Journal
DEPRESSION AND ANXIETY
Volume 31, Issue 9, Pages 765-777Publisher
WILEY
DOI: 10.1002/da.22233
Keywords
epidemiology; depression; anxiety; anxiety disorders; suicide; self-harm; panic attacks
Categories
Funding
- National Institute of Mental Health (NIMH) [R01 MH070884]
- John D. and Catherine T. MacArthur Foundation
- Pfizer Foundation
- U.S. Public Health Service [R13-MH066849, R01-MH069864, R01 DA016558]
- Fogarty International Center [FIRCA R03-TW006481]
- Pan American Health Organization
- Eli Lilly & Company Foundation
- Ortho-McNeil Pharmaceutical, Inc.
- GlaxoSmithKline
- Sanofi Aventis
- Bristol-Myers Squibb
- VICI from the Netherlands Research Foundation (NWO-ZonMW) [91812607]
- State of Sao Paulo Research Foundation (FAPESP) [03/00204-3]
- Ministry of Health
- National Center for Public Health Protection
- Shenzhen Bureau of Health
- Shenzhen Bureau of Science, Technology, and Information
- Ministry of Social Protection
- Japanese and European funds through United Nations Development Group Iraq Trust Fund (UNDG ITF)
- Israel National Institute for Health Policy and Health Services Research
- National Insurance Institute of Israel
- Japan Ministry of Health, Labour and Welfare [H13-SHOGAI-023, H14-TOKUBETSU-026, H16-KOKORO-013]
- Lebanese Ministry of Public Health
- WHO (Lebanon)
- National Institute of Health/Fogarty International Center [R03 TW006481-01]
- Sheikh Hamdan Bin Rashid Al Maktoum Award for Medical Sciences
- AstraZeneca
- Eli Lilly
- Hikma Pharm
- Pfizer
- Roche
- Sanofi-Aventis
- Servier
- Novartis
- National Institute of Psychiatry Ramon de la Fuente [INPRFMDIES 4280]
- National Council on Science and Technology [CONACyT-G30544-H]
- PanAmerican Health Organization (PAHO)
- New Zealand Ministry of Health
- Alcohol Advisory Council
- Health Research Council
- WHO (Geneva)
- WHO (Nigeria)
- Federal Ministry of Health, Abuja, Nigeria
- Health and Social Care Research and Development Division of the Public Health Agency
- Champalimaud Foundation
- Gulbenkian Foundation
- Foundation for Science and Technology (FCT)
- Ministry of Public Health
- U.S. National Institute of Mental Health [RO1-MH61905]
- National Institute of Drug Abuse (NIDA)
- Substance Abuse and Mental Health Services Administration (SAMHSA)
- Robert Wood Johnson Foundation (RWJF) [044708]
- John W. Alden Trust
- Janssen Pharmaceuticals
- Fundacao de Amparo a Pesquisa do Estado de Sao Paulo (FAPESP) [03/00204-3] Funding Source: FAPESP
- Medical Research Council [MR/K023241/1] Funding Source: researchfish
- Public Health Agency [COM/4437/11, COM/4411/10] Funding Source: researchfish
- MRC [MR/K023241/1] Funding Source: UKRI
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BackgroundVariation in the course of major depressive disorder (MDD) is not strongly predicted by existing subtype distinctions. A new subtyping approach is considered here. MethodsTwo data mining techniques, ensemble recursive partitioning and Lasso generalized linear models (GLMs), followed by k-means cluster analysis are used to search for subtypes based on index episode symptoms predicting subsequent MDD course in the World Mental Health (WMH) surveys. The WMH surveys are community surveys in 16 countries. Lifetime DSM-IV MDD was reported by 8,261 respondents. Retrospectively reported outcomes included measures of persistence (number of years with an episode, number of years with an episode lasting most of the year) and severity (hospitalization for MDD, disability due to MDD). ResultsRecursive partitioning found significant clusters defined by the conjunctions of early onset, suicidality, and anxiety (irritability, panic, nervousness-worry-anxiety) during the index episode. GLMs found additional associations involving a number of individual symptoms. Predicted values of the four outcomes were strongly correlated. Cluster analysis of these predicted values found three clusters having consistently high, intermediate, or low predicted scores across all outcomes. The high-risk cluster (30.0% of respondents) accounted for 52.9-69.7% of high persistence and severity, and it was most strongly predicted by index episode severe dysphoria, suicidality, anxiety, and early onset. A total symptom count, in comparison, was not a significant predictor. ConclusionsDespite being based on retrospective reports, results suggest that useful MDD subtyping distinctions can be made using data mining methods. Further studies are needed to test and expand these results with prospective data. (C) 2014 Wiley Periodicals, Inc.
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