Alcohol Use as Risk Factors for Older Adults’ Emergency Department Visits: A Latent Class Analysis

Although many studies addressed bullying occurrence and its associations, they often use individual variables constructed from few items that probably are inadequate to evaluate bullying severity and type. We aimed to identify involvement patterns in bullying victimization and perpetration, and its association with alcohol use, school performance, and sociodemographic variables. Baseline assessment of a randomized controlled trial were used and a latent class analysis was conducted to identify bullying patterns among 1,742 fifth-grade and 2,316 seventh-grade students from 30 public schools in São Paulo, Brazil. Data were collected using an anonymous self-reported, audio-guided questionnaire completed by the participants on smartphones. Multinomial logistic regressions were performed to verify how covariant variables affected bullying latent classes. Both grades presented the same four latent classes: low bullying, moderate bullying victimization, high bullying victimization, and high bullying victimization and perpetration. Alcohol use was associated with all bullying classes in both grades, with odds ratio up to 5.36 (95% CI 3.05; 10.38) among fifth graders from the high bullying victimization and perpetration class. Poor school performance was also strongly associated with this class (aOR = 10.12, 95%CI = 4.19; 24.41). Black/brown 5th graders were 3.35 times more likely to fit into the high bullying victimization class (95% CI 1.34; 8.37). Lack of evidence for association of sociodemographic variables and bullying latent class among seventh-grade students was found. Bullying and alcohol use are highly harmful behaviors that must be prevented. However, prevention programs should consider how racial and gender issues are influencing the way students experience violence.

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Identifying individuals with multiple non-communicable disease risk factors in Kenya: a latent class analysis

Public Health ◽

10.1016/j.puhe.2021.07.031 ◽

2021 ◽

Vol 198 ◽

pp. 180-186

Author(s):

R.S. Mkuu ◽

T.D. Gilreath ◽

A.E. Barry ◽

F.M. Nafukho ◽

J. Rahman ◽

...

Keyword(s):

Risk Factors ◽

Latent Class Analysis ◽

Latent Class ◽

Disease Risk ◽

Communicable Disease ◽

Class Analysis ◽

Non Communicable Disease

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Prevalence, risk factors and multi-group latent class analysis of lifetime anxiety disorders comorbid depressive symptoms

Journal of Affective Disorders ◽

10.1016/j.jad.2018.09.053 ◽

2019 ◽

Vol 243 ◽

pp. 360-365 ◽

Cited By ~ 1

Author(s):

Hongguang Chen ◽

Xiao Wang ◽

Yueqin Huang ◽

Guohua Li ◽

Zhaorui Liu ◽

...

Keyword(s):

Risk Factors ◽

Depressive Symptoms ◽

Anxiety Disorders ◽

Latent Class Analysis ◽

Latent Class ◽

Class Analysis

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Clusters of the Risk Markers and the Pattern of Premature Coronary Heart Disease: An Application of the Latent Class Analysis

Frontiers in Cardiovascular Medicine ◽

10.3389/fcvm.2021.707070 ◽

2021 ◽

Vol 8 ◽

Author(s):

Leila Jahangiry ◽

Mahdieh Abbasalizad Farhangi ◽

Mahdi Najafi ◽

Parvin Sarbakhsh

Keyword(s):

Risk Factors ◽

Coronary Heart Disease ◽

Latent Class Analysis ◽

High Probability ◽

Latent Class ◽

Class Analysis ◽

Risk Markers ◽

History Of ◽

Opium Addiction ◽

The Mean

Background: Coronary heart disease (CHD) is the major cause of mortality in the world with a significant impact on the younger population. The aim of this study was to identify prematurity among patients with coronary artery bypass graft surgery (CABG) based on the clustering of CHD risk factors.Methods: Patients were recruited from an existing cohort of candidates for CABG surgery named Tehran Heart Center Coronary Outcome Measurement (THC-COM). A latent class analysis (LCA) model was formed using 11 potential risk factors as binary variables: cigarette smoking, obesity, diabetes, family history of CHD, alcohol use, opium addiction, hypertension, history of stroke, history of myocardial infarction (MI), peripheral vascular disease (PVD), and hyperlipidemia (HLP). We analyzed our data to figure out how the patients are going to be clustered based on their risk factors.Results: For 566 patients who were studied, the mean age (SD) and BMI of patients were 59.1 (8.9) and 27.3 (4.1), respectively. The LCA model fit with two latent classes was statistically significant (G2 = 824.87, df = 21, p < 0.0001). The mean (SD) age of patients for Class I and Class II was 55.66 (8.55) and 60.87 (8.66), respectively. Class I (premature) was characterized by a high probability of smoking, alcohol consumption, opium addiction, and a history of MI (P < 0.05), and class II by a high probability of obesity, diabetes, and hypertension.Conclusion: Latent class analysis calculated two groups of severe CHD with distinct risk markers. The younger group, which is characterized by smoking, addiction, and the history of MI, can be regarded as representative of premature CHD.

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