 |
|
|
Font Size:
|
||||
|
|
|
||||
|
MEPS HC-213A: 2019 Prescribed MedicinesJuly 2021 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. 299 a-1), data collected by the Agency for Healthcare Research and Quality (AHRQ) and/or the National Center for Health Statistics (NCHS) may not be used for any purpose other than for the purpose for which they were supplied; any effort to determine the identity of any reported cases is prohibited by law. Therefore in accordance with the above referenced Federal Statute, it is understood that:
By using these data you signify your agreement to comply with the above stated statutorily based requirements with the knowledge that deliberately making a false statement in any matter within the jurisdiction of any department or agency of the Federal Government violates Title 18 part 1 Chapter 47 Section 1001 and is punishable by a fine of up to $10,000 or up to 5 years in prison. The Agency for Healthcare Research and Quality requests that users cite AHRQ and the Medical Expenditure Panel Survey as the data source in any publications or research based upon these data. B. Background1.0 Household Component (HC)The Medical Expenditure Panel Survey (MEPS) provides nationally representative estimates of health care use, expenditures, sources of payment, and health insurance coverage for the U.S. civilian 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 health care. Estimates can be produced for individuals, families, and selected population subgroups. The panel design of the survey, which includes 5 Rounds of interviews covering 2 full calendar years, provides data for examining person level changes in selected variables such as expenditures, health insurance coverage, and health status. Using computer assisted personal interviewing (CAPI) technology, information about each household member is collected, and the survey builds on this information from interview to interview. All data for a sampled household are reported by a single household respondent. The MEPS HC was initiated in 1996. Each year a new panel of households is selected. Because the data collected are comparable to those from earlier medical expenditure surveys conducted in 1977 and 1987, it is possible to analyze long-term trends. Each annual MEPS HC sample size is about 15,000 households. Data can be analyzed at either the person or event level. Data must be weighted to produce national estimates. The set of households selected for each panel of the MEPS HC is a subsample of households participating in the previous year's National Health Interview Survey (NHIS) conducted by the National Center for Health Statistics (NCHS). The NHIS sampling frame provides a nationally representative sample of the U.S. civilian noninstitutionalized population. In 2006, the NHIS implemented a new sample design, which included Asian persons in addition to households with Black and Hispanic persons in the oversampling of minority populations. NHIS introduced a new sample design in 2016 that discontinued oversampling of these minority groups. The linkage of the MEPS to the previous year's NHIS provides additional data for longitudinal analytic purposes. 2.0 Medical Provider Component (MPC)Upon completion of the household CAPI interview and obtaining permission from the household survey respondents, a sample of medical providers are contacted by telephone to obtain information that household respondents cannot accurately provide. This part of the MEPS is called the Medical Provider Component (MPC) and information is collected on dates of visits, diagnosis and procedure codes, charges and payments. The Pharmacy Component (PC), a subcomponent of the MPC, does not collect charges or diagnosis and procedure codes but does collect drug detail information, including National Drug Code (NDC) and medicine name, as well as amounts of payment. The MPC is not designed to yield national estimates. It is primarily used as an imputation source to supplement/replace household-reported expenditure information. 3.0 Survey Management and Data CollectionMEPS HC and MPC data are collected under the authority of the Public Health Service Act. Data are collected under contract with Westat, Inc. (MEPS HC) and Research Triangle Institute (MEPS MPC). Data sets and summary statistics are edited and published in accordance with the confidentiality provisions of the Public Health Service Act and the Privacy Act. The National Center for Health Statistics (NCHS) provides consultation and technical assistance. As soon as data collection and editing are completed, the MEPS survey data are released to the public in staged releases of summary reports, micro data files, and tables via the MEPS website. Additional information on MEPS is available from the MEPS project manager or the MEPS public use data manager at the Center for Financing Access and Cost Trends, Agency for Healthcare Research and Quality, 5600 Fishers Lane, Rockville, MD 20857 (301-427-1406). C. Technical Information1.0 General InformationThis documentation describes one in a series of public use event files from the 2019 Medical Expenditure Panel Survey (MEPS) Household Component (HC) and Medical Provider Component (MPC). Released as an ASCII data file (with related SAS, SPSS, Stata, and R programming statements) and SAS data set, SAS transport file, Stata data set, and Excel file, the 2019 Prescribed Medicines public use file provides detailed information on household-reported prescribed medicines for a nationally representative sample of the civilian noninstitutionalized population of the United States. Data from the Prescribed Medicines event file can be used to make estimates of retail prescribed medicine utilization and expenditures for calendar year 2019. The file contains 62 variables and has a logical record length of 579 with an additional 2-byte carriage return/line feed at the end of each record. As illustrated below, this file consists of MEPS survey data obtained in the 2019 portion of Round 3 and Rounds 4 and 5 for Panel 23, as well as Rounds 1, 2 and the 2019 portion of Round 3 for Panel 24 (i.e., the rounds for the MEPS panels covering calendar year 2019).
Each record on this event file represents a unique prescribed medicine event; that is, a prescribed medicine reported by the respondent as being obtained by a member of the household at any pharmacy, including mail-order or on-line. In addition to expenditures related to the prescribed medicine, each record contains household-reported characteristics. Data from this event file can be merged with other 2019 MEPS HC data files, for purposes of appending person characteristics such as demographic or health insurance coverage to each prescribed medicine record. Counts of prescribed medicine utilization are based entirely on household reports. Information from the Pharmacy Component (PC) (within the MEPS MPC, see Section B 2.0 for more details on the MPC) was used to provide expenditure and payment data, as well as details of the medication (e.g., strength, quantity, etc.). The file can be used to construct summary variables of expenditures, sources of payment, and other aspects of utilization of prescribed medicines. Aggregate annual person-level information on the use of prescribed medicines and other health services use is provided on the 2019 Full Year Consolidated Data File, where each record represents a MEPS sampled person. The following documentation offers a brief overview of the types and levels of data provided and the content and structure of the files and the codebook. It contains the following sections:
For more information on the MEPS HC sample design, see Chowdhury et al (2019). For information on the MEPS MPC design, see RTI (2019). A copy of the survey instrument used to collect the information on this file is available on the MEPS website. 2.0 Data File InformationThe 2019 Prescribed Medicines public use data set contains 293,125 prescribed medicine records. Each record represents one household-reported prescribed medicine that was purchased during calendar year 2019 at any pharmacy, including mail-order or on-line. Of the 293,125 prescribed medicine records, 289,675 records are associated with persons having a positive person-level weight (PERWT19F). The persons represented on this file had to meet either (a) or (b) below:
Persons with no prescribed medicine use for 2019 are not included on this file (but are represented on MEPS person-level files). A codebook for the Prescribed Medicines data file is provided (in file H213acb.pdf). This file includes prescribed medicine records for all household members who resided in eligible responding households and for whom at least one prescribed medicine was reported. Only prescribed medicines that were obtained in calendar year 2019 are represented on this file. This file includes prescribed medicines identified in the Prescribed Medicines (PM) section of the HC survey instrument, as well as those prescribed medicines identified in association with other medical events. Each record on this file represents a single acquisition of a prescribed medicine reported by household respondents. Some household members may have multiple acquisitions of prescribed medicines and thus will be represented in multiple records on this file. Other household members may have no reported acquisitions of prescribed medicines and thus will have no records on this file. Prior to Panel 21 Round 5 and Panel 22 Round 3, when diabetic supplies, such as syringes and insulin, were mentioned in the Other Medical Expenses (OM) section of the MEPS HC, the interviewer was directed to collect information on these items in the Prescribed Medicines section of the MEPS questionnaire. To the extent that these items are purchased without a prescription, they represent a non-prescription addition to the MEPS prescription drug expenditure and utilization data. Although these items may be purchased without a prescription, a prescription purchase may be required to obtain third party payments. Analysts are free to code and define diabetic supply/equipment and insulin events utilizing their own coding mechanism. If desired, this would enable analysts to subset the Prescribed Medicines file to exclude these types of events. Starting in Panel 21 Round 5 and Panel 22 Round 3, diabetic supply/equipment and insulin are no longer mentioned in the OM section but are mentioned and collected in the Prescribed Medicines section. Therefore, diabetic supply/equipment and insulin are collected as other Prescribed Medicines. The charges and payments are no longer collected for Prescribed Medicines in the MEPS Household Component. It should also be noted that refills are included on this file. The HC obtains information on the name of the prescribed medicine and the number of times the medicine was obtained. The data collection design for the HC does not allow separate records to be created for multiple acquisitions of the same prescribed medicine. However, in the PC, each original purchase, as well as any refill, is considered