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Trends in the prevalence of social isolation among middle and older adults in China from 2011 to 2018: the China Health and Retirement Longitudinal Study

BMC Public Health volume 24, Article number: 339 (2024) Cite this article

Abstract

Background

Epidemiological studies have shown that social isolation, which is prevalent in older adults, is associated with a range of adverse health outcomes, but the prevalence of and trends in regard to social isolation remain ambiguous in China. The aim of this study was to elucidate the trends regarding the prevalence of social isolation among middle-aged and older adults in China from 2011 to 2018 and to further identify associated risk factors.

Methods

A repeated cross-sectional study, The data were derived from panel sample data of four waves conducted from May 2011 to August 2018 in the nationally representative China Health and Retirement Longitudinal Study (CHARLS) using multistage probability sampling. Social isolation was ascertained by the five item Steptoe Social Isolation Index. The potential covariates were demographic characteristics, lifestyle factors, and health status. Linear-by-linear association was used to assess the trends in regard to social isolation over time under the influence of the potential covariates. Linear-by-linear association and an age-period-cohort analysis were used to explore the trends, and two-level (time, individual) generalized estimating equation models (GEE) linked multivariate binary logistic regression were performed to identify risk factors.

Results

A high prevalence of social isolation and a moderate upward trend from 2013 to 2018 were observed among a U-shaped trend prevalence of social isolation from 2011 to 2018 across China, with rates of 38.09% (95% CI = 36.73–39.45) in 2011, 33.66% (32.32–35.00) in 2013, 39.13% (37.59–40.67) in 2015, and 39.95% (38.59–41.31) in 2018 (p < 0.001). The prevalence of social isolation increased with age and educational attainment. Females had a higher prevalence than males. The prevalence of social isolation was found to be significantly lower in pensioners than in non-pensioners between 2011 and 2018 (p < 0.001). The prevalence of social isolation was 38.9%, 34.9%, 38.5%, and 44.08% about three times higher among those who doid not use the Internet and 13.44%, 11.64%, 12.93%, and 16.73% than among those who doid in 2011, 2013, 2015 and 2018 respectively. The participants with short (0–5 h) and long sleep (9 or more hours), and poor self-rated health had a higher prevalence of social isolation than the others. Older age, lower educational attainment, living in a rural region, lack of medical insurance or pension, lack of internet use and poor health were risk factors (p < 0.05).

Conclusions

We found a U-shaped prevalence of social isolation trends from 2011 to 2018 and revealed increasing trends from 2013 to 2018 among middle-aged and older adults in China. The findings of the study highlight the urgent need for interventions to reduce social isolation including improving sleep quality and internet skills. Disadvantaged groups in terms of age, economic status, and health status should be the focus of such interventions, especially in the era of COVID-19.

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Background

Social isolation is defined as ‘‘a state in which the individual lacks a sense of social belonging, lacks engagement with others, has a minimal number of social contacts and lacks fulfilling and quality relationships’’ [1]. It has been identified and well-documented by previous research as a social determinant of physical and mental health. Particularly for older adults living in the community, social isolation leads to numerous detrimental health effects, including not only higher rates of morbidity and mortality, but also dementia and an increased rate of falls. Though a number of studies have confirmed that social distancing can slow the rate of transmission of COVD-19 [2, 3], it also brings about health problems such as depression, anxiety and stress [4]. A German study found that the prevalence of social isolation was 12.3% across ages 18–79 years, and increased with increasing age, from 5.4% in the 18-39 age group to 21.7% in the 70-79 age group [5]. A survey in the United States reported that 35% of adults aged 45 and over felt socially isolated [6]. A systematic review showed that the prevalence of social isolation among older adults was 31.2% during the coronavirus disease 2019 (COVID-19) pandemic [7]. Overall, social isolation has a high prevalence in older age groups and is strongly associated with poor health conditions and unfavorable behaviors [8]. Undoubtedly, social isolation has become a major health issue for older adults living in the community, but the epidemiological data on social isolation have been inconsistent. While there has been an ongoing debate about the rise of social isolation based on reports of low birth rates, an ageing population and so on, but there is little evidence of rising rates of social isolation. As social networks shrink with age, with a marked increase in the proportion of older adults, it has been hypothesised that social isolation may indeed increase over time [5]. However, there have been relatively few reports on the changes in the prevalence of social isolation among older adults at the population level in recent years, especially in China [9].

Previous studies have demonstrated that the causes of social isolation are complicated. The atrophy of older adults’ social networks occurs due to factors specific to the individual, such as a decline in physical or cognitive function associated with ageing [10], and social distancing as recommended due to the COVID-19 pandemic [11]. In addition, barriers to internet access among middle-aged and older adults may exacerbate their social isolation [12]. As a result of these individual and environmental factors, the social networks of middle-aged and older adults gradually decline. A higher risk of social isolation is observed among older adults, and more than 50% of this population category are at risk of social isolation globally [13]. However, studies on social isolation trends have focused largely on loneliness and refer mainly to the frequency of social engagement or number of confidants. For example, studies have found that the mean network size decreased by about a third (one confidant), from 2.94 in 1985 to 2.08 in 2004 [14], and social isolation increased from 2003 to 2020. In particular, social engagement with family, friends and others (roommates, neighbors, acquaintances, co-workers, clients, etc.) decreased among Americans [15]. A repeated cross-sectional comparative study in Japan and England found that social isolation among older adults increased from 2010 to 2016 in Japan, which was a more severe increase than in England, where social isolation was gradually alleviated among women aged 75 and older over the same period [13]. In summary, we find that studies on trends in social isolation are not only limited and mainly focused on the United States, but also vary from country to country. However, there is a consensus that social isolation has not received adequate attention in public health, nor has its extent and trends at the national and subgroup levels been paid much attention.

