MEPS HC-067D: 2002 Hospital Inpatient Stays
October 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, ERHEVIDX, FFEEIDX)
2.6.1.3 Round Indicator (EVENTRN)
2.6.2 MPC Data Indicator (MPCDATA)
2.6.3 Hospital Inpatient Stay Event Variables
2.6.3.1 Start and End Dates of Event (IPBEGDD-IPENDYR)
2.6.3.2 Length of Stay (NUMNIGHX, NUMNIGHT)
2.6.3.3 Preceding ER Visits (EMERROOM)
2.6.3.4 Other Visit Detail (SPECCOND - VAPLACE)
2.6.3.5 Condition and Procedure Codes (IPICD1X-IPICD4X,
IPPRO1X, IPPRO2X), and Clinical Classification Codes (IPCCC1X-IPCCC4X)
2.6.3.6 Discharge Detail (DSCHPMED)
2.6.4 Flat Fee Variables (FFEEIDX, FFIPTYPE, FFBEF02,
FFTOT03)
2.6.4.1 Definition of Flat Fee Payments
2.6.4.2 Flat Fee Variable Descriptions
2.6.4.2.1 Flat Fee ID (FFEEIDX)
2.6.4.2.2 Flat Fee Type (FFIPTYPE)
2.6.4.2.3 Counts of Flat Fee Events that Cross Years
(FFBEF02, FFTOT03)
2.6.4.3 Caveats of Flat Fee Groups
2.6.5 Expenditure Data
2.6.5.1 Definition of Expenditures
2.6.5.2 Data Editing and Imputation Methodologies of
Expenditure Variables
2.6.5.2.1 General Data Editing Methodology
2.6.5.2.2 General Hot-Deck Imputation
2.6.5.2.3 Hospital Inpatient Stay Data Editing and
Imputation
2.6.5.3 Imputation Flag (IMPFLAG)
2.6.5.4 Flat Fee Expenditures
2.6.5.5 Zero Expenditures
2.6.5.6 Discount Adjustment Factor
2.6.5.7 Mother/Newborn Expenditures
2.6.5.8 Hospital Inpatient Stay/Emergency Room
Expenditures
2.6.5.9 Sources of Payment
2.6.5.10 Imputed Hospital Inpatient Stay Expenditure
Variables
2.6.5.10.1 Hospital Inpatient Facility Expenditures
(IPFSF02X-IPFOT02X, IPFXP02X, IPFTC02X)
2.6.5.10.2 Hospital Inpatient Physician Expenditures
(IPDSF02X - IPDOT02X, IPDTC02X, IPDXP02X)
2.6.5.10.3 Total Expenditures and Charges for Hospital
Inpatient Stays (IPXP02X, IPTC02X)
2.6.5.11 Rounding
3.0 Sample Weight (PERWT02F)
3.1 Overview
3.2 Details on Person Weight Construction
3.2.1 MEPS Panel 6 Weight
3.2.2 MEPS Panel 7 Weight
3.2.3 The Final Weight for 2002
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 Hospital
Inpatient Stays
4.4 Person-Based Ratio Estimates
4.4.1 Person-Based Ratio Estimates Relative to Persons
with Hospital Inpatient Use
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 (VARSTR, VARPSU)
5.0 Merging/Linking MEPS Data Files
5.1 Merging a 2002 Person-Level File to the 2002 Hospital
Inpatient Stays File
5.2 Linking the 2002 Hospital Inpatient Stays File to the
2002 Medical Conditions File and/or the 2002
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; and
- If the identity of any person or establishment
should be discovered inadvertently, then (a) no use will be made of
this knowledge, (b) the Director, Office of Management, AHRQ will be
advised of this incident, (c) the information that would identify any
individual or establishment will be safeguarded or destroyed, as
requested by AHRQ, and (d) no one else will be informed of the
discovered identity; and
- No one will attempt to link this data set with
individually identifiable records from any data sets other than the
Medical Expenditure Panel Survey or the National Health Interview
Survey.
By using these data you signify your agreement to comply
with the above stated statutorily based requirements with the knowledge that
deliberately making a false statement in any matter within the jurisdiction of
any department or agency of the Federal Government violates
Title 18
part 1 Chapter 47 Section 1001 and is punishable by a fine of up to
$10,000 or up to 5 years in prison.
The Agency for Healthcare Research and Quality requests
that users cite AHRQ and the Medical Expenditure Panel Survey as the data source
in any publications or research based upon these data.
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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 complete, 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 CD-ROM and/or 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 2002 Medical Expenditure Panel Survey (MEPS) Household
Component (HC) and Medical Provider Component (MPC). Released as an ASCII data
file (with related SAS and SPSS programming statements)and SAS transport file, the 2002 Hospital Inpatient Stays (STAZ)
public use file provides detailed information on hospital inpatient stays for a
nationally representative sample of the civilian noninstitutionalized population
of the United States. Data from the STAZ event file can be used to make
estimates of hospital inpatient stay utilization and expenditures for calendar
year 2002. As illustrated below, this file consists of MEPS survey data from the
2002 portion of Round 3 and Rounds 4 and 5 for Panel 6, as well as Rounds 1, 2
and the 2002 portion of Round 3 for Panel 7 (i.e., the rounds for the MEPS
panels covering calendar year 2002).
301 Moved Permanently
301 Moved Permanently
Hospital stay events reported in Panel 7 Round 3 and known
to have begun after December 31, 2002 are not included on this file.
Each record on the inpatient hospital event file
represents a unique hospital inpatient stay, that is, a hospital inpatient stay
reported by the household respondent. In addition to expenditures related to the
stay, each record contains household-reported medical conditions and procedures
associated with the hospitalization and information on the length of stay.
