Moraga Paula, Ketcheson David I, Ombao Hernando C, Duarte Carlos M
Department of Mathematical Sciences, University of Bath, Bath, Somerset, BA2 7AY, UK.
Computer, Electrical, and Mathematical Sciences & Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi Arabia.
Wellcome Open Res. 2020 Jun 2;5:117. doi: 10.12688/wellcomeopenres.15996.1. eCollection 2020.
The assessment of the severity and case fatality rates of coronavirus disease 2019 (COVID-19) and the determinants of its variation is essential for planning health resources and responding to the pandemic. The interpretation of case fatality rates (CFRs) remains a challenge due to different biases associated with surveillance and reporting. For example, rates may be affected by preferential ascertainment of severe cases and time delay from disease onset to death. Using data from Spain, we demonstrate how some of these biases may be corrected when estimating severity and case fatality rates by age group and gender, and identify issues that may affect the correct interpretation of the results. Crude CFRs are estimated by dividing the total number of deaths by the total number of confirmed cases. CFRs adjusted for preferential ascertainment of severe cases are obtained by assuming a uniform attack rate in all population groups, and using demography-adjusted under-ascertainment rates. CFRs adjusted for the delay between disease onset and death are estimated by using as denominator the number of cases that could have a clinical outcome by the time rates are calculated. A sensitivity analysis is carried out to compare CFRs obtained using different levels of ascertainment and different distributions for the time from disease onset to death. COVID-19 outcomes are highly influenced by age and gender. Different assumptions yield different CFR values but in all scenarios CFRs are higher in old ages and males. The procedures used to obtain the CFR estimates require strong assumptions and although the interpretation of their magnitude should be treated with caution, the differences observed by age and gender are fundamental underpinnings to inform decision-making.
评估2019冠状病毒病(COVID-19)的严重程度、病死率及其变化的决定因素对于规划卫生资源和应对疫情至关重要。由于与监测和报告相关的不同偏差,病死率(CFR)的解释仍然是一项挑战。例如,发病率可能受到重症病例的优先确诊以及从发病到死亡的时间延迟的影响。利用西班牙的数据,我们展示了在按年龄组和性别估计严重程度和病死率时,如何纠正其中一些偏差,并确定可能影响结果正确解释的问题。粗病死率通过将死亡总数除以确诊病例总数来估计。通过假设所有人群组的发病率一致,并使用经人口统计学调整的漏报率,可获得针对重症病例优先确诊进行调整后的病死率。通过将计算发病率时可能产生临床结果的病例数作为分母,来估计针对发病到死亡之间延迟进行调整后的病死率。进行敏感性分析以比较使用不同确诊水平和从发病到死亡时间的不同分布所获得的病死率。COVID-19的结果受年龄和性别的影响很大。不同的假设会产生不同的病死率值,但在所有情况下,老年人和男性的病死率都更高。用于获得病死率估计值的程序需要很强的假设,尽管对其数值的解释应谨慎对待,但按年龄和性别观察到的差异是为决策提供信息的基本依据。