Elsevier

Social Science & Medicine

Volume 130, April 2015, Pages 181-189
Social Science & Medicine

An exploratory discrete-time multilevel analysis of the effect of social support on the survival of elderly people in China

https://doi.org/10.1016/j.socscimed.2015.02.020Get rights and content

Highlights

  • Applies social support theory for the survival of the elderly in China.

  • Studies living arrangements, received and perceived support as social support.

  • The effect of social support varies according to different support resources.

  • Living with spouse and financial independence are associated with higher survival rates.

  • Perceiving care support from any resource is associated with higher survival rates.

Abstract

This study undertakes a survival analysis of elderly persons in China using Chinese Longitudinal Healthy Longevity Survey 2002–2008. Employing discrete-time multilevel models, we explored the effect of social support on the survival of elderly people in China. This study focuses on objective (living arrangements and received support) and subjective activities (perceived support) of social support, finding that the effect of different activities of social support on the survival of elderly people varies according to the availability of different support resources. Specifically, living with a spouse, financial independence, perceiving care support from any resource is associated with higher survival rates for elderly people. Separate analysis focusing on urban elderly and rural elderly revealed broadly similar results. There is a larger difference between those perceiving care support from family or social service and not perceiving care support in urban areas comparing to those in rural areas. Those who cannot pay medical expenses are the least likely to survive. The higher level of economic development in province has no significant effect on the survival of elderly people for the whole sample model and the elderly people in urban areas; however, there is a negative influence on the survival of the rural elderly people.

Introduction

A growing body of studies have illustrated that social support has beneficial effects on various health outcomes, including survival (Lyyra and Heikkinen, 2006, Uchino, 2006). Yet, previous studies also found that social support has multiple dimensions and different sources of social support can have different effects on the health outcomes and survival (Uchino, 2009, Zhou and Qian, 2008). Whereas extensive studies have investigated the relationship between different dimensions of social support and mortality for elderly people in developed countries (Holt-Lunstad et al., 2010, Uchino, 2009), few have focused on such relationships in China, a developing country with a large aging population and severely underdeveloped social security provisions in response to its rapid population aging. Unlike Western nations where elderly social support, particularly financial and medical support, are primarily provided by the state, the family has been the cornerstone of such social support for elderly people in China. On the other hand, the Chinese government has been carrying out major social welfare reforms in recent years, seeking to improve China's social security provision for its citizens, especially the elderly population. There are vast differences in social support from family and the state between rural and urban areas. For example, fertility rates are higher in rural areas than urban areas; which may increase the probability of rural elderly persons living with their children compared to urban elderly persons. This also may reduce the likelihood of rural elderly persons living in nursing homes than urban elderly persons, as nursing homes in rural areas are very rare and most of rural elderly persons living in such homes are without any living children. On the other hand, rural elderly individuals are less likely to be covered by pension than their urban counterparts, which increases the former's likelihood to rely on themselves or their family for financial support. While medical insurance coverage has dramatic increased in rural areas since 2003, nonetheless, those rural elderly persons who have medical insurance are still stuffing the high out-of-pocket medical expenditure. This could also increase their likelihood of relying on themselves or their family for medical support. Therefore, we hypothesize that the effect of social support on the survival status of older people may be different between rural and urban areas; social support from family may have a more protective effect on the survival in rural areas than urban areas; while, social support from state may have a more beneficial effect on the survival status of urban elderly people compared to rural elderly people.

A systematic study on the relationship between different dimensions of social support and the survival of Chinese elderly persons according to different availability of support resources will not only advance our knowledge and help fill the empirical gap in this field but also contribute greatly to China's social policy decision making on how to improve the well-being and the longevity of elderly persons, especially for its most vulnerable members. In this study, we examine the effects of various dimensions of social support (including living arrangements, received and perceived support) provided by different sources such as family, state or self on the survival of elderly persons. We also compare survival differences between elderly persons living in urban and rural areas to address the substantial socioeconomic and institutional rural-urban inequalities in China. This research is informed by social support theories and the results contribute to a better understanding of the relationships between social supports and the survival of elderly persons in China. The first round of nation-wide longitudinal data from the Chinese Longitudinal Healthy Longevity Survey (CLHLS) is used to examine these relationships in contemporary China.

