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July 2016
Agency for Healthcare Research and Quality
Center for Financing, Access, and Cost Trends
5600 Fishers Lane
Rockville, MD 20857
(301) 427-1406
Table of Contents
A Data Use Agreement
B. Background
1.0 Household Component
2.0 Medical Provider Component
3.0 Survey Management and Data Collection
C. Technical and Programming Information
1.0 General Information
2.0 Data File Information
2.1 Codebook Structure
2.2 Reserved Codes
2.3 Codebook Format
2.4 Variable Source and Naming Conventions
2.4.1 General
2.4.2 Expenditure and Source of Payment Variables
2.5 File Contents
2.5.1 Survey Administration Variables
2.5.1.1 Person Identifiers (DUID, PID, DUPERSID)
2.5.1.2 Record Identifiers (EVNTIDX, ERHEVIDX, FFEEIDX)
2.5.1.3 Round Indicator (EVENTRN)
2.5.1.4 Panel Indicator (PANEL)
2.5.2 MPC Data Indicator (MPCDATA)
2.5.3 Hospital Inpatient Stay Event Variables
2.5.3.1 Start and End Dates of Event (IPBEGMM-IPENDYR)
2.5.3.2 Length of Stay (NUMNIGHX, NUMNIGHT)
2.5.3.3 Preceding ER Visits (EMERROOM)
2.5.3.4 Other Visit Detail (SPECCOND - ANYOPER)
2.5.3.5 Clinical Classification Codes (IPCCC1X-IPCCC4X)
2.5.3.6 Discharge Detail (DSCHPMED)
2.5.4 Flat Fee Variables (FFEEIDX, FFIPTYPE, FFBEF14, FFTOT15)
2.5.4.1 Definition of Flat Fee Payments
2.5.4.2 Flat Fee Variable Descriptions
2.5.4.2.1 Flat Fee ID (FFEEIDX)
2.5.4.2.2 Flat Fee Type (FFIPTYPE)
2.5.4.2.3 Counts of Flat Fee Events that Cross Years (FFBEF14, FFTOT15)
2.5.4.3 Caveats of Flat Fee Groups
2.5.5 Expenditure Data
2.5.5.1 Definition of Expenditures
2.5.5.2 Data Editing and Imputation Methodologies of Expenditure Variables
2.5.5.2.1 General Data Editing Methodology
2.5.5.2.2 Imputation Methodologies
2.5.5.2.3 Hospital Inpatient Stay Data Editing and Imputation
2.5.5.3 Imputation Flag (IMPFLAG)
2.5.5.4 Flat Fee Expenditures
2.5.5.5 Zero Expenditures
2.5.5.6 Discount Adjustment Factor
2.5.5.7 Mother/Newborn Expenditures
2.5.5.8 Hospital Inpatient Stay/Emergency Room Expenditures
2.5.5.9 Sources of Payment
2.5.5.10 Imputed Hospital Inpatient Stay Expenditure Variables
2.5.5.10.1 Hospital Inpatient Facility Expenditures (IPFSF14X-IPFOT14X, IPFXP14X, IPFTC14X)
2.5.5.10.2 Hospital Inpatient Physician Expenditures (IPDSF14X - IPDOT14X, IPDXP14X, IPDTC14X)
2.5.5.10.3 Total Expenditures and Charges for Hospital Inpatient Stays (IPXP14X and IPTC14X)
2.5.5.11 Rounding
3.0 Sample Weight (PERWT14F)
3.1 Overview
3.2 Details on Person Weight Construction
3.2.1 MEPS Panel 18 Weight Development Process
3.2.2 MEPS Panel 19 Weight Development Process
3.2.3 The Final Weight for 2014
3.2.4 Coverage
3.3 Using MEPS Data for Trend Analysis
4.0 Strategies for Estimation
4.1 Developing Event-Level Estimates
4.2 Person-Based Estimates for Hospital Inpatient Stays
4.3 Variables with Missing Values
4.4 Variance Estimation (VARSTR, VARPSU)
5.0 Merging/Linking MEPS Data Files
5.1 Linking to the Person-Level File
5.2 Linking to the Prescribed Medicines File
5.3 Linking to the Medical Conditions File
References
D. Variable-Source Crosswalk
Individual identifiers have been removed from the
micro-data contained in these files. Nevertheless, under sections 308 (d) and
903 (c) of the Public Health Service Act (42 U.S.C. 242m and 42 U.S.C. 299 a-1),
data collected by the Agency for Healthcare Research and Quality (AHRQ) and/or
the National Center for Health Statistics (NCHS) may not be used for any purpose
other than for the purpose for which they were supplied; any effort to determine
the identity of any reported cases is prohibited by law.
Therefore in accordance with the above referenced
Federal Statute, it is understood that:
- No one is to use the data in this data set in any way except
for statistical reporting and analysis; and
- If the identity of any person or establishment should be
discovered inadvertently, then (a) no use will be made of this
knowledge, (b) the Director Office of Management AHRQ will be
advised of this incident, (c) the information that would
identify any individual or establishment will be safeguarded or
destroyed, as requested by AHRQ, and (d) no one else will be
informed of the discovered identity; and
- No one will attempt to link this data set with individually
identifiable records from any data sets other than the Medical
Expenditure Panel Survey or the National Health Interview
Survey.
By using these data you signify your agreement to
comply with the above stated statutorily based requirements with the knowledge
that deliberately making a false statement in any matter within the jurisdiction
of any department or agency of the Federal Government violates Title 18 part 1
Chapter 47 Section 1001 and is punishable by a fine of up to $10,000 or up to 5
years in prison.
The Agency for Healthcare Research and Quality
requests that users cite AHRQ and the Medical Expenditure Panel Survey as the
data source in any publications or research based upon these data.
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The Medical Expenditure Panel Survey (MEPS) provides
nationally representative estimates of health care use, expenditures, sources of
payment, and health insurance coverage for the U.S. civilian
non-institutionalized population. The MEPS Household Component (HC) also
provides estimates of respondents’ health status, demographic and socio-economic
characteristics, employment, access to care, and satisfaction with health care.
Estimates can be produced for individuals, families, and selected population
subgroups. The panel design of the survey, which includes 5 Rounds of interviews
covering 2 full calendar years, provides data for examining person level changes
in selected variables such as expenditures, health insurance coverage, and
health status. Using computer assisted personal interviewing (CAPI) technology,
information about each household member is collected, and the survey builds on
this information from interview to interview. All data for a sampled household
are reported by a single household respondent.
The MEPS-HC was initiated in 1996. Each year a new
panel of sample households is selected. Because the data collected are
comparable to those from earlier medical expenditure surveys conducted in 1977
and 1987, it is possible to analyze long-term trends. Each annual MEPS-HC sample
size is about 15,000 households. Data can be analyzed at either the person or
event level. Data must be weighted to produce national
estimates.
The set of households selected for each panel of the
MEPS HC is a subsample of households participating in the previous year’s
National Health Interview Survey (NHIS) conducted by the National Center for
Health Statistics. The NHIS sampling frame provides a nationally representative
sample of the U.S. civilian non-institutionalized population and reflects an
oversample of Blacks and Hispanics. In 2006, the NHIS implemented a new sample
design, which included Asian persons in addition to households with Black and
Hispanic persons in the oversampling of minority populations. The linkage of the
MEPS to the previous year’s NHIS provides additional data for longitudinal
analytic purposes.
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Upon completion of the household CAPI interview and
obtaining permission from the household survey respondents, a sample of medical
providers are contacted by telephone to obtain information that household
respondents can not accurately provide. This part of the MEPS is called the
Medical Provider Component (MPC) and information is collected on dates of visit,
diagnosis and procedure codes, charges and payments. The Pharmacy Component
(PC), a subcomponent of the MPC, does not collect charges or diagnosis and
procedure codes but does collect drug detail information, including National
Drug Code (NDC) and medicine name, as well as date filled and sources and
amounts of payment. The MPC is not designed to yield national estimates. It is
primarily used as an imputation source to supplement/replace household reported
expenditure information.
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MEPS HC and MPC data are collected under the authority
of the Public Health Service Act. Data are collected under contract with Westat,
Inc. (MEPS HC) and Research Triangle Institute (MEPS MPC). Data sets and summary
statistics are edited and published in accordance with the confidentiality
provisions of the Public Health Service Act and the Privacy Act. The National
Center for Health statistics (NCHS) provides consultation and technical
assistance.
As soon as data collection and editing are completed,
the MEPS survey data are released to the public in staged releases of summary
reports, micro data files, and tables via the MEPS Web site:
meps.ahrq.gov.
Selected data can be analyzed through MEPSnet, an on-line interactive tool
designed to give data users the capability to statistically analyze MEPS data in
a menu-driven environment.
Additional information on MEPS is available from the
MEPS project manager or the MEPS public use data manager at the Center for
Financing, Access, and Cost Trends, Agency for Healthcare Research and Quality,
5600 Fishers Lane, Rockville, MD 20857 (301-427-1406).
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This documentation describes one in a series of public
use event files from the 2014 Medical Expenditure Panel Survey (MEPS) Household
Component (HC) and Medical Provider Component (MPC). Released as an ASCII data
file (with related SAS, Stata, and SPSS programming statements) and SAS
transport file, the 2014 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 2014. The
file contains 61 variables and has a logical record length of 362 with an
additional 2-byte carriage return/line feed at the end of each record. As
illustrated below, this file consists of MEPS survey data from the 2014 portion
of Round 3 and Rounds 4 and 5 for Panel 18, as well as Rounds 1, 2 and the 2014
portion of Round 3 for Panel 19 (i.e., the rounds for the MEPS panels covering
calendar year 2014).
Hospital stay events reported in Panel 19 Round 3 and
known to have begun after December 31, 2014 are not included on this file.
Each record on the inpatient hospital event file
represents a unique hospital inpatient stay reported by the household
respondent. In addition to expenditures related to the stay, each record
contains household-reported medical conditions 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
2014 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 is provided on the MEPS 2014 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 Weight
- Strategies for Estimation
- Merging/Linking MEPS Data Files
- References
- Variable - Source Crosswalk
Any variables not found on this file but released on
previous years’ files may have been excluded because they contained only missing
data.
For more information on MEPS HC survey design, see T.
Ezzati-Rice, et al. (1998-2007) and S. Cohen (1996). For information on the MEPS
MPC design, see S. Cohen (1998). Copies of the HC and the MPC survey instruments
used to collect the information on the STAZ file are available in the Survey
Questionnaires section on the MEPS Web site at the following address:
meps.ahrq.gov.
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The 2014 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.
The 2014 STAZ public use data set contains variable
and frequency distributions for a total of 2,957 hospital inpatient stay records
reported during the 2014 portion of Round 3 and Rounds 4 and 5 for Panel 18, as
well as Rounds 1, 2, and the 2014 portion of Round 3 for Panel 19 of the MEPS
Household Component. This file includes hospital inpatient stay records for all
household survey members who resided in eligible responding households and for
whom at least one hospital inpatient stay was reported. Hospital inpatient stay
records known to have ended before January 1, 2014 or after December 31, 2014
are not included on this file. Some household members may have had multiple
hospital inpatient stays reported and, thus, will be represented in multiple
records on this file. Other household members may have had reported no hospital
inpatient stays and, thus, will have no records on this file. Of the 2,957
hospital inpatient stay records, 2,817 are associated with persons having a
positive person-level weight (PERWT14F). 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 2014. Stays that
began prior to 2014 but ended during 2014 are included on this
data file. Stays that began in 2014 but ended during 2015 are
excluded from this data file and will be included in a
subsequent 2015 IP data file. Persons with no hospital inpatient
stay events for 2014 are not included on this event-level IP
file but are represented on the person-level 2014 Full Year
Population Characteristics file.
- 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 2014 eligibility (i.e., persons with a
positive 2014 full-year person-level sampling weight (PERWT14F >
0)), or
- Be an eligible member of a family all of whose key in-scope
members have a positive person-level weight (PERWT14F > 0).
(Such a family consists of all persons with the same value for
FAMIDYR.) That is, the person must have a positive full-year
family-level weight (FAMWT14F > 0). Note that FAMIDYR and
FAMWT14F are variables on the 2014 Full Year Consolidated Data
File.
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.
To append person-level information such as demographic
or health insurance coverage to each event record, data from this file can be
merged with 2014 MEPS HC person-level data (e.g. Full Year Consolidated or Full
Year Population Characteristics file) using the person identifier, DUPERSID.
Hospital inpatient stay events can also be linked to the MEPS 2014 Medical
Conditions File and the MEPS 2014 Prescribed Medicines File. Please see Section
5.0 or the MEPS 2014 Appendix File, HC-168I, for details on how to merge MEPS
data files.
