REVIEW ARTICLE
Typology of Social Network Structures and Late-Life Depression in Low- and Middle-Income Countries
Akin Ojagbemi1, *, Oye Gureje1
Article Information
Identifiers and Pagination:
Year: 2019Volume: 15
First Page: 134
Last Page: 142
Publisher ID: CPEMH-15-134
DOI: 10.2174/1745017901915010134
Article History:
Received Date: 25/07/2019Revision Received Date: 08/10/2019
Acceptance Date: 10/10/2019
Electronic publication date: 15/11/2019
Collection year: 2019
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.
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.
