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August 2017
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 Naming
2.5 File Contents
2.5.1 Identifier Variables (DUID-CONDRN)
2.5.2 Medical Condition Variables (AGEDIAG-CCCODEX)
2.5.2.1 Priority Conditions and Injuries
2.5.2.2 Age Priority Condition Began
2.5.2.3 Follow-up Questions for Injuries and Priority Conditions
2.5.2.4 Sources for Conditions on the MEPS Conditions File
2.5.2.5 Treatment of Data from Rounds Not Occurring in 2015
2.5.2.6 Rounds in Which Conditions Were
Reported/Selected (CRND1 – CRND5)
2.5.2.7 Diagnosis, Condition, and Procedure Codes
2.5.2.8 Clinical Classification Codes
2.5.3 Utilization Variables (OBNUM – RXNUM)
3.0 Survey Sample Information
3.1 Overview
3.2 Details on Person Weight Construction
3.2.1 MEPS Panel 19 Weight Development Process
3.2.2 MEPS Panel 20 Weight Development Process
3.2.3 The Final Weight for 2015
3.2.4 Coverage
3.3 Using MEPS Data for Trend Analysis
4.0 Merging/Linking MEPS Data Files
4.1 National Health Interview Survey (NHIS)
4.2 Longitudinal Analysis
References
Appendix 1: Variable-Source Crosswalk
Appendix 2: Condition, Procedure, and Clinical Classification Code Frequencies
Appendix 3: Clinical Classification Code to ICD-9-CM Code Crosswalk
Appendix 4: List of Invalid ICD-9-CM Codes
Appendix 5: List of Conditions Asked in Priority
Conditions Enumeration Section
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
noninstitutionalized 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 noninstitutionalized 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 the data contained in
MEPS Public Use Release HC-180, which is one in a series of public use data
files to be released from the 2015 Medical Expenditure Panel Survey Household
Component (MEPS HC). Released in ASCII (with related SAS, SPSS, and Stata
programming statements and data user information) and SAS formats, this public
use file provides information on household-reported medical conditions collected
on a nationally representative sample of the civilian noninstitutionalized
population of the United States for calendar year 2015 MEPS HC. The file
contains 27 variables and has a logical record length of 87 with an additional
2-byte carriage return/line feed at the end of each record.
This documentation offers a brief overview of the
types and levels of data provided and the content and structure of the files. It
contains the following sections:
- Data File Information
- Survey Sample Information
- Merging/Linking MEPS Data Files
- Appendices
- Variable-Source Crosswalk
- Detailed ICD-9-CM Condition, Procedure, and Clinical
Classification Code Frequencies
- Clinical Classification Code to ICD-9-CM Code
Crosswalk
- List of Invalid ICD-9-CM Codes
- List of Conditions Asked in Priority Conditions
Enumeration Section
A codebook of all the variables included in the 2015
Medical Conditions File is provided in an accompanying file.
For more information on MEPS survey design, see T.
Ezzati-Rice, et al., 1998-2007 and S.Cohen, 1996. A copy of the survey instrument used to collect the information on
this file is available on the MEPS Website: meps.ahrq.gov.
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This file contains 123,227 records. Each record
represents one medical condition reported for a household survey member who
resides in an eligible responding household and who has a positive person or
family weight.
Conditions created in the Priority Condition
Enumeration (PE) section were asked in the context of “has person ever been told
by a doctor or other health care professional that they have (condition)?”
except joint pain and chronic bronchitis, which ask only about the last 12 months.
If the response is Yes (1), then a condition record is generated but only
included in this file if the condition is current. A condition is defined
as current if it is linked to an event or a condition the person is
currently experiencing (i.e., a condition selected in the Condition Enumeration
(CE) section).
Records meeting one of the following criteria are
included on the file:
In Panel 20:
- Round 1 and Round 2 records that are current conditions. A current
condition is defined as a condition linked to a 2015 event or a condition
the person is currently experiencing (i.e., a condition selected in the CE
section);
- Round 3 conditions that were linked to a 2015 event;
- Round 3 current conditions that were due to an accident or injury;
- Round 3 priority condition records that are current and the age of
diagnosis is less than or equal to the person’s age as of 12/31/2015, the
age of diagnosis is refused, don’t know, or not ascertained, or the age of
diagnosis is set to -1 (inapplicable);
- Any other current Round 3 conditions where 50 percent or more of person’s
reference period occurred in 2015.
