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

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.

Patterns, Predictors, and Prognostic Validity of Persistent Complex Bereavement Disorder Symptoms in Recently Bereaved Adults

“…LCA is a person-centered approach that enables the examination of population heterogeneity by grouping individuals into homogenous subgroups (with similar symptom-profiles), or latent classes, on the basis of dichotomously rated indicators of symptoms (Collins & Lanza, 2010). Based on prior LCA research, including research on latent classes of posttraumatic stress symptoms (Hansen, Ross, & Armour, 2017) and depressive symptoms (Ulbricht, Chrysanthopoulou, Levin, & Lapane, 2018) two alternative outcomes were anticipated. The first possible outcome was that subgroups would emerge characterized by increasing probabilities 6 (e.g., low, moderate, high probability) of endorsing all PCBD symptoms.…”

Subtypes of Depression: Latent Class Analysis in Spanish Old People with Depressive Symptoms

“…A diagnostic of MDD in this population may be complex, given the characteristics such as increased somatization, masking symptoms, confusion with frequent life situations (bereavement, changes of address, loss of physical and mental abilities), or difficulty in making a differential diagnosis with dementia [7]. For a deeper understanding of the disorder and its heterogeneity in old age, recent research has been collecting evidence of clinically relevant depression subtypes [8]. For example, Sneed, J.R., et al found two types of depression, vascular and non-vascular depression [9], in two different samples of community-dwelling old people with a diagnosed previous major depressive episode.…”

Latent class analysis of obesity‐related characteristics and associations with body mass index among young children

“…Descriptive analyses of covariates including sex, age and weight category were examined for participants, as well as the frequency of responses for each of the 15 latent class indicators across all participants in the sample. For the primary objective, LCA was conducted to identify unobserved (latent) classes based on the categorical indicators described 31 . LCA assigns each participant a ‘best’ class assignment based on their probability of belonging to each class.…”