When pooling data across multiple years of MEPS data, 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 to allow analysts to pool data from 2002 to 2018. However, analysts can no longer do this routinely because the variance structure was modified beginning in 2019. To ensure that variance strata are identified appropriately for variance estimation purposes when pooling MEPS data across several years, analysts should proceed as follows:
5.4.2 Balanced Repeated Replication MethodBRR replicate weights are not provided on this MEPS PMED PUF for the purposes of variance estimation. However, a file containing a BRR structure is 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 are derived from the final weight through a shortcut approach. Specifically, the replicate weights are not computed from the base weight, and all adjustments made in different stages of weighting are not applied independently in each replicate. Thus, the variances computed 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 1996-2024 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 1996-2024 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, education). Most of the event PUFs can also be linked to the 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) multiple conditions may be 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 PUFs. This is because the set of households selected for MEPS is a subsample of NHIS participants. For information on obtaining MEPS/NHIS link files, please see the Data File Types section of the MEPS website. 6.1 Linking to the Medical Conditions PUFThe CLNK PUF provides a link from MEPS event PUFs to the Conditions PUF. When using the CLNK PUF, analysts should keep in mind that (1) conditions are self-reported, (2) multiple conditions may be 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 Conditions PUF. 6.2 Longitudinal AnalysisPanel-specific longitudinal files can be downloaded from the Download Data Files section of the MEPS website. For both panels (Panel 28 and Panel 29), 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 on 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., & Ahrnsbrak, R. (2024, January). Outpatient prescription drugs: collection and editing in the 2021 Medical Expenditure Panel Survey. (Methodology Report #37). Agency for Healthcare Research and Quality. Chowdhury, S. R., Machlin, S. R., & Gwet, K. L. (2019, January). Sample designs of the Medical Expenditure Panel Survey Household Component, 1996-2006 and 2007-2016. (Methodology Report #33). Agency for Healthcare Research and Quality. Ding, Y. & Hill, S. C. (2022, September). Evaluating alternative benchmarks to improve identification of outlier drug prices for MEPS Prescribed Medicines (PMED) data editing. (Working Paper #22001). Agency for Healthcare Research and Quality. 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. Hill, S. C., Zuvekas, S. H., & Zodet, M. W. (2011). Implications of the accuracy of MEPS prescription drug data for health services research. Inquiry, 48(3), 242-259. Monheit, A. C., Wilson, R., & Arnett, R. H, III. (Eds.). (1999) Informing American health care policy: The dynamics of medical expenditure and insurance surveys, 1977-1996. Jossey-Bass Publishers. RTI International (2025). Medical Expenditure Panel Survey Medical Provider Component (MEPS-MPC) Methodology report 2023 data collection. Agency for Healthcare Research and Quality. Zodet, M. W., Hill, S. C., & 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. Additional ResourcesBramlett, 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. 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. & Cohen, S. B. (1985). Imputation procedures to compensate for missing responses to data items. In B. G. Cox and S. B. Cohen (Eds.), Methodological issues for health care surveys (pp. 214-234). CRC Press. Dahlhamer, J. M., Bramlett, M. D., Maitland, A., & Blumberg, S. J. (2021, February). Preliminary evaluation of nonresponse bias due to the COVID-19 pandemic on National Health Interview Survey estimates, April-June 2020. National Center for Health Statistics. 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. Rothbaum, J. & Bee, A. (2021, May). Coronavirus infects surveys, too: Survey nonresponse bias and the coronavirus pandemic. U.S. Census Bureau. Rothbaum, J. & Bee, A. (2022, September). 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. U.S. Census Bureau. Shah, B. V., Barnwell, B. G., Bieler, G. S., Boyle, K. E., Folsom, R. E., Lavange, L., Wheeless, S. C., & Williams, R. (1996). Technical manual: Statistical methods and algorithms used in SUDAAN Release 7.0. RTI International. U.S. Census Bureau (2021). Current Population Survey: 2021 Annual Social and Economic (ASEC) Supplement. U.S. Census Bureau, Bureau of Labor Statistics. 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. Appendix 1
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| Variable | Description | Source |
|---|---|---|
| DUID | Panel # + encrypted DU identifier | Assigned in sampling |
| PID | Person number | Assigned in sampling |
| DUPERSID | Sample person ID (DUID + PID) | Assigned in sampling |
| RXRECIDX | Record ID - Unique Prescribed Medicine Identifier | Constructed |
| LINKIDX | Link to condition and other event files | CAPI derived |
| DRUGIDX | Link to drugs across rounds | CAPI derived |
| PANEL | Panel indicator | Assigned in sampling |
| PURCHRD | Round in which the Rx/prescribed medicine was obtained/purchased | CAPI derived |
| Variable | Description | Source |
|---|---|---|
| RXBEGMM | Month person first used medicine | PM130_02 |
| RXBEGYRX | Year person first used medicine | PM130_01 |
| RXNAME | Medicine name (Imputed) | Imputed |
| RXDRGNAM | Multum medicine name (Imputed) | Imputed |
| RXNDC | NDC (Imputed) | Imputed |
| RXQUANTY | Quantity of Rx/prescribed medicine (Imputed) | Imputed |
| RXFORM | Dosage form (Imputed) | Imputed |
| RXFRMUNT | Quantity unit of medication (Imputed) | Imputed |
| RXSTRENG | Strength of medication (Imputed) | Imputed |
| RXSTRUNT | Unit of medication (Imputed) | Imputed |
| RXDAYSUP | Days supplied of prescribed med(Imputed) | Imputed |
| PHARTP1-PHARTP10 | Type of pharmacy prov - (1st-10th) | PM160LU |
| RXFLG | Flag variable indicating imputation source for NDC on pharmacy donor record | Constructed |
| IMPFLAG | Method of expenditure data creation | Constructed |
| PCIMPFLG | Flag indicating type of household to pharmacy prescription match | Constructed |
| DiabEquip | Other diabetic equipment or supplies | PM40 |
| INPCFLG | Flag indicating if the person has at least one record in the Pharmacy Component | Constructed |
| TC1 | Multum therapeutic class #1 | Oracle Health |
| TC1S1 | Multum therapeutic subclass #1 for TC1 | Oracle Health |
| TC1S1_1 | Multum therapeutic sub-subclass for TC1S1 | Oracle Health |
| TC1S1_2 | Multum therapeutic sub-subclass for TC1S1 | Oracle Health |
| TC1S2 | Multum therapeutic subclass #2 for TC1 | Oracle Health |
| TC1S2_1 | Multum therapeutic sub-subclass for TC1S2 | Oracle Health |
| TC1S3 | Multum therapeutic subclass #3 for TC1 | Oracle Health |
| TC1S3_1 | Multum therapeutic sub-subclass for TC1S3 | Oracle Health |
| TC2 | Multum therapeutic class #2 | Oracle Health |
| TC2S1 | Multum therapeutic subclass #1 for TC2 | Oracle Health |
| TC2S1_1 | Multum therapeutic sub-subclass for TC2S1 | Oracle Health |
