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Social capital and depression: evidence from urban elderly in China a

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Weiming Cao , Lu Li , Xudong Zhou & Chi Zhou

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Institute of Social and Family Medicine, School of Public Health, Zhejiang University, PR China b

Health Management School, Hangzhou Normal University, Zhejiang, PR China Published online: 26 Aug 2014.

Click for updates To cite this article: Weiming Cao, Lu Li, Xudong Zhou & Chi Zhou (2015) Social capital and depression: evidence from urban elderly in China, Aging & Mental Health, 19:5, 418-429, DOI: 10.1080/13607863.2014.948805 To link to this article: http://dx.doi.org/10.1080/13607863.2014.948805

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Aging & Mental Health, 2015 Vol. 19, No. 5, 418429, http://dx.doi.org/10.1080/13607863.2014.948805

Social capital and depression: evidence from urban elderly in China Weiming Caoa, Lu Lia*, Xudong Zhoua and Chi Zhoub a

Institute of Social and Family Medicine, School of Public Health, Zhejiang University, PR China; bHealth Management School, Hangzhou Normal University, Zhejiang, PR China

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(Received 28 February 2014; accepted 20 July 2014) Objectives: To study the relationship between social capital and depression among older adults from urban China and the mediating effect of social support on the influence of social capital on depression. Methods: Data were collected from face-to-face interviews targeting older adults (N D 928, response rate D 68.1%) aged over 60 years residing in Hangzhou, China, in 2013. Indicators of social capital included both cognitive (trust and reciprocity) and structural (social network and social participation) aspects. The dependent variable depression was measured by the Geriatric Depression Scale, social support was measured by the Multidimensional Scale of Perceived Social Support, and sociodemographic variables (age, education, and household income) and physical function were controlled for analysis. The data were analyzed by factor analysis and a hierarchical regression model. Results: Trust, reciprocity, and social network were significantly associated with geriatric depression after controlling. Social participation was not correlated with geriatric depression. Social support partially mediated the relationships between social capital and geriatric depression. Conclusion: This study provides new evidence that social capital effectively mediates geriatric depression directly and indirectly. The intervention of social capital on depression should therefore consider the two pathways. Future longitudinal studies should help further understand the mechanisms linking social capital and depression. Keywords: China; elderly; social capital; depression; social support; social network

Introduction China’s population has rapidly aged over the last couple of decades. In 2010, China had 178 million people of 60 years old or above, 13.26% of its total population, and 47 million older people resided in cities, with average annual increase rate of approximately 4.64% from 2001 to 2010 (Nation Bureau of Statistics, 2011). The older population was projected to grow to 25% of its total population by 2050 (Chen, 2006). At the same time, China’s massive urbanization, sustainable industrialization, rapid economic development, and family structure simplification and downsizing have caused dramatic changes in the social environment and social relations in China. These specific factors commonly resulted in an increasing series of serious problems that urban older Chinese suffer from, including decreasing social integration, physiological dysfunction, and feeling of loneliness, which often result in inferiority, fear, and depression (Zhang, Xu, Nie, Zhang, & Wu, 2012). In particular, with little social security and few pensions to ease burden in life, China’s ‘only-child’ policy has made a young couple to support as many as four older parents, which led to acute stress for the aged (Dong, Beck, & Simon, 2010). Moreover, depression has become a more common psychological problem (20.5%) among the elderly in urban China (Wang & Zhao, 2012; Zhang et al., 2012). Social capital has been used to explain the impact of the changes within the social environment and social relationships on health over the past decades. Numerous studies have shown that social capital is associated with a

*Corresponding author. Email: [email protected] Ó 2014 Taylor & Francis

variety of health outcomes such as mortality, behavioral health problems, and other self-rated health problems (Fujisawa, Hamano, & Takegawa, 2009; Harpham, Grant, & Rodriguez, 2004; Kawachi, Kennedy, Lochner, & Prothrow-Stith, 1997; Kawachi, 2006; Murayama et al., 2013; Song, 2011; Yip et al., 2007). Current research on the relationship between social capital and geriatric depression has mainly focused on the elderly population of developed countries (Aihara, Minai, Kikuchi, Aoyama, & Shimanouchi, 2009; Forsman, Nyqvist, & Wahlbeck, 2011, 2012; Murayama et al., 2013; Nyqvist, Gustavsson, & Gustafson, 2006; Pollack & von dem Knesebeck, 2004), whereas research involving the elderly in developing countries is limited (Harpham et al., 2004; Kim, Auh, Lee, & Ahn, 2013), especially with regard to elderly urban Chinese (Norstrand & Xu, 2012). Therefore, this study investigates the relationship linking social capital to depression among older urban dwelling Chinese adults, despite the fact that the concept and measurement of social capital have been developed within western societies.

