马来西亚社会经济不平等如何聚集糖尿病患者:使用非参数化无监督学习方法对区域差异的地理评估
Kurubaran Ganasegeran1,2, Mohd Rizal Abdul Manaf3, Nazarudin Safian4
1Department of Public Health Medicine, Faculty of Medicine, Universiti Kebangsaan Malaysia, 56000, Kuala Lumpur, Malaysia. medkuru@yahoo.com.
Journal of epidemiology and global health
|February 5, 2024
概括
这项研究使用聚类来确定马来西亚五个不同的当地地区群体,糖尿病患病率和社会经济因素各不相同. 这些发现使得有针对性的公共卫生干预能够有效地管理糖尿病负担.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 数据科学数据科学数据科学
背景情况:
- 准确的流行病学评估对于有效的公共卫生干预至关重要.
- 大数据和统计方法提高了对疾病分布和健康结果的理解.
- 社会经济因素显著影响健康结果,包括糖尿病患病率.
研究的目的:
- 探索与马来西亚当地社会经济不平等相关的糖尿病负担模式.
- 确定具有类似糖尿病流行率和社会经济特征的行政区集群.
- 为糖尿病控制提供有针对性的公共卫生干预信息.
主要方法:
- 在马来西亚的144个行政区应用了无监督的机器学习,特别是层次聚类.
- 利用多模式的二次数据源,包括来自271,553个人的地区性糖尿病原始率.
- 使用多变量非参数测试统计数据来评估区域特征的差异.
主要成果:
- 确定了五个统计学上显著的局部糖尿病负担集群.
- 每个群体都表现出不同程度的糖尿病患病率和不同的人口水平特征.
- 集群根据社会经济,人口和地理因素进行了区分.
结论:
- 层次聚类有效地根据糖尿病负担和当地特征分组了各地区.
- 已识别的集群为本地公共卫生战略提供了机会,以控制糖尿病.
- 了解这些模式是实施有针对性和有效的糖尿病干预措施的关键.
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