AUTHOR=Zong Xu , Wang Huaiyue 

TITLE=Do early-life circumstances predict late-life suicidal ideation? Evidence from SHARE data using machine learning

JOURNAL=Frontiers in Psychiatry

VOLUME=Volume 15 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2024.1426876

DOI=10.3389/fpsyt.2024.1426876

ISSN=1664-0640

ABSTRACT=A number of studies 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. 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 middleaged and older adults over 50.Results: There are 46, 498 participants in this study, 26, 672 (57.36%) were female and 19, 826 (42.64%) were male. XGBoost showed a strong predictive performance with an AUC of 0.80 and accuracy of 0.77. Top predictors are mainly in the domains of childhood relationships, childhood socioeconomic status, childhood health and healthcare. In particular, whether having a group of friends plays the most critical influence in suicidal ideation in old age.Discussion: 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 of middle-aged and aged individuals.