MEPS HC-010G: 1996 Office-Based Medical Provider 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 Office-Based Medical Provider Visits
2.5.3 Condition and Procedure Codes(OBICD1X-OBICD4X, OBPRO1X)
and Clinical Classification Codes (OBCCC1X-OBCCC4X)
2.5.4 Flat Fee Variables
2.5.5
Expenditure
2.6 File 2 Contents: Un-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 Variables with Missing Values
4.2 Basic Estimates of Utilization, Expenditure and Source of Payment
4.3 Estimates of the Number of Persons with Office-Based Medical Provider
Visits
4.4 Person-Based Ratio Estimates
4.4.1 Person-Based Ratio Estimates Relative to Persons with Office-Based
Medical Provider 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
5.1 Linking a Person-Level File to the Office-Based Medical Provider Visit File
5.2 Linking the Office-Based Medical Provider Visit file (HC-010G) to the
Medical Conditions File (HC-006) and/or the Prescribed Medicines File
(HC-010A)
5.3 Limitations/Caveats of RXLK (the Prescribed Medicine Link File)
5.4 Limitations/Caveats of CLNK (the Medical Conditions Link File)
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.
Return To Table Of Contents
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.
Return To Table Of Contents
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.
Return To Table Of Contents
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:
- Diagnoses 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 (diagnosis-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.
Return To Table Of Contents
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:
- A list of employers or other insurance providers identified by MEPS HC respondents who
report having private health insurance at the Round 1 interview.
- A Bureau of the Census list frame of private-sector business establishments.
- The Census of Governments from Bureau of the Census.
- An Internal Revenue Service list of the self-employed.
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
prescreening telephone interview, a mailed questionnaire, and a telephone follow-up for
nonrespondents.
Return To Table Of Contents
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.
Return To Table Of Contents
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.
Return To Table Of Contents
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 office-based provider visits for a nationally representative sample of the civilian noninstitutionalized
population of the United States and can be used to make estimates of office-based provider utilization
and expenditures for calendar year 1996. Each record represents one household-reported office-based
provider visit reported during rounds 1,2, and 3. Office-based provider 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 office-based provider visits, each record contains household reported medical
conditions and procedures associated with each visit.
Data from this event file can be merged with other MEPS HC data files, for purposes of appending
person characteristics such as demographic or health insurance coverage to each office-based provider
visit record.
Counts of office-based provider visits are based entirely on household reports. Office-based providers
were sampled into the MEPS MPC (see section B2.0). Only those providers for whom the respondent
signed a permission form were included in MPC. Information from 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 office-based provider visits. Aggregate annual person-level information on the use
of office-based providers and other health services use is provided on public use file HC-008 and 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
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
instruments used to collect the information on this file is available on the MEPS web site at the
following address: <http://www.meps.ahrq.gov>.
Return To Table Of Contents
2.0 Data File Information
This public use data set consists of two event-level data files. File 1 contains characteristics
associated with the office-based provider visit and imputed expenditure data. File 2 contains un-imputed expenditure data from both the Household and Medical Provider Components for all office-based provider visits on File 1.
Both File 1 and File 2 of this public use data set contain 100,320 office-based provider visits. Of the
100,320 records, 98,670 are associated with persons having a positive person-level weight
(WTDPER96). This file includes all records related to office-based provider visit for all household
survey respondents who resided in eligible responding households and reported at least one office-based provider visit. Each record represents one household-reported office-based provider visits that
occurred during calender year 1996. Some household respondents may have multiple visits and thus
will be represented in multiple records in the file. Other household respondents may have reported
no visits and thus will have no records on this file. These data were collected during rounds 1,2, and
3 of the MEPS HC. The persons represented on this file had to meet 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 for a particular FAMID variables) in which all eligible family
members responded for their entire period of 1996 eligibility, and at least one family member
has a positive 1996 full-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)).
Please refer to Attachment 1 for definitions of key, non-key, inscope and eligible. Person with no
office-based medical provider visit for 1996 are not included on this file (but are represented on
MEPS person-level files). A codebook for the data file is provided.
Each office-based medical provider visit record on this file includes the following: date of the visit;
types of provider seen; time spent with the provider; type of care received; types of treatments (i.e.
physical therapy, occupational therapy, speech therapy, chemotherapy, radiation therapy etc.) received
during the visit; 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 of the office-based visit expenditure; 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 (if office-based provider sampled into MPC) 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 mis-classifications 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-level characteristics such as demographic
or health insurance coverage to each record. The office-based medical provider visit file can also be
linked to the MEPS 1996 Medical Conditions File (HC-006) and MEPS 1996 Prescribed Medicines
File (HC-010A). Please see the Appendix File for details on how to link MEPS data files.
