An examination of generalized anxiety disorder and dysthymic disorder by latent class analysis

2013 ◽  
Vol 44 (8) ◽  
pp. 1701-1712 ◽  
Author(s):  
D. Rhebergen ◽  
I. M. van der Steenstraten ◽  
M. Sunderland ◽  
R. de Graaf ◽  
M. ten Have ◽  
...  

BackgroundThe nosological status of generalized anxiety disorder (GAD) versus dysthymic disorder (DD) has been questioned. The aim of this study was to examine qualitative differences within (co-morbid) GAD and DD symptomatology.MethodLatent class analysis was applied to anxious and depressive symptomatology of respondents from three population-based studies (2007 Australian National Survey of Mental Health and Wellbeing; National Comorbidity Survey Replication; and Netherlands Mental Health Survey and Incidence Study-2; together known as the Triple study) and respondents from a multi-site naturalistic cohort [Netherlands Study of Depression and Anxiety (NESDA)]. Sociodemographics and clinical characteristics of each class were examined.ResultsA three-class (Triple study) and two-class (NESDA) model best fitted the data, reflecting mainly different levels of severity of symptoms. In the Triple study, no division into a predominantly GAD or DD co-morbidity subtype emerged. Likewise, in spite of the presence of pure GAD and DD cases in the NESDA sample, latent class analysis did not identify specific anxiety or depressive profiles in the NESDA study. Next, sociodemographics and clinical characteristics of each class were examined. Classes only differed in levels of severity.ConclusionsThe absence of qualitative differences in anxious or depressive symptomatology in empirically derived classes questions the differentiation between GAD and DD.

2021 ◽  
Vol 12 ◽  
Author(s):  
Nnamdi Nkire ◽  
Kelly Mrklas ◽  
Marianne Hrabok ◽  
April Gusnowski ◽  
Wesley Vuong ◽  
...  

Introduction: With the sudden onset and global dispersal of the SARS-CoV-2 virus, many nations including Canada attempted to reduce spread of the resultant COVID-19 syndrome with self-isolation and quarantine, while seeking a cure or vaccine for this disease. Understanding impacts of self-isolation and self-quarantine on stress, anxiety, and depression will help us to mitigate these issues through appropriate development of mental health services.Methods: The sample was drawn from individuals who self-subscribed to Text4Hope, a service that delivers text messages based on a cognitive behavioral therapy framework. Text4Hope was developed to support Albertans during the COVID-19 pandemic. Subscribers were asked for demographic information and if they had to self-isolate or self-quarantine during the pandemic via a survey link. Mental health was assessed using the validated instruments: Perceived Stress Scale (PSS), Generalized Anxiety Disorder-7 item scale (GAD-7), and the Patient Health Questionnaire-9 (PHQ-9). Descriptive statistics and Chi-Square test results were derived using Statistical Package for Social Sciences (SPSS) version-26.Results: 6,041 of 32,805 Text4Hope subscribers (18.4%) completed the survey. Of these respondents, 19.2% had self-isolated or self-quarantined in Alberta as of March 31, 2020 during the COVID-19 pandemic. Post-hoc analysis using adjusted residuals suggested that individuals aged 60 years of age or older, and retirees had a higher likelihood of self-isolation or self-quarantine, compared to respondents with other age or employment characteristics. One-week prevalence rates for self-reported measures of moderate to high stress, likely Generalized Anxiety Disorder (GAD), and likely Major Depressive Disorder (MDD) were 84.9, 46.7, and 41.4%, respectively. Respondents who had to self-isolate or self-quarantine during the COVID-19 pandemic were significantly more likely to present with moderate to high stress, significant anxiety symptomatology, and significant depressive symptomatology.Conclusions: Older age and employment status were significantly associated with the likelihood of self-isolation or self-quarantine. We found elevated self-reported levels of anxiety and depression associated with self-reported COVID-19 pandemic-related self-isolation and self-quarantine activity. These findings have mental health implications both during and after the pandemic and demonstrate the need for greater focus on psychological complications of self-isolation and self-quarantine, and development of optimal ways to manage these pandemic consequences.