a unique prescribed medicine event. Therefore, for the purposes of editing, imputation, and analysis, all records in the HC were “unfolded” to create separate records for each original purchase and each refill. Please note that for multiple acquisitions of the same drug, MEPS did not collect information in the HC to distinguish between the original purchase and refills. The survey only collected data on the number of times a prescribed medicine was acquired during a round. In some cases, all purchases may have been refills of an original purchase in a prior round or prior to the survey year. Each record on this file includes the following: an identifier for each unique prescribed medicine; detailed characteristics associated with the event (e.g., national drug code (NDC), medicine name, selected Multum Lexicon variables [see Section 2.6.3 for more information on the Multum Lexicon variables included on this file], etc.); when the person first used the medicine; total expenditure and sources of payments; types of pharmacies that filled the household’s prescriptions; and a full-year person-level weight. Data from this file can be merged with previously released MEPS HC person-level data using the unique person identifier, DUPERSID, to append person characteristics such as demographic or health insurance coverage to each record. Data from this file can also be merged with the 2019 Full Year Consolidated Data File to estimate expenditures for persons with prescribed medicines. The Prescribed Medicines event file can also be linked to the MEPS 2019 Medical Conditions File and additional MEPS 2019 event files. Please see the 2019 Appendix File for details on how to link MEPS data files. 2.1 Codebook StructureFor most variables on the file, both weighted and unweighted frequencies are provided. The exceptions to this are weight variables and variance estimation variables. Only unweighted frequencies of these variables are included in the accompanying codebook file. See the Weights Variables list in section D, Variable-Source Crosswalk. The codebook and data file sequence list variables in the following order:
2.2 Reserved CodesThe following reserved code values are used:
The value -15 (CANNOT BE COMPUTED) is assigned to MEPS constructed variables in cases where there is not enough information from the MEPS instrument to calculate the constructed variables. “Not enough information” is often the result of skip patterns in the data or from missing information resulting from MEPS responses of -7 (REFUSED) or -8 (DK). Note that reserved code -8 includes cases where the information from the question was “not ascertained” or where the respondent chose “don’t know”. Generally, values of -1, -7, -8 and -15 have not been edited on this file. However, this is not true if a prescription drug name was determined to be a confidentiality risk. In these instances, the corresponding NDC was replaced with -15, the Multum Lexicon therapeutic class replaced the RXDRGNAM (Multum drug name) determined to be a confidentiality risk, and RXNAME (pharmacy drug name) was set to -15. The values of -1 and -15 can be edited by analysts by following the skip patterns in the questionnaire. The value -14 was a valid value only for the variable representing the year the household member first used the medicine (RXBEGYRX). RXBEGYRX = -14 means that when the interviewer asked the respondent the year the household member first started using the medicine, he/she responded that the household member had not yet started using the medicine (See section C, 2.6.2.1). A copy of the Household Component questionnaire can be found in the Survey Questionnaires section of the MEPS website and selecting Prescribed Medicines (PM) from the questionnaire section. 2.3 Codebook FormatThe codebook describes an ASCII data set (although the data are also being provided in a SAS data set, SAS transport file, Stata data set, and Excel file). The following codebook items are provided for each variable:
2.4 Variable Naming ConventionsIn general, variable names reflect the content of the variable. Generally, all imputed/edited variables end with an “X.” 2.4.1 GeneralVariables contained on this file were derived from the HC questionnaire itself, the MPC data collection instrument, or from the Multum Lexicon database from Cerner Multum, Inc. The source of each variable is identified in Section D, entitled “Variable-Source Crosswalk.” Sources for each variable are indicated in one of five ways:
2.4.2 Expenditure and Source of Payment VariablesOnly imputed/edited versions of the expenditure variables are provided on the file. Expenditure variables on this event file follow a standard naming convention. The 10 source of payment variables and one sum of payments variable are named consistently in the following way: The first two characters indicate the type of event: IP - inpatient stay OB - office-based visit ER - emergency room visit OP - outpatient visit HH - home health visit DV - dental visit OM - other medical equipment RX - prescribed medicine In the case of the source of payment variables, the third and fourth characters indicate: SF - self or family OF - other federal government MR - Medicare SL - state/local government MD - Medicaid WC - Workers’ Compensation PV - private insurance OT - other insurance VA - Veterans Administration/CHAMPVA TR - TRICARE XP - sum of payments The fifth and sixth characters indicate the year (19). The seventh character, “X,” indicates the variable is edited/imputed. For example, RXSF19X is the edited/imputed amount paid by self or family for the 2019 prescribed medicine expenditure. Beginning in 2019, the expenditure variables OR (other private) and OU (other public) are dropped from the file. 2.5 Data CollectionData regarding prescription drugs were obtained through the HC questionnaire and a pharmacy follow-back component (within the Medical Provider Component). 2.5.1 Methodology for Collecting Household-Reported VariablesDuring each round of the MEPS HC, respondents were asked to supply the name of any prescribed medicine they or their family members purchased or otherwise obtained during that round at any pharmacy, including mail-order or on-line. For each medicine in each round, the following information was collected: the name(s) of any health problems the medicine was prescribed for; the number of times the prescription medicine was obtained or purchased; the year and month in which the person first used the medicine; and a list of the names, addresses, and types of pharmacies that filled the household’s prescriptions. In consultation with an industry expert, outlier values for the number of times a household reported purchasing or otherwise obtaining a prescription drug in a particular round were determined by comparing the number of days a person was in the round to the number of times the person was reported to have obtained the drug in the round. For these events, a new value for the number of times a drug was purchased or otherwise obtained by a person in a round was imputed. In addition, for rounds in which a household respondent did not know/remember the number of times a certain prescribed medicine was purchased or otherwise obtained, the number of fills or refills was imputed. For those rounds that spanned two years, drugs mentioned in that round were allocated between the years based on the number of times the respondent said the drug was purchased in the respective year, the year the person started taking the drug, the length of the person’s round, the dates of the person’s round, and the number of drugs for that person in the round. 2.5.2 Methodology for Collecting Pharmacy-Reported VariablesIf the household member with the prescription gave written permission to release his or her pharmacy records, pharmacy providers identified by the household were contacted by telephone for the pharmacy follow-back component. Following an initial telephone contact, the signed permission forms and materials explaining the study were faxed (or mailed) to cooperating pharmacy providers. The materials informed the providers of all persons participating in the survey who had prescriptions filled at their place of business and requested a computerized printout of all prescriptions filled for each person. Pharmacies can choose to report information in computer assisted telephone interviews (CATI). The CATI instrument was also used to enter information from printouts. For each medication listed, the following information was requested: national drug code (NDC), medication name, strength of medicine (amount and unit), quantity (package size/amount dispensed), days supplied, and payments by source. When an NDC was provided, often the drug name and other drug characteristics were obtained from secondary proprietary data sources. 2.6 File Contents2.6.1 Survey Administration Variables2.6.1.1 Person Identifier Variables (DUID, PID, DUPERSID)The dwelling unit ID (DUID) is a seven-digit number consisting of a 2-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 dwelling unit. The ten-character variable DUPERSID uniquely identifies each person represented on the file and is the combination of the variables DUID and PID. Beginning in 2018, all ID variables begin with the 2-digit panel number. For detailed information on dwelling units and families, please refer to the documentation for the 2019 Full Year Population Characteristics File. 2.6.1.2 Record Identifier Variables (RXRECIDX, LINKIDX, DRUGIDX)The variable RXRECIDX uniquely identifies each record on the file. This 19-character variable comprises the following components: prescribed medicine drug-round-level identifier generated through the HC (positions 1-16) + enumeration number (positions 17-19). The prescribed medicine drug-round-level ID generated through the HC (positions 1-16) can be used to link a prescribed medicine event to the conditions file and to other event files, via link files, and is provided on this file as the variable LINKIDX. For more details on linking, please refer to Section 6.2 and to the 2019 Appendix File. The prescribed medicine drug-level ID generated through the HC, DRUGIDX, can be used to link drugs across rounds. DRUGIDX was first added to the file for 2009; for 1996 through 2008, the RXNDC linked drugs across rounds. The following hypothetical example illustrates the structure of these ID variables. This example illustrates a person in Rounds 1 and 2 of the household interview who reported having purchased Amoxicillin three times. The following example shows three acquisition-level records, all having the same DRUGIDX (2400002026002), for one person (DUPERSID=2400002026) in two rounds. Generally, within a round, one NDC is associated with a prescribed medicine event because matching was performed at a drug level, as opposed to an acquisition level. The LINKIDX (2400002026002103) remains the same for both records in Round 1 but varies across rounds. The RXRECIDX (2400002026002103001, 2400002026002103002, 2400002026002203001) differs for all three records.
