Predictors of depression among middle-aged and older men and women in Europe: A machine learning approach.

ABSTRACT:

Background

The high prevalence of depression in a growing aging population represents a critical public health issue. It is unclear how social, health, cognitive, and functional variables rank as risk/protective factors for depression among older adults and whether there are conspicuous differences among men and women.

Methods

We used random forest analysis (RFA), a machine learning method, to compare 56 risk/protective factors for depression in a large representative sample of European older adults (N = 67,603; ages 45-105y; 56.1% women; 18 countries) from the Survey of Health, Ageing and Retirement in Europe (SHARE Wave 6). Depressive symptoms were assessed using the EURO-D questionnaire: Scores ≥ 4 indicated depression. Predictors included a broad array of sociodemographi

SUBMITTER: Handing EP 

PROVIDER: S-EPMC9065918 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature