Routine Hospital-based SARS-CoV-2 Testing Outperforms State-based Data in Predicting Clinical Burden
Routine Hospital-based SARS-CoV-2 Testing Outperforms State-based Data in Predicting Clinical Burden
复制标题
在预测临床负担方面，基于医院的常规 SARS-CoV-2 检测优于基于州的数据
DOI：
10.1097/ede.0000000000001396
复制
发表时间：
2021
期刊：
影响因子：
5.4
通讯作者：
Wang, Siquan
中科院分区：
文献类型：
--
作者：
Covello, Leonard;Gelman, Andrew;Si, Yajuan;Wang, Siquan
在2019冠状病毒病（COVID-19）大流行期间，政府政策及医疗保健实施应对措施一直以社区报告的阳性率及阳性病例数为指导。这些数据的选择偏倚使人们质疑其作为社区实际病毒发病率指标和临床负担预测指标的有效性。在没有任何成功的公共或学术运动的全面或随机测试，我们已经开发了一种代理方法的合成随机抽样，基于病毒RNA检测的患者谁目前在医院系统内的择期手术。我们在这里提出了一种多层次回归和后分层的方法，收集和分析数据，在医院系统中的患者之间的病毒暴露，并进行统计调整，已公开估计真实的病毒发病率和趋势在社区。我们将我们的方法应用于追踪印第安纳州城乡混合环境中的病毒行为。这种方法可以很容易地在各种各样的医院环境中实施。最后，我们提供的证据表明，该模型预测严重急性呼吸综合征冠状病毒2（SARS-CoV-2）的临床负担更早，更准确地比目前公认的指标。视频摘要见，https：//links。lww。com/EDE/B859。
Throughout the coronavirus disease 2019 (COVID-19) pandemic, government policy and healthcare implementation responses have been guided by reported positivity rates and counts of positive cases in the community. The selection bias of these data calls into question their validity as measures of the actual viral incidence in the community and as predictors of clinical burden. In the absence of any successful public or academic campaign for comprehensive or random testing, we have developed a proxy method for synthetic random sampling, based on viral RNA testing of patients who present for elective procedures within a hospital system. We present here an approach under multilevel regression and poststratification to collecting and analyzing data on viral exposure among patients in a hospital system and performing statistical adjustment that has been made publicly available to estimate true viral incidence and trends in the community. We apply our approach to tracking viral behavior in a mixed urban–suburban–rural setting in Indiana. This method can be easily implemented in a wide variety of hospital settings. Finally, we provide evidence that this model predicts the clinical burden of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) earlier and more accurately than currently accepted metrics. See video abstract at, https://links. lww. com/EDE/B859.
影响因子：
0.9
作者：
Si, Yajuan;Trangucci, Rob;Gabry, Jonah;and Gelman, Andrew
通讯作者：
and Gelman, Andrew