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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
C. Technical and Programming Information
1.0 General Information
2.0 Data File Information
3.0 Linking Instructions
4.0 Adjustment of Analytic Weight Variable
5.0 Subpopulation Analysis Caveat
6.0 Further Information
Individual identifiers have been removed from the micro-data contained in these
files. Nevertheless, under sections 308 (d) and 903 (c) of the Public Health Service Act (42 U.S.C. 242m
and 42 U.S.C. 299 a-1), data collected by the Agency for Healthcare Research and Quality (AHRQ) and/or
the National Center for Health Statistics (NCHS) may not be used for any purpose other than for the
purpose for which they were supplied; any effort to determine the identity of any reported cases is
prohibited by law.
Therefore in accordance with the above referenced Federal Statute, it is understood that:
- No one is to use the data in this data set in any way except for statistical
reporting and analysis; and
- If the identity of any person or establishment should be discovered
inadvertently, then (a) no use will be made of this knowledge, (b) the Director Office of Management
AHRQ will be advised of this incident, (c) the information that would identify any individual or
establishment will be safeguarded or destroyed, as requested by AHRQ, and (d) no one else will be
informed of the discovered identity; and
- No one will attempt to link this data set with individually identifiable
records from any data sets other than the Medical Expenditure Panel Survey or the National Health
Interview Survey. 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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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
non-institutionalized population. The MEPS Household Component (HC) also provides estimates of respondents' health
status, demographic and socio-economic characteristics, employment, access to care, and satisfaction with health care.
Estimates can be produced for individuals, families, and selected population subgroups. The panel design of the
survey, which usually includes 5 Rounds of interviews covering 2 full calendar years, provides data for examining
person level changes in selected variables such as expenditures, health insurance coverage, and health status. Note
that due to the difficulty of data collection during the COVID-19 pandemic, interview completion rates in 2020 and
2021 decreased abruptly. To balance this and increase the number of completes to be comparable to previous years,
Panel 23 was extended into 2020 and 2021 with nine rounds of data collection. Panel 24 was extended into 2021 with
seven rounds of data collection. Using computer assisted personal interviewing (CAPI) technology, information about
each household member is collected, and the survey builds on this information from interview to interview. All data
for a sampled household are reported by a single household respondent.
The MEPS-HC was initiated in 1996. Each year a new panel of sample households is
selected. Because the data collected are comparable to those from earlier medical expenditure surveys
conducted in 1977 and 1987, it is possible to analyze long-term trends. 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. 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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When the household CAPI interview is completed and permission is obtained from the household survey respondents to contact their medical provider(s), a sample of these providers are 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 visit, diagnosis and procedure codes, charges and payments. The Pharmacy Component (PC), a subcomponent of the MPC, does not collect data on charges or 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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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 Ln, Rockville, MD 20857 (Ph: 301-427-1406).
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To facilitate analysis of subpopulations and/or low prevalence events, it may be
desirable to pool together (i.e. combine) more than one year of MEPS-HC data to yield sample sizes large
enough to generate reliable estimates. MEPS-HC samples in most years are not completely independent
because households are drawn from the same sample geographic areas and many persons are in the sample
for two consecutive years (see MEPS-HC Methodology Reports for more details at https://meps.ahrq.gov/mepsweb/).
Despite this lack of independence, it is valid to pool multiple years of MEPS-HC data and keep all
observations in the analysis because each year of the MEPS-HC is designed to be nationally
representative. However, to obtain appropriate standard errors when pooling years of MEPS-HC data, it is
necessary to specify a common variance structure that properly reflects the complex sample design of the
MEPS.
This HC-036 file contains the proper variance structure to use when making estimates from MEPS data that have been pooled over multiple years, specifically when one or more years are from 1996-2001 or 2019-2023. Prior to 2002, each annual MEPS public use file was released with a variance structure unique to the particular MEPS sample in that year. Also, the years 2019 and later have a common variance structure but slightly different than earlier years. The variance structure in this HC-036 file reconciles the differences in the variance units between the units on the released annual MEPS public use files.
Between 2002 and 2018, the annual MEPS public use files were released with a common variance structure that allows users to pool data from 2002 to 2018. Also, for 2019 and later years, the MEPS public use files were released with another common variance structure that allows users to pool data from 2019 and later years. However, the common variance structure for 2002 to 2018 and the common variance structure for 2019 and later years are not compatible with each other nor the structure on the annual PUFs released prior to 2002. Therefore, it is necessary to use the variance structure on this HC-036 dataset when pooling data from MEPS years prior to 2002 and also from 2019 - 2023. The following image presents some scenarios to clarify when analysts should use the variance structure in this HC-036 file.
In the first scenario, only MEPS data from years prior to 2002 are pooled together. In this case, analysts must use the variance structure in HC-036. In the second scenario, data from years prior to 2002 are pooled together with data from 2002 and forward up to 2018. The variance structure from HC-036 must be used in this circumstance as well. In the third and fourth scenarios, no data from years prior to 2002 or from 2019 to 2023 are pooled. In both of these cases, analysts should use the variance structure on the released annual public use files. In the fifth scenario, only data from 2019 to 2023 are pooled, so analysts should use the variance structure on the released annual public use files. In the last scenario, data from year 2019-2023 are pooled with other years. The variance structure from HC-036 must be used in this circumstance as well. In no circumstance should the variance structure on the annual PUFs be combined with the variance structure on the HC-036 dataset.
The variables STRA9623 (stratum of the primary sampling unit) and PSU9623 (primary sampling unit) in this HC-036 dataset provide the appropriate sample design information needed by survey procedures in software packages that implement the with-replacement Taylor series linearization method to obtain estimates of complex sample variances.
