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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. Generally, imputed/edited variables end with an “X”. 2.4.1 Variable-Source CrosswalkVariables were derived either from the HC questionnaire itself, the MPC data collection instrument, 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 the 12 source of payment variables are named in the following way: The first two characters indicate the type of event: IP - inpatient stay In the case of source of payment variables, the third and fourth characters indicate: SF - self or family In addition, the total charge variable is indicated by TC in the variable name. The fifth and sixth characters indicate the year (15). The seventh character, “X", indicates the variable is edited/imputed. For example, HHSF15X is the edited/imputed amount paid by self or family for 2015 home health 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 2015 Full-Year Population Characteristics file. 2.5.1.2 Record Identifier (EVNTIDX)EVNTIDX uniquely identifies each event (i.e., each record on the home health file) and is the variable required to link home health events to data files containing details on conditions (MEPS 2015 Medical Conditions File). For details on linking see Section 5.0 or the MEPS 2015 Appendix File, HC-178I. 2.5.1.3 Round Indicator (EVENTRN)EVENTRN indicates the round in which the home health event was reported. Please note: Rounds 3, 4, and 5 are associated with MEPS survey data collected from Panel 19. Likewise, Rounds 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 Home Health Event VariablesThis file contains variables describing home health events reported by household respondents in the Home Health Section of the MEPS HC survey questionnaire. 2.5.2.1 Date of Event (HHDATEYR, HHDATEMM)The date variables (HHDATEYR and HHDATEMM) indicate the year and month that the household respondent reported as the year and month of occurrence for this type of home health event. An artifact of the data collection for the variable HHDATEYR is that a person may have started receiving that type of home health care from that provider prior to 2015. These variables should not be interpreted as “true” start dates. 2.5.2.2 Characteristics of Event (MPCELIG-OTHCWOS)The HC questionnaire asked the respondent to indicate whether the home health provider event(s) for each month’s services were provided through an agency or an independent paid provider (SELFAGEN). The response to the SELFAGEN question dictated the skip pattern CAPI followed regarding the questions in the home health section of the HC questionnaire. The questionnaire also asked respondents if the provider was paid or whether a friend, relative, or volunteer (HHTYPE) provided the home health services. The constructed variable MPCELIG indicates whether the home health provider event was eligible for MPC data collection and the type of imputation process the event went through. MPCELIG is a more accurate variable for determining whether the event was an agency, a paid independent, or an informal care event. However, SELFAGEN is a more accurate variable for determining the home health questions asked of the respondent. For all members receiving care from an agency, hospital, or nursing home, the respondent was asked to identify the type of home health worker (CNA-SPEECTHP) they saw – for example, certified nursing assistant, home health aide, registered nurse, etc. Analysts should keep in mind that these identifications by household respondents are subjective in nature, are not mutually exclusive or collectively exhaustive, and should not be used to make certain estimates. For example, a person on one type of insurance may identify an individual providing home health care services to them as a personal care attendant while an individual having a different type of insurance coverage may identify that same worker as a home care aide. Making estimates of personal care attendants or home care aides based on their identification by household respondents and treating these types of workers as mutually exclusive groups will result in inaccurate estimates. Respondents may also have indicated that a person was seen by more than one home health care worker during a single event. For example, since an event is a month of services, a respondent may have reported that a person was seen by a nurse, a physical therapist, and/or a home health aide during a single event. Respondents were also asked to identify other non-skilled, skilled, and other workers seen during that month of care (NONSKILL-OTHCWOS). However, “other specify” variables (SKILLWOS and OTHCWOS) were not reconciled with the type of health care worker variable (CNA-SPEECTHP). In addition, the type of health care worker variables (CNA-SPEECTHP) were not reconciled with MPCELIG, SELFAGEN, or HHTYPE, so inconsistencies between these variables are possible. 2.5.2.3 Treatments, Therapies, and Services (HOSPITAL-OTHSVCOS)Regardless of the type of provider, all respondents were asked if the home health services received were due to a hospitalization (HOSPITAL), whether services were due to a medical condition (VSTRELCN), if the person was helped with daily activities (DAILYACT), if the person received companionship services (COMPANY), and whether or not the person received any other type of services (OTHSVCE and OTHSVCOS). Only if persons were reported as receiving care from a formal provider was the respondent asked if they were taught how to use medical equipment (MEDEQUIP) and whether or not they received a medical treatment (TREATMT). 2.5.2.4 Frequency of Event (FREQCY-HHDAYS)Several variables identify the frequency and length of home health events (FREQCY-MINLONG) and whether or not the same services were received during each month (SAMESVCE). Frequency of event variables (FREQCY- TMSPDAY) were used as building blocks to construct HHDAYS. HHDAYS indicates the number of days the person received care during that event (i.e., month of care). Frequency variables can be combined to get a measure of the intensity of care. For example, HHDAYS can be used in conjunction with HRSLONG and TMSPDAY to form a measure of intensity of care, that is, how many hours of care were provided in one month. 