Oral Presentation ANZOS-OSSANZ-AOCO Joint Annual Scientific Meeting 2017

Communities identify key complex drivers for obesity aligned with the Foresight Map but focus more on elements amenable to change (#118)

Jaimie A McGlashan 1 2 , Joshua Hayward 1 , Andrew Brown 1 , Brynle Owen 1 , Michael Johnstone 2 , Doug Creighton 2 , Steven Allender 1
  1. Global Obesity Centre, Deakin University, Geelong, VIC, Australia
  2. Institute for Intelligent Systems Research and Innovation , Deakin University, Geelong, VIC, Australia

Introduction:

Systems thinking methods, such as systems mapping, have been used to gain insight into the interplay of multiple causes and identify potential points for intervention. The Foresight map provides such a picture from the point of view of internationally recognised research leaders in obesity. Recent efforts to map systems from the perspective of community stakeholders present an opportunity to examine how community led systems maps compare to the Foresight map. This research measures the similarity between the Foresight obesity systems map, and a community developed map of the local drivers of obesity.

Methods:

A formal systems mapping process was conducted with 160 community members from Western Victoria to develop a systems map of the causes of childhood obesity within their region. Variables from the community-developed systems map were coded against the themes defined in the Foresight map to allow comparison of the size of themes and strength of their adjoining causal relationships. Key variables were identified in each map using network analysis techniques. These variables were compared to understand the similarity in influential variables in the systems as defined by the two groups.

Results:

Compared to the Foresight map, the community map focused more on social influences, such as social psychology (37% of variables) and built physical activity environment (19%) and less on physiological and biological factors (2%), which was the Foresight map’s largest domain (23%). Comparing the relationships between the clusters, both maps reflected a strong impact of built environments on an individual's behaviour. Network analysis showed similar strength of media and available time within both maps.

Conclusion:

The identification of social and environmental drivers over physiological factors in the community map provides evidence of the community’s focus and enthusiasm on drivers they could influence when describing the causes of obesity.