A multilevel social network approach to studying multiple disease-prevention behaviors

• Published in: Scientific reports 2025-01, Vol.15 (1), p.1718-16, Article 1718
• Main Authors: Vörös, András, Bellotti, Elisa, Nengnong, Carinthia Balabet, Passah, Mattimi, Nongrum, Quinnie Doreen, Khongwir, Charishma, van Eijk, Anna Maria, Kessler, Anne, Sarkar, Rajiv, Carlton, Jane M., Albert, Sandra
• Format: Article
• Language: English
• Subjects: 631/477/2811; 692/699; 692/700/478; Adolescent; Adult; Disease prevention; Female; Health Behavior; Households; Humanities and Social Sciences; Humans; India - epidemiology; Infectious diseases; Malaria; Malaria - epidemiology; Malaria - prevention & control; Male; Middle Aged; multidisciplinary; Opinion leaders; Prevention; Science; Science (multidisciplinary); Social behavior; Social factors; Social interactions; Social Networking; Social networks; Social organization; Social Support; Statistical models; Vector-borne diseases; Young Adult
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-025-85240-7

The effective prevention of many infectious and non-infectious diseases relies on people concurrently adopting multiple prevention behaviors. Individual characteristics, opinion leaders, and social networks have been found to explain why people take up specific prevention behaviors. However, it remains challenging to understand how these factors shape multiple interdependent behaviors. We propose a multilevel social network framework that allows us to study the effects of individual and social factors on multiple disease prevention behaviors simultaneously. We apply this approach to examine the factors explaining eight malaria prevention behaviors, using unique interview data collected from 1529 individuals in 10 hard-to-reach, malaria-endemic villages in Meghalaya, India in 2020–2022. Statistical network modelling reveals exposure to similar behaviors in one’s social network as the most important factor explaining prevention behaviors. Further, we find that households indirectly shape behaviors as key contexts for social ties. Together, these two factors are crucial for explaining the observed patterns of behaviors and social networks in the data, outweighing individual characteristics, opinion leaders, and social network size. The results highlight that social network processes may facilitate or hamper disease prevention efforts that rely on a combination of behaviors. Our approach is well suited to study these processes in the context of various diseases.