Clinical Practice & Epidemiology in Mental Health




ISSN: 1745-0179 ― Volume 15, 2019
REVIEW ARTICLE

Typology of Social Network Structures and Late-Life Depression in Low- and Middle-Income Countries



Akin Ojagbemi1, *, Oye Gureje1
1 World Health Organization (WHO) Collaborating Centre for Research and Training in Mental Health, Neurosciences, and Substance Abuse, Department of Psychiatry, University of Ibadan, Ibadan, Nigeria

Abstract

Background:

Rapid social changes and youth migration ensures a continuous drain on the social networks of the elderly in Low- and Middle-Income Countries (LMICs).

Objective:

We reviewed available literature on the relationship between social network structures and depression among community dwelling older persons in LMICs with a view to identifying patterns that might provide information for designing preventive psychosocial interventions.

Methods:

We searched the MEDLINE database through Pubmed, extracted information on the typologies of social network structures in LMICs and identified dimensions with the strongest systematic association with late-life depression, by weight, using the inverse of variance method. All analyses were conducted using the Cochrane review manager version 5.3.

Results:

Fourteen community-based surveys drawn from 16 LMIC contexts met criteria for syntheses. They included a total of 37,917 mostly female (58.8%) participants with an average age of 73.2 years. Social network size, contact with network, diversity of network, co-residency with own child, having more friends than family in the network, and prestigious standing of persons in the social network were protective structures against late-life depression. Conversely, low network diversity contributed 44.2% of the weight of all social network structures that are predictive of late-life depression.

Conclusion:

Recommendations are made for the design of new measures of social network structures in LMICs that captures the key dimensions identified. Epidemiological studies using such tools will provide more precise information for planning and prioritization of scarce resources for the prevention of late-life depression in LMICs.

Keywords: Social relationships, Social support, Socio-economic transition, Extended family systems, Socio-economic context, Socio-cultural context.


Article Information


Identifiers and Pagination:

Year: 2019
Volume: 15
First Page: 134
Last Page: 142
Publisher Id: CPEMH-15-134
DOI: 10.2174/1745017901915010134

Article History:

Received Date: 25/07/2019
Revision Received Date: 08/10/2019
Acceptance Date: 10/10/2019
Electronic publication date: 15/11/2019
Collection year: 2019

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© 2019 Ojagbemi & Gureje.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


* Address correspondence to this author at the Department of Psychiatry, College of Medicine, University of Ibadan, P.M.B 5017 (G.P.O) Ibadan, Nigeria; Tel: +234-8036737171;
E-mails: drakinjagbemi@yahoo.com; akinojagbemi@gmail.com






1. INTRODUCTION

By 2050, approximately 80% of older persons in the world will be residents of Low and Middle Income Countries (LMICs) [1Dotchin CL, Akinyemi RO, Gray WK, Walker RW. Geriatric medicine: services and training in Africa. Age Ageing 2013; 42(1): 124-8.
[http://dx.doi.org/10.1093/ageing/afs119] [PMID: 23027519]
-4Kuscu MK, Dural U, Onen P, et al. The association between individual attachment patterns, the perceived social support, and the psychological well-being of Turkish informal caregivers. Psychooncology 2009; 18(9): 927-35.
[http://dx.doi.org/10.1002/pon.1441] [PMID: 19140124]
]; depression is the most common mental healthcondition in old age [5Whiteford HA, Degenhardt L, Rehm J, et al. Global burden of disease attributable to mental and substance use disorders: findings from the Global Burden of Disease Study 2010. Lancet 2013; 382(9904): 1575-86.
[http://dx.doi.org/10.1016/S0140-6736(13)61611-6] [PMID: 23993280]
, 6Global Burden of Disease Study 2013 Collaborators, Global, regional, and national incidence, prevalence, and years lived with disability for 301 acute and chronic diseases and injuries in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 2015 Jun; 7piiS0140-6736(15)60692]. Some observational studies suggest that the prevalence and incidence of late-life depression may be much higher in LMICs than those reported for high income countries [7Gureje O, Oladeji B, Abiona T. Incidence and risk factors for late-life depression in the Ibadan Study of Ageing. Psychol Med 2011; 41(9): 1897-906.
[http://dx.doi.org/10.1017/S0033291710002643] [PMID: 21275087]
]. Consequently, research has focused on possible preventive factors for late-life depression. In particular, the observation that late-life depression may have a strong relationship with contextual factors in the social environment of older people [8Vink D, Aartsen MJ, Schoevers RA. Risk factors for anxiety and depression in the elderly: a review. J Affect Disord 2008; 106(1-2): 29-44.
[http://dx.doi.org/10.1016/j.jad.2007.06.005] [PMID: 17707515]
] has propelled hypotheses around the possible protective effect of good social network [9Schwarzbach M, Luppa M, Forstmeier S, König HH, Riedel-Heller SG. Social relations and depression in late life-a systematic review. Int J Geriatr Psychiatry 2014; 29(1): 1-21.
[http://dx.doi.org/10.1002/gps.3971] [PMID: 23720299]
].

