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MEPS HC-126G: 2009 Office-Based Medical Provider Visits (Final) November 2011 Agency for Healthcare Research and Quality Center for Financing, Access, and Cost Trends 540 Gaither Road Rockville, MD 20850 (301) 427-1406 A. Data Use Agreement Individual identifiers have been removed from the micro-data contained in these files. Nevertheless, under sections 308 (d) and 903 (c) of the Public Health Service Act (42 U.S.C. 242m and 42 U.S.C. 299 a-1), data collected by the Agency for Healthcare Research and Quality (AHRQ) and/or the National Center for Health Statistics (NCHS) may not be used for any purpose other than for the purpose for which they were supplied; any effort to determine the identity of any reported cases is prohibited by law. Therefore in accordance with the above referenced Federal Statute, it is understood that:
By using these data you signify your agreement to comply with the above stated statutorily based requirements with the knowledge that deliberately making a false statement in any matter within the jurisdiction of any department or agency of the Federal Government violates Title 18 part 1 Chapter 47 Section 1001 and is punishable by a fine of up to $10,000 or up to 5 years in prison. The Agency for Healthcare Research and Quality requests that users cite AHRQ and the Medical Expenditure Panel Survey as the data source in any publications or research based upon these data. The Medical Expenditure Panel Survey (MEPS) provides nationally representative estimates of health care use, expenditures, sources of payment, and health insurance coverage for the U.S. civilian non-institutionalized population. The MEPS Household Component (HC) also provides estimates of respondents’ health status, demographic and socio-economic characteristics, employment, access to care, and satisfaction with health care. Estimates can be produced for individuals, families, and selected population subgroups. The panel design of the survey, which includes 5 Rounds of interviews covering 2 full calendar years, provides data for examining person level changes in selected variables such as expenditures, health insurance coverage, and health status. Using computer assisted personal interviewing (CAPI) technology, information about each household member is collected, and the survey builds on this information from interview to interview. All data for a sampled household are reported by a single household respondent. The MEPS-HC was initiated in 1996. Each year a new panel of sample households is selected. Because the data collected are comparable to those from earlier medical expenditure surveys conducted in 1977 and 1987, it is possible to analyze long-term trends. Each annual MEPS-HC sample size is about 15,000 households. Data can be analyzed at either the person or event level. Data must be weighted to produce national estimates. The set of households selected for each panel of the MEPS HC is a subsample of households participating in the previous year’s National Health Interview Survey (NHIS) conducted by the National Center for Health Statistics. The NHIS sampling frame provides a nationally representative sample of the U.S. civilian non-institutionalized population and reflects an oversample of blacks and Hispanics. In 2006, the NHIS implemented a new sample design, which included Asian persons in addition to households with black and Hispanic persons in the oversampling of minority populations. MEPS further oversamples additional policy relevant sub-groups such as low income households. The linkage of the MEPS to the previous year’s NHIS provides additional data for longitudinal analytic purposes. 2.0 Medical Provider Component Upon completion of the household CAPI interview and obtaining permission from the household survey respondents, a sample of medical providers are contacted by telephone to obtain information that household respondents can not accurately provide. This part of the MEPS is called the Medical Provider Component (MPC) and information is collected on dates of visit, diagnosis and procedure codes, charges and payments. The Pharmacy Component (PC), a subcomponent of the MPC, does not collect charges or diagnosis and procedure codes but does collect drug detail information, including National Drug Code (NDC) and medicine name, as well as date filled and sources and amounts of payment. The MPC is not designed to yield national estimates. It is primarily used as an imputation source to supplement/replace household reported expenditure information. 3.0 Survey Management and Data Collection MEPS HC and MPC data are collected under the authority of the Public Health Service Act. Data are collected under contract with Westat, Inc. (MEPS HC) and Research Triangle Institute (MEPS MPC). Data sets and summary statistics are edited and published in accordance with the confidentiality provisions of the Public Health Service Act and the Privacy Act. The National Center for Health statistics (NCHS) provides consultation and technical assistance. As soon as data collection and editing are completed, the MEPS survey data are released to the public in staged releases of summary reports, micro data files, and tables via the MEPS Web site: www.meps.ahrq.gov. Selected data can be analyzed through MEPSnet, an on-line interactive tool designed to give data users the capability to statistically analyze MEPS data in a menu-driven environment. Additional information on MEPS is available from the MEPS project manager or the MEPS public use data manager at the Center for Financing, Access, and Cost Trends, Agency for Healthcare Research and Quality, 540 Gaither Road, Rockville, MD 20850 (301-427-1406). C. Technical and Programming Information This documentation describes one in a series of public use event files from the 2009 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 a SAS transport file, the 2009 Office-Based Medical Provider Visits public use event file provides detailed information on office-based provider visits for a nationally representative sample of the civilian noninstitutionalized population of the United States. Data from the office-based provider events file can be used to make estimates of office-based provider utilization and expenditures for calendar year 2009. The file contains 75 variables and has a logical record length of 277 with an additional 2-byte carriage return/line feed at the end of each record. As illustrated below, this file consists of MEPS survey data from the 2009 portion of Round 3 and Rounds 4 and 5 for Panel 13, as well as Rounds 1, 2 and the 2009 portion of Round 3 for Panel 14 (i.e., the rounds for the MEPS panels covering calendar year 2009).
