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MEPS HC-197D: 2017 Hospital Inpatient StaysJune 2019 Agency for Healthcare Research and Quality NOTE: The MEPS instrument design changed beginning in Spring of 2018, affecting Panel 23 Round 1, Panel 22 Round 3, and Panel 21 Round 5. For the Full-Year 2017 PUFs, the Panel 22 Round 3 and Panel 21 Round 5 data were transformed to the degree possible to conform to the previous design. For the Full-Year 2018 PUFs, Panel 22 Rounds 1 and 2, collected under the old design, were transformed to the degree possible to conform to the new design. Data users should be aware of possible impacts on the data and especially trend analysis for these data years due to the design transition.
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 ComponentThe Medical Expenditure Panel Survey (MEPS) provides nationally representative estimates of health care use, expenditures, sources of payment, and health insurance coverage for the U.S. civilian noninstitutionalized population. The MEPS Household Component (HC) also provides estimates of respondents’ health status, demographic and socio-economic characteristics, employment, access to care, and satisfaction with 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 sample households is selected. Because the data collected are comparable to those from earlier medical expenditure surveys conducted in 1977 and 1987, it is possible to analyze long-term trends. Each annual MEPS-HC sample size is about 15,000 households. Data can be analyzed at either the person or event level. Data must be weighted to produce national estimates. The set of households selected for each panel of the MEPS HC is a subsample of households participating in the previous year’s National Health Interview Survey (NHIS) conducted by the National Center for Health Statistics. The NHIS sampling frame provides a nationally representative sample of the U.S. civilian noninstitutionalized population and reflects an oversample of Blacks and Hispanics. In 2006, the NHIS implemented a new sample design, which included Asian persons in addition to households with Black and Hispanic persons in the oversampling of minority populations. 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 ComponentUpon 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 date filled and sources and amounts of payment. The MPC is not designed to yield national estimates. It is primarily used as an imputation source to supplement/replace household reported expenditure information. 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. Selected data can be analyzed through MEPSnet, an on-line interactive tool designed to give data users the capability to statistically analyze MEPS data in a menu-driven environment. Additional information on MEPS is available from the MEPS project manager or the MEPS public use data manager at the Center for Financing, Access, and Cost Trends, Agency for Healthcare Research and Quality, 5600 Fishers Lane, Rockville, MD 20857 (301-427-1406). C. Technical and Programming Information1.0 General InformationThis documentation describes one in a series of public use event files from the 2017 Medical Expenditure Panel Survey (MEPS) Household Component (HC) and Medical Provider Component (MPC). Released as an ASCII data file (with related SAS, Stata, and SPSS programming statements) and SAS transport file, the 2017 Hospital Inpatient Stays (STAZ) public use file provides detailed information on hospital inpatient stays for a nationally representative sample of the civilian noninstitutionalized population of the United States. Data from the STAZ event file can be used to make estimates of hospital inpatient stay utilization and expenditures for calendar year 2017. The file contains 56 variables and has a logical record length of 343 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 from the 2017 portion of Round 3 and Rounds 4 and 5 for Panel 21, as well as Rounds 1, 2 and the 2017 portion of Round 3 for Panel 22 (i.e., the rounds for the MEPS panels covering calendar year 2017).
Hospital stay events reported in Panel 22 Round 3 and known to have begun after December 31, 2017 are not included on this file. Each record on the inpatient hospital event file represents a unique hospital inpatient stay reported by the household respondent. In addition to expenditures related to the stay, each record contains the length of stay. Annual counts of hospital inpatient stay utilization are based entirely on household reports. Information from the MEPS MPC is used to supplement expenditure and payment data reported by the household and does not affect use estimates. Data from this event file can be merged with other 2017 MEPS HC data files for purposes of appending person-level data such as demographic characteristics or health insurance coverage to each hospital inpatient stay record. This file can also be used to construct summary variables of expenditures, sources of payment, and related aspects of hospital inpatient care. Aggregate annual person-level information on the use of hospital inpatient stays and other health services is provided on the MEPS 2017 Full Year Consolidated Data File, where each record represents a MEPS sampled person. This documentation offers an 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:
Any variables not found on this file but released on previous years’ files may have been excluded because they contained only missing data. For more information on MEPS HC survey design, see T. Ezzati-Rice, et al. (1998–2007) and S. Cohen (1996). For information on the MEPS MPC design, see S. Cohen (1998). Copies of the HC and the MPC survey instruments used to collect the information on the STAZ file are available in the Survey Questionnaires section on the MEPS website. 2.0 Data File InformationThe 2017 Hospital Inpatient Stays public use data set consists of one event-level data file. The file contains characteristics associated with the STAZ event and imputed expenditure data. The 2017 STAZ public use data set contains variable and frequency distributions for a total of 2,853 hospital inpatient stay records reported during the 2017 portion of Round 3 and Rounds 4 and 5 for Panel 21, as well as Rounds 1, 2, and the 2017 portion of Round 3 for Panel 22 of the MEPS Household Component. This file includes hospital inpatient stay records for all household survey members who resided in eligible responding households and for whom at least one hospital inpatient stay was reported. Hospital inpatient stay records known to have ended before January 1, 2017 or after December 31, 2017 are not included on this file. Some household members may have had multiple hospital inpatient stays reported and, thus, will be represented in multiple records on this file. Other household members may have had reported no hospital inpatient stays and, thus, will have no records on this file. Of the 2,853 hospital inpatient stay records, 2,778 are associated with persons having a positive person-level weight (PERWT17F). The persons represented on this file had to meet the following three criteria:
