A data-driven approach to identifying determinants of depression in rural Latine adolescents

There is a paucity of research focused on risk and protective factors for depression in rural Latine adolescents. The present study first identified variables commonly described in conceptual models of depression etiology and maintenance in Latine adolescents and rural populations, including demographic (i.e., age, sex), cultural (i.e., acculturation), familial (i.e., family conflict, familism), and contextual factors (i.e., socioeconomic strain, parental education level, discrimination-related stress). A machine learning approach was then used to understand the relative contributions of these variables to depression in rural Latine adolescents.

Participants (n = 670; M_age = 15.74; 53% female) were Latine adolescents in grades 9-12 recruited from a high school in a low-income rural area, who completed a battery of self-report measures. A data-driven recursive partitioning method was used to examine the joint contribution of these variables to depression severity.

Using a conditional inference framework, adolescents with low depression scores were characterized by high familism and low discrimination-related stress, whereas adolescents with high depression scores endorsed low familism. Female adolescents had higher depression severity than their male counterparts.

These findings are consistent with both conceptual models of depression in Latine youth and previous empirical studies, particularly those showing that familism and discrimination-related stress play a significant role as protective and risk factors, respectively. The identification of crucial variables using data-driven approaches could help improve screening for and treatment of depression in rural Latine youth who experience significant mental health inequities. (PsycInfo Database Record (c) 2026 APA, all rights reserved).