Subtyping late-life depression according to inflammatory and metabolic dysregulation: a prospective study.

BACKGROUND: Inflammation and metabolic dysregulation are age-related physiological changes and are associated with depressive disorder. We tried to identify subgroups of depressed older patients based on their metabolic-inflammatory profile and examined the course of depression for these subgroups. METHODS: This clinical cohort study was conducted in a sample of 364 depressed older (⩾60 years) patients according to DSM-IV criteria. Severity of depressive symptoms was monitored every 6 months and a formal diagnostic interview repeated at 2-year follow-up. Latent class analyses based on baseline metabolic and inflammatory biomarkers were performed. Adjusted for confounders, we compared remission of depression at 2-year follow-up between the metabolic-inflammatory subgroups with logistic regression and the course of depression severity over 2-years by linear mixed models. RESULTS: We identified a 'healthy' subgroup (n = 181, 49.7%) and five subgroups characterized by different profiles of metabolic-inflammatory dysregulation. Compared to the healthy subgroup, patients in the subgroup with mild 'metabolic and inflammatory dysregulation' (n = 137, 37.6%) had higher depressive symptom scores, a lower rate of improvement in the first year, and were less likely to be remitted after 2-years [OR 0.49 (95% CI 0.26-0.91)]. The four smaller subgroups characterized by a more specific immune-inflammatory dysregulation profile did not differ from the two main subgroups regarding the course of depression. CONCLUSIONS: Nearly half of the patients with late-life depressions suffer from metabolic-inflammatory dysregulation, which is also associated with more severe depression and a worse prognosis. Future studies should examine whether these depressed older patients benefit from a metabolic-inflammatory targeted treatment.

Subtyping psychological distress in the population: a semi-parametric network approach.

AIMS: The mechanisms underlying both depressive and anxiety disorders remain poorly understood. One of the reasons for this is the lack of a valid, evidence-based system to classify persons into specific subtypes based on their depressive and/or anxiety symptomatology. In order to do this without a priori assumptions, non-parametric statistical methods seem the optimal choice. Moreover, to define subtypes according to their symptom profiles and inter-relations between symptoms, network models may be very useful. This study aimed to evaluate the potential usefulness of this approach. METHODS: A large community sample from the Canadian general population (N = 254 443) was divided into data-driven clusters using non-parametric k-means clustering. Participants were clustered according to their (co)variation around the grand mean on each item of the Kessler Psychological Distress Scale (K10). Next, to evaluate cluster differences, semi-parametric network models were fitted in each cluster and node centrality indices and network density measures were compared. RESULTS: A five-cluster model was obtained from the cluster analyses. Network density varied across clusters, and was highest for the cluster of people with the lowest K10 severity ratings. In three cluster networks, depressive symptoms (e.g. feeling depressed, restless, hopeless) had the highest centrality. In the remaining two clusters, symptom networks were characterised by a higher prominence of somatic symptoms (e.g. restlessness, nervousness). CONCLUSION: Finding data-driven subtypes based on psychological distress using non-parametric methods can be a fruitful approach, yielding clusters of persons that differ in illness severity as well as in the structure and strengths of inter-symptom relationships.

The prevalence, age-of-onset and the correlates of DSM-IV psychiatric disorders in the Tianjin Mental Health Survey (TJMHS).

BACKGROUND: To effectively shape mental healthcare policy in modern-day China, up-to-date epidemiological data on mental disorders is needed. The objective was to estimate the prevalence, age-of-onset (AOO) and sociodemographic correlates of mental disorders in a representative household sample of the general population (age ⩾ 18) in the Tianjin Municipality in China. METHODS: Data came from the Tianjin Mental health Survey (TJMHS), which was conducted between July 2011 and March 2012 using a two-phase design. 11 748 individuals were screened with an expanded version of the General Health Questionnaire and 4438 subjects were selected for a diagnostic interview by a psychiatrist, using the Structured Clinical Interview for the Diagnostic and Statistical Manual - fourth edition (SCID). RESULTS: The lifetime and 1-month prevalence of any mental disorder were 23.6% and 12.8%, respectively. Mood disorders (lifetime: 9.3%; 1-month: 3.9%), anxiety disorders (lifetime: 4.5% 1-month: 3.1%) and substance-use disorders (lifetime: 8.8%; 1-month: 3.5%) were most prevalent. The median AOO ranged from 25 years [interquartile range (IQR): 23-32] for substance-use disorders to 36 years (IQR: 24-50) for mood disorders. Not being married, non-immigrant status (i.e. local 'Hukou'), being a farmer, having <6 years of education and male gender were associated with a higher lifetime prevalence of any mental disorder. CONCLUSION: Results from the current survey indicate that mental disorders are steadily reported more commonly in rapidly-developing urban China. Several interesting sociodemographic correlates were observed (e.g. male gender and non-immigrant status) that warrant further investigation and could be used to profile persons in need of preventive intervention.

Complementary and alternative medicine contacts by persons with mental disorders in 25 countries: results from the World Mental Health Surveys.

AIMS: A substantial proportion of persons with mental disorders seek treatment from complementary and alternative medicine (CAM) professionals. However, data on how CAM contacts vary across countries, mental disorders and their severity, and health care settings is largely lacking. The aim was therefore to investigate the prevalence of contacts with CAM providers in a large cross-national sample of persons with 12-month mental disorders. METHODS: In the World Mental Health Surveys, the Composite International Diagnostic Interview was administered to determine the presence of past 12 month mental disorders in 138 801 participants aged 18-100 derived from representative general population samples. Participants were recruited between 2001 and 2012. Rates of self-reported CAM contacts for each of the 28 surveys across 25 countries and 12 mental disorder groups were calculated for all persons with past 12-month mental disorders. Mental disorders were grouped into mood disorders, anxiety disorders or behavioural disorders, and further divided by severity levels. Satisfaction with conventional care was also compared with CAM contact satisfaction. RESULTS: An estimated 3.6% (standard error 0.2%) of persons with a past 12-month mental disorder reported a CAM contact, which was two times higher in high-income countries (4.6%; standard error 0.3%) than in low- and middle-income countries (2.3%; standard error 0.2%). CAM contacts were largely comparable for different disorder types, but particularly high in persons receiving conventional care (8.6-17.8%). CAM contacts increased with increasing mental disorder severity. Among persons receiving specialist mental health care, CAM contacts were reported by 14.0% for severe mood disorders, 16.2% for severe anxiety disorders and 22.5% for severe behavioural disorders. Satisfaction with care was comparable with respect to CAM contacts (78.3%) and conventional care (75.6%) in persons that received both. CONCLUSIONS: CAM contacts are common in persons with severe mental disorders, in high-income countries, and in persons receiving conventional care. Our findings support the notion of CAM as largely complementary but are in contrast to suggestions that this concerns person with only mild, transient complaints. There was no indication that persons were less satisfied by CAM visits than by receiving conventional care. We encourage health care professionals in conventional settings to openly discuss the care patients are receiving, whether conventional or not, and their reasons for doing so.

The cross-national epidemiology of specific phobia in the World Mental Health Surveys.

BACKGROUND: Although specific phobia is highly prevalent, associated with impairment, and an important risk factor for the development of other mental disorders, cross-national epidemiological data are scarce, especially from low- and middle-income countries. This paper presents epidemiological data from 22 low-, lower-middle-, upper-middle- and high-income countries. METHOD: Data came from 25 representative population-based surveys conducted in 22 countries (2001-2011) as part of the World Health Organization World Mental Health Surveys initiative (n = 124 902). The presence of specific phobia as defined by the Diagnostic and Statistical Manual of Mental Disorders, fourth edition was evaluated using the World Health Organization Composite International Diagnostic Interview. RESULTS: The cross-national lifetime and 12-month prevalence rates of specific phobia were, respectively, 7.4% and 5.5%, being higher in females (9.8 and 7.7%) than in males (4.9% and 3.3%) and higher in high- and higher-middle-income countries than in low-/lower-middle-income countries. The median age of onset was young (8 years). Of the 12-month patients, 18.7% reported severe role impairment (13.3-21.9% across income groups) and 23.1% reported any treatment (9.6-30.1% across income groups). Lifetime co-morbidity was observed in 60.5% of those with lifetime specific phobia, with the onset of specific phobia preceding the other disorder in most cases (72.6%). Interestingly, rates of impairment, treatment use and co-morbidity increased with the number of fear subtypes. CONCLUSIONS: Specific phobia is common and associated with impairment in a considerable percentage of cases. Importantly, specific phobia often precedes the onset of other mental disorders, making it a possible early-life indicator of psychopathology vulnerability.

Dimensional and discrete variations on the psychosis continuum in a Dutch crowd-sourcing population sample.

BACKGROUND: Mild psychotic experiences are common in the general population. Although transient and benign in most cases, these experiences are predictive of later mental health problems for a significant minority. The goal of the present study was to perform examinations of the dimensional and discrete variations in individuals' reporting of subclinical positive and negative psychotic experiences in a unique Dutch internet-based sample from the general population. METHODS: Positive and negative subclinical psychotic experiences were measured with the Community Assessment of Psychic Experiences in 2870 individuals. First, the prevalence of these experiences and their associations with demographics, affect, psychopathology and quality of life were investigated. Next, latent class analysis was used to identify data-driven subgroups with different symptom patterns, which were subsequently compared on aforementioned variables. RESULTS: Subclinical psychotic experiences were commonly reported. Both positive and negative psychotic experiences were associated with younger age, more negative affect, anxiety and depression as well as less positive affect and poorer quality of life. Seven latent classes ('Low psychotic experiences', 'Lethargic', 'Blunted', 'Distressed', 'Paranormal', 'Distressed/grandiose' and 'Distressed/positive psychotic experiences') were identified that demonstrated both dimensional differences in the number/severity of psychotic experiences and discrete differences in the patterns of reported experiences. CONCLUSION: Subclinical psychotic experiences show both dimensional severity variations and discrete symptom-pattern variations across individuals. To understand and capture all interindividual variations in subclinical psychotic experiences, their number, nature and context (co-occurrence patterns) should be considered at the same time. Only some psychotic experiences may lay on a true psychopathological psychosis continuum.

Using patient self-reports to study heterogeneity of treatment effects in major depressive disorder.

BACKGROUNDS: Clinicians need guidance to address the heterogeneity of treatment responses of patients with major depressive disorder (MDD). While prediction schemes based on symptom clustering and biomarkers have so far not yielded results of sufficient strength to inform clinical decision-making, prediction schemes based on big data predictive analytic models might be more practically useful. METHOD: We review evidence suggesting that prediction equations based on symptoms and other easily-assessed clinical features found in previous research to predict MDD treatment outcomes might provide a foundation for developing predictive analytic clinical decision support models that could help clinicians select optimal (personalised) MDD treatments. These methods could also be useful in targeting patient subsamples for more expensive biomarker assessments. RESULTS: Approximately two dozen baseline variables obtained from medical records or patient reports have been found repeatedly in MDD treatment trials to predict overall treatment outcomes (i.e., intervention v. control) or differential treatment outcomes (i.e., intervention A v. intervention B). Similar evidence has been found in observational studies of MDD persistence-severity. However, no treatment studies have yet attempted to develop treatment outcome equations using the full set of these predictors. Promising preliminary empirical results coupled with recent developments in statistical methodology suggest that models could be developed to provide useful clinical decision support in personalised treatment selection. These tools could also provide a strong foundation to increase statistical power in focused studies of biomarkers and MDD heterogeneity of treatment response in subsequent controlled trials. CONCLUSIONS: Coordinated efforts are needed to develop a protocol for systematically collecting information about established predictors of heterogeneity of MDD treatment response in large observational treatment studies, applying and refining these models in subsequent pragmatic trials, carrying out pooled secondary analyses to extract the maximum amount of information from these coordinated studies, and using this information to focus future discovery efforts in the segment of the patient population in which continued uncertainty about treatment response exists.

Casting wider nets for anxiety and depression: disability-driven cross-diagnostic subtypes in a large cohort.

BACKGROUND: In search of empirical classifications of depression and anxiety, most subtyping studies focus solely on symptoms and do so within a single disorder. This study aimed to identify and validate cross-diagnostic subtypes by simultaneously considering symptoms of depression and anxiety, and disability measures. METHOD: A large cohort of adults (Lifelines, n = 73 403) had a full assessment of 16 symptoms of mood and anxiety disorders, and measurement of physical, social and occupational disability. The best-fitting subtyping model was identified by comparing different hybrid mixture models with and without disability covariates on fit criteria in an independent test sample. The best model's classes were compared across a range of external variables. RESULTS: The best-fitting Mixed Measurement Item Response Theory model with disability covariates identified five classes. Accounting for disability improved differentiation between people reporting isolated non-specific symptoms ['Somatic' (13.0%), and 'Worried' (14.0%)] and psychopathological symptoms ['Subclinical' (8.8%), and 'Clinical' (3.3%)]. Classes showed distinct associations with clinically relevant external variables [e.g. somatization: odds ratio (OR) 8.1-12.3, and chronic stress: OR 3.7-4.4]. The Subclinical class reported symptomatology at subthreshold levels while experiencing disability. No pure depression or anxiety, but only mixed classes were found. CONCLUSIONS: An empirical classification model, incorporating both symptoms and disability identified clearly distinct cross-diagnostic subtypes, indicating that diagnostic nets should be cast wider than current phenomenology-based categorical systems.

Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports.

Heterogeneity of major depressive disorder (MDD) illness course complicates clinical decision-making. Although efforts to use symptom profiles or biomarkers to develop clinically useful prognostic subtypes have had limited success, a recent report showed that machine-learning (ML) models developed from self-reports about incident episode characteristics and comorbidities among respondents with lifetime MDD in the World Health Organization World Mental Health (WMH) Surveys predicted MDD persistence, chronicity and severity with good accuracy. We report results of model validation in an independent prospective national household sample of 1056 respondents with lifetime MDD at baseline. The WMH ML models were applied to these baseline data to generate predicted outcome scores that were compared with observed scores assessed 10-12 years after baseline. ML model prediction accuracy was also compared with that of conventional logistic regression models. Area under the receiver operating characteristic curve based on ML (0.63 for high chronicity and 0.71-0.76 for the other prospective outcomes) was consistently higher than for the logistic models (0.62-0.70) despite the latter models including more predictors. A total of 34.6-38.1% of respondents with subsequent high persistence chronicity and 40.8-55.8% with the severity indicators were in the top 20% of the baseline ML-predicted risk distribution, while only 0.9% of respondents with subsequent hospitalizations and 1.5% with suicide attempts were in the lowest 20% of the ML-predicted risk distribution. These results confirm that clinically useful MDD risk-stratification models can be generated from baseline patient self-reports and that ML methods improve on conventional methods in developing such models.

Symptom dimensions of affective disorders in migraine patients.

OBJECTIVE: A strong association has been established between migraine and depression. However, this is the first study to differentiate in a large sample of migraine patients for symptom dimensions of the affective disorder spectrum. METHODS: Migraine patients (n=3174) from the LUMINA (Leiden University Medical Centre Migraine Neuro-analysis Program) study and patients with current psychopathology (n=1129), past psychopathology (n=477), and healthy controls (n=561) from the NESDA (Netherlands Study of Depression and Anxiety) study, were compared for three symptom dimensions of depression and anxiety. The dimensions -lack of positive affect (depression specific); negative affect (nonspecific); and somatic arousal (anxiety specific)- were assessed by a shortened adaptation of the Mood and Anxiety Symptom Questionnaire (MASQ-D30). Within the migraine group, the association with migraine specific determinants was established. Multivariate regression analyses were conducted. RESULTS: Migraine patients differed significantly (p<0.001) from healthy controls for all three dimensions: Cohen's d effect sizes were 0.37 for lack of positive affect, 0.68 for negative affect, and 0.75 for somatic arousal. For the lack of positive affect and negative affect dimensions, migraine patients were predominantly similar to the past psychopathology group. For the somatic arousal dimension, migraine patients scores were more comparable with the current psychopathology group. Migraine specific determinants for high scores on all dimensions were high frequency of attacks and cutaneous allodynia during attacks. CONCLUSION: This study shows that affective symptoms in migraine patients are especially associated with the somatic arousal component.

Symptom-specific course trajectories and their determinants in primary care patients with Major Depressive Disorder: Evidence for two etiologically distinct prototypes.

BACKGROUND: The course-heterogeneity of Major Depressive Disorder (MDD) hampers development of better prognostic models. Although latent class growth analyses (LCGA) have been used to explain course-heterogeneity, such analyses have failed to also account for symptom-heterogeneity of depressive symptoms. Therefore, the aim was to identify more specific data-driven subgroups based on patterns of course-trajectories on different depressive symptom domains. METHODS: In primary care MDD patients (n=205), the presence of the MDD criterion symptoms was determined for each week during a year. Weekly 'mood/cognition' (MC) and 'somatic' (SOM) scores were computed and parallel processes-LCGA (PP-LCGA) was used to identify subgroups based on the course on these domains. The classes׳ associations with baseline predictors and 2-/3-year outcomes were investigated. RESULTS: PP-LCGA identified four classes: quick recovery, persisting SOM, persisting MC, and persisting SOM+MC (chronic). Persisting SOM was specifically predicted by higher baseline somatic symptomatology and somatization, and was associated with more somatic depressive symptomatology at long-term follow-up. Persisting MC was specifically predicted by higher depressive severity, thinking insufficiencies, neuroticism, loneliness and lower self-esteem, and was associated with lower mental health related quality of life and more mood/cognitive depressive symptomatology at follow-up. LIMITATIONS: The sample was small and contained only primary care MDD patients. The weekly depression assessments were collected retrospectively at 3-month intervals. CONCLUSIONS: The results indicate that there are two specific prototypes of depression, characterized by either persisting MC or persisting SOM, which have different sets of associated prognostic factors and long-term outcomes, and could have different etiological mechanisms.

Exploring the underlying structure of mental disorders: cross-diagnostic differences and similarities from a network perspective using both a top-down and a bottom-up approach.

BACKGROUND: It has been suggested that the structure of psychopathology is best described as a complex network of components that interact in dynamic ways. The goal of the present paper was to examine the concept of psychopathology from a network perspective, combining complementary top-down and bottom-up approaches using momentary assessment techniques. METHOD: A pooled Experience Sampling Method (ESM) dataset of three groups (individuals with a diagnosis of depression, psychotic disorder or no diagnosis) was used (pooled N = 599). The top-down approach explored the network structure of mental states across different diagnostic categories. For this purpose, networks of five momentary mental states ('cheerful', 'content', 'down', 'insecure' and 'suspicious') were compared between the three groups. The complementary bottom-up approach used principal component analysis to explore whether empirically derived network structures yield meaningful higher order clusters. RESULTS: Individuals with a clinical diagnosis had more strongly connected moment-to-moment network structures, especially the depressed group. This group also showed more interconnections specifically between positive and negative mental states than the psychotic group. In the bottom-up approach, all possible connections between mental states were clustered into seven main components that together captured the main characteristics of the network dynamics. CONCLUSIONS: Our combination of (i) comparing network structure of mental states across three diagnostically different groups and (ii) searching for trans-diagnostic network components across all pooled individuals showed that these two approaches yield different, complementary perspectives in the field of psychopathology. The network paradigm therefore may be useful to map transdiagnostic processes.

The effects of co-morbidity in defining major depression subtypes associated with long-term course and severity.

BACKGROUND: Although variation in the long-term course of major depressive disorder (MDD) is not strongly predicted by existing symptom subtype distinctions, recent research suggests that prediction can be improved by using machine learning methods. However, it is not known whether these distinctions can be refined by added information about co-morbid conditions. The current report presents results on this question. METHOD: Data came from 8261 respondents with lifetime DSM-IV MDD in the World Health Organization (WHO) World Mental Health (WMH) Surveys. Outcomes included four retrospectively reported measures of persistence/severity of course (years in episode; years in chronic episodes; hospitalization for MDD; disability due to MDD). Machine learning methods (regression tree analysis; lasso, ridge and elastic net penalized regression) followed by k-means cluster analysis were used to augment previously detected subtypes with information about prior co-morbidity to predict these outcomes. RESULTS: Predicted values were strongly correlated across outcomes. Cluster analysis of predicted values found three clusters with consistently high, intermediate or low values. The high-risk cluster (32.4% of cases) accounted for 56.6-72.9% of high persistence, high chronicity, hospitalization and disability. This high-risk cluster had both higher sensitivity and likelihood ratio positive (LR+; relative proportions of cases in the high-risk cluster versus other clusters having the adverse outcomes) than in a parallel analysis that excluded measures of co-morbidity as predictors. CONCLUSIONS: Although the results using the retrospective data reported here suggest that useful MDD subtyping distinctions can be made with machine learning and clustering across multiple indicators of illness persistence/severity, replication with prospective data is needed to confirm this preliminary conclusion.

Allodynia is associated with a higher prevalence of depression in migraine patients.

INTRODUCTION: There is a strong association between migraine and depression. The aim of this study is to identify migraine-specific factors involved in this association. METHODS: We conducted a cross-sectional study in a large, well-defined cohort of migraine patients (n=2533). We assessed lifetime depression using validated questionnaires, and diagnosed migraine based on the International Classification of Headache Disorders III-beta criteria. Multivariate regression analyses were conducted. RESULTS: Of the 2533 migraineurs that were eligible, 1137 (45%) suffered from lifetime depression. The following independent factors were associated with an increased depression prevalence: i) migraine-specific risk factors: high migraine attack frequency and the presence of allodynia, ii) general factors: being a bad sleeper, female gender, high BMI, being single, smoking, and a low alcohol consumption. CONCLUSION: This study identified allodynia, in addition to high migraine attack frequency, as a new migraine-specific factor associated with depression.

Are childhood and adult life adversities differentially associated with specific symptom dimensions of depression and anxiety? Testing the tripartite model.

BACKGROUND: Different types of adverse events may have general or specific effects on depression and anxiety symptomatology. We examined the effects of adversities on the dimensions of the tripartite model: general distress, anhedonic depression and anxious arousal. METHODS: Data were from 2615 individuals from the Netherlands Study for Depression and Anxiety (NESDA), with or without depressive or anxiety disorders. We analysed associations of childhood trauma, childhood life events (childhood trauma interview), and recent life events (List of Threatening Events Questionnaire, LTE-Q) with anhedonic depression, anxious arousal, and general distress (assessed by the adapted Mood and Anxiety Symptoms Questionnaire, MASQ-D30). RESULTS: We controlled for co-occurrence of adversities. Regarding childhood trauma, only emotional neglect was associated with all three symptom dimensions. Psychological and sexual abuse were associated with general distress and anxious arousal, whereas physical abuse was associated only with anxious arousal. Particularly strong associations were seen for emotional neglect with anhedonic depression and for sexual abuse with anxious arousal. Childhood life events showed no associations with symptom dimensions. The recent life events 'Serious problems with friend', 'Serious financial problems', and 'Becoming unemployed' were associated with all three dimensions. The recent life event 'death of parent/child/sibling' was associated with anxious arousal. Several associations remained significant when controlled for current diagnosis of depression or anxiety. LIMITATIONS: Our cross-sectional analyses do not allow for causal interpretation. CONCLUSIONS: Distinct childhood traumas had different effects on the symptom dimensions, whereas most recent adult life events were associated with all three symptom dimensions. Our observations help to understand the often reported associations of these adversities with depressive and anxiety symptomatology. In addition, symptom dimensions of the tripartite model were shown to capture effects of adverse events on top of those captured by diagnostic categories.

The subscale structure of the Inventory of Depressive Symptomatology Self Report (IDS-SR) in older persons.

BACKGROUND: Using symptom dimensions may be more effective than using categorical subtypes when investigating clinical outcome and underlying mechanisms of late-life depression. Therefore, this study aims to identify both the factor and subscale structure of late-life depression underlying the Inventory of Depressive Symptomatology Self Report (IDS-SR) in older persons. METHOD: IDS-SR data of 423 participants in the Netherlands Study of Depression in Older Persons (NESDO) were analyzed by exploratory (EFA) and confirmatory factor analysis (CFA). The best-fitting factor solution in a group of older persons with a major depressive disorder diagnosis in the last month (n = 229) was replicated in a control group of older persons with no or less severe depression (n = 194). Multiple group (MG-CFA) was performed to evaluate generalizability of the best-fitting factor solution across subgroups, and internal consistency coefficients were calculated for each factor. RESULTS: EFA and CFA show that a 3-factor model fits best to the data [comparative fit index (CFI) = 0.98; Tucker Lewis Index (TLI) = 0.99; and root mean square error of approximation (RMSEA) = 0.052], consisting of a 'mood', 'motivation' and 'somatic' factor with adequate internal consistencies (alpha coefficient 0.93, 0.83 and 0.70, respectively). MG-CFA shows a structurally similar factor model across subgroups. CONCLUSION: The IDS-SR can be used to measure three homogeneous symptom dimensions that are specific to older people. Application of these dimensions that may serve as subscales of the IDS-SR may benefit both clinical practice and scientific research.

Utility of the Brief Symptom Inventory (BSI) in psychiatric outpatients with intellectual disabilities.

BACKGROUND: Diagnostics and care for people with intellectual disabilities (ID) and psychiatric disorders need to be improved. This can be done by using assessment instruments to routinely measure the nature and severity of psychiatric symptoms. Up until now, in the Netherlands, assessment measures are seldom used in the psychiatric care for this population. The objective of the present paper is to evaluate the use of the Brief Symptom Inventory (BSI), a widely used standardised questionnaire in general psychiatry, in a well-defined sample of people with borderline intellectual functioning or mild ID diagnosed with one or more psychiatric disorders. METHODS: A total of 224 psychiatric outpatients with either borderline intellectual functioning or mild ID participated in this study. All participants were new patients of Kristal, Centre for Psychiatry and Intellectual Disability in the Netherlands, in the period between 1 April 2008 and 1 October 2009. All participants were assessed by a multidisciplinary team, including a certified psychiatrist. Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) criteria were applied. The mean total intelligence quotient was measured with the Wechsler Adult Intelligence Scale (WAIS-III). The BSI was administered in an assisted fashion. Utility and psychometric properties of the BSI were investigated. Internal consistency coefficients (Cronbach's alphas) were computed. Bivariate correlations between the sub-scales were computed to assess differentiation between the scales. Mean sub-scale scores were compared between different DSM-IV-TR subgroups to investigate the discriminant abilities of the scales. A confirmatory factor analysis was conducted. RESULTS: The results suggest that the BSI is practically useful. Internal consistencies ranged from 0.70 to 0.96 and thus are considered good to adequate. Sub-scale inter-correlations showed there is a degree of differentiation between the sub-scales. Discriminant validity was shown for the sub-scales depression, anxiety and phobic anxiety. Confirmatory factor analysis showed that the underlying structure of the BSI could be described by the same nine-factor model as reported in previous studies. CONCLUSIONS: As a result of the psychometric properties illustrated, this study supports the use of the BSI as a screener for psychopathology and a general outcome measure in people with ID.