Latent class-derived subgroups of depressive symptoms in a community sample of older adults: the Cache County Study

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.

Distinct latent symptom profiles in late-onset depressive symptoms in community-dwelling older adults

OBJECTIVES

To examine the symptom profiles of late-onset depressive symptoms in a sample of older adults.

METHOD

The sample included 1,192 participants from the National Alzheimer's Coordinating Center Data Set. Participants were ≥65 years old, community-dwelling, and without cognitive impairment or a prior history of depression. Depressive symptoms were assessed using the Geriatric Depression Scale, 15-item (GDS-15). Latent class analysis (LCA) was used to identify and group participants based on profiles of depressive symptoms.

RESULTS

LCA revealed three distinct symptom profiles: (1) an Anhedonia/Amotivation profile with a higher probability of endorsing a combination of low positive emotion and amotivation (6%), (2) an Amotivation/Withdrawal profile with a high probability of endorsing only amotivational depressive symptoms (35%), and (3) an asymptomatic profile with no probability of endorsing any depressive symptoms (59%). Amotivational depressive symptoms were observed across both symptomatic profiles, while depressed mood (e.g. sadness) did not predominantly characterize any profile in this sample. There were also significant differences among symptom profiles in terms of demographic and clinical characteristics.

CONCLUSIONS

Findings highlight the importance of understanding depression at the symptom pattern level. A profile-based diagnostic approach may help improve the recognition of depressive symptoms in older adults.

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).

Is depression the missing link between inflammatory mediators and cancer?

Patients with cancer are at greater risk of developing depression in comparison to the general population and this is associated with serious adverse effects, such as poorer quality of life, worse prognosis and higher mortality. Although the relationship between depression and cancer is now well established, a common underlying pathophysiological mechanism between the two conditions is yet to be elucidated. Existing theories of depression, based on monoamine neurotransmitter system dysfunction, are insufficient as explanations of the disorder. Recent advances have implicated neuroinflammatory mechanisms in the etiology of depression and it has been demonstrated that inflammation at a peripheral level may be mirrored centrally in astrocytes and microglia serving to promote chronic levels of inflammation in the brain. Three major routes to depression in cancer in which proinflammatory mediators are implicated, seem likely. Activation of the kynurenine pathway involving cytokines, increases tryptophan catabolism, resulting in diminished levels of serotonin which is widely acknowledged as being the hallmark of depression. It also results in neurotoxic effects on brain regions thought to be involved in the evolution of major depression. Proinflammatory mediators also play a crucial role in impairing regulatory glucocorticoid mediated feedback of the hypothalamic-pituitary-adrenal axis, which is activated by stress and considered to be involved in both depression and cancer. The third route is via the glutamatergic pathway, whereby glutamate excitotoxicity may lead to depression associated with cancer. A better understanding of the mechanisms underlying these dysregulated and other newly emerging pathways may provide a rationale for therapeutic targeting, serving to improve the care of cancer patients.

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.

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.

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.

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.

Classifying patients with depressive and anxiety disorders according to symptom network structures: A Gaussian graphical mixture model-based clustering

Patients with mental disorders often suffer from comorbidity. Transdiagnostic understandings of mental disorders are expected to provide more accurate and detailed descriptions of psychopathology and be helpful in developing efficient treatments. Although conventional clustering techniques, such as latent profile analysis, are useful for the taxonomy of psychopathology, they provide little implications for targeting specific symptoms in each cluster. To overcome these limitations, we introduced Gaussian graphical mixture model (GGMM)-based clustering, a method developed in mathematical statistics to integrate clustering and network statistical approaches. To illustrate the technical details and clinical utility of the analysis, we applied GGMM-based clustering to a Japanese sample of 1,521 patients (Mage = 42.42 years), who had diagnostic labels of major depressive disorder (MDD; n = 406), panic disorder (PD; n = 198), social anxiety disorder (SAD; n = 116), obsessive-compulsive disorder (OCD; n = 66), comorbid MDD and any anxiety disorder (n = 636), or comorbid anxiety disorders (n = 99). As a result, we identified the following four transdiagnostic clusters characterized by i) strong OCD and PD symptoms, and moderate MDD and SAD symptoms; ii) moderate MDD, PD, and SAD symptoms, and weak OCD symptoms; iii) weak symptoms of all four disorders; and iv) strong symptoms of all four disorders. Simultaneously, a covariance symptom network within each cluster was visualized. The discussion highlighted that the GGMM-based clusters help us generate clinical hypotheses for transdiagnostic clusters by enabling further investigations of each symptom network, such as the calculation of centrality indexes.

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.

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.

Stability and transition of depression subtypes in late life

BACKGROUND

The heterogeneity of late-life depression hampers diagnosis and treatment. Data-driven methods have identified several subtypes of depression in older persons, but the longitudinal stability of these subtypes remains unknown.

METHODS

In total 111 older persons with a major depressive disorder both at baseline and 2-year follow-up from the Netherlands Study of Depression in Older persons (NESDO) were included. Latent class analysis was performed to identify subtypes of depression at baseline and at 2-year follow-up, and latent transition analysis was used to examine the stability of these subtypes over time. Transition rates between subtypes and characteristics of groups were examined.

RESULTS

Two subtypes were identified in both baseline (T0) and follow-up data (T1), including a 'melancholic' subtype (prevalence 80.2% (T0) and 62.2% (T1)), and an 'atypical' subtype (prevalence 19.8% (T0) and 37.8% (T1)). The melancholic subtype was characterized by decreased appetite and weight and had a stability of 0.86. The atypical subtype was characterized by increased appetite and weight and had a stability of 0.93, although the discriminating power of different symptoms had decreased at T1. Mean age and education differed significantly between stable and transitioning subgroups, other characteristics did not differ between subgroups.

LIMITATIONS

Limited sample size might have hampered the analyses.

CONCLUSIONS

Subtypes of late-life depression are relatively stable, but symptoms of depression (like weight loss) seem to blur with symptoms of (patho)physiological aging. This underlines the clinical relevance of depression subtyping, but also the importance of further research into subtypes and the influence of aging.

Perceptions of traditional and modern types of depression: A cross-cultural vignette survey comparing Japanese and American undergraduate students

AIM

Depression is a heterogeneous disorder that has various subtypes. In Japan, however, a prevailing misunderstanding is that the term utsu-byo (clinical depression) indicates only the melancholic type. Consequently, a subtype called 'modern-type depression' (MTD), which has contrasting features to those of melancholic or traditional-type depression (TTD), is severely stigmatized in Japan these days. The present study conducted a cross-cultural comparison of perceptions of TTD and MTD between Japan and the USA to examine how the Japanese collectivistic culture contributes to negative biases toward MTD.

METHODS

Undergraduate students in Japan (N = 303) and the Midwestern USA (N = 272) completed the survey. They read two vignettes that described the conditions of fictional individuals with either TTD or MTD, and then reported their perceptions of each vignette.

RESULTS

Mixed analyses of variance revealed significant interactions between nation (Japan or the USA) and vignette (TTD or MTD) on most perception items. These interactions and subsequent analyses with Bonferroni corrections mainly indicate the following: (i) Japanese are more likely to suppose that conditions of MTD are milder compared with TTD; and (ii) Japanese are more likely to hold stronger aversive attitudes and weaker willingness to provide support toward people with MTD than toward those with TTD.

CONCLUSION

These results indicate that people with MTD are more likely to be accepted in the US independent culture than in the Japanese collectivistic culture. Discussion highlights that cultural diversity education potentially reduces stigma of MTD in Japan.

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.

Inflammatory markers and cortisol parameters across depressive subtypes in an older cohort

BACKGROUND

There is growing evidence that inflammatory and cortisol dysregulation are underlying pathophysiological mechanisms in the aetiology of major depressive disorder, particularly in younger adults. However, findings of biological disturbances in late-life depression have been divergent, probably due to the even greater heterogeneity of depression in older adults with aging processes influencing biological factors. Using empirically derived subtypes may enable the identification of biological disturbances underlying depression in older adults.

METHODS

Data were used from the Netherlands Study of Depression in Older Persons (NESDO) of 359 persons aged 60 years or older, with a current diagnosis of major depressive disorder (MDD). Depressive subtypes (severe atypical, severe melancholic, and moderate severe subtype) that were previously identified through latent class analysis (LCA), were examined on differences in inflammatory markers including C-reactive protein (CRP), interleukin-6 (IL-6), and neutrophil gelatinase-associated lipocalin (NGAL), as well as cortisol parameters.

RESULTS

No differences in measures for inflammation and cortisol across subtypes were observed in uncorrected or for putative confounders corrected models.

LIMITATIONS

Several subjects had missing cortisol and inflammatory data, decreasing the power. However, results did not change after imputation analysis.

DISCUSSION

In this cohort of depressed older adults, no differences in inflammation and cortisol measures between depression subtypes were observed. This is probably due to the many (patho)physiological processes that are involved in aging, thereby clouding the results.

Latent Profile Analyses of Depressive Symptoms in Younger and Older Oncology Patients

The aging of America will include a significant increase in the number of older patients with cancer, many of whom will experience significant depressive symptoms. Although geriatric depression is a well-studied construct, its symptom presentation in the context of cancer is less clear. Latent profile analysis was conducted on depressive symptoms in younger (40-64 years) and older (≥65 years) patients with cancer (N = 636). The sample was clinically heterogeneous (i.e., included all stages, dominated by advanced stage disease). Participants completed questionnaires including the Center for Epidemiological Studies Depression Scale, which was used for the latent profile analysis. A four-class pattern was supported for each age group. However, the four-class pattern was significantly different between the younger and older groups in terms of the item means within each corresponding latent class; differences were primarily driven by severity such that across classes, older adults endorsed milder symptoms. An unexpected measurement issue was uncovered regarding reverse-coded items, suggesting that they may generate unreliable scores on the Center for Epidemiological Studies Depression Scale for a significant subset of patients. The results indicate that cancer clinicians can expect to see depressive symptoms along a continuum of severity for patients of any age, with less severe symptoms among older patients.

Depressive subtypes in an elderly cohort identified using latent class analysis

BACKGROUND

Clinical findings indicate heterogeneity of depressive disorders, stressing the importance of subtyping depression for research and clinical care. Subtypes of the common late life depression are however seldom studied. Data-driven methods may help provide a more empirically-based classification of late-life depression.

METHODS

Data were used from the Netherlands Study of Depression in Older People (NESDO) derived from 359 persons, aged 60 years or older, with a current diagnosis of major depressive disorder. Latent class analysis (LCA) was used to identify subtypes of depression, using ten CIDI-based depression items. Classes were then characterized using various sociodemographic and clinical characteristics.

RESULTS

The most prevalent class, as identified by LCA, was a moderate-severe class (prevalence 46.5%), followed by a severe melancholic class (prevalence 38.4%), and a severe atypical class (prevalence 15.0%). The strongest distinguishing features between the three classes were appetite and weight and, to a lesser extent, psychomotor symptoms and loss of interest. Compared with the melancholic class, the severe atypical class had the highest prevalence of females, the lowest mean age, the highest BMI, and highest prevalence of both cardiovascular disease, and metabolic syndrome.

LIMITATIONS

The strongest distinguishing symptoms, appetite and weight, could be correlated. Further, only longitudinal studies could demonstrate whether the identified classes are stable on the long term.

DISCUSSION

In older persons with depressive disorders, three distinct subtypes were identified, similar to subtypes found in younger adults. The strongest distinguishing features were appetite and weight; moreover, classes differed strongly on prevalence of metabolic syndrome and cardiovascular disease. These findings suggest differences in the involvement of metabolic pathways across classes, which should be considered when investigating the pathogenesis and (eventually) treatment of depression in older persons.

Changes in depression among older adults in China: A latent transition analysis

BACKGROUND

Depression in late life is an important public health problem in developing countries. It is timely to investigate stability and transition patterns of depressive symptom subtypes.

METHODS

Longitudinal data were used from the China Health and Retirement Longitudinal Study (CHARLS). A total of 853 women and 930 men aged 60-96 years were recruited. Latent class and latent transition analysis (LCA/LTA) were used to identify meaningful subgroups, transitions between those classes across time, and baseline demographic features that help to predict and design tailored interventions.

RESULTS

Three depression subgroups were identified: Class 1 was labeled "Mild Depression"; Class 2 was labeled "Severe Depression" and class 3 was labeled "Lack of Positive Affect". A predominant tendency for stability appeared rather than change, meanwhile individual in Mild Depression and Severe Depression latent status both had a high probability to convert to the Lack of Positive Affect latent status. Social activities played a significant role in buffering the effect of depression, while individuals with chronic diseases, having difficulty with ADLs and smoking might be at-risk groups.

LIMITATIONS

The limitations of the present study were inherent limitation in the LTA model and some small proportion of transitions.

CONCLUSIONS

This study demonstrated a transition pattern in older adult depression within a person-centered approach. Differential treatment effects were found across baseline depression class, suggesting the benefit for tailored intervention programs to improve depression outcomes among older adults.

Towards a new conceptualization of depression in older adult cancer patients: a review of the literature

OBJECTIVES

Identifying depression in older adults with cancer presents a set of unique challenges, as it combines the confounding influences of cancer and its treatment with the developmental changes associated with aging. This paper reviews the phenomenology of depression in older adults, and individuals diagnosed with cancer.

METHOD

PsychInfo, PubMed, Web of Science, and Google Scholar databases were searched for English-language studies addressing the phenomenology, symptoms, or assessment of depression in older adults and those with cancer.

RESULTS

The Diagnostic and Statistical Manual for Mental Disorders (DSM) criteria that appear to be relevant to both older adults and cancer patients are anhedonia, concentration difficulties, sleep disturbances, psychomotor retardation/agitation, and loss of energy. Possible alternative criteria that may be important considerations included constructs such as loss of purpose, loneliness, and irritability in older adults. Among cancer patients, tearfulness, social withdrawal, and not participating in treatment despite ability to do so were identified as potentially important symptoms.

CONCLUSIONS

Current DSM criteria may not adequately assess depression in older cancer patients and alternative criteria may be important to inform the understanding and identification of depression in this population. Enhancing diagnostic accuracy of depression is important as both the over-diagnosis and under-diagnosis is accompanied with significant costs. Thus, continued research exploring the phenomenology and identifying effective indicators of depression in older cancer patients is needed.

Subtypes of depression in cancer patients: an empirically driven approach

Purpose

This study aimed to (1) identify subgroups of cancer patients with distinct subtypes of depression before the start of psychological care, (2) examine whether socio-demographic and medical characteristics distinguished these subtypes, and (3) examine whether people with distinct subtypes reported differential courses of depression during psychological care.

Method

This naturalistic, longitudinal study included cancer patients who sought psychological care at specialized psycho-oncology institutions in the Netherlands. Data were collected before psychological care (T1) and three (T2) and nine (T3) months thereafter. Latent class analysis was performed to identify depression subtypes in 243 patients at T1.

Results

Before starting psychological care, three depressive subtypes were identified, differing in severity and type of symptoms. Class 1 (47 %) with mild depression reported mostly concentration and sleep problems and fatigue. Class 2 (41 %), with slightly higher levels of depression, reported similar concentration and sleep problems and fatigue as class 1, and additionally depressed mood. Class 3 (12 %), with severe depression, reported mainly a depressed mood and, to a lesser extent but still elevated, fatigue and concentration problems. None of socio-demographic and medical characteristics significantly distinguished these subtypes. These subtypes significantly predicted the course of depression over time, with class 1 reporting moderate improvements, class 2 large improvements, and class 3 the largest improvements.

Conclusions

Results indicate the presence of three subtypes of depression in cancer patients before starting psychological care. Our findings suggest that psychological interventions could be tailored to respond to the specific subtype of depression experienced by each individual.