Incentive Experiment in Panel 13 With the encouragement of the Office of Management and Budget (OMB), an experiment was undertaken for MEPS Panel 13 (first fielded in 2008) to evaluate whether and how differential payments to household respondents might affect survey participation, the level of effort required to obtain participation, and the quality of the data collected. Each sampled household in Panel 13 was randomly assigned to one of three different levels of payment--$30, $50, or $70--with the experiment continuing through the panel’s five rounds of data collection. Households receiving the $30 payment represent the control group, since that amount had been offered to all households in the 2007 panel. To learn more about this experiment, go to the Respondent Payment Experiment – Results from Panel 13. Agency for Healthcare Research and Quality, Rockville, MD. Each record on this event file represents a unique office-based provider event; that is, an office-based provider event reported by the household respondent. Office-based events reported in Panel 14 Round 3 and known to have occurred after December 31, 2009 are not included on this file. Utilization counts of office-based provider visits are based entirely on household reports. Information from the MPC is used to supplement expenditure payment data on the office-based provider file, reported by the household, and does not affect use estimates. Data from this event file can be merged with other 2009 MEPS HC data files for purposes of appending person-level data such as demographic characteristics or health insurance coverage to each office-based provider visit record on the current file. This file can also be used to construct summary variables of expenditures, sources of payment, and related aspects of office-based provider visits for calendar year 2009. Aggregate annual person-level information on the use of office-based providers and other health services is provided on the MEPS 2009 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:
For more information on MEPS HC survey design, see T. Ezzati-Rice, et al. (1998-2007) and S. Cohen, 1996. A copy of the MEPS HC survey instruments used to collect the information on the office-based provider file is available on the MEPS Web site at the following address: www.meps.ahrq.gov. The 2009 Office-Based Medical Provider public use data set consists of one event-level data file. The file contains characteristics associated with the OB event and imputed expenditure data. The Office-Based Provider public use data set contains 154,599 office-based provider event records; of these records, 150,760 are associated with persons having a positive person-level weight (PERWT09F). This file includes office-based provider event records for all household members who resided in eligible responding households and for whom at least one office-based provider event was reported. Each record represents one household-reported office-based provider event that occurred during calendar year 2009. Office-based provider visits known to have occurred after December 31, 2009 are not included on this file. Some household members may have multiple events and thus will be represented in multiple records on this file. Other household members may have had no events reported and thus will have no records on this file. These data were collected during the 2009 portion of Round 3, and Rounds 4 and 5 for Panel 13, as well as Rounds 1, 2, and the 2009 portion of Round 3 for Panel 14 of the MEPS Household Component. The persons represented on this file had to meet either (a) or (b):
Persons with no office-based medical provider visit events for 2009 are not included on this event-level OB file but are represented on the person-level 2009 Full Year Population Characteristics file. Each office-based medical provider visit event record includes the following: date of the event; type of provider seen; type of care received; types of treatments (i.e., physical therapy, occupational therapy, speech therapy, chemotherapy, radiation therapy, etc.) received during the event; type of services (i.e., lab test, sonogram or ultrasound, x-rays, etc.) received, medicines prescribed during the event; flat fee information; imputed sources of payment; total payment and total charge of the office-based event expenditure; a full-year person-level weight; variance strata; and variance PSU. To append person-level information such as demographic or health insurance coverage to each event record, data from this file can be merged with 2009 MEPS HC person-level data (e.g. Full Year Consolidated or Full Year Population Characteristics files) using the person identifier, DUPERSID. The office-based medical provider visit events can also be linked to the MEPS 2009 Medical Conditions File and MEPS 2009 Prescribed Medicines File. Please see Section 5.0 for details on how to merge MEPS data files. For each variable on the office-based provider file, both weighted and unweighted frequencies are provided in the accompanying codebook. The codebook and data file sequence list variables in the following order:
Note that the person identifier is unique within this data year. The following reserved code values are used:
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: www.meps.ahrq.gov/survey_comp/survey_questionnaires.jsp). The office-based medical provider visits 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:
2.4 Variable Source and Naming Conventions In general, variable names reflect the content of the variable, with an 8-character limitation. All imputed/edited variables end with an "X". Variables contained on this file were derived from the HC survey questionnaire itself, derived from the MPC data collection instrument, derived from CAPI, or assigned in sampling. The source of each variable is identified in Section D "Variable - Source Crosswalk" in one of four ways:
2.4.2 Expenditure and Source of Payment Variables The names of the expenditure and source of payment variables follow a standard convention, are seven 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 sources of payment are named in the following way: The first two characters indicate the type of event:
In the case of source of payment variables, the third and fourth characters indicate:
In addition, the total charge variable is indicated by TC in the variable name. The fifth and sixth characters indicate the year (09). The seventh character, "X", indicates whether the variable is edited/imputed. For example, OBSF09X is the edited/imputed amount paid by self or family for an office-based medical provider visit expenditure incurred in 2009. 2.5.1 Survey Administration Variables 2.5.1.1 Person Identifiers (DUID, PID, DUPERSID) The dwelling unit ID (DUID) is a five-digit random number assigned after the case was sampled for MEPS. The three-digit person number (PID) uniquely identifies each person within the dwelling unit. The eight-character variable DUPERSID uniquely identifies each person represented on the file and is the combination of the variables DUID and PID. For detailed information on dwelling units and families, please refer to the documentation for the 2009 Full Year Population Characteristics. 2.5.1.2 Record Identifiers (EVNTIDX, FFEEIDX) EVNTIDX uniquely identifies each office-based medical provider visit event (i.e., each record on the office-based medical provider visits file) and is the variable required for linking office-based medical provider visit events to data files containing details on conditions and/or prescribed medicines (MEPS 2009 Medical Condition file and MEPS 2009 Prescribed Medicine file, respectively). For details on linking see Section 5.0 or the MEPS 2009 Appendix File, HC-126I. 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, pregnancy is typically covered in a flat fee arrangement where the prenatal visits, the delivery, and the postpartum visits are all covered under one flat fee dollar amount. These events (the prenatal visit, the delivery, and the postpartum visits) would have the same value for FFEEIDX. FFEEIDX identifies a flat fee payment that was identified using information from the Household Component. 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. 2.5.1.3 Round Indicator (EVENTRN) EVENTRN indicates the round in which the office-based event was reported. Please note that Rounds 3, 4, and 5 are associated with MEPS survey data collected from Panel 13. Likewise, Rounds 1, 2, and 3 are associated with data collected from Panel 14. 2.5.1.4 Panel Indicator (PANEL) PANEL is a constructed variable used to specify the panel number for the person. PANEL will indicate either Panel 13 or Panel 14 for each person on the file. Panel 13 is the panel that started in 2008, and Panel 14 is the panel that started in 2009. 2.5.2 MPC Indicator (MPCELIG, MPCDATA) MPCELIG is a constructed variable that indicates whether the office-based provider visit was eligible for MPC data collection. MPCDATA is a constructed variable that indicates whether or not MPC data was collected for the office-based provider. 2.5.3 Office-Based Medical Provider Visit Variables The file contains variables describing office-based medical provider visit events reported by respondents in the Medical Provider Visits section of the MEPS HC survey questionnaire. 2.5.3.1 Date of Visit (OBDATEYR - OBDATEDD) There are three variables that, together, indicate the day, month, and year an office-based provider visit occurred (OBDATEDD, OBDATEMM, and OBDATEYR, respectively). These variables have not been edited or imputed. 2.5.3.2 Visit Details (SEETLKPV-VSTRELCN) The questionnaire determines if, during the office-based medical provider visit, the person actually saw the provider or talked to the provider on the telephone (SEETLKPV). Also, the questionnaire establishes the kind of place the person saw the medical provider (MVPLACE). One of the answer categories for the variable MVPLACE is "Laboratory/X-Ray Facility". The importance of laboratory and x-ray events in relation to the creation of office-based medical provider events is discussed above in Section 2.0. The categories "Birthing Center" and "Indian Health Service" were added to MVPLACE starting in Panel 12. The questionnaire also establishes whether the person saw or spoke to a medical doctor (SEEDOC). If during the medical visit the patient did not see a specialty doctor (DRSPLTY), or, if the person did not see a physician (i.e., a medical doctor), the respondent was asked to identify the type of medical person seen (MEDPTYPE). Whether or not any medical doctors worked at the visit location (DOCATLOC), 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. Note that, in 2002, four new categories were added to the imputation process (13 categories in total) as opposed to nine categories in 2001. These new categories are for alternative care ("Acupuncture", "Massage Therapist", "Homeopathic/Naturopathic/Herbalist", and "Other Alternative/Complementary Care Pro"). In 2001, these alternative care categories, as indicated by the variable MEDPTYPE, were not available for all rounds; therefore, records that indicated the medical provider type was "Acupuncture", "Massage Therapist", "Homeopathic/Naturopathic/Herbalist", or "Other Alternative/Complementary Care Pro" were included in the "Other" category in the 2001 imputation process. 