MEPS HC-010E: 1996 Emergency Room Visits

MEPS HC-010E: 1996 Emergency Room Visits
Agency for Healthcare Research and Quality
Center for Financing, Access, and Cost Trends
540 Gaither Road
Rockville, MD 20850
(301) 427-1406

Individual identifiers have been removed from the microdata contained in the files on this CD-ROM. 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 18 U.S.C. 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.

This documentation describes one in a series of public use files from the Medical Expenditure Panel Survey (MEPS). The survey provides a new and extensive data set on the use of health services and health care in the United States.

MEPS is conducted to provide nationally representative estimates of health care use, expenditures, sources of payment, and insurance coverage for the U.S. civilian noninstitutionalized population. MEPS also includes a nationally representative survey of nursing homes and their residents. MEPS is cosponsored by the Agency for Healthcare Research and Quality (AHRQ) (formerly the Agency for Health Care Policy and Research (AHCPR)) and the National Center for Health Statistics (NCHS).

MEPS comprises four component surveys: the Household Component (HC), the Medical Provider Component (MPC), the Insurance Component (IC), and the Nursing Home Component (NHC). The HC is the core survey, and it forms the basis for the MPC sample and part of the IC sample. The separate NHC sample supplements the other MEPS components. Together these surveys yield comprehensive data that provide national estimates of the level and distribution of health care use and expenditures, support health services research, and can be used to assess health care policy implications.

MEPS is the third in a series of national probability surveys conducted by AHRQ on the financing and use of medical care in the United States. The National Medical Care Expenditure Survey (NMCES, also known as NMES-1) was conducted in 1977. The National Medical Expenditure Survey (NMES-2) was conducted in 1987. Beginning in 1996, MEPS continues this series with design enhancements and efficiencies that provide a more current data resource to capture the changing dynamics of the health care delivery and insurance system.

The design efficiencies incorporated into MEPS are in accordance with the Department of Health and Human Services (DHHS) Survey Integration Plan of June 1995, which focused on consolidating DHHS surveys, achieving cost efficiencies, reducing respondent burden, and enhancing analytical capacities. To accommodate these goals, new MEPS design features include linkage with the National Health Interview Survey (NHIS), from which the sampling frame for the MEPS HC is drawn, and continuous longitudinal data collection for core survey components. The MEPS HC augments NHIS by selecting a sample of NHIS respondents, collecting additional data on their health care expenditures, and linking these data with additional information collected from the respondents' medical providers, employers, and insurance providers.

The MEPS HC, a nationally representative survey of the U.S. civilian noninstitutionalized population, collects medical expenditure data at both the person and household levels. The HC collects detailed data on demographic characteristics, health conditions, health status, use of medical care services, charges and payments, access to care, satisfaction with care, health insurance coverage, income, and employment.

The HC uses an overlapping panel design in which data are collected through a preliminary contact followed by a series of five rounds of interviews over a 2½-year period. Using computer-assisted personal interviewing (CAPI) technology, data on medical expenditures and use for two calendar years are collected from each household. This series of data collection rounds is launched each subsequent year on a new sample of households to provide overlapping panels of survey data and, when combined with other ongoing panels, will provide continuous and current estimates of health care expenditures.

The sampling frame for the MEPS HC is drawn from respondents to NHIS, conducted by NCHS. NHIS provides a nationally representative sample of the U.S. civilian noninstitutionalized population, with oversampling of Hispanics and blacks.

The MEPS MPC supplements and validates information on medical care events reported in the MEPS HC by contacting medical providers and pharmacies identified by household respondents. The MPC sample includes all hospitals, hospital physicians, home health agencies, and pharmacies reported in the HC. Also included in the MPC are all office-based physicians who:

Data are collected on medical and financial characteristics of medical and pharmacy events reported by HC respondents, including:

The MPC is conducted through telephone interviews and mailed survey materials. In some instances, providers sent medical and billing records which were abstracted into the survey instruments.

The MEPS IC collects data on health insurance plans obtained through employers, unions, and other sources of private health insurance. Data obtained in the IC include the number and types of private insurance plans offered, benefits associated with these plans, premiums, contributions by employers and employees, eligibility requirements, and employer characteristics.

Establishments participating in the MEPS IC are selected through four sampling frames:

To provide an integrated picture of health insurance, data collected from the first sampling frame (employers and insurance providers) are linked back to data provided by the MEPS HC respondents. Data from the other three sampling frames are collected to provide annual national and State estimates of the supply of private health insurance available to American workers and to evaluate policy issues pertaining to health insurance.

The MEPS IC is an annual survey. Data are collected from the selected organizations through a prescreening telephone interview, a mailed questionnaire, and a telephone followup for nonrespondents.

The 1996 MEPS NHC was a survey of nursing homes and persons residing in or admitted to nursing homes at any time during calendar year 1996. The NHC gathered information on the demographic characteristics, residence history, health and functional status, use of services, use of prescription medicines, and health care expenditures of nursing home residents. Nursing home administrators and designated staff also provided information on facility size, ownership, certification status, services provided, revenues and expenses, and other facility characteristics. Data on the income, assets, family relationships, and care-giving services for sampled nursing home residents were obtained from next-of-kin or other knowledgeable persons in the community.

The 1996 MEPS NHC sample was selected using a two-stage stratified probability design. In the first stage, facilities were selected; in the second stage, facility residents were sampled, selecting both persons in residence on January 1, 1996, and those admitted during the period January 1 through December 31.

The sample frame for facilities was derived from the National Health Provider Inventory, which is updated periodically by NCHS. The MEPS NHC data were collected in person in three rounds of data collection over a 1½-year period using the CAPI system. Community data were collected by telephone using computer-assisted telephone interviewing (CATI) technology. At the end of three rounds of data collection, the sample consisted of approximately 815 responding facilities, 3,209 residents in the facility on January 1, and 2,690 eligible residents admitted during 1996.

MEPS data are collected under the authority of the Public Health Service Act. They are edited and published in accordance with the confidentiality provisions of this act and the Privacy Act. 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 and microdata files. Summary reports are released as printed documents and electronic files. Microdata files are released on CD-ROM and/or as electronic files.

Printed documents and CD-ROMs are available through the AHRQ Publications Clearinghouse. Write or call:

AHRQ Publications Clearinghouse
Attn: (publication number)
P.O. Box 8547
Silver Spring, MD 20907
800/358-9295
410/381-3150 (callers outside the United States only)
888/586-6340 (toll-free TDD service; hearing impaired only)

Be sure to specify the AHRQ number of the document or CD-ROM you are requesting. Selected electronic files are available from the Internet on the MEPS web site: <http://www.meps.ahrq.gov/>

Additional information on MEPS is available from the MEPS project manager or the MEPS public use data manager at the Center for Cost and Financing Studies, Agency for Healthcare Research and Quality.

This documentation describes one in a series of public use event files from the 1996 Medical Expenditure Panel Survey Household (HC) and Medical Provider Components(MPC) . Released as an ASCII data file and SAS transport file, this public use file provides detailed information on emergency room visits for a nationally representative sample of the civilian noninstitutionalized population of the United States and can be used to make estimates of emergency room utilization and expenditures for calendar year 1996. Each record represents one household-reported emergency room visit reported during rounds 1, 2, and 3. Emergency room events reported in Round 3 and known to have begun after December 31, 1996 are not included on this file. In addition to expenditures related to this event, each record contains household reported medical conditions and procedures associated with the emergency room visit.

Data from this event file can be merged with other MEPS HC data files, for the purpose of appending person characteristics such as demographic or health insurance coverage to each emergency room visit record.

Counts of emergency room visits are based entirely on household reports. Information from the MEPS MPC was used to supplement expenditure and payment data reported by the household.

This file can be also used to construct summary variables of expenditures, sources of payment, and related aspects of emergency room visits. Aggregate annual person-level information on the use of emergency rooms and other health services use in 1996 is provided on public use file HC-011 where each record represents a MEPS sampled person.

The following documentation offers a brief overview of the types and levels of data provided, the content and structure of the files and the codebook, and programming information. It contains the following sections:

For more information on MEPS HC survey design see S. Cohen, 1997; J. Cohen, 1997; and S. Cohen, 1996. For information on the MEPS MPC design, see S. Cohen, 1998. A copy of the survey instrument used to collect the information on this file is available on the MEPS web site at the following address: <http://www.meps.ahrq.gov>.

This public use data release consists of two event-level data files. File 1 contains characteristics associated with the emergency room visit and imputed expenditure data. File 2 contains un-imputed expenditure data from both the Household and Medical Provider Components for all emergency room visits on File 1. Please see Attachment 1 for definitions of imputed, un-imputed and pre-imputed expenditure variables.

Both files1 and 2 of this public use data set contains variables and frequency distributions for a total of 3,899 emergency room visits reported during rounds 1, 2, and 3 of the MEPS Household Component. File 1 includes emergency room visit records for all household survey respondents who resided in eligible responding households and reported at least one emergency room visit. Records where the emergency room visit was known to have begun after December 31, 1996 are not included on this file. Of these records, 3,899 were associated with persons having positive person-level weights (WTDPER96). The persons represented on this file had to meet either (a) or (b):

(a) Be classified as a key, in-scope person who responded for his or her entire period of 1996 eligibility (i.e., persons with a positive 1996 full-year person-level sampling weight (WTDPER96>0)), or

(b) Be classified as either an eligible, non-key person or an eligible out-of-scope person who responded for his or her entire period of 1996 eligibility, and belonged to a family (i.e., all persons with the same value for a particular FAMID variable) in which all eligible family members responded for their entire period of 1996 eligibility, and at least one family member has a positive 1996 full-year person weight (i.e., eligible non-key or eligible out-of-scope persons who are members of a family all of whose members have a positive 1996 full-year MEPS family-level weight (WTFAM96>0)).

Please refer to Attachment 1 for definitions of key, non-key, inscope and eligible. Persons with no emergency room visits for 1996 are not included on this file (but are represented on MEPS person-level files). A codebook for the data file is provided. For each variable on the file, both weighted and unweighted frequencies are provided in the codebook.

Each emergency room visit record on this file includes the following: date of the visit; whether or not person saw doctor; type of care received; type of services (i.e. lab test, sonogram or ultrasound, x-rays etc.) received, medicines prescribed during the visit; flat fee information, imputed sources of payment, total payment and total charge; and a full-year person-level weight.

File 2 of this public use data set is intended for analysts who want to perform their own imputations to handle missing data. This file contains one set of un-imputed expenditure information from the Medical Provider Component as well as one set of pre-imputed expenditure information from the Household Component. Both sets of expenditure data have been subject to minimal logical editing that 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 HMO's and private HMO's as payment sources. However, missing data was not imputed.

Data from these files can be merged with previously released 1996 MEPS HC person level data using the unique person identifier, DUPERSID, to append person characteristics such as demographic or health insurance characteristics to each record. Emergency room events can also be linked to the MEPS 1996 Medical Conditions File (HC-006) and the MEPS 1996 Prescribed Medicines File (HC-10A). The Appendix File contains details on how to link MEPS data files.

For each variable on these files, both weighted and unweighted frequencies are provided. The codebook and data file sequence list variables in the following order:

-13 VALUE SUPPRESSED Data suppressed due to confidentiality or legal restrictions .

Generally, values of -1,-7, -8, and -9 have not been edited on this file. The values of -1 and -9 can be edited by analysts by following the skip patterns in the questionnaire.

This codebook describes an ASCII data set (although the data are also being provided in a SAS transport file). The following codebook items are provided for each variable:

In general, variable names reflect the content of the variable, with an 8 character limitation.

For questions asked in a specific round, the end digit in the variable name reflects the round in which the question was asked. All imputed/edited variables end with an "X".

Variables contained on Files 1 and 2 were derived either from the HC questionnaire itself, the MPC data collection instrument or from the CAPI. The source of each variable is identified in Section E, entitled "Variable to Source Crosswalk". Sources for each variable are indicated in one of four ways: (1) variables which are derived from CAPI or assigned in sampling are so indicated; (2) variables which come from one or more specific questions have those question numbers and the questionnaire section indicated in the "Source" column; (3) variables constructed from multiple questions using complex algorithms are labeled "Constructed" in the "Source" column; and (4) variables which have been imputed are so indicated.

Both pre-imputed and imputed versions of the expenditure and sources of payment variables are provided on 2 separate files. Variables on Files 1 and 2 follow a standard naming convention and are 8 characters in length. Please note that pre-imputed means that a series of logical edits have been performed on the variable but missing data remains. The imputed versions incorporate the same edits but have also undergone the imputation process to account for missing data.

The pre-imputed/unimputed expenditure variables on File 2 end with an "H", if the data source was from the MEPS Household Component and end with a "M" if the data source was the MEPS Medical Provider Component. All imputed variables on File 1 end with an "X".

The total sum of payments, 12 sources of payment variables and total charge variables are named consistently in the following way:

For expenditure variables on these files, the third character indicates whether the expenditure is associated with the facility (F) or the physician (D).

In the case of the sources of payment variables, the fourth and fifth characters indicate:

The sixth and seventh characters indicate the year (96) and the last character of all imputed/edited variables is an " X".

For example ERFSF96X is the edited/imputed amount paid by self or family for the facility portion of the expenditure associated with an emergency room visit.

The dwelling unit ID (DUID) is a 5-digit random number assigned after the case was sampled for MEPS. The 3-digit person number (PID) uniquely identifies each person within the dwelling unit. The 8-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 public use file HC-011.

EVNTIDX uniquely identifies each emergency room visit (i.e. each record on the file) and is the variable required to link events to data files containing details on conditions and/or prescribed medicines (HC-006 and H-010A, respectively). For details on linking see Section XXX.

