MEPS HC-085F: 2004 Outpatient Department Visits

Individual identifiers have been removed from the microdata contained in the files in this release. Nevertheless, under sections 308 (d) and 903 (c) of the Public Health Service Act (42 U.S.C. 242m and 42 U.S.C. 299 a-1), data collected by the Agency for Healthcare Research and Quality (AHRQ) and/or the National Center for Health Statistics (NCHS) may not be used for any purpose other than for the purpose for which they were supplied; any effort to determine the identity of any reported cases is prohibited by law.

Therefore in accordance with the above referenced Federal statute, it is understood that:

By using these data you signify your agreement to comply with the above-stated statutorily based requirements, with the knowledge that deliberately making a false statement in any matter within the jurisdiction of any department or agency of the Federal Government violates Title 18 part 1 Chapter 47 Section 1001 and is punishable by a fine of up to $10,000 or up to 5 years in prison.

The Agency for Healthcare Research and Quality requests that users cite AHRQ and the Medical Expenditure Panel Survey as the data source in any publications or research based upon these data.

The Medical Expenditure Panel Survey (MEPS) provides nationally representative estimates of health care use, expenditures, sources of payment, and insurance coverage for the U.S. civilian noninstitutionalized population. MEPS is cosponsored by the Agency for Healthcare Research and Quality (AHRQ) and the National Center for Health Statistics (NCHS).

MEPS is a family of three surveys. The Household Component (HC) is the core survey and forms the basis for the Medical Provider Component (MPC) and part of the Insurance Component (IC). 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) was conducted in 1977, and the National Medical Expenditure Survey (NMES) was conducted in 1987. Since 1996, MEPS has continued 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 systems.

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 advance these goals, MEPS includes linkage with the National Health Interview Survey (NHIS) – a survey conducted by NCHS from which the sample for the MEPS HC is drawn – and enhanced 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 2 ½ -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. NHIS provides a nationally representative sample of the U.S. civilian noninstitutionalized population, with oversampling of Hispanics and blacks.

The MEPS MPC supplements and/or replaces 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 home health agencies and pharmacies reported by HC respondents. Office-based physicians, hospitals, and hospital physicians are also included in the MPC but may be subsampled at various rates, depending on burden and resources, in certain years.

Data are collected on medical and financial characteristics of medical and pharmacy events reported by HC respondents. The MPC is conducted through telephone interviews and record abstraction.

The MEPS IC collects data on health insurance plans obtained through private and public-sector employers. 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, and employer characteristics.

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

To provide an integrated picture of health insurance, data collected from the first sampling frame (employers and other insurance providers identified by MEPS HC respondents) are linked back to data provided by those respondents. Data collected from the two Census Bureau sampling frames are used to produce annual national and State estimates of the supply and cost of private health insurance available to American workers and to evaluate policy issues pertaining to health insurance. National estimates of employer contributions to group health insurance from the MEPS IC are used in the computation of Gross Domestic Product (GDP) by the Bureau of Economic Analysis.

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 follow-up for nonrespondents.

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 complete, the MEPS survey data are released to the public in staged releases of summary reports, microdata files, and compendiums of tables. Data are also released through MEPSnet, an online interactive tool developed to give users the ability to statistically analyze MEPS data in real time. Summary reports and compendiums of tables are released as printed documents and electronic files. Microdata files are released on CD-ROM and/or as electronic files.

Selected printed documents are available through the AHRQ Publications Clearinghouse. Write or call:

Additional information on MEPS is available from the MEPS project manager or the MEPS public use data manager at the Center for Financing, Access and Cost Trends, Agency for Healthcare Research and Quality, 540 Gaither Road, Rockville, MD 20850, (301 427-1406).

This documentation describes one in a series of public use event files from the 2004 Medical Expenditure Panel Survey (MEPS) Household (HC) and Medical Provider Components (MPC). Released as an ASCII data file (with related SAS and SPSS programming statements) and SAS transport file, this public use file provides detailed information on outpatient visits for a nationally representative sample of the civilian noninstitutionalized population of the United States and can be used to make estimates of outpatient utilization and expenditures for calendar year 2004. The file contains 87 variables and has a logical record length of 376 with an additional 2-byte carriage return/line feed at the end of each record. As illustrated below, this file consists of MEPS survey data obtained in the 2004 portion of Round 3 and Rounds 4 and 5 for Panel 8, as well as Rounds 1, 2 and the 2004 portion of Round 3 for Panel 9 (i.e., the rounds for the MEPS panels covering calendar year 2004).

Each record on this event file represents a unique outpatient event; that is, an outpatient event reported by the household respondent. Outpatient events reported in Panel 9 Round 3 and known to have occurred after December 31, 2004 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 outpatient visit.

Annual counts of outpatient visits are based entirely on household reports. Information from the MEPS MPC is used to supplement expenditure and payment data reported by the household, and does not affect use estimates.

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

This file can also be used to construct summary variables of expenditures, sources of payment, and related aspects of outpatient visits. Aggregate annual person-level information on the use of outpatient departments and other health services use is provided on the MEPS 2004 Full Year Consolidated Data File, where each record represents a MEPS sampled person.

