[对Covid-19病例数据质量/完整性的分析:为什么数字综合数据收集也需要用于疫情控制]
Benedikt Mj Lampl1,2, Bernhard Edenharter1, Michael Leitzmann2
1Gesundheitsamt Regensburg, Regensburg, Germany.
概括
德国COVID-19数据收集不完整,阻碍了疫苗的有效性和结果评估. 改善数字战略对于在流行病期间准确的公共卫生见解至关重要.
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:
- 与国际数据相比,在COVID-19大流行期间,德国公共卫生部门的流行病学数据在估计疫苗有效性和临床结果方面效率较低.
- 这一限制凸显了德国医疗保健系统中数据收集和使用的潜在问题.
研究的目的:
- 以回顾评估在德国COVID-19大流行期间收集的特定数据点的完整性.
- 评估有关住院,疫苗接种情况以及严重疾病和死亡风险因素的信息质量.
- 在德国数据管理所需的数字化策略的背景下讨论研究结果.
主要方法:
- 追溯观察性研究设计.
- 对关键参数的完整性进行描述性评估,包括住院治疗,疫苗接种状态和风险因素.
- 从2022年1月1日到2022年6月30日的不同时间段分析数据.
主要成果:
- 在126,920个COVID-19相关的行政程序中,超过83%的病例中,住院数据是"未收集"或"无法确定".
- 关于疫苗类型 (11.1+%),接种次数 (4.4+%) 和最后一次接种疫苗日期 (2.1+%) 的信息几乎没有记录.
- 2020年至2022年期间,随着病例数量的增加,COVID-19死亡的慢性疾病/危险因素数据的完整性下降.
结论:
- 德国目前的数据收集实践不足以对COVID-19大流行病进行强有力的流行病学分析.
- 现代技术解决方案和数字数据收集对于提高数据的意义和完整性至关重要.
- 未来的战略必须平衡有效的数据采集与严格的数据保护原则.
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