根据不同的方法估计的人类测量不合理值的频率:系统性审查和元分析
Iolanda Karla Santana Dos Santos1,2, Débora Borges Dos Santos Pereira1, Jéssica Cumpian Silva1
1Faculdade de Saúde Pública, Universidade de São Paulo, São Paulo, São Paulo, Brasil.
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|October 30, 2023
概括
人类测量数据质量不佳影响营养不良的流行和政策. 这次审查发现,与体重数据相比,身高数据的不可思议值是体重数据的两倍,强调需要强有力的数据质量指标.
科学领域:
- 公共卫生 公共卫生
- 营养科学 营养科学
- 数据质量评估数据质量评估
背景情况:
- 不准确的人类测量数据可能会扭曲营养不良患病率估计.
- 数据质量受损可能会对公共政策和规划产生负面影响.
- 对人类测量数据质量的系统评估对于可靠的健康统计数据至关重要.
研究的目的:
- 系统地审查和元分析评估和清理人类识别数据的方法.
- 使用各种数据方法来确定不可思议的体重和身高值的频率.
- 为改善人类测量数据质量提供基于证据的建议.
主要方法:
- 在多个数据库 (MEDLINE,LILACS,Embase,Scopus,Web of Science,Google Scholar) 进行全面的文献搜索,更新至2023年1月.
- 包括104项研究 (123份报告) 进行定性综合和37项研究进行定量元分析.
- 进行元分析以估计不合理值的频率 (重量:0.55%,高度:1.20%) 以95%的置信区间,评估异质性和出版偏差.
主要成果:
- 与体重测量 (0.55%) 相比,身高测量显示出不合理值的频率更高 (1.20%).
- 方法质量评分没有显著影响结果的异质性.
- 没有检测到出版偏差,这表明包含的研究的可靠综合.
结论:
- 高度数据质量比重数据质量更为关注,不合理值的比例是不可思议值的两倍.
- 采用一套全面的质量指标优于依赖单个指标来进行 robust 人类测量数据评估.
- 改进的数据质量评估方法对于准确的公共卫生监测和政策制定至关重要.
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