FFID11X uniquely identifies a flat fee group, that is, all events that were part of a flat fee payment situation. For example, if a patient receives stitches in an emergency room and comes back to have the stitches removed ten days later during an outpatient visit, both visits are covered under one flat fee dollar amount. These two events (the emergency room visit and the subsequent outpatient visit) have the same value for FFID11X. Please note that FFID11X should be used to link up all MEPS event files (excluding prescribed medicines) in order to determine the full set of events that are part of a flat fee group.

EVENTRN indicates the round in which the emergency room event was first reported.

File 1 contains 21 variables describing emergency room events reported by respondents in the Emergency Room section of the MEPS questionnaire. The questionnaire contains specific probes for determining the specific details about the emergency room event. Unless noted otherwise, the following variables are provided as unedited

When a person reported having had a visit to the emergency room, the date of the emergency room visit was reported (ERBEGYR, ERBEGMM, ERBEGDD). The questionnaire determines whether or not the person saw a medical doctor (SEEDOC). The type of care the person received (VSTCTGRY) and whether or not the visit was related to a specific condition (VSTRELCN) were also determined.

Services received during the visit included whether or not the person received lab tests (LABTEST), a sonogram or ultrasound (SONOGRAM), x-rays (XRAYS), a mammogram (MAMMOG), an MRI or CAT scan (MRI), an electrocardiogram (EKG), an electroencephalogram (EEG), a vaccination (RCVVAC), anesthesia (ANESTH), or other diagnostic tests or exams (OTHSVCE). Whether or not a surgical procedure was performed during the visit was asked (SURGPROC) and, if so, the procedure name (SURGNAME). The questionnaire determined if a medicine was prescribed for the person during the emergency room visit (MEDPRESC). See Section 5.0 for information on linking to the prescription medicine events file. Finally, it was reported if the person saw any of the same doctors or surgeons at their place of practice outside of the emergency room (DOCOUTF).

The variable ERHEVIDX is a constructed variable which identifies emergency room visits whose expenditures are included in the expenditures for the associated hospital inpatient stay. This variable was constructed by comparing date information for the reported hospital stay and all emergency room visits for the same person.

VAPLACE is a constructed variable that indicates whether the provider worked at a VA facility. This only has valid data for providers that were sampled into the Medical Provider Component. All other providers are classified as unknown.

While all emergency room events are sampled into the Medical Provider Component, not all emergency room event records have MPC data associated with them. This is dependent upon the cooperation of the household respondent to provide permission forms to contact the emergency room facility as well as the cooperation of the emergency room facility to participate in the survey. MPCDATA indicates whether or not MPC data were collected for the emergency room visit.

2.5.8 Condition and Procedure Codes (ERICD1X-ERICD3X, ERPRO1X) and Clinical Classification Codes (ERCCC1X-ERCCC3X)

Information on household reported medical conditions associated with each emergency room visit are provided on this file. There are up to three condition codes (ERICD1X-ERICD3X) and one procedure code (ERPRO1X) listed for each emergency room visit (99.6% of emergency room visits have 0-3 condition records linked). In order to obtain complete condition information associated with an event, the analyst must link to the HC-006: Medical Conditions File. Details on how to link to the MEPS Medical Conditions File (HC-006) are provided in the Appendix File. The user should note that due to confidentiality restrictions, provider reported condition information is not publicly available.

The medical conditions and procedures reported by the Household Component respondent were recorded by the interviewer as verbatim text, which were then coded to fully-specified 1996 ICD-9-CM codes, including medical condition and V codes (see Health Care Financing Administration, 1980), by professional coders. Although codes were verified and error rates did not exceed 2.5 percent for any coder, analysts should not presume this level of precision in the data; the ability of household respondents to report condition data that can be coded accurately should not be assumed (see Cox and Cohen, 1985; Cox and Iachan, 1987; Edwards, et al, 1994; and Johnson and Sanchez, 1993). For detailed information on conditions, please refer to the documentation on the HC-006 1996 Medical Conditions File. For frequencies of conditions by event type, please see the Appendix File.

The ICD-9-CM codes were aggregated into clinically meaningful categories. These categories, included on the file as ERCCC1X-ERCCC3X, were generated using Clinical Classification Software (formerly known as Clinical Classifications for Health Care Policy Research (CCHPR)), (Elixhauser, et al., 1998), which aggregates conditions and V-codes into 260 mutually exclusive categories, most of which are clinically homogeneous.

In order to preserve respondent confidentiality, nearly all of the condition codes provided on this file have been collapsed from fully-specified codes to 3-digit code categories. The reported ICD-9-CM code values were mapped to the appropriate clinical classification category prior to being collapsed to the 3-digit categories.

The condition codes (and clinical classification codes) and procedure codes linked to each emergency room visit are sequenced in the order in which the conditions were reported by the household respondent, which was in chronological order of occurrence and not in order of importance or severity. Analysts who use the HC-006 Medical Conditions file in conjunction with this emergency room visit file should note that the order of conditions on this file is not identical to that on the Medical Conditions file.

The variable NUMCOND indicates the total number of condition records which can be linked from HC-006: Medical Conditions File to each emergency room visit. For events where no condition records linked (NUMCOND=0), the condition and procedure and clinical classification code variables all have a value of -1 INAPPLICABLE. Similarly, for events without a linked second or third condition record, the corresponding second or third condition, clinical classification code and procedure code variables were set to -1 INAPPLICABLE.

In order to obtain complete condition information for events with NUMCOND greater than 3, the analyst must link to the MEPS Condition File (HC-006). Please see Section 5.0 for details on linking MEPS data files.

A flat fee is the fixed dollar amount a person is charged for a package of health care services. 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 situation. The flat fee groups represented on this file (and all of the other 1996 MEPS event files) includes flat fee groups where at least one of the health care events, as reported by the HC respondent, occurred during 1996. By definition a flat fee group can span multiple years and/or event types (e.g., hospital stay, physician office visit), and a single person can have multiple flat fee groups.

There are several variables on this file that describe a flat fee payment situation and the number of medical events that are part of a flat fee group. As noted previously, for a person, the variable FFID11X can be used to identify all events that are part of the same flat fee group. To identify such events, FFID11X should be used to link events from all MEPS event files (excluding prescribed medicines): HC-010B through HC-010H. For the emergency room visits that are not part of a flat fee payment situation, the flat fee variables described below are all set to inapplicable (-1).

FFERTYPX indicates whether the 1996 emergency room visit is the "stem" or "leaf" of a flat fee group. A stem (records with FFERTYPX = 1) is the initial medical service (event) which is followed by other medical events that are covered under the same flat fee payment. The leaf of the flat fee group (records with FFERTYPX = 2) are those medical events that are tied back to the initial medical event (the stem) in the flat fee group.

If an emergency room visit is part of a flat fee group, the variable FFTOT96 counts the total number of all known events (that occurred during 1996) covered under a single flat fee payment situation. This count includes the emergency room visit record.

