在多变量生存模型中的一个新奇的异常统计数据及其应用,以确定马拉维不寻常的五岁以下死亡率分区
Tsirizani M Kaombe1, Samuel O M Manda1,2,3
1Department of Mathematical Sciences, Faculty of Science, University of Malawi, Zomba, Malawi.
Journal of applied statistics
|June 1, 2023
概括
这项研究引入了一种新方法,用于在撒哈拉以南非洲找到高五岁以下死亡率 (U5M) 的地区. 这有助于有效地针对儿童健康干预措施,提高脆弱地区的生存率.
科学领域:
- 儿童健康 儿童健康
- 生物统计学 生物统计学
- 流行病学 流行病学
背景情况:
- 撒哈拉以南非洲的五岁以下儿童死亡率 (U5M) 仍然很高,尽管全球的死亡率有所下降.
- 识别极端U5M率的次国家区域对于有针对性的公共卫生干预至关重要.
- 现有的方法可能无法充分识别高死亡率的局部集群.
研究的目的:
- 提出一种新的群体异常值检测统计,用于识别极端低于5岁儿童死亡率的地区.
- 通过模拟研究来评估这个统计数据的性能.
- 应用该方法来确定马拉维异常高或低的U5M率的分区.
主要方法:
- 在多变量生存数据模型中开发一种新的群异常值检测统计.
- 通过模拟研究评估准确性和所需数据集大小的性能评估.
- 应用到马拉维的儿童存活率数据,识别使用Akaike信息标准 (AIC) 选择模型的极端U5M率的分区.
主要成果:
- 拟议的异常值检测统计数据在模拟中显示出高精度 (≥90%) 在检测异常高或低死亡率组.
- 该方法需要至少有50个大小为80或更大的集群的数据集,以获得可靠的性能.
- 对马拉维儿童存活率数据的分析发现,最多有7个子区具有明显高或低的五岁以下儿童死亡率.
结论:
- 新型群体异常点检测统计有效地确定了极端的五岁以下儿童死亡率的次国家区域.
- 这种方法可以指导像撒哈拉以南非洲这样的地区的针对性儿童健康干预.
- 通过适当的数据大小和统计建模,可以准确识别异常区域.
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