One caveat that should be noted is that in the case of a newborn and the hospital inpatient stay associated with the newborn’s birth, a separate hospital inpatient stay record exists on the file only if the newborn was discharged after the mother. Thus, hospital stays associated with a normal birth are generally represented on the file as a single record (i.e., the mother’s hospital inpatient stay record, covering expenditure data for both the mother and baby). In situations where the newborn was discharged after the mother, the birth event will be represented as two records: one record for the mother and one record for the baby. For newborns re-admitted to the hospital during the reference year, each subsequent re-admission will have a separate record. Each inpatient record includes the following: start and end dates of the hospital inpatient stay; number of nights in the hospital; reason entered the hospital; condition(s) associated with the hospital inpatient stay; medicines prescribed at discharge; flat fee information; imputed sources of payment; total payment and total charge for both the facility and physician portions of the hospital inpatient stay expenditure; a full-year person-level weight; variance strata; and variance PSU. To append person-level information such as demographic or health insurance coverage to each event record, data from this file can be merged with 2017 MEPS HC person-level data (e.g. Full Year Consolidated or Full Year Population Characteristics file) using the person identifier, DUPERSID. Hospital inpatient stay events can also be linked to the MEPS 2017 Medical Conditions File and the MEPS 2017 Prescribed Medicines File. Please see Section 5.0 or the MEPS 2017 Appendix File, HC-197I, for details on how to merge MEPS data files. 2.1 Codebook StructureFor most variables on the Inpatient Events file, 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 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:
Note that the person identifier is unique within this data year. 2.2 Reserved Codes
Generally, the values of -1, -7, -8, and -9 for non-expenditure variables have not been edited on this file. The values of -1 and -9 can be edited by data users/analysts by following the skip patterns in the HC survey questionnaire, located on the MEPS website. 2.3 Codebook Format
2.4 Variable Source and Naming ConventionsIn general, variable names reflect the content of the variable, with an eight-character limitation. All imputed/edited variables end with an “X”. 2.4.1 GeneralVariables on this file were derived from the HC questionnaire itself, derived from the MPC data collection instrument, derived from CAPI, or assigned in sampling. The source of each variable is identified in Section D “Variable – Source Crosswalk” in one of four ways:
2.4.2 Expenditure and Source of Payment VariablesThe names of the expenditure and source of payment variables follow a standard convention, are eight characters in length, and end in an “X” indicating edited/imputed. Please note that imputed means that a series of logical edits, as well as an imputation process to account for missing data, have been performed on the variable. The total sum of payments and 12 sources of payment variables are named in the following way: The first two characters indicate the type of event:
IP – inpatient stay For expenditure variables on the IP file, the third character indicates whether the expenditure is associated with the facility (F) or the physician (D). In the case of the source of payment variables, the fourth and fifth characters indicate:
SF – self or family In addition, the total charge variable is indicated by TC in the variable name. The sixth and seventh characters indicate the year (17). The eighth character, “X”, indicates whether the variable is edited/imputed. For example, IPFSF17X is the edited/imputed amount paid by self or family for the facility portion of the hospital inpatient stay expenditure incurred in 2017. 2.5 File Contents2.5.1 Survey Administration Variables2.5.1.1 Person Identifiers (DUID, PID, DUPERSID)The dwelling unit ID (DUID) is a five-digit random number assigned after the case was sampled for MEPS. The three-digit person number (PID) uniquely identifies each person within the dwelling unit. The eight-character variable DUPERSID uniquely identifies each person represented on the file and is the combination of the variables DUID and PID. For detailed information on dwelling units and families, please refer to the documentation for the 2017 Full Year Population Characteristics File. 2.5.1.2 Record Identifiers (EVNTIDX, ERHEVIDX, FFEEIDX)EVNTIDX uniquely identifies each hospital inpatient stay/event (i.e., each record on the STAZ file) and is the variable required to link hospital inpatient stay events to data files containing details on conditions and/or prescribed medicines (MEPS 2017 Medical Conditions File and MEPS 2017 Prescribed Medicines File, respectively). For details on linking, see Section 5.0 or the MEPS 2017 Appendix File, HC-197I. ERHEVIDX is a constructed variable identifying a STAZ record that includes the facility expenditures for the preceding emergency room visit. This variable is derived from provider-reported information on linked emergency room and inpatient stay events that matched to corresponding events reported by the household. The variable ERHEVIDX contains the EVNTIDX of the linked event. On the 2017 STAZ file, there are 440 hospital stays linked to a preceding emergency room visit, that is, there are records with a valid ERHEVIDX value. ERHEVIDX has not been reconciled with the unedited variable EMERROOM. Please note that the physician expenditures associated with the emergency room visit remain on the emergency room file. FFEEIDX is a constructed variable which uniquely identifies a flat fee group, that is, all events that were a part of a flat fee payment. For example, dialysis treatments are typically covered in a flat fee arrangement where all visits are covered under one flat fee dollar amount. These events would have the same value for FFEEIDX. 2.5.1.3 Round Indicator (EVENTRN)EVENTRN indicates the round in which the hospital inpatient stay was first reported. Please note that Rounds 3, 4, and 5 are associated with MEPS survey data collected from Panel 21. Likewise, Rounds 1, 2, and 3 are associated with data collected from Panel 22. 