Social networks are the systems of social relationships in which an individual is embedded [10Berkman LF, Syme SL. Social networks, host resistance, and mortality: a nine-year follow-up study of Alameda County residents. Am J Epidemiol 1979; 109(2): 186-204.
[http://dx.doi.org/10.1093/oxfordjournals.aje.a112674] [PMID: 425958]
]. As people age and experience many losses, their social networks also narrow. This, in turn, reduces the amount of social resources available to cope with the accumulation of stressors in old age [11Thoits PA. Sociological Approaches to Mental Illness.A Handbook for the Study of Mental Health Social Contexts, Theories, and Systems 2012; 106-24., 12Cohen S, Wills TA. Stress, social support, and the buffering hypothesis. Psychol Bull 1985; 98(2): 310-57.
[http://dx.doi.org/10.1037/0033-2909.98.2.310] [PMID: 3901065]
]. In this way, persons with limited social network are likely to be at increased risk of late-life depression. In reference to another frequently cited theory [13Berkman LF, Glass T, Brissette I, Seeman TE. From social integration to health: Durkheim in the new millennium. Soc Sci Med 2000; 51(6): 843-57.
[http://dx.doi.org/10.1016/S0277-9536(00)00065-4] [PMID: 10972429]
], social network is also seen to be influenced by culture, politics and economic factors.

In terms of structure, a person’s social network may consist of people with whom they regularly interact, the characteristics of such individuals, and the quality of interactions [9Schwarzbach M, Luppa M, Forstmeier S, König HH, Riedel-Heller SG. Social relations and depression in late life-a systematic review. Int J Geriatr Psychiatry 2014; 29(1): 1-21.
[http://dx.doi.org/10.1002/gps.3971] [PMID: 23720299]
]. Social networks have also functional characteristics by increasing the social support resources available to the individual [14Sherbourne CD, Stewart AL. The MOS social support survey. Soc Sci Med 1991; 32(6): 705-14.
[http://dx.doi.org/10.1016/0277-9536(91)90150-B] [PMID: 2035047]
]. Yet, the availability of a social network structure does not necessarily translate to function [15O’Reilly P. Methodological issues in social support and social network research. Soc Sci Med 1988; 26(8): 863-73.
[http://dx.doi.org/10.1016/0277-9536(88)90179-7] [PMID: 3287636]
]. Therefore, the two components can be studied independently.

Many LMICs are undergoing rapid socio-economic transition [16The International Migration Report 2017 2017.]. Poverty, material deprivation, armed conflicts, and general insecurity are widespread [17Ojagbemi A, Bello T, Luo Z, Gureje O. Living Conditions, Low Socioeconomic Position, and Mortality in the Ibadan Study of Aging. J Gerontol B Psychol Sci Soc Sci 2017; 72(4): 646-55.
[PMID: 27038398]
, 18Cappuccio FP, Miller MA. Cardiovascular disease and hypertension in sub-Saharan Africa: burden, risk and interventions. Intern Emerg Med 2016; 11(3): 299-305.
[http://dx.doi.org/10.1007/s11739-016-1423-9] [PMID: 27001886]
]. Health and social care is often unavailable for the majority of older people in the region [19Uwakwe R, Ibeh CC, Modebe AI, et al. The epidemiology of dependence in older people in Nigeria: prevalence, determinants, informal care, and health service utilization. A 10/66 dementia research group cross-sectional survey. J Am Geriatr Soc 2009; 57(9): 1620-7.
[http://dx.doi.org/10.1111/j.1532-5415.2009.02397.x] [PMID: 19682135]
]. Moreover, youth migration is occurring faster than ever before [20Leigh-Hunt N, Bagguley D, Bash K, et al. An overview of systematic reviews on the public health consequences of social isolation and loneliness. Public Health 2017; 152: 157-71.
[http://dx.doi.org/10.1016/j.puhe.2017.07.035] [PMID: 28915435]
, 21Challenges and Priorities of Global Mental Health in the Sustainable Development Goals (SDG) Era 2018; 24.]. Rapid youth migration, in particular, ensures a continuous drain on the social networks of older people through the erosion traditional of extended family systems [22Fokkema T, De Jong Gierveld J, Dykstra PA. Cross-national differences in older adult loneliness. J Psychol 2012; 146(1-2): 201-28.
[http://dx.doi.org/10.1080/00223980.2011.631612] [PMID: 22303621]
, 23Dykstra PA. Older adult loneliness: myths and realities. Eur J Ageing 2009; 6(2): 91-100.
[http://dx.doi.org/10.1007/s10433-009-0110-3] [PMID: 19517025]
].