Annual counts of hospital inpatient stay utilization are
based entirely on household reports. Information from the MEPS MPC is used to
supplement expenditure and payment data reported by the household, and does not
affect use estimates.
Data from this event file can be merged with other 2002
MEPS HC data files for purposes of appending person-level data such as
demographic characteristics or health insurance coverage to each hospital
inpatient stay record.
This file can also be used to construct summary variables
of expenditures, sources of payment, and related aspects of hospital inpatient
care. Aggregate annual person-level information on the use of hospital inpatient
stays and other health services use is provided on the MEPS 2002 Full Year
Consolidated Data File, where each record represents a MEPS sampled person.
This documentation offers an 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
Any variables not found on this file but released on
previous years' files were excluded because they contained only missing data.
For more information on MEPS HC survey design see S.
Cohen, 1997; J. Cohen, 1997; and S. Cohen, 1996. For information on the MEPS MPC
design, see S. Cohen, 1999. Copies of the HC and the MPC survey instruments used
to collect the information on the STAZ file are available in the Survey
Instruments section on the MEPS web site at the following address:
http://www.meps.ahrq.gov.
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2.0 Data File Information
The 2002 Hospital Inpatient Stays public use data set
consists of one event-level data file. The file contains characteristics
associated with the STAZ 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 2002 STAZ public use data set contains variable and
frequency distributions for a total of 3,944 hospital inpatient stay records
reported during the 2002 portion of Round 3 and Rounds 4 and 5 for Panel 6, as
well as Rounds 1, 2, and the 2002 portion of Round 3 for Panel 7 of the MEPS
Household Component. This file includes hospital inpatient stay records for all
household survey respondents who resided in eligible responding households and
reported at least one hospital inpatient stay. Hospital inpatient stay records
known to have ended before January 1, 2002 or after December 31, 2002 are not
included on this file. Some household respondents may have multiple hospital
inpatient stays and, thus, will be represented in multiple records on this file.
Other household respondents may have reported no hospital inpatient stays and,
thus, will have no records on this file. Of the 3,944 hospital inpatient stay
records, 3,782 are associated with persons having a positive person-level
weight (PERWT02F). The persons represented on this file had to meet the
following three criteria:
- The hospital stay had to have been reported by a
household survey respondent as an inpatient hospital stay (regardless of a
stay's length). Thus, the file contains some hospitalizations that were
reported as not including an overnight stay.
- The hospital stay had to have ended during
2002. Stays that began prior to 2002 but ended during 2002 are included
on this file. Stays that began in 2002 but ended during 2003 are
excluded from this file and will be represented on a subsequent 2003
data file. Persons with no hospital inpatient stay events for 2002 are
not included on this file but are represented on the 2002 MEPS
person-level file. A codebook for the data file is provided in files
H62CB.PDF and H62CB.ASP.
- The persons represented on this file also had to meet
either 3a or 3b:
- Be classified as a key in-scope person who
responded for his or her entire period of 2002 eligibility (i.e., persons
with a positive 2002 full-year person-level sampling weight (PERWT02F >
0)), or
- Be an eligible member of a family all of
whose key in-scope members have a positive person-level weight
(PERWT02F > 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 (FAMWT02F > 0). Note that FAMIDYR and
FAMWT02F are variables on the 2002 Population Characteristics file.
One caveat that should be noted is that in the case of a
newborn and the hospital inpatient stay associated with the newborn's birth, a
separate hospital inpatient stay record exists on the file only if the newborn
was discharged after the mother. Thus, hospital stays associated with a normal
birth are generally represented on the file as a single record (i.e., the
mother's hospital inpatient stay record, covering expenditure data for both the
mother and baby). In situations where the newborn was discharged after the
mother, the birth event will be represented as two records: one record for the
mother and one record for the baby. For newborns re-admitted to the hospital
during the reference year, each subsequent re-admission will have a separate
record.
Each inpatient record includes the following: start and
end dates of the hospital inpatient stay; number of nights in the hospital;
reason entered the hospital; condition(s) associated with the hospital inpatient
stay; medicines prescribed at discharge; flat fee information; imputed sources
of payment; total payment and total charge for both the facility and physician
portions of the hospital inpatient stay expenditure; a full-year person-level
weight; variance strata; and variance PSU.
Data from this file can be merged with the MEPS 2002 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. Hospital inpatient stay events can
also be linked to the MEPS 2002 Medical Conditions File and the MEPS 2002
Prescribed Medicines File. Please see Section 5.0 or the MEPS 2002 Appendix
File, HC-067I, 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 2002
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 Inpatient Events file, both
weighted and unweighted frequencies are provided in the codebook files
(H67DCB.PDF and H67DCB.ASP). The codebook and data file sequence list variables
in the following order:
Unique person identifiers
Unique hospital inpatient stay identifiers
Hospital inpatient stay characteristics 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, the 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 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 STAZ 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 General
Variables on this file were derived from the HC
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:
- HS - Hospital Stays Section
- FF - Flat Fee Section
- CP - Charge Payment Section;
- Variables constructed from multiple questions using
complex algorithms are labeled "Constructed" in the "Source" column; and
- Variables which have been edited or imputed are so
indicated.
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2.5.2 Expenditure and Source of Payment Variables
The names of the expenditure and source of payment
variables follow a standard convention, are eight characters in length, and end
in an "X" indicating edited/imputed. Please note that imputed means that a
series of logical edits, as well as an imputation process to account for missing
data, have been performed on the variable.
The total sum of payments and 12 sources of payment
variables 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 |
For expenditure variables on the IP file, the third
character indicates whether the expenditure is associated with the facility (F)
or the physician (D).
In the case of the source of payment variables, the fourth
and fifth characters indicate:
SF - self or family |
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 |
TR - TRICARE |
OU - other public |
|
XP - sum of payments |
In addition, the total charge variable is indicated by TC
in the variable name.
