Childhood experiences and Frailty Trajectory Among Middle-aged and Older Adults in China

Abstract

Introduction

The world has witnessed a rapid growth of elder population over the past decades. As one of the most populous country, China has encountered even greater challenges. According to the seventh national census, there are more than 264 million people aged 60 and above at present, accounting for about 18.70% of the total population (Zhang 2020b). Compared with the sixth census conducted in 2010, there is an 5.44 % increase in the proportion of older populations (Zhang 2020b), and it is estimated that, by 2050, about 487 million Chinese will age 60 years old and above (The Xinhua News Agency 2018). As people age, their health conditions tend to decline over time. Among the health problems of older adults, a prevalent but still widely ignored health indicator is frailty. 

Frailty is a unique result of gradual downstream changes of multiple physiologic functions (Fried et al. 2001; Rockwood et al. 2005). It refers to a condition that falls between healthy status and severe illness. Therefore, people might be considered frail even when they do not suffer from a specific life-threatening disease (Rockwood & Mitnitski 2007). Although frailty is based on age-associated markers like activities of daily living, it is not just a simple summation of them. It has been confirmed that frailty reflects a more comprehensive biologic syndrome, which will lead to vulnerability and decreased resistance to stressors (Fried et al. 2001; Rockwood & Mitnitski 2007; Rockwood et al. 2005). Moreover, it can predict adverse outcomes such as falls, hospitalization and mortality (Álvarez-Bustos et al. 2022; Stow et al. 2018; Taniguchi et al. 2020). 

Frailty Index (FI) is regarded as a valid measurement of frailty and has been widely used in studies in different countries. By accumulating multi-dimensional age-related deficits and then dividing by the total number of them, FI is generated as a continuous indicator ranging from 0 to 1, with higher representing higher levels of frailty (Rockwood & Mitnitski 2007). Many studies have successfully drawn results using frailty measured at a single point in time (Huang et al. 2021; Wang et al. 2020; Wang et al. 2021). However, frailty, to a great extent, is not static. It is a dynamic, time-varying indicator. Therefore, it might be more precise and practical to study the trajectory of frailty (Jung et al. 2022; Raymond et al. 2020; Yu et al. 2018).

There are a range of factors that might influence frailty trajectory. Studies have found that elements like overweight, lifestyles, socioeconomic status and social capital can significantly affect the incidence and progression of frailty at later life (Amiri et al. 2020; Liu et al. 2022; Rogers et al. 2017; Stow et al. 2021). However, most of these influencing factors only covered adulthood, whereas the life course theory believes that individual’s life is a cohort that combined with multiple stages and life events, and that each stage would have an impact on next stages and would eventually influence the status during later life (Elder et al. 2003). Therefore, experiences in childhood might also have long-term influences on health status in old age, both directly and indirectly. And it has been partially proved among empirical studies on cognition, depression and diseases in later life (Kendig et al. 2017; Yao 2021; Zhang & Lu 2021; Zhang & Zhang 2022). 

Literature showed that childhood experiences are generally divided into three parts, that is, adverse childhood experiences (ACE) (Hu 2021; Lin et al. 2022; Tao et al. 2021), childhood socio-economic status (SES) (Tao et al. 2021; Zhang & Lu 2021) and childhood health and healthcare (Pan 2020; Zhang & Lu 2021). The severer ACE elder people had experienced, the worse their health is, while better childhood SES and healthcare are the protective factors of health status in later life (Pan et al. 2021; Tao et al. 2021). 

Limited numbers of studies have been conducted to examine childhood experiences and frailty in later life. For example, several cross-sectional studies have demonstrated that childhood circumstances would affect the frailty in later life at one time point (Li et al. 2019, 2020; Mian et al. 2021). Furthermore, a recent study using data from Survey of Health, Ageing, and Retirement in Europe (SHARE) explored the impact of experiences during childhood on frailty trajectory at later life in European contexts (Linden et al. 2020). However, there are few studies which examined how they influence the change rate of frailty in the contexts of developing countries and regions such as China. 

