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Personal social networks and health among aging adults in Agincourt, South Africa: A multidimensional approach
Social Networks 55 (2018) 142–148
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Social Networks journal homepage: www.elsevier.com/locate/socnet
Personal social networks and health among aging adults in Agincourt, South Africa: A multidimensional approach
T
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Ami R. Moorea, , Victor Prybutokb, Anh Tac, Foster Ameyd a
Department of Rehabilitation and Health Services, University of North Texas, 1155 Union Circle #311456, Denton, TX 76203, United States Toulouse Graduate School, University of North Texas, 1155 Union Circle #305459, Denton, TX 76203, United States c Department of Information Technology & Decision Sciences, University of North Texas, Denton, TX 76203, United States d Middle Tennessee State University, MTSU Box 10, Murfreesboro, TN 37132, United States b
A R T I C LE I N FO
A B S T R A C T
Keywords: Social networks Aging Agincourt South Africa Health
Personal social networks (SN) affect health and wellbeing. This study used a multidimensional approach of SN and social determinants of health (SDH) to examine the association between SN and self-reported physical health among the aging population of Agincourt, South Africa. We analyzed the composition of personal SN and used a multiple linear regression analysis to examine both network dimensions and SDH that correlate with physical health. Results highlight the complexity and nuances of social relationships. A few recommendations were also made.
Personal social networks affect health and wellbeing. Berkman et al. (2000) showed that social networks (SN) influence health through support that members receive. SN positively influence health because of the meaningful relationships they provide and evidence suggests that people with diverse networks live longer (Berkman and Glass, 2000; Seeman, 1996; Cohen and Janicki-Deverts, 2009). Also, older people in SN that provide positive social support show greater well-being relative to those with less robust networks (Dominguez and Arford, 2010; McLaughlin et al., 2010). Similarly, the reliability of support provided by members in SN protects against depression among disabled older people (Allen et al., 2000) as well as having undiagnosed and uncontrolled hypertension among older people (Cornwell and Waite, 2012). Individuals in the network and the roles they play are important influences on the health and wellbeing of members (Cohen and Willis, 1985; Fiori et al., 2007; Antonucci et al., 2010). Also, although network ties change as one ages, new social networks that older people create tend to positively affect different aspects of their health. Cornwell and Laumann (2015) found in their longitudinal study of older Americans that adding new members to one’s networks improved self-reported health as well as functional and psychological health. SN are complex and members in networks have multifaceted roles. In order to understand these aspects of SN, a multidimensional approach that considers the structure, function, and quality of networks is needed to study the effect of SN on health. The goal of the multidimensional approach is to provide holistic insights into the association between SN and health (Fiori et al., 2007). This study uses a multidimensional approach to
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explore the effects of SN on health controlling for the social determinants of health (SDH) among an aging population in Agincourt, South Africa (SA). Network structure allows identification and study of network types with a focus on size, proximity, links, and frequency of contacts. Network function involves the type and frequency of support provided to members, and network quality refers to the subjective evaluation of network members (Doubova et al., 2010; Fiori et al., 2007; Wenger, 1996). Several studies have examined the effect of SN on the health of aging populations. Some examined only one dimension of the networks. For example, Doubova et al. (2010), applied mainly the structural approach to examine social network types and their association with functional dependency in older adults in Mexico. They found that older people in widowed and restricted networks reported higher levels of functional dependency and depression as well as a negative self-rated health. Litwin (1998), also, in his study of social network types and health among elderly Israelis found that older people who had resourceful diversified networks of friends and neighbors reported better health status while the least resourceful attenuated networks reported poor health. In another study, Litwin (2001) examined the association between network types and morale in old age and found that older people in friendship networks had the highest level of morale while those in exclusively family or restricted networks had the lowest level of morale. Additionally, McKibbin et al. (2016), in their study of health status and SN and resilience among older adults in rural and remote environments in Wyoming, found family networks and mental health
Corresponding author at: Department of Sociology, University of North Texas, Denton, TX 76203, United States. E-mail addresses: [email protected] (A.R. Moore), [email protected] (V. Prybutok), [email protected] (A. Ta), [email protected] (F. Amey).
https://doi.org/10.1016/j.socnet.2018.06.001
0378-8733/ © 2018 Elsevier B.V. All rights reserved.
Social Networks 55 (2018) 142–148
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networks are related to area of residence, the characteristics of the neighborhoods also influence SNs. Cornwell and Behler (2015), found that older people who resided in poor neighborhoods had smaller SN size and weaker social ties, in addition to men in those areas also having less frequent interactions. Also, Browne-Yung et al. (2013), in their study of the relationship between life course and health among lowincome people in different socioeconomic neighborhoods found that the social networks and the ability to socially network are related to the socioeconomic status of the neighborhoods. They also reported that people who live in advantaged neighborhoods do not spontaneously have access to positive social networks. This study used a multidimensional approach of SN and SDH to examine the association between SN and self-reported physical health among the aging population of Agincourt. It addresses the gap in the SN literature among older people in SA by asking the following questions: (1) What is the composition of personal SN among the aging population in Agincourt, SA? (2) Which network dimensions correlate with selfreported physical health? (3) What factors of the SDH associate with health status, controlling for social network dimensions?
status to be significant predictors of resilience. Others, such as Windsor et al. (2016) examined the associations of SN with mental health in midlife among older Australian adults using two attributes of SN (structure and quality). They found better mental health reports among older Australians in networks that are more diverse. In addition, older people with poorer self-reported health in more diverse networks were less likely to report poorer mental health. Additionally, network quality improved mental health. Specifically, high frequency of positive exchanges attenuated the negative association between self-rated health and mental health. Furthermore, Fiori et al. (2011), used network structure and function to examine patterns of social exchange and attachment characteristics in later life. They found that attachment security style was associated with network size, greater reciprocal exchange, and less giving to kin. Negative attachment styles such as dismissiveness and fearful avoidance were related to smaller non-kin networks and fewer reciprocity, respectively. Likewise, Cornwell and Waite (2009), used network function and structure in addition to some social isolation indicators and examined the relationship between social isolation and health. They reported that perceived isolation (measured by loneliness and perceived lack of support), and social disconnectedness which comprised of social network characteristics and social participation, are both related to negative physical and mental health. In fact, different social network dimensions operate through different pathways to affect health and wellbeing. While network structure provides resources and opportunities