加纳中风相对风险的空间和空间时间模式,使用贝叶斯模型方法
Abdul-Karim Iddrisu1, Sampson Appiah Takyi2, Joyseline Owusu Afriyie2
1Department of Statistics, University of Botswana, Botswana.; Department of Mathematics and Statistics, University of Energy and Natural Resources, Ghana.
概括
这项研究在加纳绘制了中风风险图,确定了高风险地区和预测因素. 结果可以指导公共卫生干预措施和用于预防中风的资源分配.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 空间分析 空间分析
背景情况:
- 卒中是全球死亡和残疾的主要原因之一.
- 加纳面临着严重的中风负担,区域风险数据有限.
- 了解空间和时间中风风险对于有针对性的干预至关重要.
研究的目的:
- 分析加纳中风相对风险的空间和空间时间分布.
- 确定中风风险较高的地区.
- 确定与中风风险相关的预测因素.
主要方法:
- 用贝叶斯空间和时空模型来估计区域性中风风险.
- 机器学习算法 (随机森林,渐变增强) 确定了中风风险预测因素.
- 使用了加纳卫生服务 (DHIMS2) 的年度中风数据 (2018-2022年).
主要成果:
- 在研究期间,中风的相对风险显著下降.
- 通过空间和时空模型,特定地区被确定为高风险地区.
- 国民总收入是一个显著的预测因素,降低了中风风险;温度和糖尿病患病率在统计上没有显著意义.
结论:
- 该研究为资源分配和高风险地区的向性中风查提供了关键数据.
- 建议改善诊断能力和监测系统,以减少偏见并加强病例确定.
- 结果与加纳的NCD政策一致,通过例行模型更新支持数据驱动的决策.
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