There can be multiple RXNDCs for a LINKIDX. All the acquisitions in the LINKIDX represent the same drug (active ingredients), but the RXNDCs may represent different manufacturers. (For more details on matching, please see Section 4.0). 2.6.1.3 Panel Variable (PANEL)PANEL is a constructed variable used to specify the panel number for the person. Panel will indicate either Panel 23 or Panel 24 for each person on the file. Panel 23 is the panel that started in 2018, and Panel 24 is the panel that started in 2019. 2.6.1.4 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, 4, and 5 are associated with MEPS survey data collection from Panel 23. Similarly, Rounds 1, 2, and 3 are associated with data collected from Panel 24. 2.6.2 Characteristics of Prescribed Medicine Events2.6.2.1 When Prescribed Medicine Was First Taken (RXBEGMM-RXBEGYRX)There are two variables which indicate when a prescribed medicine was first taken (used), as reported by the household respondent. They are the following: RXBEGMM denotes the month in which a person first started taking a medication, and RXBEGYRX reflects the year in which a person first started taking a medicine. These “first taken” questions are only asked the first time a prescription is mentioned by the household respondent. These questions are not asked about refills of the prescription in subsequent rounds. Values are carried forward from prior rounds for all medications. Users should also note that the value -14 (not yet used or taken) is not relevant for refills. The variable DRUGIDX (see Section 2.6.1.2) can be used to determine whether a medication was reported in a prior round. For purposes of confidentiality, RXBEGYRX was bottom-coded at 1934. 2.6.2.2 Prescribed Medicine Attributes (RXNAME-RXDAYSUP)For each prescribed medicine included on this file, several data items collected describe in detail the medication obtained or purchased. These data items are the following:
Days supplied was first collected and released to the public on the 2010 Prescribed Medicines file. Many pharmacies did not provide this information, and imputation was not attempted in these cases. A value of 999 indicates the medication is to be taken as needed. No edits were implemented to impose consistency between the quantity and days supplied, and no edits were implemented for very high values. The 2019 file contains multiple values of RXFORM and RXFRMUNT not found in Prescribed Medicines files in prior years. There was no reconciliation of inconsistencies or duplication between RXFORM and RXFRMUNT. Please refer to Appendices 1, 2, and 3 for definitions for RXFORM, RXFRMUNT, and RXSTRUNT abbreviations, codes and symbols. Please refer to Appendix 4 for therapeutic class code definitions. The national drug code (NDC) is an 11-digit code. The first 5 digits indicate the manufacturer of the prescribed medicine. The next 4 digits indicate the form and strength of the prescription, and the last 2 digits indicate the package size from which the prescription was dispensed. NDC values were imputed from a proprietary database to certain PC prescriptions because the NDC reported by the pharmacy provider was not valid. These records are identified by RXFLG = 3. For the years 1996-2004, AHRQ’s licensing agreement for the proprietary database precluded the release of the imputed NDC values to the public, so for these prescriptions, the household-reported name of the prescription (RXHHNAME) and the original NDC (RXNDC) and prescription name (RXNAME) reported by the pharmacy were provided on the file to allow users to do their own imputation. In addition, for the years 1996-2004, the imputed NDC values for the RXFLG = 3 cases could be accessed through the AHRQ Data Center. For those events not falling into the RXFLG = 3 category, the reserved code (-13) was assigned to the household-reported medication name (RXHHNAME). The household-reported name of the prescription (RXHHNAME) is no longer provided on this file; however, this variable may be accessed through the AHRQ Data Center as can the original pharmacy-reported name and NDC. For information on accessing data through the AHRQ Data Center, see the Data Center section of the MEPS website. Beginning with the 2013 data, the variable RXDRGNAM is included on the file. This drug name is the generic name of the drug most commonly used by prescribing physicians. It is supplied by the Multum Lexicon database. RXDRGNAM for earlier years can be found in the Multum Lexicon Addendum Files to MEPS Prescribed Medicines Files for 1996-2013. Additionally, the 2013 addendum file contains a version of RXDRGNAM that has corrected values for some records. See the documentation for the addendum files. Generally, orphan drugs and drugs AHRQ estimated were used by fewer than 200,000 people are masked to ensure confidentiality of the data, unless use of the drug does not reveal specific information about the condition treated (for example, cold remedies). For these drugs, details are generally recoded as missing and RXNAME is recoded to whatever therapeutic class information remains. Prospective researchers seeking access to restricted data must complete a MEPS Data Center application. See the Data Center section of the MEPS website. Starting in the 2018 prescribed medicines PUF, the variable DiabEquip (OTHER DIABETIC EQUIPMENT OR SUPPLIES), indicates the record is for diabetic supplies/equipment that were first reported in response to question PM40, which asks whether the person obtained “any other diabetic equipment or supplies, typically prescribed by a physician; for example, syringes, a blood glucose monitor machine, glucose meter, insulin pumps, lancets, alcohol swabs or control solution.” Imputed data on this event file, unlike other MEPS event files, may still have missing data. This is because imputed data on this file are imputed from the PC or from a proprietary database. These sources did not always include complete information for each variable but did include an NDC, which would typically enable an analyst to obtain any missing data items. For example, although there are a substantial number of missing values for the strength of the prescription that were not supplied by the pharmacist, these missing values were not imputed because this information is embedded in the NDC. 2.6.2.3 Type of Pharmacy (PHARTP1-PHARTP7)Household respondents were asked to list the type of pharmacy from which household members purchased their medications. A respondent could list multiple pharmacies associated with each member’s prescriptions in a given round or over the course of all rounds combined covering the survey year. All household-reported pharmacies are provided on this file, but there is no link in the survey or in the data file enabling users to know the type of pharmacy from which a specific prescription was obtained if multiple pharmacies are listed. The variables PHARTP1 through PHARTP7 identify the types of pharmacy providers from which the person’s prescribed medicines were purchased. The possible types of pharmacies include the following: (1) mail-order, (2) another store, (3) HMO/clinic/hospital, (4) drug store, and (5) on-line. A -1 value for PHARTPn indicates that the household did not report “nth” pharmacy. Starting with the 2018 data, the pharmacy types are those reportedly used by the person. For prior data years, the pharmacy types are those reportedly used by anyone in the Reporting Unit (RU). 2.6.2.4 Analytic Flag Variables (RXFLG-INPCFLG)There are four flag variables included on this file (RXFLG, IMPFLAG, PCIMPFLG, and INPCFLG). RXFLG indicates whether or not there was any imputation performed on this record for the NDC variable, and if imputed, from what source the NDC was imputed. If no imputation was performed, RXFLG = 1. If the imputation source was another PC record, RXFLG = 2. Similarly, if the imputation source was a secondary, proprietary database and not the PC database, RXFLG = 3. IMPFLAG indicates the method of creating the expenditure data on this record: IMPFLAG = 2 indicates complete PC data, IMPFLAG = 4 indicates fully imputed data, and IMPFLAG = 5 indicates partially imputed data. Beginning with the 2017 data, the MEPS ceased asking households to report payments for any drugs and diabetic equipment and supplies, so the values 1 and 3 are irrelevant for prescribed medicine events. PCIMPFLG indicates the type of match between a household-reported event and a PC-reported event. PCIMPFLG = 1 indicates an exact match for a specific event for a person between the PC and the HC. PCIMPFLG = 2 indicates not an exact match between the PC and HC for a specific person (i.e., a person’s household-reported event did not have a matched counterpart in the person’s corresponding PC records). PCIMPFLG assists analysts in determining which records have the strongest link to data reported by a pharmacy. It should be noted that whenever there are multiple purchases of a unique prescribed medication in a given round, MEPS did not collect information that would enable designating any single purchase as the “original” purchase at the time the prescription was first filled, and then designating other purchases as “refills.” The user needs to keep this in mind when the purchases of a medication are referred to as “refills” in the documentation. Because matching was performed at a drug level as opposed to an acquisition level, the values for PCIMPFLG are either 1 or 2. For more details on general data editing/imputation methodology, please see Section 4.0. INPCFLG denotes whether or not a household member had at least one prescription drug purchase in the PC (0 = NO, 1 = YES). 2.6.2.5 Clinical Classification Software Refined CodesInformation on household-reported medical conditions (ICD-10-CM condition codes) and aggregated clinically meaningful categories generated using Clinical Classification Software Refined (CCSR) associated with each prescribed medicine are not provided on this file. For information on ICD-10-CM condition codes and associated CCSR codes, see the MEPS 2019 Medical Conditions file and the 2019 Appendix to MEPS Event Files. 2.6.3 Multum Lexicon Variables from Cerner Multum, Inc.Each record on this file contains the following Multum Lexicon variables: RXDRGNAM generic name of the drug most commonly used by prescribing physicians PREGCAT pregnancy category variable - identifies the FDA pregnancy category to which a particular drug has been assigned TCn therapeutic classification variable - assigns a drug to one or more therapeutic/chemical categories; can have up to three categories per drug TCnSn therapeutic sub-classification variable - assigns one or more sub-categories to a more general therapeutic class category given to a drug TCnSn_n therapeutic sub sub-classification variable - assigns one or more sub sub-categories to a more general therapeutic class category and sub-category given to a drug Users should carefully review the data when conducting trend analyses or pooling years or panels because Multum’s therapeutic classification has changed across the years of the MEPS. The Multum variables on each year of the MEPS Prescribed Medicines files reflect the most recent classification available in the year the data were released. Since the release of the 1996 Prescribed Medicines file, the Multum classification has been changed by the addition of new classes and subclasses, and by changes in the hierarchy of classes. Three examples follow: 1) In the 1996-2004 Prescribed Medicines files, antidiabetic drugs are a subclass of the hormone class, but in subsequent files, the antidiabetic subclass is part of a class of metabolic drugs. 2) In the 1996-2004 files, antihyperlipidemic agents are categorized as a class with a number of subclasses including HMG-COA reductase inhibitors (statins). In subsequent files, antihyperlipidemic drugs are a subclass, and HMG-COA reductase inhibitors are a sub-subclass, in the metabolic class. 3) In the 1996-2004 files, the psychotherapeutic class comprises drugs from four subclasses: antidepressants, antipsychotics, anxiolytics/sedatives/hypnotics, and CNS stimulants. In subsequent files, the psychotherapeutic class comprises only antidepressants and antipsychotics. Changes may occur between any years. For additional information on these and other Multum Lexicon variables, as well as the Multum Lexicon database itself, please refer to the Cerner Multum file. Users should also be aware of a problem discovered with the linking between the MEPS Prescribed Medicines files and the Cerner Multum file that resulted in some incorrect therapeutic classes being assigned. In particular, some diagnostic tests and medical devices were inadvertently assigned to be in a therapeutic class when they should not have been. Specifically, from 1996-2002, some diabetic supplies were assigned to be in TC1S1 = 101 (sex hormone), and from 2003 through 2010 some diabetic supplies were assigned to be in TC1S1 = 37 (toxoids). In addition, starting in 2006, NDC 00169750111 should have been assigned to TC1 = 358 and TC1S1 = 99. Analysts should use caution when using the Cerner Multum therapeutic class variables for analysis and should always check for accuracy. Researchers using the Multum Lexicon variables are requested to cite Multum Lexicon as the data source. 2.6.4 Expenditure Variables (RXSF19X-RXXP19X)2.6.4.1 Definition of ExpendituresExpenditures on this file refer to what is paid for health care services. More specifically, expenditures in MEPS are defined as the sum of payments for care received, including out-of-pocket payments and payments made by private insurance, Medicaid, Medicare, and other sources. The definition of expenditures used in MEPS differs slightly from its predecessors, the 1987 NMES and 1977 NMCES surveys, where “charges” rather than “sum of payments” were used to measure expenditures. This change was adopted because charges became a less appropriate proxy for medical expenditures during the 1990s because of the increasingly common practice of discounting charges. Although measuring expenditures as the sum of payments incorporates discounts in the MEPS expenditure estimates, the estimates do not incorporate any manufacturer or other rebates paid to pharmacy benefit managers, health plans, Medicaid programs, or other purchasers. Another general change from the two prior surveys is that charges associated with uncollected liability, bad debt, and charitable care (unless provided by a public clinic or hospital) are not counted as expenditures, because there are no payments associated with those classifications. For details on expenditure definitions, please reference the following, “Informing American Health Care Policy” (Monheit, Wilson, Arnett, 1999). If examining trends in MEPS expenditures or performing longitudinal analysis on MEPS expenditures please refer to Section C, sub-sections 3.4 and 6.3 respectively for more information. 2.6.4.2 Sources of PaymentIn addition to total expenditures, variables are provided which itemize expenditures according to major source of payment categories. These categories are:
Pharmacies rarely report discounts. Manufacturer discounts and coupons reported by pharmacies are excluded from the total expenditure and source of payment variables, because the manufacturer is paying itself. Free drugs are included in this file, but discounts, write-offs, and free drugs at commercial pharmacies are not counted toward the total expenditure and source of payment variables, because these reflect pharmacy pricing strategies. Discounts, write-offs, and free drugs at safety net providers and government pharmacies are paid with public sector funds, are included in total expenditures, and are assigned to a public source of payment or other unclassified sources based on the type of pharmacy and the person’s insurance coverage. Prior to 2019, for cases where reported insurance coverage and sources of payment are inconsistent, the positive amount from a source inconsistent with reported insurance coverage was moved to one or both of the source categories Other Private and Other Public. Beginning in 2019, this step is removed and the inconsistency between the payment sources and insurance coverage is allowed to remain - the amounts are not moved to Other Private and Other Public categories any more. The two source of payment categories, Other Private and Other Public, are no longer available. 3.0 Sample Weight (PERWT19F)3.1 OverviewThere is a single full year person-level weight (PERWT19F) assigned to each record for each key, in-scope person who responded to MEPS for the full period of time that he or she was in-scope during 2019. A key person was either a member of a responding NHIS household at the time of interview or joined a family associated with such a household after being out-of-scope at the time of the NHIS (the latter circumstance includes newborns as well as those returning from military service, an institution, or residence in a foreign country). A person is in-scope whenever he or she is a member of the civilian noninstitutionalized portion of the U.S. population. 3.2 Details on Person Weight ConstructionThe person-level weight PERWT19F was developed in several stages. Person-level weights for Panel 23 and Panel 24 were created separately. The weighting process for each panel included an adjustment for nonresponse over time and calibration to independent population figures. The calibration was initially accomplished separately for each panel by raking the corresponding sample weights for those in-scope at the end of the calendar year to Current Population Survey (CPS) population estimates based on six variables. The six variables used in the establishment of the initial person-level control figures were: educational attainment of the reference person (no degree, high school/GED no college, some college, bachelor’s degree or higher); census region (Northeast, Midwest, South, West); MSA status (MSA, non-MSA); race/ethnicity (Hispanic; Black, non-Hispanic; Asian, non-Hispanic; and other); sex; and age. A 2019 composite weight was then formed by multiplying each weight from Panel 23 by the factor .500 and each weight from Panel 24 by the factor .500. The choice of factors reflected the relative sample sizes of the two panels, helping to limit the variance of estimates obtained from pooling the two samples. The composite weight was raked to the same set of CPS-based control totals. When the poverty status information derived from income variables became available, a final raking was undertaken, establishing control figures reflecting poverty status rather than educational attainment. Thus, control totals were established using poverty status (five categories: below poverty, from 100 to 125 percent of poverty, from 125 to 200 percent of poverty, from 200 to 400 percent of poverty, at least 400 percent of poverty) as well as the other five variables previously used in the weight calibration. 