MEPS HC-178G: 2015 Office-Based Medical Provider Visits
June 2017
Agency for Healthcare Research and Quality
Center for Financing, Access, and Cost Trends
5600 Fishers Lane
Rockville, MD 20857
(301) 427-1406
Table of Contents
A. Data Use Agreement
B. Background
1.0 Household Component
2.0 Medical Provider Component
3.0 Survey Management and Data Collection
C. Technical and Programming Information
1.0 General Information
2.0 Data File Information
2.1 Codebook Structure
2.2 Reserved Codes
2.3 Codebook Format
2.4 Variable Source and Naming Conventions
2.4.1 General
2.4.2 Expenditure and Source of Payment Variables
2.5 File Contents
2.5.1 Survey Administration Variables
2.5.1.1 Person Identifiers (DUID, PID, DUPERSID)
2.5.1.2 Record Identifiers (EVNTIDX, FFEEIDX)
2.5.1.3 Round Indicator (EVENTRN)
2.5.1.4 Panel Indicator (PANEL)
2.5.2 MPC Indicator (MPCELIG, MPCDATA)
2.5.3 Office-Based Medical Provider Visit Variables
2.5.3.1 Date of Visit (OBDATEYR - OBDATEMM)
2.5.3.2 Visit Details (SEETLKPV-VSTRELCN)
2.5.3.3 Procedures, Services, and Prescription Medicines (LABTEST-MEDPRESC)
2.5.4 Clinical Classification Codes (OBCCC1X-OBCCC4X)
2.5.5 Flat Fee Variables (FFEEIDX, FFOBTYPE, FFBEF15, FFTOT16)
2.5.5.1 Definition of Flat Fee Payments
2.5.5.2 Flat Fee Variable Descriptions
2.5.5.2.1 Flat Fee ID (FFEEIDX)
2.5.5.2.2 Flat Fee Type (FFOBTYPE)
2.5.5.2.3 Counts of Flat Fee Events that Cross Years (FFBEF15, FFTOT16)
2.5.5.3 Caveats of Flat Fee Groups
2.5.6 Expenditure Data
2.5.6.1 Definition of Expenditures
2.5.6.2 Data Editing and Imputation Methodologies of Expenditure Variables
2.5.6.2.1 General Data Editing Methodology
2.5.6.2.2 Imputation Methodologies
2.5.6.2.3 Office-Based Provider Visit Data Editing and Imputation
2.5.6.3 Capitation Imputation
2.5.6.4 Imputation Flag (IMPFLAG)
2.5.6.5 Flat Fee Expenditures
2.5.6.6 Zero Expenditures
2.5.6.7 Discount Adjustment Factor
2.5.6.8 Sources of Payment
2.5.6.9 Office-Based Expenditure Variables (OBSF15X – OBTC15X)
2.5.7 Rounding
3.0 Sample Weight (PERWT15F)
3.1 Overview
3.2 Details on Person Weight Construction
3.2.1 MEPS Panel 19 Weight Development Process
3.2.2 MEPS Panel 20 Weight Development Process
3.2.3 The Final Weight for 2015
3.2.4 Coverage
3.3 Using MEPS Data for Trend Analysis
4.0 Strategies for Estimation
4.1 Developing Event-Level Estimates
4.2 Person-Based Estimates for Office-Based Visits
4.3 Variables with Missing Values
4.4 Variance Estimation (VARSTR, VARPSU)
5.0 Merging/Linking MEPS Data Files
5.1 Linking to the Person-Level File
5.2 Linking to the Prescribed Medicines File
5.3 Linking to the Medical Conditions File
References
D. Variable-Source Crosswalk
Individual identifiers have been removed from the
micro-data contained in these files. Nevertheless, under sections 308 (d) and
903 (c) of the Public Health Service Act (42 U.S.C. 242m and 42 U.S.C. 299 a-1),
data collected by the Agency for Healthcare Research and Quality (AHRQ) and/or
the National Center for Health Statistics (NCHS) may not be used for any purpose
other than for the purpose for which they were supplied; any effort to determine
the identity of any reported cases is prohibited by law.
Therefore in accordance with the above referenced
Federal Statute, it is understood that:
- No one is to use the data in this data set in any way except
for statistical reporting and analysis; and
- If the identity of any person or establishment should be
discovered inadvertently, then (a) no use will be made of this
knowledge, (b) the Director Office of Management AHRQ will be
advised of this incident, (c) the information that would
identify any individual or establishment will be safeguarded or
destroyed, as requested by AHRQ, and (d) no one else will be
informed of the discovered identity; and
- No one will attempt to link this data set with individually
identifiable records from any data sets other than the Medical
Expenditure Panel Survey or the National Health Interview
Survey.
By using these data you signify your agreement to
comply with the above stated statutorily based requirements with the knowledge
that deliberately making a false statement in any matter within the jurisdiction
of any department or agency of the Federal Government violates Title 18 part 1
Chapter 47 Section 1001 and is punishable by a fine of up to $10,000 or up to 5
years in prison.
The Agency for Healthcare Research and Quality
requests that users cite AHRQ and the Medical Expenditure Panel Survey as the
data source in any publications or research based upon these data.
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The Medical Expenditure Panel Survey (MEPS) provides
nationally representative estimates of health care use, expenditures, sources of
payment, and health insurance coverage for the U.S. civilian
non-institutionalized population. The MEPS Household Component (HC) also
provides estimates of respondents’ health status, demographic and socio-economic
characteristics, employment, access to care, and satisfaction with health care.
Estimates can be produced for individuals, families, and selected population
subgroups. The panel design of the survey, which includes 5 Rounds of interviews
covering 2 full calendar years, provides data for examining person level changes
in selected variables such as expenditures, health insurance coverage, and
health status. Using computer assisted personal interviewing (CAPI) technology,
information about each household member is collected, and the survey builds on
this information from interview to interview. All data for a sampled household
are reported by a single household respondent.
