scholarly journals Mental disorders pattern in staff of a military unit in Iran: the role of metabolic syndrome on latent class membership

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Abbas Abbasi-Ghahramanloo ◽  
Mohammadkarim Bahadori ◽  
Esfandiar Azad ◽  
Nooredin Dopeykar ◽  
Parisa Mahdizadeh ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Mental Disorders ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Adult Population ◽  
Class Analysis ◽  
Class Membership ◽  
Anthropometric Measures ◽  
Military Unit

Abstract Introduction Mental disorders are among the most prevalent health problems of the adult population in the world. This study aimed to identify the subgroups of staff based on mental disorders and assess the independent role of metabolic syndrome (MetS) on the membership of participants in each latent class. Methods This cross-sectional study was conducted among 694 staff of a military unit in Tehran in 2017. All staff of this military unit was invited to participate in this study. The collected data included demographic characteristics, anthropometric measures, blood pressure, biochemical parameters, and mental disorders. We performed latent class analysis using a procedure for latent class analysis (PROC LCA) in SAS to identify class membership of mental disorders using Symptom Checklist-90. Results Three latent classes were identified as healthy (92.7%), mild (4.9%), and severe (2.4%) mental disorders. Having higher age significantly decreased the odds of belonging to the mild class (adjusted OR (aOR = 0.21; 95% confidence interval (CI): 0.05–0.83) compared to the healthy class. Also, obesity decreased the odds of membership in mild class (aOR = 0.10, 95% CI: 0.01–0.92) compared to healthy class. On the other hand, being female increased the odds of being in severe class (aOR = 9.76; 95% CI: 1.35–70.65) class in comparison to healthy class. Conclusion This study revealed that 7.3% of staff fell under mild and severe classes. Considering educational workshops in the workplace about mental disorders could be effective in enhancing staff’s knowledge of these disorders. Also, treatment of comorbid mental disorders may help reduce their prevalence and comorbidity.

scholarly journals The Role of Trust in Explaining Food Choice: Combining Choice Experiment and Attribute Best–Worst Scaling

Foods ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 45
Author(s):  
Ching-Hua Yeh ◽  
Monika Hartmann ◽  
Nina Langen
Keyword(s):  
Latent Class Analysis ◽  
Food Choice ◽  
Logit Model ◽  
Choice Experiment ◽  
Latent Class ◽  
Class Analysis ◽  
Class Membership ◽  
Credence Attributes ◽  
Best Worst Scaling

This paper presents empirical findings from a combination of two elicitation techniques—discrete choice experiment (DCE) and best–worst scaling (BWS)—to provide information about the role of consumers’ trust in food choice decisions in the case of credence attributes. The analysis was based on a sample of 459 Taiwanese consumers and focuses on red sweet peppers. DCE data were examined using latent class analysis to investigate the importance and the utility different consumer segments attach to the production method, country of origin, and chemical residue testing. The relevance of attitudinal and trust-based items was identified by BWS using a hierarchical Bayesian mixed logit model and was aggregated to five latent components by means of principal component analysis. Applying a multinomial logit model, participants’ latent class membership (obtained from DCE data) was regressed on the identified attitudinal and trust components, as well as demographic information. Results of the DCE latent class analysis for the product attributes show that four segments may be distinguished. Linking the DCE with the attitudinal dimensions reveals that consumers’ attitude and trust significantly explain class membership and therefore, consumers’ preferences for different credence attributes. Based on our results, we derive recommendations for industry and policy.

scholarly journals Eating behaviour, physical activity, TV exposure and sleeping habits in five year olds: a latent class analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Molly Mattsson ◽  
Deirdre M. Murray ◽  
Mairead Kiely ◽  
Fergus P. McCarthy ◽  
Elaine McCarthy ◽  
...  
Keyword(s):  
Physical Activity ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Eating Behaviour ◽  
Overweight And Obesity ◽  
Class Analysis ◽  
Class Membership ◽  
Active Play ◽  
Food Avoidance ◽  
Cluster Membership

