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July 2016
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 Data Indicator (MPCDATA)
2.5.3 Outpatient Visit Event Variables
2.5.3.1 Visit Details (OPDATEYR-VSTRELCN)
2.5.3.2 Services, Procedures, and Prescription Medicines (LABTEST– MEDPRESC)
2.5.4 Clinical Classification Codes (OPCCC1X-OPCCC4X)
2.5.5 Flat Fee Variables (FFEEIDX, FFOPTYPE, FFBEF14, FFTOT15)
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 (FFOPTYPE)
2.5.5.2.3 Counts of Flat Fee Events that Cross Years (FFBEF14, FFTOT15)
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 Outpatient 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 Imputed Outpatient Expenditure Variables
2.5.6.9.1 Outpatient Facility Expenditure Variables (OPFSF14X-OPFOT14X, OPFXP14X, OPFTC14X)
2.5.6.9.2 Outpatient Physician Expenditures (OPDSF14X – OPDOT14X, OPDXP14X, OPDTC14X)
2.5.6.9.3 Total Expenditures and Charges for Outpatient Visits (OPXP14X, OPTC14X)
2.5.6.10 Rounding
3.0 Sample Weight (PERWT14F)
3.1 Overview
3.2 Details on Person Weight Construction
3.2.1 MEPS Panel 18 Weight Development Process
3.2.2 MEPS Panel 19 Weight Development Process
3.2.3 The Final Weight for 2014
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 Outpatient 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 2014 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 SAS transport file,
this public use file provides detailed information on outpatient visits for a
nationally representative sample of the civilian noninstitutionalized population
of the United States and can be used to make estimates of outpatient utilization
and expenditures for calendar year 2014. The file contains 70 variables and has
a logical record length of 350 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 obtained in the 2014 portion of Round 3 and Rounds 4 and 5 for Panel
18, as well as Rounds 1, 2 and the 2014 portion of Round 3 for Panel 19 (i.e.,
the rounds for the MEPS panels covering calendar year 2014).
Each record on this event file represents a unique
outpatient event; that is, an outpatient event reported by the household
respondent. Outpatient events reported in Panel 19 Round 3 and known to have
occurred after December 31, 2014 are not included on this file. In
addition to expenditures related to this event, each record contains
household-reported medical conditions associated with the outpatient visit.
Annual counts of outpatient visits are based entirely
on household reports. Information from the MEPS MPC is used to supplement
expenditure and payment data reported by the household, and does not affect use
estimates.
Data from this event file can be merged with other
MEPS HC data files, for purposes of appending person characteristics such as
demographic or health insurance characteristics to each outpatient visit record.
This file can also be used to construct summary
variables of expenditures, sources of payment, and related aspects of outpatient
visits. Aggregate annual person-level information on the use of outpatient
departments and other health services is provided on the MEPS 2014 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
Any variables not found on this file but released on
previous years’ files may have been excluded because they contained only missing
data.
For more information on MEPS HC survey design, see T.
Ezzati-Rice, et al. (1998-2007) and S. Cohen, 1996. For information on the MEPS
MPC design, see S. Cohen, 1998. Copies of the HC and the MPC survey
instruments used to collect the information on the Outpatient Department Visit
file are available in the Survey Questionnaires section of the MEPS Web
site at the following address:
meps.ahrq.gov.
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The 2014 Outpatient Department Visits public use data
set consists of one event-level data file. The file contains characteristics
associated with the outpatient (OP) event and imputed expenditure data.
The 2014 outpatient public use data set contains
15,144 outpatient event records; of these records, 14,637 are associated with
persons having a positive person-level weight (PERWT14F). This file includes
outpatient event records for all household members who resided in eligible
responding households and for whom at least one outpatient event was reported.
Questions inquired whether someone in the family had a visit to an independent
lab or testing facility for x-rays or other tests. An affirmative answer to
these questions leads to the creation of an office-based provider event record
or an outpatient department event record.
Each record represents one household-reported
outpatient event that occurred during calendar year 2014. Outpatient visits
known to have occurred after December 31, 2014 are not included on this file.
Some household members may have multiple outpatient events and thus will be
represented in multiple records on this file. Other household members may have
had no outpatient events reported and thus will have no records on this file.
These data were collected during the 2014 portion of Round 3, and Rounds 4 and 5
for Panel 18, as well as Rounds 1, 2, and the 2014 portion of Round 3 for Panel
19 of the MEPS HC. The persons represented on this file had to meet either (a)
or (b) below:
- Be classified as a key in-scope person who responded for his
or her entire period of 2014 eligibility (i.e., persons with a
positive 2014 full-year person-level weight (PERWT14F > 0)), or
- Be an eligible member of a family all of whose key in-scope
members have a positive person-level weight (PERWT14F > 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 (FAMWT14F>0). Note that FAMIDYR and FAMWT14F
are variables on the 2014 Full Year Consolidated Data File.