The indicators used to assess social isolation vary widely and often include living alone, being unmarried, low participation in social activities and a lack of diversity in social networks [6], which is related to the uncertainty and incomparability of the social isolation prevalence proxy. The adverse health effects of social isolation are increasingly being recognized. In addition to the increased risk of poor physical health due to cardiovascular disease [16], premature death [17], and functional impairment [18], social isolation hinders social integration and potentially triggers mental health problems, such as depression and anxiety disorders [19, 20]. Age [21, 22], residential area [23] and educational attainment [24] are known risk factors for social isolation among demographic characteristics. Sleep problems [25] and internet use [26, 27] also have an impact on social isolation. Poor self-rated health [28], a low capacity for activities of daily living [29] and multiple chronic diseases [30] have also been documented. In addition, family characteristics [31], living arrangements [32] and children [33] might be factors. However, studies are inconclusive about the differences and risk factors in the prevalence of social isolation in relation to socio-demographic and social factors [5].

In 2020, China’s Seventh Census reported that there were 264 million Chinese adults who were more than 60 years old, representing 18.70% of the population. This percentage is expected to rise to 26.9% by 2050 [34, 35]. In parallel with the ageing population, social isolation, a state of low participation in social activities, and poor integration into social life [36] are prevalent among older adults as they experience significant life changes, including retirement, death of, or separation from family members and friends, and a decreased ability to perform activities of daily living, which has become a significant public health issue of global concern [37]. The World Health Organization’s (WHO’s) Active Ageing Initiative recognized the significance of reducing social isolation as early as 2002 [38], and it has been included as one of the four key areas for action in the WHO Healthy Ageing Ten Year Strategy (2021–2030) [39]. Cultural tradition emphasizes the importance of family and social networks in Chinese populations. It has been suggested that the association between social isolation and health may be more pronounced. However, very few studies have examined the prevalence of social isolation among middle-aged and older Chinese adults [9], and even fewer have examined the changes in its trends over time. The trends and social risk factors regarding social isolation remain ambiguous in China.

Given the dramatic changes in the use of technology as a means of social connectivity in China, it is currently not well understood whether such changes will exacerbate the trend of social isolation. This study aims to elucidate the trends regarding the prevalence of social isolation among middle-aged and older adults in China from 2011 to 2018 and to further identify the associated risk factors. The study included adults aged 45 years and older, as social functional decline can begin at this age. We hypothesised that social isolation may have worsened as the technological and economic development of society has accelerated, and our findings contribute to the growing body of literature focusing on social isolation and provide important information for the ageing population.

Methods

Data and sampling

Our study is a repeated cross-sectional study. The data were derived from the nationally representative China Health and Retirement Longitudinal Study (CHARLS), conducted by the National Development Institute of Peking University [40]. The CHARLS has been conducted four times since 2011 to assess the economic, social, and health conditions of adults aged 45 years and older in China. Stratified multistage sampling was conducted according to a probability proportionate to size [41]. A total of 150 counties in 28 provinces across China were included in the final sample. The CHARLS survey was approved by the Peking University Biomedical Ethics Economic Review Committee (IRB00001052-11015), and all of the participants provided written informed consent. A detailed description of the CHARLS has been published elsewhere [42].

To examine the trends in the prevalence of social isolation among middle-aged and older people in different years, panel data of four waves was chosen for this study from 2011 to 2018. The sample was selected from the CHARLS according to the following eligibility criteria: 1) the participants were aged 45 years or older and 2) the participants had answered all of the questions on the assessment of social isolation and its covariates. Referring to previous studies [43], the study sample was limited to participants who provided information on all indicators of social isolation. After removing participants with more than 20% missing items for social isolation, Little's MCAR tests were performed on the remaining missing values for social isolation at each wave, and none were significant, suggesting that the complete random missing condition was met and could be removed directly. In addition, for the hierarchical linear analyses, we further restricted the participants to those who provided complete information on all covariates [43]. A total of 29788 participants were included in the final analyses. Figure 1 shows the details of the participant selection.

Fig. 1
figure 1

Flowchart for selection of the final sample included in the analysis

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Measurements

Social isolation

In line with previous studies [44, 45], social isolation was ascertained using five-items similar to the Steptoe Social Isolation Index, but with slight differences [46], which asks about marital/cohabitation status, monthly contact (face-to-face, telephone and email/written) with children, and other family or friends, and participation in groups. These are the five items: 1) whether the participant was cohabiting; 2) whether the participant or his/her spouse had seen their parent or parent-in-law in the past month; 3) whether the participant had seen or contacted his/her child in the past month (including by phone, text message, post or e-mail); 4) whether the participant had interacted with his/her friends in the past month; and 5) whether the participant had participated in any activities (such as social clubs or residents’ groups, religious groups, or committees) in the past month. For the above five questions, if the answer was no, 1 point was allocated, otherwise 0 points were allocated, resulting in a total social isolation score ranging from 0 to 5. Based on the total score, participants were divided into non-isolated (score = 0–2) and isolated groups (score = 3–5), based on previous studies [47].