2.5.3.3 Treatments, Procedures, Services, and Prescription Medicines (PHYSTH-MEDPRESC) Types of treatments received during the office-based medical provider 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), psychotherapy/counseling (PSYCHOTH), and shots other than allergy (OTHSHOT). 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 a CAT scan (MRI), an electrocardiogram (EKG), an electroencephalogram (EEG), a vaccination (RCVVAC), anesthesia (ANESTH), throat swab (THRTSWAB), 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. THRTSWAB was introduced in the 2008 version of this file. 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). For a repeat visit event group, if a prescribed medicine is linked to the stem event (MEDPRESC=1), then the value of MEDPRESC is copied to the leaf events without linking the leaf events to the prescribed medicine. Beginning in 2009, MEDPRESC=1 was recoded to -9 for all leaf events. VAPLACE, a constructed variable that indicates whether the service was provided at a VA facility, was dropped from this file for confidentiality purposes beginning in 2008. Information on household-reported medical conditions and procedures associated with each office-based medical provider visit is provided on this file. There are up to four condition and CCS codes (OBICD1X-OBICD4X, OBCCC1X-OBCCC4X) and up to three procedure codes (OBPRO1X – OBPRO3X) listed for each office-based medical provider visit. In order to obtain complete condition information associated with an event, the analyst must link to the Medical Conditions File. Details on how to link to the MEPS Medical Conditions File are provided in Section 5.0. 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 2009 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 how conditions and procedures were coded, please refer to the documentation on the MEPS 2009 Medical Conditions File. For frequencies of conditions by event type, please see the MEPS 2009 Appendix File, HC-126I. The ICD-9-CM conditions were aggregated into clinically meaningful categories. These categories, included on the file as OBCCC1X-OBCCC4X, 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 mutually exclusive categories, most of which are clinically homogeneous. In order to preserve household member 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 3-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-128 documentation. The condition codes (and clinical classification codes) and procedure codes linked to each office-based medical provider visit event 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 Medical Conditions file in conjunction with this office-based medical provider visits file should note that the order of conditions on this file is not identical to that on the 2009 Medical Conditions file. Analysts should use the clinical classification codes listed in the Conditions PUF document (HC-128) and the Appendix to the Event Files (HC-126I) document when analyzing MEPS conditions data. Although there is a list of clinical classification codes and labels on the Healthcare Cost and Utilization Project (HCUP) Web site, if updates to these codes and/or labels are made on the HCUP Web site after the release of the 2009 MEPS PUFs, these updates will not be reflected in the 2009 MEPS data. 2.5.5 Flat Fee Variables (FFEEIDX, FFOBTYPE, FFBEF09, FFTOT10) 2.5.5.1 Definition of Flat Fee Payments A flat fee is the fixed dollar amount a person is charged for a package of services provided during a defined period of time. An example would be an obstetrician’s fee covering a normal delivery, and the associated pre- and post-natal care. A flat fee group is the set of medical services (i.e., events) that are covered under the same flat fee payment. The flat fee groups represented on the office-based provider file include flat fee groups where at least one of the health care events, as reported by the HC respondent, occurred during 2009. By definition, a flat fee group can span multiple years and/or event types (only outpatient department visits and physician office visits). Furthermore, a single person can have multiple flat fee groups. 2.5.5.2 Flat Fee Variable Descriptions 2.5.5.2.1 Flat Fee ID (FFEEIDX) As noted earlier in Section 2.5.1.2 "Record Identifiers," the variable FFEEIDX uniquely identifies all events that are part of the same flat fee group for a person. On any 2009 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. 2.5.5.2.2 Flat Fee Type (FFOBTYPE) FFOBTYPE indicates whether the 2009 office-based medical provider visit event is the "stem" or "leaf" of a flat fee group. A stem (records with FFOBTYPE = 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 FFOBTYPE = 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 office-based visits that are not part of a flat fee payment, the FFOBTYPE is set to -1, "INAPPLICABLE." 2.5.5.2.3 Counts of Flat Fee Events that Cross Years (FFBEF09, FFTOT10) As described in Section 2.5.5.1, a flat fee payment covers multiple events and the multiple events could span multiple years. For situations where the office-based medical provider visit occurred in 2009 as a part of a group of events, and some of the events occurred before 2009, counts of the known events are provided on the office-based medical provider visit event file record. Variables that indicate events occurred before or after 2009 are as follows:
If there are no 2008 events on the file, FFBEF09 will be omitted. Likewise, if there are no 2010 events on the file, FFTOT10 will be omitted. If there are no flat fee data related to the records in this file, FFEEIDX and FFOBTYPE will be omitted as well. Please note that the crosswalk in this document lists all possible flat fee variables. 2.5.5.3 Caveats of Flat Fee Groups Data users/analysts should note that flat fee payments are common on the office-based medical provider visits file. There are 3,403 office-based medical provider visit events that are identified as being part of a flat fee payment group. In order to correctly identify all events that are part of a flat fee group, the user should link all MEPS events, except those in the prescribed medicine file, using the variable FFEEIDX. 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 2009, but the remaining visits that were part of this flat fee group occurred in 2010. In this case, the 2009 flat fee group represented on this file would consist of one event (the stem). The 2010 leaf events that are part of this flat fee group are not represented on this file. Similarly, the household respondent may have reported a flat fee group where the initial visit began in 2008 but subsequent visits occurred during 2009. In this case, the initial visit would not be represented on the file. This 2009 flat fee group would then consist only 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. 2.5.6.1 Definition of Expenditures Expenditures on this file refer to what is paid for health care services. More specifically, expenditures in MEPS are defined as the sum of payments for care received, 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. While charge data are provided on this file, data users/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 is it directly comparable to the resource costs of those services or the expenditures defined in the 1987 NMES (for details on expenditure definitions, see Monheit et al., 1999). AHRQ has developed factors to apply to the 1987 NMES expenditure data to facilitate longitudinal analysis. These factors can be accessed via the CFACT data center. For more information, see the Data Center section of the MEPS Web site www.meps.ahrq.gov/data_stats/onsite_datacenter.jsp. If examining trends in MEPS expenditures or performing longitudinal analysis on MEPS expenditures please refer to Section C, sub-Section 3.3 for more information. 2.5.6.2 Data Editing and Imputation Methodologies of Expenditure Variables The expenditure data included on this file were derived from both the MEPS household (HC) and medical provider (MPC) components. 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 office-based medical provider visits, MPC data were used if available; otherwise HC data were used. Missing data for office-based medical provider 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. 2.5.6.2.1 General Data Editing Methodology 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. 2.5.6.2.2 Imputation Procedures For events in this file that were eligible for the MEPS-MPC (i.e. physician office visits where MPCELIG=1), a predictive mean matching imputation method was used to impute missing expenditures. This procedure uses regression models (based on events with completely reported expenditure data) to predict total expenses for each event. Then, for each event with missing payment information, a donor event with the closest predicted payment to that for the event with missing payment was used to impute the missing payment value. For events in this file that were not eligible for the MEPS-MPC (i.e. non-physician visits where MPCELIG=2), a weighted sequential hot-deck procedure was used to impute missing expenditures. This procedure uses survey data from respondents to replace missing data while taking into account the persons’ weighted distribution in the imputation process. Classification variables vary by type of provider in the hot-deck imputations, but total charge (when available) and insurance coverage are key variables in all of the imputations. Separate imputations were performed for flat fee and simple events. After the imputations were completed, visits to physician and non-physician providers were combined into this office-based medical provider visits file. 2.5.6.2.3 Office-Based Provider Visit Data Editing and Imputation Expenditures for office-based provider visits were developed in a sequence of logical edits and imputations. "Household" edits were applied to sources and amounts of payment for all events reported by HC respondents. "MPC" edits were applied to provider-reported sources and amounts of payment for records matched to household-reported events. Both sets of edits were used to correct obvious errors (as described above) in the reporting of expenditures. After the data from each source were edited, a decision was made as to whether household- or MPC-reported information would be used in the final editing and hot-deck imputations for missing expenditures. The general rule was that MPC data would be used for events where a household-reported event corresponded to an MPC-reported event (i.e., a matched event), since providers usually have more complete and accurate data on sources and amounts of payment than households. 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 2.5.5 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 persons in non-fee for service managed care plans. HMO events reported in the MPC as covered by capitation arrangements were imputed using similar completed 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. 