2.5.1.4 Panel Indicator (PANEL)PANEL is a constructed variable used to specify the panel number for the person. PANEL will indicate either Panel 21 or Panel 22 for each person on the file. Panel 21 is the panel that started in 2016, and Panel 22 is the panel that started in 2017. 2.5.2 MPC Data Indicator (MPCDATA)MPCDATA is a constructed variable which indicates whether or not MPC data were collected for the hospital inpatient stay. While all hospital inpatient events are sampled into the Medical Provider Component, not all hospital inpatient stay records have MPC data associated with them. This is dependent upon the cooperation of the household respondent to provide permission forms to contact the hospital as well as the cooperation of the hospital to participate in the survey. 2.5.3 Hospital Inpatient Stay Event VariablesThis file contains variables describing hospital inpatient stays/events reported by household respondents in the Hospital Stays section of the MEPS HC questionnaire. The questionnaire contains specific probes for determining details about the hospital inpatient stay. 2.5.3.1 Start and End Dates of Event (IPBEGMM – IPENDYR)There are two variables which indicate the month and year a hospital stay began (IPBEGMM and IPBEGYR, respectively). Similarly, there are two variables which indicate the month and year a hospital stay ended (IPENDMM and IPENDYR, respectively). These variables have not been edited. 2.5.3.2 Length of Stay (NUMNIGHX)NUMNIGHX denotes the length of a hospital inpatient stay. For stays beginning in 2016 and ending in 2017, this variable would include the nights associated with the entire visit. It was edited using the above mentioned begin and end dates of the hospital inpatient stay (Section 2.5.3.1). If the dates were unknown, then NUMNIGHX was imputed. Starting in 2017, NUMNIGHT (number of nights in the hospital) is removed from the file. Inpatient hospital stays take into account information from the Medical Provider Component (MPC), the variable NUMNIGHX may not be adjusted to reflect the entire length of stay based on the MPC. 2.5.3.3 Preceding ER Visits (EMERROOM)The variable EMERROOM was derived directly from the Hospital Inpatient Stays section of the HC survey instrument and is unedited. EMERROOM describes whether or not the hospital inpatient stay began with an emergency room visit. Data users/analysts should be aware that no attempt was made to reconcile EMERROOM with information from the Emergency Room Visits file. Furthermore, no attempt has been made to reconcile the EMERROOM variable with the edited ERHEVIDX variable (see Section 2.5.1.2). 2.5.3.4 Other Visit Detail (SPECCOND – ANYOPER)Also provided are the following unedited variables: hospital inpatient stays related to a medical condition (SPECCOND); the reason the person entered the hospital (RSNINHOS); and any operation or surgery performed while the person was in the hospital (ANYOPER). Beginning in 2017, DLVRTYPE (vaginal or Caesarean delivery) was removed from the file because of design changes. With respect to RSNINHOS, please note that while there were 282 cases where RSNINHOS = 4 (reason entered hospital – to give birth to a baby), this does not mean that there were actually 282 new births. In fact, it may have been reported that the mother went to the hospital for delivery (hence, the interviewer would have assigned the event RSNINHOS = 4), but the mother could have had, for example, false labor pains or a stillbirth. Thus, this unedited household-reported variable may be inconsistent with reported number of births (see the 2017 Full Year Population Characteristics File, Section 2.5.2 “Navigating the MEPS Data with Information on Person Disposition Status”). 2.5.3.5 Clinical Classification CodesInformation on household-reported medical conditions (ICD-10-CM condition codes) and aggregated clinically meaningful categories generated using Clinical Classification Software associated with each hospital inpatient stay are not provided on this file. The 2016 Medical Conditions public use file (PUF) was the first time ICD10 codes were provided on MEPS public use files. As a consequence of the adoption of the new condition classification system, the ICD-10 mapping to CCS codes is still under review and a final mapping is not available at the time of this file release. Users can visit the Healthcare Cost and Utilization Project (HCUP) website for more information. 2.5.3.6 Discharge Detail (DSCHPMED)DSCHPMED is derived directly from the Hospital Stays Section of the HC survey instrument. DSCHPMED indicates whether or not any medicines were prescribed at discharge. 2.5.4 Flat Fee Variables (FFEEIDX, FFIPTYPE, FFBEF17, FFTOT18)2.5.4.1 Definition of Flat Fee PaymentsA flat fee is the fixed dollar amount a person is charged for a package of health care services provided during a defined period of time. Examples would be: obstetrician’s fee covering a normal delivery, as well as pre- and post-natal care; or a surgeon’s fee covering surgical procedure and post-surgical care. A flat fee group is the set of medical services (i.e., events) that are covered under the same flat fee payment. The flat fee groups represented on the STAZ file include flat fee groups where at least one of the health care events, as reported by the HC respondent, occurred during 2017. By definition, a flat fee group can span multiple years. Furthermore, a single person can have multiple flat fee groups. 2.5.4.2 Flat Fee Variable Descriptions2.5.4.2.1 Flat Fee ID (FFEEIDX)As noted earlier in Section 2.5.1.2 “Record Identifiers,” the variable FFEEIDX uniquely identifies all events that are part of the same flat fee group for a person. On any 2017 MEPS event file, every event that was a part of a specific flat fee group will have the same value for FFEEIDX. Note that prescribed medicine and home health events are never included in a flat fee group and FFEEIDX is not a variable on those event files. 2.5.4.2.2 Flat Fee Type (FFIPTYPE)FFIPTYPE indicates whether the 2017 hospital stay is the “stem” or “leaf” of a flat fee group. A stem (records with FFIPTYPE = 1) is the initial medical service (event) which is followed by other medical events that are covered under the same flat fee payment. The leaves of the flat fee group (records with FFIPTYPE = 2) are those medical events that are tied back to the initial medical event (the stem) in the flat fee group. These “leaf” records have their expenditure variables set to zero. For the hospital inpatient stays that are not part of a flat fee payment, the FFIPTYPE is set to -1, “INAPPLICABLE.” 2.5.4.2.3 Counts of Flat Fee Events that Cross Years (FFBEF17, FFTOT18)As explained in Section 2.5.4.1, a flat fee payment covers multiple events and the multiple events could span multiple years. For situations where the hospital inpatient stay/event occurred in 2017 as a part of a group of events, and some event occurred before or after 2017, counts of the known events are provided on the STAZ record. Variables that indicate events occurred before or after 2017 are as follows:
2.5.4.3 Caveats of Flat Fee GroupsThere are 18 hospital inpatient stays/events identified as being part of a flat fee payment group. In general, every flat fee group should have an initial visit (stem) and at least one subsequent visit (leaf). There are some situations where this is not true. For some of these flat fee groups, the initial visit reported occurred in 2017, but the remaining visits that were part of this flat fee group occurred in 2018. In this case, the 2017 flat fee group would consist of one event, the stem. The 2018 events that are part of this flat fee group are not represented on the file. Similarly, the household respondent may have reported a flat fee group where the initial visit began in 2016 but subsequent visits occurred during 2017. In this case, the initial visit would not be represented on the file. This 2017 flat fee group would then only consist of one or more leaf records and no stem. Please note that the crosswalk in this document lists all possible flat fee variables. 2.5.5 Expenditure Data2.5.5.1 Definition of ExpendituresExpenditure variables 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 for each hospital stay, 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 due to the increasingly common practice of discounting. Although measuring expenditures as the sum of payments incorporates discounts in the MEPS expenditure estimates, these estimates do not incorporate any payment not directly tied to specific medical care visits, such as bonuses or retrospective payment adjustments paid by third party payers. 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 payments associated with those classifications. While charge data are provided on this file, data users/analysts should use caution when working with these data because a charge does not typically represent actual dollars exchanged for services or the resource costs of those services; nor are they directly comparable to the expenditures defined in the 1987 NMES. For details on expenditure definitions, please reference the following, “Informing American Health Care Policy” (Monheit, et al., 1999). AHRQ has developed factors to apply to the 1987 NMES expenditure data to facilitate longitudinal analysis. These factors can be accessed via the CFACT data center. For more information, see the Data Center section of the MEPS website. Expenditure data related to hospital inpatient events are broken out by facility and separately billing doctor expenditures. This file contains six categories of expenditure variables per stay: basic hospital facility expenses; expenses for doctors who billed separately from the hospital for any inpatient services provided during hospital stay; total expenses, which is the sum of the facility and physician expenses; facility charge; physician charge; and total charges, which is the sum of the facility and physician charges. If examining trends in MEPS expenditures, please refer to Section 3.3 for more information. 2.5.5.2 Data Editing and Imputation Methodologies of Expenditure VariablesThe expenditure data included on this file were derived from both the MEPS Household (HC) and Medical Provider Components (MPC). The MPC contacted medical providers identified by household respondents. The charge and payment data from medical providers were used in the expenditure imputation process to supplement missing household data. For all hospital inpatient stays, MPC data were used if available; otherwise, HC data were used. Missing data for hospital inpatient stays where HC data were not complete and MPC data were not collected, or MPC data were not complete, were imputed during the imputation process. 2.5.5.2.1 General Data Editing MethodologyLogical edits were used to resolve internal inconsistencies and other problems in the HC and MPC survey-reported data. The edits were designed to preserve partial payment data from households and providers, and to identify actual and potential sources of payment for each household-reported event. In general, these edits accounted for outliers, copayments or charges reported as total payments, and reimbursed amounts that were reported as out-of-pocket payments. In addition, edits were implemented to correct for misclassifications between Medicare and Medicaid and between Medicare HMOs and private HMOs as payment sources. These edits produced a complete vector of expenditures for some events and provided the starting point for imputing missing expenditures in the remaining events. 2.5.5.2.2 Imputation MethodologiesThe predictive mean matching imputation method was used to impute missing expenditures. This procedure uses regression models (based on events with completely reported expenditure data) to predict total expenses for each event. Then, for each event with missing payment information, a donor event with the closest predicted payment with the same pattern of expected payment sources as the event with missing payment was used to impute the missing payment value. The imputations for the flat fee events were carried out separately from the simple events. The weighted sequential hot-deck procedure was used to impute the missing total charges. This procedure uses survey data from respondents to replace missing data while taking into account the persons’ weighted distribution in the imputation process. 