The unique socio-economic and cultural contexts of LMICs suggest that the conceptualisation of social network structures is likely to be different from those of high-income countries. Few studies have considered the typologies of social network structures and how these might relate to prevalence, onset and course of late-life depression. The objective of the present study was to review the literature on the relationship between social network structures and depression among community dwelling elderly persons in LMICs and identify the most important protective typologies.

2. METHODS

2.1. Search Strategy

We searched the Medline database through PUBMED on the 13th of May 2019 using the following keywords: ‘depression OR depressive AND Social network OR Networks OR Networking’. Searches were limited to English language literature and those based on human samples who were aged 60 years or older.

2.2. Inclusion Criteria

Studies were included if; 1) participants were drawn from a country grouped as belonging to LMICs in the World Health Organization and World Bank income categories [24World Health Statistics 2015.], 2) they investigated epidemiological phenomena such as prevalence, incidence, risk or associated factors, and outcome, 3) they were conducted among community-dwelling participants, and 4) they used epidemiological or experimental study designs, such as descriptive and analytical cross-sectional studies, prospective and retrospective cohort studies, case control studies, randomized controlled trials, non-randomized controlled trials, quasi-experimental, as well as before and after studies.

For the purpose of inclusion, social network structure was defined as the organization (rather than its functions) of an older person’s social network [14Sherbourne CD, Stewart AL. The MOS social support survey. Soc Sci Med 1991; 32(6): 705-14.
[http://dx.doi.org/10.1016/0277-9536(91)90150-B] [PMID: 2035047]
]. This may include, but not limited to, a). The number of people with whom the older person regularly interacts, b). The characteristics of such individuals, c). Their relationships with the older person, and d). the quality of the interactions.

2.3. Exclusion Criteria

We excluded the following types of studies, 1) those conducted in hospitals, rehabilitation settings, nursing homes or other such institutions, 2) review papers, case series, individual case reports, expert opinions, discussion papers, and position papers; and 3) studies focusing solely on qualitative data. Study assessment for inclusion and exclusion criteria as well as subsequent data extraction was conducted based on the descriptions in the original article.

2.4. Statistical Methods

Flexible random effect meta-analyses were conducted to investigate the most important social network typologies that are protective or predictive of late-life depression. For these analyses, we included only studies reporting statistically significant effects at below 5% alpha. We then entered the log of study effect sizes as well as their corresponding Standard Errors (S.E) into the meta-analytic models. Estimates of effect sizes and S.Es were derived from data in the original article using methodologies developed by the Cochrane collaboration [25Cochrane Handbook for Systematic Reviews of Interventions Version 510 2011.]. Weighting was applied using the inverse of variance method.

To determine the extent of statistical heterogeneity, we estimated the percentage of total variation in estimates reported across studies that is due to heterogeneity, rather than chance. This was computed using the I2 test. All analyses were conducted using the Cochrane review manager (Revman) version 5.3 software [26Review Manager (RevMan) [Computer program] 2014.].