The MEPS-HC was initiated in 1996. Each year a new
panel of sample households is selected. Because the data collected are
comparable to those from earlier medical expenditure surveys conducted in 1977
and 1987, it is possible to analyze long-term trends. Each annual MEPS-HC sample
size is about 15,000 households. Data can be analyzed at either the person or
event-level. Data must be weighted to produce national
estimates.
The set of households selected for each panel of the
MEPS HC is a subsample of households participating in the previous year’s
National Health Interview Survey (NHIS) conducted by the National Center for
Health Statistics. The NHIS sampling frame provides a nationally representative
sample of the U.S. civilian non-institutionalized population and reflects an
oversample of Blacks and Hispanics. In 2006, the NHIS implemented a new sample
design, which included Asian persons in addition to households with Black and
Hispanic persons in the oversampling of minority populations. The linkage of the
MEPS to the previous year’s NHIS provides additional data for longitudinal
analytic purposes.
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Upon completion of the household CAPI interview and
obtaining permission from the household survey respondents, a sample of medical
providers are contacted by telephone to obtain information that household
respondents can not accurately provide. This part of the MEPS is called the
Medical Provider Component (MPC) and information is collected on dates of visit,
diagnosis and procedure codes, charges and payments. The Pharmacy Component
(PC), a subcomponent of the MPC, does not collect charges or diagnosis and
procedure codes but does collect drug detail information, including National
Drug Code (NDC) and medicine name, as well as date filled and sources and
amounts of payment. The MPC is not designed to yield national estimates. It is
primarily used as an imputation source to supplement/replace household reported
expenditure information.
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MEPS HC and MPC data are collected under the authority
of the Public Health Service Act. Data are collected under contract with Westat,
Inc. (MEPS HC) and Research Triangle Institute (MEPS MPC). Data sets and summary
statistics are edited and published in accordance with the confidentiality
provisions of the Public Health Service Act and the Privacy Act. The National
Center for Health Statistics (NCHS) provides consultation and technical
assistance.
As soon as data collection and editing are completed,
the MEPS survey data are released to the public in staged releases of summary
reports, micro data files, and tables via the MEPS Web site:
meps.ahrq.gov.
Selected data can be analyzed through MEPSnet, an on-line interactive tool
designed to give data users the capability to statistically analyze MEPS data in
a menu-driven environment.
Additional information on MEPS is available from the
MEPS project manager or the MEPS public use data manager at the Center for
Financing, Access, and Cost Trends, Agency for Healthcare Research and Quality,
5600 Fishers Lane, Rockville, MD 20857 (301-427-1406).
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This documentation describes one in a
series of public use event files from the 2015 Medical Expenditure Panel Survey
(MEPS) Household (HC) and Medical Provider Components (MPC). Released as an
ASCII data file (with related SAS, SPSS, and Stata programming statements) and a
SAS transport file, the 2015 Office-Based Medical Provider Visits public use
event file provides detailed information on office-based provider visits for a
nationally representative sample of the civilian noninstitutionalized population
of the United States. Data from the office-based provider events file can be
used to make estimates of office-based provider utilization and expenditures for
calendar year 2015. The file contains 56 variables and has a logical record
length of 241 with an additional 2-byte carriage return/line feed at the end of
each record. As illustrated below, this file consists of MEPS survey data from
the 2015 portion of Round 3 and Rounds 4 and 5 for Panel 19, as well as Rounds
1, 2 and the 2015 portion of Round 3 for Panel 20 (i.e., the rounds for the MEPS
panels covering calendar year 2015).
Each record on this event file represents a unique
office-based provider event; that is, an office-based provider event reported by
the household respondent. Office-based events reported in Panel 20 Round 3 and
known to have occurred after December 31, 2015 are not included on this file.
Utilization counts of office-based provider visits are based entirely on
household reports. Information from the MPC is used to supplement expenditure
payment data on the office-based provider file, reported by the household, and
does not affect use estimates.
Data from this event file can be merged with other
2015 MEPS HC data files for purposes of appending person-level data such as
demographic characteristics or health insurance coverage to each office-based
provider visit record on the current file.
This file can also be used to construct summary
variables of expenditures, sources of payment, and related aspects of
office-based provider visits for calendar year 2015. Aggregate annual
person-level information on the use of office-based providers and other health
services is provided on the MEPS 2015 Full Year Consolidated Data File, where
each record represents a MEPS sampled person.
This documentation offers a brief overview of the
types and levels of data provided, and the content and structure of the files
and the codebook. It contains the following sections:
- Data File Information
- Sample Weight
- Strategies for Estimation
- Merging/Linking MEPS Data Files
- References
- Variable-Source Crosswalk
For more information on MEPS HC survey design, see T.
Ezzati-Rice, et al. (1998-2007) and S. Cohen (1996). A copy of the MEPS HC
survey instruments used to collect the information on the office-based provider
file is available on the MEPS Web site at the following address:
meps.ahrq.gov.
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The 2015 Office-Based Medical Provider public use data
set consists of one event-level data file. The file contains characteristics
associated with the office-based (OB) event and imputed expenditure data.
The Office-Based Provider public use data set contains
172,388 office-based provider event records; of
these records, 169,170 are associated with persons
having a positive person-level weight (PERWT15F). This file includes
office-based provider event records for all household members who resided in
eligible responding households and for whom at least one office-based provider
event was reported.
Each record represents one household-reported
office-based provider event that occurred during calendar year 2015.
Office-based provider visits known to have occurred after December 31, 2015 are
not included on this file. Some household members may have multiple events and
thus will be represented in multiple records on this file. Other household
members may have had no events reported and thus will have no records on this
file. These data were collected during the 2015 portion of Round 3, and Rounds 4
and 5 for Panel 19, as well as Rounds 1, 2, and the 2015 portion of Round 3 for
Panel 20 of the MEPS Household Component. The persons represented on this file
had to meet either (a) or (b):
- Be classified as a key in-scope person who responded for his
or her entire period of 2015 eligibility (i.e., persons with a
positive 2015 full-year person-level weight (PERWT15F > 0)), or
- Be an eligible member of a family all of whose key in-scope
members have a positive person-level weight (PERWT15F > 0).
