MEPS HC-010C: 1996 Other Medical Expenses

MEPS HC-010C: 1996 Other Medical Expenses
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 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 information on the purchase of and expenditures for visual aids, medical equipment, supplies and other medical items for a nationally representative sample of the civilian noninstitutionalized population of the United States and can be used to make estimates of the utilization and expenditures associated with medical items for calendar year 1996.

This file contains one record for each type of medical item reported as being purchased or otherwise obtained by the household respondent during the specified reference period. It should be noted that references periods for reporting expenditures varies by type of medical item obtained. Expenditure data for visual aides, insulin, and diabetic supplies and equipment are collected during rounds 1, 2 and 3. Therefore, each round is a reference period. Expenditure data for other medical items, which include orthopedic items, hearing devices, medical equipment, disposable supplies, ambulance services, bathroom aides, and home alterations are collected only in round 3 and the reference period is the entire year. For details regarding reference periods see Section XX.

Data from this event file can be merged with other MEPS HC data files, at the person level, to append person characteristics such as demographic or health insurance coverage to each record on the current file. Such information can be found on public use files HC-008 and HC-011.

The purchase of medical equipment, supplies, and other medical items was not included in the Medical Provider Component (MPC), therefore all expenditure and payment data are reported by household respondents only.

Data users should be aware of the limitations of this file. These limitations include the following:

a) A record can represent one or more purchases of an item or service during a reference period. If a respondent reported spending $400 for glasses and/or contact lenses in round 2, it is unknown if the person purchased one or more pair of glasses and/or contact lenses during that round. Similarly, if $800 was spent for ambulance services, it is not known if the respondent used an ambulance once or more than once in 1996;

b) Although analysts can link conditions to the current file using DUPSERID, the specific condition requiring the purchase of medical items or services, such as an ambulance, cannot be identified. For example, if a person reported having asthma, a head injury, and a heart attack, it is not known which condition(s) required the use of an ambulance.

c) Expenditure data for insulin and diabetic supplies is not included on this file. Expenditures for these items are included on the Prescribed Medicines File (HC-010A)

This file can be used to construct summary variables of expenditures, sources of payment, and related aspects of the purchase of medical items. Aggregate annual person-level information on expenditures for other medical equipment is provided on 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 set consists of two event-level data files. File 1 contains the type of medical equipment obtained and imputed expenditure data. File 2 contains pre-imputed expenditure data from the Household Component for types medical equipment identified on File 1. Please see Attachment 1 for definitions of imputed and pre-imputed expenditure variables.

Files 1 and 2 of this public use data set contains 6,402 other medical expenditure records; of these 6,248 are associated with persons having a positive person-level weight (WTDPER96). These files include records for all household survey respondents who resided in eligible responding households and reported purchasing or otherwise obtaining at least one type of medical item, such as medical equipment, glasses, hearing devices, etc., during calendar year 1996. Some household respondents may have reported obtaining more than one type of medical item and, therefore have several records on this file. Likewise, respondents who did not report obtaining a medical item in 1996 have no records on this file. These data were collected during rounds 1, 2, and 3 of the MEPS HC. The persons represented on this file had to meet either (a) or (b) below:

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 of FAMID) 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 family-level weight (WTFAM96 >0)).

Please refer to Attachment 1 for definitions of key, non-key, inscope and eligible. Each record on this file includes the following: type of medical item obtained, number of prescribed medicines that can be linked to this file, flat fee information, imputed sources of payment, total payment and total charge for the medical item, 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 consists of one set of pre-imputed expenditure information from the Household Component. 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 data using the unique person identifier, DUPERSID, to append person characteristics such as demographic or health insurance characteristics to each record. See Section 5.0 for details on how to link MEPS data files. Although conditions can be linked to the current file, data users should note that specific conditions requiring the purchase of medical items or services, such as ambulance service, cannot be identified for records on this file.