There are 31 emergency room visits that are identified as being part of a flat fee payment group. In order to correctly identify all events that are part of a flat fee group, the user should link all MEPS event files (excluding the prescribed medicines file) using the variable FFID11X.

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 flat fee groups, the initial visit reported occurred in 1996 but the remaining visits that were part of this flat fee group occurred in 1997. In this case, the 1996 flat fee group represented on this file would consist of one event (the stem). The 1997 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 1995 but subsequent visits occurred during 1996. In this case, the initial visit would not be represented on the file. This 1996 flat fee group would then only consist of one or more leaf records and no stem. Another reason for which a flat fee group would not have a stem and a leaf record is that the stems or leaves could have been reported as different event types. In a small number of cases, there are flat fee groups that span various event types. The stem may have been reported as one event type and the leaves may have been reported as another event type. In order to determine this, the analyst must link all event files (excluding the prescribed medicines file) using the variable FFID11X to create the flat fee group.

Expenditures on this file refer to what is paid for health care services. More specifically, expenditures in MEPS are defined as the sum of payments for care received for each emergency room visit, 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 1990's due to the increasingly common practice of discounting. Although measuring expenditures as the sum of payments incorporates discounts in the MEPS expenditure estimates, the estimates do not incorporate any payment not directly tied to specific medical care visits, such as bonuses or retrospective payment adjustments by third party payers. 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 et al., 1999).

Expenditure data related to emergency room visits are broken out by facility and separately billing doctor expenditures. This file contains five categories of expenditure variables per visit: basic hospital emergency room facility expenses, expenses for doctors who billed separately from the hospital for any emergency room services provided during emergency room visit, total expenses, which is the sum of the facility and physician expenses; facility total charge and physician total charge.

The 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 was used in the expenditure imputation process to supplement missing household data. For all emergency room visits, MPC data were used if complete; otherwise, HC data were used if complete. Missing data for emergency room visits where HC data were not complete and MPC data were not collected or complete were derived through the imputation process.

Logical 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, co-payments 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 mis-classifications between Medicare and Medicaid and between Medicare HMO's and private HMO's 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.

A weighted sequential hot-deck procedure was used to impute missing expenditures as well as total charge. The procedure uses survey data from respondents to replace missing data, while taking into account the respondents' weighted distribution in the imputation process. Classification variables vary by event type in the hot-deck imputations, but total charge and insurance coverage are key variables in all of the imputations. Separate imputations were performed for nine categories of medical provider care: inpatient hospital stays, outpatient hospital department visits, emergency room visits, visits to physicians, visits to non-physician providers, dental services, home health care by certified providers, home health care by paid independents, and other medical expenses. After the imputations were finished, visits to physician and non-physician providers were combined into a single medical provider file. The two categories of home care also were combined into a single home health file.

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.

The imputation process was also used to make expenditure estimates at the event level for events that were paid on a capitated basis. The capitation imputation procedure was designed as reasonable approach to complete event level expenditures for respondents in managed care plans. This procedure was conducted in two stages. First, HMO events reported in the MPC as covered by capitation arrangements were imputed using similar HMO events paid on a fee-for-service, with total charge as a key variable. Then this completed set of MPC events was used as the donor pool for unmatched household-reported events for sample persons in HMOs. By using this strategy, capitated HMO events were imputed as if the provider were reimbursed from the HMO on a discounted fee-for-service basis.

Facility expenditures for emergency room services 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 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 hot-deck imputations for missing expenditures. The general rule was that MPC data would be used where a household reported event corresponded to a 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.

One of the more important edits separated flat fee events from simple events. This edit was necessary because groups of events covered by a flat fee (i.e. a flat fee bundle) were edited and imputed separately from individual events covered by a single charge (i.e., simple events). Most emergency room events were imputed as simple events because hospital facility charges are rarely bundled with other events. (See section 2.5.10 for more detail on flat fee groups). However, some emergency room visits were treated as free events because the respondent was admitted to a hospital through its emergency room. In these cases, emergency room charges are included in the charge for an inpatient hospital stay.

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 hot-deck imputations, while events with missing expenditure data were assigned to various recipient categories. Each event was assigned to a recipient category based on its pattern of missing data. For example, an event with a known total charge but no expenditures information was assigned to one category, while an event with a known total charge and some expenditures information was assigned to a different category. Similarly, events without a known total charge were assigned to various recipient categories based on the amount of missing data.

The logical edits produced eight recipient categories in which all events had a common pattern of missing data. Separate hot-deck imputations were performed on events in each recipient category, and the donor pool was restricted to events with complete expenditures from the MPC. The donor pool restriction was used even though some unmatched events had complete household-reported expenditures. These events were not allowed to donate information to other events because the MPC data were considered to be more reliable.

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 cost of free care would be implicitly included in paid events and explicitly included in events that should have been treated as free from provider.

Expenditures for some emergency room visits are not shown because the person was admitted to the hospital through the emergency room. These emergency room events are not free, but the expenditures are included in the inpatient stay expenditures. The variable ERHEVIDX can be used to differentiate between free emergency room care and situations where the emergency room charges have been included in the inpatient hospital charges.

The 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 payments. Thus, if the first visit in the flat fee group occurred prior to 1996, all of the events that occurred in 1996 will have zero payments. Conversely, if the first event in the flat fee group occurred at the end of 1996, the total expenditure for the entire flat fee group will be on that event, regardless of the number of events it covered after 1996.

There are some medical events reported by respondents where the payments were zero. This could occur for several reasons including (1) free care was provided, (2) bad debt was incurred, (3) care was covered under a flat fee arrangement beginning in an earlier year, or (4) follow-up visits were provided without a separate charge (e.g. after a surgical procedure). If all of the medical events for a person fell into one of these categories, then the total annual expenditures for that person would be zero.

An 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.

It is common for an emergency room visit to result in a hospitalization. However, while it is true that all of the event files can be linked by DUPERSID, there is no unique record link between inpatient stays and emergency room visits. However, where this relationship could be identified (using start and end date of the events as well as information from the provider), the expenditure associated with the emergency room visit was moved to the hospital facility expenditure (see Section 2.5.11.2). Hence, the expenditures for some emergency room visits, are included in the resulting hospitalization. In these situations, the emergency room record on this file will have its expenditure information zeroed out to avoid double-counting while its corresponding hospital inpatient stay record on HC-010D will have the combined expenditure. The variable ERHEVIDX identifies these emergency room visits whose expenditures are included in the expenditures for the following hospital inpatient stay. It should also be noted that for these cases, there is only one hospital stay associated with the emergency room stay.

In addition to total expenditures, variables are provided which itemize expenditures according to major sources of payment categories. These categories are:

7. Other Federal sources - includes Indian Health Service, Military Treatment Facilities, and other care by the Federal government

8. Other State and Local Source - includes community and neighborhood clinics, State and local health departments, and State programs other than Medicaid.

10. Other Unclassified Sources - includes sources such as automobile, homeowner's, liability, and other miscellaneous or unknown sources.

