Journal of epidemiology and global health · 2025
Identifying Subgroups At-Risk for Noncommunicable Diseases in Cambodia: A Latent Class Analysis of Behavioral and Metabolic Risk Factor Patterns.
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Comey Cassandra, Anindya Kanya, Santosa Ailiana, Kowal Paul, Chhim Srean, Sopheab Heng, Ng Nawi
Аннотация
BACKGROUND: Cambodia is experiencing a demographic shift likely to increase the burden of noncommunicable diseases (NCDs). Identifying patterns of risk factors among adults can contribute to efforts to effectively target and prevent these chronic diseases. This study aims to examine latent classes of population risk based on behavioral and metabolic risk factors for NCDs in Cambodia.
METHODS: Data from 5275 respondents aged 18 and older from the 2023 Cambodian World Health Survey Plus were used for analysis. Latent class analysis identified distinct classes of individuals with similar behavioral and metabolic risk factors. Indicator variables included tobacco and alcohol use, fruit and vegetable diet, physical activity, body mass index, blood pressure, blood glucose, and total cholesterol and triglyceride levels. Multinomial logistic regression was employed to predict latent class membership based on sociodemographic characteristics.
RESULTS: Three distinct latent classes were identified: "alcohol users with lower metabolic risk" (37.8%), "substance users with compounding unhealthy behaviors" (15.0%), and "alcohol users with higher metabolic risk" (47.2%). Men, older adults, and individuals with lower education were more likely to be substance users with compounding unhealthy behaviors and alcohol users with higher metabolic risks.
CONCLUSIONS: These findings highlight the need for targeted public health strategies to address the combined impact of multiple risk factors, particularly among men, older adults, and individuals with lower education levels.
METHODS: Data from 5275 respondents aged 18 and older from the 2023 Cambodian World Health Survey Plus were used for analysis. Latent class analysis identified distinct classes of individuals with similar behavioral and metabolic risk factors. Indicator variables included tobacco and alcohol use, fruit and vegetable diet, physical activity, body mass index, blood pressure, blood glucose, and total cholesterol and triglyceride levels. Multinomial logistic regression was employed to predict latent class membership based on sociodemographic characteristics.
RESULTS: Three distinct latent classes were identified: "alcohol users with lower metabolic risk" (37.8%), "substance users with compounding unhealthy behaviors" (15.0%), and "alcohol users with higher metabolic risk" (47.2%). Men, older adults, and individuals with lower education were more likely to be substance users with compounding unhealthy behaviors and alcohol users with higher metabolic risks.
CONCLUSIONS: These findings highlight the need for targeted public health strategies to address the combined impact of multiple risk factors, particularly among men, older adults, and individuals with lower education levels.