Development and evaluation of a risk algorithm predicting alcohol dependence after early onset of regular alcohol use
| Autor(a) principal: | |
|---|---|
| Data de Publicação: | 2023 |
| Outros Autores: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Tipo de documento: | Artigo |
| Idioma: | eng |
| Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
| Texto Completo: | http://hdl.handle.net/10362/149281 |
Resumo: | The ESEMeD project is funded by the European Commission (Contracts QLG5-1999-01042; SANCO 2004123 and EAHC 20081308). The Portuguese Mental Health Study was carried out by the Department of Mental Health, Faculty of Medical Sciences, NOVA University of Lisbon, with collaboration of the Portuguese Catholic University, and was funded by Champalimaud Foundation, Gulbenkian Foundation, Foundation for Science and Technology (FCT) and Ministry of Health. |
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Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
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7160 |
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Development and evaluation of a risk algorithm predicting alcohol dependence after early onset of regular alcohol useAdolescencealcohol usecalibrationdependencediscriminationmachine learningMedicine (miscellaneous)Psychiatry and Mental healthSDG 3 - Good Health and Well-beingThe ESEMeD project is funded by the European Commission (Contracts QLG5-1999-01042; SANCO 2004123 and EAHC 20081308). The Portuguese Mental Health Study was carried out by the Department of Mental Health, Faculty of Medical Sciences, NOVA University of Lisbon, with collaboration of the Portuguese Catholic University, and was funded by Champalimaud Foundation, Gulbenkian Foundation, Foundation for Science and Technology (FCT) and Ministry of Health.Aims: Likelihood of alcohol dependence (AD) is increased among people who transition to greater levels of alcohol involvement at a younger age. Indicated interventions delivered early may be effective in reducing risk, but could be costly. One way to increase cost-effectiveness would be to develop a prediction model that targeted interventions to the subset of youth with early alcohol use who are at highest risk of subsequent AD. Design: A prediction model was developed for DSM-IV AD onset by age 25 years using an ensemble machine-learning algorithm known as ‘Super Learner’. Shapley additive explanations (SHAP) assessed variable importance. Setting and Participants: Respondents reporting early onset of regular alcohol use (i.e. by 17 years of age) who were aged 25 years or older at interview from 14 representative community surveys conducted in 13 countries as part of WHO's World Mental Health Surveys. Measurements: The primary outcome to be predicted was onset of life-time DSM-IV AD by age 25 as measured using the Composite International Diagnostic Interview, a fully structured diagnostic interview. Findings: AD prevalence by age 25 was 5.1% among the 10 687 individuals who reported drinking alcohol regularly by age 17. The prediction model achieved an external area under the curve [0.78; 95% confidence interval (CI) = 0.74–0.81] higher than any individual candidate risk model (0.73–0.77) and an area under the precision-recall curve of 0.22. Overall calibration was good [integrated calibration index (ICI) = 1.05%]; however, miscalibration was observed at the extreme ends of the distribution of predicted probabilities. Interventions provided to the 20% of people with highest risk would identify 49% of AD cases and require treating four people without AD to reach one with AD. Important predictors of increased risk included younger onset of alcohol use, males, higher cohort alcohol use and more mental disorders. Conclusions: A risk algorithm can be created using data collected at the onset of regular alcohol use to target youth at highest risk of alcohol dependence by early adulthood. Important considerations remain for advancing the development and practical implementation of such models.NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM)Centro de Estudos de Doenças Crónicas (CEDOC)RUNBharat, ChriannaGlantz, Meyer D.Aguilar-Gaxiola, SergioAlonso, JordiBruffaerts, RonnyBunting, BrendanCaldas-de-Almeida, José MiguelCardoso, GraçaChardoul, Stephaniede Jonge, PeterGureje, OyeHaro, Josep MariaHarris, Meredith G.Karam, Elie G.Kawakami, NoritoKiejna, AndrzejKovess-Masfety, VivianeLee, SingMcGrath, John J.Moskalewicz, JacekNavarro-Mateu, FernandoRapsey, CharleneSampson, Nancy A.Scott, Kate M.Tachimori, Hisateruten Have, MargreetVilagut, GemmaWojtyniak, BogdanXavier, MiguelKessler, Ronald C.Degenhardt, Louisa2023-02-15T22:20:51Z20232023-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10362/149281eng0965-2140PURE: 53067616https://doi.org/10.1111/add.16122info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T05:31:12Zoai:run.unl.pt:10362/149281Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:53:41.704356Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
| dc.title.none.fl_str_mv |
