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

Abstract

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.

Expectativa de vida com e sem multimorbidade entre idosos brasileiros: Pesquisa Nacional de Saúde 2013

“…A co-ocorrência de doenças crônicas não é atribuída ao acaso. Frequentemente, os padrões de multimorbidade são associados a fatores sociodemográficos e qualidade de vida (PARK et al, 2019;NGUYEN et al, 2019b). Dessa forma, a integração entre a saúde pública e as estratégias clínicas que lidam com estas doenças crônicas entre idosos devem se basear nos padrões de multimorbidade, e não nas doenças isoladas em si.…”

Patterns of multimorbidity and demographic profile of latent classes in a Danish population—A register-based study

“…If the LCA is based on information on diseases this will result in categories that represent groups of individuals with different disease profiles. This technique has been used in previous studies identifying patterns of multimorbidity in different populations [ 6 – 9 ].…”

Emerging Multimorbidity Patterns and Its Linkages With Selected Health Outcomes Among Working-Agegroup Population: Findings From Odisha, India

“…Additionally, a weighted prevalence was computed for all the 18 chronic disease conditions included in the present study. To identify multimorbidity patterns among the working agegroup population a latent class analysis(LCA) approach was carried out (14,19,20). All the eighteen chronic diseases, namelyacid peptic disease, arthritis, chronic back pain, diabetes, epilepsy, lariasis, hearing disorder, heart disease, hypertension, kidney disease, lung diseases, mental disorder, osteoarthritis, skin diseases, stroke, tuberculosis, thyroid diseases and vision disorderwere included as observed indicators.…”

“…Four latent classes were included in the study. Fit indices Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC)(lowest values were indicative of the best tting model) were used to identify the optimal number of latent classes to be included in the study (14,19,20).…”