Covariates

The potential covariates were demographic characteristics (age, gender, educational attainment, residence, geographic location, medical insurance and pension), lifestyle factors (internet use, sleep duration, smoking and alcohol consumption), and health status (self-rated health, multimorbidity, and physical and mental health). Physical health was measured using Katz’s Basic Activities of Daily Living scale (BADL, Cronbach’s α = 0.881–0.889), which includes the following six items: the ability to bathe, eat, get in and out of bed, get dressed, go to the toilet, and defecate. The BADL scores were classified into three levels of impairment based on previous studies: 1) none: BADL score = 0, 2) mild: BADL score = 1, and 3) severe: BADL score = 2 [48,49,50]. Mental health was assessed using the 10-item Epidemiological Survey Center Depression Scale Short Form (CES-D-10 scale, Cronbach’s α = 0.78–0.79), which consists of 10 items, with scores ranging from 0 to 30. The scores are divided into three levels: 1) depression: score ≥ 20, 2) depressive symptoms: score 10–19, and 3) no depressive symptoms: score < 10) [51, 52]. Details of the scoring systems are presented in Table S1.

Statistical analysis

For descriptive statistics, frequencies and percentages were used for the categorical variables, and the means and standard deviations were used for the continuous variables. To eliminate the effect of differences in the internal composition of the cross-sectional data in different years on the prevalence rate, we adopted sampling weights to account for differences in sampling fractions in the panel data compared to the overall population. using direct standardization in regard to the population stratum proportions of sex. Sex-standardized rate that is a statistical adjustment applied to rates or ratios to account for differences in the distribution of a particular characteristic was calculated. A chi-squared test was used to examine the differences in the prevalence of social isolation between the subgroups according to each categorical variable. Linear-by-linear association was used to assess the trends in social isolation over time under the influence of confounding variables. To separate the layer effects over time and minimize the influence of reverse causality, two-level (time, individual) generalized estimating equation models (GEE) linked multivariate binary logistic regression were performed to test the trend difference between the subgroups, and risk factors were identified. An age-period-cohort analysis (APC) was performed to test the effects of the participant age, the survey year, and birth cohort on the prevalence of social isolation. To minimize the expected loss relative to the appropriate reference posterior distribution, the Intrinsic Estimator was used to independently estimate the effect coefficients of age, survey period, and birth cohort [53]. Stata 15.0 (StataCorp, College Station, TX, USA) and SPSS 25.0 (IBM, Armonk, NY, USA) was used to perform the data analysis. The significance level was set at a two-tailed value of p < 0.05.

Results

Participants’ characteristics

The total sample consisted of 29,788 participants, including 7,900 in 2011, 7,198 in 2013, 6,362 in 2015, and 8,382 in 2018. The mean (standard deviation) age was 60.4 (9.61) years, and the ratio of men to women was approximately 1:1 (49.1%: 50.9%). The majority (62.3%) of the participants had a primary education or less, and 17,706 (59.4%) lived in rural areas. Almost all of the participants (94.8%) had health insurance, and 19,057 (64.0%) had a pension. Surprisingly, the majority of the participants (92.3%) had not used the internet in the past month, and more than half (54.6%) of the participants slept less than 7 hours per night. In addition, 9,422 (31.6%) smoked, and 10,576 (35.5%) consumed alcohol. 13,599 (45.7%) participants had multimorbidity, 7,259 (24.4%) reported poor health, 11,133 (37.4.%) had symptoms of depression, and 2,187 (7.3%) had a severe physical impairment. The characteristics of the samples from the four waves of data are shown in Table 1.

Table 1 Characteristics of the participants classified as middle-aged or older adults (N = 29 788)a
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Trends in social isolation over time

A high prevalence of social isolation and a moderate upward trend from 2013 to 2018 were observed in a U-shaped trend prevalence of social isolation from 2011 to 2018 across China, with rates of 38.09% (95% CI = 36.73-39.45) in 2011, 33.66% (32.32-35.00) in 2013, 39.13% (37.59-40.67) in 2015, and 39.95% (38.59-41.31) in 2018 (p < 0.001).

With the exception of age and geographical location, the prevalence of social isolation showed U-shaped trends in the sub-group of the remaining socio-demographic variables, with an upward trend after a low point in 2013. It is worth noting that the prevalence of social isolation increased with age and educational attainment. The prevalence of social isolation was three to four times higher in the over-80s group than in the 45-60s group, and two to three times higher in the group with a primary school education or less than in the group with a college education or more. Females had a higher prevalence than males. In addition, the prevalence of social isolation was significantly higher in rural areas than in urban areas, and among those without health insurance or a pension than among those with health insurance or a pension, suggesting that socio-economically disadvantaged groups were a priority group for social isolation.

Similarly, the prevalence of social isolation presented a U-shaped trend from 2011 to 2018 for most lifestyle and health status subgroups, with an upward trend from 2013 to 2018. The prevalence of social isolation was almost three times higher among those who had not used the internet in the last month than among those who had and it increased significantly over time, as it did among those who slept 7-8 hours and 8-9 hours, but the prevalence of social isolation was significantly higher among those who slept less (0-5 hours) and more (9 or more hours), especially among those who slept the most. Interestingly, there was no increasing trend in the prevalence of social isolation among participants who smoked and consumed alcohol, and the prevalence of social isolation was significantly lower in the group who consumed alcohol than among those who did not, possibly because smoking and drinking alcohol are two forms of social interaction in China, and they may facilitate social networking. In the analyses of health outcomes, the prevalence of social isolation was significantly higher among the subgroup with a poor health status than among those with a good health status and was more likely to increase among participants with multimorbidity and fair or poor self-rated health than among others. Notably, although the prevalence of social isolation was high among participants with physical and mental health problems, as expected, it did not change significantly over the study period (Table 2).