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:

Generally, -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 from the HC questionnaire. The source of each variable is identified in Section E, entitled, "Variable to Source Crosswalk". Sources for each variable are indicated in one of three 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 numbers and the questionnaire section indicated in the "Source" column; and (3) variables constructed from multiple questions using complex algorithms are labeled "Constructed" in the "Source" column.

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 remain. The imputed versions incorporate the same edits but also have undergone an imputation process to account for missing data.

The pre-imputed expenditure variables on File 2 end with an "H" indicating that the data source was the MEPS Household Component. All imputed variables on File 1 end with an "X"indicating they are fully edited and imputed.

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

In the case of the source of payment variables, the third and fourth characters indicate:

The fifth and sixth characters indicate the year (96). The seventh character indicates whether or not the variable was imputed/edited (ends with 'X') or reported by the household (ends in 'H').

OMSF96X is the edited/imputed amount paid by self or family for the medical item.

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 on public use file HC-008.

FFID11X uniquely identifies a flat fee group, that is, all events that were part of a flat fee payment situation. For example, a charge for crutches following outpatient foot surgery is typically covered in a flat fee arrangement where the visit and the medical equipment are covered under one flat fee dollar amount. These events are in different files but have the same value for FFID11X. FFID11X identifies a flat fee payment situation that was identified using information from the Household Component. Please note that FFEEID11 should be used to link 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 other medical expenditure was reported. For most types of medical expenditures on this file data were collected only in round 3 and each record represents a summary of expenditures for items purchased or otherwise obtained for 1996. There are two exceptions:

a) Expenditure data for the purchase of glasses and/or contact lenses were collected in rounds 1, 2, and 3. For vision items purchased in round 3, it could not be determined if the purchases occurred in 1996 or 1997. Therefore, records with expenses reported in round 3 were only included if more than half of the person's reference period for the round was in 1996.

b) Respondents were asked whether or not they obtained insulin or diabetic supplies/equipment in rounds 1, 2, and 3. Expenditures for insulin and diabetic supplies/equipment are not included on this file, but are included on the 1996 Prescribed Medicines file (HC-010A). All records for insulin and diabetic supplies on this file have a value of -1 for all expenditure (i.e., charge and payment) variables included on File 1 and File 2 of this data set.

Other medical expenditures (OMTYPE1) include glasses/contacts, insulin, diabetic supplies, orthopedic items, hearing devices, medical equipment, disposable supplies, bathroom aids, and homes alterations. Other medical expenditures identified in OMOTHOS ( type of expenditure - other specify) have been edited to appropriate OMTYPE1 categories. The edited (OMTYPE1X, OMOTHOX) and unedited (OMETYP1, OMOTHOS) versions of both of these variables are included on this file.

A flat fee is the fixed dollar amount a person is charged for a package of services provided during a defined period of time. A flat fee group is the set of medical events (that can vary by type of event) that are covered under the same flat fee payment situation. For example, a person may have outpatient orthopedic surgery and be given crutches or other orthopedic equipment. The surgeon's fee covers the procedure as well as follow-up care and the orthopedic equipment.

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.

Data users should note that flat fee payment situations are not common on this file compared with other medical expenditures . There are only 35 records that are identified as being part of a flat fee payment group. This yields 27 separate payment 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 FFEEID11 can be used to identify all events, that are part of the same flat fee group. To identify such events, FFEEID11 should be used to link events from all MEPS event files (excluding prescribed medicines): HC-010B through HC-010H. For the other medical expenditures that are not part of a flat fee payment situation, the flat fee variables described below are all set to inapplicable (-1).

FFOMTYPX indicates whether the 1996 other medical expenditure is the "stem" or "leaf" of a flat fee group. A stem (records with FFOMTYPX = 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 FFOMTYPX = 2) are those medical events that are tied back to the initial medical event (the stem) in the flat fee 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. There are 29 event records which were reported by the household respondent as being part of a flat fee. 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 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 bundles 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 using the variable FFEEID11X to create the flat fee group.

