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MEPS HC 249: 2023 Medical Conditions

August 2025

Agency for Healthcare Research and Quality
Center for Financing, Access, and Cost Trends
5600 Fishers Lane
Rockville, MD 20857
(301) 427-1406

Table of Contents

A. Data Use Agreement
B. Background
1.0 Household Component
2.0 Medical Provider Component
3.0 Survey Management and Data Collection
C. Technical and Programming Information
1.0 General Information
2.0 Data File Information
2.1 Codebook Structure
2.2 Reserved Codes
2.3 Codebook Format
2.4 Variable Naming
2.5 File Contents
2.5.1 Identifier Variables (DUID-CONDRN)
2.5.2 Medical Condition Variables (AGEDIAG-ICD10CDX)
2.5.3 Utilization Variables (OBCOND - RXCOND)
3.0 Survey Sample Information
3.1 Discussion of Pandemic Effects on Quality of MEPS Data
3.2 Sample Weight (PERWT23F)
3.3 Details on Person Weight Construction
3.3.1 MEPS Panel 27 Weight Development Process
3.3.2 MEPS Panel 28 Weight Development Process
3.3.3 The Final Weight for 2023
3.4 Coverage
3.5 Using MEPS Data for Trend Analysis
4.0 National Health Interview Survey (NHIS)
5.0 Longitudinal Analysis
References
D. Variable-Source Crosswalk
Appendix 1 ICD10CDX and CCSR Condition Code Frequencies

A. Data Use Agreement

Individual identifiers have been removed from the micro-data contained in these files. Nevertheless, under Sections 308 (d) and 903 (c) of the Public Health Service Act (42 U.S.C. 242m and 42 U.S.C. 299 a-1), data collected by the Agency for Healthcare Research and Quality (AHRQ) and/or the National Center for Health Statistics (NCHS) may not be used for any purpose other than for the purpose for which they were supplied; any effort to determine the identity of any reported cases is prohibited by law.

Therefore in accordance with the above referenced Federal Statute, it is understood that:

No one is to use the data in this dataset in any way except for statistical reporting and analysis; and
If the identity of any person or establishment should be discovered inadvertently, then (a) no use will be made of this knowledge, (b) the Director Office of Management AHRQ will be advised of this incident, (c) the information that would identify any individual or establishment will be safeguarded or destroyed, as requested by AHRQ, and (d) no one else will be informed of the discovered identity; and
No one will attempt to link this dataset with individually identifiable records from any datasets other than the Medical Expenditure Panel Survey or the National Health Interview Survey. Furthermore, linkage of the Medical Expenditure Panel Survey and the National Health Interview Survey may not occur outside the AHRQ Data Center, NCHS Research Data Center (RDC) or the U.S. Census RDC network.

By using these data you signify your agreement to comply with the above stated statutorily based requirements with the knowledge that deliberately making a false statement in any matter within the jurisdiction of any department or agency of the Federal Government violates Title 18 part 1 Chapter 47 Section 1001 and is punishable by a fine of up to $10,000 or up to 5 years in prison.

The Agency for Healthcare Research and Quality requests that users cite AHRQ and the Medical Expenditure Panel Survey as the data source in any publications or research based upon these data.

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B. Background

1.0 Household Component

The Medical Expenditure Panel Survey (MEPS) provides nationally representative estimates of health care use, expenditures, sources of payment, and health insurance coverage for the U.S. civilian noninstitutionalized population. The MEPS Household Component (HC) also provides estimates of respondents’ health status, demographic and socioeconomic characteristics, employment, access to care, and satisfaction with care. Estimates can be produced for individuals, families, and selected population subgroups. The panel design of the survey includes five rounds of interviews covering 2 full calendar years. Information about each household member is collected through computer-assisted personal interviewing (CAPI) technology, and the survey builds on this information from interview to interview. All data for a sampled household are reported by a single household respondent.

The MEPS HC was initiated in 1996. Each year, a new panel of sample households is selected. Because the data collected are comparable to those from earlier medical expenditure surveys conducted in 1977 and 1987, it is possible to analyze long-term trends. Historically, each annual MEPS HC sample consists of up to 15,000 households. Data can be analyzed at the person, the family, or the event level. Data must be weighted to produce national estimates.

The set of households selected for each panel of the MEPS HC is a subsample of households participating in the previous year’s National Health Interview Survey (NHIS) conducted by the National Center for Health Statistics (NCHS). The NHIS sampling frame provides a nationally representative sample of the U.S. civilian noninstitutionalized population. In 2006, the NCHS implemented a new sample design for the NHIS to include households with Asian persons in addition to households with Black and Hispanic persons in the oversampling of minority populations. In 2016, NCHS introduced another sample design that discontinued the oversampling of these minority groups.