The sixth and seventh characters
indicate the year (02). The eighth character, "X",
indicates whether the variable is edited/imputed.
For example, IPFSF02X is the edited/imputed amount paid by
self or family for the facility portion of the hospital inpatient stay
expenditure incurred in 2002.
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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 2002 Full
Year Population Characteristics File.
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2.6.1.2 Record Identifiers (EVNTIDX, ERHEVIDX, FFEEIDX)
EVNTIDX uniquely identifies each hospital inpatient
stay/event (i.e., each record on the STAZ file) and is the variable required to
link hospital inpatient stay events to data files containing details on
conditions and/or prescribed medicines (MEPS 2002 Medical Conditions File and
MEPS 2002 Prescribed Medicines File, respectively). For details on linking, see
Section 5.0 or the MEPS 2002 Appendix File, HC-067I.
ERHEVIDX is a constructed variable identifying a STAZ
record that includes the facility expenditures for the preceding emergency room
visit. This variable was constructed by comparing date information for the
reported hospital stay and all emergency room visits for the same person. On the
2002 STAZ file, there are 575 hospital stays linked to a preceding emergency
room visit, that is, there are records with a valid ERHEVIDX value. ERHEVIDX has
not been reconciled with the unedited variable EMERROOM. Please note that, the
physician expenditures associated with the emergency room visit remain on the
emergency room file.
FFEEIDX is a constructed variable which uniquely
identifies a flat fee group, that is, all events that were a part of a flat fee
payment. For example, dialysis treatments are typically covered in a flat fee
arrangement where all visits are covered under one flat fee dollar amount. These
events would have the same value for FFEEIDX.
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2.6.1.3 Round Indicator (EVENTRN)
EVENTRN indicates the round in which the hospital
inpatient stay was first reported. Please note that Rounds 3, 4, and 5 are
associated with MEPS survey data collected from Panel 6. Likewise, Rounds 1, 2,
and 3 are associated with data collected from Panel 7.
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2.6.2 MPC Data Indicator (MPCDATA)
MPCDATA is a constructed variable which indicates whether
or not MPC data were collected for the hospital inpatient stay. While all
hospital inpatient events are sampled into the Medical Provider Component, not
all hospital inpatient stay records have MPC data associated with them. This is
dependent upon the cooperation of the household respondent to provide permission
forms to contact the hospital as well as the cooperation of the hospital to
participate in the survey.
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2.6.3 Hospital Inpatient Stay Event Variables
This file contains variables describing hospital inpatient
stays/events reported by household respondents in the Hospital Stays section of
the MEPS HC questionnaire. The questionnaire contains specific probes for
determining details about the hospital inpatient stay.
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2.6.3.1 Start and End Dates of Event (IPBEGDD-IPENDYR)
This file contains variables describing hospital inpatient
stays reported by household respondents in the Hospital Section of the MEPS HC
questionnaire. There are three variables which indicate the day, month, and year
a hospital stay began (IPBEGDD, IPBEGMM, IPBEGYR, respectively). Similarly,
there are three variables which indicate the day, month, and year a hospital
stay ended (IPENDDD, IPENDMM, IPENDYR, respectively). These variables have not
been edited.
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2.6.3.2 Length of Stay (NUMNIGHX, NUMNIGHT)
NUMNIGHX denotes the length of a hospital inpatient stay.
For stays beginning in 2001 and ending in 2002, this variable would include the
nights associated with the entire visit. It was edited using the above mentioned
begin and end dates of the hospital inpatient stay (Section 2.6.3.1). If the
dates were unknown, then NUMNIGHX used the number from the unedited variable
NUMNIGHT (number of nights in the hospital). If both the dates and NUMNIGHT were
missing data, then NUMNIGHX was imputed. Users should note that NUMNIGHT was
only asked for events with missing date information. Hence, it contains large
amounts of missing data and cannot be used alone but rather in conjunction with
date information.
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2.6.3.3 Preceding ER Visits (EMERROOM)
The variable EMERROOM was derived directly from the
Hospital Inpatient Stays section of the HC survey instrument and is unedited.
EMERROOM describes whether or not the hospital inpatient stay began with an
emergency room visit. Data users/analysts should be aware that no attempt was
made to reconcile EMERROOM with information from the Emergency Room Visit File.
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2.6.3.4 Other Visit Detail (SPECCOND - VAPLACE)
Also provided are the following unedited variables:
hospital inpatient stays related to a medical condition (SPECCOND); the reason
the person entered the hospital (RSNINHOS); any operation or surgery performed
while the respondent was in the hospital (ANYOPER).
With respect to RSNINHOS, please note that while there
were 507 cases where RSNINHOS = 4 (reason entered hospital - to give birth to a
baby), this does not mean that there were actually 507 new births. In
fact, it may have been reported that the mother went to the hospital for
delivery (hence, the interviewer would have assigned the event RSNINHOS = 4),
but the mother could have had, for example, false labor pains or a stillbirth.
Thus, this unedited self-reported variable may be inconsistent with reported
number of births (see the 2002 Full Year Population Characteristics File,
section 2.6.2 "Navigating the MEPS Data with Information on Person Disposition
Status").
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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2.6.3.5 Condition and Procedure Codes (IPICD1X-IPICD4X,
IPPRO1X, IPPRO2X), and Clinical Classification Codes (IPCCC1X-IPCCC4X)
Information on household reported medical conditions and
procedures associated with each hospital inpatient stay event are provided on
this file. There are up to four condition and CCC codes (IPICD1X-IPICD4X,
IPCCC1X-IPCCC4X) and up to two procedure codes (IPPRO1X and IPPRO2X) listed for
each hospital inpatient stay event. In order to obtain complete condition
information associated with an event, the data user/analyst must link to the
MEPS 2002 Medical Conditions File. Details on how to link the 2002 STAZ file to
the MEPS 2002 Medical Conditions File are provided in Section 5.2 and the MEPS
2002 Appendix File, HC-067I. The data user/analyst should note that because of
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 2002 ICD-9-CM codes, including
medical condition and V codes (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 (Cox and Cohen, 1985;
Cox and Iachan, 1987; Edwards, et al., 1994; and Johnson and Sanchez, 1993). For
detailed information on how condition and procedures were coded, please refer to
the documentation on the MEPS 2002 Medical Conditions File. For frequencies of
conditions by event type, please see the MEPS 2002 Appendix File, HC-067I.