Persons with no outpatient visit events for 2014 are
not included on this event-level OP file but are represented on the person-level
2014 Full Year Population Characteristics file.
Each outpatient visit record includes the following
information: date of the visit; whether or not the household member saw the
doctor; type of care received; type of services (i.e., lab test, sonogram or
ultrasound, x-rays, etc) received; medicines prescribed during the visit; flat
fee information; imputed sources of payment; total payment and total charge; 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 2014 MEPS HC person-level data (e.g. Full Year Consolidated or Full
Year Population Characteristics files) using the person identifier, DUPERSID.
Outpatient visit events on this file can also be linked to the MEPS 2014 Medical
Conditions File and to the MEPS 2014 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 Outpatient Department events
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 outpatient visit identifiers
- Outpatient 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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This 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 eight-character limitation. All imputed/edited variables end
with an “X”.
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Variables on this file were derived from the HC
questionnaire itself, 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 so
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;
- FF- Flat Fee section
- CP- Charge Payment section
- OP - Outpatient section
- Variables constructed from multiple questions using complex
algorithms are labeled “Constructed” in the “Source” column; and
- Variables which have been imputed are so indicated.
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The names of the expenditure and source of payment
variables follow a standard convention, are eight characters in length, and end
in an “X” indicating edited/imputed. Please note that imputed means that a
series of logical edits, as well as an imputation process to account for missing
data, have been performed on the variable.
The total sum of payments and the 12 source of payment
variables are named in the following way:
The first two characters indicate the type of event:
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
For expenditure variables on the OP file, the third
character indicates whether the expenditure (or amount paid) is associated with
the facility (F) or the physician (D).
In the case of the source of payment variables, the
fourth and fifth characters indicate:
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 sixth and seventh characters indicate the year
(14). The eighth character, “X”, indicates whether the variable is
edited/imputed.
For example, OPFSF14X is the edited/imputed amount
paid by self or family for the facility portion of the expenditure associated
with an outpatient visit.
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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 2014 Full Year Population Characteristics File.
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EVNTIDX uniquely identifies each outpatient event
(i.e., each record on the outpatient file) and is the variable required to link
outpatient events to data files containing details on conditions and/or
prescribed medicines (MEPS 2014 Medical Condition file and MEPS 2014 Prescribed
Medicines file, respectively). For details on linking see Section 5.0 or the
MEPS 2014 Appendix File, HC-168I.
FFEEIDX is a constructed variable that uniquely
identifies a flat fee group, that is, all events that were part of a flat fee
payment. For example, if a patient receives stitches during an outpatient visit
and comes back to have the stitches removed ten days later in a follow-up
outpatient visit, both visits are covered under one flat fee dollar amount.
These two events (the initial outpatient visit and the subsequent outpatient
visit) would have the same value for FFEEIDX. 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 outpatient
event was reported. Please note: Rounds 3, 4, and 5 are associated with MEPS
survey data collected from Panel 18. Likewise, Rounds 1, 2, and 3 are associated
with data collected from Panel 19.
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PANEL is a constructed variable used to specify the
panel number for the person. PANEL will indicate either Panel 18 or Panel 19 for
each person on the file. Panel 18 is the panel that started in 2013, and Panel
19 is the panel that started in 2014.
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MPCDATA is a constructed variable that indicates
whether or not MPC data were collected for the outpatient visit. While all
outpatient events are sampled into the Medical Provider Component, not all
outpatient event records have MPC data associated with them. This is dependent
upon the cooperation of the household respondent to provide permission forms to
contact the outpatient facility as well as the cooperation of the outpatient
facility to participate in the survey.
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This file contains variables describing outpatient
events reported by respondents in the Outpatient Department section of the MEPS
HC questionnaire. The questionnaire contains specific probes for determining
details about the outpatient visit. These variables have not been edited.
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When a person reported having had a visit to a
hospital outpatient department or special clinic, the year and month of the
outpatient visit was reported (OPDATEYR and OPDATEMM). Also reported is
whether the person actually saw the provider or talked to the provider on the
telephone (SEETLKPV). It also establishes whether the person saw or spoke to a
medical doctor (SEEDOC). If the person did not see a specialty doctor (DRSPLTY),
or, if the person did not see a physician (i.e., medical doctor), the respondent
was asked to identify the type of medical person that was seen (MEDPTYPE). 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.
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Through 2012, types of treatment received during the
outpatient visit were on the file and included physical therapy (PHYSTH),
occupational therapy (OCCUPTH), speech therapy (SPEECHTH), chemotherapy
(CHEMOTH), radiation therapy (RADIATTH), kidney dialysis (KIDNEYD), IV therapy
(IVTHER), drug or alcohol treatment (DRUGTRT), allergy shots (RCVSHOT),
psychotherapy/counseling (PSYCHOTH), and shots other than allergy (OTHSHOT).
Beginning in 2013, treatment variables (PHYSTH – OTHSHOT) were removed because
of design changes.
