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MEPS HC-206G:
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| Value | Definition |
|---|---|
| -1 INAPPLICABLE | Question was not asked due to skip pattern |
| -7 REFUSED | Question was asked and respondent refused to answer question |
| -8 DK | Question was asked and respondent did not know answer |
| -15 CANNOT BE COMPUTED | Value cannot be derived from data |
Generally, values of -1, -7, -8, and -15 for non-expenditure variables have not been edited on this file. The values of -1 and -15 can be edited by the data users/analysts by following the skip patterns in the HC survey questionnaire located on the MEPS website.
The office-based medical provider visits codebook describes an ASCII data set (although the data are also being provided in a SAS transport file).
| Identifier | Description |
|---|---|
| Name | Variable name |
| Description | Variable descriptor |
| Format | Number of bytes |
| Type | Type of data: numeric (indicated by NUM) or character (indicated by CHAR) |
| Start | Beginning column position of variable in record |
| End | Ending column position of variable in record |
In general, variable names reflect the content of the variable. All imputed/edited variables end with an “X”.
Beginning in 2018, as variable collection, universe, or categories are altered, the variable name will be appended with “_Myy” to indicate in which year the alterations took place. Details about these alterations can be found throughout this document.
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:
The names of the expenditure and source of payment variables follow a standard convention, 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:
IP - inpatient stay
ER - emergency room visit
HH - home health visit
OM - other medical equipment
OB - office-based visit
OP - outpatient visit
DV - dental visit
RX - prescribed medicine
In the case of source of payment variables, the third and fourth characters indicate:
SF - self or family
MR - Medicare
MD - Medicaid
PV - private insurance
VA - Veterans Administration/CHAMPVA
TR - TRICARE
OF - other federal government
SL - state/local government
WC - Workers’ Compensation
OT - other insurance
OR - other private
OU - other public
XP - sum of payments
In addition, the total charge variable is indicated by TC in the variable name.
The fifth and sixth characters indicate the year (18). The seventh character, “X”, indicates whether the variable is edited/imputed.
For example, OBSF18X is the edited/imputed amount paid by self or family for an office-based medical provider visit expenditure incurred in 2018.
The dwelling unit ID (DUID) is a seven-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 ten-character variable DUPERSID uniquely identifies each person represented on the file and is the combination of the variables DUID and PID. As part of the new CAPI design, the lengths of the ID variables have changed in the file. The additional 2 bytes in the IDs resulted from adding a 2-digit panel number to the beginning of all the IDs. Analysts wishing to pool data years 2017 and 2018 should add panel numbers to the beginning of Panel 22 Year 2017 ID variables, or remove the 2-digit panel number at the beginning of Panel 22 Year 2018 ID variables to ensure they identify the same person.
For detailed information on dwelling units and families, please refer to the documentation for the 2018 Full Year Population Characteristics.
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 2018 Medical Condition File and MEPS 2018 Prescribed Medicines File, respectively). As part of the new CAPI design, the length of the EVNTIDX has changed to 16 in the file. In addition to the 2-digit panel number added in the beginning, a 2-digit event type number is added to the end. For details on linking see Section 5.0 or the MEPS 2018 Appendix File, HC-206I.
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.
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 22. Likewise, Rounds 1, 2, and 3 are associated with data collected from Panel 23.
PANEL is a constructed variable used to specify the panel number for the person. PANEL will indicate either Panel 22 or Panel 23 for each person on the file. Panel 22 is the panel that started in 2017, and Panel 23 is the panel that started in 2018.
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 were collected for the office-based provider.
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.
There are two variables that, together, indicate the month and year an office-based provider visit occurred (OBDATEMM and OBDATEYR, respectively). These variables have not been edited or imputed.
