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MEPS HC-059G: 2001 Office-Based Medical Provider Visits
January 2004
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 General
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 MPC Indicator (MPCELIG, MPCDATA)
2.6.3 Office-Based Medical Provider Visit
Variables
2.6.3.1 Date of Visit (OBDATEYR - OBDATEDD)
2.6.3.2 Visit Details (SEETLKPV-VSTRELCN)
2.6.3.3 Treatments, Procedures, Services, and
Prescription Medicines (PHYSTH-MEDPRESC)
2. 6.3.4 VA Facility (VAPLACE)
2.6.4 Condition and Procedure Codes
(OBICD1X-OBICD4X, OBPRO1X), and Clinical Classification Codes
(OBCCC1X-OBCCC4X)
2.6.5 Flat Fee Variables (FFEEIDX, FFOBTYPE,
FFBEF01, FFTOT02)
2.6.5.1 Definition of Flat Fee Payments
2.6.5.2 Flat Fee Variable Descriptions
2.6.5.2.1 Flat Fee ID (FFEEIDX)
2.6.5.2.2 Flat Fee Type (FFOBTYPE)
2.6.5.2.3 Counts of Flat Fee
Events that Cross Years (FFBEF01, FFTOT02)
2.6.5.3 Caveats of Flat Fee Groups
2.6.6 Expenditure Data
2.6.6.1 Definition of Expenditures
2.6.6.2 Data Editing and Imputation
Methodologies of Expenditure Variables
2.6.6.2.1 General Data Editing
Methodology
2.6.6.2.2 General Hot-Deck
Imputation
2.6.6.2.3 Office-Based Provider
Visit Data Editing and Imputation
2.6.6.3 Capitation Imputation
2.6.6.4 Imputation Flag (IMPFLAG)
2.6.6.5 Flat Fee Expenditures
2.6.6.6 Zero Expenditures
2.6.6.7 Discount Adjustment Factor
2.6.6.8 Sources of Payment
2.6.6.9 Office-Based Expenditure
Variables (OBSF01X - OBTC01X)
2.6.7 Rounding
3.0 Sample Weight (PERWT01F)
3.1 Overview
3.2 Details on Person Weight Construction
3.2.1 MEPS Panel 5 Weight
3.2.2 MEPS Panel 6 Weight
3.2.3 The Final Weight for 2001
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
Office-Based Medical Provider Visit Events
4.4 Person-Based Ratio Estimates
4.4.1 Person-Based Ratio Estimates
Relative to Persons with Office-Based Medical Provider Visit
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 (VARSTR01, VARPSU01)
5.0 Merging/Linking MEPS Data Files
5.1 Linking a 2001 Person-Level File to the
2001 Office-Based Medical Provider Visits File
5.2 Linking the MEPS 2001 Office-Based Medical Provider Visits
File to the MEPS 2001 Medical Conditions File and/or the MEPS 2001
Prescribed Medicines File
5.2.1 Limitations/Caveats of RXLK (the
Prescribed Medicine Link File)
5.2.2 Limitations/Caveats of CLNK (the
Medical Conditions 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:
- No one is to use the data in this data set in any way except for
statistical reporting and analysis; andIf 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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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 on
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 2001 Medical Expenditure Panel Survey (MEPS) Household (HC) and Medical
Provider Components (MPC). Released as an ASCII data file and a SAS transport
file, the 2001 Office-Based Medical Provider Visits public use event file
provides detailed information on office-based provider visits for a nationally
representative sample of the civilian noninstitutionalized population of the
United States. Data from the office-based provider events file can be used to
make estimates of office-based provider utilization and expenditures for
calendar year 2001. As illustrated below, this file consists of MEPS survey data
from the 2001 portion of Round 3 and Rounds 4 and 5 for Panel 5, as well as
Rounds 1, 2 and the 2001 portion of Round 3 for Panel 6 (i.e., the rounds for
the MEPS panels covering calendar year 2001).
301 Moved Permanently
301 Moved Permanently
Each record on this event file represents a unique office-based provider
event; that is, an office-based provider event reported by the household
respondent. Utilization counts of office-based provider visits are based
entirely on household reports. Information from the MPC is used to supplement
expenditure payment data, on the office-based provider file, reported by the
household and does not affect use estimates.
Data from this event file can be merged with other 2001 MEPS HC data files
for purposes of appending person-level data such as demographic characteristics
or health insurance coverage to each office-based provider visit record on the
current file.
This file can also be used to construct summary variables of expenditures,
sources of payment, and related aspects of office-based provider visits for
calendar year 2001. Aggregate annual person-level information on the use of
office-based providers and other health services use is provided on the MEPS
2001 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 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 instruments used to
collect the information on the office-based provider 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 2001 Office-Based Medical Provider public use data set
consists of one event-level data file. The file contains characteristics
associated with the OB event and imputed expenditure data. For users 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 website 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 Office-Based Provider public use data set contains 147,490office-based provider event records; of these records, 144,510are associated with persons having a positive person-level weight
(PERWT01F). This file includes office-based provider event records for all
household survey respondents who resided in eligible responding households and
reported at least one office-based provider event. Each record represents one
household-reported office-based provider event that occurred during calendar
year 2001. Office-based provider visits known to have occurred after December
31, 2001 are not included on this file. Some household respondents may have
multiple events and thus will be represented in multiple records on this file.
