Title

The use of latent class analysis for identifying subtypes of depression: A systematic review.

UMMS Affiliation

Department of Quantitative Health Sciences; Lamar Soutter Library

Date

3-17-2018

Document Type

Article

Medical Subject Headings

Depressive Disorder/classification

Disciplines

Library and Information Science | Psychiatry | Psychiatry and Psychology

Abstract

Depression is a significant public health problem but symptom remission is difficult to predict. This may be due to substantial heterogeneity underlying the disorder. Latent class analysis (LCA) is often used to elucidate clinically relevant depression subtypes but whether or not consistent subtypes emerge is unclear. We sought to critically examine the implementation and reporting of LCA in this context by performing a systematic review to identify articles detailing the use of LCA to explore subtypes of depression among samples of adults endorsing depression symptoms. PubMed, PsycINFO, CINAHL, Scopus, and Google Scholar were searched to identify eligible articles indexed prior to January 2016. Twenty-four articles reporting 28 LCA models were eligible for inclusion. Sample characteristics varied widely. The majority of articles used depression symptoms as the observed indicators of the latent depression subtypes. Details regarding model fit and selection were often lacking. No consistent set of depression subtypes was identified across studies. Differences in how models were constructed might partially explain the conflicting results. Standards for using, interpreting, and reporting LCA models could improve our understanding of the LCA results. Incorporating dimensions of depression other than symptoms, such as functioning, may be helpful in determining depression subtypes.

Rights and Permissions

Psychiatry Res. 2018 Mar 17. pii: S0165-1781(17)30312-8. doi: 10.1016/j.psychres.2018.03.003. [Epub ahead of print]

Related Resources

Link to article in PubMed

Keywords

Depression, Depression subtypes, Finite mixture model, Latent class analysis

PubMed ID

29605104

Share

COinS