DSM-III major depressive disorder in the community. A latent class analysis of data from the NIMH epidemiologic catchment area programme

Associations between depression subtype and functional impairment and treatment utilization in a national sample of adolescents

BACKGROUND

Prior studies have characterized distinct major depressive episode (MDE) subtypes among adults, yet limited evidence exists regarding variation in MDE during adolescence.

METHODS

Using 2008-2016 National Survey of Drug Use and Health data, latent class analysis (LCA) was used to characterize depression subtypes (based on symptom presentation) among 9,896 youth ages 12-17 with recent first-onset MDE. Logistic regression was used to estimate associations of MDE subtype with functional outcomes and treatment utilization, adjusting for demographic characteristics and depression severity (i.e., number of MDE diagnostic criteria and recurrence status) RESULTS: A 5-class LCA model provided optimal fit. Three distinct categories of MDE symptoms generally clustered together, which we termed "somatic," "cognitive," and "self-worth;" classes were differentiated by distinct combinations of symptoms across these 3 categories. Subtypes were characterized as: Highly Symptomatic (39% of youth); Somatic & Cognitive (24%), Somatic (22%), Diffuse Symptoms (8%), and Somatic & Self-Worth (6%). The majority of youth reported at least moderate impairment across multiple domains; subtype was a significant predictor of functional impairment. Only 34% of youth received any past-year depression-related treatment; treatment utilization was significantly higher for MDE subtypes with the highest prevalences of suicidal ideation.

LIMITATIONS

Due to cross-sectional data, we cannot establish causal directionality.

CONCLUSIONS

Subtype was significantly predictive of functional impairment and treatment utilization, above and beyond number of MDE diagnostic criteria or recurrence status. Understanding distinct profiles of adolescent depression, as well as potential differential associations with impairment, can inform prevention, diagnosis, and treatment of depression among youth.

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

BACKGROUND

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

METHODS

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

RESULTS

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

LIMITATIONS

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

CONCLUSIONS

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

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.

Comorbid Anxiety and Depressive Symptoms in Children and Adolescents: A Systematic Review and Analysis

BACKGROUND

A large and extensive body of research has examined comorbid anxiety and depression in adults. Children and adolescents also frequently present with comorbid anxiety and depression; however, research and treatment require unique environmental and neurodevelopmental considerations in children. As a result, our understanding of comorbid anxiety and depression in children and adolescents is limited.

OBJECTIVE

The goal of this systematic review was to examine the current literature focused on comorbid anxiety and depression in children and adolescents. The review included theoretical conceptualizations as well as diagnostic, neurobiological, prevention, and treatment considerations. In addition, a proposed algorithm for the treatment of comorbid anxiety and depression in children/adolescents is provided.

METHODS

This systematic literature review included 3 discrete searches in Ovid SP Medline, PsycInfo, and PubMed.

RESULTS

The review included and synthesized 115 articles published between 1987 and 2015. The available evidence suggests that anxiety and depression are common in clinical populations of children and adolescents, and that comorbidity is likely underestimated in children and adolescents. Children and adolescents with comorbid anxiety and depression have unique presentations, greater symptom severity, and treatment resistance compared with those who have either disease in isolation. A dimensional approach may be necessary for the future development of diagnostic strategies and treatments for this population. Nascent neuroimaging work suggests that anxiety and depression each represents a distinct neurobiological phenotype.

CONCLUSIONS

The literature that is currently available suggests that comorbid anxiety and depression is a common presentation in children and adolescents. This diagnostic picture underscores the importance of comprehensive dimensional assessments and multimodal evidence-based approaches given the high disease severity. Future research on the neurobiology and the treatment of these common clinical conditions is warranted.

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

BACKGROUND

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

METHODS

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

RESULTS

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

LIMITATIONS

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

CONCLUSIONS

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

Subtypes of major depression: latent class analysis in depressed Han Chinese women

BACKGROUND

Despite substantial research, uncertainty remains about the clinical and etiological heterogeneity of major depression (MD). Can meaningful and valid subtypes be identified and would they be stable cross-culturally?

METHOD

Symptoms at their lifetime worst depressive episode were assessed at structured psychiatric interview in 6008 women of Han Chinese descent, age ⩾ 30 years, with recurrent DSM-IV MD. Latent class analysis (LCA) was performed in Mplus. RESULTS; Using the nine DSM-IV MD symptomatic A criteria, the 14 disaggregated DSM-IV criteria and all independently assessed depressive symptoms (n = 27), the best LCA model identified respectively three, four and six classes. A severe and non-suicidal class was seen in all solutions, as was a mild/moderate subtype. An atypical class emerged once bidirectional neurovegetative symptoms were included. The non-suicidal class demonstrated low levels of worthlessness/guilt and hopelessness. Patterns of co-morbidity, family history, personality, environmental precipitants, recurrence and body mass index (BMI) differed meaningfully across subtypes, with the atypical class standing out as particularly distinct.

CONCLUSIONS

MD is a clinically complex syndrome with several detectable subtypes with distinct clinical and demographic correlates. Three subtypes were most consistently identified in our analyses: severe, atypical and non-suicidal. Severe and atypical MD have been identified in multiple prior studies in samples of European ethnicity. Our non-suicidal subtype, with low levels of guilt and hopelessness, may represent a pathoplastic variant reflecting Chinese cultural influences.

The role of sex on stability and change of depression symptom subtypes over 20 years: a latent transition analysis

Prospective studies investigating the long-term stability of depression symptom subtypes are rare. Moreover, sex has received little attention as a predictor. This study aimed to investigate the role of sex on stability and transition patterns of depressive symptom subtypes over 20 years. The data were drawn from three follow-ups (1988, 1999, and 2008) of the longitudinal Zurich Study. Latent transition analyses were fitted to the data of 322 subjects, using depressive symptoms from the face-to-face interviews. The stable classes were characterized by psychosocial correlates. Three subtypes were identified: 'severe atypical,' 'severe typical,' and 'moderate.' While stability of the severe atypical and moderate subtype was relatively high and increased over time (70-71; 45-90%), stability of the severe typical subtype was lower (45-48%). Females had a higher risk of being in the severe atypical subtype and exhibited more transitions, particularly with respect to the severe typical subtype. In contrast, males displayed more stable subtypes. The stable severe atypical subtype was associated with comorbid eating disorders as well as psychosis syndromes, whereas the stable severe typical subtype was associated only with psychosis syndromes. Our results provide first evidence for the notion that long-term stability and transition patterns differ by sex and depression subtypes. This finding has received too little attention in previous research and should be considered in treatments.

The predictive power of subgroups: an empirical approach to identify depressive symptom patterns that predict response to treatment

BACKGROUND

Depression research has been trying to improve the response rates to treatments by identifying a valid set of differential predictor variables. Potential candidates have been proposed, one of which were different subtypes of depression. However, the results on the predictive quality of subtypes on treatment are conflicting.