Table 2 Standardised prevalence of social isolation in relation to sociodemographic characteristics, lifestyle factors, and health outcomes in middle-aged and older adults in China, 2011–2018
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Risk factors for social isolation

Table 3 presents the potential risk factors associated with the prevalence of social isolation. Demographic variables (age, gender, educational attainment, type of residence, geographical location, medical insurance and pension) were also included in model 1. The health status variables (multimorbidity, self-rated health, and physical and mental health) based on model 1 were added for model 2; and the lifestyle factors variables (internet use, sleep duration, smoking, and alcohol consumption) based on model 2 were added for model 3.

Table 3 Results of generalized estimating equation of the prevalence of social isolation according to sociodemographic characteristics, lifestyle factors, and health outcomes
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Participants who were older, had a lower level of educational attainment, lived in a rural region or a western region and had no medical insurance or pension had a higher risk of social isolation than participants in other groups. Notably, the presence of multimorbidity (OR = 0.885, 95% CI = 0.834-0.939) had a protective effect on the prevalence of social isolation, which may be related to the fact that we controlled for too many covariates, and those with multiple chronic conditions may be more likely to be cared for by family and friends when controlling for health status. In addition, participants with poor self-rated physical or mental health were more likely to be socially isolated than those in good health. As expected, participants who did not use the internet (OR = 1.632, 95% CI = 1.441-1.847) were more likely to experience social isolation than those who used the internet. Interestingly, alcohol consumption was a protective factor against social isolation (OR =0.801, 95% CI =0.752-0.852). The effect of smoking also became insignificant after adjustment. Normal sleep duration (7-8 h) was protective compared to sleep disorder, especially for long sleep.

Age-period-cohort analysis

Consistent with the results of the previous analyses, the age-period-cohort analysis also revealed an increasing trend in the prevalence of social isolation, regardless of age, survey period, or birth cohort effects after controlling for the other two effects separately (Fig. 2). The prevalence of social isolation showed a slow upward trend over time among middle-aged and older adults excluding the effects of period, age and birth, especially with increasing age, except for a brief downward trend from 2012 to 2014 (Fig. 2b). This means that the issue of social isolation needs attention, especially in the context of long-term coexistence with COVID-19, whereby the growth trend is likely to be further exacerbated, and measures to reduce social isolation should be actively applied at the societal level, especially among the older age group.

Fig. 2
figure 2

Age-period-cohort analysis of social isolation among middle-aged and older adults in China, 2011—2018

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Discussion

To the best of our knowledge, this is the first study to explore a U-shaped trend in the prevalence of social isolation from 2011 to 2018 and it revealed an increasing trend over time from 2013 to 2018 after adjusting for the covariates among middle-aged and older adults in a nationally representative dataset from China using four cross-sectional surveys over approximately 10 years. In addition, an age-period-cohort analysis confirmed this increasing trend in the prevalence of social isolation over time. A number of issues brought about by an ageing population in the context of rapid urbanization and economic development in China might exacerbate social isolation, such as the ‘rise of living alone’, the ‘epidemic of loneliness’ and dramatic lifestyle changes [54,55,56].

Similar to the findings of previous studies [47, 57], we also observed a general and gradual increase in social isolation with age. Participants aged over 60 years were more than twice as likely to experience social isolation compared to those aged 45–59 years (41.92%-77.67% vs 20.76%-25.26%). This is comparable to the prevalence of social isolation among older people in studies from other Asian countries [58], although the prevalence of social isolation did not show an upward trend in all age groups during this period. This finding may be explained by the fact that older adults have been exposed to risk factors for isolation for a longer period of time than middle-aged adults due to a greatly reduced social network as a result of old age, such as migration of children, other relatives, and friends, as well as the death or increasing disability of social network members. Some studies have also suggested that the oldest old are more likely to perceive themselves as isolated than the young old, and the source of the greater feelings of isolation and the perceived lack of social support that are more common among the oldest adults should be further investigated [6, 59].

We also confirmed that being female, having a low educational attainment and living in a rural area are risk factors for social isolation, which is consistent with most findings, although contrary evidence also exists [5, 60]. A longitudinal study in China indicated the mechanisms that the moderating effects of education and gender differences on the relationship between social isolation and depression were examined among older adults, and suggested that men with higher education may be more vulnerable to the detrimental effects of social isolation. Policy-based proposals to improve social connectedness are needed [15], especially in rural areas of China where more older adults live alone compared to in urban areas [32]. We found that the prevalence of social isolation varied strongly with the socio-economic factors of interest. The more disadvantaged economically people are in terms of education, medical insurance and pension (income), the higher the prevalence of social isolation. In addition, the increasing trends in social isolation are more pronounced with lower socioeconomic status over time. Social isolation is known to result from a complex interplay of socioeconomic power and inequalities. Our findings support the notion that those who are marginalized are more likely to experience social isolation [61]. It is well known that socio-economic status plays a dominant role in shaping the living conditions and physical environments that provide access and opportunities to develop and maintain social connections. Inequalities in social connectedness begin with education in the early years of life, with lower levels of education being consistently associated with a higher prevalence of social isolation. Therefore, more equal opportunities for a good education may improve social inclusion, as some studies have suggested, to mitigate the subsequent health inequalities of social isolation [5].

