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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier/North-Holland Biomedical Press
2020
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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. |
format | Online Article Text |
id | pubmed-7732701 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier/North-Holland Biomedical Press |
record_format | MEDLINE/PubMed |
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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