Use It for Good – News and information from IPUMS

Continuity and Change in the 2025 NHIS Data

September 15, 2026 by mpcblog

By Julia A. Rivera Drew

Collected annually since 1957, the National Health Interview Survey (NHIS) is the longest running nationally-representative health survey in the world. The NHIS questionnaire and data collection approaches have changed considerably over time, both to better capture emerging public health topics as well as to adapt the study design, questionnaire, and data collection operations to maintain a high-quality data product. The objective of this blog post is understanding the factors that contribute to year-to-year change in variables available from the NHIS with a particular focus on the 2025 NHIS data.

How is the NHIS structured?

Since 2019, the NHIS questionnaire has been a combination of (1) NHIS “core” content, collected every year; (2) NHIS “rotating core” content, collected on some predetermined occasional schedule; (3) NHIS “emerging public health” topical content, which is generally temporary and may be collected for a few consecutive years, collected occasionally, or collected once and then not again; and (4) NHIS “sponsored” content, with funding provided by other federal agencies. Refer to our user note on changes to the NHIS questionnaire implemented with the 2019 redesign for more information.

Why might specific variables be discontinued?

Changes to NHIS variables from one year to the next are by and large expected and due to intentional design choices or priorities of the question sponsors. One such intentional design choice is the implementation of rotating core content with the 2019 NHIS redesign. For example, a set of items on utilization of health care services such as dental, vision, and home health care is asked on a two-years-on-one-year-off rotation (e.g., DENTINT, “interval since last dental visit,” was collected in 2019-2020, skipped in 2021, collected in 2022-2023, skipped in 2024, and collection resumed in 2025). When a major change to NHIS core content is proposed, users are formally notified through a federal register notice (such as the recent federal notice about proposed changes to the NHIS questionnaire to be implemented in 2028).

The discontinuation of emerging public health and sponsored content is harder to predict. For example, the 2019 and 2020 questionnaires included three items of emerging public health content on using opioid use for pain, which quickly gave way in 2021 to a series of COVID-19 related measures designed to track important public health aspects of the pandemic, such as testing, infection, vaccination, social distancing, and social isolation. Sustaining sponsored content, where open-ended supplements are fielded on an annual or otherwise regular basis, is clearly distinguished from other, close-ended sponsored content in the NHIS documentation. However, even sustaining sponsors can reduce or completely end their support for long-running questions on the NHIS questionnaire with little notice, as was the case for the food security supplement items that have been sponsored by USDA for inclusion in the NHIS since 2011. The discontinuation of these items was disclosed at the beginning of 2025 and described in greater detail below.

IPUMS integrates new NHIS data into our collection and makes them available through the nhis.ipums.org website as quickly as possible. However, we frequently break the release of new NHIS data to the IPUMS NHIS website into two parts, adding continuing content first and new or modified content later. If we have not added a variable of interest to you, please consult the original NCHS documentation before concluding that it is no longer available.

Which NHIS variables have been discontinued with the release of 2025 data?

In our initial data processing of the 2025 NHIS sample, released at the end of August 2026, we observed that multiple long-standing items were absent. Below, we describe the long-standing items that we noticed were absent as of this writing.

Food Security

Notably, there were no food security items included with the 2025 data, including an additional measure on SNAP benefit receipt in the past 30 days. The absence of these measures was an expected discontinuation, documented in an updated footnote in NCHS documentation about NHIS sustaining sponsors, as described in this IPUMS blog post about the broader discontinuation of USDA-sponsored food security items on household surveys.

Immunization

Multiple items from an immunization supplement sponsored by the National Center for Immunization and Respiratory Diseases (NCIRD) were also absent from the 2025 data (refer to Table 1 below), although NCIRD is still identified as a sustaining NHIS sponsor and sponsored several continuing immunization measures. The discontinuation of some immunization measures could simply reflect the removal of items that have declined in importance with shifts in the landscape of vaccination practice, such as questions about the number of COVID-19 vaccine doses or brand of COVID-19 vaccine first received. Measures of ever having received a COVID-19 vaccination continue. Similarly, we observed removal of items asking about a specific brand name of shingles vaccine (Zostavax) but the retention of an “ever received” shingles vaccine variable.

A handful of other items continue to be of substantial public health importance (Callahan et al. 2021; Cambou et al. 2021; Meghani et al. 2023; Evensen-Martinez et al. 2026; Gibson and Greene 2020, Guardiano et al. 2024; Srinivasan, et al. 2021; Sulley et al. 2025) and their exclusion from the 2025 data was unexpected. Five long-running immunization items – three items about flu shots during pregnancy – PREGFLUYR_A (IPUMS NHIS variable PREGFLUYR), FLUSHPG1 (PREGFLUSH), and FLUSHPG2 (PREGFLUSH) – and two items about whether sample adults worked or volunteered in a health care setting – WRKHLTH (WORKVOLHCSET) and WRKDIR (WORKVOLHCPAT and WORKVOLMED) – were not included in the 2025 data.

Table 1. Long-Running Immunization Supplement Items Sponsored by the National Center for Immunization and Respiratory Diseases (NCIRD) Absent from the 2025 NHIS Questionnaire

NHIS name¹	IPUMS NHIS name	Variable Label	Year Introduced
PREGFLUYR_A	PREGFLUYR	Were you pregnant any time [during the last flu season]?	2019
FLUSHPG1	PREGFLUSH	Did you get a flu vaccination before or during your current pregnancy?	2012
FLUSHPG2	PREGFLUSH	Earlier you said you were pregnant sometime [during the last flu season]. Did you get a flu vaccination before, during, or after your pregnancy?	2012
SHTCVD19NM	CVDSHTNUM	How many COVID-19 vaccinations have you received?	2021
SHOTTYPE	CVDSHTTYPE1	[For your first shot, which] brand of COVID-19 vaccine did you receive?	2022
SHINGYEARP_A	SHOTSHNGYR	What year did you get your most recent shingles vaccine?	2022
SHINGWHEN1_A	SHOTSHNGC	Was [your last shingles vaccine] before 2017?	2022
SHINGRIX3_A	SHOTSGRXEV	Have you ever had any Shingrix® shots?	2018
SHINGRIXN3_A	SHOTSGRXNO	How many Shingrix® shots have you ever had?	2018
SHINGRIXFS_A	SHOTSGRX21	Was your most recent Shingrix® shot in [SHINGYEARP_A] your first or second Shingrix® shot?	2022
WRKHLTH	WORKVOLHCSET	Do you currently volunteer or work in a hospital, medical clinic, doctor’s office, dentist’s office, nursing home or some other health-care facility?	2009
WRKDIR	WORKVOLHCPAT, WORKVOLMED	Do you provide direct patient care as part of your routine work?	2009

¹Original NHIS name may change over time. Click on IPUMS NHIS variable name link to learn more about original NHIS variable names over time.

The immunization items that were absent from the 2025 questionnaire are not part of NHIS “core” content, meaning that they are not paid for by NCHS. Instead, they are part of an immunization supplement sponsored by NCIRD. We would expect questionnaire content to change from year-to-year to better meet public health surveillance needs. NHIS questionnaire content may also change from year-to-year because of real funding constraints and shifting sponsor priorities. The rationale for the modification or removal of content is not always clear. And it is, of course, always possible that these measures will reappear on the questionnaire in the future.

Newly Announced Proposed Changes to the NHIS Questionnaire

On August 20, 2026, NCHS published a Federal Register notice asking for public comment on proposed changes to the NHIS questionnaire – the deadline for comment is October 20. Note that the proposed changes to the NHIS questionnaire described in the Federal Register notice are distinct from the changes to the 2025 NHIS content discussed above. IPUMS NHIS is reviewing and summarizing the proposed changes and highlighting helpful resources for responding to the Federal Register notice; we will update our summary document with additional information from other organizations, data users, and our own assessments. Note that the IPUMS NHIS website provides information at a glance on the availability of specific measures, organized by topic, and makes it easy to examine changes in question phrasing over time.

References

Callahan, Alice G., Victoria H. Coleman-Cowger, Jay Schulkin, and Michael L. Power. “Racial disparities in influenza immunization during pregnancy in the United States: a narrative review of the evidence for disparities and potential interventions.” Vaccine 39, no. 35 (2021): 4938-4948.

Cambou, Mary Catherine, Timothy P. Copeland, Karin Nielsen-Saines, and James Macinko. “Insurance status predicts self-reported influenza vaccine coverage among pregnant women in the United States: a cross-sectional analysis of the National Health Interview Study Data from 2012 to 2018.” Vaccine 39, no. 15 (2021): 2068-2073. https://doi.org/10.1016/j.vaccine.2021.07.028

Evensen-Martinez, Maison, Meave Phipps, Natalia Correa, and Isain Zapata. “Wellness assessment of United States healthcare provider and healthcare-support personnel using NHIS (National Health Interview Survey).” Public Health 253 (2026): 106213.