In Panel 19:
- Round 3, Round 4, and Round 5 records that are current conditions. A
current condition is defined as a condition linked to a 2015 event or a
condition the person is currently experiencing (i.e., a condition selected
in the CE section);
- Round 1 and Round 2 condition records that are linked to a 2015 event or
a condition the person is currently experiencing in 2015 (i.e., a condition
selected in the CE section).
For most variables on the file, the codebook provides
both weighted and unweighted frequencies. 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 Appendix 1, Variable-Source Crosswalk.
Data from this file can be merged with 2015 MEPS
person-level data to append person-level characteristics such as demographic or
health insurance characteristics to each record by using DUPERSID (see Section
4.0 for details). Since each record represents a single condition reported by a
household respondent, some household members may have multiple medical conditions
and thus will be represented by multiple records on this file. Other household
members may have had no reported medical conditions and thus will have no records
on this file. Still other household members may have had a reported medical
condition that did not meet the criteria above and thus will have no records on
this file. Data from this file also can be merged to 2015 MEPS Event Files
(HC-178A, and HC-178D through HC-178H) by using the link files provided in
HC-178I. (See HC-178I documentation for details.)
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The codebook and data file list variables in the
following order:
- Unique person identifiers
- Unique condition identifiers
- Medical condition variables
- Utilization 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 |
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This codebook describes an ASCII data set and provides
the following programming identifiers for each variable:
| Identifier |
Description |
| Name |
Variable name (maximum of 8 characters) |
| Description |
Variable descriptor (maximum 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 8-character limitation. Edited variables end in an “X” and are
so noted in the variable label. (CONDIDX, which is an encrypted identifier
variable, also ends in an “X”.)
Variables contained in this delivery were derived
either from the questionnaire itself or from the CAPI. The source of each
variable is identified in Appendix 1 “Variable-Source Crosswalk.” Sources for
each variable are indicated in one of three ways: (1) variables derived from
CAPI or assigned in sampling are so indicated; (2) variables collected at one or
more specific questions have those numbers and questionnaire sections indicated
in the “SOURCE” column; and (3) variables constructed from multiple questions
using complex algorithms are labeled “Constructed” in the “SOURCE” column.
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The definitions of Dwelling Units (DUs) in the MEPS HC
are generally consistent with the definitions employed for the National Health
Interview Survey (NHIS). The dwelling unit ID (DUID) is a 5-digit random number
assigned after the case was sampled for MEPS. The person number (PID) uniquely
identifies each person within the dwelling unit.
The variable DUPERSID uniquely identifies each person
represented on the file and is the combination of the variables DUID and PID.
CONDN is the condition number and uniquely identifies
each condition reported for an individual. The range on this file for CONDN is
11-681 and the range of total records for any one person on the file is 1-57.
The variable CONDIDX uniquely identifies each
condition (i.e., each record on the file) and is the combination of DUPERSID and
CONDN. CONDIDX is always a length of 12 with DUPERSID (8) and CONDN (4)
combined. For CONDIDX, the condition number is padded with leading zeroes to
ensure consistent length.
PANEL is a constructed variable used to specify the
panel number for the interview in which the condition was reported. PANEL will
indicate either Panel 19 or Panel 20.
CONDRN indicates the round in which the condition was
first reported. For a small number of cases, conditions that actually
began in an earlier round were not reported by respondents until subsequent
rounds of data collection. During file construction, editing was performed for
these cases in order to reconcile the round in which a condition began and the
round in which the condition was first reported.
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This file contains variables describing medical
conditions reported by respondents in several sections of the MEPS
questionnaire, including the Condition Enumeration section, and all
questionnaire sections collecting information about health provider visits
and/or prescription medications (see Variable-Source Crosswalk in Appendix 1 for
details).
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Certain conditions were a priori designated as
“priority conditions” due to their prevalence, expense, or relevance to policy.
Some of these are long-term, life-threatening conditions, such as cancer,
diabetes, emphysema, high cholesterol, hypertension, ischemic heart disease, and
stroke. Others are chronic manageable conditions, including arthritis and asthma.
The only mental health condition on the priority conditions list is attention
deficit hyperactivity disorder/attention deficit disorder.
When a condition was first mentioned, respondents were
asked whether it was due to an accident or injury (INJURY=1). Only non-priority
conditions (i.e., conditions reported in a section other than PE) are eligible
to be injuries. The interviewer is prevented from selecting priority conditions
as injuries.
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The age of diagnosis (AGEDIAG) was collected for all
priority conditions, except joint pain. For confidentiality reasons, AGEDIAG is
set to Inapplicable (-1) for cancer conditions.