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

Any variables not found on this file but released on previous years’ files were excluded because they contained only missing data.

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. Copies of the HC and the MPC survey instruments used to collect the information on the Outpatient Department Visit file are available in the Survey Instrument section of the MEPS web site at the following address: http://www.meps.ahrq.gov.

The 2004 Outpatient Department Visit public use data set consists of one event-level data file. The file contains characteristics associated with the outpatient event and imputed expenditure data.

The 2004 outpatient public use data set contains 15,779 outpatient event records; of these records, 15,249 are associated with persons having a positive person-level weight (PERWT04F). This file includes outpatient event records for all household survey respondents who resided in eligible responding households and reported at least one outpatient event. Starting in 2004, new questions were added inquiring whether someone in the family had a visit to an independent lab or testing facility for x-rays or other tests. An affirmative answer to these questions would lead to the creation of an office-based provider event record or an outpatient department event record.

Each record represents one household-reported outpatient event that occurred during calendar year 2004. Outpatient visits known to have occurred after December 31, 2004 are not included on this file. Some household respondents may have multiple outpatient events and thus will be represented in multiple records on this file. Other household respondents may have reported no outpatient events and thus will have no records on this file. These data were collected during the 2004 portion of Round 3, and Rounds 4 and 5 for Panel 8, as well as Rounds 1, 2, and the 2004 portion of Round 3 for Panel 9 of the MEPS HC. The persons represented on this file had to meet either (a) or (b) below:

Persons with no outpatient visit events for 2004 are not included on this event-level OP file but are represented on the person-level 2004 Full Year Population Characteristics file.

Each outpatient visit record includes the following information: date of the visit; whether or not the survey respondent saw the 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; a full-year person-level weight; variance strata; and variance PSU.

Data from this file can be merged with the MEPS 2004 Full Year Population Characteristics file using the unique person identifier, DUPERSID, to append person characteristics, such as demographic or health insurance characteristics, to each record. Outpatient visit events on this file can also be linked to the MEPS 2004 Medical Conditions File and to the MEPS 2004 Prescribed Medicines File. Please see Section 5.0 for details on how to merge MEPS data files.

MEPS began in 1996 and several annual data files have been released. As more years of data are produced, MEPS will become increasingly valuable for examining health care trends. However, it is important to consider a variety of factors when examining trends over time using MEPS. Statistical significance tests should be conducted to assess the likelihood that observed trends are attributable to sampling variation. MEPS expenditure estimates are especially sensitive to sampling variation due to the underlying skewed distribution of expenditures. For example, 1 percent of the population accounts for about one-quarter of all expenditures. The extent to which observations with extremely high expenditures are captured in the MEPS sample varies from year to year (especially for smaller population subgroups), which can produce substantial shifts in estimates of means or totals that are simply an artifact of the sample(s). The length of time being analyzed should also be considered. In particular, large shifts in survey estimates over short periods of time (e.g. from one year to the next) that are statistically significant should be interpreted with caution, unless they are attributable to known factors such as changes in public policy or MEPS survey methodology. Looking at changes over longer periods of time can provide a more complete picture of underlying trends. Analysts may wish to consider using techniques to smooth or stabilize trend analyses of MEPS data such as pooling time periods for comparison (e.g. 1996-97 versus 1998-99), working with moving averages, or using modeling techniques with several consecutive years of MEPS data to test the fit of specified patterns over time. Finally, researchers should be aware of the impact of multiple comparisons on Type I error because performing numerous statistical significance tests of trend increases the likelihood of inappropriately concluding a change is statistically significant.

The records on this file can be linked to all other 2004 MEPS-HC public use data sets by the sample person identifier (DUPERSID).

For each variable on the Outpatient Department Events file, both weighted and unweighted frequencies are provided in the codebook (H85FCB.PDF). The codebook and data file sequence list variables in the following order:

Generally, values of -1, -7, -8, and -9 for non-expenditure variables have not been edited on this file. The values of -1 and -9 can be edited by the data users/analysts by following the skip patterns in the HC survey questionnaire (located on the MEPS web site: http://www.meps.ahrq.gov/mepsweb/survey_comp/survey.jsp).

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 eight-character limitation. All imputed/edited variables end with a "X".

Variables on this file were derived from the HC questionnaire itself, the MPC data collection instrument, derived from CAPI, or assigned in sampling. The source of each variable is identified in Section D "Variable - Source Crosswalk" in one of four ways:

The names of the expenditure and source of payment variables follow a standard convention, are eight characters in length, and end in an "X" indicating edited/imputed. Please note that imputed means that a series of logical edits, as well as an imputation process to account for missing data, have been performed on the variable.

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

For expenditure variables on the OP file, the third character indicates whether the expenditure (or amount paid) is associated with the facility (F) or the physician (D).

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

The sixth and seventh characters indicate the year (04). The eighth character, "X", indicates whether the variable is edited/imputed.

For example, OPFSF04X is the edited/imputed amount paid by self or family for the facility portion of the expenditure associated with an outpatient visit.