There are significant differences in social, cultural, and economic backgrounds between China and Europe in the past century. First and foremost, the disparities in economic development, health care resources, and social infrastructure between Europe and China in 1950s and 1960s might have significant influence on the relationships between childhood experiences and frailty trajectories in later life in the two regions. Second, Chinese population’s diet habits, healthy behavior patterns, and other lifestyles are different from those of European. Third, Chinese family structure has shifted from multi-generational household to nuclear family structure in the past few decades (e.g., average family size has declined from 4.33 in 1953 to 2.62 in 2020) (National Bureau of Statistics of China 1988; Zhang 2020a). The Chinese society has undergone great transitions as well (e.g., from an agricultural society to industrial society) since the economic reform in 1978. Under such circumstances, local evidence is required to further test the progression of frailty in later life and its antecedents from both childhood and adulthood in Chinese contexts. 

Therefore, using a nationwide longitude data, the present study aims to investigate the influence of childhood experiences on frailty trajectory among older Chinese adults. The hypotheses of this study are as follows:

H1: Severer adverse childhood experiences are associated with higher frailty levels at baseline among older adults in China. 

H2: Better family socioeconomic status in Childhood is associated with lower frailty levels at baseline among older adults in China. 

H3: Better health and healthcare in Childhood is associated with lower frailty levels at baseline among older adults in China.

H4: Severer adverse childhood experiences are associated with higher levels of change rates of frailty among older adults in China.

H5: Better family socioeconomic status in Childhood is associated with lower levels of change rates of frailty among older adults in China.

H6: Better health and healthcare in Childhood is associated with lower levels of change rates of frailty among older adults in China. 

Methods

Data

Data were drawn from the harmonized data as well as life history data of The China Health and Retirement Longitudinal Study (CHARLS), a longitudinal cohort study comprising a nationally representative sample of individuals over age 45 years in China (Zhao et al. 2014). Using multi-stage stratified probability proportional to size (PPS) sampling, CHARLS has successfully conducted four waves of nationwide data collection in 150 counties of 28 provinces, ensuring the quality and representativeness of data. The baseline survey was conducted during 2011-2012, with follow-up surveys taking place in 2013, 2015, and 2018, respectively. The harmonized data is an integrated version of the four waves of data, which was created by the USC Gateway to Global Aging Data team (Phillips et al. 2021). Besides, a life history survey was conducted during 2014 to explore the conditions during the respondents’ early life, including but not limited to their family information, raising up history, education, and health. 

In this study, we included respondents who have taken all of the four longitudinal surveys and the life history survey. At baseline, 17,708 respondents were asked, 11,988 of whom completed all the follow-up surveys occurred in 2013, 2015 and 2018. After matching and excluding who did not answer the life history survey, and who had missing values on age or age under 45 years at baseline, a total of 10,963 respondents were included in the final analytic sample.

Measurements

Dependent variable

The dependent variable in this study is frailty. According to Wang et al. (2021), 32 deficits from 6 different domains were chosen to construct FI: (1) Self-rated health. The answer was measured by a 5-point Likert scale, with 1 representing very good and 5 representing very poor. For convenience, we reversed it and recoded the values into 0-1 (0 = very good; 0.25 = good; 0.5 = fair; 0.75 = poor; 1 = very poor); (2) Activities of daily living (ADL). Six indicators (i.e., difficulty with dressing, bathing, eating, get/in bed, using the toilet, controlling urination and defecation) were used, and each of which was turned into a dichotomous variable, with 0 indicating no difficulty and 1 indicating difficulty; (3) Instrumental activities of daily living (IADL). Same as ADL, five deficits (i.e., difficulty with managing money, take medications, shop for grocery, prepare hot meal, cleaning house) were used and recoded into dichotomous variables; (4) Cognitive function. We used three cognition tests to measure the respondents’ cognitive function, including mathematical performance, orientation, and draw assign picture. These questions were recoded into variables ranging from 0 to 1, depending on the correct number. For example, in the mathematical test, respondents who answered all the five questions correctly were given a score of 0, and who were correct in four questions were given 0.2 point; (5) Chronic diseases. There was a total of 13 chronic diseases and all of them were dichotomous variable, with 1 representing having this disease diagnosed by doctors; (6) Psychological characteristics. Four questions (i.e., sleep was restless, felt lonely, could not get going, feel hopeful about the future) were used, and the last question was reversely recoded as it represents a positive domain. These 32 recoded indicators were then added up and divided by 32. thus, FI was generated with a range from 0-1, and higher scores mean higher frailty levels. Considering the missing values, we regarded 20% of the total number of deficits, or 6, as the threshold (Wang et al. 2021). If respondents had more than 6 missing values on these metrics, their FI would be considered as missing. 