Abstract Background Diet, physical activity, sedentary behaviours, and sleep time are considered major contributory factors of the increased prevalence of childhood overweight and obesity. The aims of this study were to (1) identify behavioural clusters of 5 year old children based on lifestyle behaviours, (2) explore potential determinants of class membership, and (3) to determine if class membership was associated with body measure outcomes at 5 years of age. Methods Data on eating behaviour, engagement in active play, TV watching, and sleep duration in 1229 5 year old children from the Cork BASELINE birth cohort study was obtained through in-person interviews with parent. Latent class analysis was used to identify behavioural clusters. Potential determinants of cluster membership were investigated using multinomial logistic regression. Associations between the identified classes and cardio metabolic body measures were examined using multivariate logistic and linear regression, with cluster membership used as the independent variable. Results 51% of children belonged to a normative class, while 28% of children were in a class characterised by high scores on food avoidance scales in combination with low enjoyment of food, and 20% experienced high scores on the food approach scales. Children in both these classes had lower conditional probabilities of engaging in active play for at least 1 hour per day and sleeping for a minimum of 10 h, and higher probability of watching TV for 2 hours or more, compared to the normative class. Low socioeconomic index (SEI) and no breastfeeding at 2 months were found to be associated with membership of the class associated with high scores on the food avoidance scale, while lower maternal education was associated with the class defined by high food approach scores. Children in the class with high scores on the food approach scales had higher fat mass index (FMI), lean mass index (LMI), and waist-to-height ratio (WtHR) compared to the normative class, and were at greater risk of overweight and obesity. Conclusion Findings suggest that eating behaviour appeared to influence overweight and obesity risk to a greater degree than activity levels at 5 years old. Further research of how potentially obesogenic behaviours in early life track over time and influence adiposity and other cardio metabolic outcomes is crucial to inform the timing of interventions.

Delirium superimposed on dementia: defining disease states and course from longitudinal measurements of a multivariate index using latent class analysis and hidden Markov chains

2011 ◽  
Vol 23 (10) ◽  
pp. 1659-1670 ◽  
Author(s):  
Antonio Ciampi ◽  
Alina Dyachenko ◽  
Martin Cole ◽  
Jane McCusker
Keyword(s):  
Mental Disorders ◽  
Markov Chains ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Hidden Markov ◽  
The Elderly ◽  
Population Heterogeneity ◽  
Class Analysis ◽  
Hidden Markov Chains ◽  
Time Point

ABSTRACTBackground: The study of mental disorders in the elderly presents substantial challenges due to population heterogeneity, coexistence of different mental disorders, and diagnostic uncertainty. While reliable tools have been developed to collect relevant data, new approaches to study design and analysis are needed. We focus on a new analytic approach.Methods: Our framework is based on latent class analysis and hidden Markov chains. From repeated measurements of a multivariate disease index, we extract the notion of underlying state of a patient at a time point. The course of the disorder is then a sequence of transitions among states. States and transitions are not observable; however, the probability of being in a state at a time point, and the transition probabilities from one state to another over time can be estimated.Results: Data from 444 patients with and without diagnosis of delirium and dementia were available from a previous study. The Delirium Index was measured at diagnosis, and at 2 and 6 months from diagnosis. Four latent classes were identified: fairly healthy, moderately ill, clearly sick, and very sick. Dementia and delirium could not be separated on the basis of these data alone. Indeed, as the probability of delirium increased, so did the probability of decline of mental functions. Eight most probable courses were identified, including good and poor stable courses, and courses exhibiting various patterns of improvement.Conclusion: Latent class analysis and hidden Markov chains offer a promising tool for studying mental disorders in the elderly. Its use may show its full potential as new data become available.

scholarly journals Application of Latent Class Analysis to Identify Metabolic Syndrome Components Patterns in adults: Tehran Lipid and Glucose study

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Noushin Sadat Ahanchi ◽  
Farzad Hadaegh ◽  
Abbas Alipour ◽  
Arash Ghanbarian ◽  
Fereidoun Azizi ◽  
...  
Keyword(s):  
Metabolic Syndrome ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Class Analysis ◽  
Metabolic Syndrome Components

scholarly journals Exploring symptoms of somatization in chronic widespread pain: latent class analysis and the role of personality

2017 ◽  
Vol Volume 10 ◽  
pp. 1733-1740 ◽  
Author(s):  
Andrea Burri ◽  
Peter Hilpert ◽  
Peter McNair ◽  
Frances Williams
Keyword(s):  
Latent Class Analysis ◽  
Latent Class ◽  
Chronic Widespread Pain ◽  
Widespread Pain ◽  
Class Analysis

scholarly journals Bourdieu in de buurt : Sociale klassendebat en de klassenstructuur van Rotterdam

Sociologie ◽  
2020 ◽  
Vol 15 (2) ◽  
pp. 117-147
Author(s):  
Gijs Custers ◽  
Godfried Engbersen
Keyword(s):  
Latent Class Analysis ◽  
Latent Class ◽  
Research Approach ◽  
Class Analysis ◽  
Social And Cultural Capital ◽  
New Class ◽  
Social Mix ◽  
The Social ◽  
New Research

Abstract Studies by Savage et al. (2013) and Vrooman, Gijsberts and Boelhouwer (2014) introduce new class typologies that combine Bourdieu’s work with latent class analysis. This paper identifies this new research approach as Bourdieusian latent class analysis. We discuss the role of these studies within the social class debate and we review the merits and limitations of this approach. In addition, we show how the class structure of Rotterdam can be empirically established by studying the distribution of economic, social and cultural capital. We use the Neighbourhood Profile data (N = 14,040; 71 neighbourhoods) to develop a class typology that includes eight social groups. This class typology complements conventional indicators of neighbourhood socioeconomic status and can be used to study ‘social mix’ and gentrification.

scholarly journals A primer to latent profile and latent class analysis

2021 ◽  
Author(s):  
Johannes Bauer
Keyword(s):  
Individual Differences ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Class Analysis ◽  
Class Membership ◽  
External Variables ◽  
Response Profiles ◽  
Person Centered Approach ◽  
Conceptual Overview ◽  
Latent Profile

This chapter gives an applied introduction to latent profile and latent class analysis (LPA/LCA). LPA/LCA are model-based methods for clustering individuals in unobserved groups. Their primary goals are probing whether and, if so, how many latent classes can be identified in the data, and to estimate the proportional size and response profiles of these classes in the population. Moreover, latent class membership can serve as predictor or outcome for external variables. Substantively, LPA/LCA adopt a person-centered approach that is useful for analyzing individual differences in prerequisites, processes, or outcomes of learning. The chapter provides a conceptual overview of LPA/LCA, a nuts-and-bolts discussion of the steps and decisions involved in their application, and illustrative examples using freely available data and the R statistical environment.

scholarly journals Latent class analysis of co-morbidity in the Adult Psychiatric Morbidity Survey in England 2007: implications for DSM-5 and ICD-11

2011 ◽  
Vol 41 (10) ◽  
pp. 2201-2212 ◽  
Author(s):  
S. Weich ◽  
O. McBride ◽  
D. Hussey ◽  
D. Exeter ◽  
T. Brugha ◽  
...  
Keyword(s):  
Latent Class Analysis ◽  
Personality Disorders ◽  
Attention Deficit Disorder ◽  
Latent Class ◽  
Adult Population ◽  
Psychiatric Morbidity ◽  
Self Report ◽  
Class Analysis ◽  
Post Traumatic Stress ◽  
Co Morbidity

BackgroundPsychiatric co-morbidity is complex and ubiquitous. Our aim was to describe the extent, nature and patterning of psychiatric co-morbidity within a representative sample of the adult population of England, using latent class analysis.MethodData were used from the 2007 Adult Psychiatric Morbidity Survey, a two-phase national household survey undertaken in 2007 comprising 7325 participants aged 16 years and older living in private households in England. The presence of 15 common mental health and behavioural problems was ascertained using standardized clinical and validated self-report measures, including three anxiety disorders, depressive episode, mixed anxiety depressive disorder, psychosis, antisocial and borderline personality disorders, eating disorders, post-traumatic stress disorder, attention deficit disorder, alcohol and drug dependencies, problem gambling and attempted suicide.ResultsA four-class model provided the most parsimonious and informative explanation of the data. Most participants (81.6%) were assigned to a non-symptomatic or ‘Unaffected’ class. The remainder were classified into three qualitatively different symptomatic classes: ‘Co-thymia’ (12.4%), ‘Highly Co-morbid’ (5.0%) and ‘Addictions’ (1.0%). Classes differed in mean numbers of conditions and impairments in social functioning, and these dimensions were correlated.ConclusionsOur findings confirm that mental disorders typically co-occur and are concentrated in a relatively small number of individuals. Conditions associated with the highest levels of disability, mortality and cost – psychosis, suicidality and personality disorders – are often co-morbid with more common conditions. This needs to be recognized when planning services and when considering aetiology.

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