2.5.6.4 Imputation Flag (IMPFLAG) IMPFLAG is a six-category variable that indicates if the event contains complete Household Component (HC) or Medical Provider Component (MPC) data, was fully or partially imputed, or was imputed in the capitated imputation process (for OP and 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 payments. Thus, if the first visit in the flat fee group occurred prior to 2009, all of the events that occurred in 2009 will have zero payments. Conversely, if the first event in the flat fee group occurred at the end of 2009, the total expenditure for the entire flat fee group will be on that event, regardless of the number of events it covered after 2009. See Section 2.5.5 for details on the flat fee variables. There are some medical events reported by respondents where the payments were zero. Zero payment events can occur in MEPS for the following reasons: (1) the visit was covered under a flat fee arrangement (flat fee payments are included only on the first event covered by the arrangement), (2) there was no charge for a follow-up visit, (3) the provider was never paid directly for services provided by an individual, insurance plan, or other source, (4) charges were included in another bill, or (5) event was paid through government or privately funded research or clinical trials. 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". 2.5.6.7 Discount Adjustment Factor 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:
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 persons who were not enrolled in Medicaid, but were presumed eligible by a provider who ultimately received payments from the public payer. 2.5.6.9 Office-Based Expenditure Variables (OBSF09X - OBTC09X) OBSF09X - OBOT09X are the 12 sources of payment. The 12 sources of payment are: self/family (OBSF09X), Medicare (OBMR09X), Medicaid (OBMD09X), private insurance (OBPV09X), Veterans /CHAMPVA (OBVA09X), TRICARE (OBTR09X), other Federal sources (OBOF09X), State and Local (non-federal) government sources (OBSL09X), Workers’ Compensation (OBWC09X), other private insurance (OBOR09X), other public insurance (OBOU09X), and other insurance (OBOT09X). OBXP09X is the sum of the 12 sources of payment for the Office Based expenditures, and OBTC09X is the total charge. Expenditure variables have been rounded to the nearest penny. Person-level expenditure information released on the MEPS 2009 Person-Level Expenditure File will be rounded to the nearest dollar. It should be noted that using the MEPS 2009 event files to create person-level totals will yield slightly different totals than that 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 2009 Appendix File, HC-126I, for details on such rounding differences. There is a single full year person-level weight (PERWT09F) 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 2009. 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. 3.2 Details on Person Weight Construction The person-level weight PERWT09F was developed in several stages. Person-level weights for Panel 13 and Panel 14 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 2009 composite weight was then formed by multiplying each weight from Panel 13 by the factor .52 and each weight from Panel 14 by the factor .48. 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 13 was developed using the 2008 full year weight for an individual as a "base" weight for survey participants present in 2008. For key, in-scope RU members who joined an RU some time in 2009 after being out-of-scope in 2008, the 2008 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 2009. These control figures were derived by scaling back the population totals obtained from the March 2010 CPS to correspond to a national estimate for the civilian noninstitutionalized population provided by the Census Bureau for December 2009. 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. Key, responding persons not in-scope on December 31, 2009 but in-scope earlier in the year retained, as their final Panel 13 weight, the weight after the nonresponse adjustment. The person-level weight for MEPS Panel 14 was developed using the MEPS Round 1 person-level weight as a "base" weight. For key, in-scope RU members who joined an RU after Round 1, the Round 1 family weight served as a "base" weight. The weighting process included an adjustment for nonresponse over Round 2 and the 2009 portion of Round 3 as well as raking to the same population control figures for December 2009 used for the MEPS Panel 13 weights. The same five variables employed for Panel 13 raking (census region, MSA status, race/ethnicity, sex, and age) were used for Panel 14 raking. Similarly, for Panel 14, key, responding persons not in-scope on December 31, 2009 but in-scope earlier in the year retained, as their final Panel 14 weight, the weight after the nonresponse adjustment. Note that the MEPS Round 1 weights 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 CPS data base of the corresponding year (i.e., 2008 for Panel 13 and 2009 for Panel 14). 