Independent variable

Three kinds of independent variables were selected in this study, that is, adverse childhood experiences (ACE), childhood socioeconomic status, and childhood health and healthcare.

ACE represents a series of adversities the respondents experienced before 17 years old in terms of family, friendship, community and so on. Therefore, based on the life history data and previous studies, we selected 17 indicators as the criteria of ACE (Lin et al. 2022). Overall, ACE includes deficits in several domains: abuse and neglect (i.e., emotional neglect and physical abuse, domestic violence), death (i.e., death of parents, sibling), illness and disability (parental disability and household mental illness), living environment outside the home (i.e., unsafe neighborhood and bullying), substance abuse, parental separation or divorce, and incarcerated household member. After converting these indicators into dichotomous variable (1 = yes, 0 = no), we adopted the method of calculating the mean value. If the respondents had missing values on more than 20% of the above indicators, their ACE scores would be considered as missing. In this way, ACE was transformed into a variable ranging from 0 to 1, with greater values indicating severer ACE. Sensitivity analysis has been conducted to test different coding method of ACE (e.g., sum method), generating robust results. 

Childhood SES was measured by the self-rated SES: “Compared to the average family in the same community/village before age 17, how was your family’s financial situation?”, which has been proved to be highly valid in previous literature (Zhang & Lu 2021). This was measured by a 5-point Likert scale, and we reversed and recoded it into 0-4: 0 = a lot worse off than them, 2 = same as them, 4 = a lot better off than them.

As for childhood health and healthcare, self-rated health has been affirmed to have strong correlation to retrospective childhood physical and psychological problems (Kendig et al. 2017). However, it is based on comparisons with other people in the same community, which in turn can be affected by the local health and medical standards. Therefore, we chose to use both subjective and objective measurements. 

The subjective one was measured by the self-reported health: “Before or when you were 15 years old, compared to other children of the same age, how was your health status?”. Similar to childhood SES, we reversed and recoded it into 0-4, with higher score indicating better health. As for the objective health and healthcare, we accumulated 4 dichotomized variables: confined to bed or home for a month or more (0 = yes, 1 = no), hospitalized for a month or more (0 = yes, 1 = no), received any vaccinations (0 = no, 1 = yes), had a usual source of care (0 = no, 1 = yes), and the higher the value, the better the childhood health and healthcare (Pan 2020).

Covariates

Sociodemographic characteristics such as gender (1 = male, 0 = female), marital status (1 = married, 0 = other marital status), education (1 = had received formal education or higher, 0 = illiterate), place of residence (1 = rural, 0 = urban), and hukou status (1 = agricultural, 0 = non-agricultural) at baseline were recoded as dichotomous variables. Baseline age, BMI, and household per capita consumption were also measured. Following the Chinese standard (National Health Commission of the People's Republic of China 2006), BMI were further recoded as dummy variables with normal weight as the reference group (e.g., normal weight vs. overweight, normal weight vs. obesity, normal weight vs. underweight). Moreover, the log value of household per capita consumption was calculated. In addition, lifestyles (drinking and smoking) were also included by asking the respondents whether they have drunk any alcohol last year (1 = yes, 0 = no), and whether they still smoke cigarettes (1 = yes, 0 = no).

Data analysis

In this study, the frequency analysis was conducted by STATA 16SE. Furthermore, we used MPLUS 8.7 to conduct latent growth curve models to determine the relationship between childhood experiences and frailty trajectory. First, an unconditional model was estimated to identify the frailty trajectory. Maximum likelihood estimation with robust standard errors (MLR) was used, which can adjust to non-normality and non-independence of observations, and handle missingness (Yuan & Bentler 2000; Yuan et al. 2012). To examine the model fit, we take several fit indices into account: The Chi-square value (χ2), the comparative fit index (CFI), the Tucker-Lewis index (TLI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR). Second, by constructing a conditional model, the effects of ACE, childhood SES, childhood health and healthcare on both intercept (baseline level) and slope (rate of change) of frailty trajectory were tested, when baseline covariates were controlled. 