that help members fulfil their role expectations and hence gives them a sense of purpose in life and increases their self-esteem (Berkman et al., 2000; Windsor et al., 2016; Thoits, 2011), network function has been shown to provide resources that help members cope with adversity (Windsor et al., 2016). Social support, a characteristic of network function, is a buffer of stress (Kawachi and Berkman, 2001; Thoits, 2011). However, interactions with members of social network can be a source of strain and tension when support is unhealthy and this ultimately negatively impacts health and well-being (Lincoln, 2000; Rook et al., 2012). Social network quality encompasses the subjective evaluation of the networks, how network members perceive the function and structure of the networks (Doubova et al., 2010; Fiori et al., 2007). While positive quality interactions with network members have been shown to be beneficial to the health of members throughout their lives (Glick and Rose, 2011; Hartup and Stevens, 1997), negative quality of social ties can have harmful effects on health (Umberson and Montez, 2010). Other important factors that significantly impact health are the SDH. They are “the structural determinants and conditions in which people are born, grow, live, work and age” (Marmot et al., 2008). They encompass socioeconomic factors (e. g. education, employment), physical environment (e. g. neighborhood features that may affect health), childhood development factors that may affect health in later years (e. g. health during childhood), etc. While several studies have examined the SDH (Braveman et al., 2005; Braveman and Gottlieb, 2014; Sadana et al., 2016), no studies to our knowledge, have examined SN and health including the SDH in the same context, using a large national data of an African population in general, and of South Africans in particular. It is important to examine SN and health controlling for SDH because SDH do affect both health and SN. For example, it is well established that one’s health and socioeconomic conditions during childhood affect health in later life (Haas, 2007; Cohen et al., 2010; Zimmer et al., 2016; Pakpahan et al., 2016). However, the relationship between childhood health and socioeconomic status and adult health may be mediated by educational attainment and adult socioeconomic condition (Pakpahan et al., 2016). Also, area of residence does affect SN (Meert, 2000; Morton et al., 2008; Lannoo et al., 2011) which ultimately affect health. Meert (2000) and Morton et al. (2008) reported that socioeconomically disadvantaged people in rural areas tend to rely more on their social networks for resources such as food relative to their counterparts in urban areas. While resources provided by social
Methods This study used data from the Health and Aging in Africa: A longitudinal Study of an INDEPTH Community in SA (HAALSI) Cohort from Agincourt, to examine social network dimensions on self-reported health status, controlling for SDH. South Africans aged 40 years and older who were living in Agincourt (the Mpumalanga province) were sampled for the study, following the existing framework of the Agincourt Health and Socio-Demographic Surveillance System (AHDSS) (Berkman, 2016). Agincourt is a rural area located in North East of SA. As reported by Kahn et al. (2012), Agincourt is undergoing multiple interrelated transitions. While fertility rate is declining, child mortality is rising because of HIV/AIDS. There is also an increase in cardio metabolic disease across different age groups. The dataset used for this study is the baseline data collected between 2014 and 2015. Measures The outcome variable is self-rated health status. This variable is assessed by a question that asked respondents to rate their health today. The response is a Likert scale that describes their health conditions: 1 = very good; 2 = good; 3 = moderate; 4 = bad; and 5 = very bad. We reverse coded the responses whereby high numbers correspond to good health and low numbers to bad health (5 = very good and 1 = very bad). Social network variables: Respondents were asked to generate the names of 6 adults with whom they have been in communication either in person or by telephone or by internet in the past 6 months, starting with the person who is most important to them for any reason. They were also told that these adults may live inside or outside of their households, they could be friends, relatives, acquaintances, neighbors, colleagues, advisors, caretakers, religious leaders, counselors or anyone else and must be alive. The structure of the SN is composed of (a) the type of relationship with people in the network, (b) the gender of network members, (c) the frequency of in-person interaction, and (d) physical proximity of contacts. The variable that assessed relationship with network members asked respondents how they were related to the persons they named as network members. The responses were (1) no relationship, (2) acquaintances, (3) married to each other/ living together, (4) relatives but not married to each other, (5) friends with each other, (6) co-workers, (7) involved in a club/ organization, and (8) other. We created 6 categories which fall under 2 main types of SN (kin and non-kin). People who made up the kin category are: (1) married or living together and (2) relatives but not married to each other. The nonkin category is composed of (3) friends, (4) acquaintances, (5) coworkers and club members, and (6) others. This classification follows 143
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levels of agreement with the statements (4—Strongly agree and 1—Strongly disagree). Cronbach’s alpha for this index was 0.87. The other three items relate to respondents’ feelings about the village. The first asked: (e). In general, how much do you like your village? The answer choices are: 1—Like it a lot; 2—Like it; 3—Dislike it; and 4—Dislike it a lot. We regrouped the answers into two categories: 1—Like it a lot and like it and 2—Dislike it and Dislike it a lot. The second item, (f) In general, how safe is your village?), also has four answer choices: 1—Extremely safe; 2—Safe; 3—Not safe; and 4—Extremely unsafe. The answers ‘extremely safe’ and ‘safe’ were collapsed into one category and ‘not safe’ and ‘extremely unsafe’ into another. The third variable, (g) Do you feel as if you are really part of (a member of) this village?), has two answers, Yes and No. We included variables that assess respondents’ experiences in the past days that may have affected their health. Seven items assessed respondents’ experiences of events that may have led them to have posttraumatic stress issues. These items asked if respondents had any experience that was so frightening, horrible, or upsetting that, in the past 30 days: (a) they had avoided being reminded of this experience by staying away from certain places, people or activities; (b) they lost interest in activities that were once important or enjoyable; (c) they began to feel more isolated or distant from other people; (d) they found it hard to have love or affection for other people; (e) they began to feel that there was no point in planning for the future; (f) they had more trouble than usual falling asleep or staying asleep; and (g) they had become jumpy or got easily startled by ordinary noises or movements. These items were summed up into an index called “sum PTSD.” Responses were 1 (Yes) and 2 (No). Cronbach’s alpha for this index is 0.83. Another important social determinant of health variable included in the study was health during childhood. As mentioned earlier, this variable is a strong predictor of health but may be mediated by educational attainment and adult socioeconomic condition (Pakpahan et al., 2016). Respondents were asked about how they would rate their health during childhood. Responses are 1—Very good, 2—Good, 3—Moderate, 4—Bad, and 5—Very bad. These were reverse coded such that 5 = Very good and 1 = Very bad. Lastly, demographic and socioeconomic variables such as education, marital status, gender, age, employment status, total number of children ever had, number of deceased children, etc. were also included in the study. A multicollinearity test was conducted among the variables. It is noteworthy to report that we excluded social network size from the analysis because of multicollinearity issue or high variance inflation factors (VIF). We used multiple linear regression to analyze the effects of personal SN and the determinants of health on self-reported physical health status among aging people in Agincourt, SA.
past research such as Fischer and Shavit (1995) and Offer and Fischer (2018). The gender of network members was measured as male or female. For the frequency of in-person interaction, respondents were asked how often they interacted with network members in-person in the past 6 months. Responses were (1) every day or almost every day, (2) a few times per week, (3) once a week, (4) a few times per month (5) once per month, (6) a few times in past 6 months, and (7) not at all. We reverse coded the responses whereby higher numbers correspond to frequent interactions and lower numbers correspond to less frequent interaction (7 = every day/ almost every day and 1 = not at all). The physical proximity of network members was measured by a variable that asked respondents to list where contact lived, namely, (1) in your household, (2) in your village, (3) within Agincourt but not village, (4) in SA but not Agincourt, and (5) in another country. The responses were regrouped into 3 categories: (1) in your household, (2) in your village (3) live elsewhere. We measured network function by four variables: (a) emotional support, (b) physical support, (c) informational support, and (d) financial support. Respondents were asked how often they received each type of support from network members over the past 6 months. The answer choices were (1) every day or almost every day, (2) a few times per week, (3) once a week, (4) a few times per month, (5) once per month, (6) a few times in past 6 months, and (7) not at all. We reverse coded the responses whereby higher numbers correspond to frequent interactions and lower numbers correspond to less frequent interaction (7 = every day/ almost every day and 1 = not at all) of people in networks. Lastly, network quality was assessed by one variable that measured the relationship quality among network members. The question asked respondents how often they physically fought with, verbally argued with, and were humiliated by the network members. This represents negative exchanges. This is the only question that relates to the quality of personal SN in the dataset. Responses were a seven-point scale from (1) every day or almost every day to (7) not at all). We reverse coded the responses whereby higher numbers correspond to frequent interactions and lower numbers correspond to less frequent interaction (7 = every day/ almost every day and 1 = not at all). SDH include variables that describe the social environment in which respondents lived, variables that assess the impact of their lifetime experiences, and socioeconomic and demographic variables. We created variables that tap into the perception of respondents’ social environment (Agincourt) derived from literature on community trust (Zarychta, 2015; Subramanien et al., 2002), and sense of place and community attachment (Williams and Kitchen, 2012). These variables are predictors of health as shown in Subramanien et al. (2002) in their study of social trust and self-rated health within American communities. They reported a negative relationship between levels of community trust and poor health. Zarychta (2015) also reported an association between community trust and household health in rural areas in Honduras, whereby trusting households that are surrounded by trusting neighbors reported better health status. With regard to sense of place, Williams and Kitchen (2012) found a strong association between sense of place and mental health among residents of Hamilton, Ontario. We also used seven items that assess respondents’ perceptions of the social environment. Four of these items described respondents’ perceptions of the people in the village and three described respondents’ feelings toward the village. We created a perception of people in the village index by summing up the following four questions: (a) If there is a problem in the village, most people in this village work together to deal with it. (b). Most people in this village are willing to help their neighbors. (c.) Most people in this village can be trusted, and (d.) Most people in this village do favors for each other (such as, watching your house when you are gone, taking care of other people’s children in an emergency, or lending people small things, or other actions like this). The response is a Likert scale which describes the levels of agreement with the statements: 1—Strongly agree to 4—Strongly disagree. We reverse coded the answers such that higher scores correspond to greater
Results Table 1 shows that the mean age of the sample was about 62 years. Study participants were mostly female (53.6%), belonged to a religious denomination (82.4%), were either overweight or obese (57.8%) and a little over half were married (50.9%). Almost 46% of the respondents had no formal education and only about 4% had a higher education. The respondents had an average of about 5 children. Over half of them had at least one deceased child (58.4%). A significant number of respondents felt that they were part of the village (82.7%), the village was safe or extremely safe (95.7%), and liked the village (97.8%). Table 2 shows the composition and size of the networks. Respondents had more kin in each of their networks than friends and others. Specifically, they had relatives who were not their spouses or people they were cohabiting with. They also frequently interacted more with these relatives regardless of the physical proximity of place of residence, except for spouses/cohabitants who were living within the households. In addition, they had negative interactions more with these relatives, followed by their spouses or cohabitants than friends and others in their networks. There were more female members in the 144
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networks than males, except for co-workers. While the mean size of the functional support network has the greatest number of people (3), the smallest network size is the network of people respondents frequently interacted with in the household (mean = 0.8). Table 3 presents multiple linear regression results of the analysis. There is a statistically significant positive effect of contacts who were spouses, contacts who were friends and financial support on self-reported health. Specifically, respondents whose network members consisted largely of spouses and also friends reported better health relative to those whose contacts were others. However, study participants whose contacts mainly either lived in the household or village reported worse health compared to those whose members lived elsewhere. Additionally, emotional support negatively affected self-reported health. Adding the determinants of health changed some of the significant results (Model 2). In Model 2, there is still a positive effect of contacts who were spouses and financial support on physical health, and a negative effect of contacts living within the household, emotional support on self-reported physical health. Also, the statistically significant effect of contacts who were friends on self-reported health disappeared once we controlled for the SDH. However, the coefficients are mostly reduced for all the variables in Model 1. In some cases, previous significant effects are now absent (e.g., contacts who are friends). This suggests that the effects observed in Model 1 are mainly transmitted through the additional variables in Model 2. For example, contacts who are partners or friends and live in household or the village could have effects on health depending on how much respondents like the village or how they perceive the village. In other words, the relationships with contacts do not work in a vacuum but within the wider social context. In the same vein, the effect for frequency of in-person contact in Model 1, appeared to have been suppressed by the additional variables in Model 2. Again, this points to the importance of the overall social context of this network variable. In addition, there is a positive effect of perception of people in the village, finding the village safe, being satisfied with life, working full time on report of physical health. For instance, people who felt that the village was safe or extremely safe had better physical health relative to those who felt the village was unsafe. Furthermore, respondents who worked full time reported good health than those who did not work full time. Nevertheless, there is a negative effect of feeling part of the village, being underweight, feeling depressed, and age on self-reported physical health.
Table 1 Descriptive characteristics of variables.
Self-reported general Health Number of children ever had (mean) Number of children deceased (mean) Age Health during childhood Satisfaction with life Sum of 7 negative Exp Sex Female Male Marital status Married Unmarried Education No education At most 8th grade 9th grade - high school Higher education Religion Christianity Other religion No religion (reference) Employment status Working full time No Weight status Under-weight Normal Overweight and obese Do you like your village (a lot + like it) Yes No Village is safe (Extremely safe + safe) Yes No Feel part of village Yes No Felt depressed Yes No Having at least one child deceased Yes No Work full time Yes No Married or living with partner Yes No
N
Mean/Max
Percent
s.d.
5059 4732 4728 5017 5055 4856 4941
3.67/5 4.84/30 1.36/17 62.38/105 4.47/5 6.74/10 0.53/7
– – – – – – –
1.04 2.67 1.74 13.1 0.97 2.39 1.27
2714 2345
– –
53.60% 46.40%
2575 2480
– –
50.90% 49.10%
2306 1958 566 212
– – – –
45.70% 38.80% 11.20% 4.20%
3781 386 888
– –
74.80% 7.60% 17.60%
505 4541
– –
10.00% 90.00%
258 1719 2712
– – –
5.50% 36.70% 57.80%
4828 107
– –
97.80% 2.20%
4723 214
– –
95.70% 4.30%
4576 362
– –
92.70% 7.30%
564 4495
– –
11.10% 88.90%
2763 1965
– –
58.40% 41.60%
505 4541
– –
10% 90%
2575 2484
– –
50.90% 49.10%
Discussion This paper examined the effects of multiple dimensions of SN on self-reported health, controlling for SDH, using the baseline data of Health and Aging in Africa: A longitudinal Study of an INDEPTH
Table 2 Composition of Networks. Variables Network Structure Proportion of male Proportion of female Proximity of contact in household in village outside village Function Emotional support Physical support Informational support Financial support Quality Fighting, verbal argument, criticism, humiliation *
N
M
Re
Fr
Ac
Co
Other
Average # in SN
6431 8640
14.7% 17.9%
61.6% 64.2%
16.0% 10.6%
3.2% 3.8%
3.6% 2.3%
0.9% 1.2%
1.3 1.7
4230 6592 4354
54.4% 1.4% 2.1%
43.0% 63.1% 83.4%
0.7% 24.4% 7.1%
1.5% 5.0% 3.2%
0.2% 3.9% 3.8%
0.2% 2.2% 0.4%
0.8 1.3 0.9
14,134 13,464 14,447 12,026
17.2% 18.0% 16.9% 19.1%
62.2% 61.6% 62.9% 60.6%
13.1% 13.0% 12.9% 13.1%
3.6% 3.7% 3.6% 3.8%
2.8% 2.6% 2.7% 2.4%
1.1% 1.1% 1.0% 1.0%
3.0 3.0 3.0 3.0
1518
29.1%
55.0%
11.3%
1.6%
1.6%
1.4%
2.8
N: Network Size, M: Married or living with each other, Re: Relatives, Fr: Friends, Ac: Acquaintances, Co: Co-workers and Clubs. 145
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Table 3 Regression results of self-reported health status on social networks and social determinants of health variables. Model 1
Unstandardized coefficient
Standard Error
p-value
Intercept Network structure Frequency of in person interactions Relationship with network members Contacts are married or living with Contacts are relatives Contacts are friends Contacts are acquaintances Contacts are co-workers and clubs Contacts are others Proximity of Contacts Contacts live in household Contacts live in village Contacts live elsewhere Gender of Contacts Contacts are male Contacts are female Network function Emotional support Physical support Informational support Financial support Network quality Frequency of fighting R2 = 0.036, F = 13.1144***, N = 4575.
3.503
0.17
0.000
0.007
0.004
0.056
0.549*** 0.063 0.337* −0.175 0.018 –
0.181 0.166 0.171 0.138 0.204 –
0.002 0.703 0.049 0.355 0.931 –
−0.184** −0.130* –
0.064 0.056 –
0.004 0.019 –
0.015 –
0.015 –
0.332 –
−0.019*** 0.005 −0.001 0.018***
0.004 0.004 0.004 0.004
0.000 0.253 0.765 0.000
0.009
0.006
0.122
Model 2
Unstandardized coefficient
Standard Error
p-value
Intercept Network structure Frequency of in person contacts Relationship with network members Contacts are married or living with Contacts are relatives Contacts are friends Contacts are acquaintances Contacts are Co-workers and Clubs Contacts are others Proximity of Contacts Contacts live in household Contacts live in village Contacts live elsewhere Gender of Contacts Contacts are male Contacts are female Network function Emotional support Physical support Informational support Financial support Network quality Frequency of fighting Determinants of health variables Perception of people in village index How much like the village (a lot + like it) How much like the village (dislike it a lot + dislike it) Village is safe (Extremely safe + safe) Village is safe (Extremely unsafe + unsafe) Feel part of village (Yes) Feel part of village (No) Underweight Overweight + obese Normal weight Health during childhood Satisfaction with life Felt depressed (Yes) Felt depressed (No) Number of children ever had Number of children deceased Work full time Did not work full time At most 8th grade High school Tertiary or University
1.934
0.231
0
0.009*
0.004
0.014
0.145* −0.02 −0.003 0.027 −0.235 –
0.062 0.147 0.152 0.168 0.182 –
0.019 0.894 0.985 0.874 0.874 –
−0.159** −0.095 –
0.054 0.052 –
0.007 0.066 –
0.012 –
0.014 –
0.389 –
−0.010** 0.007 −0.010** 0.011**
0.003 0.004 0.004 0.004
0.002 0.098 0.004 0.004
−0.003
0.006
0.543
0.120*** 0.178 – 0.197** – −0.152** – −0.145* −0.011 – 0.022 0.138*** −0.410*** – 0.006 0.023 0.138** – −0.022 0.036 −0.019
0.008 0.098 – 0.072 – 0.054 – 0.063 0.03 – 0.014 0.006 0.044 – 0.005 0.03 0.047 – 0.031 0.051 0.074
0 0.07 – 0.007 – 0.005 – 0.022 0.726 – 0.127 0 0 – 0.305 0.447 0.003 – 0.489 0.483 0.801
(continued on next page) 146
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Table 3 (continued) Model 2
Unstandardized coefficient
Standard Error
p-value
No Education Male Female Christianity Other religion No religion Age Married or living with partner Not married R2 = 0.291, F = 48.361***, N = 3925.
– 0.032 – −0.072 −0.076 – −0.015*** −0.03 –
– 0.034 – 0.041 0.061 – 0.001 0.048 –
– 0.349 – 0.077 0.216 – 0 0.532 –
HIV/AIDS on the younger population may be another burden shouldered by respondents. This study, is the first to our knowledge, to use three different SN dimensions –structure, function, and quality—to assess self-reported physical health among an older population in South Africa. In fact, as described by the convoy model and social support and relationship theories, social network members provide different roles and functions which ultimately affects health (Antonucci et al., 2010; Antonucci and Akiyama, 1987). While some of the results are similar to previous findings such as a positive association between receipt of financial support and physical health, other findings are intriguing. For example, there was a negative association between feeling part of the village and health as well as negative effects of receipt of emotional and informational support on physical health. More studies should investigate this relationship including length of residence as a factor as well as context in which support is provided. This study has some limitations. First, the data are cross-sectional and we cannot assess the convoy of social relationships and their long term effects on health as social relationships change with time. The cross-sectional data may also cause bias via reverse causality. Second, the data set does not allow the identification of the level of support provided. Is the support consistent or low? This is important because studies have shown a link between levels of social support and some health conditions (Birditt and Antonucci, 2008; Walter-Ginzburg et al., 2002). Third, we believe that the social network quality question is loaded and should have been separated because the question, although it conveys a negative quality of social relationship (fighting, arguing, and being humiliated), all of the conditions do not translate into the same level interaction (arguing may be different from being humiliated). Fourth, the variable that describes the relationship with network members did not make a distinction between kin who are children and other family members as vertical family ties such as parents-children have special normative expectations relative to other family ties (Viry, 2012). Despite these limitations, the study contributes to the extant literature on social relationships, aging, and health. The INDEPTH Community in SA (HAALSI) Cohort survey used for this paper is a strength as it has variables that allow for the analysis of three different social network dimensions as well as SDH. While the study shed some light on the complexity of social relations and their effects on the health of older people in Agincourt, it is important that future studies examine specific contexts of social support among understudied aging populations in Africa. As the aging population is rapidly growing on the continent, knowing the dynamics of SN and their impact on the health of the elderly, especially in rural areas where valued resources are scant, will help not only family and loved ones with older relatives, but also, care providers to devise the best types of support.
Community in SA (HAALSI). This study showed that the aging population had more kin (non-spouses or spouses) in each of their networks and interacted more with these family members regardless of the physical proximity of place of residence compared to friend, acquaintances, and other network types. This finding is consistent with previous research that shows that family or immediate kin tends to be the closest or the most important members of personal SN, especially among people in low-income countries (Bastani, 2007; Moore and Prybutok, 2014). Also, since Agincourt is a rural area, residents may have close personal ties with family members who provide for their essential daily needs. However, the negative effects of receipt of emotional and informational support as well as having network members living within the households on physical health is puzzling as one will intuitively think these kinds of support should help improve health. However, this could be explained by the nature of the ties of the relationships between respondents and their network members. First, the networks are composed mostly of kin which represent closed network ties. Second, as reported by Viry et al. (2009), these ties are generally strong with emotional implication, and provide binding social capital which tends to lead to social control. Hence, network members might have become too emotionally controlling for the respondents. Nevertheless, Walter-Ginzburg et al. (2002), also found this contrary relationship between support and mortality in their study of old-old Israelis whereby those with more support did not show a reduced mortality. It is also possible that the effects of networks are more nuanced than appears. For example, since this research is based on cross-sectional data, it is not possible to establish the temporal order of poor health and network characteristics. In this situation, it makes sense to suggest a reverse causation namely, that poor health leads to seeking help from others who then become identified as network members (Booth et al., 2014). All these findings highlight the complexity and nuances of social relationships. Thus, it is important to understand the context in which support is provided (Birditt and Antonucci, 2008). Additionally, while the positive effect of perception of people in the village (perception of place), finding the village safe (community trust), and being satisfied with life and self-reported health are unsurprising, the negative association between feeling part of the village and health is unexpected. According to research on perception of place, cognitive and emotional responses to a place may influence the physiological and behavioral response to the place (Scannell and Gifford, 2010; Lengen and Kistermann, 2012; Hystad and Carpiano, 2012). Also, perception of a place has been found to be closely linked to health outcomes than objective indicators of the quality of the place (Wen et al., 2006). Thus, respondents overwhelmingly having a positive sense of place reported good health outcome. However, the negative association between feeling part of the village and health may be explained by the cumulative effects of neighborhood hazards on older adults (Robert and Li, 2001), as these older people may have been daily exposed to whatever may be deleterious in Agincourt. Also, as the aging population in Agincourt has experienced a significant growth (from 1% to 2%) but a worsening of mortality (Kahn et al., 2006), these changes might have negative impact on respondents’ health. Also, the devastating impact of
Declaration of interest None.