Gibson, Diane M., and Jessica Greene. “Risk for Severe COVID-19 Illness Among Health Care Workers Who Work Directly with Patients: Gibson et al.” Journal of General Internal Medicine 35, no. 9 (2020): 2804-2806.

Guardiano, Megan, Timothy A. Matthews, Wendie Robbins, and Jian Li. “Comparison of Working Conditions Between Immigrant and Non-immigrant Healthcare Workers in the United States: Evidence From the National Health Interview Survey.” Safety and Health at Work 15, no. 4 (2024): 491-495.

Meghani, Mehreen, Hilda Razzaghi, Katherine E. Kahn, Mei-Chuan Hung, Anup Srivastav, Peng-jun Lu, Sascha Ellington et al. “Surveillance systems for monitoring vaccination coverage with vaccines recommended for pregnant women, United States.” Journal of Women’s Health 32, no. 3 (2023): 260-270.

Srinivasan, Mithuna, Xi Cen, Brandy Farrar, Jennifer A. Pooler, and Talia Fish. “Food Insecurity Among Health Care Workers In The US: Study examines food insecurity among health care workers in the United States.” Health Affairs 40, no. 9 (2021): 1449-1456.

Sulley, Saanie, David Adzrago, Cameron K. Ormiston, and Faustine Williams. “Mental health outcomes among US healthcare workers before, during, and after the 2019 global respiratory pandemic: A population-based study.” Journal of affective disorders (2025): 120342.

Comparing Census Bureau and IPUMS USA Inflation Adjustments to American Community Survey Income Variables

August 18, 2026 by mpcblog

By Kari Williams & Isabel Pastoor

Researchers working with variables that report income must account for the changing value of a dollar across time. IPUMS USA, and other IPUMS data collections, include resources that streamline the process of adjusting monetary variables into constant dollars to facilitate comparison. These tools are very useful for researchers who are initiating research projects and exclusively using IPUMS data; however, these tools can make it difficult to directly compare, for example, IPUMS and the original U.S. Census Bureau versions of public use microdata sample (PUMS) data. This blog post provides an overview of how the Census Bureau reports income values in the American Community Survey (ACS) PUMS data, the adjustment factors provided by the Census Bureau, and how those map onto variables and ACS microdata extracts from IPUMS USA.

Want to skip the gory details? The key points are summarized in the next paragraph and in Table 1.

TL;DR

The Census Bureau does not automatically adjust for inflation in the PUMS files. They provide an adjustment factor, available as the variable ADJINC in the original Census Bureau PUMS files, that users can apply manually. This adjustment factor is intended to account for the differential reference period of respondents based on the rolling sample design of the ACS and, in multi-year files, to account for inflation across the five-year (or three-year) period. In contrast, IPUMS USA does automatically adjust for inflation across multi-year periods. However, IPUMS does not automatically apply or recommend using the within-year reference period adjustment because it does not adequately account for the differences in reference periods between respondents within a single year of the ACS. Users who wish to apply this adjustment can use the IPUMS USA variable ADJUST, which provides the within-year adjustment factor only (i.e., it does not include the multi-year inflation adjustment as that is automatically applied by IPUMS to multi-year ACS PUMS samples).

Table 1: Comparing U.S. Census Bureau and IPUMS USA Adjustment of Income Variables, 1-Year and 5-Year ACS PUMS

	Census Bureau	IPUMS
Inflation Across Years	1-Year: N/A	1-Year: N/A
Inflation Across Years	5-Year: Apply manually, included in ADJINC variable	5-Year: Applied automatically
Within-Year Reference Period Adjustment	1-Year: Apply manually, entirety of ADJINC variable	1-Year: Not recommended; apply manually using ADJUST variable
Within-Year Reference Period Adjustment	5-Year: Apply manually, included in ADJINC variable	5-Year: Not recommended; apply manually using ADJUST variable

IPUMS Time Use Leadership Receives Ellen Galinsky Generative Researcher Award

July 17, 2026July 16, 2026 by mpcblog

By Kari Williams & Stacy Nordstrom

Drs. Sarah Flood, Liana Sayer, and Melissa Milkie presented with the Ellen Galinsky Generative Researcher Award
Since 1993, IPUMS has worked to preserve and harmonize population data and make them freely accessible to researchers. This includes time diary data from the American Time Use Survey (ATUS) as well as international and historical time diary data. Last month, Drs. Sarah Flood and Liana Sayer, co-PIs of IPUMS Time Use, along with their long-time collaborator Dr. Melissa Milkie, received the Ellen Galinsky Generative Researcher Award, presented by the Work and Family Researchers Network.