Social support refers to emotional support, tangible support such as financial assistance, information support and companionship that elderly people receive (Langford, 1997). It has been found to be positively associated with better well-being and lower level social support is linked with higher mortality risks (Holt-Lunstad et al., 2010, Uchino, 2006). This is because as a psychosocial factor, social support provides a buffer against stress and protects people from potential adverse effects in stressful situations (Uchino, 2006). As a physiological factor, social support helps facilitate healthier behaviours and medical regimes and results in better health outcomes (Uchino, 2009). These factors are particularly important for elderly people as they are more reliant on resources available in the area of residence than younger adults (Robert and Li, 2001). The right type and sources of social support may enhance their capacity to cope with stress, improve their health outcomes and subsequently lower their mortality risks (Uchino, 2006).

Social support for elderly persons includes both objective (such as living arrangements and received support) and subjective activities (such as perceived support) (Zhou and Qian, 2008). Living with their children or other family members, elderly individuals should have regular interactions with their family members and receive physical and emotional support (Zhou and Qian, 2008). Received support is another objective activity, which refers to the reported receipt of support resources (Uchino, 2009). In terms of subjective activity of social support (perceived support), elderly people may believe that they would be taken care by their family members, social networks or community when they are sick; even for those live independently elderly people, having such belief could shape their positive attitudes to buffer the effect of stressful events (Zhou and Qian, 2008). The effect of these two activities (dimensions) of social support on the health outcomes or survival status may vary according to different availability of support resources. The following section summarizes existing findings on the relationship between these dimensions of social support and mortality according to different sources of social support.

As the objective activity of social support, living arrangements are important to the health of elderly persons because they play a role in providing social support and determining social roles associated with different household structures (Waite and Hughes, 1999). Studies have suggested that living arrangements have a great impact on mortality risks, when comparing people living alone, as a married couple with or without children, as a cohabitating non-married couple or with other persons, and living in private or nursing homes (Davis et al., 1997, Herm et al., 2013, Koskinen et al., 2007).

Living with a spouse has consistently been found to be beneficial for the survival of elderly people (Waite and Hughes, 1999). The explanation has largely been attributed to the protective effect of marriage as marriage provides provision of social/economic support; healthier individuals are more likely to marry or stay married into the married group; and better health behaviours of married individuals may be in turn the result of a direct effect of marriage (Manzoli et al., 2007).

For the other living arrangements, elderly women living alone only exhibit slightly higher mortality risk than married women but the mortality of elderly men living alone is more unfavourable compared to those living with a spouse (Herm et al., 2013, Koskinen et al., 2007). Davis et al. (1997) argued that elderly people who live alone or with spouse are more likely to develop better coping mechanisms and contingency plans (use of formal health services) than those in other living arrangements. Elderly persons living in institutions or nursing homes have higher mortality rates than those living in private households (Herm et al., 2013). The results for elderly people living with adult children in developed countries are rather mixed depending on the gender of the elderly person and their marital status (Herm et al., 2013, Waite and Hugfies, 1999).

In terms of living arrangements in China, Li et al. (2009) found that elderly people living with their spouse are associated with a greater health advantage than elderly people living in other living arrangements. Intergenerational co-residence or living with adult children has been a traditional living arrangement in China for elderly people to facilitate family care and support for elderly persons or out of necessity due to financial constraints of elderly people. As a return for such support, many elderly people are expected to carry out household chores for the entire family or care for grandchildren, both of which may lead to a decline in their physical function (Wang et al., 2009). Moreover, their wellbeing and quality of life may be affected by the potentially complicated family relationships such as intergenerational conflicts (Zhou and Qian, 2008). It appears that elderly persons living in a nursing home are more satisfied with their quality of life than those living with children (Zhou and Qian, 2008). Elderly individuals living alone (who are more likely to be of a higher socioeconomic status) tend to enjoy a better quality of life than those living with children (Sereny, 2011).