(Such a family consists of all persons with the same value for
FAMIDYR.) That is, the person must have a positive full-year
family-level weight (FAMWT15 >0). Note that FAMIDYR and FAMWT15
are variables on the 2015 Full Year Consolidated Data File.
Persons with no office-based medical provider visit
events for 2015 are not included on this event-level OB file but are represented
on the person-level 2015 Full Year Population Characteristics file.
Each office-based medical provider visit event record
includes the following: date of the event; type of provider seen; type of care
received; type of services (i.e., lab test, sonogram or ultrasound, x-rays,
etc.) received, medicines prescribed during the event; flat fee information;
imputed sources of payment; total payment and total charge of the office-based
event expenditure; a full-year person-level weight; variance strata; and
variance PSU.
To append person-level information such as demographic
or health insurance coverage to each event record, data from this file can be
merged with 2015 MEPS HC person-level data (e.g. Full Year Consolidated or Full
Year Population Characteristics file) using the person identifier, DUPERSID. The
office-based medical provider visit events can also be linked to the MEPS 2015
Medical Conditions File and MEPS 2015 Prescribed Medicines File. Please see
Section 5.0 for details on how to merge MEPS data files.
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For most variables on the office-based provider file,
both weighted and unweighted frequencies are provided in the accompanying
codebook. The exceptions to this are weight variables and variance estimation
variables. Only unweighted frequencies of these variables are included in the
accompanying codebook file. See the Weights Variables list in Section D,
Variable-Source Crosswalk. The codebook and data file sequence list variables in
the following order:
- Unique person identifiers
- Unique office-based medical provider visit event identifiers
- Office-based medical provider visit characteristic variables
- Clinical Classification Software (CCS) codes
- Imputed expenditure variables
- Weight and variance estimation variables
Note that the person identifier is unique within this data year.
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The following reserved code values are used:
| Value |
Definition |
| -1 INAPPLICABLE |
Question was not asked due to skip pattern |
| -7 REFUSED |
Question was asked and respondent refused to answer question |
| -8 DK |
Question was asked and respondent did not know answer |
| -9 NOT ASCERTAINED |
Interviewer did not record the data |
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).
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The office-based medical provider visits codebook describes an ASCII data set (although the data are also being provided in a SAS
transport file). The following codebook items are provided for each variable:
| Identifier |
Description |
| Name |
Variable name (maximum of 8 characters) |
| Description |
Variable descriptor (maximum of 40 characters) |
| Format |
Number of bytes |
| Type |
Type of data: numeric (indicated by NUM) or character (indicated by CHAR) |
| Start |
Beginning column position of variable in record |
| End |
Ending column position of variable in record |
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In general, variable names reflect the content of the
variable, with an 8-character limitation. All imputed/edited variables end with
an “X”.
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Variables contained on this file were derived from the
HC survey questionnaire itself, derived from the MPC data collection instrument,
derived from CAPI, or assigned in sampling. The source of each variable is
identified in Section D Variable-Source Crosswalk in one of four ways:
- Variables derived from CAPI or assigned in sampling are
indicated as “CAPI derived” or “Assigned in sampling,”
respectively;
- Variables which come from one or more specific questions
have those questionnaire sections and question numbers indicated
in the “Source” column; questionnaire sections are identified
as:
- MV – Office-Based Medical Provider Visits section
- FF – Flat Fee section
- CP – Charge Payment section
- Variables constructed from multiple questions using complex
algorithms are labeled “Constructed” in the “Source” column; and
- Variables which have been edited or imputed are so
indicated.
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The names of the expenditure and source of payment
variables follow a standard convention, are seven characters in length, and end
in an “X” indicating edited/imputed. Please note that imputed means that a
series of logical edits, as well as an imputation process to account for missing
data, have been performed on the variable.
The total sum of payments and the 12 sources of
payment are named in the following way:
The first two characters indicate the type of event:
IP - inpatient stay
ER - emergency room visit
HH - home health visit
OM - other medical equipment
OB - office-based visit
OP - outpatient visit
DV - dental visit
RX - prescribed medicine
In the case of source of payment variables, the third and fourth characters indicate:
SF - self or family
MR - Medicare
MD - Medicaid
PV - private insurance
VA - Veterans Administration/CHAMPVA
TR - TRICARE
OF - other federal government
SL - state/local government
WC - Workers’ Compensation
OT - other insurance
OR - other private
OU - other public
XP - sum of payments
In addition, the total charge variable is indicated by TC in the variable name.
The fifth and sixth characters indicate the year (15).
The seventh character, “X", indicates whether the variable is edited/imputed.
For example, OBSF15X is the edited/imputed amount paid
by self or family for an office-based medical provider visit expenditure
incurred in 2015.
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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.
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EVNTIDX uniquely identifies each office-based medical
provider visit event (i.e., each record on the office-based medical provider
visits file) and is the variable required for linking office-based medical
provider visit events to data files containing details on conditions and/or
prescribed medicines (MEPS 2015 Medical Condition file and MEPS 2015 Prescribed
Medicines file, respectively). For details on linking see Section 5.0 or the
MEPS 2015 Appendix File, HC-178I.
FFEEIDX is a constructed variable that uniquely
identifies a flat fee group, that is, all events that were part of a flat fee
payment. For example, pregnancy is typically covered in a flat fee arrangement
where the prenatal visits, the delivery, and the postpartum visits are all
covered under one flat fee dollar amount. These events (the prenatal visit, the
delivery, and the postpartum visits) would have the same value for FFEEIDX.
FFEEIDX identifies a flat fee payment that was identified using information from
the Household Component. A “mixed” flat fee group could contain both outpatient
and office-based visits. Only outpatient and office-based events are allowed in
a mixed bundle. Please note that FFEEIDX should be used to link up the
outpatient and office-based events in order to determine the full set of events
that are part of a flat fee group.
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EVENTRN indicates the round in which the office-based
event was reported. Please note that Rounds 3, 4, and 5 are associated with MEPS
survey data collected from Panel 19. Likewise, Rounds 1, 2, and 3 are associated
with data collected from Panel 20.
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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.