Services received during the visit included whether or
not the person received lab tests (LABTEST), a sonogram or ultrasound
(SONOGRAM), x-rays (XRAYS), a mammogram (MAMMOG), an MRI or CAT scan (MRI), an
electrocardiogram (EKG), an electroencephalogram (EEG), a vaccination (RCVVAC),
anesthesia (ANESTH), a throat swab (THRTSWAB), and 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 outpatient visit is provided on this file. There are up to
four CCS codes (OPCCC1X-OPCCC4X) listed for each outpatient 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 information on conditions
associated with an event, the analyst must link to the Medical Conditions File.
Please see Section 5.0 for details on how to link this file to the Medical
Conditions File. 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 2014 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 conditions, please refer to the documentation
on the Medical Conditions File.
The ICD-9-CM condition codes were aggregated into
clinically meaningful categories. These categories, included on the file as
OPCCC1X-OPCCC4X, 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
outpatient visit are sequenced in the order in which the conditions were
reported by the household respondent, which was in order of input into the
database and not in order of importance or severity. Data users/analysts who use
the MEPS 2014 Medical Conditions file in conjunction with this outpatient visit
file should note that the order of conditions on this file is not identical to
that on the Medical Conditions file.
Analysts should use the clinical classification codes
listed in the Conditions PUF (HC-170) document and the Appendix to the Event
Files (HC-168I) document when analyzing MEPS conditions data. Although there is
a list of clinical classification codes and labels on the Healthcare Cost and
Utilization Project (HCUP) Web site, if updates to these codes and/or labels are
made on the HCUP Web site after the release of the 2014 MEPS PUFs, these updates
will not be reflected in the 2014 MEPS data.
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A flat fee is the fixed dollar amount a person is
charged for a package of health care services provided during a defined period
of time. Examples would be: an obstetrician’s fee covering a normal delivery, as
well as pre- and post-natal care; or a surgeon’s fee covering surgical procedure
along with post-surgical care. A flat fee group is the set of medical services
(i.e., events) that are covered under the same flat fee payment. The flat fee
groups represented on this file include flat fee groups where at least one of
the health care events, as reported by the HC respondent, occurred during 2014.
By definition a flat fee group can span multiple years. 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 2014 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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FFOPTYPE indicates whether the 2014 outpatient visit
is the “stem” or “leaf” of a flat fee group. A stem (records with FFOPTYPE = 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 FFOPTYPE = 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 outpatient visits that are
not part of a flat fee payment, the FFOPTYPE is set to -1, “INAPPLICABLE.”
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As described above, a flat fee payment covers multiple
events and the multiple events could span multiple years. For situations where
the outpatient visit occurred in 2014 as a part of a group of events, and some
of the events occurred before or after 2014, counts of the known events are
provided on the outpatient visit record. Variables indicating events that
occurred before or after 2014 are as follows:
FFBEF14 – total number of pre-2014 events in the same
flat fee group as the 2014 outpatient visit. This count would not include the
2014 outpatient visit(s).
FFTOT15 – the number of 2015 outpatient visits
expected to be in the same flat fee group as the outpatient visit record that
occurred in 2014.
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There are 224 outpatient visits that are identified as
being part of a flat fee payment group. In general, every flat fee group should
have an initial visit (stem) and at least one subsequent visit (leaf). There are
some situations where this is not true. For some of these flat fee groups, the
initial visit reported occurred in 2014 but the remaining visits that were part
of this flat fee group occurred in 2015. In this case, the 2014 flat fee group
represented on this file would consist of one event (the stem). The 2015 events
that are part of this flat fee group are not represented on the file. Similarly,
the household respondent may have reported a flat fee group where the initial
visit began in 2013 but subsequent visits occurred during 2014. In this case,
the initial visit would not be represented on the file. This 2014 flat fee group
would then only consist of one or more leaf records and no stem. 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
outpatient services. More specifically, expenditures in MEPS are defined as the
sum of payments for care received for each outpatient visit, including
out-of-pocket payments and payments made by private insurance, Medicaid,
Medicare, and other sources. The definition of expenditures used in MEPS differs
slightly from its predecessors, the 1987 NMES and 1977 NMCES surveys, where
“charges” rather than sum of payments were used to measure expenditures. This
change was adopted because charges became a less appropriate proxy for medical
expenditures during the 1990s due to the increasingly common practice of
discounting. Although measuring expenditures as the sum of payments incorporates
discounts in the MEPS expenditure estimates, the estimates do not incorporate
any payment not directly tied to specific medical care visits, such as bonuses
or retrospective payment adjustments 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. For details
on expenditure definitions, please reference 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
meps.ahrq.gov/data_stats/onsite_datacenter.jsp.
Expenditure data related to outpatient visits are
broken out by facility and separately billing doctor expenditures. This file
contains six categories of expenditure variables per visit: basic hospital
outpatient facility expenses; expenses for doctors who billed separately from
the outpatient facility for any services provided during the outpatient visit;
total expenses, which is the sum of the facility and physician expenses;
facility charge; physician charge; and total charges, which is the sum of the
facility and physician charges. If examining trends in MEPS expenditures, please
refer to Section 3.3 for more information.
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The expenditure data included on this file were
derived from both the MEPS Household (HC) and the Medical Provider Components
(MPC). The MPC contacted medical providers identified by household respondents.
The charge and payment data from medical providers were used in the expenditure
imputation process to supplement missing household data. For all outpatient
visits, MPC data were used if available; otherwise, HC data were used. Missing
data for outpatient 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. 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. The imputations for the flat
fee events were carried out separately from the simple events.
Expenditures for services provided by separately
billing doctors in hospital settings were also edited and imputed. These
expenditures are shown separately from hospital facility charges for hospital
inpatient, outpatient, and emergency room care.
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Facility expenditures for outpatient services were
developed in a sequence of logical edits and imputations. “Household” edits were
applied to sources and amounts of payment for all events reported by HC
respondents. “MPC” edits were applied to provider-reported sources and amounts
of payment for records matched to household-reported events. Both sets of edits
were used to correct obvious errors in the reporting of expenditures. After the
data from each source were edited, a decision was made as to whether household-
or MPC-reported information would be used in the final editing and predictive
mean matching imputations for missing expenditures. The general rule was that
MPC data would be used where a household-reported event corresponded to an
MPC-reported event (i.e., a matched event), since providers usually have more
complete and accurate data on sources and amounts of payment than households.
One of the more important edits separated flat fee
events from simple events. This edit was necessary because groups of events
covered by a flat fee (i.e., a flat fee bundle) were edited and imputed
separately from individual events covered by a single charge (i.e., simple
events). (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 for the predictive mean matching imputations, while
events with missing expenditure data were assigned to various recipient
categories. Each event with missing expenditure data was assigned to a recipient
category based on the extent of its missing charge and expenditure data. For
example, an event with a known total charge but no expenditure information was
assigned to one category, while an event with a known total charge and partial
expenditure information was assigned to a different category. Similarly, events
without a known total charge and no or partial expenditure information were
assigned to various recipient categories.
The logical edits produced eight recipient categories
in which all events had a common extent of missing data. Separate predictive
mean matching imputations were performed on events in each recipient category.
For outpatient 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.
Expenditures for services provided by separately
billing doctors in hospital settings were also edited and imputed. These
expenditures are shown separately from hospital facility charges for hospital
inpatient, outpatient, and emergency room.
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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 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 facility payments, physician’s expenditures may still
be present. Thus, if the first visit in the flat fee group occurred prior to
2014, all of the events that occurred in 2014 will have zero payments.
Conversely, if the first event in the flat fee group occurred at the end of
2014, the total expenditure for the entire flat fee group will be on that event,
regardless of the number of events it covered after 2014. See Section 2.5.5 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 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 Source – 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 from 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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This file contains two sets of imputed expenditure
variables: facility expenditures and physician expenditures.
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Outpatient visit expenses include all expenses for
treatment, services, tests, diagnostic and laboratory work, x-rays, and similar
charges, as well as any physician services included in the hospital outpatient
visit charge.
OPFSF14X – OPFOT14X are the 12 sources of payment. The
12 sources of payment are: self/family (OPFSF14X), Medicare (OPFMR14X), Medicaid
(OPFMD14X), private insurance (OPFPV14X), Veterans Administration/CHAMPVA
(OPFVA14X), TRICARE (OPFTR14X), other federal sources (OPFOF14X), state and
local (non-federal) government sources (OPFSL14X), Workers’ Compensation
(OPFWC14X), other private insurance (OPFOR14X), other public insurance
(OPFOU14X), and other insurance (OPFOT14X). OPFXP14X is the sum of the 12
sources of payment for the outpatient facility expenditures, and OPFTC14X is the
total charge. Please note that where an outpatient visit record is linked to a
hospital inpatient stay record, all facility sources of payment variables, as
well as, OPFTC14X have been zeroed out.
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Separately billing doctor (SBD) expenses typically
cover services provided to patients in hospital settings by providers like
anesthesiologists, radiologists, and pathologists, whose charges are often not
included in the outpatient facility bill.
For physicians who bill separately (i.e., outside the
outpatient facility bill), a separate data collection effort within the Medical
Provider Component was performed to obtain the same set of expenditure
information from each separately billing doctor. It should be noted that there
could be several separately billing doctors associated with a medical event. For
example, an outpatient visit could have a radiologist and a pathologist
associated with it. If their services are not included in the outpatient visit
bill then this is one medical event with 2 separately billing doctors. The
imputed expenditure information associated with the separately billing doctors
was summed to the event-level and is provided on the file. OPDSF14X – OPDOT14X
are the 12 sources of payment, OPDXP14X is the sum of the 12 sources of
payments, and OPDTC14X is the physician(s) total charge.
Data users/analysts need to take into consideration
whether to analyze facility and SBD expenditures separately, combine them within
service categories, or collapse them across service categories (e.g., combine
SBD expenditures with expenditures for physician visits to offices and/or
outpatient departments).