3.2.1 MEPS Panel 23 Weight Development ProcessThe person-level weight for MEPS Panel 23 was developed using the 2018 full year weight for an individual as a “base” weight for survey participants present in 2018. For key, in-scope members who joined an RU some time in 2019 after being out-of-scope in 2018, the initially assigned person-level weight was the corresponding 2018 family weight. The weighting process included an adjustment for person-level nonresponse over Rounds 4 and 5 as well as raking to population control totals for December 2019 for key, responding persons in-scope on December 31, 2019. These control figures were derived by projecting forward the population distribution obtained from the March 2019 CPS to reflect the December 31, 2019 estimated population total (estimated based on Census projections for January 1, 2020). Variables used for person-level raking included: educational attainment of the reference person (no degree, high school/GED no college, some college, bachelor’s degree or higher); census region (Northeast, Midwest, South, West); MSA status (MSA, non-MSA); race/ethnicity (Hispanic; Black, non-Hispanic; Asian, non-Hispanic; and other); sex; and age. The final weight for key, responding persons who were not in-scope on December 31, 2019 but were in-scope earlier in the year is the person weight after the nonresponse adjustment. Note that the 2018 full-year weight that was used as the base weight for Panel 23 was derived as follows; adjustment of the MEPS Round 1 weight for nonresponse over the remaining data collection rounds in 2018; and raking the resulting nonresponse adjusted weight to December 2017 population control figures. It should also be noted that rather than projecting the March 2019 CPS population distribution estimates forward, the standard approach for MEPS has been to scale back from the following year’s CPS estimates. In this case, it would have been the March 2020 CPS estimates. However, there was evidence that the onset of the Covid-19 pandemic in March 2020 in the U.S. affected estimates associated with income and education (Rothbaum & Bee, 2020). Since education was planned as one of the variables to be used for raking, it was decided to use the 2019 March CPS data to establish the population estimates for the FY 2019 weights. 3.2.2 MEPS Panel 24 Weight Development ProcessThe person-level weight for MEPS Panel 24 was developed using the 2019 MEPS Round 1 person-level weight as a “base” weight. For key, in-scope members who joined an RU after Round 1, the Round 1 family weight served as a “base” weight. The weighting process included an adjustment for nonresponse over the remaining data collection rounds in 2019 as well as raking to the same population control figures for December 2019 used for the MEPS Panel 23 weights for key, responding persons in-scope on December 31, 2019. The same six variables employed for Panel 23 raking (educational attainment of the reference person, census region, MSA status, race/ethnicity, sex, and age) were used for Panel 24 raking. Again, the final weight for key, responding persons who were not in-scope on December 31, 2019 but were in-scope earlier in the year was the person weight after the nonresponse adjustment. Note that the MEPS Round 1 weights for Panel 24 incorporated the following components: the original household probability of selection for the NHIS and for the NHIS sample reserved for MEPS; adjustment for NHIS nonresponse; the probability of selection of NHIS responding households for MEPS; an adjustment for nonresponse at the dwelling unit level for Round 1; and poststratification to U.S. civilian noninstitutionalized population estimates at the family and person levels obtained from the corresponding March CPS databases. 3.2.3 The Final Weight for 2019The final raking of those in-scope at the end of the year has been described above. In addition, the composite weights of two groups of persons who were out-of-scope on December 31, 2019 were adjusted for expected undercoverage. Specifically, the weights of those who were in-scope some time during the year, out-of-scope on December 31, and entered a nursing home during the year and still residing in a nursing home at the end of the year were poststratified to an estimate of the number of persons who were residents of Medicare and Medicaid certified nursing homes for part of the year (approximately 3-9 months) during 2014. This estimate was developed from data on the Minimum Data Set (MDS) of the Center for Medicare and Medicaid Services (CMS). The weights of persons who died while in-scope were poststratified to corresponding estimates derived using data obtained from the Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS), Underlying Cause of Death, 1999-2018 on CDC WONDER Online Database, released in 2020, the latest available data at the time. Separate decedent control totals were developed for the “65 and older” and “under 65” civilian noninstitutionalized populations. Overall, the weighted population estimate for the civilian noninstitutionalized population for December 31, 2019 is 323,833,996 (PERWT19F >0 and INSC1231=1). The sum of person-level weights across all persons assigned a positive person-level weight is 327,396,693. 3.3 CoverageThe target population for MEPS in this file is the 2019 U.S. civilian noninstitutionalized population. However, the MEPS sampled households are a subsample of the NHIS households interviewed in 2017 (Panel 23) and 2018 (Panel 24). New households created after the NHIS interviews for the respective Panels and consisting exclusively of persons who entered the target population after 2017 (Panel 23) or after 2018 (Panel 24) are not covered by MEPS. Neither are previously out-of-scope persons who join an existing household but are unrelated to the current household residents. Persons not covered by a given MEPS panel thus include some members of the following groups: immigrants; persons leaving the military; U.S. citizens returning from residence in another country; and persons leaving institutions. The set of uncovered persons constitutes only a small segment of the MEPS target population. 3.4 Using MEPS Data for Trend AnalysisMEPS began in 1996, and the utility of the survey for analyzing health care trends expands with each additional year of data; however, there are a variety of methodological and statistical considerations when examining trends over time using MEPS. Tests of statistical significance should be conducted to assess the likelihood that observed trends may be attributable to sampling variation. The length of time being analyzed should also be considered. In particular, large shifts in survey estimates over short periods of time (e.g. from one year to the next) that are statistically significant should be interpreted with caution unless they are attributable to known factors such as changes in public policy, economic conditions, or MEPS survey methodology. With respect to methodological considerations, beginning with the 2007 data, the rules MEPS uses to identify outlier prices for prescription medications became much less stringent than in prior years. Starting with the 2007 Prescribed Medicines file, there was: less editing of prices and quantities reported by pharmacies, more variation in prices for generics, lower mean prices for generics, higher mean prices for brand name drugs, greater differences in prices between generic and brand name drugs, and a somewhat lower proportion of spending on drugs by families, as opposed to third-party payers. Starting with the 2008 Prescribed Medicines file, improvements in the data editing changed the distribution of payments by source: (1) more spending on Medicare beneficiaries is by private insurance, rather than Medicare, and (2) less out-of-pocket payments and more Medicaid payments among Medicaid enrollees. Starting with the 2009 data, additional improvements increased public program amounts and reduced out-of-pocket payments and, for Medicare beneficiaries with both Part D and Medicaid, decreased Medicare payments and increased Medicaid and other state and local government payments. Starting with the 2017 data, changes in the price imputation procedures for specialty drugs with missing payment information resulted in higher total expenditures; therefore, users should be cautious in the types of comparisons they make about prescription drug spending before and after 2007, 2008, 2009, and 2017. In addition, some therapeutic class codes have changed over time. In 2013 MEPS survey operations introduced an effort to obtain more complete information about health care utilization from MEPS respondents with full implementation in 2014. This effort resulted in improved data quality and reduced underreporting in the second half of 2013 and throughout 2014. Respondents tended to report more visits, especially non-physician visits, by sample members and the new approach appeared particularly effective among those subgroups with relatively