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MPCELIG is a constructed variable that indicates
whether the office-based provider visit was eligible for MPC data collection.
MPCDATA is a constructed variable that indicates whether or not MPC data were
collected for the office-based provider.
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The file contains variables describing office-based
medical provider visit events reported by respondents in the Medical Provider
Visits section of the MEPS HC survey questionnaire.
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There are two variables that, together, indicate the
month and year an office-based provider visit occurred (OBDATEMM and OBDATEYR,
respectively). These variables have not been edited or imputed.
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The questionnaire determines if, during the
office-based medical provider visit, the person actually saw the provider or
talked to the provider on the telephone (SEETLKPV). The questionnaire also
establishes whether the person saw or spoke to a medical doctor (SEEDOC). If the
person talked to a medical doctor, the respondent is asked to specify the type (DRSPLTY)
and other health professional type (MEDPTYPE) is set to -1, “INAPPLICABLE”. If
during the medical visit the patient did not see a specialty doctor (DRSPLTY),
or, if the person did not see a physician (i.e., a medical doctor), the
respondent was asked to identify the type of medical person seen (MEDPTYPE).
Whether or not any medical doctors worked at the visit location (DOCATLOC), the
type of care the person received (VSTCTGRY), and whether or not the visit or
telephone call was related to a specific condition (VSTRELCN) were also
determined. Note that response categories with small frequencies may have been
recoded to other categories for confidentiality reasons. Through 2012, the
questionnaire established the kind of place the person saw the medical provider
(MVPLACE). Beginning in 2013, MVPLACE was removed because of design changes.
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Services received during the visit included whether or
not the person received lab tests (LABTEST), a sonogram or ultrasound
(SONOGRAM), x-rays (XRAYS), a mammogram (MAMMOG), an MRI or a CAT scan (MRI), an
electrocardiogram (EKG), an electroencephalogram (EEG), a vaccination (RCVVAC),
anesthesia (ANESTH), throat swab (THRTSWAB), or other diagnostic tests or exams
(OTHSVCE). Minimal editing was done across treatment, services, and procedures
to ensure consistency across “inapplicable,” “not ascertained,” “don’t know,”
“refused,” and “no services received” values.
Whether or not a surgical procedure was performed during the visit was asked (SURGPROC).
Finally, the questionnaire determined if a medicine
was prescribed for the person during the visit (MEDPRESC). For a repeat visit
event group, if a prescribed medicine is linked to the stem event (MEDPRESC=1),
then the value of MEDPRESC is copied to the leaf events without linking the leaf
events to the prescribed medicine. Beginning in 2009, MEDPRESC=1 was recoded to
-9 for all leaf events.
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Information on household-reported medical conditions
associated with each office-based medical provider visit is provided on this
file. There are up to four CCS codes (OBCCC1X-OBCCC4X) listed for each
office-based medical provider 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 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 analyst must link to the Medical Conditions File.
Details on how to link to the MEPS Medical Conditions File are provided in
Section 5.0. The user should note that due to confidentiality restrictions,
provider-reported condition information is not publicly available.
The medical conditions 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
condition and V-codes (see 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 (see 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 conditions were aggregated into
clinically meaningful categories. These categories, included on the file as
OBCCC1X-OBCCC4X, were generated using Clinical Classification Software [formerly
known as Clinical Classifications for Health Care Policy Research (CCHPR), (Elixhauser,
et al., 1998)], which aggregates conditions and V-codes into mutually exclusive
categories, most of which are clinically homogeneous.
The clinical classification codes linked to each
office-based medical provider visit event are sequenced in the order in which
the conditions were reported by the household respondent, which was in order of
input into the database and not in order of importance or severity. Data
users/analysts who use the Medical Conditions file in conjunction with this
office-based medical provider visits file should note that the order of
conditions on this file is not identical to that on the 2015 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 document (HC-178I) 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.
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A flat fee is the fixed dollar amount a person is
charged for a package of services provided during a defined period of time. An
example would be an obstetrician’s fee covering a normal delivery, and the
associated pre- and post-natal care. A flat fee group is the set of medical
services (i.e., events) that are covered under the same flat fee payment. The
flat fee groups represented on the office-based provider file include flat fee
groups where at least one of the health care events, as reported by the HC
respondent, occurred during 2015. By definition, a flat fee group can span
multiple years and/or event types (only outpatient department visits and
physician office visits). Furthermore, a single person can have multiple flat
fee groups.
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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.
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FFOBTYPE indicates whether the 2015 office-based
medical provider visit event is the “stem” or “leaf” of a flat fee group. A stem
(records with FFOBTYPE = 1) is the initial medical service (event) which is
followed by other medical events that are covered under the same flat fee
payment. The leaves of the flat fee group (records with FFOBTYPE = 2) are those
medical events that are tied back to the initial medical event (the stem) in the
flat fee group. These “leaf” records have their expenditure variables set to
zero. For the office-based visits that are not part of a flat fee payment, the
FFOBTYPE is set to -1, “INAPPLICABLE.”
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As described in Section 2.5.5.1, a flat fee payment
covers multiple events and the multiple events could span multiple years. For
situations where the office-based medical provider visit occurred in 2015 as a
part of a group of events, and some of the events occurred before 2015, counts
of the known events are provided on the office-based medical provider visit
event file record. Variables that indicate events occurred before or after 2015
are as follows:
FFBEF15 - total number of pre-2015 events in the same
flat fee group as the 2015 office-based medical provider visit. This count would
not include the 2015 office-based medical visit(s).
FFTOT16 - the number of 2016 office-based events
expected to be in the same flat fee group as the office-based medical provider
visit event(s) that occurred in 2015.
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Data users/analysts should note that flat fee payments
are common on the office-based medical provider visits file. There are 4,025
office-based medical provider visit events that are identified as being part
of a flat fee payment group. In order to correctly identify all events that are
part of a flat fee group, the user should link all MEPS events, except those in
the prescribed medicine file, using the variable FFEEIDX. In general, every flat
fee group should have an initial visit (stem) and at least one subsequent visit
(leaf). There are some situations where this is not true. For some of these flat
fee groups, the initial visit reported occurred in 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 leaf events that are part of this flat fee group are not
represented on this file. Similarly, the household respondent may have reported
a flat fee group where the initial visit began in 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 consist only of one or more
leaf records and no stem. Another reason for which a flat fee group would not
have a stem and at least one leaf record is that the stem or leaves could have
been reported as different event types. Outpatient and office-based medical
provider visits are the only two event types allowed in a single flat fee group.