The questionnaire establishes whether the person saw or spoke to a medical doctor (SEEDOC_M18). If the person talked to a medical doctor, the respondent is asked to specify the type (DRSPLTY_M18) and other health professional type (MEDPTYPE_M18) is set to -1, “INAPPLICABLE”. If during the medical visit the patient did not see a specialty doctor (DRSPLTY_M18) 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_M18). 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_M18) were also determined. Note that response categories with small frequencies may have been recoded to other categories for confidentiality reasons. Starting in 2018, the variable SEETLKPV is removed.
Services received during the visit included whether or not the person received lab tests (LABTEST_M18), a sonogram or ultrasound (SONOGRAM_M18), x-rays (XRAYS_M18), a mammogram (MAMMOG_M18), an MRI or a CAT scan (MRI_M18), an electrocardiogram/an electroencephalogram (EKG_M18), or a vaccination (RCVVAC_M18). Minimal editing was done across treatment, services, and procedures to ensure consistency across “inapplicable,” “don’t know,” “refused,” and “no services received” values. Due to design changes, beginning in 2017, EEG (an electroencephalogram) was combined with EKG; ANESTH (anesthesia) and THRTSWAB (throat swab) were removed. Beginning in 2018, OTHSVCE is removed.
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), the value of MEDPRESC is copied to the leaf events without linking the leaf events to the prescribed medicine, then MEDPRESC=1 was recoded to -15 for all leaf events.
Information on household-reported medical conditions (ICD-10-CM condition codes) and aggregated clinically meaningful categories generated using Clinical Classification Software Refined (CCSR) for each office-based medical provider visit are not provided on this file. For information on the ICD-10-CM condition codes and associated CCSR codes, see the MEPS 2018 Medical Conditions File.
A flat fee is the fixed dollar amount a person is charged for a package of services provided during a defined period of time. 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 2018. 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.
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 2018 MEPS event file, every event that was a part of a specific flat fee group will have the same value for FFEEIDX. Note that prescribed medicine and home health events are never included in a flat fee group and FFEEIDX is not a variable on those event files.
FFOBTYPE indicates whether the 2018 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.”
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 2018 as a part of a group of events, and some of the events occurred before 2018, counts of the known events are provided on the office-based medical provider visit event file record. Variables that indicate events occurring before or after 2018 are as follows:
Data users/analysts should note that flat fee payments are common on the office-based medical provider visits file. There are 2,652 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 2018, but the remaining visits that were part of this flat fee group occurred in 2019. In this case, the 2018 flat fee group represented on this file would consist of one event (the stem). The 2019 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 2017 but subsequent visits occurred during 2018. In this case, the initial visit would not be represented on the file. This 2018 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. Please note that the crosswalk in this document lists all possible flat fee variables.
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. Currently, charges associated with uncollected liability, bad debt, and charitable care (unless provided by a public clinic or hospital) are not counted as expenditures because there are no payments associated with those classifications. While charge data are provided on this file, data users/analysts should use caution when working with these data because a charge does not typically represent actual dollars exchanged for services or the resource costs of those services, nor 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 website. If examining trends in MEPS expenditures, please refer to Section 3.3 for more information.
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.
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.
The predictive mean matching imputation method was used to impute missing expenditures. This procedure uses regression models (based on events with completely reported expenditure data) to predict total expenses for each event. Then, for each event with missing payment information, a donor event with the closest predicted payment with the same pattern of expected payment sources as the event with missing payment was used to impute the missing payment value. Within each event type file, separate imputations were performed for flat fee and simple events. Separate imputations were performed for visits to physicians (where MPCELIG=1) and visits to non-physician providers (where MPCELIG=2). After the imputations were finished, visits to physician and non-physician providers were combined into a single medical provider file.
The weighted sequential hot-deck procedure was used to impute the missing total charges. This procedure uses survey data from respondents to replace missing data while taking into account the persons’ weighted distribution in the imputation process.
