Adolescent depression and anxiety—which are linked with many negative life outcomes—are prevalent around the world, particularly in low-income countries such as those in Sub-Saharan Africa (SSA). We used network analysis to examine the topology, stability, and centrality of depression and anxiety symptoms. We analyzed data from a large community sample (N = 2,192) of Kenyan adolescents aged 13-18, using the Patient Health Questionnaire and the Generalized Anxiety Disorder Screener. We identified the central symptoms of the depression and anxiety symptom networks, and we compared the structure and connectivity of these networks between low-symptom and elevated-symptom sub-samples. Our findings indicate the most central depression symptoms were “self-blame” and “depressed mood”, while the strongest depression symptom associations were “self-blame” ––“depressed mood” and “trouble concentrating” ––“little interest/pleasure”. Similarly, the most central anxiety symptoms were “too much worry” and “uncontrollable worry”, while the strongest anxiety symptom associations were “too much worry” ––“uncontrollable worry” and “trouble relaxing” ––“restlessness”. We found a statistical difference in the network structure between low-symptom and elevated-symptom adolescents. The low- symptom sample had higher network connectivity scores for both depression (global strength difference = 0.30; low-symptom = 0.49; high-symptom = 0.19; p = .003) and anxiety symptoms (global strength difference = 1.04; low-symptom = 1.57; high-symptom = 0.53; p < .001). This is the first report that uses network analysis techniques to identify central symptoms of adolescent depression and anxiety in SSA. Our findings illustrate how network analysis may inform the understanding of psychopathology within cultures and suggest promising treatment targets.

Keywords: Depression, anxiety, network analysis, developmental psychopathology, Sub Saharan Africa

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