To ensure confidentiality, age of diagnosis was
top-coded to 85. This corresponds with the age top-coding in person-level PUFs.
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When a respondent reported that a condition resulted
from an accident or injury (INJURY=1), respondents were asked during the round in
which the injury was first reported whether the accident/injury occurred at work
(ACCDNWRK). This question was not asked about persons aged 15 and younger; the
condition had ACCDNWRK coded to inapplicable (-1) for those persons.
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The records on this file correspond with medical
condition records collected by CAPI and stored on a person’s MEPS conditions
roster. Conditions can be added to the MEPS conditions roster in several ways. A
condition can be reported in the Priority Condition Enumeration (PE) section in
which persons are asked if they have been diagnosed with specific conditions.
The condition can be identified as the reason reported by the household
respondent for a particular medical event (hospital stay, outpatient visit,
emergency room visit, home health episode, prescribed medication purchase,
or medical provider visit). Finally, the condition may be reported by the
household-level respondent as a condition “bothering” the person during the
reference period (see question CE03). Conditions reported in the PE section that
are not current are not included on this file.
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Prior to the 2008 file, priority conditions reported
during Rounds 1 and 2 of the second year panel were included on the file even if
the conditions were not related to an event or reported as a serious condition
occurring in the second year of the panel. Beginning in 2008, priority
conditions are included on the file only if they are current conditions. A
current condition is defined as a condition linked to an event or a condition
the person is currently experiencing (i.e., a condition selected in the
Condition Enumeration (CE) section). Conditions from Rounds 1 and 2 that are not
included in the 2015 file are available in the 2014 Medical Conditions File. Note
that, for some Rounds 1 and 2 records, data may not be available on the previous
year’s file. This situation can occur when a person does not have a positive
person or family weight in the first year but is assigned a positive weight in
the subsequent year. The situation can also occur if the condition is a priority
condition for which no events were reported in the first year but are reported
in the second year. For 2015,100 conditions from Panel 19 Rounds 1 and 2 are
included on the 2015 Medical Conditions File for persons who did not appear on
the previous year’s file.
Note: Priority conditions are generally chronic
conditions. Even though a person may not have reported an event in 2015 due to
the condition, or reported generally experiencing the condition in 2015,
analysts should consider that the person is probably still experiencing the
condition. If a Panel 19 person reported a priority condition in Round 1 or 2
and did not have an event for the condition in Round 3, 4, or 5, the condition
will not be included on the 2015 Medical Conditions File.
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A set of constructed variables indicates the round in
which the condition was first reported (CONDRN), and the subsequent round(s) in
which the condition was selected (CRND1 – CRND5). The condition may be reported
or selected when the person reports an event that occurred due to the condition,
or the condition may be selected as a serious condition that is not linked to
any events. For example, consider a condition for which CRND1 = 0, CRND2 = 1,
and CRND3 = 1. For non-priority conditions, this sequence of CRND indicators on
a condition record implies that the condition was not present during Round 1
(CRND1 = 0), was first mentioned during Round 2, and was selected during Round
3. For priority conditions, it is necessary to look at CONDRN rather than CRND#
to determine in which round the condition was first reported. In addition to the
scenario above, this sequence of CRND indicators may imply for priority
conditions that the condition was reported in the PE section in Round 1 but was
not connected with an event, and not selected in the CE section as a current
condition until Rounds 2 and 3.
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The medical conditions and procedures reported by the
Household Component respondent were recorded by the interviewer as verbatim
text, which was then coded by professional coders to fully-specified ICD-9-CM
codes, including medical condition and V codes (see Health Care Financing
Administration, 1980). Although codes were verified and error rates did not
exceed 2 percent, analysts should not presume this level of precision in the
data; the ability of household respondents to report condition data that can be
coded accurately should not be assumed (see Cox and Iachan, 1987; Edwards, et
al, 1994; and Johnson and Sanchez, 1993). Some condition information is collected
in the Medical Provider Component of MEPS. However, since it is not available for
everyone in the sample, it is not used to supplement, replace, or verify
household-reported condition data.
Data analysts should also use caution when working
with the procedure codes on this file. Procedure codes are gathered in the same
manner as the conditions data, i.e., reports by household respondents. The
survey does not prompt respondents for procedures, so procedures are
under-reported. In addition, the ability of household respondents to accurately
report procedures should not be assumed. Analysts should not use available data
on procedures to make estimates of frequencies of specific procedures or to
extrapolate to national estimates.
