Proof-of-concept of a data-driven approach to estimate the associations of comorbid mental and physical disorders with global health-related disability

Ymkje anna De vries, Jordi Alonso, Somnath Chatterji, Peter De jonge, Joran Lokkerbol, John j. Mcgrath, Maria v. Petukhova, Nancy a. Sampson, Erik Sverdrup, Daniel v. Vigo, Stefan Wager, Ali Al‐hamzawi, Guilherme Borges, Ronny Bruffaerts, Brendan Bunting, Stephanie Chardoul, Elie g. Karam, Andrzej Kiejna, Viviane Kovess‐masfety, Fernando Navarro‐mateuAkin Ojagbemi, Marina Piazza, José Posada‐villa, Carmen Sasu, Kate m. Scott, Hisateru Tachimori, Margreet ten Have, Yolanda Torres, Maria carmen Viana, Manuel Zamparini, Zahari Zarkov, Ronald c. Kessler

Research output: Contribution to journalArticlepeer-review

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Abstract

Objective: 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.
Original languageEnglish
Article numbere2003
Pages (from-to)1-16
Number of pages16
JournalInternational Journal of Methods in Psychiatric Research
Volume33
Issue number1
Early online date29 Dec 2023
DOIs
Publication statusPublished (in print/issue) - 31 Mar 2024

Bibliographical note

Funding Information:
The World Mental Health (WMH) Survey Initiative is supported by the
United States National Institute of Mental Health (NIMH; R01 MH070884), the John D. and
Catherine T. MacArthur Foundation, the Pfizer Foundation, the United States Public Health
Service (R13-MH066849, R01-MH069864, and R01 DA016558), the Fogarty International
Center (FIRCA R03-TW006481), the Pan American Health Organization, Eli Lilly and
Company, Ortho-McNeil Pharmaceutical Inc., GlaxoSmithKline, and Bristol-Myers Squibb.
We thank the staff of the WMH Data Collection and Data Analysis Coordination Centres for
assistance with instrumentation, fieldwork, and consultation on data analysis. None of the
funders had any role in the design, analysis, interpretation of results, or preparation of this
paper. The views and opinions expressed in this report are those of the authors and should not
be construed to

Publisher Copyright:
© 2023 The Authors. International Journal of Methods in Psychiatric Research published by John Wiley & Sons Ltd.

Keywords

  • Causal Forest
  • Comorbidity
  • Disability
  • Global Burden of Disease
  • Mental disorders

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