3.2.3 The Final Weight for 2009 The composite weights of two groups of persons who were out-of-scope on December 31, 2009 were poststratified. Specifically, the weights of those who were in-scope some time during the year, out-of-scope on December 31, and entered a nursing home during the year were poststratified to a corresponding control total obtained from the 1996 MEPS Nursing Home Component. Those who died while in-scope during 2009 were poststratified to corresponding estimates derived using data obtained from the Medicare Current Beneficiary Survey (MCBS) and Vital Statistics information provided by the National Center for Health Statistics (NCHS). Separate decedent control totals were developed for the "65 and older" and "under 65" civilian noninstitutionalized populations. Overall, the weighted population estimate for the civilian noninstitutionalized population for December 31, 2009 is 302,964,200 (PERWT09F>0 and INSC1231=1). The sum of the person-level weights across all persons assigned a positive person-level weight is 306,660,588. The target population for MEPS in this file is the 2009 U.S. civilian noninstitutionalized population. However, the MEPS sampled households are a subsample of the NHIS households interviewed in 2007 (Panel 13) and 2008 (Panel 14). New households created after the NHIS interviews for the respective Panels and consisting exclusively of persons who entered the target population after 2007 (Panel 13) or after 2008 (Panel 14) are not covered by MEPS. Neither are previously out-of-scope persons who join an existing household but are unrelated to the current household residents. Persons not covered by a given MEPS panel thus include some members of the following groups: immigrants; persons leaving the military; U.S. citizens returning from residence in another country; and persons leaving institutions. The set of uncovered persons constitutes only a small segment of the MEPS target population. 3.3 Using MEPS Data for Trend Analysis MEPS began in 1996, and the utility of the survey for analyzing health care trends expands with each additional year of data. 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 may be attributable to sampling variation. The length of time being analyzed should also be considered. In particular, large shifts in survey estimates over short periods of time (e.g. from one year to the next) that are statistically significant should be interpreted with caution, unless they are attributable to known factors such as changes in public policy, economic conditions, or MEPS survey methodology. 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 analyses of trends using MEPS data such as comparing pooled time periods (e.g. 1996-97 versus 2004-05), working with moving averages, or using modeling techniques with several consecutive years of MEPS data to test the fit of specified patterns over time. Finally, researchers should be aware of the impact of multiple comparisons on Type I error. Without making appropriate allowance for multiple comparisons, undertaking numerous statistical significance tests of trends increases the likelihood of concluding that a change has taken place when one has not. 4.1 Developing Event-Level Estimates The data in this file can be used to develop national 2009 event level estimates for the U.S. civilian noninstitutionalized population on office-based medical provider visits as well as expenditures, and sources of payment for these visits. Estimates of total visits are the sum of the weight variable (PERWT09F) across relevant event records while estimates of other variables must be weighted by PERWT09F to be nationally representative. The tables below contain event-level estimates for selected variables. Selected Event Level Estimates Office-Based Visits
Office-Based Expenditures (SEETLKPV = 1)
Office-Based Expenditures: Physician Visits (SEEDOC = 1 & SEETLKPV = 1)
*OBXP09X=-1 (inapplicable) for all phone call events (SEETLKPV=2). ** Zero payment events can occur in MEPS for the following reasons: (1) the visit was covered under a flat fee arrangement (flat fee payments are included only on the first event covered by the arrangement), (2) there was no charge for a follow-up visit, (3) the provider was never paid directly for services provided by an individual, insurance plan, or other source, (4) charges were included in another bill, or (5) event was paid through government or privately funded research or clinical trials. 4.2 Person-Based Estimates for Office-Based Visits To enhance analyses of office-based visits, analysts may link information about office-based visits by sample persons in this file to the annual full year consolidated file (which has data for all MEPS sample persons), or conversely, link person-level information from the full year consolidated file to this event level file (see Section 5.0 below for more details). Both this file and the full year consolidated file may be used to derive estimates for persons with office-based care and annual estimates of total expenditures. However, if the estimate relates to the entire population, this file cannot be used to calculate the denominator, as only those persons with at least one office-based event are represented on this data file. Therefore, the full year consolidated file must be used for person-level analyses that include both persons with and without office-based care. 4.3 Variables with Missing Values 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 2.5.6. 4.4 Variance Estimation (VARSTR, VARPSU) MEPS has a complex sample design. To obtain estimates of variability (such as the standard error of sample estimates or corresponding confidence intervals) for MEPS estimates, analysts need to take into account the complex sample design of MEPS for both person-level and family-level analyses. Several methodologies have been developed for estimating standard errors for surveys with a complex sample design, including the Taylor-series linearization method, balanced repeated replication, and jackknife replication. Various software packages provide analysts with the capability of implementing these methodologies. Replicate weights have not been developed for the MEPS data. Instead, the variables needed to calculate appropriate standard errors based on the Taylor-series linearization method are included on this file as well as all other MEPS public use files. Software packages that permit the use of the Taylor-series linearization method include SUDAAN, Stata, SAS (version 8.2 and higher), and SPSS (version 12.0 and higher). For complete information on the capabilities of each package, analysts should refer to the corresponding software user documentation. Using the Taylor-series linearization method, variance estimation strata and the variance estimation PSUs within these strata must be specified. The variance strata variable is named VARSTR, while the variance PSU variable is named VARPSU. Specifying a "with replacement" design in a computer software package, such as SUDAAN, provides 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), one can expect at least 100 degrees of freedom for the 2009 full year data associated with the corresponding estimates of variance. Prior to 2002, MEPS variance strata and PSUs were developed independently from year to year, and the last two characters of the strata and PSU variable names denoted the year. However, beginning with the 2002 Point-in-Time PUF, the variance strata and PSUs were developed to be compatible with MEPS data associated with the NHIS sample design used through 2005. Such data 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. As a result of the change in the NHIS sample design in 2006, a new set of variance strata and PSUs have been established for variance estimation purposes for use with MEPS Panel 12 and subsequent MEPS panels. There were 165 variance strata associated with both MEPS Panel 13 and Panel 14, providing a substantial number of degrees of freedom for subgroups as well as the nation as a whole. Each variance stratum contains either two or three variance estimation PSUs. 5.0 Merging/Linking MEPS Data Files Data from this file can be used alone or in conjunction with other files for different analytic purposes. This section summarizes various scenarios for merging/linking MEPS event files. The set of households selected for MEPS is a subsample of those participating in the National Health Interview Survey (NHIS), thus, each MEPS panel can also be linked back to the previous year’s NHIS public use data files. For information on obtaining MEPS/NHIS link files please see www.meps.ahrq.gov/data_stats/more_info_download_data_files.jsp. 5.1 Linking to the Person-Level File Merging characteristics of interest from person-level file (e.g., MEPS 2009 Full Year Consolidated File) expands the scope of potential estimates. For example, to estimate the total number of office-based medical provider visits of persons with specific demographic characteristics (such as age, race, sex, and education), population characteristics from a person-level file need to be merged onto the office-based medical provider visits file. This procedure is illustrated below. The MEPS 2009 Appendix File, HC-126I, provides additional detail on how to merge MEPS data files.
The following is an example of SAS code, which completes these steps:
5.2 Linking to the Prescribed Medicines File The RXLK file provides a link from the MEPS event files to the Prescribed Medicine Event File. When using RXLK, data users/analysts should keep in mind that one office-based visit can link to more than one prescribed medicine record. Conversely, a prescribed medicine event may link to more than one office-based visit or different types of events. When this occurs, it is up to the data user/analyst to determine how the prescribed medicine expenditures should be allocated among those medical events. For detailed linking examples, including SAS code, data users/analysts should refer to the MEPS 2009 Appendix File, HC-126I. 5.3 Linking to the Medical Conditions File The CLNK provides a link from MEPS event files to the 2009 Medical Conditions File. When using the CLNK, data users/analysts should keep in mind that (1) conditions are household-reported, (2) there may be multiple conditions associated with an office-based medical provider visit, and (3) a condition may link to more than one office-based medical provider visit or any other type of visit. Users should also note that not all office-based medical provider visits link to the condition file. Cohen, S.B. (1996). The Redesign of the Medical Expenditure Panel Survey: A Component of the DHHS Survey Integration Plan. Proceedings of the COPAFS Seminar on Statistical Methodology in the Public Service. Cox, B.G. and Cohen, S.B. (1985). Chapter 6: A Comparison of Household and Provider Reports of Medical Conditions. In Methodological Issues for Health Care Surveys. Marcel Dekker, New York. Cox, B. 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. Ezzati-Rice, T.M., Rohde, F., Greenblatt, J., Sample Design of the Medical Expenditure Panel Survey Household Component, 1998–2007. Methodology Report No. 22. March 2008. Agency for Healthcare Research and Quality, Rockville, MD. 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. (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. VARIABLE-SOURCE CROSSWALK Survey Administration Variables
Medical Provider Visits Variables
Flat Fee Variables
Imputed Expenditure Variables
Weights
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