Professional coders followed specific guidelines in
coding missing values to the ICD-9-CM diagnosis condition and procedure
variables. The ICD-9-CM diagnosis condition variable (ICD9CODX) was coded -9
where the verbatim text fell into one of three categories: (1) the text
indicated that the condition was unknown (e.g., DK); (2) the text indicated the
condition could not be diagnosed by a doctor (e.g., doctor doesn’t know); or (3)
the specified condition was not codeable and a procedure could not be discerned
from the text. ICD9CODX was coded -1 where the verbatim text strictly denoted a
procedure and not a condition. The ICD-9-CM procedure variable (ICD9PROX) was
coded -9 where the verbatim text strictly denoted a procedure, but the text was
not specific enough to assign a procedure code. ICD9PROX was set to -1 where the
text strictly specified a condition and not a procedure.
In order to preserve confidentiality, nearly all of
the diagnosis condition codes provided on this file have been collapsed from
fully-specified codes to 3-digit code categories. Table 1 in Appendix 2 provides
unweighted and weighted frequencies for all ICD-9-CM condition code values
reported on the file. In this table, values that reflect this collapsing have an
asterisk in the label indicating that the 3-digit category includes all the
subclassifications within that category. For example, the ICD9CODX value of 034
“Strep Throat/Scarlet Fev *” includes the fully-specified subclassifications
034.0 and 034.1; the value 296 “Affective Psychoses*” includes the
fully-specified subclassifications 296.0 through 296.99. Approximately 8 percent
of the records on this file were edited further by collapsing two or more
3-digit codes into one 3-digit code. Some values of ICD9CODX have been recoded
to Not Ascertained (-9) for confidentiality reasons.
For confidentiality purposes, ICD-9-CM codes are
recoded to broader codes by clinicians for conditions that occur fewer than 20
times within a year’s conditions file and for clinically rare conditions. A
condition is deemed clinically rare if it appears on the National Institutes of
Health’s list of rare diseases. Additional factors used to determine recoding
include age, gender, and population estimates. Each year, a few conditions on the
final file fall below the confidentiality threshold. This is due to the
multistage file development process. The confidentiality recoding is performed
on the preliminary version of the Conditions file each year. This preliminary
version is used in the development of other event PUFs and, in turn, these event
PUFs are used in the development of the final conditions file. During this
process, some records from the preliminary file are dropped because only records
that are relevant to the current data year are reflected in the final Conditions
PUF.
Most of the procedure codes were collapsed from
fully-specified codes to 2-digit category codes. Table 2 in Appendix 2 provides
unweighted and weighted frequencies for ICD9PROX, and this type of collapsing is
identified by an asterisk in the variable label. For example, the ICD9PROX value
of 81 “Joint Repair*” includes subclassifications 81.0 through 81.99. Less than
1 percent of records were further edited to combine two or more 2-digit
categories.
Note that, through FY 2012, for conditions related to
certain medical events, the ICD-9-CM codes on this file were also released in
the Prescribed Medicines, Emergency Room Visits, Office-based Medical Provider
Visits, Outpatient Department Visits, and Inpatient Hospital Stays Event
Files. Because the ICD-9-CM codes have been collapsed, it is possible for there
to be duplicate ICD-9-CM condition or procedure codes linked to a single medical
event when different fully-specified codes are collapsed into the same code. For
information on merging data on this file with the 2015 MEPS Event Files
(HC-178A, and HC-178D through HC-178H) refer to the link files provided in
HC-178I, and see HC-178I documentation for details.
Each year certain ICD-9-CM codes may be ‘retired’ from
use. Since 2012, these codes are removed from the ‘history’ table (Appendix 3)
prior to condition coding processing and listed separately for reference (see
List of Invalid ICD-9-CM Codes in Appendix 4).
In a small number of cases, diagnosis, condition, and
procedure codes were further recoded to -9 if they denoted a pregnancy for a
person younger than 16 or older than 44. Less than one-tenth of 1 percent of
records were recoded in this manner on the 2015 Medical Conditions File. The
person’s age was determined by linking the 2015 Medical Conditions File to the
2014 and 2015 Person-Level Use PUFs. If the person’s age is under 16 or over 44
in the round in which the condition or procedure was reported, the appropriate
condition or procedure code was recoded to -9.
