Local data and upstream reporting as sources of error in the administrative data undercount of Covid 19
Local data and upstream reporting as sources of error in the administrative data undercount of Covid 19
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DOI：
10.1080/13645579.2021.1909337
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发表时间：
2021-04-05
影响因子：
3.3
通讯作者：
Dubrow, Joshua K.
中科院分区：
文献类型：
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作者：
Dubrow, Joshua K.
Covid 19大流行阐明了数据在公共政策制定中的作用，即社会的数据交流，以及探索当地数据来源的重要性，以揭示从一开始就肯定是病例和死亡人数不足的错误。我注意到四个相互关联的错误来源。前两个问题在任何定量数据收集项目中都是常见的：(1)表示、测量和数据处理；(2)来自资源不均等的地方和国家数据提供者的数据标准化问题。Covid 19特别揭示了(3)政府至少在公开展示这些数据方面进行干预的可能性；(4)由紧张的数据收集环境造成的数据链中的人为错误。为了找出错误，我们应该看看各国的压力和收集这些数据的当地背景，以及上游报告程序。
The Covid 19 pandemic illuminates the role data has in public policy-making, i.e. datafication of society, and the importance of exploring the local sources of data to reveal errors in what has assuredly been from the beginning an undercount of cases and deaths. I note four interrelated error sources. The first two are common to any quantitative data collection project: (1) representation, measurement, and data processing; and (2) problems of data standardization from unequally resourced local and national data providers. Covid 19 casts a special light on (3) the possibility of government intervention in at least the public presentation of these data; and (4) human errors in the data chain caused by a stressful data collection environment. To identify errors, we should look to national pressures and the local contexts from which these data are collected and the upstream reporting process.