Machine Learning to Investigate Life-Course Social Determinants of Loneliness among Older Adults in the US, England, Israel, and 27 European Countries during the Pandemic
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Background
Loneliness in later life is common and shaped by social determinants, with the COVID-19 pandemic and regional contexts further influencing disparities. Yet the role of life-course social determinants in predicting loneliness during the pandemic across countries remains unclear.
Methods
We used data from the Health and Retirement Study, the English Longitudinal Study of Ageing, and the Survey of Health, Ageing and Retirement in Europe, including 47,016 participants aged 65+ (mean age: 75). Surveys were conducted before and during the pandemic (April–August 2020). Six machine learning (ML) algorithms incorporating 18 predictors spanning childhood, pre-pandemic, and pandemic periods were applied to predict loneliness. Model optimization and interpretability employed cross-validation, hyperparameter tuning, and SHAP values.
Findings
CatBoost showed the best performance, with AUC scores of 0.718 in Europe and Israel, 0.693 in England, and 0.632 in the US. Key predictors common across regions included being single, female, parental education, and COVID-19-related adversity. Regional differences emerged: pre-pandemic income and financial support during the pandemic were stronger predictors in the US; receipt of public pension and parental occupation at age 10 were more influential in England; and parental education was especially important in Europe and Israel.
Interpretation
This study is the first to apply ML to assess life-course determinants of loneliness during the pandemic across 30 countries. Findings reveal both consistent and context-specific predictors, highlighting the value of early-life information in identifying high-risk groups and guiding targeted, context-sensitive interventions during public health crises.
Funding
European Research Council, Academy of Finland, and others.