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Data users/analysts interested in total expenditures
should use the variable OPXP14X, which includes both facility and physician
amounts. Those interested in total charges should use the variable OPTC14X,
which includes both facility and physician charges (see Section 2.5.6.1 for an
explanation of the “charge” concept).
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Expenditure variables have been rounded to the nearest
penny. Person-level expenditure information released on the MEPS 2014
Person-Level Use and Expenditure File were rounded to the nearest dollar.
It should be noted that using the MEPS 2014 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
(PERWT14F) assigned to each record for each key, in-scope person who responded
to MEPS for the full period of time that he or she was in-scope during 2014. A
key person 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 PERWT14F was developed in
several stages. First, person-level weights for Panel 18 and Panel 19 were
created separately. The weighting process for each panel included an adjustment
for nonresponse over time and calibration to independent population 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 2014 composite weight was then formed by multiplying
each weight from Panel 18 by the factor .500 and each weight from Panel 19 by
the factor .500. The choice of factors reflected the relative sample sizes of
the two panels, helping to limit the variance of estimates obtained from pooling
the two samples. The composite weight was raked to the same set of CPS-based
control totals. When the poverty status information derived from income
variables became available, a final raking was undertaken on the previously
established weight variable. Control totals were established using poverty
status (five categories: below poverty, from 100 to 125 percent of poverty, from
125 to 200 percent of poverty, from 200 to 400 percent of poverty, at least 400
percent of poverty) as well as the other five variables previously used in the
weight calibration.
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The person-level weight for MEPS Panel 18 was
developed using the 2013 full year weight for an individual as a “base” weight
for survey participants present in 2013. For key, in-scope members who joined an
RU some time in 2014 after being out-of-scope in 2013, the initially assigned
person-level weight was the corresponding 2013 family weight. The weighting
process included an adjustment for person-level nonresponse over Rounds 4 and 5
as well as raking to population control totals for December 2014 for key,
responding persons in-scope on December 31, 2014. These control totals were
derived by scaling back the population distribution obtained from the March 2015
CPS to reflect the December 31, 2014 estimated population total (estimated based
on Census projections for January 1, 2015). Variables used for person-level
raking included: census region (Northeast, Midwest, South, West); MSA status (MSA,
non-MSA); race/ethnicity (Hispanic; Black, non-Hispanic; Asian, non-Hispanic;
and other); sex; and age. (Poverty status is not included in this version
of the MEPS full year database because of the time required to process the
income data collected and then assign persons to a poverty status category). The
final weight for key, responding persons who were not in-scope on December 31,
2014 but were in-scope earlier in the year was the person weight after the
nonresponse adjustment.
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The person-level weight for MEPS Panel 19 was
developed using the 2014 MEPS Round 1 person-level weight as a “base” weight.
For key, in-scope members who joined an RU after Round 1, the Round 1 family
weight served as a “base” weight. The weighting process included an adjustment
for nonresponse over the remaining data collection rounds in 2014 as well as
raking to the same population control figures for December 2014 used for the
MEPS Panel 18 weights for key, responding persons in-scope on December 31, 2014.
The same five variables employed for Panel 18 raking (census region, MSA status,
race/ethnicity, sex, and age) were used for Panel 19 raking. Again, the final
weight for key, responding persons who were not in-scope on December 31, 2014
but were in-scope earlier in the year was the person weight after the
nonresponse adjustment.
Note that the MEPS Round 1 weights for both panels
incorporated the following components: a weight reflecting the original
household probability of selection for the NHIS and an adjustment for NHIS
nonresponse; a factor representing the proportion of the 16 NHIS panel-quarter
combinations eligible for MEPS; the oversampling of certain subgroups for MEPS
among the NHIS household respondents eligible for MEPS; ratio-adjustment to NHIS-based
national population estimates at the household (occupied DU) level; adjustment
for nonresponse at the DU level for Round 1; and poststratification to U.S.
civilian noninstitutionalized population estimates at the family and person
level obtained from the corresponding March CPS databases.
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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, 2014 were poststratified.
Specifically, the weights of those who were in-scope some time during the year,
out-of-scope on December 31, and entered a nursing home during the year were
poststratified to a corresponding control total obtained from the 1996 MEPS
Nursing Home Component. The weights of persons who died while in-scope during
2014 were poststratified to corresponding estimates derived using data obtained
from the Medicare Current Beneficiary Survey (MCBS) and Vital Statistics
information provided by the National Center for Health Statistics (NCHS).
Separate decedent control totals were developed for the “65 and older” and
“under 65” civilian noninstitutionalized decedent populations.
Overall, the weighted population estimate for the
civilian noninstitutionalized population for December 31, 2014 is 314,906,436
(PERWT14F>0 and INSC1231 = 1). The sum of the person-level weights across all
persons assigned a positive person-level weight is 318,440,423.
