Font Size:
|
||||||
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 GeneralVariables on this file were derived from the HC questionnaire itself, derived from the MPC data collection instrument, derived from CAPI, or assigned in sampling. The source of each variable is identified in Section D “Variable - Source Crosswalk” in one of four ways:
2.4.2 Expenditure and Source of Payment VariablesThe names of the expenditure and source of payment variables follow a standard convention, are eight characters in length, and end in an “X” indicating edited/imputed. Please note that imputed means that a series of logical edits, as well as an imputation process to account for missing data, have been performed on the variable. The total sum of payments and the 12 source of payment variables are named in the following way: The first two characters indicate the type of event:
For expenditure variables on the ER file, the third character indicates whether the expenditure is associated with the facility (F) or the physician (D). In the case of the source of payment variables, the fourth and fifth characters indicate:
In addition, the total charge variable is indicated by TC in the variable name. The sixth and seventh characters indicate the year (15). The eighth character, “X”, indicates whether the variable is edited/imputed. For example, ERFSF15X is the edited/imputed amount paid by self or family for the facility portion of the expenditure associated with an emergency room visit. 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 2015 Full Year Population Characteristics file. 2.5.1.2 Record Identifiers (EVNTIDX, ERHEVIDX, FFEEIDX)EVNTIDX uniquely identifies each emergency room visit/event (i.e., each record on the Emergency Room Visits file) and is the variable required to link emergency room events to data files containing details on conditions and/or prescribed medicines (MEPS 2015 Medical Conditions File and the MEPS 2015 Prescribed Medicines File, respectively). For details on linking, see Section 5.0 or the MEPS 2015 Appendix File, HC-178I. ERHEVIDX is a constructed variable identifying an EROM record that has its facility expenditures represented on an associated hospital inpatient stay record. This variable is derived from provider-reported information on linked emergency room and inpatient stay events that matched to corresponding events reported by the household. The variable ERHEVIDX contains the EVNTIDX of the linked event. On the 2015 EROM file, there are 386 emergency room events linked to subsequent hospital stays. Please note that where the emergency room visit is associated with a hospital stay (and its expenditures and charges are included with the hospital stay), the physician expenditures associated with the emergency room visit remain on the Emergency Room Visits file. FFEEIDX is a constructed variable which uniquely identifies a flat fee group, that is, all events that were a part of a flat fee payment. 2.5.1.3 Round Indicator (EVENTRN)EVENTRN indicates the round in which the emergency room visit was reported. Please note: Rounds 3, 4, and 5 are associated with MEPS survey data collected from Panel 19. Likewise, Round 1, 2, and 3 are associated with data collected from Panel 20. 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 19 or Panel 20 for each person on the file. Panel 19 is the panel that started in 2014, and Panel 20 is the panel that started in 2015. 2.5.2 MPC Data Indicator (MPCDATA)MPCDATA is a constructed variable which indicates whether or not MPC data were collected for the emergency room visit. While all emergency room events are sampled into the Medical Provider Component, not all emergency room event records have MPC data associated with them. This is dependent upon the cooperation of the household respondent to provide permission forms to contact the emergency room facility as well as the cooperation of the emergency room facility to participate in the survey. 2.5.3 Emergency Room Visit Event VariablesThis file contains variables describing emergency room visits/events reported by household respondents in the Emergency Room section of the MEPS HC questionnaire. The questionnaire contains specific probes for determining details about the emergency room event. These variables have not been edited. 2.5.3.1 Visit Details (ERDATEYR-VSTRELCN)When a person reported having had a visit to the emergency room, the year and month of the emergency room visit was recorded (ERDATEYR and ERDATEMM respectively). The type of care the person received (VSTCTGRY) and whether or not the visit was related to a specific condition (VSTRELCN) were also determined. Through 2012, whether or not the person saw a medical doctor (SEEDOC) was included on the file. Beginning in 2013, SEEDOC was removed because of design changes. 