The stem may have been reported as an outpatient department visit and the leaves
may have been reported as office-based medical provider visits. Please note that
the crosswalk in this document lists all possible flat fee variables.
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Expenditures on this file refer to what is paid for
health care services. More specifically, expenditures in MEPS are defined as the
sum of payments for care received, including out-of-pocket payments and payments
made by private insurance, Medicaid, Medicare, and other sources. The definition
of expenditures used in MEPS differs slightly from its predecessors: the 1987
NMES and 1977 NMCES surveys where “charges” rather than sum of payments were
used to measure expenditures. This change was adopted because charges became a
less appropriate proxy for medical expenditures during the 1990s due to the
increasingly common practice of discounting. Although measuring expenditures as
the sum of payments incorporates discounts in the MEPS expenditure estimates,
the estimates do not incorporate any payment not directly tied to specific
medical care visits, such as bonuses or retrospective payment adjustments paid
by third party payers. Currently, charges associated with uncollected liability,
bad debt, and charitable care (unless provided by a public clinic or hospital)
are not counted as expenditures because there are no payments associated with
those classifications. While charge data are provided on this file, data
users/analysts should use caution when working with these data because a charge
does not typically represent actual dollars exchanged for services or the
resource costs of those services, nor is it directly comparable to the resource
costs of those services or the expenditures defined in the 1987 NMES (for
details on expenditure definitions, see Monheit et al., 1999). AHRQ has
developed factors to apply to the 1987 NMES expenditure data to facilitate
longitudinal analysis. These factors can be accessed via the CFACT data center.
For more information, see the Data Center section of the MEPS Web site
meps.ahrq.gov/data_stats/onsite_datacenter.jsp.
If examining trends in MEPS expenditures, please refer to Section 3.3 for
more information.
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The expenditure data included on this file were
derived from both the MEPS household (HC) and medical provider (MPC) components.
The MPC contacted medical providers identified by household respondents. The
charge and payment data from medical providers were used in the expenditure
imputation process to supplement missing household data. For all office-based
medical provider visits, MPC data were used if available; otherwise HC data were
used. Missing data for office-based medical provider visits where HC data were
not complete and MPC data were not collected, or MPC data were not complete,
were derived through the imputation process.
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Logical edits were used to resolve internal
inconsistencies and other problems in the HC and MPC survey-reported data. The
edits were designed to preserve partial payment data from households and
providers, and to identify actual and potential sources of payment for each
household-reported event. In general, these edits accounted for outliers,
co-payments or charges reported as total payments, and reimbursed amounts that
were reported as out-of-pocket payments. In addition, edits were implemented to
correct for misclassifications between Medicare and Medicaid and between
Medicare HMOs and private HMOs as payment sources. These edits produced a
complete vector of expenditures for some events, and provided the starting point
for imputing missing expenditures in the remaining events.
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The predictive mean matching imputation method was
used to impute missing expenditures. This procedure uses regression models
(based on events with completely reported expenditure data) to predict total
expenses for each event. Then, for each event with missing payment information,
a donor event with the closest predicted payment with the same pattern of
expected payment sources as the event with missing payment was used to impute
the missing payment value. Within each event type file, separate imputations
were performed for flat fee and simple events. Separate imputations were
performed for visits to physicians (where MPCELIG=1) and visits to non-physician
providers (where MPCELIG=2). After the imputations were finished, visits to
physician and non-physician providers were combined into a single medical
provider file.
The weighted sequential hot-deck procedure was used to
impute the missing total charges. This procedure uses survey data from
respondents to replace missing data while taking into account the persons’
weighted distribution in the imputation process.
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Expenditures for office-based provider visits were
developed in a sequence of logical edits and imputations. “Household” edits were
applied to sources and amounts of payment for all events reported by HC
respondents. “MPC” edits were applied to provider-reported sources and amounts
of payment for records matched to household-reported events. Both sets of edits
were used to correct obvious errors (as described above) in the reporting of
expenditures. After the data from each source were edited, a decision was made
as to whether household- or MPC-reported information would be used in the final
editing and in the predictive mean matching imputations for missing
expenditures. The general rule was that MPC data would be used for events where
a household-reported event corresponded to an MPC-reported event (i.e., a
matched event), since providers usually have more complete and accurate data on
sources and amounts of payment than households.
One of the more important edits separated flat fee
events from simple events. This edit was necessary because groups of events
covered by a flat fee (i.e., a flat fee bundle) were edited and imputed
separately from individual events covered by a single charge (i.e., simple
events). (See Section 2.5.5 for more details on flat fee groups).
Logical edits also were used to sort each event into a
specific category for the imputations. Events with complete expenditures were
flagged as potential donors while events with missing expenditure data were
assigned to various recipient categories. Each event with missing expenditure
data was assigned to a recipient category based on the extent of its missing
charge and expenditure data. For example, an event with a known total charge but
no expenditure information was assigned to one category, while an event with a
known total charge and partial expenditure information was assigned to a
different category. Similarly, events without a known total charge and no or
partial expenditure information were assigned to various recipient categories.
The logical edits produced eight recipient categories
in which all events had a common extent of missing data. Separate imputations
were performed on events in each recipient category. For office-based events,
the donor pool was restricted to events with complete expenditures from the MPC.
The donor pool included “free events” because, in some
instances, providers are not paid for their services. These events represent
charity care, bad debt, provider failure to bill, and third party payer
restrictions on reimbursement in certain circumstances. If free events were
excluded from the donor pool, total expenditures would be over-counted because
the distribution of free events among complete events (donors) would not be
represented among incomplete events (recipients).
