MEPS HC-077C: 2003 Other Medical Expenses
Agency for Healthcare Research and Quality
Center for Financing, Access, and Cost Trends
540 Gaither Road
Rockville, MD 20850
(301) 427-1406
TABLE OF CONTENTS
A. Data Use Agreement
B. Background
1.0 Household Component
2.0 Medical Provider Component
3.0 Insurance Component
4.0 Survey Management
C. Technical and Programming Information
1.0 General Information
2.0 Data File Information
2.1 Using MEPS Data for Trend and Longitudinal
Analysis
2.2 Codebook Structure
2.3 Reserved Codes
2.4 Codebook Format
2.5 Variable Source and Naming Conventions
2.5.1 Variable-Source Crosswalk
2.5.2 Expenditure and Source of Payment
Variables
2.6 File Contents
2.6.1 Survey Administration Variables
2.6.1.1 Person Identifiers (DUID, PID, DUPERSID)
2.6.1.2 Record Identifiers (EVNTIDX, FFEEIDX)
2.6.1.3 Round Indicator (EVENTRN)
2.6.2 Other Medical Type Variables (OMTYPEX,
OMTYPE, OMOTHOX, OMOTHOS)
2.6.3 Flat Fee Variables (FFEEIDX, FFOMTYPE,
FFBEF03, FFTOT04)
2.6.3.1 Definition of Flat Fee Payments
2.6.3.2 Flat Fee Variable Descriptions
2.6.3.2.1 Flat Fee ID (FFEEIDX)
2.6.3.2.2 Flat Fee Type (FFOMTYPE)
2.6.3.2.3 Counts of Flat Fee Events
that Cross Years (FFBEF03, FFTOT04)
2.6.3.3 Caveats of Flat Fee Groups
2.6.4 Expenditure Data
2.6.4.1 Definition of Expenditures
2.6.4.2 Data Editing and Imputation Methodologies
of Expenditure Variables
2.6.4.2.1 General Data Editing
Methodology
2.6.4.2.2 General Hot-Deck Imputation
2.6.4.2.3 Other Medical Expenses Data
Editing and Imputation
2.6.4.3 Imputation Flag Variable (IMPFLAG)
2.6.4.4 Flat Fee Expenditures
2.6.4.5 Zero Expenditures
2.6.4.6 Sources of Payment
2.6.4.7 Other Medical Expenditure Variables
(OMSF03X-OMTC03X)
2.6.4.8 Rounding
3.0 Sample Weight (PERWT03F)
3.1 Overview
3.2 Details on Person Weight Construction
3.2.1 MEPS Panel 7 Weight
3.2.2 MEPS Panel 8 Weight
3.2.3 The Final Weight for 2003
3.2.4 Coverage
4.0 Strategies for Estimation
4.1 Variables with Missing Values
4.2 Basic Estimates of Utilization, Expenditures,
and Sources of Payment
4.3 Estimates of the Number of Persons with Other
Medical Expense Events
4.4 Person-Based Ratio Estimates
4.4.1 Person-Based Ratio Estimates Relative to
Persons with Other Medical Expense Events
4.4.2 Person-Based Ratio Estimates Relative to
the Entire Population
4.5 Sampling Weights for Merging Previous Releases
of MEPS Household Data with this Event File
4.6 Variance Estimation (VARPSU, VARSTR)
5.0 Merging/Linking MEPS Data Files
5.1 Merging a Person-Level File to the Other
Medical Expenses File
5.2 Linking the 2003 Other Medical Expenses File
to the 2003 Prescribed Medicine File
5.2.1 Limitations/Caveats of RXLK (the
Prescribed Medicine Link File)
References
D. Variable-Source Crosswalk
A. Data Use Agreement
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:
1. |
No one is to use the data in this data set in
any way except for statistical reporting and
analysis; and |
2. |
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 |
3. |
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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B. Background
The Medical Expenditure Panel Survey (MEPS) provides
nationally representative estimates of health care use, expenditures, sources of
payment, and insurance coverage for the U.S. civilian noninstitutionalized
population. MEPS is cosponsored by the Agency for Healthcare Research and
Quality (AHRQ) and the National Center for Health Statistics (NCHS).
MEPS is a family of three surveys. The Household Component
(HC) is the core survey and forms the basis for the Medical Provider Component
(MPC) and part of the Insurance Component (IC). Together these surveys yield
comprehensive data that provide national estimates of the level and distribution
of health care use and expenditures, support health services research, and can
be used to assess health care policy implications.
MEPS is the third in a series of national probability
surveys conducted by AHRQ on the financing and use of medical care in the United
States. The National Medical Care Expenditure Survey (NMCES, also known as
NMES-1) was conducted in 1977 and the National Medical Expenditure Survey
(NMES-2) in 1987. Since 1996, MEPS continues this series with design
enhancements and efficiencies that provide a more current data resource to
capture the changing dynamics of the health care delivery and insurance systems.
The design efficiencies incorporated into MEPS are in
accordance with the Department of Health and Human Services (DHHS) Survey
Integration Plan of June 1995, which focused on consolidating DHHS surveys,
achieving cost efficiencies, reducing respondent burden, and enhancing
analytical capacities. To advance these goals, MEPS includes linkage with the
National Health Interview Survey (NHIS) - a survey conducted by NCHS from which
the sample for the MEPS HC is drawn - and enhanced longitudinal data collection
for core survey components. The MEPS HC augments NHIS by selecting a sample of
NHIS respondents, collecting additional data on their health care expenditures,
and linking these data with additional information collected from the
respondents’ medical providers, employers, and insurance providers.
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1.0 Household Component
The MEPS HC, a nationally representative survey of the
U.S. civilian noninstitutionalized population, collects medical expenditure data
at both the person and household levels. The HC collects detailed data on
demographic characteristics, health conditions, health status, use of medical
care services, charges and payments, access to care, satisfaction with care,
health insurance coverage, income, and employment.
The HC uses an overlapping panel design in which data are
collected through a preliminary contact followed by a series of five rounds of
interviews over a 2 ½-year period. Using computer-assisted personal interviewing
(CAPI) technology, data on medical expenditures and use for two calendar years
are collected from each household. This series of data collection rounds is
launched each subsequent year on a new sample of households to provide
overlapping panels of survey data and, when combined with other ongoing panels,
will provide continuous and current estimates of health care expenditures.