METHODS

The analyzed data consisted of Hamilton Depression Rating Scales (HAM-D17) of 879 depressive inpatients, which were recruited in a naturalistic multicenter study. Mean length of stay was 9.9 weeks. In a first step, a Latent Class Analysis (LCA) was conducted to classify the patients into smaller groups. In a second step, the class variable was included in a Linear Mixed Effects model to predict the same patients' response to treatment.

RESULTS

Five classes were obtained from LCA, showing substantially different symptom profiles. One of the classes, with a symptom profile similar to melancholic depression, showed substantially slower response to treatment (i.e., estimated time to remission; 11.3 weeks) than the remaining classes in the study (6.6-8.6 weeks).

LIMITATIONS

The applied measurement instrument, the HAM-D17, did not include items for two additional, frequently found subtypes of depression: psychotic and atypical depression. Thus, these subtypes could not emerge in the LCA. Furthermore, there was no systematic variation of treatment in the data. Thus, a differential effect of the classes on treatment could not be measured.

CONCLUSIONS

The classification of patients according to their symptom profiles seems to be a potent predictor for treatment response. However, the obtained symptom patterns are not completely congruent with the theoretically proposed subgroups. Against the background of the results, dividing melancholic depression in a rather cognitive and vegetative subtype may be promising.

Anxious forms of depression

Previous research has described distinctive features for anxious and nonanxious forms of major depression. The concept of "mixed anxiety depression disorder" (MADD) refers to a milder degree of the anxious form of depression, since the depressive symptoms fall short of the number required for a diagnosis of major depression. It is argued that this can be thought of as a subclinical form of anxious depression, rather than a separate disorder in its own right. In view of its substantial prevalence in general medical settings, its associated disability and its public health importance, it deserves to be recognized, and seen as being continuous with the more severe forms of anxious depression. It will therefore be included in the Field Trials of the version of the ICD-11 (where ICD is the International Classification of Disease) intended for primary care. It is argued that current anxiety, depression (without anxiety), and anxious depression would cover most of the psychologically distressed patients seen in general medical settings, using a pseudodimensional system.

Symptom-based subtypes of depression and their psychosocial correlates: a person-centered approach focusing on the influence of sex

BACKGROUND

Reducing the complexity of major depressive disorder by symptom-based subtypes constitutes the basis of more specific treatments. To date, few studies have empirically derived symptom subtypes separated by sex, although the impact of sex has been widely accepted in depression research.

METHODS

The community-based sample included 373 males and 443 females from the Zurich Program for Sustainable Development of Mental Health Services (ZInEP) manifesting depressive symptoms in the past 12 months. Latent Class Analysis (LCA) was performed separately by sex to extract sex-related depression subtypes. The subtypes were characterized by psychosocial characteristics.

RESULTS

Three similar subtypes were found in both sexes: a severe typical subtype (males: 22.8%; females: 35.7%), a severe atypical subtype (males: 17.4%; females: 22.6%), and a moderate subtype (males: 25.2%; females: 41.8%). In males, two additional subgroups were identified: a severe irritable/angry-rejection sensitive (IARS) subtype (30%) comprising the largest group, and a small psychomotor retarded subtype (4%). Males belonging to the severe typical subtype exhibited the lowest masculine gender role orientation, while females of the typical subtype showed more anxiety disorders. The severe atypical subtype was associated with eating disorders in both sexes and with alcohol/drug abuse/dependence in females. In contrast, alcohol/drug abuse/dependence was associated with the severe IARS subtype in males.

LIMITATIONS

The study had a cross-sectional design, allowing for no causal inferences.

CONCLUSIONS

This study contributes to a better understanding of sex-related depression subtypes, which can be well distinguished on the basis of symptom profiles. This provides the base for future research investigating the etiopathogenesis and effective treatment of the heterogeneous depression disorder.

Latent subtypes of depression in a community sample of older adults: can depression clusters predict future depression trajectories?

Identifying sources of heterogeneity in late life depression remains an important focus of psychiatric investigation. Community samples are particularly informative since many older adults have clinically significant depressive symptoms but fail to meet criteria for major depression and older adults generally do not seek treatment for their depressive symptoms. The primary data used for these analyses were those collected in a community-based survey of over 3000 adults age 65 or older followed for up to ten years. Depressive symptoms were measured by the Center for Epidemiologic Studies-Depression scale (CES-D). Latent class analysis was used to identify clusters of participants based on their symptom profiles at baseline. Mixed models were used to examine trajectories of CES-D scores based on cluster assignment. A model with three unique clusters best fit the data. Cluster 1 (59%) had a low probability of any symptom endorsement. Cluster 2 (31%) endorsed as a group some negative affect and somatic symptoms but their endorsement of low positive affect did not differ from Cluster 1. Participants in Cluster 3 (10%) had a higher probability of endorsement of all symptoms compared to Clusters 1 and 2. The results did not appreciably differ when symptom severity was included. Cluster assignment was a significant predictor of change in CES-D score over the ten-year follow-up period, and the effects over time differed by sex. Depressive symptom profiles predict the longitudinal course of depression in a community sample of older adults, findings that are important especially in primary care settings.

Diagnostic heterogeneity in psychiatry: towards an empirical solution

The launch of the 5th version of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) has sparked a debate about the current approach to psychiatric classification. The most basic and enduring problem of the DSM is that its classifications are heterogeneous clinical descriptions rather than valid diagnoses, which hampers scientific progress. Therefore, more homogeneous evidence-based diagnostic entities should be developed. To this end, data-driven techniques, such as latent class- and factor analyses, have already been widely applied. However, these techniques are insufficient to account for all relevant levels of heterogeneity, among real-life individuals. There is heterogeneity across persons (p:for example, subgroups), across symptoms (s:for example, symptom dimensions) and over time (t:for example, course-trajectories) and these cannot be regarded separately. Psychiatry should upgrade to techniques that can analyze multi-mode (p-by-s-by-t) data and can incorporate all of these levels at the same time to identify optimal homogeneous subgroups (for example, groups with similar profiles/connectivity of symptomatology and similar course). For these purposes, Multimode Principal Component Analysis and (Mixture)-Graphical Modeling may be promising techniques.

Cross-informant agreement on child and adolescent withdrawn behavior: a latent class approach

Withdrawn behavior (WB) relates to many developmental outcomes, including pervasive developmental disorders, anxiety, depression, psychosis, personality disorders and suicide. No study has compared the latent profiles of different informants' reports on WB. This study uses multi-informant latent class analyses (LCA) of the child behavior checklist (CBCL), teacher report form (TRF) and youth self-report (YSR) to examine phenotypic variance in WB. LCA was applied to the CBCL, TRF and YSR of 2,031 youth (ages 6-18); of which 276 children were clinically-referred. A 4-class solution for the CBCL and 3-class solutions for the YSR and TRF were optimal. The CBCL yielded low symptoms, predominantly shy or secretive moderate symptoms, and all symptoms classes. The TRF lacked the moderate--secretive class, and the YSR lacked the moderate--shy class. Agreement was low. LCA shows similar structure of withdrawn behavior across informants but characterizations of moderate WB vary.

