scholarly journals Patterns of multiple health risk–behaviours in university students and their association with mental health: application of latent class analysis

2016 ◽  
Vol 36 (8) ◽  
pp. 163-170 ◽  
Author(s):  
M. Y. Kwan ◽  
K. P. Arbour-Nicitopoulos ◽  
E. Duku ◽  
G. Faulkner
Keyword(s):  
Mental Health ◽  
High Risk ◽  
Health Risk ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Life Stage ◽  
Class Analysis ◽  
Risk Behaviours ◽  
Health Risk Behaviours ◽  
Multiple Health

Introduction University and college campuses may be the last setting where it is possible to comprehensively address the health of a large proportion of the young adult population. It is important that health promoters understand the collective challenges students are facing, and to better understand the broader lifestyle behavioural patterning evident during this life stage. The purpose of this study was to examine the clustering of modifiable health-risk behaviours and to explore the relationship between these identified clusters and mental health outcomes among a large Canadian university sample. Methods Undergraduate students (n = 837; mean age = 21 years) from the University of Toronto completed the National College Health Assessment survey. The survey consists of approximately 300 items, including assessments of student health status, mental health and health-risk behaviours. Latent class analysis was used to identify patterning based on eight salient health-risk behaviours (marijuana use, other illicit drug use, risky sex, smoking, binge drinking, poor diet, physical inactivity, and insufficient sleep). Results A three-class model based on student behavioural patterns emerged: “typical,” “high-risk” and “moderately healthy.” Results also found high-risk students reporting significantly higher levels of stress than typical students (χ2(1671) = 7.26, p < .01). Conclusion Students with the highest likelihood of engaging in multiple health-risk behaviours reported poorer mental health, particularly as it relates to stress. Although these findings should be interpreted with caution due to the 28% response rate, they do suggest that interventions targeting specific student groups with similar patterning of multiple health-risk behaviours may be needed.

Application of latent class analysis in assessing the mental health of medical students during the COVID-19 Epidemic (Preprint)

2020 ◽  
Author(s):  
Fei Wang
Keyword(s):  
Mental Health ◽  
Medical Students ◽  
High Risk ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Risk Group ◽  
Multinomial Logistic Regression ◽  
High Risk Group ◽  
Class Analysis ◽  
Related Factors

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.

scholarly journals Application of Latent Class Analysis in Assessing the Mental Health of Medical Students During the COVID-19 Epidemic

2021 ◽  
Author(s):  
Zhuang Liu ◽  
Yue Zhang ◽  
Ran Zhang ◽  
Rongxun Liu ◽  
Lijuan Liang ◽  
...  
Keyword(s):  
Mental Health ◽  
Medical Students ◽  
High Risk ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Risk Group ◽  
Multinomial Logistic Regression ◽  
High Risk Group ◽  
Class Analysis ◽  
Related Factors

Abstract 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. 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.

scholarly journals FRI0223 SUBPHENOTYPES OF ANCA ASSOCIATED VASCULITIS IDENTIFIED BY LATENT CLASS ANALYSIS

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 694.3-695
Author(s):  
K. Wójcik ◽  
A. Ćmiel ◽  
A. Masiak ◽  
Z. Zdrojewski ◽  
R. Jeleniewicz ◽  
...  
Keyword(s):  
High Risk ◽  
Latent Class Analysis ◽  
Relapse Rate ◽  
Clinical Characteristics ◽  
Latent Class ◽  
Middle Age ◽  
Organ Involvement ◽  
Class Analysis ◽  
Fourth Class ◽  
Anca Associated Vasculitis

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

scholarly journals Latent Class Analysis to Represent Social Determinant of Health Risk Groups in the Medicaid Cohort of the District of Columbia

Medical Care ◽  
2020 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Melissa L. McCarthy ◽  
Zhaonian Zheng ◽  
Marcee E. Wilder ◽  
Angelo Elmi ◽  
Paige Kulie ◽  
...  
Keyword(s):  
Health Risk ◽  
Latent Class Analysis ◽  
Latent Class ◽  
District Of Columbia ◽  
Risk Groups ◽  
Social Determinant ◽  
Class Analysis ◽  
Social Determinant Of Health

scholarly journals Gender differences in the prevalence and clustering of multiple health risk behaviours in young adults

2016 ◽  
Vol 72 (9) ◽  
pp. 2098-2113 ◽  
Author(s):  
George Kritsotakis ◽  
Maria Psarrou ◽  
Maria Vassilaki ◽  
Zacharenia Androulaki ◽  
Anastas E. Philalithis
Keyword(s):  
Gender Differences ◽  
Young Adults ◽  
Health Risk ◽  
Risk Behaviours ◽  
Health Risk Behaviours ◽  
Multiple Health
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