失踪和算法偏见:来自美国国家疫情报告系统的例子,2009-2019年
Emily Diemer1,2, Elena N Naumova3
1Tufts University Friedman School of Nutrition Science and Policy, 150 Harrison Avenue, Boston, MA, 02111, USA. Emily.sanchez@tufts.edu.
Journal of public health policy
|May 3, 2024
概括
公共卫生监测中的算法偏差可能发生在缺少有关疾病和暴露持续时间的数据时. 这项研究发现,缺少的数据系统地误估了食源性疾病爆发的持续时间,影响了监测的准确性.
科学领域:
- 流行病学 流行病学
- 公共卫生监督 公共卫生监督
- 数据科学数据科学数据科学
背景情况:
- 算法偏见在公共卫生监测中越来越令人担忧.
- 通常缺乏具体的例子来说明这种偏见.
- 常见的假设是,暴露和疾病时期一致.
研究的目的:
- 调查食物传播疾病爆发 (FBDO) 持续时间估计中的算法偏差.
- 检查缺失的疾病和暴露持续时间数据对监测算法的影响.
- 为提供具体的例子,说明数据缺失如何引入偏见.
主要方法:
- 从2009年至2019年期间,美国国家疫情报告系统 (NORS) 分析了9407起由食物传播的疾病爆发.
- 使用完整与不完整的日期和时间信息进行疫情持续时间的比较.
- 统计分析以检测系统地高估或低估FBDO持续时间.
主要成果:
- 由于缺少开始和结束日期,在FBDO持续时间估计中检测到算法偏差.
- 对于缺乏暴露日期的疫情,平均疾病持续时间是完整数据的5.3倍 (p < 0.001).
- 拥有完整数据的FBDO约60%显示暴露期在疾病发作之前结束.
结论:
- 缺乏结构数据,特别是关于疾病和暴露时间的数据,可能导致公共卫生监测中的算法偏差.
- 现代监控系统需要加强调查能力,以识别和解决数据缺失问题.
- 准确估计疫情持续时间对于有效的公共卫生应对和资源分配至关重要.
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