Next Article in Journal
Effect of Prehabilitation on the 6-Minute Walk Test and Length of Hospital Stay in Frail Older People: A Meta-Analysis of Randomized Controlled Trials
Previous Article in Journal
A Novel Macro-Level Model in Evaluating Health and Safety Training Based on Virtual Reality
Previous Article in Special Issue
Determinants of Caregiving Subgroups for Mexican American Caregivers Assisting Older Adults at Home and Their Influence on Perceived Stress
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 22
Issue 9
10.3390/ijerph22091379
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Race, Social Context, and Caregiving Intensity: Impact on Depressive Symptoms Among Spousal Caregivers

by
Florence U. Johnson
1,*,
Melissa Plegue
2,
Namratha Boddakayala
3 and
Sheria G. Robinson-Lane
1
1
School of Nursing, University of Michigan, Ann Arbor, MI 48109, USA
2
School of Medicine, University of Michigan, Ann Arbor, MI 48109, USA
3
School of Information, University of Michigan, Ann Arbor, MI 48109, USA
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2025, 22(9), 1379; https://doi.org/10.3390/ijerph22091379
Submission received: 26 June 2025 / Revised: 27 August 2025 / Accepted: 27 August 2025 / Published: 3 September 2025
(This article belongs to the Special Issue Family Caregiving of Older Adults)
Browse Figures
Review Reports Versions Notes

Abstract

Understanding the factors that influence the mental health of family caregivers is essential. This study examines the impact of caregiving intensity, operationalized as caregiving hours, on depressive symptoms, with a focus on racial differences and gender effects. We analyzed data from n = 2112 unique spousal caregivers across 6622 person-years of observations from the Health and Retirement Study (HRS) longitudinal data (2008–2014). We estimated the impact of caregiving hours on depressive symptoms, controlling for race, gender, and education. Random intercepts accounted for household-level variance. We assessed interaction terms to evaluate differential effects across racial groups. Depressive symptoms were positively associated with caregiving hours β = 1.74, SE = 0.24, suggesting that increasing caregiving hours is likely to lead to emotional distress. We observed a significant negative interaction effect among African American caregivers ( β   = −1.25, p = 0.013), indicating that increased caregiving hours led to a reduction in depressive symptoms. Gender was not significantly associated with caregiving hours ( β = −0.36, p = 0.715). The random effects model demonstrated substantial household-level variation (var(_cons) = 266.07, p < 0.0001). Findings highlight racial differences in the effects of caregiving hours on depressive symptoms and point to the importance of culturally responsive interventions designed to mitigate depressive symptoms among caregivers. Future research should explore protective factors that mitigate psychological distress and promote resilience.

1. Introduction

More than 11 million unpaid family caregivers in the United States provided nearly 18.4 billion hours of care to those living with dementia in their homes [1]. Research shows that around 10% of the caregivers provide care to their spouses with Alzheimer’s Disease and Related Dementias (ADRD) [2], and 30% of the ADRD caregivers are 65 or older [1]. As Baby Boomers continue to age, we expect the number of people living with dementia to increase, which will intensify the dependence on spousal caregivers [3]. This shifting of the demographic highlights the importance of grasping and confronting the mental health issues associated with spousal caregiving, especially for Black spousal caregivers [3]. These caregivers tend to provide care for more extended periods compared to those caring for individuals with other chronic conditions [1]. Spousal caregivers of individuals with dementia frequently face significant challenges that negatively affect their mental well-being and the quality of care they provide [3,4]. These caregivers often lack training in essential disease management skills, such as assisting with activities of daily living or managing behavior issues, which contributes to the challenges of managing the complex care tasks associated with dementia [5,6]. The emotional demands of caregiving, which include tasks such as assisting with mobility and personal hygiene, can be overwhelming, particularly for older caregivers who may have health challenges of their own [5,7,8,9].
Although caregiving can be meaningful, research has found it to be associated with significant psychosocial and emotional strain, placing the caregiver at risk for experiencing depressive symptoms, anxiety, and a decline in overall well-being [10,11,12]. Mental health challenges are especially prevalent in the Black and Hispanic communities; however, the associated mental health-related issues in these groups remain understudied [13]. Building on previous research, this study focuses on the contextual factors that are available in the HRS dataset and can impact caregiving, such as gender, education level, and ethno-racial identity [14,15]. These factors are associated with health inequities—i.e., avoidable and unfair differences in health status, access to healthcare, and health outcomes [16]. For Black and Hispanic caregivers, these inequities add stress to their already heavy caregiving loads [17]. Systemic factors, including racism, socioeconomic inequality, and limited access to supportive resources, may exacerbate the stress of caregiving [18,19].
Medical expenses and the need for supplementary paid support services contribute to financial strain among caregivers with limited resources [10,11,12,13,14,15,16,17,18,19,20,21,22]. Gender also plays a crucial role, with women, who tend to be primary caregivers, typically experiencing higher caregiving burden and associated mental health challenges due to societal norms and expectations for prioritizing familial needs over their own and being the backbone of the family [23,24]. Education levels can impact caregivers’ ability to navigate healthcare systems and access resources, with higher education often correlating with better coping strategies and advocacy skills [25,26]. Access-related challenges exacerbate depressive symptoms and stress [17,27].
Despite the growing body of research on caregiver burden and mental health outcomes [20,23], the unique experiences of Black and Hispanic spousal caregivers remain understudied [17,28,29], leaving critical gaps in our understanding of how to support their mental health with responsive cultural interventions that address the stressors commonly faced by Black and Hispanic spousal caregivers [17,18]. One final gap in the literature, given the variation in caregiving intensity across groups, is the relationship between caregiving hours and depressive symptoms [30,31]. Using the Health and Retirement Study (HRS) longitudinal data, we examined the impact of caregiving intensity and depressive symptoms on spousal caregivers. To gain a deeper understanding of caregiver mental health disparities, we stratified the analysis by gender and race [17,21,23].
We hypothesize that as caregiving hours increase, self-reported depressive symptoms will increase, and there will be differences based on race and ethnicity. We expect that Black spousal caregivers will verbalize higher levels of depressive symptoms as their caregiving hours increase. Our overarching goal is to leverage a nationally representative dataset to improve our understanding of racial differences in spousal caregiving and their relationship to the mental health of caregivers.

2. Materials and Methods

2.1. Data

The data used are from the Health and Retirement Study (HRS). HRS is a nationally representative longitudinal survey of noninstitutionalized individuals aged 50 and older, surveying more than 22,000 people in the United States every two years. If the participant (hereafter referred to as the partner) is married or living with a spouse/partner, the spouse/partner (hereafter referred to as the spouse) is recruited into the study and surveyed, regardless of their age. They used a multistage, area-clustered probability design with an overall response rate of 80% to choose the primary sample [32].
The RAND Health and Retirement Study (RAND HRS) Longitudinal File 2018 is the primary dataset used. It is a streamlined and easy-to-use product from the HRS core and exit interviews [33]. This dataset captures comprehensive sociodemographic, health, and functioning information, including data on households with married or partnered individuals. It encompasses variables such as income, assets, and medical expenditures, facilitating research on retirement, health insurance, savings, and economic well-being [34]. Using Wave 9 (2008–2014) of the RAND HRS for this secondary analysis made it possible to build a substantial longitudinal dataset that tracked each couple member (spouse and partner) over a six-year period [34]. The dataset also included validated measures of cognitive functioning and depressive symptoms [35,36].

2.2. HRS Population and Sample Derivation

HRS included a representative sample of older respondents (ages 50 years and older) who met the following inclusion criteria from the original HRS wave 9 study sample: they completed at least two adjacent waves of the HRS from 2000 to 2014 [32]. Wave 9 served as the primary point of reference; the researchers selected participants who consistently participated in the study across all years for the longitudinal data because their participation ensured reliable, longitudinal data [32]. They had a spouse or partner (of any age) who also responded to the two or more adjacent surveys. HRS defined people who assisted their partners with at least one activity of daily living (ADL) or instrumental activity of daily living (iADL) task as spousal caregivers, and their partners also recognized them as caregivers. The collection of demographic data occurred during the first survey [37].
The in-person supplemental study, Aging, Demographics, and Memory Study (ADAMS) gathered demographic information on both study participants’ cognition [38]. The HRS dataset sample includes both members of the spousal dyads; when the study person is diagnosed with ADRD, the person providing care is referred to as the “spousal caregiver”, and the partner with ADRD as the care recipient. Our final analytic sample of 2112 spousal caregivers included participants who contributed to two longitudinal observations during the 2008–2014 data period, self-identified as spouses, and assisted their spouse with ADL or iADL.

2.3. Outcome (Depressive Symptoms)

The primary outcome of depression in this study was the number of caregiver depressive symptoms, based on the Center for Epidemiologic Studies Depression (CES-D) scale, which is a valid and reliable way to measure depression [39]. The researchers at HRS used CESD-8, a modified version of the CESD scale that contains eight items; the CESD-8 is typically scored on a four-point Likert scale [40]. This study used a dichotomous scoring system for simplicity and ease of interpretation. HRS researchers asked respondents to answer ‘yes’ or ‘no’ to questions related to depressive symptoms. Each ‘yes’ response was coded as ‘one’ and each ‘no’ response as ‘zero,’ except for two positively worded items: ‘I felt cheerful’ and ‘I loved life.’ For these two items, a ‘yes’ response was coded as ‘zero’ and a ‘no’ response as ‘one’ [41].
The total score was calculated by summing the coded responses, with a maximum possible score of 8. HRS also asked the respondents if they experienced feelings of depression, found that everything took effort, had sleep disturbance, felt alone, felt unhappy, and struggled to get going. A total score of 5 or more was indicative of depressive symptoms.

