Comprehensive Summary
This study by Faro et al. aims to improve the scalability and accuracy of screening for anxiety and depression by developing simplified decision-rule tools based on the standard questionnaires GAD-7 (Generalized Anxiety Disorder Scale) and PHQ-9 (Patient Health Questionnaire). Using cross-sectional data from a large sample of 20,585 Brazilian adults across all states, the authors used classification and regression tree (CART) models for the individual items of GAD-7 and PHQ-9, and then trained decision trees using bootstrapping, cross-validation, and external validation datasets. For the GAD-7, a decision rule using only 2 items was able to classify minimal/mild anxiety vs severe anxiety with high accuracy (≈ 86.1% for minimal/mild; 85.1% for severe). For the PHQ-9, a rule using 3 items classified minimal/mild vs severe depression with ~81.7% and ~78.8% accuracy respectively. The intermediate categories (moderate / moderately severe) were harder to classify accurately, but the simplified tools performed robustly for identifying “low risk” vs “high risk.” Faro et al. argue that these simplified, item-based decision rules offer a practical alternative to full length questionnaires or very short forms (like GAD-2 / PHQ-2), because they balance brevity with retention of symptom-diversity and severity discrimination. They suggest such tools could enable cost-effective, scalable screening of anxiety and depression — especially useful in low- and middle-income countries (LMICs) or settings with limited mental-health resources. They do also note that no sociodemographic factors were kept in the final rules, only symptoms, which may help with generalization.
Outcomes and Implications
This work is important because depression and anxiety are highly prevalent worldwide, yet widespread, efficient, and accurate screening remains challenging, particularly in resource-limited settings. By showing that a minimal number of questionnaire items can reliably classify severity, the study reduces barriers to large-scale mental health surveillance and early detection. Clinically, these simplified decision rules could be deployed in primary care, community health programs, or public health screening initiatives to quickly identify individuals at high risk of severe anxiety or depression and allows for timely intervention. Because the rules use only a few items, they reduce respondent burden and could facilitate more frequent or mass screening. The authors imply that with further external validation and implementation research, such tools could become part of standard screening protocols in the near to medium term, especially in LMICs or under-resourced health systems.