The dwelling unit ID (DUID) is a five-digit random number assigned after the case was sampled for MEPS. The three-digit person number (PID) uniquely identifies each person within the dwelling unit. The eight-character variable DUPERSID uniquely identifies each person represented on the file and is the combination of the variables DUID and PID. For detailed information on dwelling units and families, please refer to the documentation for the 2004 Full Year Population Characteristics File.

EVNTIDX uniquely identifies each outpatient event (i.e., each record on the outpatient file) and is the variable required to link outpatient events to data files containing details on conditions and/or prescribed medicines (MEPS 2004 Medical Condition file and MEPS 2004 Prescribed Medicine file, respectively). For details on linking see Section 5.0 or the MEPS 2004 Appendix File, HC-085I.

FFEEIDX is a constructed variable that uniquely identifies a flat fee group, that is, all events that were part of a flat fee payment. For example, if a patient receives stitches during an outpatient visit and comes back to have the stitches removed ten days later in a follow-up outpatient visit, both visits are covered under one flat fee dollar amount. These two events (the initial outpatient visit and the subsequent outpatient visit) would have the same value for FFEEIDX. A "mixed" flat fee group could contain both outpatient and office-based visits. Only outpatient and office-based events are allowed in a mixed bundle. Please note that FFEEIDX should be used to link up the outpatient and office-based events in order to determine the full set of events that are part of a flat fee group.

EVENTRN indicates the round in which the outpatient event was reported. Please note: Rounds 3, 4, and 5 are associated with MEPS survey data collected from Panel 8. Likewise, Rounds 1, 2, and 3 are associated with data collected from Panel 9.

MPCDATA is a constructed variable that indicates whether or not MPC data were collected for the outpatient visit. While all outpatient events are sampled into the Medical Provider Component, not all outpatient 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 outpatient facility as well as the cooperation of the outpatient facility to participate in the survey.

This file contains variables describing outpatient events reported by respondents in the Outpatient Department section of the MEPS HC questionnaire. The questionnaire contains specific probes for determining details about the outpatient visit. These variables have not been edited.

When a person reported having had a visit to a hospital outpatient department or special clinic, the date of the outpatient visit was reported (OPDATEYR, OPDATEMM, OPDATEDD). Also reported is whether the person actually saw the provider or talked to the provider on the telephone (SEETLKPV). It also establishes whether the person saw or spoke to a medical doctor (SEEDOC). If the person did not see a specialty doctor (DRSPLTY), or, if the person did not see a physician (i.e., medical doctor), the respondent was asked to identify the type of medical person that was seen (MEDPTYPE). The type of care the person received (VSTCTGRY), and whether or not the visit or telephone call was related to a specific condition (VSTRELCN) were also determined.

Types of treatment received during the outpatient visit include physical therapy (PHYSTH), occupational therapy (OCCUPTH), speech therapy (SPEECHTH), chemotherapy (CHEMOTH), radiation therapy (RADIATTH), kidney dialysis (KIDNEYD), IV therapy (IVTHER), drug or alcohol treatment (DRUGTRT), allergy shots (RCVSHOT), and psychotherapy/counseling (PSYCHOTH). 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). Minimal editing was done across treatment, services, and procedures to ensure consistency across "inapplicable," "not ascertained," "don't know," "refused," and "no services received" values. Whether or not a surgical procedure was performed during the visit was asked (SURGPROC). Finally, the questionnaire determined if a medicine was prescribed for the person during the visit (MEDPRESC).

VAPLACE is a constructed variable that indicates whether the outpatient department or clinic was a VA facility. This variable only has valid data for providers that were sampled into the Medical Provider Component. All other providers are classified as "No".

Information on household-reported medical conditions and procedures associated with each outpatient visit is provided on this file. There are up to four condition and CCS codes (OPICD1X-OPICD4X, OPCCC1X-OPCCC4X) and up to three procedure codes (OPPRO1X-OPPRO3X) listed for each outpatient visit. In order to obtain complete information on conditions and procedures associated with an event, the analyst must link to the Medical Conditions File. Please see Section 5.0 for details on how to link this file to the Medical Conditions 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 2004 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, data users/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 Medical Conditions File.

The ICD-9-CM condition codes were aggregated into clinically meaningful categories. These categories, included on the file as OPCCC1X-OPCCC4X, 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 263 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 three-digit code categories. The reported ICD-9-CM code values were mapped to the appropriate clinical classification category prior to being collapsed to the three-digit categories. Similarly, the procedure codes have been collapsed from fully-specified codes to two-digit code categories. Because of this collapsing, it is possible for there to be duplicate ICD-9-CM condition or procedure codes linked to a single medical event when different fully-specified codes are collapsed into the same code. For more information on ICD-9-CM codes, see the HC-087 documentation.

The condition and procedure codes (and clinical classification codes) linked to each outpatient visit are sequenced in the order in which the conditions were reported by the household respondent, which was in order of input into the database and not in order of importance or severity. Data users/analysts who use the MEPS 2004 Medical Conditions file in conjunction with this outpatient visit file should note that the order of conditions on this file is not identical to that on the Medical Conditions file.

A flat fee is the fixed dollar amount a person is charged for a package of health care services provided during a defined period of time. Examples would be: an obstetrician’s fee covering a normal delivery, as well as pre- and post-natal care; or a surgeon’s fee covering surgical procedure along with post-surgical care. A flat fee group is the set of medical services (i.e., events) that are covered under the same flat fee payment. The flat fee groups represented on this file include flat fee groups where at least one of the health care events, as reported by the HC respondent, occurred during 2004. By definition a flat fee group can span multiple years. Furthermore, a single person can have multiple flat fee groups.

As noted earlier in Section 2.6.1.2 "Record Identifiers," the variable FFEEIDX uniquely identifies all events that are part of the same flat fee group for a person. On any 2004 MEPS event file, every event that is part of a specific flat fee group will have the same value for FFEEIDX. Note that prescribed medicine and home health events are never included in a flat fee group and FFEEIDX is not a variable on those event files.

FFOPTYPE indicates whether the 2004 outpatient visit is the "stem" or "leaf" of a flat fee group. A stem (records with FFOPTYPE = 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 FFOPTYPE = 2) are those medical events that are tied back to the initial medical event (the stem) in the flat fee group. These "leaf" records have their expenditure variables set to zero. For the outpatient visits that are not part of a flat fee payment, the FFOPTYPE is set to -1, "INAPPLICABLE."

As described above, a flat fee payment covers multiple events and the multiple events could span multiple years. For situations where the outpatient visit occurred in 2004 as a part of a group of events, and some of the events occurred before or after 2004, counts of the known events are provided on the outpatient visit record. Variables indicating events that occurred before or after 2004 are as follows:

If there are no 2003 events on the file, FFBEF04 will be omitted. Likewise, if there are no 2005 events on the file, FFTOT05 will be omitted. If there are no flat fee data related to the records in this file, FFEEIDX and FFOPTYPE will be omitted as well. Please note that the crosswalk in this document lists all possible flat fee variables.

There are 392 outpatient visits that are identified as being part of a flat fee payment group. In general, every flat fee group should have an initial visit (stem) and at least one subsequent visit (leaf). There are some situations where this is not true. For some of these flat fee groups, the initial visit reported occurred in 2004 but the remaining visits that were part of this flat fee group occurred in 2005. In this case, the 2004 flat fee group represented on this file would consist of one event (the stem). The 2005 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 2003 but subsequent visits occurred during 2004. In this case, the initial visit would not be represented on the file. This 2004 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 at least one leaf record is that the stem or leaves could have been reported as different event types. Outpatient and Office-based medical provider visits are the only two event types allowed in a single flat fee group. The stem may have been reported as an outpatient department visit and the leaves may have been reported as office-based medical provider visits.

Expenditures on this file refer to what is paid for outpatient services. More specifically, expenditures in MEPS are defined as the sum of payments for care received for each outpatient 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 1990s 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 paid 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., 2000). AHRQ has developed factors to apply to the 1987 NMES expenditure data to facilitate longitudinal analysis. These factors can be assessed via the CFACT data center. For more information, see the data center section of the MEPS web site http://www.meps.ahrq.gov.

Expenditure data related to outpatient visits are broken out by facility and separately billing doctor expenditures. This file contains six categories of expenditure variables per visit: basic hospital outpatient facility expenses; expenses for doctors who billed separately from the outpatient facility for any services provided during the outpatient visit; total expenses, which is the sum of the facility and physician expenses; facility charge; physician charge; and total charges, which is the sum of the facility and physician charges. If examining trends in MEPS expenditures or performing longitudinal analysis on MEPS expenditures, please refer to section C, sub-section 2.1 for more information.

The expenditure data included on this file were derived from both the MEPS Household (HC) and the Medical Provider Components (MPC). The MPC contacted medical providers identified by household respondents. The charge and payment data from medical providers were used in the expenditure imputation process to supplement missing household data. For all outpatient visits, MPC data were used if available; otherwise, HC data were used. Missing data for outpatient visits where HC data were not complete and MPC data were not collected, or MPC data were not 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 misclassifications between Medicare and Medicaid and between Medicare HMOs and private HMOs as payment sources. These edits produced a complete vector of expenditures for some events, and provided the starting point for imputing missing expenditures in the remaining events.

A weighted sequential hot-deck procedure was used to impute for missing expenditures as well as total charge. This 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. Within each event type file, separate imputations were performed for flat fee and simple events. 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.

Facility expenditures for outpatient 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 an MPC-reported event (i.e., a matched event), since providers usually have more complete and accurate data on sources and amounts of payment than households.

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). (See Section 2.6.5 for more details on flat fee groups).

Logical edits also were used to sort each event into a specific category for the imputations. Events with complete expenditures were flagged as potential donors for the hot-deck imputations, while events with missing expenditure data were assigned to various recipient categories. Each event with missing expenditure data was assigned to a recipient category based on the extent of its missing charge and expenditure data. For example, an event with a known total charge but no expenditure information was assigned to one category, while an event with a known total charge and partial expenditure information was assigned to a different category. Similarly, events without a known total charge and no or partial expenditure information were assigned to various recipient categories.