2.3.1. Main Predictors of Interest (Care Intensity)

Our primary predictor of interest, care intensity, was operationalized in this study as the number of hours a week a spouse spends helping the person living with ADRD. HRS calculated caregiving hours by asking the study subject two specific questions: How many days did your spouse assist you last month, and how many hours per day does your spouse help you during those days? The answer could range from 0 to 8 h. The spousal caregiver was asked the same questions about caregiving hours as the study subject. Our study team categorized spousal caregiving hours into none, 1–13 (low), and greater than 13 (high) to facilitate descriptive comparisons of demographics with care intensity.

2.3.2. Covariates

Age, education, gender, and marital status were included as covariates since these factors are associated with both care intensity and depressive symptoms.

2.3.3. Analysis

We generated descriptive statistics for all study variables. To comprehensively assess the effects of race, age, education, and caregiving hours on self-reported depressive symptoms, we estimated several models. A linear mixed model was used to examine the influence of gender, education, race, and caregiving hours on depressive symptoms, accounting for repeated measures across time. To explore whether the relationship between caregiving hours and depressive symptoms varied by race, we included an interaction term between caregiving hours and caregiver race. We used maximum likelihood estimation to fit the mixed-effects model, which included a random intercept for each caregiver to account for within-subject correlation due to multiple years of data. Predictive margins were calculated and plotted at varying levels of caregiving hours to visualize the interaction effects and to compare depressive symptom trajectories across racial groups. Data management and analysis were performed using STATA 16.

3. Results

We identified an eligible cohort of 2112 unique spouses across 6622 person-years in which they cared for partners identified as having ADRD. The majority of the spouses were non-Hispanic White (NH White) (60.9%), followed by African American (20.1%), Hispanic (11.5%), and other (7.4%); Table 1. Most of the caregivers were female (59.6%), with less than a “High School (HS) education” (36.2%), and fewer spouses had “some college” (17%). Spouses in the “high” caregiving level category exhibited the highest average depressive symptoms (mean = 2.06, standard deviation (SD) = 2.18), while those in the “none” category had the lowest (mean = 1.76, SD = 2.05) (Table 2). The linear mixed model analysis (Table 3) revealed key findings regarding the impact of caregiving hours on depressive symptoms, differentiated by race. NH White caregivers showed a significant positive association between caregiving hours and depressive symptoms, with a marginal effect (dy/dx = 0.00347, p < 0.0001). Similarly, Hispanic caregivers also showed a positive association between caregiving hours and depressive symptoms (dy/dx = 0.00316, p = 0.021). Findings also demonstrated that as caregiving hours increased, depressive symptoms increased for both NH White and Hispanic caregivers. This suggests that caregiver intensity may function inversely across racial groups. African American caregivers had no significant association (dy/dx = 0.00034, p = 0.807) with depressive symptoms; these caregivers did not show a significant association, unlike their counterparts (Figure 1). Figure 2 illustrates the relationship between caregiving hours and depressive symptoms stratified by race, education, and gender.

4. Discussion

This study underscores the complex link between depressive symptoms and care intensity, demonstrating substantial racial disparities in caregiving reactions to emotional distress. Although depressive symptoms were generally associated with increased caregiving hours, African American caregivers exhibited a contrasting trend, reporting fewer depressive symptoms as caregiving hours increased. These results suggest that cultural, psychological, and structural factors may influence caregiving impact distinctively across ethnic groups. Other studies have noted that Black women commonly use positive reappraisal as an adaptive coping strategy, which may diminish depressive symptoms [42,43,44].
The emotional toll of caregiving may be more pronounced among NH White and Hispanic caregivers, who exhibit stronger associations between caregiving intensity and depressive symptoms (p = 0.021) [45]. In contrast, African Americans and individuals from other racial groups demonstrate a weaker or non-significant association [46]. The absence of significant impacts for African Americans and the “other” race groups could be indicative of caregiving dynamics that differ across cultural contexts [47]. Disparities in caregiving practices, coping methods, and social support networks, as well as larger systemic inequities in healthcare and family dynamics, could explain these differences [48]. The lack of gender differences suggests that both men and women experience similar caregiving demands, which highlights the importance of gender-neutral caregiving policies [49].
In their recent qualitative findings, Robinson-Lane et al. [43] reinforce the idea that Black caregivers frequently draw on culturally rooted resilience strategies, such as adaptability, proactive planning, and reliance on supportive environments. However, the cultural frameworks—exemplified by the strong Black woman schema—may also exacerbate the underreporting of mental health issues, hence complicating the recognition and treatment of caregiver mental health. To lessen the psychological impact of caregiving, future research should look into protective characteristics, especially in groups where emotional distress is already high. Caregiver stress can be better understood by examining the role of resilience mechanisms, including social support, coping mechanisms, and access to mental health resources. To create equitable support networks for diverse caregiving populations and address gaps in caregiving experiences, it is essential to understand these dynamics across racial and cultural contexts. Developing culturally responsive interventions is critical.

5. Conclusions

Our study results indicate a link between caregiving hours and depressive symptoms in Hispanic and NH White caregivers, but not among African American caregivers. To effectively support the mental health of spousal caregivers, interventions should be accessible and culturally sensitive to honor the values, coping strategies, and lived experiences of those they aim to serve, especially African American caregivers. Regular mental health screenings should include checks for depressive symptoms, utilizing the resilience-building strategies developed by Black caregiving groups. It should be noted that although our study did not find a significant association between caregiving hours and depressive symptoms among African American caregivers, this does not mean that they might not face unique challenges as caregivers and still need tailored support. Addressing structural inequities, such as limited access to resources and training, financial constraints, and examining the interplay of cultural identity, gender norms that typically assign caregiving responsibilities to women, and disengaged social support networks, is essential for future research and for monitoring resilience and distress over time.

6. Limitations

One limitation of this study is that we did not consider participants’ mental health before caregiving because we were interested in how caregiving impacted mental health and not overall mental health status. Finally, bias is possible due to the nature of longitudinal studies, as participants may drop out over time, while new participants are added. Additionally, we addressed missing data using a listwise deletion approach. This limitation may have reduced our sample size, which could have decreased our statistical power and thereby hindered our ability to detect significant associations.

Author Contributions

Conceptualization, F.U.J., M.P. and S.G.R.-L.; methodology, F.U.J., M.P. and S.G.R.-L.; software, F.U.J.; validation, F.U.J., M.P., N.B. and S.G.R.-L.; formal analysis, F.U.J. and M.P.; resources, F.U.J. and S.G.R.-L.; data curation, F.U.J.; writing—original draft preparation, F.U.J.; writing—review and editing, F.U.J., M.P. and S.G.R.-L.; visualization, F.U.J. and N.B.; supervision, F.U.J. and S.G.R.-L.; funding acquisition, F.U.J.; All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the National Institutes of Health (NIH)/National Institute of Nursing Research (NINR) grant #T32NR016914-01, Complexity: Innovations in Promoting Health and Safety (PI: M. Titler). The HRS is sponsored by the National Institute on Aging (NIA) (grant number NIA U01AG009740) and conducted by the University of Michigan. The NIA (P30AG059300, K01AG065420) to S.G.R., MCCFAD National Institute on Aging grant number P30AG059300, and the Michigan Alzheimer’s Disease Center Dr. Antonia Lenstra Fund.

Institutional Review Board Statement

The Institutional Review Board of the University of Michigan deemed this study “exempt.” Ethical review and approval were waived for this secondary data analysis. Our study used publicly available and anonymized secondary data collected from the HRS, which included informed consent from all participants.

Informed Consent Statement

This secondary data analysis study did not involve human subjects; therefore, it did not require any additional informed consent.

Data Availability Statement

The data are publicly available at https://hrsdata.isr.umich.edu/ (accessed on 17 June 2025).

Acknowledgments

This work was supported by the National Institute for Occupational Safety and Health of the Centers for Disease Control and Prevention (CDC) [T42OH008455]. The HRS is supported by the NIA of the NIH [U01AG009740]. The content is solely the authors’ responsibility and does not necessarily represent the official views of the CDC or NIH. The National Clinician Scholar Program. The University of Michigan School of Nursing. We utilized Microsoft Copilot to support structural refinement and clarity of the paper; it was not used in the conceptualization or development of the research study and manuscript.

Conflicts of Interest

The authors declare that they have no conflicts of interest. The funders had no part in the study’s design, data collection, analysis, or interpretation, manuscript writing, or publication decision.

Abbreviations

The following abbreviations are used in this manuscript:
ADRDAlzheimer’s Disease and Related Dementias
HRSHealth and Retirement Study
RAND HRSRAND Health and Retirement Study
ADLsActivities of daily living
iADLsInstrumental activities of daily living
ADAMSAging, Demographics, and Memory Study
CESDCenter for Epidemiologic Studies Depression
NH WhiteNon-Hispanic White
HSHigh School
SDStandard Deviation
GEDGeneral Educational Development
NIHNational Institutes of Health
NINRNational Institute of Nursing Research
NIANational Institute on Aging
CDCCenters for Disease Control and Prevention

