比较多种推算方法以解决免疫信息系统中缺失的患者人口统计数据:回顾性队列研究
Sara Brown1, Ousswa Kudia1, Kaye Kleine1
1Scientific Services - Analytics, Scientific Technologies Corporation (United States), 411 S 1st St, Phoenix, AZ, 85004, United States, 1 480-745-8500.
JMIR public health and surveillance
|August 26, 2025
概括
多种归算方法如MICE和miceforest有效地管理免疫监测中缺少的种族/种族数据,保护人口统计数据并提高公共卫生干预的准确性. 对于大型数据集, Miceforest 提供了更高的计算效率.
科学领域:
- 公共卫生监测
- 生物统计学
- 卫生信息学
背景情况:
- 免疫信息系统 (IIS) 和监测数据对于公共卫生至关重要,但往往缺少数据,可能会影响疫苗覆盖率评估,并阻碍解决健康差异的努力.
- 准确评估疫苗覆盖率对于有效的公共卫生计划和干预措施至关重要,尤其是在旨在减少不平等的情况下.
研究的目的:
- 在大规模公共卫生监测数据集中处理缺少的种族和种族数据时,评估三种多重归因方法的性能.
- 根据其保存人口分布的能力,计算效率以及它们对评估种族/种族和流感疫苗接种状态之间的关联的影响来比较这些归算方法.
主要方法:
- 一项回顾性队列研究分析了来自西弗吉尼亚州免疫信息系统的2021-2022年流感疫苗接种和人口统计数据 (N=2,302,036),其中种族 (15%) 和种族 (34%) 缺失显著.
- 应用了三种多重归算技术 (MICE,Iterative-Imputer,miceforest) 来生成15个归算数据集.
- 通过使用G统计和概率比率统计来比较人口分布保存,计算效率和空间聚类模式来评估性能.
主要成果:
- 所有的归算方法都显示了种族归算的显著空间聚类. 与Iterative-Imputer相比,MICE和miceforest的人口比例分布保持得更好.
- 计算效率差异很大:MICE需要14个小时,Iterative-Imputer需要2分钟,miceforest需要10分钟进行15次推算.
- 计算后的分析显示分层流感疫苗接种率下降 (0. 87% - 18%) 和总体下降26%至19%,突出显示缺少数据对估计的影响.
结论:
- MICE和miceforest提供了可靠的方法来归纳缺失的人口数据,比Iterative-Imputer更有效地减轻偏差. 通过基于云的处理,Miceforest提供了更高的计算效率,特别是在大型数据集中.
- 归算方法的选择对研究结果产生重大影响,强调需要仔细选择.
- 估计的疫苗接种率大幅下降强调了缺少的数据如何掩盖真正的差异. 建议定期使用归算方法来改善健康公平评估,并指导有针对性的公共卫生干预.
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