Two additional sources 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:

11. Other Private - any type of private insurance payments reported for persons not reported to have any private health insurance coverage during the year as defined in MEPS; and

12. Other Public - Medicaid payments reported for persons who were not reported to be enrolled in the Medicaid program at any time during the year.

Though relatively small in magnitude, users should exercise caution when interpreting the expenditures associated with these two additional sources of payment. While these payments stem from apparent inconsistent responses to health insurance and sources 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 program.

Users should also note that the Other Public and Other private sources of payment categories only exist on File 1 for imputed expenditure data since they were created through the editing/imputation process. File 2 reflect 10 sources of payment as they were collected through the survey instrument.

This file contains 2 sets of imputed expenditure variables: facility expenditures and physician expenditures.

Emergency Room Facility Expenditures (ERFSF96X-ERFOT96X, ERFTC96X, ERFXP96X)

Emergency room expenses include all expenses for treatment, services, tests, diagnostic and laboratory work, x-rays, and similar charges, as well as any physician services included in the emergency room charge.

Emergency room expenditures were obtained primarily through the MPC. If the physician charges were included in the emergency room visit bill, then this expenditure is included in the facility expenditure variables. The imputed facility expenditures are provided on this file. ERFSF96X - ERFOT96X are the 12 sources of payment, ERFTC96X is the total charge, and ERFXP96X is the sum of the 12 sources of payments for the facility expenditure. The 12 sources of payment are: self/family, Medicare, Medicaid, private insurance, Veterans Administration, CHAMPUS/CHAMPVA, other federal, state/local governments, Workman's Compensation, other private insurance, other public insurance and other insurance.

Emergency Room Physician Expenditures (ERDSF96X - ERDOT96X, ERDTC96X, ERDXP96X)

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 emergency room visit bills.

For physicians who bill separately (i.e. outside the emergency room visit 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, an emergency room visit could have a radiologist, and an internist associated with it. If their services are not included in the emergency room visit bill then this is one medical event with 2 separately billing doctors. The imputed expenditure information associated with the separately billing doctors was summed to the event level and is provided on the file. ERDSF96X - ERDOT96X are the 12 sources of payment, ERDXP96X is the sum of the 12 sources of payments, and ERDTC96X is the total charge.

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). Analysts interested in total expenditure should use the variable EREXP96X, which includes both the facility and physician amounts.

Expenditure variables on this file, HC-010E, have been rounded to the nearest penny. Person-level expenditure information released on HC-011 were rounded to the nearest dollar. It should be noted that using the MEPS event files HC-010A through HC-010H to create person-level totals will yield slightly different totals than those found on HC-011. These differences are due to rounding only. Moreover, in some instances, the number of persons having expenditures on the event files (HC-010A - HC-010H) for a particular source of payment may differ from the number of persons with expenditures on the person-level expenditure file (HC-011) for that source of payment. This difference is also artifact of rounding only. Please see the Appendix File for details on such rounding differences.

The variables IMPERFSF - IMPERCHG identify records where sources of payment and total charge for the facility portion of the expenditure have been imputed using the methodologies outlined in this document. The variable IMPERNUM indicates the number of physician records associated with the emergency room visit where the physician portion of the expenditures have been imputed. It is not available for individual sources of payment.

When a record was identified as being the leaf of a flat fee group, the values of all imputation flags were set to "0" (not imputed) since they were not included in the imputation process.

Both imputed and pre-imputed expenditure data is provided on this file. Pre-imputed means that only a series of logical edits were applied to both the HC and MPC data to correct for several problems including outliers, co-payments or charges reported as total payments, and reimbursed amounts counted as out-of-pocket payments. Edits were also implemented to correct for mis-classifications between Medicare and Medicaid and between Medicare HMO's and private HMO's as payment sources as well as a number of other data inconsistencies that could be resolved through logical edits. Missing data were not imputed.

As described previously, there are several components that went into creating the total medical expenditure variable: household reported expenditure data and provider reported expenditure data. Both sets of expenditure data are provided in their pre-imputed form and have not gone through the same level of quality control as their imputed counterpart. This means that (in some instances) there are large amounts of missing data. The household and provider reported facility pre-imputed expenditure data are provided on this file (ERSF96H - EROT96H and ERFSF96M-ERFOT96M respectively).

The user shall note that there exist only 10 sources of payment variables in the pre-imputed expenditure data, while the imputed expenditure data on File 1 contains 12 sources of payment variables. The additional two sources of payment (which are not reported as separate sources of payment through the data collection) are Other Private and Other Public. These sources of payment categories were constructed to resolve apparent inconsistencies between individuals' reported insurance coverage and their sources of payment for specific events.

The user should also note that the variable HHSFFIDX, which is the original flat fee identifier that was derived during the household interview, should only be used if the user interested in performing their own expenditure imputation.

There is a single full year person-level weight (WTDPER96) included on this file. A person-level weight was assigned to each emergency room visit reported by a key, in-scope person who responded to MEPS for the full period of time that he or she was in-scope during 1996. A key person either was a member of an NHIS household at the time of the NHIS interview, or became a member of such a household after being out-of-scope at the time of the 1995 NHIS (examples of the latter situation include newborns and persons returning from military service, an institution, or living outside the United States). A person is in-scope whenever he or she is a member of the civilian noninstitutionalized portion of the U.S. population.

The person-level weight WTDPER96 was developed using the MEPS Round 1 person-level weight as a base weight (for key, in-scope respondents who joined an RU after Round 1, the Round 1 RU weight served as a base weight). The weighting process included an adjustment for nonresponse over Round 2 and the 1996 portion of Round 3, as well as poststratification to population control figures for December 1996 (these figures were derived by scaling the population totals obtained from the March 1997 Current Population Survey (CPS) to reflect the Census Bureau estimated population distribution across age and sex categories as of December, 1996). Variables used in the establishment of person-level poststratification control figures included: poverty status (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); census region (Northeast, Midwest, South, West); MSA status (MSA, non-MSA); race/ethnicity (Hispanic, black but non-Hispanic, and other); sex; and age. Overall, the weighted population estimate for the civilian non-institutionalized population for December 31, 1996 is 265,439,511 persons. The inclusion of key, in-scope persons who were not in-scope on December 31,1996 brings the estimated total number of persons represented by the MEPS respondents over the course of the year up to 268,905,490 (WTDPER96 > 0). The weighting process included poststratification to population totals obtained from the 1996 Medicare Current Beneficiary Survey (MCBS) for the number of deaths among Medicare beneficiaries in 1996, and poststratification to population totals obtained from the 1996 MEPS Nursing Home Component for the number of individuals admitted to nursing homes.

The MEPS Round 1 weights incorporated the following components: the original household probability of selection for the NHIS; ratio-adjustment to NHIS national population estimates at the household (occupied dwelling unit) level; adjustment for nonresponse at the dwelling unit level for Round 1; and poststratification to figures at the family- and person-level obtained from the March 1996 CPS database.

