Latent class analysis of the feared situations of social anxiety disorder: A population-based study

2016 ◽  
Vol 33 (12) ◽  
pp. 1178-1187 ◽  
Author(s):  
Hugo Peyre ◽  
Nicolas Hoertel ◽  
Fabrice Rivollier ◽  
Benjamin Landman ◽  
Kibby McMahon ◽  
...  
Keyword(s):  
Anxiety Disorder ◽  
Social Anxiety ◽  
Latent Class Analysis ◽  
Social Anxiety Disorder ◽  
Latent Class ◽  
Population Based ◽  
Class Analysis ◽  
Population Based Study

Typical symptom change patterns and their predictors in patients with social anxiety disorder: A latent class analysis

2020 ◽  
Vol 71 ◽  
pp. 102200 ◽  
Author(s):  
Uwe Altmann ◽  
Romina Gawlytta ◽  
Jürgen Hoyer ◽  
Falk Leichsenring ◽  
Eric Leibing ◽  
...  
Keyword(s):  
Anxiety Disorder ◽  
Social Anxiety ◽  
Latent Class Analysis ◽  
Social Anxiety Disorder ◽  
Latent Class ◽  
Class Analysis ◽  
Symptom Change ◽  
Typical Symptom

scholarly journals Estimates and Determinants of SARS-Cov-2 Seroprevalence and Infection Fatality Ratio Using Latent Class Analysis: The Population-Based Tirschenreuth Study in the Hardest-Hit German County in Spring 2020

Viruses ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1118
Author(s):  
Ralf Wagner ◽  
David Peterhoff ◽  
Stephanie Beileke ◽  
Felix Günther ◽  
Melanie Berr ◽  
...  
Keyword(s):  
Latent Class Analysis ◽  
Latent Class ◽  
Care Home ◽  
Case Fatality ◽  
The Elderly ◽  
Population Based ◽  
Class Analysis ◽  
Population Based Study ◽  
Targeted Testing ◽  
June July

SARS-CoV-2 infection fatality ratios (IFR) remain controversially discussed with implications for political measures. The German county of Tirschenreuth suffered a severe SARS-CoV-2 outbreak in spring 2020, with particularly high case fatality ratio (CFR). To estimate seroprevalence, underreported infections, and IFR for the Tirschenreuth population aged ≥14 years in June/July 2020, we conducted a population-based study including home visits for the elderly, and analyzed 4203 participants for SARS-CoV-2 antibodies via three antibody tests. Latent class analysis yielded 8.6% standardized county-wide seroprevalence, a factor of underreported infections of 5.0, and 2.5% overall IFR. Seroprevalence was two-fold higher among medical workers and one third among current smokers with similar proportions of registered infections. While seroprevalence did not show an age-trend, the factor of underreported infections was 12.2 in the young versus 1.7 for ≥85-year-old. Age-specific IFRs were <0.5% below 60 years of age, 1.0% for age 60–69, and 13.2% for age 70+. Senior care homes accounted for 45% of COVID-19-related deaths, reflected by an IFR of 7.5% among individuals aged 70+ and an overall IFR of 1.4% when excluding senior care home residents from our computation. Our data underscore senior care home infections as key determinant of IFR additionally to age, insufficient targeted testing in the young, and the need for further investigations on behavioral or molecular causes of the fewer infections among current smokers.

scholarly journals Patterns and associated factors of diabetes self-management: Results of a latent class analysis in a German population-based study

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248992
Author(s):  
Marcus Heise ◽  
Astrid Fink ◽  
Jens Baumert ◽  
Christin Heidemann ◽  
Yong Du ◽  
...  
Keyword(s):  
Latent Class Analysis ◽  
Latent Class ◽  
Population Based ◽  
Self Management ◽  
Marginal Effect ◽  
Type I ◽  
Class Analysis ◽  
Population Based Study ◽  
Percentage Points ◽  
Management Behaviour

Objective Few studies on diabetes self-management considered the patterns and relationships of different self-management behaviours (SMB). The aims of the present study are 1) to identify patterns of SMB among persons with diabetes, 2) to identify sociodemographic and disease-related predictors of SMB among persons with diabetes. Research design and methods The present analysis includes data of 1,466 persons (age 18 to 99 years; 44.0% female; 56.0% male) with diabetes (type I and II) from the population-based study German Health Update 2014/2015 (GEDA 2014/2015-EHIS). We used latent class analysis in order to distinguish different patterns of self-management behaviours among persons with diabetes. The assessment of SMB was based on seven self-reported activities by respondents (dietary plan, diabetes-diary, diabetes health pass, self-assessment of blood glucose, self-examination of feet, retinopathy-screenings and assessment of HbA1c). Subsequent multinomial latent variable regressions identified factors that were associated with self-management behaviour. Results Latent class analysis suggested a distinction between three patterns of SMB. Based on modal posterior probabilities 42.8% of respondents showed an adherent pattern of diabetes self-management with above-average frequency in all seven indicators of SMB. 32.1% showed a nonadherent pattern with a below-average commitment in all seven forms of SMB. Another 25.1% were assigned to an ambivalent type, which showed to be adherent with regard to retinopathy screenings, foot examinations, and the assessment of HbA1c, yet nonadherent with regard to all other forms of SMB. In multivariable regression analyses, participation in Diabetes Self-Management Education programs (DSME) was the most important predictor of good self-management behaviour (marginal effect = 51.7 percentage points), followed by attentiveness towards one’s personal health (31.0 percentage points). Respondents with a duration of illness of less than 10 years (19.5 percentage points), employed respondents (7.5 percentage points), as well as respondents with a high socioeconomic status (24.7 percentage points) were more likely to show suboptimal forms of diabetes self-management. Discussion In the present nationwide population-based study, a large proportion of persons with diabetes showed suboptimal self-management behaviour. Participation in a DSME program was the strongest predictor of good self-management. Results underline the need for continual and consistent health education for patients with diabetes.

scholarly journals Cyberbullying and Social Anxiety: A Latent Class Analysis among Spanish Adolescents

2020 ◽  
Vol 17 (2) ◽  
pp. 406 ◽  
Author(s):  
María C. Martínez-Monteagudo ◽  
Beatriz Delgado ◽  
Cándido J. Inglés ◽  
Raquel Escortell
Keyword(s):  
Social Anxiety ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Negative Evaluation ◽  
Class Analysis ◽  
Social Avoidance ◽  
Social Maladjustment ◽  
Social Situations ◽  
Bully Victims ◽  
Student Profiles

Cyberbullying is a common social maladjustment that has negative repercussions on the wellbeing and development of adolescents, but numerous questions remain as to the relationship between cyberbullying and social anxiety in adolescence. This study analyzes cyberbullying profiles (screening of harassment among peers) and assesses whether these profiles vary with respect to the level of social anxiety (social anxiety scale for adolescents). The sample consisted of 1412 Spanish secondary education students aged 12 to 18 (M = 14.36, SD = 1.65). Latent class analysis and ANOVA were performed. Analyses revealed three profiles: high cyberbullying (high victimization, aggression, and aggression-victimization), low cyberbullying (moderate victimization, aggression, and aggression-victimization), and non-cyberbullying. The cyberbullying patterns varied significantly for all social anxiety subscales. Students with the high cyberbullying profile (bully–victims) presented high scores on social avoidance and distress in social situations in general with peers, whereas these students presented lower levels of fear of negative evaluation and distress and social avoidance in new situations as compared to the low cyberbullying (rarely victim/bully) and non-involved student profiles. Implications for psychologists and educational counselors and cyberbullying preventive interventions are discussed.

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 ◽  
...  
Keyword(s):  
Mental Health ◽  
Anxiety Disorder ◽  
Latent Class Analysis ◽  
Generalized Anxiety Disorder ◽  
Clinical Characteristics ◽  
Latent Class ◽  
Depressive Symptomatology ◽  
Dysthymic Disorder ◽  
Generalized Anxiety ◽  
Class Analysis

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.

Latent class analysis of the Apathy Scale does not identify subtypes of apathy in general population-based older persons

2016 ◽  
Vol 31 (9) ◽  
pp. 1021-1028 ◽  
Author(s):  
Isis Groeneweg-Koolhoven ◽  
Lotte J. Huitema ◽  
Margot W. M. de Waal ◽  
Max L. Stek ◽  
Jacobijn Gussekloo ◽  
...  
Keyword(s):  
General Population ◽  
Latent Class Analysis ◽  
Older Persons ◽  
Latent Class ◽  
Population Based ◽  
Class Analysis
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