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2.0 Medical Provider Component

When the household CAPI interview is completed, and permission is obtained from the sample members to contact their medical provider(s), a sample of these providers is contacted by telephone to obtain information that household respondents cannot accurately provide. This part of the MEPS is called the Medical Provider Component (MPC), and it collects information on dates of visits, diagnosis and procedure codes, and charges and payments. The Pharmacy Component (PC), a subcomponent of the MPC, does not collect data on charges or on diagnosis and procedure codes, but it does collect detailed information on drugs, including the National Drug Code (NDC) and medicine name, as well as amounts of payment. The MPC is not designed to yield national estimates. It is primarily used as an imputation source to supplement/replace household reported expenditure information.

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3.0 Survey Management and Data Collection

MEPS HC and MPC data are collected under the authority of the Public Health Service Act. The MEPS HC data are collected under contract with Westat, Inc. and the MEPS MPC data are collected under contract with Research Triangle Institute. Datasets and summary statistics are edited and published in accordance with the confidentiality provisions of the Public Health Service Act and the Privacy Act. The NCHS provides consultation and technical assistance.

As soon as the MEPS data are collected and edited, they are released to the public in stages of microdata files and tables via the MEPS website and datatools.ahrq.gov.

Additional information on MEPS is available from the MEPS project manager or the MEPS public use data manager at the Center for Financing, Access, and Cost Trends, Agency for Healthcare Research and Quality, 5600 Fishers Lane, Rockville, MD 20857 (301-427-1406).

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C. Technical and Programming Information

1.0 General Information

This documentation describes the Medical Conditions Public Use File HC 249 (hereafter referred to as the Conditions PUF), which is one in a series of public use data files to be released from the 2023 Medical Expenditure Panel Survey Household Component (MEPS HC). It was released as an ASCII file (with related SAS, SPSS, R, and Stata programming statements and data user information), and as a SAS dataset, a SAS transport file, a Stata dataset, and an Excel file. The Conditions PUF provides information on household-reported medical conditions collected from a nationally representative sample of the U.S. civilian noninstitutionalized population for calendar year 2023 MEPS HC. It contains 29 variables and has a logical record length of 110 with an additional 2-byte carriage return/line feed at the end of each record.

This documentation offers a brief overview of the types and levels of data provided and a detailed description of the content and structure of the files. It is organized into the following sections:

Data File Information (Section 2.0)
Survey Sample Information (Section 3.0)
National Health Interview Survey (Section 4.0)
Longitudinal Analysis (Section 5.0)
Variable-Source Crosswalk (Section D)
Appendix: ICD10CDX and CCSR Condition Code Frequencies (Appendix 1)

A codebook of all the variables included in the 2023 Conditions PUF is provided in an accompanying file.

For more information on the MEPS sample design, see Chowdhury et al. (2019). A copy of the survey instrument used to collect the information in this PUF is available on the MEPS website.

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2.0 Data File Information

This PUF contains 63,656 records. Each record represents one current medical condition reported for a household survey member who resides in an eligible responding household and who has a positive person or family weight. A condition is defined as current if it is linked to an event during 2023. Conditions in the Priority Condition Enumeration (PE) section are asked in the context of “has (person) ever been told by a doctor or other health care professional that they have (condition)?” except joint pain and chronic bronchitis, which asks only about the last 12 months. Persons with a response of Yes (1) to a priority condition question for whom the condition is not current as defined above will not have a record for that condition in this PUF.

This PUF consists of MEPS survey data obtained in Rounds 3, 4, and 5 of Panel 27, and Rounds 1, 2, and 3 of Panel 28, the rounds for the MEPS panels covering calendar year 2023.

For most variables on the file, the codebook provides both weighted and unweighted frequencies. The exceptions to this are weight variables and variance estimation variables. Only unweighted frequencies of these variables are included in the accompanying codebook file. See the Weights Variables list in Section D: Variable-Source Crosswalk.

Person-level data (e.g., demographic or health insurance characteristics) from the 2023 MEPS Full Year Consolidated file (HC 251) can be merged to the records in this file using DUPERSID. Since each record represents a single condition reported by a household respondent, some household members may have multiple medical conditions and thus will be represented by multiple records on this file. Other household members may have had no reported medical conditions and thus will have no records on this file. Still other household members may have had a reported medical condition that did not meet the criteria above and thus will have no records on this file. Data from this file also can be merged to 2023 MEPS Event Files (HC 248A, and HC 248D through HC 248H) by using the link files provided in HC 248I. (See HC 248I documentation for details.)

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2.1 Codebook Structure

The codebook and data file list variables in the following order:

Unique person identifiers
Unique condition identifiers
Medical condition variables
Utilization variables
Weight and variance estimation variables

Note that the person identifier is unique within this data year.

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2.2 Reserved Codes

This Conditions PUF contains several reserved code values.

Table 1
*Reserved Code Values, Labels, and Definitions*
Value	Label	Definition
-1	Inapplicable	Question was not asked due to skip pattern
-7	Refused	Question was asked and respondent refused to answer question
-8	Don’t know	Question was asked and respondent did not know answer or the information could not be ascertained
-15	Cannot be computed	Value cannot be derived from data

The value Cannot be Computed (-15) was assigned to the MEPS constructed variables when there was not enough information from the instrument to calculate the constructed variables. Not having enough information is often the result of skip patterns in the data or of missing information stemming from the responses Refused (-7) or Don’t Know (-8). Note that, in addition to Don’t Know, reserved code -8 also includes cases for which the information from the question was Not Ascertained.

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2.3 Codebook Format

This codebook describes an ASCII dataset (although the data are also being provided in an Excel file, a Stata dataset, a SAS dataset, and a SAS transport file), and provides the programming identifiers for each variable.

Table 2
*Variable Programming Identifiers*
Identifier	Description
Name	Variable name
Description	Variable descriptor
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, the variable names reflect the content of the variable, with an 8-character limitation. Edited variables end in an “X” and are so noted in the variable label. (CONDIDX, which is an encrypted identifier variable, also ends in an “X”.)

As the collection, universe, or categories of variables were altered, the variable names have been appended with “_Myy” to indicate the collection year in which the alterations took place. These alterations are described in detail throughout this document.

Variables in this Conditions PUF were derived either from the questionnaire itself or from the CAPI. The source of each variable is identified in Section D: Variable-Source Crosswalk. Sources for each variable are indicated in one of three ways:

Variables derived from CAPI or assigned in sampling are so indicated;
Variables collected at one or more specific questions have those numbers and questionnaire sections indicated in the “Source” column; and
Variables constructed from multiple questions by using complex algorithms are labeled “Constructed” in the “Source” column.

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2.5 File Contents

2.5.1 Identifier Variables (DUID-CONDRN)

The definitions of Dwelling Units (DUs) in the MEPS HC are generally consistent with the definitions employed for the NHIS. The Dwelling Unit ID (DUID) is a 7-digit ID number consisting of a 2-digit panel number followed by a 5-digit random number assigned after the case was sampled for MEPS. A 3-digit person number (PID) uniquely identifies each person within the DU. The variable DUPERSID is the combination of the variables DUID and PID. IDs begin with a 2-digit panel number.

CONDN is the condition number and uniquely identifies each condition reported for an individual. A 3-digit CONDN beginning with “9” reflects a condition that was added during the editing process. Note that conditions are not added every year during editing. The range in this PUF for CONDN is 1-46.

The variable CONDIDX uniquely identifies each condition (i.e., each record in this PUF) and is the combination of DUPERSID and CONDN. CONDIDX has a length of 13 with DUPERSID (10) and CONDN (3) combined.

PANEL is a constructed variable used to specify the panel number for the interview in which the condition was reported. PANEL will indicate Panel 27 or Panel 28. The panel number is included as the first two digits of the DUID and DUPERSID.

CONDRN indicates the round in which the condition was first reported. For a small number of cases, conditions that actually began in an earlier round were not reported by respondents until subsequent rounds of data collection. During file construction, editing was performed for these cases in order to reconcile the round in which a condition began and the round in which the condition was first reported.

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2.5.2 Medical Condition Variables (AGEDIAG-ICD10CDX)

This PUF contains variables describing medical conditions reported by respondents in several sections of the MEPS questionnaire, and all questionnaire sections collecting information about health provider visits and/or prescription medications (see Variable-Source Crosswalk in Section D for details).

Priority Conditions and Injuries

Certain conditions were a priori designated as “priority conditions” due to their prevalence, expense, or relevance to policy. Some of these are long-term, life-threatening conditions, such as cancer, diabetes, emphysema, high cholesterol, hypertension, ischemic heart disease, and stroke. Others are chronic, manageable conditions, including arthritis and asthma. The only mental health condition on the priority conditions list is attention deficit hyperactivity disorder/attention deficit disorder.

When a condition was first mentioned, respondents were asked whether it was due to an accident or injury (INJURY=1). Only non-priority conditions (i.e., conditions reported in a section other than PE) are eligible to be injuries. The interviewer is prevented from selecting priority conditions as injuries.

Complete List of Conditions Asked in Priority Conditions Enumeration Section:

Arthritis
Asthma
ADHD/ADD
Cancer
Chronic bronchitis
COVID-19 and Long COVID
Diabetes
Emphysema
Heart Disease (including coronary heart disease, angina, myocardial infarction, and other unspecified heart disease)
High blood pressure, including multiple diagnoses
High cholesterol
Joint pain
Stroke

Age Priority Condition Began

The age of diagnosis (AGEDIAG) was collected for all priority conditions, except joint pain. For confidentiality reasons, AGEDIAG is set to Inapplicable (-1) for cancer conditions.

To ensure confidentiality, age of diagnosis was top-coded to 85. This corresponds with the age top-coding in person-level PUFs.

Follow-up Questions for Injuries

When a respondent reported that a condition resulted from an accident or injury (INJURY=1), respondents were asked during the round in which the injury was first reported whether the accident/injury occurred at work (ACCDNWRK). This question was not asked about persons aged 15 and younger; the condition had ACCDNWRK coded to inapplicable (-1) for those persons.

Sources for Conditions on the MEPS Conditions File

The records in this PUF correspond with medical condition records collected by CAPI and stored on a person’s MEPS conditions roster. Conditions can be added to the MEPS conditions roster in several ways. A condition can be reported in the Priority Condition Enumeration (PE) section in which persons are asked if they have been diagnosed with specific conditions. The condition can be identified as the reason reported by the household respondent for a particular medical event (hospital stay, outpatient visit, emergency room visit, home health episode, prescribed medication purchase, or office-based medical provider visit). Some condition information is collected in the MPC of MEPS. However, since it is not available for everyone in the sample, it is not used to supplement, replace, or verify household-reported condition data. Conditions reported in the PE section that are not current are not included in this PUF.

Treatment of Data from Rounds Not Occurring in 2023

Prior to the 2008 PUF, priority conditions reported during Rounds 1 and 2 of the second year panel were included in the PUF even if the conditions were not related to an event or reported as a serious condition occurring in the second year of the panel. Beginning in 2008, priority conditions are included in the PUF only if they are also current conditions. From 2008-2017, a current condition was defined as a condition linked to an event or a condition the person was currently experiencing (i.e., a condition selected in the Condition Enumeration (CE) section). However, starting in Panel 21 Round 5 and Panel 22 Round 3, a current condition is defined only as a condition linked to a current year event. Conditions from Panel 27 Rounds 1-3 that are not included in the 2023 PUF may be available in the 2022 Conditions PUF if the person had a positive person or family weight in 2022.

Note: Priority conditions are generally chronic conditions. Even though a person may not have reported an event in 2023 due to the condition, analysts should consider that the person may still be experiencing the condition. If a Panel 27 person reported a priority condition in Round 1 or 2 and did not have an event for the condition in Round 3, 4, or 5, the condition will not be included on the 2023 Medical Conditions PUF.

Rounds in Which Conditions Were Reported/Selected (CRND1 - CRND9)

A set of constructed variables indicates the round in which the condition was first reported (CONDRN), and the subsequent round(s) in which the condition was selected (CRND1 - CRND9). The condition may be reported or selected when the person reports an event that occurred due to the condition. For example, consider a condition for which CRND1 = 0, CRND2 = 1, and CRND3 = 1. For non-priority conditions (conditions not asked in the PE section), this sequence of indicators on a condition record implies that the condition was not present during Round 1 (CRND1 = 0), was first mentioned during Round 2 (CRND2 = 1, CONDRN = 2), and was selected again during Round 3 (CRND3 = 1). For priority conditions, this sequence of indicators implies that the condition was reported in the PE section in Round 1 (CONDRN = 1, CRND1 = 0) but was not connected with an event until Rounds 2 and 3 (CRND2 = 1, CRND3 = 1). Because priority conditions are asked in the context of “has (person) ever been told by a doctor or other health care professional that they have (condition)?” except joint pain and chronic bronchitis, which ask only about the last 12 months, a priority condition might not be selected in the round in which it was first reported. A condition is current if it is linked to an event that occurred in 2023.

Diagnosis Codes

Medical conditions reported by the HC respondent were recorded by the interviewer using a condition pick-list with ICD-10-CM codes already assigned to conditions in the list. Reported conditions not in the pick-list were recorded as verbatim text and then were coded to ICD-10-CM codes (ICD10CDX) by professional coders.

Coders followed specific guidelines in coding missing values to the ICD-10-CM diagnosis condition variable when a verbatim text string could not be matched to an ICD-10-CM code through the pick-list. ICD10CDX was coded -15 (Cannot be Computed) where the verbatim text fell into one of three categories: (1) the text indicated that the condition was unknown (e.g., DK); (2) the text indicated the condition could not be diagnosed by a doctor (e.g., doctor doesn’t know); or (3) the specified condition was not codable. If the text indicated a procedure and the condition associated with the procedure could be discerned from the text, the condition itself was coded. For example, “cataract surgery” would be coded as the condition “other cataract” (ICD10CDX is set to code “H26”). If the condition could not be discerned (e.g. “outpatient surgery”), ICD10CDX was set to -15.

In order to preserve confidentiality, all of the conditions provided in this PUF have been collapsed to 3-digit diagnosis code categories rather than the fully-specified ICD-10-CM code. For example, the ICD10CDX value of J02 “Acute pharyngitis” includes the fully-specified subclassifications J020 and J029; the value F31 “Bipolar disorder” includes the fully-specified subclassifications F3110 through F319. Table 1 in Appendix 1 provides unweighted and weighted frequencies for all ICD-10-CM condition code values reported in the PUF. Less than 1 percent of the ICD-10-CM codes in this PUF were edited further by collapsing two or more 3-digit codes into one 3-digit code. This includes clinically rare conditions that were recoded to broader codes by clinicians. A condition is determined to be clinically rare if it appears on the National Institutes of Health’s list of rare diseases.

For confidentiality purposes, approximately 11.5% of ICD-10-CM codes were recoded to -15 (Cannot be Computed) for conditions where the frequency was fewer than 40 for the total unweighted population in the PUF or less than 400,000 for the weighted population. Additional factors used to determine recoding include age and sex.

In a small number of cases, diagnosis and condition codes were recoded to Cannot be Computed (-15) if they denoted a pregnancy for a person younger than 18 or older than 44. Less than one-tenth of 1 percent of records were recoded in this manner on the 2023 Conditions PUF. The person’s age was determined by linking the 2023 Conditions PUF to the 2022 and 2023 Population Characteristics PUF. If the person’s age is under 18 or over 44 in the round in which the pregnancy-related condition was reported, the pregnancy-related condition code was recoded to Cannot be Computed (-15).

Users should note that because of the design of the survey, most deliveries (i.e., births) are coded as pregnancies. For more accurate estimates for deliveries, analysts should use RSNINHOS “Reason Entered Hospital” found on the Hospital Inpatient Stays PUF (HC 248D).

Conditions PUF data can be merged with the 2023 Event PUFs using the 2023 Condition-Event Link PUF (HC 248I). Because the conditions have been collapsed to 3-digit diagnosis code categories rather than the fully-specified ICD-10-CM code, it is possible for there to be duplicate ICD-10-CM condition codes linked to a single medical event when different fully-specified conditions are collapsed to the same 3-digit code.

Conditions were reported in several sections of the HC questionnaire (see Variable-Source Crosswalk in Section D). Labels for all values of ICD10CDX, as shown in Table 1 of Appendix 1, are provided in the SAS programming statements included in this release (see the H249SU.TXT file).

Clinical Classification Software Refined

Clinical Classification Software Refined (CCSR) codes are used alongside ICD-10-CM diagnosis codes to group medical conditions into clinically meaningful categories. Although ICD-10-CM diagnosis codes can map to multiple CCSR codes, for the purposes of this PUF, one ICD-10-CM diagnosis code may map to up to four CCSR categories (CCSR1X, CCSR2X, CCSR3X, and CCSR4X using the v2024.1 release of the CCSR for ICD-10-CM diagnoses. The CCSR codes on this PUF are listed in alphabetical order and do not indicate a primary and secondary diagnosis. For more information on CCSR, visit the user guide for CCSR.

For confidentiality purposes, approximately 7.6% of the CCSR categories were collapsed into a broader code for the appropriate body system where the frequency was less than 40 for the total unweighted population in the file or less than 400,000 for the weighted population. For example, BLD001 (Nutritional Anemia), may be recoded to BLD000 (Disease of Blood and Disorders Involving Immune Mechanism), thus revealing only the body system. Less than one-tenth of 1% of CCSR codes were recoded to Cannot be Computed (-15) based on frequencies of ICD10CDX and CCSR pairs.

Table 2 in Appendix 1 provides unweighted and weighted frequencies for CCSR combinations reported in this PUF.

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2.5.3 Utilization Variables (OBCOND - RXCOND)

The variables OBCOND, OPCOND, HHCOND, IPCOND and ERCOND indicate that at least one 2023 event can be linked to the condition record on the current file, i.e., office-based, outpatient, home health, inpatient hospital stays and emergency room visits. Note that the HHCOND variable includes all home health types, including informal care, and OBCOND and OPCOND include telehealth visits. The variable RXCOND is an indicator of any prescribed medicine purchase associated with the condition.

These event indicators were derived from Expenditure Event PUFs (HC 248G, HC 248F, HC 248H, HC 248D, HC 248E, and HC 248A). Events associated with conditions include all utilization that occurred between January 1, 2023 and December 31, 2023.

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3.0 Survey Sample Information

3.1 Discussion of Pandemic Effects on Quality of MEPS Data

Modification to the MEPS sample design because of the pandemic ended in 2022. Concerns of potential bias due to these modifications should no longer apply to data collected in this PUF. However, like most other surveys, MEPS has been substantially affected by the pandemic. As a result of these changes, potential bias continues to be a concern. One effect of the pandemic is the significantly lower response rates (Section 3.2), and these lower rates might differentially exclude households more likely to experience IP stays. The demographic shifts on MEPS between 2019 and 2022 suggest a more educated, higher-income, older MEPS sample. (For more detail, see Section 3.1 of the 2020 Consolidated PUF, Section 3.1 of the 2021 Consolidated PUF, and Section 3.1.2 of the 2022 Consolidated PUF.)

Analyses undertaken to examine the quality of the MEPS FY 2023 data compare health care utilization and health insurance coverage for the MEPS target population between the panels fielded. These comparisons were undertaken for the full sample and the three age groups of 0-17, 18-64, and 65+. These comparisons found no abnormal differences between the two panels. Analyses across years also suggest a rebound to pre-pandemic utilization levels for most key event types.

The various actions taken in the development of the person-level weights for the MEPS FY 2023 data were designed to limit the potential for response bias. However, analysts of the MEPS FY 2023 data should continue to exercise caution when interpreting estimates and assessing analyses, especially for data collected from 2020 through 2022. This includes the comparison of such estimates to those of other years and corresponding trend analyses.

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3.2 Sample Weight (PERWT23F)

There is a single full-year person-level weight (PERWT23F) assigned to each record for each Key, in-scope person who responded to MEPS for the full period of time that they were in scope during 2023. A Key person was either a member of a responding NHIS household at the time of the interview or joined a family associated with such a household after being out of scope at the time of the NHIS (the latter circumstance includes newborns as well as those returning from military service, an institution, or residence in a foreign country). A person is in scope whenever they are a member of the civilian noninstitutionalized portion of the U.S. population.

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3.3 Details on Person Weight Construction

The person-level weight PERWT23F was developed in several stages. First, a person-level weight for Panel 27 was created, including an adjustment for nonresponse over time and raking. The raking involved adjusting to several sets of marginal control totals reflecting Current Population Survey (CPS) population estimates based on six variables. The six variables used in the establishment of the initial person-level control figures were: educational attainment of the reference person (three categories: no degree; high school/GED only or some college; bachelor’s or a higher degree); Census region (Northeast, Midwest, South, West); MSA status (MSA, non-MSA); race/ethnicity (Hispanic; Black, non-Hispanic; Asian, non-Hispanic; and other); sex; and age (0-18, 19-25, 26-34, 35-44, 45-64, and 65 or older). (Note for confidentiality reasons, the MSA status variables are no longer released for public use.) The person-level weight for Panel 28 was created similarly. Secondly, a composite weight was formed by multiplying each weight from Panel 27 by the factor .40 and each weight from Panel 28 by the factor .60. The choice of factors reflected the relative effective sample sizes of the two panels, helping to limit the variance of estimates obtained from pooling both samples. Weights for the 2023 Population Characteristics PUF were then developed by raking the composite weight to the same set of CPS-based control totals.

The approach for establishing the 2023 Consolidated PUF weight is as follows. When poverty status information derived from MEPS income variables becomes available, a final raking is undertaken. The full sample weight appearing on the Population Characteristics PUF for a given year is re-raked, replacing educational attainment with poverty status while retaining the other five raking variables previously indicated. Specifically, control totals based on CPS estimates of poverty status (five categories: below poverty, from 100 to 125 percent of poverty, from 125 to 200 percent of poverty, from 200 to 400 percent of poverty, at least 400 percent of poverty) as well as age, race/ethnicity, sex, region, and MSA status are used to calibrate weights.

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3.3.1 MEPS Panel 27 Weight Development Process

The person-level weight for MEPS Panel 27 was developed by using the 2022 full-year weight as a “base” weight for survey participants present in 2023.