Social capital and its association with depression Social capital has become a popular construct over the past decade. Previous studies have characterized social capital as the resources embedded within social network (Bourdieu, 1986; Coleman, 1988). According to Putnam, Leonardi, and Nanetti (1994), social capital refers to the ‘features of social organization, such as trust, networks

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Aging & Mental Health and norms that can improve the efficacy of society by facilitating coordinated actions.’ However, the dimensions and aspects of social capital remain unclear, despite social capital being used in a growing number of public health papers. Typically, social capital could be divided into cognitive social capital (trust, reciprocity) and structural social capital (social participation) (Kawachi, 2006; Putnam et al., 1994). In addition, social capital was defined as ‘resources embedded in a social structure that are accessed and/or mobilized in purposive actions’ (Lin, Fu, & Hsung, 2001), which was generally called the ‘network resources’ approach, and used the Position Generator to measure a person’s access to expressive and instrumental resources. Because social network was thought of as a crucial element of the structure from social ties (Berkman & Kawachi, 2000), we defined social network as part of structural social capital in this study. When most studies used one approach, the state of being included in both types of measures in studies of social capital could deepen the understanding of the potential mechanisms by which social capital exerts influence on depression. In order to further examine the relationship between social capital and geriatric depression, this study would measure both dimensions of social capital, including cognitive and structural aspects. There is conflicting evidence about the relationship between depression and the sub-dimensions of social capital of the aged. A large number of studies suggested that geriatric depression was related to the sub-dimensions of social capital: trust (Forsman et al., 2011; Norstrand & Xu, 2012; Pollack & von dem Knesebeck, 2004), reciprocity (Pollack & von dem Knesebeck, 2004), social participation (Chiao, Weng, & Botticello, 2011; Kim et al., 2013; Yuasa, Ukawa, Ikeno, & Kawabata, 2013), and social networks (Moore, Daniel, Gauvin, & Dube, 2009; Moore et al., 2011; Song & Lin, 2009). Meanwhile, other studies reported that geriatric depression was not associated with sub-dimensions of social capital: trust (Pollack & von dem Knesebeck, 2004; Yuasa et al., 2013), reciprocity (Norstrand & Xu, 2012), social participation (Bassett & Moore, 2013; Fujisawa et al., 2009), or social networks (Bassett & Moore, 2013). These inconsistencies may have occurred for various reasons including using different scales that included partial aspects (i.e. trust, reciprocity, social network, and social participation) in different countries. Inconsistencies within the literature clearly indicate that further study on the relationship between social capital and depression is much needed. Furthermore, existing research works are mainly focused on developed countries, since social capital theory was developed in western countries. Given the Eastern cultural background, it should be investigated whether there are differences in the relationship between social capital and geriatric depression from urban China and western countries. Social support and its association with depression Social support, also thought to be a vital construct in the past decades, was broadly defined as the existence or