2.5.5.2.3 Hospital Inpatient Stay Data Editing and ImputationFacility expenditures for hospital inpatient stays were developed in a sequence of logical edits and imputations. “Household” edits were applied to sources and amounts of payment for all events reported by HC respondents. “MPC” edits were applied to provider-reported sources and amounts of payment for records matched to household-reported events. Both sets of edits were used to correct obvious errors (as described above) in the reporting of expenditures. After the data from each source were edited, a decision was made as to whether household- or MPC-reported information would be used in the final editing and imputations for missing expenditures. The general rule was that MPC data would be used for events where a household-reported event corresponded to an MPC-reported event (i.e., a matched event), since providers usually have more complete and accurate data on sources and amounts of payment than households. Separate imputations were performed for flat fee and simple events. Most hospital inpatient stays were imputed as simple events because facility charges for an inpatient hospital stay are rarely grouped with other events. (See Section 2.5.4 for more details on flat fee groups.) Logical edits also were used to sort each event into a specific category for the imputations. Events with complete expenditures were flagged as potential donors for the predictive mean matching imputations, while events with missing expenditure data were assigned to various recipient categories. Each event with missing expenditure data was assigned to a recipient category based on the extent of its missing charge and expenditure data. For example, an event with a known total charge but no expenditure information was assigned to one category, while an event with a known total charge and partial expenditure information was assigned to a different category. Similarly, events without a known total charge and no or partial expenditure information were assigned to various recipient categories. The logical edits produced eight recipient categories in which all events had a common extent of missing data. However, for predictive mean matching imputations, the recipients were grouped into four categories based on the known status of total charge and the sources of payment: 1) Known charge but unknown payment status of at least one potential paying source, 2) Unknown charge and unknown payment status of at least one potential paying source, 3) Known charge and known status of all payment sources, and 4) Unknown charge and known status of all payment sources. Separate predictive mean matching imputations were performed on events in each recipient group. For hospital inpatient events, the donor pool was restricted to events with complete expenditures from the MPC. The donor pool included “free events” because, in some instances, providers are not paid for their services. These events represent charity care, bad debt, provider failure to bill, and third party payer restrictions on reimbursement in certain circumstances. If free events were excluded from the donor pool, total expenditures would be over-counted because the distribution of free events among complete events (donors) would not be represented among incomplete events (recipients). Expenditures for services provided by separately billing doctors in hospital settings were also edited and imputed. These expenditures are shown separately from hospital facility charges for hospital inpatient, outpatient, and emergency room care. 2.5.5.3 Imputation Flag (IMPFLAG)IMPFLAG is a six-category variable that indicates if the event contains complete Household Component (HC) or Medical Provider Component (MPC) data, was fully or partially imputed, or was imputed in the capitated imputation process (for OP and OB events only). The following list identifies how the imputation flag is coded; the categories are mutually exclusive. IMPFLAG = 0 not eligible for imputation (includes zeroed out and flat fee leaf events) IMPFLAG = 1 complete HC data IMPFLAG = 2 complete MPC data IMPFLAG = 3 fully imputed IMPFLAG = 4 partially imputed IMPFLAG = 5 complete MPC data through capitation imputation (not applicable to IP events) 2.5.5.4 Flat Fee ExpendituresThe approach used to count expenditures for flat fees was to place the expenditure on the first visit of the flat fee group. The remaining visits have zero facility payments, while physician’s expenditures may be still present. Thus, if the first visit in the flat fee group occurred prior to 2017, all of the events that occurred in 2017 will have zero payments. Conversely, if the first event in the flat fee group occurred at the end of 2017, the total expenditure for the entire flat fee group will be on that event, regardless of the number of events it covered after 2017. See Section 2.5.4 for details on the flat fee variables. 2.5.5.5 Zero ExpendituresThere are some medical events reported by respondents where the payments were zero. Zero payment events can occur in MEPS for the following reasons: (1) the stay was covered under a flat fee arrangement (flat fee payments are included only on the first event covered by the arrangement), (2) there was no charge for a follow-up stay, (3) the provider was never paid by an individual, insurance plan, or other source for services provided, (4) the charges were included in another bill, or (5) the event was paid for through government or privately-funded research or clinical trials. 2.5.5.6 Discount Adjustment FactorAn adjustment was also applied to some HC-reported expenditure data because an evaluation of matched HC/MPC data showed that respondents who reported that charges and payments were equal were often unaware that insurance payments for the care had been based on a discounted charge. To compensate for this systematic reporting error, a weighted sequential hot-deck imputation procedure was implemented to determine an adjustment factor for HC-reported insurance payments when charges and payments were reported to be equal. As for the other imputations, selected predictor variables were used to form groups of donor and recipient events for the imputation process. 2.5.5.7 Mother/Newborn ExpendituresExpenditure data for newborns were edited to exclude discharges after birth when the newborn left the hospital before or on the same day as the mother. As a result, inpatient expenditures reported for 2017 births were usually applied to the mother and not treated as separate expenditures for the infant. However, if a newborn was discharged at a later date than the mother’s discharge date, then the hospitalization was treated as a separate hospital stay for the newborn. This means that, in most cases, expenditure data for the newborn is included on the mother’s record. A separate record for the newborn only exists if the newborn was discharged after the mother. In this case, the expenditure for the newborn is on the newborn’s record. 