The logical edits produced eight recipient categories in which all events had a common extent of missing data. Separate hot-deck imputations were performed on events in each recipient category. For hospital inpatient and emergency room events, the donor pool was restricted to events with complete expenditures from the MPC. Due to the low ratio of donors to recipients for hospital outpatient and office based events, there were no donor pool restrictions.

The donor pool included "free events" because, in some instances, providers are not paid for their services. These events represent charity care, bad debt, provider failure to bill, and third party payer restrictions on reimbursement in certain circumstances. If free events were excluded from the donor pool, total expenditures would be over-counted because the distribution of free events among complete events (donors) would not be represented among incomplete events (recipients).

For office-based and outpatient events, the donor pool also included events originally reported by providers as paid on a capitated basis. To obtain the fee-for-service (FFS) equivalent payments for these capitated events, a "capitation imputation" was implemented (see the next section). Once imputed with the FFS equivalent payments, these events became donors for all other incomplete events, particularly for events reported by the household as services covered under managed care plans.

The imputation process was also used to estimate expenditures at the event level for events that were paid on a per month per person (capitated) basis. The capitation imputation procedure was designed as a reasonable approach to complete event-level expenditures for respondents in non-fee for service managed care plans. 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 fully completed set of MPC events was used in the donor pool for the main imputation process for cases 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.

IMPFLAG is a six-category variable that indicates if the event contains complete Household Component (HC) or Medical Provider Component (MPC) data, was fully or partially imputed, or was imputed in the capitated imputation process (for OP and MV events only). The following list identifies how the imputation flag is coded; the categories are mutually exclusive.

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 facility payments, physician’s expenditures may be still present. Thus, if the first visit in the flat fee group occurred prior to 2004, all of the events that occurred in 2004 will have zero payments. Conversely, if the first event in the flat fee group occurred at the end of 2004, the total expenditure for the entire flat fee group will be on that event, regardless of the number of events it covered after 2004. See Section 2.6.5 for details on the flat fee variables.

There are some outpatient 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, or (3) care for these events was covered under a flat fee arrangement with all expenditures appearing on the stem event. 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.

The file also contains a small number of events involving a telephone call rather than a visit to the medical provider (SEETLKPV = 2). The expenditure variables for telephone calls have a value of -1 "INAPPLICABLE".

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.

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

Two additional source of payment variables were created to classify payments for events with apparent inconsistencies between insurance coverage and sources of payment based on data collected in the survey. These variables include:

Though these two sources are relatively small in magnitude, data users/analysts should exercise caution when interpreting the expenditures associated with these two additional sources of payment. While these payments stem from apparent inconsistent responses to health insurance and source of payment questions in the survey, some of these inconsistencies may have logical explanations. For example, private insurance coverage in MEPS is defined as having a major medical plan covering hospital and physician services. If a MEPS sampled person did not have such coverage but had a single service type insurance plan (e.g., dental insurance) that paid for a particular episode of care, those payments may be classified as "other private". Some of the "other public" payments may stem from confusion between Medicaid and other state and local programs or may be from persons who were not enrolled in Medicaid, but were presumed eligible by a provider who ultimately received payments from the public payer.

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

Outpatient visit 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 hospital outpatient visit charge.

OPFSF04X - OPFOT04X are the 12 sources of payment. The 12 sources of payment are: self/family (OPFSF04X), Medicare (OPFMR04X), Medicaid (OPFMD04X), private insurance (OPFPV04X), Veterans Administration (OPFVA04X), TRICARE (OPFTR04X), other Federal sources (OPFOF04X), State and Local (non-federal) government sources (OPFSL04X), Worker’s Compensation (OPFWC04X), other private insurance (OPFOR04X), other public insurance (OPFOU04X), and other insurance (OPFOT04X). OPFXP04X is the sum of the 12 sources of payment for the Outpatient Facility expenditures, and OPFTC04X is the total charge. Please note that where an outpatient visit record is linked to a hospital inpatient stay record, all facility sources of payment variables, as well as, OPFTC04X have been zeroed out.

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 the outpatient facility bill.

For physicians who bill separately (i.e., outside the outpatient facility bill), a separate data collection effort within the Medical Provider Component was performed to obtain the 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 outpatient visit could have a radiologist and a pathologist associated with it. If their services are not included in the outpatient 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. OPDSF04X - OPDOT04X are the 12 sources of payment, OPDXP04X is the sum of the 12 sources of payments, and OPDTC04X is the physician(s) total charge.

Data users/analysts need to take into consideration whether to analyze facility and SBD expenditures separately, combine them within service categories, or collapse them across service categories (e.g., combine SBD expenditures with expenditures for physician visits to offices and/or outpatient departments).

Data users/analysts interested in total expenditures should use the variable OPXP04X, which includes both facility and physician amounts. Those interested in total charges should use the variable OPTC04X, which includes both facility and physician charges (see Section 2.6.6.1 for an explanation of the "charge" concept).

