No Man Is an Island: Another Worthwhile Approach to the Study of Mental Disorders Using Social Network Analysis.

In this issue, Bryant and colleagues (1) present results from a study of bushfire survivors that examined associations between social structures and mental health indices, namely, posttraumatic stress disorder (PTSD) and depressive symptoms. The study used social network analysis to establish a variety of social connections within the network of 558 respondents, 2–4years after thedisaster. This empirical approach adds to many different ways that social support and social relationships have been examined in the context of coping with disasters (2). The social network analysis approach is based on network and graph theories (3). Itsmodern history can be traced to the early works of Moreno and Jennings in the 1930s, in which the term “sociometry” was coined (4). Social network analysis enables investigators to understand group patterns and interdependent relationships within a network. The visual representation of graphs and maps shows how individuals are connected with each other within clusters of similar properties and across clusters. Different lines between individuals within the network map may indicate features such as centrality, intensiveness, inclusiveness, or exclusiveness. In basic terms, close ties with others include those nominated by the participant (termed “egos”), those nominated by other participants (“alters”), and mutual (reciprocal) ties nominated by a pair of participants (1). Beyond basic constructs, complex structures based on triads may include a clique, which is a group of people interconnected with every member of the group; a brokerage, in which a person is connected to two individuals who have no direct ties between them; and a cyclic configuration (i.e., indirect reciprocity) suggesting that they are part of a connected social network.More complex structures exist beyond basic triads (1). Social network analysis can, in a visual manner, map complex social relationships and their connections to mental health outcomes such as depression (5). Statistically, the approach used by Bryant et al. is similar to logistic regression, and it is termed autologistic actor attribute model (ALAAM) (6). The ALAAM can act as logistic regression, predicting dichotomous outcomes from individual-level factors. However, unlike logistic regression, the ALAAM takes into account interdependence of social network relationships. ALAAMs can show how various configurations of network ties are associated with individual outcomes (6). This novel approach opens a new venue in the field of large-scale disaster mental health studies. Disaster and trauma investigations tend to look at individual-level assessments, and even if they use community-level approaches (7, 8), these studies tend to neglect the interdependence level of social networks. Collectively experienced disasters affect entire communities, and hence such events influence both the individuals’ postdisaster social networks and interrelations within the affected communities (2). Bryant and colleagues’ results using the ALAAMwere almost identical to those using logistic regression at the individual level. However, the interesting results were found at the network level (interdependent level) with relation to depression and PTSD. First, the participants who reported fewer ties with others were at greater risk for probable depression. This finding confirmed the importance of the network size effect, often reported by studies of social support andmentalhealth (2). However, being linked to others with depression predicted that an individual was also likely to report depressive symptoms. This interesting result maypoint to anemotional contagioneffect (9); being socially linked to survivorswhoaredepressedmayhave a contagious effect, thus creating a vicious circle. This finding is relevant for both community psychiatry and individual-level interventions, as it reiteratesadoptingasystemicviewonadepressed patient by probing at his or her social network connections. For example, during times of threat, individuals may often choose to “socially share” with others their anxieties and emotions in an attempt to alleviate distress, or they may seek reassurance from significant members of their social networks (10). This is a manifestation of the classic fear affiliation effect (11). The detrimental consequences of such behaviors have been found to increase the likelihood of depression (12). It is also important to note thatBryant and colleagues’data collection took place 2–4 years after the disaster. Studies of This study highlights the importance of the individual within a social network and thus adds deeper insights to contributions of social networks and social support in coping with disasters.