Spousal Caregiving Types and Cognitive Trajectories Among Middle-Aged and Older Adults in China
Abstract
Objectives: This study investigates the longitudinal association between spousal caregiving types and cognitive trajectories among middle-aged and older Chinese adults, with a focus on gender differences. Method: Data from the China Health and Retirement Longitudinal Study (CHARLS) (2011–2018) were analyzed, covering 4568 couples aged 45 and above. Caregiving types were Activities of Daily Living (ADLs) caregiving, Instrumental Activities of Daily Living (IADLs) caregiving, and both ADLs/IADLs caregiving. Cognitive function was assessed using tests of memory, orientation, numeric ability, and visuospatial ability. Latent growth curve modeling was used to examine associations between caregiving types and cognitive trajectories. Results: Spouses who only provided IADLs caregiving had slower cognitive decline compared to those who did not provide caregiving, regardless of gender. However, those who provided ADLs caregiving had faster cognitive decline compared to non-caregivers, especially in women. Discussion: These findings highlight the importance of caregiving types as well as the gendered effects of caregiving on cognitive trajectories.
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Biographies
Yu Guo is a Ph.D. candidate at the Institute for Population and Development Studies at Xi’an Jiaotong University. Her research interests include aging, cognitive function, and public policy in China, with a particular focus on the dyadic relationships among couples.
Zhenmei Zhang is a professor in the Department of Sociology at Michigan State University. Her research encompasses aging and the life course, family and health, and racial/ethnic disparities in health. Specifically, her work has focused on how social relationships across the life course contribute to health inequalities in later life in both the United States and China.
Quanbao Jiang is a professor at the Institute for Population and Development Studies at Xi’an Jiaotong University. His research focuses on population analysis and public policy, aging, gender imbalances, and low fertility rates.
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