2.5.3.2 Services, Procedures, and Prescription Medicines (LABTEST-MEDPRESC)Services received during the visit included whether or not the person received lab tests (LABTEST), a sonogram or ultrasound (SONOGRAM), x-rays (XRAYS), a mammogram (MAMMOG), an MRI or CAT scan (MRI), an electrocardiogram (EKG), an electroencephalogram (EEG), a vaccination (RCVVAC), anesthesia (ANESTH), throat swab (THRTSWAB), or other diagnostic tests or exams (OTHSVCE). Whether or not a surgical procedure was performed during the visit was asked (SURGPROC). The questionnaire determined if a medicine was prescribed for the person during the emergency room visit (MEDPRESC). See Section 5.2 for information on linking to the prescribed medicines events file. 2.5.4 Clinical Classification Codes (ERCCC1X-ERCCC4X)Information on household-reported medical conditions associated with each emergency room visit is provided on this file. There are up to four CCS codes (ERCCC1X-ERCCC4X) listed for each emergency room visit, as shown in the crosswalk of this document. The file includes the number of CCS codes reported in the data year, which may be fewer than the maximum four for CCS codes. Because the maximum number of conditions associated with an event can change from year to year, the number of reported CCS codes also can change from year to year. Starting with the 2013 file, the ICD-9-CM condition and procedure codes variables are omitted. In order to obtain complete condition information associated with an event, the data user/analyst must link to the MEPS 2015 Medical Conditions File. Details on how to link the 2015 EROM event file to the MEPS 2015 Medical Conditions File are provided in Section 5.3 and in the MEPS 2015 Appendix File, HC-178I. The data user/analyst should note that because of confidentiality restrictions, provider-reported condition information is not publicly available. The medical conditions reported by the Household Component respondent were recorded by the interviewer as verbatim text, which were then coded to fully-specified 2015 ICD-9-CM codes, including medical conditions and V codes (Health Care Financing Administration, 1980) by professional coders. Although codes were verified and error rates did not exceed 2 percent for any coder, data users/analysts should not presume this level of precision in the data; the ability of household respondents to report condition data that can be coded accurately should not be assumed (Cox and Cohen, 1985; Cox and Iachan, 1987; Edwards, et al, 1994; and Johnson and Sanchez, 1993). For detailed information on how conditions were coded, please refer to the documentation on the MEPS 2015 Medical Conditions File. For frequencies of conditions by event type, please see the MEPS 2015 Appendix File, HC-178I. The ICD-9-CM condition codes were aggregated into clinically meaningful categories. These categories, included on the file as ERCCC1X-ERCCC4X, were generated using Clinical Classification Software [formerly known as Clinical Classifications for Health Care Policy Research (CCHPR)], (Elixhauser, et al., 1998), which aggregates conditions and V-codes into mutually exclusive categories, most of which are clinically homogeneous. The clinical classification codes linked to each emergency room visit are sequenced in the order in which the conditions were reported by the household respondent, which was in order of input into the database and not in order of importance or severity. Data users/analysts who use the MEPS 2015 Medical Conditions File in conjunction with this emergency room visits file should note that the order of conditions on this file is not identical to that on the Medical Conditions file. Analysts should use the clinical classification codes listed in the Conditions PUF document (HC-180) and the Appendix to the Event Files (HC-178I) document when analyzing MEPS conditions data. Although there is a list of clinical classification codes and labels on the Healthcare Cost and Utilization Project (HCUP) Web site, if updates to these codes and/or labels are made on the HCUP Web site after the release of the 2015 MEPS PUFs, these updates will not be reflected in the 2015 MEPS data. 2.5.5 Flat Fee Variables (FFEEIDX, FFERTYPE, FFBEF15, FFTOT16)2.5.5.1 Definition of Flat Fee PaymentsA flat fee is the fixed dollar amount a person is charged for a package of health care services provided during a defined period of time. Examples would be: obstetrician’s fee covering a normal delivery, as well as pre- and post-natal care; or a surgeon’s fee covering a surgical procedure and post-surgical care. A flat fee group is the set of medical services (i.e., events) that are covered under the same flat fee payment. The flat fee groups represented on this file include flat fee groups where at least one of the health care events, as reported by the HC respondent, occurred during 2015. By definition, a flat fee group can span multiple years. Furthermore, a single person can have multiple flat fee groups. 2.5.5.2 Flat Fee Variable Descriptions2.5.5.2.1 Flat Fee ID (FFEEIDX)As noted earlier in Section 2.5.1.2 “Record Identifiers,” the variable FFEEIDX uniquely identifies all events that are part of the same flat fee group for a person. On any 2015 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. 2.5.5.2.2 Flat Fee Type (FFERTYPE)FFERTYPE indicates whether the 2015 emergency room visit is the “stem” or “leaf” of a flat fee group. A stem (records with FFERTYPE = 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 FFERTYPE = 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 emergency room visits that are not part of a flat fee payment, the FFERTYPE is set to –1, “INAPPLICABLE.” 2.5.5.2.3 Counts of Flat Fee Events that Cross Years (FFBEF15, FFTOT16)As described in Section 2.5.5.1, a flat fee payment may cover multiple events, and the multiple events could span multiple years. For situations where the emergency room event occurred in 2015 as part of a group of events, and some events occurred before or after 2015, counts of the known events are provided on the emergency room event record. Variables indicating events that occurred before or after 2015 are as follows: FFBEF15 – total number of pre-2015 events in the same flat fee group as the 2015 emergency room visit(s). This count would not include the 2015 emergency room visit(s). FFTOT16 –the number of 2016 emergency room visits, expected to be in the same flat fee group as the emergency room event that occurred in 2015. 