Although no increasing trend in the prevalence of social isolation was found among the middle-aged and older adults with the worst mental and physical health, we found that the worse people’s health status, the higher the risk of social isolation. For example, in the depressed group, the prevalence of social isolation over 10 years ranged from 47.70% to 55.21% ; and in the group with severe limitations in activities of daily living, the prevalence was as high as 53.83%-63.98%, with one in two people being socially isolated in this group. When controlling for various confounding factors, the prevalence of social isolation among people in poor health is 1.348-1.466 times higher than among people in good health. Previous studies have confirmed the significant relationship between social isolation and well-being [28,29,30, 62], which is mutually reinforcing. The main explanatory power of the relationship lies with health being a consistent predictor in the literature. This may be because deteriorating health, particularly in the form of chronic conditions and functional disability, is a major determinant of quality of life in later life [63]. However, the mechanisms by which health and social isolation operate are not clear, e.g. which types of social relationships are more relevant to health and the pathways of causality require further investigation.

In addition, the prevalence of social isolation was found to be three times higher among those who did not use the internet than among those who did, and after controlling for confounders, the risk of social isolation was 1.443 times higher among those who used the internet, which is consistent with previous studies showing that internet use alleviates social isolation [26, 27]. In other words, internet-based contact with the outside world provides social support, participation in activities of interest, and increased self-confidence [59]. However, more than 90% of participants had not used the internet in the previous month, indicating that internet usage was not a common habit among middle-aged and older adults before the COVID-19 pandemic. This may be because it was difficult for some middle-aged and older adults to acquire the basic skills needed to use the internet, and they may not have been able to afford frequent operating system changes [12]. Therefore, skills training in the use of smart devices may be appropriate for this age group. Our findings are similar to a recent cohort study of older adults in Taiwan [25] that found that social isolation was associated with poorer self-reported sleep quality after controlling for demographic, health, cognitive, and depressive factors. In addition, we found that long sleep (9 or more hours) was strongly associated with social isolation, which has only been reported in one previous study [64]. We support the study's point of view that people who are more socially isolated report spending more time in bed in the morning. Interestingly, unlike previous studies [17, 62], we found that smoking was no longer a significant factor influencing social isolation, whereas alcohol consumption was a protective factor against participants’ social isolation after controlling for other variables. A possible reason for this finding is the drinking culture in China, where drinking alcohol is the most common social activity and may strengthen social and business ties [65].

Inevitably, there are some limitations of the current study that need to be highlighted. Firstly, we censored a larger sample of missing variables, which may have made the results of the study more conservative. Furthermore, the trends in social isolation are cross-sectional and only show differences between individuals [66], The within-individual changes in social isolation over time may differ from the cross-sectional trends, and this is a direction for future study [67]. Second, due to the limitations of the secondary database, we only considered individual factors of the study population and ignored the influence of macro-environmental factors, such as policies at the societal level, which will be the focus of the next study. Third, the results of the APC analysis may be biased due to the problem of over-control, and we will continue to focus on the most important influencing factors. In addition, this study examined trends in social isolation among middle-aged and older adults before the COVID-19 pandemic, with the aim of providing insights into the development of policies to address social isolation among older adults during the pandemic.

Conclusions

In conclusion, we found a U-shaped trend in the prevalence of social isolation from 2011 to 2018 with an increasing trend from 2013 to 2018 among middle-aged and older adults in China, and we provided an in-depth examination of the relationships between social isolation and demographic, lifestyle, and health status. The findings of the study highlight the urgent need for interventions to reduce social isolation including improving sleep quality and internet skills. Disadvantaged groups in terms of age, economic status, and health status should be the focus of such interventions, especially in the era of COVID-19.

Availability of data and materials

Researchers may obtain the datasets after sending a data user agreement to the CHARLS team. https://charls.pku.edu.cn/.

References

  1. Nicholson NR. A review of social isolation: an important but underassessed condition in older adults. J Primary Prevent. 2012;33:137–52.

    Article  Google Scholar 

  2. Thu TPB, Ngoc PNH, Hai NM, Tuan LA. Effect of the social distancing measures on the spread of COVID-19 in 10 highly infected countries. Sci Total Environ. 2020;742:140430.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Block P, Hoffman M, Raabe IJ, Dowd JB, Rahal C, Kashyap R, et al. Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world. Nat Hum Behav. 2020;4:588–96.

    Article  PubMed  Google Scholar 

  4. Mautong H, Gallardo-Rumbea JA, Alvarado-Villa GE, Fernández-Cadena JC, Andrade-Molina D, Orellana-Román CE, et al. Assessment of depression, anxiety and stress levels in the Ecuadorian general population during social isolation due to the COVID-19 outbreak: a cross-sectional study. BMC Psychiatry. 2021;21:212.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Röhr S, Wittmann F, Engel C, Enzenbach C, Witte AV, Villringer A, et al. Social factors and the prevalence of social isolation in a population-based adult cohort. Soc Psychiatry Psychiatr Epidemiol. 2022;57:1959–68.

    Article  PubMed  Google Scholar 

  6. Cornwell EY, Waite LJ. Measuring social isolation among older adults using multiple indicators from the NSHAP study. J Gerontol B Psychol Sci Soc Sci. 2009;64B(Supplement 1):i38-46.