| TC2S1_2 | Multum therapeutic sub-subclass for TC2S1 | Oracle Health |
| TC2S2 | Multum therapeutic subclass #2 for TC2 | Oracle Health |
| TC3 | Multum therapeutic class #3 | Oracle Health |
| TC3S1 | Multum therapeutic subclass #1 for TC3 | Oracle Health |
| TC3S1_1 | Multum therapeutic sub-subclass for TC3S1 | Oracle Health |
| RXSF24X | Amount paid, self or family (Imputed) | Edited/Imputed |
| RXMR24X | Amount paid, Medicare (Imputed) | Edited/Imputed |
| RXMD24X | Amount paid, Medicaid (Imputed) | Edited/Imputed |
| RXPV24X | Amount paid, private insurance (Imputed) | Edited/Imputed |
| RXVA24X | Amount paid, Veterans Administration/CHAMPVA (Imputed) | Edited/Imputed |
| RXTR24X | Amount paid, TRICARE (Imputed) | Edited/Imputed |
| RXOF24X | Amount paid, other federal (Imputed) | Edited/Imputed |
| RXSL24X | Amount paid, state and local government (Imputed) | Edited/Imputed |
| RXWC24X | Amount paid, worker’s compensation (Imputed) | Edited/Imputed |
| RXOT24X | Amount paid, other insurance (Imputed) | Edited/Imputed |
| RXXP24X | Sum of payments RXSF24X - RXOU24X (Imputed) | Edited/Imputed |
| Variable | Description | Source |
|---|---|---|
| PERWT24F | Expenditure file person weight, 2024 | Constructed |
| VARSTR | Variance estimation stratum, 2024 | Constructed |
| VARPSU | Variance estimation PSU, 2024 | Constructed |
| Dosage form | Definition |
|---|---|
| -7 | refused |
| -8 | don’t know |
| -15 | cannot be computed |
| ACC | accessory |
| ACETONIDE | acetonide |
| ACT | actuation |
| ADR | acetic acid drop |
| AE | aerosol |
| AEPB | aerosol powder, breath activated |
| AER | aerosol |
| AER SPRAY | aerosol spray |
| AERA | aerosol with adapter |
| AERB | aerosol, breath activated |
| AERO | aerosol |
| AEROP | aerosol powder |
| AEROSOL | aerosol |
| AERS | aerosol, solution |
| AJKT | auto-injector kit |
| ALM | * |
| AMI | * |
| AMO | * |
| AMP | ampule |
| ARA | aerosol liquid w/adapter (inhaler) |
| ARD | aerosol solid w/adapter |
| ARO | aerosol solid |
| ASS | * |
| AUIJ | * |
| AUTO INJ | auto-injection |
| BACK SUPPORT BELT | back support belt |
| BAG | bag |
| BAL | balm |
| BALM | balm |
| BAN | bandage |
| BANDAGE | bandage |
| BAR | bar |
| BATTERY | battery |
| BENCH | bench |
| BLO | block |
| BOT | bottle |
| BOTTLE | bottle |
| BOX | box |
| BOXES | boxes |
| BRACE | brace |
| BRIEF | brief |
| BUT | butterfly |
| C | capsules, or cream (varies) |
| C12 | 12-hour extended-release capsule |
| C12A | * |
| C24 | 24-hour extended-release capsule |
| CA | capsule |
| CANE | cane |
| CAP | capsule, caplets |
| CAP-CAPLETS | caplets |
| CAP-CAPSULE | capsule |
| CAP DR | delayed-release capsule |
| CAP ER | extended-release capsule |
| CAP SA | slow-acting capsule |
| CAPLET | caplet |
| CAPLT | caplet |
| CAPS | capsules |
| CAPSULE | capsule |
| CAPSULE SA | slow-acting capsule |
| CAT | catheter |
| CATHETER | catheter |
| CC | cubic centimeter |
| CER | capsule,
extended-release tablet, extended-release |
| CHAMBER | chamber |
| CHER | * |
| CHEW | chewable tablet |
| CHEW TAB | chewable tablet |
| CHEW TABS | chewable tablets |
| CHEWABLE | chewable |
| CHW | chewable tablets |
| CLEANSER | cleanser |
| COLLAR | collar |
| COMBO | * |
| COMPOUND | compound |
| CON | condom |
| CONC | concentrate |
| CONDOM | condom |
| CONTAINER | container |
| COS | * |
| COT | * |
| COTTON | cotton |
| CP12 | capsule, extended-release, 12-hour |
| CP24 | capsule, extended-release, 24-hour |
| CPCR | capsule, extended-release |
| CPDR | capsule, delayed-release |
| CPEP | capsule, delayed-release particles |
| CPSP | capsule sprinkle |
| CPSR | slow-release capsule |
| CR | cream |
| CRE | cream |