Users should note that because of the design of the
survey, most deliveries (i.e., births) are coded as pregnancies. For more
accurate estimates for deliveries, analysts should use RSNINHOS “Reason Entered
Hospital” found on the Hospital Inpatient Stays Public Use File (HC-178D).
Conditions and procedures were reported in the same
sections of the HC questionnaire (see Variable-Source Crosswalk in Appendix 1).
Labels for all values of the variables ICD9CODX and ICD9PROX, as shown in Tables
1 and 2 of Appendix 2, are provided in the SAS programming statements included
in this release (see the H180SU.TXT file).
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ICD-9-CM condition codes have been aggregated into
clinically meaningful categories that group similar conditions (CCCODEX).
CCCODEX was generated using Clinical Classification Software (formerly known as
Clinical Classifications for Health Care Policy Research (CCHPR)), which
aggregates conditions and V-codes into mutually exclusive categories, most of
which are clinically homogeneous (Elixhauser, et al, 2000). Appendix 3 lists the
ICD-9-CM codes that have been aggregated for each clinical classification
category.
The reported ICD-9-CM condition code values were mapped
to the appropriate clinical classification category prior to being collapsed to
3-digit ICD-9-CM condition codes. The result is that every record which has an
ICD-9-CM diagnosis code also has a clinical classification code.
For confidentiality purposes, ICD-9-CM codes are
recoded to broader codes by clinicians for conditions that occur fewer than 20
times within a year’s conditions file and for clinically rare
conditions. Additional factors used to determine recoding include age, gender,
and population estimates.
CCS codes are assigned to the original fully-specified
ICD-9-CM codes. When the original ICD-9-CM codes undergo recoding, no changes
are made to the assigned CCS codes.
As with ICD9CODX and ICD9PROX, professional coders
followed specific guidelines in setting CCCODEX to a missing value. CCCODEX was
coded -9 where the verbatim text fell into one of three categories: (1) the text
indicated that the condition was unknown (e.g., DK); (2) the text indicated the
condition could not be diagnosed by a doctor (e.g., doctor doesn’t know); or (3)
the specified condition was not codeable and a procedure could not be discerned
from the text. CCCODEX was coded -1 where the verbatim text strictly denotes a
procedure and not a condition.
A small number (less than 1 percent) of clinical
classification codes have been edited for confidentiality purposes. Table 3 in
Appendix 2 provides weighted and unweighted frequencies for CCCODEX. Labels for
all values of the variable CCCODEX, as shown in Table 3, are provided in the SAS
programming statements included in this release (see the H180SU.TXT file).
In a small number of cases, clinical classification
codes were further recoded to -9 if they denoted a pregnancy for a person
younger than 16 or older than 44. Less than one-tenth of 1 percent of records
were recoded in this manner on the 2015 Medical Conditions File. The person’s
age was determined by linking the 2015 Medical Conditions File to the 2014 and
2015 Person-Level Use PUFs. If the person’s age is under 16 or over 44 in the
round in which the condition was reported, the appropriate clinical
classification code was recoded to -9.
Note that, prior to 2004 the range for the variable
CCCODEX was 001 through 260. In 2004, revisions to the coding of mental
disorders were implemented. The codes 650 through 663 replaced 065 through 075
in 2004.Beginning in 2007, the mental disorders codes were reorganized again.
Alcohol and substance abuse disorders were broken into separate categories, and
miscellaneous mental disorders were renumbered.
Analysts should use the clinical classification codes
listed in the Conditions PUF document (HC-180) and the Appendix to the Event
Files document (HC-178I) when analyzing MEPS conditions data. Although there is
a list of clinical classification codes and labels on the Healthcare Cost and
Utilization Project (HCUP) Website, if updates to these codes and/or labels are
made on the HCUP Website after the release of the 2015 MEPS PUFs, these updates
will not be reflected in the 2015 MEPS data.
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The variables OBNUM, OPNUM, HHNUM, IPNUM, ERNUM, and
RXNUM indicate the total number of 2015 events that can be linked to each
condition record on the current file, i.e., office-based, outpatient, home
health, inpatient hospital stays, emergency room visits, and prescribed
medicines, respectively.
These counts of events were derived from Expenditure
Event Public Use Files (HC-178G, HC-178F, HC-178H, HC-178D, HC-178E, and
HC-178A). Events associated with conditions include all utilization that occurred
between January 1, 2015 and December 31, 2015.
Because persons can be seen for more than one
condition per visit, these frequencies will not match the person or event-level
utilization counts. For example, if a person had one inpatient hospital stay and
was treated for a fractured hip, a fractured shoulder, and a concussion, each of
these conditions has a unique record in this file and IPNUM=1 for each record.