The ICD-9-CM condition codes were aggregated into
clinically meaningful categories. These categories, included on the file as
IPCCC1X-IPCCC4X, 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 three-digit categories. Details on this procedure can be
found in the 2002 MEPS Medical Conditions File.
The condition (and clinical classification codes) and
procedure codes linked to each hospital inpatient stay 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 MEPS 2002 Medical Conditions File in
conjunction with this hospital inpatient stay event file should note that the
order of conditions on this file is not identical to that on the Medical
Conditions file.
The user should also note that because of the design of
the HC survey instrument, most hospital stays that are reported as being for a
delivery (RSNINHOS=4) link to condition codes that are for pregnancy rather than
a delivery. In addition, RSNINHOS has not been reconciled with the ICD-9-CM
condition codes, the procedure codes, or the CCC codes that are on the file.
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2.6.3.6 Discharge Detail (DSCHPMED)
DSCHPMED is derived directly from the Hospital Stays
Section of the HC survey instrument. DSCHPMED indicates whether or not any
medicines were prescribed at discharge.
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2.6.4 Flat Fee Variables (FFEEIDX, FFIPTYPE, FFBEF02,
FFTOT03)
2.6.4.1 Definition of Flat Fee Payments
A flat fee is the fixed dollar amount a person is charged
for a package of health care services provided during a defined period of time.
Examples would be: obstetrician's fee covering a normal delivery, as well as
pre- and post-natal care; or a surgeon's fee covering surgical procedure and
post-surgical care. A flat fee group is the set of medical services (i.e.,
events) that are covered under the same flat fee payment. The flat fee groups
represented on the STAZ file include flat fee groups where at least one of the
health care events, as reported by the HC respondent, occurred during 2002. By
definition, a flat fee group can span multiple years. Furthermore, a single
person can have multiple flat fee groups.
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2.6.4.2 Flat Fee Variable Descriptions
2.6.4.2.1 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 2002 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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2.6.4.2.2 Flat Fee Type (FFIPTYPE)
FFIPTYPE indicates whether the 2002 hospital stay is the
"stem" or "leaf" of a flat fee group. A stem (records with FFIPTYPE = 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 FFIPTYPE = 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 hospital inpatient stays
that are not part of a flat fee payment, the FFIPTYPE is set to -1,
"INAPPLICABLE."
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2.6.4.2.3 Counts of Flat Fee Events that Cross Years
(FFBEF02, FFTOT03)
As explained in Section 2.6.4.1, a flat fee payment covers
multiple events and the multiple events could span multiple years. For
situations where the hospital inpatient stay/event occurred in 2002 as a part of
a group of events, and some event occurred before or after 2002, counts of the
known events are provided on the STAZ record. Variables that indicate events
occurred before or after 2002 are as follows:
FFBEF02 - total number of pre-2002 events in the same
flat fee group as the 2002 hospital inpatient stay(s). This count would not
include 2002 hospital inpatient stay(s).
FFTOT03 - the number of 2003 hospital inpatient stays
expected to be in the same flat fee group as the hospital inpatient stay
that occurred in 2002. Because there were no 2003 events expected for any
flat fee group, this variable was omitted from the 2002 IP file.
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2.6.4.3 Caveats of Flat Fee Groups
There are 13 hospital inpatient stays/events 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 2002, but the remaining
visits that were part of this flat fee group occurred in 2003. In this case, the
2002 flat fee group would consist of one event, the stem. The 2003 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 2001 but subsequent visits occurred during 2002. In this case, the
initial visit would not be represented on the file. This 2002 flat fee group
would then only consist of one or more leaf records and no stem.
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2.6.5 Expenditure Data
2.6.5.1 Definition of Expenditures
Expenditure variables 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 for each hospital stay, 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 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 are they directly comparable to the
expenditures defined in the 1987 NMES. For details on expenditure definitions,
please reference the following, "Informing American Health Care Policy" (Monheit,
et al., 1999). AHRQ has developed factors to apply to the 1987 NMES expenditure
data to facilitate longitudinal analysis. These factors can be assessed via the
CFACT data center. For more information, see the Data Center section of the MEPS
web site http://www.meps.ahrq.gov.
Expenditure data related to hospital inpatient events are
broken out by facility and separately billing doctor expenditures. This file
contains six categories of expenditure variables per stay: basic hospital
facility expenses; expenses for doctors who billed separately from the hospital
for any inpatient services provided during hospital stay; total expenses, which
is the sum of the facility and physician expenses; facility charge; physician
charge; and total charges, which is the sum of the facility and physician
charges. If examining trends in MEPS expenditures or performing longitudinal
analysis on MEPS expenditures, please refer to section C, sub-section 2.1 for
more information.
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2.6.5.2 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 Components (MPC). The MPC
contacted medical providers identified by household respondents. The charge and
payment data from medical providers were used in the expenditure imputation
process to supplement missing household data. For all hospital inpatient stays,
MPC data were used if available; otherwise, HC data were used. Missing data for
hospital inpatient stays where HC data were not complete and MPC data were not
collected, or MPC data were not complete, were imputed during the imputation
process.
