4th International Conference on Primary Health Care & 2nd Euro Nursing Congress
September 15-16 2025 | Virtual Event
Antonio CasellaIdaeho
Sapienza University of Rome, Italy
Childhood obesity is a complex issue than can’t be tackled but using a holistic and multidisciplinary
approach and, among the quantitative methods, using Network Analysis. Its application
is wide and only rarely applied to social issues but, where used, it showed its resolving
power. The aim of this research is to create a comprehensive framework that clearly shows
the multifactorial aspect of CO and keeps together five families of influences: genetics, socioeconomic
status, social network, environment and impact of policies. Using the bi-partite
network technique it is possible to visualize not only the directly responsible factors of CO but
also their secondary causes and, overall, to get a clear image of how these factors simultaneously
interact. Unlike almost every study on CO, using this approach, based on an extensive
literature review and a specifically made survey, has been realized a visual product that, on
one side keeps together the literature in an extremely synthetic layout, on the other side gives
the scientist the possibility to communicate complexity in a simplified way outside academic
context. This technique is particularly useful when prevention policies need to be designed or
evaluated, since it gives the possibility to create simulation models based on system dynamics,
such as Causal Loop Diagrams or Agent Based Models.
Antonio Casella is a junior researcher committed to social sustainability issues. His areas of interest are health,
labour trends, and migration. With a master’s degree in Sociology from Sapienza University of Rome, he collaborates
with Italian and international research institutes such as Eurispes, Republikon Intezet (Hungary), Mediterranean
Dialogue (Spain), and Sapienza. In his research journey, he combines qualitative and quantitative methods,
from ethnography to advanced statistical approaches, to manage complexity and create dynamic models.