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For most variables on the Inpatient Events file, both
weighted and unweighted frequencies are provided in the accompanying codebook
file. The exceptions to this are weight variables and variance estimation
variables. Only unweighted frequencies of these variables are included in the
accompanying codebook file. See the Weights Variables list in Section D,
Variable-Source Crosswalk. The codebook and data file sequence list variables in
the following order:
- Unique person identifiers
- Unique hospital inpatient stay identifiers
- Hospital inpatient stay characteristics variables
- Clinical Classification Software (CCS) codes
- Imputed expenditure variables
- Weight and variance estimation variables
Note that the person identifier is unique within this
data year.
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The following reserved code values are used:
| Value |
Definition |
| -1 INAPPLICABLE |
Question was not asked due to skip pattern |
| -7 REFUSED |
Question was asked and respondent refused to answer question |
| -8 DK |
Question was asked and respondent did not know answer |
| -9 NOT ASCERTAINED |
Interviewer did not record the data |
Generally, 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:
meps.ahrq.gov/survey_comp/survey_questionnaires.jsp).
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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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In general, variable names reflect the content of the
variable, with an eight-character limitation. All imputed/edited variables end
with an “X”.
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Variables on this file were derived from the HC
questionnaire itself, 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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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
ER - emergency room visit
HH - home health visit
OM - other medical equipment
OB - office-based visit
OP - outpatient visit
DV - dental visit
RX - prescribed medicine
For expenditure variables on the 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
MR - Medicare
MD - Medicaid
PV - private insurance
VA - Veterans Administration/CHAMPVA
TR - TRICARE
OF - other federal government
SL - state/local government
WC - Workers’ Compensation
OT - other insurance
OR - other private
OU - other public
XP - sum of payments
In addition, the total charge variable is indicated by
TC in the variable name.
The sixth and seventh characters indicate the year
(14). The eighth character, “X”, indicates whether the variable is
edited/imputed.
For example, IPFSF14X is the edited/imputed amount
paid by self or family for the facility portion of the hospital inpatient stay
expenditure incurred in 2014.
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The dwelling unit ID (DUID) is a five-digit random
number assigned after the case was sampled for MEPS. The three-digit person
number (PID) uniquely identifies each person within the dwelling unit. The
eight-character variable DUPERSID uniquely identifies each person represented on
the file and is the combination of the variables DUID and PID. For detailed
information on dwelling units and families, please refer to the documentation
for the 2014 Full Year Population Characteristics File.
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EVNTIDX uniquely identifies each 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 2014 Medical Conditions File and
MEPS 2014 Prescribed Medicines File, respectively). For details on linking, see
Section 5.0 or the MEPS 2014 Appendix File, HC-168I.
ERHEVIDX is a constructed variable identifying a STAZ
record that includes the facility expenditures for the preceding emergency room
visit. This variable is derived from provider-reported information on linked
emergency room and inpatient stay events that matched to corresponding events
reported by the household. The variable ERHEVIDX contains the EVNTIDX of the
linked event. On the 2014 STAZ file, there are 413 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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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 18. Likewise, Rounds 1, 2,
and 3 are associated with data collected from Panel 19.
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PANEL is a constructed variable used to specify the
panel number for the person. PANEL will indicate either Panel 18 or Panel 19 for
each person on the file. Panel 18 is the panel that started in 2013, and Panel
19 is the panel that started in 2014.
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MPCDATA is a constructed variable 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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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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There are two variables which indicate the month and
year a hospital stay began (IPBEGMM and IPBEGYR, respectively). Similarly, there
are two variables which indicate the month and year a hospital stay ended (IPENDMM
and IPENDYR, respectively). These variables have not been edited.
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NUMNIGHX denotes the length of a hospital inpatient
stay. For stays beginning in 2013 and ending in 2014, 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
2.5.3.1). 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.
Inpatient hospital stays take into account information
from the Medical Provider Component (MPC), the variable NUMNIGHX may not be
adjusted to reflect the entire length of stay based on the MPC.
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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 Visits File.
Furthermore, no attempt has been made to reconcile the unedited EMERROOM
variable with the edited ERHEVIDX variable (see Section 2.5.1.2).
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Also provided are the following unedited variables:
hospital inpatient stays related to a medical condition (SPECCOND); the reason
the person entered the hospital (RSNINHOS); vaginal or Caesarean delivery (DLVRTYPE);
and any operation or surgery performed while the person was in the hospital (ANYOPER).
Through 2012, receive an epidural or spinal for pain (EPIDURAL) was included on
the file. Beginning in 2013, EPIDURAL was removed because of design changes.
With respect to RSNINHOS, please note that while there
were 378 cases where RSNINHOS = 4 (reason entered hospital – to give birth to a
baby), this does not mean that there were actually 378 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 household-reported variable may be inconsistent with
reported number of births (see the 2014 Full Year Population Characteristics
File, Section 2.5.2 “Navigating the MEPS Data with Information on Person
Disposition Status”). In addition, RSNINHOS has not been edited to ensure
alignment with the CCS codes associated with an event.
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Information on household-reported medical conditions
associated with each hospital inpatient stay event is provided on this file.
There are up to four CCS codes (IPCCC1X-IPCCC4X) listed for each hospital
inpatient stay event, as shown in the crosswalk of this document. The file
includes the number of CCS codes reported in the data year, which may be fewer
than the maximum four CCS codes. Because the maximum number of conditions
associated with an event can change from year to year, the number of reported
CCS codes also can change from year to year. Starting with the 2013 file, the
ICD-9-CM condition and procedure codes variables have been omitted.
In order to obtain complete condition information
associated with an event, the data user/analyst must link to the MEPS 2014
Medical Conditions File. Details on how to link the 2014 STAZ file to the MEPS
2014 Medical Conditions File are provided in Section 5.3 and the MEPS 2014
Appendix File, HC-168I. The data user/analyst should note that provider-reported
condition information is not publicly available because of confidentiality
restrictions.