Other household respondents may have reported no events and thus will have no
records on this file. These data were collected during the 2001 portion of Round
3, and Rounds 4 and 5 for Panel 5, as well as Rounds 1, 2, and the 2001 portion
of Round 3 for Panel 6 of the MEPS Household Component. The persons represented
on this file had to meet either (a) or (b):
-
Be classified as a key in-scope person who
responded for his or her entire period of 2001 eligibility (i.e.,
persons with a positive 2001 full-year person-level weight (PERWT01F >
0)), or
- Be an eligible member of a family all of whose key in-scope members
have a positive person-level weight (PERWT01F > 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 (FAMWT01F
>0). Note that FAMIDYR and FAMWT01F are variables on the 2001
Population Characteristics file.
Persons with no office-based medical provider visit events for 2001 are not
included on this file but are represented on the 2001 MEPS person-level file. A
codebook for the data file is provided in files H55CB.PDF and H55CB.ASP.
Each office-based medical provider visit event record includes the following:
date of the event; type of provider seen; time spent with the provider; type of
care received; types of treatments (i.e., physical therapy, occupational
therapy, speech therapy, chemotherapy, radiation therapy, etc.) received during
the event; type of services (i.e., lab test, sonogram or ultrasound, x-rays,
etc.) received, medicines prescribed during the event; flat fee information,
imputed sources of payment, total payment and total charge of the office-based
event expenditure; and a full-year person-level weight.
Data from this file can be merged with the MEPS 2001 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. The office-based medical provider visit events
can also be linked to the MEPS 2001 Medical Conditions File and MEPS 2001
Prescribed Medicines File. 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 expenditures 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 trends
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.
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2.2 Codebook Structure
For each variable on the office-based provider file, both
weighted and unweighted frequencies are provided in the codebook (files
H59GCB.PDF and H59GCB.ASP). The codebook and data file sequence list variables
in the following order:
Unique person identifiers
Unique office-based medical provider visit event identifier
Office-based medical provider visit characteristic variables
ICD-9-CM condition and procedure codes
Clinical Classification Software (CCS) codes
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 Format
The office-based medical provider visits codebook describes an ASCII data set
(although the data are also being provided in a SAS transport file). The
following codebook items are provided for each variable:
| Identifier |
Description |
| Name |
Variable name (maximum of 8 characters) |
| Description |
Variable descriptor (maximum of 40 characters) |
| Format |
Number of bytes |
| Type |
Type of data: numeric (indicated by NUM) or character (indicated by CHAR) |
| Start |
Beginning column position of variable in record |
| End |
Ending column position of variable in record |
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2.5 Variable Source and Naming
Conventions
In general, variable names reflect the content of the
variable, with an 8-character limitation. All imputed/edited variables end with
an "X".
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2.5.1 General
Variables contained on this file were derived from the HC survey
questionnaire itself, derived from the MPC data collection instrument, derived
from CAPI, or assigned in sampling. The source of each variable is identified in
Section D "Variable - Source Crosswalk" in one of four ways:
- Variables derived from CAPI or assigned in sampling are indicated as
"CAPI derived" or "Assigned in sampling," respectively;
- Variables which come from one or more specific questions have those
questionnaire sections and question numbers indicated in the "Source"
column; questionnaire sections are identified as:
- MV - Office-Based Medical Provider Visits
section
- FF - Flat Fee section
- CP - Charge Payment section;
- Variables constructed from multiple questions using complex algorithms
are labeled "Constructed" in the "Source" column; and
- Variables 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 the 12 sources 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 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 |
OR - other private |
| TR - 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 (01). The seventh character,
"X", indicates whether the variable is edited/imputed.
For example, OBSF01X is the edited/imputed amount paid by self or family for
an office-based medical provider visit expenditure incurred in 2001.
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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 2001 Full Year
Population Characteristics.
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2.6.1.2 Record Identifiers (EVNTIDX, FFEEIDX)
EVNTIDX uniquely identifies each office-based medical provider visit event
(i.e., each record on the office-based medical provider visits file) and is the
variable required for linking office-based medical provider visit events to data
files containing details on conditions and/or prescribed medicines (MEPS 2001
Medical Condition file and MEPS 2001 Prescribed Medicine file, respectively).
For details on linking see Section 5.0 or the MEPS 2001 Appendix File, HC-059I.
FFEEIDX is a constructed variable that uniquely identifies a flat fee group,
that is, all events that were part of a flat fee payment. For example, pregnancy
is typically covered in a flat fee arrangement where the prenatal visits, the
delivery, and the postpartum visits are all covered under one flat fee dollar
amount. These events (the prenatal visit, the delivery, and the postpartum
visits) would have the same value for FFEEIDX. FFEEIDX identifies a flat fee
payment that was identified using information from the Household Component. A
"mixed" flat fee group could contain both outpatient and office-based visits.
Only outpatient and office-based events are allowed in a mixed bundle. Please
note that FFEEIDX should be used to link up the outpatient and office-based
events in order to determine the full set of events that are part of a flat fee
group.
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2.6.1.3 Round Indicator (EVENTRN)
EVENTRN indicates the round in which the outpatient event was reported.
Please note that Rounds 3, 4, and 5 are associated with MEPS survey data
collected from Panel 5. Likewise, Rounds 1, 2, and 3 are associated with data
collected from Panel 6.