Results

Descriptive statistics

As shown in Table 1, among the respondents, the mean age was 58.29 years, and 53% of them were female. The majority of the sample was married (83.6%). More than 80% of them held an agricultural hukou, while only 65.8% really lived in rural area. The percentage of respondents who had received formal education (elementary school) or higher was 53.2%. Approximately a half of respondents were within normal weight (44.1%), while others were underweight (5.4%), overweight (24.0%), and obese (9.8%) respectively. As for lifestyles at baseline, less than one third respondents had the habit of drinking (33.1%) and smoking (29.0%). 

During childhood, the mean score of ACE was 0.13. Self-rated childhood SES had a mean score of 1.45, indicating that a large proportion of respondents tended to regard themselves poorer than others. In specific, about 40.2% respondents thought they were somewhat or a lot worse off than others, whereas only 8.6% thought they were better off than their counterparts. For childhood health and healthcare, the average score of objective and subjective evaluation was 3.66 and 2.33 respectively, demonstrating that their actual health and health care level was better than what they recognized before.

[Insert Table 1 about here]

Frailty trajectory and childhood circumstances

We built an unconditional linear latent growth curve model to identify the trajectory of FI (Figure 1). The model fit indexes showed a significant Chi-square test (χ² (5) = 337.135, p < .001). The significant Chi-square estimates might because that it is sensitive to large sample size (Hair et al. 2010; Kline 2011). Furthermore, all of the other fit indices suggested a good model fit (RMSEA = 0.078, CFI = 0.975, TLI = 0.969, SRMR = 0.036). Overall, there was an increasing pattern of the trajectory (intercept = .152, p < .001; slope = .016, p < .001). In other words, the FI scores increased significantly during the survey period.

[Insert Fig. 1 about here]

After taking all childhood variables and covariates into account, the model fit was also adequate (χ² (37) = 380.327, p < .001, RMSEA = 0.035, CFI = 0.980, TLI = 0.962, SRMR = 0.017). As shown in Table 2 and Figure 2, ACE, childhood SES, childhood objective health and healthcare, and childhood self-rated health were all found to be significantly associated with frailty at baseline (ACE: b (SE) = .133 [.011], β (SE)= .171 [.013], p < .001; childhood SES: b (SE) = -.006 [.001], β (SE)= -.074 [.013], p < .001; childhood objective health and healthcare: b (SE) = -.008 [.002], β (SE)= -.057 [.013], p < .001; childhood self-rated health: b (SE) = -.009 [.001], β (SE) = -.112 [.013], p < .001). However, self-rated health was not associated with the rate of change of frailty (b (SE) =.000 [.000], β (SE)= -.022 [.021], p = .304). ACE, childhood SES, and objective health and healthcare were significantly associated with the slope of frailty (ACE: b (SE) = .018 [.004], β (SE)= .112 [.023], p < .001; childhood SES: b (SE) = -.001 [.000], β (SE)= -.057 [.021], p < .01; objective health and healthcare: b (SE) = -.002 [.001], β (SE) = -.058 [.024], p < .05). 

(Insert Fig. 2 about here)

As for covariates (see Table 2), age, gender, marital status, place of residence, hukou status, education, drinking, underweight, overweight, and obesity were associated with the baseline level of frailty, whereas only age, gender, overweight, and obesity were still associated with the slope of the trajectory. While smoking was not significantly associated with the intercept, it had a significant influence on the change rate of frailty. Finally, household per capita consumption was not associated with neither intercept nor slope of frailty trajectory.

[Insert Table 2 about here]

Discussion

In this present study, we investigated the effects of ACE, childhood SES, and childhood health and healthcare on frailty trajectory among Chinese middle-aged and elder people from a life course perspective. Instead of examining the impact of childhood on later life frailty at one time point, our study revealed the impact of childhood experiences on both intercept and slope of frailty trajectory. Moreover, we made new contributions to the literature by employing a nationally representative sample in the Chinese context, which could further provide empirical evidences for practices in developing countries and regions. 