The award recognizes work-family researchers who have contributed breakthrough thinking to the work-family field via theory, measures, and/or data sets that led to expansive application, innovation, and diffusion, including the sharing of research opportunities in the spirit of open science. The award committee highlighted Flood’s foundational contributions to public data infrastructure and long history of demystifying complex time diary data, as well as Sayer’s innovative scholarship on gender and social class inequalities in time use over the life course.

Family Interrelationships Variables in IPUMS MEPS

July 31, 2026July 1, 2026 by mpcblog

By Etienne Breton

Health and family are inextricably tied. Their interplay is complex and dynamic, ranging from biological transmissions to the presence or absence of familial support over the life course. Elucidating these associations often requires vast datasets collected over multiple decades – to account for the ever-changing health and family circumstances of our lives. Researchers interested in investigating these questions at scale may now add a new tool to their toolkit: IPUMS family interrelationship variables are now available in IPUMS MEPS!

Also known as family pointers, these variables identify the location of a person’s probable co-resident spouse and/or parent(s) in the household. They increase reproducibility, flexibility and ease of use when analyzing family units and relationships within households. Whether interested in studying simple parent-child dyads or complex multigenerational arrangements, users may now seamlessly attach characteristics of in-household family members to a person’s records in MEPS.

IPUMS has pioneered the development of family pointers on nationally-representative samples of households and individuals, and these variables have since been added to most of our data collection projects. Their recent addition to IPUMS MEPS presents exciting opportunities owing to the unique richness of the MEPS data, which includes the possibility to eventually expand these pointers to a panel format.

How do the IPUMS MEPS family pointers compare to those in other IPUMS data collections?

The construction of these family interrelationship variables is comparable with other IPUMS microdata collections centered in the US: these are IPUMS USA¹, IPUMS CPS, IPUMS ATUS and IPUMS NHIS. The logic underpinning both common and project-specific codes is best described in the rule variables (as exemplified in the variables descriptions for MEPS: SPRULE and MOMRULE). These variables detail how pointers were attributed to certain individuals and not others, which further allows users to adjust the strictness of pointer attributions.

Let us provide a very brief overview of these procedures. In IPUMS MEPS, as in other IPUMS data collections, the assignment of family pointers and the corresponding rule variables rely primarily on information provided by the variable RELATE (denoting relationship to the householder or household reference person), and additionally on information from variables AGE, SEX and MARSTAT (marital status). The vast majority of family pointers are assigned using direct links established by RELATE (i.e., when a respondent is listed as the child or spouse of the householder). In IPUMS MEPS, these direct attributions represent between 94.7% and 98.9% of all assigned pointers depending on the year and the family pointer variable under consideration.

There remains, therefore, cases that RELATE does not directly solve. For instance, RELATE identifies persons who are grandchildren of the householder but does not specify who are the parents of those grandchildren among all children of the householder. In such clear but indirect cases, our codes algorithmically assign parent-child and spouse-spouse links based on information from RELATE as well as respondents’ age and marital status. These assignments are not probabilistic but instead follow a predefined logic which relies on a small number of well-defined assumptions². Crucially, the values of the rules variables listed above correspond to how direct (first digit) and unambiguous (second digit) each case is, with lower numbers indicating more direct and/or unambiguous cases. This means that users can rely on these rule variables to tailor the levels of directness and clarity they prefer for assigning family pointers.

Note that MEPS data are collected in a panel format: they encompass five interview rounds carried out over two calendar years. Currently, we provide family pointers for person records reported at the annual-level (or full-year consolidated files); variables reported at this level may differ from individual round-level observations, for which we do not yet offer family pointers. These variables should, therefore, be interpreted as reflecting household membership and family interrelationships within households as of December 31 of the survey year under consideration. The vast majority of family pointers are assigned using direct links established by RELATE (i.e., when a respondent is listed as the child or spouse of the householder)³.