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2.6.2 MPC Indicator (MPCELIG, MPCDATA)
MPCELIG is a constructed variable that indicates whether the office-based
provider visit was eligible for MPC data collection. MPCDATA is a constructed
variable that indicates whether or not MPC data was collected for the
office-based provider.
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2.6.3 Office-Based Medical
Provider Visit Variables
The file contains variables describing office-based medical
provider visit events reported by respondents in the Medical Provider Visits
section of the MEPS HC survey questionnaire.
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2.6.3.1 Date of Visit (OBDATEYR - OBDATEDD)
There are three variables that, together, indicate the day, month, and year
an office-based provider visit occurred (OBDATEDD, OBDATEMM, OBDATEYR,
respectively). These variables have not been edited or imputed.
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2.6.3.2 Visit Details (SEETLKPV-VSTRELCN)
The questionnaire determines if during the office-based
medical provider visit the person actually saw the provider or talked to the
provider on the telephone (SEETLKPV). It also establishes whether the person saw
or spoke to a medical doctor or not (SEEDOC). If the person did not see a
physician (i.e., a medical doctor), the respondent was asked to identify the
type of medical person seen (MEDPTYPE). Whether or not any medical doctors
worked at the visit location (DOCATLOC), the type of care the person received (VSTCTGRY),
and whether or not the visit or telephone call was related to a specific
condition (VSTRELCN) were also determined.
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2.6.3.3 Treatments, Procedures, Services, and
Prescription Medicines (PHYSTH-MEDPRESC)
Types of treatments received during the office-based medical provider visit
include 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), and psychotherapy/counseling (PSYCHOTH). Services
received during the visit included whether or not the person received lab tests
(LABTEST), a sonogram or ultrasound (SONOGRAM), x-rays (XRAYS), a mammogram (MAMMOG),
an MRI or a CAT scan (MRI), an electrocardiogram (EKG), an electroencephalogram
(EEG), a vaccination (RCVVAC), anesthesia (ANESTH), or other diagnostic tests or
exams (OTHSVCE). Minimal editing was done across treatment, services, and
procedures to ensure consistency across "inapplicable," "not ascertained,"
"don't know," "refused," and "no services received" values. Whether or not a
surgical procedure was performed during the visit was asked (SURGPROC). Finally,
the questionnaire determined if a medicine was prescribed for the person during
the visit (MEDPRESC).
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2.6.3.4 VA Facility (VAPLACE)
VAPLACE is a constructed variable that indicates whether the service was
provided at a VA facility. This variable only has valid data for providers that
were sampled into the Medical Provider Component. All other providers are
classified as "No".
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2.6.4 Condition and Procedure
Codes (OBICD1X-OBICD4X, OBPRO1X), and Clinical Classification Codes
(OBCCC1X-OBCCC4X)
Information on household-reported medical conditions and procedures
associated with each office-based medical provider visit are provided on this
file. There are up to four condition and CCS codes (OBICD1X-OBICD4X,
OBCCC1X-OBCCC4X) and one procedure code (OBPRO1X) listed for each office-based
medical provider visit. In order to obtain complete condition information
associated with an event, the analyst must link to the Medical Conditions File.
Details on how to link to the MEPS Medical Conditions File are provided in
Section 5.0. The user should note that due to confidentiality restrictions,
provider-reported condition information is not publicly available.
The medical conditions and procedures reported by the Household Component
respondent were recorded by the interviewer as verbatim text, which were then
coded to fully-specified 2001 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 2.5 percent for any
coder, data users/analysts should not presume this level of precision in the
data; the ability of household respondents to report condition data that can be
coded accurately should not be assumed (see Cox and Cohen, 1985; Cox and Iachan,
1987; Edwards, et al, 1994; and Johnson and Sanchez, 1993). For detailed
information on how conditions and procedures were coded, please refer to the
documentation on the MEPS 2001 Medical Conditions File. For frequencies of
conditions by event type, please see the MEPS 2001 Appendix File, HC-059I.
The ICD-9-CM codes were aggregated into clinically meaningful categories.
These categories, included on the file as OBCCC1X-OBCCC4X, were generated using
Clinical Classification Software [formerly known as Clinical Classifications for
Health Care Policy Research (CCHPR), (Elixhauser, et al., 1998)], which
aggregates conditions and V-codes into 260 mutually exclusive categories, most
of which are clinically homogeneous.
In order to preserve respondent confidentiality, nearly all of the condition
codes provided on this file have been collapsed from fully-specified codes to
three-digit code categories. The reported ICD-9-CM code values were mapped to
the appropriate clinical classification category prior to being collapsed to the
3-digit categories. Details on this procedure can be found in the 2001 MEPS
Medical Conditions File.
The condition codes (and clinical classification codes) and procedure codes
linked to each office-based medical provider visit event are sequenced in the
order in which the conditions were reported by the household respondent, which
was in order of input into the database and not in order
of importance or severity. Data users/analysts who use the Medical Conditions
file in conjunction with this office-based medical provider visits file should
note that the order of conditions on this file is not identical to that on the
2001 Medical Conditions file.
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2.6.5 Flat Fee Variables (FFEEIDX,
FFOBTYPE, FFBEF01, FFTOT02)
2.6.5.1 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. An example would be an
obstetrician's fee covering a normal delivery, and the associated pre- and
post-natal care. A flat fee group is the set of medical services (i.e., events)
that are covered under the same flat fee payment. The flat fee groups
represented on the office-based provider file include flat fee groups where at
least one of the health care events, as reported by the HC respondent, occurred
during 2001. By definition, a flat fee group can span multiple years and/or
event types (only outpatient department visits and physician office visits).