The sampling frame for the MEPS HC is drawn from
respondents to NHIS. NHIS provides a nationally representative sample of the
U.S. civilian noninstitutionalized population, with oversampling of Hispanics
and blacks.
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2.0 Medical Provider Component
The MEPS MPC supplements and/or replaces information on
medical care events reported in the MEPS HC by contacting medical providers and
pharmacies identified by household respondents. The MPC sample includes all home
health agencies and pharmacies reported by HC respondents. Office-based
physicians, hospitals, and hospital physicians are also included in the MPC but
may be subsampled at various rates, depending on burden and resources, in
certain years.
Data are collected on medical and financial
characteristics of medical and pharmacy events reported by HC respondents. The
MPC is conducted through telephone interviews and record abstraction.
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3.0 Insurance Component
The MEPS IC collects data on health insurance plans
obtained through private and public-sector employers. Data obtained in the IC
include the number and types of private insurance plans offered, benefits
associated with these plans, premiums, contributions by employers and employees,
eligibility requirements, and employer characteristics.
Establishments participating in the MEPS IC are selected
through three sampling frames:
A list of employers or other insurance providers
identified by MEPS HC respondents who report having private health insurance
at the Round 1 interview. |
A Bureau of the Census list frame of private-sector
business establishments. |
The Census of Governments from Bureau of the Census. |
To provide an integrated picture of health insurance, data
collected from the first sampling frame (employers and insurance providers
identified by MEPS HC respondents) are linked back to data provided by those
respondents. Data from the two Census Bureau sampling frames are used to produce
annual national and state estimates of the supply and cost of private health
insurance available to American workers and to evaluate policy issues pertaining
to health insurance. National estimates of employer contributions to group
insurance from the MEPS IC are used in the computation of Gross Domestic Product
(GDP) by the Bureau of Economic Analysis.
The MEPS IC is an annual survey. Data are collected from
the selected organizations through a prescreening telephone interview, a mailed
questionnaire, and a telephone follow-up for nonrespondents.
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4.0 Survey Management
MEPS data are collected under the authority of the Public
Health Service Act. They are edited and published in accordance with the
confidentiality provisions of this act and the Privacy Act. 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, microdata files and compendiums of tables. Data are released through
MEPSnet, an online interactive tool developed to give users the ability to
statistically analyze MEPS data in real time. Summary reports and compendiums of
tables are released as printed documents and electronic files. Microdata files
are released as electronic files.
Selected printed documents are available through the AHRQ
Publications Clearinghouse. Write or call:
AHRQ Publications Clearinghouse
Attn: (publication number)
P.O. Box 8547
Silver Spring, MD 20907
800-358-9295
410-381-3150 (callers outside the United States
only)
888-586-6340 (toll-free TDD service; hearing
impaired only)
Be sure to specify the AHRQ number of the document you are
requesting.
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, 540 Gaither
Road, Rockville, MD 20850 (301-427-1406).
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C. Technical and Programming Information
1.0 General Information
This documentation describes one in a series of public use
event files from the 2003 Medical Expenditure Panel Survey (MEPS) Household
Component (HC). Released as an ASCII data file (with related SAS and SPSS
programming statements) and a SAS transport
file, the 2003 Other Medical public use event file provides information on the
purchases of and expenditures for visual aids, medical equipment, supplies, and
other medical items for a nationally representative sample of the civilian
noninstitutionalized population of the United States. Data from the Other
Medical event file can be used to make estimates of the Other Medical event
utilization and expenditures associated with medical items for calendar year
2003. As illustrated below, this file consists of MEPS survey data obtained in
the 2003 portion of Round 3, and Rounds 4 and 5 for Panel 7, as well as Rounds
1, 2, and the 2003 portion of Round 3 for Panel 8 (i.e., the rounds for the MEPS
panels covering calendar year 2003).
301 Moved Permanently
301 Moved Permanently
a. |
A record can represent one or more purchases of an
item or service during a reference period. For example, if a respondent
reported spending $400 for glasses and/or contact lenses in Round 2, it is
unknown if the person purchased one or more pair of glasses and/or contact
lenses during that round. Similarly, if $800 were spent for ambulance
services, it is not known if the respondent used an ambulance once or more
than once in 2003. |
b. |
Expenditure data for insulin and diabetic supplies
are not included on this file, but are included on the 2003 Prescribed
Medicines File. All records for insulin and diabetic supplies on this file
have a value of –1, "INAPPLICABLE," for all expenditure (i.e., charge and
payment) variables. |
The following 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 Weights
Strategies for Estimation
Merging/Linking MEPS Data Files
References
Variable - Source Crosswalk
For more information on MEPS HC survey design, see S.
Cohen, 1997; J. Cohen, 1997; and S. Cohen, 1996. A copy of the MEPS HC survey
instrument used to collect the information on the dental file is available on
the MEPS web site at the following address: <http://www.meps.ahrq.gov>.
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2.0 Data File Information
The 2003 Other Medical Expenses public use data set
consists of one event-level data file. The file contains characteristics
associated with the Other Medical event and imputed expenditure data. For data
users/analysts wanting to impute expenditures, pre-imputed data are available
through the Center for Financing, Access and Cost Trends (CFACT) Data Center.
Please visit the CFACT Data Center web site for details: <http://www.meps.ahrq.gov/mepsweb/data_stats/onsite_datacenter.jsp>.
The data user/analyst is forewarned that the imputation of expenditures will
necessitate a sizable commitment of resources: financial, staff, and time.
The 2003 Other Medical public use data set contains 9,671
other medical expenditure records; of these records, 9,416 are associated with
persons having a positive person-level weight (PERWT03F). This file includes
records for all household survey respondents who resided in eligible responding
households and reported purchasing or otherwise obtaining at least one type of
medical item, such as medical equipment, glasses, hearing devices, etc., during
calendar year 2003. Some household respondents may have reported obtaining more
than one type of medical item and, therefore, have several records on this file.
Likewise, respondents who did not report obtaining a medical item in 2003 have
no records on this file. These data were collected during the 2003 portion of
Round 3, and Rounds 4 and 5 for Panel 7, as well as Rounds 1, 2, and the 2003
portion of Round 3 for Panel 8 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 2003 eligibility (i.e.,
persons with a positive 2003 full-year person-level weight (PERWT03F >
0)), or
|
- Be an eligible member of a family all of whose
key in-scope members have a positive person-level weight (PERWT03F >
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 (FAMWT03F > 0). Note that FAMIDYR and FAMWT03F are
variables on the 2003 Population Characteristics file.
|
Persons with no other medical events for 2003 are not
included on this event-level OM file but are represented on the person-level 2003 Full Year Population Characteristics file.
Each record includes the following: type of medical item
obtained; flat fee information; imputed sources of payment; total payment and
total charge for the medical item; and a full-year person-level weight.
Data from this file can be merged with the MEPS 2003 Full
Year Population Characteristics File using the unique person identifier,
DUPERSID, to append person-level information, such as demographic or health
insurance characteristics, to each record. Please see section 5.0 for details on
how to merge MEPS data files.
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2.1 Using MEPS Data for Trend and Longitudinal
Analysis
MEPS began in 1996 and several annual data files have been
released. As more years of data are produced, MEPS will become increasingly
valuable for examining health care trends. 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 are attributable to sampling variation. MEPS expenditure estimates are
especially sensitive to sampling variation due to the underlying skewed
distribution of expenditures. For example, 1 percent of the population accounts
for about one-quarter of all expenditures. The extent to which observations with
extremely high expenditures are captured in the MEPS sample varies from year to
year (especially for smaller population subgroups), which can produce
substantial shifts in estimates of means or totals that are simply an artifact
of the sample(s). 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 or MEPS survey methodology. 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 smooth or stabilize trend
analyses of MEPS data such as pooling time periods for comparison (e.g. 1996-97
versus 1998-99), 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 because performing numerous statistical significance
tests of trends increases the likelihood of inappropriately concluding a change
is statistically significant.
The records on this file can be
linked to all other 2003 MEPS-HC public use data sets by the sample person
identifier (DUPERSID).
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2.2 Codebook Structure
For each variable on the Other Medical event file, both
weighted and unweighted frequencies are provided in the codebook (files
H77CCB.PDF and H77CCB.ASP). The codebook and data file sequence list variables
in the following order:
Unique person identifier |
Unique other medical expenses identifier |
Type of other medical expenses |
Imputed expenditure variables |
Weight and variance estimation variables |
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2.3 Reserved Codes
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: <http://www.meps.ahrq.gov/mepsweb/survey_comp/survey.jsp>).
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2.4 Codebook Formating
The 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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2.5 Variable Source and
Naming Conventions
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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2.5.1 Variable-Source Crosswalk
Variables were derived from the HC survey questionnaire or
from the CAPI. The source of each variable is identified in Section D "Variable
- Source Crosswalk" in one of four ways:
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; questionnaire sections are identified as: |
- EV – Event Roster 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 that have been edited or imputed are so
indicated. |
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2.5.2 Expenditure and Source
of Payment Variables
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 12 source of payment are
named in the following way:
The first two characters indicate the type of event:
IP - inpatient stay |
OB - office-based visit |
ER - emergency room visit |
OP - outpatient visit |
HH - home health visit |
DV - dental visit |
OM - other medical equipment |
RX - prescribed medicine |
In the case of the source of payment variables, the third
and fourth characters indicate:
SF - self or family |
OF - other Federal Government |
MR - Medicare |
SL - State/local government |
MD - Medicaid |
WC - Workers’ Compensation |
PV - private insurance |
OT - other insurance |
VA - Veterans Administration |
OR - other private |
R - TRICARE |
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 (03). The
seventh character, "X", indicates whether the variable is edited/imputed.
For example, OMSF03X is the edited/imputed amount paid by
self or family for 2003 other medical equipment and expenditures.
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2.6 File Contents
2.6.1 Survey Administration
Variables
2.6.1.1 Person Identifiers (DUID,
PID, DUPERSID)
The dwelling unit ID (DUID) is a five-digit random number
assigned after the case was sampled for MEPS. The three-digit person number
(PID) uniquely identifies each person within the dwelling unit. The
eight-character variable DUPERSID uniquely identifies each person represented on
the file and is the combination of the variables DUID and PID. For detailed
information on dwelling units and families, please refer to the documentation
for the 2003 Full Year Population Characteristics File.
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2.6.1.2 Record Identifiers (EVNTIDX,
FFEEIDX)
EVNTIDX uniquely identifies each other medical expense
event (i.e., each record on the OME file) and is the variable required to link
other medical events to data files containing details on prescribed medicines
(the MEPS 2003 Prescribed Medicines File). For details on
linking, see Section 5.0, or the MEPS 2003 Appendix File, HC-077I.
FFEEIDX is a constructed variable that uniquely identifies
a flat fee group, that is, all events that were part of a flat fee payment.
FFEEIDX identifies a flat fee payment that was identified using information from
the Household Component.
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2.6.1.3 Round Indicator (EVENTRN)
EVENTRN indicates the round in which the other medical
event was reported. For most types of other medical expenditures on this file,
data were collected only in Round 5 for Panel 7 and Round 3 for Panel 8; each
record represents a summary of expenditures for items purchased or otherwise
obtained for 2003. There are two exceptions:
1. |
Expenditure data for the purchase of glasses
and/or contact lenses were collected in Rounds 3, 4, and 5 for Panel 7
and Rounds 1, 2, and 3 for Panel 8. For vision items purchased in Round
3 for Panel 8, it could not be determined if the purchases occurred in
2003 or 2004. Therefore, records with expenses reported in Round 3 were
only included if the number of glasses purchased in 2003 was greater
than or equal to the number of purchases in 2004. |
2. |
Respondents were asked whether or not they
obtained insulin or diabetic supplies/equipment in Rounds 3, 4, and 5
for Panel 7 and Rounds 1, 2, and 3 for Panel 8. The reported purchases
of these medical items are included on this file while the actual
expenditures for insulin and diabetic supplies/equipment are not
included. Rather, these expenditures are included on the 2003 Prescribed
Medicines file. All records for insulin and diabetic supplies on this
file have a value of –1, "INAPPLICABLE", for each expenditure (i.e.,
charge and payment) variable. |
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2.6.2 Other Medical Type
Variables (OMTYPEX, OMTYPE, OMOTHOX, OMOTHOS)
Other medical expenditures (OMTYPE) include glasses
or contact lenses, insulin, diabetic equipment/supplies, ambulance services,
orthopedic items, hearing devices, prostheses, bathroom aids, medical equipment,
disposable supplies, and alterations/modifications (to homes). When the
interviewer did not know how to categorize types of medical item expenditures,
these items were specified in the variable OMOTHOS (OMTYPE other specify). As a
part of the editing process, other medical expenditures identified in OMOTHOS
have been edited to appropriate OMTYPE categories. The edited (OMTYPEX, OMOTHOX)
and unedited (OMTYPE, OMOTHOS) versions of both of these variables are included
on this file.
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2.6.3 Flat Fee Variables
(FFEEIDX, FFOMTYPE, FFBEF03, FFTOT04)
2.6.3.1 Definition of Flat Fee Payments
A flat fee is the fixed dollar amount a person is charged
for a package of services provided during a defined period of time. A flat fee
group is the set of medical services that are covered under the same flat fee
payment. The flat fee groups represented on the Other Medical file include flat
fee groups where at least one of the other medical events, as reported by the HC
respondent, occurred during 2003. By definition, a flat fee group can span
multiple years. Furthermore, a single person can have multiple flat fee groups.
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2.6.3.2 Flat Fee Variable Descriptions
2.6.3.2.1 Flat Fee ID (FFEEIDX)
As noted earlier in Section 2.6.1.2 "Record Identifiers,"
the variable FFEEIDX uniquely identifies all events that are part of the same
flat fee group for a person. On any 2003 MEPS event file, every event that is
part of a specific flat fee group will have the same value for FFEEIDX. Note
that prescribed medicine and home health events are never included in a flat fee
group and none of the flat fee variables are on those event files.
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2.6.3.2.2 Flat Fee Type (FFOMTYPE)
FFOMTYPE indicates whether the 2003 other medical
expenditure is the "stem" or "leaf" of a flat fee group. A stem (records with
FFOMTYPE = 1) is the initial other medical service event, which is followed by
other medical expense events that are covered under the same flat fee payment.
The leaves of the flat fee group (records with FFOMTYPE = 2) are those other
medical events that are tied back to the initial event (the stem) in the flat
fee group. These "leaf" records have their expenditure variables set to zero.
For the other medical events that are not part of a flat fee payment, the
FFOMTYPE is set to -1, "INAPPLICABLE".
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2.6.3.2.3 Counts of Flat Fee Events that Cross Years (FFBEF03, FFTOT04)
As described in Section 2.6.3.1, a flat fee payment covers
multiple events and the multiple events could span multiple years. For
situations where the medical item was obtained in 2003 as part of a group of
events, and some of the events occurred before or after 2003, counts of the
known events are provided on the other medical record. Variables that indicate
events occurring before or after 2003 are the following:
FFBEF03 – indicates total number of 2002 events in the
same flat fee group as the medical item that was obtained in 2003. This
count would not include the medical item obtained in 2003.
FFTOT04 – indicates the number of 2004 medical events,
including the purchase of any additional medical items, expected to be in
the same flat fee group as the medical item obtained in 2003.
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2.6.3.3 Caveats of Flat Fee Groups
Data users/analysts should note that flat fee payments are
not common on the Other Medical file. There are only 15 records that are identified as being part of a flat fee payment
group. In general, every flat fee group should have an initial visit (stem) and
at least one subsequent visit (leaf). There are some situations where this is
not true. For some of these flat fee groups, the initial visit reported occurred
in 2003, but the remaining visits that were part of this flat fee group occurred
in 2004. In this case, the 2003 flat fee group represented on this file would
consist of one event (the stem). The 2004 "leaf events" that are part of this
flat fee group are not represented on the file. Similarly, the household
respondent may have reported a flat fee group where the initial visit began in
2002 but subsequent visits occurred during 2003. In this case, the initial visit
would not be represented on the file. This 2003 flat fee group would then only
consist of one or more leaf records and no stem.
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2.6.4 Expenditure Data
2.6.4.1 Definition of
Expenditures
Expenditures on this file refer to what is paid for the
medical item. More specifically, expenditures in MEPS are defined as the sum of
payments for each medical item that was obtained, including out-of-pocket
payments and payments made by private insurance, Medicaid, Medicare and other
sources. The definition of expenditures used in MEPS differs slightly from its
predecessors: the 1987 NMES and 1977 NMCES surveys where "charges" rather than
sum of payments were used to measure expenditures. This change was adopted
because charges became a less appropriate proxy for medical expenditures during
the 1990s due to the increasingly common practice of discounting. Although
measuring expenditures as the sum of payments incorporates discounts in the MEPS
expenditure estimates, these estimates do not incorporate any payment not
directly tied to specific medical care events, such as bonuses or retrospective
payment adjustments paid by third party payers. Another general change from the
two prior surveys is that charges associated with uncollected liability, bad
debt, and charitable care (unless provided by a public clinic or
hospital) are not counted as expenditures because there are no payments
associated with those classifications. While charge data are provided on this
file, data users/analysts should use caution when working with this data because
a charge does not typically represent actual dollars exchanged for services or
the resource costs of those services, nor are they directly comparable to the
expenditures defined in the 1987 NMES. For details on expenditure definitions,
please refer to the following, "Informing American Health Care Policy" (Monheit
et al., 2000). AHRQ has developed factors to apply to the 1987 NMES expenditure
data to facilitate longitudinal analysis. These factors can be assessed via the
CFACT data center. For more information see the Data Center section of the MEPS
web site at <http://www.meps.ahrq.gov/mepsweb/data_stats/onsite_datacenter.jsp>. If examining trends in
MEPS expenditures or performing longitudinal analysis on MEPS expenditures,
please refer to section C, sub-section 2.1 for more information.
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2.6.4.2 Data Editing and Imputation Methodologies of
Expenditure Variables
The general methodology used for editing and imputing
expenditure data is described below. The MPC did not include either the dental
events or other medical expenditures (such as glasses, contact lenses, and
hearing devices). Therefore, although the general procedures remain the same for
dental and other medical expenditures, editing and imputation methodologies were
applied only to household-reported data. Please see below for details on the
differences between these editing/imputation methodologies. Separate imputations
were performed for flat fee and simple events, as well.