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availability of people on whom one can rely and people who let one know that one is cared about, valued, and loved (Sarason, Levine, Basham, & Sarason, 1983). It was hypothesized that social support could prevent or modulate responses to stressful events that are damaging mental health (Berkman & Kawachi, 2000). A large body of previous literature showed that social support played a vital role in depression via emotional economic, informational, and instrumental support among elderly people not only in developed society (Forsman et al., 2011; Grav, Hellzen, Romild, & Stordal, 2012), but also in developing society, especially in China (Chi & Chou, 2001; Chou & Chi, 2003; Wang & Zhao, 2012; Zimmer & Chen, 2012). It was observed that social support has a significant impact on depression from a cross-sectional study among elderly people in Hong Kong (Chi & Chou, 2001). Furthermore, the reciprocal relationship between social support and depressive symptoms was found from a longitudinal study of a representative community sample of the elderly population in Hong Kong (Chou & Chi, 2003). Social capital, social support, and depression Previous studies showed that social support and social capital (network) differ with respect to both the concept and pathway (Berkman & Kawachi, 2000; Song & Lin, 2009). Berkman and Kawachi (2000) thought that social capital usually emphasizes individual stock of social resources regardless of whether individuals are under stress, while social support generally focuses on functional aspect of social resources for individuals under stress. The pathways by which social capital and social support influence mental health can be described by two alternative, although not mutually exclusive, causal models (Figure 1) (Berkman & Kawachi, 2000). Song and Lin (2009) suggested that social capital and social support are two independent relationship-based causes of disease, which require different instruments of measurement. Therefore, this study aims to examine the association linking individual-level social capital, social support, and depression in the elderly in urban China and discusses the possible impacts of social capital and social support on geriatric depression. We propose two modeling hypotheses: (1) social capital has a direct positive effect on depression net of social support and (2) social capital has an indirect negative effect on depression via social support, under which there are two sub-modeling hypotheses: (2a) higher social capital, higher social support and (2b) higher social support, lower depression. In this study, the model includes two equations and two dependent variables: Equation (1) is social capital (X1: trust, reciprocity, social participation, social network), exogenous variables, and social support (Y2) for depression (Y1) of the regression equation, exogenous variables including sociodemographic variables (X2: age, gender), social-economic variables (X3: education level, household income), and physical function (X4). Equation (2) is social capital and exogenous variables for social support (Y2) of the regression equation.

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Figure 1. Pathways of social capital and social support on geriatric depression.

Specific equations are as follows: Y1 ¼ f ðX1 þ X2 þ X3 þ X4 þ Y2Þ;

(1)

Y2 ¼ f ðX1 þ X2 þ X3 þ X4Þ:

(2)

Methods Sample A cross-sectional population-based survey was carried out from May to August 2013, in Hangzhou, Zhejiang province, China, to investigate the relationship between social capital, social support, and geriatric depression. Hangzhou is a relatively developed area in China, with its urban per capita gross domestic product (GDP) being 14,013 US dollars in 2013, whereas 15,000 dollars for the developed countries in 2012. A two-stage stratified cluster sampling method was applied. To select the sites, first, three districts were randomly selected from Hangzhou’s 13 districts to represent high, middle, or low level of urbanization; second, one sub-district was randomly selected from each of the three districts; third, two communities were randomly selected from each of the three sub-districts to represent high or low development level, resulting a total of six communities selected for the survey. The target population included those living in the six selected communities, who were 60 years old or above, willing to participate in the study, and able to read, write, and communicate in Chinese language, and had no cognitive

disorder. A total of 1351 respondents were enrolled in the survey and accepted a face-to-face interview. A group of 10 college students from the School of Health Management, Zhejiang University conducted the interview after training. A total of 1062 questionnaires were collected, among which 134 were incomplete due to temporary physical or mental conditions (42), unwilling to spend time for the interview (55), other unexplained reasons (37), with the acceptance rate being 68.1%. The study was approved by an institutional Ethics Review Board. All the subjects were voluntary to join in this survey with informed consents and filled in the questionnaire anonymously. Participants were told that they had right to withdraw from this study at any time.

Measures Measurement of depression Depression was measured by the 30-item Geriatric Depression Scale (GDS) (Yesavage et al., 1983). The subject was asked to answer ‘Yes’ or ‘No’ to 10 positive questions such as ‘Are you basically satisfied with your life?’, ‘Are you hopeful about the future?’, and ‘Are you in good spirits most of time?’, with a ‘No’-answer suggesting depression. The remaining 20 items were negative questions such as ‘Do you feel helpless?’, ‘Do you feel that your life is empty?’, and ‘Do you often get bored?’, with the positive answer indicating depression.