The medical conditions reported by the Household
Component respondent were recorded by the interviewer as verbatim text, which
was then coded by professional coders to fully-specified 2014 ICD-9-CM codes,
including medical condition and V-codes (Health Care Financing Administration,
1980). Although codes were verified and error rates did not exceed 2 percent for
any coder, data users/analysts should not presume this level of precision in the
data; the ability of household respondents to report condition data that can be
coded accurately should not be assumed (Cox and Cohen, 1985; Cox and Iachan,
1987; Edwards, et al., 1994; and Johnson and Sanchez, 1993). For detailed
information on how conditions were coded, please refer to the documentation on
the MEPS 2014 Medical Conditions File. For frequencies of conditions by event
type, please see the MEPS 2014 Appendix File, HC-168I.
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
mutually exclusive categories, most of which are clinically homogeneous.
The clinical classification 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 2014 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 CCS
codes that are on the file.
Analysts should use the clinical classification codes
listed in the Conditions PUF document (HC-170) and the Appendix to the Event
Files (HC-168I) document when analyzing MEPS conditions data. Although there is
a list of clinical classification codes and labels on the Healthcare Cost and
Utilization Project (HCUP) Web site, if updates to these codes and/or labels are
made on the HCUP Web site after the release of the 2014 MEPS PUFs, these updates
will not be reflected in the 2014 MEPS data.
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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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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 2014. By
definition, a flat fee group can span multiple years. Furthermore, a single
person can have multiple flat fee groups.
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As noted earlier in Section 2.5.1.2 “Record
Identifiers,” the variable FFEEIDX uniquely identifies all events that are part
of the same flat fee group for a person. On any 2014 MEPS event file, every
event that was a part of a specific flat fee group will have the same value for
FFEEIDX. Note that prescribed medicine and home health events are never included
in a flat fee group and FFEEIDX is not a variable on those event files.
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FFIPTYPE indicates whether the 2014 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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As explained in Section 2.5.4.1, 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 2014 as a part of
a group of events, and some event occurred before or after 2014, counts of the
known events are provided on the STAZ record. Variables that indicate events
occurred before or after 2014 are as follows:
FFBEF14 – total number of pre-2014 events in the same
flat fee group as the 2014 hospital inpatient stay(s). This count would not
include 2014 hospital inpatient stay(s).
FFTOT15 – the number of 2015 hospital inpatient stays
expected to be in the same flat fee group as the hospital inpatient stay that
occurred in 2014.
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There are 9 hospital inpatient stays/events identified
as being part of a flat fee payment group. In general, every flat fee group
should have an initial visit (stem) and at least one subsequent visit (leaf).
There are some situations where this is not true. For some of these flat fee
groups, the initial visit reported occurred in 2014, but the remaining visits
that were part of this flat fee group occurred in 2015. In this case, the 2014
flat fee group would consist of one event, the stem. The 2015 events that are
part of this flat fee group are not represented on the file. Similarly, the
household respondent may have reported a flat fee group where the initial visit
began in 2013 but subsequent visits occurred during 2014. In this case, the
initial visit would not be represented on the file. This 2014 flat fee group
would then only consist of one or more leaf records and no stem. Please note
that the crosswalk in this document lists all possible flat fee variables.
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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.
Currently, charges associated with uncollected liability, bad debt, and
charitable care (unless provided by a public clinic or hospital) are not counted
as expenditures because there are no payments associated with those
classifications. While charge data are provided on this file, data
users/analysts should use caution when working with these data because a charge
does not typically represent actual dollars exchanged for services or the
resource costs of those services; nor 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
accessed via the CFACT data center. For more information, see the Data Center
section of the MEPS Web site
meps.ahrq.gov/data_stats/onsite_datacenter.jsp.
Expenditure data related to 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, please refer to Section 3.3
for more information.
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The expenditure data included on this file were
derived from both the MEPS Household (HC) and Medical Provider 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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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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The predictive mean matching imputation method was
used to impute missing expenditures. This procedure uses regression models
(based on events with completely reported expenditure data) to predict total
expenses for each event. Then, for each event with missing payment information,
a donor event with the closest predicted payment with the same pattern of
expected payment sources as the event with missing payment was used to impute
the missing payment value. The imputations for the flat fee events were carried
out separately from the simple events.
The weighted sequential hot-deck procedure was used to
impute the missing total charges. This procedure uses survey data from
respondents to replace missing data while taking into account the persons’
weighted distribution in the imputation process.
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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 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 2.5.4 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 predictive mean matching imputations, while
events with missing expenditure data were assigned to various recipient
categories. Each event with missing expenditure data was assigned to a recipient
category based on the extent of its missing charge and expenditure data. For
example, an event with a known total charge but no expenditure information was
assigned to one category, while an event with a known total charge and partial
expenditure information was assigned to a different category. Similarly, events
without a known total charge and no or partial expenditure information were
assigned to various recipient categories.
The logical edits produced eight recipient categories
in which all events had a common extent of missing data. Separate predictive
mean matching imputations were performed on events in each recipient category.
For hospital inpatient events, the donor pool was restricted to events with
complete expenditures from the MPC.
The donor pool included “free events” because, in some
instances, providers are not paid for their services. These events represent
charity care, bad debt, provider failure to bill, and third party payer
restrictions on reimbursement in certain circumstances. If free events were
excluded from the donor pool, total expenditures would be over-counted because
the distribution of free events among complete events (donors) would not be
represented among incomplete events (recipients).
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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IMPFLAG is a six-category variable that indicates if
the event contains complete Household Component (HC) or Medical Provider
Component (MPC) data, was fully or partially imputed, or was imputed in the
capitated imputation process (for OP and OB events only). The following list
identifies how the imputation flag is coded; the categories are mutually
exclusive.
- IMPFLAG = 0 not eligible for imputation (includes zeroed out and flat fee leaf events)
- IMPFLAG = 1 complete HC data
- IMPFLAG = 2 complete MPC data
- IMPFLAG = 3 fully imputed
- IMPFLAG = 4 partially imputed
- IMPFLAG = 5 complete MPC data through capitation imputation (not applicable to IP events)
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The approach used to count expenditures for flat fees
was to place the expenditure on the first visit of the flat fee group. The
remaining visits have zero facility payments, while physician’s expenditures may
be still present. Thus, if the first visit in the flat fee group occurred prior
to 2014, all of the events that occurred in 2014 will have zero payments.
Conversely, if the first event in the flat fee group occurred at the end of
2014, the total expenditure for the entire flat fee group will be on that event,
regardless of the number of events it covered after 2014. See Section 2.5.4 for
details on the flat fee variables.
