Using NHIS for the Study of Aging, Health, and the Life Course
The NHIS is well-suited for a broad range of aging, health, and life course research applications, including on topics such as mental health, chronic conditions, functional limitations, health behaviors, public program participation, mortality, and health care access and utilization. The NHIS data are unique among long-running, population-representative data resources for the inclusion of multiple validated mental health measures, information on sexual orientation, topical supplements and items designed to capture emerging public health content, and a publicly available linkage with mortality information from the National Death Index (NDI). A sampling of recent publications include studies using the NHIS data from IPUMS to investigate whether engaging in moderate to vigorous activity can mitigate the mortality risks of fall injuries for older adults (Adeyemi et al., 2025); the association between health care cost burdens and the frequency of depressive/anxious feelings in older adults (Choi, et al. 2024); and the link between housing assistance and health care access for older adults with and without Alzheimer’s Disease and Related Dementias (Fenelon and Chen 2025).
Analytic Considerations for Researchers
The NHIS data are a rich data resource suitable for the study of many aging and health-related topics, but interested researchers should keep a few analytic considerations in mind when they use these data. First, the NHIS is administered to a sample of the community-dwelling (i.e., civilian, non-institutionalized) population, making it unsuitable for the study of institutionalized populations such as nursing home residents. Second, geographic detail in the NHIS public use data about where NHIS survey participants live is limited to region (northeast, midwest, south, and west), potentially limiting the utility of the public use NHIS data for research questions requiring more detailed information about where people live. Third, the NHIS uses a stratified design to select its sample, requiring users to account for the complex survey design to correctly produce population-representative estimates using the data. Please see the IPUMS NHIS user note on variance estimation for more information and sample code.
Example Studies Using IPUMS Data
Adeyemi, Oluwaseun, Tracy Chippendale, Olugbenga Ogedegbe, Dowin Boatright, and Joshua Chodosh. "Activity Intensity and All-Cause Mortality Following Fall Injury Among Older Adults: Results from a 12-Year National Survey." In Healthcare, vol. 13, no. 19, p. 2530. MDPI, 2025. https://doi.org/10.3390/healthcare13192530
Choi, Namkee G., C. Nathan Marti, Bryan Y. Choi, and Mark M. Kunik. "Healthcare cost burden and self-reported frequency of depressive/anxious feelings in older adults." Journal of gerontological social work 67, no. 3 (2024): 349-368. https://doi.org/10.1080/01634372.2024.2326683
Fenelon, Andrew, and Jie Chen. "Housing assistance and health care access among older adults with Alzheimer’s Disease and related dementias: evidence from Multisource linked Survey-administrative data." The American Journal of Geriatric Psychiatry (2025). https://doi.org/10.1016/j.jagp.2025.11.007