Return To Table Of Contents
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 office-based medical provider visit identifier
Other survey administration variables
Office-based medical provider characteristic variables
ICD-9 codes
Clinical Classification Software codes
Imputed expenditure variables
Weight and variance estimation variables
File 2
Unique person identifiers
Unique office-based medical provider visit identifier
Pre-imputed expenditure variables
Return To Table Of Contents
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.
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.
Return To Table Of Contents
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 |
Return To Table Of Contents
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 an "X".
Return To Table Of Contents
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.
Return To Table Of Contents
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
7 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 an imputation process to account for missing data.
The pre-imputed/unimputed expenditure variables on File 2 end with an "H", if the data source was
from the MEPS HC and ends with a "M" if the data source was the MEPS MPC. 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
In the case of source of payment variables, the third and fourth characters indicate:
SF - self or family
OF - other Federal Government
XP - sum of payments
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
The fifth and sixth characters indicate the year (96). The last character indicates whether it is
edited/imputed ( X) or came from household (H) or MPC (M).
For example, OBSF96X is the edited/imputed amount paid by self or family for an office-based
medical provider expenditure incurred in 1996.
Return To Table Of Contents
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, pregnancy is typically covered in a flat fee arrangement where the prenatal
visits, the delivery, and the postpartum visits are all covered under one flat fee dollar amount. These
three events (the prenatal visit, the delivery, and the postpartum visits) have the same value for
FFID11X. Please note that FFID11X should be used to link up all MEPS event files (excluding
prescribed medicines) in order to determine the full set of events that are part of a flat fee group.
EVENTRN indicates the round in which the office-based medical provider visit was first reported.
Return To Table Of Contents
2.5.2 Characteristics of Office-Based Medical Provider Visits
File 1 contains 35 variables describing office-based medical provider visits reported by respondents
in the Medical Provider Visits section of the MEPS questionnaire. The questionnaire contains specific
probes for determining specific details about the medical provider visit. Unless noted otherwise, the
following variables provided as unedited).
Date of Office-Based Provider Visit (OBDATEYR-OBDATEDD)
The event date variables (OBDATEYR, OBDATEMM, and OBDATEDD) indicate the year, month,
and date that the household respondent reported having had a medical provider event.
Visit Details (SEETLKPV-VSTRELCN)
The questionnaire determines if during the office-based medical provider visit whether the person
actually saw the provider or talked to the provider on the telephone (SEETLKPV). It also establishes
if the person was referred by another physician or medical provider (REFERDBY), and whether the
person saw or spoke to a medical doctor or not (SEEDOC). If the person did not see a physician (i.e.,
a medical doctor), the respondent was asked to identify the type of medical person seen
(MEDPTYPE). The respondent was also asked how much time was spent with the medical provider
(TIMESPNT). Whether or not any medical doctors worked at the visit location (DOCATLOC), the
type of care the person received (VSTCTGRY), and whether or not the visit or telephone call was
related to a specific condition (VSTRELCN) were also determined.
Treatments, Services, Procedures, and Prescription Medicines (PHYSTH-MEDPRESC)
Types of treatments received during the office-based medical provider visit include physical therapy
(PHYSTH), occupational therapy (OCCUPTH), speech therapy (SPEECHTH), chemotherapy
(CHEMOTH), radiation therapy (RADIATTH), kidney dialysis (KIDNEYD), IV therapy (IVTHER),
drug or alcohol treatment (DRUGTRT), allergy shots (RCVSHOT), and psychotherapy/counseling
(PSYCHOTH). Services received during the visit included whether or not the person received lab
tests (LABTEST), a sonogram or ultrasound (SONOGRAM), x-rays (XRAYS), a mammogram
(MAMMOG), an MRI or a CAT scan (MRI), an electrocardiogram (EKG), an electroencephalogram
(EEG), a vaccination (RCVVAC), anesthesia (ANESTH), or other diagnostic tests or exams
(OTHSVCE). Minimal editing was done across treatment, services, and procedures to ensure
consistency across inapplicables, not ascertained, don't know, refused, and no services received
values. Whether or not a surgical procedure was performed during the visit was asked (SURGPROC)
and, if so, the procedure name (SURGNAME). Finally, the questionnaire determined if a medicine
was prescribed for the person during the visit (MEDPRESC).
Other Visit Details (VAPLACE)
VAPLACE is a constructed variable that indicates whether the provider worked at a VA facility. This
variable only has valid data for providers that were sampled into the Medical Provider Component.
All other providers are classified as unknown.
MPC Indicator (MPCELIG, MPCDATA)
MPCELIG is constructed variable that indicates whether the office-based provider visit was eligible
for MPC data collection. MPCDATA is a constructed variable that indicates whether or not MPC data
was collected for the office-based provider visit.
