Long-Term Impact of Grandchild Caregiving Trajectories on Depression in Middle-Aged and Older Chinese People: A Longitudinal Study

Abstract

There has been little research investigating the effects of caregiving for grandchildren on grandparents’ mental health from a dynamic perspective. The aim of this study was to evaluate the effects on caregivers’ depression of changes in grandparenting intensity. The study population included 8,157 respondents obtained from the China Health and Retirement Longitudinal Study (CHARLS). Latent growth mixture modeling was used to group respondents into five classes of trajectory of caregiving intensity as follows: “sharply decreasing”, “never or rarely”, “slowly decreasing”, “increasing”, and “continuously high”. A generalized additive mixed model (GAMM) and a marginal structural model (MSM) both associated the “continuously high” and “sharply decreasing” intensities with depression. “Continuously high” intensity significantly increased the risk of depression in the male group only. Further research should be conducted to analyze the deep-seated mechanisms of association between grandparenting and mental health, in different cultural contexts and among subgroups with different characteristics.

Keywords

grandparenting CES-D score depression dynamic impact

Introduction

Intergenerational relationships within families have received considerable attention from the public and from academia around the world, and grandchild caregiving is a topical issue (Di Gessa et al., 2016). Under the speedily increasing pressure of social development, grandparents’ taking responsibility for grandchild caregiving can enhance their parents’ participation in employment, and assist with the integration of family and work life (Ates, 2017). In aging populations, it is inevitable for many families. Whether and how grandchild caregiving affects grandparents’ health, both physiologically and psychologically, are important questions for investigation (Butler & Zakari, 2005).

There is ample literature on the impact of grandchild caregiving on grandparents’ health. However, conclusions vary greatly, especially regarding the impact on mental health. Some studies have reported that grandparents who provide care to grandchildren are more likely to have greater life satisfaction and fewer symptoms of depression (Ku et al., 2013; Zeng et al., 2020). Conversely, other studies have reported that grandchild caregiving exerts mental pressure on caregivers (Band-Winterstein et al., 2019; Cheng & Wu, 2015; Hayslip & Kaminski, 2005). Variability in such study findings can arise from different characteristics of individuals, families, cultures, and research limitations (sample size, cross-sectional design rather than longitudinal or cohort analysis, etc.).

Caring for grandchildren gives grandparents a new role, which differs from that of caring for their own children. This new role may bring emotional benefits to grandparents, increase their self-esteem, improve family functioning, and generate a sense of achievement (Hughes et al., 2007; Pruchno & McKenney, 2002). However, when caregiving intensity reaches a certain level, caregivers may feel pressured, strained in their roles, and suffer loss of health (Edwards et al., 2002; Longo et al., 2020; Rozario et al., 2004). Caregiving grandparents face a risk of depression, due to pressure on their limited resources and energy. They may experience a loss of freedom, reduced self-identity, decreased social activity, and unstable emotions while caregiving (Hayslip & Kaminski, 2005; Xu et al., 2017). Furthermore, previous studies, of caregivers of different ages and genders with different levels of family resources and social status, found different rates of depression and emotional benefits (Bai et al., 2020; Peterson, 2017; Whitley et al., 2016; Zeng et al., 2020).

The impacts of grandchild caregiving on grandparents’ mental health are associated with their cultural context (B. Hayslip et al., 2019). For example, in the United States, grandparental involvement in caregiving for grandchildren is usually the result of family dysfunction (Baker & Silverstein, 2008; Ross et al., 2015). In China, caring for grandchildren is common as a long-standing cultural tradition (Xu, 2019); it is often regarded as an important responsibility of grandparents. Previous researches have indicated that the cultural context is significant for parental efficacy, authoritative parenting style, grandchild negative interpersonal dynamics, role satisfaction, well-being, and attachment to the grandchild (B. J. Hayslip et al., 2019; Wang et al., 2019). More than half of older Chinese people are primary grandchild caregivers (Ko & Hank, 2014); the number will continue to increase under the irreversible speed of population aging in China (Zeng & Hesketh, 2016). Furthermore, in China, population mobility is a prominent issue. Being left behind is a serious problem in rural areas. More than 60 million children in rural China have been left behind by parents moving to cities for work (Ge et al., 2019). Consequently, rural grandparents may face more pressure to provide caregiving. China's two-child policy has gradually been liberalized since 2016. While this will benefit the sex ratio, it will also bring greater pressure to caregivers of children (Qian et al., 2020; Zeng & Hesketh, 2016). Sociodemographic and policy changes increase the importance of studying the effects of grandchild caregiving on China's middle-aged and older population.

The direction and significance of the impact are of caregiving mainly influenced by caregivers’ sociodemographic characteristics (such as gender and marital status), health status, family structure (e.g., “sandwich” families), and caregiving intensity, according to recent studies of associations between caring for grandchildren and grandparents’ mental health (Giang et al., 2019; Xu, 2019; Zeng et al., 2020). Although there is well-established research on grandchild caregiving in China, this has several limitations. First, most studies have used cross-sectional data or have had short observation windows; a cross-sectional design is not appropriate for issues such as the stability of research development, the role of dynamic influences, and the evaluation of causal inferences. Second, grandparents’ physical functions might not have been well controlled in some studies. Third, many studies concentrated on evaluating average effects (i.e., the difference in average results assigned to intervention and control individuals); changes in intensity during caregiving periods were not sufficiently considered. During grandparents’ caregiving, their mental status may change along with intensity, the growth of grandchildren, improvement or decline of family status, and the ability of the grandparents themselves to provide caregiving. Analysis from a dynamic perspective is more appropriate and could provide specific risk evaluations for different subgroups (e.g., grandmothers/grandfathers, rural/urban caregivers) that would be valuable for policymakers, social organizations, and families in providing more targeted support and monitoring.

The Present Study

For investigating the effects on grandparents’ mental health of caregiving for grandchildren, there has been little research from a dynamic perspective, based on longitudinal data, with an observation window of longer than five years. The aim of this study was to evaluate the effect of dynamic changes in grandchild caregiving intensity on caregivers’ depression symptoms using national panel data from China. Based on the determined trajectory of caregiving intensity, respondents were divided into groups. Their physical function was controlled during analysis, as a time-varying confounder. We also investigated how the association between grandparenting intensity and depression varied between different subgroups, especially in terms of gender and rural/urban residence.

Two hypotheses were tested. First, the trajectory of grandparenting intensity is associated with depression symptoms among middle-aged and older caregivers; long-term high-intensity caregiving is a risk factor for depression. Second, the impact on depression of a given trajectory operates differently according to gender and place of residence (urban/rural).

