Quantifying a systems map: network analysis of a childhood obesity causal loop diagram

Causal loop diagrams developed by groups capture a shared understanding of complex problems and provide a visual tool to guide interventions. This paper explores the application of network analytic methods as a new way to gain quantitative insight into the structure of an obesity causal loop diagram to inform intervention design. Identification of the structural features of causal loop diagrams is likely to provide new insights into the emergent properties of complex systems and analysing central drivers has the potential to identify leverage points. The results found the structure of the obesity causal loop diagram to resemble commonly observed empirical networks known for efficient spread of information. Known drivers of obesity were found to be the most central variables along with others unique to obesity prevention in the community. While causal loop diagrams are often specific to single communities, the analytic methods provide means to contrast and compare multiple causal loop diagrams for complex problems

Social network analysis of stakeholder networks from two community-based obesity prevention interventions

IntroductionStudies of community-based obesity prevention interventions have hypothesized that stakeholder networks are a critical element of effective implementation. This paper presents a quantitative analysis of the interpersonal network structures within a sub-sample of stakeholders from two past successful childhood obesity prevention interventions.MethodsParticipants were recruited from the stakeholder groups (steering committees) of two completed community-based intervention studies, Romp &amp; Chomp (R&amp;C), Australia (2004-2008) and Shape Up Somerville (SUS), USA (2003-2005). Both studies demonstrated significant reductions of overweight and obesity among children. Members of the steering committees were asked to complete a retrospective social network questionnaire using a roster of other committee members and free recall. Each participant was asked to recall the people with whom they discussed issues related to childhood obesity throughout the intervention period, along with providing the closeness and level of influence of each relationship.ResultsNetworks were reported by 13 participants from the SUS steering committee and 8 participants from the R&amp;C steering committee. On average, participants nominated 16 contacts with whom they discussed issues related to childhood obesity through the intervention, with approximately half of the relationships described as &lsquo;close&rsquo; and 30% as &lsquo;influential&rsquo;. The &lsquo;discussion&rsquo; and &lsquo;close&rsquo; networks had high clustering and reciprocity, with ties directed to other steering committee members, and to individuals external to the committee. In contrast, influential ties were more prominently directed internal to the steering committee, with higher network centralization, lower reciprocity and lower clustering.Discussion and conclusionSocial network analysis provides a method to evaluate the ties within steering committees of community-based obesity prevention interventions. In this study, the network characteristics between a sub-set of stakeholders appeared to be supportive of diffused communication. Future work should prospectively examine stakeholder network structures in a heterogeneous sample of community-based interventions to identify elements most strongly associated with intervention effectiveness.<br /

The use of network analysis to evaluate community-based obesity prevention interventions

This thesis explored the complex components of community-based obesity prevention interventions; leadership committees, systems maps, and their interdependencies. The thesis proposed an array of techniques from network analysis, and analysed both retrospective and prospective trials. The findings offer recommendations for the formation of synergised collaboration in future complex systems interventions

Distribution of node in and out degree (number of in and out bound edges for each node) for the community developed obesity CLD.

<p>Distribution of node in and out degree (number of in and out bound edges for each node) for the community developed obesity CLD.</p

Community developed CLD of obesity drivers displayed as a directed network.

<p>Community developed CLD of obesity drivers displayed as a directed network.</p

Individual node metrics: In-degree, Out-degree and Betweenness centrality interpretations for a causal loop diagram.

<p>Individual node metrics: In-degree, Out-degree and Betweenness centrality interpretations for a causal loop diagram.</p

Variables with the highest betweenness centrality- the ‘mediators’ of the causal loop diagram shown (a) by node size in the network, (b) the distribution of values and (c) a table of values for nodes with the highest betweenness centrality.

<p>Variables with the highest betweenness centrality- the ‘mediators’ of the causal loop diagram shown (a) by node size in the network, (b) the distribution of values and (c) a table of values for nodes with the highest betweenness centrality.</p

Summary of network statistics for the CLD.

<p>Summary of network statistics for the CLD.</p

Structural network measures and their proposed interpretation for causal loop diagrams and intervention planning.

<p>Structural network measures and their proposed interpretation for causal loop diagrams and intervention planning.</p

A summary of the relationships to and from the variables with the highest in-degree and out-degree in the system, respectively.

<p>A summary of the relationships to and from the variables with the highest in-degree and out-degree in the system, respectively.</p