| CREA | cream |
| CREAM | cream |
| CRM | cream |
| CRY | crystal |
| CRYS | crystals |
| CRYSTAL | crystal |
| CS24 | * |
| CSDR | * |
| CTB | chewable tablets |
| CTG | cartridge |
| CURVE | curve |
| CUTTER | cutter |
| DEV | device |
| DEVI | device |
| DEVICE | device |
| DIA | diaper |
| DIAPER | diaper |
| DIAPHRAGM | diaphragm |
| DIHYDROCHLOR | dihydrochloride |
| DIPROPION | dipropionate |
| DIS | disk, or dermal infusion system |
| DISC | DISC |
| DISK | disk |
| DISKUS | diskus |
| DISPOSABLE | disposable |
| DOS PAK | dose pack |
| DPRH | diaphragm |
| DR | drop |
| DRC | delayed-release capsule |
| DRE | dressing |
| DRESSING | dressing |
| DRO | drop |
| DROP | drop |
| DROPS | drops |
| DROPS OPTH OTI | ophthalmic/otic drops |
| DROPS SUSP | drops suspension |
| DRP | drop |
| DRPS | drops |
| DSK | disk |
| DSPK | tablets in a dose pack |
| DSPT | tablet, dispersible |
| DT | tablet, disintegrating |
| EAM | * |
| EAR DROP | ear drop |
| EAR DROPS | ear drops |
| EAR DRP | ear drop |
| EAR SUSP | ear suspension |
| EC TABS | enteric-coated tablets |
| ECC | enteric-coated capsules |
| ECO | * |
| ECT | enteric-coated tablets |
| ELI | elixir |
| ELIX | elixir |
| ELIXER | elixir |
| ELIXIR | elixir |
| ELX | elixir |
| EMERGENCY KIT | emergency kit |
| EMO | emollient |
| EMU | emulsion |
| EMUL | emulsion |
| EMULSION | emulsion |
| ENE | enema |
| ENEM | enema |
| ENEMA | enema |
| ER | * |
| ERC | capsule, extended-release |
| ERSUS | suspension, extended-release |
| ERT | tablet, extended-release |
| ERTA | extended-release tablets |
| ERTC | tablet, chewable, extended-release |
| ESI | * |
| EST | * |
| ETA | * |
| EXHU | * |
| EXTN CAP | extended-release capsule |
| EXTRACT | extract |
| EYE DRO | eye drop |
| EYE DROP | eye drop |
| EYE DROPS | eye drops |
| EYE DRP | eye drop |
| EYE EMU | * |
| EYE OIN | eye ointment |
| EYE SO | eye solution |
| EYEDRO | eye drop |
| FIL | film |
| FILM | film |
| FILM ER | film, extended-release |
| FILMTAB | filmtab |
| FILMTABS | filmtabs |
| FLI | film |
| FLOWMETER | flowmeter |
| FOA | foam |
| FOAM | foam |
| GAU | gauze |
| GAUZE | gauze |
| GEF | effervescent granules |
| GEL | gel |
| GELC | * |
| GEL CAP | gel capsule |
| GELS | gel-forming solution |
| GER | granule, extended-release |
| GFS | gel-forming solution |
| GLOVE | glove |
| GRA | granules |
| GRAN | granules |
| GRANULES | granules |
| GRAR | granules for reconstitution |
| GRR | grams |
| GTT | drops |
| GUL | * |
| GUM | gum |
| HFA | * |
| HOSE | medical hosiery |
| HU | capsule |
| HYDROBROMIDE | hydrobromide |
| ICR | control-release insert |
| IMPL | implant |
| IMPLANT | implant |
| IN | injectable |
| INH | inhalant, inhaler |
| INH-INHALANT | inhalant |
| INH-INHALER | inhaler |
| INHA | inhaler |
| INH AER | inhalant aerosol |
| INHAL | inhalant |
| INHAL SOL | inhalant solution |
| INHALER | inhaler |
| INHL | inhalant |
| INJ | injectable |
| INJECTION (S) | injection(s) |
| INSERT | insert |
| INST | insert |
| INSULIN | insulin |
| IPA | * |
| IUD | intrauterine device |
| IV | intravenous |
| JEL | jelly |
| JELLY | jelly |
| KI | * |
| KIT | kit |
| L | lotion |
| LAN | * |
| LANCET | lancet |
| LANCETS | lancets |
| LI | liquid |
| LINIMENT | liniment |
| LIP | * |
| LIQ | liquid |
| LIQD | liquid |
| LIQUID | liquid |
| LO | * |
| LOLLIPOP | lollipop |
| LOT | lotion |
| LOTION | lotion |
| LOTN | lotion |
| LOZ | lozenge |
| LOZENGE | lozenge |
| LOZG | lozenge |
| LPOP | lollipop |
| LQCR | liquid, extended-release |
| MALEATE | maleate |
| MASK | mask |
| MCG | microgram |
| MEQ | milliequivalent |
| METER | meter |
| MG | milligram |
| MIS | miscellaneous |