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2.6.5.2.1 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,
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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2.6.5.2.2 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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2.6.5.2.3 Hospital Inpatient Stay Data Editing and
Imputation
Facility expenditures for hospital inpatient stays 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 a 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.
Separate imputations were performed for flat fee and
simple events. Most hospital inpatient stays were imputed as simple events
because facility charges for an inpatient hospital stay are rarely grouped with
other events. (See Section 2.6.4 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. For office
based and outpatient events, the donor pool was restricted to events with
complete expenditures from the MPC. Due to the ratio of donors to recipients,
for hospital inpatient and emergency room events, there were no donor pool
restrictions.
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) would not be
represented among incomplete events (recipients).
Expenditures for services provided by separately billing
doctors in hospital settings were also edited and imputed. These expenditures
are shown separately from hospital facility charges for hospital inpatient,
outpatient, and emergency room care.
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2.6.5.3 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 (not applicable to IP events)
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2.6.5.4 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 facility payments, while physician's expenditures may be still
present. Thus, if the first visit in the flat fee group occurred prior to 2002,
all of the events that occurred in 2002 will have zero payments. Conversely, if
the first event in the flat fee group occurred at the end of 2002, the total
expenditure for the entire flat fee group will be on that event, regardless of
the number of events it covered after 2002. See Section 2.6.4 for details on the
flat fee variables.
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2.6.5.5 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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2.6.5.6 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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2.6.5.7 Mother/Newborn Expenditures
Expenditure data for newborns were edited to exclude
discharges after birth when the newborn left the hospital before or on the same
day as the mother. As a result, inpatient expenditures reported for 2002 births
were usually applied to the mother and not treated as separate expenditures for
the infant. However, if a newborn was discharged at a later date than the
mother's discharge date, then the hospitalization was treated as a separate
hospital stay for the newborn.
This means that, in most cases, expenditure data for the
newborn is included on the mother's record. A separate record for the newborn
only exists if the newborn was discharged after the mother. In this case, the
expenditure for the newborn is on the newborn's record.
In addition, the user should note that for the purposes of
the expenditure imputation, deliveries were identified using the variable
RSNINHOS and pregnancy ID which has not been reconciled with pregnancy and
delivery ICD-9-CM codes on this file as well as on the Medical Conditions File.
As mentioned previously, in most instances where RSNINHOS = 4 (delivery), the
ICD-9-CM code indicates a pregnancy rather than a delivery.
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2.6.5.8 Hospital Inpatient Stay/Emergency Room
Expenditures
Although a person may have indicated that there was an
emergency room visit that preceded this hospital stay (EMERROOM), there was no
verification that, in fact, the emergency room visit was actually recorded
within the Emergency Room Section of the questionnaire.
While it is true that all of the event files can be linked
by DUPERSID, there is no unique record link between hospital inpatient stays and
emergency room visits. That is, a person could have one hospital inpatient stay
and three emergency room visits during the calendar year. While the hospital
inpatient stay record may indicate that it was preceded by an emergency room
visit, there is no unique record link to the appropriate (of the three)
emergency room visit.
However, wherever relationships could be identified (using
MPC start and end date of the events as well as other information from the
provider), the facility expenditure associated with the emergency room visit was
moved to the hospital facility expenditure. Hence, for some hospital stays,
facility expenditures for a preceding emergency room visit are included. In
these situations, the corresponding emergency room record on the MEPS 2002
Emergency Room Visit File will have its facility expenditure information zeroed
out to avoid double-counting. The variable ERHEVIDX identifies these hospital
stays whose expenditures include the facility expenditures for the preceding emergency room visit (see ERHEVIDX in
Section 2.6.1.2). It should also be noted that for these cases, there is only
one hospital stay associated with the emergency room stay.
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2.6.5.9 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 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 health insurance coverage and sources of payment based on data collected in
the survey. These variables include:
- Other Private - any type of private insurance
payments reported for persons not reported to have any private health
insurance coverage during the year as defined in MEPS, and
- Other Public - Medicare/Medicaid payments reported
for persons who were not reported to be enrolled in the Medicare/Medicaid
program at any time during the year.
Though these two sources are relatively small in
magnitude, data users/analysts should exercise caution when interpreting the
expenditures associated with these two additional sources of payment. While
these payments stem from apparent inconsistent responses to health insurance and
source of payment questions in the survey, some of these inconsistencies may
have logical explanations. For example, private insurance coverage in MEPS is
defined as having a major medical plan covering hospital and physician services.
If a MEPS sampled person did not have such coverage but had a single service
type insurance plan (e.g., dental insurance) that paid for a particular episode
of care, those payments may be classified as "other private." Some of the "other
public" payments may stem from confusion between Medicaid and other state and
local programs or may be from persons who were not enrolled in Medicaid, but
were presumed eligible by a provider who ultimately received payments from the
public payer.
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2.6.5.10 Imputed Hospital Inpatient Stay Expenditure
Variables
This file contains two sets of imputed expenditure
variables: facility expenditures and physician expenditures.
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2.6.5.10.1 Hospital Inpatient Facility Expenditures
(IPFSF02X-IPFOT02X, IPFXP02X, IPFTC02X)
Hospital facility expenses include all expenses for direct
hospital care, including room and board, diagnostic and laboratory work, x-rays,
and similar charges, as well as any physician services included in the hospital
charge.
IPFSF02X - IPFOT02X are the 12 sources of payment. The 12
sources of payment are: self/family (IPFSF02X), Medicare (IPFMR02X), Medicaid
(IPFMD02X), private insurance (IPFPV02X), Veterans Administration (IPFVA02X),
TRICARE (IPFTR02X), other Federal sources (IPFOF02X), State and Local
(non-federal) government sources (IPFSL02X), Worker's Compensation (IPFWC02X),
other private insurance (IPFOR02X), other public insurance (IPFOU02X), and other
insurance (IPFOT02X). IPFXP02X is the sum of the 12 sources of payment for the
Hospital Facility expenditures, and IPFTC02X is the total charge.