Aging & Mental Health

0.715 in this study. Response to each of these questions ranged from ‘strongly agree’ (scored 5) to ‘strongly disagree’ (scored 1). Then the scores were summed up and dichotomized in the analysis as 1 D ‘high reciprocity’ if the sum score is at or above the mean and 0 D ‘low trust’ if otherwise.

The score of GDS ranged from 0 to 30 (the higher the score, the more severe geriatric depression), which has been widely used among the aged worldwide (Chan, Leung, Lee, Cheng, & Wu, 1994; Garcıa-Pe~ na et al., 2008). The Chinese version of the 30-item GDS has been used in Chinese cultural situations, with Cronbach’s a coefficient being 0.89, sensitivity 0.706, and specificity 0.701 (Chan et al., 1994). In this study, Cronbach’s a coefficient was 0.875.

Social network Social network was measured by the Position Generator (Lin et al., 2001), which assessed the respondent’s connections with persons in specific occupations for capturing social network. This study used an occupational prestige scale (Bian, 2004), which contained a list of 20 occupations with Chinese specific prestige scores ranging from 0 to 95 (Bian, 2004). Respondents indicated if they knew someone from their families, friends, or acquaintances in each listed occupation. From prestige scores, the value of resources potentially accessible to each respondent was extracted. Three variables were thus calculated: diversity, upper reach ability, and range. Network diversity refers to the total number of occupations a respondent reported (ranging from 0 to 20). Upper reach ability was the uppermost resource a respondent could reach in his or her social network (ranging from 0 to 95). Network range was calculated as the difference between the highest and the lowest prestige occupation scores (ranging from 0 to 94). Factor analysis of the three variables led to social network, a one-factor solution reported in previous studies (Moore et al., 2009, 2011; Song, 2011; Song & Lin, 2009; Yue, Li, Jin, & Feldman, 2013). Table 1 shows that the factor loadings of the three indicators are greater than 0.885, Kaiser-Meyer-Olkin (KMO) value being 0.701, Cronbach’s a coefficient being 0.902, and Barlett’s test of sphericity being acceptable in this study. At last, social network scores were equally divided into high, middle, and low groups, with low group being a reference group.

Measurement of social capital Trust

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A single question was used to assess ‘generalized trust’: ‘Generally speaking, would you say that most people could be trusted?’. Participants were requested to select one response from a 5-point Likert scale, ranging from ‘strongly agree’ (scored 5) to ‘strongly disagree’ (scored 1). The scores of the ‘trust’ in the analysis were dichotomized as ‘high trust’ (trust D 54) and ‘low trust’ (trust D 13). A similar approach was used in the previous literature (Pollack & von dem Knesebeck, 2004). Reciprocity The following questions were used to assess ‘reciprocity’: ‘Do you think villagers concern issues that not only relate to themselves, but also relate to others?’, ‘Do you think villagers will provide help if someone really needs it?’, ‘Would you lend money to your neighbor if he/she needs it to see a doctor?’, and ‘Would you like to support a project that might not benefit you most, but benefit other villagers?’. Given the context of Chinese culture, these items were adapted from World Bank Social Capital Scale, whose validity on ‘reciprocity’ have been previously reported in China (Yip et al., 2007; Zhang, Wang, Wang, & Hsiao, 2006), with Cronbach’s a coefficient being

Table 1. Social network: occupation and occupational prestige scores in the Position Generator method. Occupation

Score

Occupation

Score

Occupation

Score

Occupation

Score

Administrative staff Policemen

53

20

52

Manufacturing workers Sales

15

Scientific researchers

95

Government officials

80

University teachers

91

77

Engineering technicians Lawyers

86

Primary and secondary school teachers General managers

71

86

Business clerks

Medical doctors

86

Accountants

Variables Range of prestige score Network diversity Upper reachability Eigen value Explained variance (%)

48

Waiters

11

64

Professional nurses Drivers

25

6

58

Cookers

24

Babysitters/ part-timers Farmers

Mean (SD)

Factor loading

43.82 (32.54)

0.951

4.35 (3.66) 62.84 (30.62) 2.51 83.67

0.907 0.885

Cronbach’s a

0.902

1

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Social participation Social participation was measured by the number of groups/organizations that the participant joined in the past five years, including hobby/cultural clubs, religious groups, women’s groups, sports club, volunteer organizations, and political parties. Each group was coded with 0 representing ‘non-member/inactive member’ and 1 representing ‘active member’ (Han, Kim, & Lee, 2013). The sum score was categorized in the analysis as high participation group (responding to score 1, if sum score 1), low participation group (responding to score 0, if otherwise), as previously reported in Asian populations (Han et al., 2013).

