IPUMS Health Surveys – Use It for Good

Measuring Food Security with U.S. Federal Data

October 13, 2025October 10, 2025 by mpcblog

By Kari Williams & Isabel Pastoor

The U.S. Department of Agriculture (USDA) defines a household as being food secure when all household members at all times have access to “enough food for an active, healthy life;” it sets a minimum threshold for food security of “ready availability of nutritionally adequate and safe foods” and the “assured ability to acquire acceptable foods in socially acceptable ways” (USDA Economic Research Service, 2025). The USDA provides survey modules for assessing food security in the U.S. (see Table 1), which are used in a number of federal surveys.

Following the recent announcement by the USDA that they plan to cease data collection for the Food Security supplement fielded as part of the December Current Population Survey, we are highlighting data sources for studying food security in the U.S. Table 2 provides an overview of a number of federal data sources that can be used to study aspects of food security in the U.S. This list of data sources is not exhaustive; we have prioritized data available through IPUMS and other long-running and large-scale population surveys. Additional sources covering shorter time periods or more specific focal populations can be found from the USDA’s Food Security in the United States Documentation page and the Food Access Research Atlas.

Even More IPUMS Data Available in the SDA Online Data Analysis Tool

June 15, 2025December 19, 2024 by mpcblog

By Daniel Backman

Beyond offering the ability to create and download customized datasets from the IPUMS microdata collections, we also support web-based analysis of the data through the SDA (Survey Documentation and Analysis) online data analysis tool. SDA empowers users to analyze IPUMS data directly from their web browsers without the need for additional software or advanced programming skills. Whether you’re a seasoned researcher or a student exploring data for the first time, the SDA tool makes it easier than ever to unlock insights from our datasets. If you’re a current SDA user and ready to get started, check out the new datasets from IPUMS CPS and IPUMS MEPS. Otherwise, read on to learn more about SDA and how to use this tool to analyze IPUMS data.

About IPUMS & SDA

What is SDA?

The SDA tool is a web-based interface that allows you to generate frequency tables, cross-tabulations, and summary statistics; create customized data visualizations, including bar charts, line graphs, and scatter plots; perform regression analysis; and export results as a CSV file for presentations or further analysis.

SDA increases the accessibility of data by allowing users to analyze data through a web-interface without needing to use (or purchase!) statistical software. There is detailed guidance on how to use the tool for analyses and how to manipulate variables. Additionally, it provides exceptionally fast real-time processing of data, making it ideal for use in the classroom or other interactive settings. See our data training exercises page for exercises that will guide you through using SDA to analyze IPUMS data.

Introducing the MEPS Variable Builder!

June 17, 2025November 4, 2024 by mpcblog

By Julia A. Rivera Drew

Earlier this year, IPUMS MEPS launched a new feature – the MEPS Variable Builder – to make it dramatically easier to create customized person-level variables that summarize information from the medical event and condition records and add them to your IPUMS extract. If you have ever thought about using the MEPS event and condition data but didn’t know where to begin because of the complexity of the data, the MEPS Variable Builder is for you!

The Medical Expenditure Panel Survey Household Component (MEPS-HC, referred to MEPS here) provides comprehensive information on characteristics of people residing in responding households, as well as information about their medical encounters during the calendar year – e.g., office-based provider visits, emergency room (ER) visits, and hospitalizations – and medical conditions associated with those medical encounters. This unique combination of information makes the MEPS data ideal for research questions that need detailed health care utilization and/or expenditure data alongside individual-level correlates of health. However, these rich data can be difficult to work with, creating barriers for researchers who wish to use the MEPS data.

IPUMS MEPS created the MEPS Variable Builder to enable users to easily build person-level variables summarizing information from the MEPS-HC event and medical condition records, also known as “event summary variables.” Using a point-and-click interface, researchers can create custom event summary variables that count the number of events or sum expenditures across event records, filtered on selected characteristics of events and/or medical conditions. Users can then include these custom event summary variables in their IPUMS extract. At this time, the variable builder does not include prescribed medicines data.

In this blog post, we run through an example where we create a variable that is the sum of all expenditures paid for by Workers’ Compensation for medical visits due to a workplace injury.

Introducing the MEPS Prescribed Medicines Data

May 28, 2025November 20, 2023 by mpcblog

By Julia A. Rivera Drew

The Household Component of the Medical Expenditure Panel Survey (MEPS), administered by the Agency for Healthcare Research and Quality (AHRQ), is a short panel survey collecting information for a nationally representative sample of the civilian, noninstitutionalized population. Since 1996, the MEPS has collected information on demographic and socioeconomic characteristics; health status; medical conditions; and health care access, utilization, and expenditures.

