Abstract

BackgroundDepression and anxiety are significant contributors to the global burden of disease among young people. Accurate data on the prevalence of these conditions are crucial for the equitable distribution of resources for planning and implementing effective programs. This study aimed to culturally adapt and validate data collection tools for measuring depression and anxiety at the population level.MethodsThe study was conducted in Kathmandu, Nepal, a diverse city with multiple ethnicities, languages, and cultures. Ten focus group discussions with 56 participants and 25 cognitive interviews were conducted to inform adaptations of the Patient Health Questionnaire adapted for Adolescents (PHQ-A) and Generalized Anxiety Disorder (GAD-7). To validate the tools, a cross-sectional survey of 413 adolescents (aged 12–19) was conducted in three municipalities of Kathmandu district. Trained clinical psychologists administered the Kiddie Schedule for Affective Disorders and Schizophrenia (K-SADS-DSM 5 version) to survey participants.ResultsA number of cultural adaptations were required, such as changing statements into questions, using a visual scale (glass scale) to maintain uniformity in responses, and including a time frame at the beginning of each item. For younger adolescents aged 12 to 14 years, a PHQ-A cut-off of > = 13 had a sensitivity of 0.93, specificity of 0.80, positive predictive value (PPV) of 0.33, and negative predictive value (NPV) of 0.99. For older adolescents aged 15–19, a cut-off of > = 11 had a sensitivity of 0.89, specificity of 0.70, PPV of 0.32, and NPV of 0.97. For GAD-7, a cut-off of > = 8 had a sensitivity of 0.70 and specificity of 0.67 for younger adolescents and 0.71 for older adolescents, with a PPV of 0.39 and NPV of 0.89. The individual symptom means of both PHQ-A and GAD-7 items showed moderate ability to discriminate between adolescents with and without depression and anxiety.ConclusionThe PHQ-A and GAD-7 demonstrate fair psychometric properties for screening depression but performed poorly for anxiety, with high rates of false positives. Even when using clinically validated cut-offs, population prevalence rates would be inflated by 2–4 fold with these tools, requiring adjustment when interpreting epidemiological findings.

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