Division of Nutrition Epidemiology and Data Science, Friedman School of Nutrition Science and Policy, Tufts University, 150 Harrison Avenue, Boston, MA 02111, USA.
Int J Environ Res Public Health. 2022 Jan 25;19(3):1321. doi: 10.3390/ijerph19031321.
Critical temporal changes such as weekly fluctuations in surveillance systems often reflect changes in laboratory testing capacity, access to testing or healthcare facilities, or testing preferences. Many studies have noted but few have described day-of-the-week (DoW) effects in SARS-CoV-2 surveillance over the major waves of the novel coronavirus 2019 pandemic (COVID-19). We examined DoW effects by non-pharmaceutical intervention phases adjusting for wave-specific signatures using the John Hopkins University's (JHU's) Center for Systems Science and Engineering (CSSE) COVID-19 data repository from 2 March 2020 through 7 November 2021 in Middlesex County, Massachusetts, USA. We cross-referenced JHU's data with Massachusetts Department of Public Health (MDPH) COVID-19 records to reconcile inconsistent reporting. We created a calendar of statewide non-pharmaceutical intervention phases and defined the critical periods and timepoints of outbreak signatures for reported tests, cases, and deaths using Kolmogorov-Zurbenko adaptive filters. We determined that daily death counts had no DoW effects; tests were twice as likely to be reported on weekdays than weekends with decreasing effect sizes across intervention phases. Cases were also twice as likely to be reported on Tuesdays-Fridays (RR = 1.90-2.69 [95%CI: 1.38-4.08]) in the most stringent phases and half as likely to be reported on Mondays and Tuesdays (RR = 0.51-0.93 [0.44, 0.97]) in less stringent phases compared to Sundays; indicating temporal changes in laboratory testing practices and use of healthcare facilities. Understanding the DoW effects in daily surveillance records is valuable to better anticipate fluctuations in SARS-CoV-2 testing and manage appropriate workflow. We encourage health authorities to establish standardized reporting protocols.
关键的时间变化，如监测系统的每周波动，通常反映了实验室检测能力、检测或医疗设施的可及性，或检测偏好的变化。许多研究都注意到了，但很少有研究描述了新型冠状病毒 2019 大流行（COVID-19）期间主要波次的 SARS-CoV-2 监测中的周日效应。我们通过调整特定波次特征的非药物干预阶段，使用约翰霍普金斯大学（JHU）的中心系统科学与工程（CSSE）COVID-19 数据存储库，从 2020 年 3 月 2 日至 2021 年 11 月 7 日，在美国马萨诸塞州米德尔塞克斯县检查了周日效应。我们交叉参考了 JHU 的数据与马萨诸塞州公共卫生部（MDPH）的 COVID-19 记录，以协调不一致的报告。我们创建了全州非药物干预阶段的日历，并使用柯尔莫哥洛夫-祖尔贝肯自适应滤波器确定了报告测试、病例和死亡的爆发特征的关键时期和时间点。我们发现，每日死亡人数没有周日效应；与周末相比，工作日报告的检测数量增加了一倍，而且随着干预阶段的推进，效果大小逐渐减小。在最严格的阶段，病例也有两倍的可能性在周二至周五（RR = 1.90-2.69 [95%CI：1.38-4.08]）报告，在宽松阶段，周一和周二（RR = 0.51-0.93 [0.44，0.97]）报告的可能性减半，与周日相比；这表明实验室检测实践和使用医疗设施的时间变化。了解日常监测记录中的周日效应对于更好地预测 SARS-CoV-2 检测的波动和管理适当的工作流程非常有价值。我们鼓励卫生当局建立标准化的报告协议。
