MEPS HC-010F:
1996 Outpatient Department Visits
Agency for Healthcare Research and Quality
Center for Financing, Access, and Cost Trends
540 Gaither Road
Rockville, MD 20850
(301) 427-1406
Table of Contents
A. Data Use Agreement
B. Background
1.0 Household Component
2.0 Medical Provider Component
3.0 Insurance Component
4.0 Nursing Home Component
5.0 Survey Management
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
2.4.1 General
2.4.2 Expenditure and Sources of Payment Variables
2.5 File 1 Contents
2.5.1 Survey Administration Variables
2.5.2 Characteristics of Outpatient Visits
(OPBEGYY-VAPLACE)
2.5.3 MPC Data Indicator
(MPCDATA)
2.5.4 Conditions and Procedures Codes (OPICD1X-OPICD4X,
OPPRO1X) and Clinical Classification Codes (OPCCC1X-OPCCC4X)
2.5.5 Record Count Variable
(NUMCOND)
2.5.6 Flat Fee Variables
2.5.7 Expenditure Data
2.5.8 Imputed Outpatient Expenditure Variables
2.6 File 2 Contents: Pre-imputed Expenditure Variables
3.0 Sample Weights and Variance Estimation Variables (WTDPER96-VARPSU96)
3.1 Details on Person Weights Construction
4.0 Strategies for Estimation
4.1 Variable with Missing Values
4.2 Basic Estimates of Utilization, Expenditures and Sources of Payment
4.3 Estimates of the Number of Persons with Outpatient Visits
4.4 Person-Based Ratio Estimates
4.4.1 Person-Based Ratio Estimates Relative to Persons with Outpatient
Visits
4.4.2 Person-Based Ratio Estimates Relative to the Entire Population
4.5 Sampling Weights for Merging Previous Releases of MEPS Household Data
with the Current Data File
4.6 Variance Estimation
5.0 Merging/Linking MEPS Data Files
6.0 Programming Information
References
Attachment 1
D. Codebooks
(link to separate file)
E. Variable-Source Crosswalk
A. Data Use Agreement
Individual identifiers have been removed from the microdata contained in the files on this CD-ROM.
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.
- 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.
- 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 18 U.S.C. 1001 and is
punishable by a fine of up to $10,000 or up to 5 years in prison.
The Agency for Healthcare Research and Quality requests that users cite AHRQ and the Medical
Expenditure Panel Survey as the data source in any publications or research based upon these data.
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B. Background
This documentation describes one in a series of public use files from the Medical Expenditure Panel
Survey (MEPS). The survey provides a new and extensive data set on the use of health services and
health care in the United States.
MEPS is conducted to provide nationally representative estimates of health care use, expenditures,
sources of payment, and insurance coverage for the U.S. civilian noninstitutionalized population.
MEPS also includes a nationally representative survey of nursing homes and their residents. MEPS
is cosponsored by the Agency for Healthcare Research and Quality (AHRQ) (formerly the Agency
for Health Care Policy and Research (AHCPR)) and the National Center for Health Statistics
(NCHS).
MEPS comprises four component surveys: the Household Component (HC), the Medical Provider
Component (MPC), the Insurance Component (IC), and the Nursing Home Component (NHC). The
HC is the core survey, and it forms the basis for the MPC sample and part of the IC sample. The
separate NHC sample supplements the other MEPS components. Together these surveys yield
comprehensive data that provide national estimates of the level and distribution of health care use
and expenditures, support health services research, and can be used to assess health care policy
implications.
MEPS is the third in a series of national probability surveys conducted by AHRQ on the financing
and use of medical care in the United States. The National Medical Care Expenditure Survey
(NMCES, also known as NMES-1) was conducted in 1977. The National Medical Expenditure
Survey (NMES-2) was conducted in 1987. Beginning in 1996, MEPS continues this series with
design enhancements and efficiencies that provide a more current data resource to capture the
changing dynamics of the health care delivery and insurance system.
The design efficiencies incorporated into MEPS are in accordance with the Department of Health
and Human Services (DHHS) Survey Integration Plan of June 1995, which focused on consolidating
DHHS surveys, achieving cost efficiencies, reducing respondent burden, and enhancing analytical
capacities. To accommodate these goals, new MEPS design features include linkage with the
National Health Interview Survey (NHIS), from which the sampling frame for the MEPS HC is
drawn, and continuous longitudinal data collection for core survey components. The MEPS HC
augments NHIS by selecting a sample of NHIS respondents, collecting additional data on their health
care expenditures, and linking these data with additional information collected from the respondents'
medical providers, employers, and insurance providers.
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1.0 Household Component
The MEPS HC, a nationally representative survey of the U.S. civilian noninstitutionalized
population, collects medical expenditure data at both the person and household levels. The HC
collects detailed data on demographic characteristics, health conditions, health status, use of medical
care services, charges and payments, access to care, satisfaction with care, health insurance coverage,
income, and employment.
The HC uses an overlapping panel design in which data are collected through a preliminary contact
followed by a series of five rounds of interviews over a 2½-year period. Using computer-assisted
personal interviewing (CAPI) technology, data on medical expenditures and use for two calendar
years are collected from each household. This series of data collection rounds is launched each
subsequent year on a new sample of households to provide overlapping panels of survey data and,
when combined with other ongoing panels, will provide continuous and current estimates of health
care expenditures.
The sampling frame for the MEPS HC is drawn from respondents to NHIS, conducted by NCHS.
NHIS provides a nationally representative sample of the U.S. civilian noninstitutionalized
population, with oversampling of Hispanics and blacks.
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2.0 Medical Provider Component
The MEPS MPC supplements and validates information on medical care events reported in the
MEPS HC by contacting medical providers and pharmacies identified by household respondents.
The MPC sample includes all hospitals, hospital physicians, home health agencies, and pharmacies
reported in the HC. Also included in the MPC are all office-based physicians who:
- were identified by the household respondent as providing care for HC respondents
receiving Medicaid.
- were selected through a 75-percent sample of HC households receiving care through an
HMO (health maintenance organization) or managed care plan.
- were selected through a 25-percent sample of the remaining HC households.
Data are collected on medical and financial characteristics of medical and pharmacy events reported
by HC respondents, including:
- Conditions and procedures coded according to ICD-9-CM (9th Revision, International
Classification of Diseases) and DSM-IV (Fourth Edition, Diagnostic and Statistical
Manual of Mental Disorders).
- Physician procedure codes classified by CPT-4 (Common Procedure Terminology,
Version 4).
- Inpatient stay codes classified by DRGs (condition and procedure related groups).
- Prescriptions coded by national drug code (NDC), medication name, strength, and
quantity dispensed.
- Charges, payments, and the reasons for any difference between charges and payments.
The MPC is conducted through telephone interviews and mailed survey materials. In some instances,
providers sent medical and billing records which were abstracted into the survey instruments.
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3.0 Insurance Component
The MEPS IC collects data on health insurance plans obtained through employers, unions, and other
sources of private health insurance. Data obtained in the IC include the number and types of private
insurance plans offered, benefits associated with these plans, premiums, contributions by employers
and employees, eligibility requirements, and employer characteristics.
Establishments participating in the MEPS IC are selected through four sampling frames:
To provide an integrated picture of health insurance, data collected from the first sampling frame
(employers and insurance providers) are linked back to data provided by the MEPS HC respondents.
Data from the other three sampling frames are collected to provide annual national and State
estimates of the supply of private health insurance available to American workers and to evaluate
policy issues pertaining to health insurance.
The MEPS IC is an annual survey. Data are collected from the selected organizations through a pre-screening telephone interview, a mailed questionnaire, and a telephone follow-up for
nonrespondents.
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4.0 Nursing Home Component
The 1996 MEPS NHC was a survey of nursing homes and persons residing in or admitted to nursing
homes at any time during calendar year 1996. The NHC gathered information on the demographic
characteristics, residence history, health and functional status, use of services, use of prescription
medicines, and health care expenditures of nursing home residents. Nursing home administrators
and designated staff also provided information on facility size, ownership, certification status,
services provided, revenues and expenses, and other facility characteristics. Data on the income,
assets, family relationships, and care-giving services for sampled nursing home residents were
obtained from next-of-kin or other knowledgeable persons in the community.
The 1996 MEPS NHC sample was selected using a two-stage stratified probability design. In the
first stage, facilities were selected; in the second stage, facility residents were sampled, selecting both
persons in residence on January 1, 1996, and those admitted during the period January 1 through
December 31.
The sample frame for facilities was derived from the National Health Provider Inventory, which is
updated periodically by NCHS. The MEPS NHC data were collected in person in three rounds of
data collection over a 1½-year period using the CAPI system. Community data were collected by
telephone using computer-assisted telephone interviewing (CATI) technology. At the end of three
rounds of data collection, the sample consisted of 815 responding facilities, 3,209 residents in the
facility on January 1, and 2,690 eligible residents admitted during 1996.
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5.0 Survey Management
MEPS data are collected under the authority of the Public Health Service Act. They are edited and
published in accordance with the confidentiality provisions of this act and the Privacy Act. NCHS
provides consultation and technical assistance.
As soon as data collection and editing are completed, the MEPS survey data are released to the
public in staged releases of summary reports and microdata files. Summary reports are released as
printed documents and electronic files. Microdata files are released on CD-ROM and/or as electronic
files.
