How Socio-economic Inequalities Cluster People with Diabetes in Malaysia: Geographic Evaluation of Area Disparities Using a Non-parameterized Unsupervised Learning Method.

Autor: Ganasegeran K; Department of Public Health Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, 56000, Kuala Lumpur, Malaysia. medkuru@yahoo.com.; Clinical Research Center, Seberang Jaya Hospital, Ministry of Health Malaysia, 13700, George Town, Penang, Malaysia. medkuru@yahoo.com., Abdul Manaf MR; Department of Public Health Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, 56000, Kuala Lumpur, Malaysia. mrizal@ppukm.ukm.edu.my., Safian N; Department of Public Health Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, 56000, Kuala Lumpur, Malaysia., Waller LA; Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA, 30322, USA., Mustapha FI; Public Health Division, Perak State Health Department, Ministry of Health Malaysia, 30000, Ipoh, Perak, Malaysia., Abdul Maulud KN; Earth Observation Centre (EOC), Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor Darul Ehsan, Malaysia.; Department of Civil Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor Darul Ehsan, Malaysia., Mohd Rizal MF; Earth Observation Centre (EOC), Institute of Climate Change, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor Darul Ehsan, Malaysia.
Jazyk: angličtina
Zdroj: Journal of epidemiology and global health [J Epidemiol Glob Health] 2024 Mar; Vol. 14 (1), pp. 169-183. Date of Electronic Publication: 2024 Feb 05.
DOI: 10.1007/s44197-023-00185-2
Abstrakt: Accurate assessments of epidemiological associations between health outcomes and routinely observed proximal and distal determinants of health are fundamental for the execution of effective public health interventions and policies. Methods to couple big public health data with modern statistical techniques offer greater granularity for describing and understanding data quality, disease distributions, and potential predictive connections between population-level indicators with areal-based health outcomes. This study applied clustering techniques to explore patterns of diabetes burden correlated with local socio-economic inequalities in Malaysia, with a goal of better understanding the factors influencing the collation of these clusters. Through multi-modal secondary data sources, district-wise diabetes crude rates from 271,553 individuals with diabetes sampled from 914 primary care clinics throughout Malaysia were computed. Unsupervised machine learning methods using hierarchical clustering to a set of 144 administrative districts was applied. Differences in characteristics of the areas were evaluated using multivariate non-parametric test statistics. Five statistically significant clusters were identified, each reflecting different levels of diabetes burden at the local level, each with contrasting patterns observed under the influence of population-level characteristics. The hierarchical clustering analysis that grouped local diabetes areas with varying socio-economic, demographic, and geographic characteristics offer opportunities to local public health to implement targeted interventions in an attempt to control the local diabetes burden.
(© 2024. The Author(s).)
Databáze: MEDLINE
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