Furthermore, a single person can have multiple flat fee groups.
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2.6.5.2 Flat Fee
Variable Descriptions
2.6.5.2.1 Flat Fee ID (FFEEIDX)
As noted earlier in Section 2.5.1.2 "Record Identifiers," the variable
FFEEIDX uniquely identifies all events that are part of the same flat fee group
for a person. On any 2001 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 FFEEIDX is not a variable on those event files.
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2.6.5.2.2 Flat Fee
Type (FFOBTYPE)
FFOBTYPE indicates whether the 2001 office-based medical provider visit event
is the "stem" or "leaf" of a flat fee group. A stem (records with FFOBTYPE = 1)
is the initial medical service (event) which is followed by other medical events
that are covered under the same flat fee payment. The leaves of the flat fee
group (records with FFOBTYPE = 2) are those medical events that are tied back to
the initial medical event (the stem) in the flat fee group. These "leaf" records
have their expenditure variables set to zero. For the office-based visits that
are not part of a flat fee payment, the FFOBTYPE is set to -1, "INAPPLICABLE."
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2.6.5.2.3 Counts of
Flat Fee Events that Cross Years (FFBEF01, FFTOT02)
As described in Section 2.6.5.1, a flat fee payment covers
multiple events and the multiple events could span multiple years. For
situations where the office-based medical provider visit occurred in 2001 as a
part of a group of events, and some of the events occurred before 2001, counts
of the known events are provided on the office-based medical provider visit
event file record. Variables that indicate events occurred before or after 2001
are as follows:
FFBEF01 - total number of pre-2001 events in the same flat fee group as
the 2001 office-based medical provider visit. This count would not include
the 2001 office-based medical visit(s).
FFTOT02 - indicates the number of 2002 office-based events expected to be
in the same flat fee group as the office-based medical provider visit
event(s) that occurred in 2001.
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2.6.5.3 Caveats of Flat Fee Groups
Data users/analysts should note that flat fee payments are common on the
office-based medical provider visits file. There are 3,875office-based medical provider visit events that are identified as
being part of a flat fee payment group. In order to correctly identify all
events that are part of a flat fee group, the user should link all MEPS events,
except those in the prescribed medicine file, using the variable FFEEIDX. In
general, every flat fee group should have an initial visit (stem) and at least
one subsequent visit (leaf). There are some situations where this is not true.
For some of these flat fee groups, the initial visit reported occurred in 2001,
but the remaining visits that were part of this flat fee group occurred in 2002.
In this case, the 2001 flat fee group represented on this file would consist of
one event (the stem). The 2002 leaf events that are part of this flat fee group
are not represented on this file. Similarly, the household respondent may have
reported a flat fee group where the initial visit began in 2000 but subsequent
visits occurred during 2001. In this case, the initial visit would not be
represented on the file. This 2001 flat fee group would then consist only of one
or more leaf records and no stem. Another reason for which a flat fee group
would not have a stem and at least one leaf record is that the stem or leaves
could have been reported as different event types. Outpatient and Office-based
medical provider visits are the only two event types allowed in a single flat
fee group. The stem may have been reported as an outpatient department visit and
the leaves may have been reported as office-based medical provider visits.
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2.6.6 Expenditure Data
2.6.6.1 Definition of Expenditures
Expenditures on this file refer to what is paid for health care services.
More specifically, expenditures in MEPS are defined as the sum of payments for
care received, including out-of-pocket payments and payments made by private
insurance, Medicaid, Medicare and other sources. The definition of expenditures
used in MEPS differs slightly from its predecessors: the 1987 NMES and 1977
NMCES surveys where "charges" rather than sum of payments were used to measure
expenditures. This change was adopted because charges became a less appropriate
proxy for medical expenditures during the 1990's 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.
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 is it directly comparable
to the resource costs of those services or the expenditures defined in the 1987
NMES (for details on expenditure definitions, see Monheit et al., 1999). AHRQ
has developed factors to apply to the 1987 NMES expenditure data to facilitate
longitudinal analysis. These factors can be assessed via the CFACT data center.
For more information, see the Data Center section of the MEPS web site <http://www.meps.ahrq.gov>.
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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2.6.6.2 Data Editing and Imputation
Methodologies of Expenditure Variables
The expenditure data included on this file were derived from both the MEPS
household (HC) and medical provider (MPC) components. The MPC contacted medical
providers identified by household respondents. The charge and payment data from
medical providers were used in the expenditure imputation process to supplement
missing household data. For all office-based medical provider visits, MPC data
were used if complete; otherwise HC data were used if complete. Missing data for
office-based medical provider visits where HC data were not complete and MPC
data were not collected or complete were derived through the imputation process.
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2.6.6.2.1 General Data Editing Methodology
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 mis-classifications
between Medicare and Medicaid and between Medicare HMOs and private HMOs as
payment sources. These edits produced a complete vector of expenditures for some
events, and provided the starting point for imputing missing expenditures in the
remaining events.
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2.6.6.2.2 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 event type 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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2.6.6.2.3 Office-Based Provider Visit Data
Editing and Imputation
Expenditures for office-based provider visits were developed
in a sequence of logical edits and imputations. "Household" edits were applied
to sources and amounts of payment for all events reported by HC respondents. "MPC"
edits were applied to provider-reported sources and amounts of payment for
records matched to household-reported events. Both sets of edits were used to
correct obvious errors (as described above) in the reporting of expenditures.
After the data from each source were edited, a decision was made as to whether
household- or MPC-reported information would be used in the final editing and
hot-deck imputations for missing expenditures. The general rule was that MPC
data would be used for events where a household-reported event corresponded to
an MPC-reported event (i.e., a matched event), since providers usually have more
complete and accurate data on sources and amounts of payment than households.
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 event type 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.
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.
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
2.6.5 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 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 eight recipient categories in
which all events had a common extent of missing data. Separate hot-deck
imputations were performed on events in each recipient category, and the donor
pool was restricted to events with complete expenditures from the MPC. The donor
pool restriction was used even though some unmatched events had complete
household-reported expenditures. These events were not allowed to donate
information to other events because the MPC data were considered to be more
reliable.
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 event among complete events (donors) is not represented
among incomplete events (recipients).
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2.6.6.3 Capitation Imputation
The imputation process was also used to make expenditure estimates at the
event level for events that were paid on a capitated basis. The capitation
imputation procedure was designed as a reasonable approach to complete
event-level expenditures for respondents in managed care plans. The procedure
was conducted in two stages. First, HMO events reported in the MPC as covered by
capitated arrangements were imputed using similar MPC HMO events that were paid
on a fee-for-service basis, with total charge as a key variable. Then, this
completed set of MPC events was used as the donor pool for unmatched
household-reported events for sample persons 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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2.6.6.4 Imputation Flag (IMPFLAG)
IMPFLAG is a six-category variable that indicates if the
event contains complete Household Component (HC) or Medical Provider Component (MPC)
data, was fully or partially imputed, or was imputed in the capitated imputation
process (for OP and 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
IMPFLAG=3 fully imputed
IMPFLAG=4 partially imputed
IMPFLAG=5 complete MPC data through
capitation imputation
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2.6.6.5 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
2001, all of the events that occurred in 2001 will have zero payments.
Conversely, if the first event in the flat fee group occurred at the end of
2001, the total expenditure for the entire flat fee group will be on that event,
regardless of the number of events it covered after 2001. See Section 2.6.5 for
details on the flat fee variables.
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2.6.6.6 Zero Expenditures
There are some medical events reported by respondents where the payments were
zero. This could occur for several reasons including (1) free care was provided,
(2) bad debt was incurred, (3) care was covered under a flat fee arrangement
beginning in an earlier year, or (4) follow-up visits were provided without a
separate charge (e.g., after a surgical procedure). 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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2.6.6.7 Discount
Adjustment Factor
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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2.6.6.8 Sources of Payment
In addition to total expenditures, variables are provided which itemize
expenditures according to major source of payment categories. These categories
are:
- Out-of-pocket by user (self) or family,Medicare,Medicaid,Private Insurance,Veterans Administration, 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 relatively small in magnitude, data users/analysts should exercise
caution when interpreting the expenditures associated with these two additional
sources of payment. While these payments stem from apparent inconsistent
responses to health insurance and source of payment questions in the survey,
some of these inconsistencies may have logical explanations. For example,
private insurance coverage in MEPS is defined as having a major medical plan
covering hospital and physician services. If a MEPS sampled person did not have
such coverage but had a single service type insurance plan (e.g., dental
insurance) that paid for a particular episode of care, those payments may be
classified as "other private". Some of the "other public" payments may stem from
confusion between Medicaid and other state and local programs or may be persons
who were not enrolled in Medicaid, but were presumed eligible by a provider who
ultimately received payments from the public payer.
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2.6.6.9 Office-Based Expenditure Variables
(OBSF01X - OBTC01X)
OBSF01X - OBOT01X are the 12 sources of payment. OBTC01X is the total charge,
and OBXP01X is the sum of the 12 sources of payment for the Office Based
expenditures. The 12 sources of payment are: self/family (OBSF01X), Medicare
(OBMR01X), Medicaid (OBMD01X), private insurance (OBPV01X), Veterans
Administration (OBVA01X), TRICARE (OBTR01X), other Federal sources (OBOF01X),
State and Local (non-federal) government sources (OBSL01X), Worker's
Compensation (OBWC01X), other private insurance (OBOR01X), other public
insurance (OBOU01X), and other insurance (OBOT01X).
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2.6.7 Rounding
Expenditure variables have been rounded to the nearest penny. Person-level
expenditure information released on the MEPS 2001 Person-Level Expenditure File
will be rounded to the nearest dollar. It should be noted that using the MEPS
2001 event files to create person-level totals will yield slightly
different totals than that 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 2001 Appendix File,
HC-059I, for details on such rounding differences.
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3.0 Sample
Weight (PERWT01F)
3.1 Overview
There is a single full year person-level weight (PERWT01F)
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 2001. 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 PERWT01F was developed in several
stages. Person-level weights for Panels 5 and 6 were created separately. The
weighting process for each panel included an adjustment for nonresponse over
time and poststratification. Poststratification was achieved initially by
controlling to Current Population Survey (CPS) population estimates based on
five variables. The five variables used in the establishment of the initial
person-level poststratification control figures were: census region (Northeast,
Midwest, South, West); MSA status (MSA, non-MSA); race/ethnicity (Hispanic,
black but non-Hispanic, and other); sex; and age. A 2001 composite weight was
then formed by multiplying each weight from Panel 5 by the factor (1/3) and each
weight from Panel 6 by the factor (2/3). 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 then
poststratified to the same set of CPS-based control totals. When poverty status
information derived from income variables became available, a final
poststratification was done on the previously established weight variable.