Printed documents and CD-ROMs are available through the AHRQ Publications Clearinghouse.
Write or call:
AHRQ Publications Clearinghouse
Attn: (publication number)
P.O. Box 8547
Silver Spring, MD 20907
800/358-9295
410/381-3150 (callers outside the United States only)
888/586-6340 (toll-free TDD service; hearing impaired only)
Be sure to specify the AHRQ number of the document or CD-ROM you are requesting. Selected
electronic files are available from the Internet on the MEPS web site: <http://www.meps.ahrq.gov/>
Additional information on MEPS is available from the MEPS project manager or the MEPS public
use data manager at the Center for Cost and Financing Studies, Agency for Healthcare Research and
Quality.
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C. Technical and Programming Information
1.0 General Information
This documentation describes one in a series of public use event files from the 1996 Medical
Expenditure Panel Survey Household (HC) and Medical Provider Components (MPC). Released
as an ASCII data file and SAS transport file, this public use file provides detailed information on
outpatient visits for a nationally representative sample of the civilian noninstitutionalized population
of the United States and can be used to make estimates of outpatient utilization and expenditures for
calendar year 1996. Each record represents one household-reported outpatient visit reported during
rounds 1, 2, and 3. Outpatient visits reported in Round 3 and known to have begun after December
31, 1996 are not included on this file. In addition to expenditures related to this event, each record
contains household reported medical conditions and procedures associated with the outpatient visit.
Data from this event file can be merged with other MEPS HC data files, for the purpose of
appending person characteristics such as demographic or health insurance characteristics to each
outpatient visit record.
Counts of outpatient visits are based entirely on household reports. Information from the MEPS
MPC was used to supplement expenditure and payment data reported by the household.
This file can be also used to construct summary variables of expenditures, sources of payment, and
related aspects of outpatient visits. Aggregate annual person-level information on the use of
outpatient departments and other health services use is provided on public use file HC-011, where
each record represents a MEPS sampled person.
The following documentation offers a brief overview of the types and levels of data provided, the
content and structure of the files and the codebook, and programming information. It contains the
following sections:
Data File Information
Sample Weights and Variance Estimation Variables
Merging MEPS Data Files
Programming Information
References
Definitions
Codebook
Variable to Source Crosswalk
For more information on MEPS HC survey design see S. Cohen, 1997; J. Cohen, 1997; and S.
Cohen, 1996. For information on the MEPS MPC design, see S. Cohen, 1998. A copy of the survey
instrument used to collect the information on this file is available on the MEPS web site at the
following address: <http://www.meps.ahrq.gov>.
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2.0 Data File Information
This public use data set consists of 2 event-level data files. File 1 contains characteristics associated
with the outpatient visit and imputed expenditure data. File 2 contains unimputed expenditure data
from both the Household and Medical Provider Components for all outpatient visits on File 1.
Please see Attachment 1 for definitions of imputed, un-imputed and pre-imputed expenditure
variables.
Both files 1 and 2 of this public use data set contains variables and frequency distribution for a total
of 9,957 outpatient visits reported during rounds 1, 2, and 3 of the MEPS Household Component.
This file includes records of outpatient visits for all household survey respondents who resided in
eligible responding households and who reported at least one outpatient visit. Records where the
outpatient visit was known to have occurred after December 31, 1996 are not included on this file.
Of these records, 9,793 were associated with persons having positive person-level weights
(WTDPER96). The persons represented on this file had to meet criteria for either (a) or (b):
(a) Be classified as a key in-scope person who responded for his or her entire period
of 1996 eligibility (i.e., persons with a positive 1996 full-year person-level sampling
weight (WTDPER96>0)), or
(b) Be classified as either an eligible non-key person or an eligible out-of-scope
person who responded for his or her entire period of 1996 eligibility, and belonged
to a family (i.e., all persons with the same value of FAMID) in which all eligible
family members responded for their entire period of 1996 eligibility, and at least one
family member has a positive 19996 fill-year person weight (i.e., eligible non-key or
eligible out-of-scope persons who are members of a family all of whose members
have a positive 1996 full-year MEPS family-level weight (WTFAM96>0)).
For each variable on the file, both weighted and unweighted frequencies are provided in the
codebook.
Each record of the outpatient visit on this file includes the following information: date of the visit;
whether or not the survey respondent saw the doctor; type of care received; type of services (i.e. lab
test, sonogram or ultrasound, x-rays, etc) received, medicines prescribed during the visit; flat fee
information, imputed sources of payment, total payment and total charge; and a full-year person-level
weight.
File 2 of this public use data set is intended for analysts who want to perform their own imputations
to handle missing data. This file contains one set of un-imputed expenditure information from the
Medical Provider Component as well as one set of pre-imputed expenditure information from the
Household Component. Both sets of expenditure data have been subject to minimal logical editing
that 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 HMO's and
private HMO's as payment sources. However, missing data was not imputed.
Data from these files can be merged with previously released 1996 MEPS HC person level data
using the unique person identifier, DUPERSID, to append person characteristics such as
demographic or health insurance characteristics to each record. The outpatient visits on this file can
also be linked to the MEPS 1996 Medical Conditions File (HC-006) and to the MEPS Prescribed
Medicines File (HC-010A). Please see the Appendix file for details on how to link MEPS data files.
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2.1 Codebook Structure
For each variable on these files, both weighted and unweighted frequencies are provided. The
codebook and data file sequence list variables in the following order:
File 1
Unique person identifiers
Unique outpatient visit identifiers
Other survey administration variables
Outpatient visit event-level variables
ICD-9 codes
Clinical Classification Software codes
Imputed expenditure variables
Weight and variance estimation variables
File 2
Unique person identifiers
Unique outpatient visit event-level identifiers
Pre-imputed and unimputed expenditure variables
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2.2 Reserved Codes
The following reserved code values are used:
VALUE DEFINITION
-1 INAPPLICABLE Question was not asked due to skip pattern.
-2 DETERMINED IN A PREVIOUS ROUND
-3 NO DATA IN ROUND
-5 NEVER WILL KNOW
-6 INAPPLICABLE Not asked due to person being under age 5
-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.
-10 HOURLY WAGE VALUE SUPPRESSED
-11 Not a priority condition; data not collected.
-12 Condition-level information not applicable in round.
-13 VALUE SUPPRESSED Data suppressed due to confidentiality or legal restrictions.
Generally, -1,-7, -8, and -9 have not been edited on this file. The values of -1 and -9 can be edited
by analysts by following the skip patterns in the questionnaire.
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2.3 Codebook Format
This 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 40
characters) |
| Format |
Number of bytes |
| Type |
Type of data: numeric (indicated by
NUM) or character (indicated by CHAR) |
| Start |
Beginning column position of
variable in record |
| End |
Ending column position of variable
in record |
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2.4 Variable Naming
In general, variable names reflect the content of the variable, with an 8 character limitation. For
questions asked in a specific round, the end digit in the variable name reflects the round in which
the question was asked. All imputed/edited variables end with a "X".
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2.4.1 General
Variables contained on Files 1 and 2 were derived either from the HC questionnaire itself, the MPC
data collection instrument or from the CAPI. The source of each variable is identified in Section E,
entitled, "Variable to Source Crosswalk". Sources for each variable are indicated in one of four
ways: (1) variables which are derived from CAPI or assigned in sampling are so indicated; (2)
variables which come from one or more specific questions have those numbers and the questionnaire
section indicated in the "Source" column; (3) variables constructed from multiple questions using
complex algorithms are labeled "Constructed" in the "Source" column; and (4) variables which have
been imputed are so indicated.
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2.4.2 Expenditure and Sources of Payment Variables
Both pre-imputed and imputed versions of the expenditure and sources of payment variables are
provided on 2 separate files. Variables on Files 1 and 2 follow a standard naming convention and
are 8 characters in length. Please note that pre-imputed means that a series of logical edits have been
performed on the variable but missing data remains. The imputed versions incorporate the same
edits but have also undergone the imputation process to account for missing data.
The pre-imputed expenditure variables on File 2 end with an "H", if the data source was from the
MEPS Household Component and ends with a "M" if the data source was the MEPS Medical
Provider Component. All imputed variables on File 1 end with an "X".
The total sum of payments, 12 sources of payment variables and total charge variables are named
consistently in the following way:
The first two characters indicate the type of event:
IP - inpatient stay
OB - office-based visit
ER - emergency room visit
OP - outpatient visit
HH - home health visit
DV - dental visit
OM - other medical equipment
RX - prescribed medicine
For expenditure variables on these files, the third character indicates whether the expenditure (or
amount paid) is associated with the facility (F) or the physician (P).
In the case of the sources of payment variables, the fourth and fifth characters indicate:
SF - self or family
OF - other Federal Government
MR - Medicare
SL - State/local government
MD - Medicaid
WC - Worker's Compensation
PV - private insurance
OT - other insurance
VA - Veterans
OR - other private
CH - CHAMPUS/CHAMPVA
OU - other public
XP - sum of payments
The sixth and seventh characters indicate the year (96) and the last character of all imputed/edited
variables is an " X".
For example, OPFSF96X is the edited/imputed amount paid by self or family for the facility portion
of the expenditure associated with an outpatient visit.