Fertility rates are higher in rural areas than urban areas, which may increase the probability of rural elderly persons living with their children compared to urban elderly people. Urban elderly people tend to have higher socioeconomic status than their rural counterparts, which may increase the likelihood to living alone or with spouse to enjoy a better quality of life. Nursing homes are very rare and less developed in rural areas than in urban areas. Most of rural elderly people living in nursing care are without any living children; while in contrast, in urban areas nursing homes have been extended to people who can afford to pay. Therefore, we hypothesize that living with children has no advantage in increased survival compared to living alone or living with spouse, and those elderly persons in a nursing home may have increased survival compared to other living arrangements. We also hypothesize that the different living arrangements contribute different effects on elderly people's survival in rural and urban areas. For the rural elderly persons, we hypothesize that those elderly people in a nursing home may have no benefit effect in increased survival compared to other living arrangements; for the urban elderly people, living alone, living with spouse or living in nursing homes could have advantage in increased survival compared to living with children.

Received support is another objective activity of social support, which refers to the actual transfer of advice, aid, and other support resources offered by providers (Wethington and Kessler, 1986). The results from testing the relationship between received support and mortality are highly variable, as received support has been found to produce both beneficial and detrimental effects on health or mortality (Uchino, 2009). Uchino (2009) summarized some explanations for such conflicting results. For example, as one of the many coping options, the effectiveness of received support may depend heavily on the context (Berg and Upchurch, 2007). The type of support received in a particularly stressful context may determine whether its influence is positive or negative. The effectiveness of received support could also be affected by conflict in relationships. For instance, a number of laboratory studies suggest that the coexistence of positivity and negativity in relationships can decrease the efficacy of received support (Holt-Lunstad et al., 2007). Received support could also contribute to a drop in self-esteem or invasion of one's independence. Results from a study (Bolger and Kessler, 2000) suggested that received support may be supportive only if it is not noticed by the recipient.

In terms of the resources that elderly people receive in China, it is a common practice for elderly individuals to receive financial assistance and medical support from family members. It is not only a reflection, but an expectation formally and informally endorsed by the state. This is particularly true for rural elderly persons, as most are not in any pension schemes which would not allow them to be financially independent. Krause et al. (1998) suggest that receiving economic assistance from family members is associated with greater psychological distress among Chinese elderly people. This is because “relations with family members may at times be difficult, and providing economic assistance when resources are scarce may create feelings of burden and promote interpersonal tension” (Krause et al., 1998; pp. 66). On the other hand, as we discussed previously, some elderly persons have to carry out household chores for the entire family or care for grandchildren in order to expect financial or other support from their children. This may lead to a decline in their physical function; hence increase the risk of mortality. Indeed, some researches on the health of elderly people in China find that economically dependent elderly persons are more likely to develop disabilities (Gu and Xu, 2007), less likely to report good health (Feng et al., 2013a), and exhibit lower life satisfaction and poorer mental health (Li et al., 2007). The rapidly declining mortality rate and fertility rate in China suggest that fewer adult children now need to economically support their elderly parents for a long period. This may create more tension and conflicts within the household, and may require elderly people to carry out more household chores by their children which could be detrimental to their health and survival. Hence, we hypothesize that economically dependent elderly people have worse survival rates. These effects may be different between rural and urban elderly people. We hypothesize that received financial assistance and medical support from family members may be worse in survival of rural elderly people than urban elderly people as rural elderly people are less likely to be covered by pension and medical insurance and more likely to rely on their family members as resources than their urban counterparts.