Return To Table Of Contents
2.5.3 Condition and Procedure Codes(OBICD1X-OBICD4X, OBPRO1X) and Clinical
Classification Codes (OBCCC1X-OBCCC4X)
Information on household reported medical
conditions and procedures associated with each office-based medical
provider visit are provided on this file. There are up to four
condition codes (OBICD1X-OBICD4X), one procedure code (OBPRO1X),
and up to four clinical classification
codes (OBCCC1X-OBCCC4X) listed for each office-based medical provider
visit (83.1 % of office-based medical provider visits have 0-4
condition records linked). In order to obtain complete condition
information associated with an event, the analyst must link to
the HC-006 Medical
Conditions File. Details on how to link to the MEPS Medical Conditions
File (HC-006) are provided in the Appendix File. The user should
note that due to confidentiality restrictions, provider reported
condition information are 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
Conditions File. For frequencies of conditions by event type, please see HC-010I: the Appendix File.
The ICD-9-CM codes were aggregated into clinically meaningful categories. These categories,
included on the file as OBCCC1X-OBCCC4X, were generated using Clinical Classification Software
(formerly known as Clinical Classifications for Health Care Policy Research (CCHPR)), (Elixhauser,
et al., 1998), which aggregates conditions and V-codes into 260 mutually exclusive categories, most
of which are clinically homogeneous.
In order to preserve respondent confidentiality, nearly all of the condition codes provided on this file
have been collapsed from fully-specified codes to 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 condition codes (and clinical classification codes) and procedure codes linked to each office-based medical provider visit event are sequenced in the order in which the conditions were reported
by the household respondent, which was in 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 office-based medical provider visit file should note that the order of conditions on this file is not
identical to that on Medical Conditions file.
Record Count Variable (NUMCOND)
The variable NUMCOND indicates the total number of condition and procedure records which can
be linked from HC-006: Medical Conditions File to each office-based medical provider visit record.
For visits where no condition records linked (NUMCOND=0), the condition, procedure and clinical
classification code variables all have a value of -1 INAPPLICABLE. Similarly, for visits without a
linked second, third or fourth condition record, the corresponding second, third or fourth diagnosis
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 MEPS Condition Files (HC-006). See Section 5.0 for details on linking
MEPS data files.
Return To Table Of Contents
2.5.4 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: obstetrician's fee covering a normal delivery, as well as pre- and post-natal care.
A flat fee group is the set of medical services (i.e., events) that are covered under the same flat fee
payment situation. The flat fee groups represented on this file (and all of the other 1996 MEPS event
files), include 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), and a single person 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 office-based visit that are not part of a flat fee
payment situation, the flat fee variables described below are all set to inapplicable (-1).
Flat Fee Type (FFOBTYPX)
FFOBTYPX indicates whether the 1996 office-based medical provider visit is the "stem" or "leaf"
of a flat fee group. A stem (records with FFOBTYPX = 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 FFOBTYPX = 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 a office-based medical provider 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 office-based medical provider visit record in the count.
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 office-based medical provider visit is part of a
group of events, and some of the events occurred before 1996, counts of the known events are
provided on the office-based medical provider 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 office-based medical provider visit record. This count would not include
1996 office-based medical provider visit.
FFOB97 indicates whether or not there are 1997 office-based medical
provider visits in the same flat fee group as the 1996 office-based medical
provider visit record.
FFTOT97 -- indicates whether or not there any 1997 medical events in the
same flat fee group as the 1996 office-based medical provider visit record.
Caveats of Flat Fee Groups
The user should note that flat fee payment situations are common with respect to office-based
medical provider visits. There are 3,271 office-based medical provider 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, except the prescribed medicine file (HC-010A), using the variable FFID11X.
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
this 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, except the prescribed medicine file (HC-010A), using the variable FFID11X to create the flat fee group.
Return To Table Of Contents
2.5.5 Expenditure Data
Definition of Expenditures
Expenditures on this file refer to what is paid for health care services. More specifically,
expenditures in MEPS are defined as the sum of payments for care received, including out of
pocket payments and payments made by private insurance, Medicaid, Medicare and other sources.
The definition of expenditures used in MEPS differs slightly from its predecessors: the 1987
NMES and 1977 NMCES surveys where "charges" rather than sum of payments were used to
measure expenditures. This change was adopted because charges became a less appropriate proxy
for medical expenditures during the 1990's due to the increasingly common practice of
discounting. Although measuring expenditures as the sum of payments incorporates discounts in
the MEPS expenditure estimates, 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).
Return To Table Of Contents
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
Medical Provider Components (MPC). The MPC contacted medical providers identified by
household respondents. The charge and payment data from medical providers was used in the
expenditure imputation process to supplement missing household data. For all office-based
medical provider visits, MPC data were used if complete; otherwise HC data were used if
complete. Missing data for office-based medical provider visits where HC data were not complete
and MPC data were not collected or complete were derived through the imputation process.
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.
Capitation Imputation
The imputation process was also used to make expenditure estimates at the event level for events
that were paid on a capitated basis. The capitation imputation procedure was designed as a
reasonable approach to complete event level expenditures for respondents in managed care plans.
The procedure was conducted in two stages. First, HMO events reported in the MPC as covered
by capitated arrangements were imputed using similar MPC HMO events that were paid on a fee-for-service basis, with total charge as a key variable. Then, this completed set of MPC events was
used as the donor pool for unmatched household-reported events for sample persons' in HMOs.
By using this strategy, capitated HMO events were imputed as if the provider were reimbursed
from the HMO on a discounted fee-for-service basis.
Return To Table Of Contents
Imputation Methodology for Office-based Medical Provider Visits
Expenditures on visits of office-based medical providers 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. Many physician visits were
imputed as flat fee events because the charges covered a package of health care services. In some
cases, all of the services were provided in the physician's office. In other cases, the physician
provided services in multiple settings such as his or her office and a hospital.
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 expenditure information was assigned to one category, while an
event with a known total charge and some expenditure 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. Expenditures
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
either the HC or the MPC. For most MPC-eligible event types, 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. However, this restriction was relaxed in order to increase the size
of the donor pool for physician visits with missing expenditures and because household reported
data for physician visits was in general more reliable than for hospital-based events..
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.
Return To Table Of Contents
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.
Return To Table Of Contents
Sources of Payment
In addition to total expenditures, variables are provided which itemize expenditures according to
major source 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 source of payment questions in
the survey, some of these inconsistencies may have logical explanations. For example, private
insurance coverage in MEPS is defined as having a major medical plan covering hospital and
physician services. If a MEPS sampled person did not have such coverage but had a single
service type insurance plan (e.g. dental insurance) that paid for a particular episode of care, those
payments may be classified as "other private". Some of the "other public" payments may stem
from confusion between Medicaid and other state and local programs or may be persons who were
not enrolled in Medicaid, but were presumed eligible by a provider who ultimately received
payments from the program.
Users should also note that the Other Public and Other private source of payment categories only
exist on File 1 for imputed expenditure data since they were created through the
editing/imputation process. File 2 reflect 10 sources of payment as they were collected through
the survey instrument.
Return To Table Of Contents
Imputed Office- Based Expenditure Variables (OBSF96X - OBXP96X)
There are 13 expenditure variables included on this event file. All of these expenditures have
gone through an editing and imputation process and have been rounded to the second decimal
place. There is a sum of payments variable (OBXP96X) which for each office-based medical
provider visit sums all the expenditures from the various source of payment. The 12 sources of
payment expenditure variables for each office-based medical provider visit are the following:
amount paid by self or family (OBSF96X), amount paid by Medicare (OBMR96X), amount paid
by Medicaid (OBMD96X), amount paid by private insurance (OBPV96X), amount paid by
Veterans Administration (OBVA96X), amount paid by CHAMPUS/CHAMPVA (OBCH96X),
amount paid other federal sources (OBOF96X), amount paid by state and local (non-federal)
government sources (OBSL96X), amount paid by Worker's Compensation (OBWC96X), and
amount paid by some other source of insurance (OBOT96X). As mentioned previously, there are
two additional expenditure variables called OBOR96X and OBOU96X (other private and other
public respectively). These two expenditure variables were created to maintain consistency
between what the household reported as their private and public insurance status for
hospitalization and physician coverage.
Rounding
Expenditure variables on file, HC-010G, 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.
Return To Table Of Contents
Imputation Flags (IMPOBSLF - IMPOBCHG)
The variables IMPOBSLF-IMPOBCHG identify records where the office-based provider expense
have been imputed using the methodologies outlined in this document. When a record was
identified as being the leaf of a flat fee or it was a telephone visit, the values of all imputation
flags were set to "0" (not imputed) since they were not included in the imputation process.
Return To Table Of Contents
2.6 File 2 Contents: Un-imputed Expenditure Variables
Both imputed and pre-imputed expenditure data are 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 several
problems including 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
misclassifications between Medicare and Medicaid and between Medicare HMO's and private
HMO's as payment sources as well as number of other data inconsistencies that could be resolved
through logical edits. Missing data were not imputed.
The user should 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 source of payment
variables. The additional two sources of payments (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 user should also note that the variable HHSFFIDX, which is the original flat fee identifier that
was derived during the household interview, should be used only if user is interested in
performing their own expenditure imputation.
Return To Table Of Contents
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 office-based medical provider visit 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.
Return To Table Of Contents
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.