3. RESULTS

The combined database searches identified a total of 890 records. From an initial screening of the titles and abstracts of these records, we identified 24 articles that might have contained information relevant to the review. After retrieving and reading through the full texts of all 24 articles, a further 10 articles were excluded for reasons presented in Fig. (1): four studies [2Dong X, Beck T, Simon MA. The associations of gender, depression and elder mistreatment in a community-dwelling Chinese population: the modifying effect of social support. Arch Gerontol Geriatr 2010; 50(2): 202-8.
[http://dx.doi.org/10.1016/j.archger.2009.03.011] [PMID: 19398133]
, 27Baiyewu O, Yusuf AJ, Ogundele A. Depression in elderly people living in rural Nigeria and its association with perceived health, poverty, and social network. Int Psychogeriatr 2015; 27(12): 2009-15.
[http://dx.doi.org/10.1017/S1041610215001088] [PMID: 26265242]
-29Quatrin LB, Galli R, Moriguchi EH, Gastal FL, Pattussi MP. Collective efficacy and depressive symptoms in Brazilian elderly. Arch Gerontol Geriatr 2014; 59(3): 624-9.
[http://dx.doi.org/10.1016/j.archger.2014.08.001] [PMID: 25183439]
] investigated constructs other than social network structures, three studies [3Axinn WG, Ghimire DJ, Williams NE, Scott KM. Associations between the social organization of communities and psychiatric disorders in rural Asia. Soc Psychiatry Psychiatr Epidemiol 2015; 50(10): 1537-45.
[http://dx.doi.org/10.1007/s00127-015-1042-1] [PMID: 25796491]
, 4Kuscu MK, Dural U, Onen P, et al. The association between individual attachment patterns, the perceived social support, and the psychological well-being of Turkish informal caregivers. Psychooncology 2009; 18(9): 927-35.
[http://dx.doi.org/10.1002/pon.1441] [PMID: 19140124]
, 30Perkins JM, Nyakato VN, Kakuhikire B, et al. Food insecurity, social networks and symptoms of depression among men and women in rural Uganda: a cross-sectional, population-based study. Public Health Nutr 2018; 21(5): 838-48.
[http://dx.doi.org/10.1017/S1368980017002154] [PMID: 28988551]
] were based on general, rather than elderly, population samples (and did not present results specific to older participants in their samples), two studies [31Chao SF. Assessing social support and depressive symptoms in older Chinese adults: a longitudinal perspective. Aging Ment Health 2011; 15(6): 765-74.
[http://dx.doi.org/10.1080/13607863.2011.562182] [PMID: 21838514]
, 32Chi I, Chou KL. Social support and depression among elderly Chinese people in Hong Kong. Int J Aging Hum Dev 2001; 52(3): 231-52.
[http://dx.doi.org/10.2190/V5K8-CNMG-G2UP-37QV] [PMID: 11407488]
] were conducted among high income populations, and one study [33Gallegos-Carrillo K, Mudgal J, Sánchez-García S, et al. Social networks and health-related quality of life: A population based study among older adults. Salud Publica Mex 2009; 51(1): 6-13.
[http://dx.doi.org/10.1590/S0036-36342009000100004] [PMID: 19180307]
] was reported in Spanish.

Fig. (1)
Preferred reporting items for systematic reviews and meta-analyses flow chart for the study of typologies of social network structures and late-life depression in low- and middle-income countries.


3.1. Appraisal of Studies Meeting Inclusion Criteria

In all, 14 studies met criteria for syntheses. The studies were drawn from across 16 LMIC contexts. They included a total of 37,917 mostly female (58.8%) participants with an average age of 73.2 years (Table 1).

Table 1
Participants characteristics in studies of social network structures and late-life depression in low- and middle-income countries.


Key information about the studies is presented in Table 2. They were published between April 2008 and August 2016. About 30% of included studies were from China. Twelve (85.7%) had a cross-sectional design. The remaining two studies were prospective observations of between two- [34Monserud MA, Wong R. Depressive Symptoms Among Older Mexicans: The Role of Widowhood, Gender, and Social Integration. Res Aging 2015; 37(8): 856-86.
[http://dx.doi.org/10.1177/0164027514568104] [PMID: 25651596]
] and three-years [7Gureje O, Oladeji B, Abiona T. Incidence and risk factors for late-life depression in the Ibadan Study of Ageing. Psychol Med 2011; 41(9): 1897-906.
[http://dx.doi.org/10.1017/S0033291710002643] [PMID: 21275087]
] follow-up. Only three studies used standardised clinical interviews for the ascertainment of depression [7Gureje O, Oladeji B, Abiona T. Incidence and risk factors for late-life depression in the Ibadan Study of Ageing. Psychol Med 2011; 41(9): 1897-906.
[http://dx.doi.org/10.1017/S0033291710002643] [PMID: 21275087]
, 34Monserud MA, Wong R. Depressive Symptoms Among Older Mexicans: The Role of Widowhood, Gender, and Social Integration. Res Aging 2015; 37(8): 856-86.
[http://dx.doi.org/10.1177/0164027514568104] [PMID: 25651596]
, 35Thiyagarajan JA, Prince M, Webber M. Social support network typologies and health outcomes of older people in low and middle income countries--a 10/66 Dementia Research Group population-based study. Int Rev Psychiatry 2014; 26(4): 476-85.
[http://dx.doi.org/10.3109/09540261.2014.925850] [PMID: 25137114]
]. The remaining 78.6% relied on various rating scales.

Table 2
Characteristics of studies of social network structures and late-life depression in low- and middle-income countries: citations are repeated if study investigated more than one social network structure typologies.


Included studies clearly described the source population and sampling frame. Appropriate analytical techniques were used. Half of the studies derived beta values for the association between social network structures and late-life depression. The remaining studies derived other valid measures of study effect (Table 2). The corresponding S.E or 95% Confidence Intervals of the reported effect sizes were also presented.

3.2. Ascertainment of Social Network Structures

In Table 2, various standardised questionnaires and rating scales were used to ascertain types of social network structures. Apart from an aggregate score on the Lubben’s Social Network Scale [36Lubben J. Assessing social networks among elderly populations. J Fam Med Community Health 1988; 1988: 42-52.
[http://dx.doi.org/10.1097/00003727-198811000-00008]
], six broad typologies of social network structures were investigated across studies. These include:

  1. Social network size, defined in Aung et al. [37Aung MN, Moolphate S, Aung TN, Katonyoo C, Khamchai S, Wannakrairot P. The social network index and its relation to later-life depression among the elderly aged ≥80 years in Northern Thailand. Clin Interv Aging 2016; 11: 1067-74.
    [http://dx.doi.org/10.2147/CIA.S108974] [PMID: 27540286]
    ] as the number of persons in the social network. This was assessed using the Cohen’s index [38Cohen S, Doyle WJ, Skoner DP, Rabin BS, Gwaltney JM Jr. Social ties and susceptibility to the common cold. JAMA 1997; 277(24): 1940-4.
    [http://dx.doi.org/10.1001/jama.1997.03540480040036] [PMID: 9200634]
    ].
  2. Social network composition, defined in Singh et al. [39Singh L, Singh PK, Arokiasamy P. Social Network and Mental Health Among Older Adults in Rural Uttar Pradesh, India: A Cross-Sectional Study. J Cross Cult Gerontol 2016; 31(2): 173-92.
    [http://dx.doi.org/10.1007/s10823-016-9286-0] [PMID: 26879450]
    ] as comprising either children network, relatives’ network, friends’ network or confidant network. The same construct was classified in Chen et al. [40Chen YY, Wong GH, Lum TY, et al. Neighborhood support network, perceived proximity to community facilities and depressive symptoms among low socioeconomic status Chinese elders. Aging Ment Health 2016; 20(4): 423-31.
    [http://dx.doi.org/10.1080/13607863.2015.1018867] [PMID: 25775108]
    ] as either having family, relatives, friends or health/social care workers in the network.
  3. Social network interaction, This was defined using the Lubben’s Social Network Scale [36Lubben J. Assessing social networks among elderly populations. J Fam Med Community Health 1988; 1988: 42-52.
    [http://dx.doi.org/10.1097/00003727-198811000-00008]
    ] in Kim et al. [41Kim T, Nguyen ET, Yuen EJ, Nguyen T, Sorn R, Nguyen GT. Differential Role of Social Connectedness in Geriatric Depression Among Southeast Asian Ethnic Groups. Prog Community Health Partnersh 2015; 9(4): 483-93.
    [http://dx.doi.org/10.1353/cpr.2015.0075] [PMID: 26639374]
    ]. It was also described as the frequency of reciprocal visits between the older persons and other people in their network [42Caetano SC, Silva CM, Vettore MV. Gender differences in the association of perceived social support and social network with self-rated health status among older adults: A population-based study in Brazil. BMC Geriatr 2013; 13: 122.
    [http://dx.doi.org/10.1186/1471-2318-13-122] [PMID: 24229389]
    ]. Further, the interaction construct was defined in other studies as having a meeting with someone in the network [43Tong HM, Lai DW, Zeng Q, Xu WY. Effects of social exclusion on depressive symptoms: elderly Chinese living alone in Shanghai, China. J Cross Cult Gerontol 2011; 26(4): 349-64.
    [http://dx.doi.org/10.1007/s10823-011-9150-1] [PMID: 21814741]
    ], telephone conversation [43Tong HM, Lai DW, Zeng Q, Xu WY. Effects of social exclusion on depressive symptoms: elderly Chinese living alone in Shanghai, China. J Cross Cult Gerontol 2011; 26(4): 349-64.
    [http://dx.doi.org/10.1007/s10823-011-9150-1] [PMID: 21814741]
    ], satisfaction with contact [44Wahlin Å, Palmer K, Sternäng O, Hamadani JD, Kabir ZN. Prevalence of depressive symptoms and suicidal thoughts among elderly persons in rural Bangladesh. Int Psychogeriatr 2015; 27(12): 1999-2008.
    [http://dx.doi.org/10.1017/S104161021500109X] [PMID: 26250141]
    ] or regularity of contact [7Gureje O, Oladeji B, Abiona T. Incidence and risk factors for late-life depression in the Ibadan Study of Ageing. Psychol Med 2011; 41(9): 1897-906.
    [http://dx.doi.org/10.1017/S0033291710002643] [PMID: 21275087]
    ].
  4. Geographical proximity, This was defined by co-residency with own children (whether biological or foster) or not [34Monserud MA, Wong R. Depressive Symptoms Among Older Mexicans: The Role of Widowhood, Gender, and Social Integration. Res Aging 2015; 37(8): 856-86.
    [http://dx.doi.org/10.1177/0164027514568104] [PMID: 25651596]
    , 44Wahlin Å, Palmer K, Sternäng O, Hamadani JD, Kabir ZN. Prevalence of depressive symptoms and suicidal thoughts among elderly persons in rural Bangladesh. Int Psychogeriatr 2015; 27(12): 1999-2008.
    [http://dx.doi.org/10.1017/S104161021500109X] [PMID: 26250141]
    , 45Sicotte M, Alvarado BE, León EM, Zunzunegui MV. Social networks and depressive symptoms among elderly women and men in Havana, Cuba. Aging Ment Health 2008; 12(2): 193-201.
    [http://dx.doi.org/10.1080/13607860701616358] [PMID: 18389399]
    ]. Co-residency with other family members rather than own children was an additional definition of proximity provided in Monserud and Wong [34Monserud MA, Wong R. Depressive Symptoms Among Older Mexicans: The Role of Widowhood, Gender, and Social Integration. Res Aging 2015; 37(8): 856-86.
    [http://dx.doi.org/10.1177/0164027514568104] [PMID: 25651596]
    ]. For non-co-resident respondents, geographical proximity was further expounded by whether respondents had adult children who lived in the same neighborhood [34Monserud MA, Wong R. Depressive Symptoms Among Older Mexicans: The Role of Widowhood, Gender, and Social Integration. Res Aging 2015; 37(8): 856-86.
    [http://dx.doi.org/10.1177/0164027514568104] [PMID: 25651596]
    ], or lived more than half an hour from the older person [46Webster NJ, Antonucci TC, Ajrouch KJ, Abdulrahim S. Social networks and health among older adults in Lebanon: the mediating role of support and trust. J Gerontol B Psychol Sci Soc Sci 2015; 70(1): 155-66.
    [http://dx.doi.org/10.1093/geronb/gbu149] [PMID: 25324295]
    ]. The emotional proximity of social network was a supplementary category of geographical proximity defined in Webster et al. [46Webster NJ, Antonucci TC, Ajrouch KJ, Abdulrahim S. Social networks and health among older adults in Lebanon: the mediating role of support and trust. J Gerontol B Psychol Sci Soc Sci 2015; 70(1): 155-66.
    [http://dx.doi.org/10.1093/geronb/gbu149] [PMID: 25324295]
    ] as the relative distance between the older persons and people in their network.
  5. Social network diversity, Summarised in Thiyagarajan et al. [35Thiyagarajan JA, Prince M, Webber M. Social support network typologies and health outcomes of older people in low and middle income countries--a 10/66 Dementia Research Group population-based study. Int Rev Psychiatry 2014; 26(4): 476-85.
    [http://dx.doi.org/10.3109/09540261.2014.925850] [PMID: 25137114]
    ] as integrated on non-integrated networks based on results of the Practical Assessment of Network Types [47Wenger GC, Shahtahmasebi S. Survivors: Support network variation and sources of help in rural communities. J Cross Cult Gerontol 1991; 6(1): 41-82.
    [PMID: 24390432]
    ]. Diversity was also defined in Sicotte et al. [45Sicotte M, Alvarado BE, León EM, Zunzunegui MV. Social networks and depressive symptoms among elderly women and men in Havana, Cuba. Aging Ment Health 2008; 12(2): 193-201.
    [http://dx.doi.org/10.1080/13607860701616358] [PMID: 18389399]
    ] by the number of different types of relationships the older person had with people in their network.
  6. Social standing of the network, defined in Cao et al. [48Cao W, Li L, Zhou X, Zhou C. Social capital and depression: evidence from urban elderly in China. Aging Ment Health 2015; 19(5): 418-29.
    [http://dx.doi.org/10.1080/13607863.2014.948805] [PMID: 25155221]
    ] as the older person’s connection with family members, friends or acquaintances in a list of 20 specific occupations with varying prestige scores. To achieve this, a position generator procedure was applied to occupational prestige scale scores [49Bian Y. Source and functions of urbanites’ social capital: A network approach. Soc Sci China 2004; 3: 136-46.].

3.3. Association of Types of Social Network Structures with Late-life Depression

In Table 2, social network interaction was the most cited broad typology of social network structures. The construct was investigated in five studies [7Gureje O, Oladeji B, Abiona T. Incidence and risk factors for late-life depression in the Ibadan Study of Ageing. Psychol Med 2011; 41(9): 1897-906.
[http://dx.doi.org/10.1017/S0033291710002643] [PMID: 21275087]
, 41Kim T, Nguyen ET, Yuen EJ, Nguyen T, Sorn R, Nguyen GT. Differential Role of Social Connectedness in Geriatric Depression Among Southeast Asian Ethnic Groups. Prog Community Health Partnersh 2015; 9(4): 483-93.
[http://dx.doi.org/10.1353/cpr.2015.0075] [PMID: 26639374]
-44Wahlin Å, Palmer K, Sternäng O, Hamadani JD, Kabir ZN. Prevalence of depressive symptoms and suicidal thoughts among elderly persons in rural Bangladesh. Int Psychogeriatr 2015; 27(12): 1999-2008.
[http://dx.doi.org/10.1017/S104161021500109X] [PMID: 26250141]
]. However, three of the five studies of social network interaction showed no statistical effect on depression. Also notable in Table 2 are the results of two longitudinal observations in relation to social network interaction [7Gureje O, Oladeji B, Abiona T. Incidence and risk factors for late-life depression in the Ibadan Study of Ageing. Psychol Med 2011; 41(9): 1897-906.
[http://dx.doi.org/10.1017/S0033291710002643] [PMID: 21275087]
] and geographical proximity [34Monserud MA, Wong R. Depressive Symptoms Among Older Mexicans: The Role of Widowhood, Gender, and Social Integration. Res Aging 2015; 37(8): 856-86.
[http://dx.doi.org/10.1177/0164027514568104] [PMID: 25651596]
].

The disaggregated factors in the dimensions of social network structures demonstrating systematic association with late life depression are presented in Fig. (2). In the decreasing order, the most precise protective factors were large network size, regular contact with network, high network diversity, having a co-resident child, having more friends than family in the network, and prestigious standing of persons in the social network. However, large network size and regular contact with network contributed more than one-third (33.8%) of all systematic associations with late life depression. Conversely, 44.2% of all predictive factors for late-life depression was contributed by low network diversity.

Fig. (2)
Independently associated social network structures with prevalent depression in older persons from low- and middle-income countries (ranked by the inverse of variance method).


4. DISCUSSION

The present study found that six broad dimensions of social network structures demonstrate significant protective effects against late-life depression in LMICs. In descending order of weight contributed, the dimensions were represented by social network size, contact with network, diversity of network, co-residency with own child, having more friends than family in the network, and prestigious standing of persons in the social network. However, large network size and regular contact with network contributed more than one-third (33.8%) of all protective structures against late life depression. Conversely, 44.2% of all social network structures that are predictive of late-life depression was contributed by low network diversity.

The finding that prestigious standing of persons in the network contributed much less (7.9%) to the pooled weight of protective structures against late-life depression may be due to the complex nature of the position generator procedure [50Lin N, Fu YC, Hsung RM. The Position Generator: Measurement techniques for investigations of social capital. Social capital: Theory and research 2001.]. The social standing index generated by this procedure is an aggregate score comprising the number of occupations endorsed by respondents, the highest prestige score of occupations so endorsed and the range of prestige scores. Each of these components have been found to have their own unique effects on wellbeing [51Cheng ST, Lee CK, Chan AC, Leung EM, Lee JJ. Social network types and subjective well-being in Chinese older adults. J Gerontol B Psychol Sci Soc Sci 2009; 64(6): 713-22.
[http://dx.doi.org/10.1093/geronb/gbp075] [PMID: 19820232]
]. For example, while the endorsement of higher number of occupations demonstrate strong relationship with health, prestige scores assigned to those occupations may sometimes show non-significant effects [52Moore S, Daniel M, Gauvin L, Dubé L. Not all social capital is good capital. Health Place 2009; 15(4): 1071-7.
[http://dx.doi.org/10.1016/j.healthplace.2009.05.005] [PMID: 19482506]
]. Another possible reason for an observed small effect of social standing on late-life depression is the observation that social stratifications based on occupation may be more relevant to high-income countries [53Grusky DB. The Past, Present, and Future of Social Inequality.Social Stratification: Class, Race, and Gender in Sociological Perspective 2001; 3-51.]. This is as large numbers of community-dwelling older people in LMICs engage in subsistence farming and other elementary occupations [54Ojagbemi A, Bello T, Gureje O. Gender differential in social and economic predictors of incident major depressive disorder in the Ibadan Study of Ageing. Soc Psychiatry Psychiatr Epidemiol 2018; 53(4): 351-61.
[http://dx.doi.org/10.1007/s00127-018-1500-7] [PMID: 29468523]
].

A third observation in the present review is that individual studies on the relationship between social network and late-life depression in LMICs have often selected only a few dimensions of social network structures for investigation. In this way, studies appear less comprehensive in their approach to the social network. The result of this is the observed variability in findings. Inconsistencies in the magnitude and direction of associations make it difficult to decide on the most appropriate targets for preventive interventions based on social network characteristics. Greater precision in findings will provide the information needed by policy makers to better streamline scarce resources for the prevention of late-life depression in LMICs.

It would seem that the most comprehensive assessment of the dimension of social network structures in the present review was provided by Chan et al. [55Chan MF, Zeng W. Investigating factors associated with depression of older women in Macau. J Clin Nurs 2009; 18(21): 2969-77.
[http://dx.doi.org/10.1111/j.1365-2702.2009.02867.x] [PMID: 19732244]
] since the authors relied on the summary score on the Lubben’s Social Network Scale [36Lubben J. Assessing social networks among elderly populations. J Fam Med Community Health 1988; 1988: 42-52.
[http://dx.doi.org/10.1097/00003727-198811000-00008]
]. Analyzing from the dimensions of social network structures identified in the present study, the Lubben’s scale demonstrates relatively superior face validity by providing some assessment of four out of the six broad dimensions identified: social network size, diversity, interaction and geographical proximity. However, the relative predictive weight of association of the Lubbens’s scale summary score with depression in the present study was low. While this finding may also suggest weaknesses in the primary study [55Chan MF, Zeng W. Investigating factors associated with depression of older women in Macau. J Clin Nurs 2009; 18(21): 2969-77.
[http://dx.doi.org/10.1111/j.1365-2702.2009.02867.x] [PMID: 19732244]
], it provides some evidence of inadequate known group validity of the Lubben’s social network scale.

We note that the validity and reliability of available measures of social networks have long been a source of concern in the literature [15O’Reilly P. Methodological issues in social support and social network research. Soc Sci Med 1988; 26(8): 863-73.
[http://dx.doi.org/10.1016/0277-9536(88)90179-7] [PMID: 3287636]
, 56Butts CT. Social network analysis: A methodological introduction. Asian J Soc Psychol 2008; 11: 13-41.
[http://dx.doi.org/10.1111/j.1467-839X.2007.00241.x]
]. Future research should strive towards attaining the goal of finding the best measurement modalities for social network structures and functions in specific populations and disease contexts. The optimal tool for assessing social network structures relevant to late-life depression in LMICs may yet be unavailable. Such a tool should ideally produce measures that are easy to interpret and are comprehensive enough to assess the dimensions of social network structures identified in the present study.

The observations made in this limited review should be interpreted within limitations of the quality of available data. Judgments about the contributions of the different facets of the social network to late-life depression using meta-analytic weighting may be affected by methodological variability in the included studies. In this regard, it should be noted that a significant heterogeneity (I2=88%, p<0.001) was recorded when studies reporting significant protective effects of social network structures on late-life depression were combined. On a closer look at our data, the significant statistical heterogeneity found in studies reporting protective effects of the social network was due to the effect size outlier reported when examining the effect of the prestigious social standing of members of the network on late-life depression. The study by Cao and colleagues [48Cao W, Li L, Zhou X, Zhou C. Social capital and depression: evidence from urban elderly in China. Aging Ment Health 2015; 19(5): 418-29.
[http://dx.doi.org/10.1080/13607863.2014.948805] [PMID: 25155221]
] conducted among 928 older residents in Hangzhou China was the only citation in the series to use the occupational prestige scale as a meaningful index of social network structure. Even though this study [48Cao W, Li L, Zhou X, Zhou C. Social capital and depression: evidence from urban elderly in China. Aging Ment Health 2015; 19(5): 418-29.
[http://dx.doi.org/10.1080/13607863.2014.948805] [PMID: 25155221]
] contributed only 7.9% of the weight of all studies reporting significant protective effect of social network structures, its effect size is about half of the combined effect sizes reported by the other studies in the series.

The present mini review has other limitations. We have carried out a rapid review of the literature on social network structures and late-life depression in LMICs. As such, the search strategy has focused on records in the MEDLINE database. Records in other important databases, such as PsychInfo and Embase have not been considered. Also, manual searches of the reference list of included studies as well as grey literature were not implemented. Nevertheless, the MEDLINE is, perhaps, the largest repository of biomedical literature. To ensure that ahead of print articles are not missed, we implemented our search of the MEDLINE database through Pubmed.

CONCLUSION

Health care resources are limited in LMICs. As such, accurate information about the contribution of different types of social network structures that may be protective for late-life depression in these countries may be helpful in the planning and prioritization of policies and allocation of scarce resources. New measures that include the broad dimensions of social network structures identified in the present study are needed. Such new tools should be deployed in future large-scale surveys of the mental health and wellbeing of elderly persons living in LMICs. While cross-sectional surveys are important, the use of such tools in longitudinal observations will provide even more robust information about the relative contributions of social network structures and functions to the mental health of older people in LMICs.

CONSENT FOR PUBLICATION

Not applicable.

FUNDING

None.

CONFLICT OF INTEREST

The authors declare no conflict of interest, financial or otherwise.

ACKNOWLEDGEMENTS

AO performed literature search, data extraction and analyses, AO and OG wrote the first draft of the paper. Both authors approved the final draft for submission.

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(National Central University, Taiwan)


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