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2.6.5.10.2 Hospital Inpatient Physician Expenditures
(IPDSF02X - IPDOT02X, IPDTC02X, IPDXP02X)
Separately billing doctor (SBD) expenses typically cover
services provided to patients in hospital settings by providers like
anesthesiologists, radiologists, and pathologists, whose charges are often not
included in hospital bills.
For medical doctors who bill separately (i.e., outside the
hospital bill), a separate data collection effort within the Medical Provider
Component was performed to obtain this same set of expenditure information from
each separately billing doctor. It should be noted that there could be several
separately billing doctors associated with a medical event. For example, a
hospital inpatient stay could have a radiologist, anesthesiologist, pathologist
and a surgeon associated with it. If their services are not included in the
hospital bill then this is one medical event with four separately billing
doctors. The imputed expenditure information associated with the separately
billing doctors for a hospital inpatient stay is combined (i.e., the
expenditures incurred by the radiologist + anesthesiologist + pathologist +
surgeon) and is provided on the file. IPDSF02X - IPDOT02X are the 12 sources of
payment; IPDXP02X is the sum of the 12 sources of payments; and IPDTC02X is the
physician's total charge.
Data users/analysts need to take into consideration
whether to analyze facility and SBD expenditures separately, combine them within
service categories, or collapse them across service categories (e.g., combine
SBD expenditures with expenditures for physician visits to offices and/or
outpatient departments).
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2.6.5.10.3 Total Expenditures and Charges for Hospital
Inpatient Stays (IPXP02X, IPTC02X)
Data users/analysts interested in total expenditures
should use the variable IPXP02X, which includes both facility and physician
amounts. Those interested in total charges should use the variable IPTC02X,
which includes both facility and physician charges (see Section 2.6.5.1 for an
explanation of the "charge" concept).
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2.6.5.11 Rounding
Expenditure variables have been rounded to the nearest
penny. Person-level expenditure information released on the MEPS 2002
Person-Level Use and Expenditure File were rounded to the nearest dollar. It
should be noted that using the MEPS 2002 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 MEPS 2002 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 2002 Appendix File, HC-067I, for details on such rounding differences.
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3.0 Sample Weight (PERWT02F)
3.1 Overview
There is a single full year person-level weight (PERWT02F)
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 2002. 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 PERWT02F was developed in several
stages. Person-level weights for Panels 6 and 7 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, black but
non-Hispanic, Asian but non- Hispanic and other); sex; and age. A 2002 composite
weight was then formed by multiplying each weight from Panel 6 by the factor .55
and each weight from Panel 7 by the factor .45. 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 6 Weight
The person-level weight for MEPS Panel 6 was developed
using the 2001 full year weight for an individual as a "base" weight for survey
participants present in 2001. For key, in-scope respondents who joined an RU
some time in 2002 after being out-of-scope in 2001, the 2001 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 2002. These control
figures were derived by scaling back the population totals obtained from the
March 2002 CPS to reflect the December 2002 CPS estimated population
distribution across age and sex categories as of December 2002. 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, 2002 is 284,568,843. Key,
responding persons not in-scope on December 31, 2002 but in-scope earlier in the
year retained, as their final Panel 6 weight, the weight after the nonresponse
adjustment.
Return To Table Of Contents
3.2.2 MEPS Panel 7 Weight
The person-level weight for MEPS Panel 7 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 2002 portion of Round 3 as well as raking to the same
population control figures for December 2002 used for the MEPS Panel 6 weights.
The same five variables employed for Panel 6 raking (census region, MSA status,
race/ethnicity, sex, and age) were used for Panel 7 raking. Similarly, for Panel
7, key, responding persons not in-scope on December 31, 2002 but in-scope
earlier in the year retained, as their final Panel 7 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 raking to figures at the family and person level obtained from the March
2002 CPS data base.
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3.2.3 The Final Weight for 2002
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, black but
non-Hispanic, Asian but non- Hispanic and other); sex; and age. Overall, the
weighted population estimate for the civilian noninstitutionalized population
for December 31, 2002 is 284,568,843 (PERWT02F>0 and INSC1231=1). The weights of
some persons out-of-scope on December 31, 2002 were also calibrated, this time
using poststratification. Specifically, the weights of persons out-of-scope on
December 31, 2002 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 2002 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 288,181,763.
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3.2.4 Coverage
The target population for MEPS in this file is the 2002
U.S. civilian noninstitutionalized population. However, the MEPS sampled
households are a subsample of the NHIS households interviewed in 2000 (Panel 6)
and 2001 (Panel 7). New households created after the NHIS interviews for the
respective Panels and consisting exclusively of persons who entered the target
population after 2000 (Panel 6) or after 2001 (Panel 7) 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 hospital inpatient care
and to allow for estimates of number of persons with hospital inpatient
utilization for 2002.
Return To Table Of Contents
4.1 Variables with Missing Values
It is essential that the data user/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 data user/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 data user/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 expenditure)
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 hospital
inpatient stays, expenditures and sources of payment, the value in each record
contributing to the estimates must be multiplied by the weight (PERWT02F)
contained on that record.
Example 1
For example, the total number of hospital inpatient stays,
regardless of the length of the hospital stay, for the civilian
noninstitutionalized population of the U.S. in 2002, is estimated as the sum of
the weight (PERWT02F) across all records. That is,
301 Moved Permanently
301 Moved Permanently
= 29,534,629. |
(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 at the hospital inpatient stay level (where the visit has
a total expense greater than 0), should be
calculated as the weighted mean of the facility bill and doctor's bill paid by
self/family. That is,
301 Moved Permanently
301 Moved Permanently
= $218.16 |
(2) |
where Xj = IPFSF02Xj +
IPDSF02Xj and
301 Moved Permanently
301 Moved Permanently
= 29,131,913 |
for all records with IPXP02Xj > 0.