References

  1. Centers for Disease Control and Prevention. Caregiving. Available online: https://www.cdc.gov/caregiving/index.html (accessed on 17 June 2025).
  2. Alzheimer’s Association. Alzheimer’s Disease Facts and Figures Special Report: American Perspectives on Early Detection of Alzheimer’s Disease in the Era of Treatment. 2025. Available online: https://www.alz.org/getmedia/3d226bf2-0690-48d0-98ac-d790384f4ec2/alzheimers-facts-and-figures-special-report.pdf (accessed on 8 August 2025).
  3. Hazzan, A.A.; Dauenhauer, J.; Follansbee, P.; Hazzan, J.O.; Allen, K.; Omobepade, I. Family Caregiver Quality of Life and the Care Provided to Older People Living with Dementia: Qualitative Analyses of Caregiver Interviews. BMC Geriatr. 2022, 22, 86. [Google Scholar] [CrossRef]
  4. Unson, C.; Njoku, A.; Bernard, S.; Agbalenyo, M. Racial and Ethnic Disparities in Chronic Stress among Male Caregivers. Int. J. Environ. Res. Public Health 2023, 20, 6188. [Google Scholar] [CrossRef] [PubMed]
  5. Mueller, A.; Thao, L.; Condon, O.; Liebzeit, D.; Fields, B. A Systematic Review of the Needs of Dementia Caregivers Across Care Settings. Home Health Care Manag. Pract. 2022, 34, 133–147. [Google Scholar] [CrossRef]
  6. Peterson, K.; Hahn, H.; Lee, A.J.; Madison, C.A.; Atri, A. In the Information Age, Do Dementia Caregivers Get the Information They Need? Semi-Structured Interviews to Determine Informal Caregivers’ Education Needs, Barriers, and Preferences. BMC Geriatr. 2016, 16, 164. [Google Scholar] [CrossRef]
  7. Burgdorf, J.G.; Reckrey, J.; Russell, D. “Care for Me, Too”: A Novel Framework for Improved Communication and Support Between Dementia Caregivers and the Home Health Care Team. Gerontologist 2023, 63, 874–886. [Google Scholar] [CrossRef] [PubMed]
  8. Nguyen, H.; Eccleston, C.E.; Doherty, K.V.; Jang, S.; McInerney, F. Communication in Dementia Care: Experiences and Needs of Carers. Dementia 2022, 21, 1381–1398. [Google Scholar] [CrossRef]
  9. Rutkowski, R.A.; Ponnala, S.; Younan, L.; Weiler, D.T.; Bykovskyi, A.G.; Werner, N.E. A Process-Based Approach to Exploring the Information Behavior of Informal Caregivers of People Living with Dementia. Int. J. Med. Inform. 2021, 145, 104341. [Google Scholar] [CrossRef]
  10. Ali, A.M.; Alkhamees, A.A.; Hallit, S.; Al-Dwaikat, T.N.; Khatatbeh, H.; Al-Dossary, S.A. The Depression Anxiety Stress Scale 8: Investigating Its Cutoff Scores in Relevance to Loneliness and Burnout among Dementia Family Caregivers. Sci. Rep. 2024, 14, 13075. [Google Scholar] [CrossRef]
  11. Blom, C.; Reis, A.; Lencastre, L. Caregiver Quality of Life: Satisfaction and Burnout. Int. J. Environ. Res. Public Health 2023, 20, 6577. [Google Scholar] [CrossRef]
  12. Hellis, E.; Mukaetova-Ladinska, E.B. Informal Caregiving and Alzheimer’s Disease: The Psychological Effect. Medicina 2022, 59, 48. [Google Scholar] [CrossRef]
  13. Johnson, F. Examining the Relationship Between Community-Based Support Services Use and Mental Health in Black Family Caregivers of Persons Living with Dementia. Ph.D Thesis, University of Michigan, Ann Arbor, MI, USA, June 2023. [Google Scholar]
  14. Bayram, E.; Wyman-Chick, K.A.; Costello, R.; Ghodsi, H.; Rivera, C.S.; Solomon, L.; Kane, J.P.M.; Litvan, I. Sex Differences for Social Determinants Associated with Lewy Body Dementia Onset and Diagnosis. Neurodegener. Dis. 2025, 25, 1–13. [Google Scholar] [CrossRef]
  15. Ikanga, J.; Reyes, A.; Zhao, L.; Hill-Jarrett, T.G.; Hammers, D.; Epenge, E.; Esambo, H.; Kavugho, I.; Esselakoy, C.; Gikelekele, G.; et al. Exploring Factors Contributing to Caregiver Burden in Family Caregivers of Congolese Adults with Suspected Dementia. Int. J. Geriatr. Psychiatry 2023, 38, e6004. [Google Scholar] [CrossRef]
  16. Rad, J. Health Inequities: A Persistent Global Challenge from Past to Future. Int. J. Equity Health 2025, 24, 148. [Google Scholar] [CrossRef]
  17. Clair, C.A.; Tobin, K.E.; Taylor, J.L. Caregiver Burden and Its Limitations in Describing Black Caregivers’ Experience. Curr. Geriatr. Rep. 2023, 12, 228–234. [Google Scholar] [CrossRef]