Are depression and frailty overlapping syndromes in mid- and late-life? A latent variable analysis

BACKGROUND

Depression and frailty both predict disability and morbidity in later life. However, it is unclear to what extent these common geriatric syndromes represent overlapping constructs.

OBJECTIVE

To examine the joint relationship between the constructs of depression and frailty.

METHODS

Data come from 2004-2005 wave of the Baltimore Epidemiologic Catchment Area Study, and the analysis is limited to participants 40 years and older, with complete data on frailty and depression indicators (N = 683). Depression was measured using the Diagnostic Interview Schedule, and frailty was indexed by modified Fried criteria. A series of confirmatory latent class analyses were used to assess the degree to which depression and frailty syndromes identify the same populations. A latent kappa coefficient (κl) was also estimated between the constructs.

RESULTS

Confirmatory latent class analyses indicated that depression and frailty represent distinct syndromes rather than a single construct. The joint modeling of the two constructs supported a three-class solution for depression and two-class solution for frailty, with 2.9% categorized as severely depressed, 19.4% as mildly depressed, and 77.7% as not depressed, and 21.1% categorized as frail and 78.9% as not frail. The chance-corrected agreement statistic indicated moderate correspondence between the depression and frailty constructs (κl: 66, 95% confidence interval: 0.58-0.74).

CONCLUSIONS

Results suggest that depression and frailty are interrelated concepts, yet their operational criteria identify substantively overlapping subpopulations. These findings have implications for understanding factors that contribute to the etiology and prognosis of depression and frailty in later life.

Gender differences in symptomatic profiles of depression: results from the São Paulo Megacity Mental Health Survey

BACKGROUND

Few studies have investigated symptomatic subtypes of depression and their correlates by gender.

METHODS

Data are from the São Paulo Megacity Mental Health Survey. Symptom profiles of 1207 subjects (864 women; 343 men) based upon symptoms of the worst depressive episode in lifetime were examined through latent class analysis. Correlates of gender-specific latent classes were analyzed by logistic regression.

RESULTS

For both men and women, a 3-class model was the best solution. A mild class was found in both genders (41.1% in women; 40.1% in men). Gender differences appeared in the most symptomatic classes. In women, they were labeled melancholic (39.3%) and atypical (19.5%), differing among each other in somatic/vegetative symptoms. The melancholic class presented inhibition and eating/sleeping symptoms in the direction of decreasing, whereas the atypical class had increased appetite/weight, and hypersomnia. For men, symptoms that differentiate the two most symptomatic classes were related to psychomotor activity: a melancholic/psychomotor retarded (40.4%) and agitated depression (19.6%). The highest between-class proportion of agitation and racing thoughts was found among men in the agitated class, with similarity to bipolar mixed state.

LIMITATIONS

Analyses were restricted to those who endorsed questions about their worst lifetime depressive episode; the standardized assessment by lay interviewers; the small male sample size.

CONCLUSIONS

The construct of depression of current classifications is heterogeneous at the symptom level, where gender different subtypes can be identified. These symptom profiles have potential implications for the nosology and the therapeutics of depression.

Twenty-year depressive trajectories among older women

CONTEXT

Despite the frequent occurrence of depressive symptoms among older adults, especially women, little is known about the long-term course of late-life depressive symptoms.

OBJECTIVE

To characterize the natural course of depressive symptoms among older women (from the young old to the oldest old) followed up for almost 20 years.

DESIGN

Using latent-class growth-curve analysis, we analyzed women enrolled in an ongoing prospective cohort study (1988 through 2009).

SETTING

Clinic sites in Baltimore, Maryland; Minneapolis, Minnesota; the Monongahela Valley near Pittsburgh, Pennsylvania; and Portland, Oregon.

PARTICIPANTS

We studied 7240 community-dwelling women 65 years or older.

MAIN OUTCOME MEASURE

The Geriatric Depression Scale short form (score range, 0-15) was used to routinely assess depressive symptoms during the follow-up period.

RESULTS

Among older women, we identified 4 latent classes during 20 years, with the predicted probabilities of group membership totaling 27.8% with minimal depressive symptoms, 54.0% with persistently low depressive symptoms, 14.8% with increasing depressive symptoms, and 3.4% with persistently high depressive symptoms. In an adjusted model for latent class membership, odds ratios (ORs) for belonging in the increasing depressive symptoms and persistently high depressive symptoms classes, respectively, compared with a group having minimal depressive symptoms were substantially and significantly (P < .05) elevated for the following variables: baseline smoking (ORs, 4.69 and 7.97), physical inactivity (ORs, 2.11 and 2.78), small social network (ORs, 3.24 and 6.75), physical impairment (ORs, 8.11 and 16.43), myocardial infarction (ORs, 2.09 and 2.41), diabetes mellitus (ORs, 2.98 and 3.03), and obesity (ORs, 1.86 and 2.90).

CONCLUSIONS

During 20 years, almost 20% of older women experienced persistently high depressive symptoms or increasing depressive symptoms. In addition, these women had more comorbidities, physical impairment, and negative lifestyle factors at baseline. These associations support the need for intervention and prevention strategies to reduce depressive symptoms into the oldest-old years.

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

OBJECTIVE

We sought to identify possible subgroups of elders that varied in depressive symptomatology and to examine symptom patterns and health status differences between subgroups.

METHODS

The Cache County memory study is a population-based epidemiological study of dementia with 5092 participants. Depressive symptoms were measured with a modified version of the diagnostic interview schedule-depression. There were 400 nondemented participants who endorsed currently (i.e., in the past 2 weeks) experiencing at least one of the three "gateway" depressive symptoms and then completed a full depression interview. Responses to all nine current depressive symptoms were modeled using the latent class analysis.

RESULTS

Three depression subgroups were identified: a significantly depressed subgroup (62%), with the remainder split evenly between a subgroup with low probability of all symptoms (21%), and a subgroup with primarily psychomotor changes, sleep symptoms, and fatigue (17%). Latent class analysis derived subgroups of depressive symptoms and Diagnostic and statistical manual of mental disorders, fourth edition depression diagnostic group were nonredundant. Age, gender, education, marital status, early or late onset, number of episodes, current episode duration, and functional status were not significant predictors of depression subgroup. The first subgroup was more likely to be recently bereaved and had less physical health problems, whereas the third subgroup were less likely to be using antidepressants compared with the second subgroup.

CONCLUSIONS

There are distinct subgroups of depressed elders, which are not redundant with the Diagnostic and statistical manual of mental disorders, fourth edition classification scheme, offering an alternative diagnostic approach to clinicians and researchers. Future work will examine whether these depressive symptom profiles are predictive of incident dementia and earlier mortality.

Examining variation in depressive symptoms over the life course: a latent class analysis

BACKGROUND

Older adults have the lowest prevalence and incidence of major depressive disorder, although it has been hypothesized that this finding is due in part to differences in expression of psychopathology in later life. The aim of this study was to examine variation in depressive symptomatology in the general population across the lifespan.

