克服难民环境中的分母问题,以健康和移民数据的碎片化电子记录:基于预测的方法
Stella Erdmann1, Rosa Jahn2, Sven Rohleder3
1Institute of Medical Biometry, University of Heidelberg, Im Neuenheimer Feld 130.3, 69120, Heidelberg, Germany. erdmann@imbi.uni-heidelberg.de.
BMC medical research methodology
|April 2, 2024
概括
这项研究开发了一种新的方法来解决难民健康数据中的分母问题,通过将患者数量与中心占用量联系起来. 这种方法提高了难民人口中疾病频率估计的准确性.
科学领域:
- 公共卫生 公共卫生
- 流行病学 流行病学
- 医疗信息学 医疗信息学
背景情况:
- 难民环境中的流行病学研究面临挑战,原因是缺乏风险人口数据 (分母问题).
- 准确的分母数据对于可靠的疾病发病率计算和健康监测至关重要.
研究的目的:
- 开发和评估一种实证方法,以减轻难民健康监测中的分母问题.
- 评估难民中心占用率,患者数量和消化系统疾病之间的关系.
主要方法:
- 来自初级保健监测系统的个人患者数据与移民当局的占用数据相匹配.
- 使用回归模型分析了患者人数,占用率和疾病发病率之间的关系,对年龄,性别和中心类型进行控制.
- 为缺失的分母数据组件做出预测.
主要成果:
- 这项研究分析了来自德国21个难民接待中心的445个"中心月"的数据 (2017年11月至2021年7月).
- 使用占用数据作为分母导致较低的疾病发病率估计,而不是仅使用患者人数,避免过高估计.
- 开发的模型为疾病频率提供了经验基础的预测,使得更准确的比较.
结论:
- 模拟患者和占用人数之间的比率有效地解决了难民健康监测中的分母问题.
- 这种经验方法可以更准确地估计疾病的频率,并在不同的环境和时间段进行比较.
- 通过提供更强大的分母,这些发现支持在人道主义环境中改进健康监测策略.
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