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The target population for MEPS in this file is the
2014 U.S. civilian noninstitutionalized population. However, the MEPS sampled
households are a subsample of the NHIS households interviewed in 2012 (Panel 18)
and 2013 (Panel 19). New households created after the NHIS interviews for the
respective panels and consisting exclusively of persons who entered the target
population after 2012 (Panel 18) or after 2013 (Panel 19) are not covered by
MEPS. Neither are previously out-of-scope persons who join an existing household
but are unrelated to the current household residents. Persons not covered by a
given MEPS panel thus include some members of the following groups: immigrants;
persons leaving the military; U.S. citizens returning from residence in another
country; and persons leaving institutions. The set of uncovered persons
constitutes only a small segment of the MEPS target population.
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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 2013
MEPS introduced an effort to obtain more complete information about health care
utilization from MEPS respondents with full implementation in 2014. This effort
likely resulted in improved data quality and a reduction in underreporting in FY
2014, and could have some modest impact on analyses involving trends in
utilization across years.
There are also statistical factors to consider in
interpreting trend analyses. Looking at changes over longer periods of time can
provide a more complete picture of underlying trends. Analysts may wish to
consider using techniques to evaluate, smooth, or stabilize analyses of trends
using MEPS data such as comparing pooled time periods (e.g. 1996-97 versus
2013-14), working with moving averages, or using modeling techniques with
several consecutive years of MEPS data to test the fit of specified patterns
over time. Finally, researchers should be aware of the impact of multiple
comparisons on Type I error. Without making appropriate allowance for multiple
comparisons, undertaking numerous statistical significance tests of trends
increases the likelihood of concluding that a change has taken place when one
has not.
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The data in this file can be used to develop national
2014 event-level estimates for the U.S. civilian noninstitutionalized population
on outpatient visits as well as expenditures, and sources of payment for these
visits. Estimates of total visits are the sum of the weight variable (PERWT14F)
across relevant event records while estimates of other variables must be
weighted by PERWT14F to be nationally representative. The tables below contain
event-level estimates for selected variables.
Selected Event-Level Estimates
Outpatient Visits
| Estimate of Interest |
Variable Name |
Estimate (SE) |
Estimate Excluding
Zero Payment Events (SE)** |
Total number of outpatient
visits (including phone call
events, in millions)* |
PERWT14F |
168.6 (9.36) |
162.9 (8.99) |
Total number of outpatient
visits in person and not by
telephone (SEETLKPV=1, in
millions) |
PERWT14F |
167.8 (9.30) |
162.9 (8.99) |
Total number of in-person
visits to doctor
(SEETLKPV=1 &
SEEDOC=1, in millions) |
PERWT14F |
67.8 (4.35) |
66.0 (4.27) |
Proportion of outpatient visits
with expenditures > 0** |
OPXP14X |
0.966 (0.0040) |
-- |
Outpatient Expenditures (SEETLKPV = 1)
| Estimate of Interest |
Variable Name |
Estimate (SE) |
Estimate Excluding
Zero Payment Events (SE)** |
Mean total payments per visit
(all sources) |
OPXP14X |
$927 ($57.7) |
$955 ($58.7) |
Mean out-of-pocket payment
per visit |
OPDSF14X
+OPFSF14X |
$54 ($3.8) |
$56 ($3.9) |
Mean proportion of total
expenditures paid by private
insurance per visit |
(OPDPV14X+
OPFPV14X)
/OPXP14X |
-- |
0.353 (0.0146) |
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 |
OPXP14X |
$1,534 ($103.9) |
$1,575 ($106.7) |
Mean out-of-pocket payment
per visit where person saw
medical doctor |
OPDSF14X
+OPFSF14X |
$91 ($8.6) |
$93 ($8.8) |
Mean proportion of total
expenditures per visit paid by
private insurance where person
saw medical doctor |
(OPDPV14X
+OPFPV14X)
/OPXP14X |
-- |
0.359 (0.0212) |
*OPXP14X = -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 hospital outpatient visits,
analysts may link information about outpatient visits by sample persons in this
file to the annual full year consolidated file (which has data for all MEPS
sample persons), or conversely, link person-level information from the full year
consolidated file to this event-level file (see Section 5 below for more
details). Both this file and the full year consolidated file may be used to
derive estimates for persons with outpatient 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 outpatient 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 hospital outpatient 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, 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:
- 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. Each MEPS
panel can also be linked back to the previous year’s National Health Interview
Survey public use data files. For information on obtaining MEPS/NHIS link files
please see
meps.ahrq.gov/data_stats/more_info_download_data_files.jsp.
Return To Table Of Contents
Merging characteristics of interest from other MEPS
files (e.g., MEPS 2014 Full Year Consolidated File) expands the scope of
potential estimates. For example, to estimate the total number of outpatient
visits for persons with specific characteristics (e.g., age, race, sex, and
education), population characteristics from a person-level file need to be
merged onto the outpatient visit file. This procedure is illustrated below. The
MEPS 2014 Appendix File, HC-168I, provides additional detail on how to merge
MEPS data files.