| MISC | miscellaneous |
| MIST | mist |
| MONITOR | monitor |
| MONOH | * |
| MOUTHWASH | mouthwash |
| NAS | nasal spray |
| NASAL | nasal |
| NASAL INHALER | nasal inhaler |
| NASAL POCKET HL | nasal inhaler, pocket |
| NASAL SOLN | nasal solution |
| NASAL SPR | nasal spray |
| NASAL SPRAY | nasal spray |
| NDL | needle |
| NE | nebulizer |
| NEB | nebulizer |
| NEBU | nebulization solution |
| NEBULIZER | nebulizer |
| NEEDLE | needle |
| NEEDLES | needles |
| NHL | * |
| NMA | enema |
| NMO | nanomole, millimicromole |
| NOP | * |
| NOS | * |
| NOSE DROPS | nose drops |
| ODR | ophthalmic drop (ointment) |
| ODT | oral disintegrating tablet |
| OIL | oil |
| OIN | ointment |
| OINT | ointment |
| OINT TOP | topical ointment |
| OINTA | ointment with applicator |
| OINTMENT | ointment |
| OLN | * |
| OMB | * |
| ONT | ointment |
| OP | ophthalmic solution |
| OP DROPS | ophthalmic drops |
| OP SOL | ophthalmic solution |
| OPA | * |
| OPH | ophthalmic |
| OPH S | ophthalmic solution or suspension |
| OPH SOL | ophthalmic solution |
| OPH SOLN | ophthalmic solution |
| OPHT SOL | ophthalmic solution |
| OPHTH DROP (S) | ophthalmic drops |
| OPHTH OINT | ophthalmic ointment |
| OPHTH SOLN | ophthalmic solution |
| OPT SLN | ophthalmic solution |
| OPT SOL | ophthalmic solution |
| OPTH | ophthalmic solution or suspension or ointment |
| OPTH S | ophthalmic solution or suspension |
| OPTH SLN | ophthalmic solution |
| OPTH SOL | ophthalmic solution |
| OPTH SUSP | ophthalmic suspension |
| OPTIC | optic |
| ORA | * |
| ORAL | oral |
| ORAL INHL | oral inhalant |
| ORAL INHALER | oral inhaler |
| ORAL PWD | oral powder |
| ORAL RINSE | oral rinse |
| ORAL SOL | oral solution |
| ORAL SUS | oral suspension |
| ORAL SUSP | oral suspension |
| ORM | * |
| OSE | * |
| OTHER | other |
| OTI | otic solution |
| OTIC | otic |
| OTIC SOL | otic solution |
| OTIC SOLN | otic solution |
| OTIC SUS | otic suspension |
| OTIC SUSP | otic suspension |
| PA | tablet pack, pad or patch (varies) |
| PAC | pack |
| PACK | pack |
| PAD | pad |
| PADS | pads |
| PAK | pack |
| PAS | paste |
| PASTE | paste |
| PAT | patch |
| PATCH | patch |
| PATCHES | patches |
| PCH | patch |
| PDI | powder for injection |
| PDR | powder |
| PDS | powder for reconstitution |
| PEDIATRIC DROPS | pediatric drops |
| PEL | pellets |
| PEN | pen |
| PI1 | powder for injection, 1 month |
| PI3 | powder for injection, 3 months |
| PIH | powder for inhalation |
| PKG | package |
| PKT | packet |
| PLASTER | plaster |
| PLEDGETS | pledgets |
| PLLT | pellet |
| PNKT | * |
| PO-SYRUP | syrup by mouth (oral syrup) |
| POD | POD |
| POPSICLE | popsicle |
| POUCH | pouch |
| POW | powder |
| POWD | powder |
| POWDER | powder |
| POWDER FOR SOLUTION | * |
| POWDER/SUSPENS | powder/suspension |
| PRO | prophylactic |
| PRSY | * |
| PSKT | * |
| PST | paste |
| PSTE | paste |
| PT24 | patch, 24-hour |
| PT72 | patch, 72-hour |
| PTCH | patch |
| PTTW | patch, biweekly |
| PTWK | patch, weekly |
| PULVULE | pulvule |
| PWD | powder |
| PWD F/SOL | powder for solution |
| PWDI | powder for injection |
| PWDIE | powder for injection, extended-release |
| PWDR | powder for reconstitution |
| PWDRD | powder for reconstitution, delayed-release |
| RAL | * |
| RCTL SUPP | rectal suppository |
| RECTAL CREAM | rectal cream |
| REDITABS | reditabs |
| REF | * |
| RIN | rinse |
| RING | ring |
| RINSE | rinse |
| RMO | * |
| ROLL | roll |
| RTL | * |
| S | syrup, suspension, solution (varies) |
| SA CAPS | slow-acting capsules |
| SA TAB | slow-acting tablet |
| SA TABLETS | slow-acting tablets |
| SA TABS | slow-acting tablets |
| SAL | salve |
| SALIC | * |
| SCRUB | scrub |
| SE | * |
