Latent Class Analysis of Lifestyle Risk Factors and Association with Overweight and/or Obesity in Children and Adolescents: Systematic Review

2020 ◽  
Author(s):  
Rafaela Liberali ◽  
Flavia Del Castanhel ◽  
Emil Kupek ◽  
Maria Alice Altenburg de Assis
Keyword(s):  
Risk Factors ◽  
Systematic Review ◽  
Latent Class Analysis ◽  
Children And Adolescents ◽  
Latent Class ◽  
Class Analysis ◽  
Lifestyle Risk Factors ◽  
Lifestyle Risk ◽  
Obesity In Children

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

Public Health ◽  
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

scholarly journals Physical Activity and Sedentary Activity Patterns Among Children and Adolescents: A Latent Class Analysis Approach

2011 ◽  
Vol 8 (4) ◽  
pp. 457-467 ◽  
Author(s):  
Carrie D. Patnode ◽  
Leslie A. Lytle ◽  
Darin J. Erickson ◽  
John R. Sirard ◽  
Daheia J. Barr-Anderson ◽  
...  
Keyword(s):  
Physical Activity ◽  
Latent Class Analysis ◽  
Children And Adolescents ◽  
Latent Class ◽  
Multinomial Logistic Regression ◽  
Activity Patterns ◽  
Moderate Activity ◽  
Class Analysis ◽  
Sedentary Behaviors ◽  
Sedentary Activity

Background:While much is known about the overall levels of physical activity and sedentary activity among youth, few studies have attempted to define clusters of such behaviors. The purpose of this study was to identify and describe unique classes of youth based on their participation in a variety of physical activity and sedentary behaviors.Methods:Latent class analysis was used to characterize segments of youth based on patterns of self-reported and accelerometer-measured participation in 12 behaviors. Children and adolescents (N = 720) from 6th-11th grade were included in the analysis. Differences in class membership were examined using multinomial logistic regression.Results:Three distinct classes emerged for boys and girls. Among boys, the 3 classes were characterized as “Active” (42.1%), “Sedentary” (24.9%), and “Low Media/Moderate Activity” (33.0%). For girls, classes were “Active” (18.7%), “Sedentary” (47.6%), and “Low Media/Functional Activity” (33.7%). Significant differences were found between the classes for a number of demographic indicators including the proportion in each class who were classified as overweight or obese.Conclusions:The behavioral profiles of the classes identified in this study can be used to suggest possible audience segments for intervention and to tailor strategies appropriately.

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

2019 ◽  
Vol 243 ◽  
pp. 360-365 ◽  
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

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

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.

Risk Factors Explaining Military Deaths From Suicide, 2008–2017: A Latent Class Analysis

2021 ◽  
pp. 0095327X2110469
Author(s):  
Scott D. Landes ◽  
Janet M. Wilmoth ◽  
Andrew S. London ◽  
Ann T. Landes
Keyword(s):  
Mental Health ◽  
Risk Factors ◽  
Latent Class Analysis ◽  
Military Service ◽  
Latent Class ◽  
Service Providers ◽  
Service Members ◽  
Latent Classes ◽  
Class Analysis ◽  
Military Service Members

Military suicide prevention efforts would benefit from population-based research documenting patterns in risk factors among service members who die from suicide. We use latent class analysis to analyze patterns in identified risk factors among the population of 2660 active-duty military service members that the Department of Defense Suicide Event Report (DoDSER) system indicates died by suicide between 2008 and 2017. The largest of five empirically derived latent classes was primarily characterized by the dissolution of an intimate relationship in the past year. Relationship dissolution was common in the other four latent classes, but those classes were also characterized by job, administrative, or legal problems, or mental health factors. Distinct demographic and military-status differences were apparent across the latent classes. Results point to the need to increase awareness among mental health service providers and others that suicide among military service members often involves a constellation of potentially interrelated risk factors.

scholarly journals Patterns of comorbidities in hospitalised cancer survivors for palliative care and associated in-hospital mortality risk: A latent class analysis of a statewide all-inclusive inpatient data