By
summing IPNUM for these records, the total inpatient hospital stays would be
three when actually there was only one inpatient hospital stay for that person
and three conditions were treated. These variables are useful for determining the
number of inpatient hospital stays for head injuries, hip fractures, etc.
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There is a single full year person-level weight
(PERWT15F) 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 2015. A
key person was either a member of a responding NHIS household at the time of the
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 PERWT15F was developed in
several stages. First, person-level weights for Panel 19 and Panel 20 were created
separately. The weighting process for each panel included adjustments 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 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
2015 composite weight was then formed by multiplying each weight from Panel 19 by
the factor .460 and each weight from Panel 20 by the factor .540. Using such
factors to form composite weights serves to limit the variance of estimates
obtained from pooling the two samples. The resulting composite weight was raked
to the same set of CPS-based control totals. Then, when the poverty status
information (derived from the MEPS income variables) became available, another
raking was undertaken, using dimensions reflecting poverty status in addition to
the previously mentioned five variables. 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. Thus, the raking for the final weight reflected
poverty status as well as the other five variables previously used in the weight
calibration.
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The person-level weight for an individual in MEPS
Panel 19 was developed using the 2014 full year weight as a “base” weight for each
survey participant present in 2014. For key, in-scope members who joined an RU
some time in 2015 after being out-of-scope in 2014, the initially assigned
person-level weight was the corresponding 2014 family weight. The weighting
process included an adjustment for person-level nonresponse over Rounds 4 and 5
as well as raking to population control figures for December 2015 for key,
responding persons in-scope on December 31, 2015. These control figures were
derived by scaling back the population distribution obtained from the March
2016 CPS to reflect the December 31, 2015 estimated population total (estimated
based on Census projections for January 1, 2016). 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. The final weight for key, responding
persons who were not in-scope on December 31, 2015 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 an individual in MEPS
Panel 20 was developed using the 2015 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 2015 as
well as raking to the same population control figures for December 2015 used for
the MEPS Panel 19 weights for key, responding persons in-scope on December 31,
2015. The same five variables employed for Panel 19 raking (census region, MSA
status, race/ethnicity, sex, and age) were used for Panel 20 raking. Again, the
final weight for key, responding persons who were not in-scope on December 31,
2015 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, 2015 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
2015 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 populations.
Overall, the weighted population estimate for the
civilian noninstitutionalized population for December 31, 2015 is
317,629,239(PERWT15F>0 and INSC1231=1). The sum of the person-level weights
across all persons assigned a positive person-level weight is 321,423,251.
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The target population for MEPS in this file is the
2015 U.S. civilian noninstitutionalized population. However, the MEPS sampled
households are a subsample of the NHIS households interviewed in 2013(Panel 19)
and 2014(Panel 20). New households created after the NHIS interviews for the
respective panels and consisting exclusively of persons who entered the target
population after 2013(Panel 19) or after 2014(Panel 20) 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.
Return To Table Of Contents
MEPS began in 1996, and the utility of the survey for
analyzing health care trends expands with each additional year of data; however,
there are a variety of methodological and statistical considerations when
examining trends over time using MEPS. Examining changes over longer periods of
time can provide a more complete picture of underlying trends. 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 survey methodology.
In 2013 MEPS survey operations introduced an effort to
obtain more complete information about health care utilization from MEPS
respondents with full implementation in 2014. This effort resulted in improved
data quality and a reduction in underreporting in the second half of 2013 and
throughout 2014. Respondents tended to report more visits, especially
non-physician visits, by sample members and the new approach appeared
particularly effective among those subgroups with relatively large numbers of
visits, such as the elderly, Medicare beneficiaries, and people with multiple
chronic conditions, disabilities, or poor health. Reported spending on visits
also tended to increase, especially for such subgroups.
Changes to the MEPS survey instrument should also be
considered when analyzing trends. For example, as a result of improved methods
for collecting priority conditions data implemented in 2007, prevalence measures
prior to 2007 are not comparable to those from 2007 and beyond for many of these
conditions. Data users should review relevant sections of the documentation for
descriptions of these types of changes before undertaking trend analyses.