| SER | extended-release suspension |
| SET | set |
| SGL | soft b23gel cap |
| SHA | shampoo |
| SHAM | shampoo |
| SHAMPOO | shampoo |
| SHMP | shampoo |
| SHOE | shoe |
| SLT | sublingual tablet |
| SL TAB | sublingual tablet |
| SO | solution |
| SOA | soap |
| SOAJ | * |
| SOCT | * |
| SOL | solution |
| SOLG | gel-forming solution |
| SOLN | solution |
| SOLR | solution, reconstituted |
| SOLUTION | solution |
| SOLU | solution |
| SOPN | * |
| SOSY | * |
| SOTJ | * |
| SP | spray |
| SPG | sponge |
| SPN | * |
| SPONGE | sponge |
| SPR | spray |
| SPRAY | spray |
| SQU | * |
| SRER | * |
| SRN | syringe |
| ST | * |
| STA | * |
| STAT | immediately |
| STK | stick |
| STOCKING | stocking |
| STP | strip |
| STR | strip |
| STRIP | strip |
| STRIPS | strips |
| STRP | strip |
| SU | suspension, solution, suppository, powder, or granules for reconstitution (varies) |
| SUB | sublingual |
| SUBL | tablet, sublingual |
| SUBLINGUAL | sublingual |
| SUER | * |
| SUP | suppository |
| SUPN | * |
| SUPP | suppository |
| SUPPOSITORIES | suppositories |
| SUPPOSITORY | suppository |
| SUS | suspension |
| SUS/LIQ | suspension/liquid |
| SUSP | suspension |
| SUSPEN | suspension |
| SUSPENDED RELEASE CAPLET | suspended-release caplet |
| SUSPENSION | suspension |
| SUSR | suspension, reconstituted |
| SUSY | * |
| SWA | swab |
| SWAB | swab |
| SWABS | swabs |
| SYG | * |
| SYP | syrup |
| SYR | syrup |
| SYRG | syringe |
| SYRINGE | syringe |
| SYRP | syrup |
| SYRUP | syrup |
| T | tablet |
| T12 | 12-hour extended-release tablet |
| T12A | 12-hour extended-release tablet |
| T24 | 24-hour extended-release tablet |
| T24A | 24-hour extended-release tablet |
| TA | tablet |
| TAB | tablet |
| TAB CHEW | chewable tablet |
| TAB DR | delayed-release tablet |
| TAB EC | enteric-coated tablet |
| TAB SL | slow-acting tablet |
| TAB SUBL | sublingual tablet |
| TABL | tablet |
| TABLET | tablet |
| TABLET CUTTER | tablet cutter |
| TABLET SPLITTER | tablet splitter |
| TABLETS | tablets |
| TABS | tablets |
| TAM | tampon |
| TAP | tape |
| TAPE | tape |
| TB | tablet |
| TB12 | tablet, extended-release 12-hour |
| TB24 | tablet, extended-release 24-hour |
| TBCH | chewable tablet |
| TBCR | tablet, extended-release |
| TBDD | * |
| TBDP | tablet, dispersible |
| TBEC | tablet, delayed-release |
| TBED | * |
| TBEF | tablet, effervescent |
| TBPK | * |
| TBS | tablets |
| TBSL | sublingual tablet |
| TBSO | tablet, soluble |
| TBSR | slow-release tablet |
| TC | tablet, chewable |
| TCP | tablet, coated particles |
| TDM | extended-release film |
| TDR | orally disintegrating tablets |
| TDS | transdermal system |
| TEF | effervescent tablet |
| TER | extended-release tablet |
| TERF | film, extended-release |
| TES | test |
| TEST | test |
| TEST STRIP | test strip |
| TEST STRIPS | test strips |
| TIN | tincture |
| TINC | tincture |
| TOP CREAM | topical cream |
| TOP OINT | topical ointment |
| TOP SOL | topical solution |
| TOP SOLN | topical solution |
| TOPICAL | topical |
| TOPICAL CREAM | topical cream |
| TOPICAL GEL | topical gel |
| TOPICAL OINTMENT | topical ointment |
| TOPICAL SOLUTION | topical solution |
| TOPICAL-UNSPECIFIED | topical-unspecified |
| TRO | troche |
| TROC | troche |
| TROCHE | troche |
| TTB | time-release tablet |
| TUB | tube |
| TUBE | tube |
| UNDERWEAR | underwear |
| UNIT DOSE | unit dose |
| UNT | unit |
| VAGINAL CREAM | vaginal cream |
| VAGINAL RING | * |
| VAPORIZER | vaporizer |
| VIA | vial |
| VIAL | vial |
| VIAL(S) | vial(s) |
| VIL | vial |
| WAB | * |
| WAF | wafer |
| WAFR | wafer |
| WALKER | walker |
| WASH | wash |
| WIPES | wipes |
| Z-PAK | z-pak |
* No definition for the dosage form.