2.5.5.8 Hospital Inpatient Stay/Emergency Room ExpendituresAlthough a person may have indicated that there was an emergency room visit that preceded this hospital stay (EMERROOM), there was no verification that, in fact, the emergency room visit was actually recorded within the Emergency Room section of the questionnaire. While it is true that all of the event files can be linked by DUPERSID, there is no unique record link between hospital inpatient stays and emergency room visits. That is, a person could have one hospital inpatient stay and three emergency room visits during the calendar year. While the hospital inpatient stay record may indicate that it was preceded by an emergency room visit, there is no unique record link to the appropriate (of the three) emergency room visit. However, when relationships could be identified (using the MPC start and end date of the events as well as other information from the provider), where the facility expenditures associated with the preceding emergency room visit were included in the hospital facility expenditures, the corresponding emergency room record on the MEPS 2017 Emergency Room Visits File will have its facility expenditure information zeroed out to avoid double-counting. The variable ERHEVIDX identifies these hospital stays whose expenditures include the facility expenditures for the preceding emergency room visit (see ERHEVIDX in Section 2.5.1.2). It should also be noted that for these cases, there is only one hospital stay associated with the emergency room stay. 2.5.5.9 Sources of PaymentIn addition to total expenditures, variables are provided which itemize expenditures according to major source of payment categories. These categories are:
Two additional source of payment variables were created to classify payments for events with apparent inconsistencies between health insurance coverage and sources of payment based on data collected in the survey. These variables include:
Though these two sources are relatively small in magnitude, data users/analysts should exercise caution when interpreting the expenditures associated with these two additional sources of payment. While these payments stem from apparent inconsistent responses to health insurance and source of payment questions in the survey, some of these inconsistencies may have logical explanations. For example, private insurance coverage in MEPS is defined as having a major medical plan covering hospital and physician services. If a MEPS sampled person did not have such coverage but had a single service type insurance plan (e.g., dental insurance) that paid for a particular episode of care, those payments may be classified as “other private.” Some of the “other public” payments may stem from confusion between Medicaid and other state and local programs or may be from persons who were not enrolled in Medicaid, but were presumed eligible by a provider who ultimately received payments from the public payer. 2.5.5.10 Imputed Hospital Inpatient Stay Expenditure VariablesThis file contains two sets of imputed expenditure variables: facility expenditures and physician expenditures. 2.5.5.10.1 Hospital Inpatient Facility Expenditures (IPFSF17X – IPFOT17X, IPFXP17X, IPFTC17X)Hospital facility expenses include all expenses for direct hospital care, including room and board, diagnostic and laboratory work, x-rays, and similar charges, as well as any physician services included in the hospital charge. IPFSF17X – IPFOT17X are the 12 sources of payment. The 12 sources of payment are: self/family (IPFSF17X), Medicare (IPFMR17X), Medicaid (IPFMD17X), private insurance (IPFPV17X), Veterans Administration/CHAMPVA (IPFVA17X), TRICARE (IPFTR17X), other federal sources (IPFOF17X), state and local (non-federal) government sources (IPFSL17X), Workers’ Compensation (IPFWC17X), other private insurance (IPFOR17X), other public insurance (IPFOU17X), and other insurance (IPFOT17X). IPFXP17X is the sum of the 12 sources of payment for the Hospital Facility expenditures, and IPFTC17X is the total charge. Wherever an emergency room visit record is linked to a hospital inpatient stay record (identified by the variable ERHEVIDX, see Section 2.5.1.2), the facility source of payment variables on the emergency room visit record were zeroed out because the emergency room expenditures were already included in the hospital facility source of payment variables. 2.5.5.10.2 Hospital Inpatient Physician Expenditures (IPDSF17X – IPDOT17X, IPDXP17X, IPDTC17X)Separately billing doctor (SBD) expenses typically cover services provided to patients in hospital settings by providers like anesthesiologists, radiologists, and pathologists, whose charges are often not included in hospital bills. For medical doctors who bill separately (i.e., outside the hospital bill), a separate data collection effort within the Medical Provider Component was performed to obtain this same set of expenditure information from each separately billing doctor. It should be noted that there could be several separately billing doctors associated with a medical event. For example, a hospital inpatient stay could have a radiologist, anesthesiologist, pathologist and a surgeon associated with it. If their services are not included in the hospital bill then this is one medical event with four separately billing doctors. The imputed expenditure information associated with the separately billing doctors for a hospital inpatient stay is combined (i.e., the expenditures incurred by the radiologist + anesthesiologist + pathologist + surgeon) and is provided on the file. IPDSF17X – IPDOT17X are the 12 sources of payment; IPDXP17X is the sum of the 12 sources of payments; and IPDTC17X is the physician’s total charge. Data users/analysts need to take into consideration whether to analyze facility and SBD expenditures separately, combine them within service categories, or collapse them across service categories (e.g., combine SBD expenditures with expenditures for physician visits to offices and/or outpatient departments). 2.5.5.10.3 Total Expenditures and Charges for Hospital Inpatient Stays (IPXP17X and IPTC17X)Data users/analysts interested in total expenditures should use the variable IPXP17X, which includes both facility and physician amounts. Those interested in total charges should use the variable IPTC17X, which includes both facility and physician charges (see Section 2.5.5.1 for an explanation of the “charge” concept). 2.5.5.11 RoundingExpenditure variables have been rounded to the nearest penny. Person-level expenditure information released on the MEPS 2017 Person-Level Use and Expenditure File were rounded to the nearest dollar. It should be noted that using the MEPS 2017 event files to create person-level totals will yield slightly different totals than those found on the full year consolidated file. These differences are due to rounding only. Moreover, in some instances, the number of persons having expenditures on the MEPS 2017 event files for a particular source of payment may differ from the number of persons with expenditures on the person-level expenditure file for that source of payment. This difference is also an artifact of rounding only. 3.0 Sample Weight (PERWT17F)3.1 OverviewThere is a single full year person-level weight (PERWT17F) 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 2017. A key person either was 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 PERWT17F was developed in several stages. First, person-level weights for Panel 21 and Panel 22 were created separately. The weighting process for each panel included an adjustment for nonresponse over time and calibration to independent population totals. 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 2017 composite weight was then formed by multiplying each weight from Panel 21 by the factor .500 and each weight from Panel 22 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 on the previously established weight variable. Control totals were established using poverty status (five categories: below poverty, from 100 to 125 percent of poverty, from 125 to 200 percent of poverty, from 200 to 400 percent of poverty, at least 400 percent of poverty) as well as the other six variables previously used in the weight calibration. 3.2.1 MEPS Panel 21 Weight Development ProcessThe person-level weight for MEPS Panel 21 was developed using the 2016 full year weight for an individual as a “base” weight for survey participants present in 2016. For key, in-scope members who joined an RU some time in 2017 after being out-of-scope in 2016, the initially assigned person-level weight was the corresponding 2016 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 2017 for key, responding persons in-scope on December 31, 2017. These control totals were derived by scaling back the population distribution obtained from the March 2018 CPS to reflect the December 31, 2017 estimated population total (estimated based on Census projections for January 1, 2018). 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. (Poverty status is not included in this version of the MEPS full year database because of the time required to process the income data collected and then assign persons to a poverty status category). The final weight for key, responding persons who were not in-scope on December 31, 2017 but were in-scope earlier in the year was the person weight after the nonresponse adjustment. Note that the 2016 full-year weight that was used as the base weight for Panel 21 was derived as follows; adjustment of the MEPS Round 1 weight for nonresponse over the remaining data collection rounds in 2016; and raking the resulting nonresponse adjusted weight to December 2016 population control figures. 3.2.2 MEPS Panel 22 Weight Development ProcessThe person-level weight for MEPS Panel 22 was developed using the 2017 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 2017 as well as raking to the same population control figures for December 2017 used for the MEPS Panel 21 weights for key, responding persons in-scope on December 31, 2017. The same six variables employed for Panel 21 raking (educational attainment of the reference person, census region, MSA status, race/ethnicity, sex, and age) were used for Panel 22 raking. Again, the final weight for key, responding persons who were not in-scope on December 31, 2017 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 22 incorporated the following components: the original household probability of selection for the NHIS; proportion of 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 level obtained from the corresponding March CPS databases. 3.2.3 The Final Weight for 2017The 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, 2017 were poststratified. Specifically, the weights of those who were in-scope some time during the year, out-of-scope on December 31, and entered a nursing home during the year were adjusted to compensate for expected undercoverage for this subpopulation. The weights of persons who died while in-scope during 2017 were poststratified to corresponding estimates derived using data obtained from the Medicare Current Beneficiary Survey (MCBS) and Vital Statistics information provided by the National Center for Health Statistics (NCHS). Separate decedent control totals were developed for the “65 and older” and “under 65” civilian noninstitutionalized decedent populations. Overall, the weighted population estimate for the civilian noninstitutionalized population for December 31, 2017 is 321,529,965 (PERWT17F>0 and INSC1231=1). The sum of the person-level weights across all persons assigned a positive person-level weight is 324,779,909. 3.2.4 CoverageThe target population for MEPS in this file is the 2017 U.S. civilian noninstitutionalized population. However, the MEPS sampled households are a subsample of the NHIS households interviewed in 2015 (Panel 21) and 2016 (Panel 22). New households created after the NHIS interviews for the respective panels and consisting exclusively of persons who entered the target population after 2015 (Panel 21) or after 2016 (Panel 22) 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. 3.3 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, in 2013 MEPS introduced an effort to obtain more complete information about health care utilization from MEPS respondents with full implementation in 2014. This effort likely resulted in improved data quality and a reduction in underreporting starting in FY 2014 and could have some modest impact on analyses involving trends in utilization across years. 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 using techniques to evaluate, smooth, or stabilize analyses of trends using MEPS data such as comparing pooled time periods (e.g. 1996–97 versus 2011–13), 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 Strategies for Estimation4.1 Developing Event-Level EstimatesThe data in this file can be used to develop national 2017 event-level estimates for the U.S. civilian noninstitutionalized population on inpatient hospital stays as well as expenditures, and sources of payment for these stays. Estimates of total stays are the sum of the weight variable (PERWT17F) across relevant event records while estimates of other variables must be weighted by PERWT17F to be nationally representative. The tables below contain event-level estimates for selected variables. Selected Event-Level Estimates
* Zero payment events can occur in MEPS for the following reasons: (1) the stay was covered under a flat fee arrangement (flat fee payments are included only on the first event covered by the arrangement), (2) there was no charge for a follow-up stay, (3) the provider was never paid by an individual, insurance plan, or other source for services provided, (4) the charges were included in another bill, or (5) the event was paid for through government or privately-funded research or clinical trials. 4.2 Person-Based Estimates for Hospital Inpatient StaysTo enhance analyses of hospital inpatient stays, analysts may link information about inpatient stays by sample persons in this file 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 5 below for more details). Both this file and the full year consolidated file may be used to derive estimates for persons with hospital inpatient care and annual estimates of total expenditures. 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 inpatient event 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 inpatient care. 4.3 Variables with Missing ValuesIt is essential that the data user/analyst examine all variables for the presence of negative values used to represent missing values. For continuous or discrete variables, where means or totals may be taken, it may be necessary to set minus values to values appropriate to the analytic needs. That is, the data user/analyst should either impute a value or set the value to one that will be interpreted as missing by the software package used. For categorical and dichotomous variables, the data user/analyst may want to consider whether to recode or impute a value for cases with negative values or whether to exclude or include such cases in the numerator and/or denominator when calculating proportions. Methodologies used for the editing/imputation of expenditure variables (e.g., sources of payment, flat fee, and zero expenditure) are described in Section 2.5.5. 4.4 Variance Estimation (VARSTR, VARPSU)The 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. However, the capability of employing the Balanced Repeated Replication (BRR) methodology is also provided if needed to develop variances for more complex estimators. 4.4.1 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 public use files. Software packages that permit the use of the Taylor-series linearization method include SUDAAN, Stata, SAS (version 8.2 and higher), and SPSS (version 12.0 and higher). For complete information on the capabilities of each package, analysts should refer to the corresponding software user documentation. Using the Taylor-series linearization method, variance estimation strata and the variance estimation PSUs within these strata must be specified. The 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, the most recent change in the NHIS sample design took place in 2016, effectively changing the MEPS design beginning with calendar year 2017, where Panel 22 is based on the new NHIS design while Panel 21 is based on the old one. There were 117 variance strata formed for Panel 22. With the 165 strata available from Panel 21, there are a total of 282 variance strata appearing on the 2017 Full Year PUF. 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 have four digit values with a “2” as the first digit. Those associated with the previous design have “1” as the first of four digits. If analyses call for pooling MEPS data across several years, in order to ensure that variance strata are identified appropriately for variance estimation purposes, one can proceed as follows:
4.4.2 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 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, 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 file. For more information about creating BRR replicates, users can refer to the documentation for the HC-036BRR pooled linkage file. 5.0 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 event files. The set of households selected for MEPS is a subsample of those participating in the National Health Interview Survey (NHIS), thus, each MEPS panel can also be linked back to the previous year’s NHIS public use data files. For information on obtaining MEPS/NHIS link files please see the MEPS website. 5.1 Linking to the Person-Level FileMerging characteristics of interest from other MEPS files (e.g., MEPS 2017 Full-Year Consolidated File) expands the scope of potential estimates. For example, to estimate the total number of hospital inpatient stays for 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 hospital inpatient stays file. This procedure is illustrated below. The MEPS 2017 Appendix File, HC-197I, provides additional detail on how to merge MEPS data files.
The following is an example of SAS code that completes these steps: PROC SORT DATA=HCXXX(KEEP=DUPERSID AGE31X AGE42X
AGE53X SEX RACEV1X EDUCYR HIDEG) OUT=PERSX; PROC SORT DATA=STAZ; DATA NEWSTAZ; 5.2 Linking to the Prescribed Medicines FileThe prescribed medicines-event link (RXLK) file provides a link from the MEPS event files to the Prescribed Medicine Event File. When using RXLK, data users/analysts should keep in mind that one inpatient stay can link to more than one prescribed medicine record. Conversely, a prescribed medicine event may link to more than one inpatient stay visit or different types of events. When this occurs, it is up to the data user/analyst to determine how the prescribed medicine expenditures should be allocated among those medical events. For detailed linking examples, including SAS code, data users/analysts should refer to the MEPS 2017 Appendix File, HC-197I. 5.3 Linking to the Medical Conditions FileThe conditions-event link file (CLNK) provides a link from MEPS event files to the 2017 Medical Conditions File. When using the CLNK, data users/analysts should keep in mind that (1) conditions are household-reported, (2) there may be multiple conditions associated with a hospital inpatient stay, and (3) a condition may link to more than one hospital inpatient stay or any other type of visit. Data users/analysts should also note that not all hospital inpatient stays link to the medical conditions file. ReferencesCohen, S.B. (1998). Sample Design of the 1996 Medical Expenditure Panel Survey Medical Provider Component. Journal of Economic and Social Measurement. Vol. 24, 25–53. Cohen, S.B. (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. Ezzati-Rice, T.M., Rohde, F., Greenblatt, J., Sample Design of the Medical Expenditure Panel Survey Household Component, 1998–2007. Methodology Report No. 22. March 2008. Agency for Healthcare Research and Quality, Rockville, MD. Fay, R.E. (1989). Theory and Application of Replicate Weighting for Variance Calculations. Proceedings of the Survey Research Methods Sections, ASA, 212–217. Monheit, A.C., Wilson, R., and Arnett, III, R.H. (Editors). Informing American Health Care Policy. (1999). Jossey-Bass Inc., San Francisco. Shah, B.V., Barnwell, B.G., Bieler, G.S., Boyle, K.E., Folsom, R.E., Lavange, L., Wheeless, S.C., and Williams, R. (1996). Technical Manual: Statistical Methods and Algorithms Used in SUDAAN Release 7.0, Research Triangle Park, NC: Research Triangle Institute. D. Variable-Source CrosswalkVARIABLE-SOURCE CROSSWALK
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