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Social support Social support was measured by the Multidimensional Scale of Perceived Social Support (MSPSS) (Zimet, Dahlem, Zimet, & Farley, 1988). The scale included 12 items such as ‘My family really tries to help me’, ‘I have friends with whom I can share my joys and sorrows’, and ‘There is a special person who is around when I am in need’. The responses ranged from ‘strongly agree’ (scored 5) to ‘strongly disagree’ (scored 1) in each item. The sum score of social support was obtained by adding up all the individual item scores. The Chinese version of MSPSS has been widely used in Chinese samples, with Cronbach’s a coefficient being 0.89 (Chou, 2000) and it was 0.863 in this survey. Covariates Covariates in this study included gender, age, education level, household income, and physical function. Education level was characterized as ‘illiterate’, ‘primary school’, ‘middle school’, and ‘high school and above’, with the illiterate group as a reference group. Annual household income (in RMB) was divided into groups of 024,999, 25,00049,999, 50,00074,999, and 75,000 or more, with the lowest income group as the reference. Physical function was measured by the 10-item physical function part of SF-36 scale (the MOS 36-item Short Form Health Survey). Response ranged from ‘strongly agree’ (scored 3) to ‘strongly disagree’ (scored 1) in each item. The sum score of physical function was calculated according to the established scoring algorithms, the higher the sum scores, the better the physical function status. The Chinese version of the SF-36 scale has been widely used in Chinese samples, with Cronbach’s a coefficient being 0.87 (Lam, Tse, Gandek, & Fong, 2005; Li, Wang, & Shen, 2003; Ngai, Cheung, Lam, Chiu, & Fung, 2012).

Statistical analyses SPSS 17.0 statistical software (SPSS Inc., Chicago, IL, USA) was used to analyze the data. The characteristics of the elderly sample (Table 2) and dichotomized social capital and sociodemographic variables were described

Table 2. Sociodemographic characteristics. Total (N D 928) Variate Gender Male Female Age 6069 7079 80 Educationa Illiterate Primary education Middle education High education or above Annual household income in RMB (US$1 D 6.5RMB) 024,999 25,00049,999 50,00074,999 75,000 Physical function (mean § SD)

N (%) 346 (37.2) 582 (62.8) 508 (54.7) 263 (28.3) 158 (17.0) 143 (15.4) 237 (25.5) 311 (33.5) 238 (25.6)

193 (20.8) 290 (31.2) 317 (34.1) 129 (13.9) 81.6 § 21.6

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Education defined as completion of: primary 16 years, middle 79 years, high 1012 years.

in line with analysis purpose. t-test and variance analysis were used to assess the association linking depression and social capital, social support, and key sociodemographic variables, while Spearman correlation analysis was applied to evaluate the relationship between geriatric depression and social capital and social support. A hierarchical regression analysis was performed to explore the impact of social capital on geriatric depression. The indicator for depression was tested according to three different models. The base model was built up, in which geriatric depression was the dependent variable and social capital factors were independent variables (Model 1). Considering social capital and social support as key factors and referring to other studies (Chou & Chi, 2005; Zhang et al., 2012), the exogenous variables (age, gender, education, household income, and physical function) were added simultaneously in a block (Model 2), rather than stepwise based on significant relationship to depression. Lastly, social support factors were added and analyzed simultaneously with all variables (Model 3). In order to examine if social support was associated with social capital, the indicator for social support was tested according to two models. One was the base model built up to have social support as the dependent variable and social capital factors as independent variables, and the other had the exogenous variables added. We used multicollinearity and related analyses to ensure that no high degree of correlation between variables was allowed (tolerance >0.2; variance inflation factors

Social capital and depression: evidence from urban elderly in China.

To study the relationship between social capital and depression among older adults from urban China and the mediating effect of social support on the ...
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