2.5.5.3 Caveats of Flat Fee GroupsThere are 13 emergency room visits that are identified as being part of a flat fee payment group. In general, every flat fee group should have an initial visit (stem) and at least one subsequent visit (leaf). There are some situations where this is not true. For some flat fee groups, the initial visit reported occurred in 2015, but the remaining visits that were part of this flat fee group occurred in 2016. In this case, the 2015 flat fee group represented on this file would consist of one event, the stem. The 2016 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 2014 but subsequent visits occurred during 2015. In this case, the initial visit would not be represented on the file. This 2015 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. 2.5.6 Expenditure Data2.5.6.1 Definition of ExpendituresExpenditures 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 for each emergency room visit, including out-of-pocket payments and payments made by private insurance, Medicaid, Medicare, and other sources. The definition of expenditures used in MEPS differs slightly from its predecessors: the 1987 NMES and 1977 NMCES surveys where “charges” rather than sum of payments were used to measure expenditures. This change was adopted because charges became a less appropriate proxy for medical expenditures during the 1990s due to the increasingly common practice of discounting. Although measuring expenditures as the sum of payments incorporates discounts in the MEPS expenditure estimates, the estimates do not incorporate any payment not directly tied to specific medical care visits, such as bonuses or retrospective payment adjustments 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 are they directly comparable to the expenditures defined in the 1987 NMES. For details on expenditure definitions, please reference “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 meps.ahrq.gov/data_stats/onsite_datacenter.jsp. Expenditure data related to emergency room visits are broken out by facility and separately billing doctor expenditures. This file contains six categories of expenditure variables per visit: basic hospital emergency room facility expenses; expenses for doctors who billed separately from the hospital for any emergency room services provided during the emergency room visit; total expenses, which is the sum of the facility and physician expenses; facility charge; physician charge; and total charges, which is the sum of the facility and physician charges. If examining trends in MEPS expenditures, please refer to Section 3.3 for more information. 2.5.6.2 Data Editing and Imputation Methodologies of Expenditure VariablesThe expenditure data included on this file were derived from both the MEPS Household (HC) and Medical Provider Components (MPC). The MPC contacted medical providers identified by household respondents. The charge and payment data from medical providers were used in the expenditure imputation process to supplement missing household data. For all emergency room visits, MPC data were used if available; otherwise, HC data were used. Missing data for emergency room visits, where HC data were not complete and MPC data were not collected, or MPC data were not complete, were imputed through the imputation process. 2.5.6.2.1 General Data Editing MethodologyLogical edits were used to resolve internal inconsistencies and other problems in the HC and MPC survey-reported data. The edits were designed to preserve partial payment data from households and providers, and to identify actual and potential sources of payment for each household-reported event. In general, these edits accounted for outliers, copayments or charges reported as total payments, and reimbursed amounts that were reported as out-of-pocket payments. In addition, edits were implemented to correct for misclassifications between Medicare and Medicaid and between Medicare HMOs and private HMOs as payment sources. These edits produced a complete vector of expenditures for some events, and provided the starting point for imputing missing expenditures in the remaining events. 2.5.6.2.2 Imputation MethodologiesThe 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. The imputations for the flat fee events were carried out separately from the simple events. 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. 2.5.6.2.3 Emergency Room Visit Data Editing and ImputationFacility expenditures for emergency room services were developed in a sequence of logical edits and imputations. “Household” edits were applied to sources and amounts of payment for all events reported by HC respondents. “MPC” edits were applied to provider-reported sources and amounts of payment for records matched to household-reported events. Both sets of edits were used to correct obvious errors in the reporting of expenditures. After the data from each source were edited, a decision was made as to whether household- or MPC-reported information would be used in the final editing and predictive mean matching imputations for missing expenditures. The general rule was that MPC data would be used where a household-reported event corresponded to an MPC-reported event (i.e., a matched event), since providers usually have more complete and accurate data on sources and amounts of payment than households. One