Analysts may also wish to consider using statistical
techniques to smooth or stabilize analyses of trends using MEPS data such as
comparing pooled time periods (e.g. 1996-97 versus 2011-13), 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, statistical significance tests should be
conducted to assess the likelihood that observed trends are not attributable to
sampling variation. In addition, 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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Data from the current file can be used alone or in
conjunction with other files. Merging characteristics of interest from
person-level files expands the scope of potential estimates. See HC-178I for
instructions on merging the Conditions File to the Medical Event
Files. Person-level characteristics can be merged to this Conditions File using
the following procedure:
- Sort the person-level file by person identifier, DUPERSID. Keep only
DUPERSID and the variables to be merged onto the Conditions File.
- Sort the Conditions File by person identifier, DUPERSID.
- Merge both files by DUPERSID, and output all records in the Conditions
File.
- If PERS contains the person-level variables, and COND is the Conditions
File, the following code can be used to add person-level variables to the
person’s conditions in the Condition-level file.
PROC SORT DATA=PERS(KEEP=DUPERSID AGE SEX EDUYRDG
EDUCYR HIDEG EDRECODE)
OUT=PERSX; BY DUPERSID;
RUN;
PROC SORT DATA=COND; BY DUPERSID;
RUN;
DATA COND;
MERGE COND (IN=A) PERSX(IN=B); BY DUPERSID;
IF A;
RUN;
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Data from this file can be used alone or in
conjunction with other files for different analytic purposes. Each MEPS panel can
also be linked back to the previous years’ National Health Interview Survey
public use data files. For information on obtaining MEPS/NHIS link files please
see meps.ahrq.gov/data_stats/more_info_download_data_files.jsp.
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Panel-specific longitudinal files are available for
downloading in the data section of the MEPS Web site. For each panel, the
longitudinal file comprises MEPS survey data obtained in Rounds 1 through 5 of
the panel and can be used to analyze changes over a two-year period. Variables
in the file pertaining to survey administration, demographics, employment,
health status, disability days, quality of care, patient satisfaction, health
insurance, and medical care use and expenditures were obtained from the MEPS
full-year Consolidated files from the two years covered by that panel.
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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. 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.
Evaluation of National Health Interview Survey Diagnostic Reporting. National
Center for Health Statistics, Vital Health 2(120). 1994.
Elixhauser, A., Steiner, C. A., Whittington, C. A.,
and McCarthy, E. Clinical Classifications for health policy research: Hospital
inpatient statistics, 1995. Healthcare Cost and Utilization project, HCUP-3
research Note. Rockville, MD: Agency for Healthcare Research and Quality; 2000.
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. (Department of Health and Human Services Pub. No
(PHS) 80-1260). Department of Health and Human Services: U.S. Public Health
Services.
Johnson, Ayah E., and Sanchez, Maria Elena. (1993),
“Household and Medical Reports on Medical Conditions: National Medical
Expenditure Survey.” Journal of Economic and Social Measurement, 19,
199-223.
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Unique Identifier Variables
| Variable |
Label |
Source1 |
| DUID |
Dwelling Unit ID |
Assigned In Sampling |
| PID |
Person Number |
Assigned In Sampling |
| DUPERSID |
Person ID (DUID + PID) |
Assigned In Sampling |
| CONDN |
Condition Number |
CAPI Derived |
| CONDIDX |
Condition ID |
CAPI Derived |
| PANEL |
Panel Number |
Constructed |
| CONDRN |
Condition Round Number |
CAPI Derived |
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Medical Condition Variables
| Variable |
Label |
Source1 |
| AGEDIAG |
Age When Diagnosed |
PE section |
| CRND1 |
Has Condition Information In Round 1 |
Constructed |
| CRND2 |
Has Condition Information In Round 2 |
Constructed |
| CRND3 |
Has Condition Information In Round 3 |
Constructed |
| CRND4 |
Has Condition Information In Round 4 |
Constructed |
| CRND5 |
Has Condition Information In Round 5 |
Constructed |
| INJURY |
Was Condition Due To Accident/Injury |
CN01A |
| ACCDNWRK |
Did Accident Occur At Work |
CN07 |
| ICD9CODX |
ICD-9-CM Code For Condition - Edited |
CE05, HS04, ER04, OP09, MV09, HH05, PM09
(Edited) |
| ICD9PROX |
ICD-9-CM Code For Procedure - Edited |
CE05, HS04, ER04, OP09, MV09, HH05, PM09
(Edited) |
| CCCODEX |
Clinical Classification Code - Edited |
Constructed/Edited |
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Utilization Variables
| Variable |
Label |
Source1 |
| HHNUM |
# Home Health Events Assoc. w/ Condition |
Constructed |
| IPNUM |
# Inpatient Events Assoc. w/ Condition |
Constructed |
| OPNUM |
# Outpatient Events Assoc. w/ Condition |
Constructed |
| OBNUM |
# Office-Based Events Assoc. w/ Condition |
Constructed |
| ERNUM |
# ER Events Assoc. w/ Condition |
Constructed |
| RXNUM |
# Prescribed Medicines Assoc. w/ Cond. |
Constructed |
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Weights and Variance Estimation Variables
| Variable |
Label |
Source1 |
| PERWT15F |
Expenditure File Person Weight, 2015 |
Constructed |
| VARSTR |
Variance Estimation Stratum, 2015 |
Constructed |
| VARPSU |
Variance Estimation PSU, 2015 |
Constructed |
1See the Household Component section under Survey Questionnaires on the MEPS home page for information on the MEPS HC questionnaire sections shown in the Source column (e.g., CN, PE).
