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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: meps.ahrq.gov/survey_comp/survey_questionnaires.jsp). 2.3 Codebook Format
2.4 Variable Source and Naming ConventionsIn general, variable names reflect the content of the variable, with an eight-character limitation. All imputed/edited variables end with an “X”. 2.4.1 Variable-Source CrosswalkVariables were derived from the HC survey questionnaire or from the CAPI. 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 VariablesThe 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 12 source of payment variables are named in the following way: The first two characters indicate the type of event:
In the case of the 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 (14). The seventh character, “X” , indicates whether the variable is edited/imputed. For example, OMSF14X is the edited/imputed amount paid by self or family for 2014 other medical equipment and expenditures. 2.5 File Contents2.5.1 Survey Administration Variables2.5.1.1 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 2014 Full-Year Population Characteristics File. 2.5.1.2 Record Identifiers (EVNTIDX, FFEEIDX)EVNTIDX uniquely identifies each other medical expense event (i.e., each record on the OME file) and is the variable required to link other medical events to data files containing details on prescribed medicines (MEPS 2014 Prescribed Medicines File). For details on linking, see Section 5.0, or the MEPS 2014 Appendix File, HC-168I. FFEEIDX is a constructed variable that uniquely identifies a flat fee group, that is, all events that were part of a flat fee payment. FFEEIDX identifies a flat fee payment that was identified using information from the Household Component. 2.5.1.3 Round Indicator (EVENTRN)EVENTRN indicates the round in which the other medical event was reported. For most types of other medical expenditures on this file, data were collected only in Round 5 for Panel 18 and Round 3 for Panel 19; each record represents a summary of expenditures for items purchased or otherwise obtained for 2014. There is one exception: Expenditure data for the purchase of glasses and/or contact lenses were collected in Rounds 3, 4, and 5 for Panel 18 and Rounds 1, 2, and 3 for Panel 19. For vision items purchased in Panel 19 Round 3, it could not be determined if the purchases occurred in 2014 or 2015. Therefore, records with expenses reported in Round 3 were only included if the number of purchases in 2014 was greater than or equal to the number of purchases in 2015. 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 18 or Panel 19 for each person on the file. Panel 18 is the panel that started in 2013, and Panel 19 is the panel that started in 2014. 2.5.2 Other Medical Type Variables (OMTYPEX, OMTYPE, OMOTHOX, OMOTHOS)Other medical expenditures (OMTYPE) include glasses or contact lenses, ambulance services, orthopedic items, hearing devices, prostheses, bathroom aids, medical equipment, disposable supplies, and alterations/modifications (to homes). When the interviewer did not know how to categorize types of medical item expenditures, these items were specified in the variable OMOTHOS (OMTYPE other specify). As a part of the editing process, other medical expenditures identified in OMOTHOS have been edited to appropriate OMTYPE categories. The edited (OMTYPEX, OMOTHOX) and unedited (OMTYPE, OMOTHOS) versions of both of these variables are included on this file. Records for purchases of insulin and diabetic supplies in a round were included in the Other Medical Expenses event files for 1996-2004. Beginning with the 2005 file, it was decided to exclude these records from the Other Medical Expenses event file since the expenditures have always been included on the Prescribed Medicines file. The Prescribed Medicines file is a more appropriate source for estimates of both utilization and expenditures for insulin and diabetic supplies. As a consequence, there are no records on this file where the variable OMTYPEX = 2 or 3 (the values used in 1996-2004 to identify records for purchases of insulin and diabetic supplies, respectively).
2.5.3 Flat Fee Variables (FFEEIDX, FFOMTYPE, FFBEF14, FFTOT15)2.5.3.1 Definition of Flat Fee PaymentsA flat fee is the fixed dollar amount a person is charged for a package of services provided during a defined period of time. A flat fee group is the set of medical services that are covered under the same flat fee payment. The flat fee groups represented on the Other Medical Expenses event file include flat fee groups where at least one of the other medical events, as reported by the HC respondent, occurred during 2014. By definition, a flat fee group can span multiple years. Furthermore, a single person can have multiple flat fee groups. 2.5.3.2 Flat Fee Variable Descriptions2.5.3.2.1 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 2014 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 none of the flat fee variables are on those event files. 2.5.3.2.2 Flat Fee Type (FFOMTYPE)FFOMTYPE indicates whether the 2014 other medical expenditure is the “stem” or “leaf” of a flat fee group. A stem (records with FFOMTYPE = 1) is the initial other medical service event, which is followed by other medical expense events that are covered under the same flat fee payment. The leaves of the flat fee group (records with FFOMTYPE = 2) are those other medical events that are tied back to the initial event (the stem) in the flat fee group. These “leaf” records have their expenditure variables set to zero. For the other medical events that are not part of a flat fee payment, the FFOMTYPE is set to -1, “INAPPLICABLE”. 2.5.3.2.3 Counts of Flat Fee Events that Cross Years (FFBEF14, FFTOT15)As described in Section 2.5.3.1, a flat fee payment
covers multiple events and the multiple events could span multiple years. For
situations where the medical item was obtained in 2014 as part of a group of
events, and some of the events occurred before or after 2014, counts of the
known events are provided on the other medical record. FFBEF14 – indicates total number of 2013 events in the same flat fee group as the medical item that was obtained in 2014. This count would not include the medical item obtained in 2014. FFTOT15 – indicates the number of 2015 medical events, including the purchase of any additional medical items, expected to be in the same flat fee group as the medical item obtained in 2014. 2.5.3.3 Caveats of Flat Fee GroupsData users/analysts should note that flat fee payments are not common on the Other Medical Expenses file. There are 25 records that are identified as being part of a flat fee payment group. In general, every flat fee group should have an initial visit (stem) and at least one subsequent visit (leaf). There are some situations where this is not true. For some of these flat fee groups, the initial visit reported occurred in 2014, but the remaining visits that were part of this flat fee group occurred in 2015. In this case, the 2014 flat fee group represented on this file would consist of one event (the stem). The 2015 “leaf events” that are part of this flat fee group are not represented on the file. Similarly, the household respondent may have reported a flat fee group where the initial visit began in 2013 but subsequent visits occurred during 2014. In this case, the initial visit would not be represented on the file. This 2014 flat fee group would then only consist of one or more leaf records and no stem. Please note that the crosswalk in this document lists all possible flat fee variables. 2.5.4 Condition, Procedure, and Clinical Classification CodesConditions data are not collected for Other Medical events; therefore, this file cannot be linked to the Conditions File. 2.5.5 Expenditure Data2.5.5.1 Definition of ExpendituresExpenditures on this file refer to what is paid for the medical item. More specifically, expenditures in MEPS are defined as the sum of payments for each medical item that was obtained, including out-of-pocket payments and payments made by private insurance, Medicaid, Medicare and other sources. The definition of expenditures used in MEPS differs slightly from its predecessors: the 1987 NMES and 1977 NMCES surveys where “charges” rather than sum of payments were used to measure expenditures. This change was adopted because charges became a less appropriate proxy for medical expenditures during the 1990s due to the increasingly common practice of discounting. Although measuring expenditures as the sum of payments incorporates discounts in the MEPS expenditure estimates, these estimates do not incorporate any payment not directly tied to specific medical care events, 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 these data because a charge does not typically represent actual dollars exchanged for services or the resource costs of those services, nor are they directly comparable to the expenditures defined in the 1987 NMES. For details on expenditure definitions, please refer to the following, “Informing American Health Care Policy” (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 at meps.ahrq.gov/data_stats/onsite_datacenter.jsp. If examining trends in MEPS expenditures, please refer to Section C, sub-Section 3.3 for more information. 