School-readiness profiles of children with language impairment: linkages to home and classroom experiences

BACKGROUND

This study represents an effort to advance our understanding of the nature of school readiness among children with language impairment (LI), a population of children acknowledged to be at risk of poor academic achievement. The academic, social-emotional, and behavioural competencies with which children arrive at kindergarten affect the nature of their future educational experiences, and their overall academic achievement.

AIMS

To examine whether there are reliable profiles that characterize children with LI just prior to kindergarten entrance, and the extent to which profile membership is associated with characteristics of children's homes and preschool experiences. Questions addressed were twofold: (1) To what extent are there reliable profiles of children with LI with respect to their school readiness? (2) To what extent is children's profile membership associated with characteristics of their homes and preschool classrooms?

METHODS & PROCEDURES

Participants were 136 children with LI from early childhood special education classrooms. We utilized latent class analysis (LCA) to classify individuals into profiles based on individual responses on school readiness measures. We then used multilevel hierarchical generalized linear models to examine the relations between profile membership and children's home/classroom experiences.

OUTCOMES & RESULTS

LCA analyses revealed that a four-profile solution was the most appropriate fit for the data and that classroom experiences were predictive of these profiles, such that children in classrooms with more instructional/emotional support were more likely to be placed in profiles characterized by higher school readiness skills.

CONCLUSIONS & IMPLICATIONS

These results suggest that the school readiness profiles of young children with LI are associated with the quality of children's classroom experiences, and that high-quality classroom experiences can be influential for ensuring that young children with LI arrive in kindergarten ready to learn.

Normal Aging or Depression? A Qualitative Study on the Differences Between Subsyndromal Depression and Depression in Very Old People

PURPOSE OF THE STUDY

The aim of this study was to make a qualitative comparison of experiences of being in very old people with subsyndromal depression (SSD), in relation to the experiences of very old people with syndromal depression or nondepression. Through investigation and deeper understanding of the interface between depressive disease and normal aging, clinicians might give more accurate prevention or treatment to those very old persons who need such help.

DESIGN AND METHODS

Semistructured qualitative interviews were conducted for 27 individuals of 87-88 years of age, who were categorized in the 3 strata of nondepressive, SSD, and syndromal depression. Transcripts were analyzed using qualitative content analysis within each stratum and later with a comparison between the strata.

RESULTS

The content analysis resulted in 4 themes in people with SSD, as defined by a self-report depression screening instrument, giving a comprehensive picture of SSD in very old people, and also showed qualitative differences between the SSD, syndromal depression, and nondepressive groups. A main finding was that SSD differs qualitatively from syndromal depression but not clearly from nondepression.

IMPLICATIONS

The results might indicate that SSD in very old people is not related to pathology but to normal aging, even though the condition correlates with negative health parameters. Overlooking certain psychosocial aspects of living in the very old may pose a risk of both underdiagnosis and overdiagnosis in the spectrum of depressive disorders.

What characterizes late-life depression?

This article analyzes late-life depression, looking carefully at what defines a person as elderly, the incidence of late-life depression, complications and differences in symptoms between young and old patients with depression, subsyndromal depression, bipolar depression in the elderly, the relationship between grief and depression, along with sleep disturbances and suicidal ideation.

The Impact of Social Resources on Depressive Symptoms in Racially and Ethnically Diverse Older Adults

The objectives of this study were (1) to develop an empirical typology of physical health risks in racially and ethnically diverse older adults and (2) to examine whether the impact of social resources on depressive symptoms differs across the identified health risk groups (low, moderate, and high risks). The data source was the Survey of Older Floridians, a statewide survey of older adults aged 65 and older ( n = 1,432). Latent profile analysis with multiple indicators of physical health (chronic conditions, functional disability, and self-rated health) was used to identify three health risk groups (low, moderate, and high risks). The direct and interactive effects of the health risk group membership and social resources (social support and religious service attendance) on depressive symptoms were found. Of particular interest was that the positive impact of social support was most pronounced in the moderate health risk group.