Latent Class Analysis in health research

Kongsted, Alice; Nielsen, Anne Molgaard

doi:10.1016/j.jphys.2016.05.018

Cited by 154 publications

(131 citation statements)

References 16 publications

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“…Therefore, we performed latent class analysis (LCA) based on the hypothesis that certain chronic diseases cluster. LCA identifies probabilistic rather than deterministic subgroups based on responses to a set of observed variables, and assumes that the pattern is explained by unobserved categorical latent variables of K classes [26,27]. Our objectives were: 1) to identify multimorbidity patterns in the general Korean population aged over 50 years using nationally representative survey data; and, 2) to explore whether such patterns were associated with certain sociodemographic characteristics and quality-of-life.…”

Section: Introductionmentioning

confidence: 99%

Use of latent class analysis to identify multimorbidity patterns and associated factors in Korean adults aged 50 years and older

Park

Lee

2019

Preprint

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IntroductionMultimorbidity associated with significant disease and economic burdens is common among the aged. We identified chronic disease multimorbidity patterns in Koreans 50 years of age or older, and explored whether such patterns were associated with particular sociodemographic factors and health-related quality-of-life.MethodsThe multimorbidity patterns of 10 chronic diseases (hypertension, dyslipidemia, stroke, osteoarthritis, tuberculosis, asthma, allergic rhinitis, depression, diabetes mellitus, and thyroid disease) were identified via latent class analysis of data on 8,370 Korean adults aged 50+ years who participated in the sixth Korean National Health and Nutrition Examination Survey (2013-2015). The associations between multimorbidity patterns, and sociodemographic factors and health-related quality of life, were subjected to regression analysis.ResultsThree patterns of multimorbidity were identified: 1) a relatively healthy group (60.4% of the population); 2) a ‘cardiometabolic conditions’ group (27.8%); and, 3) an ‘arthritis, asthma, allergic rhinitis, depression, and thyroid disease’ group (11.8%). The female (compared to male) gender was associated with an increased likelihood of membership of thecardiometabolic conditionsgroup (odds ratio [OR]=1.32, 95% confidence interval [CI]=1.15-1.51) and (to a much greater extent) thearthritis, asthma, allergy, depression, and thyroid diseasegroup (OR=4.32, 95% CI=3.30-5.66). Low socioeconomic status was associated with membership of the two multimorbidity classes. Membership of thearthritis, asthma, allergy, depression, and thyroid diseasegroup was associated with a significantly poorer health-related quality-of-life than was membership of the other two groups.ConclusionThe co-occurrence of chronic diseases was not attributable to chance. Multimorbidity patterns were associated with sociodemographic factors and quality-of-life. Our results suggest that targeted, integrated public health and clinical strategies dealing with chronic diseases should be based on an understanding of multimorbidity patterns; this would improve the quality-of-life of vulnerable multimorbid adults.

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Section: Introductionmentioning

confidence: 99%

Use of latent class analysis to identify multimorbidity patterns and associated factors in Korean adults aged 50 years and older

Park

Lee

2019

Preprint

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“…When using continuous variables for clustering, LCA is also often referred to as latent profile analysis (LPA, see also Masyn, 2013; or latent class cluster analysis (LCCA, see also Masyn, 2013). The purpose of LCA is to identify a number of subgroups that describe the underlying scoring patterns in the data, estimate the prevalence of the subgroups, and estimate each individual's probability of belonging to each subgroup (Kongsted & Nielsen, 2017). LCA differs from other techniques, such as principal component analysis or cluster analysis, by fitting "a model to the data rather than providing an ad hoc classification of the given data" (Van de Pol, Holleman, Kamoen, Krouwel, & De Vreese, 2014, p. 402).…”

Section: Analytic Methodsmentioning

confidence: 99%

“…Some people might be more concerned that online businesses honor the social contract and handle their data safely, while others are less concerned (Li, 2012). Because individuals' feeling of a shared social contract is a complex construct (a latent variable) that is difficult to measure directly (Kongsted & Nielsen, 2017), we use a latent class analysis (LCA, Oberski, 2016). Such a method acknowledges the complexity of the social contract construct, and enables us to identify different subgroups that have different perceptions toward the social contract and estimate the prevalence of these groups (Kongsted & Nielsen, 2017).…”

mentioning

confidence: 99%

“…Because individuals' feeling of a shared social contract is a complex construct (a latent variable) that is difficult to measure directly (Kongsted & Nielsen, 2017), we use a latent class analysis (LCA, Oberski, 2016). Such a method acknowledges the complexity of the social contract construct, and enables us to identify different subgroups that have different perceptions toward the social contract and estimate the prevalence of these groups (Kongsted & Nielsen, 2017). Hence, conducting an LCA leads to a conceptual and empirical breakdown of people's feelings toward privacy (resulting in a typology which adds to previous work, e.g., Sheehan, 2002).…”

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confidence: 99%

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Breaching the contract? Using social contract theory to explain individuals’ online behavior to safeguard privacy

2019

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This study applies social contract theory to examine whether perceptions of a social contract explains adaptive behavior to safeguard online privacy. We (1) identify and (2) estimate the prevalence of subgroups that differ in their perceived "social contract" (based on privacy concerns, trust, and risk), and (3) measure how this perceived social contract affects adaptive online behavior. Using a representative two-wave panel survey (N = 1,222), we distinguished five subgroups of internet users; the highly-concerned, wary, ambivalent, neutral (the largest group), and carefree users. The former three were more likely to adapt their behavior than the latter two subgroups. We argue that the implied social contract represents an important construct that helps to identify whether individuals engage in privacy protection behavior.

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“…LPA is a form of latent variable mixture modeling that aims at identifying different latent clusters, classes, or subgroups of individuals with similar response patterns based upon a set of categorical or continuous cluster indicators [58,59]. The main advantage of LPA over common cluster analytic techniques such as cluster analysis is that LPA is model-based, whereas hierarchical and most non-hierarchical techniques of cluster analysis are not [60]. In the present study, LPA was used to derive classes or subgroups of students who share similar profiles of mathematics achievement emotions.…”

Section: Latent Profile Analysismentioning

confidence: 99%

A Validation Study of the Control-Value Theory within the Domain of Mathematics at High School: A Latent Profile Analysis

Abd‐El‐Fattah¹

2018

J Psychol Psychother

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The present study aimed at validating the control-value theory within the domain of mathematics at high school level. It adopts a person-centered approach to explore whether high school students can be grouped into different achievement emotions profiles within the domain of mathematics and whether these profiles differ in mathematics achievement and mathematics tracking. The sample of the study included 355 high school students (185 males and 170 females) in Egypt. Students responded to the Learning-Related Emotions Scales-Mathematics-Arabic version (LRES-M-AR) in addition to a question concerning their intention to pursue a mathematics track at high school.A latent profile analysis showed three different achievement emotions profiles within the domain of mathematics: positive emotions, negative emotions, and positive emotions with high boredom. Students belong to the positive emotions profile had higher mathematics achievement and reported more mathematics tracking than students belong to the negative emotions profile or the positive emotions with high boredom profile. Students belong to the negative emotions profile had higher mathematics achievement and reported more mathematics tracking than students belong to the positive emotions with high boredom profile. Citation: Abd-El-Fattah SM (2018) A Validation Study of the Control-Value Theory within the Domain of Mathematics at High School: A Latent Profile Analysis. J Psychol Psychother 8: 348.

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Latent Class Analysis in health research

Cited by 154 publications

References 16 publications

Use of latent class analysis to identify multimorbidity patterns and associated factors in Korean adults aged 50 years and older

Use of latent class analysis to identify multimorbidity patterns and associated factors in Korean adults aged 50 years and older

Breaching the contract? Using social contract theory to explain individuals’ online behavior to safeguard privacy

A Validation Study of the Control-Value Theory within the Domain of Mathematics at High School: A Latent Profile Analysis

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