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

ABSTRACT:

Background

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.

Methods

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 socioe

SUBMITTER: Zong X 

PROVIDER: S-EPMC11602597 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature