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Role of Ascertainment Bias in Determining Case Fatality Rate of COVID-19

机译:确定偏差在确定Covid-19的情况下的作用

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Case fatality rate (CFR) is used to calculate mortality burden of COVID-19 under different scenarios, thus informing risk-benefit balance of interventions both pharmaceutical and nonpharmaceutical. However, observed CFR is driven by testing: as more low-risk cases are identified, observed CFR will decline. This report quantifies test bias by modeling observed CFR as log-log-linear function of test density (tests per population) in 163 countries. CFR declined almost 20% (e.g. from 5% to 4%) for each doubling of test density ( p 0.0001); this association did not vary by continent (interaction p 0.10) although at any given test density CFR was higher in Europe or North America than in Asia or Africa. This effect of test density on observed CFR is adequate to hide all but the largest true differences in case survivorship. Published estimates of CFR should specify test density, and comparisons should correct for it such as by applying the provided model.
机译:病例死亡率(CFR)用于在不同情景下计算Covid-19的死亡负担,从而了解干预措施的风险效益余额两种药品和非药物。 然而,观察到的CFR是通过测试驱动的:由于鉴定了更低的风险情况,观察到的CFR将下降。 本报告通过将观察到的CFR为163个国家的测试密度(每人检验)的对数对数线性函数来量化测试偏压。 CFR在测试密度加倍下降近20%(例如5%至4%)(P <0.0001); 这种关联并没有因大陆而变化(相互作用P&GT; 0.10),尽管在任何给定的测试密度CFR在欧洲或北美比亚洲或非洲更高。 这种测试密度对观察到的CFR的这种影响是充分利用,以隐藏所有的真实差异,以免求生存。 公布的CFR估计应指定测试密度,并且比较应校正它,例如通过应用提供的模型。

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