large numbers of visits, such as the elderly, Medicare beneficiaries, and people with multiple chronic conditions, disabilities, or poor health. Reported spending on visits also tended to increase, especially for such subgroups. The aforementioned changes in the NHIS sample design in 2016 could also potentially affect trend analyses. The new NHIS sample design is based on more up-to-date information related to the distribution of housing units across the U.S. As a result, it can be expected to better cover the full U.S. civilian, noninstitutionalized population, the target population for MEPS as well as many of its subpopulations. Better coverage of the target population helps to reduce potential bias in both NHIS and MEPS estimates. Another change with the potential to affect trend analyses involved modifications to the MEPS instrument design and data collection process. These were introduced in the Spring of 2018 and thus affected data beginning with Round 1 of Panel 23, Round 3 of Panel 22, and Round 5 of Panel 21. Since the Full Year 2017 PUFs were established from data collected in Rounds 1-3 of Panel 22 and Rounds 3-5 of Panel 21, they reflected two different instrument designs. In order to mitigate the effect of such differences within the same full year file, the Panel 22, Round 3 data and the Panel 21 Round 5 data were transformed to make them as consistent as possible with data collected under the previous design. The changes in the instrument were designed to make the data collection effort more efficient and easy to administer. In addition, expectations were that data on some items, such as those related to health care events, would be more complete with the potential of identifying more events. Increases in service use reported since the implementation of these changes are consistent with these expectations. There are also statistical factors to consider in interpreting trend analyses. Looking at changes over longer periods of time can provide a more complete picture of underlying trends. Analysts may wish to consider techniques to evaluate, smooth, or stabilize analyses of trends such as comparing pooled time periods (e.g. 1996-97 versus 2011-12), working with moving averages, or using modeling techniques with several consecutive years of MEPS data to test the fit of specified patterns over time. Finally, researchers should be aware of the impact of multiple comparisons on Type I error. Without making appropriate allowance for multiple comparisons, undertaking numerous statistical significance tests of trends increases the likelihood of concluding that a change has taken place when one has not. 4.0 General Data Editing and Imputation MethodologyThe general approach to preparing the household prescription data for this file was to utilize the PC prescription data to impute information collected from pharmacy providers to the household drug mentions. A matching program was adopted to link PC drugs and the corresponding drug information to household drug mentions. To improve the quality of these matches, all drugs on the household and pharmacy files were coded using a proprietary database on the basis of the medication names provided by the household respondent and pharmacy, and, when available, the NDC provided in the pharmacy follow-back component. The matching process was done at a drug (active ingredient) level, as opposed to an acquisition level. Considerable editing was done prior to the matching to correct data inconsistencies in both data sets and to fill in missing data and correct outliers on the pharmacy file. Drug price-per-unit outliers were analyzed on the pharmacy file by first identifying the average wholesale unit price (AWUP) of the drug by linkage through the NDC to a secondary data file. In general, prescription drug unit prices were deemed to be outliers by comparing unit prices reported in the pharmacy database to the AWUP reported in the secondary data file and were edited, as necessary. Beginning with the 2007 data, the rules used to identify outlier prices for prescription medications in the PC changed. New outlier thresholds were established based on the distribution of the ratio of retail unit prices relative to the AWUP in the 2006 MarketScan Outpatient Pharmaceutical Claims database. The new thresholds vary by patent status, whereas in prior years they did not. These changes improve data quality in three ways: (1) the distribution of prices in the MEPS better benchmarks to MarketScan, overall and by patent status (Zodet et al. 2010), (2) fewer pharmacy-reported payments and quantities (for example, number of pills) are edited, and (3) imputed prices reflect prices paid, rather than AWUPs. As a result, compared with earlier years of the MEPS, starting with 2007 there is more variation in prices for generics, lower mean prices for generics, higher mean prices for brand name drugs, greater differences in prices between generic and brand name drugs, and a somewhat lower proportion of spending on drugs by families, as opposed to third-party payers. Pharmacy reports of free antibiotics were not edited as if they were outliers. Beginning with the 2010 data, some additional free drugs obtained through commercial pharmacies were not edited. Beginning with the 2009 data, three changes in editing sources of payment data were made to improve data quality, based on a validation study (Hill et al., 2011). Two changes were made in editing fills for which pharmacies reported partial payment data. First, if the third party amount was missing and the third party payer was a public payer, then pharmacy reports of zero out-of-pocket amounts were preserved rather than imputed. Second, somewhat tighter outlier thresholds were implemented for the fills with partial payment data, and somewhat looser outlier thresholds were implemented for fills with complete payment data. Another change affected Medicare beneficiaries with both Part D and Medicaid coverage--reported Medicaid and other state and local program payments were no longer edited to be Medicare payments. Beginning with the 2010 data, improvements in the payment imputation methods for pharmacy data (1) better utilize pharmacy-reported quantities to impute missing payment amounts, and (2) preserve within-NDC variation in the prices on the records for which third party payment amounts are imputed. Beginning with the 2017 data, higher imputed prices were allowed. Imputed prices are capped to prevent the creation of unreasonable prices in cases with unreasonable quantity data. For the 2017 data, the cap was raised to account for the rising prices of specialty drugs. While there are relatively few cases for which the cap is relevant, these are expensive drugs, and this change in editing procedures accounts for more than 95% of the increase in total expenditures for prescribed medicines relative to 2016. Beginning with the 2011 data, the imputation of the number of fills for a drug was improved. In the 2011 data, for 10% of household-reported drugs the respondent did not know or remember the number of times the drug was obtained during the round. For missing and implausible values, a hot-deck procedure imputed a new number of acquisitions, drawing from the donor pool of drugs with valid values. Prior to 2011, the imputation method gave greater weight to donors with more acquisitions in the round. The new method conditions on insurance status, age, and geography, as well as drug. In the 2017 data for Panel 22 Round 3 and Panel 21 Round 5, more implausibly high numbers of fills were reported than in prior years, and so there was more extensive imputation of number of fills. Drug matches between household drug mentions and pharmacy drug events for a person in the PC were based on drug code, medication name, and the round in which the drug was reported. The matching of household drug mentions to pharmacy drugs was performed so that the most detailed and accurate information for each prescribed medicine event was obtained. The matching program assigned scores to potential matches. Numeric variables required exact matches to receive a high score, while partial scores could be assigned to matches between character variables, such as prescription name, depending on the degree of similarity in the spelling and sound of the medication names. Household drug mentions that were deemed exact matches to PC drugs for the same person in the same round required sufficiently high scores to reflect a high quality match. Initially, exact matches were used only once and were taken out of the donor pool from that point on (i.e., these matches were made without replacement). For remaining persons with pharmacy data from any round and unmatched household drugs, additional matches are made with replacement across rounds. Any refill of a household drug mention that had been matched to a pharmacy drug event was matched to the same pharmacy drug event. All remaining unmatched household drug mentions for persons either in or out of the PC were statistically matched to the entire pharmacy donor base with replacement by medication name, drug code, type of third party coverage, health conditions, age, sex, and other characteristics of the individual. PC records containing an NDC imputed without an exact match on a generic code were omitted from the donor