Can Machine Learning Help Us Understand Social Connection and Its Impact on Health?

The role of social connection in influencing health outcomes has become an increasingly discussed topic globally for people across the lifespan.A prescient report by the National Academies of Sciences, Engineering, and Medicine (NASEM) released in February 2020 outlined the need for health systems to address loneliness and social isolation among older adults. 1 And very soon after that, a global pandemic ensued, making experiences of social isolation and loneliness commonplace. 2As we emerge from the pandemic, attention on this subject continues, with the 2023 Surgeon General Advisory and the World Health Organization Commission on Social Connection calling for cross-sector collaboration and interventions to enhance social connection.Personalized strategies that account for the nuanced differences in people's social lives and address their particular needs are necessary.Elsewhere in JAMA Network Open, Bu and Fancourt 3 advance our understanding of how social connection can be further characterized using machine learning and how these clusters impact health outcomes.They used data from a nationally representative sample of 7706 individuals (aged 50 years) in England from 2008 to 2009 to 2010 to 2011. 3 The survey contained detailed questions on different dimensions of social connection, including (1) structure (size and diversity of the network), ( 2) function (how well social relationships meet emotional and practical needs), and (3) quality (positive and negative perceptions of relationships).These 3 dimensions informed by the Holt-Lundstad et al 4 framework were highlighted in the 2020 NASEM report, are an instrumental step toward a comprehensive understanding of social connection.Their analysis revealed the presence of 5 clusters, ranging from highly connected (33% of the sample), where each of the 3 dimensions was strong, to disconnected (13% of the sample), where each dimension was poor.