Control totals were established based on 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 poststratification variables.
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3.2.1 MEPS Panel 5 Weight
The person-level weight for MEPS Panel 5 was developed using
the 2000 full year weight for an individual as a "base" weight for survey
participants present in 2000. For key, in-scope respondents who joined an RU
some time in 2001 after being out-of-scope in 2000, the 2000 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 poststratification to population control figures for December 2001.
These control figures were derived by scaling back the population totals
obtained from the March 2001 CPS to reflect the December 2001 CPS estimated
population distribution across age and sex categories as of December 2001.
Variables used in the establishment of person-level poststratification control
figures included: census region (Northeast, Midwest, South, West); MSA status (MSA,
non-MSA); race/ethnicity (Hispanic, black but non-Hispanic, and other); sex; and
age. Overall, the weighted population estimate for the civilian
noninstitutionalized population on December 31, 2001 is 280,791,812. Key,
responding persons not in-scope on December 31, 2001 but in-scope earlier in the
year retained, as their final Panel 5 weight, the weight after the nonresponse
adjustment.
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3.2.2 MEPS Panel 6 Weight
The person-level weight for MEPS Panel 6 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
2001 portion of Round 3 as well as poststratification to the same population
control figures for December 2001 used for the MEPS Panel 5 weights. The same
five variables employed for Panel 5 poststratification (census region, MSA
status, race/ethnicity, sex, and age) were used for Panel 6 poststratification.
Similarly, for Panel 6, key, responding persons not in-scope on December 31,
2001 but in-scope earlier in the year retained, as their final Panel 6 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 2001 CPS data base.
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3.2.3 The Final Weight for
2001
Variables used in the establishment of person-level
poststratification 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, black but non-Hispanic, and other); sex; and age. Overall, the
weighted population estimate for the civilian noninstitutionalized population
for December 31, 2001 is 280,791,812 (PERWT01F>0 and INSC1231=1). The weights of
some persons out-of-scope on December 31, 2001 were also poststratified.
Specifically, the weights of persons out-of-scope on December 31, 2001 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 2001 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 284,247,327.
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3.2.4 Coverage
The target population for MEPS in this file is the 2001 U.S.
civilian noninstitutionalized population. However, the MEPS sampled households
are a subsample of the NHIS households interviewed in 1999 (Panel 5) and 2000
(Panel 6). New households created after the NHIS interviews for the respective
Panels and consisting exclusively of persons who entered the target population
after 1999 (Panel 5) or after 2000 (Panel 6) 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 office-based medical
provider visits and to allow for estimates of number of persons with
office-based medical provider visits in 2001.
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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 2.6.5.
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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 office-based medical
provider visits utilization, expenditures, and sources of payment, the value in
each record contributing to the estimates must be multiplied by the weight
(PERWT01F) contained on that record.
Example 1
For example, the total number of office-based medical provider visits for the
civilian noninstitutionalized population of the U.S. in 2001 is estimated as the
sum of the weight (PERWT01F) across all office-based medical provider visit
records. That is,
301 Moved Permanently
301 Moved Permanently
= 1,354,573,765 |
(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 per office-based medical provider visit (where the visit has a total
expense greater than 0) should be calculated as the
weighted mean of the office-based provider's bill paid by self/family. That is,
301 Moved Permanently
301 Moved Permanently
= $21.09 |
(2) |
where
301 Moved Permanently
301 Moved Permanently
= 1,271,946,878 and Xj = OBSF01Xj
for all records with OBXP01Xj > 0.
This gives $21.09 as the estimated mean amount of out-of-pocket payment of
expenditures associated with office-based medical provider visits and
1,271,946,878 as an estimate of the total number of office-based medical
provider visits with expenditure. Both of these estimates are for the civilian
noninstitutionalized population of the U.S. in 2001.
Example 3
Another example would be to estimate the mean proportion of total
expenditures (where event expense is greater than 0) paid by private insurance
for office-based medical provider visits. This should be calculated as the
weighted mean of the proportion of total expenditures paid by private insurance
at the provider visit level. That is
301 Moved Permanently
301 Moved Permanently
= 0.4250 |
(3) |
where
301 Moved Permanently
301 Moved Permanently
= 1,271,946,878 and Yj = OBPV01Xj / OBXP01Xj
for all office-based medical provider visits with OBXP01Xj >
0.
This gives 0.4250 as the estimated mean proportion of total expenditures paid
by private insurance for office-based medical provider visits with expenditures
for the civilian noninstitutionalized population of the U.S. in 2001.
Return To Table Of Contents
4.3 Estimates of the Number of
Persons with Office-Based Medical Provider Visit Events
When calculating an estimate of the total number of persons with office-based
medical provider visits, users can use a person-level file or the current file.
However, the current file must be used when the measure of interest is defined
at the event level. For example, to estimate the number of office-based medical
provider visits in person and not by telephone, the current file must be used.
This would be estimated as,
301 Moved Permanently
301 Moved Permanently
across all
unique persons i on this file |
(4) |
where
Wi is the sampling weight (PERWT01F) for
person i
and
Xi = 1 if SEETLKPVj = 1 for any office-based
medical provider visit of person i
= 0 otherwise.