The variables BRR1 – BRR128 in the HC-036BRR dataset (https://meps.ahrq.gov/mepsweb/data_stats/download_data_files_detail.jsp?cboPufNumber=HC-036BRR)
provide a comparable replicate sample design structure. These replicates can be incorporated in software
package survey procedures that implement the balanced repeated replication (BRR) method to produce
estimates of complex sample variances.
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Released as an ASCII data file (with SAS®, STATA®,
SPSS®,and R user statements) and in SAS Transport file, a SAS V9 file, a XLSX file, and a Stata file,
the HC-036 file contains 471,911 records corresponding to the number of unique persons in MEPS from 1996-2023, with
the exception of Panel 22 persons who appear in both the 2017 and 2018 HC files. The format for DUPERSID changed in
2018 requiring that HC-036 include these persons twice, one time with their 2017 DUPERSID and then again with their
2018 DUPERSID. All records contain the standard MEPS-HC person level ID variables (DUPERSID + PANEL), as well as the
pooled variance estimation structure (STRA9623 and PSU9623).
There is a record for each unique person appearing in any of the 1996-2023 MEPS HC full year
person level public use files: HC-012, HC-020, HC-028, HC-038, HC-050, HC-060, HC-070, HC-079, HC-089, HC-097, HC-105,
HC-113, HC-121, HC-129, HC-138, HC-147, HC-155, HC-163, HC-171, HC-181, HC-192, HC-201, HC-209, HC-216, HC-224, HC-233, HC-243, and HC-251. These data sets have a combined total of 878,642 records; however, as each person may appear in one or two of these data sets, the number of records with unique DUPERSID in HC-036 (471,911) is fewer than the total number of records on the annual files.
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The following steps should be taken to create a pooled analysis dataset.
- Create a dataset for each year containing the person- and/or event-level
records of all persons to be included in the analysis. Keep the unique person identifier (DUPERSID and
PANEL), the person-level sampling weight, any classification variables (e.g., sex, race/ethnicity) and
response variables (e.g., total expenditure amount, number of prescription drug purchases, etc.) to be
used in the data analysis.
- Reconcile the discrepancies in variable names. For all years, most variable
names on the annual public use files contain a 2-digit year suffix. For instance, in the 1997
consolidated person-level file (HC-020) the panel variable is called PANEL97, the total annual
expenditure amount variable is called TOTEXP97 and the sampling weight variable is called WTDPER97.
But in the 2003 dataset (HC-079) these same variables are named PANEL03, TOTEXP03 and PERWT03F,
respectively, and in the 1996 dataset (HC-012) the total expenditure and sampling weight variables are
named TOTEXP96 and WTDPER96, respectively, and the panel variable is missing (users should assign a
value of 1 for each record in HC-012). Starting in 2005, the panel variable is simply named PANEL (no
year suffix). As illustrated below, the variable names must be made consistent before pooling the
data.
- Create a pooled analysis dataset by simply combining the individual-year
datasets (e.g., the records from the 1996 and 1997 files). In other words, the number of records in
the pooled file will equal the sum of the record counts for the individual annual files being
pooled.
- Attach the pooled variance structure to the pooled analysis dataset by merging the variables STRA9623 and PSU9623 from this HC-036 file to the pooled analysis dataset by DUPERSID and PANEL keeping all records in the pooled analysis dataset only. Depending on the software being used to manage the datasets, the pooled analysis dataset may need to be sorted by DUPERSID and PANEL prior to merging. This step will add two additional variables to the pooled file (STRA9623 and PSU9623) but have no impact on the number of records.
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It is generally recommended that analysts adjust the analytic weight variable by
dividing it by the number of years being pooled. The sum of these adjusted weights represents the
average annual population size for the pooled period (rather than the sum of the population sizes across
multiple years that would result from unadjusted weights). Although this adjustment will have no effect
on estimated means, proportions or regression coefficients because the weight variable is being divided
by a constant (i.e. number of years), estimates of totals based on adjusted weights will reflect an
"average annual" basis rather than the entire pooled period. On the other hand, if the objective is to
produce an estimated total for the entire pooled period (e.g. total medical expenditures across multiple
years rather than average per year), then the analytic weight variable should not be divided by the
number of years in the pooled period.
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When pooling data over several years to increase sample sizes for small subdomains
of the population (e.g., obtaining the total and mean expenditures for prescription drugs among children
with asthma), users must be careful to maintain the integrity of the MEPS survey design. The MEPS design
is accounted for by the full set of survey stratum and PSU values on both the annual files and this
HC-036 pooled linkage variance estimate file.1 When users create
analytic subfiles that contain only respondents in the subdomain of interest (e.g., children with
asthma), it is very unlikely that there will be all combinations of stratum and PSU that properly
account for the MEPS survey design in a linearized estimate of the sampling variances. Therefore, the
following approach is recommended for analyzing subpopulations in MEPS:
- Construct a flag variable for all survey respondents that can be used to
identify persons in the subdomain of interest,
- Using a with-replacement design option for a Taylor Series procedure in a
complex survey design statistical software package, read in records from all respondents (i.e., not
just those in the subdomain of interest) and specify the analytic subdomain using the flag variable
(see step 1 above).2
For any question regarding the HC-036 file or pooling
of data, please contact Sadeq Chowdhury by email at: sadeq.chowdhury@ahrq.hhs.gov or
Fred Rohde by email at: frederick.rohde@ahrq.hhs.gov.
1The MEPS design is also accounted for by the full set of replicates
in the HC-036BRR data
set https://meps.ahrq.gov/mepsweb/data_stats/download_data_files_detail.jsp?cboPufNumber=HC-036BRR
.
2The syntax for specifying survey designs and analytic subdomains
varies across software
packages (see section IB at https://meps.ahrq.gov/mepsweb/survey_comp/clustering_faqs.jsp
for examples).
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