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2.6.4.2.1 General Data
Editing Methodology
Logical edits were used to resolve internal
inconsistencies and other problems in the HC survey-reported data. The edits
were designed to preserve partial payment data from households and providers,
and to identify actual and potential sources of payment for each
household-reported event. In general, these edits accounted for outliers,
copayments or charges reported as total payments, and reimbursed amounts that
were reported as out-of-pocket payments. In addition, edits were implemented to
correct for misclassifications between Medicare and Medicaid and between
Medicare HMOs and private HMOs as payment sources. These edits produced a
complete vector of expenditures for some events, and provided the starting point
for imputing missing expenditures in the remaining events.
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2.6.4.2.2 General Hot-Deck
Imputation
A weighted sequential hot-deck procedure was used to
impute for missing expenditures, as well as total charge. This procedure uses
survey data from respondents to replace missing data, while taking into account
the respondents’ weighted distribution in the imputation process. Classification
variables vary by event type in the hot-deck imputations, but total charge and
insurance coverage are key variables in all of the imputations. Separate
imputations were performed for nine categories of medical provider care:
inpatient hospital stays, outpatient hospital department visits, emergency room
visits, visits to physicians, visits to non-physician providers, dental
services, home health care by certified providers, home health care by paid
independents, and other medical expenses. Within each file, separate imputations
were performed for flat fee and simple events. After the imputations were
finished, visits to physician and non-physician providers were combined into a
single medical provider file. The two categories of home care also were combined
into a single home health file.
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2.6.4.2.3 Other Medical
Expenses Data Editing and Imputation
Expenditures on other medical equipment and services were
developed in a sequence of logical edits and imputations. The household edits
were used to correct obvious errors in the reporting of expenditures, and to
identify actual and potential sources of payments. Some of the edits were global
(i.e., applied to all events). Others were hierarchical and mutually exclusive.
One of the more important edits separated flat fee events from simple events.
This edit was necessary because groups of events covered by a flat fee (i.e., a
flat fee bundle) were edited and imputed separately from individual events each
covered by a single charge (i.e., simple events). Other medical services were
imputed as flat fee events if the charges covered a package of health care
services (e.g., optical), and all of the services were part of the same event
type (i.e., a pure bundle). If a bundle contained any OM events with any other
types of events, the services were treated as simple events in the imputations
(See Section 2.6.3 for more detail on the definition and imputation of events in
flat fee bundles.)
Logical edits were used to sort each event into a specific
category for the imputations. Events with complete expenditures were flagged as
potential donors for the hot-deck imputations, while events with missing
expenditure data were assigned to various recipient categories. Each event with
missing expenditure data was assigned to a recipient category based on the
extent of its missing charge and expenditure data. For example, an event with a
known total charge but no expenditure information was assigned to one category,
while an event with a known total charge and partial expenditure information was
assigned to a different category. Similarly, events without a known total charge
and no or partial expenditure information were assigned to various recipient
categories.
The logical edits produced nine recipient categories for
events with missing data. Eight of the categories were for events with a common
pattern of missing data and a primary payer other than Medicaid. Medicaid events
were imputed separately because persons on Medicaid rarely know the provider’s
charge for services or the amount paid by the state Medicaid program. As a
result, the total charge for Medicaid-covered services was imputed and
discounted to reflect the amount that a state program might pay for the care.
Separate hot-deck imputations were used to impute missing
data in each of the other eight recipient categories. The donor pool included
"free events" because in some instances, providers are not paid for their
services. These events represent charity care, bad debt, provider failure to
bill, and third party payer restrictions on reimbursement in certain
circumstances. If free events were excluded from the donor pool, total
expenditures would be over-counted because the distribution of free events among
complete events (donors) is not represented among incomplete events
(recipients).
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2.6.4.3 Imputation Flag
Variable (IMPFLAG)
IMPFLAG is a six-category variable that indicates if the
event contains complete Household Component (HC) or Medical Provider Component
(MPC) data, was fully or partially imputed, or was imputed in the capitated
imputation process (for OP and MV events only). The following list identifies
how the imputation flag is coded; the categories are mutually exclusive.
IMPFLAG=0 not eligible for imputation (includes zeroed out and flat fee leaf events)
IMPFLAG=1 complete HC data
IMPFLAG=2 complete MPC data (not applicable to OM events)
IMPFLAG=3 fully imputed
IMPFLAG=4 partially imputed
IMPFLAG=5 complete MPC data through capitation imputation (not applicable to OM events)
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2.6.4.4 Flat Fee Expenditures
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 2003, all of the events that occurred in 2003 will have zero
payments. Conversely, if the first event in the flat fee group occurred at the
end of 2003, the total expenditure for the entire flat fee group will be on that
event, regardless of the number of events it covered after 2003. See section
2.6.3 for details on the flat fee variables.
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2.6.4.5 Zero Expenditures
Some respondents reported obtaining medical items where
the payments were zero. This could occur for several reasons including (1) item
or service was free, (2) bad debt was incurred, or (3) the item was covered
under a flat fee arrangement beginning in an earlier year. If all of the medical
events for a person fell into one of these categories, then the total annual
expenditures for that person would be zero.
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2.6.4.6 Sources of Payment
In addition to total expenditures, variables are provided
which itemize expenditures according to major source of payment categories.
These categories are:
1. |
Out-of-pocket by user (self) or family, |
2. |
Medicare, |
3. |
Medicaid, |
4. |
Private Insurance, |
5. |
Veterans Administration, excluding TRICARE, |
6. |
TRICARE, |
7. |
Other Federal sources - includes Indian Health
Service, Military Treatment Facilities, and other care by the Federal
government, |
8. |
Other State and Local Source - includes community and
neighborhood clinics, State and local health departments, and State programs
other than Medicaid, |
9. |
Workers’ Compensation, and |
10. |
Other Unclassified Sources - includes sources such as
automobile, homeowner’s, and liability insurances, 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:
11. |
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 |
12. |
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 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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2.6.4.7 Other Medical
Expenditure Variables (OMSF03X-OMTC03X)
Other medical expenditure data were obtained only through
the Household Component Survey. For cases with missing expenditure data, other
medical expenditures were imputed using the procedures described above. However,
please note that expenditure data for insulin and diabetic supplies are not
included on this file, but are included on the 2003 Prescribed Medicines File.
Missing expenditure data associated with these records were not imputed. Charge
and Payment variables in these cases carry a value of –1, "INAPPLICABLE".