  18. Robinson-Lane, S.G.; Johnson, F.U.; Tuyisenge, M.J.; Kirch, M.; Christensen, L.L.; Malani, P.N.; Solway, E.; Singer, D.C.; Kullgren, J.T.; Koumpias, A.M. Racial and Ethnic Variances in Preparedness for Aging in Place among US Adults Ages 50–80. Geriatr. Nurs. 2023, 54, 357–364. [Google Scholar] [CrossRef]
  19. Egede, L.E.; Walker, R.J.; Williams, J.S. Addressing Structural Inequalities, Structural Racism, and Social Determinants of Health: A Vision for the Future. J. Gen. Intern. Med. 2024, 39, 487–491. [Google Scholar] [CrossRef] [PubMed]
  20. Phillips, R.; Durkin, M.; Engward, H.; Cable, G.; Iancu, M. The Impact of Caring for Family Members with Mental Illnesses on the Caregiver: A Scoping Review. Health Promot. Int. 2023, 38, daac049. [Google Scholar] [CrossRef]
  21. Willert, B.; Minnotte, K.L. Informal Caregiving and Strains: Exploring the Impacts of Gender, Race, and Income. Appl. Res. Qual. Life 2021, 16, 943–964. [Google Scholar] [CrossRef]
  22. Greenfield, J.C.; Hasche, L.; Bell, L.M.; Johnson, H. Exploring How Workplace and Social Policies Relate to Caregivers’ Financial Strain. J. Gerontol. Soc. Work 2018, 61, 849–866. [Google Scholar] [CrossRef] [PubMed]
  23. Xiong, C.; Biscardi, M.; Astell, A.; Nalder, E.; Cameron, J.I.; Mihailidis, A.; Colantonio, A. Sex and Gender Differences in Caregiving Burden Experienced by Family Caregivers of Persons with Dementia: A Systematic Review. PLoS ONE 2020, 15, e0231848. [Google Scholar] [CrossRef]
  24. Stall, N.M.; Shah, N.R.; Bhushan, D. Unpaid Family Caregiving—The Next Frontier of Gender Equity in a Postpandemic Future. JAMA Health Forum 2023, 4, e231310. [Google Scholar] [CrossRef]
  25. Arnone, J. Caregiver Burden and Mental Health: Millennial Caregivers. Online J. Issues Nurs. 2024, 29. [Google Scholar] [CrossRef]
  26. Kim, B.; Wister, A.; Mitchell, B.; Li, L.; Kadowaki, L. Healthcare System Navigation Difficulties among Informal Caregivers of Older Adults: A Logistic Regression Analysis of Social Capital, Caregiving Support and Utilization Factors. BMC Health Serv. Res. 2024, 24, 1159. [Google Scholar] [CrossRef]
  27. Berk, M.; Köhler-Forsberg, O.; Turner, M.; Penninx, B.W.J.H.; Wrobel, A.; Firth, J.; Loughman, A.; Reavley, N.J.; McGrath, J.J.; Momen, N.C.; et al. Comorbidity between Major Depressive Disorder and Physical Diseases: A Comprehensive Review of Epidemiology, Mechanisms and Management. World Psychiatry 2023, 22, 366–387. [Google Scholar] [CrossRef]
  28. Sehar, U.; Rawat, P.; Choudhury, M.; Boles, A.; Culberson, J.; Khan, H.; Malhotra, K.; Basu, T.; Reddy, P.H. Comprehensive Understanding of Hispanic Caregivers: Focus on Innovative Methods and Validations. J. Alzheimer’s Dis. Rep. 2023, 7, 557–574. [Google Scholar] [CrossRef]
  29. Tran, J.T.; Theng, B.; Serag, H.; Raji, M.; Tzeng, H.-M.; Shih, M.; Lee, W.-C. (Miso) Cultural Diversity Impacts Caregiving Experiences: A Comprehensive Exploration of Differences in Caregiver Burdens, Needs, and Outcomes. Cureus 2023, 15, e46537. [Google Scholar] [CrossRef]
  30. Kolodziej, I.W.K.; Coe, N.B.; Van Houtven, C.H. The Impact of Care Intensity and Work on the Mental Health of Family Caregivers: Losses and Gains. J. Gerontol. Ser. B 2022, 77, S98–S111. [Google Scholar] [CrossRef] [PubMed]
  31. Liu, H.; Lou, W.Q.V. Continuity and Changes in Three Types of Caregiving and the Risk of Depression in Later Life: A 2-Year Prospective Study. Age Ageing 2017, 46, 827–832. [Google Scholar] [CrossRef] [PubMed]
  32. Fisher, G.G.; Hassan, H.; Faul, J.D.; Rodgers, W.L.; Weir, D.R. Health and Retirement Study: Imputation of Cognitive Functioning Measures, 1992–2014 (Final Release Version); Survey Research Center, University of Michigan: Ann Arbor, MI, USA, 2017; Available online: https://hrs.isr.umich.edu/sites/default/files/biblio/COGIMPdd.pdf (accessed on 23 June 2025).