of the more important edits separated flat fee events from simple events. This edit was necessary because groups of events covered by a flat fee (i.e., a flat fee bundle) were edited and imputed separately from individual events covered by a single charge (i.e., simple events). Most emergency room events were imputed as simple events because hospital facility charges are rarely bundled with other events. (See Section 2.5.5 for more details on flat fee groups). However, some emergency room visits were treated as free events because the person was admitted to a hospital through its emergency room. In these cases, emergency room charges are included in the charge for an inpatient hospital stay. Logical edits also were used to sort each event into a specific category for the imputations. Events with complete expenditures were flagged as potential donors for the predictive mean matching imputations, while events with missing expenditure data were assigned to various recipient categories. Each event with missing expenditure data was assigned to a recipient category based on the extent of its missing charge and expenditure data. For example, an event with a known total charge but no expenditure information was assigned to one category, while an event with a known total charge and partial expenditure information was assigned to a different category. Similarly, events without a known total charge and no or partial expenditure information were assigned to various recipient categories. The logical edits produced eight recipient categories in which all events had a common extent of missing data. Separate predictive mean matching imputations were performed on events in each recipient category. For emergency room 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). Expenditures for some emergency room visits are not shown because the person was admitted to the hospital through the emergency room. These emergency room events are not free, but the expenditures are included in the inpatient stay expenditures. The variable ERHEVIDX can be used to differentiate between free emergency room care and situations where the emergency room charges have been included in the inpatient hospital charges. Expenditures for services provided by separately billing doctors in hospital settings were also edited and imputed. These expenditures are shown separately from hospital facility charges for hospital inpatient, outpatient, and emergency room care. 2.5.6.3 Imputation Flag (IMPFLAG)IMPFLAG is a six-category variable that indicates if the event contains complete Household Component (HC) or Medical Provider Component (MPC) data, was fully or partially imputed, or was imputed in the capitated imputation process (for OP and OB events only). The following list identifies how the imputation flag is coded; the categories are mutually exclusive. IMPFLAG = 0 not eligible for imputation (includes zeroed out and flat fee leaf events) IMPFLAG = 1 complete HC data IMPFLAG = 2 complete MPC data IMPFLAG = 3 fully imputed IMPFLAG = 4 partially imputed IMPFLAG = 5 complete MPC data through capitation imputation (not applicable to ER events) 2.5.6.4 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 facility payments, while physician’s expenditures may still be present. Thus, if the first visit in the flat fee group occurred prior to 2015, all of the events that occurred in 2015 will have zero payments. Conversely, if the first event in the flat fee group occurred at the end of 2015, the total expenditure for the entire flat fee group will be on that event, regardless of the number of events it covered after 2015. See Section 2.5.5 for details on the flat fee variables. 2.5.6.5 Zero ExpendituresThere 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 by an individual, insurance plan, or other source for services provided, (4) charges were included in the bill for a subsequent hospital admission (emergency room events only), or (5) the event was paid for through government or privately-funded research or clinical trials. 2.5.6.6 Discount Adjustment FactorAn 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. 2.5.6.7 Emergency Room/Hospital Inpatient Stay ExpendituresIt is common for an emergency room visit to result in a hospital stay. While it is true that all of the event files can be linked by DUPERSID, there is no unique record link between hospital inpatient stays and emergency room visits. However, wherever this relationship could be identified (using the MPC start and end dates of the events as well as other information from the provider), the facility expenditure associated with the emergency room visit is included in the hospital facility expenditure. Hence, the expenditures (and charges) for some emergency room visits are included in the resulting hospitalization. In these situations, the emergency room record on this file will have its expenditure (and charge) information zeroed out to avoid double-counting while its corresponding hospital inpatient stay record on the MEPS 2015 Hospital Inpatient Stays File will have the combined expenditures. Please note that any physician expenditures associated with emergency room events remain on the Emergency Room Visits event file. The variable ERHEVIDX identifies the emergency room visits whose facility expenditures are included in the expenditures for the following hospital inpatient stay. It should also be noted that for these cases there is only one emergency room visit associated with the hospital room stay. 2.5.6.8 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 health insurance coverage and sources of payment based on data collected in the survey. These variables include:
Though these two sources are relatively small in magnitude, data users/analysts should exercise caution when interpreting the expenditures associated with these two additional sources of payment. While these payments stem from apparent inconsistent responses to health insurance and source of payment questions in the survey, some of these inconsistencies may have logical explanations. For example, private insurance coverage in MEPS is defined as having a major medical plan covering hospital and physician services. If a MEPS sampled person did not have such coverage but had a single service type insurance plan (e.g., dental insurance) that paid for a particular episode of care, those payments may be classified as “other private.” Some of the “other public” payments may stem from confusion between Medicaid and other state and local programs or may be from persons who were not enrolled in Medicaid, but were presumed eligible by a provider who ultimately received payments from the public payer. 2.5.6.9 Imputed Emergency Room Expenditure VariablesThis file contains two sets of imputed expenditure variables: facility expenditures and physician expenditures. 2.5.6.9.1 Emergency Room Facility Expenditures (ERFSF15X-ERFOT15X, ERFXP15X, ERFTC15X)Emergency room expenses include all expenses for treatment, services, tests, diagnostic and laboratory work, x-rays, and similar charges, as well as any physician services included in the emergency room charge. ERFSF15X - ERFOT15X are the 12 sources of payment. The 12 sources of payment are: self/family (ERFSF15X), Medicare (ERFMR15X), Medicaid (ERFMD15X), private insurance (ERFPV15X), Veterans Administration/CHAMPVA (ERFVA15X), TRICARE (ERFTR15X), other federal sources (ERFOF15X), state and local (non-federal) government sources (ERFSL15X), Worker’s Compensation (ERFWC15X), other private insurance (ERFOR15X), other public insurance (ERFOU15X), and other insurance (ERFOT15X). ERFXP15X is the sum of the 12 sources of payment for the emergency room expenditures, and ERFTC15X is the total charge. Please note that where an emergency room visit record is linked to a hospital inpatient stay record, all facility sources of payment variables, as well as ERFTC15X, have been zeroed out. 2.5.6.9.2 Emergency Room Physician Expenditures (ERDSF15X - ERDOT15X, ERDXP15X, ERDTC15X)Separately billing doctor (SBD) expenses typically cover services provided to patients in hospital settings by providers like anesthesiologists, radiologists, and pathologists, whose charges are often not included in emergency room visit bills. For physicians who bill separately (i.e., outside the emergency room visit bill), a separate data collection effort within the Medical Provider Component was performed to obtain this same set of expenditure information from each separately billing doctor. It should be noted that there could be several separately billing doctors associated with a medical event. For example, an emergency room visit could have a radiologist and an internist associated with it. If their services are not included in the emergency room visit bill then this is one medical event with two separately billing doctors. The imputed expenditure information associated with the separately billing doctors was summed to the event level and is provided on the file. ERDSF15X - ERDOT15X are the 12 sources of payment, ERDXP15X is the sum of the 12 sources of payments, and ERDTC15X is the physician’s total charge. Data users/analysts need to take into consideration whether to analyze facility and SBD expenditures separately, combine them within service categories, or collapse them across service categories (e.g., combine SBD expenditures with expenditures for physician visits to offices and/or outpatient departments). 2.5.6.9.3 Total Expenditures and Charges for Emergency Room Visits (ERXP15X, ERTC15X)Data users/analysts interested in total expenditure should use the variable ERXP15X, which includes both the facility and physician amounts. Those interested in total charges should use the variable ERTC15X, which includes both facility and physician charges (see Section 2.5.6.1 for an explanation of the “charge” concept). However, please note that where the emergency room visit is linked to a hospital inpatient stay record, ERFTC15X has been zeroed out. Thus, ERTC15X may be equal to “0” or the doctor total charge (ERDTC15X). 2.5.7 RoundingThe expenditure variables have been rounded to the nearest penny. Person-level expenditure information released on the MEPS 2015 Person-Level Use and Expenditure File were rounded to the nearest dollar. It should be noted that using the MEPS 2015 event files to create person-level totals will yield slightly different totals than those found on the full year 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 expenditures file for that source of payment. This difference is also an artifact of rounding only. 3.0 Sample Weight (PERWT15F)3.1 OverviewThere is a single full year person-level weight (PERWT15F) 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 2015. 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 PERWT15F was developed in several stages. Person-level weights for Panel 19 and Panel 20 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 2015 composite weight was then formed by multiplying each weight from Panel 19 by the factor .460 and each weight from Panel 20 by the factor .540. 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 19 Weight Development ProcessThe person-level weight for MEPS Panel 19 was developed using the 2014 full year weight for an individual as a “base” weight for survey participants present in 2014. For key, in-scope members who joined an RU some time in 2015 after being out-of-scope in 2014, the initially assigned person-level weight was the corresponding 2014 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 2015 for key, responding persons in-scope on December 31, 2015. These control figures were derived by scaling back the population distribution obtained from the March 2016 CPS to reflect the December 31, 2015 estimated population total (estimated based on Census projections for January 1, 2016). 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, 2015 but were in-scope earlier in the year was the person weight after the nonresponse adjustment. 3.2.2 MEPS Panel 20 Weight Development ProcessThe person-level weight for MEPS Panel 20 was developed using the 2015 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 2015 as well as raking to the same population control figures for December 2015 used for the MEPS Panel 19 weights for key, responding persons in-scope on December 31, 2015. The same five variables employed for Panel 19 raking (census region, MSA status, race/ethnicity, sex, and age) were used for Panel 20 raking. Again, the final weight for key, responding persons who were not in-scope on December 31, 2015 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 databases. 3.2.3 The Final Weight for 2015The 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, 2015 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 2015 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, 2015 is 317,629,239 (PERWT15F>0 and INSC1231=1). The sum of person-level weights across all persons assigned a positive person-level weight is 321,423,251. 3.2.4 CoverageThe target population for MEPS in this file is the 2015 U.S. civilian noninstitutionalized population. However, the MEPS sampled households are a subsample of the NHIS households interviewed in 2013 (Panel 19) and 2014 (Panel 20). New households created after the NHIS interviews for the respective panels and consisting exclusively of persons who entered the target population after 2013 (Panel 19) or after 2014 (Panel 20) 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 2011-12), 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 Estimation4.1 Developing Event-Level EstimatesThe data in this file can be used to develop national 2015 event-level estimates for the U.S. civilian noninstitutionalized population on emergency room visits as well as expenditures, and sources of payment for these visits. Estimates of total visits are the sum of the weight variable (PERWT15F) across relevant event records while estimates of other variables must be weighted by PERWT15F to be nationally representative. The tables below contain event-level estimates for selected variables. Selected Event-Level Estimates
* Zero payment events can occur in MEPS for the following reasons: (1) the stay 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 stay, (3) the provider was never paid by an individual, insurance plan, or other source for services provided, (4) charges were included in the bill for a subsequent hospital admission (emergency room events only), or (5) the event was paid for through government or privately-funded research or clinical trials. 4.2 Person-Based Estimates for Emergency Room VisitsTo enhance analyses of emergency room visits, analysts may link information about emergency room 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 below for more details). Both this file and the full year consolidated file may be used to derive estimates for persons with emergency room 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 emergency room 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 emergency room care. 4.3 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., sources of payment, flat fee, and zero expenditures) are described in Section 2.5.6. 4.4 Variance Estimation (VARPSU, VARSTR)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. Replicate weights have not been developed for the MEPS data. Instead, the variables needed to calculate appropriate standard errors based on the Taylor-series linearization method are included on this file as well as all other MEPS public use files. Software packages that permit the use of the Taylor-series linearization method include SUDAAN, Stata, SAS (version 8.2 and higher), and SPSS (version 12.0 and higher). For complete information on the capabilities of each package, analysts should refer to the corresponding software user documentation. Using the Taylor-series linearization method, variance estimation strata and the variance estimation PSUs within these strata must be specified. The 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 year’s 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 a person-level file (e.g., MEPS 2015 Full Year Consolidated File) expands the scope of potential estimates. For example, to estimate the total number of emergency room visits for persons with specific demographic characteristics (e.g., age, race, sex, and education), population characteristics from a person-level file need to be merged onto the emergency room visit file. This procedure is illustrated below. The MEPS 2015 Appendix File, HC-178I, provides additional details on how to merge MEPS data files.
