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

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.

Unmet Healthcare Needs Among Elderly Malaysians.

PURPOSE: Older people often have chronic diseases which require a continuity of care over the long term. Countries undergoing population aging need to ensure that older people are receiving the care they need. This study assesses the prevalence of, reasons for, and factors associated with unmet healthcare needs among older people individuals in Malaysia. PATIENTS AND METHODS: This cross-sectional study used data collected during 2018-2020 from 1204 older adults aged 60 and older selected from Selangor state, Malaysia. A comprehensive face-to-face interview based on the Bahasa Malaysia version of the Japan Gerontological Evaluation Study (JAGES-BM) questionnaire was administered to gain information on unmet healthcare needs, socioeconomic factors, health-related factors, and measures of function (activities of daily living, depression, visual impairment, hearing impairment, memory impairment, and walking impairment). Multivariate logistic regression was used to analyze factors associated with their unmet healthcare needs. RESULTS: Overall, the percentage of older people respondents with unmet healthcare needs is 6.6%. The most reported reasons for forgoing or delaying healthcare were lack of knowledge about healthcare and financial barriers to care. The inability to travel alone (adjusted odds ratio [aOR] 2.51), being overweight (aOR 1.88), and having self-reported depression (aOR 2.23) were each associated with a higher likelihood of having unmet healthcare needs in their daily life. CONCLUSION: The prevalence of unmet healthcare needs among older people in this part of Malaysia is lower than that reported in some other countries. However, it is possible to further reduce unmet healthcare needs by improving people's knowledge and attitudes about appropriate healthcare utilization, strengthening financial protection measures and providing support to people at high risk of having unmet healthcare needs.