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Getting RID of the blues: Formulating a Risk Index for Depression (RID) using structural equation modeling

Version 2 2024-06-06, 09:26
Version 1 2017-10-19, 10:30
journal contribution
posted on 2024-06-06, 09:26 authored by JF Dipnall, Julie PascoJulie Pasco, Michael BerkMichael Berk, Lana WilliamsLana Williams, Seetal DoddSeetal Dodd, Felice JackaFelice Jacka, D Meyer
Objective: While risk factors for depression are increasingly known, there is no widely utilised depression risk index. Our objective was to develop a method for a flexible, modular, Risk Index for Depression using structural equation models of key determinants identified from previous published research that blended machine-learning with traditional statistical techniques. Methods: Demographic, clinical and laboratory variables from the National Health and Nutrition Examination Study (2009–2010, N = 5546) were utilised. Data were split 50:50 into training:validation datasets. Generalised structural equation models, using logistic regression, were developed with a binary outcome depression measure (Patient Health Questionnaire-9 score ⩾ 10) and previously identified determinants of depression: demographics, lifestyle-environs, diet, biomarkers and somatic symptoms. Indicative goodness-of-fit statistics and Areas Under the Receiver Operator Characteristic Curves were calculated and probit regression checked model consistency. Results: The generalised structural equation model was built from a systematic process. Relative importance of the depression determinants were diet (odds ratio: 4.09; 95% confidence interval: [2.01, 8.35]), lifestyle-environs (odds ratio: 2.15; 95% CI: [1.57, 2.94]), somatic symptoms (odds ratio: 2.10; 95% CI: [1.58, 2.80]), demographics (odds ratio:1.46; 95% CI: [0.72, 2.95]) and biomarkers (odds ratio:1.39; 95% CI: [1.00, 1.93]). The relationships between demographics and lifestyle-environs and depression indicated a potential indirect path via somatic symptoms and biomarkers. The path from diet was direct to depression. The Areas under the Receiver Operator Characteristic Curves were good (logistic:training = 0.850, validation = 0.813; probit:training = 0.849, validation = 0.809). Conclusion: The novel Risk Index for Depression modular methodology developed has the flexibility to add/remove direct/indirect risk determinants paths to depression using a structural equation model on datasets that take account of a wide range of known risks. Risk Index for Depression shows promise for future clinical use by providing indications of main determinant(s) associated with a patient’s predisposition to depression and has the ability to be translated for the development of risk indices for other affective disorders

History

Journal

Australian and New Zealand Journal of Psychiatry

Volume

51

Pagination

1121-1133

Location

England

ISSN

0004-8674

eISSN

1440-1614

Language

English

Publication classification

C Journal article, C1 Refereed article in a scholarly journal

Copyright notice

2017, Royal Australian and New Zealand college of Psychiatrists

Issue

11

Publisher

SAGE PUBLICATIONS LTD