METHOD

Data came from three sites of the Epidemiologic Catchment Area (ECA) Project (n=10 529). Depressive symptoms during the past 6 months were assessed using the Diagnostic Interview Schedule (DIS). Latent class analysis (LCA) was used to identify homogeneous groups of depressive symptomatology based on 16 individual symptoms, and to examine variation in the prevalence and composition of depression classes across age groups.

RESULTS

The DIS symptoms fit a four-class model composed of non-depressed (83.2%), mild depression (11.6%), severe depression (1.9%), and despondent (3.2%) groups. Relative to the non-depressed class, older age was inversely associated with being in the mild or severe depression class. The profile of the latent classes was similar across age groups with the exception of the despondent class, which was not well differentiated among the youngest adults and was not inversely associated with age.

CONCLUSIONS

The symptom profiles of depression are similar across age with the exception of the despondent class, which is more differentiated from severe depression among older adults. The findings demonstrate the benefit of examining individual symptoms rather than broad symptom groups for understanding the natural history of depression over the lifespan.

Age differences in symptom expression in patients with major depression

OBJECTIVE

The aim of the study was to compare symptom expression in primarily middle-aged (<60) and older (60+) patients who were depressed and determine if symptom profiles differed by age.

METHODS

Patients diagnosed with major depression (N = 664) were screened using the Center for Epidemiologic Studies--Depression scale and sections of the Diagnostic Interview Schedule. Patients were separated into homogeneous clusters based on symptom endorsement using latent class analysis.

RESULTS

Older patients were less likely to endorse crying spells, sadness, feeling fearful, being bothered, or feeling life a failure but were more likely to endorse poor appetite and loss of interest in sex. Older patients were also less likely to report enjoying life, feeling as good as others, feeling worthless, wanting to die, and thinking about suicide. In two latent class models with depressive symptoms as indicators, three-class models best fit the data. Profiles supported heterogeneity in symptom expression. Clusters differed by age when other demographic, clinical, health, and social variables were controlled but did not support age-specific symptom profiles. Overall, older patients had later age of onset, had fewer lifetime spells, were more likely to have ever received electroconvulsive therapy (ECT), and were less likely to have comorbid anxiety. Older patients also had more cognitive impairment, health conditions, and mobility limitations but had higher levels of subjective social support and had experienced fewer stressful life events.

CONCLUSIONS

There are age differences in symptom endorsement between younger/middle-aged and older patients with major depression. The data, however, did not identify a symptom profile unique to late-life depression.

Latent class analysis of manic and depressive symptoms in a population-based sample in São Paulo, Brazil

BACKGROUND

Current diagnostic criteria cannot capture the full range of bipolar spectrum. This study aims to clarify the natural co-segregation of manic-depressive symptoms occurring in the general population.

METHODS

Using data from the Sao Paulo Catchment Area Study, latent class analysis (LCA) was applied to eleven manic and fourteen depressive symptoms assessed through CIDI 1.1 in 1464 subjects from a community-based study in Sao Paulo, Brazil. All manic symptoms were assessed, regardless of presence of euphoria or irritability, and demographics, services used, suicidality and CIDI/DSM-IIIR mood disorders used to external validate the classes.

RESULTS

The four obtained classes were labeled Euthymics (EU; 49.1%), Mild Affectives (MA; 31.1%), Bipolars (BIP; 10.7%), and Depressives (DEP; 9%). BIP and DEP classes represented bipolar and depressive spectra, respectively. Compared to DEP class, BIP exhibited more atypical depressive characteristics (hypersomnia and increase in appetite and/or weight gain), risk of suicide, and use of services. Depressives had rates of atypical symptoms and suicidality comparable to oligosymptomatic MA class subjects.

LIMITATIONS

The use of lay interviewers and DSM-IIIR diagnostic criteria, which are more restrictive than the currently used DSM-IV TR.

CONCLUSIONS

Findings of high prevalence of bipolar spectrum and of atypical symptoms and suicidality as indicators of bipolarity are of great clinical importance, due to different treatment needs, and higher severity. Lifetime sub-affective and syndromic manic symptoms are clinically significant, arguing for the need of revising DSM bipolar spectrum categories.

Searching for valid psychiatric phenotypes: discrete latent variable models

INTRODUCTION

A primary challenge in psychiatric genetics is the lack of a completely validated system of classification for mental disorders. Appropriate statistical methods are needed to empirically derive more homogenous disorder subtypes.

METHODS

Using the framework of Robins and Guze's ('Establishment of diagnostic validity in psychiatric illness: its application to schizophrenia', American Journal of Psychiatry, 1970, 126(7), 983-987) five phases, latent variable models to derive and validate diagnostic groups are described. A process of iterative validation is proposed through which refined phenotypes would facilitate research on genetics, pathogenesis, and treatment, which would in turn aid further refinement of disorder definitions.

CONCLUSIONS

Latent variable methods are useful tools for defining and validating psychiatric phenotypes. Further methodological research should address sample size issues and application to iterative validation.

The emerging modern face of mood disorders: a didactic editorial with a detailed presentation of data and definitions

The present work represents a detailed description of our current understanding and knowledge of the epidemiology, etiopathogenesis and clinical manifestations of mood disorders, their comorbidity and overlap, and the effect of variables such as gender and age. This review article is largely based on the 'Mood disorders' chapter of the Wikibooks Textbook of Psychiatry http://en.wikibooks.org/wiki/Textbook_of_Psychiatry/Mood_Disorders.

Statistical validation of endophenotypes using a surrogate endpoint analytic analogue

Endophenotypes, which involve the same biological pathways as diseases but presumably are closer to the relevant gene actions than diagnostic phenotypes, have emerged as an important concept in the genetic studies of complex diseases. In this report, we develop a formal statistical methodology for validating endophenotypes. The proposed method was motivated by the conditioning strategy used for surrogate endpoints commonly seen in clinical research. We define an endophenotype to be "a trait for which a test of null hypothesis of no genetic heritability implies the corresponding null hypothesis based on the phenotype of interest". An index, the proportion of heritability explained, is used as an operational criterion of validation. Statistical inferences on this index are also developed. Usefulness of the proposed method is demonstrated through computer simulations and a study of assessing the Continuous Performance Test as an endophenotype of the schizophrenia spectrum.

Profiles of depressive symptoms in older adults diagnosed with major depression: latent cluster analysis

OBJECTIVE

To explore the underlying structure of symptom presentation in older adults with major depression by identifying homogeneous clusters of individuals based on symptom profiles.

DESIGN

Secondary data analysis using latent class cluster analysis.

SETTING

Clinical Research Center for the Study of Depression in Later Life conducted at Duke University.

PARTICIPANTS

Three hundred sixty-six patients age 60+ who met Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria for major depression and were enrolled in a longitudinal naturalistic treatment study.

MEASUREMENTS

Responses to the 10 items of the Montgomery-Asberg Depression Rating Scale at the time of study enrollment.