For Key, in-scope members who joined an RU at some time in 2023 after being out of scope in 2022, the initially assigned person-level weight was the corresponding 2022 family weight. The weighting process also included an adjustment for person-level nonresponse over Rounds 4 and 5 as well as raking to the population control figures for December 2023 for Key, responding persons in scope on December 31, 2023. These control totals were derived by scaling back the population distribution obtained from the March 2024 CPS to reflect the December 31, 2023 estimated population total (estimated based on Census projections for January 1, 2024). Variables used for person-level raking included: education of the reference person (no degree, high school/GED only or some college, bachelor’s or a higher degree); Census region (Northeast, Midwest, South, West); MSA status (MSA, non-MSA); race/ethnicity (Hispanic; Black, non-Hispanic; Asian, non-Hispanic; and other); sex; and age. (Note for confidentiality reasons, the MSA status variables are no longer released for public use.) The final weight for Key, responding persons who were not in scope on December 31, 2023 but were in scope earlier in the year was the nonresponse-adjusted person weight without raking.

Note that the 2022 full-year weight that was used as the base weight for Panel 27 was derived using the 2022 MEPS Round 1 weight and reflected adjustment for nonresponse over the remaining data collection rounds in 2022 as well as raking to the December 2022 population control figures.

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3.3.2 MEPS Panel 28 Weight Development Process

The person-level weight for Panel 28 was developed using the 2023 Round 1 person-level weight as a “base” weight. The Round 1 weights incorporated the following components: the original household probability of selection for the NHIS and for the NHIS subsample reserved for the MEPS, an adjustment for NHIS nonresponse, the probability of selection for MEPS from the NHIS, an adjustment for nonresponse at the dwelling unit level for Round 1, and raking to control figures at the person level obtained from the March CPS of the corresponding year. For Key, in-scope members who joined an RU after Round 1, the Round 1 DU weight served as a “base” weight.

The weighting process also included an adjustment for nonresponse over the remaining data collection rounds in 2023 as well as raking to the same population control figures for December 2023 that were used for the Panel 27 weight for Key, responding persons in scope on December 31, 2023. The same six variables used for Panel 27 raking (education level of the reference person, Census region, MSA status, race/ethnicity, sex, and age) were also used for Panel 28 raking. Similar to Panel 27, the Panel 28 final weight for Key, responding persons who were not in scope on December 31, 2023 but were in scope earlier in the year was the nonresponse-adjusted person weight without raking.

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3.3.3 The Final Weight for 2023

The final raking of those in scope at the end of the year has been described above. In addition, the composite weights of two groups of persons who were out of scope on December 31, 2023 were adjusted for expected undercoverage. Specifically, the weights of those who were out of scope on December 31, 2023, but in scope at some time during the year and were residing in a nursing home at the end of the year were poststratified to an estimate of the number of persons who were residents of Medicare- and Medicaid-certified nursing homes for part of the year (approximately 3-9 months) during 2014. This estimate was developed from data on the Minimum Data Set (MDS) of the Center for Medicare and Medicaid Services (CMS). The weights of persons who died while in scope were poststratified to corresponding estimates derived using data obtained from the Centers for Disease Control and Prevention (CDC), National Center for Health Statistics (NCHS), Provisional Mortality Statistics, 2018 through Last Week on CDC WONDER Online Database, released in 2024, the latest available data at the time. Separate decedent control totals were developed for the “65 and older” and “under 65” civilian noninstitutionalized populations.

Overall, the weighted population estimate for the civilian noninstitutionalized population for December 31, 2023 is 330,710,135 (PERWT23F >0 and INSC1231=1). The sum of person-level weights across all persons assigned a positive person-level weight is 334,530,273.

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3.4 Coverage

The target population associated with MEPS is the 2023 U.S. civilian noninstitutionalized population. However, the MEPS sampled households are a subsample of the NHIS households interviewed in 2021 (Panel 27) and 2022 (Panel 28). New households created after the NHIS interviews for the respective panels and consisting exclusively of persons who entered the target population after 2021 (Panel 27) or after 2022 (Panel 28) are not covered by the 2023 MEPS. Nor are previously out of scope persons who joined an existing household but are not related to the current household residents. Persons not covered by a given MEPS panel thus include some members of the following groups: immigrants, persons leaving the military, U.S. citizens returning from residence in another country, and persons leaving institutions. Those not covered represent a small proportion of the MEPS target population.

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3.5 Using MEPS Data for Trend Analysis

For analysts using the MEPS data for trend analysis, we note that there are uncertainties associated with 2020, 2021, and 2022 data quality for reasons discussed throughout Section 3. Evaluations of important MEPS estimates suggest that they are of reasonable quality. Nevertheless, analysts are advised to exercise caution in interpreting these estimates, particularly in terms of trend analyses, since access to health care was substantially affected by the pandemic, as were related factors such as health insurance and employment status for many persons.