This file is constructed for efficient estimation of utilization, expenditure, and sources of payment for hospital emergency room visits and to allow for estimates of number of persons with emergency room visits for 1996.

It is essential that the analyst examine all variables for the presence of negative values used to represent missing values. For example, a record with a value of -8 for the first ICD9condition code (ERICD1X) indicates that the condition was reported as unknown.

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 analyst should either impute a value or set the value to one that will be interpreted as missing by the computing language used. For categorical and dichotomous variables, the analyst may want to consider whether to recode or impute a value for cases with negative values or whether to exclude or include such cases in the numerator and/or denominator when calculating proportions.

Methodologies used for the editing/imputation of expenditure variables (e.g. sources of payment flat fee, hospital/er, and zero expenditures) are described in section 2.5.11.2.

While the examples described below illustrate the use of event level data in constructing person-level total expenditures, these estimates can also be derived from the person-level expenditure file unless the characteristic of interest is event specific.

In order to produce national estimates related to emergency room utilization, expenditures and sources of payment, the value in each record contributing to the estimates must be multiplied by the weight (WTDPER96) contained on that record.

For example, the total number of emergency room visits, for the civilian non-institutionalized population of the U.S. in 1996, is estimated as the sum of the weight (WTDPER96) across all emergency room visit records. That is,

Various estimates can be produced based on specific variables and subsets of records.

For example, the estimate for the mean out-of-pocket payment per emergency room visit should be calculated as the weighted average of the facility bill and doctor's bill paid by self/family. That is,

This gives $44.57 as the estimated mean amount of out-of-pocket payment of expenditures associated with emergency room visits and 41,310,356 as an estimate of the total number of emergency room visits with expenditures. Both of these estimates are for the civilian non-institutionalized population of the U.S. in 1996.

Another example would be to estimate the average proportion of total expenditures paid by private insurance for emergency room visits with expenditures. This should be calculated as the weighted average of the proportion of total expenditures paid by private insurance at the event level. That is

This gives 0.4319 as the estimated mean proportion of total expenditures paid by private insurance for emergency room visits with expenditures for the civilian non-institutionalized population of the U.S. in 1996.

When calculating an estimate of the total number of persons with emergency room visits, users can use a person-level file (MEPS HC-011: Person-level Expenditures and Utilization) or the current file. However, the current file must be used, when the measure of interest is defined at the event level. For example, to estimate the number of persons with emergency room visits where the patient sees a doctor, the current file must be used. This would be estimated as,

4.4.1 Person-Based Ratio Estimates Relative to Persons with Emergency Room Use

This file may be used to derive person-based ratio estimates. However, when calculating ratio estimates where the denominator is persons, care should be taken to properly define the unit of analysis as person-level. For example, the mean expense for persons with emergency room visit is estimated as,

If the ratio relates to the entire population, this file cannot be used to calculate the denominator, as only those persons with at least one emergency room visit are represented on this data file. In this case MEPS File HC-011, which has data for all sampled persons, must be used to estimate the total number of persons (i.e. those with use and those without use). For example, to estimate the proportion of civilian non-institutionalized population of the U.S. with at least one emergency room visit where s/he saw a doctor, the numerator would be derived from data on the current file, and the denominator should be derived from data on the MEPS HC-011, the person-level file. That is,

Prior to estimation users will need to correct the 75 records with a missing value for SEEDOC.

4.5 Sampling Weights for Merging Previous Releases of MEPS Household Data with the Current Data File

There have been several previous releases of MEPS Household Survey public use data. Unless a variable name common to several tapes is provided, the sampling weights contained on these data files are file-specific. The file-specific weights reflect minor adjustments to eligibility and response indicators due to birth, death, or institutionalization among respondents.

In general, for estimates from a MEPS data file that do not require merging with variables from other MEPS data files, the sampling weight(s) provided on that data file are the appropriate weight(s). When merging a MEPS Household data file to another, the major analytical variable (i.e. the dependent variable) determines the correct sampling weight to use.

To obtain estimates of variability (such as the standard error of sample estimates or corresponding confidence intervals) for estimates based on MEPS survey data, one needs to take into account the complex sample design of MEPS. Various approaches can be used to develop such estimates of variance including use of the Taylor series or various replication methodologies. Replicate weights have not been developed for the MEPS 1996 data. Variables needed to implement a Taylor series estimation approach are described in the paragraph below.

Using a Taylor Series approach, variance estimation strata and the variance estimation PSUs within these strata must be specified. The corresponding variables on the MEPS full year utilization database are VARSTR96 and VARPSU96, respectively. Specifying a "with replacement" design in a computer software package such as SUDAAN (Shah, 1996) should provide 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 actual number available. For MEPS sample estimates for characteristics generally distributed throughout the country (and thus the sample PSUs), there are over 100 degrees of freedom associated with the corresponding estimates of variance. The following illustrates these concepts using two examples from Section 4.2.

Using a Taylor Series approach, specifying VARSTR96 and VARPSU96 as the variance estimation strata and PSUs (within these strata) respectively and specifying a "with replacement" design in the computer software package SUDAAN will yield an estimate of standard error of $2.71 for the estimated mean of out-of-pocket payment.

Using a Taylor Series approach, specifying VARSTR96 and VARPSU96 as the variance estimation strata and PSUs (within these strata) respectively and specifying a "with replacement" design in the computer software package SUDAAN will yield an estimate of standard error of 0.0118 for the weighted mean proportion of total expenditures paid by private insurance.

Data from this file can be used alone or in conjunction with other files. This section provides instructions for linking the hospital stays files with other MEPS public use files, including: the conditions file, the prescribed medicines file, and a person-level file.

Merging characteristics of interest from person-level files (e.g., HC-008: 1996 Population Characteristics and Utilization Data, or HC-011: 1996 Use and Expenditure File) expands the scope of potential estimates. For example, to estimate the total number of hospital inpatient stays for persons with specific characteristics (e.g., age, race, and sex), population characteristics from a person-level file need to be merged onto the emergency room visit file. This procedure is illustrated below. The Appendix File (HC-010I) provides additional detail on how to merge MEPS data files.

1. Create data set PERS by sorting the person-level file, HC003, by the person identifier, DUPERSID. Keep only variables to be merged on to the emergency room visit file and DUPERSID.

2. Create data set EROM by sorting the emergency room visit file by person identifier, DUPERSID.

3. Create final data set NEWEROM by merging these two files by DUPERSID, keeping only records on the emergency room visit file.

5.2 Linking the Emergency Room Visits File (HC-010F) to the Medical Conditions File (HC-006) and/or the Prescribed Medicines File (HC-010A)

Due to survey design issues, there are limitations/caveats that an analyst must keep in mind when linking the different files. Those limitations/caveats are listed below. For detailed linking examples, including SAS code, analysts should refer to the Appendix File.

The RXLK file provides a link from the MEPS event files to the prescribed medicine records on HC-010A. When using RXLK, analysts should keep in mind that one emergency room visit can link to more than one prescribed medicine record. Conversely, a prescribed medicine event may link to more than one emergency room visit or different types of events. When this occurs, it is up to the analyst to determine how the prescribed medicine expenditures should be allocated among those medical events.