RESULTS

The authors identified four latent clusters of older adults with major depression. Patients in Cluster 1 (47.2%) had mean scores of average severity for reported and apparent sadness and lassitude and low mean scores for reduced appetite. Patients in Cluster 2 (27.1%) had higher mean scores compared with Cluster 1 for all items, and particularly for apparent sadness. Patients in Cluster 3 (18.9%) had the lowest mean scores for both apparent and reported sadness, but a similar profile compared with Cluster 1 for inner tension, reduced sleep, reduced appetite, and concentration difficulties. Cluster 4 (6.8%) had the highest mean scores for each item. Both apparent and reported sadness accounted for a large amount of variance among the four clusters. Patients in Cluster 4 were more likely to have 12 or less years of education and/or one or more functional limitations.

CONCLUSION

The heterogeneity in symptom presentation among older adults diagnosed with major depression can potentially inform the development of DSM-V.

Subtypes of depression in a nationally representative sample

BACKGROUND

Continued research efforts aim to elucidate the heterogeneity in depression. The identification of meaningful and valid subtypes has implications for research and clinical practice. Based on patterns of depressive symptomatology, this study identified a typology of depressive syndromes using data from a large, nationally representative survey.

METHODS

Analyses were based on a subsample of 12,180 respondents from the 2001-2002 Wave of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). Latent class analysis was applied to the DSM-IV 'A' criteria for major depression to identify homogenous subtypes or classes of depressive syndromes. Associations between the emergent latent classes and demographic and clinical characteristics were assessed.

RESULTS

Three clinically relevant subtypes were identified, in addition to a class who reported few depressive symptoms: severely depressed (40.9%), psychosomatic (30.6%), cognitive-emotional (10.2%) and non-depressed (18.3%). The odds of experiencing negative life events, psychiatric disorders, and having a family background of major depression were significantly higher for the severely depressed, psychosomatic and cognitive-emotional classes, compared to the non-depressed class. Several unique differences between the latent classes also emerged.

LIMITATIONS

Methodological shortcomings included: reliance on lay interviewer-administered structured interviews to determine diagnoses; basing sample selection on the endorsement of screener items; and, using measures of 'any anxiety disorder', 'any mood disorder', and 'any personality disorder' to determine psychiatric disorder prevalence rates.

CONCLUSIONS

Significant heterogeneity in depressive symptomatology exists in this U.S. sample. Profiling symptom patterns is potentially useful as a first step in developing tailored intervention and treatment programmes.

Development and validation of an infant morbidity index using latent variable models

Birth defect, abnormal condition of the newborn, developmental delay or disability and low birth weight are four major infant morbidity outcomes. Most studies have focused on assessment of the effects of risk factors on each of these outcomes or of the relationship among these outcomes or both. Little attention has been paid to the development of a composite index, which is a summary construct of infant morbidity outcomes. In this paper, we develop extended latent variable (LV) models and modified Gauss-Newton algorithms for multiple multinomial morbidity outcomes with complete responses. By assuming the marginal distribution of the LV to be log-normal, we model the conditional probability of each outcome as a nonlinear function of the LV, which has properties similar to the logistic function. The estimated generalized nonlinear least-square method is used to solve equations for parameters of interest. The models are applied to an infant morbidity data set. A new single variable, called infant morbidity index (IMI) that functions as a summary of four infant morbidity outcomes and represents propensity for infant morbidity, is developed. The validity of this index is then assessed in detail. It is shown that the IMI is correlated with each of the individual outcomes, with infant mortality and with a face-valid index of morbidity outcomes, and can be used in future research as a measure of propensity for infant morbidity.

Fatigue in the U.S. Workforce: Prevalence and Implications for Lost Productive Work Time

OBJECTIVE

The objective of this study was to estimate fatigue prevalence and associated health-related lost productive time (LPT) in U.S. workers.

METHODS

Fatigue prevalence, LPT due to fatigue, and LPT for any health-related reason (in hours and dollars) were measured in a national cross-sectional telephone survey of U.S. workers.

RESULTS

The 2-week period prevalence of fatigue was 37.9%. Of workers with fatigue, 65.7% reported health-related LPT compared with 26.4% of those without fatigue. Workers with fatigue cost employers 136.4 billion dollars annually in health-related LPT, an excess of 101.0 billion dollars compared with workers without fatigue. Fatigue frequently co-occurs with other conditions and, when present, is associated with a threefold increase, on average, in the proportion of workers with condition-specific LPT.

CONCLUSIONS

Fatigue is prevalent in the U.S. workforce. When occurring with other health conditions, it is associated with significantly more condition-specific LPT.

A Latent Class Analysis of Injury Patterns Among Rear-Seated, Seat-Belted Children

BACKGROUND

The objective of the study was to determine the constellation of injury patterns in rear-seated, seat-belt-restrained children using data-driven latent class methodology novel to injury prevention research.

METHODS

A cross-sectional probability sample of rear-seated, belt-restrained children aged 5 to 15 years in crashes was obtained via insurance claims records and a telephone survey. Eight body regions of AIS 2 or greater injury (concussion, nonconcussive head injury, face, chest, abdomen, neck/spine/back, upper extremity, lower extremity) were determined, and a latent class model was fit to determine whether underlying "injury clusters" were present.

RESULTS

A three-class model appears to best fit to observed data: an "abdominal/spine" cluster that contained 11% of the population, a "concussion" cluster that contained 56% of the population, and a residual "mixture" cluster that contained the remaining 33% of the population. When compared with the mixture cluster, the abdomen/spine cluster was associated with 4 to 8 year old children, lap-only belt restraint, frontal impacts, and minivans. The concussion cluster was also more common among 4 to 8 and 9 to 12 year olds, with side or rear impact crashes, and with pickup trucks.

CONCLUSIONS

Latent class analysis allows injury clusters to be estimated from the data, not predetermined by the investigator, and suggests that distinct mechanisms of abdominal injury and concussive head injury exist in a population-based sample of children in motor vehicle crashes.

Depression syndromes with risk of alcohol dependence in adulthood: a latent class analysis

Using prospectively gathered data, we assessed whether depression is associated a risk for late-onset alcohol dependence, and whether that relationship differed by gender. The baseline interview was completed in 1981 (mean age=41.7 years, standard deviation (S.D.)=17.0, range 18-86) on a probability sample of Baltimore residents as part of the Epidemiologic Catchment Area Program. Between 1993 and 1996, the original cohort was traced (73% of the survivors were re-interviewed, n=1920). Baseline depression items were subjected to gender-specific latent class analyses prior to exploring associations between class membership and two classifications of alcohol dependence: (1) lifetime prevalence, and (2) new onset assessed at follow-up. A depression syndrome class was identified (24% of the females and 20% of the males). The odds of lifetime alcohol dependence among those in the depressive syndrome class was significantly elevated for both sexes, relative to the non-depressed class. However, no appreciable association was found for depressive syndrome with the development of alcohol dependence. In this sample of middle-aged adults, the evidence supports an association for the presence of a depressive syndrome with lifetime alcohol dependence, but not for the new onset of alcohol dependence. Other predictors of alcohol dependence identified in the analyses are discussed.

Using a Bayesian latent growth curve model to identify trajectories of positive affect and negative events following myocardial infarction

Positive and negative affect data are often collected over time in psychiatric care settings, yet no generally accepted means are available to relate these data to useful diagnoses or treatments. Latent class analysis attempts data reduction by classifying subjects into one of K unobserved classes based on observed data. Latent class models have recently been extended to accommodate longitudinally observed data. We extend these approaches in a Bayesian framework to accommodate trajectories of both continuous and discrete data. We consider whether latent class models might be used to distinguish patients on the basis of trajectories of observed affect scores, reported events, and presence or absence of clinical depression.

The identification of OCD-related subgroups based on comorbidity

BACKGROUND

Individuals with obsessive-compulsive disorder (OCD) frequently have other psychiatric disorders. This study employed latent class analysis (LCA) to explore whether there are underlying clinical constructs that distinguish "OCD-related" subgroups.

METHODS

The study included 450 subjects, case and control probands and their first-degree relatives, and LCA was used to derive empirically based subgroups of 10 disorders: OCD, obsessive-compulsive personality disorder (OCPD), recurrent major depressive disorder (RMDD), separation anxiety disorder, panic disorder or agoraphobia (PD/AG), tic disorders (TD), generalized anxiety disorder (GAD), somatoform disorders (hypochondriasis or body dysmorphic disorder), pathologic skin picking or nail biting (PSP/NB), and eating disorders (EDs). The derived classes were compared on several clinical variables.

RESULTS

The best fitting model is a four-class structure: minimal disorder, predominant RMDD and GAD, "highly comorbid," and PD/AG and TD. The nature and number of disorders represented suggests that the first classes are distributed ordinarily on a dimension of severity, and the fourth class is qualitatively distinct. Support for this structure is based on the number of disorders, age at onset of OCD, neuroticism, and extraversion.

CONCLUSIONS

In this OCD enriched sample, LCA identified four classes of disorder. These classes appear to conform to two subgroups that may prove useful in investigating the etiology of OCD.

Toward validation of atypical depression in the community: results of the Zurich cohort study

AIMS

This paper (1) examines the validity of the atypical subtype of depression in a community-based longitudinal cohort study, (2) presents estimates of the prevalence and sex differences of DSM-IV atypical depression and a newly more broadly defined atypical syndrome in the community and (3) compares the clinical correlates and treatment patterns of those with atypical depression with other depressives.

METHODS

The Zurich cohort study is comprised of 591 subjects selected from a population-based cohort of young adults representative of the canton of Zurich in Switzerland, who were screened in 1978 with the Symptom Checklist 90-R [L.R. Derogatis (1977)] and followed prospectively with five interviews between 1979 and 1993. Atypical depression was defined on a spectrum ranging from atypical major to minor to atypical depressive symptoms alone.

RESULTS

The rate of DSM-IV atypical major depressive episodes in this community is 4.8% and for major atypical depression syndrome is 7.3%. Whereas there was no marked sex difference for nonatypical features, there was a significant female preponderance for DSM-IV and broadly defined atypical depressive subtypes. Systematic investigation of the diagnostic criteria for atypical depression revealed that a nonhierarchical definition of atypical depression with respect to mood reactivity yielded as valid a syndromic definition as the current hierarchy based on mood reactivity as an essential feature. Very high comorbidity (odd ratios>2.0) was found with seasonality, bipolar II, social phobia, binge eating, neurasthenia and sociopathy.

LIMITATIONS

Atypical depression was not defined à priori, its criteria were derived from two sections of the Zurich interview.

CONCLUSIONS

Atypical depression has high population prevalence and substantial significance in terms of clinical severity, impairment, and service use. The intriguing finding that the sex difference in depression may be attributed to atypical features of depression will need further investigation. Overall, our data indicate that the atypical subtype of depression is a valid entity based on evidence from such traditional indicators of validity as inclusion criteria and indicators of course. However, there are some problems with discriminatory validity from other disorders. Although comorbidity with these disorders may in part reflect an operational artifact of symptom overlap, further work needs to be done in distinguishing atypical depression from bipolar II.

Methods for evaluating the performance of diagnostic tests in the absence of a gold standard: a latent class model approach

In many areas of medical research, 'gold standard' diagnostic tests do not exist and so evaluating the performance of standardized diagnostic criteria or algorithms is problematic. In this paper we propose an approach to evaluating the operating characteristics of diagnoses using a latent class model. By defining 'true disease' as our latent variable, we are able to estimate sensitivity, specificity and negative and positive predictive values of the diagnostic test. These methods are applied to diagnostic criteria for depression using Baltimore's Epidemiologic Catchment Area Study Wave 3 data.

The subtypes of major depression in a twin registry

OBJECTIVE

The subtypes of major depression (MD) remain incompletely understood. While there is consensus about the existence of MD with 'typical' vegetative features, further data are required to evaluate the existence of MD with atypical features.

METHOD

Assessment of MD symptomatology in year prior to interview was available in 6846 individual twins from a population-based twin registry. The nine 'A' criteria for DSM-IV MD were unpacked so that the nature of sleep disturbance, appetite and weight changes, and motoric alterations were recorded. Latent class analysis was used to create an empirical typology of MD.

RESULTS

Seven latent classes appeared to provide the best representation of the data. The most severe of these classes had interpretable profiles corresponding to typical MD, atypical MD, and 'minor' but seemingly important depressive states. These classes were generally more deviant than a comparison group for nearly all available validators. There tended to be a gradient with the typical class being most extreme, minor depressive classes the least extreme, and the atypical class having an intermediate position.

CONCLUSIONS

Our findings support the existence of atypical depression as a phenomenological subtype of MD. Besides the differences in symptom patterns, there are many more similarities than differences across a range of external validators. Similar to other reports, we found evidence of the importance and morbidity of depressive symptomatology that does not meet the DSM-IV MD thresholds.

Neuropsychiatric disturbance in Alzheimer's disease clusters into three groups: the Cache County study

OBJECTIVE

We investigated the frequency and inter-relationship of neuropsychiatric disturbances in a population sample of persons suffering from Alzheimer's disease (AD).

METHOD

Screening 5,092 elderly residents (90% of the population aged 65 and older) of Cache County, Utah, for dementia, we identified 198 persons with AD using a comprehensive neuropsychiatric examination protocol. This examination included the Neuropsychiatric Inventory (NPI), a widely used measure of dementia-associated neuropsychiatric disturbances.

RESULTS

Overall, 60% of individuals with AD reported one or more neuropsychiatric symptoms. A latent class analysis revealed that these participants could be classified into three groups (classes) based on their neuropsychiatric symptom profile. The largest class included cases with no neuropsychiatric symptoms (40%) or with a mono-symptomatic disturbance (19%). A second class (28%) exhibited a predominantly affective syndrome, while a third class (13%) had a psychotic syndrome.

CONCLUSION

Data from this first US population-based study of AD-associated neuropsychiatric disturbances suggest that a significant majority of persons with AD suffer from one or more neuropsychiatric disturbance. Based on phenomenological study, the spectrum of neuropsychiatric symptoms in AD can be empirically classified into three groups: an affective syndrome, a psychotic syndrome and other neuropsychiatric disturbance. The biologic and predictive validity of this classification merits further investigation.