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There are some medical events reported by respondents
where the payments were zero. Zero payment events can occur in MEPS for the
following reasons: (1) the stay was covered under a flat fee arrangement (flat
fee payments are included only on the first event covered by the arrangement),
(2) there was no charge for a follow-up stay, (3) the provider was never paid by
an individual, insurance plan, or other source for services provided, (4) the
charges were included in another bill, or (5) the event was paid for through
government or privately-funded research or clinical trials.
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An adjustment was also applied to some HC-reported
expenditure data because an evaluation of matched HC/MPC data showed that
respondents who reported that charges and payments were equal were often unaware
that insurance payments for the care had been based on a discounted charge. To
compensate for this systematic reporting error, a weighted sequential hot-deck
imputation procedure was implemented to determine an adjustment factor for
HC-reported insurance payments when charges and payments were reported to be
equal. As for the other imputations, selected predictor variables were used to
form groups of donor and recipient events for the imputation process.
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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 2014 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.
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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 the 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 2014
Emergency Room Visits 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 2.5.1.2). 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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In addition to total expenditures, variables are
provided which itemize expenditures according to major source of payment
categories. These categories are:
- Out-of-pocket by User or Family,
- Medicare,
- Medicaid,
- Private Insurance,
- Veterans Administration/CHAMPVA,
- 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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This file contains two sets of imputed expenditure
variables: facility expenditures and physician expenditures.
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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.
IPFSF14X – IPFOT14X are the 12 sources of payment. The
12 sources of payment are: self/family (IPFSF14X), Medicare (IPFMR14X), Medicaid
(IPFMD14X), private insurance (IPFPV14X), Veterans Administration/CHAMPVA
(IPFVA14X), TRICARE (IPFTR14X), other federal sources (IPFOF14X), state and
local (non-federal) government sources (IPFSL14X), Workers’ Compensation
(IPFWC14X), other private insurance (IPFOR14X), other public insurance
(IPFOU14X), and other insurance (IPFOT14X). IPFXP14X is the sum of the 12
sources of payment for the Hospital Facility expenditures, and IPFTC14X is the
total charge.
Wherever an emergency room visit record is linked to a
hospital inpatient stay record (identified by the variable ERHEVIDX, see Section
2.5.1.2), the facility source of payment variables on the emergency room visit
record were zeroed out because the emergency room expenditures were already
included in the hospital facility source of payment variables.
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Separately billing doctor (SBD) expenses typically
cover services provided to patients in hospital settings by providers like
anesthesiologists, radiologists, and pathologists, whose charges are often not
included in 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. IPDSF14X – IPDOT14X are the 12 sources of
payment; IPDXP14X is the sum of the 12 sources of payments; and IPDTC14X is the
physician’s total charge.
Data users/analysts need to take into consideration
whether to analyze facility and SBD expenditures separately, combine them within
service categories, or collapse them across service categories (e.g., combine
SBD expenditures with expenditures for physician visits to offices and/or
outpatient departments).
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Data users/analysts interested in total expenditures
should use the variable IPXP14X, which includes both facility and physician
amounts. Those interested in total charges should use the variable IPTC14X,
which includes both facility and physician charges (see Section 2.5.5.1 for an
explanation of the “charge” concept).
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Expenditure variables have been rounded to the nearest
penny. Person-level expenditure information released on the MEPS 2014
Person-Level Use and Expenditure File were rounded to the nearest dollar. It
should be noted that using the MEPS 2014 event files to create person-level
totals will yield slightly different totals than those found on the full year
consolidated file. These differences are due to rounding only. Moreover, in some
instances, the number of persons having expenditures on the MEPS 2014 event
files for a particular source of payment may differ from the number of persons
with expenditures on the person-level expenditure file for that source of
payment. This difference is also an artifact of rounding only.
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There is a single full year person-level weight
(PERWT14F) assigned to each record for each key, in-scope person who responded
to MEPS for the full period of time that he or she was in-scope during 2014. A
key person either was a member of a responding NHIS household at the time of
interview, or joined a family associated with such a household after being
out-of-scope at the time of the NHIS (the latter circumstance includes newborns
as well as those returning from military service, an institution, or residence
in a foreign country). A person is in-scope whenever he or she is a member of
the civilian noninstitutionalized portion of the U.S. population.
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The person-level weight PERWT14F was developed in
several stages. First, person-level weights for Panel 18 and Panel 19 were
created separately. The weighting process for each panel included an adjustment
for nonresponse over time and calibration to independent population totals. The
calibration was initially accomplished separately for each panel by raking the
corresponding sample weights for those in-scope at the end of the calendar year
to Current Population Survey (CPS) population estimates based on five variables.
The five variables used in the establishment of the initial person-level control
figures were: census region (Northeast, Midwest, South, West); MSA status (MSA,
non-MSA); race/ethnicity (Hispanic; Black, non-Hispanic; Asian, non-Hispanic;
and other); sex; and age. A 2014 composite weight was then formed by
multiplying each weight from Panel 18 by the factor .500 and each weight from
Panel 19 by the factor .500. The choice of factors reflected the relative sample
sizes of the two panels, helping to limit the variance of estimates obtained
from pooling the two samples. The composite weight was raked to the same set of
CPS-based control totals. When the poverty status information derived from
income variables became available, a final raking was undertaken on the
previously established weight variable. Control totals were established using
poverty status (five categories: below poverty, from 100 to 125 percent of
poverty, from 125 to 200 percent of poverty, from 200 to 400 percent of poverty,
at least 400 percent of poverty) as well as the other five variables previously
used in the weight calibration.
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The person-level weight for MEPS Panel 18 was
developed using the 2013 full year weight for an individual as a “base” weight
for survey participants present in 2013. For key, in-scope members who joined an
RU some time in 2014 after being out-of-scope in 2013, the initially assigned
person-level weight was the corresponding 2013 family weight. The weighting
process included an adjustment for person-level nonresponse over Rounds 4 and 5
as well as raking to population control totals for December 2014 for key,
responding persons in-scope on December 31, 2014. These control totals were
derived by scaling back the population distribution obtained from the March 2015
CPS to reflect the December 31, 2014 estimated population total (estimated based
on Census projections for January 1, 2015). Variables used for person-level
raking included: census region (Northeast, Midwest, South, West); MSA status (MSA,
non-MSA); race/ethnicity (Hispanic; Black non-Hispanic; Asian non-Hispanic;
and other); sex; and age. (Poverty status is not included in this version
of the MEPS full year database because of the time required to process the
income data collected and then assign persons to a poverty status category). The
final weight for key, responding persons who were not in-scope on December 31,
2014 but were in-scope earlier in the year was the person weight after the
nonresponse adjustment.
