A research group led by Professor Naoki Kondo and doctoral student Rom Lunar of the Graduate School of Medicine, Kyoto University, and Assistant Professor Koichiro Shiba from Boston University used a novel machine-learning algorithm that estimated how the association between social isolation and all-cause mortality varied depending on an older adult’s characteristics.
From this analysis, we found that the link between social isolation and death was stronger among people who were older, men, with fewer school years, retired or never had a job, smokers, people who did not participate in annual physical examinations, and were already socially isolated prior to the start of the study. We also found that the combination of low education and being in the middle- and high-income tertiles was associated with the greatest reduction in survival days due to social isolation, with this pattern being stronger among women. Lastly, our simulations showed that education- and income-based disparities in survival may decrease if social isolation is eliminated from the population. Moreover around 11,000 deaths linked to social isolation among older adults with low education could be prevented annually.
With this in mind, we believe that population-level interventions reducing social isolation would not only improve health on average but also mitigate existing health disparities.
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