    Article  PubMed Central  Google Scholar 

  7. Su Y, Rao W, Li M, Caron G, D’Arcy C, Meng X. Prevalence of loneliness and social isolation among older adults during the COVID-19 pandemic: a systematic review and meta-analysis. Int Psychogeriatr. 2023;35:229–41.

    Article  PubMed  Google Scholar 

  8. Hämmig O. Health risks associated with social isolation in general and in young, middle and old age. PLoS One. 2019;14:e0219663.

    Article  PubMed  PubMed Central  Google Scholar 

  9. Luo F, Guo L, Thapa A, Yu B. Social isolation and depression onset among middle-aged and older adults in China: Moderating effects of education and gender differences. J Affect Disord. 2021;283:71–6.

    Article  PubMed  Google Scholar 

  10. Röhr S, Löbner M, Gühne U, Heser K, Kleineidam L, Pentzek M, et al. Changes in social network size are associated with cognitive changes in the oldest-old. Front Psychiatry. 2020;11:330.

    Article  PubMed  PubMed Central  Google Scholar 

  11. Plagg B, Engl A, Piccoliori G, Eisendle K. Prolonged social isolation of the elderly during COVID-19: between benefit and damage. Arch Gerontol Geriatr. 2020;89:104086.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Fan Q. Utilizing ICT to prevent loneliness and social isolation of the elderly: a literature review. Cuadernos de Trabajo Social. 2016;29:185–200.

    Article  Google Scholar 

  13. Tsuji T, Saito M, Ikeda T, Aida J, Cable N, Koyama S, et al. Change in the prevalence of social isolation among the older population from 2010 to 2016: a repeated cross-sectional comparative study of Japan and England. Arch Gerontol Geriatr. 2020;91:104237.

    Article  PubMed  Google Scholar 

  14. McPherson M, Smith-Lovin L, Brashears ME. Social isolation in America: changes in core discussion networks over two decades. Am Sociol Rev. 2006;71:353–7.

    Article  Google Scholar 

  15. Kannan VD, Veazie PJ. US trends in social isolation, social engagement, and companionship ⎯ nationally and by age, sex, race/ethnicity, family income, and work hours, 2003–2020. SSM Population Health. 2023;21:101331.

    Article  PubMed  Google Scholar 

  16. Xia N, Li H. Loneliness, social isolation, and cardiovascular health. Antioxid Redox Signal. 2018;28:837–51.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Holt-Lunstad J, Smith TB, Baker M, Harris T, Stephenson D. Loneliness and social isolation as risk factors for mortality: a meta-analytic review. Perspect Psychol Sci. 2015;10:227–37.

    Article  PubMed  Google Scholar 

  18. Guo L, An L, Luo F, Yu B. Social isolation, loneliness and functional disability in Chinese older women and men: a longitudinal study. Age Ageing. 2021;50:1222–8.

    Article  PubMed  Google Scholar 

  19. Taylor HO, Taylor RJ, Nguyen AW, Chatters L. Social isolation, depression, and psychological distress among older adults. J Aging Health. 2018;30:229–46.

    Article  PubMed  Google Scholar 

  20. Santini ZI, Jose PE, York Cornwell E, Koyanagi A, Nielsen L, Hinrichsen C, et al. Social disconnectedness, perceived isolation, and symptoms of depression and anxiety among older Americans (NSHAP): a longitudinal mediation analysis. Lancet Public Health. 2020;5:e62-70.

    Article  PubMed  Google Scholar 

  21. Holt-Lunstad J. The potential public health relevance of social isolation and loneliness: prevalence, epidemiology, and risk factors. Public Policy Aging Rep. 2017;27:127–30.

    Article  Google Scholar 

  22. Cudjoe TKM, Roth DL, Szanton SL, Wolff JL, Boyd CM, Thorpe RJ Jr. The epidemiology of social isolation: national health and aging trends study. J Gerontol B. 2020;75:107–13.

    Article  Google Scholar 

  23. Plain C. Rates of social isolation vary by rurality and demographics - School of Public Health - University of Minnesota. School of Public Health. 2019. https://www.sph.umn.edu/news/rates-of-social-isolation-vary-by-rurality-and-demographics/. Accessed 31 Oct 2022.

  24. Holt-Lunstad J. A pandemic of social isolation? World Psychiatry. 2021;20:55–6.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Yu B, Steptoe A, Niu K, Ku P-W, Chen L-J. Prospective associations of social isolation and loneliness with poor sleep quality in older adults. Qual Life Res. 2018;27:683–91.

    Article  PubMed  Google Scholar 

  26. Miguel I, da Luz HA. Internet use for active aging: a systematic literature review. In: 2017 12th Iberian Conference on Information Systems and Technologies (CISTI). 2017. p. 1–6.

    Google Scholar 

  27. Beneito-Montagut R, Cassián-Yde N, Begueria A. What do we know about the relationship between internet-mediated interaction and social isolation and loneliness in later life? Qual Ageing Older Adults. 2018;19:14–30.

    Article  Google Scholar 

  28. Chatters LM, Taylor HO, Nicklett EJ, Taylor RJ. Correlates of objective social isolation from family and friends among older adults. Healthcare. 2018;6:24.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Buchman AS, Boyle PA, Wilson RS, James BD, Leurgans SE, Arnold SE, et al. Loneliness and the rate of motor decline in old age: the rush memory and aging project, a community-based cohort study. BMC Geriatr. 2010;10:2–8.