If a medical item was obtained as part of a flat fee group, the variable FFTOT96 counts the total number of all known events that occurred during 1996 that are covered under a single flat fee payment situation.

As described above, a flat fee payment situation covers multiple events and the multiple events could span multiple years. For situations where the medical item was obtained in 1996 as part of a group of events and some of the events occurred before or after 1996, counts of the known events are provided on the other medical expenditure record. Indicator variables are provided if some of the events occurred after 1996. These variables are:

FFBEF96 -- total number of pre-1996 events in the same flat fee group as the medical item that was obtained in 1996. This count would not include the medical item obtained in 1996.

FFOM97 indicates whether or not medical items were obtained in 1997 as part of the same flat fee group as the medical item that was obtained in 1996.

FFTOT97 -- indicates whether or not there are 1997 medical events, including the purchase of the medical item, in the same flat fee group as the medical item obtained in 1996.

Expenditures on this file refer to what is paid for the medical item. More specifically, expenditures in MEPS are defined as the sum of payments for each medical item that was obtained, 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. Measuring expenditures as the sum of payments incorporates discounts in the MEPS expenditure estimates. 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. While charge data are provided on this file, analysts should use caution when working with this 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 see Monheit et al, 1999 ).

The general methodology used for editing and imputing expenditure data is described below. Neither the dental events nor other medical expenditures (such as glasses, contact lenses, and hearing devices) were included in the Medical Provider Component (MPC). Therefore, although the general procedures remain the same, for dental and other medical expenditures, editing and imputation methodologies were applied only to household-reported data. Specific methodologies for editing and imputing dental expenditures follows the General Imputation Methodology section.

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, 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. 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 for missing expenditures as well as total charge. The procedure uses survey data from respondents to correct for missing non-respondent data, while preserving the respondents' weighted distribution in the imputed data. 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 on other medical equipment and services were developed in a sequence of logical edits and imputations. The household edits were used to correct obvious errors in the reporting of expenditures, and to identify actual and potential sources of payments. Some of the edits were global (i.e., applied to all events). Others were hierarchical and mutually exclusive.

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 nine recipient categories for events with missing data. Eight of the categories were for events with a common pattern of missing data and a primary payer other than Medicaid. These events were imputed separately because persons on Medicaid rarely know the provider's charge for services or the amount paid the state Medicaid program. As a result the total charge for Medicaid-covered services was imputed and discounted to reflect the amount that a state program might pay for the care.

Separate hot-deck imputations were used to impute for missing data in each of the other eight recipient categories. 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. Whenever possible missing data were imputed from donors with the same other medical expenditure type, age (<45 and 45 and older), and region.

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.

Some respondents reported obtaining medical items where the payments were zero. This could occur for several reasons including (1) item or service was free, (2) bad debt was incurred, or (3) the item was covered under a flat fee arrangement beginning in an earlier year.

In addition to total expenditures, variables are provided which itemize expenditures according to major source 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 particular persons that appear inconsistent due to differences between survey questions on health insurance coverage and sources of payment for medical events. 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 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 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 source of payment categories only exist on File 1 for imputed expenditure data since they were created through the editing/imputation process. File 2 reflects source of payment as it was collected through the survey.

Other medical expenditure data were obtained through the HC Questionnaire. The imputed expenditures are provided on this file. OMSF96X - OMOT96X are the 12 sources of payment, OMXP96X is the sum of the 12 sources of payment variables, and OMTC96X is the total charge for the medical item. 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.

Expenditure variables on File 1 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 that found on HC-011. These differences are due to rounding only. Please see the Appendix File for details on rounding differences.

The variables IMPOMSLF - IMPOMCHG identify records where the expenditures have been imputed using the methodologies outlined in this document. Records identified as being the leaf of a flat fee or OMTYPE=2 or 3, 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 are provided in this data set; pre-imputed data data are found on File 2. Pre-imputed means that only a series of logical edits were applied to both 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. Edits were also implemented to correct for misclassifications 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.

