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MEPS Home Medical Expenditure Panel Survey
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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:

  1. No one is to use the data in this data set in any way except for statistical reporting and analysis.
  2. 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.
  3. 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:

  • 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 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.

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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.

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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.

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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

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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. 

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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.

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D. Codebooks (link to separate file)

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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

 

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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)

 

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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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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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