Similar to previous studies (Chamberlain et al. 2016; Jung et al. 2022; Verghese et al. 2021), we have identified an increasing pattern of frailty trajectory. When considering childhood conditions, significant impacts of all these three childhood antecedents were found on frailty at baseline (Li et al. 2019, 2020; Mian et al. 2021), and childhood SES also had a strong relationship with the trajectory of frailty (Linden et al. 2020). Furthermore, Linden et al. (2020) did not found a significant association between ACE and frailty trajectory. However, the findings of this study showed that ACE did play a more than important role in the progression of frailty in China. This might because that we adopted a more comprehensive measurement of ACE by including both neighborhood and school environment. The findings of this study added new evidence that, the objective indicators of childhood health and healthcare, rather than the subjective indicators, were significantly associated with the change rate of frailty in later life. 

Furthermore, consistent with prior studies (Jung et al. 2022; Landré et al. 2020; Raymond et al. 2020), we found respondents who were older, female, overweight, and obese would have a higher baseline level as well as a steeper trajectory of frailty. Besides, compared with those married counterparts, those who were not married at baseline (e.g., divorced and widowed) were more likely to be frail. Characteristics like  living in rural area, having an agricultural hukou, getting less education, and underweight would contribute to higher frail score at baseline, and smoking would contribute to the risk of frailty development, which have also been confirmed in previous studies (Chu et al. 2021; Jung et al. 2022; Raymond et al. 2020; Yu et al. 2018).

Our findings confirmed the application of the life course theory in Chinese contexts and highlighted the importance of childhood in frailty development in later life. Additionally, this study has several implications for both policy and intervention. First, ACE, childhood SES, and childhood health and healthcare are effective screening tools for frailty in later life. Therefore, policy makers or social workers should make use of them to identify risk populations. Second, social service programs of health education, and health self-management could be particularly important for middle-aged adults and older adults who are older, being females, being widowed or divorced, living in rural areas, having agricultural hukou status, having overweight/obesity, and those with relatively low educational attainments. Such programs could play an important role in mitigating the progression of frailty and further reduce morbidity and mortality in later life. Furthermore, networks of child protection and assistance should be better established to intervene the potential risk and protective factors. Specifically, for those who have already experienced childhood adversities, timely intervention is in great need to protect them from being frailer in later life. Finally, as childhood conditions are also integrated with family, community, school and society, it is essential to develop social supportive sources for children in multiple social contexts. In a word, the integration of child service and aged-care service should be built from a life course perspective, especially considering that the benefits/risks in childhood conditions could have a long-term impact on the welfare at older age. 

Despite contributions and strengths, the present study also has several limitations. First, we dropped respondents who did not participate in all these five surveys, which might cause selection bias. Second, since childhood conditions were all based on respondents’ retrospections, the measurements might suffer from information inaccuracy, especially among those who were elder, even though multiple studies using 2014 CHARLS data had shown adequate validity of these childhood indicators we used. Finally, apart from the direct impact of childhood circumstances on frailty trajectory, indirect pathways might also exist. Therefore, future research can further study the mediation or moderation role of adulthood conditions in the above relationships. 

Declarations

Acknowledgements: This analysis is based on the China Health and Retirement Longitudinal Study (CHARLS). We thank the CHARLS research team and field team for collecting the data and making the data publicly accessible.

Ethical approval was obtained from the Ethical Review Committee of Peking University.

Data availability: The data that support the findings of this study are available from the corresponding author, N.L., upon reasonable request.

Funding: This work was supported by fund for building world-class universities (disciplines) of Renmin University of China.

Declaration of contribution of authors: Yuqi Yan contributed to statistical analysis, original draft preparation and writing, and revision. Liqing Cai contributed to statistical analysis and revision. Nan Lu contributed to study design, supervision, data analysis, and paper writing and revision. 

Statements and Declarations: The authors have no competing interests to declare that are relevant to the content of this article.

Author Biography: 

Yuqi Yan is a bachelor student of the Department of Social Work and Social Policy, School of Sociology and Population Studies, Renmin University of China.

Liqing Cai is a master student of the Department of Social Work and Social Policy, School of Sociology and Population Studies, Renmin University of China.

Nan Lu (PhD) is an associate professor of the Department of Social Work and Social Policy, School of Sociology and Population Studies, Renmin University of China. 

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Tables

Table 1. Descriptive statistics (N=10,963).

Table 2. Latent growth curve model of frailty trajectory (N=10,963).

Note. *p < 0.05. **p < 0.01. ***p < 0.001.