Clustering the Economic Status via Partitioning around Medoid and Its Association with Common Non-communicable Diseases.

Abstract

During the last decades, the role of economic status and wealth-related variables in relation to the mortality and incidence of a wide range of diseases have received increased attention. This study focused on clustering the economic status of a population-based study using partitioning around the medoid (PAM) and then investigating the association between the obtained economic clusters and the incidence of non-communicable diseases (NCDs). The present study was based on data from Shahrekord Cohort Study (SCS). This study considered nine NCDs, including cardiac disease, myocardial infarction, diabetes, hypertension, stroke, all types of malignancies, chronic lung disease, depression, and obesity, among 7034 participants aged 35 and 70 from the urban population of Sharekord (IRAN) in 2022. Four quantitative and four qualitative variables were used to cluster the economic status. The NbClust package was used to determine the optimal number of clusters, and the K-med package in R software (version 4.2.1) was used for PAM clustering. Descriptive statistics were reported as frequency (%) or median (IQR), and statistical analysis was performed using the Chi square test and Mann-Whitney test in SPSS software (version 19.0). P<0.05 was considered statistically significant. The estimated optimal number of clusters was two. The first cluster contained individuals with good economic status, while the second cluster contained those with a moderate economic status. The findings indicated that individuals with a good economic status had significantly higher rates of cardiac disease (7.2% versus 5.3%, P<0.001), stroke (1.3% versus 0.6%, P<0.001), diabetes (12.8% versus 9.1%, P<0.001), hypertension (21.6% versus 15.6%, P<0.001), depression (P<0.001), and obesity (P=0.03). The findings of the present study showed that economic status was significantly associated with the majority of NCDs.