Expenditure variables have been rounded to the nearest penny. Person-level expenditure information released on the MEPS 2004 Person-Level and Expenditure File were rounded to the nearest dollar. It should be noted that using the MEPS 2004 event files to create person-level totals will yield slightly different totals than those found on the person-level expenditure file. These differences are due to rounding only. Moreover, in some instances, the number of persons having expenditures on the event files for a particular source of payment may differ from the number of persons with expenditures on the person-level expenditure file for that source of payment. This difference is also an artifact of rounding only. Please see the MEPS 2004 Appendix File, HC-085I, for details on such rounding differences.

There is a single full year person-level weight (PERWT04F) 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 2004. A key person either was a member of an NHIS household at the time of the NHIS interview, or became a member of a family associated with such a household after being out-of-scope at the time of the NHIS (the latter circumstance includes newborns as well as 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 PERWT04F was developed in several stages. Person-level weights for Panels 8 and 9 were created separately. The weighting process for each panel included an adjustment for nonresponse over time and calibration to independent population figures. The calibration was initially accomplished separately for each panel by raking the corresponding sample weights to Current Population Survey (CPS) population estimates based on five variables. The five variables used in the establishment of the initial person-level control figures were: census region (Northeast, Midwest, South, West); MSA status (MSA, non-MSA); race/ethnicity (Hispanic, non-Hispanic with black as sole reported race, non-Hispanic with Asian as sole reported race,and other); sex; and age. A 2004 composite weight was then formed by multiplying each weight from Panel 8 by the factor .49 and each weight from Panel 9 by the factor .51. The choice of factors reflected the relative sample sizes of the two panels, helping to limit the variance of estimates obtained from pooling the two samples. The composite weight was again raked to the same set of CPS-based control totals. When poverty status information derived from income variables became available, a final raking was undertaken on the previously established weight variable. Control totals were established using poverty status (five categories: below poverty, from 100 to 125 percent of poverty, from 125 to 200 percent of poverty, from 200 to 400 percent of poverty, at least 400 percent of poverty) as well as the original five variables used in the previous calibrations.

The person-level weight for MEPS Panel 8 was developed using the 2003 full year weight for an individual as a "base" weight for survey participants present in 2003. For key, in-scope respondents who joined an RU some time in 2004 after being out-of-scope in 2003, the 2003 family weight associated with the family the person joined served as a "base" weight. The weighting process included an adjustment for nonresponse over Rounds 4 and 5 as well as raking to population control figures for December 2004. These control figures were derived by scaling back the population totals obtained from the March 2004 CPS to correspond to a national estimate for the civilian noninstitutionalized population provided by the Census Bureau for December 2004. Variables used in the establishment of person-level control figures included: census region (Northeast, Midwest, South, West); MSA status (MSA, non-MSA); race/ethnicity (Hispanic, black but non-Hispanic, Asian but non-Hispanic, and other); sex; and age. Overall, the weighted population estimate for the civilian noninstitutionalized population on December 31, 2004 is 289,659,890. Key, responding persons not in-scope on December 31, 2004 but in-scope earlier in the year retained, as their final Panel 8 weight, the weight after the nonresponse adjustment.

The person-level weight for MEPS Panel 9 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 family weight served as a "base" weight. The weighting process included an adjustment for nonresponse over round 2 and the 2004 portion of Round 3 as well as raking to the same population control figures for December 2004 used for the MEPS Panel 8 weights. The same five variables employed for Panel 8 raking (census region, MSA status, race/ethnicity, sex, and age) were used for Panel 9 raking. Similarly, for Panel 9, key, responding persons not in-scope on December 31, 2004 but in-scope earlier in the year retained, as their final Panel 9 weight, the weight after the nonresponse adjustment.

Note that the MEPS Round 1 weights (for both panels with one exception as noted below) incorporated the following components: the original household probability of selection for the NHIS; ratio-adjustment to NHIS-based 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 2004 CPS data base.

Variables used in the establishment of person-level 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, non-Hispanic with black as sole reported race, non-Hispanic with Asian as sole reported race, and other); sex; and age. Overall, the weighted population estimate for the civilian noninstitutionalized population for December 31, 2004 is 289,659,890 (PERWT04F>0 and INSC1231=1). The weights of some persons out-of-scope on December 31, 2004 were also calibrated, this time using poststratification. Specifically, the weights of persons out-of-scope on December 31, 2004 who were in-scope some time during the year and also entered a nursing home during the year were poststratified to a corresponding control total obtained from the 1996 MEPS Nursing Home Component. The weights of persons who died while in-scope during 2004 were poststratified to corresponding estimates derived using data obtained from the Medicare Current Beneficiary Survey (MCBS) and Vital Statistics information provided by the National Center for Health Statistics (NCHS). Separate control totals were developed for the "65 and older" and "under 65" civilian noninstitutionalized populations. The sum of the person-level weights across all persons assigned a positive person level weight is 293,527,003.

The target population for MEPS in this file is the 2004 U.S. civilian noninstitutionalized population. However, the MEPS sampled households are a subsample of the NHIS households interviewed in 2002 (Panel 8) and 2003 (Panel 9). New households created after the NHIS interviews for the respective Panels and consisting exclusively of persons who entered the target population after 2002 (Panel 8) or after 2003 (Panel 9) are not covered by MEPS. Neither are previously out-of-scope persons who join an existing household but are unrelated to the current household residents. Persons not covered by a given MEPS panel thus include some members of the following groups: immigrants; persons leaving the military; U.S. citizens returning from residence in another country; and persons leaving institutions. The set of uncovered persons constitutes only a small segment of the MEPS target population.