Return To Table Of Contents
4.0 Strategies for Estimation
This file is constructed for efficient estimation of utilization, expenditure, and sources of payment
for office-based medical provider visits and to allow for estimates of number of persons with
office-based medical provider visits for 1996.
Return To Table Of Contents
4.1 Variables with Missing Values
It is essential that the analyst examine all variables for the presence of negative values used to
represent missing values. For example, a record with a value of -8 for the first ICD9
condition/procedure code (OBICD1X) 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 the editing/imputation of expenditure variables (e.g. sources of payment
flat fee, and zero expenditures) are described in section 2.5.5.
Return To Table Of Contents
4.2 Basic Estimates of Utilization, Expenditure and Source of Payment
While the examples described below illustrate the use of event level data in constructing person-level total expenditures, these estimates can also be derived from the person-level expenditure file
unless the characteristic of interest is event specific.
In order to produce national estimates related to office-based medical provider visits, 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 office-based medical provider visits, for the civilian non-institutionalized population of the U.S. in 1996, is estimated as the sum of the weight
(WTDPER96) across all office-based medical provider records. That is,
Sum of Wj = 1,296,710,368 (1)
Example 2:
Various estimates can be produced based on specific variables and subsets of records. For
example, the estimate for the mean out-of-pocket payment per office-based medical provider visit
should be calculated as the weighted average of the office-based provider's bill paid by
self/family. That is,
X bar =(Sum of WjXj) / (Sum of Wj)= $20.75, (2)
where Xj = OBSF96Xj and Sum of Wj=1,159,521,672
for all office-based medical provider records with OBXP96Xj > 0.
This gives $20.75 as the estimated mean amount of out-of-pocket payment of expenditures
associated with office-based medical provider visit and 1,159,521,672 as an estimate of the total
number of office-based medical provider visits with expenditure. 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 office-based medical provider visits. This should be calculated as the
weighted average of proportion of total expenditures paid by private insurance. That is
Y bar =(Sum of WjYj) / (Sum of Wj)=0.4138, (3)
where Yj= OBPV96Xj / OBXP96Xi and Sum of Wj=1,159,521,672,
for all office-based medical provider recorders with OBXP96Xj > 0.
This gives 0.4138 as the estimated mean proportion of total expenditures paid by private
insurance for office-based medical provider visits with expenditures for the civilian non-institutionalized population of the U.S. in 1996.
Return To Table Of Contents
4.3 Estimates of the Number of Persons with Office-Based Medical Provider
Visits
When calculating an estimate of the total number of persons with office-based medical provider
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 office-based medical
provider visits in person and not by telephone, 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 SEETLKPV EQ 1 for any visits of person i
= 0 otherwise.
Prior to estimation users will need to take into consideration that 149 records have a missing value
for SEETLKPV .
Return To Table Of Contents
4.4 Person-Based Ratio Estimates
4.4.1 Person-Based Ratio Estimates Relative to Persons with Office-Based
Medical Provider 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 up to person-level. For example, the mean expense for persons with office-based medical
provider 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 OBXP96Xj across all visits for person
i.
Return To Table Of Contents
4.4.2 Person-Based Ratio Estimates Relative to the Entire Population
If the ratio relates to the entire population, this file cannot be used to calculate the denominator, as
only those persons with at least one office-based medical provider visits 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 visits and those without visits). For example,
to estimate the proportion of civilian non-institutionalized population of the U.S. with at least one
in person office-based medical provider visit, the numerator would be derived from data on the
current file, and the denominator should be derived from data on the 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 SEETLKPVj EQ 1 for any visits of person i on the office-based medical
provider visits file
= 0 otherwise for all remaining persons on the MEPS HC-011 file.
Prior to estimation users will need to take into consideration that 149 records have a missing value
for SEETLKPV.
Return To Table Of Contents
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.
Return To Table Of Contents
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 are described in the paragraph below.
Using a Taylor Series approach, variance estimation strata and the variance estimation PSUs within
these strata must be specified. The corresponding variables on the MEPS full year utilization
database are 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 the
computer software package SUDAAN will yield an estimate of standard error of $0.59 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 the
computer software package SUDAAN will yield an estimate of standard error of 0.0091 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 this file can be used alone or in conjunction with other files. This section provides
instructions for linking the office-based medical provider visits with other MEPS public use files,
including the conditions file, the prescribed medicines file, and a person-level file.
Return To Table Of Contents
5.1 Linking a Person-Level File to the Office-Based Medical Provider Visit File
Merging characteristics of interest from other MEPS files (e.g., HC-008: 1996 Full Year Population
Characteristics File or HC-010: 1996 Prescribed Medicines File) expands the scope of potential
estimates. For example, to estimate the total number of office-based medical provider visits of
persons with specific characteristics (e.g., age, race, and sex), population characteristics from a
person-level file need to be merged onto the office-based medical provider file. This procedure is
illustrated below. The Appendix File (HC-010I) provides additional details on how to merge MEPS
data files.