| Code | Description |
|---|---|
| -1 | inapplicable |
| -7 | refused |
| -8 | don’t know |
| -15 | cannot be computed |
| AEROSOLS | aerosols |
| ALCOHOL PADS | alcohol pads |
| BLISTERS | * |
| CAPLT | caplet |
| CAPS | capsule |
| CC | cubic centimeter |
| DEVICE | device |
| DISKUS INHALERS | diskus inhalers |
| EA | each |
| G | gram |
| GELC | * |
| GM | gram |
| GR | gram |
| INH | inhaler |
| INHALERS | inhalers |
| L | liter |
| LANCETS | lancets |
| LOZ | lozenge |
| MCL | microliter |
| MCM | micrometer |
| MCN | * |
| MG | milligram |
| ML | milliliter |
| MONITOR | monitor |
| NASAL SPRAYS | nasal sprays |
| NDL | * |
| NEBULIZERS | nebulizers |
| OTHER | other |
| PA | * |
| PADS | pads |
| PEN NEEDLES | * |
| PT | pint |
| SPRAYS | sprays |
| SRN | * |
| SUP | * |
| SWABS | swabs |
| TEST STRIPS | test strips |
| TROCHES | troches |
| OZ | ounce |
| QT | quart |
| TAB | tablet |
* No description for the code.
| Abbreviations, codes, and symbols | Definition |
|---|---|
| -7 | refused |
| -8 | don’t know |
| -15 | cannot be computed |
| % | percent |
| %/OTHER | percent/other |
| 09 | compound |
| 9HR | 9hr |
| 24HR | 24hr |
| 91 | other, specify |
| ACT | actuation |
| ACTIVATION | activation |
| ACTUATION | actuation |
| BLIST | blister |
| B CELL | b cell |
| CC | cubic centimeters |
| CM2 | square centimeter |
| DAYS | days |
| DOSE | dose |
| DROP | drop |
| DRP | drop |
| EL | ELISA (enzyme-linked immunosorbent assay) |
| G | gram |
| G/G | gram/gram |
| G/ML | gram/milliliter |
| GM | gram |
| GM/SCOOP | * |
| GR | grain |
| HR or HRS | hour, hours |
| INH | inhalation |
| IU | international unit |
| IU/ML | international unit/milliliter |
| MCG | microgram |
| MCG/24HR | microgram/24hr |
| MCG/DOSE | microgram/dose |
| MCG/MCG/ACT | microgram/microgram/actuation |
| MCG/MCG/DOSE | microgram/microgram/dose |
| MCG/OTHER | microgram/other |
| MEQ | milliequivalent |
| MEQ/ML | milliequivalent/milliliter |
| MG | milligram |
| MG/G | milligram/gram |
| MG/IU | milligram/international unit |
| MG/MG/ACT | * |
| MG/MG/ML | milligram/milligram/milliliter |
| MG/ML/MG | milligram/milliliter/milligram |
| MG/ML/MG/ML | milligram/milliliter/milligram/milliliter |
| MG/ML/ML | milligram/milliliter/milliliter |
| MG/OTHER | milligram/other |
| ML | milliliter |
| ML/MCG | milliliter/microgram |
| ML/MG | milliliter/milligram |
| ML/ML | milliliter/milliliter |
| MM | millimeter |
| MMU | millimass units |
| MU | * |
| OTHER | other |
| OTHER/ML | other/milliliter |
| OZ | ounce |
| PACKET | packet |
| PFU | plaque-forming units |
| SPRAY | spray |
| SQ CM | square centimeter |
| U/GM | units/gram |
| U/ML/ML | units/milliliter/milliliter |
| U OR UNIT | units |
| U/ML/U/ML | units/milliliter/units/milliliter |
| UNT | unit |
| UT/ML | * |
| VIAL | vial |
* No definition for the abbreviations, codes, and symbols.
| 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 nonsteroidal 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 angioedema 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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