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 in the predictive mean matching 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 while events with missing expenditure data were assigned to various recipient categories. Each event with missing expenditure data was assigned to a recipient category based on the extent of its missing charge and expenditure data. For example, an event with a known total charge but no expenditure information was assigned to one category, while an event with a known total charge and partial expenditure information was assigned to a different category. Similarly, events without a known total charge and no or partial expenditure information were assigned to various recipient categories.
The logical edits produced eight recipient categories in which all events had a common extent of missing data. However, for predictive mean matching imputations, the recipients were grouped into four categories based on the known status of total charge and the sources of payment: 1) Known charge but unknown payment status of at least one potential paying source, 2) Unknown charge and unknown payment status of at least one potential paying source, 3) Known charge and known status of all payment sources, and 4) Unknown charge and known status of all payment sources. Separate imputations were performed on events in each recipient group. For office-based events, the donor pool was restricted to events with complete expenditures from the MPC.
The donor pool included “free events” because, in some instances, providers are not paid for their services. These events represent charity care, bad debt, provider failure to bill, and third party payer restrictions on reimbursement in certain circumstances. If free events were excluded from the donor pool, total expenditures would be over-counted because the distribution of free events among complete events (donors) would not be represented among incomplete events (recipients).
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 weighted sequential hot-deck procedure was 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.
IMPFLAG is a six-category variable that indicates if the event contains complete Household Component (HC) or Medical Provider Component (MPC) data, was fully or partially imputed, or was imputed in the capitated imputation process (for OP and OB events only). The following list identifies how the imputation flag is coded; the categories are mutually exclusive.
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 2018, all of the events that occurred in 2018 will have zero payments. Conversely, if the first event in the flat fee group occurred at the end of 2018, the total expenditure for the entire flat fee group will be on that event, regardless of the number of events it covered after 2018. 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) the charges were included in another bill, or (5) the event was paid through government or privately funded research or clinical trials.
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 persons who were not enrolled in Medicaid, but were presumed eligible by a provider who ultimately received payments from the public payer.
OBSF18X - OBOT18X are the 12 sources of payment. The 12 sources of payment are: self/family (OBSF18X), Medicare (OBMR18X), Medicaid (OBMD18X), private insurance (OBPV18X), Veterans /CHAMPVA (OBVA18X), TRICARE (OBTR18X), other federal sources (OBOF18X), state and local (non-federal) government sources (OBSL18X), Workers’ Compensation (OBWC18X), other private insurance (OBOR18X), other public insurance (OBOU18X), and other insurance (OBOT18X). OBXP18X is the sum of the 12 sources of payment for the office-based expenditures, and OBTC18X is the total charge.
Expenditure variables have been rounded to the nearest penny. Person-level expenditure information released on the MEPS 2018 Person-Level Use and Expenditure File will be rounded to the nearest dollar. It should be noted that using the MEPS 2018 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.
There is a single full year person-level weight (PERWT18F) 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 2018. A key person either was a member of a responding NHIS household at the time of interview, or joined a family associated with such a household after being out-of-scope at the time of the NHIS (the latter circumstance includes newborns as well as those returning from military service, an institution, or residence in a foreign country). A person is in-scope whenever he or she is a member of the civilian noninstitutionalized portion of the U.S. population.
The person-level weight PERWT18F was developed in several stages. First, person-level weights for Panel 22 and Panel 23 were created separately. The weighting process for each panel included an adjustment for nonresponse over time and calibration to independent population totals. The calibration was initially accomplished separately for each panel by raking the corresponding sample weights for those in-scope at the end of the calendar year to Current Population Survey (CPS) population estimates based on six variables. The six variables used in the establishment of the initial person-level control figures were: educational attainment of the reference person (no degree, high school/GED no college, some college, bachelor’s degree or higher); census region (Northeast, Midwest, South, West); MSA status (MSA, non-MSA); race/ethnicity (Hispanic; Black, non-Hispanic; Asian, non-Hispanic; and other); sex; and age. A 2018 composite weight was then formed by multiplying each weight from Panel 22 by the factor .490 and each weight from Panel 23 by the factor .510. The choice of factors reflected the relative sample sizes of the two panels, helping to limit the variance of estimates obtained from pooling the two samples. The composite weight was raked to the same set of CPS-based control totals. When the poverty status information derived from income variables became available, a final raking was undertaken, establishing control figures reflecting poverty status rather than educational attainment. Thus, control totals were established using poverty status (five categories: below poverty, from 100 to 125 percent of poverty, from 125 to 200 percent of poverty, from 200 to 400 percent of poverty, at least 400 percent of poverty) as well as the other five variables previously used in the weight calibration.