Perceived support is a subjective perception which refers to one's potential access to social support (Uchino, 2009). Findings reveal a consistent association between perceived support and lower mortality rates (Berkman et al., 1992, Brummett et al., 2001, Uchino, 2009). Perceived support helps reduce negative emotional and behavioural responses to stressful events (Lyyra and Heikkinen, 2006) and often times is connected with proactive coping, healthier behavioural choices and cooperation with medical regimes (Aspinwall and Taylor, 1997, DiMatteo, 2004). It has been found to be more important in buffering the effect of stressful events (Wethington and Kessler, 1986), acts as a better indicator for people's psychological well-being (van der Zee et al., 1997), and provides more beneficial physical health outcomes than actual received support (Uchino, 2009).

For elderly people in China, the thoughts of being cared for when sick provide a sense of security that could have important beneficial effects on their survival outcomes. Studies have suggested that perceived support contributes to a better quality of life (Zhou and Qian, 2008) and better self-rated health (Feng et al., 2013a, Feng et al., 2013b) for Chinese elderly individuals. This creates a perceived quality of intergenerational relationships which act as a good reflection on perceived social support from adult children while also greatly affecting older Chinese parents' psychological well-being (Chen and Silverstein, 2000). Therefore, we hypothesize that elderly people who perceive they have care and support when sick have higher survival rates than those without any sources of care. This hypothesis may hold true for both rural and urban elderly people.

A main reason many Chinese elderly people depend so heavily on family as their social support is due to the unequal and inadequate provision of institutional social support. In China, social security systems are severely underdeveloped and unevenly distributed, particularly in rural areas (Lei and Lin, 2009). Less than 5% of rural elderly individuals are covered by pension schemes compared to two thirds of the urban elderly (Han and Wei, 2012). This causes the majority of the rural elderly people to either seek financial support from family or continue working into (extreme) old age (Cai et al., 2012).

Health care is another major component in social security support. Since the economic reforms in China, the government reduced spending on health care, which has greatly exacerbated the inequalities between urban and rural areas in China (Li, 2010). The urban-rural health care inequality has persisted because many of the recent health reforms have focused on urban areas (Li, 2010). In rural areas, although the coverage of the New Cooperative Medical System (NCMS) have extended from 12.6% in 2003 to 92.5% in 2008 (National Health Services Survey, 2008), it has not improved rural residents' formal access to medical care due to high out-of-pocket payments (Lei and Lin, 2009). According to the 2005 WHO report, over 60% of patients in rural areas were unable to afford the cost needed to remain in hospital to complete treatment (WHO, 2005). Gu et al. (2009) showed that access to healthcare increased odds of healthy survival by 22–68% after controlling for demographic and socioeconomic variables in China. The rural-urban disparity in healthcare may lead to differential survival status between elderly people from these two societies.

The institutional provision of social support also exhibits considerable inequality between provinces. Since the fiscal decentralization and increased local financial autonomy after the economic reform, each province is allowed to implement its own social security policy for the contribution rates and benefits of social security. The quality and quantity of the social security resources depends on the level of economic development of each province. This results in great variations between coastal and inland areas (Brixi et al., 2011). Feng et al. (2013b) found that elderly people in provinces with higher levels of health facilities are more likely to report adequate medical support. On the other hand, family support also displays geographical disparity with the highest family support found in the most and least developed provinces, and the weakest family support from mid-level developed urban regions (Lin, 2002). Such geographical inequality in institutional social support and family may contribute to the different mortality risks between provinces.

This paper aims to build on that of Feng et al. (2013a), but instead of using self-rated health as the outcome, survival is analysed in discrete-time multilevel models (Barber et al., 2000). More specifically, the paper investigates the effects of different dimensions of social support on survival for elderly people including: living arrangements, received support, and perceived support according to different support resources (mainly from state, family and self). Urban-rural difference on the relationship between social support and survival status are also investigated. The study is informed by social support theory and the results will fill the empirical gap in the relationship between social support and survival in a developing country where social security provision has yet to catch up with the speed and degree of aging.