- Create data set PERSX by sorting the Full Year Consolidated
file by the person identifier, DUPERSID. Keep only variables to
be merged onto the outpatient visit file and DUPERSID.
- Create data set OPAT by sorting the outpatient visit file by
person identifier, DUPERSID.
- Create final data set NEWOPAT by merging these two files by
DUPERSID, keeping only records on the outpatient visit 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) OUT=PERSX;
BY DUPERSID;
RUN;
PROC SORT DATA=OPAT;
BY DUPERSID;
RUN;
DATA NEWOPAT;
MERGE OPAT(IN=A) PERSX(IN=B);
BY DUPERSID;
IF A;
RUN;
Return To Table Of Contents
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
outpatient event can link to more than one prescribed medicine record.
Conversely, a prescribed medicine event may link to more than one outpatient
event 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 2014 Appendix File,
HC-168I.
Return To Table Of Contents
The condition-event link (CLNK) file provides a link
from MEPS event files to the 2014 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
outpatient visit, and (3) a condition may link to more than one outpatient visit
or any other type of visit. Users should also note that not all outpatient
visits link to the medical conditions file.
Return To Table Of Contents
Cohen, S.B. (1998). Sample Design of the 1996 Medical
Expenditure Panel Survey Medical Provider Component. Journal of Economic and
Social Measurement. Vol 24, 25-53.
Cohen, S.B. (1996). The Redesign of the Medical
Expenditure Panel Survey: A Component of the DHHS Survey Integration Plan.
Proceedings of the COPAFS Seminar on Statistical Methodology in the Public
Service.
Cox, B.G. and Cohen, S.B. (1985). Chapter 6: A
Comparison of Household and Provider Reports of Medical Conditions. In
Methodological Issues for Health Care Surveys. Marcel Dekker, New York.
Cox, B. and Iachan, R. (1987). A Comparison of
Household and Provider Reports of Medical Conditions. Journal of the American
Statistical Association 82(400):1013-18.
Edwards, W.S., Winn, D.M., Kurlantzick V., et al.
(1994). Evaluation of National Health Interview Survey Diagnostic Reporting.
National Center for Health Statistics, Vital Health 2(120).
Elixhauser A., Steiner C.A., Whittington C.A., and
McCarthy E. Clinical Classifications for Health Policy Research: Hospital
Inpatient Statistics, 1995. Healthcare Cost and Utilization Project, HCUP-3
Research Note. Rockville, MD: Agency for Health Care Policy and Research; 1998.
AHCPR Pub. No. 98-0049.
Ezzati-Rice, T.M., Rohde, F., Greenblatt, J., Sample
Design of the Medical Expenditure Panel Survey Household Component, 1998–2007.
Methodology Report No. 22. March 2008. Agency for Healthcare Research and
Quality, Rockville, MD.
Health Care Financing Administration (1980).
International Classification of Diseases, 9th Revision, Clinical
Modification (ICD-CM). Vol. 1. (DHHS Pub. No. (PHS) 80-1260). DHHS: U.S. Public
Health Services.
Johnson, A.E. and Sanchez, M.E. (1993). Household and
Medical Provider Reports on Medical Conditions: National Medical Expenditure
Survey, 1987. Journal of Economic and Social Measurement. Vol. 19,
199-233.
Monheit, A.C., Wilson, R., and Arnett, III, R.H.
(Editors). Informing American Health Care Policy. (1999). Jossey-Bass
Inc, San Francisco.
Shah, B.V., Barnwell, B.G., Bieler, G.S., Boyle, K.E.,
Folsom, R.E., Lavange, L., Wheeless, S.C., and Williams, R. (1996). Technical
Manual: Statistical Methods and Algorithms Used in SUDAAN Release 7.0,
Research Triangle Park, NC: Research Triangle Institute.