OMSF03X - OMOT03X are the 12 sources of payment. OMTC03X
is the total charge, and OMXP03X is the sum of the 12 sources of payment for the
other medical expenditures. The 12 source of payment are: self/family (OMSF03X),
Medicare (OMMR03X), Medicaid (OMMD03X), private insurance (OMPV03X), Veterans
Administration (OMVA03X), TRICARE (OMTR03X), other Federal sources (OMOF03X),
State and Local (non-federal) government sources (OMSL03X), Worker’s
Compensation (OMWC03X), other private insurance (OMOR03X), other public
insurance (OMOU03X), and other insurance (OMOT03X).
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2.6.4.8 Rounding
Expenditure variables on the 2003 other medical file have
been rounded to the nearest penny. Person-level expenditure information released
on the MEPS 2003 Person-Level Expenditure File will be rounded to the nearest
dollar. It should be noted that using the MEPS event files to create
person-level totals will yield slightly different totals than those found on the
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. Please see the
MEPS 2003 Appendix File, HC-077I, for details on rounding differences.
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3.0 Sample Weight (PERWT03F)
3.1 Overview
There is a single full year person-level weight (PERWT03F)
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 2003. A key person
either was a member of an NHIS household at the time of the NHIS interview, or
became a member of a family associated with such a household after being
out-of-scope at the time of the NHIS (examples of the latter situation include
newborns and persons returning from military service, an institution, or living
outside the United States). 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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3.2 Details on Person Weight
Construction
The person-level weight PERWT03F was developed in several
stages. Person-level weights for Panels 7 and 8 were created separately. The
weighting process for each panel included an adjustment for nonresponse over
time and calibration to independent population figures. The calibration was
initially accomplished separately for each panel by raking the corresponding
sample weights 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, non-Hispanic with
black as sole reported race, non-Hispanic with Asian as sole reported race, and other); sex; and age. A 2003 composite weight was
then formed by multiplying each weight from Panel 7 by the factor .49 and each
weight from Panel 8 by the factor .51. 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 again
raked to the same set of CPS-based control totals. When 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 (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 original five variables used in
the previous calibrations.
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3.2.1 MEPS Panel 7 Weight
The person-level weight for MEPS Panel 7 was developed
using the 2002 full year weight for an individual as a "base" weight for survey
participants present in 2002. For key, in-scope respondents who joined an RU
some time in 2003 after being out-of-scope in 2002, the 2002 family weight
associated with the family the person joined served as a "base" weight. The
weighting process included an adjustment for nonresponse over Rounds 4 and 5 as
well as raking to population control figures for December 2003. These control
figures were derived by scaling back the population totals obtained from the
March 2004 CPS to correspond to a national estimate for the civilian
noninstitutionalized population provided by the Census Bureau for December 2003.
Variables used in the establishment of person-level control figures included:
census region (Northeast, Midwest, South, West); MSA status (MSA, non-MSA);
race/ethnicity (Hispanic, black but non-Hispanic, Asian but non-Hispanic, and other); sex; and age. Overall, the weighted
population estimate for the civilian noninstitutionalized population on December
31, 2003 is 286,779,677. Key, responding persons not in-scope on December 31,
2003 but in-scope earlier in the year retained, as their final Panel 7 weight,
the weight after the nonresponse adjustment.
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3.2.2 MEPS Panel 8 Weight
The person-level weight for MEPS Panel 8 was developed
using the MEPS Round 1 person-level weight as a "base" weight. For key, in-scope
respondents 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 Round 2 and the 2003 portion of Round 3 as well as raking to the same
population control figures for December 2003 used for the MEPS Panel 7 weights.
The same five variables employed for Panel 7 raking (census region, MSA status,
race/ethnicity, sex, and age) were used for Panel 8 raking. Similarly, for Panel
8, key, responding persons not in-scope on December 31, 2003 but in-scope
earlier in the year retained, as their final Panel 8 weight, the weight after
the nonresponse adjustment.
Note that the MEPS Round 1 weights (for both panels with
one exception as noted below) incorporated the following components: the
original household probability of selection for the NHIS; ratio-adjustment to
NHIS-based national population estimates at the household (occupied dwelling
unit) level; adjustment for nonresponse at the dwelling unit level for Round 1;
and poststratification to figures at the family and person level obtained from
the March 2003 CPS data base.
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3.2.3 The Final Weight for
2003
Variables used in the establishment of person-level
control figures included: poverty status (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); census region (Northeast, Midwest,
South, West); MSA status (MSA, non-MSA); race/ethnicity (Hispanic, non-Hispanic
with black as sole reported race, non-Hispanic with Asian as sole reported race, and other); sex; and age. Overall, the weighted
population estimate for the civilian noninstitutionalized population for
December 31, 2003 is 286,779,677 (PERWT03F>0 and INSC1231=1). The weights of
some persons out-of-scope on December 31, 2003 were also calibrated, this time
using poststratification. Specifically, the weights of persons out-of-scope on
December 31, 2003 who were in-scope some time during the year and also 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 2003 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 control totals were developed for the "65
and older" and "under 65" civilian noninstitutionalized populations. The sum of
the person-level weights across all persons assigned a positive person level
weight is 290,604,436.
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3.2.4 Coverage
The target population for MEPS in this file is the 2003
U.S. civilian noninstitutionalized population. However, the MEPS sampled
households are a subsample of the NHIS households interviewed in 2001 (Panel 7)
and 2002 (Panel 8). New households created after the NHIS interviews for the
respective Panels and consisting exclusively of persons who entered the target
population after 2001 (Panel 7) or after 2002 (Panel 8) 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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4.0 Strategies for Estimation
This file is constructed for efficient estimation of
utilization, expenditures, and sources of payment for other medical expenditures
and to allow for estimates of number of persons who obtained medical items in
2003.
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4.1 Variables with Missing
Values
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 computing language 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 2.6.4.
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4.2 Basic Estimates of
Utilization, Expenditures, and Sources of Payment
While the examples described below illustrate the use of
event-level data in constructing person-level total expenditures, these
estimates can also be derived from the person-level expenditure file unless the
characteristic of interest is event specific.
In order to produce national estimates related to other
medical expense utilization, expenditures, and sources of payment, the value in
each record contributing to the estimates must be multiplied by the weight
(PERWT03F) contained on that record.
