As babies and children grow older, they grow taller. But as adults grow even older, they often lose height, due to declining bone density, spinal disc compression, or changes in posture that are measured as changes in height (Miall et al., 1967; Cline et al., 1989; Masunari et al., 2012; Shimizu et al., 2020). This blog post provides information about the relationship between age and height in the U.S. population in the 21st century, using data from the National Health Interview Survey (NHIS) from IPUMS NHIS.
Income is a well-established determinant of health across the life course, influencing morbidity, mortality, and functional aging. Income data provide critical insights into material conditions that underpin health inequality over the life course. Analyses can examine income profiles across population subgroups—by gender, race, nativity, or education—and investigate longitudinal changes that precede or accompany retirement, disability, or health decline.
Paid care work forms the backbone of the U.S. health and social care systems, encompassing a wide range of occupations responsible for delivering clinical care, personal assistance, and preventive services. Despite its central role in population health and economic functioning, paid care work remains challenging to measure systematically. Care jobs are often distributed across diverse settings, span multiple occupational classifications, and involve complex combinations of clinical, relational, and administrative tasks that are not always well captured in traditional labor statistics. As a result, researchers require data infrastructure that links detailed employment information with demographic and contextual characteristics to fully understand the paid care workforce
Unpaid caregiving for older adults—most often performed by family members or friends—is an essential yet undercounted part of the care economy. The American Time Use Survey (ATUS), accessible through IPUMS Time Use, is one of the best tools available for studying unpaid care. ATUS includes 24-hour time diaries from U.S. residents, capturing how they allocate their time across, for example, work, care, leisure, and household activities. Researchers can measure time spent caring for children, adults, or household members with health needs, and distinguish between direct care (such as feeding or assisting with mobility) and supervisory or emotional support.
Understanding how health changes with age and why those changes differ so dramatically across populations requires data that are both large in scale and rich in context. The new Aging and Health Launchpad is designed to support researchers using large-scale data to study topics related to health and aging, from caregiving and workforce participation to healthcare access and cognitive health. The goal is simple but ambitious: to demonstrate how large-scale data can be used to answer pressing questions about aging, health, and inequality across the life course. This initiative blends the Life Course Center’s research specializations with our expertise building and supporting the use of powerful population data resources.