Return To Table Of Contents
VARIABLE-SOURCE CROSSWALK
FOR MEPS HC-168F: 2014 OUTPATIENT DEPARTMENT 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 |
| MPCDATA |
MPC data flag |
Constructed |
Return To Table Of Contents
Outpatient Department Visit Variables
| Variable |
Description |
Source |
| OPDATEYR |
Event date – year |
CAPI derived |
| OPDATEMM |
Event date – month |
CAPI derived |
| SEETLKPV |
Did person visit provider in person or telephone |
OP02 |
| SEEDOC |
Did person talk to MD this visit/phone call |
OP04 |
| DRSPLTY |
OPAT doctor specialty |
OP04A |
| MEDPTYPE |
Type of medical person talked to on visit date |
OP05 |
| VSTCTGRY |
Best category for care person received on visit date |
OP07 |
| VSTRELCN |
This visit/phone call related to spec condition |
OP08 |
| LABTEST |
This visit did person have lab tests |
OP11 |
| SONOGRAM |
This visit did person have sonogram or ultrasound |
OP11 |
| XRAYS |
This visit did person have x-rays |
OP11 |
| MAMMOG |
This visit did person have a mammogram |
OP11 |
| MRI |
This visit did person have an MRI/Catscan |
OP11 |
| EKG |
This visit did person have an EKG or ECG |
OP11 |
| EEG |
This visit did person have an EEG |
OP11 |
| RCVVAC |
This visit did person receive a vaccination |
OP11 |
| ANESTH |
This visit did person receive anesthesia |
OP11 |
| THRTSWAB |
This visit did P have a throat swab |
OP11 |
| OTHSVCE |
This visit did person have other diagnostic tests or exams |
OP11 |
| SURGPROC |
Was surgical procedure performed on person this visit |
OP12 |
| MEDPRESC |
Any medicine prescribed for person during visit |
OP14 |
| OPCCC1X |
Modified Clinical Classification Code |
Constructed/ Edited |
| OPCCC2X |
Modified Clinical Classification Code |
Constructed/ Edited |
| OPCCC3X |
Modified Clinical Classification Code |
Constructed/ Edited |
| OPCCC4X |
Modified Clinical Classification Code |
Constructed/ Edited |
Return To Table Of Contents
Flat Fee Variables
| Variable |
Description |
Source |
| FFOPTYPE |
Flat fee bundle |
Constructed |
| FFBEF14 |
Total # of visits in FF before 2014 |
FF05 |
| FFTOT15 |
Total # of visits in FF after 2014 |
FF10 |
Return To Table Of Contents
Imputed Total Expenditure Variables
| Variable |
Description |
Source |
| OPXP14X |
Total expenditure for event (OPFXP14X+OPDXP14X) |
Constructed |
| OPTC14X |
Total charge for event (OPFTC14X+OPDTC14X) |
Constructed |
| OPFSF14X |
Facility amount paid, self/family (Imputed) |
CP Section
(Edited) |
| OPFMR14X |
Facility amount paid, Medicare (Imputed) |
CP Section
(Edited) |
| OPFMD14X |
Facility amount paid, Medicaid (Imputed) |
CP Section
(Edited) |
| OPFPV14X |
Facility amount paid, private insurance (Imputed) |
CP Section
(Edited) |
| OPFVA14X |
Facility amount paid, Veterans/CHAMPVA (Imputed) |
CP Section
(Edited) |
| OPFTR14X |
Facility amount paid, TRICARE (Imputed) |
CP Section
(Edited) |
| OPFOF14X |
Facility amount paid, other federal (Imputed) |
CP Section
(Edited) |
| OPFSL14X |
Facility amount paid, state & local government (Imputed) |
CP Section
(Edited) |
| OPFWC14X |
Facility amount paid, workers’ compensation (Imputed) |
CP Section
(Edited) |
| OPFOR14X |
Facility amount paid, other private insurance (Imputed) |
Constructed |
| OPFOU14X |
Facility amount paid, other public insurance (Imputed) |
Constructed |
| OPFOT14X |
Facility amount paid, other insurance (Imputed) |
CP Section
(Edited) |
| OPFXP14X |
Facility sum payments OPFSF14X –OPFOT14X |
Constructed |
| OPFTC14X |
Total facility charge (Imputed) |
CP Section
(Edited) |
| OPDSF14X |
Doctor amount paid, self/family (Imputed) |
Constructed |
| OPDMR14X |
Doctor amount paid, Medicare (Imputed) |
Constructed |
| OPDMD14X |
Doctor amount paid, Medicaid (Imputed) |
Constructed |
| OPDPV14X |
Doctor amount paid, private insurance (Imputed) |
Constructed |
| OPDVA14X |
Doctor amount paid, Veterans/CHAMPVA (Imputed) |
Constructed |
| OPDTR14X |
Doctor amount paid, TRICARE (Imputed) |
Constructed |
| OPDOF14X |
Doctor amount paid, other federal (Imputed) |
Constructed |
| OPDSL14X |
Doctor amount paid, state & local government (Imputed) |
Constructed |
| OPDWC14X |
Doctor amount paid, workers’ compensation (Imputed) |
Constructed |
| OPDOR14X |
Doctor amount paid, other private insurance (Imputed) |
Constructed |
| OPDOU14X |
Doctor amount paid, other public insurance (Imputed) |
Constructed |
| OPDOT14X |
Doctor amount paid, other insurance (Imputed) |
Constructed |
| OPDXP14X |
Doctor sum payments OPDSF14X –OPDOT14X |
Constructed |
| OPDTC14X |
Total doctor charge (Imputed) |
Constructed |
| IMPFLAG |
Imputation status |
Constructed |
Return To Table Of Contents
Weights
| Variable |
Description |
Source |
| PERWT14F |
Expenditure file person weight, 2014 |
Constructed |
| VARSTR |
Variance estimation stratum, 2014 |
Constructed |
| VARPSU |
Variance estimation PSU, 2014 |
Constructed |
Return To Table Of Contents
|
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