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Social Networks journal homepage: www.elsevier.com/locate/socnet
Personal social networks and health among aging adults in Agincourt, South Africa: A multidimensional approach
T
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Ami R. Moorea, , Victor Prybutokb, Anh Tac, Foster Ameyd a
Department of Rehabilitation and Health Services, University of North Texas, 1155 Union Circle #311456, Denton, TX 76203, United States Toulouse Graduate School, University of North Texas, 1155 Union Circle #305459, Denton, TX 76203, United States c Department of Information Technology & Decision Sciences, University of North Texas, Denton, TX 76203, United States d Middle Tennessee State University, MTSU Box 10, Murfreesboro, TN 37132, United States b
A R T I C LE I N FO
A B S T R A C T
Keywords: Social networks Aging Agincourt South Africa Health
Personal social networks (SN) affect health and wellbeing. This study used a multidimensional approach of SN and social determinants of health (SDH) to examine the association between SN and self-reported physical health among the aging population of Agincourt, South Africa. We analyzed the composition of personal SN and used a multiple linear regression analysis to examine both network dimensions and SDH that correlate with physical health. Results highlight the complexity and nuances of social relationships. A few recommendations were also made.
Personal social networks affect health and wellbeing. Berkman et al. (2000) showed that social networks (SN) influence health through support that members receive. SN positively influence health because of the meaningful relationships they provide and evidence suggests that people with diverse networks live longer (Berkman and Glass, 2000; Seeman, 1996; Cohen and Janicki-Deverts, 2009). Also, older people in SN that provide positive social support show greater well-being relative to those with less robust networks (Dominguez and Arford, 2010; McLaughlin et al., 2010). Similarly, the reliability of support provided by members in SN protects against depression among disabled older people (Allen et al., 2000) as well as having undiagnosed and uncontrolled hypertension among older people (Cornwell and Waite, 2012). Individuals in the network and the roles they play are important influences on the health and wellbeing of members (Cohen and Willis, 1985; Fiori et al., 2007; Antonucci et al., 2010). Also, although network ties change as one ages, new social networks that older people create tend to positively affect different aspects of their health. Cornwell and Laumann (2015) found in their longitudinal study of older Americans that adding new members to one’s networks improved self-reported health as well as functional and psychological health. SN are complex and members in networks have multifaceted roles. In order to understand these aspects of SN, a multidimensional approach that considers the structure, function, and quality of networks is needed to study the effect of SN on health. The goal of the multidimensional approach is to provide holistic insights into the association between SN and health (Fiori et al., 2007). This study uses a multidimensional approach to
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explore the effects of SN on health controlling for the social determinants of health (SDH) among an aging population in Agincourt, South Africa (SA). Network structure allows identification and study of network types with a focus on size, proximity, links, and frequency of contacts. Network function involves the type and frequency of support provided to members, and network quality refers to the subjective evaluation of network members (Doubova et al., 2010; Fiori et al., 2007; Wenger, 1996). Several studies have examined the effect of SN on the health of aging populations. Some examined only one dimension of the networks. For example, Doubova et al. (2010), applied mainly the structural approach to examine social network types and their association with functional dependency in older adults in Mexico. They found that older people in widowed and restricted networks reported higher levels of functional dependency and depression as well as a negative self-rated health. Litwin (1998), also, in his study of social network types and health among elderly Israelis found that older people who had resourceful diversified networks of friends and neighbors reported better health status while the least resourceful attenuated networks reported poor health. In another study, Litwin (2001) examined the association between network types and morale in old age and found that older people in friendship networks had the highest level of morale while those in exclusively family or restricted networks had the lowest level of morale. Additionally, McKibbin et al. (2016), in their study of health status and SN and resilience among older adults in rural and remote environments in Wyoming, found family networks and mental health
Corresponding author at: Department of Sociology, University of North Texas, Denton, TX 76203, United States. E-mail addresses: [email protected] (A.R. Moore), [email protected] (V. Prybutok), [email protected] (A. Ta), [email protected] (F. Amey).
https://doi.org/10.1016/j.socnet.2018.06.001
0378-8733/ © 2018 Elsevier B.V. All rights reserved.
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networks are related to area of residence, the characteristics of the neighborhoods also influence SNs. Cornwell and Behler (2015), found that older people who resided in poor neighborhoods had smaller SN size and weaker social ties, in addition to men in those areas also having less frequent interactions. Also, Browne-Yung et al. (2013), in their study of the relationship between life course and health among lowincome people in different socioeconomic neighborhoods found that the social networks and the ability to socially network are related to the socioeconomic status of the neighborhoods. They also reported that people who live in advantaged neighborhoods do not spontaneously have access to positive social networks. This study used a multidimensional approach of SN and SDH to examine the association between SN and self-reported physical health among the aging population of Agincourt. It addresses the gap in the SN literature among older people in SA by asking the following questions: (1) What is the composition of personal SN among the aging population in Agincourt, SA? (2) Which network dimensions correlate with selfreported physical health? (3) What factors of the SDH associate with health status, controlling for social network dimensions?
status to be significant predictors of resilience. Others, such as Windsor et al. (2016) examined the associations of SN with mental health in midlife among older Australian adults using two attributes of SN (structure and quality). They found better mental health reports among older Australians in networks that are more diverse. In addition, older people with poorer self-reported health in more diverse networks were less likely to report poorer mental health. Additionally, network quality improved mental health. Specifically, high frequency of positive exchanges attenuated the negative association between self-rated health and mental health. Furthermore, Fiori et al. (2011), used network structure and function to examine patterns of social exchange and attachment characteristics in later life. They found that attachment security style was associated with network size, greater reciprocal exchange, and less giving to kin. Negative attachment styles such as dismissiveness and fearful avoidance were related to smaller non-kin networks and fewer reciprocity, respectively. Likewise, Cornwell and Waite (2009), used network function and structure in addition to some social isolation indicators and examined the relationship between social isolation and health. They reported that perceived isolation (measured by loneliness and perceived lack of support), and social disconnectedness which comprised of social network characteristics and social participation, are both related to negative physical and mental health. In fact, different social network dimensions operate through different pathways to affect health and wellbeing. While network structure provides resources and opportunities that help members fulfil their role expectations and hence gives them a sense of purpose in life and increases their