This file is constructed for efficient estimation of utilization, expenditures, and sources of payment for outpatient care and to allow for estimates of the number of persons with outpatient visits during 2004.

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 expenditure) are described in Section 2.6.6.

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 outpatient visits, expenditures and sources of payment, the value in each record contributing to the estimates must be multiplied by the weight (PERWT04F) contained on that record.

For example, the total number of outpatient visits, for the civilian noninstitutionalized population of the U.S. in 2004 is estimated as the sum of the weight (PERWT04F) across all outpatient visit 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 for outpatient visits (where the visit has a total expense greater than 0) should be calculated as the weighted mean of the facility bill and doctor’s bill paid by self/family. That is,

This gives $41.49 as the estimated mean amount of out-of-pocket payment of expenditures associated with outpatient visits and 144,774,547 as an estimate of the total number of such outpatient visits with expenditures. Both of these estimates are for the civilian noninstitutionalized population of the U.S. in 2004.

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

This gives 0.4538 as the estimated mean proportion of total expenditures paid by private insurance for outpatient visits with expenditure for the civilian noninstitutionalized population of the U.S. in 2004.

When calculating an estimate of the total number of persons with outpatient visits, users can use a person-level file 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 noninstitutionalized population of the U.S. with outpatient visits where the patient sees a doctor, this event file must be used. This would be estimated as

This file may be used to derive person-based ratio estimates. However, when calculating ratio estimates where the denominator is at the person level, care should be taken to properly define and estimate the unit of analysis as person-level. For example, the mean expense for persons with outpatient visits 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 outpatient visit are represented on this data file. In this case, the Full Year Consolidated File, 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 the civilian noninstitutionalized population of the U.S. with at least one outpatient visit where the person saw a doctor, the numerator would be derived from data on this event file, and the denominator would be derived from data on the person-level file. That is,

There have been several previous releases of MEPS Household Survey public use data. Unless a variable name common to several files 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 with 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 2004 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 VARSTR and VARPSU, respectively. Prior to 2003, MEPS variance strata and PSUs were developed independently from year to year, and the last two characters of the strata and PSU variable names denoted the year. However, beginning with the 2003 Point-in-Time PUF, the variance strata and PSUs have been developed to be compatible with all future PUFs. Thus, data from future years can be pooled and the variance strata and PSU variables provided can be used without modification for variance estimation purposes for estimates covering multiple years of data. There are 203 variance estimation strata, each stratum with either two or three variance estimation PSUs. 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 VARSTR and VARPSU as the variance estimation strata and PSUs (within these strata), respectively, and specifying a "with replacement" design in a computer software package (i.e., SUDAAN) will yield standard error estimates of $3.22 and 0.0152 for the estimated mean out-of-pocket payment and the estimated mean proportion of total expenditures paid by private insurance, respectively.

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

Merging characteristics of interest from other MEPS files (e.g., MEPS 2004 Full Year Population Characteristics File) expands the scope of potential estimates. For example, to estimate the total number of outpatient visits for persons with specific characteristics (e.g., age, race, sex, and education), population characteristics from a person-level file need to be merged onto the outpatient visit file. This procedure is illustrated below. The MEPS 2004 Appendix File, HC-085I, provides additional detail on how to merge MEPS data files.

Due to survey design issues, there are limitations/caveats that data users/analysts must keep in mind when linking the different files. Those limitations/caveats are listed below. For detailed linking examples, including SAS code, data users/analysts should refer to the MEPS 2004 Appendix File, HC-085I.

The RXLK file provides a link from the MEPS event files to the 2004 Prescribed Medicine File. When using RXLK, data users/analysts should keep in mind that one outpatient visit can link to more than one prescribed medicine record. Conversely, a prescribed medicine event may link to more than one outpatient visit or different types of events. When this occurs, it is up to the data users/analysts 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 2004 Medical Conditions File. When using the CLNK, data users/analysts should keep in mind that (1) conditions are self-reported, (2) there may be multiple conditions associated with an outpatient visit, and (3) a condition may link to more than one outpatient visit or any other type of visit. Users should also note that not all outpatient visits link to the medical conditions file.

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, 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.

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.

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. 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, 9th 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.

Monheit, A.C., Wilson, R., and Arnett, III, R.H. (Editors). Informing American Health Care Policy. (2000). 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.