This study uses the three-wave (2002, 2005 and 2008) Chinese Longitudinal Healthy Longevity Survey (CLHLS). The CLHLS is the first nation-wide longitudinal survey with the largest sample of oldest-old ever conducted in a developing country. The first wave was conducted in 1998 in half of the counties/cities randomly selected from 22 provinces1 in China with an attempt to interview all centenarians in the sampled counties/cities. For each centenarian with a pre-designated random code, one nearby octogenarian and one nonagenarian were randomly interviewed with pre-designated age and sex. The ‘nearby’ refers to the same village or street, or the same town, county or city, when applicable. The sampling strategy was designed to ensure comparable numbers of randomly selected male and female octogenarians and nonagenarians at each age from 80 to 99. After the second wave in 2000, the younger elderly people (those who are 60–79) were included in the third wave in 2002. There were 4889 younger elderly people (60–79), 4239 octogenarians, 3747 nonagenarians, and 3189 centenarians in the 2002 wave. Of the total 16,064 elderly persons from the 2002 wave, 8175 were re-interviewed in 2005 and 5874 elderly persons had died before the 2005 survey whose information was collected from their next-of-kin. In all, 2129 respondents from the 2002 waves are not present in the 2005 survey, of which 2015 have unknown information and 114 are known as deceased, but with missing information at the time of their death. We excluded those with missing information from the analysis at the onset. In the 2008 survey, 4190 were re-interviewed, 2513 had died before the survey and 1472 respondents were lost to follow-up. The latter are included in the study but did not contribute any information beyond 2005. To produce unbiased results, we assumed the missing observations on the outcome did not depend on the response after accounting for the predictors to meet the assumption of Missing at Random (MAR) (Rubin, 1976). For those who were lost in the 2008 follow up waves, we found that only their age, urban/rural residence and family income exhibited significant differences from those who stayed in the 2008 wave (all other individual predictor variables showed no significant differences in predicting those lost to follow up (including self-rated health in 2005)). As a result, all these variables are included in the models to help satisfy the MAR assumption. Of course, as the data is missing, these assumptions cannot be fully evaluated. This study is exempt from ethnics approval since it is an analysis of secondary data.

The dependent variable is whether the respondent remained alive or died during each 6 month period between 2002 and 2008. This length of time was chosen to balance between detail and aggregation. Table 1 illustrates the aggregated form for the survival status of the 13,935 elderly people as a life table. The rows of the table represent the 6 month time periods, which include the initial time [brackets] and exclude the concluding time (parentheses). The first column is the risk set and shows the number of people who are known to be alive at the start of each period. This is reduced due to people's deaths and follow up attrition. The second column is the number of people known to have died in each period and is used to calculate the hazard ratio for the risk of dying in each particular period. The final column is the survival function which cumulates the period-to-period hazards of mortality together to assess the probability that a randomly selected elderly person will survive and not experience death. We omitted the final two six-month periods from the analysis since the low numbers of deaths suggest not all deaths are reported.

The predictor variables include four groups: individual demographic and socioeconomic characteristics (age, gender, urban/rural residence, education and income; family per capita income; categorised by quartiles), dimensions of social support (living arrangements, received financial support, received medical expense and perceived care support when sick), individual health status (self-rated health), and province economic development (province mean household income2). The distribution of these variables at baseline is shown in Table 2 (the separated distribution of urban-rural areas are also presented). For those variables with missing values, we used a multiple imputation method to estimate the results based on 10 random imputations (Goldstein, 2009).

Given the hierarchical structure of the data, the appropriate model is a discrete time multilevel survival analysis (Barber et al., 2000, Goldstein et al., 2002). The discrete time approach has two advantages over continuous-time methods. First, the assumption in the continuous-time methods is the effect of a predictor on event occurrence is constant over time; second, non-proportional hazards and time-varying covariates are allowed in a discrete time approach.