Return To Table Of Contents
4.4 Person-Based Ratio
Estimates
4.4.1 Person-Based Ratio
Estimates Relative to Persons with Office-Based Medical Provider Visit
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 office-based medical provider
visits is estimated as,
301 Moved Permanently
301 Moved Permanently
across all unique persons i on this file |
(5) |
where
Wi is the sampling weight (PERWT01F) for
person i
and
Zi =
301 Moved Permanently
301 Moved Permanently
OBXP01Xj
across all office-based medical provider visits for
person i.
Return To Table Of Contents
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 office-based
medical provider visit 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 visits
and those without visits). For example, to estimate the proportion of the
civilian noninstitutionalized population of the U.S. with at least one in-person
office-based medical provider visit, the numerator would be derived from data on
the current file, and the denominator should be derived from data on the
person-level file. That is,
301 Moved Permanently
301 Moved Permanently
across all unique persons i on the person-level file |
(6) |
where
Wi is the sampling weight (PERWT01F) for
person i
and
Zi = 1 if SEETLKPVj = 1 for any
office-based medical provider visit of
person i.
= 0 otherwise.
Return To Table Of Contents
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.
Return To Table Of Contents
4.6 Variance Estimation
(VARSTR01, VARPSU01)
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 2001 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 VARSTR01 and VARPSU01,
respectively. 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.
Return To Table Of Contents
Examples 2 and 3 from Section 4.2
Using a Taylor Series approach, specifying VARSTR01 and VARPSU01 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 $0.51 and 0.0071 for the
estimated mean of out-of-pocket payment and the estimated mean proportion of
total expenditures paid by private insurance, respectively.
Return To Table Of Contents
5.0 Merging/Linking MEPS Data Files
Data from this office-based medical provider visits file can
be used alone or in conjunction with other files. This section provides
instructions for linking the office-based medical provider visits with other
MEPS public use files, including the conditions file, the prescribed medicines
file, and a person-level file.
Return To Table Of Contents
5.1 Linking a 2001
Person-Level File to the 2001 Office-Based Medical Provider Visits File
Merging characteristics of interest from person-level file
(e.g., MEPS 2001 Full Year Population Characteristics File) expands the scope of
potential estimates. For example, to estimate the total number of office-based
medical provider visits of persons with specific demographic characteristics
(such as age, race, and sex), population characteristics from a person-level
file need to be merged onto the office-based medical provider visits file. This
procedure is illustrated below. The MEPS 2001 Appendix File, HC-059I provides
additional detail on how to merge MEPS data files.
- Create data set PERSX by sorting the 2001 Full Year Population
Characteristics File, by the person identifier, DUPERSID. Keep only
variables to be merged onto the office-based medical provider visits file
and DUPERSID.Create data set OBMP by sorting the office-based medical provider
visits file by person identifier, DUPERSID.
- Create final data set NEWOBMP by merging these two files by DUPERSID,
keeping only records on the office-based medical provider visits file.
The following is an example of SAS code, which completes
these steps:
PROC SORT DATA=2001 Full Year Population Characteristics file (KEEP=DUPERSID
AGE31X AGE42X AGE53X SEX RACEX)
OUT=PERSX;
BY DUPERSID;
RUN;
PROC SORT DATA=OBMP;
BY DUPERSID;
RUN;
DATA NEWOBMP;
MERGE OBMP (IN=A) PERSX(IN=B);
BY DUPERSID;
IF A;
RUN;
Return To Table Of Contents
5.2 Linking the MEPS 2001 Office-Based Medical
Provider Visits File to the MEPS 2001 Medical Conditions File and/or the
MEPS 2001 Prescribed Medicines 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 2001 Appendix File,
HC-059I.
Return To Table Of Contents
5.2.1 Limitations/Caveats of
RXLK (the Prescribed Medicine Link File)
The RXLK file provides a link from MEPS event files to the 2001 Prescribed
Medicine File. When using RXLK, data users/analysts should keep in mind that one
office-based medical visit can link to more than one prescribed medicine record.
Conversely, a prescribed medicine event may link to more than one office-based
medical visits or different types of events. When this occurs, it is up to the
analyst to determine how the prescribed medicine expenditures should be
allocated among those medical events.
Return To Table Of Contents
5.2.2 Limitations/Caveats of
CLNK (the Medical Conditions Link File)
The CLNK provides a link from MEPS event files to the 2001 Medical Conditions
File. When using the CLNK, data users/analysts should keep in mind that (1)
conditions are self-reported and (2) there may be multiple conditions associated
with an office-based medical provider visit. Users should also note that not all
office-based medical provider visits link to the condition file.
Return To Table Of Contents
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, 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.
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.
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.