Variable	Description	Source
OPDATEYR	Event date - year	CAPI derived
OPDATEMM	Event date - month	CAPI derived
OPDATEDD	Event date - day	CAPI derived
SEETLKPV	Did person visit provider in person or telephone	OP02
SEEDOC	Did person talk to MD this visit/phone call	OP04
DRSPLTY	OPAT doctor specialty	OP04A
MEDPTYPE	Type of medical person person talked to on visit date	OP05
VSTCTGRY	Best category for care person received on visit date	OP07
VSTRELCN	This visit/phone call related to spec condition	OP08
PHYSTH	This visit did person have physical therapy	OP10
OCCUPTH	This visit did person have occupational therapy	OP10
SPEECHTH	This visit did person have speech therapy	OP10
CHEMOTH	This visit did person have chemotherapy	OP10
RADIATTH	This visit did person have radiation therapy	OP10
KIDNEYD	This visit did person have kidney dialysis	OP10
IVTHER	This visit did person have IV therapy	OP10
DRUGTRT	This visit did person have treatment for drug/alcohol	OP10
RCVSHOT	This visit did person receive an allergy shot	OP10
PSYCHOTH	This visit did person have psychotherapy/counseling	OP10
LABTEST	This visit did person have lab tests	OP11
SONOGRAM	This visit did person have sonogram or ultrasound	OP11
XRAYS	This visit did person have x-rays	OP11
MAMMOG	This visit did person have a mammogram	OP11
MRI	This visit did person have an MRI/Catscan	OP11
EKG	This visit did person have an EKG or ECG	OP11
EEG	This visit did person have an EEG	OP11
RCVVAC	This visit did person receive a vaccination	OP11
ANESTH	This visit did person receive anesthesia	OP11
OTHSVCE	This visit did person have other diagnostic tests or exams	OP11
SURGPROC	Was surgical procedure performed on person this visit	OP12
MEDPRESC	Any medicine prescribed for person during visit	OP14
VAPLACE	VA facility flag	Constructed
OPICD1X	3-digit ICD-9-CM condition code	Edited
OPICD2X	3-digit ICD-9-CM condition code	Edited
OPICD3X	3-digit ICD-9-CM condition code	Edited
OPICD4X	3-digit ICD-9-CM condition code	Edited
OPPRO1X	2-digit ICD-9-CM procedure code	Edited
OPPRO2X	2-digit ICD-9-CM procedure code	Edited
OPPRO3X	2-digit ICD-9-CM procedure code	Edited
OPCCC1X	Modified Clinical Classification Code	Constructed/Edited
OPCCC2X	Modified Clinical Classification Code	Constructed/Edited
OPCCC3X	Modified Clinical Classification Code	Constructed/Edited
OPCCC4X	Modified Clinical Classification Code	Constructed/Edited

Variable	Description	Source
OPXP04X	Total expenditure for event (OPFXP04X+OPDXP04X)	Constructed
OPTC04X	Total charge for event (OPFTC04X+OPDTC04X)	Constructed
OPFSF04X	Facility amount paid, self/family (Imputed)	CP Section (Edited)
OPFMR04X	Facility amount paid, Medicare (Imputed)	CP Section (Edited)
OPFMD04X	Facility amount paid, Medicaid (Imputed)	CP Section (Edited)
OPFPV04X	Facility amount paid, private insurance (Imputed)	CP Section (Edited)
OPFVA04X	Facility amount paid, Veterans Administration (Imputed)	CP Section (Edited)
OPFTR04X	Facility amount paid, TRICARE (Imputed)	CP Section (Edited)
OPFOF04X	Facility amount paid, other federal (Imputed)	CP Section (Edited)
OPFSL04X	Facility amount paid, state & local government (Imputed)	CP Section (Edited)
OPFWC04X	Facility amount paid, workers’ compensation (Imputed)	CP Section (Edited)
OPFOR04X	Facility amount paid, other private insurance (Imputed)	Constructed
OPFOU04X	Facility amount paid, other public insurance (Imputed)	Constructed
OPFOT04X	Facility amount paid, other insurance (Imputed)	CP Section (Edited)
OPFXP04X	Facility sum payments OPFSF04X-OPFOT04X	Constructed
OPFTC04X	Total facility charge (Imputed)	CP Section (Edited)
OPDSF04X	Doctor amount paid, self/family (Imputed)	Constructed
OPDMR04X	Doctor amount paid, Medicare (Imputed)	Constructed
OPDMD04X	Doctor amount paid, Medicaid (Imputed)	Constructed
OPDPV04X	Doctor amount paid, private insurance (Imputed)	Constructed
OPDVA04X	Doctor amount paid, Veterans Administration (Imputed)	Constructed
OPDTR04X	Doctor amount paid, TRICARE (Imputed)	Constructed
OPDOF04X	Doctor amount paid, other federal (Imputed)	Constructed
OPDSL04X	Doctor amount paid, state & local government (Imputed)	Constructed
OPDWC04X	Doctor amount paid, workers’ compensation (Imputed)	Constructed
OPDOR04X	Doctor amount paid, other private insurance (Imputed)	Constructed
OPDOU04X	Doctor amount paid, other public insurance (Imputed)	Constructed
OPDOT04X	Doctor amount paid, other insurance (Imputed)	Constructed
OPDXP04X	Doctor sum payments OPDSF04X-OPDOT04X	Constructed
OPDTC04X	Total doctor charge (Imputed)	Constructed
IMPFLAG	Imputation status	Constructed

Connect With Us

Sign up for Email Updates

Agency for Healthcare Research and Quality