Methods

Sample and Data

The data used in this study were derived from four rounds of the China Health and Retirement Longitudinal Study (CHARLS), conducted in 2011 (baseline), 2013, 2015, and 2018. CHARLS is a biennial/triennial survey conducted by the National School of Development of Peking University. It collects high-quality microdata from middle-aged and older individuals (≥45 years) in China. The individual questionnaire includes basic demographics and information on family transfers; health status and functioning; healthcare and insurance; employment, retirement and pension; income and consumption; household assets, etc. (Zhao et al., 2014). Details on the sampling method and questionnaire are available from the official website.

Data from 2011, 2013, and 2015 were extracted from the Harmonized CHARLS database, developed by the Center for Economic and Social Research of the University of Southern California (www.g2aging.org) by integrating three waves of high-quality CHARLS data with few missing values. Data from 2018, as well as information on caregiving intensity (weeks per year and hours per week) in each wave, were integrated based on the Harmonized CHARLS codebook.

Sample Inclusion

The baseline data (2011) consisted of 17,708 respondents (response rate 80.5%). Respondents from all follow-up waves (N = 11,988) were involved. After excluding cases with missing values for the dependent and independent variables (Center for Epidemiological Studies Depression Score [CES-D] and caregiving intensity, respectively) and those without children, the data from 8,157 individuals were retained for analysis (Figure 1). The baseline characteristics (2011) of the respondents are presented in Table 1. The mean age was 57 years. There were more female (55.02%) than male (44.98%) respondents. Uneducated respondents constituted 26.39% of the sample; most respondents (63.25%) had an educational level of junior high school or below. There were 1,084 (13.29%) respondents who were divorced or widowed. There were 61.53% of respondents living together with their children; only 34.42% received financial aid from their children. There were more rural (66.64%) than urban respondents (33.36%). The mean number of living children of the respondents was three.

Figure 1.

Flowchart of participant selection.

Table 1.

Descriptive Analysis of the Respondents in the Baseline Wave (2011, N = 8,157).

	Mean ± SD/N (%)
Age	57.41 ± 8.19
Gender
Male	3669 (44.98%)
Female	4488 (55.02%)
Education level
No formal education (illiterate)	2153 (26.39%)
Junior High school and below	5159 (63.25%)
High school, technical secondary school, junior college	821 (10.06%)
Bachelor and above	24 (0.29%)
Marital status
Married	7073 (86.71%)
Divorced or widowed	1084 (13.29%)
Hukou type
Agricultual	6800 (83.36%)
Nonagricultural	1301 (15.95%)
Unified residence	55 (0.67%)
Do not have hukou	1 (0.01%)
ADL-5
0	7000 (86.69%)
1	625 (7.74%)
2	212 (2.63%)
3	121 (1.50%)
4	64 (0.79%)
5	53 (0.66%)
Public health insurance coverage
Covered	7662 (94.15%)
Not covered	476 (5.85%)
Income group
Low	2628 (37.30%)
Middle	1932 (27.42%)
Relatively high	1201 (17.05%)
High	1284 (18.23%)
Any children coresiding
Yes	5018 (61.53%)
No	3138 (38.47%)
Economic aid from children
Yes	2804 (34.42%)
No	5343 (65.58%)
Residence
Urban	5436 (66.64%)
Rural	2721 (33.36%)
Number of living children	2.70 ± 1.25

Note: ADL-5 = 5-item summary of any difficulty with activities of daily living.

Imputation of Missing Values

To avoid reduced statistical power and bias caused by the exclusion of missing values, multiple imputation (MI) was conducted to impute missing covariates based on five replications and a chained equation approach (van Buuren & Groothuis-Oudshoorn, 2011). The analysis was conducted separately for each complete data set, and the results were pooled across the imputed data sets. In this way, MI was able to explicitly incorporate the uncertainty about the true value of imputed variables (Austin et al., 2021). The number of missing values and the MI evaluation are shown in Supplementary Table S1.

Measures

Dependent Variable

The outcome measure was the 10-item CES-D score, with responses scale ranging from 0 (rarely or never) to 3 (most or all the time). The summed scores ranged from 0 to 30; a high score indicates negative feelings in the respondent over the past week (Radloff, 1977; Boey, 1999; Diego et al., 2001).

Independent Variables

The independent variable was the trajectory of caregiving intensity, which was captured by latent growth mixture models (LGMMs). The respondents were asked whether they had taken care of their grandchildren in the past year, and to report the total amount of time (hours) that they provided care. The trajectory groups were classified (using LGMMs), based on changes in caregiving time (see Statistical analysis).

Covariates

Covariates were selected on the basis of their association with an independent variable and their impact on the change of the association between the independent variable and dependent variable. Age and gender were included as fixed covariates to be controlled. Other covariates were included as potential confounders in the final models if they changed the estimates of the effect of grandchild care provision intensity on the CES-D score by more than 10% or were significantly associated with the CES-D score (Jaddoe et al., 2014). Confounders were selected based on a generalized estimating equation, as the data were repeated measurements. The final covariates included age, gender, marital status, hukou status, rural or urban residence, education, income, co-residence with any children, number of living children, financial aid from children, and a five-item summary of difficulty with activities of daily living (ADL-5). Associations between each confounder and the CES-D score are shown in Supplementary Tables S2 and S3.

Statistical Analysis

LGMMs were utilized to identify grandparenting groups that had different discrete growth trajectories during the four survey rounds, and to test predictors of membership in these classes (Muthen & Muthen, 2000). LGMMs effectively estimate individual change over time and to investigate the existence of latent trajectories (where individuals have trajectories that are unobserved or latent). It was difficult to directly categorize respondents according to caregiving time because it may change dramatically over time. Therefore, it was more appropriate to use LGMMs to categorize the respondents. Because LGMMs cannot accept a variable that has a variance greater than the maximum (1,000,000) and minimum caring time of zero made the variance infinite, caregiving time in the four waves was first pooled and categorized. We considered that if there were too few groups, the differences would be indistinguishable. Therefore, we divided caregiving time into 10 groups, using deciles (Supplementary Table S4). Unlike traditional fixed-effects approaches, in which relations among variables are fixed across individuals, latent trajectory approaches model the variation across individuals in growth parameters, such as intercept and slope (Curran & Hussong, 2003; Curran & Willoughby, 2003). Such continuous latent growth parameters incorporate information from multiple indicators (repeated measures of an outcome). We compared one- to six-class unconditional LGMMs models and assessed their relative fit with conventional indices. To determine the appropriate class solution, we examined the Bayesian information criterion (BIC), the Akaike information criterion (AIC), entropy values, and the Lo–Mendell–Rubin likelihood ratio test (LRT).

To explore the impact of caregiving trajectories on depression, we employed two types of model. First, generalized additive mixed models (GAMMs) were utilized with all covariates controlled. GAMMs have the advantages of relaxed independence assumptions and the ability to accommodate repeated measures (Lin & Zhang, 1999). In addition, GAMMs can eliminate pseudo replications, improve model fit, increase confidence interval reliability and provide better local estimates of impacts and intercepts than other models (McKeown & Sneddon, 2014).

Second, marginal structural models (MSMs) were utilized via inverse probability of treatment weighting (IPTW). Weight at each time point was determined not only by the confounder at the previous level but also at the current level. MSM is highly effective in controlling time-dependent confounding factors in observational studies (VanderWeele, 2009). The respondents’ physical status not only influenced the outcome (CES-D score) but also affected subsequent grandparenting intensity (Robins et al., 2000). Thus, we used ADL-5 to reflect the respondents’ physical function. We treated it as an intermediate variable (between caregiving trajectory and CES-D score); we controlled for it as a time-varying confounder. All other covariates were also controlled.

Four groups of sensitivity analyses were conducted. First, we repeated the procedure for GAMMs and MSMs without imputing missing values. Second, we excluded 10% of the respondents (the oldest 5% and the youngest 5%). Third, because of the high proportion of missing values for ADL-5, we replaced ADL-5 with self-rated health (missing proportion, <1%), then repeated the MSMs. Fourth, we repeated the MSMs using non-imputed data, adjusting for ADL-5 and income as time-varying confounders with >5% missing data. In this step, the missing values were imputed by IPTW before predicting outcomes.

Subgroup analysis was performed, based on the GAMMs and MSMs to identify specific respondents whose depression status was highly or significantly impacted by their grandparenting trajectory. The subgroups included gender (female/male) and place of residence (urban/rural).

A two-sided test (α < 0.05) was employed to determine statistical significance. Mplus version 7.4 (http://www.statmodel.com) and R version 3.6.3 (R Foundation for Statistical Computing, Vienna, Austria) were used for statistical analysis.

Results

Trajectory of Grandparenting Intensity

The results of the LGMMs are shown in Table 2 . We compared two- to six-class unconditional models for grandparenting intensity and examined the BIC, AIC, entropy values, and the LRT. We sought a model with lower values for the criterion indices, higher entropy values, and significant p-values for the LRT test. The results suggested that the five-class solution was the best.

Table 2.

Fit Indices for two- to six-Class Growth Mixture Models for Caregiving Duration.

	AIC	BIC	Entropy	Lo–Mendell–Rubin test p-value
Two classes	154855.78	154918.84	0.96	<.001
Three classes	151103.77	151187.85	0.97	.001
Four classes	148576.34	148681.44	0.97	<.001
Five classes	146746.33	146872.45	0.95	<.001
Six classes	144816.12	144963.26	0.96	1.000

Note: AIC = Akaike information criterion; BIC = Bayesian information criterion.

We estimated the means of each class in each survey wave, then defined the five classes as: “sharply decreasing” (from high- to low intensity), “never or rarely” (care for grandchildren), “slowly decreasing” (from moderate- to low intensity), “increasing” (from low- to high intensity), and “continuously high” (high intensity) (Figure 2). The mean and standard error of the CES-D scores for each class are shown in Table 3. The classes continuously high and sharply decreasing had higher CES-D scores.

Figure 2.

Classes of grandchildren caregiving trajectory based on latent class growth model. The definition of the classes: Class 1, sharply decreasing; Class 2, never or rarely; Class 3, slowly decreasing; Class 4, increasing; and Class 5, continuously high.

Table 3.

Mean CES-D Scores of Different Grandparenting Classes in Four Survey Rounds (N = 8,157 in Each Wave).

	2011	2013	2015	2018
Never or rarely caring for grandchildren	8.32 ± 6.29	7.92 ± 5.78	8.09 ± 6.40	8.81 ± 6.60
Increasing	7.94 ± 6.15	7.87 ± 5.73	8.07 ± 6.54	8.93 ± 6.50
Continuously high	8.98 ± 6.12	8.71 ± 6.32	8.82 ± 6.56	9.29 ± 6.82
Slowly decreasing	8.50 ± 6.29	7.81 ± 5.81	8.23 ± 6.43	8.58 ± 6.40
Sharply decreasing	9.25 ± 6.61	8.59 ± 5.62	8.93 ± 6.12	9.41 ± 6.52

Note: CES-D = Center for Epidemiological Studies Depression Score.

Association Between Grandchildren Caregiving and Depression

Table 4 shows the associations between depression and grandparenting trajectories, determined through GAMM and MSM analysis. After controlling for covariates, the GAMM results showed that the “continuously high” (β = 0.55, p = .004) and “sharply decreasing” (β = 0.44, p = .047) intensities were positively associated with depression. The “increasing” and “slowly decreasing” intensities were not significantly associated with depression. The MSM results were consistent with the GAMM results, with “continuously high” (β = 0.74, p < .001) and “sharply decreasing” (β = 0.83, p < .001) intensities having significant impacts, after controlling for ADL-5 (as a time-varying confounder) and other covariates.

Table 4.

Associations Between Caregiving Trajectory and CES-D Score Estimated by GAMMs and MSMs (N = 8,157).

	GAMMs^a		MSM^b
	β (95%CI)	P-value	β (95%CI)	P-value
Increasing	0.09 (−0.16, 0.34)	0.477	−0.05 (−0.23, 0.12)	.544
Continuously high	0.55 (0.18, 0.93)	0.004	0.74 (0.46, 1.01)	<.001
Slowly decreasing	−0.10 (−0.44, 0.24)	0.559	0.10 (−0.14, 0.35)	.398
Sharply decreasing	0.44 (0.01, 0.87)	0.047	0.83 (0.52, 1.14)	<.001

Note: ADL-5 = 5-item Activities of Daily Living score; CI = confidence interval; GAMM = generalized additive mixed models; MSM, marginal structural model; IPTW, inverse probability of treatment weighting.

The adjusted covariates included age, gender, marital status, hukou status, rural or urban residence, education, income, co-residence with any children, financial aid from children, number of living children, and a ADL-5.

Respondents’ physical function (ADL-5) was adjusted as a time-varying confounder and used to perform IPTW to predict exposure and outcome status in each time point. The other adjusted covariates included age, gender, marital status, hukou status, rural or urban residence, education, income, co-residence with any children, number of living children and financial aid from children.

We conducted four sensitivity analysis that are as follows: (1) repeating the procedure for GAMMs and MSMs without imputing missing values; (2) repeating the procedure for GAMMs and MSMs, excluding the oldest 5% and youngest 5% of respondents; (3) replacing ADL-5 with self-rated health status in the MSMs; and (4) using IPTW to impute missing ADL-5 and income values in the MSMs. The results (Table 5) were almost consistent with those presented in Table 4. In other words, the direction and magnitude of the effects remained similar, whereas only several associations became not statistically significant. Sensitivity analysis increased the reliability and robustness of our conclusions.

Table 5.

Sensitivity and Subgroup Analysis.

	Increasing		Continuously high		Slowly decreasing		Sharply decreasing
	GAMMs	MSM	GAMMs	MSM	GAMMs	MSM	GAMMs	MSM
Sensitivity analysis
1.Without imputed missing values^a	0.16 (−0.11, 0.43)	0.02 (−0.19 ,0.22)	0.60** (0.20, 1.00)	0.78*** (0.46, 1.10)	−0.04 (-0.40, 0.31)	0.09 (−0.20,0.37)	0.50* (0.04, 0.95)	0.79*** (0.43, 1.15)
2.With the oldest and youngest participants removed^a	0.05 (−0.21,0.31)	−0.08 (−0.26,0.10)	0.46* (0.08, 0.84)	0.71*** (0.43, 0.99)	−0.21 (−0.57, 0.14)	0.09 (−0.16, 0.34)	0.41 (−0.03, 0.86)	0.90*** (0.58, 1.21)
3. Using self-rated health to replace ADL-5^b	–	−0.15 (−0.32,0.03)	–	0.69*** (0.42, 0.97)	–	−0.03 (−0.27 ,0.21)	–	0.49** (0.19, 0.79)
4. Adjusting for ADL-5 and income as time-varying confounders using non-imputed data^c	–	0.06 (−0.15, 0.27)	–	0.80*** (0.47, 1.12)	–	0.09 (−0.19, 0.37)	–	0.80*** (0.43, 1.16)
Subgroup analysis
1. Gender
Male^d	0.20 (−0.15, 0.54)	−0.13 (−0.38, 0.11)	0.91 ** (0.33, 1.50)	0.70** (0.26, 1.14)	−0.20 (−0.66, 0.27)	−0.14 (−0.46, 0.19)	0.36 (−0.27, 0.98)	0.54 (0.09, 0.99)
Female^d	-0.02 (−0.37, 0.34)	−0.28* (−0.52, −0.04)	0.32 (−0.17, 0.82)	0.16 (−0.18, 0.51)	−0.06 (−0.53,0.42)	−0.01 (−0.35, 0.33)	0.46 (−0.13, 1.05)	0.61** (0.20, 1.02)
2. Residence
Rural^e	0.02 (−0.29, 0.34)	−0.06 (−0.27, 0.16)	0.60* (0.12, 1.08)	0.85*** (0.50, 1.20)	0.01 (−0.41, 0.43)	0.20 (−0.10, 0.50)	0.45 (−0.08, 0.98)	0.74*** (0.36, 1.12)
Urban^e	0.18 (−0.24, 0.60)	0.07 (−0.21, 0.35)	0.50 (−0.10, 1.09)	0.76*** (0.34, 1.19)	−0.26 (−0.82, 0.29)	−0.01 (−0.40, 0.38)	0.40 (−0.34, 1.13)	1.01*** (0.49, 1.52)

Note: ADL-5 = 5-item Activities of Daily Living score; CI = confidence interval; GAMM = generalized additive mixed model; MSM, marginal structural model; IPTW, inverse probability of treatment weighting.

In GAMMs, the adjusted covariates included age, gender, marital status, hukou status, rural or urban residence, education, income, co-residence with any children, financial aid from children, number of living children, and a five-item summary of difficulty with activities of daily living (ADL-5). In the MSMs, ADL-5 was adjusted as a time-varying confounder, for which IPTW was used to impute missing values, and predict exposure and outcome status at each time point. Other covariates were adjusted in the MSMs.

In MSMs, self-rated health status was adjusted as a time-varying confounder, for which IPTW was used to impute missing values, and predict exposure and outcome status at each time point. Other covariates were also adjusted.

In MSMs, ADL-5 and income were adjusted as time-varying confounders, for which IPTW was used to impute missing values, and predict exposure and outcome status at each time point.

All covariates were adjusted, except for gender.

All covariates were adjusted, except for rural or urban residence.

*p < .05; **p < .01; ***p < .001.

Subgroup Analysis

Using the GAMMs and MSMs, we performed two subgroup analyses, for gender and place of residence (Table 5). When grandparenting intensity remained continuously high, grandfathers faced a high risk of depression (GAMM: β = 0.91, p = .002; MSM: β = 0.70, p = .002) when compared with the “never or rarely” caregiving group; this impact was not significant for grandmothers. MSM analysis found the “increasing” trajectory to be negatively associated and the “sharply decreasing” trajectory positively associated with depression in grandmothers, but not significantly so in grandfathers. There is an obvious sex variance in the association between caregiving and mental status. MSM analysis found the impacts of different caring types to be consistent between rural and urban respondents; there was no variance indicated between these two groups.

Discussion

This study is the first to explore the associations between trajectories of grandparenting intensity and depression in grandparents in China. Our dynamic perspective concentrated on the long-term effects on depression of caregiving for grandchildren, and took consideration of changes in caregiving intensity. Our analyses used GAMMs, which are appropriate for analyzing repeated-measurement data and handling auto-correlation, and MSMs, which are effective at controlling for time-variant exposures and covariates. As expected, the results indicate that long-term, continuously high-intensity grandparenting increases the risk of depression in grandparents. When grandparenting intensity decreases continuously from high to low, the risk of depression also increases. We found that grandfathers and grandmothers have different risks of depression under varying caregiving intensities.

Caring for grandchildren for a moderate period of time may increase a grandparent's sense of responsibility, self-identification, and importance within their family, which can decrease the risk of depression (Tsai et al., 2013; Xu, 2019; Zhou et al., 2017). Grandparents who provide caregiving at increasing intensity levels are more likely to have fewer depression symptoms than those who do not provide care at all (Zeng Y, 2020). Our results show that the “increasing” and “slowly decreasing” trajectories are not significantly associated with depression. This also supports the contention that caregiving intensity within a moderate range is not harmful to grandparents’ mental health to a certain extent. However, when caregiving intensity remains continuously high, the risk of depression increases. Many of grandparents with high intensity of caregiving are primary caregivers. In other words, grandparents who are secondary caregivers may benefit in terms of better mental health; but if this activity becomes excessively burdensome, the positive effect may transform into a negative one (Arpino & Gomez-Leon, 2020). This is consistent with role strain theory, in which continuously intensive caregiving can be physically taxing of aging adults and deplete their resources. Increased depression risk may result from grief and disappointment associated with the need to perform multiple roles simultaneously (e.g., worker, parent, and volunteer), less social support, and lessened resourcefulness, wherein social support and resourcefulness moderated the relationship between caregiving stress, caregiver strain, and depressive symptoms (Chen & Liu, 2012; Jendrek, 1994; Minkler, 1999; Musil et al., 2009; Xu, 2019). Furthermore, as that found in a longitudinal study, with the assumption of caregiving, caregiver stress, depression, family strain, and family problems all increased (Musil et al., 2011).