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List of Invalid ICD-9-CM Diagnosis Codes
| Diagnosis Code |
Description |
| 41.4 |
Escherichia coli [E. coli] infection in
conditions classified elsewhere and of unspecified site |
| 173 |
Other malignant neoplasm of skin of lip |
| 173.1 |
Other malignant neoplasm of skin of eyelid,
including canthus |
| 173.2 |
Other malignant neoplasm of skin of ear and
external auditory canal |
| 173.3 |
Other malignant neoplasm of skin of other and
unspecified parts of face |
| 173.4 |
Other malignant neoplasm of scalp and skin of
neck |
| 173.5 |
Other malignant neoplasm of skin of trunk,
except scrotum |
| 173.6 |
Other malignant neoplasm of skin of upper
limb, including shoulder |
| 173.7 |
Other malignant neoplasm of skin of lower
limb, including hip |
| 173.8 |
Other malignant neoplasm of other specified
sites of skin |
| 173.9 |
Other malignant neoplasm of skin, site
unspecified |
| 284.1* |
Pancytopenia |
| 286.5 |
Hemorrhagic disorder due to intrinsic
circulating anticoagulants |
| 310.8 |
Other specified nonpsychotic mental disorders
following organic brain damage |
| 425.1* |
Hypertrophic obstructive cardiomyopathy |
| 444 |
Embolism and thrombosis of abdominal aorta |
| 512.8* |
Other spontaneous pneumothorax |
| 516.3 |
Idiopathic fibrosing alveolitis |
| 518.5* |
Pulmonary insufficiency following trauma and surgery |
| 596.8 |
Other specified disorders of bladder |
| 631 |
Other abnormal product of conception |
| 718.60* |
Unspecified intrapelvic protrusion of
acetabulum, site unspecified |
| 747.3 |
Anomalies of pulmonary artery |
| 793.1* |
Nonspecific (abnormal) findings on
radiological and other examination of lung field |
| 795.5* |
Nonspecific reaction to tuberculin skin test
without active tuberculosis |
| 997.4** |
Digestive system complications |
| 998.0* |
Postoperative shock |
| 999.4 |
Anaphylactic shock due to serum |
| 999.5** |
Other serum reaction |
| V12.2 |
Personal history of endocrine, metabolic, and
immunity disorders |
| V13.8 |
Personal history of other specified diseases
|
| V19.1 |
Family history of other eye disorders |
| V40.3* |
Other behavioral problems |
List of Invalid ICD-9-CM Procedure Codes
| Procedure Code |
Description |
| 02.2* |
Ventriculostomy |
Notes:
* These diagnosis codes were discussed at the March
9-10, 2011 ICD-9-CM Coordination and Maintenance Committee meeting and were not
finalized in time to include in the FY 2012 IPPS/LTCH PPS proposed rule. They
were deleted on October 1, 2011.
** The code title has changed from the proposed rule.
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LIST OF CONDITIONS ASKED IN PRIORITY CONDITIONS ENUMERATION SECTION
- Angina/Angina Pectoris
- Arthritis
- Asthma
- Attention Deficit Hyperactivity Disorder (ADHD)/Attention Deficit Disorder (ADD)
- Cancer/Malignancy
- Chronic Bronchitis
- Coronary Heart Disease
- Diabetes/Sugar Diabetes
- Emphysema
- Heart Attack/Myocardial Infarction (MI)
- High Cholesterol
- Hypertension/High Blood Pressure
- Joint Pain
- Other Heart Disease (not coronary heart disease, angina, or heart attack)
- Stroke/Transient Ischemic Attack (TIA)/Mini-stroke
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