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

Park, Hyesook; Lee, Hye Ah

doi:10.1371/journal.pone.0216259

Cited by 49 publications

(69 citation statements)

References 63 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…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][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

Background Multimorbidity is an increasing public health concern and is associated with a range of further adverse outcomes. Identification of disease patterns as well as characteristics of populations affected by multimorbidity is important for prevention strategies to identify those at risk. Aim The aim of the study was to identify and describe demographic characteristics of multimorbidity classes in three age groups (16-44 years, 45-64 years, and 65+ years). Methods Based on register information on 47 chronic diseases and conditions, we used latent class analysis to identify multimorbidity classes in a random sample of the Danish population (n = 470,794). Information on sociodemographic characteristics (age, sex, region of origin, educational level, employment status, and marital status) was obtained from registers and linked to the study population. Age-and sex-adjusted multinomial logistic regression models were used to examine associations between multimorbidity classes and sociodemographic characteristics. Results We identified seven classes among individuals in the age groups 45-64 years and 65+ years and five classes in the age group 16-44 years. Overall, the classes were similar in the three age groups, but varied in size, i.e. the class 'No or few diseases' was larger in the younger age group. The class 'Many diseases' (a class with both somatic diseases and mental illnesses) was only seen in individuals aged 45-64 years and 65+ years. There were social inequalities in odds of belonging to the multimorbidity classes compared to the healthier class. These social inequalities varied but were especially strong in the classes named 'Many diseases' and 'Mental illness, epilepsy'.

show abstract

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

show abstract

“…Nonetheless, our findings and those from other countries have some similarities. For instance, the relatively healthy group is the majority, and the percentage is approximately 50–70% [ 12 , 13 , 15 , 16 ]. In our study, the relatively healthy group accounted for 58.92% of the whole study population.…”

Section: Discussionmentioning

confidence: 99%

“…The cognitively impaired group demonstrated a significantly higher mortality [ 12 ]. Moreover, a Korean study discovered three disease patterns: a relatively healthy group (60.4%); a cardiometabolic conditions group (27.8%); and an arthritis, asthma, allergic rhinitis, depression, and thyroid disease group (11.8%) [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Trends of Multimorbidity Patterns over 16 Years in Older Taiwanese People and Their Relationship to Mortality

Yeh

Wei

et al. 2022

IJERPH

View full text Add to dashboard Cite

Understanding multimorbidity patterns is important in finding a common etiology and developing prevention strategies. Our aim was to identify the multimorbidity patterns of Taiwanese people aged over 50 years and to explore their relationship with health outcomes. This longitudinal cohort study used data from the Taiwan Longitudinal Study on Aging. The data were obtained from wave 3, and the multimorbidity patterns in 1996, 1999, 2003, 2007, and 2011 were analyzed separately by latent class analysis (LCA). The association between each disease group and mortality was examined using logistic regression. Four disease patterns were identified in 1996, namely, the cardiometabolic (18.57%), arthritis–cataract (15.61%), relatively healthy (58.92%), and multimorbidity (6.9%) groups. These disease groups remained similar in the following years. After adjusting all the confounders, the cardiometabolic group showed the highest risk for mortality (odds ratio: 1.237, 95% confidence interval: 1.040–1.472). This longitudinal study reveals the trend of multimorbidity among older adults in Taiwan for 16 years. Older adults with a cardiometabolic multimorbidity pattern had a dismal outcome. Thus, healthcare professionals should put more emphasis on the prevention and identification of cardiometabolic multimorbidity.

show abstract

“…Cognitive function was measured according to the Mini-Mental State Examination (MMSE): the higher the score (0-30), the better the cognitive status is [35]. MMSE scores were divided into three categories to distinguish people with severe (0-17), mild (18)(19)(20)(21)(22)(23), and no cognitive impairment (24)(25)(26)(27)(28)(29)(30) [36]. Functional status was assessed according to the ability to perform five of the activities of daily living (ADLs) (eating, dressing, bathing, toileting, transferring) [37].…”

Section: Study Population and Measuresmentioning

confidence: 99%

“…Some scholars used this approach to identify profiles of health by considering functional, cognitive and psychological indicators [12-14, 16, 17, 22], with some evaluating socioeconomic differences among the health profiles [12,13,17,22] and others predicting the health care expenditures of people belonging to different groups [14,16]. Other researchers have applied a personcentered approach to identify profiles within a single aspect of health, such as morbidities [15,19,25], physical status [21], and depression [20], by considering several outcomes of the same health dimension. According to the existing literature, LCA could be used to identify groups of individuals requiring specific forms of health care and to predict their health care needs and expenditures.…”

Section: Introductionmentioning

confidence: 99%

Health profiles and socioeconomic characteristics of nonagenarians residing in Mugello, a rural area in Tuscany (Italy)

et al. 2020

View full text Add to dashboard Cite

Background: Health, as defined by the WHO, is a multidimensional concept that includes different aspects. Interest in the health conditions of the oldest-old has increased as a consequence of the phenomenon of population aging. This study investigates whether (1) it is possible to identify health profiles among the oldest-old, taking into account physical, emotional and psychological information about health, and (2) there are demographic and socioeconomic differences among the health profiles. Methods: Latent Class Analysis with covariates was applied to the Mugello Study data to identify health profiles among the 504 nonagenarians residing in the Mugello district (Tuscany, Italy) and to evaluate the association between socioeconomic characteristics and the health profiles resulting from the analysis. Results: This study highlights four groups labeled according to the posterior probability of determining a certain health characteristic: "healthy", "physically healthy with cognitive impairment", "unhealthy", and "severely unhealthy". Some demographic and socioeconomic characteristics were found to be associated with the final groups: older nonagenarians are more likely to be in worse health conditions; men are in general healthier than women; more educated individuals are less likely to be in extremely poor health conditions, while the lowest-educated are more likely to be cognitively impaired; and office or intellectual workers are less likely to be in poor health conditions than are farmers. Conclusions: Considering multiple dimensions of health to determine health profiles among the oldest-old could help to better evaluate their care needs according to their health status.

show abstract

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

Cited by 49 publications

References 63 publications

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

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

Trends of Multimorbidity Patterns over 16 Years in Older Taiwanese People and Their Relationship to Mortality

Health profiles and socioeconomic characteristics of nonagenarians residing in Mugello, a rural area in Tuscany (Italy)

Contact Info

Product

Resources

About