A schematic formulation is as follows:logit(pijk)=logit(pijk1pijk)=β0+i=213αiTijk+β1X1ijk+β2X2jk+β3X3k++vk+ujkvkN(0, σv2);ujkN(0, σu2)where the observed response pijk is being alive (coded 1) at period i for individual j in province k. The logit of being alive in the period is linearly related to the effects of elapsed time since the first interview (captured by the set of coefficients associated with the 12 period dummies,Tijk), individual variables which may be time-variant (X1ijk) or time-invariant(X2jk) (gender, education and urban/rural residence), and province level variables (X3k). The parameter β0 represents the overall intercept log-odds across provinces for being alive, when all the predictor variables are set to zero. The α’s represents the effect of a passage of each 6 month time period. The terms β1 and β2 capture the ‘micro’ individual characteristic effects, and β3 captures the ‘macro’ province effect. The inclusion of time in the specification models accounts for the (log) underlying hazard of death (the probability of dying in a six-month period). The other parameters then assess how the hazard is modified by the other predictor variables. The vk terms are the random effects attributable to province-level residual differences after taking account of the fixed effects of time, individual effects, and province effects; the ujk terms are the random effects for individuals after taking account of time, individual effects and province effects. These random effects are on the logit scale and are assumed to be normally distributed with mean of 0 and variance of  σv2 and  σu02 respectively. Since this study is a survival analysis, and the elderly people who have passed away are not included in the next period, there are no random effects between individual level and the variance of  σu02 is 0 (Although there appears to be a three-level data structure of periods within individuals within provinces, there is a no variation at the individual-level 2 because death is an absorbing end state not a repeated event. The model therefore effectively has a two-level structure as the individual variance is zero).

We apply full Bayesian MCMC methods to estimate the models. Full Bayesian procedures allow the exact estimation of parameters without approximations and with full uncertainty propagation so that the imprecision associated with one parameter is taken into account in characterising the uncertainty of another parameter (Jones and Subramanian, 2012). In particular both the fixed and random parameters are estimated on the basis that the other set of estimates are unknown. Moreover, an important output from this approach is the Deviance Information Criterion (DIC, Spiegelhalter et al., 2002) which allows the comparison of the goodness-of –fit for a sequence of models that takes account of model complexity. This is defined in terms of the effective degrees of freedom consumed in the fitting of the model which is estimated as a by-product of the MCMC estimation. Finally, the confidence intervals for the estimates of the random parameters do not presume normality as they are allowed to be sampled from a positively skewed Gamma distribution.

The models were estimated using the MLwiN 2.27 software (Rasbash et al., 2009), using the default settings of a burn-in of 500 and a monitoring chain of 5000 simulations following the good-practice recommendations of Draper (2006). The convergence to a distribution was sped up by orthogonal parameterisation and hierarchical centring at the highest level three (Brown, 2012). Visual inspection of the traces of estimates showed that the defaults were sufficient for convergence and characterising the posterior distribution of the estimates, which was confirmed by a range of convergence diagnostics. Default priors were used to start the chains; these are designed to have the least possible effect on the resultant converged estimates.

Section snippets

Results

Table 2 presents descriptive information for the predictor variables at the base line for the current sample. The results indicate family is the dominant source of social support. Less than 20% of respondents live alone or in a nursing home. More than 90% of elderly individuals perceived care and support from their family members in times of sickness. On the other hand, 68.8% and 74.9% of respondents received finance and medical support from family respectively. There are vast differences in

Discussion and conclusion

Informed by social support theory, we systematically examined the effects of different dimensions of social support on the survival of elderly persons in China. Results show that the effect of different activities of social support on the survival status of elderly people varies according to the availability of different support resources. The empirical results have important implications for policy makers on how to improve the longevity of the rapidly growing elderly population and reduce

Acknowledgement

This research is self-funded and we are grateful to CLHLS (CLHLS datasets used in this study are jointly supported by NIH (R01AG023627), National Natural Sciences Foundation of China (71110107025, 71233001) and UNFPA.) for providing us with the data set. We would like to thank Susan Elliott and three anonymous reviewers for their helpful comments and suggestions. We are also grateful to Prof Jane Falkingham and Dr Athina Vlachantoni's helpful suggestions on the revisions.

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