Does 1 + 2 = 8? Automating QA/QC for Tabular Data

May 21, 2026May 21, 2026 by mpcblog

By Tracy Kugler and Tsu Zhu

The problem with OCR and numbers

To extract data tables from census reports only available as print documents, IPUMS IHGIS uses optical character recognition (OCR) software to automate the conversion of scanned images into digital representations of letters and numbers. OCR software has made great strides in accuracy for textual information by using dictionaries of known words to interpret uncertain letters. However, dictionaries do not help in distinguishing uncertain numerical digits. While a dictionary can suggest that the third character in “wh_t” should be an ‘a’ and not an ‘o’, there is no simple way to tell whether the third digit in “45_” should be a 3 or an 8. To ensure that IHGIS data are accurate, we must have confidence that each number has been recognized correctly and matches the number in the source document.

To address this gap, we developed an R package that leverages IHGIS structured metadata to identify logical relationships between cell counts and row/column totals and determine where cells don’t add up as expected. Often, a given cell participates in multiple relationships, which allows the package to use patterns among discrepancies to pinpoint and correct errors. The package can automatically identify and correct up to 95% of error cells, depending on the structure of relationships.

Identifying relationships from structured metadata

The R package currently relies on structured metadata generated by earlier stages in the IHGIS data processing pipeline to identify sum and total relationships among rows and columns. After tables are OCR’ed from source documents, we use a customized markup framework to generate metadata. We then convert the marked up files into CSV files with a standard structure, which serve as input to the quality assurance/quality control (QA/QC) process. The CSV files include hierarchical labels for categories on the columns and geographic units on the rows. Within the labels, blanks are used to indicate totals. The package identifies a column/row with a blank header cell as the sum of other columns/rows that share the same non-blank label(s) and have sub-category labels corresponding to the blank.

IPUMS Announces 2025 Research Award Recipients

May 19, 2026May 13, 2026 by mpcblog

IPUMS is excited to announce the winners of its annual IPUMS Research Awards. These awards honor both published research and nominated graduate student papers from 2025 that use IPUMS data to advance or deepen our understanding of social and demographic processes.

The 2025 competition awarded prizes for both published research and graduate student research (published or unpublished) in eight categories:

IPUMS USA: data from the U.S. decennial censuses (including full count data for 1850-1950) and American Community Survey Data
IPUMS CPS: monthly data from the Current Population Survey (back to 1976) and Annual Social and Economic supplement (back to 1962)
IPUMS International: harmonized data from censuses and labor force surveys around the world, contributed by more than 100 international statistical office partners, for 1960-forward
IPUMS Health Surveys: harmonized data from the U.S. National Health Interview Survey (NHIS) for 1963 onward and Medical Expenditure Panel Survey (MEPS) for 1996 onward
IPUMS Spatial: Census summary tables and GIS data from the US (IPUMS NHGIS) and around the world (IPUMS IHGIS), and measures of contextual determinants of health (IPUMS CDOH)
IPUMS Global Health: harmonized health survey data from around the world, including harmonized versions of the Demographic and Health Surveys (IPUMS DHS), Multiple Indicator Cluster Surveys (IPUMS MICS), and the Performance Monitoring for Action (IPUMS PMA)
IPUMS Time Use: time diary data from the American Time User Survey (IPUMS ATUS), historical and contemporary time use data from the U.S. (IPUMS AHTUS), and around the world (IPUMS MTUS)
IPUMS Excellence in Research: The IPUMS mission of democratizing data is strengthened by broad representation among our data users and the research that we highlight. This award was created to recognize the diversity of scholars doing innovative research with IPUMS data. This category includes submissions from all IPUMS data collections.

The award committee received and reviewed hundreds of nominations for our 2025 competition. From these publications the award committees selected the 2025 honorees.

IPUMS DHS Goes Global

May 4, 2026 by mpcblog

By Miriam L. King and Sula Sarkar

IPUMS DHS now includes integrated variables for 84 counties (up from 51) and nearly 350 samples (up from 233), including new data from Latin America, Eastern Europe, Oceania, the Caribbean, and Central and East Asia. Providing DHS data in a form that facilitates micro-analyses across countries is one of IPUMS’ greatest strengths, so researchers will be excited to learn that they can now do even more! Our latest data release expands the scope of IPUMS DHS beyond its initial coverage of Africa, the Middle East, and South Asia and adds the latest samples for 12 countries previously in the database. Figure 1 shows the full geographic scope of IPUMS DHS, as well as highlighting newly added countries and previously included countries with new samples.

Figure 1: Countries included in IPUMS DHS