2019 ◽  
Vol 33 (10) ◽  
pp. 1272-1281 ◽  
Author(s):  
Lan Luo ◽  
Wei Du ◽  
Shanley Chong ◽  
Huibo Ji ◽  
Nicholas Glasgow
Keyword(s):  
Risk Factors ◽  
Palliative Care ◽  
Hospital Mortality ◽  
Cancer Survivors ◽  
Latent Class Analysis ◽  
Cancer Patients ◽  
Mortality Risk ◽  
Latent Class ◽  
Class Analysis ◽  
Hospital Deaths

Background: At the end of life, cancer survivors often experience exacerbations of complex comorbidities requiring acute hospital care. Few studies consider comorbidity patterns in cancer survivors receiving palliative care. Aim: To identify patterns of comorbidities in cancer patients receiving palliative care and factors associated with in-hospital mortality risk. Design, Setting/Participants: New South Wales Admitted Patient Data Collection data were used for this retrospective cohort study with 47,265 cancer patients receiving palliative care during the period financial year 2001–2013. A latent class analysis was used to identify complex comorbidity patterns. A regression mixture model was used to identify risk factors in relation to in-hospital mortality in different latent classes. Results: Five comorbidity patterns were identified: ‘multiple comorbidities and symptoms’ (comprising 9.1% of the study population), ‘more symptoms’ (27.1%), ‘few comorbidities’ (39.4%), ‘genitourinary and infection’ (8.7%), and ‘circulatory and endocrine’ (15.6%). In-hospital mortality was the highest for ‘few comorbidities’ group and the lowest for ‘more symptoms’ group. Severe comorbidities were associated with elevated mortality in patients from ‘multiple comorbidities and symptoms’, ‘more symptoms’, and ‘genitourinary and infection’ groups. Intensive care was associated with a 37% increased risk of in-hospital deaths in those presenting with more ‘multiple comorbidities and symptoms’, but with a 22% risk reduction in those presenting with ‘more symptoms’. Conclusion: Identification of comorbidity patterns and risk factors for in-hospital deaths in cancer patients provides an avenue to further develop appropriate palliative care strategies aimed at improving outcomes in cancer survivors.

Factors Associated With Multimorbidity Patterns in Older Adults in England: Findings From the English Longitudinal Study of Aging (ELSA)

2019 ◽  
Vol 32 (9) ◽  
pp. 1120-1132 ◽  
Author(s):  
Hai Nguyen ◽  
Kia-Chong Chua ◽  
Alexandru Dregan ◽  
Silia Vitoratou ◽  
Ivet Bayes-Marin ◽  
...  
Keyword(s):  
Risk Factors ◽  
Older Adults ◽  
Longitudinal Study ◽  
Latent Class ◽  
Future Research ◽  
Factors Associated ◽  
Lifestyle Risk Factors ◽  
Class B ◽  
Lifestyle Risk ◽  
English Longitudinal Study

Objective: We aimed to identify the patterns of multimorbidity in older adults and explored their association with sociodemographic and lifestyle risk factors. Method: The sample included 9,171 people aged 50+ from Wave 2 of the English Longitudinal Study of Aging (ELSA). Latent Class Analysis (LCA) was performed on 26 chronic diseases to determine clusters of common diseases within individuals and their association with sociodemographic and lifestyle risk factors. Result: Three latent classes were identified: (a) a cardiorespiratory/arthritis/cataracts class, (b) a metabolic class, and (c) a relatively healthy class. People aged 70 to 79 were 9.91 times (95% Confidence Interval [CI] = [5.13, 19.13]) more likely to be assigned to the cardiorespiratory/arthritis/cataracts class, while regular drinkers and physically inactive people were 0.33 times (95% CI = [0.24, 0.47]) less likely to be assigned to this class. Conclusion: Future research should investigate these patterns further to gain more insights into the needs of people with multimorbidity.

scholarly journals Work Disability among Employees with Diabetes: Latent Class Analysis of Risk Factors in Three Prospective Cohort Studies

PLoS ONE ◽  
2015 ◽  
Vol 10 (11) ◽  
pp. e0143184 ◽  
Author(s):  
Marianna Virtanen ◽  
Jussi Vahtera ◽  
Jenny Head ◽  
Rosemary Dray-Spira ◽  
Annaleena Okuloff ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cohort Studies ◽  
Latent Class Analysis ◽  
Prospective Cohort ◽  
Work Disability ◽  
Latent Class ◽  
Class Analysis ◽  
Prospective Cohort Studies
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