The person-level weight for MEPS Panel 22 was developed using the 2017 full year weight for an individual as a “base” weight for survey participants present in 2017. For key, in-scope members who joined an RU some time in 2018 after being out-of-scope in 2017, the initially assigned person-level weight was the corresponding 2017 family weight. The weighting process included an adjustment for person-level nonresponse over Rounds 4 and 5 as well as raking to population control totals for December 2018 for key, responding persons in-scope on December 31, 2018. These control totals were derived by scaling back the population distribution obtained from the March 2019 CPS to reflect the December 31, 2018 estimated population total (estimated based on Census projections for January 1, 2019). Variables used for person-level raking included: educational attainment of the reference person (no degree, high school/GED no college, some college, bachelor’s degree or higher); census region (Northeast, Midwest, South, West); MSA status (MSA, non-MSA); race/ethnicity (Hispanic; Black, non-Hispanic; Asian, non-Hispanic; and other); sex; and age. (Poverty status is not included in this version of the MEPS full year database because of the time required to process the income data collected and then assign persons to a poverty status category). The final weight for key, responding persons who were not in-scope on December 31, 2018 but were in-scope earlier in the year was the person weight after the nonresponse adjustment.
Note that the 2017 full-year weight that was used as the base weight for Panel 22 was derived as follows; adjustment of the MEPS Round 1 weight for nonresponse over the remaining data collection rounds in 2017; and raking the resulting nonresponse adjusted weight to December 2017 population control figures.
The person-level weight for MEPS Panel 23 was developed using the 2018 MEPS Round 1 person-level weight as a “base” weight. For key, in-scope members who joined an RU after Round 1, the Round 1 family weight served as a “base” weight. The weighting process included an adjustment for nonresponse over the remaining data collection rounds in 2018 as well as raking to the same population control figures for December 2018 used for the MEPS Panel 22 weights for key, responding persons in-scope on December 31, 2018. The same six variables employed for Panel 22 raking (educational attainment of the reference person, census region, MSA status, race/ethnicity, sex, and age) were used for Panel 23 raking. Again, the final weight for key, responding persons who were not in-scope on December 31, 2018 but were in-scope earlier in the year was the person weight after the nonresponse adjustment.
Note that the MEPS Round 1 weights for Panel 23 incorporated the following components: the original household probability of selection for the NHIS; proportion of the NHIS sample reserved for MEPS; adjustment for NHIS nonresponse; the probability of selection of NHIS responding households for MEPS; an adjustment for nonresponse at the dwelling unit level for Round 1; and poststratification to U.S. civilian noninstitutionalized population estimates at the family and person level obtained from the corresponding March CPS databases.
The final raking of those in-scope at the end of the year has been described above. In addition, the composite weights of two groups of persons who were out-of-scope on December 31, 2018 were adjusted for expected undercoverage. 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 and still residing in a nursing home at the end of the year were adjusted to compensate for expected undercoverage for this subpopulation. The weights of persons who died while in-scope during 2018 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, 2018 is 322,920,490 (PERWT18F>0 and INSC1231=1). The sum of the person-level weights across all persons assigned a positive person-level weight is 326,327,888.
The target population for MEPS in this file is the 2018 U.S. civilian noninstitutionalized population. However, the MEPS sampled households are a subsample of the NHIS households interviewed in 2016 (Panel 22) and 2017 (Panel 23). New households created after the NHIS interviews for the respective panels and consisting exclusively of persons who entered the target population after 2016 (Panel 22) or after 2017 (Panel 23) 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.
MEPS began in 1996, and the utility of the survey for analyzing health care trends expands with each additional year of data; however, there are a variety of methodological and statistical considerations when examining trends over time using MEPS. Tests of statistical significance should be conducted to assess the likelihood that observed trends may be attributable to sampling variation. The length of time being analyzed should also be considered. In particular, large shifts in survey estimates over short periods of time (e.g. from one year to the next) that are statistically significant should be interpreted with caution unless they are attributable to known factors such as changes in public policy, economic conditions, or MEPS survey methodology.
With respect to methodological considerations, in 2013 MEPS introduced an effort to obtain more complete information about health care utilization from MEPS respondents with full implementation in 2014. This effort likely resulted in improved data quality and a reduction in underreporting starting in FY 2014 and could have some modest impact on analyses involving trends in utilization across years. The change in the NHIS sample design in 2016 could also potentially affect trend analyses. For example, coverage of the MEPS target population would be expected to have increased, so subpopulations whose coverage rates were particularly increased would have increased contributions from undercovered portions of their subpopulation.
Another change with the potential to affect trend analyses involved modifications to the MEPS instrument design and data collection process. These were introduced in the Spring of 2018 and thus affected data beginning with Round 1 of Panel 23, Round 3 of Panel 22, and Round 5 of Panel 21. Since the Full Year 2017 PUFs were established from data collected in Rounds 1-3 of Panel 22 and Rounds 3-5 of Panel 21, they reflected two different instrument designs. In order to mitigate the effect of such differences within the same full year file, the Panel 22, Round 3 data and the Panel 21 Round 5 data were transformed to make them as consistent as possible with data collected under the previous design. The changes were designed to make the data collection effort more efficient and easy to administer. In addition, expectations were that data on some items, such as those related to health care events, would be more complete with the potential of identifying more events. Increases in service use reported since the implementation of these changes are consistent with these expectations.
There are also statistical factors to consider in interpreting trend analyses. Looking at changes over longer periods of time can provide a more complete picture of underlying trends. Analysts may wish to consider using techniques to evaluate, smooth, or stabilize analyses of trends using MEPS data such as comparing pooled time periods (e.g. 1996-97 versus 2011-13), working with moving averages, or using modeling techniques with several consecutive years of MEPS data to test the fit of specified patterns over time. Finally, researchers should be aware of the impact of multiple comparisons on Type I error. Without making appropriate allowance for multiple comparisons, undertaking numerous statistical significance tests of trends increases the likelihood of concluding that a change has taken place when one has not.
The data in this file can be used to develop national 2018 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 (PERWT18F) across relevant event records while estimates of other variables must be weighted by PERWT18F to be nationally representative. The tables below contain event-level estimates for selected variables.
Selected Event-Level Estimates
| Estimate of Interest | Variable Name | Estimate (SE) | Estimate Excluding Zero Payment Events (SE)* |
|---|---|---|---|
| Total number of office-based medical provider visits (in millions) | PERWT18F | 2,085.9 (58.64) | 2,037.0 (57.05) |
| Total number of in-person visits to doctor (SEEDOC_M18=1, in millions) | PERWT18F | 1,072.8 (30.49) | 1,048.3 (29.85) |
| Proportion of office-based medical provider visits with expenditures > 0* | OBXP18X | 0.977 (0.0015) | ------------- |
| Estimate of Interest | Variable Name | Estimate (SE) | Estimate Excluding Zero Payment Events (SE)* |
|---|---|---|---|
| Mean total payments per visit (all sources) | OBXP18X | $246 ($5.0) | $252 ($5.2) |
| Mean out-of-pocket payment per visit | OBSF18X | $43 ($1.8) | $44 ($1.9) |
| Mean proportion of total expenditures paid by private insurance per visit | OBPV18X/ OBXP18X | ------------- | 0.343 (0.0075) |
| Estimate of Interest | Variable Name | Estimate (SE) | Estimate Excluding Zero Payment Events (SE)* |
|---|---|---|---|
| Mean total payments per visit where person saw medical doctor | OBXP18X | $298 ($8.0) | $305 ($8.2) |
| Mean out-of-pocket payment per visit where person saw medical doctor | OBSF18X | $46 ($3.0) | $47 ($3.1) |
| Mean proportion of total expenditures per visit paid by private insurance where person saw medical doctor | OBPV18X/ OBXP18X | ------------- | 0.358 (0.0076) |
* 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) the charges were included in another bill, or (5) the event was paid through government or privately funded research or clinical trials.
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.
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 software package 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.
The MEPS is based on a complex sample design. To obtain estimates of variability (such as the standard error of sample estimates or corresponding confidence intervals) for MEPS estimates, analysts need to take into account the complex sample design of MEPS for both person-level and family-level analyses. Several methodologies have been developed for estimating standard errors for surveys with a complex sample design, including the Taylor-series linearization method, balanced repeated replication, and jackknife replication. Various software packages provide analysts with the capability of implementing these methodologies. MEPS analysts most commonly use the Taylor Series approach. However, the capability of employing the Balanced Repeated Replication (BRR) methodology is also provided if needed to develop variances for more complex estimators.
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 variables VARSTR and VARPSU on this MEPS data file serve to identify the sampling strata and primary sampling units required by the variance estimation programs. Specifying a “with replacement” design in one of the previously mentioned computer software packages will provide estimated standard errors appropriate for assessing the variability of MEPS survey estimates. It should be noted that the number of degrees of freedom associated with estimates of variability indicated by such a package may not appropriately reflect the number available. For variables of interest distributed throughout the country (and thus the MEPS sample PSUs), one can generally expect to have at least 100 degrees of freedom associated with the estimated standard errors for national estimates based on this MEPS database.
Prior to 2002, MEPS variance strata and PSUs were developed independently from year to year, and the last two characters of the strata and PSU variable names denoted the year. However, beginning with the 2002 Point-in-Time PUF, the variance strata and PSUs were developed to be compatible with all future PUFs until the NHIS design changed. Thus, when pooling data across years 2002 through the Panel 11 component of the 2007 files, the variance strata and PSU variables provided can be used without modification for variance estimation purposes for estimates covering multiple years of data. There were 203 variance estimation strata, each stratum with either two or three variance estimation PSUs.
From Panel 12 of the 2007 files, a new set of variance strata and PSUs were developed because of the introduction of a new NHIS design. There are 165 variance strata with either two or three variance estimation PSUs per stratum, starting from Panel 12. Therefore, there are a total of 368 (203+165) variance strata in the 2007 Full Year file as it consists of two panels that were selected under two independent NHIS sample designs. Since both MEPS panels in the Full Year files from 2008 through 2016 are based on the next NHIS design, there are only 165 variance strata. These variance strata (VARSTR values) have been numbered from 1001 to 1165 so that they can be readily distinguished from those developed under the former NHIS sample design in the event that data are pooled for several years.
As discussed, a complete change was made to the NHIS sample design in 2016, effectively changing the MEPS design beginning with calendar year 2017. Both Panels 22 and 23 reflect this new design. There were 117 variance strata originally formed under this new design intended for use until the next fully new NHIS design was implemented. They appear in the various MEPS data sets associated with 2017 as well as for the 2018 Point-in-Time PUF involving both Panels 22 and 23. However, it was later learned that the NHIS sample design was further modified in 2018, calling for a reconstruction of the previously established variance strata. Technically, this reconstruction would not be required until the MEPS 2019 PUFs were to be constructed. However, some analysts pool MEPS data across several years. In order to accommodate such pooling, the modification to the MEPS variance structure is being implemented initially for this 2018 FY PUF. Only a handful of variance strata have been affected with some pooling of previous strata being necessary. There are now 110 variance strata established for MEPS, compared to the 117 previously established. In order to make the pooling of data across multiple years of MEPS more straightforward, the numbering system for the variance strata has changed. Those strata associated with the new design have four digit values with a “2” as the first digit. Those associated with the previous design have “1” as the first of four digits.
If analyses call for pooling MEPS data across several years, in order to ensure that variance strata are identified appropriately for variance estimation purposes, one can proceed as follows:
BRR replicate weights are not provided on this MEPS PUF for the purposes of variance estimation. However, a file containing a BRR replication structure is made available so users can form replicate weights, if desired, from the final MEPS weight to compute variances of MEPS estimates using either BRR or Fay’s modified BRR (Fay 1989) methods. The replicate weights are useful to compute variances of complex non-linear estimators for which a Taylor linear form is not easy to derive and not available in commonly used software. For instance, it is not possible to calculate the variances of a median or the ratio of two medians using the Taylor linearization method. For these types of estimators, users may calculate a variance using BRR or Fay’s modified BRR methods. However, it should be noted that the replicate weights have been derived from the final weight through a shortcut approach. Specifically, the replicate weights are not computed starting with the base weight and all adjustments made in different stages of weighting are not applied independently in each replicate. Thus, the variances computed using this one-step BRR do not capture the effects of all weighting adjustments that would be captured in a set of fully developed BRR replicate weights. The Taylor Series approach does not fully capture the effects of the different weighting adjustments either.
The dataset, HC-036BRR, contains the information necessary to construct the BRR replicates. It contains a set of 128 flags (BRR1—BRR128) in the form of half sample indicators, each of which is coded 0 or 1 to indicate whether the person should or should not be included in that particular replicate. These flags can be used in conjunction with the full-year weight to construct the BRR replicate weights. For analysis of MEPS data pooled across years, the BRR replicates can be formed in the same way using the HC-036 file. For more information about creating BRR replicates, users can refer to the documentation for the HC-036BRR pooled linkage file.
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 the MEPS website.
Merging characteristics of interest from a person-level file (e.g., MEPS 2018 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 2018 Appendix File, HC-206I, provides additional detail on how to merge MEPS data files.
The following is an example of SAS code that completes these steps:
PROC SORT DATA=HCXXX (KEEP=DUPERSID AGE31X AGE42X
AGE53X SEX RACEV1X EDUCYR HIDEG) OUT=PERSX;
BY DUPERSID;
RUN;
PROC SORT DATA=OBMP;
BY DUPERSID;
RUN;
DATA NEWOBMP;
MERGE OBMP (IN=A) PERSX(IN=B);
BY DUPERSID;
IF A;
RUN;
The prescribed medicines-event link (RXLK) file provides a link from the MEPS event files to the Prescribed Medicines Event File. When using the 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 2018 Appendix File, HC-206I.
The condition-event link (CLNK) file provides a link from MEPS event files to the 2018 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.
Chowdhury, S.R., Machlin, S.R., Gwet, K.L. Sample Designs of the Medical Expenditure Panel Survey Household Component, 1996–2006 and 2007–2016. Methodology Report #33. January 2019. Agency for Healthcare Research and Quality, Rockville, MD.
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.
Fay, R.E. (1989). Theory and Application of Replicate Weighting for Variance Calculations. Proceedings of the Survey Research Methods Sections, ASA, 212-217.
Monheit, A.C., Wilson, R., and Arnett, III, R.H. (Editors). Informing American Health Care Policy. (1999). Jossey-Bass Inc, San Francisco.
Shah, B.V., Barnwell, B.G., Bieler, G.S., Boyle, K.E., Folsom, R.E., Lavange, L., Wheeless, S.C., and Williams, R. (1996). Technical Manual: Statistical Methods and Algorithms Used in SUDAAN Release 7.0, Research Triangle Park, NC: Research Triangle Institute.
| Variable | Description | Source |
|---|---|---|
| DUID | Panel # + Encrypted DU identifier | Assigned in sampling |
| PID | Person number | Assigned in sampling |
| DUPERSID | Person ID (DUID + PID) | Assigned in sampling |
| EVNTIDX | Event ID | Assigned in sampling |
| EVENTRN | Event round number | CAPI derived |
| PANEL | Panel number | Constructed |
| FFEEIDX | Flat fee ID | CAPI derived |
| MPCELIG | MPC eligibility flag | Constructed |
| MPCDATA | MPC data flag | Constructed |
| Variable | Description | Source |
|---|---|---|
| OBDATEYR | Event date – year | CAPI derived |
| OBDATEMM | Event date – month | CAPI derived |
| SEEDOC_M18 | Did P talk to MD this visit | MV10 |
| DRSPLTY_M18 | MVIS doctor’s specialty | MV20 |
| MEDPTYPE_M18 | Type of med person P talked to on visit dt | MV30 |
| DOCATLOC | Any MD work at location where P saw prov | MV40 |
| VSTCTGRY | Best category for care P recv on visit dt | MV50 |
| VSTRELCN_M18 | This visit related to spec cond | MV60 |
| LABTEST_M18 | This visit did P have lab tests | MV90 |
| SONOGRAM_M18 | This visit did P have sonogram or ultrsd | MV90 |
| XRAYS_M18 | This visit did P have x-rays | MV90 |
| MAMMOG_M18 | This visit did P have a mammogram | MV90 |
| MRI_M18 | This visit did P have an MRI/Catscan | MV90 |
| EKG_M18 | This visit did P have an EKG, EEG or ECG | MV90 |
| RCVVAC_M18 | This visit did P receive a vaccination | MV90 |
| SURGPROC | Was surg proc performed on P this visit | MV80 |
| MEDPRESC | Any medicines prescribed for P this visit | MV110 |
| Variable | Description | Source |
|---|---|---|
| FFOBTYPE | Flat fee bundle | Constructed |
| FFBEF18 | Total # of visits in FF before 2018 | FF50 |
| FFTOT19 | Total # of visits in FF after 2018 | FF60 |
| Variable | Description | Source |
|---|---|---|
| OBSF18X | Amount paid, self/family (imputed) | CP Section (Edited) |
| OBMR18X | Amount paid, Medicare (imputed) | CP Section (Edited) |
| OBMD18X | Amount paid, Medicaid (imputed) | CP Section (Edited) |
| OBPV18X | Amount paid, private insurance (imputed) | CP Section (Edited) |
| OBVA18X | Amount paid, Veterans/CHAMPVA (imputed) | CP Section (Edited) |
| OBTR18X | Amount paid, TRICARE (imputed) | CP Section (Edited) |
| OBOF18X | Amount paid, other federal (imputed) | CP Section (Edited) |
| OBSL18X | Amount paid, state & local government (imputed) | CP Section (Edited) |
| OBWC18X | Amount paid, workers’ compensation (imputed) | CP Section (Edited) |
| OBOR18X | Amount paid, other private insurance (imputed) | Constructed |
| OBOU18X | Amount paid, other public insurance (imputed) | Constructed |
| OBOT18X | Amount paid, other insurance (imputed) | CP Section (Edited) |
| OBXP18X | Sum of OBSF18X – OBOT18X (imputed) | Constructed |
| OBTC18X | Household reported total charge (imputed) | CP Section (Edited) |
| IMPFLAG | Imputation status | Constructed |
| Variable | Description | Source |
|---|---|---|
| PERWT18F | Expenditure file person weight, 2018 | Constructed |
| VARSTR | Variance estimation stratum, 2018 | Constructed |
| VARPSU | Variance estimation PSU, 2018 | Constructed |
|
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