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

Linking Multi-Dimensional Religiosity in Childhood and Later Adulthood: Implications for Later Life Health

This study examines religiosity patterns across childhood and later adulthood and their associations with later-life health using an experimental module from the 2016 Health and Retirement Study (N = 1649; Mean Age = 64.0). Latent class analysis is used to categorize individuals by commonalities in religious attendance, religious identity, and spiritual identity. Cross-sectional and longitudinal associations are then explored using probable depression, disability, and mortality as health indicators. Results reveal complex patterns, often characterized by declining attendance and fluctuating identity. Relationships with health appear stronger in cross-sectional analyses, suggesting that some associations may be non-causal. Individuals with consistently strong religiosity show significantly better psychological health compared to their relatively non-religious counterparts. Moreover, the absence of religiosity in later adulthood is associated with an increased risk of mortality. Overall, the findings support the promotion of religiosity whilst acknowledging individual variations and highlighting the need for more individualistic approaches to the study of religion and health.

Heterogeneity of Cognition in Older Adults with Remitted Major Depressive Disorder: A Latent Profile Analysis

OBJECTIVES

To identify data-driven cognitive profiles in older adults with remitted major depressive disorder (rMDD) with or without mild cognitive impairment (MCI) and examine how the profiles differ regarding demographic, clinical, and neuroimaging measures.

DESIGN

Secondary cross-sectional analysis using latent profile analysis.

SETTING

Multisite clinical trial in Toronto, Canada.

PARTICIPANTS

One hundred seventy-eight participants who met DSM-5 criteria for rMDD without MCI (rMDD-MCI; n = 60) or with MCI (rMDD + MCI; n = 118).

MEASUREMENTS

Demographic, clinical, neuroimaging measures, and domain scores from a neuropsychological battery assessing verbal memory, visuospatial memory, processing speed, working memory, language, and executive function.

RESULTS

We identified three latent profiles: Profile 1 (poor cognition; n = 75, 42.1%), Profile 2 (intermediate cognition; n = 75, 42.1%), and Profile 3 (normal cognition; n = 28, 15.7%). Compared to participants with Profile 3, those with Profile 1 or 2 were older, had lower education, experienced a greater burden of medical comorbidities, and were more likely to have MCI. The profiles did not differ on the severity of residual symptoms, age of onset of rMDD, number of depressive episodes, psychotropic medication, cerebrovascular risk, ApoE4 carrier status, or family history of depression, dementia, or Alzheimer's disease. The profiles differed in cortical thickness of 15 regions, with the most prominent effects for left precentral and pars opercularis, and right inferior parietal and supramarginal.

CONCLUSION

Older patients with rMDD can be grouped cross-sectionally based on data-driven cognitive profiles that differ from the absence or presence of a diagnosis of MCI. Future research should determine the differential risk for dementia of these data-driven subgroups.

Depression Severity, Slow- versus Fast-Wave Neural Activity, and Symptoms of Melancholia

Melancholia is a major and severe subtype of depression, with only limited data regarding its association with neurological phenomena. To extend the current understanding of how particular aspects of melancholia are correlated with brain activity, electroencephalographic data were collected from 100 adults (44 males and 56 females, all aged 18 y or more) and investigated for the association between symptoms of melancholia and the ratios of alpha/beta activity and theta/beta activity at parietal-occipital EEG sites PO1 and PO2. The results indicate differences in these associations according to the depressive status of participants and the particular symptom of melancholia. Depressed participants exhibited meaningfully direct correlations between alpha/beta and theta/beta activity and the feeling that "Others would be better off if I was dead" at PO1, whereas non-depressed participants had significant inverse correlations between theta/beta activity and "Feeling useless and not needed" and "I find it hard to make decisions" at PO1. The results are discussed in terms of the relative levels of fast-wave (beta) versus slow-wave (alpha, theta) activity exhibited by depressed and non-depressed participants in the parietal-occipital region and the cognitive activities that are relevant to that region.

Clustering of Social Determinants of Health as an Indicator of Meaningful Subgroups within an African American Population: Application of Latent Class Analysis

BACKGROUND

Health disparities between people who are African American (AA) versus their White counterparts have been well established, but disparities among AA people have not. The current study introduces a systematic method to determine subgroups within a sample of AA people based on their social determinants of health.

METHODS

Health screening data collected in the West Side of Chicago, an underserved predominantly AA area, in 2018 were used. Exploratory latent class analysis was used to determine subgroups of participants based on their responses to 16 variables, each pertaining to a specific social determinant of health.

RESULTS

Four unique clusters of participants were found, corresponding to those with "many unmet needs", "basic unmet needs", "unmet healthcare needs", and "few unmet needs".

CONCLUSION

The findings support the utility of analytically determining meaningful subgroups among a sample of AA people and their social determinants of health. Understanding the differences within an underserved population may contribute to future interventions to eliminate health disparities.

Characterization of depression subtypes and their relationships to stressor profiles among middle-aged and older adults: An analysis of the canadian longitudinal study on aging (CLSA)

The current diagnostic criteria for depression do not sufficiently reflect its heterogeneous clinical presentations. Associations between adverse childhood experiences (ACEs), allostatic load (AL), and depression subtypes have not been extensively studied. Depression subtypes were determined based on clinical presentations, and their relationships to AL biomarkers and ACEs were elucidated in a sample of middle-aged and older adults. Participants from the Canadian Longitudinal Study on Aging who screened positive for depression were included (n = 3966). Depression subtypes, AL profiles and ACE profiles were determined with latent profile analyses, and associations between them were determined using multinomial logistic regression. Four depression subtypes were identified: positive affect, melancholic, typical, and atypical. Distinct associations between depression subtypes, stressor profiles and covariates were observed. Among the subtypes compared to positive affect, atypical subtype had the most numerous significant associations, and the subtypes had unique relationships to stressor profiles. Age, sex, smoking status, chronic conditions, marital status, and physical activity were significant covariates. The present study describes distinct associations between depression subtypes and measures of stress (objective and self-reported), as well as related factors that differentiate subtypes. The findings may inform more targeted and integrated clinical management strategies for depression in individuals exposed to multiple stressors.

Profiles of met and unmet care needs in the oldest-old primary care patients with depression - results of the AgeMooDe study

BACKGROUND

Unmet care needs have been associated with an increased risk of depression in old age. Currently, the identification of profiles of met and unmet care needs associated with depressive symptoms is pending. Therefore, this exploratory study aimed to identify profiles of care needs and analyze associated factors in oldest-old patients with and without depression.

METHODS

The sample of 1092 GP patients aged 75+ years is based on the multicenter study "Late-life depression in primary care: needs, health care utilization and costs (AgeMooDe)". Depression (i.e. clinically meaningful depressive symptoms) was determined using the Geriatric Depression Scale (GDS) (cutoff score ≥ 4). Needs of patients were assessed using the Camberwell Assessment of Need for the Elderly (CANE). Associated sociodemographic and clinical factors were examined, and latent class analysis identified the need profiles.

RESULTS

The main result of the study indicates three need profiles: 'no needs', 'met physical needs', and 'unmet social needs'. Members of the 'met physical needs' (OR = 3.5, 95 %-CI: 2.5-4.9) and 'unmet social needs' (OR = 17.4, 95 %-CI: 7.7-39.7) profiles were significantly more likely to have depression compared to members of the 'no needs' profile.

LIMITATIONS

Based on the cross-sectional design, no conclusions can be drawn about the causality or direction of the relationships between the variables.

CONCLUSIONS

The study results provide important insights for the establishment of needs-based interventions for GPs. Particular attention should be paid to the presence of unmet social needs in the oldest-old GP patients with underlying depressive symptoms.

Latent class of depressive symptoms of and its determinants: A cross-sectional study among Iranian University students

Background

According to the report of the World Health Organization, mental disorders are one of the 10 most important causes of disability in the world. This study was conducted with the aim of determining the number and frequency of latent classes of depression and its determinants in Isfahan university of medical students.

Materials and Methods

A total of 1408 medical students from Isfahan University of Medical Sciences, Iran, were enrolled in the study in 2017. The symptoms and severity of depression were assessed using the standard Hospital Anxiety and Depression scale questionnaire. Latent class analysis was applied to seven symptoms of depression, all of which had four levels. Latent class subgroups were compared using the Chi-square test and analysis of variance test. The regression model was used to check the relationship between identified classes and related factors. Analyzes were done using SPSS-21 and Mplus7 software.

Results

In this study, three latent classes were identified, that is, the group of healthy people, the group of borderline people, and the group of people suspected of depression. The prevalence of identified latent classes among medical students is 0.52, 0.32, and 0.16%, respectively. The regression results showed that compared to the healthy group, the factors affecting depression in the borderline and suspicious group were increasing age, female gender, interest in the field of study, physical activity, history of depression, and history of anxiety.

Conclusion

The three classes that were identified based on the students' answers to the depression symptoms questions differed only based on severity. The history of depression and anxiety were the strongest predictors of latent classes of depression.

Transdiagnostic clustering of self-schema from self-referential judgements identifies subtypes of healthy personality and depression

Introduction

The heterogeneity of depressive and anxiety disorders complicates clinical management as it may account for differences in trajectory and treatment response. Self-schemas, which can be determined by Self-Referential Judgements (SRJs), are heterogeneous yet stable. SRJs have been used to characterize personality in the general population and shown to be prognostic in depressive and anxiety disorders.

Methods

In this study, we used SRJs from a Self-Referential Encoding Task (SRET) to identify clusters from a clinical sample of 119 patients recruited from the Institute of Mental Health presenting with depressive or anxiety symptoms and a non-clinical sample of 115 healthy adults. The generated clusters were examined in terms of most endorsed words, cross-sample correspondence, association with depressive symptoms and the Depressive Experiences Questionnaire and diagnostic category.

Results

We identify a 5-cluster solution in each sample and a 7-cluster solution in the combined sample. When perturbed, metrics such as optimum cluster number, criterion value, likelihood, DBI and CHI remained stable and cluster centers appeared stable when using BIC or ICL as criteria. Top endorsed words in clusters were meaningful across theoretical frameworks from personality, psychodynamic concepts of relatedness and self-definition, and valence in self-referential processing. The clinical clusters were labeled "Neurotic" (C1), "Extraverted" (C2), "Anxious to please" (C3), "Self-critical" (C4), "Conscientious" (C5). The non-clinical clusters were labeled "Self-confident" (N1), "Low endorsement" (N2), "Non-neurotic" (N3), "Neurotic" (N4), "High endorsement" (N5). The combined clusters were labeled "Self-confident" (NC1), "Externalising" (NC2), "Neurotic" (NC3), "Secure" (NC4), "Low endorsement" (NC5), "High endorsement" (NC6), "Self-critical" (NC7). Cluster differences were observed in endorsement of positive and negative words, latency biases, recall biases, depressive symptoms, frequency of depressive disorders and self-criticism.

Discussion

Overall, clusters endorsing more negative words tended to endorse fewer positive words, showed more negative biases in reaction time and negative recall bias, reported more severe depressive symptoms and a higher frequency of depressive disorders and more self-criticism in the clinical population. SRJ-based clustering represents a novel transdiagnostic framework for subgrouping patients with depressive and anxiety symptoms that may support the future translation of the science of self-referential processing, personality and psychodynamic concepts of self-definition to clinical applications.

Association between attention performance and the different dimensions of DSM-5 depression symptoms

Objective

Depressive symptoms can be assessed with self-reported questionnaires, such as the Patient Health Questionary-9 (PHQ-9). Previous studies have suggested that the PHQ-9 items can be grouped into somatic and non-somatic clusters. However, the classification of the PHQ-9 item "concentration difficulties" into somatic or non-somatic is still controversial. This controversy may be explained by difficulties experienced by subjects in accurately evaluating their attention problems. The primary objective of this study was to determine the correlation between objective attentional performance and the two clusters of depressive symptoms in hospital employees working in stressful conditions.

Methods

The participants filled out the PHQ-9 to identify their depressive symptoms. Based on the PHQ-9, the somatic or non-somatic symptoms were measured without considering the question about subjective concentration difficulties. Then, a brief version of the Continuous Visual Attention Test (CVAT) was applied to assess four attentional subdomains. The CVAT is a Go/No-go task that measures number of correct responses (focused attention), number of incorrect responses (behavior-inhibition), average reaction time of correct responses (RT-alertness), and variability of reaction time (VRT-sustained attention). The entire task lasted 90 s. Correlation analyses assessed the relationships between attentional performance and the two dimensions of depressive symptoms.

Results

After applying the inclusion/exclusion criteria, 359 individuals were selected. Their age ranged from 20 to 70 years (mean = 40.5, SD = 10.37), and the majority was female (67.6%). A predominance in somatic depressive symptoms was present in 231 (64%) participants, whereas 59 (16%) showed a predominance of non-somatic symptoms. Sixty-nine participants (20%) did not show any predominance. Higher somatic scores were associated with higher RTs, whereas higher non-somatic scores were related to an increase in the number of incorrect responses.

Conclusion

The predominance of the somatic cluster was related to lower alertness, whereas the predominance of non-somatic cluster was associated with impulsivity/hyperactivity. This result may explain the difficulties associated with correctly classifying the item concentration difficulties. A brief attentional task can be used as an auxiliary tool to correctly identify the different dimensions of attention that are associated with different clusters of depressive symptoms.

Patterns of Women's exposure to psychological violence: A global examination of low- and middle-income countries

Introduction

Under Sustainable Development Goal 5, prevalence of intimate partner violence (IPV) is a globally reportable indicator. There is a lack of consensus on how to measure and report psychological IPV, affecting prevalence estimates and cross-country comparability. We examine similarities and differences in the patterning of women's experiences of psychological abuse in low- and middle-income countries (LMICs) to inform common cut points.

Methods

Data include 13,452 ever-partnered women from six LMICs participating in the WHO multi-country study on women's health and domestic violence against women and 306,101 from 47 LMICs participating in the Demographic and Health Surveys. A confirmatory latent class analysis (LCA) approach was applied to identify the optimal class structure using the 3 DHS and 4 WHO psychological IPV items, assessed the impact of physical and sexual IPV on class structure, and tested class generalizability across countries. We validated the three-class solution by regressing the classes on physical IPV, sexual IPV, controlling behaviors, and injury due to domestic violence. We used item response theory (IRT) methods to assess item-level characteristics of the items.

Results

Analysis confirmed the three-class structure in most countries. Addition of physical and sexual IPV did not change overall class structure or improve discrimination or homogeneity of the items. The three-class structure was invariant within most WHO-classified regions. Operationalized classes informed by the LCA resulted in prevalences of roughly 90% low-to-no class, 7% moderate-intensity class, and 3% high-intensity class. Classes showed convergent validity with all outcomes tested. IRT analysis revealed good discriminations but substantial information overlaps over a narrow range of the latent psychological violence construct.

Conclusions

This study confirms the three-class pattern but suggests some differences across countries. and regions. We suggest cut points distinguishing violent from non-violent acts and demarcating levels of severity for future study. Findings offer evidence-based guidance to rectify challenges.

Subgroups of depressive symptoms in Korean police officers: A latent class analysis

The prevalence of depressive symptoms is common among police officers; however, studies that identify the patterns of depressive symptoms in police officers and occupational characteristics related to the specific subgroups of depressive symptoms are scarce. A total of 493 police officers in South Korea participated in this descriptive cross-sectional study between October and December 2019. Depressive symptoms were measured using the Patient Health Questionnaire-9. Latent class analysis was used to identify the subgroups of depressive symptoms. To identify the characteristics and predictors of the subgroup, χ2 tests, analysis of variance, and multinomial logistic regression analysis were performed. Four latent classes of depressive symptoms were identified: "at-risk" (10.8%), "anhedonic" (21.5%), "somatic" (17.2%), and "minimal" (50.5%). Compared to the minimal group, drinking behaviors were higher in the at-risk group (odds ratio [OR] = 1.10, 95% confidence interval [CI] [1.03, 1.11]), and working hours were greater in the somatic group (OR = 1.01, 95% CI [1.00, 1.02]). Additionally, sleep quality (OR = 1.35, 95% CI [0.82, 2.22]) and fatigue (OR = 1.02, 95% CI [1.00, 1.04]) were found to be related in the anhedonic group. This study identified the heterogeneity of depressive symptoms among police officers. It is necessary to accurately identify the factors associated with the depression subgroups of police officers to develop support strategies and prevent an increase in their depression severity. The association between risk factors such as working hours and drinking behaviors might inform strategies to reduce depression in police offers.

Subtypes of early childhood caries predict future caries experience

OBJECTIVES

To test whether postulated subtypes of early childhood caries (ECC) are predictive of subsequent caries experience in a population-based cohort of Swedish children.

METHODS

The study included children aged between 3 and 5 years at study entry with dental records available for at least 5 years of follow-up. Dental record data were retrieved from the Swedish Quality Registry for Caries and Periodontal disease (SKaPa) for the initial and follow-up visits. Participants who had ECC at study entry were assigned to one of five ECC subtypes (termed classes 1-5) using latent class modelling of tooth surface-level caries experience. Subsequent experience of caries was assessed using the decayed, missing and filled surfaces indices (dmfs/DMFS) at follow-up visits, and compared between ECC subtypes using logistic and negative binomial regression modelling.

RESULTS

The study included 128 355 children who had 3 or more dental visits spanning at least 5 years post-baseline. Of these children, 31 919 had caries at the initial visit. Baseline ECC subtype was associated with differences in subsequent disease experience. As an example, 83% of children who had a severe form of ECC at age 5 went on to have caries in the permanent dentition by the end of the study, compared to 51% of children who were caries-free at age 5 (adjusted odds ratio of 4.9 for new disease at their third follow-up).

CONCLUSIONS

ECC subtypes assigned at a baseline visit are associated with differences in subsequent caries experience in both primary and permanent teeth. This suggests that the development and future validation of an ECC classification can be used in addition to current prediction tools to help identify children at high risk of developing new caries lesions throughout childhood and adolescence.

Factors associated with the intensity of anxiety and depression symptoms in health workers of two centres of reference for COVID 19 patient care in Antioquia, Colombia - A latent class analysis

OBJECTIVE

To classify the staff of two reference institutions for COVID-19 care in Antioquia according to the intensity of anxiety and depression symptoms, and to determine the factors associated with these classes.

METHODS

Cross-sectional study in which the GAD-7, PHQ-9, fear of COVID-19, and the Copenhagen Burnout scale were used. Latent class analysis was performed to identify the classes, and the factors associated with these were determined using multinomial logistic regression.

RESULTS

486 people participated. The three-class model had the best fit: class I with low scores on the scales; class II with mild degrees of anxiety and depression, and intermediate levels of fear of COVID-19 and perceived stress; and class III with moderate and severe degrees of anxiety, depression, and perceived stress. The factors associated with belonging to class III were age (OR = 0.94; 95%CI, 0.91-0.96), change of residence to avoid exposing relatives (OR = 4.01; 95%CI, 1.99-8.09), and a history of depressive disorder (OR = 3.10; 95%CI, 1.27-7.56), and anxiety (OR = 5.5; 95%CI, 2.36-12.90). Factors associated with class II were age (OR = 0.97; 95%CI, 0.95-0.99), history of depressive disorder (OR = 3.41; 95%CI, 1.60-7.25), living with someone at risk of death from COVID-19 (OR = 1.86; 95%CI, 1.19-2.91), family member being healthcare staff (OR = 1.58; 95%CI, 1.01-2.47), and change of residence to avoid exposing relatives (OR = 1.99; 95%CI, 1.11-3.59).

CONCLUSIONS

Three classes of participants were obtained, two of them with anxiety and depression symptoms. Younger age and a history of mental disorder were factors associated with the two classes of symptomatic patients; other factors may be causes or consequences of the symptoms.

Depression Subtypes in Systolic Heart Failure: A Secondary Analysis From a Randomized Controlled Trial

BACKGROUND

Heart failure (HF) is associated with an elevated risk of morbidity, mortality, hospitalization, and impaired quality of life. One potential contributor to these poor outcomes is depression. Yet the effectiveness of treatments for depression in patients with HF is mixed, perhaps due to the heterogeneity of depression.

METHODS

This secondary analysis applied latent class analysis (LCA) to data from a clinical trial to classify patients with systolic HF and comorbid depression into LCA subtypes based on depression symptom severity, and then examined whether these subtypes predicted treatment response and mental and physical health outcomes at 12 months follow-up.

RESULTS

In LCA of 629 participants (mean age 63.6 ± 12.9; 43% females), we identified 4 depression subtypes: mild (prevalence 53%), moderate (30%), moderately severe (12%), and severe (5%). The mild subtype was characterized primarily by somatic symptoms of depression (e.g., energy loss, sleep disturbance, poor appetite), while the remaining LCA subtypes additionally included nonsomatic symptoms of depression (e.g., depressed mood, anhedonia, worthlessness). At 12 months, LCA subtypes with more severe depressive symptoms reported significantly greater improvements in mental quality of life and depressive symptoms compared to the LCA mild subtype, but the incidence of cardiovascular- and noncardiovascular-related readmissions, and mortality was similar among all subtypes.

CONCLUSIONS

In patients with depression and systolic heart failure those with the LCA mild depression subtype may not meet full criteria for major depressive disorder, given the overlap between HF and somatic symptoms of depression. We recommend requiring depressed mood or anhedonia as a necessary symptom for major depressive disorder in patients with HF.

An overview of clustering methods with guidelines for application in mental health research

Cluster analyzes have been widely used in mental health research to decompose inter-individual heterogeneity by identifying more homogeneous subgroups of individuals. However, despite advances in new algorithms and increasing popularity, there is little guidance on model choice, analytical framework and reporting requirements. In this paper, we aimed to address this gap by introducing the philosophy, design, advantages/disadvantages and implementation of major algorithms that are particularly relevant in mental health research. Extensions of basic models, such as kernel methods, deep learning, semi-supervised clustering, and clustering ensembles are subsequently introduced. How to choose algorithms to address common issues as well as methods for pre-clustering data processing, clustering evaluation and validation are then discussed. Importantly, we also provide general guidance on clustering workflow and reporting requirements. To facilitate the implementation of different algorithms, we provide information on R functions and libraries.

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).

Heterogeneity and associated factors of patients with polycystic ovary syndrome health behaviors: a latent class analysis

OBJECTIVE

Using latent class to analyze whether there are subtypes of health behaviors in patients with PCOS can be addressed using targeted interventions.

METHODS

October 2021 to June 2022, 471 PCOS patients were surveyed using the Health Promoting Lifestyle Profile Questionnaire. Latent class analysis (LCA) was used to identify subgroups of PCOS patients. Subsequent multinomial latent variable regressions identified factors that were associated with health behaviors.

RESULTS

A three-class subtypes was the optimum grouping classification: (1)High healthy behavior risk; (2)high healthy responsibility and physical activity risk; (3)low healthy behavior risk. The multinomial logistic regression analysis revealed that (1)Single (OR = 2.061,95% CI = 1.207-3.659), Education level is primary school or below (OR = 4.997,95%CI = 1.732-14.416), participants is student (OR = 0.362,95%=0.138-0.948), participants with pregnancy needs (OR = 1.869,95%=1.009-3.463) were significantly more likely to be in the high healthy behavior risk subtypes; (2)The older the age (OR = 0.953,95%=0.867-1.047) and the larger the WC (OR = 0.954,95%=0.916-0.993), participants is married (OR = 1.126,95%=0.725-1.961), participants is employed ( OR = 1.418,95%=0.667-3.012) were significantly more likely to be in the high health responsibility and physical activity risk subtypes.

CONCLUSION

Patients with PCOS are a heterogeneous population with potential subtypes that may be suitable for customized multi-level care and targeted interventions.

Intraindividual phenotyping of depression in high-risk youth: An application of a multilevel hidden Markov model

BACKGROUND

Traditionally, depression phenotypes have been defined based on interindividual differences that distinguish between subgroups of individuals expressing distinct depressive symptoms often from cross-sectional data. Alternatively, depression phenotypes can be defined based on intraindividual differences, differentiating between transitory states of distinct symptoms profiles that a person transitions into or out of over time. Such within-person phenotypic states are less examined, despite their potential significance for understanding and treating depression.

METHODS

The current study used intensive longitudinal data of youths (N = 120) at risk for depression. Clinical interviews (at baseline, 4, 10, 16, and 22 months) yielded 90 weekly assessments. We applied a multilevel hidden Markov model to identify intraindividual phenotypes of weekly depressive symptoms for at-risk youth.

RESULTS

Three intraindividual phenotypes emerged: a low-depression state, an elevated-depression state, and a cognitive-physical-symptom state. Youth had a high probability of remaining in the same state over time. Furthermore, probabilities of transitioning from one state to another did not differ by age or ethnoracial minority status; girls were more likely than boys to transition from a low-depression state to either the elevated-depression state or the cognitive-physical symptom state. Finally, these intraindividual phenotypes and their dynamics were associated with comorbid externalizing symptoms.

CONCLUSION

Identifying these states as well as the transitions between them characterizes how symptoms of depression change over time and provide potential directions for intervention efforts.

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.

Associations between specific depressive symptoms and psychosocial functioning in psychotherapy

BACKGROUND

Psychotherapy for depression aims to reduce symptoms and to improve psychosocial functioning. We examined whether some symptoms are more important than others in the association between depression and functioning over the course of psychotherapy treatment.

METHODS

We studied associations between specific symptoms of depression (PHQ-9) and change in social and occupational functioning (SOFAS), both with structural equation models (considering liabilities of depression and each specific symptom) and with logistic regression models (considering the risk for individual patients). The study sample consisted of adult patients (n = 771) from the Finnish Psychotherapy Quality Registry (FPQR) who completed psychotherapy treatment between September 2018 and September 2021.

RESULTS

Based on our results of logistic regression analyses and SEM models, the baseline measures of depression symptoms were not associated with changes in functioning. Changes in depressed mood or hopelessness, problems with sleep, feeling tired, and feeling little interest or pleasure were associated with improved functioning during psychotherapy. The strongest evidence for symptom-specific effects was found for the symptom of depressed mood or hopelessness.

LIMITATIONS

Due to our naturalistic study design containing only two measurement points, we were unable to study the causal relationship between symptoms and functioning.

CONCLUSIONS

Changes in certain symptoms during psychotherapy may affect functioning independently of underlying depression. Knowledge about the dynamics between symptoms and functioning could be used in treatment planning or implementation. Depressed mood or hopelessness appears to have a role in the dynamic relationship between depression and functioning.

Deep phenotyping towards precision psychiatry of first-episode depression - the Brain Drugs-Depression cohort

BACKGROUND

Major Depressive Disorder (MDD) is a heterogenous brain disorder, with potentially multiple psychosocial and biological disease mechanisms. This is also a plausible explanation for why patients do not respond equally well to treatment with first- or second-line antidepressants, i.e., one-third to one-half of patients do not remit in response to first- or second-line treatment. To map MDD heterogeneity and markers of treatment response to enable a precision medicine approach, we will acquire several possible predictive markers across several domains, e.g., psychosocial, biochemical, and neuroimaging.

METHODS

All patients are examined before receiving a standardised treatment package for adults aged 18-65 with first-episode depression in six public outpatient clinics in the Capital Region of Denmark. From this population, we will recruit a cohort of 800 patients for whom we will acquire clinical, cognitive, psychometric, and biological data. A subgroup (subcohort I, n = 600) will additionally provide neuroimaging data, i.e., Magnetic Resonance Imaging, and Electroencephalogram, and a subgroup of patients from subcohort I unmedicated at inclusion (subcohort II, n = 60) will also undergo a brain Positron Emission Tomography with the [¹¹C]-UCB-J tracer binding to the presynaptic glycoprotein-SV2A. Subcohort allocation is based on eligibility and willingness to participate. The treatment package typically lasts six months. Depression severity is assessed with the Quick Inventory of Depressive Symptomatology (QIDS) at baseline, and 6, 12 and 18 months after treatment initiation. The primary outcome is remission (QIDS ≤ 5) and clinical improvement (≥ 50% reduction in QIDS) after 6 months. Secondary endpoints include remission at 12 and 18 months and %-change in QIDS, 10-item Symptom Checklist, 5-item WHO Well-Being Index, and modified Disability Scale from baseline through follow-up. We also assess psychotherapy and medication side-effects. We will use machine learning to determine a combination of characteristics that best predict treatment outcomes and statistical models to investigate the association between individual measures and clinical outcomes. We will assess associations between patient characteristics, treatment choices, and clinical outcomes using path analysis, enabling us to estimate the effect of treatment choices and timing on the clinical outcome.

DISCUSSION

The BrainDrugs-Depression study is a real-world deep-phenotyping clinical cohort study of first-episode MDD patients.

TRIAL REGISTRATION

Registered at clinicaltrials.gov November 15th, 2022 (NCT05616559).

Bottom-up transdiagnostic personality subtypes are associated with state psychopathology: A latent profile analysis

Introduction

Personality-based profiling helps elucidate associations between psychopathology symptoms and address shortcomings of current nosologies. The objective of this study was to bracket the assumption of a priori diagnostic class borders and apply the profiling approach to a transdiagnostic sample. Profiles resembling high-functioning, undercontrolled, and overcontrolled phenotypes were expected to emerge.

Methods

We used latent profile analysis on data from a sample of women with mental disorders (n = 313) and healthy controls (n = 114). 3-5 profile solutions were compared based on impulsivity, perfectionism, anxiety, stress susceptibility, mistrust, detachment, irritability, and embitterment. The best-fitting solution was then related to measures of depression, state anxiety, disordered eating, and emotion regulation difficulties to establish clinical significance.

Results

A 5-profile solution proved best-fitting. Extracted profiles included a high-functioning, a well-adapted, an impulsive and interpersonally dysregulated, an anxious and perfectionistic, and an emotionally and behaviorally dysregulated class. Significant differences were found in all outcome state measures, with the emotionally and behaviorally dysregulated class exhibiting the most severe psychopathology.

Discussion

These results serve as preliminary evidence of the predictive nature and clinical utility of personality-based profiles. Selected personality traits should be considered in case formulation and treatment planning. Further research is warranted to replicate the profiles and assess classification stability and profiles' association with treatment outcome longitudinally.

Development and Validation of a New Gadget Addiction Scale (Screen Dependency Scale) among Pre-School Children in Malaysia

BACKGROUND

Excessive screen time in young children is associated with many harmful consequences including screen dependency. Research has shown a worrying prevalence of media-related dependency among adolescents and pre-school children. There are a few available questionnaires among adolescents but none for pre-school children. This study aimed to design and validate a questionnaire to assess screen dependency among pre-school children aged 4 to 6 years old.

METHODOLOGY

A cross-sectional two-phase study was carried out to develop the scale. In phase 1, a preliminary parent-report measure questionnaire was developed in Bahasa Malaysia. Later, it was sent to four experts for content validity followed by face validity. In Phase 2, a total of 386 parents of pre-school children aged 4 to 6 years old, split into two samples, were involved in the field study for exploratory factor analysis (EFA) and confirmatory factor analysis (CFA).

RESULT

Sample 1 was used to perform EFA to determine the factorial structure of the SDS. All items with a factor loading of >0.4 were included. Sample 2 was used to perform the CFA. RMSEA and CFI analysis showed that the SDS has a good fit and confirms the dimensional structure found via EFA. The final questionnaire consists of 15 items with a 4 factors' structure and has excellent internal consistency reliability.

CONCLUSIONS

The Screen Dependency Scale (SDS) is a reliable and valid questionnaire to detect screen dependency among pre-school children aged 4 to 6 years old in Malaysia.

Latent class analysis of behavior across dog breeds reveal underlying temperament profiles

Latent class analysis (LCA) is a type of modeling analysis approach that has been used to identify unobserved groups or subgroups within multivariate categorical data. LCA has been used for a wide array of psychological evaluations in humans, including the identification of depression subtypes or PTSD comorbidity patterns. However, it has never been used for the assessment of animal behavior. Our objective here is to identify behavioral profile-types of dogs using LCA. The LCA was performed on a C-BARQ behavioral questionnaire dataset from 57,454 participants representing over 350 pure breeds and mixed breed dogs. Two, three, and four class LCA models were developed using C-BARQ trait scores and environmental covariates. In our study, LCA is shown as an effective and flexible tool to classify behavioral assessments. By evaluating the traits that carry the strongest relevance, it was possible to define the basis of these grouping differences. Groupings can be ranked and used as levels for simplified comparisons of complex constructs, such as temperament, that could be further exploited in downstream applications such as genomic association analyses. We propose this approach will facilitate dissection of physiological and environmental factors associated with psychopathology in dogs, humans, and mammals in general.

Clinical and biological subtypes of late-life depression

BACKGROUND

Late-life depression (LDD) results from multiple psychosocial and neurobiological changes occurring in later life. The current study investigated how patterns of clinical symptoms and brain structural features are classified into LDD subtypes.

METHOD

Self-report scale of depression, behavioral rating of affective symptoms, and brain structural imaging of white matter change and cortical thickness were assessed in 541 older adults with no cognitive impairment or mild cognitive impairment. Latent profile analysis was used to identify distinct subtypes of depression.

RESULTS

The latent profile analysis identified four classes with mild to severe depressive symptoms and two classes with minimal symptoms. While the classes primarily differed in the overall severity, the combinatory patterns of clinical symptoms and neuropathological signature distinguished the classes with similar severity. The classes were distinguished in terms of whether or not neurodegenerative risk accompanied the corresponding depressive symptoms. The presence of the negative self-scheme and cortical thinning pattern notably characterized the subtypes of LDD.

LIMITATIONS

The underlying etiologies of the biological subtypes are still speculative, and the current study lacks clinical history that differentiates late- and early-onset depression.

CONCLUSIONS

Our finding provides insight in identifying heterogeneities of depressive disorder in later life and suggests that self-report and behavioral symptom profile in combination with white matter lesion and cortical thickness effectively characterizes distinct subtypes of LDD.

Network structure of time-varying depressive symptoms through dynamic time warp analysis in late-life depression

OBJECTIVES

Late-life major depressive disorder (MDD) can be conceptualized as a complex dynamic system. However, it is not straightforward how to analyze the covarying depressive symptoms over time in case of sparse panel data. Dynamic time warping (DTW) analysis may yield symptom networks and dimensions both at the patient and group level.

METHODS

In the Netherlands Study of Depression in Older People (NESDO) depressive symptoms were assessed every 6 months using the 30-item Inventory of Depressive Symptomatology (IDS) with up to 13 assessments per participant. Our sample consisted of 182 persons, aged ≥ 60 years, with an IDS total score of 26 or higher at baseline. Symptom networks dimensions, and centrality metrics were analyzed using DTW and Distatis analyses.

RESULTS

The mean age was 69.8 years (SD 7.1), with 69.0% females, and a mean IDS score of 38.0 (SD = 8.7). DTW enabled visualization of an idiographic symptom network in a single NESDO participant. In the group-level nomothetic approach, four depressive symptom dimensions were identified: "core symptoms", "lethargy/somatic", "sleep", and "appetite/atypical". Items of the "internalizing symptoms" dimension had the highest centrality, whose symptom changes over time were most similar to those changes of other symptoms.

CONCLUSIONS

DTW revealed symptom networks and dimensions based on the within-person symptom changes in older MDD patients. Its centrality metrics signal the most influential symptoms, which may aid personalized care.

Individual differences in symptoms of maternal depression and associations with parenting behavior

Links between global levels of maternal depressive symptoms and parenting behavior in early childhood are well established. However, depression is a heterogeneous disorder and little is known about how individual differences in depression symptoms may be differentially associated with different types of parenting behavior. We aimed to uncover nuance in the relationship between depression and parenting behavior by examining individual differences in symptoms of maternal depression and associations with parenting behavior with 2- and 3-year-old children. Participants included 714 diverse, low-income mothers and their 2-year-old children. Maternal depression symptoms were self-reported at child age 2. Three domains of parenting behavior (harsh, positive, and disengaged) were coded from mother-child interactions at ages 2 and 3. Individual differences in maternal depressive symptoms at child age 2 comprised five profiles: low, interpersonal rejection, moderate, high depressed affect and physical, and severe. Women with the high depressed affect and physical profile demonstrated the greatest risk for parenting challenges with higher levels of harsh parenting at child age 2 compared to all other profiles and higher levels of disengaged parenting at child age 3 compared to the low, moderate, and severe profiles. Unexpectedly, positive parenting did not differ by maternal depression profile at either age. There is wide heterogeneity in symptoms of depression among mothers of 2-year-old children that is clinically relevant for different dimensions of parenting. Physical and depressed affect symptoms in particular may present risk for harsh parenting. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Heterogeneity of quality of life in young people attending primary mental health services

AIMS

The utility of quality of life (QoL) as an outcome measure in youth-specific primary mental health care settings has yet to be determined. We aimed to determine: (i) whether heterogeneity on individual items of a QoL measure could be used to identify distinct groups of help-seeking young people; and (ii) the validity of these groups based on having clinically meaningful differences in demographic and clinical characteristics.

METHODS

Young people, at their first presentation to one of five primary mental health services, completed a range of questionnaires, including the Assessment of Quality of Life-6 dimensions adolescent version (AQoL-6D). Latent class analysis (LCA) and multivariate multinomial logistic regression were used to define classes based on AQoL-6D and determine demographic and clinical characteristics associated with class membership.

RESULTS

1107 young people (12-25 years) participated. Four groups were identified: (i) no-to-mild impairment in QoL; (ii) moderate impairment across dimensions but especially mental health and coping; (iii) moderate impairment across dimensions but especially on the pain dimension; and (iv) poor QoL across all dimensions along with a greater likelihood of complex and severe clinical presentations. Differences between groups were observed with respect to demographic and clinical features.

CONCLUSIONS

Adding multi-attribute utility instruments such as the AQoL-6D to routine data collection in mental health services might generate insights into the care needs of young people beyond reducing psychological distress and promoting symptom recovery. In young people with impairments across all QoL dimensions, the need for a holistic and personalised approach to treatment and recovery is heightened.

Unsupervised Machine Learning to Identify Depressive Subtypes

OBJECTIVES

This study evaluated an unsupervised machine learning method, latent Dirichlet allocation (LDA), as a method for identifying subtypes of depression within symptom data.

METHODS

Data from 18,314 depressed patients were used to create LDA models. The outcomes included future emergency presentations, crisis events, and behavioral problems. One model was chosen for further analysis based upon its potential as a clinically meaningful construct. The associations between patient groups created with the final LDA model and outcomes were tested. These steps were repeated with a commonly-used latent variable model to provide additional context to the LDA results.

RESULTS

Five subtypes were identified using the final LDA model. Prior to the outcome analysis, the subtypes were labeled based upon the symptom distributions they produced: psychotic, severe, mild, agitated, and anergic-apathetic. The patient groups largely aligned with the outcome data. For example, the psychotic and severe subgroups were more likely to have emergency presentations (odds ratio [OR] = 1.29; 95% confidence interval [CI], 1.17-1.43 and OR = 1.16; 95% CI, 1.05-1.29, respectively), whereas these outcomes were less likely in the mild subgroup (OR = 0.86; 95% CI, 0.78-0.94). We found that the LDA subtypes were characterized by clusters of unique symptoms. This contrasted with the latent variable model subtypes, which were largely stratified by severity.

CONCLUSIONS

This study suggests that LDA can surface clinically meaningful, qualitative subtypes. Future work could be incorporated into studies concerning the biological bases of depression, thereby contributing to the development of new psychiatric therapeutics.

Delirium diagnosis without a gold standard: Evaluating diagnostic accuracy of combined delirium assessment tools

Background

Fluctuating course of delirium and complexities of ICU care mean delirium symptoms are hard to identify or commonly confused with other disorders. Delirium is difficult to diagnose, and clinicians and researchers may combine assessments from multiple tools. We evaluated diagnostic accuracy of different combinations of delirium assessments performed in each enrolled patient.

Methods

Data were obtained from a previously conducted cross-sectional study. Eligible adult patients who remained admitted to ICU for >24 hours with at least one family member present were consecutively enrolled as patient-family dyads. Clinical delirium assessments (Intensive Care Delirium Screening Checklist [ICSDC] and Confusion Assessment Method-ICU [CAM-ICU]) were completed twice daily by bedside nurse or trained research assistant, respectively. Family delirium assessments (Family Confusion Assessment Method and Sour Seven) were completed once daily by family members. We pooled all delirium assessment tools in a single two-class latent model and pairwise (i.e., combined, clinical or family assessments) Bayesian analyses.

Results

Seventy-three patient-family dyads were included. Among clinical delirium assessments, the ICDSC had lower sensitivity (0.72; 95% Bayesian Credible [BC] interval 0.54–0.92) and higher specificity (0.90; 95%BC, 0.82–0.97) using Bayesian analyses compared to pooled latent class analysis and CAM-ICU had higher sensitivity (0.90; 95%BC, 0.70–1.00) and higher specificity (0.94; 95%BC, 0.80–1.00). Among family delirium assessments, the Family Confusion Assessment Method had higher sensitivity (0.83; 95%BC, 0.71–0.92) and higher specificity (0.93; 95%BC, 0.84–0.98) using Bayesian analyses compared to pooled latent class analysis and the Sour Seven had higher specificity (0.85; 95%BC, 0.67–0.99) but lower sensitivity (0.64; 95%BC 0.47–0.82).

Conclusions

Results from delirium assessment tools are often combined owing to imperfect reference standards for delirium measurement. Pairwise Bayesian analyses that explicitly accounted for each tool’s (performed within same patient) prior sensitivity and specificity indicate that two combined clinical or two combined family delirium assessment tools have fair diagnostic accuracy.

Depressive symptom profiles predict dementia onset and brain pathology in older persons. The AGES-Reykjavik study

Late-life depression (LLD) increases risk for dementia and brain pathology, but possibly this is only true for one or more symptom profiles of LLD. In 4354 participants (76 ± 5 years; 58% female) from the Age, Gene/Environment Susceptibility (AGES)-Reykjavik Study, we identified five LLD symptom profiles, based on the Geriatric Depression Scale-15 (no LLD (57%); apathy (31%); apathy with emptiness (2%), mild LLD (8%) and severe LLD (2%)). Cox regression analyses showed that severe LLD, mild LLD and apathy increased risk of dementia up to 12 years, compared to no LLD. Additionally, hippocampal volume loss and white matter lesion increase, were assessed on 1.5 T MR images, at baseline and after 5 years follow-up. Only severe LLD showed increased WML volume over time, but not on hippocampal volume loss. WML increase over time mediated partially the relation between mild LLD and dementia but not for the other symptom profiles. It appears that hippocampal atrophy and LLD are independent predictors for dementia incidence, whereas for mild LLD the risk for dementia is partially mediated by WML changes.

Major Depression and Its Recurrences: Life Course Matters

Major depression is one of the most prevalent and debilitating personal and public health conditions worldwide. Less appreciated is that depression's tremendous burdens are not shared equally among all who become depressed. Some will suffer recurrences over the rest of their lives, whereas half or more will never have a recurrence. Based on these two distinctive life course prototypes, we propose a subtype distinction for research on the origins and lifetime course of major depression. A pressing goal is to determine at the time of depression's first onset who will follow which clinical trajectory. The lack of recognition of this distinction has resulted in many obstacles, including conceptual biases, methodological oversights, and definitional dead ends. Current theories are reviewed and compared. The implications for contemporary diagnostic controversies, reevaluating research on treatment and prevention, and enhancing the predictive strength of traditionally weak indicators of recurrences and recurrent depression are discussed. Expected final online publication date for the Annual Review of Clinical Psychology, Volume 18 is May 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Depression symptom clusters in adolescents: A latent class analysis in a clinical sample

BACKGROUND

Major depression is clinically heterogeneous. We aimed to identify classes of depressed adolescents with different symptom presentations and examine if these were differentially associated with illness severity, functioning, engagement with treatment, and clinical outcomes.

METHOD

Baseline depression symptoms of 454 depressed adolescents (age 11-17) from the IMPACT trial were subjected to latent class analysis. We compared classes on self-reported symptoms and social impairment at baseline and follow-up and their engagement in treatment.

RESULTS

We identified three classes of participants which differed in the number and pattern of depression symptoms; Class 1-Severe- (37.2%)-endorsed almost all symptoms and were most functionally impaired; Class 2-Moderate- (41.9%)-endorsed fewer symptoms with high suicidal ideation, self-harm, and worthlessness; Class 3-Somatic (20.9%)-endorsed fewest symptoms, with high somatic symptoms. Groups did not differ on engagement, therapeutic alliance, or post-treatment symptom reduction. Adolescents in the severe and moderate subgroups reported symptom reductions after treatment ended, whilst those in the somatic subgroup did not.

CONCLUSIONS

At presentation, high somatic features in depressed adolescents, rather than severity, or impairment levels, may indicate lower liability for responding to psychological treatment.

Predicting transitions between longitudinal classes of post-traumatic stress disorder, adjustment disorder and well-being during the COVID-19 pandemic: protocol of a latent transition model in a general Dutch sample

BACKGROUND

A growing body of literature shows profound effects of the COVID-19 pandemic on mental health, among which increased rates of post-traumatic stress disorder (PTSD) and adjustment disorder (AD). However, current research efforts have largely been unilateral, focusing on psychopathology and not including well-being, and are dominated by examining average psychopathology levels or on disorder absence/presence, thereby ignoring individual differences in mental health. Knowledge on individual differences, as depicted by latent subgroups, in the full spectrum of mental health may provide valuable insights in how individuals transition between health states and factors that predict transitioning from resilient to symptomatic classes. Our aim is to (1) identify longitudinal classes (ie, subgroups of individuals) based on indicators of PTSD, AD and well-being in response to the pandemic and (2) examine predictors of transitioning between these subgroups.

METHODS AND ANALYSIS

We will conduct a three-wave longitudinal online survey study of n≥2000 adults from the general Dutch population. The first measurement occasion takes place 6 months after the start of the pandemic, followed by two follow-up measurements with 6 months of intervals. Latent transition analysis will be used for data analysis.

ETHICS AND DISSEMINATION

Ethical approval has been obtained from four Dutch universities. Longitudinal study designs are vital to monitor mental health (and predictors thereof) in the pandemic to develop preventive and curative mental health interventions. This study is carried out by researchers who are board members of the Dutch Society for Traumatic Stress Studies and is part of a pan-European study (initiated by the European Society for Traumatic Stress Studies) examining the impact of the pandemic in 11 countries. Results will be published in peer-reviewed journals and disseminated at conferences, via newsletters, and media appearance among (psychotrauma) professionals and the general public.

Heterogenous Subtypes of Late-Life Depression and Their Cognitive Patterns: A Latent Class Analysis

BACKGROUND

Late-life depression (LLD), characterized by cognitive deficits, is considered heterogeneous across individuals. Previous studies have identified subtypes with diverse symptom profiles, but their cognitive patterns are unknown. This study aimed to investigate the subtypes of LLD and the cognitive profile of each group.

METHODS

In total, 109 depressed older adults were enrolled. We performed latent class analysis using Geriatric Depression Scale items as indicators to generate latent classes. We compared the sociodemographic and clinical characteristics with cognitive functions between groups and conducted regression analysis to investigate the association between class membership and variables with significant differences.

RESULTS

Two classes were identified: the "pessimistic" group was characterized by pessimistic thoughts and the "worried" group with a relatively high prevalence of worry symptoms. The two groups did not differ in sociodemographic characteristics. The "pessimistic" group showed a higher rate of past history of depression and lower age of onset. The "worried" group had more physical comorbidities and a higher rate of past history of anxiety. The "pessimistic" group was more impaired in general cognitive function, executive function, information processing speed, and attention. Lower general and executive functions were associated with the membership in the "pessimistic" group.

CONCLUSIONS

Subjects with pessimistic symptoms and subjects with a propensity to worry may form two distinct subtypes of late-life depression with different cognitive profiles. Further, the cognitive evaluation of subjects with pessimistic symptoms is of utmost importance.

Perfectionism and Emotional Intelligence: A Person-Centered Approach

This study examined the relationship between perfectionistic concerns (PC) and perfectionistic strivings (PS) with the subcomponents of emotional intelligence (EI) through a latent class person-centered approach. A sample of 1582 Ecuadorian adolescents (619 females) aged from 12 to 18 was employed. The trait meta-mood scale-24 (TMMS-24) and the child and adolescent perfectionism scale (CAPS) were used, respectively, for assessing three subcomponents of EI (i.e., emotional attention, emotional clarity, and mood repair) and two perfectionist dimensions (PC and PS). A three-class solution (High perfectionism, moderate perfectionism, and nonperfectionism) was identified by using latent class analysis. High perfectionism significantly scored higher on emotional attention in comparison with the moderate and nonperfectionism classes, with small and moderate effect sizes. Overall, results suggest that people with high perfectionism might be at greater risk of developing maladaptive emotional self-regulation strategies, such as rumination, because of their tendency to excessively attend their negative mood states.

Refining our understanding of depressive states and state transitions in response to cognitive behavioural therapy using latent Markov modelling

BACKGROUND

It is increasingly recognized that existing diagnostic approaches do not capture the underlying heterogeneity and complexity of psychiatric disorders such as depression. This study uses a data-driven approach to define fluid depressive states and explore how patients transition between these states in response to cognitive behavioural therapy (CBT).

METHODS

Item-level Patient Health Questionnaire (PHQ-9) data were collected from 9891 patients with a diagnosis of depression, at each CBT treatment session. Latent Markov modelling was used on these data to define depressive states and explore transition probabilities between states. Clinical outcomes and patient demographics were compared between patients starting at different depressive states.

RESULTS

A model with seven depressive states emerged as the best compromise between optimal fit and interpretability. States loading preferentially on cognitive/affective v. somatic symptoms of depression were identified. Analysis of transition probabilities revealed that patients in cognitive/affective states do not typically transition towards somatic states and vice-versa. Post-hoc analyses also showed that patients who start in a somatic depressive state are less likely to engage with or improve with therapy. These patients are also more likely to be female, suffer from a comorbid long-term physical condition and be taking psychotropic medication.

CONCLUSIONS

This study presents a novel approach for depression sub-typing, defining fluid depressive states and exploring transitions between states in response to CBT. Understanding how different symptom profiles respond to therapy will inform the development and delivery of stratified treatment protocols, improving clinical outcomes and cost-effectiveness of psychological therapies for patients with depression.

Patterns of Changes in Oncological Care due to COVID-19: Results of a Survey of Oncological Nurses and Physicians from the Region of Hanover, Germany

BACKGROUND

Healthcare staff is confronted with intensive decisional conflicts during the pandemic. Due to the specific burden of this moral distress in oncology, the investigation aimed at quantification of these conflicts and identification of risk factors that determine the extent and severity of these conflicts. We examined the heterogeneity of changes in oncology care due to COVID-19.

METHODS

We conducted a survey of oncological physicians and nurses in the region of Hanover, Germany in the second half of 2020. Overall, N = 200 respondents, 54% nurses, were included in the sample. Indicators of changes in oncology care were used to determine profiles of changes. To characterize these profiles, a diverse set of variables, including decision conflicts, uncertainty, age, gender, work experience, changes in communication with patients, psychological distress, work stress, process organization, and personnel resources, was obtained. Latent class analysis was conducted to determine these latent profiles.

RESULTS

We found that three distinct profiles best described the overall changes in oncology care due to COVID-19 in our sample, with each profile being associated with specific characteristics: (1) "Few Changes in Oncology Care" profile with 33% of participants belonging to this profile, (2) "Medium Changes in Oncology Care" profile with 43% of participants, and (3) "Severe Changes in Oncology Care" profile (24%). Participants from these profiles significantly differed regarding their age, work experience, occupational group, the prevalence of decision conflicts, decision uncertainty, quality of communication with patients, and quality of process organization.

CONCLUSIONS

Distinct profiles of change in oncology care due to COVID-19 can be identified. Most participants reported small to medium changes, while some participants also reported severe changes. Profiles also differed regarding their associated characteristics. As such, specific consequences for better pandemic preparedness can be derived based on the current study. Future studies should investigate the patterns of changes in routine care due to COVID-19.

Latent profile analysis of cognitive decline and depressive symptoms after intracerebral hemorrhage

BACKGROUND

Cognitive impairment and depressive symptoms are highly prevalent after Intracerebral Hemorrhage (ICH). We leveraged Latent Profile Analysis (LPA) to identify profiles for cognitive decline and depression onset after ICH. We also investigated differences in clinical, genetic and neuroimaging characteristics across patients' profiles.

METHODS

We analyzed data from the ICH study conducted at Massachusetts General Hospital between January 1998 and December 2019. We collected information from electronical health records, follow-up interviews, CT and MRI imaging, and APOE genotype. We conducted LPA and multinomial logistic regression analyses to: 1) identify distinct profiles for cognitive decline and depression onset after ICH; 2) identify clinical, neuroimaging and genetic factors predicting individuals' likelihood to express a specific profile.

RESULTS

We followed 784 ICH survivors for a median of 45.8 months. We identified four distinct profiles in cognitive and depressive symptoms after ICH: low depression and dementia risk, early-onset depression and dementia, late-onset depression and dementia, high depression with low dementia risk. Cerebral small vessel disease severity and APOE genotype were specifically associated with the late-onset profile (both p < 0.05). Acute hematoma characteristics (size, intraventricular extension) and functional disability were specifically associated with the early-onset profile (all p < 0.05).

CONCLUSION

We identified four distinct profiles for cognitive and depressive symptoms after ICH, each displaying specific associations with individual patients' clinical, genetic and neuroimaging data. These associations reflect separate biological mechanisms influencing dementia and depression risk after ICH. Our findings support employing LPA in future ICH studies, and is likely applicable to stroke survivors at large.

Morbidity profiles in Europe and Israel: international comparisons from 20 countries using biopsychosocial indicators of health via latent class analysis

Aim

I examined health/morbidity profiles across 20 countries, determined their associated demographic characteristics and risk factors and compared the distribution of these health/morbidity profiles across countries.

Subject and methods

I used population-based data drawn from the European Social Survey (N = 20092, 52% female, ages 40+) covering 20 mostly European countries (Austria, Belgium, Czechia, Denmark, Finland, France, Germany, Great Britain, Hungary, Ireland, Israel, Lithuania, Netherlands, Norway, Poland, Portugal, Slovenia, Spain, Sweden and Switzerland) from 2014. Diverse indicators of health/morbidity were used, including self-rated health, self-rated disability, self-reported health problems and mental health symptoms using the CES-D. Latent class analysis was conducted to determine health/morbidity profiles across countries.

Results

I found that four distinct health profiles best describe overall health/morbidity status in the international sample, each associated with specific demographic and behavioural risk factors: ‘healthy’ profile (62% of participants), ‘unhappy but healthy’ profile (14%), ‘high morbidity, mostly physical’ profile (16%) and ‘high morbidity, mostly psychological’ profile (8%). With few exceptions, participants from Northern Europe and Western Europe were more likely to belong to the ‘healthy’ and the ‘unhappy but healthy’ profiles, whereas participants from Eastern Europe were more likely to belong to the ‘high morbidity, mostly physical’ profile. Distribution of the ‘high morbidity, mostly psychological’ profile appeared to be more uniform across regions.

Conclusions

Distinct morbidity/health profiles could be identified across countries, and countries varied regarding the relative distribution of these profiles. Specific prevention and treatment consequences associated with each profile are discussed. Future studies should further investigate the patterns of overall health and morbidity in Europe’s populations.

Identifying subtypes of depression in clinician-annotated text: a retrospective cohort study

Current criteria for depression are imprecise and do not accurately characterize its distinct clinical presentations. As a result, its diagnosis lacks clinical utility in both treatment and research settings. Data-driven efforts to refine criteria have typically focused on a limited set of symptoms that do not reflect the disorder's heterogeneity. By contrast, clinicians often write about patients in depth, creating descriptions that may better characterize depression. However, clinical text is not commonly used to this end. Here we show that clinically relevant depressive subtypes can be derived from unstructured electronic health records. Five subtypes were identified amongst 18,314 patients with depression treated at a large mental healthcare provider by using unsupervised machine learning: severe-typical, psychotic, mild-typical, agitated, and anergic-apathetic. Subtypes were used to place patients in groups for validation; groups were found to be associated with future outcomes and characteristics that were consistent with the subtypes. These associations suggest that these categorizations are actionable due to their validity with respect to disease prognosis. Moreover, they were derived with automated techniques that might theoretically be widely implemented, allowing for future analyses in more varied populations and settings. Additional research, especially with respect to treatment response, may prove useful in further evaluation.

[Factors associated with the intensity of anxiety and depression symptoms in health workers of two centres of reference for COVID 19 patient care in Antioquia, Colombia - a latent class analysis]

Objective: To classify the staff of two reference institutions for COVID-19 care in Antioquia according to the intensity of anxiety and depression symptoms, and to determine the factors associated with these classes.Methods:Cross-sectional study in which the GAD-7, PHQ-9, fear of COVID-19, and the Copenhagen Burnout scale were used. Latent class analysis was performed to identify the classes, and the factors associated with these were determined using multinomial logistic regression.Results: 486 people participated. The three-class model had the best fit: class I with low scores on the scales; class II with mild degrees of anxiety and depression, and intermediate levels of fear of COVID-19 and perceived stress; and class III with moderate and severe degrees of anxiety, depression, and perceived stress. The factors associated with belonging to class III were age (OR=0.94; 95%CI, 0.91-0.96), change of residence to avoid exposing relatives (OR=4.01; 95%CI, 1.99-8.09), and a history of depressive disorder (OR=3.10; 95%CI, 1.27-7.56), and anxiety (OR=5.5; 95%CI, 2.36-12.90). Factors associated with class II were age (OR=0.97; 95%CI, 0.95-0.99), history of depressive disorder (OR=3.41; 95%CI, 1.60-7.25), living with someone at risk of death from COVID-19 (OR=1.86; 95%CI, 1.19-2.91), family member being healthcare staff (OR=1.58; 95%CI, 1.01-2.47), and change of residence to avoid exposing relatives (OR=1.99; 95%CI, 1.11-3.59).Conclusions: Three classes of participants were obtained, two of them with anxiety and depression symptoms. Younger age and a history of mental disorder were factors associated with the two classes of symptomatic patients; other factors may be causes or consequences of the symptoms.

Depression subtypes and their response to cognitive behavioral therapy: A latent transition analysis

BACKGROUND

Depression is a heterogeneous condition, with multiple possible symptom-profiles leading to the same diagnosis. Descriptive depression subtypes based on observation and theory have so far proven to have limited clinical utility.

AIM

To identify depression subtypes and to examine their time-course and prognosis using data-driven methods.

METHODS

Latent transition analysis was applied to a large (N = 8380) multi-service sample of depressed patients treated with cognitive behavioral therapy (CBT) in outpatient clinics. Patients were classed into initial latent states based on their responses to the Patient Health Questionnaire-9 of depression symptoms, and transition probabilities to other states during treatment were quantified. Qualitatively similar states were clustered into overarching depression subtypes and we statistically compared indices of treatment engagement and outcomes between subtypes using post hoc analyses.

RESULTS

Fourteen latent states were clustered into five depression subtypes: mild (2.7%), severe (9.8%), cognitive-affective (23.7%), somatic (21.4%), and typical (42.4%). These subtypes had high temporal stability, and the most common transitions during treatment were from severe toward milder states within the same subtype. Differential response to treatment was evident, with the highest improvement rate (63.6%) observed in the cognitive-affective subtype.

CONCLUSION

Replicated evidence indicates that depression subtypes are temporally stable and associated with differential response to CBT.

Subpopulations of Addictive Behaviors in Different Sample Types and Their Relationships with Gender, Personality, and Well-Being: Latent Profile vs. Latent Class Analysis

BACKGROUND

Relatively strong theoretical assumptions and previous studies concerning co-occurring addictive behaviors suggest a subpopulation representing general proclivity to behavioral addictions (BAs), and there are gender-specific subpopulations. This study aimed to compare latent profile analysis (LPA) and latent class analysis (LCA) as the methods of investigating different clusters of BAs in the general student population and among students positively screened for at least one BA. Participants and procedure: Analyses of six BAs (study, shopping, gaming, Facebook, pornography, and food) and their potential antecedents (personality) and consequences (well-being) were conducted on a full sample of Polish undergraduate students (N = 1182) and a subsample (n = 327) of students including individuals fulfilling cutoff for at least one BA.

RESULTS

LPA on the subsample mostly replicated the previous four profiles found in the full sample. However, LCA on a full sample did not replicate previous findings using LPA and showed only two classes: those with relatively high probabilities on all BAs and low probabilities. LCA on the subsample conflated profiles identified with LPA and classes found with LCA in the full sample.

CONCLUSIONS

LCA on dichotomized scores (screened positively vs. negatively) were less effective in identifying clear patterns of interrelationships between BAs based on relatively strong theoretical assumptions and found in previous research. BAs can be investigated on the whole spectrum of behavior, and person-centered analyses might be more useful when they are based on continuous scores. This paper provides more detailed analyses of the four basic clusters of BAs, prevalence, and co-occurrence of particular BAs within and between them, their gender and personality risk factors, relationships to well-being, and their interrelationships as emerging from the results of this and previous studies.

Depression symptom profiles and long-term response to cognitive behavioral therapy plus contingency management for smoking cessation

BACKGROUND

Depression is heterogeneous in nature and using diagnostic categories limits insight into understanding psychopathology and its impact on treatment efficacy. This secondary analysis sought to: 1) identify distinct subpopulations of cigarette users with depression, and 2) examine their response to cognitive-behavioral treatment (CBT) + contingency management (CM) for smoking cessation at one year.

METHOD

The sample comprised 238 (74 % females) adults who smoke receiving CBT only or CBT + CM. A latent class analysis was conducted on baseline depressive symptoms measured using the Beck Depression Inventory-II. Generalized estimating equations assessed the main and interactive effects of class, time, treatment, and sex on smoking abstinence.

RESULTS

Three distinct classes were identified: C1 (n= 76/238), characterized by mild depression, loss of energy, pessimism, and criticism, C2 (n= 100/238) presenting moderate severity and decreased appetite, and C3 (n= 62/238) showing severe depression, increased appetite, and feelings of punishment. There was a significant cluster × treatment interaction, which indicated additive effects of CM over CBT alone for Class 1 and 2. Persons in Class 1 and 2 were 3.60 [95 % CI: 1.62, 7.97] and 2.65 [95 % CI: 1.19, 5.91] times more likely to be abstinent if CBT + CM was delivered rather than CBT only. No differential sex effects were observed on treatment response according to cluster.

CONCLUSIONS

Profiling depression symptom subtypes of cigarette users may be more informative to improve CM treatment response than merely focusing on total scores.

A distress-continuum, disorder-threshold model of depression: a mixed-methods, latent class analysis study of slum-dwelling young men in Bangladesh

BACKGROUND

Binary categorical approaches to diagnosing depression have been widely criticized due to clinical limitations and potential negative consequences. In place of such categorical models of depression, a 'staged model' has recently been proposed to classify populations into four tiers according to severity of symptoms: 'Wellness;' 'Distress;' 'Disorder;' and 'Refractory.' However, empirical approaches to deriving this model are limited, especially with populations in low- and middle-income countries.

METHODS

A mixed-methods study using latent class analysis (LCA) was conducted to empirically test non-binary models to determine the application of LCA to derive the 'staged model' of depression. The study population was 18 to 29-year-old men (n = 824) from an urban slum of Bangladesh, a low resource country in South Asia. Subsequently, qualitative interviews (n = 60) were conducted with members of each latent class to understand experiential differences among class members.

RESULTS

The LCA derived 3 latent classes: (1) Severely distressed (n = 211), (2) Distressed (n = 329), and (3) Wellness (n = 284). Across the classes, some symptoms followed a continuum of severity: 'levels of strain', 'difficulty making decisions', and 'inability to overcome difficulties.' However, more severe symptoms such as 'anhedonia', 'concentration issues', and 'inability to face problems' only emerged in the severely distressed class. Qualitatively, groups were distinguished by severity of tension, a local idiom of distress.

CONCLUSIONS

The results indicate that LCA can be a useful empirical tool to inform the 'staged model' of depression. In the findings, a subset of distress symptoms was continuously distributed, but other acute symptoms were only present in the class with the highest distress severity. This suggests a distress-continuum, disorder-threshold model of depression, wherein a constellation of impairing symptoms emerge together after exceeding a high level of distress, i.e., a tipping point of tension heralds a host of depression symptoms.

Circadian depression: A mood disorder phenotype

Major mood syndromes are among the most common and disabling mental disorders. However, a lack of clear delineation of their underlying pathophysiological mechanisms is a major barrier to prevention and optimised treatments. Dysfunction of the 24-h circadian system is a candidate mechanism that has genetic, behavioural, and neurobiological links to mood syndromes. Here, we outline evidence for a new clinical phenotype, which we have called 'circadian depression'. We propose that key clinical characteristics of circadian depression include disrupted 24-h sleep-wake cycles, reduced motor activity, low subjective energy, and weight gain. The illness course includes early age-of-onset, phenomena suggestive of bipolarity (defined by bidirectional associations between objective motor and subjective energy/mood states), poor response to conventional antidepressant medications, and concurrent cardiometabolic and inflammatory disturbances. Identifying this phenotype could be clinically valuable, as circadian-targeted strategies show promise for reducing depressive symptoms and stabilising illness course. Further investigation of underlying circadian disturbances in mood syndromes is needed to evaluate the clinical utility of this phenotype and guide the optimal use of circadian-targeted interventions.

Confirming diagnostic categories within a depression continuum: Testing extra-linearity of risk factors and a latent class analysis

BACKGROUND

Dimensions are recommended as replacements for diagnostic categories of depression, but clinicians continue to use categories. Categories are appropriate if major, underlying changes in symptom structure occur above a clinical cut-off on a depression continuum.

METHODS

Cross-sectional surveys of Chinese undergraduates (n = 39,446) 2014-2018 measured self-reported depressive symptoms, associated psychopathology and etiological risk factors using standardised instruments. We created a continuum using PHQ-9 scores and tested linear and extra-linear contrasts in associated psychopathology, and etiology. We carried out latent class analyses (LCA).

RESULTS

Most symptoms showed linear increase, but depressed mood, anhedonia, and suicidal ideation showed marked increase at the severe end of the continuum. There was extra-linear increase in associated psychotic symptoms, other psychopathology, age, low family income, chronic pain and physical illness, childhood physical and sexual abuse, and neglect. Four LCs corresponding to Melancholic, Severe melancholic, Non-melancholic, and Mild depression were confirmed, but only above a clinical cut-off along the continuum. Etiological risk factors did not differentiate between classes but showed overall dramatic increase in impact above threshold of clinical severity.

LIMITATIONS

Only one self-report instrument was used (PHQ-9) to measure depression and diagnoses were not validated by clinical interviews.

CONCLUSIONS

Categories are necessary to describe the dramatic changes in underlying structure and symptom associations above a clinical threshold of severity. These result from extra-linear impact of etiological risk factors at the severe end of the depression continuum. Although the study confirmed melancholic and non-melancholic subtypes, further investigation should investigate etiological factors that determine this subdivision.