Document Type : Original Article(s)
Authors
1 Student Research Committee, Shahrekord University of Medical Sciences, Shahrekord, Iran
2 Modeling in Health Research Center, Shahrekord University of Medical Sciences, Shahrekord, Iran
Abstract
Background: 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).
Methods: 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.
Results: 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).
Conclusion: The findings of the present study showed that economic status was significantly associated with the majority of NCDs.
Keywords
- Bodenmann P, Favrat B, Wolff H, Guessous I, Panese F, Herzig L, et al. Screening primary-care patients forgoing health care for economic reasons. PLoS One. 2014;9:e94006. doi: 10.1371/journal.pone.0094006. PubMed PMID: 24699726; PubMed Central PMCID: PMCPMC3974836.
- Mode NA, Evans MK, Zonderman AB. Race, Neighborhood Economic Status, Income Inequality and Mortality. PLoS One. 2016;11:e0154535. doi: 10.1371/journal.pone.0154535. PubMed PMID: 27171406; PubMed Central PMCID: PMCPMC4865101.
- He P, Luo Y, Hu X, Gong R, Wen X, Zheng X. Association of socioeconomic status with hearing loss in Chinese working-aged adults: A population-based study. PLoS One. 2018;13:e0195227. doi: 10.1371/journal.pone.0195227. PubMed PMID: 29596478; PubMed Central PMCID: PMCPMC5875885.
- Kish JK, Yu M, Percy-Laurry A, Altekruse SF. Racial and ethnic disparities in cancer survival by neighborhood socioeconomic status in Surveillance, Epidemiology, and End Results (SEER) Registries. J Natl Cancer Inst Monogr. 2014;2014:236-43. doi: 10.1093/jncimonographs/lgu020. PubMed PMID: 25417237; PubMed Central PMCID: PMCPMC4841168.
- Agyekum AK, Adde KS, Aboagye RG, Salihu T, Seidu AA, Ahinkorah BO. Unmet need for contraception and its associated factors among women in Papua New Guinea: analysis from the demographic and health survey. Reprod Health. 2022;19:113. doi: 10.1186/s12978-022-01417-7. PubMed PMID: 35527266; PubMed Central PMCID: PMCPMC9080214.
- Najafi F, Soltani S, Karami Matin B, Kazemi Karyani A, Rezaei S, Soofi M, et al. Socioeconomic - related inequalities in overweight and obesity: findings from the PERSIAN cohort study. BMC Public Health. 2020;20:214. doi: 10.1186/s12889-020-8322-8. PubMed PMID: 32046684; PubMed Central PMCID: PMCPMC7014739.
- Jooste S, Mabaso M, Taylor M, North A, Shean Y, Simbayi LC. Socio-economic differences in the uptake of HIV testing and associated factors in South Africa. BMC Public Health. 2021;21:1591. doi: 10.1186/s12889-021-11583-1. PubMed PMID: 34445996; PubMed Central PMCID: PMCPMC8390264.
- Omondi I, Odiere MR, Rawago F, Mwinzi PN, Campbell C, Musuva R. Socioeconomic determinants of Schistosoma mansoni infection using multiple correspondence analysis among rural western Kenyan communities: Evidence from a household-based study. PLoS One. 2021;16:e0253041. doi: 10.1371/journal.pone.0253041. PubMed PMID: 34161354; PubMed Central PMCID: PMCPMC8221481.
- Eyler L, Hubbard A, Juillard C. Assessment of economic status in trauma registries: A new algorithm for generating population-specific clustering-based models of economic status for time-constrained low-resource settings. Int J Med Inform. 2016;94:49-58. doi: 10.1016/j.ijmedinf.2016.05.004. PubMed PMID: 27573311.
- Webster TJ. Malaysian economic development, leading industries and industrial clusters. The Singapore Economic Review. 2014;59:1450044. doi: 10.1142/S0217590814500441.
- Kassambara A. Practical guide to cluster analysis in R: Unsupervised machine learning. Sthda; 2017
- Farazdaghi M, Razeghi M, Sobhani S, Raeisi-Shahraki H, Alipour Haghighi M, Farazdaghi M, et al. Knee impairments: Comparison between new clinical classification by cluster analysis and movement system impairment model. J Bodyw Mov Ther. 2022;30:210-20. doi: 10.1016/j.jbmt.2022.02.003. PubMed PMID: 35500973.
- Reza Farazdaghi M, Razeghi M, Sobhani S, Raeisi Shahraki H, Motealleh A. A New Clustering Method for Knee Movement Impairments using Partitioning Around Medoids Model. Iran J Med Sci. 2020;45:451-62. doi: 10.30476/ijms.2019.82033. PubMed PMID: 33281262; PubMed Central PMCID: PMCPMC7707633.