2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 694.3-695
Author(s):  
K. Wójcik ◽  
A. Ćmiel ◽  
A. Masiak ◽  
Z. Zdrojewski ◽  
R. Jeleniewicz ◽  
...  

Background:ANCA associated vasculitides (AAV) are a heterogeneous group of rare diseases with unknown etiology and the clinical spectrum ranging from life-threatening systemic disease, through single organ involvement to minor isolated skin changes. Thus there is an unmet need for phenotype identification especially among patients with granulomatosis with polyangiitis GPA, patients with microscopic polyangiitis MPA group seems to be more uniform. Recently, based on previous clustering analysis and clinical, histopathological, serological and prognostic aspects three subcategories of AAV have been proposed and named as: non-severe AAV, severe PR3-AAV and severe MPO-AAV [1].Objectives:In line with these attempts to subcategorize AAV we decided to use latent class analysis (LCA) on a large multicenter cohort of polish AAV patients from POLVAS [2] registry to identify potential new subphenotypes or confirm already proposed ones.Methods:Latent Class Analysis (LCA) approach was used as a model based clustering method of objects described by dichotomous (e.g., gender; ANCA status – cANCA, pANCA; organ involvement - skin, eye, ENT, respiratory, heart, GI, renal, urinary, CNS, peripheral nerves) and polytomous (number of relapses) variables supported by quantitative covariates (e.g., age at diagnosis, CRP at diagnosis, maximal serum creatinine concentration ever).Results:Results of LCA on our AAV group returned four class model of AAV subphenotypes, confirming existence of the previously proposed by Mahr at al. [1] and revealed fourth – previously not described clinically relevant subphenotype. To this fourth class - belong patients only with GPA, diagnosed at young age, with multiorgan involvement, high relapse rate and relatively high risk of death.Table 1.AAV subcategorization – summary of clinical characteristics and ANCA specificityLCA Class 1LCA Class 2LCA Class 3LCA Class 4No of patients13019410297AAV typeMainly GPAMainly GPAmainly MPAOnly GPAAge at diagnosisMiddle ageMiddle ageOldYoungMale/female ratio1:22:11:11:1Main organ involvementENT, respiratory, eyeRenal, respiratory, ENTRenal, respiratory, skinMultiorgan involvementRelapse rateintermediateintermediatelowhighModified class description (based on ref. [1])Non severe AAVSevere PR3 AAVSevere MPO AAVSevere non-renalPR3 AAVConclusion:Based on multiple clinical and serological variables LCA methodology identified 4-class subphenotypes model of AAV. Fourth-class is a new clinically important subphenotype including exclusively PR3-positive young AAV patients with multiorgan involvement, high risk of relapse and distinct mortality.References:[1]Mahr A, Specks U, Jayne D. Subclassifying ANCA-associated vasculitis: a unifying view of disease spectrum. Rheumatol Oxf Engl 2019;58:1707–9.https://doi.org/10.1093/rheumatology/kez148.[2]Wójcik K, Wawrzycka-Adamczyk K, Włudarczyk A, Sznajd J, Zdrojewski Z, Masiak A, i in. Clinical characteristics of Polish patients with ANCA-associated vasculitides—retrospective analysis of POLVAS registry. Clinical Rheumatology. 1 wrzesień 2019;38(9):2553–63.Disclosure of Interests:Krzysztof Wójcik: None declared, Adam Ćmiel: None declared, Anna Masiak: None declared, Zbigniew Zdrojewski: None declared, Radoslaw Jeleniewicz: None declared, Maria Majdan Consultant of: Roche, Amgen, Speakers bureau: Roche, Amgen, Iwona Brzosko: None declared, Marek Brzosko: None declared, Piotr Głuszko: None declared, Małgorzata Stasiek: None declared, Małgorzata Wisłowska: None declared, Joanna Kur-Zalewska: None declared, Marta Madej: None declared, Anna Hawrot-Kawecka: None declared, Hanna Storoniak: None declared, Barbara Bułło-Piontecka: None declared, Alicja Dębska-Ślizień: None declared, Eugeniusz Kucharz: None declared, Katarzyna Jakuszko: None declared, Jacek Musiał: None declared


Author(s):  
Bruce G Taylor ◽  
Weiwei Liu ◽  
Elizabeth A. Mumford

The purpose of this study is to understand the availability of employee wellness programs within law enforcement agencies (LEAs) across the United States, including physical fitness, resilience/wellness, coping skills, nutrition, mental health treatment, and substance use treatment. The research team investigated whether patterns of LEA wellness programming are identifiable and, if so, what characteristics describe these patterns. We assess using latent class analysis whether there are distinct profiles of agencies with similar patterns offering different types of wellness programs and explore what characteristics distinguish agencies with certain profiles of wellness programming. Data were from a nationally representative sample of 1135 LEAs: 80.1% municipal, 18.6% county and 1.3% other agencies (state-level and Bureau of Indian Affairs LEAs). We found that many agencies (62%) offer no wellness programming. We also found that 23% have comprehensive wellness programming, and that another group of agencies specialize in specific wellness programming. About 14% of the agencies have a high probability of providing resilience coping skill education, mental health and/or substance use treatment services programming. About 1% of the agencies in the United States limit their programming to fitness and nutrition, indicating that fitness and nutrition programs are more likely to be offered in concert with other types of wellness programs. The analyses revealed that agencies offering broad program support are more likely to be large, municipal LEAs located in either the West, Midwest or Northeast (compared with the southern United States), and not experiencing a recent budget cut that impacted wellness programming.


2020 ◽  
Author(s):  
Fei Wang

BACKGROUND The novel coronavirus disease 2019 (COVID-19) is a global public health emergency that has caused worldwide concern. The mental health of medical students under the COVID-19 epidemic has attracted much attention. OBJECTIVE This study aims to identify subgroups of medical students based on mental health status and explore the influencing factors during the COVID-19 epidemic in China. METHODS A total of 29,663 medical students were recruited during the epidemic of COVID-19 in China. Latent class analysis of the mental health of medical students was performed using M-plus software to identify subtypes of medical students. The latent class subtypes were compared using the chi-square test. Multinomial logistic regression was used to examine associations between identified classes and related factors. RESULTS In this study, three distinct subgroups were identified, namely, the high-risk group, the low-risk group and the normal group. Therefore, medical students can be divided into three latent classes, and the number of students in each class is 4325, 9321 and 16,017. The multinomial logistic regression results showed that compared with the normal group, the factors influencing mental health in the high-risk group were insomnia, perceived stress, family psychiatric disorders, fear of being infected, drinking, individual psychiatric disorders, sex, educational level and knowledge of COVID-19, according to the intensity of influence from high to low. CONCLUSIONS Our findings suggested that latent class analysis can be used to categorize different medical students according to their mental health subgroup during the outbreak of COVID-19. The main factors influencing the high-risk group and low-risk group are basic demographic characteristics, disease history, COVID-19 related factors and behavioral lifestyle, among which insomnia and perceived stress have the greatest impact. School administrative departments could utilize more specific measures on the basis of different subgroups, and provide targeted measures.


2021 ◽  
pp. 0095327X2110469
Author(s):  
Scott D. Landes ◽  
Janet M. Wilmoth ◽  
Andrew S. London ◽  
Ann T. Landes

Military suicide prevention efforts would benefit from population-based research documenting patterns in risk factors among service members who die from suicide. We use latent class analysis to analyze patterns in identified risk factors among the population of 2660 active-duty military service members that the Department of Defense Suicide Event Report (DoDSER) system indicates died by suicide between 2008 and 2017. The largest of five empirically derived latent classes was primarily characterized by the dissolution of an intimate relationship in the past year. Relationship dissolution was common in the other four latent classes, but those classes were also characterized by job, administrative, or legal problems, or mental health factors. Distinct demographic and military-status differences were apparent across the latent classes. Results point to the need to increase awareness among mental health service providers and others that suicide among military service members often involves a constellation of potentially interrelated risk factors.


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