Application of latent class analysis to estimate susceptibility to adverse health outcomes based on several risk factors

Abstract

Background: The study demonstrates the use of latent class analysis (LCA) to segregate population in two latent classes e.g. susceptible or not susceptible to adverse health outcomes according to the observed risk factors as a method of medical diagnosis. Methods: The present study uses a secondary data set on 420 patients referred to the University of California, Los Angeles (UCLA). Adult Cardiac Imaging and Hemodynamics Laboratories for Dobutamine stress echocardiography (DSE) between March1991 & March1996. LCA is used for estimating the individual item-response probabilities in each latent group and also the latent class sizes. The observed variables or indicators of the latent subgroups are the common risk factors viz. history of smoking, history of cardiac issues etc. The interaction effect of hypertension & diabetes is also included in the analysis. Results: Based on the behaviour of the estimates of latent class model parameters, the unobserved groups are identified and named. Proportion of individuals falling in each latent class are approximately 0.20 & 0.80 respectively. The susceptibility to adverse health outcomes in future is the most in male individuals having a positive history of hypertension and/or diabetes, as the corresponding indicators have higher positive item-response probabilities (0.72 & 0.83 respectively) than the rest. Conclusions: The study briefly explains the application of LCA for identifying subgroups according to susceptibility to adverse health effects in a large population. Assessment of common risk factors in predicting latent class sizes provides estimates of probabilities for being a member in each class. The importance of the combined effect of hypertension & diabetes in predicting future health problems related to cardiac issues is highlighted. Class assignments of individuals according to their pattern of response are also listed.

Identifying the latent classes of modifiable risk behaviours among diabetic and hypertensive individuals in Northeastern India: a population-based cross-sectional study

“…In a study by Dey et al (2016), based on seven observed risk factors, LCA was used to classify individuals into two latent classes 'susceptible to adverse health outcomes groups' and 'not susceptible to adverse health outcomes groups'. 39 Another study in China applied LCA to 10 observed complications and comorbidities of patients with type 2 diabetes and categorised the individuals into four latent classes, namely 'complications and comorbidity groups', 'high risk of complications group', 'high risk of comorbidities and Cardiovascular Disease groups' and 'diabetes without complications and comorbidities group'. 40 A study in West Azerbaijan province among the patients with hypertension aged 50 years and above used four indicators, such as dietary patterns, physical activity, tobacco use and high BP control to categorise the patients with hypertension into three latent classes.…”

“…Several studies have adopted the LCA approach to identify risk factors clustering within the heterogeneous population. In a study by Dey et al (2016), based on seven observed risk factors, LCA was used to classify individuals into two latent classes ‘susceptible to adverse health outcomes groups’ and ‘not susceptible to adverse health outcomes groups’ 39. Another study in China applied LCA to 10 observed complications and comorbidities of patients with type 2 diabetes and categorised the individuals into four latent classes, namely ‘complications and comorbidity groups’, ‘high risk of complications group’, ‘high risk of comorbidities and Cardiovascular Disease groups’ and ‘diabetes without complications and comorbidities group’ 40.…”

Latent Class Multi-Label Classification to Identify Subclasses of Disease for Improved Prediction

“…Dey et al proposed a latent class model for identifying subgroups according to health effects in a large population. It used LCA to produce a clustering of patients [12] by using random sampling without replacement, assuming local independence between observed variables, and each patient is assigned to one class where Bayesian Information Criteria is used to select the best number of classes in the model.…”

Safety Profiles of mRNA COVID-19 Vaccines Using World Health Organization Global Scale Database (VigiBase): A Latent Class Analysis

“…However, factors that are associated with unknown adverse events (AEs) following mRNA COVID-19 vaccination, such as age, gender, and existing diseases or high-risk subgroups, have not been well established [ 9 – 11 ]. The identification of patterns and factors can provide insights into preventive measures and management strategies concerning unknown AEFIs [ 12 ].…”

“…We aimed to (1) identify the patterns of serious AEFI profiles after COVID-19 vaccination within the VigiBase, the World Health Organization (WHO)’s international database of suspected adverse drug reactions, and (2) describe potential factors to identify subgroups in mRNA COVID-19 vaccine reports with similar serious AEFI profiles. To achieve these aims, we applied latent class analysis (LCA), a statistical method used to identify the relationships among a set of unobserved dichotomous or polytomous variables that can be viewed as indicators [ 12 ]. This study was conducted in accordance with the Helsinki Declaration.…”