2.5.5.2 Data Editing and Imputation Methodologies of Expenditure VariablesThe general methodology used for editing and imputing expenditure data is described below. The MPC did not include either the dental events or other medical expenditures (such as glasses, contact lenses, and hearing devices). Therefore, although the general procedures remain the same for dental and other medical expenditures, editing and imputation methodologies were applied only to household-reported data. Please see below for details on the differences between these editing/imputation methodologies. Separate imputations were performed for flat fee and simple events, as well. 2.5.5.2.1 General Data Editing MethodologyLogical edits were used to resolve internal inconsistencies and other problems in the HC survey-reported data. The edits were designed to preserve partial payment data from households and providers, and to identify actual and potential sources of payment for each household-reported event. In general, these edits accounted for outliers, copayments or charges reported as total payments, and reimbursed amounts that were reported as out-of-pocket payments. In addition, edits were implemented to correct for misclassifications between Medicare and Medicaid, and between Medicare 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. 2.5.5.2.2 Imputation MethodologiesThe predicted 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 the missing payment was used to impute the missing payment value. The imputations for the flat fee events were carried out separately from the simple events. A 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. 2.5.5.2.3 Other Medical Expenses Data Editing and ImputationExpenditures on other medical equipment and services were developed in a sequence of logical edits and imputations. The household edits were used to correct obvious errors in the reporting of expenditures, and to identify actual and potential sources of payments. Some of the edits were global (i.e., applied to all events). Others were hierarchical and mutually exclusive. 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 each covered by a single charge (i.e., simple events). Other medical services were imputed as flat fee events if the charges covered a package of health care services (e.g., optical), and all of the services were part of the same event type (i.e., a pure bundle). If a bundle contained any OM events with any other types of events, the services were treated as simple events in the imputations (See Section 2.5.3 for more detail on the definition and imputation of events in flat fee bundles.) Logical edits were used to sort each event into a specific category for the imputations. Events with complete expenditures were flagged as potential donors for the predictive mean 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 nine recipient categories for events with missing data. Eight of the categories were for events with a common pattern of missing data and a primary payer other than Medicaid. Medicaid events were imputed separately because persons on Medicaid rarely know the provider’s charge for services or the amount paid by the state Medicaid program. As a result, the total charge for Medicaid-covered services was imputed and discounted to reflect the amount that a state program might pay for the care. Separate predictive mean imputations were used to impute missing data in each of the eight recipient categories. The donor pool included “free events” because in some instances, providers are not paid for their services. These events represent charity care, bad debt, provider failure to bill, and third party payer restrictions on reimbursement in certain circumstances. If free events were excluded from the donor pool, total expenditures would be over-counted because the distribution of free events among complete events (donors) is not represented among incomplete events (recipients). 2.5.5.3 Imputation Flag Variable (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:
2.5.5.4 Flat Fee ExpendituresThe 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 2014, all of the events that occurred in 2014 will have zero payments. Conversely, if the first event in the flat fee group occurred at the end of 2014, the total expenditure for the entire flat fee group will be on that event, regardless of the number of events it covered after 2014. See Section 2.5.3 for details on the flat fee variables. 2.5.5.5 Zero ExpendituresSome respondents reported persons obtaining medical items where the payments were zero. This could occur for several reasons including (1) the item or service was free, (2) bad debt was incurred, or (3) the item was covered under a flat fee arrangement beginning in an earlier year. If all of the medical events for a person fell into one of these categories, then the total annual expenditures for that person would be zero. 2.5.5.6 Sources of PaymentIn 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 relatively small in magnitude, data users/analysts should exercise caution when interpreting the expenditures associated with these two additional sources of payment. While these payments stem from apparent inconsistent responses to health insurance and source of payment questions in the survey, some of these inconsistencies may have logical explanations. For example, private insurance coverage in MEPS is defined as having a major medical plan covering hospital and physician services. If a MEPS sampled person did not have such coverage but had a single service type insurance plan (e.g., dental insurance) that paid for a particular episode of care, those payments may be classified as “other private.” Some of the “other public” payments may stem from confusion between Medicaid and other state and local programs or may be from persons who were not enrolled in Medicaid, but were presumed eligible by a provider who ultimately received payments from the public payer. 2.5.5.7 Other Medical Expenditure Variables (OMSF14X-OMTC14X)Other medical expenditure data were obtained only through the Household Component Survey. For cases with missing expenditure data, other medical expenditures were imputed using the procedures described above. OMSF14X – OMOT14X are the 12 sources of payment.
