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July 2013
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 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.
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 noninstitutionalized
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 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. 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. Each annual MEPS‑HC sample size is about 15,000 households. Data can be analyzed
at either the person or 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 and reflects an oversample of blacks and Hispanics. In 2006, the NHIS implemented a new sample design, which
included Asian persons in addition to households with black and Hispanic persons in the oversampling of minority populations.
MEPS further oversamples additional policy relevant subgroups such as low income households. The linkage of the MEPS to
the previous year’s NHIS provides additional data for longitudinal analytic purposes.
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Upon completion of the household CAPI interview and obtaining permission from the household survey
respondents, a sample of medical providers are contacted by telephone to obtain information that household respondents
can not accurately provide. This part of the MEPS is called the Medical Provider Component (MPC) and information is
collected on dates of visit, diagnosis and procedure codes, charges and payments. The Pharmacy Component (PC), a
subcomponent of the MPC, does not collect charges or diagnosis and procedure codes but does collect drug detail information,
including National Drug Code (NDC) and medicine name, as well as date filled and sources and 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. Data are
collected under contract with Westat, Inc. Data sets and summary statistics are edited and published in accordance with
the confidentiality provisions of the Public Health Service Act and the Privacy Act. The National Center for Health
statistics (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, micro data files, and tables via the MEPS web site:
www.meps.ahrq.gov. Selected data can be analyzed through MEPSnet, an online interactive tool designed to
give data users the capability to statistically analyze MEPS data in a menu-driven environment.
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, 540 Gaither
Road, Rockville, MD 20850 (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
http://www.meps.ahrq.gov). 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 and where one or more years are from 1996‑2001. Prior to 2002, each annual MEPS
public use file was released with a variance structure unique to the particular MEPS sample in that year. 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.
Starting in 2002, the annual MEPS public use files were released with a common variance structure
that allows users to pool data from 2002 and forward. This common variance structure is neither compatible with the
structure on the annual PUFs released prior to 2002 nor is it compatible with the structure on this HC‑036 dataset.
Therefore, it is only necessary to use the variance structure on this HC‑036 dataset when pooling data from MEPS years
prior to 2002. The following scenarios provide some guidelines for 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. The variance structure from HC‑036 must be used in this circumstance as well.
In the last two scenarios, no data from years prior to 2002 are pooled. In both of these cases, analysts should use the
variance structure on the released annual public use files. In no circumstance should the variance structure on the annual
PUFs be combined with the variance structure on the HC‑036 dataset.
The variables STRA9611 (stratum of the primary sampling unit) and PSU9611 (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 (
http://www.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® and SPSS® user statements) and in SAS Transport
version, the HC‑036 file contains 278,203 records corresponding to the number of unique persons in MEPS from
1996‑2011. These records contain the standard MEPS‑HC person level ID variables (DUPERSID + PANEL), as well as the pooled
variance estimation structure (STRA9611 and PSU9611).
There is a record for each unique person appearing in any of the 1996‑2011 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, and HC‑141. Sixteen data sets have a combined total of 509,427 records; however, as each person
may appear in one or two of these data sets, the number of unique persons (278,203) 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 STRA9611 and PSU9611 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 (STRA9611 and PSU9611) 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 (
http://www.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 http://www.meps.ahrq.gov/mepsweb/survey_comp/standard_errors.jsp
for examples).
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