HHSFFIDX is the original flat fee identifier that was derived during the household interview. This identifier should only be used if the analyst is interested in performing their own expenditure imputation.

The user shall note that there are 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 File 2 also includes a variable indicating uncollected liability. Uncollected liability was not used in imputation.

There is a single full year person-level weight (WTDPER96) included on this file. A person-level weight was 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 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 other medical expenditures and to allow for estimates of number of persons who obtained medical items in 1996.

It is essential that the analyst examine all variables for the presence of negative values used to represent missing values. For continuous or discrete variables, where means or totals may be 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, and zero expenditures) are described in Section 2.5.4.2.

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 other medical expense utilization, expenditure 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 other medical expense events, for the civilian non-institutionalized population of the U.S. in 1996 is estimated as the sum of the weight (WTDPER96) across all other medical expense event records. That is,

Subsetting to records based on characteristics of interest expands the scope of potential estimates. For example, the estimate for the mean out-of-pocket payment per other medical expense event should be calculated as the weighted average of amount paid by self/family. That is,

This gives $122.39 as the estimated mean amount of out-of-pocket payment of expenditures associated with other medical expense events and 66,954,766 as an estimate of the total number of other medical expense events with expenditure. 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 per other medical expense event. This should be calculated as the weighted mean of the proportion of the total other medical expense paid by private insurance at the other medical expense event level. That is,

This gives 0.1756 as the estimated mean proportion of total expenditures paid by private insurance for other medical expense events with expenditure for the civilian non-institutionalized population of the U.S. in 1996.

When calculating an estimate of the total number of persons with other medical expense events, users can use a person-level file (MEPS HC-011: Person Level Expenditures and Utilization) or this event file. However, this event file must be used when the measure of interest is defined at the event level. For example, to estimate the number of persons in the civilian non-institutionalized population of the U.S. with a medical expense for ambulance service in 1996, this event file must be used. This would be estimated as

4.4.1 Person-Based Ratio Estimates Relative to Persons with Other Medical Expense Events

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 and estimate the unit of analysis up to person level. For example, the mean expense for persons with other medical expense events 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 other medical expense event 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 other medical expense event for ambulance services received in 1996, the numerator would be derived from data on this event file, and the denominator would be derived from data on the MEPS HC-011 person-level file. That is,

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.

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 provided in the file and 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 a computer software package SUDAAN will yield an estimate of standard error of $4.10 for the estimated mean 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 a computer software package SUDAAN will yield an estimate of standard error of 0.0073 for the weighted mean proportion of total expenditures paid by private insurance.

Data from the other medical expenditure event file can be used alone or in conjunction with other files. Merging characteristics of interest from other MEPS files (e.g., HC-008: 1996 Full Year Population Characteristics File) expands the scope of potential estimates. For example, to estimate the expenditures for medical equipment, visual aids, etc. for persons with specific characteristics such as age, race, and sex, population characteristics from a person-level file need to be merged onto the dental file. This procedure is shown below. The Appendix File HC:010I provides examples of how to merge other MEPS files to the dental and other event files.

1. Create data set PERSX by sorting the person level file, HC008, by the person identifier, DUPERSID. Keep only variables to be merged on to the dental file and DUPERSID.

2. Create data set OMEXP by sorting the other medical expenditures file by person identifier, DUPERSID.

3. Create final data set NEWOME by merging these two files by DUPERSID, keeping only records on the dental file.

The following are the technical specifications for the HC-010C 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 8: Imputation Procedures to Compensate for Missing Responses to Data Items. In Methodological Issues for Health Care Surveys. Marcel Dekker, New York.

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.