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The person-level weight for MEPS Panel 19 was
developed using the 2014 MEPS Round 1 person-level weight as a “base”
weight. For key, in-scope members who joined an RU after Round 1, the Round 1
family weight served as a “base” weight. The weighting process included an
adjustment for nonresponse over the remaining data collection rounds in 2014 as
well as raking to the same population control figures for December 2014 used for
the MEPS Panel 18 weights for key, responding persons in-scope on December 31,
2014. The same five variables employed for Panel 18 raking (census region, MSA
status, race/ethnicity, sex, and age) were used for Panel 19 raking. Again, the
final weight for key, responding persons who were not in-scope on December 31,
2014 but were in-scope earlier in the year was the person weight after the
nonresponse adjustment.
Note that the MEPS Round 1 weights for both panels
incorporated the following components: a weight reflecting the original
household probability of selection for the NHIS and an adjustment for NHIS
nonresponse; a factor representing the proportion of the 16 NHIS panel-quarter
combinations eligible for MEPS; the oversampling of certain subgroups for MEPS
among the NHIS household respondents eligible for MEPS; ratio-adjustment to NHIS-based
national population estimates at the household (occupied DU) level; adjustment
for nonresponse at the DU level for Round 1; and poststratification to U.S.
civilian noninstitutionalized population estimates at the family and person
level obtained from the corresponding March CPS databases.
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The final raking of those in-scope at the end of the
year has been described above. In addition, the composite weights of two groups
of persons who were out-of-scope on December 31, 2014 were poststratified.
Specifically, the weights of those who were in-scope some time during the year,
out-of-scope on December 31, and entered a nursing home during the year were
poststratified to a corresponding control total obtained from the 1996 MEPS
Nursing Home Component. The weights of persons who died while in-scope
during 2014 were poststratified to corresponding estimates derived using data
obtained from the Medicare Current Beneficiary Survey (MCBS) and Vital
Statistics information provided by the National Center for Health Statistics (NCHS).
Separate decedent control totals were developed for the “65 and older” and
“under 65” civilian noninstitutionalized decedent populations.
Overall, the weighted population estimate for the
civilian noninstitutionalized population for December 31, 2014 is 314,906,436
(PERWT14F>0 and INSC1231=1). The sum of the person-level weights across all
persons assigned a positive person-level weight is 318,440,423.
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The target population for MEPS in this file is the
2014 U.S. civilian noninstitutionalized population. However, the MEPS sampled
households are a subsample of the NHIS households interviewed in 2012 (Panel 18)
and 2013 (Panel 19). New households created after the NHIS interviews for the
respective panels and consisting exclusively of persons who entered the target
population after 2012 (Panel 18) or after 2013 (Panel 19) are not covered by
MEPS. Neither are previously out-of-scope persons who join an existing household
but are unrelated to the current household residents. Persons not covered by a
given MEPS panel thus include some members of the following groups: immigrants;
persons leaving the military; U.S. citizens returning from residence in another
country; and persons leaving institutions. The set of uncovered persons
constitutes only a small segment of the MEPS target population.
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MEPS began in 1996, and the utility of the survey for
analyzing health care trends expands with each additional year of data. However,
it is important to consider a variety of factors when examining trends over time
using MEPS. Statistical significance tests should be conducted to assess the
likelihood that observed trends may be attributable to sampling variation. The
length of time being analyzed should also be considered. In particular, large
shifts in survey estimates over short periods of time (e.g. from one year to the
next) that are statistically significant should be interpreted with caution
unless they are attributable to known factors such as changes in public policy,
economic conditions, or MEPS survey methodology.
With respect to methodological considerations, in 2013
MEPS introduced an effort to obtain more complete information about health care
utilization from MEPS respondents with full implementation in 2014. This effort
likely resulted in improved data quality and a reduction in underreporting in FY
2014 and could have some modest impact on analyses involving trends in
utilization across years.
There are also statistical factors to consider in
interpreting trend analyses. Looking at changes over longer periods of
time can provide a more complete picture of underlying trends. Analysts may wish
to consider using techniques to evaluate, smooth, or stabilize analyses of
trends using MEPS data such as comparing pooled time periods (e.g. 1996-97
versus 2013-14), working with moving averages, or using modeling techniques with
several consecutive years of MEPS data to test the fit of specified patterns
over time. Finally, researchers should be aware of the impact of multiple
comparisons on Type I error. Without making appropriate allowance for multiple
comparisons, undertaking numerous statistical significance tests of trends
increases the likelihood of concluding that a change has taken place when one
has not.
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The data in this file can be used to develop national
2014 event-level estimates for the U.S. civilian noninstitutionalized population
on inpatient hospital stays as well as expenditures, and sources of payment for
these stays. Estimates of total stays are the sum of the weight variable
(PERWT14F) across relevant event records while estimates of other variables must
be weighted by PERWT14F to be nationally representative. The tables below
contain event-level estimates for selected variables.
Selected Event-Level Estimates
Hospital Stays
| Estimate of Interest |
Variable Name |
Estimate (SE) |
Estimate Excluding
Zero Payment Events (SE)* |
| Total number of inpatient hospital stays (in millions) |
PERWT14F |
28.4 (1.17) |
28.2 (1.17) |
| Total number of nights in hospital across all stays (in millions) |
NUMNIGHX |
146.6 (10.85) |
146.1 (10.86) |
| Average number of nights per stay |
NUMNIGHX |
5.2 (0.31) |
5.2 (0.31) |
| Average number of nights per stay (NUMNIGHX > 0) |
NUMNIGHX |
5.3 (0.32) |
5.3 (0.32) |
Hospital Expenditures
| Estimate of Interest |
Variable Name |
Estimate (SE) |
Estimate Excluding
Zero Payment Events (SE)* |
| Mean total payments per stay |
IPXP14X |
$13,450 ($575.3) |
$13,525 ($576.9) |
| Mean out-of-pocket payment per stay |
IPDSF14X
+IPFSF14X |
$351 ($36.0) |
$353 ($36.3) |
Mean proportion of total expenditures per stay
paid by private insurance |
(IPDPV14X+
IPFPV14X)
/IPXP14X |
-- |
0.298 (0.0155) |
| Mean total payments per night (NUMNIGHX > 0) |
IPXP14X/
NUMNIGHX |
$4,443 ($205.7) |
$4,468 ($205.7) |
* Zero payment events can occur in MEPS for the
following reasons: (1) the stay was covered under a flat fee arrangement (flat
fee payments are included only on the first event covered by the arrangement),
(2) there was no charge for a follow-up stay, (3) the provider was never paid by
an individual, insurance plan, or other source for services provided, (4) the
charges were included in another bill, or (5) the event was paid for through
government or privately-funded research or clinical trials.