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2.5 File 1 Contents
2.5.1 Survey Administration Variables
Person Identifiers (DUID, PID, DUPERSID)
The dwelling unit ID (DUID) is a 5-digit random number assigned after the case was sampled for
MEPS. The 3-digit person number (PID) uniquely identifies each person within the dwelling unit.
The 8-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 on public use file (HC-008).
Record Identifiers (EVNTIDX, FFID11X, EVENTRN)
EVNTIDX uniquely identifies each event (i.e. each record on the file) and is the variable required
to link events to data files containing details on conditions and/or prescribed medicines (HC-006 and
H-010A, respectively). For details on linking see Section 5.0.
FFID11X uniquely identifies a flat fee group, that is, all events that were part of a flat fee payment
situation. For example, if a patient receives stitches in an outpatient visit and comes back to have the
stitches removed ten days later in a follow-up outpatient visit, both visits are covered under one flat
fee dollar amount. These two events (the initial outpatient visit and the subsequent outpatient visit)
have the same value for FFID11X. Please note that FFID11X should be used to link up all MEPS
event files (excluding prescribed medicines: HC-010A) in order to determine the full set of events
that are part of a flat fee group.
EVENTRN indicates the round in which the outpatient visit was first reported.
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2.5.2 Characteristics of Outpatient Visits (OPBEGYY-VAPLACE)
File 1 contains 47 variables describing outpatient visits reported by respondents in the Outpatient
Department section of the MEPS Household questionnaire. The questionnaire contains specific
probes for gathering details about the outpatient visit. Unless noted otherwise, the following
variables are provided as unedited.
Visit Details (OPBEGYR - VSTRELCN)
When a person reported having had a visit to a hospital outpatient department or special clinic, the
date of the outpatient visit was reported (OPBEGYR, OPBEGMM, OPBEGDD). The user should
note that all records on this file are for in-person outpatient department visits. Phone visits are not
on this file. Also reported were: if the person was referred by another physician or medical provider
(REFERDBY), and if during the visit the person talked to the medical provider in person or over the
telephone (SEEDOC). If the person did not see a physician (i.e., medical doctor), the respondent
was asked to identify the type of medical person that was seen (MEDPTYPE). The amount of time
actually spent with the medical provider (TIMESPNT), the type of care the person received
(VSTCTGRY), and whether or not the visit or telephone call was related to a specific condition
(VSTRELCN) were also determined.
Treatment, Services, Procedures, and Prescription Medicines (PHYSTH - DOCOUTF)
Types of treatment received during
the outpatient visit include physical therapy (PHYSTH), occupational
therapy (OCCUPTH), speech therapy (SPEECHTH), chemotherapy (CHEMOTH),
radiation therapy (RADIATTH), kidney dialysis (KIDNEYD), IV therapy
(IVTHER), drug or
alcohol treatment (DRUGTRT), allergy shots (RCVSHOT), and psychotherapy/counseling
(PSYCHOTH). Services received during the visit included whether
or not the person received lab tests (LABTEST), a sonogram or
ultrasound (SONOGRAM), x-rays (XRAYS), a mammogram
(MAMMOG), an MRI or CAT scan (MRI), an electrocardiogram (EKG),
an electroencephalogram (EEG), a vaccination (RCVVAC), anesthesia
(ANESTH), or other diagnostic tests or exams
(OTHSVCE). Whether or not a surgical procedure was performed during
the visit was asked (SURGPROC) and, if so, the procedure name
(SURGNAME). Finally, The questionnaire
determined if a medicine was prescribed for the person during the
visit (MEDPRESC) and if the person saw any of the same doctors
or surgeons at their place of practice outside of the outpatient
department or clinic (DOCOUTF).
Other Visit Details (VAPLACE)
VAPLACE is a constructed variable that indicates whether the outpatient department or clinic was
a VA facility. This variable only has valid data for providers that were sampled into the Medical
Provider Component. All other providers are classified as unknown
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2.5.3 MPC Data Indicator (MPCDATA)
While all hospital outpatient visits are sampled into the Medical Provider Component, not all
outpatient visits records have MPC data associated with them. This is dependent upon the
cooperation of the household respondent to provide permission forms to contact the outpatient
facility as well as the cooperation of the outpatient facility to participate in the survey. MPCDATA
is a constructed variable which indicates whether or not MPC data were collected for the outpatient
visit.
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2.5.4 Conditions and Procedures Codes (OPICD1X-OPICD4X, OPPRO1X) and
Clinical Classification Codes (OPCCC1X-OPCCC4X)
Information on household reported medical
conditions and procedures associated with each outpatient visit
is provided on this file. There are up to four condition codes
(OPICD1X-OPICD4X)
and 1 procedure code (OPPRO1X) listed for each outpatient visit
(99.5 % of the outpatient visits have 0-4 condition records linked).
In order to obtain complete information on conditions and
procedures associated with an event, the analyst must link to the
HC-006 Medical Conditions File. Please see Section 5.0 for details
on how to link this file to the MEPS Medical Conditions File
(HC-006). The user should note that due to confidentiality restrictions,
provider-reported condition
information is not publicly available.
The medical conditions reported by the Household Component respondent were recorded by the
interviewer as verbatim text, which were then coded to fully-specified 1996 ICD-9-CM codes,
including medical condition and V codes (see Health Care Financing Administration, 1980), by
professional coders. Although codes were verified and error rates did not exceed 2.5 percent for any
coder, analysts should not presume this level of precision in the data; the ability of household
respondents to report condition data that can be coded accurately should not be assumed (see Cox
and Cohen, 1985; Cox and Iachan, 1987; Edwards, et al, 1994; and Johnson and Sanchez, 1993).
For detailed information on conditions, please refer to the documentation on HC-006 1996 Medical
Condition File.
The ICD-9-CM conditions and procedures codes were aggregated into clinically meaningful
categories. These categories, included on the file as OPCCC1X-OPCCC4X, were generated using
Clinical Classification Software (formerly known as Clinical Classifications for Health Care Policy
Research (CCHPR)), (Elixhauser, et al., 1998), which aggregates conditions and V-codes into 260
mutually exclusive categories, most of which are clinically homogeneous.
In order to preserve respondent confidentiality, nearly all of the condition codes provided on this file
have been collapsed from fully-specified codes to 3-digit code categories. The reported ICD-9-CM
code values were mapped to the appropriate clinical classification category prior to being collapsed
to the 3-digit categories.
The conditions and procedures codes (and clinical classification codes) linked to each outpatient visit
are sequenced in the order in which the conditions were reported by the household respondent, which
was in chronological order of occurrence and not in order of importance or severity. Analysts who
use the HC-006 Medical Conditions file in conjunction with this outpatient visit file should note that
the order of conditions on this file is not identical to that on the Medical Conditions file.
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2.5.5 Record Count Variable (NUMCOND)
The variable NUMCOND indicates the total number of condition records which can be linked from
HC-006: Medical Conditions File to each outpatient visit record. For events where no condition
records linked (NUMCOND=0), the conditions and procedures and clinical classification code
variables all have a value of -1 INAPPLICABLE. Similarly, for events without a linked second or
third condition record, the corresponding second or third conditions and procedures and clinical
classification code variable was set to -1 INAPPLICABLE.
In order to obtain complete condition information for events with NUMCOND greater than 4, the
analyst must link to the Medical Conditions File: HC-006. See Section 5.0 for details on linking
MEPS data files.
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2.5.6 Flat Fee Variables
Definition of Flat Fee Payments
A flat fee is the fixed dollar amount a person is charged for a package of health care services.
Examples would be: an obstetrician's fee covering a normal delivery, as well as pre- and post-natal
care; or a surgeon's fee covering surgical procedure along with 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 situation.
The flat fee groups represented on this file (and all of the other 1996 MEPS event files), includes flat
fee groups where at least one of the health care events, as reported by the HC respondent, occurred
during 1996. By definition a flat fee group can span multiple years and/or event types (e.g., hospital
stay, physician office visit), moreover, a single persons can have multiple flat fee groups.
Flat Fee Variable Descriptions
There are several variables on this file that describe a flat fee payment situation and the number of
medical events that are part of a flat fee group. As noted previously, for a person, the variable
FFID11X can be used to identify all events, that are part of the same flat fee group. To identify such
events, FFID11X should be used to link events from all MEPS event files (excluding prescribed
medicines): HC-010B through HC-010H. For the outpatient visits that are not part of a flat fee
payment situation, the flat fee variables described below are all set to -1 INAPPLICABLE.
Flat Fee Type (FFOPTYPX)
FFOPTYPX indicates whether the 1996 outpatient visit is the "stem" or "leaf" of a flat fee group.
A stem (records with FFOPTYPX = 1) is the initial medical service (event) which is followed by
other medical events that are covered under the same flat fee payment. The leaf of the flat fee group
(records with FFOPTYPX = 2) are those medical events that are tied back to the initial medical event
(the stem) in the flat fee group.
Total Number of 1996 Events in Group (FFTOT96)
If an outpatient visit is part of a flat fee group, the variable FFTOT96 counts the total number of all
known events (that occurred during 1996) covered under a single flat fee payment situation. This
count includes the outpatient visit record in the count.