The “sharply decreasing” grandparenting intensity is also associated with increased risk of depression. Previous studies have shown that caring for grandchildren may increase grandparents’ sense of participation and responsibility within their families, and is negatively associated with feelings of loneliness, sadness, and receiving unkindness from others. The emotional attachments between grandparents and grandchildren have been described as unique in that the relationship is exempt from the psycho-emotional intensity and responsibility that exist in parent–child relationships, and many grandparents are in need of their grandchildren's love and company (Tsai et al., 2013). In addition, on the basis of the social exchange viewpoint, grandparents who provide more grandchild care gain emotional rewards from helping their adult children and interacting with their grandchildren (Lin et al., 2011). However, as their grandchildren age and commence study and employment, their need for grandparental support reduces, with their emotional rewards continuously decreasing. The consequent reduction in contact between grandparents and grandchildren can result in the grandparent feeling lonely, grief, and alienated, which increases the risk of depression (Sims & Rofail, 2013).

Our study showed that grandfathers who continuously provided high-intensity care had a greater risk of depression; grandmothers were more sensitive to both the positive effect of increasing intensity and the negative effect of sharply decreasing intensity, which illustrates the centrality of gender variation in shaping family roles. It may be that male caregivers have greater needs for social activity and feel more pressure to working or earn money to support their families (Chen et al., 2018). In addition, in traditional Chinese families, grandfathers tend to play a role as nonnormative caregivers, such as fun-seeker, playmate, and companion rather than fulfilling more intensive responsibilities, such as feeding, bathing, and dressing (Lo & Liu, 2009). Given that cultural expectations are for grandfathers not to engage in nurturing roles—they often do not do so when grandmothers are present—a lack of support and guidance might make caregiving more difficult and stressful for them. Compared with grandmothers, grandfathers may experience additional role strain or even social stigma if they are heavily involved in grandparenting, which deviates from the traditional norm. Therefore, the same amount of grandparenting may be more psychologically stressful to grandfathers than grandmothers (Xu, 2019). Furthermore, grandfathers might fulfill caregiving roles while dealing with role deprivations related to retirement or widowhood (a double jeopardy) (Chen & Liu, 2012). Grandmothers seem to be more adaptable than grandfathers as primary caregivers of grandchildren; they can obtain more benefit from caregiving, but may feel lonely out of the caring role. Recognizing their importance could motivate them to interpret their depressive symptoms more seriously and to engage in self-management so as to keep themselves mentally healthy, enabling them to continue to provide caregiving for grandchildren (Musil et al., 2017).

There was no significant variation in the impact of caregiving on depression between rural and urban areas. Several studies have indicated that caregiving frequency, by itself, is not harmful or beneficial to the emotional and cognitive health of grandparents in rural China. However, it appears to be harmful in the context of custodial care with less financial support from adult children (Silverstein & Zhang, 2020; Silverstein & Zuo, 2021). Thus, family support is essential for protecting the mental health of elderly caregivers in both rural and urban China. Between 2011 and 2018, we found a steep decline in the proportion of grandparents residing with their children. This may have been due to a transformation of the family model. In China, living with their children is a traditional family pattern for older adults. However, due to the one-child policy, unbalanced economic development, and accelerated urbanization, the number of empty nester families has increased rapidly in recent decades (Zhou et al., 2015). Older caregivers who are left behind require more material and emotional support from their families, as well as from society.

Limitations

Our study has several limitations. First, we used secondary data from which we were unable to capture certain information, such as grandparents’ caregiving experiences with their first grandchildren, how long they were involved in grandchild caregiving before the baseline survey and why they stopped caregiving. In particular, the lack of data on grandparents’ personality traits and preferences did not permit further in-depth analysis of the effects of these aspects on depression. Second, we excluded some respondents from our sample because of a lack of follow-up information (or because key information was missing), which could, to a certain extent, affect the accuracy of our conclusions, especially regarding subgroups. Furthermore, the proportions of missing values for some variables (especially income and ADL-5) were relatively high. We could not consider the missing values as random, due to the substantial amount of attrition over time. However, we conducted several sensitivity analyses—MSM with non-imputed data, with key variables (income and ADL-5) imputed on the basis of IPTW—which that our results were robust, increasing the credibility of our conclusions. Third, there was a possibility of recall bias, as all data were self-reported. For example, physical function, which is a key factor associated with the respondents’ caregiving capability and depression, could be captured only from self-reported answers; it may not adequately reflect the respondent's situation. Fourth, although we controlled the variable “co-resident with adult children”, we could not qualify the relationship between the children and grandchildren. Furthermore, we could not identify the number of grandchildren or whether and why they cease receiving grandparental caregiving.

Implications

Strong intergenerational bonds are a defining tradition in China. A way of life that involves caring for grandchildren is often perceived normatively by grandparents, not as an isolated event but as an essential part of a dynamic system of family exchange (Chen & Liu, 2012).

The policy implication is the importance, for parents requiring high-intensity caregiving for children, of seeking external assistance and support from their families, communities, and society (Hayslip et al., 2015). Caregivers themselves should also improve the management of their mental health by participating in support groups, enrolling in formally designed programs to improve psychosocial functioning and the quality of relationships with the grandchildren cared for, and finding ways to access both formal and informal support to ease the stress of caregiving and improve their psychological adjustment (Hayslip et al., 2014).

Conclusion

Our study sheds light on the effects on depression of dynamic changes in grandparenting among the middle-aged and older population. It is also the first study to introduce GAMMs and MSMs for analysis of the impacts of caregiving for grandchildren. The robust conclusions drawn from this study can supplement limited existing research on intergenerational caregiving, which is a complex social phenomenon. This study proved that long-term high-intensity grandparenting has a negative impact on grandparents’ mental health, especially for grandfathers. The risk of depression caused by sharply decreasing grandparenting intensity should also receive attention; loneliness should be treated as an important health issue. Future studies in different cultural contexts should further analyze the long-term impact on grandparents’ physical and mental health of providing caregiving for grandchildren. Mechanisms underlying the associations between grandparenting and mental health should be explored. Further analysis is required to determine, for grandparents with different characteristics, the sensitivity of the association between their mental health and their caregiving.