For office-based and outpatient events, the donor pool
also included events originally reported by providers as paid on a capitated
basis. To obtain the fee-for-service (FFS) equivalent payments for these
capitated events, a “capitation imputation” was implemented (see the next
section). Once imputed with the FFS equivalent payments, these events became
donors for all other incomplete events, particularly for events reported by the
household as services covered under managed care plans.
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The imputation process was also used to estimate
expenditures at the event-level for events that were paid on a per month per
person (capitated) basis. The capitation imputation procedure was designed as a
reasonable approach to complete event-level expenditures for persons in non-fee
for service managed care plans. HMO events reported in the MPC as covered by
capitation arrangements were imputed using similar completed HMO events paid on
a fee-for-service, with total charge as a key variable. Then this fully
completed set of MPC events was used in the donor pool for the main imputation
process for cases in HMOs. By using this strategy, capitated HMO events were
imputed as if the provider were reimbursed from the HMO on a discounted
fee-for-service basis.
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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
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The approach used to count expenditures for flat fees
was to place the expenditure on the first visit of the flat fee group. The
remaining visits have zero payments. Thus, if the first visit in the flat fee
group occurred prior to 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.
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There are some medical events reported by respondents
where the payments were zero. Zero payment events can occur in MEPS for the
following reasons: (1) the visit was covered under a flat fee arrangement (flat
fee payments are included only on the first event covered by the arrangement),
(2) there was no charge for a follow-up visit, (3) the provider was never paid
directly for services provided by an individual, insurance plan, or other
source, (4) the charges were included in another bill, or (5) the event was paid
through government or privately funded research or clinical trials.
The file also contains a small number of events
involving a telephone call rather than a visit to the medical provider (SEETLKPV
= 2). The expenditure variables for telephone calls have a value of -1
“INAPPLICABLE”.
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An adjustment was also applied to some HC-reported
expenditure data because an evaluation of matched HC/MPC data showed that
respondents who reported that charges and payments were equal were often unaware
that insurance payments for the care had been based on a discounted charge. To
compensate for this systematic reporting error, a weighted sequential hot-deck
imputation procedure was implemented to determine an adjustment factor for
HC-reported insurance payments when charges and payments were reported to be
equal. As for the other imputations, selected predictor variables were used to
form groups of donor and recipient events for the imputation process.
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In addition to total expenditures, variables are
provided which itemize expenditures according to major source of payment
categories. These categories are:
- Out-of-pocket by User (self) or Family,
- Medicare,
- Medicaid,
- Private Insurance,
- Veterans Administration/CHAMPVA, excluding TRICARE
- TRICARE,
- Other Federal Sources - includes Indian Health Service,
military treatment facilities, and other care by the federal
government,
- Other State and Local Sources - includes community and
neighborhood clinics, state and local health departments, and
state programs other than Medicaid,
- Workers’ Compensation, and
- Other Unclassified Sources - includes sources such as
automobile, homeowner’s, and liability insurance, and other
miscellaneous or unknown sources.
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:
- Other Private - any type of private insurance payments
reported for persons not reported to have any private health
insurance coverage during the year as defined in MEPS, and
- Other Public – Medicare/Medicaid payments reported for
persons who were not reported to be enrolled in the
Medicare/Medicaid program at any time during the year.
Though these two sources are relatively small
in magnitude, data users/analysts should exercise caution when interpreting the
expenditures associated with these two additional sources of payment. While
these payments stem from apparent inconsistent responses to health insurance and
source of payment questions in the survey, some of these inconsistencies may
have logical explanations. For example, private insurance coverage in MEPS is
defined as having a major medical plan covering hospital and physician services.
If a MEPS sampled person did not have such coverage but had a single service
type insurance plan (e.g., dental insurance) that paid for a particular episode
of care, those payments may be classified as “other private”. Some of the “other
public” payments may stem from confusion between Medicaid and other state and
local programs or may be persons who were not enrolled in Medicaid, but were
presumed eligible by a provider who ultimately received payments from the public
payer.
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OBSF15X - OBOT15X are the 12 sources of payment. The
12 sources of payment are: self/family (OBSF15X), Medicare (OBMR15X), Medicaid
(OBMD15X), private insurance (OBPV15X), Veterans /CHAMPVA (OBVA15X), TRICARE
(OBTR15X), other federal sources (OBOF15X), state and local (non-federal)
government sources (OBSL15X), Workers’ Compensation (OBWC15X), other private
insurance (OBOR15X), other public insurance (OBOU15X), and other insurance
(OBOT15X). OBXP15X is the sum of the 12 sources of payment for the office-based
expenditures, and OBTC15X is the total charge.
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Expenditure variables have been rounded to the nearest
penny. Person-level expenditure information released on the MEPS 2015
Person-Level Use and Expenditure File will be 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
person-level expenditure file. These differences are due to rounding only.
Moreover, in some instances, the number of persons having expenditures on the
event files for a particular source of payment may differ from the number of
persons with expenditures on the person-level expenditure file for that source
of payment. This difference is also an artifact of rounding only.
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There 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 either was a member of a responding NHIS household at the time of
interview, or joined a family associated with such a household after being
out-of-scope at the time of the NHIS (the latter circumstance includes newborns
as well as those returning from military service, an institution, or residence
in a foreign country). A person is in-scope whenever he or she is a member of
the civilian noninstitutionalized portion of the U.S. population.
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The person-level weight PERWT15F was developed in
several stages. First, 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 totals. The
calibration was initially accomplished separately for each panel by raking the
corresponding sample weights for those in-scope at the end of the calendar year
to Current Population Survey (CPS) population estimates based on 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.
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The 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 totals 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.
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The 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 data bases.
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The final raking of those in-scope at the end of the
year has been described above. In addition, the composite weights of two groups
of persons who were out-of-scope on December 31, 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 decedent 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 the person-level weights across
all persons assigned a positive person-level weight is 321,423,251.
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The 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.
