Literature Collection

Although psychological science has contributed enormously to our understanding of the development of psychopathology and to interventions to treat psychiatric disorders, it has not successfully reduced the burden of mental illness in American communities. Factors include the lack of a mandate of population impact, stigma attached to mental health intervention, difficulty scaling up interventions, and problems in financing prevention. The concept of primary mental health care is introduced as a possible solution; components could include universal reach, universal brief interventions, screening and referral, ongoing support to all individuals, an infrastructure of specialized services, and an integrated data system. Promising examples of the primary mental health care approach are described, including PROSPER, Communities That Care, Triple P, Family Check-Up, HealthySteps, Family Connects, and Community Navigation. For these efforts to succeed, challenges must be overcome in labor force participation, reaching the full population, novel approaches to delivery of interventions, cultural adaptation, community-level interventions, financing, and scientific inquiry.

BACKGROUND: The prevalence of depression among older adults places a considerable strain on healthcare systems due to a shortage of psychiatrists for professional evaluations. This study reports a machine learning (ML) model to assist in screening for geriatric depression, enabling primary care practitioners to detect and respond to cases effectively and on time. METHODS: Data from the 2011-2018 National Health and Nutrition Examination Survey (NHANES) were used. To identify relevant variables for depression screening, features were selected using Least Absolute Shrinkage and Selection Operator (LASSO) regression. Synthetic Minority Over-sampling Technique (SMOTE) was applied to address class imbalance in the training set. Seven ML algorithms were employed to develop predictive screening models. Model performance was evaluated using standard ML metrics and clinically relevant impact measures, ensuring a comprehensive assessment. To improve interpretability, Shapley Additive exPlanations (SHAP) were used to visualize the contribution of each feature. RESULTS: The analysis included 3802 participants, with 933 (24.54 %) identified as having depression. Thirteen key variables were selected for model development. The Extreme Gradient Boosting (XGBoost) model demonstrated the best performance, with an accuracy of 0.82 in test set and a maximum Area Under the Curve (AUC) of 0.88 in Receiver Operating Characteristic (ROC) analysis. Sleep disorders, gender, poverty-income ratio (PIR), serum albumin levels, and segmented neutrophil count were identified as the influential predictors. CONCLUSION: The ML model developed for screening depression in older adults showed strong predictive performance and clinical applicability, supporting health workers in the early identification and management of depression.