  33. Bugliari, D.; Campbell, N.; Chan, C.; Hayden, O.; Hurd, M.; Main, R.; Mallett, J.; McCullough, C.; Meijer, E.; Moldoff, M.; et al. RAND HRS Longitudinal File 1992–2018 (Version 1); RAND Center for the Study of Aging: Santa Monica, CA, USA, 2016; Available online: https://hrsdata.isr.umich.edu/sites/default/files/documentation/other/1615843861/randhrs1992_2018v1.pdf (accessed on 17 June 2025).
  34. Bugliari, D.; Campbell, N.; Chan, C.; Hayden, O.; Hurd, M.; Main, R.; Mallett, J.; McCullough, C.; Meijer, E.; Moldoff, M.; et al. RAND HRS Data File, Version P; RAND Center for the Study of Aging: Santa Monica, CA, USA, 2016; Available online: https://hrsonline.isr.umich.edu/modules/meta/rand/randhrsp/randhrs_P.pdf (accessed on 17 June 2025).
  35. Langa, K.M.; Llewellyn, D.J.; Lang, I.A.; Weir, D.R.; Wallace, R.B.; Kabeto, M.U.; Huppert, F.A. Cognitive Health among Older Adults in the United States and in England. BMC Geriatr. 2009, 9, 23. [Google Scholar] [CrossRef]
  36. Gianattasio, K.Z.; Wu, Q.; Glymour, M.M.; Power, M.C. Comparison of Methods for Algorithmic Classification of Dementia Status in the Health and Retirement Study. Epidemiology 2019, 30, 291–302. [Google Scholar] [CrossRef] [PubMed]
  37. Sonnega, A.; Faul, J.D.; Ofstedal, M.B.; Langa, K.M.; Phillips, J.W.; Weir, D.R. Cohort Profile: The Health and Retirement Study (HRS). Int. J. Epidemiol. 2014, 43, 576–585. [Google Scholar] [CrossRef]
  38. Heeringa, S.G.; Fisher, G.G.; Hurd, M.; Langa, K.M.; Ofstedal, M.B.; Plassman, B.L.; Rodgers, W.L.; Weir, D.R. Aging, Demographics, and Memory Study (ADAMS): Sample Design, Weighting and Analysis for ADAMS; University of Michigan, Institute for Social Research: Ann Arbor, MI, USA, 2009; Available online: https://hrsonline.isr.umich.edu/sitedocs/userg/adams/ADAMSSampleWeights_Nov2007.pdf (accessed on 17 June 2025).
  39. Radloff, L.S. The CES-D Scale: A Self-Report Depression Scale for Research in the General Population. Appl. Psychol. Meas. 1977, 1, 385–401. [Google Scholar] [CrossRef]
  40. Huang, J.; Perrin, N.; Li, J. Depressive Symptoms and Cognitive Function in Older Adults with Subjective Cognitive Decline: Longitudinal Findings From 2010 to 2020. Alzheimer’s Amp Dement. 2024, 20, e090034. [Google Scholar] [CrossRef]
  41. Steffick, D. Documentation of Affective Functioning Measures in the Health and Retirement Study; Institute for Social Research, University of Michigan: Ann Arbor, MI, USA, 2000. [Google Scholar] [CrossRef]
  42. Robinson-Lane, S.G. Adapting to Chronic Pain: A Focused Ethnography of Black Older Adults. Geriatr. Nurs. 2020, 41, 468–473. [Google Scholar] [CrossRef]
  43. Robinson-Lane, S.G.; Johnson, F.U.; Tuyisenge, M.J.; Jackson, J.; Qurashi, T.; Tripathi, P.; Giordani, B. “It Isn’t What I Had to Do, It’s What I Get to Do”: The Experiences of Black Family Caregivers Managing Dementia. J. Fam. Nurs. 2024, 30, 304–316. [Google Scholar] [CrossRef] [PubMed]
  44. Monte, V.; Pham, M.D.; Tsai, W. Microaggressions and General Health among Black and Asian Americans: The Moderating Role of Cognitive Reappraisal. Cult. Divers. Ethn. Minor. Psychol. 2025, 31, 88–96. [Google Scholar] [CrossRef] [PubMed]
  45. Sörensen, S.; Pinquart, M. Racial and Ethnic Differences in the Relationship of Caregiving Stressors, Resources, and Sociodemographic Variables to Caregiver Depression and Perceived Physical Health. Aging Ment. Health 2005, 9, 482–495. [Google Scholar] [CrossRef]
  46. Cohen, S.A.; Mendez-Luck, C.A.; Greaney, M.L.; Azzoli, A.B.; Cook, S.K.; Sabik, N.J. Differences in Caregiving Intensity Among Distinct Sociodemographic Subgroups of Informal Caregivers: Joint Effects of Race/Ethnicity, Gender, and Employment. J. Gerontol. Nurs. 2021, 47, 23–32. [Google Scholar] [CrossRef] [PubMed]
  47. Losada-Baltar, A.; Falzarano, F.B.; Hancock, D.W.; Márquez-González, M.; Pillemer, K.; Huertas-Domingo, C.; Jiménez-Gonzalo, L.; Fernandes-Pires, J.A.; Czaja, S.J. Cross-National Analysis of the Associations Between Familism and Self-Efficacy in Family Caregivers of People With Dementia: Effects on Burden and Depression. J. Aging Health 2024, 36, 403–413. [Google Scholar] [CrossRef]