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MEPS began in 1996, and the utility of the survey for
analyzing health care trends expands with each additional year of data. However,
it is important to consider a variety of factors when examining trends over time
using MEPS. Statistical significance tests should be conducted to assess the
likelihood that observed trends may be attributable to sampling variation. The
length of time being analyzed should also be considered. In particular, large
shifts in survey estimates over short periods of time (e.g. from one year to the
next) that are statistically significant should be interpreted with caution
unless they are attributable to known factors such as changes in public policy,
economic conditions, or MEPS survey methodology.
With respect to methodological considerations, in 2014
MEPS introduced an effort to obtain more complete information about health care
utilization from MEPS respondents with full implementation in 2015. This effort
likely resulted in improved data quality and a reduction in underreporting in FY
2015 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.
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The data in this file can be used to develop national
2015 event-level estimates for the U.S. civilian noninstitutionalized population
on office-based medical provider visits as well as expenditures, and sources of
payment for these visits. Estimates of total visits are the sum of the weight
variable (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
Office-Based Visits
| Estimate of Interest |
Variable Name |
Estimate (SE) |
Estimate Excluding
Zero Payment Events (SE)** |
| Total number of
office-based medical provider visits (including
phone call events*, in millions) |
PERWT15F |
1,885.6 (57.45) |
1,793.2 (55.60) |
| Total number of
office-based medical provider visits in person
and not by telephone (SEETLKPV=1, in millions) |
PERWT15F |
1,870.1 (57.09) |
1,792.9 (55.60) |
| Total number of
in-person visits to doctor (SEETLKPV=1 & SEEDOC=1,
in millions) |
PERWT15F |
1,036.7 (28.61) |
1,002.6 (27.94) |
| Proportion of
office-based medical provider visits with
expenditures > 0** |
OBXP15X |
0.951 (0.0023) |
-- |
Office-Based Expenditures (SEETLKPV = 1)
| Estimate of Interest |
Variable Name |
Estimate (SE) |
Estimate Excluding
Zero Payment Events (SE)** |
| Mean total payments
per visit (all sources) |
OBXP15X |
$209 ($3.6) |
$218 ($3.8) |
| Mean out-of-pocket
payment per visit |
OBSF15X |
$30 ($1.0) |
$31 ($1.0) |
| Mean proportion of
total expenditures paid by private insurance per
visit |
OBPV15X/OBXP15X |
-- |
0.356 (0.0072) |
Office-Based Expenditures: Physician Visits (SEEDOC = 1 & SEETLKPV = 1)
| Estimate of Interest |
Variable Name |
Estimate (SE) |
Estimate Excluding
Zero Payment Events (SE)** |
| Mean total payments
per visit where person saw medical doctor |
OBXP15X |
$248 ($5.6) |
$256 ($5.8) |
| Mean out-of-pocket
payment per visit where person saw medical
doctor |
OBSF15X |
$32 ($1.3) |
$33 ($1.4) |
| Mean proportion of
total expenditures per visit paid by private
insurance where person saw medical doctor |
OBPV15X/
OBXP15X |
-- |
0.363 (0.0074) |
*OBXP15X = -1 (inapplicable) for all phone call events (SEETLKPV = 2).
** Zero payment events can occur in MEPS for the
following reasons: (1) the visit was covered under a flat fee arrangement (flat
fee payments are included only on the first event covered by the arrangement),
(2) there was no charge for a follow-up visit, (3) the provider was never paid
directly for services provided by an individual, insurance plan, or other
source, (4) the charges were included in another bill, or (5) the event was paid
through government or privately funded research or clinical trials.
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To enhance analyses of office-based visits, analysts
may link information about office-based visits by sample persons in this file to
the annual full year consolidated file (which has data for all MEPS sample
persons), or conversely, link person-level information from the full year
consolidated file to this event-level file (see Section 5.0 below for more
details). Both this file and the full year consolidated file may be used to
derive estimates for persons with office-based care and annual estimates of
total expenditures. However, if the estimate relates to the entire population,
this file cannot be used to calculate the denominator, as only those persons
with at least one office-based event are represented on this data file.
Therefore, the full year consolidated file must be used for person-level
analyses that include both persons with and without office-based care.
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It is essential that the analyst examine all variables
for the presence of negative values used to represent missing values. For
continuous or discrete variables, where means or totals may be taken, it may be
necessary to set minus values to values appropriate to the analytic needs. That
is, the analyst should either impute a value or set the value to one that will
be interpreted as missing by the software package used. For categorical
and dichotomous variables, the analyst may want to consider whether to recode or
impute a value for cases with negative values or whether to exclude or include
such cases in the numerator and/or denominator when calculating proportions.
Methodologies used for the editing/imputation of
expenditure variables (e.g., sources of payment, flat fee, and zero
expenditures) are described in Section 2.5.6.
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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, 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:
- When pooling any year from 2002 or later, one can use the
variance strata numbering as is.
- When pooling any year from 1996 to 2001 with any year from
2002 or later, use the H36 file.
- A new H36 file will be constructed in the future to allow
pooling of 2007 and later years with 1996 to 2006.
Return To Table Of Contents
Data from this file can be used alone or in
conjunction with other files for different analytic purposes. This section
summarizes various scenarios for merging/linking MEPS event files. The set of
households selected for MEPS is a subsample of those participating in the
National Health Interview Survey (NHIS), thus, each MEPS panel can also be
linked back to the previous year’s NHIS public use data files. For information
on obtaining MEPS/NHIS link files please see
meps.ahrq.gov/data_stats/more_info_download_data_files.jsp.
Return To Table Of Contents
Merging 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
office-based medical provider visits of persons with specific demographic
characteristics (such as age, race, sex, and education), population
characteristics from a person-level file need to be merged onto the office-based
medical provider visits file. This procedure is illustrated below. The MEPS 2015
Appendix File, HC-178I, provides additional detail on how to merge MEPS data
files.
- Create data set PERSX by sorting the 2015 Full Year
Consolidated File by the person identifier, DUPERSID. Keep only
variables to be merged onto the office-based medical provider
visits file and DUPERSID.
- Create data set OBMP by sorting the office-based medical
provider visits file by person identifier, DUPERSID.
- Create final data set NEWOBMP by merging these two files by
DUPERSID, keeping only records on the office-based medical
provider visits file.