Is clinical depression distinct from subthreshold depressive symptoms? A review of the continuity issue in depression research

Resolving whether subthreshold depressive symptoms exist on a continuum with unipolar clinical depression is important for progress on both theoretical and applied issues. To date, most studies have found that individuals with subthreshold depressive symptoms resemble cases of major depressive disorder along many important dimensions (e.g., in terms of patterns of functional impairment, psychiatric and physical comorbidity, familiality, sleeping EEG, and risk of future major depression). However, such manifest similarities do not rule out the possibility of a latent qualitative difference between subthreshold and diagnosable depression. Formal taxonomic analyses, intended to resolve the possibility of a latent qualitative distinction, have so far yielded contradictory findings. Several large-sample latent class analyses (LCA) have identified latent clinical and nonclinical classes of unipolar depression, but LCA is vulnerable to identification of spurious classes. Paul Meehl's taxometric methods provide a potentially conservative alternative way to identify latent classes. The one comprehensive taxometric analysis reported to date suggests that self-report depression symptoms occur along a latent continuum but exclusive reliance on self-report depression measures and incomplete information regarding sample base rates of depression makes it difficult to draw strong inferences from that report. We conclude that although most of the evidence at this time appears to favor both a manifest and latent continuum of unipolar depression symptomatology, several important issues remain unresolved. Complete resolution of the continuity question would be speeded by the application of both taxometric techniques and LCA to a single large sample with a known base rate of lifetime diagnosed depressives.

Latent class analysis of child behavior checklist anxiety/depression in children and adolescents

OBJECTIVE

Comorbidity of psychiatric problems such as anxiety and depression poses challenges to treatment and research. This study tested whether problem items from the Anxious/Depressed scale of the Child Behavior Checklist (CBCL) can be separated into distinct anxiety and depression classes or are continuously distributed throughout a population.

METHOD

A CBCL was completed by a parent or guardian of each of 1,987 children and adolescents selected to represent nonreferred children in the United States, as well as by a parent or guardian of each of a demographically matched sample of 1,987 clinically referred children and adolescents. Problem items from the Anxious/Depressed scale of the CBCL were subjected to latent class analysis.

RESULTS

Analyses revealed three levels of problem presentation in both samples. Children in the nonreferred sample were classified as having no problems, mild problems, or moderate anxiety/depression problems. Children and adolescents in the referred group were classified as having mild, moderate, or severe levels of problems. No pure anxiety or depression classes were found, only classes containing a mixture of both anxiety and depressive problems. Age, gender, and sample differences were found in class groupings, with nonreferred adolescent girls showing elevated levels of problems.

CONCLUSIONS

Results suggest that the comorbid conditions of anxiety and depression, as assessed by the CBCL anxiety/depression problem items, can be thought of as part of the same continuum of problems. Implications for assessment and treatment utilization are discussed.

Comorbidity and mixed anxiety-depressive disorder: clinical curiosity or pathophysiological need?

The paper reviews available epidemiological evidence for the existence of and the implications of comorbidity of anxiety and depressive disorders and mixed anxiety-depressive (MAD) disorders. Using epidemiolological evidence of prevalence and incidence and data relating to time-course of illness, risk factor and outcome, it is concluded: (1) that anxiety-depression comorbidity is quite frequent in epidemiological and clinical settings throughout the world; (2) this comorbidity is diagnosis-specific and is associated with increased vulnerabilities and risks as well as poorer outcome and marked disabilities; and (3) no such evidence was found for MAD disorders. Contrary to what was predicted, the prevalence of MAD disorders was quite low even when using the more recent criteria of the Diagnostic and Statistical Manual of Mental Disorders, 4th edition. (4) Furthermore, there was quite a heterogeneous pattern in terms of risk, severity and outcome making it questionable whether this disorder, as currently defined, is a clinical entity. These findings are discussed in terms of two perspectives, the 'lumpers' with their dimensional view and the 'splitters' with their categorical view. It is concluded that although comorbidity of threshold anxiety and depressive disorders seems to be an important phenomenon, no such evidence is provided for MAD disorders. Copyright 2001 John Wiley & Sons, Ltd.

Latent class model diagnosis

In many areas of medical research, such as psychiatry and gerontology, latent class variables are used to classify individuals into disease categories, often with the intention of hierarchical modeling. Problems arise when it is not clear how many disease classes are appropriate, creating a need for model selection and diagnostic techniques. Previous work has shown that the Pearson chi 2 statistic and the log-likelihood ratio G2 statistic are not valid test statistics for evaluating latent class models. Other methods, such as information criteria, provide decision rules without providing explicit information about where discrepancies occur between a model and the data. Identifiability issues further complicate these problems. This paper develops procedures for assessing Markov chain Monte Carlo convergence and model diagnosis and for selecting the number of categories for the latent variable based on evidence in the data using Markov chain Monte Carlo techniques. Simulations and a psychiatric example are presented to demonstrate the effective use of these methods.

Understanding the heterogeneity of depression through the triad of symptoms, course and risk factors: a longitudinal, population-based study

BACKGROUND

There is an ongoing research effort to test if depression is a homogeneous clinical syndrome and to identify valid and useful subtypes based on the number and nature of depressive symptoms. This study summarizes the patterns of depressive symptoms evident in a prospective study of the general population and examines the validity of potential subtypes by studying their course and etiologic heterogeneity.

METHODS

A general population sample of 1920 adults (aged 18-96) from the Baltimore Epidemiologic Catchment Area (ECA) follow-up study (1981 to 1993/6) were examined. Data on diagnoses, symptoms, course and risk factors were collected using the Diagnostic Interview Schedule (DIS). Latent class analysis was applied to summarize symptom patterns. Course characteristics and risk factor profiles were compared among potential subtypes based on the number of symptom groups or symptom patterns. Logistic regression models were used to examine the etiologic heterogeneity among potential subtypes based on symptoms.

RESULTS

The number of symptom groups gave the most efficient insight into differential etiologic processes. Severe depression (7-9 symptom groups) was associated with female gender, family history of depression but not with stressful life events before the onset of the first episode. Moderate (5-6 symptom groups) and mild depression (3-4 symptom groups) were associated with family history of depression, stressful life events before the onset, but not with female gender. The latent class model generated patterns of depressive psychopathology as follows: anhedonia, suicidal, psychomotor, and severely depressed subtypes. The Anhedonia subtype showed a course and risk factor profile distinct from the others.

LIMITATIONS

The measurement of psychopathology was based on self-reported DIS interviews instead of psychiatric assessments. Recall or report bias cannot be excluded in the ascertainment of family history and stressful life events.

CONCLUSIONS

Depression is heterogeneous, even below the threshold of syndromal diagnosis. The severity of an episode appears to be more informative than the pattern of symptoms, with the possible exception of a putative anhedonic subtype.

