Development and evaluation of a risk algorithm predicting alcohol dependence after early onset of regular alcohol use.
Por:
Bharat C, Glantz MD, Aguilar-Gaxiola S, Alonso J, Bruffaerts R, Bunting B, Caldas-de-Almeida JM, Cardoso G, Chardoul S, de Jonge P, Gureje O, Haro JM, Harris MG, Karam EG, Kawakami N, Kiejna A, Kovess-Masfety V, Lee S, McGrath JJ, Moskalewicz J, Navarro-Mateu F, Rapsey C, Sampson NA, Scott KM, Tachimori H, Ten Have M, Gemma Vilagut Saiz, Wojtyniak B, Xavier M, Kessler RC and Degenhardt L
Publicada:
1 may 2023
Ahead of Print:
7 ene 2023
Resumen:
AIM: 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 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 lifetime 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% across 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 (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. CONCLUSION: 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.
Filiaciones:
Bharat C:
National Drug and Alcohol Research Centre (NDARC), University of New South Wales Australia, Sydney, NSW, Australia
Glantz MD:
Department of Epidemiology, Services, and Prevention Research (DESPR), National Institute on Drug Abuse (NIDA), National Institute of Health (NIH), Bethesda, Maryland, USA
Aguilar-Gaxiola S:
Center for Reducing Health Disparities, UC Davis Health System, Sacramento, California, USA
Alonso J:
Health Services Research Unit, IMIM-Hospital del Mar Medical Research Institute, Barcelona, Spain
Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
Department of Life and Health Sciences, Pompeu Fabra University (UPF), Barcelona, Spain
Bruffaerts R:
Universitair Psychiatrisch Centrum - Katholieke Universiteit Leuven (UPC-KUL), Campus Gasthuisberg, Leuven, Belgium
Bunting B:
School of Psychology, Ulster University, Londonderry, United Kingdom
Caldas-de-Almeida JM:
Lisbon Institute of Global Mental Health and Chronic Diseases Research Center (CEDOC), NOVA Medical School|Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Lisbon, Portugal
Cardoso G:
Lisbon Institute of Global Mental Health and Chronic Diseases Research Center (CEDOC), NOVA Medical School|Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Lisbon, Portugal
Chardoul S:
Institute for Social Research, University of Michigan, Ann Arbor, Michigan, USA
de Jonge P:
Department of Developmental Psychology, University of Groningen, Groningen, The Netherlands
Gureje O:
Department of Psychiatry, University College Hospital, Ibadan, Nigeria
Haro JM:
Research, Teaching and Innovation Unit, Parc Sanitari Sant Joan de Déu, Sant Boi de Llobregat, Barcelona, Spain. Centre for Biomedical Research on Mental Health (CIBERSAM), Madrid, Spain
Harris MG:
School of Public Health, The University of Queensland, Herston, QLD, Australia
Queensland Centre for Mental Health Research, The Park Centre for Mental Health, QLD, 4072, Australia
Karam EG:
Department of Psychiatry and Clinical Psychology, St George Hospital University Medical Center, Balamand University, Faculty of Medicine, Beirut, Lebanon
Institute for Development, Research, Advocacy and Applied Care (IDRAAC), Beirut, Lebanon
Kawakami N:
Department of Mental Health, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
Kiejna A:
Institute of Psychology, University of Lower Silesia, Wroclaw, Poland
Kovess-Masfety V:
Ecole des Hautes Etudes en Santé Publique (EHESP), EA 4057, Paris Descartes University, Paris, France
Lee S:
Department of Psychiatry, Chinese University of Hong Kong, Tai Po, Hong Kong
McGrath JJ:
Queensland Centre for Mental Health Research, The Park Centre for Mental Health, QLD, 4072, Australia
Queensland Brain Institute, The University of Queensland, St Lucia, QLD, Australia. National Centre for Register-based Research, Aarhus University, Aarhus V, 8000, Denmark
Moskalewicz J:
Institute of Psychiatry and Neurology, Warsaw, Poland
Navarro-Mateu F:
Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
Department of Basic Psychology and Methodology, University of Murcia, Murcia, Spain
Murcia Biomedical Research Institute (IMIB-Arrixaca), Murcia, Spain
Unidad de Docencia, Investigación y Formación en Salud Mental, Servicio Murciano de Salud, Murcia, Spain
Rapsey C:
Department of Psychological Medicine, University of Otago, Dunedin, Otago, New Zealand
Sampson NA:
Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USA
Scott KM:
Department of Psychological Medicine, University of Otago, Dunedin, Otago, New Zealand
Tachimori H:
Endowed Course for Health System Innovation, Keio University School of Medicine, Tokyo, Japan. Department of Clinical Data Science, Clinical Research & Education Promotion Division, National Center of Neurology and Psychiatry, Tokyo, Japan
Ten Have M:
Trimbos-Instituut, Netherlands Institute of Mental Health and Addiction, Utrecht, Netherlands
Gemma Vilagut Saiz:
Health Services Research Unit, IMIM-Hospital del Mar Medical Research Institute, Barcelona, Spain
Instituto de Salud Carlos III, Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain
Wojtyniak B:
Centre of Monitoring and Analyses of Population Health, National Institute of Public Health-National Research Institute, Warsaw, Poland
Xavier M:
Lisbon Institute of Global Mental Health and Chronic Diseases Research Center (CEDOC), NOVA Medical School|Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Lisbon, Portugal
Kessler RC:
Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts, USA
Degenhardt L:
National Drug and Alcohol Research Centre (NDARC), University of New South Wales Australia, Sydney, NSW, Australia
Open Access
|