The CLNK provides a link from MEPS event files to the Medical Conditions File (HC-006). When using the CLNK, analysts should keep in mind that (1) conditions are self-reported and (2) there may be multiple conditions associated with a hospital inpatient stay. Users should also note that not all hospital inpatient stays link to the condition file.

The following are the technical specifications for the HC-010E data files, which are provided in ASCII and SAS formats.

Cohen, 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. (1997). Sample Design of the 1996 Medical Expenditure Panel Survey Household Component. Rockville (MD): Agency for Health Care Policy and Research; 1997. MEPS Methodology Report, No. 2. AHCPR Pub. No. 97-0027.

Cohen, J.W. (1997). Design and Methods of the Medical Expenditure Panel Survey Household Component. Rockville (MD): Agency for Health Care Policy and Research; 1997. MEPS Methodology Report, No. 1. AHCPR Pub. No. 97-0026.

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). Chapter 6: A Comparison of Household and Provider Reports of Medical Conditions. In Methodological Issues for Health Care Surveys. Marcel Dekker, New York.

Cox, B.G. and Cohen, S.B. (1985). Chapter 8: Imputation Procedures to Compensate for Missing Responses to Data Items. In Methodological Issues for Health Care Surveys. Marcel Dekker, New York.

Cox, B. and Iachan, R. (1987). A Comparison of Household and Provider Reports of Medical Conditions. Journal of the American Statistical Association 82(400):1013-18.

Edwards, W.S., Winn, D.M., Kurlantzick V., et al. (1994). Evaluation of National Health Interview Survey Diagnostic Reporting. National Center for Health Statistics, Vital Health 2(120).

Elixhauser A., Steiner C.A., Whittington C.A., and McCarthy E. Clinical Classifications for Health Policy Research: Hospital Inpatient Statistics, 1995. Healthcare Cost and Utilization Project, HCUP-3 Research Note. Rockville, MD: Agency for Health Care Policy and Research; 1998. AHCPR Pub. No. 98-0049.

Health Care Financing Administration (1980). International Classification of Diseases, 9^th Revision, Clinical Modification (ICD-CM). Vol. 1. (DHHS Pub. No. (PHS) 80-1260). DHHS: U.S. Public Health Services.

Johnson, A.E. and Sanchez, M.E. (1993). Household and Medical Provider Reports on Medical Conditions: National Medical Expenditure Survey, 1987. Journal of Economic and Social Measurement. Vol. 19, 199-233.

Moeller J.F., Stagnitti, M., Horan, E., et al. Data Collection and Editing Procedures for Prescribed Medicines in the 1996 Medical Expenditure Panel Survey Household Component. Rockville (MD): Agency for Healthcare Research and Quality; 2000. MEPS Methodology Report (forthcoming).

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.

Dwelling Units, Reporting Units, Families, and Persons The definitions of Dwelling Units (DUs) and Group Quarters in the MEPS Household Survey are generally consistent with the definitions employed for the National Health Interview Survey. The dwelling unit ID (DUID) is a five-digit random ID number assigned after the case was sampled for MEPS. The person number (PID) uniquely identifies all persons within the dwelling unit. The variable DUPERSID is the combination of the variables DUID and PID.

A Reporting Unit (RU) is a person or group of persons in the sampled dwelling unit who are related by blood, marriage, adoption or other family association, and who are to be interviewed as a group in MEPS. Thus, the RU serves chiefly as a family-based "survey operations" unit rather than an analytic unit. Regardless of the legal status of their association, two persons living together as a "family" unit were treated as a single reporting unit if they chose to be so identified.

Unmarried college students under 24 years of age who usually live in the sampled household, but were living away from home and going to school at the time of the Round 1 MEPS interview, were treated as a Reporting Unit separate from that of their parents for the purpose of data collection. These variables can be found on MEPS person level files.

In-Scope  A person was classified as in-scope (INSCOPE) if he or she was a member of the U.S. civilian, non-institutionalized population at some time during the Round 1 interview. This variable can be found on MEPS person level files.

Keyness The term "keyness" is related to an individual's chance of being included in MEPS. A person is key if that person is appropriately linked to the set of 1995 NHIS sampled households designated for inclusion in MEPS. Specifically, a key person either was a member of an NHIS household at the time of the NHIS interview, or became a member of such a household after being out-of-scope prior to joining that household (examples of the latter situation include newborns and persons returning from military service, an institution, or living outside the United States).

A non-key person is one whose chance of selection for the NHIS (and MEPS) was associated with a household eligible but not sampled for the NHIS, who happened to have become a member of a MEPS reporting unit by the time of the MEPS Round 1 interview. MEPS data, (e.g., utilization and income) were collected for the period of time a non-key person was part of the sampled unit to permit family level analyses. However, non-key persons who leave a sample household would not be recontacted for subsequent interviews. Non-key individuals are not part of the target sample used to obtain person level national estimates.

It should be pointed out that a person may be key even though not part of the civilian, non-institutionalized portion of the U.S population. For example, a person in the military may be living with his or her civilian spouse and children in a household sampled for the 1995 NHIS. The person in the military would be considered a key person for MEPS. However, such a person would not receive a person-level sample weight so long as he or she was in the military. All key persons who participated in the first round of the 1996 MEPS received a person level sample weight except those who were in the military. The variable indicating "keyness" is KEYNESS. This variable can be found on MEPS person level files.

Eligibility The eligibility of a person for MEPS pertains to whether or not data were to be collected for that person. All key, in-scope persons of a sampled RU were eligible for data collection. The only non-key persons eligible for data collection were those who happened to be living in the same RU as one or more key persons, and their eligibility continued only for the time that they were living with a key person. The only out-of-scope persons eligible for data collection were those who were living with key in-scope persons, again only for the time they were living with a key person. Only military persons meet this description. A person was considered eligible if they were eligible at any time during Round 1. The variable indicating "eligibility" is ELIGRND1, where 1 is coded for persons eligible for data collection for at least a portion of the Round 1 reference period, and 2 is coded for persons not eligible for data collection at any time during the first round reference period. This variable can be found on MEPS person level files.

Pre-imputed - This means that only a series of logical edits were applied to the HC data to correct for several problems including outliers, copayments or charges reported as total payments, and reimbursed amounts counted as out of pocket payments. Missing data remains.

Unimputed - This means that only a series of logical edits were applied to the MPC data to correct for several problems including outliers, copayments or charges reported as total payments, and reimbursed amounts counted as out of pocket payments. This data was used as the imputation source to account for missing HC data.

Imputation -Imputation is more often used for item missing data adjustment through the use of predictive models for the missing data, based on data available on the same (or similar) cases. Hot-deck imputation creates a data set with complete data for all nonrespondent cases, often by substituting the data from a respondent case that resembles the nonrespondent on certain known variables.