Example 1
For example, the total number of other medical expense
events for "GLASSES OR CONTACT LENSES" (OMTYPEX=1), for the civilian
noninstitutionalized population of the U.S. in 2003 is estimated as the sum of
the weight (PERWT03F) across all other medical expense event records with
OMTYPEX=1. That is,
Sum of Wj = 54,226,815 for all records with OMTYPEXj = 1. (1)
Example 2
Subsetting to records based on characteristics of interest
expands the scope of potential estimates. For example, the estimate for the mean
out-of-pocket payment on "GLASSES OR CONTACT LENSES" (where the visit has a
total expense greater than 0) should be calculated as the weighted mean of
amount paid by self/family. That is,
(Sum of WjXj)/(Sum of Wj) = $142.42 (2)
where
Sum of Wj =
53,270,486 and Xj = OMSF03Xj
for all records with OMTYPEXj = 1 and OMXP03Xj > 0.
This gives $142.42 as the estimated mean amount of
out-of-pocket payment of expenditures associated with "GLASSES OR CONTACT
LENSES" events and 53,270,486 as an estimate of the total number of such other
medical expense events with expenditure. Both of these estimates are for the
civilian noninstitutionalized population of the U.S. in 2003.
Example 3
Another example would be to estimate the average
proportion of total expenditures (where event expense is greater than 0) paid by
private insurance per "GLASSES OR CONTACT LENSES" event. This should be
calculated as the weighted mean of the proportion of the total "GLASSES OR
CONTACT LENSES" expense paid by private insurance at the other medical expense
event-level. That is,
(Sum of WjYj)/(Sum of Wj) = 0.1654 (3)
where
Sum of Wj =
53,270,486 and Yj = OMPV03Xj/ OMXP03Xj
for all records with OMTYPEXj = 1 and
OMXP03Xj > 0.
This gives 0.1654 as the estimated mean proportion of
total expenditures paid by private insurance for "GLASSES OR CONTACT LENSES"
events with expenditure for the civilian noninstitutionalized population of the
U.S. in 2003.
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4.3 Estimates of the Number of Persons with Other
Medical Expense Events
When calculating an estimate of the total number of
persons with other medical expense events, users can use a person-level file or
this event file. However, this event file must be used when the measure of
interest is defined at the event-level. For example, to estimate the number of
persons in the civilian noninstitutionalized population of the U.S. with a
medical expense for ambulance service in 2003, this event file must be used.
This would be estimated as
Sum of WiXi across all
unique persons i on this file (4)
where
Wi is the sampling weight (PERWT03F)for person i
and
Xi = 1 if OMTYPEXi= 4 for
any other medical expense record of person i
= 0 otherwise.
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4.4 Person-Based Ratio
Estimates
4.4.1 Person-Based Ratio
Estimates Relative to Persons with Other Medical Expense Events
This file may be used to derive person-based ratio
estimates. However, when calculating ratio estimates where the denominator is
persons, care should be taken to properly define and estimate the unit of
analysis up to person-level. For example, the mean expense for persons with
other medical expense events is estimated as,
(Sum of WiZi)/(Sum of Wi)across all unique persons i on this file (5)
where
Wiis the sampling weight(PERWT03F)for person i
and
Zi= Sum of OMXP03Xi across all
other medical events for person i.
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4.4.2 Person-Based Ratio
Estimates Relative to the Entire Population
If the ratio relates to the entire population, this file
cannot be used to calculate the denominator, as only those persons with at least
one other medical expense event are represented on this data file. In this case,
the Full Year Consolidated File, which has data for all sampled persons, must be
used to estimate the total number of persons (i.e., those with events and those
without events).
For example, to estimate the proportion of civilian
noninstitutionalized population of the U.S. with at least one other medical
event for ambulance services, the numerator would be derived from data on this
event file, and the denominator would be derived from data on the person-level
file. That is,
(Sum of WiZi)/(Sum of Wi) across all unique persons i on the MEPS HC person-level file (6)
where
Wi is the sampling weight (PERWT03F) for person i
and
Zi = 1 if OMTYPEXj = 4
for any other medical expense record of person i
= 0 otherwise.
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4.5 Sampling Weights for
Merging Previous Releases of MEPS Household Data with this Event File
There have been several previous releases of MEPS
Household Survey public use data. Unless a variable name common to several files
is provided, the sampling weights contained on these data files are
file-specific. The file-specific weights reflect minor adjustments to
eligibility and response indicators due to birth, death, or institutionalization
among respondents.
For estimates from a MEPS data file that do not require
merging with variables from other MEPS data files, the sampling weight(s)
provided on that data file are the appropriate weight(s). When merging a MEPS
Household data file to another, the major analytical variable (i.e., the
dependent variable) determines the correct sampling weight to use.
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4.6 Variance Estimation (VARPSU,
VARSTR)
To obtain estimates of variability (such as the standard
error of sample estimates or corresponding confidence intervals) for estimates
based on MEPS survey data, one needs to take into account the complex sample
design of MEPS. Various approaches can be used to develop such estimates of
variance including use of the Taylor Series or various replication
methodologies. Replicate weights have not been developed for the MEPS 2003 data.
Variables needed to implement a Taylor Series estimation approach are provided
in the file and are described in the paragraph below.
Using a Taylor Series approach, variance estimation strata
and the variance estimation PSUs within these strata must be specified. The
corresponding variables on the MEPS full year utilization database are VARSTR
and VARPSU, respectively. 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 have been developed to be
compatible with all future PUFs. Thus, data from future years can be pooled and
the variance strata and PSU variables provided can be used without modification
for variance estimation purposes for estimates covering multiple years of data.
There are 203 variance estimation strata, each stratum with either two or three
variance estimation PSUs. Specifying a "with replacement" design in a
computer software package such as SUDAAN (Shah, 1996) should provide 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
actual number available. For MEPS sample estimates for characteristics generally
distributed throughout the country (and thus the sample PSUs), there are over
100 degrees of freedom associated with the corresponding estimates of variance.
The following illustrates these concepts using two examples from Section 4.2.
Examples 2 and 3 from Section 4.2
Using a Taylor Series approach, specifying VARSTR and
VARPSU as the variance estimation strata and PSUs (within these strata)
respectively and specifying a "with replacement" design in a computer software
package (i.e., SUDAAN) will yield standard error estimates of $2.89 and 0.0069
for the estimated mean of out-of-pocket payment and the estimated mean
proportion of total expenditures paid by private insurance respectively.