Based on information provided by a family respondent about each family member at each interview, AHRQ produces a dataset of all reported fills of prescribed medicines purchased by family members during the calendar year (including refills). For example, if a prescription was filled monthly, there would be 12 records for that specific prescribed medicine (DRUGID) in the annual file. The prescribed medicines data includes information such as the medication name (RXNAME), national drug code (RXNDC), therapeutic classification (MULTC1), when the person began taking the medication (RXBEGMM and RXBEGYR), amounts paid (RXFEXPTOT), and source of payment (RXFEXPSRC).

IPUMS MEPS provides a harmonized and integrated version of the MEPS Household Component data, including data from the prescribed medicines files.

Guidance for Pooling Multiple Years of NHIS Data

June 16, 2025November 4, 2022 by mpcblog

By Julia A. Rivera Drew

Introduction

Depending on their research question, analysts will commonly pool multiple years of the National Health Interview Survey (NHIS) data together in order to increase sample sizes of particular subpopulations of interest, such as bisexual adults, immigrants, or pregnant women. The complex design of the NHIS, however, requires analysts to take additional steps to correctly construct and analyze pooled NHIS datasets. Moreover, planned changes to the NHIS design implemented in 2019, as well as changes made in response to the COVID-19 pandemic, require additional special handling to correctly analyze datasets combining multiple years of NHIS data. The objectives of this blog post are to: (1) share tips to correctly construct and analyze pooled NHIS datasets and (2) identify resources for more information.

Tips to Correctly Construct and Analyze Pooled NHIS Datasets

Create a pooled sampling weight to use with your pooled dataset.

In general, when pooling multiple years of NHIS data together, you will need to create a new sampling weight to use with the pooled sample. To create this new sampling weight, divide the appropriate sampling weight by the number of years within each distinct sample design period. For example, if one wished to estimate the number of children living in families with low or very low food security (FSSTAT) using pooled 2020-2021 NHIS data (e.g., similar to this report on children and food insecurity), one would need to create a new sampling weight by dividing the sampling weight identified under the “weights” tab for FSSTAT, SAMPWEIGHT, by the number of years pooled together from the same sampling design period (in this case, two). The sum of the pooled weights would then represent the average annual population size for the pooled time period, rather than the total cumulative population size for the pooled time period. For any given combination of variables, refer to information under the “weights” tab for the variables included in your analysis to help select the appropriate sampling weight. The distinct NHIS sample design periods are 1963-1974, 1975-1984, 1985-1994, 1995-2005, 2006-2015, 2016-2018, and 2019-present.

Using the MEPS-HC to Study Change in Adult Mental Health

June 13, 2025January 11, 2022 by mpcblog

By Julia A. Rivera Drew and Natalie Del Ponte

The Medical Expenditure Panel Survey-Household Component, or MEPS-HC, data are an invaluable resource for studying short-term trajectories in health, including adult mental health. An integrated series of the MEPS-HC data is available at IPUMS MEPS. Collected on the Self-Administered Questionnaire and, starting in 2019, the Preventive Self-Administered Questionnaire, the MEPS-HC includes two validated adult mental health scales. The Kessler Psychological Distress Scale (K6) and the two-item Patient Health Questionnaire depression screener (PHQ-2) are asked twice per panel, during interview rounds 2 and 4 (see Table 1). There are also two validated scales measuring health-related quality of life (HRQOL) that capture several interrelated health domains, including mental health. These include the Short Form-12 (SF-12) in 2000-2016 and the Veterans RAND-12 (VR-12) starting in 2017 (see Table 2 for VR-12 measures). For more information on the SF-12, see the section on SF-12 scoring on MCS and for more information on the VR-12, see ADDAYA.

An Age-Based Approach to Disability

June 13, 2025August 10, 2021 by mpcblog

By Solvejg Wastvedt and Yash Singh

Disability is dynamic: it evolves over time and interacts with environmental and societal factors. Due to the complex nature of disability, researchers conceptualize and measure disability differently depending on their research question and available data. For instance, an identical condition might evolve differently for a child facing food insecurity compared to one that has stable access to food. Similarly, a physical limitation for a worker in New York City may have a vastly different impact compared to a similar worker in rural Iowa. Disability can be viewed as the relational concept between individuals with physical or mental impairment and the environment.¹ ² This complexity makes measuring disability a challenging task. The following post aims to help researchers understand and use disability measurements available in IPUMS data collections.