Factor mixture analysis of DSM-IV symptoms of major depression in a treatment seeking clinical population

Profiles of depressive symptoms in Peru: An 8-year analysis in population-based surveys

Background Profiles of depressive symptoms have been described due to heterogeneity in symptomatology and presentation. In our study, we estimate depressive symptom profiles and relate these symptom profiles to risk factors in the Peruvian population. Methods We carried out an observational study based on the Peruvian Demographic and Health Survey (2014-2022). Men and women aged 15 years and older living in urban and rural areas in all regions of Peru were included. The Patient Health Questionnaire-9 was used to define depressive symptom profiles. We estimated latent class models to define the profiles and performed a Poisson regression analysis to determine the associated factors. Results A total of 259,655 participants were included. The three-class model was found to be the most appropriate, and the classes were defined according to the severity of depressive symptoms (moderate-severe symptoms, mild symptoms, and without depressive symptoms). Also, it was found that the three classes identified have not changed during the years of evaluations, presenting very similar prevalence over the years. In addition, women are more likely than men to belong to a class with more severe depressive symptoms; and the older the age, the higher the probability of belonging to a class with greater severity of depressive symptoms. Conclusions Our study found that at the population level in Peru, depressive symptoms are grouped into three classes according to the intensity of the symptomatology present (no symptoms, mild symptoms and moderate-severe symptoms).

Course of Subtypes of Late-Life Depression Identified by Bipartite Network Analysis During Psychosocial Interventions

Importance

Approximately half of older adults with depression remain symptomatic at treatment end. Identifying discrete clinical profiles associated with treatment outcomes may guide development of personalized psychosocial interventions.

Objective

To identify clinical subtypes of late-life depression and examine their depression trajectory during psychosocial interventions in older adults with depression.

Design, Setting, and Participants

This prognostic study included older adults aged 60 years or older who had major depression and participated in 1 of 4 randomized clinical trials of psychosocial interventions for late-life depression. Participants were recruited from the community and outpatient services of Weill Cornell Medicine and the University of California, San Francisco, between March 2002 and April 2013. Data were analyzed from February 2019 to February 2023.

Interventions

Participants received 8 to 14 sessions of (1) personalized intervention for patients with major depression and chronic obstructive pulmonary disease, (2) problem-solving therapy, (3) supportive therapy, or (4) active comparison conditions (treatment as usual or case management).

Main Outcomes and Measures

The main outcome was the trajectory of depression severity, assessed using the Hamilton Depression Rating Scale (HAM-D). A data-driven, unsupervised, hierarchical clustering of HAM-D items at baseline was conducted to detect clusters of depressive symptoms. A bipartite network analysis was used to identify clinical subtypes at baseline, accounting for both between- and within-patient variability across domains of psychopathology, social support, cognitive impairment, and disability. The trajectories of depression severity in the identified subtypes were compared using mixed-effects models, and time to remission (HAM-D score ≤10) was compared using survival analysis.

Results

The bipartite network analysis, which included 535 older adults with major depression (mean [SD] age, 72.7 [8.7] years; 70.7% female), identified 3 clinical subtypes: (1) individuals with severe depression and a large social network; (2) older, educated individuals experiencing strong social support and social interactions; and (3) individuals with disability. There was a significant difference in depression trajectories (F2,2976.9 = 9.4; P < .001) and remission rate (log-rank χ22 = 18.2; P < .001) across clinical subtypes. Subtype 2 had the steepest depression trajectory and highest likelihood of remission regardless of the intervention, while subtype 1 had the poorest depression trajectory.

Conclusions and Relevance

In this prognostic study, bipartite network clustering identified 3 subtypes of late-life depression. Knowledge of patients' clinical characteristics may inform treatment selection. Identification of discrete subtypes of late-life depression may stimulate the development of novel, streamlined interventions targeting the clinical vulnerabilities of each subtype.

Variations in sociodemographic and health-related factors are linked to distinct clusters of individuals with depression based on the PHQ-9 instrument: NHANES 2007-2018

BACKGROUND

Depression is a heterogeneous disease. Identification of latent depression subgroups and differential associations across these putative groups and sociodemographic and health-related factors might pave the way toward targeted treatment of individuals.

METHODS

We used model-based clustering to identify relevant subgroups of 2900 individuals with moderate to severe depression (defined as scores ≥10 on the PHQ-9 instrument) from the NHANES cross-sectional survey. We used ANOVA and chi-squared tests to assess associations between cluster membership and sociodemographics, health-related variables, and prescription medication use.

RESULTS

We identified six latent clusters of individuals, three based on depression severity and three differentially loaded by somatic and mental components of the PHQ-9. The Severe mental depression cluster had the most individuals with low education and income (P < 0.05). We observed differences in the prevalence of numerous health conditions, with the Severe mental depression cluster showing the worst overall physical health. We observed marked differences between the clusters regarding prescription medication use: the Severe mental depression cluster had the highest use of cardiovascular and metabolic agents, while the Uniform severe depression cluster showed the highest use of central nervous system and psychotherapeutic agents.

LIMITATIONS

Due to the cross-sectional design we cannot make conclusions about causal relationships. We used self-reported data. We did not have access to a replication cohort.

CONCLUSIONS

We show that socioeconomic factors, somatic diseases, and prescription medication use are differentially associated with distinct and clinically relevant clusters of individuals with moderate to severe depression.

Learning Individualized Treatment Rules for Multiple-Domain Latent Outcomes

For many mental disorders, latent mental status from multiple-domain psychological or clinical symptoms may perform as a better characterization of the underlying disorder status than a simple summary score of the symptoms, and they may also serve as more reliable and representative features to differentiate treatment responses. Therefore, in order to address the complexity and heterogeneity of treatment responses for mental disorders, we provide a new paradigm for learning optimal individualized treatment rules (ITRs) by modeling patients' latent mental status. We first learn the multi-domain latent states at baseline from the observed symptoms under a restricted Boltzmann machine (RBM) model, through which patients' heterogeneous symptoms are represented using an economical number of latent variables and yet remains flexible. We then optimize a value function defined by the latent states after treatment by exploiting a transformation of the observed symptoms based on the RBM without modeling the relationship between the latent mental states before and after treatment. The optimal treatment rules are derived using a weighted large margin classifier. We derive the convergence rate of the proposed estimator under the latent models. Simulation studies are conducted to test the performance of the proposed method. Finally, we apply the developed method to real world studies and we demonstrate the utility and advantage of our method in tailoring treatments for patients with major depression, and identify patient subgroups informative for treatment recommendations.

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

Major depressive disorder (MDD) is one of the most disabling disorders and the one that most contributes to disability. When it occurs in older people, it is an additional burden to their potential physical and cognitive deficiencies, making MDD an important public health problem that supposes a large investment in health. There is a clear lack of consistency between the subtypes of depression found in the literature, ranging from two to seven classes, with three being the most commonly found non-melancholic, melancholic and psychotic, or putative psychotics. The aim of this research is to add knowledge to the profiles of depressive symptoms in a representative sample of older Spanish people, and to study the possible relationship of these symptom profiles with variables that have traditionally been related to depression. Spanish data from the sixth wave of SHARE were used, with 612 Spanish older adults living in Spain. A routine of several LCAs with a different number of classes was performed to answer this first aim to classify Spanish adults with depression symptoms. The results pointed out the presence of three different classes among the participants in the study: psychosomatic (11.12%), melancholic (14.21%), and anhedonic (74.67%). This work represents a step forward to understand the heterogeneity of major depressive disorder, facilitating the diagnosis, and subsequent treatment of older adults.

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

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.

Using a hybrid subtyping model to capture patterns and dimensionality of depressive and anxiety symptomatology in the general population

BACKGROUND

Researchers have tried to identify more homogeneous subtypes of major depressive disorder (MDD) with latent class analyses (LCA). However, this approach does no justice to the dimensional nature of psychopathology. In addition, anxiety and functioning-levels have seldom been integrated in subtyping efforts. Therefore, this study used a hybrid discrete-dimensional approach to identify subgroups with shared patterns of depressive and anxiety symptomatology, while accounting for functioning-levels.

METHODS

The Comprehensive International Diagnostic Interview (CIDI) 1.1 was used to assess previous-year depressive and anxiety symptoms in the Netherlands Mental Health Survey and Incidence Study-1 (NEMESIS-1; n=5583). The data were analyzed with factor analyses, LCA and hybrid mixed-measurement item response theory (MM-IRT) with and without functioning covariates. Finally, the classes' predictors (measured one year earlier) and outcomes (measured two years later) were investigated.

RESULTS

A 3-class MM-IRT model with functioning covariates best described the data and consisted of a 'healthy class' (74.2%) and two symptomatic classes ('sleep/energy' [13.4%]; 'mood/anhedonia' [12.4%]). Factors including older age, urbanicity, higher severity and presence of 1-year MDD predicted membership of either symptomatic class vs. the healthy class. Both symptomatic classes showed poorer 2-year outcomes (i.e. disorders, poor functioning) than the healthy class. The odds of MDD after two years were especially increased in the mood/anhedonia class.

LIMITATIONS

Symptoms were assessed for the past year whereas current functioning was assessed.

CONCLUSIONS

Heterogeneity of depression and anxiety symptomatology are optimally captured by a hybrid discrete-dimensional subtyping model. Importantly, accounting for functioning-levels helps to capture clinically relevant interpersonal differences.

World Health Organization disability assessment schedule 2.0: An international systematic review

PURPOSE

This systematic review examines research and practical applications of the World Health Organization Disability Assessment Schedule (WHODAS 2.0) as a basis for establishing specific criteria for evaluating relevant international scientific literature. The aims were to establish the extent of international dissemination and use of WHODAS 2.0 and analyze psychometric research on its various translations and adaptations. In particular, we wanted to highlight which psychometric features have been investigated, focusing on the factor structure, reliability, and validity of this instrument.

METHOD

Following Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology, we conducted a search for publications focused on "whodas" using the ProQuest, PubMed, and Google Scholar electronic databases.

RESULTS

We identified 810 studies from 94 countries published between 1999 and 2015. WHODAS 2.0 has been translated into 47 languages and dialects and used in 27 areas of research (40% in psychiatry).

CONCLUSIONS

The growing number of studies indicates increasing interest in the WHODAS 2.0 for assessing individual functioning and disability in different settings and individual health conditions. The WHODAS 2.0 shows strong correlations with several other measures of activity limitations; probably due to the fact that it shares the same disability latent variable with them. Implications for Rehabilitation WHODAS 2.0 seems to be a valid, reliable self-report instrument for the assessment of disability. The increasing interest in use of the WHODAS 2.0 extends to rehabilitation and life sciences rather than being limited to psychiatry. WHODAS 2.0 is suitable for assessing health status and disability in a variety of settings and populations. A critical issue for rehabilitation is that a single "minimal clinically important .difference" score for the WHODAS 2.0 has not yet been established.

The identification of symptom-based subtypes of depression: A nationally representative cohort study

BACKGROUND

In recent years, researchers have used various techniques to elucidate the heterogeneity in depressive symptoms. This study seeks to resolve the extent to which variations in depression reflect qualitative differences between symptom categories and/or quantitative differences in severity.

METHODS

Data were used from the Netherlands Mental Health Survey and Incidence Study-2, a nationally representative face-to-face survey of the adult general population. In a subsample of respondents with a lifetime key symptom of depression at baseline and who participated in the first two waves (n=1388), symptom profiles at baseline were based on symptoms reported during their worst lifetime depressive episode. Depressive symptoms and DSM-IV diagnoses were assessed with the Composite International Diagnostic Interview 3.0. Three latent variable techniques (latent class analysis, factor analysis, factor mixture modelling) were used to identify the best subtyping model.

RESULTS

A latent class analysis, adjusted for local dependence between weight change and appetite change, described the data best and resulted in four distinct depressive subtypes: severe depression with anxiety (28.0%), moderate depression with anxiety (29.3%), moderate depression without anxiety (23.6%) and mild depression (19.0%). These classes showed corresponding clinical correlates at baseline and corresponding course and outcome indicators at follow-up (i.e., class severity was linked to lifetime mental disorders at baseline, and service use for mental health problems and current disability at follow-up).

LIMITATIONS

Although the sample was representative of the population on most parameters, the findings are not generalisable to the most severely affected depressed patients.

CONCLUSIONS

Depression could best be described in terms of both qualitative differences between symptom categories and quantitative differences in severity. In particular anxiety was a distinguishing feature within moderate depression. This study stresses the central position anxiety occupies in the concept of depression.

Initial interpretation and evaluation of a profile-based classification system for the anxiety and mood disorders: Incremental validity compared to DSM-IV categories

Limitations in anxiety and mood disorder diagnostic reliability and validity due to the categorical approach to classification used by the Diagnostic and Statistical Manual of Mental Disorders (DSM) have been long recognized. Although these limitations have led researchers to forward alternative classification schemes, few have been empirically evaluated. In a sample of 1,218 outpatients with anxiety and mood disorders, the present study examined the validity of Brown and Barlow's (2009) proposal to classify the anxiety and mood disorders using an integrated dimensional-categorical approach based on transdiagnostic emotional disorder vulnerabilities and phenotypes. Latent class analyses of 7 transdiagnostic dimensional indicators suggested that a 6-class (i.e., profile) solution provided the best model fit and was the most conceptually interpretable. Interpretation of the classes was further supported when compared with DSM diagnoses (i.e., within-class prevalence of diagnoses, using diagnoses to predict class membership). In addition, hierarchical multiple regression models were used to demonstrate the incremental validity of the profiles; class probabilities consistently accounted for unique variance in anxiety and mood disorder outcomes above and beyond DSM diagnoses. These results provide support for the potential development and utility of a hybrid dimensional-categorical profile approach to anxiety and mood disorder classification. In particular, the availability of dimensional indicators and corresponding profiles may serve as a useful complement to DSM diagnoses for both researchers and clinicians.

Cancer-related fatigue in breast cancer patients: factor mixture models with continuous non-normal distributions

OBJECTIVE

Fatigue is one of the most prevalent and significant symptoms experienced by breast cancer patients. This study aimed to investigate potential population heterogeneity in fatigue symptoms of the patients using the innovative non-normal mixture modeling.

METHODS

A sample of 197 breast cancer patients completed the brief fatigue inventory and other measures on cancer symptoms. Non-normal factor mixture models were analyzed and compared using the normal, t, skew-normal, and skew-t distributions. Selection of the number of latent classes was based on the Bayesian information criterion (BIC). The identified classes were validated by comparing their demographic profiles, clinical characteristics, and cancer symptoms using a stepwise distal outcome approach.

RESULTS

The observed fatigue items displayed slight skewness but evident negative kurtosis. Factor mixture models using the normal distribution pointed to a 3-class solution. The t distribution mixture models showed the lowest BIC for the 2-class model. The restored class (52.5 %) exhibited moderate severity (item mean = 2.8-3.2) and low interference (item mean = 1.1-1.9). The exhausted class (47.5 %) displayed high levels of fatigue severity and interference (item mean = 5.8-6.6). Compared to the restored class, the exhausted class reported significantly higher perceived stress, anxiety, depression, pain, sleep disturbance, and lower quality of life.

CONCLUSIONS

The non-normal factor mixture models suggest two distinct subgroups of patients on their fatigue symptoms. The presence of the exhausted class with exacerbated symptoms calls for a proactive assessment of the symptoms and development of tailored interventions for this subgroup.