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5.0 Merging/Linking MEPS Data
Files
Data from this file can be used alone or in conjunction
with other files. This section provides instructions, or the details on where to
find the instructions, for linking the 2003 other medical provider events with
other 2003 MEPS public use files, including the 2003 prescribed medicines file
and a 2003 person-level file.
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5.1 Merging a Person-Level
File to the Other Medical Expenses File
Merging characteristics of interest from other MEPS files
(e.g., 2003 Full Year Population Characteristics File or 2003 Prescribed
Medicines) expands the scope of potential estimates. For example, to estimate
the expenditures for medical equipment, visual aids, etc. for persons with
specific demographic characteristics (such as age, race, and sex), population
characteristics from a person-level file need to be merged onto the Other
Medical event file. This procedure is shown below. The MEPS 2003 Appendix File,
HC-077I, provides additional details on how to merge other MEPS data files.
1. |
Create data set PERSX by sorting the 2003 Full
Year Population Characteristics File, by the person identifier, DUPERSID.
Keep only variables to be merged onto the other medical events file and
DUPERSID. |
2. |
Create data set OMEXP by sorting the other medical
event file by person identifier, DUPERSID. |
3. |
Create final data set NEWOME by merging these two
files by DUPERSID, keeping only records on the other medical event file. |
The following is an example of SAS code, which
completes these steps:
PROC SORT DATA=HCXXX (KEEP=DUPERSID AGE31X AGE42X
AGE53X SEX RACEX EDUCYR) OUT=PERSX;
BY DUPERSID;
RUN; |
PROC SORT DATA=OMEXP;
BY DUPERSID;
RUN; |
DATA NEWOME;
MERGE OMEXP (IN=A) PERSX(IN=B);
BY DUPERSID;
IF A;
RUN; |
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5.2 Linking the 2003 Other
Medical Expenses File to the 2003 Prescribed Medicine File
Due to survey design issues, there are limitations/caveats
that data users/analysts must keep in mind when linking the different files.
These limitations/caveats are listed below. For detailed linking examples,
including SAS code, data users/analysts should refer to the MEPS 2003 Appendix
File, HC-077I.
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5.2.1 Limitations/Caveats of
RXLK (the Prescribed Medicine Link File)
The RXLK file provides a link from the MEPS event files to
the prescribed medicine records on the 2003 Prescribed Medicine Event File. When
using RXLK, data users/analysts should keep in mind that one other medical
record can link to more than one prescribed medicine record. Conversely, a
prescribed medicine event may link to more than one other medical record. When
this occurs, it is up to the data user/analyst to determine how the prescribed
medicine expenditures should be allocated among those other medical expenses.
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References
Cohen, S.B. (1997). Sample Design of the 1996 Medical
Expenditure Panel Survey Household Component. Rockville (MD): Agency for Health
Care Policy and Research; 1997. MEPS Methodology Report, No. 2. AHCPR Pub. No. 97-0027.
Cohen, J.W. (1997). Design and Methods of the Medical
Expenditure Panel Survey Household Component. Rockville (MD): Agency for Health
Care Policy and Research; 1997. MEPS Methodology Report, No. 1. AHCPR Pub. No. 97-0026.
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.
Monheit, A.C., Wilson, R., and Arnett, III, R.H. (Editors)
(1999). Informing American Health Care Policy. Jossey-Bass Inc., San Francisco.
Shah, B.V., Barnwell, B.G., Bieler, G.S., Boyle, K.E.,
Folsom, R.E., Lavange, L., Wheeless, S.C., and Williams, R. (1996). Technical
Manual: Statistical Methods and Algorithms Used in SUDAAN Release 7.0, Research Triangle Park, NC: Research Triangle Institute.
D. Variable-Source Crosswalk
Variable-Source Crosswalk
FOR MEPS HC-077C: 2003 OTHER MEDICAL EXPENSES
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 |
FFEEIDX |
Flat fee ID |
CAPI derived |
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Other Medical Events Variables
Variable |
Description |
Source |
OMTYPEX |
Other medical expense type – edited |
EV03 (edited) |
OMTYPE |
Other medical expense type |
EV03 |
OMOTHOX |
OMTYPE other specify – edited |
EV03A (edited) |
OMOTHOS |
OMTYPE other specify |
EV03A |
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Flat Fee Variables
Variable |
Description |
Source |
FFOMTYPE |
Flat Fee Bundle |
Constructed |
FFBEF03 |
Total # of visits in FF before 2003 |
FF05 |
FFTOT04 |
Total # of visits in FF after 2003 |
FF10 |
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Imputed Expenditure Variables
Variable |
Description |
Source |
OMSF03X |
Amount paid, self/family (Imputed) |
CP Section (Edited) |
OMMR03X |
Amount paid, Medicare (Imputed) |
CP Section (Edited) |
OMMD03X |
Amount paid, Medicaid (Imputed) |
CP Section (Edited) |
OMPV03X |
Amount paid, private insurance (Imputed) |
CP Section (Edited) |
OMVA03X |
Amount paid, Veterans Administration (Imputed) |
CP Section (Edited) |
OMTR03X |
Amount paid, TRICARE (Imputed) |
CP Section (Edited) |
OMOF03X |
Amount paid, other federal (Imputed) |
CP Section (Edited) |
OMSL03X |
Amount paid, state & local government (Imputed) |
CP Section (Edited) |
OMWC03X |
Amount paid, worker’s compensation (Imputed) |
CP Section (Edited) |
OMOR03X |
Amount paid, other private insurance (Imputed) |
Constructed |
OMOU03X |
Amount paid, other public insurance (Imputed) |
Constructed |
OMOT03X |
Amount paid, other insurance (Imputed) |
CP Section (Edited) |
OMXP03X |
Sum of payments OMSF03X–OMOT03X (Imputed) |
Constructed |
OMTC03X |
Household reported total charge (Imputed) |
CP Section (Edited) |
IMPFLAG |
Imputation status |
Constructed |
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Weights
Variable |
Description |
Source |
PERWT03F |
Expenditure file person weight, 2003 |
Constructed |
VARSTR |
Variance estimation stratum, 2003 |
Constructed |
VARPSU |
Variance estimation PSU, 2003 |
Constructed |
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