pool. Beginning with the 2008 Prescribed Medicines file, the criteria for matching were changed to allow multiple NDCs for the same drug reported by pharmacies (for example, different manufacturers) to match to one drug reported by the household. Beginning with the 2010 data, the matching process was improved for diabetic supplies to better utilize pharmacy reports of the diversity of supplies individuals purchased. Some matches have inconsistencies between the PC donor’s potential sources of payment and those of the HC recipient, and these were resolved. Beginning with the 2008 data, the method used to resolve inconsistencies in potential payers was changed to better reflect the distribution of sources of payment among the acquisitions with consistent sources of payment. This change (1) reduced Medicare payments and increased private payments among Medicare beneficiaries, and (2) reduced out-of-pocket payments and increased Medicaid payments among Medicaid enrollees. In addition, Medicare, Medicaid, and private drug expenditures better benchmark totals in the National Health Expenditure Accounts. Also beginning with the 2011 data, many aspects of the specifications were modified so that imputations and edits better reflect Medicare Part D donut hole rules and Medicare Part B coverage of a few medications and diabetic supplies. Discounts on brand name drugs in the donut hole do not count towards total expenditures and are not included in source of payment variables. For more information on the MEPS Prescribed Medicines editing and imputation procedures, please see Hill et al, 2014, Methodology Report. 4.1 RoundingExpenditure variables on the 2019 Prescribed Medicines file have been rounded to the nearest penny. Person-level expenditure variables released on the 2019 Full Year Consolidated Data File were rounded to the nearest dollar. It should be noted that using the 2019 MEPS event files to create person-level totals will yield slightly different totals than those found on the 2019 Full Year Consolidated data file. These differences are due to rounding only. Moreover, in some instances, the number of persons having expenditures on the 2019 event files for a particular source of payment may differ from the number of persons with expenditures on the 2019 Full Year Consolidated data file for that source of payment. This difference is also an artifact of rounding only. 4.2 Edited/Imputed Expenditure Variables (RXSF19X-RXXP19X)There are 11 expenditure variables included on this event file. All of these expenditures have gone through an editing and imputation process and have been rounded to the second decimal place. There is a sum of payments variable (RXXP19X) which, for each prescribed medicine event, sums all the expenditures from the various sources of payment. The 10 sources of payment expenditure variables for each prescribed medicine event are the following: amount paid by self or family (RXSF19X), amount paid by Medicare (RXMR19X), amount paid by Medicaid (RXMD19X), amount paid by private insurance (RXPV19X), amount paid by the Veterans Administration/CHAMPVA (RXVA19X), amount paid by TRICARE (RXTR19X), amount paid by other federal sources (RXOF19X), amount paid by state and local (non-federal) government sources (RXSL19X), amount paid by Worker’s Compensation (RXWC19X), and amount paid by some other source of insurance (RXOT19X). Please see Section 2.6.4 for details on all sources of payment variables. 5.0 Strategies for Estimation5.1 Developing Event-Level EstimatesThe data in this file can be used to develop national 2019 event-level estimates for the U.S. civilian noninstitutionalized population on prescribed medicine purchases (events) as well as expenditures, and sources of payment for these purchases. Estimates of total number of purchases are the sum of the weight variable (PERWT19F) across relevant event records while estimates of other variables must be weighted by PERWT19F to be nationally representative. The tables below contain event-level estimates for selected variables. Selected Event (Purchase) Level Estimates All Prescribed Medicine Purchases
Example by Drug Type: Statins (TC1S1_1 = 173 or TC1S1_2 = 173 or TC1S2_1 = 173 or TC1S3_1 = 173 or TC2S1_1 = 173 or TC2S1_2 = 173)
5.2 Person-Based Estimates for Prescribed Medicine PurchasesTo enhance analyses of prescribed medicine purchases, analysts may link information about prescribed medicine purchases to the annual full year consolidated file (which has data for all MEPS sample persons), or conversely, link person-level information from the full year consolidated file to this event-level file (see Section 6 below for more details). Both this file and the full year consolidated file may be used to derive estimates for persons with prescribed medicine purchases and annual estimates of total expenditures for these purchases; however, if the estimate relates to the entire population, this file cannot be used to calculate the denominator, as only those persons with at least one prescribed medicine purchase are represented on this data file. Therefore, the full year consolidated file must be used for person-level analyses that include both persons with and without prescribed medicine events. 5.3 Variables with Missing ValuesIt is essential that the analyst examine all variables for the presence of negative values used to represent missing values. For continuous or discrete variables, whose means or totals may be calculated, the analyst should either impute a value or set the value such that it will be interpreted as missing by the software package used. For categorical and dichotomous variables, the analyst may want to consider whether to recode or impute a value for cases with negative values or whether to exclude or include such cases in the numerator and/or denominator when calculating proportions. Methodologies used for the editing/imputation of expenditure variables (e.g., total expenditures and sources of payment) are described in Section 4.2. 5.4 Variance Estimation (VARSTR, VARPSU)MEPS has a complex sample design. To obtain estimates of variability (such as the standard error of sample estimates or corresponding confidence intervals) for MEPS estimates, analysts need to take into account the complex sample design of MEPS for both person-level and family-level analyses. Several methodologies have been developed for estimating standard errors for surveys with a complex sample design, including the Taylor-series linearization method, balanced repeated replication, and jackknife replication. Various software packages provide analysts with the capability of implementing these methodologies. MEPS analysts most commonly use the Taylor Series approach. Although this data file does not contain replicate weights, the capability of employing replicate weights constructed using the Balanced Repeated Replication (BRR) methodology is also provided if needed to develop variances for more complex estimators (see Section 5.4.2). 5.4.1 Taylor-series Linearization MethodThe variables needed to calculate appropriate standard errors based on the Taylor-series linearization method are included on this and all other MEPS public use files. Software packages that permit the use of the Taylor-series linearization method include SUDAAN, R, Stata, SAS (version 8.2 and higher), and SPSS (version 12.0 and higher). For complete information on the capabilities of a package, analysts should refer to the corresponding software user documentation. Using the Taylor-series linearization method, variance estimation strata and the variance estimation PSUs within these strata must be specified. The variables VARSTR and VARPSU on this MEPS data file serve to identify the sampling strata and primary sampling units required by the variance estimation programs. Specifying a “with replacement” design in one of the previously mentioned computer software packages will provide estimated standard errors appropriate for assessing the variability of MEPS survey estimates. It should be noted that the number of degrees of freedom associated with estimates of variability indicated by such a package may not appropriately reflect the number available. For variables of interest distributed throughout the country (and thus the MEPS sample PSUs), one can generally expect to have at least 100 degrees of freedom associated with the estimated standard errors for national estimates based on this MEPS database. Prior to 2002, MEPS variance strata and PSUs were developed independently from year to year, and the last two characters of the strata and PSU variable names denoted the year. However, beginning with the 2002 Point-in-Time PUF, the variance strata and PSUs were developed to be compatible with all future PUFs until the NHIS design changed. Thus, when pooling data across years 2002 through the Panel 11 component of the 2007 files, the variance strata and PSU variables provided can be used without modification for variance estimation purposes for estimates covering multiple years of data. There were 203 variance estimation strata, each stratum with either two or three variance estimation PSUs. From Panel 12 of the 2007 files, a new set of variance strata and PSUs were 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, starting from Panel 12. Therefore, there are a total of 368 (203+165) variance strata in the 2007 Full Year file as it consists of two panels that were selected under two independent NHIS sample designs. Since both MEPS panels in the Full Year files from 2008 through 2016 are based on the next NHIS design, there are only 165 variance strata. These variance strata (VARSTR values) have been numbered from 1001 to 1165 so that they can be readily distinguished from those developed under the former NHIS sample design in the event that data are pooled for several years. As discussed, a complete change was made to the NHIS sample design in 2016, effectively changing the MEPS design beginning with calendar year 2017. There were 117 variance strata originally formed under this new design intended for use until the next fully new NHIS design was implemented. In order to make the pooling of data across multiple years of MEPS more straightforward, the numbering system for the variance strata has changed. Those strata associated with the new design (implemented in 2016) were numbered from 2001 to 2117. However, the new NHIS sample design implemented in 2016, was further modified in 2018. With the modification in the 2018 NHIS sample design, the MEPS variance structure for the 2019 Full Year file has also had to be modified, reducing the number of variance strata to 105. Consistency was maintained with the prior structure in that the 2019 Full Year file variance strata were also numbered within the range of values from 2001-2117, although there are now gaps in the values assigned within this range. Retaining this numbering system permits analysts interested in pooling MEPS data across several years to do so without incurring logistical issues related to variance estimation while permitting the establishment of useful estimates of variability for estimates based on data obtained over several years of data collection. To obtain appropriate standard errors when pooling MEPS data across multiple years, it is necessary to specify a common variance structure. Prior to 2002, each annual MEPS public use file was released with a variance structure unique to the particular MEPS sample in that year. However, starting in 2002, the annual MEPS public use files were released with a common variance structure that allows users to pool data from 2002 and forward. To ensure that variance strata are identified appropriately for variance estimation purposes when pooling MEPS data across several years, one can proceed as follows:
5.4.2 Balanced Repeated Replication (BRR) MethodBRR replicate weights are not provided on this MEPS PUF for the purposes of variance estimation. However, a file containing a BRR replication structure is made available so that the users 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 to compute variances of complex non-linear estimators for which a Taylor linear form is not easy to derive and not available in commonly used software. For instance, it is not possible to calculate the variances of a median or the ratio of two medians using the Taylor linearization method. For these types of estimators, users may calculate a variance using BRR or Fay’s modified BRR methods. However, it should be noted that the replicate weights have been derived from the final weight through a shortcut approach. Specifically, the replicate weights are not computed starting with the base weight and all adjustments made in different stages of weighting are not applied independently in each replicate. Thus, the variances computed 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 1996-2018 Replicates for Variance Estimation File, contains the information necessary to construct the BRR replicates. It contains 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 analysis of MEPS data pooled across years, the BRR replicates can be formed in the same way using the HC-036, MEPS 1996-2018 Pooled Linkage Variance Estimation File. For more information about creating BRR replicates, users 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 file can be used alone or in conjunction with other files for different analytic purposes. This section summarizes various scenarios for merging/linking MEPS files. Each MEPS panel can also be linked back to the previous year’s National Health Interview Survey public use data files. For information on obtaining MEPS/NHIS link files please see the data files section of the MEPS website. 6.1 Linking to the Person-Level FileMerging characteristics of interest from the person-level file (e.g., MEPS 2019 Full Year Consolidated File) expands the scope of potential estimates. For example, to estimate the total number of prescribed medicine purchases of persons with specific demographic characteristics (such as age, race, sex, and education), population characteristics from a person-level file need to be merged onto the prescribed medicines file. This procedure is illustrated below. The MEPS 2019 Appendix File, HC-213I, provides additional detail on how to merge MEPS data files.
The following is an example of SAS code, which completes these steps: PROC SORT DATA=IN.HCXXX (KEEP=DUPERSID AGE31X AGE42X
AGE53X SEX RACEV1X EDUCYR HIDEG) OUT=PERSX; PROC SORT DATA=IN.HCXXXA; DATA NEWPMEDS; 6.2 Linking to the Medical Conditions FileThe condition-event link file (CLNK) provides a link from MEPS event files to the 2019 Medical Conditions File. When using the CLNK, data users/analysts should keep in mind that (1) conditions are self-reported, (2) there may be multiple conditions associated with a prescribed medicine purchase, and (3) a condition may link to more than one prescribed medicine purchase or any other type of purchase. Users should also note that not all prescribed medicine purchases link to the condition file. 6.3 Longitudinal AnalysisPanel-specific longitudinal files are available for downloading in the data section of the MEPS website. For each panel, the longitudinal file comprises MEPS survey data obtained in Rounds 1 through 5 of the panel and can be used to analyze changes over a two-year period. Variables in the file pertaining to survey administration, demographics, employment, health status, disability days, quality of care, patient satisfaction, health insurance, and medical care use and expenditures were obtained from the MEPS full-year Consolidated files from the two years covered by that panel. For more details or to download the data files, please see Longitudinal Weight Files on the MEPS website. ReferencesChowdhury, S.R., Machlin, S.R., Gwet, K.L. Sample Designs of the Medical Expenditure Panel Survey Household Component, 1996–2006 and 2007–2016. Methodology Report #33. January 2019. Agency for Healthcare Research and Quality, Rockville, MD. Cohen, S.B. (1996). The Redesign of the Medical Expenditure Panel Survey: A Component of the DHHS Survey Integration Plan. Proceedings of the COPAFS Seminar on Statistical Methodology in the Public Service. Cox, B.G. and Cohen, S.B. (1985). Imputation Procedures to Compensate for Missing Responses to Data Items. In D.B. Owen and R.G. Cornell (Eds.), Methodological Issues for Health Care Surveys (pp. 214-234). New York, NY: Marcel Dekker. Fay, R.E. (1989). Theory and Application of Replicate Weighting for Variance Calculations. Proceedings of the Survey Research Methods Sections, ASA, 212-217. Hill, S.C., Roemer, M., and Stagnitti, M.N. (2014). Outpatient Prescription Drugs: Data Collection and Editing in the 2011 Medical Expenditure Panel Survey. (Methodology Report No. 29). Rockville, MD: Agency for Healthcare Research and Quality. Hill, S.C., Zuvekas, S.H., and Zodet, M.W. (2011). Implications of the Accuracy of MEPS Prescription Drug Data for Health Services Research. Inquiry 48(3), 242-259. Moeller J.F., Stagnitti, M., Horan, E., et al. (2001). Outpatient Prescription Drugs: Data Collection and Editing in the 1996 Medical Expenditure Panel Survey (HC-010A) (MEPS Methodology Report No. 12, AHRQ Pub. No. 01-0002). Rockville, MD: Agency for Healthcare Research and Quality. Monheit, A.C., Wilson, R., and Arnett, III, R.H. (Eds.). (1999) Informing American Health Care Policy. San Francisco, CA: Jossey-Bass Inc. RTI International (2019). Medical Provider Component (MEPS-MPC) Methodology Report 2017 Data Collection. Rockville, MD. Agency for Healthcare Research and Quality. Shah, B.V., Barnwell, B.G., Bieler, G.S., Boyle, K.E., Folsom, R.E., Lavange, L., Wheeless, S.C., and Williams, R. (1996). Technical Manual: Statistical Methods and Algorithms Used in SUDAAN Release 7.0. Research Triangle Park, NC: Research Triangle Institute. Zodet, M.W., Hill, S.C., and Miller, E. Comparison of Retail Drug Prices in the MEPS and MarketScan: Implications for MEPS Editing Rules. Agency for Healthcare Research and Quality Working Paper No. 10001, February 2010. D. Variable-Source CrosswalkFOR MEPS HC-213A: 2019 Prescribed Medicines EventsSurvey Administration Variables
Prescribed Medicines Events Variables
Weights
Appendix 1Definitions for RXFORM, Dosage Form
* No definition for the dosage form. Appendix 2Definitions for RXFRMUNT, Quantity Unit of Medication
* No description for the code. Appendix 3Definitions for RXSTRUNT, Unit of Medication
* No definition for the abbreviations, codes and symbols. Appendix 4Definitions of Therapeutic Class Code
|
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Back
Email Updates