Variable	Description	Source
ERDATEYR	Event date - year	CAPI derived
ERBEGMM	Event date – month	CAPI derived
ERBEGDD	Event date – day	CAPI derived
SEEDOC	Did P see medical doctor during this visit	ER01
VSTCTGRY	Best category for EROM care on visit	ER02
VSTRELCN	Was this visit related to special health condition	ER03
LABTEST	During the visit did the P have lab tests	ER05
SONOGRAM	During this visit did P have sonog/ultras	ER05
XRAYS	During this visit did P have x–rays	ER05
MAMMOG	During this visit did P have mammogram	ER05
MRI	During this visit did P have MRI/CATSCAN	ER05
EKG	During this visit did P have EKG or ECG	ER05
EEG	During this visit did P have EEG	ER05
RCVVAC	During this visit did P receive vaccination	ER05
ANESTH	During this visit did P receive anesthesia	ER05
OTHSVCE	During this visit did P have OTH TSTS/EXM	ER05
SURGPROC	Surgical procedure performed on P during visit	ER06
SURGNAME	Surgical procedure name in categories	ER07
MEDPRESC	This visit were any medicines prescribed for P	ER08
DOCOUTF	Did person see any ER docs outside of ER	ER10
VAPLACE	Emergency room is a VA facility	Constructed
ERICD1X	3-digit ICD-9 condition code	Edited
ERICD2X	3-digit ICD-9 condition code	Edited
ERICD3X	3-digit ICD-9 condition code	Edited
ERPRO1X	2-digit ICD-9 condition code	Edited
ERCCC1X	Modified Clinical Classification Code	Constructed/Edited
ERCCC2X	Modified Clinical Classification Code	Constructed/Edited
ERCCC3X	Modified Clinical Classification Code	Constructed/Edited
NUMCOND	Total number of COND records linked to this event	Constructed

Variable	Description	Source
FFERTYPX	Edited flat fee stem or leaf	FF01, FF02
FFER96	Total # ER visits in flat fee in 1996	FF02
FFTOT96	Total # visits in flat fee for 1996	FF02 (edited)
EREXP96X	Total expenditure for emergency room visit	Constructed
ERTC96X	Total charge for emergency room visit	Constructed
ERFSF96X	Facility amount paid, family (imputed)	CP11 (Edited/Imputed)
ERFMR96X	Facility amount paid, Medicare (imputed)	CP09 (Edited/Imputed)
ERFMD96X	Facility amount paid, Medicaid (imputed)	CP07 (Edited/Imputed)
ERFPV96X	Facility amount paid, private insurance (imputed)	CP07 (Edited/Imputed)
ERFVA96X	Facility amount paid, Veterans (imputed)	CP07 (Edited/Imputed)
ERFCH96X	Facility amount paid, CHAMP/CHAMPVA (imputed)	CP07 (Edited/Imputed)
ERFOF96X	Facility amount paid, other federal (imputed)	CP07 (Edited/Imputed)
ERFSL96X	Facility amount paid, state/local govt. (imputed)	CP07 (Edited/Imputed)
ERFWC96X	Facility amount paid, Worker’s Comp (imputed)	CP07 (Edited/Imputed)
ERFOR96X	Facility amount paid, other private (imputed)	Constructed
ERFOU96X	Facility amount paid, other public (imputed)	Constructed
ERFOT96X	Facility amount paid, other insurance (imputed)	CP07 (Edited/Imputed)
ERFXP96X	Facility sum of payments ERFSF96X – ERFOT96X	Constructed
ERFTC96X	Facility total charge (imputed)	CP09 (Edited/Imputed)
IMPERFSF	Imputation flag for ERFSF96X	Constructed
IMPERFMR	Imputation flag for ERFMR96X	Constructed
IMPERFMD	Imputation flag for ERFMD96X	Constructed
IMPERFPV	Imputation flag for ERFPV96X	Constructed
IMPERFVA	Imputation flag for ERFVA96X	Constructed
IMPERFCH	Imputation flag for ERFCH96X	Constructed
IMPERFOF	Imputation flag for ERFOF96X	Constructed
IMPERFSL	Imputation flag for ERFSL96X	Constructed
IMPERFWC	Imputation flag for ERFWC96X	Constructed
IMPERFOR	Imputation flag for ERFOR96X	Constructed
IMPERFOU	Imputation flag for ERFOU96X	Constructed
IMPERFOT	Imputation flag for ERFOT96X	Constructed
IMPERCHG	Imputation flag for ERFTC96X	Constructed
IMPERNUM	Number of Dr. records imputed per provider	Constructed

Variable	Description	Source
ERDSF96X	Doctor amount paid, family (imputed)	CP11 (Edited/Imputed)
ERDMR96X	Doctor amount paid, Medicare (imputed)	CP09 (Edited/Imputed)
ERDMD96X	Doctor amount paid, Medicaid (imputed)	CP07 (Edited/Imputed)
ERDPV96X	Doctor amount paid, private insurance (imputed)	CP07 (Edited/Imputed)
ERDVA96X	Doctor amount paid, Veterans (imputed)	CP07 (Edited/Imputed)
ERDCH96X	Doctor amount paid, CHAMP/CHAMPVA (imputed)	CP07 (Edited/Imputed)
ERDOF96X	Doctor amount paid, other federal (imputed)	CP07 (Edited/Imputed)
ERDSL96X	Doctor amount paid, state/local govt. (imputed)	CP07 (Edited/Imputed)
ERDWC96X	Doctor amount paid, Worker’s Comp (imputed)	CP07 (Edited/Imputed)
ERDOR96X	Doctor amount paid, other private (imputed)	Constructed
ERDOU96X	Doctor amount paid, other public (imputed)	Constructed
ERDOT96X	Doctor amount paid, other insurance (imputed)	CP07 (Edited/Imputed)
ERDXP96X	Doctor sum of payments ERDSF96X – ERDOT96X	Constructed
ERDTC96X	Doctor total charge (imputed)	CP09 (Edited/Imputed)

Variable	Description	Source
ERSF96M	MPC reported amount paid, family (unimputed)	Question# 8a
ERMR96M	MPC reported amount paid, Medicare (unimputed)	Question# 8b
ERMD96M	MPC reported amount paid, Medicaid (unimputed)	Question# 8c
ERPV96M	MPC reported amount paid, private insurance (unimputed)	Question# 8d
ERVA96M	MPC reported amount paid, Veterans (unimputed)	Question# 8e
ERCH96M	MPC reported amount paid, CHAMP/CHAMPVA (unimputed)	Question# 8f
EROF96M	MPC reported amount paid, other federal (unimputed)	Question# 8g
ERSL96M	MPC reported amount paid, state/local govt. (unimputed)	Question# 8g
ERWC96M	MPC reported amount paid, Worker’s Comp (unimputed)	Question# 8g
EROT96M	MPC reported amount paid, other insurance (unimputed)	Question# 8g
ERTC96M	MPC reported total charge (unimputed)	Question# 9

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