预测结核病例发现效率高的社区,以优化巴基斯坦的资源分配:比较负二项空间滞后模型与贝叶斯机器学习模型的性能
Christina Mergenthaler1,2, Jake D Mathewson1, Stephanie Lako1
1Centre for Applied Spatial Epidemiology, KIT Royal Tropical Institute, Amsterdam, The Netherlands.
一个更简单的负二项回归 (NBR) 模型在预测结核病 (TB) 热点方面几乎与复杂的贝叶斯式机器学习 (BML) 模型一样有效. 这一发现支持使用统计模型指导巴基斯坦积极病例发现 (ACF) 努力.
科学领域:
- 流行病学 流行病学
- 公共卫生 公共卫生
- 生物统计学 生物统计学
背景情况:
- 据估计,巴基斯坦每年可能仍有183,000例结核病 (TB) 病例未被诊断.
- 针对高风险人群的活跃病例发现 (ACF) 是提高结核病诊断率的关键.
- 开发预测模型可以优化对结核病控制资源的分配.
研究的目的:
- 将负二项回归 (NBR) 模型的预测精度与贝叶斯机器学习 (BML) 模型进行比较.
- 在巴基斯坦的活跃病例发现 (ACF) 设置中确定结核病阳性的预测因素.
- 评估统计模型对针对结核病热点的有用性.
主要方法:
- 从414个ACF事件 (2020年9月 - 2022年1月) 的横截面数据的回顾性分析.
- 开发一个包含空间自相对应的负二项回归 (NBR) 模型.
- 使用根平均平方误差 (RMSE) 和Akaike信息标准 (AIC) 的NBR和BML模型的比较.
主要成果:
- 在21,227名游客中检测到407例 (1.9%) 细菌学确认的结核病例.
- 空间滞后变量在NBR模型中显著解释了结核病阳性率的变化.
- 无论是NBR还是BML模型,在分区层面都显示出类似的预测性表现,NBR的适合度略高 (AIC).
结论:
- 统计模型对于预测结核病热点来指导ACF规划是有效的.
- 一个更简单的NBR模型为TB预测提供了与更复杂的BML模型相匹配的性能.
- 模型预测在不同的框架中是强大的,支持它们用于服务不足地区的目标ACF.
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