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Counts of Flat Fee Events that Cross Years (FFBEF96 FFTOT97)
As described above, a flat fee payment situation covers multiple events and the multiple events could
span multiple years. For situations where a 1996 outpatient visit is part of a group of events, and
some of the events occurred either before 1996, counts of the known events are provided on the
outpatient visit file record. An indicator variable is provided if some of the events occurred after
1996. These variables are:
FFBEF96 -- total number of pre-1996 events in the same flat fee group as the 1996
outpatient visit record. This count would not include the 1996 outpatient visit.
FFOP97 indicates whether or not there are 1997 outpatient visits in the same flat
fee group as the1996 outpatient visit record.
FFTOT97 -- indicates whether or not there are 1997 medical events in the same flat
fee group as the 1996 outpatient visit record.
Caveats of Flat Fee Groups
There are 442 outpatient visits that are identified as being part of a flat fee payment group. In order
to correctly identify all events that are part of a flat fee group, the user should link all MEPS event
files using the variable FFID11X (excluding the prescribed medicines file).
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 1996 but the remaining visits that were part of this flat fee group occurred
in 1997. In this case, the 1996 flat fee group represented on this file would consist of one event (the
stem). The 1997 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 1995
but subsequent visits occurred during 1996. In this case, the initial visit would not be represented
on the file. This 1996 flat fee group would then only consist of one or more leaf records and no
stem. Another reason for which a flat fee group would not have a stem and a leaf record is that the
stems or leaves could have been reported as different event types. In a small number of cases, there
are flat fee groups that span various event types. The stem may have been reported as one event type
and the leaves may have been reported as another event type. In order to determine this, the analyst
must link all event files (excluding the prescribed medicines file) using the variable FFID11X to
create the flat fee group.
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2.5.7 Expenditure Data
Definition of Expenditures
Expenditures on this file refer to what is paid for in health care services. More specifically,
expenditures in MEPS are defined as the sum of payments for care received for each outpatient visit,
including out of pocket payments and payments made by private insurance, Medicaid, Medicare and
other sources. The definition of expenditures used in MEPS differs slightly from its predecessors:
the 1987 NMES and 1977 NMCES surveys where "charges" rather than sum of payments were used
to measure expenditures. This change was adopted because charges became a less appropriate proxy
for medical expenditures during the 1990's due to the increasingly common practice of discounting.
Although measuring expenditures as the sum of payments incorporates discounts in the MEPS
expenditure estimates, these estimates do not incorporate any payment not directly tied to specific
medical care visits, such as bonuses or retrospective payment adjustments paid by third party payers.
Another general change from the two prior surveys is that charges associated with uncollected
liability, bad debt, and charitable care (unless provided by a public clinic or hospital) are not counted
as expenditures because there are no payments associated with those classifications. For details on
expenditure definitions, please reference the following: "Informing American Health Care Policy"
(Monheit, et al., 1999).
Expenditure data related to outpatient visits are broken out by facility and separately billing doctor
expenditures. This file contains five categories of expenditure variables per visit: basic hospital
outpatient facility expenses, expenses for doctors who billed separately from the outpatient facility
for any services provided during the outpatient visit, total expenses, which is the sum of the facility
and physician expenses; facility total charge and doctor total charge.
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Data Editing/Imputation Methodologies of Expenditure Variables
General Imputation Methodology
The expenditure data included on this file were derived from both the MEPS Household (HC) and
the 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 outpatient visits,
MPC data were used if complete; otherwise, HC data were used if complete. Missing data for
outpatient visits where HC data were not complete and MPC data were not collected or complete
were derived through the imputation process.
Logical edits were used to resolve internal inconsistencies and other problems in the HC and MPC
survey-reported data. The edits were designed to preserve partial payment data from households and
providers, and to identify actual and potential sources of payment for each household-reported event.
In general, these edits accounted for outliers, co-payments or charges reported as total payments,
and reimbursed amounts that were reported as out of pocket payments. In addition, edits were
implemented to correct for mis-classifications between Medicare and Medicaid and between
Medicare HMO's and private HMO's 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.
A weighted sequential hot-deck procedure was used to impute for missing expenditures as well as
total charge. The procedure uses survey data from respondents to replace missing data, while taking
into account the respondents' weighted distribution in the imputation process. Classification
variables vary by event type in the hot-deck imputations, but total charge and insurance coverage are
key variables in all of the imputations. Separate imputations were performed for nine categories of
medical provider care: inpatient hospital stays, outpatient hospital department visits, emergency
room visits, visits to physicians, visits to non-physician providers, dental services, home health care
by certified providers, home health care by paid independents, and other medical expenses. After
the imputations were finished, visits to physician and non-physician providers were combined into
a single medical provider file. The two categories of home care also were combined into a single
home health file.
Expenditures for services provided by separately billing doctors in hospital settings were also edited
and imputed. These expenditures are shown separately from hospital facility charges for hospital
inpatient, outpatient, and emergency room care.
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Capitation Imputation
The imputation process was also used to make expenditure estimates at the event level for events that
were paid on a capitated basis. The capitation imputation procedure was designed as reasonable
approach to complete event level expenditures for respondents in managed care plans. This
procedure was conducted in two stages. First, HMO events reported in the MPC as covered by
capitation arrangements were imputed using similar HMO events paid on a fee-for-service, with total
charge as a key variable. Then this completed set of MPC events was used as the donor pool for
unmatched household-reported events for sample persons in HMOs. By using this strategy, capitated
HMO events were imputed as if the provider were reimbursed from the HMO on a discounted fee-for-service basis.
Imputation Methodology for Outpatient Department Visits
Facility expenditures for outpatient visits were developed in a sequence of logical edits and
imputations. "Household" edits were applied to sources and amounts of payment for all events
reported by HC respondents. "MPC" edits were applied to provider-reported sources and amounts
of payment for records matched to household-reported events. Both sets of edits were used to correct
obvious errors in the reporting of expenditures. After the data from each source were edited, a
decision was made as to whether household- or MPC-reported information would be used in the final
editing and hot-deck imputations for missing expenditures. The general rule was that MPC data
would be used for matched events, 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 outpatient visits were
imputed as simple events because hospital facility charges are rarely bundled with other events. (See
section 2.5.6 for more details on the definition of flat fee groups.)
Logical edits also were used to sort each event into a specific category for the imputations. Events
with complete expenditures were flagged as potential donors for the hot-deck imputations, while
events with missing expenditure data were assigned to various recipient categories. Each event was
assigned to a recipient category based on its pattern of missing data. For example, an event with a
known total charge but no expenditures information was assigned to one category, while an event
with a known total charge and some expenditures information was assigned to a different category.
Similarly, events without a known total charge were assigned to various recipient categories based
on the amount of missing data.
The logical edits produced eight recipient categories for events with missing data. Imputing
expenditures for some of these events was problematic, however, because the providers were not
reimbursed on a fee-for-service basis. Therefore, expenditures for services provided in capitated or
staff model health maintenance organizations (HMOs) were imputed prior to the main imputations.
Expenditures for the remaining events were imputed through separate hot-deck imputations for each
of the eight recipient categories. The donor pool in these imputations was restricted to events with
complete expenditures from the MPC, although some unmatched events had complete household-reported expenditures. Unmatched household events with complete data were not allowed to donate
information to other events because the MPC data were considered to be more reliable.
The donor pool included "free events" because, in some instances, providers are not paid for their
services. These events represent charity care, bad debt, provider failure to bill, and third party payer
restrictions on reimbursement in certain circumstances. If free events were excluded from the donor
pool, total expenditures would be over-counted because the cost of free care would be implicitly
included in paid events and explicitly included in events that should have been treated as free from
provider.
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Flat Fee Expenditures
The approach used to count expenditures for flat fees was to place the expenditure on the first visit
of the flat fee group. The remaining visits have zero payments. Thus, if the first visit in the flat fee
group occurred prior to 1996, all of the events that occurred in 1996 will have zero payments.
Conversely, if the first event in the flat fee group occurred at the end of 1996, the total expenditure
for the entire flat fee group will be on that event, regardless of the number of events it covered after
1996.
Zero Expenditures
There are some medical events reported by respondents where the payments were zero. This could
occur for several reasons including (1) free care was provided, (2) bad debt was incurred, (3) care
was covered under a flat fee arrangement beginning in an earlier year, or (4) follow-up visits were
provided without a separate charge (e.g. after a surgical procedure). If all of the medical events for
a person fell into one of these categories, then the total annual expenditures for that person would
be zero.
Discount Adjustment Factor
An adjustment was also applied to some HC reported expenditure data because an evaluation of
matched HC/MPC data showed that respondents who reported that charges and payments were equal
were often unaware that insurance payments for the care had been based on a discounted charge. To
compensate for this systematic reporting error, a weighted sequential hot-deck imputation procedure
was implemented to determine an adjustment factor for HC reported insurance payments when
charges and payments were reported to be equal. As for the other imputations, selected predictor
variables were used to form groups of donor and recipient events for the imputation process.