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
D. Variable-Source Crosswalk
VARIABLE-SOURCE CROSSWALK
FOR MEPS HC-059G: 2001 OFFICE-BASED MEDICAL PROVIDER VISITS
Survey Administration Variables
Variable |
Description |
Source |
DUID |
Dwelling unit ID |
Assigned in sampling |
PID |
Person number |
Assigned in sampling |
DUPERSID |
Person ID (DUID + PID) |
Assigned in sampling |
EVNTIDX |
Event ID |
Assigned in sampling |
EVENTRN |
Event round number |
CAPI derived |
FFEEIDX |
Flat fee ID |
CAPI derived |
MPCELIG |
MPC eligibility flag |
Constructed |
MPCDATA |
MPC data flag |
Constructed |
Return To Table Of Contents
Medical Provider Visits Variables
Variable |
Description |
Source |
OBDATEYR |
Event date - year |
CAPI derived |
OBDATEMM |
Event date - month |
CAPI derived |
OBDATEDD |
Event date - day |
CAPI derived |
SEETLKPV |
Did Person visit provider in person or telephone |
MV01 |
SEEDOC |
Did Person talk to MD this visit/phone call |
MV03 |
MEDPTYPE |
Type of medical person Person talked to on visit date |
MV04 |
DOCATLOC |
Any MD work at location where Person saw provider |
MV06 |
VSTCTGRY |
Best category for care Person received on visit date |
MV07 |
VSTRELCN |
Was this visit/phone call related to spec hlth cond |
MV08 |
PHYSTH |
This visit did Person have physical therapy |
MV10 |
OCCUPTH |
This visit did Person have occupational therapy |
MV10 |
SPEECHTH |
This visit did Person have speech therapy |
MV10 |
CHEMOTH |
This visit did Person have chemotherapy |
MV10 |
RADIATTH |
This visit did Person have radiation therapy |
MV10 |
KIDNEYD |
This visit did Person have kidney dialysis |
MV10 |
IVTHER |
This visit did Person have IV therapy |
MV10 |
DRUGTRT |
This visit did Person have treatment for drug/alcohol |
MV10 |
RCVSHOT |
This visit did Person receive an allergy shot |
MV10 |
PSYCHOTH |
Did Person have psychotherapy/counseling |
MV10 |
LABTEST |
This visit did Person have lab tests |
MV11 |
SONOGRAM |
This visit did Person have sonogram or ultrasound |
MV11 |
XRAYS |
This visit did Person have x-rays |
MV11 |
MAMMOG |
This visit did Person have a mammogram |
MV11 |
MRI |
This visit did Person have an MRI/Catscan |
MV11 |
EKG |
This visit did Person have an EKG or ECG |
MV11 |
EEG |
This visit did Person have an EEG |
MV11 |
RCVVAC |
This visit did Person receive a vaccination |
MV11 |
ANESTH |
This visit did Person receive anesthesia |
MV11 |
OTHSVCE |
This visit did Person have other diagnostic test/exam |
MV11 |
SURGPROC |
Was surgical procedure performed on Person this visit |
MV12 |
MEDPRESC |
Any medicines prescribed for Person this visit |
MV14 |
VAPLACE |
VA Facility Flag |
Constructed |
OBICD1X |
3-digit ICD-9-CM condition code |
Edited |
OBICD2X |
3-digit ICD-9-CM condition code |
Edited |
OBICD3X |
3-digit ICD-9-CM condition code |
Edited |
OBICD4X |
3-digit ICD-9-CM condition code |
Edited |
OBPRO1X |
2-digit ICD-9-CM procedure code |
Edited |
OBCCC1X |
Modified Clinical Classification Code (CCS) |
Constructed/Edited |
OBCCC2X |
Modified Clinical Classification Code (CCS) |
Constructed/Edited |
OBCCC3X |
Modified Clinical Classification Code (CCS) |
Constructed/Edited |
OBCCC4X |
Modified Clinical Classification Code (CCS) |
Constructed/Edited |
Return To Table Of Contents
Flat Fee Variables
Variable |
Description |
Source |
FFOBTYPE |
Flat fee bundle |
Constructed |
FFBEF01 |
Total # of visits in FF before 2001 |
FF05 |
FFTOT02 |
Total # of visits in FF after 2001 |
FF10 |
Return To Table Of Contents
Imputed Expenditure Variables
Variable |
Description |
Source |
OBSF01X |
Amount paid, self/family (imputed) |
CP Section (Edited) |
OBMR01X |
Amount paid, Medicare (imputed) |
CP Section (Edited) |
OBMD01X |
Amount paid, Medicaid (imputed) |
CP Section (Edited) |
OBPV01X |
Amount paid, private insurance (imputed) |
CP Section (Edited) |
OBVA01X |
Amount paid, Veterans Administration (imputed) |
CP Section (Edited) |
OBTR01X |
Amount paid, TRICARE (imputed) |
CP Section (Edited) |
OBOF01X |
Amount paid, other federal (imputed) |
CP Section (Edited) |
OBSL01X |
Amount paid, state & local government (imputed) |
CP Section (Edited) |
OBWC01X |
Amount paid, workers' compensation (imputed) |
CP Section (Edited) |
OBOR01X |
Amount paid, other private insurance (imputed) |
Constructed |
OBOU01X |
Amount paid, other public insurance (imputed) |
Constructed |
OBOT01X |
Amount paid, other insurance (imputed) |
CP Section (Edited) |
OBXP01X |
Sum of OBSF01X - OBOT01X (imputed) |
Constructed |
OBTC01X |
Household reported total charge (imputed) |
CP Section (Edited) |
IMPFLAG |
Imputation status |
Constructed |
Return To Table Of Contents
Weights
Variable |
Description |
Source |
PERWT01F |
Final person level weight, 2001 |
Constructed |
VARSTR01 |
Variance estimation stratum, 2001 |
Constructed |
VARPSU01 |
Variance estimation PSU, 2001 |
Constructed |
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