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
FFOMTYPX	Edited flat fee group (stem or leaf)	Constructed
FFOM96	Total # OM events in FF in 1996	FF02
FFTOT96	Total # OM events (pure/mixed) in flat fee for 1996	FF02 (Edited)
FFBEF96	Number OM events in flat fee before 1996	FF05
FFOM97	# OM events in flat fee: Rd3, 1997	FF10 (Edited)
FFTOT97	# events (pure/mixed)in flat fee: Rd3,1997	FF10
OMSF96X	Amount paid, family (Imputed)	CP11 (Edited/Imputed)
OMMR96X	Amount paid, Medicare (Imputed)	CPO7 (Edited/Imputed)
OMMD96X	Amount paid, Medicaid (Imputed)	CPO7 (Edited/Imputed)
OMPV96X	Amount paid, private (Imputed)	CPO7 (Edited/Imputed)
OMVA96X	Amount paid, Veterans (Imputed)	CPO7 (Edited/Imputed)
OMCH96X	Amount paid, CHAMPUS/CHAMPVA (Imputed)	CPO7 (Edited/Imputed)
OMOF96X	Amount paid, other federal (Imputed)	CPO7 (Edited/Imputed)
OMSL96X	Amount paid, state and local gov’t (Imputed)	CPO7 (Edited/Imputed)
OMWC96X	Amount paid, worker’s comp (Imputed)	CPO7 (Edited/Imputed)
OMOR96X	Amount paid, other private (Imputed)	Constructed
OMOU96X	Amount paid, other public (Imputed)	Constructed
OMOT96X	Amount paid, other insurance (Imputed)	Constructed
OMXP96X	Sum of payments OMSLF96X– OMWC96X (Imputed)	Constructed
OMTC96X	Household reported total charge (Imputed)	CP09 (Edited/Imputed)
IMPOMSLF	Imputation flag for OMSLF96X	Constructed
IMPOMMCR	Imputation flag for OMMCR96X	Constructed
IMPOMMCD	Imputation flag for OMMCD96X	Constructed
IMPOMPRV	Imputation flag for OMPRV96X	Constructed
IMPOMVA	Imputation flag for OMVA96X	Constructed
IMPOMCHM	Imputation flag for OMCHM96X	Constructed
IMPOMOFD	Imputation flag for OMOFD96X	Constructed
IMPOMSTL	Imputation flag for OMSTL96X	Constructed
IMPOMWCP	Imputation flag for OMWCP96X	Constructed
IMPOMOPR	Imputation flag for OMOR96X	Constructed
IMPOMOPU	Imputation flag for OMOU96X	Constructed
IMPOMOTH	Imputation flag for OMOT96X	Constructed
IMPOMCHG	Imputation flag for OMTC96X	Constructed

Variable	Description	Source
OMSF96H	Household reported amt. paid, family (Pre-imputed)	CP11 (Edited)
OMMR96H	Household reported amt. paid, Medicare (Pre-imputed)	CPO9 (Edited)
OMMD96H	Household reported amt. paid, Medicaid (Pre-imputed)	CPO7 (Edited)
OMPV96H	Household (Pre-imputed) reported amt. paid, private insurance	CPO7 (Edited)
OMVA96H	Household reported amt. paid, Veterans (Pre-imputed)	CPO7 (Edited)
OMCH96H	Household reported amt. paid, CHAMPUS/CHAMPVA (Pre-imputed)	CPO7 (Edited)
OMOF96H	Household reported amt.paid, other federal (Pre-imputed)	CPO7 (Edited)
OMSL96H	Household reported amt. paid, state and local gov’t (Pre-imputed)	CPO7 (Edited)
OMWC96H	Household reported amt. paid, worker’s comp (Pre-imputed)	CPO7 (Edited)
OMOT96H	Household reported amt. paid, other insurance (Pre-imputed)	CPO7 (Edited)
OMUC96H	Household reported uncollected liability (Pre-imputed)	CPO7 (Edited)
OMTC96H	Household reported total charge (Pre-imputed)	CP09 (Edited)

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