- Shokri E, Razeghi M, Raeisi Shahraki H, Jalli R, Motealleh A. The Use of Cluster Analysis by Partitioning around Medoids (PAM) to Examine the Heterogeneity of Patients with Low Back Pain within Subgroups of the Treatment Based Classification System. J Biomed Phys Eng. 2023;13:89-98. doi: 10.31661/jbpe.v0i0.2001-1047. PubMed PMID: 36818010; PubMed Central PMCID: PMCPMC9923237.
- Alqurneh A, Mustapha A, Sharef NM. A partitioning-based approach for clustering COVID-19 drugs and co-medication for safe use. International Journal of Integrated Engineering. 2020;12:224-32. doi: 10.30880/ijie.2020.12.05.028.
- Maharlouei N, Sarkarinejad A, Shahraki HR, Rezaianzadeh A, Lankarani KB. Socioeconomic Status and Child Developmental Delay: A Prospective Cohort Study. Shiraz E-Medical Journal. 2021;22. doi: 10.5812/semj.100166.
- Zarean E, Looha MA, Amini P, Ahmadi A, Dugue PA. Sleep characteristics of middle-aged adults with non-alcoholic fatty liver disease: findings from the Shahrekord PERSIAN cohort study. BMC Public Health. 2023;23:312. doi: 10.1186/s12889-023-15251-4. PubMed PMID: 36774488; PubMed Central PMCID: PMCPMC9922458.
- Ahmadi A, Shirani M, Khaledifar A, Hashemzadeh M, Solati K, Kheiri S, et al. Non-communicable diseases in the southwest of Iran: profile and baseline data from the Shahrekord PERSIAN Cohort Study. BMC Public Health. 2021;21:2275. doi: 10.1186/s12889-021-12326-y. PubMed PMID: 34903205; PubMed Central PMCID: PMCPMC8670056.
- Ahmadi A, Taji F, Shahraki HR. Comparing health service reception in individuals with and without non-communicable diseases before and during the COVID-19 pandemic: Shahrekord cohort study. Iranian Journal of Endocrinology and Metabolism. 2023;24:92-100. Persian.
- Maechler M. Finding groups in data: Cluster analysis extended Rousseeuw et al. R package version. 2019;2:242-8.
- Charrad M, Ghazzali N, Boiteau V, Niknafs A. NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set. Journal of Statistical Software. 2014;61:1-36. doi: 10.18637/jss.v061.i06.
- Kaufman L, Rousseeuw PJ. Finding groups in data: an introduction to cluster analysis. New Jersey: John Wiley & Sons; 2009.
- Mtintsilana A, Craig A, Mapanga W, Dlamini SN, Norris SA. Association between socio-economic status and non-communicable disease risk in young adults from Kenya, South Africa, and the United Kingdom. Sci Rep. 2023;13:728. doi: 10.1038/s41598-023-28013-4. PubMed PMID: 36639432; PubMed Central PMCID: PMCPMC9839722.
- Reddy MM, Zaman K, Yadav R, Yadav P, Kumar K, Kant R. Prevalence, Associated Factors, and Health Expenditures of Noncommunicable Disease Multimorbidity-Findings From Gorakhpur Health and Demographic Surveillance System. Front Public Health. 2022;10:842561. doi: 10.3389/fpubh.2022.842561. PubMed PMID: 35462842; PubMed Central PMCID: PMCPMC9019118.
- Marthias T, Anindya K, Ng N, McPake B, Atun R, Arfyanto H, et al. Impact of non-communicable disease multimorbidity on health service use, catastrophic health expenditure and productivity loss in Indonesia: a population-based panel data analysis study. BMJ Open. 2021;11:e041870. doi: 10.1136/bmjopen-2020-041870. PubMed PMID: 33597135; PubMed Central PMCID: PMCPMC7893673.
- Lotfi MH, Amiri F, Forouzannia SK, Fallahzadeh H, Shekari H. The Association Between Socio-Economic Factors and Coronary Artery Disease in Yazd Province: A Case-Control Study. Journal of Community Health Research. 2014;3:168-76. Persian.
- Kundu J, Chakraborty R. Socio-economic inequalities in burden of communicable and non-communicable diseases among older adults in India: Evidence from Longitudinal Ageing Study in India, 2017-18. PLoS One. 2023;18:e0283385. doi: 10.1371/journal.pone.0283385. PubMed PMID: 36996071; PubMed Central PMCID: PMCPMC10062644.
- Biswas T, Islam MS, Linton N, Rawal LB. Socio-Economic Inequality of Chronic Non-Communicable Diseases in Bangladesh. PLoS One. 2016;11:e0167140. doi: 10.1371/journal.pone.0167140. PubMed PMID: 27902760; PubMed Central PMCID: PMCPMC5130253.
)