A latent class analysis of psychotic symptoms in the general population

2017 ◽  
Vol 52 (6) ◽  
pp. 573-584 ◽  
Author(s):  
Baptiste Pignon ◽  
Hugo Peyre ◽  
Andrei Szöke ◽  
Pierre A Geoffroy ◽  
Benjamin Rolland ◽  
...  
Keyword(s):  
General Population ◽  
Latent Class Analysis ◽  
Mental Health Treatment ◽  
Latent Class ◽  
Psychotic Disorders ◽  
Social Deprivation ◽  
Psychotic Symptoms ◽  
Class Analysis ◽  
Clinical Correlates ◽  
High Prevalence

Objective: Individuals with psychotic symptoms may actually correspond to various subgroups, characterized by different patterns of psychotic symptoms as well as specific sociodemographic and clinical correlates. We aimed to identify groups of individuals from the general population with specific patterns of psychotic symptoms. Methods: In a 38,694-subject survey, a latent class analysis was performed to identify subgroups based on the distribution of seven psychotic symptoms taken from the Mini International Neuropsychiatric Interview. The different classes were subsequently compared according to sociodemographic and clinical correlates. Results: The best fit was obtained with a four-class solution, including the following: (1) a class with a low prevalence of all psychotic symptoms (‘LOW’, 85.9%); (2) a class with a high prevalence of all psychotic symptoms (‘HAL + DEL’, 1.7%); and classes with a high prevalence of (3) hallucinations (‘HAL’, 4.5%) or (4) delusions (‘DEL’, 7.9%). The HAL + DEL class displayed higher rates of history of trauma, social deprivation and migrant status, while the HAL and DEL classes displayed intermediate rates between HAL + DEL and LOW. HAL + DEL displayed the highest rates of psychotic and non-psychotic disorders and the use of mental health treatment, while HAL and DEL displayed intermediate rates of these disorders between HAL + DEL and LOW. In comparison to the HAL class, psychotic and substance use disorders were more frequent in the DEL class, while anxiety and mood disorders were less frequent. Conclusion: These findings support the hypothesis of a continuum model relating the level of psychotic symptoms to the level of global psychopathology.

Empirical derived AUD sub types in the US general population: A latent class analysis

2013 ◽  
Vol 38 (11) ◽  
pp. 2782-2786 ◽  
Author(s):  
Martina Casey ◽  
Gary Adamson ◽  
Maurice Stringer
Keyword(s):  
General Population ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Class Analysis ◽  
The Us

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

scholarly journals Occurence of Internet Addiction in a General Population Sample: A Latent Class Analysis

2013 ◽  
Vol 20 (4) ◽  
pp. 159-166 ◽  
Author(s):  
Hans-Jürgen Rumpf ◽  
Ad A. Vermulst ◽  
Anja Bischof ◽  
Nadin Kastirke ◽  
Diana Gürtler ◽  
...  
Keyword(s):  
General Population ◽  
Latent Class Analysis ◽  
Internet Addiction ◽  
Latent Class ◽  
Population Sample ◽  
Class Analysis ◽  
General Population Sample

scholarly journals A Latent Class Analysis of Health Lifestyles in Relation to Suicidality among Adolescents in Mauritius

2021 ◽  
Vol 18 (13) ◽  
pp. 6934
Author(s):  
Fanny Hoogstoel ◽  
Sékou Samadoulougou ◽  
Vincent Lorant ◽  
Fati Kirakoya-Samadoulougou
Keyword(s):  
Latent Class Analysis ◽  
Parental Support ◽  
Latent Class ◽  
Suicide Attempts ◽  
Prevalence Ratio ◽  
Class Analysis ◽  
Suicidal Thoughts ◽  
Health Lifestyles ◽  
School Based ◽  
High Prevalence

Suicidality, which includes suicidal thoughts, planning, and suicide attempts, results mainly from a combination of psychological, sociological, and environmental factors. Despite a high prevalence of suicidality among adolescents in Africa, only a few studies have considered these factors simultaneously. The objective of the study was to identify the prevalence of suicidality, to draw up profiles of concomitant risks, and to examine the associations between these profiles and suicidality in Mauritius. This study used data from the 2017 Mauritian Global School-based Student Health Survey including 3012 adolescents with a mean age of 14.9 ± 1.4 years. Factors related to lifestyle such as consumptions of alcohol and tobacco, physical activity, violence, parental support, anxiety, and loneliness were considered. A latent class analysis was performed to identify the profiles. Finally, a modified Poisson regression analysis with generalized estimating equations, adjusted with sociodemographic characteristics, was used to assess the association between these profiles and suicidality. Overall, more than one in ten adolescents had at least one of the suicidality behaviors. Three profiles were identified: 1 = “low risk group” (63.9%); 2 = “problems with violence” (15.2%); 3 = “problems with violence, alcohol, tobacco and psychological distress” (20.9%). Profiles 2 and 3 were mainly made up of males. Adolescents under 15 represented the majority of individuals in profile 2. Finally, the risk of suicidality was higher in adolescents belonging to profiles 2 and 3 compared to profile 1 for the three suicidality behaviors (profile 3: Prevalence ratio (PR) for suicidal thoughts = 1.26, 95% CI = 1.19–1.34; PR for planning = 1.23, 95% CI = 1.17–1.30; PR for attempt = 1.23, 95% CI = 1.17–1.29). This study highlights the high prevalence of suicidality and a list of concomitant risks, emphasizing this suicidality in Mauritian adolescents. Therefore, these results recommend focusing preventive efforts toward a simultaneous consideration of these factors.

Categorization and validation of handedness using latent class analysis

2004 ◽  
Vol 16 (4) ◽  
pp. 212-218 ◽  
Author(s):  
Milan Dragovic
Keyword(s):  
General Population ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Hand Preference ◽  
Classification Criteria ◽  
Future Research ◽  
Class Analysis ◽  
Exact Number ◽  
Different Types ◽  
Good Agreement

Background:A view that handedness is not a dichotomous, i.e. left–right, phenomenon is shared by majority of researchers. However, there are different opinions about the exact number of hand-preference categories and criteria that should be used for their classification.Objectives:This study examined hand-preference categories using the latent class analysis (LCA) and validated them against two external criteria (i.e. hand demonstration test and a series of arbitrary cut-off points).Method:The Edinburgh Handedness Inventory was applied to 354 individuals randomly selected from the general population, and the obtained data were analysed using the LatentGOLD software.Results:Three discrete hand-preference clusters were identified, i.e. left-, right- and mixed-handed category. Further subdivision of hand-preference clusters resulted in a non-parsimonious subcategorization of individuals. There was a good agreement between the LCA-based classification and classification based on hand-preference demonstration tests. The highest agreement between the LCA model and the different types of arbitrary classification criteria ranged between 0 ± 50 and 0 ± 70 of the laterality quotient.Conclusions:The study findings supported the view that handedness is not a bimodal phenomenon. However, definitions and subcategorizations of mixed-handedness using the cut-off points that are outside of the recommended range may lead to misclassification of cases. It is hoped that the categorization and validation of handedness developed in the context of this study will make future research in this area less dependent on arbitrary values and criteria.

A Latent Class Analysis of Manic and Psychotic Symptoms

2020 ◽  
Vol 208 (9) ◽  
pp. 721-728
Author(s):  
Baptiste Pignon ◽  
Hugo Peyre ◽  
Emmanuel Noblins ◽  
Benjamin Rolland ◽  
Pierre Thomas ◽  
...  
Keyword(s):  
Latent Class Analysis ◽  
Latent Class ◽  
Psychotic Symptoms ◽  
Class Analysis
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