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To enhance analyses of hospital inpatient stays,
analysts may link information about inpatient stays by sample persons in this
file to the annual full year consolidated file (which has data for all MEPS
sample persons), or conversely, link person-level information from the full year
consolidated file to this event-level file (see Section 5 below for more
details). Both this file and the full year consolidated file may be used to
derive estimates for persons with hospital inpatient care and annual estimates
of total expenditures. However, if the estimate relates to the entire
population, this file cannot be used to calculate the denominator, as only those
persons with at least one inpatient event are represented on this data file.
Therefore, the full year consolidated file must be used for person-level
analyses that include both persons with and without inpatient care.
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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 software package 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 2.5.5.
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The MEPS has a complex sample design. To obtain
estimates of variability (such as the standard error of sample estimates or
corresponding confidence intervals) for MEPS estimates, analysts need to take
into account the complex sample design of MEPS for both person-level and
family-level analyses. Several methodologies have been developed for estimating
standard errors for surveys with a complex sample design, including the
Taylor-series linearization method, balanced repeated replication, and jackknife
replication. Various software packages provide analysts with the capability of
implementing these methodologies. Replicate weights have not been developed for
the MEPS data. Instead, the variables needed to calculate appropriate standard
errors based on the Taylor-series linearization method are included on this file
as well as all other MEPS public use files. Software packages that permit the
use of the Taylor-series linearization method include SUDAAN, Stata, SAS
(version 8.2 and higher), and SPSS (version 12.0 and higher). For complete
information on the capabilities of each package, analysts should refer to the
corresponding software user documentation.
Using the Taylor-series linearization method, variance
estimation strata and the variance estimation PSUs within these strata must be
specified. The variables VARSTR and VARPSU on this MEPS data file serve to
identify the sampling strata and primary sampling units required by the variance
estimation programs. Specifying a “with replacement” design in one of the
previously mentioned computer software packages will provide estimated standard
errors appropriate for assessing the variability of MEPS survey estimates. It
should be noted that the number of degrees of freedom associated with estimates
of variability indicated by such a package may not appropriately reflect the
number available. For variables of interest distributed throughout the country
(and thus the MEPS sample PSUs), one can generally expect to have at least 100
degrees of freedom associated with the estimated standard errors for national
estimates based on this MEPS database.
Prior to 2002, MEPS variance strata and PSUs were
developed independently from year to year, and the last two characters of the
strata and PSU variable names denoted the year. However, beginning with the 2002
Point-in-Time PUF, the variance strata and PSUs were developed to be compatible
with all future PUFs until the NHIS design changed. Thus, when pooling data
across years 2002 through the Panel 11 component of the 2007 files, the variance
strata and PSU variables provided can be used without modification for variance
estimation purposes for estimates covering multiple years of data. There were
203 variance estimation strata, each stratum with either two or three variance
estimation PSUs.
From Panel 12 of the 2007 files, a new set of variance
strata and PSUs were developed because of the introduction of a new NHIS design.
There are 165 variance strata with either two or three variance estimation PSUs
per stratum, starting from Panel 12. Therefore, there are a total of 368
(203+165) variance strata in the 2007 Full Year file as it consists of two
panels that were selected under two independent NHIS sample designs. Since both
MEPS panels in the Full Year 2008 file and beyond are based on the new NHIS
design, there are only 165 variance strata. These variance strata (VARSTR
values) have been numbered from 1001 to 1165 so that they can be readily
distinguished from those developed under the former NHIS sample design in the
event that data are pooled for several years.
If analyses call for pooling MEPS data across several
years, in order to ensure that variance strata are identified appropriately for
variance estimation purposes, one can proceed as follows:
- When pooling any year from 2002 or later, one can use the
variance strata numbering as is.
- When pooling any year from 1996 to 2001 with any year from
2002 or later, use the H36 file.
- A new H36 file will be constructed in the future to allow
pooling of 2007 and later years with 1996 to 2006.
Return To Table Of Contents
Data from this file can be used alone or in
conjunction with other files for different analytic purposes. This section
summarizes various scenarios for merging/linking MEPS event files. The set of
households selected for MEPS is a subsample of those participating in the
National Health Interview Survey (NHIS), thus, each MEPS panel can also be
linked back to the previous year’s NHIS public use data files. For information
on obtaining MEPS/NHIS link files please see
meps.ahrq.gov/data_stats/more_info_download_data_files.jsp.
Return To Table Of Contents
Merging characteristics of interest from other MEPS
files (e.g., MEPS 2014 Full-Year Consolidated File) expands the scope of
potential estimates. For example, to estimate the total number of 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 2014 Appendix File, HC-168I, provides additional
detail on how to merge MEPS data files.
- Create data set PERSX by sorting the MEPS 2014 Full Year
Consolidated 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
RACEV1X EDUYRDG EDRECODE) 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
The prescribed medicines-event link (RXLK) file
provides a link from the MEPS event files to the Prescribed Medicine Event File.
When using RXLK, data users/analysts should keep in mind that one inpatient stay
can link to more than one prescribed medicine record. Conversely, a prescribed
medicine event may link to more than one inpatient stay visit or different types
of events. When this occurs, it is up to the data user/analyst to determine how
the prescribed medicine expenditures should be allocated among those medical
events. For detailed linking examples, including SAS code, data users/analysts
should refer to the MEPS 2014 Appendix File, HC-168I.
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The conditions-event link file (CLNK) provides a link
from MEPS event files to the 2014 Medical Conditions File. When using the CLNK,
data users/analysts should keep in mind that (1) conditions are
household-reported, (2) there may be multiple conditions associated with 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
Cohen, S.B. (1998). Sample Design of the 1996 Medical
Expenditure Panel Survey Medical Provider Component. Journal of Economic and
Social Measurement. Vol. 24, 25-53.
Cohen, S.B. (1996). The Redesign of the Medical
Expenditure Panel Survey: A Component of the DHHS Survey Integration Plan.