This gives $218.16 as the estimated mean amount of
out-of-pocket payment of expenditures associated with hospital inpatient stays
(discharges) and 29,131,913 as an estimate of the total number of hospital
inpatient stays with expenditures. Both of these estimates are for the civilian
noninstitutionalized population of the U.S. in 2002.
Example 3
Another example would be to estimate the mean proportion
of total expenditures paid by private insurance for hospital inpatient stays
with expenditures. This should be calculated as the weighted mean of the
proportion of total expenditures paid by private insurance at the event level.
That is,
301 Moved Permanently
301 Moved Permanently
= 0.3842 |
(3) |
where Yj = (IPFPV02Xj + IPDPV02Xj)/IPXP02Xj
and
301 Moved Permanently
301 Moved Permanently
= 29,131,913 |
for all records with IPXP02Xj > 0.
This gives 0.3842 as the estimated mean proportion of
total expenditures paid by private insurance for hospital inpatient stays with
expenditures for the civilian noninstitutionalized population of the U.S. in
2002.
Return To Table Of Contents
4.3 Estimates of the Number of Persons with Hospital
Inpatient Stays
When calculating an estimate of the total number of
persons with hospital inpatient stays, 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. discharged from a
hospital in 2002 with at least one hospital stay of 10 or more nights, this
event 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
(PERWT02F) for person i
and
Xi = 1 if NUMNIGHXj GE 10 for
any stay 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 Hospital Inpatient Use
This file may be used to derive person-based ratio
estimates. However, when calculating ratio estimates where the denominator is at
the person level, care should be taken to properly define and estimate the unit
of analysis as person-level. For example, the mean expense for persons with
hospital inpatient stays is estimated as:
301 Moved Permanently
301 Moved Permanently
across all unique persons i on this file |
(5) |
where
Wi is the sampling weight
(PERWT02F) for person i
and
Zi =
301 Moved Permanently
301 Moved Permanently
IPXP02Xj across all
stays 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 hospital inpatient stay 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 use and those
without use). For example, the proportion of the civilian noninstitutionalized
population of the U.S. with at least one hospital inpatient stay of four or more
days would be estimated as:
301 Moved Permanently
301 Moved Permanently
across all unique persons i on the person-level file |
(6) |
where
Wi is the sampling weight
(PERWT02F) for person i
and
Zi = 1 if NUMNIGHXj GE
4 for any stay 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 with 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 (VARSTR, VARPSU)
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 2002 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, et al, 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 $19.33 and 0.0128
for the estimated mean 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 the MEPS 2002 Hospital Inpatient Stays File can
be used alone or in conjunction with other files. This section provides
instructions for linking the hospital stays file with other MEPS public use
files, namely, the person-level file, the prescribed medicines file, and the
medical conditions file.
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5.1 Merging a 2002 Person-Level File to the 2002 Hospital
Inpatient Stays File
Merging characteristics of interest from other MEPS files(e.g., MEPS 2002 Full-Year Population Characteristics File) expands
the scope of potential estimates. For example,to estimate the total number of hospital inpatient stays for persons with
specific demographic characteristics (such as, age, race, sex, and education),
population characteristics from a person-level file need to be merged onto the
hospital inpatient stays file. This procedure is illustrated below. The MEPS
2002 Appendix File, HC-067I, provides additional detail on how to merge MEPS
data files.
- Create data set PERSX by sorting the MEPS 2002 Full
Year Population Characteristics File by the person identifier, DUPERSID.
Keep only variables to be merged onto the hospital inpatient stays file,
and DUPERSID.
- Create data set STAZ by sorting the hospital
inpatient stays file by person identifier, DUPERSID.
- Create final data set NEWSTAZ by merging these two
files by DUPERSID, keeping only records on the hospital inpatient stays
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=STAZ;
BY DUPERSID;
RUN;
DATA NEWSTAZ;
MERGE STAZ (IN=A) PERSX(IN=B);
BY DUPERSID;
IF A;
RUN;
Return To Table Of Contents
5.2 Linking the 2002 Hospital Inpatient Stays File to the
2002 Medical Conditions File and/or the 2002 Prescribed Medicines File
Due to survey design issues, data users/analysts must keep
limitations and caveats in mind when linking the different files. Those
limitations/caveats are listed below. For detailed linking examples, including
SAS code, data users/analysts should refer to the MEPS 2002 Appendix File,
HC-067I.
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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
records on the 2002 Prescribed Medicine File. When using RXLK, data
users/analysts should keep in mind that one hospital inpatient stay could link
to more than one prescribed medicine record. Conversely, a prescribed medicine
event may link to more than one hospital inpatient stay or different types of
events. When this occurs, it is up to the data user/analyst to determine how the
prescribed medicine expenditures should be allocated among those medical events.
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5.2.2 Limitations/Caveats of CLNK (the Medical Conditions
Link File)
The CLNK provides a link from MEPS event files to the 2002
Medical Conditions File. When using the CLNK, data users/analysts should keep in
mind that (1) conditions are self-reported, (2) there may be multiple conditions
associated with a hospital inpatient stay, and (3) a condition may link to more
than one hospital inpatient stay or any other type of visit. Data users/analysts
should also note that not all hospital inpatient stays link to the medical
conditions file.
Return To Table Of Contents
References
Cohen, S.B. (1999). Sample Design of the 1996 Medical
Expenditure Panel Survey Medical Provider Component. Journal of
Economic and Social Measurement. Vol. 24, 25-53.
Cohen, S.B. (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.
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: Health
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.
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D. Variable-Source Crosswalk
VARIABLE-SOURCE CROSSWALK
FOR MEPS HC-067D: 2002 HOSPITAL INPATIENT
STAYS
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 |
ERHEVIDX |
Event ID for corresponding emergency room visit |
Constructed |
FFEEIDX |
Flat fee ID |
CAPI derived |
MPCDATA |
MPC Data Flag |
Constructed |
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Characteristics of Hospital Inpatient Stays
Variable |
Description |
Source |
IPBEGYR |
Event start date - year |
CAPI derived |
IPBEGMM |
Event start date - month |
CAPI derived |
IPBEGDD |
Event start date - day |
CAPI derived |
IPENDYR |
Event end date - year |
CAPI derived |
IPENDMM |
Event end date - month |
CAPI derived |
IPENDDD |
Event end date - day |
CAPI derived |
NUMNIGHX |
# of nights in hospital - Edited/Imputed |
(Edited/Imputed) |
NUMNIGHT |
Number of nights stayed at provider |
HS01 |
EMERROOM |
Did stay begin with emergency room visit |
HS02 |
SPECCOND |
Hospital stay related to condition |
HS03 |
RSNINHOS |
Reason entered hospital |
HS05 |
ANYOPER |
Any operations or surgeries performed |
HS06 |
VAPLACE |
VA facility flag |
Constructed |
IPICD1X |
3 digit ICD-9-CM condition code |
Edited |
IPICD2X |
3 digit ICD-9-CM condition code |
Edited |
IPICD3X |
3 digit ICD-9-CM condition code |
Edited |
IPICD4X |
3 digit ICD-9-CM condition code |
Edited |
IPPRO1X |
2 digit ICD-9-CM procedure code |
Edited |
IPPRO2X |
2 digit ICD-9-CM procedure code |
Edited |
IPCCC1X |
Modified Clinical Classification Code |
Constructed/Edited |
IPCCC2X |
Modified Clinical Classification Code |
Constructed/Edited |
IPCCC3X |
Modified Clinical Classification Code |
Constructed/Edited |
IPCCC4X |
Modified Clinical Classification Code |
Constructed/Edited |
DSCHPMED |
Medicines prescribed at discharge |
HS08 |
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Flat Fee Variables
Variable |
Description |
Source |
FFIPTYPE |
Flat Fee Bundle |
Constructed |
FFBEF02 |
Total # of visits in FF before 2002 |
FF05 |
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Imputed Total Expenditure Variables
Variable |
Description |
Source |
IPXP02X |
Total expenditure for event (IPFXP02X+IPDXP02X) |
Constructed |
IPTC02X |
Total charge for event (IPFTC02X+IPDTC02X) |
Constructed |
Return To Table Of Contents
Imputed Facility Expenditure Variables
Variable |
Description |
Source |
IPFSF02X |
Facility amount paid, self/family (Imputed) |
CP Section (Edited) |
IPFMR02X |
Facility amount paid, Medicare (Imputed) |
CP Section (Edited) |
IPFMD02X |
Facility amount paid, Medicaid (Imputed) |
CP Section (Edited) |
IPFPV02X |
Facility amount paid, private insurance (Imputed) |
CP Section (Edited) |
IPFVA02X |
Facility amount paid, Veterans Administration (Imputed) |
CP Section (Edited) |
IPFTR02X |
Facility amount paid, TRICARE (Imputed) |
CP Section (Edited) |
IPFOF02X |
Facility amount paid, other federal (Imputed) |
CP Section (Edited) |
IPFSL02X |
Facility amount paid state & local government (Imputed) |
CP Section (Edited) |
IPFWC02X |
Facility amount paid, workers' compensation (Imputed) |
CP Section (Edited) |
IPFOR02X |
Facility amount paid, other private (Imputed) |
Constructed |
IPFOU02X |
Facility amount paid, other pub (Imputed) |
Constructed |
IPFOT02X |
Facility amount paid, other insurance (Imputed) |
CP Section (Edited) |
IPFXP02X |
Facility sum payments IPFSF02X - IPFOT02X |
Constructed |
IPFTC02X |
Total facility charge (Imputed) |
CP Section (Edited) |
Return To Table Of Contents
Imputed Separately Billing Physician
Expenditure Variables
Variable |
Description |
Source |
IPDSF02X |
Doctor amount paid, family (Imputed) |
Constructed |
IPDMR02X |
Doctor amount paid, Medicare (Imputed) |
Constructed |
IPDMD02X |
Doctor amount paid, Medicaid (Imputed) |
Constructed |
IPDPV02X |
Doctor amount paid, private insurance (Imputed) |
Constructed |
IPDVA02X |
Doctor amount paid, Veterans Administration (Imputed) |
Constructed |
IPDTR02X |
Doctor amount paid, TRICARE (Imputed) |
Constructed |
IPDOF02X |
Doctor amount paid, other federal (Imputed) |
Constructed |
IPDSL02X |
Doctor amount paid, state & local government (Imputed) |
Constructed |
IPDWC02X |
Doctor amount paid, workers' compensation (Imputed) |
Constructed |
IPDOR02X |
Doctor amount paid, other private insurance (Imputed) |
Constructed |
IPDOU02X |
Doctor amount paid, other public insurance (Imputed) |
Constructed |
IPDOT02X |
Doctor amount paid, other insurance (Imputed) |
Constructed |
IPDXP02X |
Doctor sum payments IPDSF02X-IPDOT02X |
Constructed |
IPDTC02X |
Total doctor charge (Imputed) |
Constructed |
IMPFLAG |
Imputation status |
Constructed |
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Weights
Variable |
Description |
Source |
PERWT02F |
Expenditure file person weight, 2002 |
Constructed |
VARSTR |
Variance estimation stratum, 2002 |
Constructed |
VARPSU |
Variance estimation PSU, 2002 |
Constructed |
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