The quality of social determinants data in the electronic health record: a systematic review.
The quality of social determinants data in the electronic health record: a systematic review.
复制标题
DOI：
10.1093/jamia/ocab199
复制
发表时间：
2021-12-28
期刊：
影响因子：
--
通讯作者：
Weiskopf NG
中科院分区：
文献类型：
--
作者：
Cook LA;Sachs J;Weiskopf NG
这项研究的目的是收集和综合有关在处理与健康的社会决定因素(SDoH)相关的变量时遇到的数据质量问题的证据。我们对有关社会决定因素、研究和数据质量的文献进行了系统的回顾，然后使用内容分析过程反复确定文献中的主题。与SDoH数据相关的最常见的质量问题是可信性(n = 31，41%)。与种族和民族有关的因素拥有最多的文献(n = 40，53%)。第一个主题，在62%(n = 47)的文章中提到，偏见或有效性问题通常是由数据质量问题引起的。最常见的有效性问题是错误分类偏差(n = 23，30%)。第二个主题是，许多文章建议了一些方法来缓解由糟糕的社会决定因素数据质量导致的问题。我们将这些建议归类为5条建议：避免完整的案例分析，推算数据，依赖多种来源，使用经过验证的软件工具，以及深思熟虑地选择地址。数据质量问题的类型因变量而异，每个问题都与特定形式的分析错误相关。SDoH数据质量方面遇到的问题很少是随机分布的。来自拉美裔患者的数据比来自其他种族/民族的数据更容易出现似是而非和错误分类的问题。考虑数据质量和基于证据的质量改进方法可能有助于防止偏见，并提高使用SDoH数据进行的研究的有效性。
The aim of this study was to collect and synthesize evidence regarding data quality problems encountered when working with variables related to social determinants of health (SDoH). We conducted a systematic review of the literature on social determinants research and data quality and then iteratively identified themes in the literature using a content analysis process. The most commonly represented quality issue associated with SDoH data is plausibility (n = 31, 41%). Factors related to race and ethnicity have the largest body of literature (n = 40, 53%). The first theme, noted in 62% (n = 47) of articles, is that bias or validity issues often result from data quality problems. The most frequently identified validity issue is misclassification bias (n = 23, 30%). The second theme is that many of the articles suggest methods for mitigating the issues resulting from poor social determinants data quality. We grouped these into 5 suggestions: avoid complete case analysis, impute data, rely on multiple sources, use validated software tools, and select addresses thoughtfully. The type of data quality problem varies depending on the variable, and each problem is associated with particular forms of analytical error. Problems encountered with the quality of SDoH data are rarely distributed randomly. Data from Hispanic patients are more prone to issues with plausibility and misclassification than data from other racial/ethnic groups. Consideration of data quality and evidence-based quality improvement methods may help prevent bias and improve the validity of research conducted with SDoH data.
登录
查看更多内容
影响因子：
13.8
作者：
Cottrell EK;Hendricks M;Dambrun K;Cowburn S;Pantell M;Gold R;Gottlieb LM
通讯作者：
Gottlieb LM
影响因子：
2.9
作者：
Chen, Yuying;Lin, Hui-Yi;DeVivo, Michael J.
通讯作者：
DeVivo, Michael J.
影响因子：
3.7
作者：
Curriero FC;Kulldorff M;Boscoe FP;Klassen AC
通讯作者：
Klassen AC
影响因子：
3.4
作者：
Bigback, Kristyn M.;Hoopes, Megan;Weiser, Thomas
通讯作者：
Weiser, Thomas
DOI：
10.1177/1077558720935733
发表时间：
2021-10
期刊：
Medical care research and review : MCRR
影响因子：
--
作者：
Grafova IB;Jarrín OF
通讯作者：
Jarrín OF