Proceedings of the COPAFS Seminar on Statistical Methodology in the Public
Service.
Cox, B.G. and Cohen, S.B. (1985). Chapter 6: A
Comparison of Household and Provider Reports of Medical Conditions. In
Methodological Issues for Health Care Surveys. Marcel Dekker, New York.
Cox, B. and Iachan, R. (1987). A Comparison of
Household and Provider Reports of Medical Conditions. Journal of the American
Statistical Association 82(400):1013-18.
Edwards, W.S., Winn, D.M., Kurlantzick V., et al.
(1994). Evaluation of National Health Interview Survey Diagnostic Reporting.
National Center for Health Statistics, Vital Health 2(120).
Elixhauser, A., Steiner, C.A., Whittington, C.A., and
McCarthy, E. Clinical Classifications for Health Policy Research: 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.
Ezzati-Rice, T.M., Rohde, F., Greenblatt, J., Sample
Design of the Medical Expenditure Panel Survey Household Component, 1998–2007.
Methodology Report No. 22. March 2008. Agency for Healthcare Research and
Quality, Rockville, MD.
Health Care Financing Administration (1980).
International Classification of Diseases, 9th Revision, Clinical
Modification (ICD-CM). Vol. 1. (DHHS Pub. No. (PHS) 80-1260). DHHS: U.S. Public
Health Services.
Johnson, A.E. and Sanchez, M.E. (1993). Household and
Medical Provider Reports on Medical Conditions: National Medical Expenditure
Survey, 1987. Journal of Economic and Social Measurement. Vol. 19,
199-233.
Monheit, A.C., Wilson, R., and Arnett, III, R.H.
(Editors). Informing American Health Care Policy. (1999). Jossey-Bass
Inc., San Francisco.
Shah, B.V., Barnwell, B.G., Bieler, G.S., Boyle, K.E.,
Folsom, R.E., Lavange, L., Wheeless, S.C., and Williams, R. (1996). Technical
Manual: Statistical Methods and Algorithms Used in SUDAAN Release 7.0,
Research Triangle Park, NC: Research Triangle Institute.
Return To Table Of Contents
VARIABLE-SOURCE CROSSWALK
FOR MEPS HC-168D: 2014 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 |
| PANEL |
Panel Number |
Constructed |
| 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 |
| IPENDYR |
Event end date – year |
CAPI derived |
| IPENDMM |
Event end date – month |
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 |
| DLVRTYPE |
Vaginal or Caesarean delivery |
HS06A |
| ANYOPER |
Any operations or surgeries performed |
HS06 |
| 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 |
| FFBEF14 |
Total # of visits in FF before 2014 |
FF05 |
| FFTOT15 |
Total # of visits in FF after 2014 |
FF10 |
Return To Table Of Contents
Imputed Total Expenditure Variables
| Variable |
Description |
Source |
| IPXP14X |
Total expenditure for event (IPFXP14X+IPDXP14X) |
Constructed |
| IPTC14X |
Total charge for event (IPFTC14X+IPDTC14X) |
Constructed |
Return To Table Of Contents
Imputed Facility Expenditure Variables
| Variable |
Description |
Source |
| IPFSF14X |
Facility amount paid, self/family (Imputed) |
CP Section (Edited) |
| IPFMR14X |
Facility amount paid, Medicare (Imputed) |
CP Section (Edited) |
| IPFMD14X |
Facility amount paid, Medicaid (Imputed) |
CP Section (Edited) |
| IPFPV14X |
Facility amount paid, private insurance (Imputed) |
CP Section (Edited) |
| IPFVA14X |
Facility amount paid, Veterans/CHAMPVA (Imputed) |
CP Section (Edited) |
| IPFTR14X |
Facility amount paid, TRICARE (Imputed) |
CP Section (Edited) |
| IPFOF14X |
Facility amount paid, other federal (Imputed) |
CP Section (Edited) |
| IPFSL14X |
Facility amount paid state & local government (Imputed) |
CP Section (Edited) |
| IPFWC14X |
Facility amount paid, workers’ compensation (Imputed) |
CP Section (Edited) |
| IPFOR14X |
Facility amount paid, other private (Imputed) |
Constructed |
| IPFOU14X |
Facility amount paid, other pub (Imputed) |
Constructed |
| IPFOT14X |
Facility amount paid, other insurance (Imputed) |
CP Section (Edited) |
| IPFXP14X |
Facility sum payments IPFSF14X – IPFOT14X |
Constructed |
| IPFTC14X |
Total facility charge (Imputed) |
CP Section (Edited) |
Return To Table Of Contents
Imputed Separately Billing Physician Expenditure Variables
| Variable |
Description |
Source |
| IPDSF14X |
Doctor amount paid, family (Imputed) |
Constructed |
| IPDMR14X |
Doctor amount paid, Medicare (Imputed) |
Constructed |
| IPDMD14X |
Doctor amount paid, Medicaid (Imputed) |
Constructed |
| IPDPV14X |
Doctor amount paid, private insurance (Imputed) |
Constructed |
| IPDVA14X |
Doctor amount paid, Veterans/CHAMPVA (Imputed) |
Constructed |
| IPDTR14X |
Doctor amount paid, TRICARE (Imputed) |
Constructed |
| IPDOF14X |
Doctor amount paid, other federal (Imputed) |
Constructed |
| IPDSL14X |
Doctor amount paid, state & local government (Imputed) |
Constructed |
| IPDWC14X |
Doctor amount paid, workers’ compensation (Imputed) |
Constructed |
| IPDOR14X |
Doctor amount paid, other private insurance (Imputed) |
Constructed |
| IPDOU14X |
Doctor amount paid, other public insurance (Imputed) |
Constructed |
| IPDOT14X |
Doctor amount paid, other insurance (Imputed) |
Constructed |
| IPDXP14X |
Doctor sum payments IPDSF14X–IPDOT14X |
Constructed |
| IPDTC14X |
Total doctor charge (Imputed) |
Constructed |
| IMPFLAG |
Imputation status |
Constructed |
Return To Table Of Contents
Weights
| Variable |
Description |
Source |
| PERWT14F |
Expenditure file person weight, 2014 |
Constructed |
| VARSTR |
Variance estimation stratum, 2014 |
Constructed |
| VARPSU |
Variance estimation PSU, 2014 |
Constructed |
Return To Table Of Contents
|
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