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;
BY DUPERSID;
RUN;
PROC SORT DATA=OBMP;
BY DUPERSID;
RUN;
DATA NEWOBMP;
MERGE OBMP (IN=A) PERSX(IN=B);
BY DUPERSID;
IF A;
RUN;
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The prescribed medicines-event link (RXLK) file
provides a link from the MEPS event files to the Prescribed Medicines Event
File. When using the RXLK, data users/analysts should keep in mind that one
office-based visit can link to more than one prescribed medicine record.
Conversely, a prescribed medicine event may link to more than one office-based
visit or different types of events. When this occurs, it is up to the data
user/analyst to determine how the prescribed medicine expenditures should be
allocated among those medical events. For detailed linking examples, including
SAS code, data users/analysts should refer to the MEPS 2015 Appendix File,
HC-178I.
Return To Table Of Contents
The condition-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
office-based medical provider visit, and (3) a condition may link to more than
one office-based medical provider visit or any other type of visit. Users should
also note that not all office-based medical provider visits link to the
condition file.
Return To Table Of Contents
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.
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VARIABLE-SOURCE CROSSWALK
FOR MEPS HC-178G: 2015 OFFICE-BASED MEDICAL PROVIDER VISITS
Survey Administration Variables
| Variable |
Description |
Source |
| DUID |
Dwelling unit ID |
Assigned in sampling |
| PID |
Person number |
Assigned in sampling |
| DUPERSID |
Person ID (DUID + PID)
|
Assigned in sampling |
| EVNTIDX |
Event ID |
Assigned in sampling |
| EVENTRN |
Event round number
|
CAPI derived |
| PANEL |
Panel number |
Constructed |
| FFEEIDX |
Flat fee ID |
CAPI derived |
| MPCELIG |
MPC eligibility flag |
Constructed |
| MPCDATA |
MPC data flag |
Constructed |
Return To Table Of Contents
Medical Provider Visits Variables
| Variable |
Description |
Source |
| OBDATEYR |
Event date – year |
CAPI derived |
| OBDATEMM |
Event date – month
|
CAPI derived |
| SEETLKPV |
Did P visit prov in
person or telephone |
MV01 |
| SEEDOC |
Did P talk to MD this
visit/phone call |
MV03 |
| DRSPLTY |
MVIS doctor’s
specialty |
MV03A |
| MEDPTYPE |
Type of med person P
talked to on vst dt |
MV04 |
| DOCATLOC |
Any MD work at
location where P saw prov |
MV06 |
| VSTCTGRY |
Best category for care
P recv on vst dt |
MV07 |
| VSTRELCN |
This vst/phone call
related to spec cond |
MV08 |
| LABTEST |
This visit did P have
lab tests |
MV11 |
| SONOGRAM |
This visit did P have
sonogram or ultrsd |
MV11 |
| XRAYS |
This visit did P have
x-rays |
MV11 |
| MAMMOG |
This visit did P have
a mammogram |
MV11 |
| MRI |
This visit did P have
an MRI/Catscan |
MV11 |
| EKG |
This visit did P have
an EKG or ECG |
MV11 |
| EEG |
This visit did P have
an EEG |
MV11 |
| RCVVAC |
This visit did P
receive a vaccination |
MV11 |
| ANESTH |
This visit did P
receive anesthesia |
MV11 |
| THRTSWAB |
This visit did P have
throat swab |
MV11 |
| OTHSVCE |
This visit did P have
oth diag test/exam |
MV11 |
| SURGPROC |
Was surg proc
performed on P this visit |
MV12 |
| MEDPRESC |
Any medicines
prescribed for P this visit |
MV14 |
| OBCCC1X |
Modified Clinical
Classification Code |
Constructed/Edited |
| OBCCC2X |
Modified Clinical
Classification Code |
Constructed/Edited |
| OBCCC3X |
Modified Clinical
Classification Code |
Constructed/Edited |
| OBCCC4X |
Modified Clinical
Classification Code |
Constructed/Edited |
Return To Table Of Contents
Flat Fee Variables
| Variable |
Description |
Source |
| FFOBTYPE |
Flat fee bundle |
Constructed |
| FFBEF15 |
Total # of visits in
FF before 2015 |
FF05 |
| FFTOT16 |
Total # of visits in
FF after 2015 |
FF10 |
Return To Table Of Contents
Imputed Expenditure Variables
| Variable |
Description |
Source |
| OBSF15X |
Amount paid,
self/family (imputed) |
CP Section (Edited) |
| OBMR15X |
Amount paid, Medicare
(imputed) |
CP Section (Edited) |
| OBMD15X |
Amount paid, Medicaid
(imputed) |
CP Section (Edited) |
| OBPV15X |
Amount paid, private
insurance (imputed) |
CP Section (Edited) |
| OBVA15X |
Amount paid, Veterans/CHAMPVA
(imputed) |
CP Section (Edited) |
| OBTR15X |
Amount paid, TRICARE
(imputed) |
CP Section (Edited) |
| OBOF15X |
Amount paid, other
federal (imputed) |
CP Section (Edited) |
| OBSL15X |
Amount paid, state &
local government (imputed) |
CP Section (Edited) |
| OBWC15X |
Amount paid, workers’
compensation (imputed) |
CP Section (Edited) |
| OBOR15X |
Amount paid, other
private insurance (imputed) |
Constructed |
| OBOU15X |
Amount paid, other
public insurance (imputed) |
Constructed |
| OBOT15X |
Amount paid, other
insurance (imputed) |
CP Section (Edited) |
| OBXP15X |
Sum of OBSF15X –
OBOT15X (imputed) |
Constructed |
| OBTC15X |
Household reported
total charge (imputed) |
CP Section (Edited) |
| IMPFLAG |
Imputation status |
Constructed |
Return To Table Of Contents
Weights Variables
| Variable |
Description |
Source |
| PERWT15F |
Expenditure file
person weight, 2015 |
Constructed |
| VARSTR |
Variance estimation
stratum, 2015 |
Constructed |
| VARPSU |
Variance estimation
PSU, 2015 |
Constructed |
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|
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