Development and evaluation of a risk algorithm predicting alcohol dependence after early onset of regular alcohol use |
| title |
Development and evaluation of a risk algorithm predicting alcohol dependence after early onset of regular alcohol use |
| spellingShingle |
Development and evaluation of a risk algorithm predicting alcohol dependence after early onset of regular alcohol use Bharat, Chrianna Adolescence alcohol use calibration dependence discrimination machine learning Medicine (miscellaneous) Psychiatry and Mental health SDG 3 - Good Health and Well-being |
| title_short |
Development and evaluation of a risk algorithm predicting alcohol dependence after early onset of regular alcohol use |
| title_full |
Development and evaluation of a risk algorithm predicting alcohol dependence after early onset of regular alcohol use |
| title_fullStr |
Development and evaluation of a risk algorithm predicting alcohol dependence after early onset of regular alcohol use |
| title_full_unstemmed |
Development and evaluation of a risk algorithm predicting alcohol dependence after early onset of regular alcohol use |
| title_sort |
Development and evaluation of a risk algorithm predicting alcohol dependence after early onset of regular alcohol use |
| author |
Bharat, Chrianna |
| author_facet |
Bharat, Chrianna Glantz, Meyer D. Aguilar-Gaxiola, Sergio Alonso, Jordi Bruffaerts, Ronny Bunting, Brendan Caldas-de-Almeida, José Miguel Cardoso, Graça Chardoul, Stephanie de Jonge, Peter Gureje, Oye Haro, Josep Maria Harris, Meredith G. Karam, Elie G. Kawakami, Norito Kiejna, Andrzej Kovess-Masfety, Viviane Lee, Sing McGrath, John J. Moskalewicz, Jacek Navarro-Mateu, Fernando Rapsey, Charlene Sampson, Nancy A. Scott, Kate M. Tachimori, Hisateru ten Have, Margreet Vilagut, Gemma Wojtyniak, Bogdan Xavier, Miguel Kessler, Ronald C. Degenhardt, Louisa |
| author_role |
author |
| author2 |
Glantz, Meyer D. Aguilar-Gaxiola, Sergio Alonso, Jordi Bruffaerts, Ronny Bunting, Brendan Caldas-de-Almeida, José Miguel Cardoso, Graça Chardoul, Stephanie de Jonge, Peter Gureje, Oye Haro, Josep Maria Harris, Meredith G. Karam, Elie G. Kawakami, Norito Kiejna, Andrzej Kovess-Masfety, Viviane Lee, Sing McGrath, John J. Moskalewicz, Jacek Navarro-Mateu, Fernando Rapsey, Charlene Sampson, Nancy A. Scott, Kate M. Tachimori, Hisateru ten Have, Margreet Vilagut, Gemma Wojtyniak, Bogdan Xavier, Miguel Kessler, Ronald C. Degenhardt, Louisa |
| author2_role |
author author author author author author author author author author author author author author author author author author author author author author author author author author author author author author |
| dc.contributor.none.fl_str_mv |
NOVA Medical School|Faculdade de Ciências Médicas (NMS|FCM) Centro de Estudos de Doenças Crónicas (CEDOC) RUN |
| dc.contributor.author.fl_str_mv |
Bharat, Chrianna Glantz, Meyer D. Aguilar-Gaxiola, Sergio Alonso, Jordi Bruffaerts, Ronny Bunting, Brendan Caldas-de-Almeida, José Miguel Cardoso, Graça Chardoul, Stephanie de Jonge, Peter Gureje, Oye Haro, Josep Maria Harris, Meredith G. Karam, Elie G. Kawakami, Norito Kiejna, Andrzej Kovess-Masfety, Viviane Lee, Sing McGrath, John J. Moskalewicz, Jacek Navarro-Mateu, Fernando Rapsey, Charlene Sampson, Nancy A. Scott, Kate M. Tachimori, Hisateru ten Have, Margreet Vilagut, Gemma Wojtyniak, Bogdan Xavier, Miguel Kessler, Ronald C. Degenhardt, Louisa |
| dc.subject.por.fl_str_mv |
Adolescence alcohol use calibration dependence discrimination machine learning Medicine (miscellaneous) Psychiatry and Mental health SDG 3 - Good Health and Well-being |
| topic |
Adolescence alcohol use calibration dependence discrimination machine learning Medicine (miscellaneous) Psychiatry and Mental health SDG 3 - Good Health and Well-being |
| description |
The ESEMeD project is funded by the European Commission (Contracts QLG5-1999-01042; SANCO 2004123 and EAHC 20081308). The Portuguese Mental Health Study was carried out by the Department of Mental Health, Faculty of Medical Sciences, NOVA University of Lisbon, with collaboration of the Portuguese Catholic University, and was funded by Champalimaud Foundation, Gulbenkian Foundation, Foundation for Science and Technology (FCT) and Ministry of Health. |
| publishDate |
2023 |
| dc.date.none.fl_str_mv |
2023-02-15T22:20:51Z 2023 2023-01-01T00:00:00Z |
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info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/article |
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article |
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publishedVersion |
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http://hdl.handle.net/10362/149281 |
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http://hdl.handle.net/10362/149281 |
| dc.language.iso.fl_str_mv |
eng |
| language |
eng |
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0965-2140 PURE: 53067616 https://doi.org/10.1111/add.16122 |
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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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