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Predicting the risk of future depression among school-attending adolescents in Nigeria using a model developed in Brazil.

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. Ther...

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Autores principales: Brathwaite, Rachel, Rocha, Thiago Botter-Maio, Kieling, Christian, Kohrt, Brandon A., Mondelli, Valeria, Adewuya, Abiodun O., Fisher, Helen L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier/North-Holland Biomedical Press 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7732701/
https://www.ncbi.nlm.nih.gov/pubmed/33113451
http://dx.doi.org/10.1016/j.psychres.2020.113511
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author Brathwaite, Rachel
Rocha, Thiago Botter-Maio
Kieling, Christian
Kohrt, Brandon A.
Mondelli, Valeria
Adewuya, Abiodun O.
Fisher, Helen L.
author_facet Brathwaite, Rachel
Rocha, Thiago Botter-Maio
Kieling, Christian
Kohrt, Brandon A.
Mondelli, Valeria
Adewuya, Abiodun O.
Fisher, Helen L.
author_sort Brathwaite, Rachel
collection PubMed
description 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.
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spelling pubmed-77327012020-12-16 Predicting the risk of future depression among school-attending adolescents in Nigeria using a model developed in Brazil. Brathwaite, Rachel Rocha, Thiago Botter-Maio Kieling, Christian Kohrt, Brandon A. Mondelli, Valeria Adewuya, Abiodun O. Fisher, Helen L. Psychiatry Res Article 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. Elsevier/North-Holland Biomedical Press 2020-12 /pmc/articles/PMC7732701/ /pubmed/33113451 http://dx.doi.org/10.1016/j.psychres.2020.113511 Text en © 2020 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Brathwaite, Rachel
Rocha, Thiago Botter-Maio
Kieling, Christian
Kohrt, Brandon A.
Mondelli, Valeria
Adewuya, Abiodun O.
Fisher, Helen L.
Predicting the risk of future depression among school-attending adolescents in Nigeria using a model developed in Brazil.
title Predicting the risk of future depression among school-attending adolescents in Nigeria using a model developed in Brazil.
title_full Predicting the risk of future depression among school-attending adolescents in Nigeria using a model developed in Brazil.
title_fullStr Predicting the risk of future depression among school-attending adolescents in Nigeria using a model developed in Brazil.
title_full_unstemmed Predicting the risk of future depression among school-attending adolescents in Nigeria using a model developed in Brazil.
title_short Predicting the risk of future depression among school-attending adolescents in Nigeria using a model developed in Brazil.
title_sort predicting the risk of future depression among school-attending adolescents in nigeria using a model developed in brazil.
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7732701/
https://www.ncbi.nlm.nih.gov/pubmed/33113451
http://dx.doi.org/10.1016/j.psychres.2020.113511
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