self-esteem (Berkman et al., 2000; Windsor et al., 2016; Thoits, 2011), network function has been shown to provide resources that help members cope with adversity (Windsor et al., 2016). Social support, a characteristic of network function, is a buffer of stress (Kawachi and Berkman, 2001; Thoits, 2011). However, interactions with members of social network can be a source of strain and tension when support is unhealthy and this ultimately negatively impacts health and well-being (Lincoln, 2000; Rook et al., 2012). Social network quality encompasses the subjective evaluation of the networks, how network members perceive the function and structure of the networks (Doubova et al., 2010; Fiori et al., 2007). While positive quality interactions with network members have been shown to be beneficial to the health of members throughout their lives (Glick and Rose, 2011; Hartup and Stevens, 1997), negative quality of social ties can have harmful effects on health (Umberson and Montez, 2010). Other important factors that significantly impact health are the SDH. They are “the structural determinants and conditions in which people are born, grow, live, work and age” (Marmot et al., 2008). They encompass socioeconomic factors (e. g. education, employment), physical environment (e. g. neighborhood features that may affect health), childhood development factors that may affect health in later years (e. g. health during childhood), etc. While several studies have examined the SDH (Braveman et al., 2005; Braveman and Gottlieb, 2014; Sadana et al., 2016), no studies to our knowledge, have examined SN and health including the SDH in the same context, using a large national data of an African population in general, and of South Africans in particular. It is important to examine SN and health controlling for SDH because SDH do affect both health and SN. For example, it is well established that one’s health and socioeconomic conditions during childhood affect health in later life (Haas, 2007; Cohen et al., 2010; Zimmer et al., 2016; Pakpahan et al., 2016). However, the relationship between childhood health and socioeconomic status and adult health may be mediated by educational attainment and adult socioeconomic condition (Pakpahan et al., 2016). Also, area of residence does affect SN (Meert, 2000; Morton et al., 2008; Lannoo et al., 2011) which ultimately affect health. Meert (2000) and Morton et al. (2008) reported that socioeconomically disadvantaged people in rural areas tend to rely more on their social networks for resources such as food relative to their counterparts in urban areas. While resources provided by social
Methods This study used data from the Health and Aging in Africa: A longitudinal Study of an INDEPTH Community in SA (HAALSI) Cohort from Agincourt, to examine social network dimensions on self-reported health status, controlling for SDH. South Africans aged 40 years and older who were living in Agincourt (the Mpumalanga province) were sampled for the study, following the existing framework of the Agincourt Health and Socio-Demographic Surveillance System (AHDSS) (Berkman, 2016). Agincourt is a rural area located in North East of SA. As reported by Kahn et al. (2012), Agincourt is undergoing multiple interrelated transitions. While fertility rate is declining, child mortality is rising because of HIV/AIDS. There is also an increase in cardio metabolic disease across different age groups. The dataset used for this study is the baseline data collected between 2014 and 2015. Measures The outcome variable is self-rated health status. This variable is assessed by a question that asked respondents to rate their health today. The response is a Likert scale that describes their health conditions: 1 = very good; 2 = good; 3 = moderate; 4 = bad; and 5 = very bad. We reverse coded the responses whereby high numbers correspond to good health and low numbers to bad health (5 = very good and 1 = very bad). Social network variables: Respondents were asked to generate the names of 6 adults with whom they have been in communication either in person or by telephone or by internet in the past 6 months, starting with the person who is most important to them for any reason. They were also told that these adults may live inside or outside of their households, they could be friends, relatives, acquaintances, neighbors, colleagues, advisors, caretakers, religious leaders, counselors or anyone else and must be alive. The structure of the SN is composed of (a) the type of relationship with people in the network, (b) the gender of network members, (c) the frequency of in-person interaction, and (d) physical proximity of contacts. The variable that assessed relationship with network members asked respondents how they were related to the persons they named as network members. The responses were (1) no relationship, (2) acquaintances, (3) married to each other/ living together, (4) relatives but not married to each other, (5) friends with each other, (6) co-workers, (7) involved in a club/ organization, and (8) other. We created 6 categories which fall under 2 main types of SN (kin and non-kin). People who made up the kin category are: (1) married or living together and (2) relatives but not married to each other. The nonkin category is composed of (3) friends, (4) acquaintances, (5) coworkers and club members, and (6) others. This classification follows 143
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levels of agreement with the statements (4—Strongly agree and 1—Strongly disagree). Cronbach’s alpha for this index was 0.87. The other three items relate to respondents’ feelings about the village. The first asked: (e). In general, how much do you like your village? The answer choices are: 1—Like it a lot; 2—Like it; 3—Dislike it; and 4—Dislike it a lot. We regrouped the answers into two categories: 1—Like it a lot and like it and 2—Dislike it and Dislike it a lot. The second item, (f) In general, how safe is your village?), also has four answer choices: 1—Extremely safe; 2—Safe; 3—Not safe; and 4—Extremely unsafe. The answers ‘extremely safe’ and ‘safe’ were collapsed into one category and ‘not safe’ and ‘extremely unsafe’ into another. The third variable, (g) Do you feel as if you are really part of (a member of) this village?), has two answers, Yes and No. We included variables that assess respondents’ experiences in the past days that may have affected their health. Seven items assessed respondents’ experiences of events that may have led them to have posttraumatic stress issues. These items asked if respondents had any experience that was so frightening, horrible, or upsetting that, in the past 30 days: (a) they had avoided being reminded of this experience by staying away from certain places, people or activities; (b) they lost interest in activities that were once important or enjoyable; (c) they began to feel more isolated or distant from other people; (d) they found it hard to have love or affection for other people; (e) they began to feel that there was no point in planning for the future; (f) they had more trouble than usual falling asleep or staying asleep; and (g) they had become jumpy or got easily startled by ordinary noises or movements. These items were summed up into an index called “sum PTSD.” Responses were 1 (Yes) and 2 (No). Cronbach’s alpha for this index is 0.83. Another important social determinant of health variable included in the study was health during childhood. As mentioned earlier, this variable is a strong predictor of health but may be mediated by educational attainment and adult socioeconomic condition (Pakpahan et al., 2016). Respondents were asked about how they would rate their health during childhood. Responses are 1—Very good, 2—Good, 3—Moderate, 4—Bad, and 5—Very bad. These were reverse coded such that 5 = Very good and 1 = Very bad. Lastly, demographic and socioeconomic variables such as education, marital status, gender, age, employment status, total number of children ever had, number of deceased children, etc. were also included in the study. A multicollinearity test was conducted among the variables. It is noteworthy to report that we excluded social network size from the analysis because of multicollinearity issue or high variance inflation factors (VIF). We used multiple linear regression to analyze the effects of personal SN and the determinants of health on self-reported physical health status among aging people in Agincourt, SA.
past research such as Fischer and Shavit (1995) and Offer and Fischer (2018). The gender of network members was measured as male or female. For the frequency of in-person interaction, respondents were asked how often they interacted with network members in-person in the past 6 months. Responses were (1) every day or almost every day, (2) a few times per week, (3) once a week, (4) a few times per month (5) once per month, (6) a few times in past 6 months, and (7) not at all. We reverse coded the responses whereby higher numbers correspond to frequent interactions and lower numbers correspond to less frequent interaction (7 = every day/ almost every day and 1 = not at all). The physical proximity of network members was measured by a variable that asked respondents to list where contact lived, namely, (1) in your household, (2) in your village, (3) within Agincourt but not village, (4) in SA but not Agincourt, and (5) in another country. The responses were regrouped into 3 categories: (1) in your household, (2) in your village (3) live elsewhere. We measured network function by four variables: (a) emotional support, (b) physical support, (c) informational support, and (d) financial support. Respondents were asked how often they received each type of support from network members over the past 6 months. The answer choices were (1) every day or almost every day, (2) a few times per week, (3) once a week, (4) a few times per month, (5) once per month, (6) a few times in past 6 months, and (7) not at all. We reverse coded the responses whereby higher numbers correspond to frequent interactions and lower numbers correspond to less frequent interaction (7 = every day/ almost every day and 1 = not at all) of people in networks. Lastly, network quality was assessed by one variable that measured the relationship quality among network members. The question asked respondents how often they physically fought with, verbally argued with, and were humiliated by the network members. This represents negative exchanges. This is the only question that relates to the quality of personal SN in the dataset. Responses were a seven-point scale from (1) every day or almost every day to (7) not at all). We reverse coded the responses whereby higher numbers correspond to frequent interactions and lower numbers correspond to less frequent interaction (7 = every day/ almost every day and 1 = not at all). SDH include variables that describe the social environment in which respondents lived, variables that assess the impact of their lifetime experiences, and socioeconomic and demographic variables. We created variables that tap into the perception of respondents’ social environment (Agincourt) derived from literature on community trust (Zarychta, 2015; Subramanien et al., 2002), and sense of place and community attachment (Williams and Kitchen, 2012). These variables are predictors of health as shown in Subramanien et al. (2002) in their study of social trust and self-rated health within American communities. They reported a negative relationship between levels of community trust and poor health. Zarychta (2015) also reported an association between community trust and household health in rural areas in Honduras, whereby trusting households that are surrounded by trusting neighbors reported better health status. With regard to sense of place, Williams and Kitchen (2012) found a strong association between sense of place and mental health among residents of Hamilton, Ontario. We also used seven items that assess respondents’ perceptions of the social environment. Four of these items described respondents’ perceptions of the people in the village and three described respondents’ feelings toward the village. We created a perception of people in the village index by summing up the following four questions: (a) If there is a problem in the village, most people in this village work together to deal with it. (b). Most people in this village are willing to help their neighbors. (c.) Most people in this village can be trusted, and (d.) Most people in this village do favors for each other (such as, watching your house when you are gone, taking care of other people’s children in an emergency, or lending people small things, or other actions like this). The response is a Likert scale which describes the levels of agreement with the statements: 1—Strongly agree to 4—Strongly disagree. We reverse coded the answers such that higher scores correspond to greater
Results Table 1 shows that the mean age of the sample was about 62 years. Study participants were mostly female (53.6%), belonged to a religious denomination (82.4%), were either overweight or obese (57.8%) and a little over half were married (50.9%). Almost 46% of the respondents had no formal education and only about 4% had a higher education. The respondents had an average of about 5 children. Over half of them had at least one deceased child (58.4%). A significant number of respondents felt that they were part of the village (82.7%), the village was safe or extremely safe (95.7%), and liked the village (97.8%). Table 2 shows the composition and size of the networks. Respondents had more kin in each of their networks than friends and others. Specifically, they had relatives who were not their spouses or people they were cohabiting with. They also frequently interacted more with these relatives regardless of the physical proximity of place of residence, except for spouses/cohabitants who were living within the households. In addition, they had negative interactions more with these relatives, followed by their spouses or cohabitants than friends and others in their networks. There were more female members in the 144
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networks than males, except for co-workers. While the mean size of the functional support network has the greatest number of people (3), the smallest network size is the network of people respondents frequently interacted with in the household (mean = 0.8). Table 3 presents multiple linear regression results of the analysis. There is a statistically significant positive effect of contacts who were spouses, contacts who were friends and financial support on self-reported health. Specifically, respondents whose network members consisted largely of spouses and also friends reported better health relative to those whose contacts were others. However, study participants whose contacts mainly either lived in the household or village reported worse health compared to those whose members lived elsewhere. Additionally, emotional support negatively affected self-reported health. Adding the determinants of health changed some of the significant results (Model 2). In Model 2, there is still a positive effect of contacts who were spouses and financial support on physical health, and a negative effect of contacts living within the household, emotional support on self-reported physical health. Also, the statistically significant effect of contacts who were friends on self-reported health disappeared once we controlled for the SDH. However, the coefficients are mostly reduced for all the variables in Model 1. In some cases, previous significant effects are now absent (e.g., contacts who are friends). This suggests that the effects observed in Model 1 are mainly transmitted through the additional variables in Model 2. For example, contacts who are partners or friends and live in household or the village could have effects on health depending on how much respondents like the village or how they perceive the village. In other words, the relationships with contacts do not work in a vacuum but within the wider social context. In the same vein, the effect for frequency of in-person contact in Model 1, appeared to have been suppressed by the additional variables in Model 2. Again, this points to the importance of the overall social context of this network variable. In addition, there is a positive effect of perception of people in the village, finding the village safe, being satisfied with life, working full time on report of physical health. For instance, people who felt that the village was safe or extremely safe had better physical health relative to those who felt the village was unsafe. Furthermore, respondents who worked full time reported good health than those who did not work full time. Nevertheless, there is a negative effect of feeling part of the village, being underweight, feeling depressed, and age on self-reported physical health.
Table 1 Descriptive characteristics of variables.
Self-reported general Health Number of children ever had (mean) Number of children deceased (mean) Age Health during childhood Satisfaction with life Sum of 7 negative Exp Sex Female Male Marital status Married Unmarried Education No education At most 8th grade 9th grade - high school Higher education Religion Christianity Other religion No religion (reference) Employment status Working full time No Weight status Under-weight Normal Overweight and obese Do you like your village (a lot + like it) Yes No Village is safe (Extremely safe + safe) Yes No Feel part of village Yes No Felt depressed Yes No Having at least one child deceased Yes No Work full time Yes No Married or living with partner Yes No
N
Mean/Max
Percent
s.d.
5059 4732 4728 5017 5055 4856 4941
3.67/5 4.84/30 1.36/17 62.38/105 4.47/5 6.74/10 0.53/7
– – – – – – –
1.04 2.67 1.74 13.1 0.97 2.39 1.27
2714 2345
– –
53.60% 46.40%
2575 2480
– –
50.90% 49.10%
2306 1958 566 212
– – – –
45.70% 38.80% 11.20% 4.20%
3781 386 888
– –
74.80% 7.60% 17.60%
505 4541
– –
10.00% 90.00%
258 1719 2712
– – –
5.50% 36.70% 57.80%
4828 107
– –
97.80% 2.20%
4723 214
– –
95.70% 4.30%
4576 362
– –
92.70% 7.30%
564 4495
– –
11.10% 88.90%
2763 1965
– –
58.40% 41.60%
505 4541
– –
10% 90%
2575 2484
– –
50.90% 49.10%
Discussion This paper examined the effects of multiple dimensions of SN on self-reported health, controlling for SDH, using the baseline data of Health and Aging in Africa: A longitudinal Study of an INDEPTH
Table 2 Composition of Networks. Variables Network Structure Proportion of male Proportion of female Proximity of contact in household in village outside village Function Emotional support Physical support Informational support Financial support Quality Fighting, verbal argument, criticism, humiliation *
N
M
Re
Fr
Ac
Co
Other
Average # in SN
6431 8640
14.7% 17.9%
61.6% 64.2%
16.0% 10.6%
3.2% 3.8%
3.6% 2.3%
0.9% 1.2%
1.3 1.7
4230 6592 4354
54.4% 1.4% 2.1%
43.0% 63.1% 83.4%
0.7% 24.4% 7.1%
1.5% 5.0% 3.2%
0.2% 3.9% 3.8%
0.2% 2.2% 0.4%
0.8 1.3 0.9
14,134 13,464 14,447 12,026
17.2% 18.0% 16.9% 19.1%
62.2% 61.6% 62.9% 60.6%
13.1% 13.0% 12.9% 13.1%
3.6% 3.7% 3.6% 3.8%
2.8% 2.6% 2.7% 2.4%
1.1% 1.1% 1.0% 1.0%
3.0 3.0 3.0 3.0
1518
29.1%
55.0%
11.3%
1.6%
1.6%
1.4%
2.8
N: Network Size, M: Married or living with each other, Re: Relatives, Fr: Friends, Ac: Acquaintances, Co: Co-workers and Clubs. 145
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Table 3 Regression results of self-reported health status on social networks and social determinants of health variables. Model 1
Unstandardized coefficient
Standard Error
p-value
Intercept Network structure Frequency of in person interactions Relationship with network members Contacts are married or living with Contacts are relatives Contacts are friends Contacts are acquaintances Contacts are co-workers and clubs Contacts are others Proximity of Contacts Contacts live in household Contacts live in village Contacts live elsewhere Gender of Contacts Contacts are male Contacts are female Network function Emotional support Physical support Informational support Financial support Network quality Frequency of fighting R2 = 0.036, F = 13.1144***, N = 4575.
3.503
0.17
0.000
0.007
0.004
0.056
0.549*** 0.063 0.337* −0.175 0.018 –
0.181 0.166 0.171 0.138 0.204 –
0.002 0.703 0.049 0.355 0.931 –
−0.184** −0.130* –
0.064 0.056 –
0.004 0.019 –
0.015 –
0.015 –
0.332 –
−0.019*** 0.005 −0.001 0.018***
0.004 0.004 0.004 0.004
0.000 0.253 0.765 0.000
0.009
0.006
0.122
Model 2
Unstandardized coefficient
Standard Error
p-value
Intercept Network structure Frequency of in person contacts Relationship with network members Contacts are married or living with Contacts are relatives Contacts are friends Contacts are acquaintances Contacts are Co-workers and Clubs Contacts are others Proximity of Contacts Contacts live in household Contacts live in village Contacts live elsewhere Gender of Contacts Contacts are male Contacts are female Network function Emotional support Physical support Informational support Financial support Network quality Frequency of fighting Determinants of health variables Perception of people in village index How much like the village (a lot + like it) How much like the village (dislike it a lot + dislike it) Village is safe (Extremely safe + safe) Village is safe (Extremely unsafe + unsafe) Feel part of village (Yes) Feel part of village (No) Underweight Overweight + obese Normal weight Health during childhood Satisfaction with life Felt depressed (Yes) Felt depressed (No) Number of children ever had Number of children deceased Work full time Did not work full time At most 8th grade High school Tertiary or University
1.934
0.231
0
0.009*
0.004
0.014
0.145* −0.02 −0.003 0.027 −0.235 –
0.062 0.147 0.152 0.168 0.182 –
0.019 0.894 0.985 0.874 0.874 –
−0.159** −0.095 –
0.054 0.052 –
0.007 0.066 –
0.012 –
0.014 –
0.389 –
−0.010** 0.007 −0.010** 0.011**
0.003 0.004 0.004 0.004
0.002 0.098 0.004 0.004
−0.003
0.006
0.543
0.120*** 0.178 – 0.197** – −0.152** – −0.145* −0.011 – 0.022 0.138*** −0.410*** – 0.006 0.023 0.138** – −0.022 0.036 −0.019
0.008 0.098 – 0.072 – 0.054 – 0.063 0.03 – 0.014 0.006 0.044 – 0.005 0.03 0.047 – 0.031 0.051 0.074
0 0.07 – 0.007 – 0.005 – 0.022 0.726 – 0.127 0 0 – 0.305 0.447 0.003 – 0.489 0.483 0.801
(continued on next page) 146
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Table 3 (continued) Model 2
Unstandardized coefficient
Standard Error
p-value
No Education Male Female Christianity Other religion No religion Age Married or living with partner Not married R2 = 0.291, F = 48.361***, N = 3925.
– 0.032 – −0.072 −0.076 – −0.015*** −0.03 –
– 0.034 – 0.041 0.061 – 0.001 0.048 –
– 0.349 – 0.077 0.216 – 0 0.532 –
HIV/AIDS on the younger population may be another burden shouldered by respondents. This study, is the first to our knowledge, to use three different SN dimensions –structure, function, and quality—to assess self-reported physical health among an older population in South Africa. In fact, as described by the convoy model and social support and relationship theories, social network members provide different roles and functions which ultimately affects health (Antonucci et al., 2010; Antonucci and Akiyama, 1987). While some of the results are similar to previous findings such as a positive association between receipt of financial support and physical health, other findings are intriguing. For example, there was a negative association between feeling part of the village and health as well as negative effects of receipt of emotional and informational support on physical health. More studies should investigate this relationship including length of residence as a factor as well as context in which support is provided. This study has some limitations. First, the data are cross-sectional and we cannot assess the convoy of social relationships and their long term effects on health as social relationships change with time. The cross-sectional data may also cause bias via reverse causality. Second, the data set does not allow the identification of the level of support provided. Is the support consistent or low? This is important because studies have shown a link between levels of social support and some health conditions (Birditt and Antonucci, 2008; Walter-Ginzburg et al., 2002). Third, we believe that the social network quality question is loaded and should have been separated because the question, although it conveys a negative quality of social relationship (fighting, arguing, and being humiliated), all of the conditions do not translate into the same level interaction (arguing may be different from being humiliated). Fourth, the variable that describes the relationship with network members did not make a distinction between kin who are children and other family members as vertical family ties such as parents-children have special normative expectations relative to other family ties (Viry, 2012). Despite these limitations, the study contributes to the extant literature on social relationships, aging, and health. The INDEPTH Community in SA (HAALSI) Cohort survey used for this paper is a strength as it has variables that allow for the analysis of three different social network dimensions as well as SDH. While the study shed some light on the complexity of social relations and their effects on the health of older people in Agincourt, it is important that future studies examine specific contexts of social support among understudied aging populations in Africa. As the aging population is rapidly growing on the continent, knowing the dynamics of SN and their impact on the health of the elderly, especially in rural areas where valued resources are scant, will help not only family and loved ones with older relatives, but also, care providers to devise the best types of support.
Community in SA (HAALSI). This study showed that the aging population had more kin (non-spouses or spouses) in each of their networks and interacted more with these family members regardless of the physical proximity of place of residence compared to friend, acquaintances, and other network types. This finding is consistent with previous research that shows that family or immediate kin tends to be the closest or the most important members of personal SN, especially among people in low-income countries (Bastani, 2007; Moore and Prybutok, 2014). Also, since Agincourt is a rural area, residents may have close personal ties with family members who provide for their essential daily needs. However, the negative effects of receipt of emotional and informational support as well as having network members living within the households on physical health is puzzling as one will intuitively think these kinds of support should help improve health. However, this could be explained by the nature of the ties of the relationships between respondents and their network members. First, the networks are composed mostly of kin which represent closed network ties. Second, as reported by Viry et al. (2009), these ties are generally strong with emotional implication, and provide binding social capital which tends to lead to social control. Hence, network members might have become too emotionally controlling for the respondents. Nevertheless, Walter-Ginzburg et al. (2002), also found this contrary relationship between support and mortality in their study of old-old Israelis whereby those with more support did not show a reduced mortality. It is also possible that the effects of networks are more nuanced than appears. For example, since this research is based on cross-sectional data, it is not possible to establish the temporal order of poor health and network characteristics. In this situation, it makes sense to suggest a reverse causation namely, that poor health leads to seeking help from others who then become identified as network members (Booth et al., 2014). All these findings highlight the complexity and nuances of social relationships. Thus, it is important to understand the context in which support is provided (Birditt and Antonucci, 2008). Additionally, while the positive effect of perception of people in the village (perception of place), finding the village safe (community trust), and being satisfied with life and self-reported health are unsurprising, the negative association between feeling part of the village and health is unexpected. According to research on perception of place, cognitive and emotional responses to a place may influence the physiological and behavioral response to the place (Scannell and Gifford, 2010; Lengen and Kistermann, 2012; Hystad and Carpiano, 2012). Also, perception of a place has been found to be closely linked to health outcomes than objective indicators of the quality of the place (Wen et al., 2006). Thus, respondents overwhelmingly having a positive sense of place reported good health outcome. However, the negative association between feeling part of the village and health may be explained by the cumulative effects of neighborhood hazards on older adults (Robert and Li, 2001), as these older people may have been daily exposed to whatever may be deleterious in Agincourt. Also, as the aging population in Agincourt has experienced a significant growth (from 1% to 2%) but a worsening of mortality (Kahn et al., 2006), these changes might have negative impact on respondents’ health. Also, the devastating impact of
Declaration of interest None.
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