2.5.3 Flat Fee VariablesA 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. Because MEPS does not collect flat fee information about home health events, no flat fee variables are included in this file. 2.5.4 Condition, Procedure, and Clinical Classification CodesInformation on household-reported medical conditions and procedures (including condition codes, procedure codes, and clinical classification codes) associated with each home health event are NOT provided on this file. To obtain complete condition information associated with an event, the analyst must link to the 2015 Medical Conditions File. Details on how to link to the MEPS 2015 Medical Conditions File are provided in the MEPS 2015 Appendix File, HC-178I. 2.5.5 Expenditure Data2.5.5.1 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, 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 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. However, please note, the MPC included home health events provided by an agency and did not include home health care provided by paid independent providers. Although the general procedures remain the same for all home health events, there were some differences in the editing and imputation methodologies applied to those events followed in the MPC and those events not followed in the MPC. Analysts should note that home health care provided by friends, family, or volunteers was assumed to be free and was not included in any imputation process. Please see below for details on the differences between these editing/imputation methodologies. Home health expenditure data for agency, hospital, and nursing home providers were collected exclusively from the MPC (i.e., household respondents were not asked to report home health expenditures from these types of providers). The MPC contacted 100 percent of the agency, hospital, and nursing home health providers identified by household respondents. Since paid independent home health providers were not included in the MPC, all expenditure data from these providers were collected from household respondents. 2.5.5.2.1 General Data Editing MethodologyLogical edits were used to resolve internal inconsistencies and other problems in the HC and the MPC survey-reported data. The edits were designed to preserve partial payment data from households and providers, and to identify actual and potential sources of payment for each household-reported event. In general, these edits accounted for outliers, co-payments or charges reported as total payments, and reimbursed amounts that were reported as out-of-pocket payments. In addition, edits were implemented to correct for mis-classifications 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 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 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 Home Health Data Editing and ImputationExpenditures for home health events were developed in a sequence of logical edits and imputations. (Analysts should note that home health care provided by friends, family, or volunteers was assumed not to have associated expenditures and was not included in any imputation process. All expenditures for home health care provided by informal care providers were assigned “–1” (Inapplicable) because those types of events were skipped out of (never asked) the questions regarding expenditures.) “Household” edits were applied to sources and amounts of payment for all household-reported events for paid independent providers and unmatched agency providers. “MPC” edits were applied to provider-reported sources and amounts of payment for records matched to household-reported events for all agency home health providers. Both sets of edits were used to correct obvious errors in the reporting of expenditures. Imputations for independent paid providers and for agencies were conducted separately. Within this file, separate imputations were performed for simple events. Logical edits were used to sort each event into a specific category for the imputations. Events with complete expenditures were flagged as potential donors while events with missing expenditure data were assigned to various recipient categories. Each event with missing expenditure data was assigned to a recipient category based on the extent of its missing charge and expenditure data. For example, an event with a known total charge but no expenditure information was assigned to one category, while an event with a known total charge and partial expenditure information was assigned to a different category. Similarly, events without a known total charge and no or partial expenditure information were assigned to various recipient categories. Expenditures were imputed using a predictive mean matching method. The donor pool in these imputations includes events with complete expenditures from the HC for HHP and restricted to the MPC for HHA. As stated previously, home health care provided by friends, family, or volunteers (informal, MPCELIG = 3) was assumed not to have expenditures associated with it and was not included in any imputation process.) 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. The following list identifies how the imputation flag is coded; the categories are mutually exclusive. IMPFLAG = 0 not eligible for imputation (includes zeroed out 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 HH) 2.5.5.4 Flat Fee ExpendituresA 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. Because MEPS does not collect flat fee information about home health events, there are no flat fee expenditure data included in this file. 2.5.5.5 Zero ExpendituresThere are some medical events reported by respondents for which the payments were zero. This could occur for several reasons including (1) free care was provided, (2) bad debt was incurred, (3) follow-up events were provided without a separate charge (e.g., after a surgical procedure), or (4) the event was paid for through government or privately-funded research or clinical trials. 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. All expenditures for home health care provided by informal care providers (family, friends, or volunteers, MPCELIG = 3) were assigned “-1” (Inapplicable) because those types of events were skipped out of (never asked) questions regarding expenditures. 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 Home Health Expenditure Variables (HHSF15X - HHXP15X)Home health agency, hospital, and nursing home events are sampled at a rate of 100% for the MPC. Households were not asked any expenditure-related questions regarding these types of events; therefore, there are no household-reported expenditure data for these events. Conversely, paid independent providers are not included in the MPC. Household-reported responses are the only data available for these types of events. All expenditure data for paid independent providers are fully imputed from household-reported expenditures. There are no expenditure data for informal care providers. Informal care (MPCELIG = 3, unpaid care provided by family, friends, or volunteers) was assigned “-1”, (Inapplicable), in all expenditure categories. The constructed variable MPCELIG is provided on this file. MPCELIG indicates whether the home health provider event was eligible for MPC data collection, and MPCELIG determines the imputation process applied to that event. All of these expenditures have gone through an editing and imputation process and have been rounded to the nearest penny. HHSF15X – HHOT15X are the 12 sources of payment. HHXP15X is the sum of the 12 sources of payment for the home health expenditures, and HHTC15X is the total charge. The 12 sources of payment are: self/family (HHSF15X), Medicare (HHMR15X), Medicaid (HHMD15X), private insurance (HHPV15X), Veterans Administration/CHAMPVA (HHVA15X), TRICARE (HHTR15X), other federal sources (HHOF15X), state and local (non-federal) government sources (HHSL15X), Workers’ Compensation (HHWC15X), other private insurance (HHOR15X), other public insurance (HHOU15X), and other insurance (HHOT15X). Analysts can determine if a home health event was provided by an agency or by some other paid independent provider by subsetting the variable MPCELIG to the appropriate and desired value. 2.5.5.8 RoundingExpenditure variables on the 2015 home health event file have been rounded to the nearest penny. Person-level expenditure information released on the MEPS 2015 Full-Year Consolidated File was rounded to the nearest dollar. It should be noted that using the 2015 MEPS event files to create person-level totals will yield slightly different totals than those 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 (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 2011-13), working with moving averages, or using modeling techniques with several consecutive years of MEPS data to test the fit of specified patterns over time. Finally, researchers should be aware of the impact of multiple comparisons on Type I error. Without making appropriate allowance for multiple comparisons, undertaking numerous statistical significance tests of trends increases the likelihood of concluding that a change has taken place when one has not. 4.0 Strategies for Estimation4.1 Developing Event-Level EstimatesThe data in this file can be used to develop national 2015 event-level (i.e., monthly) estimates for the U.S. civilian noninstitutionalized population on expenditures and sources of payment for home health care medical provider visits. The weight assigned to each home health care medical provider event reported is the person-level weight of the person who was visited. If a person had several events reported, each event is assigned that individual’s person-level weight. Estimates must be weighted by PERWT15F to be nationally representative. For example, the appropriate estimate for the overall mean out-of-pocket payment per month of care is computed 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 = PERWT15Fj (full-year person weight for the person associated with event j) and Estimates and corresponding standard errors (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_com p/standard_errors.jsp. The tables below contain the event-level estimates for several key variables on this file. Informal care (MPCELIG = 3) is not included in the tables because, by definition, there are no payments for those events and, therefore, no expenditure data are collected. Selected Event-Level Estimates
*Zero payment events can occur in MEPS for the following reasons: (1) there was no charge for a follow-up event, (2) the provider was never paid by an individual, insurance plan, or other source for services provided, (3) the charges were included in another bill, or (4) the event was paid for through government or privately-funded research or clinical trials. 4.2 Person-Based Estimates for Home Health CareTo enhance analyses of home health care, analysts may link information about the home health care received 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. Both this file and the full-year consolidated file may be used to derive estimates relative to persons with home health 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 month in which home health care was provided are represented on this data file. Therefore, the full-year consolidated file must be used for person-level analyses that include both those with and without home health 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 and zero expenditures) are described in Section 2.5.5.2. 4.4 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 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 provides instructions, or the details on where to find the instructions, for linking the 2015 home health provider events with other 2015 MEPS public use files, including the 2015 person-level and conditions 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 2015 MEPS files (e.g., the 2015 Full-Year Consolidated File or the 2015 Prescribed Medicines File) expands the scope of potential estimates. For example, to estimate the total number of home health provider events of persons with specific characteristics (e.g., age, race, and sex), population characteristics from a person-level file need to be merged onto the home health visits event file. This procedure is illustrated below. The MEPS 2015 Appendix File, HC-178I, provides additional details on how to merge 2015 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 EDRECODE EDUYRDG EDUCYR HIDEG) PROC SORT DATA = HVIS; DATA NEWHVIS; 5.2 Linking to the Prescribed Medicines FileThe RXLK file provides a link from 2015 MEPS event files to the 2015 Prescribed Medicines File. Because prescribed medicines data are not collected for home health events, this Home Health event file cannot be linked to the 2015 Prescribed Medicines File. 5.3 Linking to the Medical Conditions FileThe CLNK file provides a link from 2015 MEPS event files to the 2015 Medical Conditions File. When using the CLNK file, data users/analysts should keep in mind that (1) conditions are household reported and (2) there may be multiple conditions associated with a home health provider event. Data users/analysts should also note that not all home health provider events link to the conditions file. For detailed linking examples, including SAS code, data users/analysts should refer to the MEPS 2015 Appendix File, HC-178I. 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 8: Imputation Procedures to Compensate for Missing Responses to Data Items. In Methodological Issues for Health Care Surveys. Marcel Dekker, New York. 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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