    Article  Google Scholar 

  30. Wister AV, Li L, Mitchell BA. A study of social isolation, multimorbidity and multiple role demands among middle-age adults based on the canadian longitudinal study on aging. Int J Aging Hum Dev. 2022;94:312–43.

    Article  PubMed  Google Scholar 

  31. Förster F, Luppa M, Pabst A, Heser K, Kleineidam L, Fuchs A, et al. The role of social isolation and the development of depression. A comparison of the widowed and married oldest old in Germany. Int J Environ Res Public Health. 2021;18:6986.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Henning-Smith C, Tuttle M, Tanem J, Jantzi K, Kelly E, Florence LC. Social isolation and safety issues among rural older adults living alone: perspectives of meals on wheels programs. J Aging Soc Policy. 2022;28:1–20.

  33. Grenade L, Boldy D. Social isolation and loneliness among older people: issues and future challenges in community and residential settings. Aust Health Review. 2008;32:468–78.

    Article  Google Scholar 

  34. China Daily. Guideline seeks better lives for elderly. 2022. http://english.www.gov.cn/policies/policywatch/202201/05/content_WS61d4ef7dc6d09c94e48a324a.html. Accessed 16 Sep 2022.

  35. Fang EF, Xie C, Schenkel JA, Wu C, Long Q, Cui H, et al. A research agenda for ageing in China in the 21st century (2nd edition): Focusing on basic and translational research, long-term care, policy and social networks. Ageing Res Rev. 2020;64:101174.

    Article  PubMed  PubMed Central  Google Scholar 

  36. Irvine KN, Fisher D, Marselle MR, Currie M, Colley K, Warber SL. Social isolation in older adults: a qualitative study on the social dimensions of group outdoor health walks. Int J Environ Res Public Health. 2022;19:5353.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Qi X, Pei Y, Wang K, Han S, Wu B. Social isolation, loneliness and accelerated tooth loss among Chinese older adults: a longitudinal study. Community Dent Oral Epidemiol. 2022;51:cdoe.12727.

    Google Scholar 

  38. Dogra S, Dunstan DW, Sugiyama T, Stathi A, Gardiner PA, Owen N. Active Aging and Public Health: Evidence, Implications, and Opportunities. Annu Rev Public Health. 2022;43:439–59.

    Article  PubMed  Google Scholar 

  39. World Health Organization. Decade of Healthy Ageing (2021–2030). 2020. https://www.who.int/initiatives/decade-of-healthy-ageing. Accessed 29 Aug 2022.

  40. Zhao Y, Hu Y, Smith JP, Strauss J, Yang G. Cohort profile: the China Health and Retirement Longitudinal Study (CHARLS). Int J Epidemiol. 2014;43:61–8.

    Article  PubMed  Google Scholar 

  41. Chen X, Wang Y, Strauss J, Zhao Y. China Health and Retirement Longitudinal Study (CHARLS). In: Gu D, Dupre ME, editors. Encyclopedia of Gerontology and Population Aging. Cham: Springer International Publishing; 2021. p. 948–56.

    Chapter  Google Scholar 

  42. Zhao Y, Strauss J, Chen X, et al. China health and retirement longitudinal study wave 4 user’s guide. Beijing: National School of Development, Peking University; 2020. p. 5–6.

  43. Luo M, Li L. Social isolation trajectories in midlife and later-life: patterns and associations with health. Int J Geriatr Psychiatry. 2022;37.37(5):10.1002/gps.5715.

  44. Shankar A, McMunn A, Banks J, Steptoe A. Loneliness, social isolation, and behavioral and biological health indicators in older adults. Health Psychol. 2011;30:377–85.

    Article  PubMed  Google Scholar 

  45. Cr G, L W, C C. Social isolation and loneliness as risk factors for the progression of frailty: the English Longitudinal Study of Ageing. Age and ageing. 2018;47:392–7.

  46. Donovan NJ, Blazer D. Social isolation and loneliness in older adults: review and commentary of a national academies report. Am J Geriatr Psychiatry. 2020;28:1233–44.

    Article  PubMed  PubMed Central  Google Scholar 

  47. Zhou Z, Lin C, Ma J, Towne SD, Han Y, Fang Y. The association of social isolation with the risk of stroke among middle-aged and older adults in China. Am J Epidemiol. 2019;188:1456–65.

    Article  PubMed  Google Scholar 

  48. He M, Ma J, Ren Z, Zhou G, Gong P, Liu M, et al. Association between activities of daily living disability and depression symptoms of middle-aged and older Chinese adults and their spouses: a community based study. J Affect Disord. 2019;242:135–42.

    Article  PubMed  Google Scholar 

  49. Feng Z, Li Q, Zhou L, Chen Z, Yin W. The relationship between depressive symptoms and activity of daily living disability among the elderly: results from the China Health and Retirement Longitudinal Study (CHARLS). Public Health. 2021;198:75–81.

    Article  CAS  PubMed  Google Scholar 

  50. Ma WJ, He B, Wang YH, Huang W, Zhou J. Relationships between Kazakh elders’ disability severity and informal care time in far north-western low-income areas in China: the mediating roles of caregiver health and home-based care quality. Int J Nurs Pract. 2022;28:e13022.

    Article  PubMed  Google Scholar 

  51. Andresen EM, Malmgren JA, Carter WB, Patrick DL. Screening for depression in well older adults: evaluation of a short form of the CES-D. Am J Prev Med. 1994;10:77–84.

    Article  CAS  PubMed  Google Scholar 

  52. Boey KW. Cross-validation of a short form of the CES-D in Chinese elderly. Int J Geriatr Psychiatry. 1999;14:608–17.

    Article  CAS  PubMed  Google Scholar 

  53. Bernardo JM, Bayarri MJ, Berger JO, Dawid AP. Intrinsic Estimation. Bayesian Stat. 2003;7:465–76.

    Google Scholar 

  54. Carr D. Aging alone? International perspectives on social integration and isolation. J Gerontol B. 2019;74:1391–3.

    Article  Google Scholar 

  55. Hou B, Nazroo J, Banks J, Marshall A. Are cities good for health? A study of the impacts of planned urbanization in China. Int J Epidemiol. 2019;48:1083–90.

    Article  PubMed  Google Scholar 

  56. Ren X, Lu C. Effect of children’s support on depression among older adults living alone or with a spouse: a comparative analysis between urban and rural areas of China. Sustainability. 2021;13:6315.

    Article  Google Scholar 

  57. Golaszewski NM, LaCroix AZ, Godino JG, Allison MA, Manson JE, King JJ, et al. Evaluation of social isolation, loneliness, and cardiovascular disease among older women in the US. JAMA Netw Open. 2022;5:e2146461.

    Article  PubMed  PubMed Central  Google Scholar 

  58. Ibrahim R, Abolfathi Momtaz Y, Hamid TA. Social isolation in older Malaysians: prevalence and risk factors: social isolation in older Malaysians. Psychogeriatrics. 2013;13:71–9.

    Article  PubMed  Google Scholar 

  59. Chen YR-R, Schulz PJ. The effect of information communication technology interventions on reducing social isolation in the elderly: a systematic review. J Med Internet Res. 2016;18:e4596.

    Article  Google Scholar 

  60. Victor C, Scambler S, Bond J, Bowling A. Being alone in later life: loneliness, social isolation and living alone. Rev Clin Gerontol. 2000;10:407–17.

    Article  Google Scholar 

  61. Keister LA. Wealth in America: trends in wealth inequality. Cambridge: Cambridge University Press; 2002.

  62. Kobayashi LC, Steptoe A. Social isolation, loneliness, and health behaviors at older ages: longitudinal cohort study. Ann Behav Med. 2018;52:582–93.

    Article  PubMed  PubMed Central  Google Scholar 

  63. Chappell NL, Badger M. Social isolation and weil-being. J Gerontol. 1989;44:S169-176.

    Article  CAS  PubMed  Google Scholar 

  64. Benson JA, McSorley VE, Hawkley LC, Lauderdale DS. Associations of loneliness and social isolation with actigraph and self-reported sleep quality in a national sample of older adults. Sleep Res Soc. 2021;44:zsaa140.

    Article  Google Scholar 

  65. Cheng HG, Deng F, Xiong W, Phillips MR. Prevalence of alcohol use disorders in mainland China: a systematic review. Addiction. 2015;110:761–74.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Eckhard J. Indicators of social isolation: a comparison based on survey data from Germany. Soc Indic Res. 2018;139:963–88.

    Article  Google Scholar 

  67. Valtorta N, Hanratty B. Loneliness, isolation and the health of older adults: do we need a new research agenda? J R Soc Med. 2012;105:518–22.

    Article  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors gratefully acknowledge the China Health and Retirement Longitudinal Study (CHARLS) team for providing the datasets. We would like to thank all of the volunteers and participants involved in the CHARLS research.

Funding

This study was supported by the National Natural Science Foundation of China (Grant number: 71804029).

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Author notes

  1. Yanwei Lin and Tingting Zhu contributed equally to this work and should be considered co-first authors.

Authors and Affiliations

  1. Department of Social Medicine and Health Management, School of Public Health, Guangdong Medical University, Dongguan, China

    Yanwei Lin

  2. Department of Epidemiology and Health Statistics, School of Public Health, Guangdong Medical University, Dongguan, China

    Tingting Zhu & Xiaoyan Zhang

  3. Institute of Public Health and Wellness, Guangdong Medical University, 1#, Xincheng Avenue, Songshanhu District, Dongguan, Guangdong, 523808, China

    Yanwei Lin, Tingting Zhu, Xiaoyan Zhang & Zhirong Zeng

  4. Care Policy and Evaluation Centre (CPEC), London School of Economics and Political Science, London, WC2A2AE, UK

    Yanwei Lin

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  4. Zhirong Zeng
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Contributions

Yanwei Lin, Zhirong Zeng and Tingting Zhu had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Yanwei Lin and Tingting Zhu contributed equally to this work and are considered co–first authors.

Corresponding author

Correspondence to Zhirong Zeng.

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Ethics approval and consent to participate

Ethical approval for collecting data on human subjects was obtained and renewed annually at the Peking University Institutional Review Board (IRB00001052-11015). All the participants provided signed informed consent at the time of participation. All methods in this study on humans described in the manuscript were performed in accordance with national law and the Helsinki Declaration of 1975.

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The authors declare no competing interests.

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Supplementary Information

Additional file 1: Table S1.

Variable assignment.

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Lin, Y., Zhu, T., Zhang, X. et al. Trends in the prevalence of social isolation among middle and older adults in China from 2011 to 2018: the China Health and Retirement Longitudinal Study. BMC Public Health 24, 339 (2024). https://doi.org/10.1186/s12889-024-17734-4

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BMC Public Health

ISSN: 1471-2458

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