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dc.contributor.authorde Vries, Ymkje Annaen_US
dc.contributor.authorAlonso, Jordien_US
dc.contributor.authorChatterji, Somnathen_US
dc.contributor.authorde Jonge, Peteren_US
dc.contributor.authorLokkerbol, Joranen_US
dc.contributor.authorMcGrath, John J.en_US
dc.contributor.authorPetukhova, Maria V.en_US
dc.contributor.authorSampson, Nancy A.en_US
dc.contributor.authorSverdrup, Eriken_US
dc.contributor.authorVigo, Daniel V.en_US
dc.contributor.authorWager, Stefanen_US
dc.contributor.authorAl-Hamzawi, Alien_US
dc.contributor.authorBorges, Guilhermeen_US
dc.contributor.authorBruffaerts, Ronnyen_US
dc.contributor.authorBunting, Brendanen_US
dc.contributor.authorChardoul, Stephanieen_US
dc.contributor.authorKaram, Elie G.en_US
dc.contributor.authorKiejna, Andrzejen_US
dc.contributor.authorKovess-Masfety, Vivianeen_US
dc.contributor.authorNavarro-Mateu, Fernandoen_US
dc.contributor.authorOjagbemi, Akinen_US
dc.contributor.authorPiazza, Marinaen_US
dc.contributor.authorPosada-Villa, Joséen_US
dc.contributor.authorSasu, Carmenen_US
dc.contributor.authorScott, Kate M.en_US
dc.contributor.authorTachimori, Hisateruen_US
dc.contributor.authorHave, Margreet Tenen_US
dc.contributor.authorTorres, Yolandaen_US
dc.contributor.authorViana, Maria Carmenen_US
dc.contributor.authorZamparini, Manuelen_US
dc.contributor.authorZarkov, Zaharien_US
dc.contributor.authorKessler, Ronald C.en_US
dc.description.abstractObjective: The standard method of generating disorder-specific disability scores has lay raters make rankings between pairs of disorders based on brief disorder vignettes. This method introduces bias due to differential rater knowledge of disorders and inability to disentangle the disability due to disorders from the disability due to comorbidities. Methods: We propose an alternative, data-driven, method of generating disorder-specific disability scores that assesses disorders in a sample of individuals either from population medical registry data or population survey self-reports and uses Generalized Random Forests (GRF) to predict global (rather than disorder-specific) disability assessed by clinician ratings or by survey respondent self-reports. This method also provides a principled basis for studying patterns and predictors of heterogeneity in disorder-specific disability. We illustrate this method by analyzing data for 16 disorders assessed in the World Mental Health Surveys (n = 53,645). Results: Adjustments for comorbidity decreased estimates of disorder-specific disability substantially. Estimates were generally somewhat higher with GRF than conventional multivariable regression models. Heterogeneity was nonsignificant. Conclusions: The results show clearly that the proposed approach is practical, and that adjustment is needed for comorbidities to obtain accurate estimates of disorder-specific disability. Expansion to a wider range of disorders would likely find more evidence for heterogeneity.en_US
dc.publisherWiley Online Libraryen_US
dc.subjectCausal foresten_US
dc.subjectGlobal burden of diseaseen_US
dc.subjectMental disordersen_US
dc.titleProof-of-concept of a data-driven approach to estimate the associations of comorbid mental and physical disorders with global health-related disabilityen_US
dc.typeJournal Articleen_US
dc.contributor.affiliationFaculty of Medicineen_US
dc.relation.ispartoftextInternational Journal of Methods in Psychiatric Researchen_US
Appears in Collections:Faculty of Medicine
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