OMTC14X is the total charge, and OMXP14X is the sum of the 12 sources of payment
for the other medical expenditures. The 12 sources of payment are: self/family
(OMSF14X), Medicare (OMMR14X), Medicaid (OMMD14X), private insurance (OMPV14X),
Veterans Administration/CHAMPVA (OMVA14X), TRICARE (OMTR14X), other federal
sources (OMOF14X), state and local 2.5.5.8 RoundingExpenditure variables on the 2014 Other Medical event file have been rounded to the nearest penny. Person-level expenditure information released on the MEPS 2014 Full Year Consolidated File will be rounded to the nearest dollar. It should be noted that using the MEPS event files to create person-level totals will yield slightly different totals than those found on the consolidated 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. 3.0 Sample Weight (PERWT14F)3.1 OverviewThere is a single full-year person-level weight (PERWT14F) 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 2014. A key person was either 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. 3.2 Details on Person Weight ConstructionThe person-level weight PERWT14F was developed in several stages. Person-level weights for Panel 18 and Panel 19 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 for those in-scope at the end of the calendar year 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; Black, non-Hispanic; Asian, non-Hispanic; and other); sex; and age. A 2014 composite weight was then formed by multiplying each weight from Panel 18 by the factor .500 and each weight from Panel 19 by the factor .500. 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 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 other five variables previously used in the weight calibration. 3.2.1 MEPS Panel 18 Weight Development ProcessThe person-level weight for MEPS Panel 18 was developed using the 2013 full-year weight for an individual as a “base” weight for survey participants present in 2013. For key, in-scope members who joined an RU some time in 2014 after being out-of-scope in 2013, the initially assigned person-level weight was the corresponding 2013 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 2014 for key, responding persons in-scope on December 31, 2014. These control figures were derived by scaling back the population distribution obtained from the March 2015 CPS to reflect the December 31, 2014 estimated population total (estimated based on Census projections for January 1, 2015). Variables used for person-level raking included: 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, 2014 but were in-scope earlier in the year was the person weight after the nonresponse adjustment. 3.2.2 MEPS Panel 19 Weight Development ProcessThe person-level weight for MEPS Panel 19 was developed using the 2014 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 2014 as well as raking to the same population control figures for December 2014 used for the MEPS Panel 18 weights for key, responding persons in-scope on December 31, 2014. The same five variables employed for Panel 18 raking (census region, MSA status, race/ethnicity, sex, and age) were used for Panel 19 raking. Again, the final weight for key, responding persons who were not in-scope on December 31, 2014 but were in-scope earlier in the year was the person weight after the nonresponse adjustment. Note that the MEPS Round 1 weights for both panels incorporated the following components: a weight reflecting the original household probability of selection for the NHIS and an adjustment for NHIS nonresponse; a factor representing the proportion of the 16 NHIS panel-quarter combinations eligible for MEPS; the oversampling of certain subgroups for MEPS among the NHIS household respondents eligible for MEPS; ratio-adjustment to NHIS-based national population estimates at the household (occupied DU) level; adjustment for nonresponse at the DU 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 data bases. 3.2.3 The Final Weight for 2014The 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, 2014 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. The weights of persons who died while in-scope during 2014 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, 2014 is 314,906,436 (PERWT14F>0 and INSC1231=1). The sum of person-level weights across all persons assigned a positive person-level weight is 318,440,423. 3.2.4 CoverageThe target population for MEPS in this file is the 2014 U.S. civilian noninstitutionalized population. However, the MEPS sampled households are a subsample of the NHIS households interviewed in 2012 (Panel 18) and 2013 (Panel 19). New households created after the NHIS interviews for the respective Panels and consisting exclusively of persons who entered the target population after 2012 (Panel 18) or after 2013 (Panel 19) 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 AnalysisMEPS 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. 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 in FY 2014 and could have some modest impact on analyses involving trends in utilization across years. 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 2013-14), 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. 4.0 Strategies for EstimationThis file is constructed for estimation of utilization, expenditures, and sources of payment for other medical expenditures and to allow for estimates for the number of persons who obtained medical items in 2014. 4.1 Basic Estimates of Utilization, Expenditures, and Sources of PaymentIn contrast to the other types of event files, the unit and/or period of time covered are not consistent across all records within this file. More specifically, this file contains round-specific expenditure data on purchases of eyeglasses or contact lenses and annual data on certain other types of medical equipment, supplies, and services (see description below and OMTYPEX variable in codebook for more details). Data are not collected on the actual number of purchases of the items/services represented on this file, so it is not possible to estimate the average expenditure per unit of service. 4.1.1 Type of Records on File (OMTYPEX)Records for purchases of insulin and diabetic supplies were included in the Other Medical Expenses event files for 1996-2004. Beginning with the 2005 file, these records were excluded from the Other Medical Expenses event file since the expenditures have always been included on the Prescribed Medicines file. The Prescribed Medicines file is a more appropriate source for estimates of both utilization and expenditures for insulin and diabetic supplies. As a consequence, there are no records on this file where the variable OMTYPEX = 2 or 3 (the values used in 1996-2004 to identify records for purchases of insulin and diabetic supplies, respectively). Eyeglasses and contact lenses: Each record on this
file where OMTYPEX = 1 contains information on total expenditures during a
specific round for eyeglasses and/or contact lenses Other medical equipment, supplies and services: Each of the records in this file where OMTYPEX does not equal 1 contains person-specific information on annual expenditures for a specific category of medical equipment and supplies asked about in the survey. Estimates of the total number of persons with expenditures for an item during the year are the sum of the weight variable (PERWT14F) across relevant records (e.g., for ambulance services, records where OMTYPEX = 4). Estimates of expenditure variables must be weighted by PERWT14F to be nationally representative. For example, the estimate for the total expenditures for ambulance services paid out of pocket is produced by summing the product of the variables PERWT14F and OMSF14X across all the events in the file where OMTYPEX = 4 as follows (the subscript ‘j’ identifies each event and represents a numbering of events from 1 through the total number of events in the file): Σ Wj Xj, where Wj = PERWT14Fj (full-year weight
for the person associated with event j) and The estimate for the total annual expenditures for ambulance services paid out of pocket per person with that type of expense is produced as follows (the subscript ‘j’ identifies each event and represents a numbering of events from 1 through the total number of events in the file): Σ Wj Xj/Σ Wj, where Wj = PERWT14Fj (full-year weight
for the person associated with event j) and This type of estimate and corresponding standard error (SE) can be derived using an appropriate computer software package for complex survey analysis such as SAS, Stata, SUDAAN or SPSS. For information please see meps.ahrq.gov/survey_comp/standard_errors.jsp. Variables are contained on the full-year annual file for aggregate expenditures across all of these types of services/items (OMTYPEX = 4-11 or 91), but it is necessary to use this file to produce an annual estimate for a specific category of service. Small sample sizes make it advisable to pool multiple years of MEPS data to produce statistically reliable estimates for some of the items. 4.2 Variables with Missing ValuesIt 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 negative 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., source of payment, flat fee, and zero expenditures) are described in Section 2.5.5.2. 4.3 Variance Estimation (VARPSU, VARSTR)The 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 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 2008 file and beyond are based on the new 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. 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:
5.0 Merging/Linking MEPS Data FilesData 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. Each MEPS panel can also be linked back to the previous years’ National Health Interview Survey public use data files. For information on obtaining MEPS/NHIS link files please see meps.ahrq.gov/data_stats/more_info_download_data_files.jsp. 5.1 Linking to the Person-Level FileMerging characteristics of interest from other MEPS files (e.g., 2014 Full-Year Consolidated File or 2014 Prescribed Medicines) expands the scope of potential estimates. For example, to estimate the expenditures for medical equipment, visual aids, etc. for persons with specific demographic characteristics (such as age, race, and sex), population characteristics from a person-level file need to be merged onto the Other Medical Expenses event file. This procedure is shown below. The MEPS 2014 Appendix File, HC-168I, provides additional details on how to merge other MEPS data files.
The following is an example of SAS code which completes these steps: PROC SORT DATA=HCXXX (KEEP=DUPERSID AGE31X AGE42X 5.2 Linking to the Prescribed Medicines FileThe RXLK file provides a link from the MEPS event files to the 2014 Prescribed Medicine Event File. When using RXLK, data users/analysts should keep in mind that one other medical record can link to more than one prescribed medicine record. Conversely, a prescribed medicine event may link to more than one other medical record. 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 2014 Appendix File, HC-168I. 5.3 Linking to the Medical Conditions FileConditions data are not collected for Other Medical events; therefore, this file cannot be linked to the Conditions File. ReferencesCohen, 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. 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 Monheit, A.C., Wilson, R., and Arnett, III, R.H. (Editors) (1999). Informing American Health Care Policy. 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. D. Variable-Source CrosswalkVARIABLE-SOURCE CROSSWALK
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