Supplemental Material

sj-docx-1-ahd-10.1177_00914150221084644 - Supplemental material for Long-Term Impact of Grandchild Caregiving Trajectories on Depression in Middle-Aged and Older Chinese People: A Longitudinal Study

Supplemental material, sj-docx-1-ahd-10.1177_00914150221084644 for Long-Term Impact of Grandchild Caregiving Trajectories on Depression in Middle-Aged and Older Chinese People: A Longitudinal Study by Haomiao Li, Li Gan, Dong (Roman) Xu and Jiangyun Chen in The International Journal of Aging and Human Development

Footnotes

Acknowledgments

This analysis uses data or information (2011, 2013, and 2015) from the Harmonized CHARLS dataset and Codebook, Version C as of April 2018 developed by the Gateway to Global Aging Data. The development of the Harmonized CHARLS was funded by the National Institute on Ageing (R01 AG030153, RC2 AG036619, R03 AG043052). For more information, please refer to . We also thank the CHARLS research team and field team for collecting the data and making the data publicly accessible. Peking University's Ethical Review Committee approved the study protocol.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interests with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship,and/or publication of this article: This work was supported by the Key Laboratory of Philosophy and Social Sciences of Colleges and Universities in Guangdong Province: Public Health Policy Research & Evaluation (2015WSYS0010), Guangdong Basic and Applied Basic Research Foundation (2021A1515110743), Guangzhou Public Health Service System Construction Research Foundation (2021–2023), National Natural Science Foundation of China (71874104), and the Fundamental Research Funds for the Central Universities (2042021kf0124).

ORCID iDs

Haomiao Li

Jiangyun Chen

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Haomiao Li is a lecturer in the School of Political Science and Public Administration at Southern Medical University. He holds a PhD in social medicine and health management from the School of Medicine and Health Management, Huazhong University of Science and Technology. His main research interests include: healthy aging, social science and health policy.

Li Gan is a student in the School of Health Management at Southern Medical University, majoring in Public Service Management (Hospital Management).

Dong(Roman) Xu is a professor in the School of Health Management at Southern Medical University. He holds a PhD in Global Health from the University of Washington School of Public Health (Seattle, USA). He is an associate editor of Implementation Science Communications, Tropical Medicine & International Health. His main research interests include: global health, methods and application of implementation science, chronic disease management, use of mobile technology in health services, and measurement and improvement of quality of primary health services.

Jiangyun Chen is a lecturer in the School of Health Management at Southern Medical University. She holds a PhD in social medicine and health management from the School of Medicine and Health Management, Huazhong University of Science and Technology. She is the reviewer for International journal for equity in health, Journal of epidemiology & community health. Her main research interests include: healthy aging, primary health services, family health, tobacco control.

References

Arpino

Gomez-Leon

(2020). Consequences on depression of combining grandparental childcare with other caregiving roles [journal article]. Aging & Mental Health, 24(8), 1263–1270. http://doi.org/10.1080/13607863.2019.1584788

Ates

(2017). Does grandchild care influence grandparents’ self-rated health? Evidence from a fixed effects approach [journal article]. Social Science & Medicine, 190(2017), 67–74. http://doi.org/10.1016/j.socscimed.2017.08.021

Austin

P. C.

White

I. R.

Lee

D. S.

van Buuren

(2021). Missing data in clinical research: A tutorial on multiple imputation [journal article; review]. Canadian Journal of Cardiology, 37(9), 1322–1331. http://doi.org/10.1016/j.cjca.2020.11.010

Bai

Bian

Zhang

Cao

(2020). The impact of social support on the health of the rural elderly in China [journal article; research support, Non-U.S. Gov't]. International Journal of Environmental Research and Public Health, 17(6). http://doi.org/10.3390/ijerph17062004

Baker

L. A.

Silverstein

(2008). Preventive health behaviors among grandmothers raising grandchildren [comparative study; journal article; research support, N.I.H., extramural]. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 63(5), S304–S311. http://doi.org/10.1093/geronb/63.5.s304

Band-Winterstein

Edelstein

O. E.

Bachner

Y. G.

(2019). Correlates of depression in caregivers of ultra-orthodox frail older adults [journal article]. The American Journal of Orthopsychiatry, 89(6), 654–664. http://doi.org/10.1037/ort0000337

Boey

K. W.

(1999). Cross-validation of a short form of the CES-D in Chinese elderly [journal article]. International Journal of Geriatric Psychiatry, 14(8), 608–617. http://doi.org/10.1002/(sici)1099-1166(199908)14:8<608::aid-gps991>3.0.co;2-z

Butler

F. R.

Zakari

(2005). Grandparents parenting grandchildren: Assessing health status, parental stress, and social supports [journal article; review]. Journal of Gerontological Nursing, 31(3), 43–54. http://doi.org/10.3928/0098-9134-20050301-09

Chen

Liu

(2012). The health implications of grandparents caring for grandchildren in China [comparative study; journal article; research support, N.I.H., extramural]. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 67(1), 99–112. http://doi.org/10.1093/geronb/gbr132

10.

Chen

Y. C.

Wang

Cooper

McBride

Chen

Wang

Lai

C. Y.

Montemuro

L. C.

Morrow-Howell

(2018). A research note on challenges of cross-national aging research: An example of productive activities across three countries [journal article]. Research on Aging, 40(1), 54–71. http://doi.org/10.1177/0164027516678997

11.

Cheng

(2015). Caring for grandchildren and depressive symptoms among grandparents in China urban and rural areas. The Gerontologist, 552, 375. http://doi.org/10.1093/geront/gnv168.04

12.

Curran

P. J.

Hussong

A. M.

(2003). The use of latent trajectory models in psychopathology research [journal article; research support, U.S. Gov't, P.H.S.; review]. Journal of Abnormal Psychology, 112(4), 526–544. http://doi.org/10.1037/0021-843X.112.4.526

13.

Curran

P. J.

Willoughby

M. T.

(2003). Implications of latent trajectory models for the study of developmental psychopathology [journal article; research support, U.S. Gov't, P.H.S.]. Development and Psychopathology, 15(3), 581–612. http://doi.org/10.1017/s0954579403000300

14.

Diego

M. A.

Field

Hernandez-Reif

(2001). CES-D depression scores are correlated with frontal EEG alpha asymmetry [journal article; research support, Non-U.S. Gov't; research support, U.S. Gov't, P.H.S.]. Depression and Anxiety, 13(1), 32–37.

15.

Di Gessa

Glaser

Tinker

(2016). The impact of caring for grandchildren on the health of grandparents in Europe: A lifecourse approach [journal article; research support, N.I.H., extramural; research support, Non-U.S. Gov't]. Social Science & Medicine, 152, 166–175. http://doi.org/10.1016/j.socscimed.2016.01.041

16.

Edwards

A. B.

Zarit

S. H.

Stephens

M. A.

Townsend

(2002). Employed family caregivers of cognitively impaired elderly: An examination of role strain and depressive symptoms [journal article; research support, U.S. Gov't, P.H.S.]. Aging & Mental Health, 6(1), 55–61. http://doi.org/10.1080/13607860120101149

17.

Song

Clancy

R. F.

Qin

(2019). Studies on left-behind children in China: reviewing paradigm shifts [journal article; review]. New Directions for Child and Adolescent Development, 2019(163), 115–135. http://doi.org/10.1002/cad.20267

18.

Giang

L. T.

Pham

Phi

P. M.

(2019). Productive activities of the older people in Vietnam [journal article]. Social Science & Medicine, 229, 32–40. http://doi.org/10.1016/j.socscimed.2018.09.054

19.

Hayslip

Fruhauf

C. A.

Dolbin-MacNab

M. L.

(2019). Grandparents raising grandchildren: what have We learned over the past decade? [journal article; review]. The Gerontologist, 59(3), e152–e163. http://doi.org/10.1093/geront/gnx106

20.

Hayslip

B. J.

Blumenthal

Garner

(2014). Health and grandparent-grandchild well-being: One-year longitudinal findings for custodial grandfamilies [journal article]. Journal of Aging And Health, 26(4), 559–582. http://doi.org/10.1177/0898264314525664

21.

Hayslip

B. J.

Blumenthal

Garner

(2015). Social support and grandparent caregiver health: One-year longitudinal findings for grandparents raising their grandchildren [journal article]. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 70(5), 804–812. http://doi.org/10.1093/geronb/gbu165

22.

Hayslip

B. J.

Kaminski

P. L.

(2005). Grandparents raising their grandchildren: A review of the literature and suggestions for practice [journal article; review]. The Gerontologist, 45(2), 262–269. http://doi.org/10.1093/geront/45.2.262

23.

Hayslip

B. J.

Toledo

R. M.

Henderson

C. E.

Rodriguez

R. M.

Caballero

V. D.

(2019). Cross-Cultural differences in the experience of grandparent-grandchild relationships and related psychosocial outcomes [journal article]. International Journal of Aging & Human Development, 89(2), 151–171. http://doi.org/10.1177/0091415018769468

24.

Hughes

M. E.

Waite

L. J.

LaPierre

T. A.

Luo

(2007). All in the family: The impact of caring for grandchildren on grandparents’ health [journal article; research support, N.I.H., extramural]. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 62(2), S108–S119. http://doi.org/10.1093/geronb/62.2.s108

25.

Jaddoe

V. W.

de Jonge

L. L.

Hofman

Franco

O. H.

Steegers

E. A.

Gaillard

(2014). First trimester fetal growth restriction and cardiovascular risk factors in school age children: Population based cohort study [journal article; research support, Non-U.S. Gov't]. BMJ, 348, g14. http://doi.org/10.1136/bmj.g14

26.

Jendrek

M. P.

(1994). Grandparents who parent their grandchildren: Circumstances and decisions [journal article; research support, Non-U.S. Gov't]. The Gerontologist, 34(2), 206–216. http://doi.org/10.1093/geront/34.2.206

27.

P. C.

Hank

(2014). Grandparents caring for grandchildren in China and Korea: Findings from CHARLS and KLoSA [comparative study; journal article; research support, Non-U.S. Gov't]. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 69(4), 646–651. http://doi.org/10.1093/geronb/gbt129

28.

L. E.

Stearns

S. C.

Van Houtven

C. H.

Lee

S. D.

Dilworth-Anderson

(2013). Impact of Caring for Grandchildren on the Health of Grandparents in Taiwan. Journals of Gerontology, Series B: Psychological Sciences and Social Sciences, 68(6), 1009–1021. http://doi.org/10.1093/geronb/gbt090

29.

Lin

J. P.

Chang

T. F.

Huang

C. H.

(2011). Intergenerational relations and life satisfaction among older women in Taiwan [journal article]. International Journal of Social Welfare, 20, S47–S58.

30.

Lin

X. H.

Zhang

D. W.

(1999). Inference in generalized additive mixed models by using smoothing splines. Journal of the Royal Statistical Society Series B-Statistical Methodology, 61(2), 381–400. http://doi.org/10.1111/1467-9868.00183

31.

Liu

Y. H.

(2009). Quality of life among older grandparent caregivers: a pilot study. Journal of Advanced Nursing, 65(7), 1475–1484. http://doi.org/10.1111/j.1365-2648.2009.05000.x

32.

Longo

U. G.

Matarese

Arcangeli

Alciati

Candela

Facchinetti

Marchetti

De Marinis

M. G.

Denaro

(2020). Family caregiver strain and challenges when caring for orthopedic patients: A systematic review [journal article; review]. Journal of Clinical Medicine, 9(5). http://doi.org/10.3390/jcm9051497

33.

McKeown

G. J.

Sneddon

(2014). Modeling continuous self-report measures of perceived emotion using generalized additive mixed models [journal article]. Psychological Methods, 19(1), 155–174. http://doi.org/10.1037/a0034282

34.

Minkler

(1999). Intergenerational households headed by grandparents: contexts, realities, and implications for policy. Journal of Aging Studies, 13(2), 199–218. http://doi.org/https://doi.org/10.1016/S0890-4065(99)80051-6

35.

Musil

Warner

Zauszniewski

Wykle

Standing

(2009). Grandmother caregiving, family stress and strain, and depressive symptoms. [journal article]. Western Journal of Nursing Research, 31(3), 389–408. https://doi.org/10.1177/0193945908328262

36.

Musil

C. M.

Givens

S. E.

Jeanblanc

A. B.

Zauszniewski

J. A.

Warner

C. B.

Toly

V. B.

(2017). Grandmothers and self-management of depressive symptoms [journal article; research support, N.I.H., extramural]. Archives of Psychiatric Nursing, 31(3), 234–240. http://doi.org/10.1016/j.apnu.2016.11.008

37.

Musil

C. M.

Gordon

N. L.

Warner

C. B.

Zauszniewski

J. A.

, Standing

, & Wykle

(2011). Grandmothers and caregiving to grandchildren: Continuity, change, and outcomes over 24 months. [journal article; research support, N.I.H., extramural]. The Gerontologist, 51(1), 86–100. https://doi.org/10.1093/geront/gnq061

38.

Muthen

L. K.

(2000). Integrating person-centered and variable-centered analyses: Growth mixture modeling with latent trajectory classes [journal article; research support, Non-U.S. Gov't; research support, U.S. Gov't, P.H.S.]. Alcoholism-Clinical and Experimental Research, 24(6), 882–891.

39.

Peterson

T. L.

(2017). Changes in health perceptions among older grandparents raising adolescent grandchildren [journal article]. Social Work in Public Health, 32(6), 394–406. http://doi.org/10.1080/19371918.2017.1327389

40.

Pruchno

R. A.

McKenney

(2002). Psychological well-being of black and white grandmothers raising grandchildren: Examination of a two-factor model [comparative study; journal article; research support, U.S. Gov't, P.H.S.]. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 57(5), 444–P452. http://doi.org/10.1093/geronb/57.5.p444

41.

Qian

Liu

X. Y.

Fang

Zhang

Gao

(2020). Investigating fertility intentions for a second child in contemporary China based on user-generated content [journal article; research support, Non-U.S. Gov't]. International Journal of Environmental Research and Public Health, 17(11). http://doi.org/10.3390/ijerph17113905

42.

Radloff

L. S.

(1977). The CES-D scale A self-report depression scale for research in the general population. Applied Psychological Measurement, 1(3), 385–401.

43.

Robins

J. M.

Hernan

M. A.

Brumback

(2000). Marginal structural models and causal inference in epidemiology [journal article; research support, U.S. Gov't, P.H.S.]. Epidemiology, 11(5), 550–560. http://doi.org/10.1097/00001648-200009000-00011

44.

Ross

M. E. T.

Kang

Cron

(2015). Psychological profile, salivary cortisol, C-reactive protein, and perceived health of grandmothers With childrearing responsibility. Journal of Family Issues, 36(14), 1904–1927. http://doi.org/10.1177/0192513X14540157

45.

Rozario

P. A.

Morrow-Howell

Hinterlong

J. E.

(2004). Role enhancement or role strain - assessing the impact of multiple productive roles on older caregiver well-being. Research on Aging, 26(4), 413–428. http://doi.org/10.1177/0164027504264437

46.

Silverstein

Zhang

(2020). Grandparents’ financial contributions to grandchildren in rural China: The role of remittances, household structure, and patrilineal culture [journal article; research support, N.I.H., extramural; research support, Non-U.S. Gov't]. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 75(5), 1042–1052. http://doi.org/10.1093/geronb/gbz009

47.

Silverstein

Zuo

(2021). Grandparents caring for grandchildren in rural China: Consequences for emotional and cognitive health in later life [journal article; research support, N.I.H., extramural; research support, Non-U.S. Gov't]. Aging & Mental Health, 25(11), 2042–2052. http://doi.org/10.1080/13607863.2020.1852175

48.

Sims

Rofail

(2013). The experiences of grandparents who have limited or no contact with their grandchildren [journal article]. Journal of Aging Studies, 27(4), 377–386. http://doi.org/10.1016/j.jaging.2013.09.002

49.

Tsai

F. J.

Motamed

Rougemont

(2013). The protective effect of taking care of grandchildren on elders’ mental health? Associations between changing patterns of intergenerational exchanges and the reduction of elders’ loneliness and depression between 1993 and 2007 in Taiwan [journal article]. BMC Public Health, 13, 567. http://doi.org/10.1186/1471-2458-13-567

50.

van Buuren

Groothuis-Oudshoorn

(2011). Mice: multivariate imputation by chained equations in R. Journal of Statistical Software, 45(3), 1–67.

51.

VanderWeele

T. J.

(2009). Marginal structural models for the estimation of direct and indirect effects [journal article]. Epidemiology, 20(1), 18–26. http://doi.org/10.1097/EDE.0b013e31818f69ce

52.

Wang

C. D.

Hayslip

B. J.

Sun

Zhu

(2019). Grandparents as the primary care providers for their grandchildren: A cross-cultural comparison of Chinese and U.S. Samples [comparative study; journal article]. International Journal of Aging & Human Development, 89(4), 331–355. http://doi.org/10.1177/0091415018824722

53.

Whitley

D. M.

Lamis

D. A.

Kelley

S. J.

(2016). Mental health stress, family resources and psychological distress: A longitudinal mediation analysis in african American grandmothers raising grandchildren [journal article]. Journal of Clinical Psychology, 72(6), 563–579. http://doi.org/10.1002/jclp.22272

54.

(2019). Physical and mental health of Chinese grandparents caring for grandchildren and great-grandparents [journal article; research support, N.I.H., extramural]. Social Science & Medicine, 229, 106–116. http://doi.org/10.1016/j.socscimed.2018.05.047

55.

Tang

L. W.

Dong

X. Q.

(2017). Grandparent caregiving and psychological well-being Among Chinese American older adults-The roles of caregiving burden and pressure [journal article]. The Journals of Gerontology. Series A, Biological Sciences and Medical Sciences, 72(suppl_1), S56–S62. http://doi.org/10.1093/gerona/glw186

56.

Zeng

Chen

Y. C.

Lum

(2020). Longitudinal impacts of grandparent caregiving on cognitive, mental, and physical health in China. Aging & Mental Health, 1–8. http://doi.org/10.1080/13607863.2020.1856779

57.

Zeng

Chen

Y. C.

Lum

(2020). Longitudinal impacts of grandparent caregiving on cognitive, mental, and physical health in China [journal article]. Aging & Mental Health, 25,11(2021): 2053–2060. http://doi.org/10.1080/13607863.2020.1856779

58.

Zeng

Hesketh

(2016). The effects of China's universal two-child policy [historical article; journal article; research support, Non-U.S. Gov't; review]. Lancet, 388(10054), 1930–1938. http://doi.org/10.1016/S0140-6736(16)31405-2

59.

Zhao

Smith

J. P.

Strauss

Yang

(2014). Cohort profile: The China health and retirement longitudinal study (CHARLS) [journal article; research support, N.I.H., extramural; research support, Non-U.S. Gov't]. International Journal of Epidemiology, 43(1), 61–68. http://doi.org/10.1093/ije/dys203

60.

Zhou

Chu

Medina

Jiang

Zheng

Liu

Rozelle

(2015). Non-use of health care service among empty-nest elderly in shandong, China: A cross-sectional study [journal article; research support, Non-U.S. Gov't]. BMC Health Services Research, 15, 294. http://doi.org/10.1186/s12913-015-0974-1

61.

Zhou

Mao

Lee

Chi

(2017). The impact of caring for grandchildren on Grandparents’ physical health outcomes: The role of intergenerational support [journal article]. Research on Aging, 39(5), 612–634. http://doi.org/10.1177/0164027515623332

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.02 MB