Spatial Analysis of patterns of Multimorbidity in the Thai Cohort Study Using Latent Class Analysis

被引：0
作者：
Xiyu Feng [1 ]
Haribondhu Sarma [1 ]
Sam-ang Seubsman [2 ]
Adrian Sleigh [1 ]
Matthew Kelly [1 ]
机构：
[1] National Centre of Epidemiology and Population Health, The Australian National University, Building 62, Mills Road, Acton, Canberra
[2] School of Human Ecology, Sukhothai Thammathirat Open University, Nonthaburi
来源：
Journal of Epidemiology and Global Health | / 15卷 / 1期
关键词：
Chronic conditions; Latent class analysis; Multimorbidity patterns; Spatial analysis; Thai Cohort study;
D O I：
10.1007/s44197-025-00352-7
中图分类号：
学科分类号：
摘要：
Objective: The study aims to determine patterns of multimorbidity among common non-communicable diseases (NCDs) in Thailand. Study design: Cross-sectional analysis. Methods: This study obtained self-reported data from 42,785 participants of the Thai Cohort Study (TCS) via mailed questionnaires. Information was collected on eight chronic conditions. Common multimorbidity (co-occurrence of two or more chronic conditions) patterns were identified and classified using latent class analysis (LCA). Multinomial models assessed associations with demographic and lifestyle factors, testing linear trends with P for trend (p-trend). The spatial analysis was used to identify potential clusters and high-risk areas of the age-adjusted prevalence of multimorbidity at the study area. Results: Four clusters were identified: “Relatively healthy” class (87.32%, reference), “Metabolic syndromes” class (10.20%), “Cardiometabolic disorders” class (1.53%), and “Multi-system conditions” class (0.95%) (percentages meaning proportion of this group). Older age and males were associated with an increased risk of multimorbidity. Attaining a university-level education was found to be a protective factor for in the classes of multimorbidity. Furthermore, engaging in housework appeared to be associated with a reduced risk of developing cardiometabolic conditions and multi-system disorders. Spatial analysis indicated that the high age-adjusted prevalence of “Cardiometabolic disorders” class tended to be clustered in central Thailand. Conclusion: Multimorbidity patterns were related to sociodemographic factors and lifestyles, and geographic characteristics. Future research should focus on classifying and comparing multimorbidity among different populations such as different age groups and genders in various locations. This would help in formulating targeted health policies and interventions to reduce their health burden. © The Author(s) 2025.
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