  48. Parenteau, A.M.; Boyer, C.J.; Campos, L.J.; Carranza, A.F.; Deer, L.K.; Hartman, D.T.; Bidwell, J.T.; Hostinar, C.E. A Review of Mental Health Disparities during COVID-19: Evidence, Mechanisms, and Policy Recommendations for Promoting Societal Resilience. Dev. Psychopathol. 2023, 35, 1821–1842. [Google Scholar] [CrossRef]
  49. Martínez-Santos, A.-E.; Facal, D.; Vicho De La Fuente, N.; Vilanova-Trillo, L.; Gandoy-Crego, M.; Rodríguez-González, R. Gender Impact of Caring on the Health of Caregivers of Persons with Dementia. Patient Educ. Couns. 2021, 104, 2165–2169. [Google Scholar] [CrossRef] [PubMed]
Ijerph 22 01379 g001
Figure 1. Marginal effects of caregiving hours on depressive symptoms by race. Number of observations: 6622. Expression: linear prediction (fixed portion) with respect to variable cg_sp_wklyhrs (caregiving hours).
Figure 1. Marginal effects of caregiving hours on depressive symptoms by race. Number of observations: 6622. Expression: linear prediction (fixed portion) with respect to variable cg_sp_wklyhrs (caregiving hours).
Ijerph 22 01379 g001
Ijerph 22 01379 g002
Figure 2. Predictive margins of spousal race with 95% confidence intervals (CIs).
Figure 2. Predictive margins of spousal race with 95% confidence intervals (CIs).
Ijerph 22 01379 g002
Table 1. Spouse characteristics (n = 2112).
Table 1. Spouse characteristics (n = 2112).
CharacteristicCategoryFrequency (n)Percentage (%)
Race/EthnicityNH White128860.98
African American42520.12
Hispanic24311.51
Other1567.39
SexFemale125959.61
Male85340.39
EducationLess than HS76436.17
GED or HS graduate75735.84
Some college35816.95
College and above23311.03
Total 2112100.00
Table 2. Time-varying characteristics by caregiving level.
Table 2. Time-varying characteristics by caregiving level.
Spousal DepressionNone (Mean ± SD) n = 4233Low (Mean ± SD) n = 931High (Mean ± SD) n = 1458
Depression1.76 ± 2.051.96 ± 2.192.06 ± 2.18
ADLs0.44 ± 1.010.43 ± 0.980.34 ± 0.83
iADLs0.36 ± 0.900.32 ± 0.830.25 ± 0.68
Diagnosis duration (n)n = 2744n = 704n = 1217
Diagnosis duration2.73 ± 3.782.77 ± 3.422.97 ± 3.75
Table 3. Linear mixed model results.
Table 3. Linear mixed model results.
ParameterEstimateStd. Err.[95% Conf. Internal]
Caregiving Hours (Race = African American)−1.24990.5053−2.2403 to −0.2596
Caregiving Hours (Race = NH White)Ref
Caregiving Hours (Race = Hispanic)−0.30040.5469−1.3724 to 0.7716
Caregiving Hours (Race = Other)−0.37800.7127−1.7749 to 1.0190
Depressive symptoms2.0868630.08176721.932602 to 2.253438
Gender (Male)−0.36180.9906−2.3034 to 1.5798
White (Caregiving Hours = 0)Ref
Black (Caregiving Hours = 0)−0.23811.5849−3.3444 to 2.8682
Hispanic (Caregiving Hours = 0)0.95642.0072−2.9778 to 4.8905
Other (Caregiving Hours = 0)−1.37942.4826−6.2452 to 3.4863
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Johnson, F.U.; Plegue, M.; Boddakayala, N.; Robinson-Lane, S.G. Race, Social Context, and Caregiving Intensity: Impact on Depressive Symptoms Among Spousal Caregivers. Int. J. Environ. Res. Public Health 2025, 22, 1379. https://doi.org/10.3390/ijerph22091379

AMA Style

Johnson FU, Plegue M, Boddakayala N, Robinson-Lane SG. Race, Social Context, and Caregiving Intensity: Impact on Depressive Symptoms Among Spousal Caregivers. International Journal of Environmental Research and Public Health. 2025; 22(9):1379. https://doi.org/10.3390/ijerph22091379

Chicago/Turabian Style

Johnson, Florence U., Melissa Plegue, Namratha Boddakayala, and Sheria G. Robinson-Lane. 2025. "Race, Social Context, and Caregiving Intensity: Impact on Depressive Symptoms Among Spousal Caregivers" International Journal of Environmental Research and Public Health 22, no. 9: 1379. https://doi.org/10.3390/ijerph22091379

APA Style

Johnson, F. U., Plegue, M., Boddakayala, N., & Robinson-Lane, S. G. (2025). Race, Social Context, and Caregiving Intensity: Impact on Depressive Symptoms Among Spousal Caregivers. International Journal of Environmental Research and Public Health, 22(9), 1379. https://doi.org/10.3390/ijerph22091379

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop