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2 "Latent class analysis"
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Original Article
The patterns of lifestyle, metabolic status, and obesity among hypertensive Korean patients: a latent class analysis
Suyoung Kim, Seon Cho, Eun-Hee Nah
Epidemiol Health. 2020;42:e2020061.   Published online August 31, 2020
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  • 3 Citations
AbstractAbstract AbstractSummary PDFSupplementary Material
This study aimed to identify latent classes in hypertensive patients based on the clustering of factors including lifestyle risk factors, metabolic risk factors, and obesity in each sex.
This cross-sectional study included 102,780 male and 103,710 female hypertensive patients who underwent health check-ups at 16 centers in Korea, in 2018. A latent class analysis approach was used to identify subgroups of hypertensive patients. Multinomial logistic regression was performed to examine the association between latent classes and comorbidities of hypertension.
A four-class model provided the best fit for each sex. The following latent classes were identified: Class I (male: 16.9%, female: 1.7%; high risk of lifestyle behaviors [HB] with metabolic disorders and obesity [MO]), Class II (male: 32.4%, female: 47.1%; low risk of lifestyle behaviors [LB] with MO), Class III (male: 15.3%, female: 1.8%; HB with metabolic disorders and normal weight [MNW]), Class IV (male: 35.5%, female: 49.4%; LB with MNW). Lifestyle patterns in the latent classes were classified as high-risk or low-risk according to smoking and high-risk drinking among male, and presented complex patterns including physical inactivity alone or in combination with other factors, among female. Stage 2 hypertensive or diabetic individuals were likely to belong to classes including obesity (HB-MO, LB-MO) in both sexes, and additionally belonged to the HB-MNW class in male.
Metabolic disorders were included in all latent classes, with or without lifestyle risk factors and obesity. Hypertensive females need to manage obesity, and hypertensive males need to manage lifestyle risk factors and obesity. Sex-specific lifestyle behaviors are important for controlling hypertension.
Korean summary
고혈압 환자에서 이질적인 집단을 확인하기 위해, 생활습관, 대사이상 및 비만에 기반한 잠재계층분석을 실시한 결과, 각 성별에서 4개 계층으로 분류되었다. 모든 계층에는 대사이상 상태가 포함되었으며, 고위험 생활습관과 비만(HB-MO), 저위험 생활습관과 비만(LB-MO), 고위험 생활습관과 정상체중(HB-MNW), 저위험 생활습관과 정상체중(LB-MNW)으로 유형화하였다. 생활습관 및 비만 여부와 상관없이 모든 잠재계층에 대사이상 상태가 포함된 점으로 고혈압과 대사상태의 긴밀한 관련성을 확인하였으며, 성별에 따라 이질적인 생활습관 패턴(여성에서는 비만 관리와 남성에서는 비만과 생활습관 개선을 강조)을 확인하였다.


Citations to this article as recorded by  
  • Patterns of unhealthy behaviours during adolescence and subsequent anxiety and depression in adulthood: a prospective register linkage study of the HUNT survey and health registries
    Annette Løvheim Kleppang, Mario Vianna Vettore, Ingeborg Hartz, Siri Håvås Haugland, Tonje Holte Stea
    International Journal of Behavioral Nutrition and Physical Activity.2023;[Epub]     CrossRef
  • Factors and at-risk group associated with hypertension self-management patterns among people with physical disabilities: a latent class analysis
    Hye Jin Nam, Ju Young Yoon
    BMC Public Health.2022;[Epub]     CrossRef
  • Analysis of Fruit Consumption and the Korean Healthy Eating Index of Adults Using the 2018 Korea National Health and Nutrition Examination Survey
    Sun A Choi, Sung Suk Chung, Jeong Ok Rho
    Journal of the Korean Society of Food Science and Nutrition.2021; 50(10): 1124.     CrossRef
Assessing measurement error in surveys using latent class analysis: application to self-reported illicit drug use in data from the Iranian Mental Health Survey
Kazem Khalagi, Mohammad Ali Mansournia, Afarin Rahimi-Movaghar, Keramat Nourijelyani, Masoumeh Amin-Esmaeili, Ahmad Hajebi, Vandad Sharif, Reza Radgoodarzi, Mitra Hefazi, Abbas Motevalian
Epidemiol Health. 2016;38:e2016013.   Published online April 10, 2016
  • 14,218 View
  • 255 Download
AbstractAbstract PDF
Latent class analysis (LCA) is a method of assessing and correcting measurement error in surveys. The local independence assumption in LCA assumes that indicators are independent from each other condition on the latent variable. Violation of this assumption leads to unreliable results. We explored this issue by using LCA to estimate the prevalence of illicit drug use in the Iranian Mental Health Survey. The following three indicators were included in the LCA models: five or more instances of using any illicit drug in the past 12 months (indicator A), any use of any illicit drug in the past 12 months (indicator B), and the self-perceived need of treatment services or having received treatment for a substance use disorder in the past 12 months (indicator C). Gender was also used in all LCA models as a grouping variable. One LCA model using indicators A and B, as well as 10 different LCA models using indicators A, B, and C, were fitted to the data. The three models that had the best fit to the data included the following correlations between indicators: (AC and AB), (AC), and (AC, BC, and AB). The estimated prevalence of illicit drug use based on these three models was 28.9%, 6.2% and 42.2%, respectively. None of these models completely controlled for violation of the local independence assumption. In order to perform unbiased estimations using the LCA approach, the factors violating the local independence assumption (behaviorally correlated error, bivocality, and latent heterogeneity) should be completely taken into account in all models using well-known methods.

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