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Sources of Payment
In addition to total expenditures, variables are provided which itemize expenditures according to
major sources of payment categories. These categories are:
1. Out of pocket by user or family
2. Medicare
3. Medicaid
4. Private Insurance
5. Veteran's Administration, excluding CHAMPVA
6. CHAMPUS or CHAMPVA
7. Other Federal sources - includes Indian Health Service, Military Treatment Facilities,
and other care by the Federal government
8. Other State and Local Source - includes community and neighborhood clinics, State and
local health departments, and State programs other than Medicaid.
9. Worker's Compensation
10. Other Unclassified Sources - includes sources such as automobile, homeowner's,
liability, and other miscellaneous or unknown sources.
Two additional sources of payment variables were created to classify payments for events with
apparent inconsistencies between insurance coverage and sources of payment based on data collected
in the survey. These variables include:
11. Other Private - any type of private
insurance payments reported for persons not reported to have
any private health insurance coverage during the year as defined
in MEPS; and
12. Other Public - Medicaid payments reported for persons who were not reported to be
enrolled in the Medicaid program at any time during the year.
Though relatively small in magnitude, users 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 sources 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
program.
Users should also note that the Other Public and Other private sources of payment categories only
exist on File 1 for imputed expenditure data since they were created through the editing/imputation
process. File 2 reflects 10 sources of payment as it was collected through the survey.
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2.5.8 Imputed Outpatient Expenditure Variables
This file contains 2 sets of imputed expenditure variables: facility expenditures and physician
expenditures.
Outpatient Facility Expenditures (OPFSF96X-OPFOT96X, OPFTC96X, OPFXP96X)
Outpatient visit expenses include all expenses for treatment, services, tests, diagnostic and laboratory
work, x-rays, and similar charges, as well as any physician services included in the hospital
outpatient visit charge.
Outpatient visit expenditures were obtained primarily through the MPC. If the physician charges
were included in the outpatient visit bill, then this expenditure is included in the facility expenditure
variables. The imputed facility expenditures are provided on this file. OPFSF96X - OPFOT96X are
the 12 sources of payment, OPFTC96X is the facility total charge, and OPFXP96X is the sum of the
12 sources of payments for the facility expenditure. The 12 sources of payment are: self/family,
Medicare, Medicaid, private insurance, Veterans Administration, CHAMPUS/CHAMPVA, other
federal, state/local governments, Workman's Compensation, other private insurance, other public
insurance and other insurance.
Outpatient Physician Expenditures (OPDSF96X - OPDOT96X, OPDTC96X,
OPDXP96X)
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 outpatient facility bill.
For physicians who bill separately (i.e. outside the outpatient facility 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, an outpatient visit could
have a radiologist and a pathologist associated with it. If their services are not included in the
outpatient visit bill then this is one medical event with 2 separately billing doctors. The imputed
expenditure information associated with the separately billing doctors was summed to the event level
and is provided on the file. OPDSF96X - OPDOT96X are the 12 sources of payment, OPDXP96X
is the sum of the 12 sources of payments, and OPDTC96X is the physician total charge.
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). Analysts interested in total expenditure should use the variable OPEXP96X, which
includes both the facility and physician amounts.
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Rounding
Expenditure variables on file, HC-010F, have been rounded to the nearest penny. Person level
expenditure information released on HC-011 were rounded to the nearest dollar. It should be noted
that using the MEPS event files HC-010A through HC-010H to create person level totals will yield
slightly different totals than that those found on HC-011. These differences are due to rounding only.
Moreover, in some instances, the number of persons having expenditures on the event files (HC-010A HC-010H) for a particular source of payment may differ from the number of persons with
expenditures on the person level expenditure file (HC-011) for that source of payment. This
difference is also an artifact of rounding only. Please see the Appendix File for details on such
rounding differences.
Imputation Flags (IMPOPFSF-IMPOPCHG)
The variables IMPOPFSF - IMPOPCHG identify records where sources of payment and total charge
for the facility portion of the expenditure have been imputed using the methodologies outlined in this
document. The variable IMPOPNUM indicates the number of physician records associated with the
outpatient visit where the physician portion of the expenditures have been imputed. It is not available
for individual sources of payment.
When a record was identified as being the leaf of a flat fee group, the values of all imputation flags
were set to "0" (not imputed) since they were not included in the imputation process.
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2.6 File 2 Contents: Pre-imputed Expenditure Variables
Both imputed and pre-imputed expenditure data is provided on this file. Pre-imputed means that
only a series of logical edits were applied to both the HC and MPC data to correct for, among other
things, outliers, co-payments or charges reported as total payments, and reimbursed amounts counted
as out of pocket payments. Edits were also implemented to correct for mis-classifications between
Medicare and Medicaid and between Medicare HMO's and private HMO's as payment sources as
well as a number of other data inconsistencies that could be resolved through logical edits. Missing
data were not imputed.
As described previously, there are two components that went into creating the total medical
expenditure variable: household reported expenditure data and provider reported expenditure data.
Both of expenditure data are provided in their pre-imputed form and have not gone through the same
level of quality control as their imputed counterpart. This means that (in some instances) there are
large amounts of missing data. The household and provider reported facility pre-imputed expenditure data are provided on this file (OPSF96H - OPOT96H and OPFSF96M-OPFOT96M
respectively).
The user shall note that there exist only 10 sources of payment variables in the pre-imputed
expenditure data, while the imputed expenditure data on File 1 contains 12 sources of payment
variables. The additional two sources of payment (which are not reported as separate sources of
payment through the data collection) are Other Private and Other Public. These sources of payment
categories were constructed to resolve apparent inconsistencies between individuals' reported
insurance coverage and their sources of payment for specific events.
The users should also note the variable HHSFFIDX, which is the original flat fee identifier that was
derived during the household interview, should be used only if they are interested in performing their
own expenditure imputation.
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3.0 Sample Weights and Variance Estimation Variables (WTDPER96-VARPSU96)
Overview
There is a single full year person-level weight (WTDPER96) included on this file. A person-level
weight was assigned to each hospital inpatient stay reported by a key, in-scope person who
responded to MEPS for the full period of time that he or she was in scope during 1996. A key person
either was a member of an NHIS household at the time of the NHIS interview, or became a member
of such a household after being out-of-scope at the time of the 1995 NHIS (examples of the latter
situation include newborns and persons returning from military service, an institution, or living
outside the United States). A person is in scope whenever he or she is a member of the civilian
noninstitutionalized portion of the U.S. population.
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3.1 Details on Person Weights Construction
The person-level weight WTDPER96 was developed using the MEPS Round 1 person-level weight
as a base weight (for key, in scope respondents who joined an RU after Round 1, the Round 1 RU
weight served as a base weight). The weighting process included an adjustment for nonresponse
over Round 2 and the 1996 portion of Round 3, as well as poststratification to population control
figures for December 1996 (these figures were derived by scaling the population totals obtained from
the March 1997 Current Population Survey (CPS) to reflect the Census Bureau estimated population
distribution across age and sex categories as of December, 1996).
Variables used in the establishment of person-level poststratification control figures included:
poverty status (below poverty, from 100 to 125 percent of poverty, from 125 to 200 percent of
poverty, from 200 to 400 percent of poverty, at least 400 percent of poverty); census region
(Northeast, Midwest, South, West); MSA status (MSA, non-MSA); race/ethnicity (Hispanic, black
but non-Hispanic, and other); sex; and age. Overall, the weighted population estimate for the civilian
non-institutionalized population for December 31, 1996 is 265,439,511 persons. The inclusion of
key, in scope persons who were not in scope on December 31,1996 brings the estimated total number
of persons represented by the MEPS respondents over the course of the year up to 268,905,490
(WTDPER96 > 0). The weighting process included poststratification to population totals obtained
from the 1996 Medicare Current Beneficiary Survey (MCBS) for the number of deaths among
Medicare beneficiaries in 1996, and poststratification to population totals obtained from the 1996
MEPS Nursing Home Component for the number of individuals admitted to nursing homes.
The MEPS Round 1 weights incorporated the following components: the original household
probability of selection for the NHIS; ratio-adjustment to NHIS national population estimates at the
household (occupied dwelling unit) level; adjustment for nonresponse at the dwelling unit level for
Round 1; and poststratification to figures at the family- and person-level obtained from the March
1996 CPS database.
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4.0 Strategies for Estimation
This file is constructed for efficient estimation of utilization, expenditure, and sources of payment
for outpatient care and to allow for estimates of the number of persons with outpatient visits during
1996.
4.1 Variable with Missing Values
It is essential that the analyst examine all variables for the presence of negative values used to
represent missing values. For example, a record with a value of -8 for the first ICD9 condition code
(OPICD1X) indicates that the condition was reported as unknown.
For continuous or discrete variables, where means or totals may be taken, it may be necessary to set
minus values to values appropriate to the analytic needs. That is, the analyst should either impute
a value or set the value to one that will be interpreted as missing by the computing language used.
For categorical and dichotomous variables, the analyst may want to consider whether to recode or
impute a value for cases with negative values or whether to exclude or include such cases in the
numerator and/or denominator when calculating proportions. Methodologies used for
editing/imputation of expenditure variables(e.g. sources of payment, flat fee, and zero expenditures)
are described in Section 2.5.7.
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4.2 Basic Estimates of Utilization, Expenditures and Sources of Payment
While the examples described below illustrate the use of event level data in constructing person-level
expenditures, these estimates can also be derived from the person-level expenditure file unless the
characteristic of interest is event specific.
In order to produce national estimates related to outpatient visits, expenditure and sources of
payment, the value in each record contributing to the estimates must be multiplied by the weight
(WTDPER96) contained on that record.
Example 1:
For example, the total number of outpatient visits, for the civilian non-institutionalized population
of the U.S. in 1996, is estimated as the sum of the weight (WTDPER96) across all records. That is,
Sum of Wj = 125,819,128 (1)
Various estimates can be produced based on specific variables and subsets of records.
Example 2:
For example, the estimate for the average amount of out-of-pocket payment at the event level for
outpatient visits with expenditures should be calculated as the weighted average of the facility bill
and doctor's bill paid by self/family. That is,
X bar = (Sum of WjXj) / (Sum of Wj) = $29.22 (2)
where Xj = OPFSF96Xj + OPDSF96Xj and Sum of Wj = 115,742,669
for all records with OPEXP96Xj > 0 .
This gives $29.22 as the estimated average amount of out-of-pocket payment of expenditures
associated with outpatient visits and 115,742,669 as an estimate of the total number of outpatient
visits with expenditures. Both of these estimates are for the civilian non-institutionalized population
of the U.S. in 1996.
Example 3:
Another example would be to estimate the average proportion of total expenditures paid by private
insurance for outpatient visits with expenditures. This should be calculated as the weighted average
of proportion of total expenditures paid by private insurance at the event level. That is
Y bar = (Sum of WjYj) / (Sum of Wj) = 0.4682, (3)
where Yj = (OPFPV96Xj + OPDPV96Xi) / OPEXP96Xj and Sum of Wj = 115,742,669
for all records with OPEXP96Xj > 0.
This gives 0.4682 as the estimated average proportion of total expenditures paid by private insurance
for outpatient visits with expenditures for the civilian non-institutionalized population of the U.S.
in 1996.
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4.3 Estimates of the Number of Persons with Outpatient Visits
When calculating an estimate of the total number of persons with outpatient visits, users can use a
person-level file (MEPS HC-011: Person Level Expenditures and Utilization) or the current file.
However, the current file must be used, when the measure of interest is defined at the event level.
For example, to estimate the number of persons with outpatient visits where patient see a doctor, the
current file must be used. This would be estimated as,
Sum of WiXi across all unique persons i on this file, (4)
where
Wi is the sampling weight(WTDPER96) for person i
and
Xi = 1 if SEEDOC EQ 1 for any event of person i
= 0 otherwise.
Prior to estimation users will need to take into consideration the 242 records with a missing value
for SEEDOC.
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4.4 Person-Based Ratio Estimates
4.4.1 Person-Based Ratio Estimates Relative to Persons with Outpatient Visits
This file may be used to derive person-based ratio estimates. However, when calculating ratio
estimates where the denominator is persons, care should be taken to properly define the unit of
analysis as person level. For example, the mean expense for persons with outpatient visits is
estimated as
(Sum of WiZi) / (Sum of Wi) across all unique persons i on this file, (5)
where
Wi is the sampling weight(WTDPER96) for person i
and
Zi = Sum of OPXP96Xj across all visits for person
i.
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4.4.2 Person-Based Ratio Estimates Relative to the Entire Population
If the ratio relates to the entire population, this file cannot be used to calculate the denominator, as
only those persons with at least one outpatient visit are represented on this data file. In this case
MEPS File HC-011, which has data for all sampled persons, must be used to estimate the total
number of persons (i.e. those with use and those without use). For example, to estimate the
proportion of civilian non-institutionalized population of the U.S. with at least one outpatient visit
where s/he saw a doctor, the numerator would be derived from data on the current file, and the
denominator should be derived from data on the MEPS HC-011 person-level file. That is,
(Sum of WiZi) / (Sum of Wi) across all unique persons i on the MEPS HC-011 file, (6)
where
Wi is the sampling weight(WTDPER96) for person i
and
Zi = 1 if SEEDOCj EQ 1 for any visit of person i on the outpatient visit file
= 0 otherwise for all remaining persons on the MEPS HC-011 file.
Prior to estimation users will need to take into consideration the 242 records with a missing value
for SEEDOC.
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4.5 Sampling Weights for Merging Previous Releases of MEPS Household Data
with the Current Data File
There have been several previous releases of MEPS Household Survey public use data. Unless a
variable name common to several tapes is provided, the sampling weights contained on these data
files are file-specific. The file-specific weights reflect minor adjustments to eligibility and response
indicators due to birth, death, or institutionalization among respondents.
For estimates from a MEPS data file that do not require merging with variables from other MEPS
data files, the sampling weight(s) provided on that data file are the appropriate weight(s). When
merging a MEPS Household data file to another, the major analytical variable (i.e. the dependent
variable) determines the correct sampling weight to use.
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4.6 Variance Estimation
To obtain estimates of variability (such as the standard error of sample estimates or corresponding
confidence intervals) for estimates based on MEPS survey data, one needs to take into account the
complex sample design of MEPS. Various approaches can be used to develop such estimates of
variance including use of the Taylor series or various replication methodologies. Replicate weights
have not been developed for the MEPS 1996 data. Variables needed to implement a Taylor series
estimation approach is described in the paragraph below.
Using a Taylor Series approach, variance estimation strata and the variance estimation PSUs within
these strata must be specified. The corresponding variables on the MEPS full year utilization
database are VARSTR96 and VARPSU96, respectively. Specifying a "with replacement" design
in a computer software package such as SUDAAN (Shah, 1996) should provide standard errors
appropriate for assessing the variability of MEPS survey estimates. It should be noted that the
number of degrees of freedom associated with estimates of variability indicated by such a package
may not appropriately reflect the actual number available. For MEPS sample estimates for
characteristics generally distributed throughout the country (and thus the sample PSUs), there are
over 100 degrees of freedom associated with the corresponding estimates of variance. The following
illustrates these concepts using two examples from Section 4.2.
Example 2 from Section 4.2
Using a Taylor Series approach, specifying VARSTR96 and VARPSU96 as the variance estimation
strata and PSUs (within these strata) respectively and specifying a "with replacement" design in a
computer software package SUDAAN will yield an estimate of standard error of $2.59 for the
estimated mean of out-of-pocket payment.
Example 3 from Section 4.2
Using a Taylor Series approach, specifying VARSTR96 and VARPSU96 as the variance estimation
strata and PSUs (within these strata) respectively and specifying a "with replacement" design in a
computer software package SUDAAN will yield an estimate of standard error of 0.0197 for the
weighted mean proportion of total expenditures paid by private insurance.
Return To Table Of Contents
5.0 Merging/Linking MEPS Data Files
Data from the current can be used alone or in conjunction with other files. This section provides
instructions for linking the outpatient visits file with other MEPS public use files, including: the
conditions file, the prescribed medicines file, and a person-level file.
Linking a Person-Level File to the Outpatient Visit File
Merging characteristics of interest from person-level files (e.g., HC-008: 1996 Population
Characteristics and Utilization Data, or HC-011: 1996 Use and Expenditure File) expands the scope
of potential estimates. For example, to estimate the total number of hospital outpatient visits for
persons with specific characteristics (e.g., age, race, and sex), population characteristics from a
person-level file need to be merged onto the outpatient department file. This procedure is illustrated
below. The Appendix File (HC-010I) provides additional detail on how to merge MEPS data files.
Create data set PERS by sorting the person-level file, HC003, by the person identifier,
DUPERSID. Keep only variables to be merged on to the outpatient visit file and
DUPERSID.
Create data set OPAT by sorting the outpatient visit file by person identifier, DUPERSID.
Create final date set NEWOPAT by merging these two files by DUPERSID, keeping only
records on the outpatient visit file.
The following is an example of SAS code which completes these steps:
PROC SORT DATA=HC003(KEEP=DUPERSID AGE SEX RACEX)
OUT=PERSX;
BY DUPERSID;
RUN;
PROC SORT DATA=OPAT;
BY DUPERSID;
RUN;
DATA NEWOPAT;
MERGE EROM (IN=A) PERSX(IN=B);
BY DUPERSID;
IF A;
RUN;
Linking the Outpatient Department Visit (HC-010F) to the Medical Conditions File
(HC-006) and/or the Prescribed Medicines File (HC-010A)
Due to survey design issues, there are limitations/caveats that an analyst must keep in mind when
linking the different files. Those limitations/caveats are listed below. For detailed linking examples,
including SAS code, analysts should refer to the Appendix File.
Return To Table Of Contents
Limitations/Caveats of RXLK (the Prescribed Medicine Link File)
The RXLK file provides a link from
the MEPS event files to the prescribed medicine records on HC-010A.
When using RXLK, analysts should keep in mind that one hospital
outpatient visit can link to more than one prescribed medicine
record. Conversely, a prescribed medicine event may link to
more than one hospital outpatient visit or different types of events.
When this occurs, it is up to the analyst to determine how the
prescribed medicine expenditures should be allocated among those
medical events.
Limitations/Caveats of CLNK (the Medical Conditions Link File)
The CLNK provides a link from MEPS event files to the Medical Conditions File (HC-006). When
using the CLNK, analysts should keep in mind that (1) conditions are self-reported and (2) there may
be multiple conditions associated with a hospital outpatient visit. Users should also note that not all
hospital outpatient visits link to the condition file.