The MEPS began in 1996, and the utility of the survey for analyzing health care trends expands with each additional year of data; however, when examining trends over time using the MEPS, the length of time being analyzed should be considered. In particular, large shifts in survey estimates over short periods of time (e.g. from one year to the next) that are statistically significant should be interpreted with caution unless they are attributable to known factors such as changes in public policy, economic conditions, or the MEPS methodology.

With respect to methodological considerations, changes in data collection methods, such as interviewer training, were introduced in 2013 to obtain more complete information about health care utilization from MEPS respondents; the changes were fully implemented in 2014. This effort likely resulted in improved data quality and a reduction in underreporting starting in the second half of 2013 and continuing throughout the 2014 full year files; the changes have also had some impact on analyses involving trends in utilization across years. The changes in the NHIS sample design in 2016 and 2018 could also potentially affect trend analyses. The new NHIS sample design is based on more up-to-date information related to the distribution of housing units across the United States. As a result, it can be expected to better cover the full civilian noninstitutionalized population, the target population for MEPS, as well as many of its subpopulations. Better coverage of the target population helps to reduce the potential for bias in both NHIS and MEPS estimates.

Another change with the potential to affect trend analyses involved major modifications to the MEPS instrument design and data collection process, particularly in the events sections of the instrument. These were introduced in the spring of 2018 and thus affected data beginning with Round 1 of Panel 23, Round 3 of Panel 22, and Round 5 of Panel 21. Since the full year 2017 MEPS files were established from data collected in Rounds 1-3 of Panel 22 and Rounds 3-5 of Panel 21, they reflected two instrument designs. To mitigate the effect of such differences within the same full-year file, the Panel 22 Round 3 data and the Panel 21 Round 5 data were transformed to make them as consistent as possible with data collected under the previous design. The changes in the instrument were designed to make the data collection effort more efficient and easier to administer. In addition, expectations were that data on some items, such as those related to health care events, would be more complete with the potential of identifying more events. Increases in service use reported since the implementation of these changes are consistent with these expectations. Analysts should be aware of the possible impacts of these changes on the data and especially trend analyses that include the year 2018 because of the design transition.

Process changes, such as data editing and imputation, may also affect trend analyses. For example, analysts should refer to Section 2.5.11: Utilization, Expenditures, and Sources of Payment Variables in the Consolidated PUF (HC 251) and, for more detail, to the documentation for the prescription drug file (HC 248A) when analyzing prescription drug spending over time. As always, it is recommended that, before conducting trend analyses, analysts should review relevant sections of the documentation for descriptions of these types of changes that might affect the interpretation of changes over time.

To smooth or stabilize trend analyses based on the MEPS data, analysts may also wish to consider using statistical techniques such as comparing pooled time periods (e.g. 1996-1997 versus 2011-2012), working with moving averages, or using modeling techniques with several consecutive years of the data.

Finally, statistical significance tests should be conducted to assess the likelihood that observed trends are not attributable to sampling variation. In addition, researchers should be aware of the impact of multiple comparisons on Type I error. Without making appropriate allowance for multiple comparisons, conducting numerous statistical significance tests of trends will increase the likelihood of concluding that a change has taken place when one has not.

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4.0 National Health Interview Survey (NHIS)

Data from this PUF can be used alone or in conjunction with other PUFs for different analytic purposes. Each MEPS panel can also be linked back to the previous years’ NHIS public use data files. For information on MEPS/NHIS link files please see the AHRQ website.

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5.0 Longitudinal Analysis

Panel-specific longitudinal files can be downloaded from the data section of the MEPS website. For each panel, the longitudinal file comprises the MEPS survey data obtained in Rounds 1 through 5 of the panels and can be used to analyze changes over a two-year period. Variables in the PUF pertaining to survey administration, demographics, employment, health status, disability days, quality of care, patient satisfaction, health insurance, and medical care use and expenditures were obtained from the MEPS Consolidated PUF from the years covered by that panel. For more details or to download the data files, please see Longitudinal Data Files at the AHRQ website.

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References

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Dahlhamer, J.M., Bramlett, M.D., Maitland, A., & Blumberg, S.J. (2021). Preliminary evaluation of nonresponse bias due to the COVID-19 pandemic on National Health Interview Survey estimates, April-June 2020. Hyattsville, MD: National Center for Health Statistics.

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Rothbaum, J. & Bee, A. (2022, September 13). How Has the Pandemic Continued to Affect Survey Response? Using Administrative Data to Evaluate Nonresponse in the 2022 Current Population Survey Annual Social and Economic Supplement. Washington, DC: U.S. Census Bureau.

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D. Variable-Source Crosswalk