Predicting the risk of future depression among school-attending adolescents in Nigeria using a model developed in Brazil. (December 2020) - Brathwaite, Rachel; Rocha, Thiago Botter-Maio; Kieling, Christian; Kohrt, Brandon A.; Mondelli, Valeria; Adewuya, Abiodun O.; Fisher, Helen L.
Highlights: Pelotas model predicted depression among nigerian adolescents better than chance. The model was able to discriminate adolescents with and without depression. The model was not well-calibrated indicating poor overall performance. Updating the model with context-specific factors may improve depression prediction. ABSTRACT: Depression commonly emerges in adolescence and is a major public health issue in low- and middle-income countries where 90% of the world's adolescents live. Thus efforts to prevent depression onset are crucial in countries like Nigeria, where two-thirds of the population are aged under 24. Therefore, we tested the ability of a prediction model developed in Brazil to predict future depression in a Nigerian adolescent sample. Data were obtained from school students aged 14–16 years in Lagos, who were assessed in 2016 and 2019 for depression using a self-completed version of the Mini International Neuropsychiatric Interview for Children and Adolescents. Only the 1, 928 students free of depression at baseline were included. Penalized logistic regression was used to predict individualized risk of developing depression at follow-up for each adolescent based on the 7 matching baseline sociodemographic predictors from the Brazilian model. Discrimination between adolescents who did and did not develop depression was better than chance (area under the curve = 0.62 (bootstrap-corrected 95% CI: 0.58–0.66). However, the model was not well-calibrated even after adjustment of the intercept, indicating poorer overall performance compared to the original Brazilian cohort. Updating the model with context-specific factors may improve prediction of depression in this setting.