Return To Table Of Contents
6.0 Programming Information
The following are the technical specifications for the HC-010F data files, which are provided in
ASCII and SAS formats.
ASCII versions:
File Name: HC10FF1.DAT
Number of Observations: 9,957
Number of Variables: 104
Record Length: 398
Record Format: fixed
Record Identifier and Sort Key: EVNTIDX
File Name: HC10FF2.DAT
Number of Observations: 9,957
Number of Variables: 30
Record Length: 207
Record Format: fixed
Record Identifier and Sort Key: EVNTIDX
SAS Transport versions:
File Name: HC10FF1.SSP
SAS Name: HC10FF1
Number of Observations: 9,957
Number of Variables: 104
Record Identifier and Sort Key: EVNTIDX
File Name: HC10FF2.SSP
SAS Name: HC10FF2
Number of Observations: 9,957
Number of Variables: 30
Record Identifier and Sort Key: EVNTIDX
Return To Table Of Contents
References
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. (1997). Sample Design of the 1996 Medical Expenditure Panel Survey Household
Component. Rockville (MD): Agency for Health Care Policy and Research; 1997. MEPS
Methodology Report, No. 2. AHCPR Pub. No. 97-0027.
Cohen, J.W. (1997). Design and Methods of the Medical Expenditure Panel Survey Household
Component. Rockville (MD): Agency for Health Care Policy and Research; 1997. MEPS
Methodology Report, No. 1. AHCPR Pub. No. 97-0026.
Cohen, S.B. (1996). The Redesign of the Medical Expenditure Panel Survey: A Component of the
DHHS Survey Integration Plan. Proceedings of the COPAFS Seminar on Statistical Methodology
in the Public Service.
Cox, B.G. and Cohen, S.B. (1985). Chapter 6: A Comparison of Household and Provider Reports
of Medical Conditions. In Methodological Issues for Health Care Surveys. Marcel Dekker, New
York.
Cox, B.G. and Cohen, S.B. (1985). Chapter 8: Imputation Procedures to Compensate for Missing
Responses to Data Items. In Methodological Issues for Health Care Surveys. Marcel Dekker, New
York.
Cox, B. and Iachan, R. (1987). A Comparison of Household and Provider Reports of Medical
Conditions. Journal of the American Statistical Association 82(400):1013-18.
Edwards, W.S., Winn, D.M., Kurlantzick V., et al. (1994). Evaluation of National Health Interview
Survey Diagnostic Reporting. National Center for Health Statistics, Vital Health 2(120).
Elixhauser A., Steiner C.A., Whittington C.A., and McCarthy E. Clinical Classifications for Health
Policy Research: Hospital Inpatient Statistics, 1995. Healthcare Cost and Utilization Project, HCUP-3 Research Note. Rockville, MD: Agency for Health Care Policy and Research; 1998. AHCPR Pub.
No. 98-0049.
Health Care Financing Administration (1980). International Classification of Diseases, 9th Revision,
Clinical Modification (ICD-CM). Vol. 1. (DHHS Pub. No. (PHS) 80-1260). DHHS: U.S. Public
Health Services.
Johnson, A.E. and Sanchez, M.E. (1993). Household and Medical Provider Reports on Medical
Conditions: National Medical Expenditure Survey, 1987. Journal of Economic and Social
Measurement. Vol. 19, 199-233.
Moeller J.F., Stagnitti, M., Horan, E., et al. Data Collection and Editing Procedures for Prescribed
Medicines in the 1996 Medical Expenditure Panel Survey Household Component. Rockville (MD):
Agency for Healthcare Research and Quality; 2000. MEPS Methodology Report (forthcoming).
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
Attachment 1
Definitions
Dwelling Units, Reporting Units, Families, and Persons The definitions of Dwelling Units
(DUs) and Group Quarters in the MEPS Household Survey are generally consistent with the
definitions employed for the National Health Interview Survey. The dwelling unit ID (DUID) is a
five-digit random ID number assigned after the case was sampled for MEPS. The person number
(PID) uniquely identifies all persons within the dwelling unit. The variable DUPERSID is the
combination of the variables DUID and PID.
A Reporting Unit (RU) is a person or group of persons in the sampled dwelling unit who are related
by blood, marriage, adoption or other family association, and who are to be interviewed as a group
in MEPS. Thus, the RU serves chiefly as a family-based "survey operations" unit rather than an
analytic unit. Regardless of the legal status of their association, two persons living together as a
"family" unit were treated as a single reporting unit if they chose to be so identified.
Unmarried college students under 24 years of age who usually live in the sampled household, but
were living away from home and going to school at the time of the Round 1 MEPS interview, were
treated as a Reporting Unit separate from that of their parents for the purpose of data collection.
These variables can be found on MEPS person level files.
In-Scope A person was classified as in-scope (INSCOPE) if he or she was a member of the U.S.
civilian, non-institutionalized population at some time during the Round 1 interview. This variable
can be found on MEPS person level files.
Keyness The term "keyness" is related to an individual's chance of being included in MEPS. A
person is key if that person is appropriately linked to the set of 1995 NHIS sampled households
designated for inclusion in MEPS. Specifically, a key person either was a member of an NHIS
household at the time of the NHIS interview, or became a member of such a household after being
out-of-scope prior to joining that household (examples of the latter situation include newborns and
persons returning from military service, an institution, or living outside the United States).
A non-key person is one whose chance of selection for the NHIS (and MEPS) was associated with
a household eligible but not sampled for the NHIS, who happened to have become a member of a
MEPS reporting unit by the time of the MEPS Round 1 interview. MEPS data, (e.g., utilization and
income) were collected for the period of time a non-key person was part of the sampled unit to
permit family level analyses. However, non-key persons who leave a sample household would not
be recontacted for subsequent interviews. Non-key individuals are not part of the target sample used
to obtain person level national estimates.
It should be pointed out that a person may be key even though not part of the civilian, non-institutionalized portion of the U.S population. For example, a person in the military may be living
with his or her civilian spouse and children in a household sampled for the 1995 NHIS. The person
in the military would be considered a key person for MEPS. However, such a person would not
receive a person-level sample weight so long as he or she was in the military. All key persons who
participated in the first round of the 1996 MEPS received a person level sample weight except those
who were in the military. The variable indicating "keyness" is KEYNESS. This variable can be
found on MEPS person level files.
Eligibility The eligibility of a person for MEPS pertains to whether or not data were to be collected
for that person. All key, in-scope persons of a sampled RU were eligible for data collection. The
only non-key persons eligible for data collection were those who happened to be living in the same
RU as one or more key persons, and their eligibility continued only for the time that they were living
with a key person. The only out-of-scope persons eligible for data collection were those who were
living with key in-scope persons, again only for the time they were living with a key person. Only
military persons meet this description. A person was considered eligible if they were eligible at any
time during Round 1. The variable indicating "eligibility" is ELIGRND1, where 1 is coded for
persons eligible for data collection for at least a portion of the Round 1 reference period, and 2 is
coded for persons not eligible for data collection at any time during the first round reference period.
This variable can be found on MEPS person level files.
Pre-imputed - This means that only a series of logical edits were applied to the HC data to correct
for several problems including outliers, copayments or charges reported as total payments, and
reimbursed amounts counted as out of pocket payments. Missing data remains.
Unimputed - This means that only a series of logical edits were applied to the MPC data to correct
for several problems including outliers, copayments or charges reported as total payments, and
reimbursed amounts counted as out of pocket payments. This data was used as the imputation source
to account for missing HC data.
Imputation -Imputation is more often used for item missing data adjustment through the use of
predictive models for the missing data, based on data available on the same (or similar) cases. Hot-deck imputation creates a data set with complete data for all nonrespondent cases, often by
substituting the data from a respondent case that resembles the nonrespondent on certain known
variables.
Return To Table Of Contents
D. Codebooks
(link to separate file)
Return To Table Of Contents
E. Variable-Source Crosswalk
FOR MEPS HC-010F: 1996 OUTPATIENT DEPARTMENT VISITS
File 1:
| Variable |
Description |
Source |
DUID |
Dwelling unit ID (encrypted) |
Assigned in sampling |
PID |
Person number (encrypted) |
Assigned in sampling |
DUPERSID |
Sample person ID (encrypted) |
Assigned in sampling |
|
EVNT ID |
Assigned in Sampling |
EVENTRN |
Event Round number |
CAPI Derived |
FFID11X |
Flat Fee ID |
CAPI Derived |
MPCDATA |
Medical Provider ID |
CAPI Derived |
Return To Table Of Contents
Outpatient Department Visit Variables
| Variable |
Description |
Source |
OPDATEYR |
Event date - year |
CAPI derived |
OPDATEMM |
Event date - month |
CAPI derived |
OPDATEDD |
Event date - day |
CAPI derived |
REFERDBY |
Patient referred for this visit by another physician |
OP03 |
SEEDOC |
Did Patient talk to MD this visit/phone call |
OP04 |
MEDPTYPE |
Type of MED person Patient talked to on visit date |
OP05 |
TIMESPNT |
Time Patient spent with doctor/medical person |
OP06 |
VSTCTGRY |
Best category for care Patient received on visit |
OP07 |
VSTRELCN |
This visit/phone call related to specific condition |
OP08 |
PHYSTH |
This visit did Patient have physical therapy |
OP10 |
OCCUPTH |
This visit did Patient have occupational therapy |
OP10 |
SPEECHTH |
This visit did Patient have speech therapy |
OP10 |
CHEMOTH |
This visit did Patient have chemotherapy |
OP10 |
RADIATTH |
This visit did Patient have radiation therapy |
OP10 |
KIDNEYD |
This visit did Patient have kidney dialysis |
OP10 |
IVTHER |
This visit did Patient have IV therapy |
OP10 |
DRUGTRT |
This visit did Patient have treatment for drugs or alcohol |
OP10 |
RCVSHOT |
This visit did Patient receive an allergy shot |
OP10 |
PSYCHOTH |
Did Patient have psychotherapy/counseling? |
OP10 |
LABTEST |
This visit did Patient have lab tests |
OP11 |
SONOGRAM |
This visit did Patient have sonogram or ultrasound |
OP11 |
XRAYS |
This visit did Patient have x-rays |
OP11 |
MAMMOG |
This visit did Patient have a mammogram |
OP11 |
MRI |
This visit did Patient have an MRI |
OP11 |
EKG |
This visit did Patient have an EKG or ECG |
OP11 |
EEG |
This visit did Patient have a CATSCAN |
OP11 |
RCVVAC |
This visit did Patient receive a vaccination |
OP11 |
ANESTH |
This visit did Patient receive anesthesia |
OP11 |
OTHSVCE |
This visit did Patient have other diagnostic tests/exams |
OP11 |
SURGPROC |
Was surgical procedure performed on Patient this visit |
OP12 |
SURGNAME |
Surgical procedure name in categories |
OP13 |
MEDPRESC |
Any medicines prescribed for Patient this visit |
OP14 |
DOCOUTF |
Any doctor/surgeon also seen outside of provider |
OP16 |
VAPLACE |
Outpatient clinic is a VA facility |
Constructed |
OPICD1X |
3-digit ICD-9 condition code |
Edited |
OPICD2X |
3-digit ICD-9 condition code |
Edited |
OPICD3X |
3-digit ICD-9 condition code |
Edited |
OPICD4X |
3-digit ICD-9 condition code |
Edited |
OPPRO1X |
2-digit ICD-9 procedure code |
Edited |
OPCCC1X |
Modified Clinical Classification Code |
Constructed/Edited |
OPCCC2X |
Modified Clinical Classification Code |
Constructed/Edited |
OPCCC3X |
Modified Clinical Classification Code |
Constructed/Edited |
OPCCC4X |
Modified Clinical Classification Code |
Constructed/Edited |
NUMCOND |
Total number of COND records linked to this event |
Constructed |
Return To Table Of Contents
Expenditure Variables
| Variable |
Description |
Source |
FFOPTYPX |
Edited flat fee stem or leaf |
FF01, FF02 |
|
Total # OP visits in flat fee in 1996 |
FF02 |
FFTOT96 |
Total # visits (pure/mixed) in flat fee for 1996 |
FF02 (edited) |
FFBEF96 |
Total # of visits in flat fee before 1996 |
FF05 |
FFOP97 |
Number of OP visits in flat fee: Rd3, 1997 |
FF10 (edited) |
FFTOT97 |
Number of visits in flat fee for Rd3, 1997 |
FF10 |
OPEXP96X |
|
Constructed |
OPTCH96X |
Total charge for outpatient department visit |
Constructed |
OPFSF96X |
Facility amount paid, family (imputed) |
|
OPFMR96X |
Facility amount paid, Medicare (imputed) |
|
OPFMD96X |
Facility amount paid, Medicaid (imputed) |
CP07 (Edited/Imputed) |
OPFPV96X |
Facility amount paid, private insurance (imputed) |
|
OPFVA96X |
Facility amount paid, Veterans (imputed) |
|
OPFCH96X |
Facility amount paid, CHAMP/CHAMPVA (imputed) |
|
OPFOF96X |
Facility amount paid, other federal (imputed) |
|
OPFSL96X |
Facility amount paid, state/local govt. (imputed) |
|
|
|
CP07 (Edited/Imputed) |
|
|
Constructed |
|
|
Constructed |
|
|
CP07 (Edited/Imputed) |
OPFXP96X |
Facility sum of payments OPFSF96X – OPFOT96X |
Constructed |
OPFTC96X |
Facility total charge (imputed) |
CP09 (Edited/Imputed) |
IMPOPFSF |
Imputation flag for OPFSF96X |
Constructed |
IMPOPFMR |
Imputation flag for OPFMR96X |
Constructed |
IMPOPFMD |
Imputation flag for OPFMD96X |
|
IMPOPFPV |
Imputation flag for OPFPV96X |
Constructed |
IMPOPFVA |
Imputation flag for OPFVA96X |
Constructed |
IMPOPFCH |
Imputation flag for OPFCH96X |
Constructed |
IMPOPFOF |
Imputation flag for OPFOF96X |
Constructed |
IMPOPFSL |
Imputation flag for OPFSL96X |
Constructed |
IMPOPFWC |
Imputation flag for OPFWC96X |
Constructed |
|
Imputation flag for OPFOR96X |
Constructed |
IMPOPFOU |
Imputation flag for OPFOU96X |
Constructed |
IMPOPFOT |
Imputation flag for OPFOT96X |
Constructed |
IMPOPCHG |
Imputation flag for OPFTC96X |
Constructed |
IMPOPNUM |
Number of Dr. records imputed per facility provider |
Constructed |
OPDSF96X |
Doctor amount paid, family (imputed) |
|
OPDMR96X |
Doctor amount paid, Medicare (imputed) |
|
OPDMD96X |
Doctor amount paid, Medicaid (imputed) |
|
OPDPV96X |
Doctor amount paid, private insurance (imputed) |
|
OPDVA96X |
Doctor amount paid, Veterans (imputed) |
|
OPDCH96X |
Doctor amount paid, CHAMP/CHAMPVA (imputed) |
|
OPDOF96X |
Doctor amount paid, other federal (imputed) |
|
OPDSL96X |
Doctor amount paid, state/local govt. (imputed) |
|
|
|
CP07 (Edited/Imputed) |
|
|
Constructed |
|
|
Constructed |
|
|
CP07 (Edited/Imputed) |
OPDXP96X |
Doctor sum of payments OPDSF96X – OPDOT96X |
Constructed |
OPDTC96X |
Doctor total charge (imputed) |
CP09 (Edited/Imputed) |
Return To Table Of Contents
Weights
| Variable |
Description |
Source |
WTDPER96 |
Person weight full-year 1996 (poverty/mortality adjusted) |
Constructed |
VARPSU96 |
Variance estimation PSU 1996 |
Constructed |
VARSTR96 |
Variance estimation stratum |
Constructed |
Return To Table Of Contents
File 2:
Survey Administration and ID
Variables
| Variable |
Description |
Source |
DUID |
Dwelling unit ID (encrypted) |
Assigned in sampling |
PID |
Person number (encrypted) |
Assigned in sampling |
DUPERSID |
Sample person ID (encrypted) |
Assigned in sampling |
|
EVNT ID: DUPERSID + Event number |
Assigned in Sampling |
HHSFFIDX |
Household reported flat fee ID |
CAPI Derived |
Return To Table Of Contents
Pre-imputed Expenditure Variables
| Variable |
Description |
Source |
OPMR96H |
Household reported amount paid, Medicare (pre-imputed) |
|
OPMD96H |
Household reported amount paid, Medicaid (pre-imputed) |
|
OPPV96H |
Household reported amount paid, private insurance (pre-imputed) |
|
OPVA96H |
Household reported amount paid, Veterans (pre-imputed) |
|
OPCH96H |
Household reported amount paid, CHAMP/CHAMPVA (pre-imputed) |
|
OPOF96H |
Household reported amount paid, other federal (pre-imputed) |
|
OPSL96H |
Household reported amount paid, state/local govt. (pre-imputed) |
|
|
|
CP07 (Edited) |
|
|
CP07 (Edited) |
OPTC96H |
Household reported total charge (pre-imputed) |
CP09 (Edited) |
Return To Table Of Contents
| Variable |
Description |
Source |
OPSF96M |
MPC reported amount paid, family (unimputed) |
Question #8a |
OPMR96M |
MPC reported amount paid, Medicare (unimputed) |
|
OPMD96M |
MPC reported amount paid, Medicaid (unimputed) |
|
OPPV96M |
MPC reported amount paid, private insurance (unimputed) |
|
OPVA96M |
MPC reported amount paid, Veterans (unimputed) |
|
OPCH96M |
MPC reported amount paid, CHAMP/CHAMPVA (unimputed) |
|
OPOF96M |
MPC reported amount paid, other federal (unimputed) |
|
OPSL96M |
MPC reported amount paid, state/local govt. (unimputed) |
|
OPTC96M |
MPC reported total charge (unimputed) |
|
Return To Table Of Contents
Weights
| Variable |
Description |
Source |
WTDPER96 |
Person weight full-year 1996 (poverty/mortality adjusted) |
Constructed |
VARPSU96 |
Variance estimation PSU 1996 |
Constructed |
VARSTR96 |
Variance estimation stratum |
Constructed |
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