The following is an example of SAS code which completes these steps: PROC SORT DATA=HCXXX (KEEP=DUPERSID AGE31X AGE42X
AGE53X SEX RACEV1X EDUYRDG EDRECODE EDUCYR HIDEG) OUT=PERSX; PROC SORT DATA=EROM; DATA NEWEROM; 5.2 Linking to the Prescribed Medicines FileThe prescribed medicines-event link (RXLK) file provides a link from the MEPS event files to the 2015 Prescribed Medicines Event File. When using RXLK, data users/analysts should keep in mind that one emergency room visit can link to more than one prescribed medicine record. Conversely, a prescribed medicine event may link to more than one emergency room 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 2015 Appendix File, HC-178I. 5.3 Linking to the Medical Conditions FileThe conditions-event link (CLNK) file provides a link from MEPS event files to the 2015 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 emergency room visit, and (3) a condition may link to more than one emergency room visit or any other type of visit. Data users/analysts should also note that not all emergency room visits link to the medical conditions file. ReferencesCohen, S.B. (1998). Sample Design of the 1996 Medical Expenditure Panel Survey Medical Provider Component. Journal of Economic and Social Measurement. Vol. 24, 25-53. Cohen, S.B. (1996). The Redesign of the Medical Expenditure Panel Survey: A Component of the DHHS Survey Integration Plan. Proceedings of the COPAFS Seminar on Statistical Methodology in the Public Service. Cox, B.G. and Cohen, S.B. (1985). Chapter 6: A Comparison of Household and Provider Reports of Medical Conditions. In Methodological Issues for Health Care Surveys. Marcel Dekker, New York. Cox, B. and Iachan, R. (1987). A Comparison of Household and Provider Reports of Medical Conditions. Journal of the American Statistical Association. 82(400):1013-18. Edwards, W.S., Winn, D.M., Kurlantzick V., et al. (1994). Evaluation of National Health Interview Survey Diagnostic Reporting. National Center for Health Statistics, Vital Health 2(120). Elixhauser A., Steiner C.A., Whittington C.A., and McCarthy E. Clinical Classifications for Health Policy Research: Hospital Inpatient Statistics, 1995. Healthcare Cost and Utilization Project, HCUP-3 Research Note. Rockville, MD: Agency for Health Care Policy and Research; 1998. AHCPR Pub. No. 98-0049. Ezzati-Rice, T.M., Rohde, F., Greenblatt, J., Sample Design of the Medical Expenditure Panel Survey Household Component, 1998–2007. Methodology Report No. 22. March 2008. Agency for Healthcare Research and Quality, Rockville, MD. Health Care Financing Administration (1980). International Classification of Diseases, 9th Revision, Clinical Modification (ICD-CM). Vol. 1. (DHHS Pub. No. (PHS) 80-1260). DHHS: U.S. Public Health Services. Johnson, A.E. and Sanchez, M.E. (1993). Household and Medical Provider Reports on Medical Conditions: National Medical Expenditure Survey, 1987. Journal of Economic and Social Measurement. Vol. 19, 199-233. Monheit, A.C., Wilson, R., and Arnett, III, R.H. (Editors). Informing American Health Care Policy. (1999). Jossey-Bass Inc., San Francisco. Shah, B.V., Barnwell, B.G., Bieler, G.S., Boyle, K.E., Folsom, R.E., LaVange, L., Wheeless, S.C., and Williams, R. (1996). Technical Manual: Statistical Methods and Algorithms Used in SUDAAN Release 7.0, Research Triangle Park, NC: Research Triangle Institute. D. Variable-Source CrosswalkVARIABLE-SOURCE CROSSWALK
|
|||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||