A number of studies have demonstrated that suicidal ideation in late life is associated with early-life circumstances. However, the importance of early-life circumstances in predicting suicidal ideation is not entirely clear. This study aims to use a machine learning approach to evaluate the importance of 32 early-life circumstances from six domains in predicting suicidal ideation in old age.
The data in this study come from a cross-national longitudinal survey, the Survey of Health, Aging and Retirement in Europe (SHARE). Participants recalled information on early-life circumstances in SHARE wave 7 and reported suicidal ideation in SHARE wave 8. The XGBoost model was employed to evaluate the importance of 32 circumstances in six domains (early-life socioeconomic status, early-life health and healthcare, early-life relationship, etc.) in predicting the suicidal ideation of middle-aged and older adults over 50.
There were 46,498 participants in this study, of which 26,672 (57.36%) were females and 19,826 (42.64%) were males. XGBoost showed a strong predictive performance, with an area under the curve of 0.80 and accuracy of 0.77. Top predictors were mainly in the domains of childhood relationship, childhood socioeconomic status, childhood health, and healthcare. In particular, having a group of friends most critically influences suicidal ideation in old age.
These findings suggest that early-life circumstances may modestly predict suicidal ideation in late life. Preventive measures can be taken to lower the risk of suicidal ideation in middle-aged and older individuals.