1. 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 office-based medical
provider visit file and DUPERSID.
2. Create data set OBMP by sorting the office-based medical provider visit file by person
identifier, DUPERSID.
3. Create final date set NEWOBMP by merging these two files by DUPERSID, keeping
only records on the office-based medical provider 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=OBMP;
BY DUPERSID;
RUN;
DATA NEWOBMP;
MERGE OBMP (IN=A) PERSX(IN=B);
BY DUPERSID;
IF A;
RUN;
Return To Table Of Contents
5.2 Linking the Office-Based Medical Provider Visit file (HC-010G) 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. This limitations/caveats are listed below. For detailed linking examples
including SAS code, analyst should refer to HC-010I: the Appendix file.
Return To Table Of Contents
5.3 Limitations/Caveats of RXLK (the Prescribed Medicine Link File)
The RXLK file provides a link from the prescribed medicine records on HC-010A to the other event
files (HC010B - HC010H). When using RXLK, analysts should keep in mind that one office-based
medical visit can link to more than one prescribed medicine record. Conversely, a prescribed
medicine event may link to more than one office-based medical visits or different types of events.
When this occurs, it is up to the analyst to determine how the prescribed medicine expenditures
should be allocated among those medical events.
Return To Table Of Contents
5.4 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 office-based medical provider visit. Users should also note
that not all office-based medical provider visits link to the condition file.
Return To Table Of Contents
6.0 Programming Information
The following are the technical specifications for the HC-010G data files, which are provided in
ASCII and SAS formats.
ASCII versions:
File Name: HC10GF1.DAT
Number of Observations: 100,320
Number of Variables: 89
Record Length: 285
Record Format: fixed
Record Identifier and Sort Key: EVNTIDX
File Name: HC10GF2.DAT
Number of Observations: 100,320
Number of Variables: 31
Record Length: 215
Record Format: fixed
Record Identifier and Sort Key: EVNTIDX
SAS Transport versions:
File Name: HC10GF1.SSP
SAS Name: HC10GF1
Number of Observations: 100,320
Number of Variables: 89
Record Identifier and Sort Key: EVNTIDX
File Name: HC10GF2.SSP
SAS Name: HC10GF2
Number of Observations: 100,320
Number of Variables: 31
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 a group of persons in the sampled dwelling unit who is related
by blood, marriage, adoption or other family association, and who is 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, persons returning from an institution, or persons 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 that was 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, co-payments 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, co-payments or charges reported as total payments, and
reimbursed amounts counted as out-of-pocket payments. These data were used as the imputation
source to account for missing HC data.
Imputation - A method of estimating values for cases with missing data. Hot-deck imputation
creates a data set with complete data for all nonrespondent cases, 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-010G: 1996 OFFICE-BASED MEDICAL PROVIDER VISITS
File 1:
Survey Administration Variables
|
Variable |
Description |
Source |
|
DUID |
Dwelling unit ID (encrypted) |
Assigned in sampling |
|
PID |
Person number (encrypted) |
|
|
DUPERSID |
Sample person ID (DUID + PID) |
Assigned in sampling |
|
EVNTIDX |
Event ID |
Assigned in sampling |
|
EVENTRN |
Event round number |
CAPI derived |
|
FFID11X |
Flat fee ID |
CAPI derived |
|
MPCELIG |
MPC eligibility flag |
|
|
MPCDATA |
MPC data flag |
|
Return To Table Of Contents
Medical Provider Visits Variables
|
Variable |
Description |
Source |
|
OBDATEYR |
Event date – year |
CAPI derived |
|
OBDATEMM |
Event date – month |
CAPI derived |
|
OBDATEDD |
Event date – day |
CAPI derived |
|
SEETLKPV |
Did P visit provider in person or telephone |
MV01 |
|
REFERDBY |
P referred for this visit another physician |
MV02 |
|
SEEDOC |
Did P talk to MD this visit/phone call |
MV03 |
|
MEDPTYPE |
Type of medical person P talked to on visit date |
MV04 |
|
TIMESPNT |
Time spent with doctor/medical person |
MV05 |
|
DOCATLOC |
Any MDs work at location where P saw provider |
MV06 |
|
VSTCTGRY |
Best category for care P received on visit date |
MV07 |
|
VSTRELCN |
This visit/phone call related to specific condition |
MV08 |
|
PHYSTH |
This visit did P have physical therapy |
MV10 |
|
OCCUPTH |
This visit did P have occupational therapy |
MV10 |
|
SPEECHTH |
This visit did P have speech therapy |
MV10 |
|
CHEMOTH |
This visit did P have chemotherapy |
MV10 |
|
RADIATTH |
This visit did P have radiation therapy |
MV10 |
|
KIDNEYD |
This visit did P have kidney dialysis |
MV10 |
|
IVTHER |
This visit did P have IV therapy |
MV10 |
|
DRUGTRT |
This visit did P have treatment for drug or alcohol |
MV10 |
|
RCVSHOT |
This visit did P receive an allergy shot |
MV10 |
|
PSYCHOTH |
Did P have psychotherapy/counseling |
MV10 |
|
LABTEST |
This visit did P have lab tests |
MV11 |
|
SONOGRAM |
This visit did P have sonogram or ultrasound |
MV11 |
|
XRAYS |
This visit did P have x-rays |
MV11 |
|
MAMMOG |
This visit did P have a mammogram |
MV11 |
|
MRI |
This visit did P have MRI |
MV11 |
|
EKG |
This visit did P have EKG or ECG |
MV11 |
|
EEG |
During this visit did P have a CATSCAN |
MV11 |
|
RCVVAC |
This visit did P receive a vaccination |
MV11 |
|
ANESTH |
During this visit did P receive anesthesia |
MV11 |
|
OTHSVCE |
This visit did P have other diagnostic tests/exams |
MV11 |
|
SURGPROC |
Was surgical procedure performed on P this visit |
MV12 |
|
SURGNAME |
Surgical procedure name in categories |
MV13 |
|
MEDPRESC |
Any medicines prescribed for P this visit |
MV14 |
|
VAPLACE |
VA Facility Flag |
Constructed |
|
OBICD1X |
3-digit ICD-9 condition code |
Edited |
|
OBICD2X |
3-digit ICD-9 condition code |
Edited |
|
OBICD3X |
3-digit ICD-9 condition code |
Edited |
|
OBICD4X |
3-digit ICD-9 condition code |
Edited |
|
OBPRO1X |
2-digit ICD-9 procedure code |
Edited |
|
OBCCC1X |
Modified Clinical Classification Code |
Constructed/Edited |
|
OBCCC2X |
Modified Clinical Classification Code |
Constructed/Edited |
|
OBCCC3X |
Modified Clinical Classification Code |
Constructed/Edited |
|
OBCCC4X |
Modified Clinical Classification Code |
Constructed/Edited |
|
NUMCOND |
Total number of COND records linked to this event |
Constructed |
Return To Table Of Contents
Imputed Expenditure Variables
|
Variable |
Description |
Source |
|
FFOBTYPX |
Edited Flat fee stem or leaf |
FF01,FF02 (Edited) |
|
FFOB96 |
Total # OB visits in flat fee in 1996 |
FF02 |
|
FFTOT96 |
Total # visits in flat fee for 1996 |
FF02 (Edited) |
|
FFBEF96 |
Total # visits in flat fee before 1996 |
FF05 |
|
FFOB97 |
Number of OB visits in flat fee: Rd3, 1997 |
FF10 (Edited) |
|
FFTOT97 |
Number of visits in flat fee for Rd3, 1997 |
FF10 |
|
OBSF96X |
Amount paid, family (imputed) |
CP11 (Edited/Imputed) |
|
OBMR96X |
Amount paid, Medicare (imputed) |
CP09 (Edited/Imputed) |
|
OBMD96X |
Amount paid, Medicaid (imputed) |
CP07 (Edited/Imputed) |
|
OBPV96X |
Amount paid, Private Insurance (imputed) |
CP07 (Edited/Imputed) |
|
OBVA96X |
Amount paid, Veterans (imputed) |
CP07 (Edited/Imputed) |
|
OBCH96X |
Amount paid, CHAMP/CHAMPVA (imputed) |
CP07 (Edited/Imputed) |
|
OBOF96X |
Amount paid, other federal (imputed) |
CP07 (Edited/Imputed) |
|
OBSL96X |
Amount paid, state/local govt. (imputed) |
CP07 (Edited/Imputed) |
|
OBWC96X |
Amount paid, Worker’s Comp (imputed) |
CP07 (Edited/Imputed) |
|
OBOR96X |
Amount paid, other private (imputed) |
Constructed |
|
OBOU96X |
Amount paid, other public (imputed) |
Constructed |
|
OBOT96X |
Amount paid, other insurance (imputed) |
CP07 (Edited/Imputed) |
|
OBXP96X |
Sum of payments OBSF96X – OBOT96X |
Constructed |
|
OBTC96X |
Total charge (imputed) |
CP09 (Edited/Imputed) |
|
IMPOBSLF |
Imputation flag for OBSF96X |
Constructed |
|
IMPOBMCR |
Imputation flag for OBMR96X |
Constructed |
|
IMPOBMCD |
Imputation flag for OBMD96X |
Constructed |
|
IMPOBPRV |
Imputation flag for OBPV96X |
Constructed |
|
IMPOBVA |
Imputation flag for OBVA96X |
Constructed |
|
IMPOBCHM |
Imputation flag for OBCH96X |
Constructed |
|
IMPOBOFD |
Imputation flag for OBOF96X |
Constructed |
|
IMPOBSTL |
Imputation flag for OBSL96X |
Constructed |
|
IMPOBWCP |
Imputation flag for OBWC96X |
Constructed |
|
IMPOBOPR |
Imputation flag for OBOR96X |
Constructed |
|
IMPOBOPU |
Imputation flag for OBOU96X |
Constructed |
|
IMPOBOT |
Imputation flag for OBOT96X |
Constructed |
|
IMPOBCHG |
Imputation flag for OBTC96X |
Constructed |
Return To Table Of Contents
Weights
|
Variable |
Description |
Source |
|
WTDPER96 |
Person weight full-year 1996 (poverty adjusted) |
Constructed |
|
VARPSU96 |
Variance estimation PSU 1996 |
Constructed |
|
VARSTR96 |
Variance estimation stratum |
Constructed |
Return To Table Of Contents
File 2:
Survey Administration Variables
|
Variable |
Description |
Source |
|
DUID |
Dwelling unit ID |
Assigned in sampling |
|
PID |
Person number |
Assigned in sampling |
|
DUPERSID |
Sample person ID (DUID + PID) |
Assigned in sampling |
|
EVNTIDX |
Event ID |
Assigned in Sampling |
|
HHSFFIDX |
Household reported flat fee ID |
CAPI derived |
Return To Table Of Contents
Pre-imputed Expenditure Variables
|
Variable |
Description |
Source |
|
OBSF96H |
Household reported amount paid, family (pre-imputed) |
CP11 (Edited/Imputed) |
|
OBMR96H |
Household reported amount paid, Medicare (pre-imputed) |
CP09 (Edited/Imputed) |
|
OBMD96H |
Household reported amount paid, Medicaid (pre-imputed) |
CP07 (Edited/Imputed) |
|
OBPV96H |
Household reported amount paid, Private Insurance (pre-imputed) |
CP07 (Edited/Imputed) |
|
OBVA96H |
Household reported amount paid, Veterans (pre-imputed) |
CP07 (Edited/Imputed) |
|
OBCH96H |
Household reported amount paid, CHAMP/CHAMPVA (pre-imputed) |
CP07 (Edited/Imputed) |
|
OBOF96H |
Household reported amount paid, other federal (pre-imputed) |
CP07 (Edited/Imputed) |
|
OBSL96H |
Household reported amount paid, state/local govt. (pre-imputed) |
CP07 (Edited/Imputed) |
|
OBWC96H |
Household reported amount paid, Worker’s Comp (pre-imputed) |
CP07 (Edited/Imputed) |
|
OBOT96H |
Household reported amount paid, other insurance (pre-imputed) |
CP07 (Edited/Imputed) |
|
OBUN96H |
Household reported amount paid, uncollected liability (pre-imputed) |
|
|
OBTC96H |
Household reported total charge (pre-imputed) |
CP09 (Edited/Imputed) |
Return To Table Of Contents
|
Variable |
Description |
Source |
|
OBSF96M |
MPC reported amount paid, family (unimputed) |
Question# 8a |
|
OBMR96M |
MPC reported amount paid, Medicare (unimputed) |
Question# 8b |
|
OBMD96M |
MPC reported amount paid, Medicaid (unimputed) |
Question# 8c |
|
OBPV96M |
MPC reported amount paid, Private Insurance (unimputed) |
Question# 8d |
|
OBVA96M |
MPC reported amount paid, Veterans (unimputed) |
Question# 8e |
|
OBCH96M |
MPC reported amount paid, CHAMP/CHAMPVA (unimputed) |
Question# 8f |
|
OBOF96M |
MPC reported amount paid, other federal (unimputed) |
Question# 8g |
|
OBSL96M |
MPC reported amount paid, state/local govt. (unimputed) |
Question# 8g |
|
OBWC96M |
MPC reported amount paid, Worker’s Comp (unimputed) |
Question# 8g |
|
OBOT96M |
MPC reported amount paid, other insurance (unimputed) |
Question# 8g |
|
OBTC96M |
MPC reported total charge (unimputed) |
Question# 9 |
Return To Table Of Contents
Weights
|
Variable |
Description |
Source |
|
WTDPER96 |
Person weight full-year 1996 (poverty adjusted) |
Constructed |
|
VARPSU96 |
Variance estimation PSU 1996 |
Constructed |
|
VARSTR96 |
Variance estimation stratum |
Constructed |
Return To Table Of Contents
Return To MEPS Homepage |
Back
Email Updates
