Archives of medical research · 2026
Depressive Symptom Predictors in Older Mexican Adults: Interaction Structures and Nonlinear Effects from Machine Learning Explainability.
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González-Ramírez Itzel Amaranta, Murillo-Zamora Efrén, Dávila-Uribe Diego, Fernandez-Villa Julio, García-Pérez Angelica Elizabeth, Sánchez-García Sergio
Аннотация
BACKGROUND: Depression among older adults remains a critical public health issue in Latin America, where aging populations face compounded social and health vulnerabilities.
OBJECTIVE: To characterize psychosocial and clinical predictors of depressive symptoms among older Mexican adults using LASSO-guided logistic regression, Random Forest classification, and post-hoc explainability methods.
METHODS: A cross-sectional analysis was conducted using baseline data from a cohort (n = 1,252) of adults aged ≥60 affiliated to the Instituto Mexicano del Seguro Social (IMSS) in Mexico City. Depressive symptoms were assessed with the 35-item Center for Epidemiologic Studies Depression scale (CESD-R). A hybrid method combining LASSO-guided logistic regression and Random Forest (RF) classification was applied, followed by post-hoc explainability analysis using SHAP values, Friedman's H-statistic, and Accumulated Local Effects (ALE) plots.
RESULTS: Low perceived social support, social isolation risk, and male sex were consistently identified as influential predictors. Logistic regression showed moderate discrimination (AUC-ROC = 0.668) and satisfactory calibration; the RF model yielded significantly lower discrimination (AUC-ROC = 0.588; DeLong's test p = 0.016). Sex and age operated largely through interactions with other predictors (H = 0.867 and 0.604, respectively). ALE plots identified a U-shaped association between age and depression risk and dose-response gradients for diabetes and hypertension complications.
CONCLUSIONS: Post-hoc explainability methods revealed interaction structures and nonlinear effects undetectable by conventional regression. The LSNS-6 and MOS-SSS can serve as first-line screening tools in routine geriatric consultations. Risk stratification is further informed by comorbidity burden, particularly the presence of type 2 diabetes or hypertension complications, rather than diagnosis alone.
OBJECTIVE: To characterize psychosocial and clinical predictors of depressive symptoms among older Mexican adults using LASSO-guided logistic regression, Random Forest classification, and post-hoc explainability methods.
METHODS: A cross-sectional analysis was conducted using baseline data from a cohort (n = 1,252) of adults aged ≥60 affiliated to the Instituto Mexicano del Seguro Social (IMSS) in Mexico City. Depressive symptoms were assessed with the 35-item Center for Epidemiologic Studies Depression scale (CESD-R). A hybrid method combining LASSO-guided logistic regression and Random Forest (RF) classification was applied, followed by post-hoc explainability analysis using SHAP values, Friedman's H-statistic, and Accumulated Local Effects (ALE) plots.
RESULTS: Low perceived social support, social isolation risk, and male sex were consistently identified as influential predictors. Logistic regression showed moderate discrimination (AUC-ROC = 0.668) and satisfactory calibration; the RF model yielded significantly lower discrimination (AUC-ROC = 0.588; DeLong's test p = 0.016). Sex and age operated largely through interactions with other predictors (H = 0.867 and 0.604, respectively). ALE plots identified a U-shaped association between age and depression risk and dose-response gradients for diabetes and hypertension complications.
CONCLUSIONS: Post-hoc explainability methods revealed interaction structures and nonlinear effects undetectable by conventional regression. The LSNS-6 and MOS-SSS can serve as first-line screening tools in routine geriatric consultations. Risk stratification is further informed by comorbidity burden, particularly the presence of type 2 diabetes or hypertension complications, rather than diagnosis alone.