Psychiatric epidemiology

Statisticians working in psychiatric epidemiology regularly confront a variety of problems that are rare in other branches of epidemiology. These problems range from the frequent absence of 'objective' biological markers and consequent reliance on informant and self-reports of considerable fallability, to the perhaps corresponding frequent use of multivariate statistical methods such as latent variable modelling. Equally, such difficulties can be regarded as challenges that offer the statistician a critical role.

Latent class and factor analysis of DSM-IV ADHD: a twin study of female adolescents

OBJECTIVE

In an attempt to validate the current DSM-IV criteria for attention-deficit/hyperactivity disorder (ADHD) in females and to determine whether symptoms are continuously distributed or categorically discrete, the authors performed factor and latent class analysis on ADHD symptom data from a large general population of adolescent female twins (1,629 pairs).

METHOD

A structured diagnostic assessment of DSM-IV ADHD was completed with at least one parent of 1,629 pairs by telephone. ADHD symptoms from 1,549 pairs were subjected to latent class and factor analysis.

RESULTS

Latent class and factor analyses were consistent with the presence of separate continuous domains of inattention (ATT), hyperactivity-impulsivity (H-I), and combined ATT with H-I problems. Severe latent classes corresponding to the predominantly inattentive, predominantly hyperactive-impulsive, and combined types were identified with lifetime prevalence estimates of 4.0%, 2.2%, and 3.7%, respectively. Membership in the severe ATT class predicted academic problems, family problems, and referral to health care providers. Membership in the H-I and combined classes also predicted impaired social relationships.

CONCLUSIONS

These results suggest that DSM-IV ADHD subtypes can be thought of as existing on separate continua of inattention, hyperactivity-impulsivity, and combined type problems. Membership in any of there severe ADHD latent classes did not preclude academic excellence, but it was associated with different types of impairment and health care-seeking behavior. These data have implications in the areas of diagnosis, classification, treatment, and research.

The 'research magnificent' in late life: psychiatric epidemiology and the primary health care of older adults

OBJECTIVE

Primary care occupies a strategic positive in the evaluation, treatment, and prevention of the mental disturbances of later life. This article highlights four themes that are crucial to understanding mental disturbances among older adults: 1) subsyndromal depression, 2) coexisting depression and anxiety, 3) comorbidity of depression and chronic medical conditions, and 4) risk factors for cognitive impairment.

METHOD

The literature was selectively reviewed for each theme to ask the central question, "What can primary care physicians learn about mental disturbances of their older patients from epidemiologic and community studies?"

RESULTS

The primary care setting itself is an important venue for an examination of aging issues and mental health. Workers in the "middle ground of psychiatric epidemiology"--primary health care--have not yet reached a full appreciation for the value of research in the primary care setting for enhancing our understanding of the mental disturbances of late life, and how these intersect with other salient factors.

CONCLUSIONS

Primary care physicians and others who work in primary care should advocate for further mental health integration and research in primary care. Research is needed that will lead to new ways of maximizing the health and quality of life of older adults and their families.

Boundaries of major depression: an evaluation of DSM-IV criteria

OBJECTIVE

Little is known about the boundaries between major depression and milder subsyndromal depressive states. With respect to depressive symptoms, does DSM-IV "carve nature at its joints"?

METHOD

In personally interviewed female twins from a population-based registry, the authors examined whether a range of values along three dimensions of the depressive syndrome assessed in the last year (number of symptoms listed in DSM-III-R under diagnostic criterion A for major depressive episode, level of severity or impairment required to score symptoms as present, and duration of episode) predicted future depressive episodes in the index twin and risk of major depression in the co-twin.

RESULTS

An increasing number of criterion A symptoms predicted, in a monotonic fashion, a greater risk for future depressive episodes in the index twin as well as a greater risk for major depression in the co-twin. No such consistent relationship was seen with duration of episode. For severity, a single monotonic function predicted risk in the co-twin, while index twins with severe impairment had a substantially higher risk for future episodes than did those with less severe impairment. Four or fewer criterion A symptoms, syndromes composed of symptoms involving no or minimal impairment, and episodes of less than 14 days' duration all significantly predicted both future depressive episodes in the index twin and risk of major depression in the co-twin.

CONCLUSIONS

The authors found little empirical support for the DSM-IV requirements for 2 weeks' duration, five symptoms, or clinically significant impairment. Most functions appeared continuous. These results suggest that major depression--as articulated by DSM-IV--may be a diagnostic convention imposed on a continuum of depressive symptoms of varying severity and duration.

Development of a scale to measure symptoms of anxiety and depression in the general UK population: the psychiatric symptom frequency scale

OBJECTIVES

The psychiatric symptom frequency (PSF) scale was developed to assess symptoms of anxiety and depression (i.e. affective symptoms) experienced over the past year in the general population. This study aimed to examine the distribution of PSF scores, internal consistency, and factor structure and to investigate relationships between total scores for this scale and other indicators of poor mental health.

PARTICIPANTS

The Medical Research Council national survey of health and development, a class stratified cohort study of men and women followed up from birth in 1946, with the most recent interview at age 43 when the PSF scale was administered.

MAIN RESULTS

The PSF scale showed high internal consistency between the 18 items (Cronbach's alpha = 0.88). Ratings on items of the scale reflected one predominant factor, incorporating both depression and anxiety, and two additional factors of less statistical importance, one reflecting sleep problems and the other panic and situational anxiety. Total scores were calculated by adding 18 items of the scale, and high total scores were found to be strongly associated with reports of contact with a doctor or other health professional and use of prescribed medication for "nervous or emotional trouble or depression," and with suicidal ideas.

CONCLUSIONS

The PSF is a useful and valid scale for evaluating affective symptoms in the general population. It is appropriate for administration by lay interviewers with minimal training, is relatively brief, and generates few missing data. The total score is a flexible measure which can be used in continuous or binary form to suit the purposes of individual investigations, and provides discrimination at lower as well as upper levels of symptom severity.

Identification of symptomatologic patterns common to major psychoses: proposal for a phenotype definition

Our study was designed to identify the underlying symptomatologic structure common to major psychoses as a preliminary step for a phenotype definition. We investigated 1,004 inpatients affected by mood disorders or the schizophrenia spectrum (DSM-III-R) using the OPCRIT checklist (operational criteria checklist for psychotic illness). Symptomatologic structure was extracted by factor analytic techniques and factor scores were first obtained on 500 subjects. A CFA (confirmatory factor analysis) was then conducted on the remaining 504 subjects to evaluate fitness of the model. We identified four factors: excitement, depression, disorganization, and delusion. These factors accounted for 54.6% of the total variance of the OPCRIT checklist symptomatologic subset of 38 items. CFA indices showed a good fit for the model. We identified symptomatologic structures common to major psychoses. The factors identified were confirmed in an independent sample. Two of these symptomatologic structures are partially overlapping with categorical diagnoses (excitement and depression), and two constitute independent psychopathologic traits (delusion and disorganization). The use of "factor-derived scores" in genetic research may add a dimensional definition to the diagnostic subdivision of major psychoses.