Methods for modelling excess mortality across England during the COVID-19 pandemic
Methods for modelling excess mortality across England during the COVID-19 pandemic
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DOI：
10.1177/09622802211046384
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发表时间：
2021-10-23
影响因子：
2.3
通讯作者：
De Angelis, Daniela
中科院分区：
文献类型：
--
作者：
Barnard, Sharmani;Chiavenna, Chiara;De Angelis, Daniela
超额死亡率是衡量2019冠状病毒大流行规模的重要指标。它既包括大流行直接造成的死亡，也包括遏制措施的意外后果造成的死亡，例如延迟获得医疗服务或推迟向人口提供医疗保健服务。2020年和2021年，在英格兰，多个小组提出了大流行期间超额死亡率的衡量标准。本文描述了在五种不同的估计超额死亡率的方法中使用的数据和方法，并比较了它们的估计。描述了估计超额死亡率的基本原则，以及五种方法之间的关键共性和差异。其中两个是基于登记日期的：一个准泊松模型，具有偏移量和5年平均值；三种基于发生日期：无抵消的泊松模型、欧洲监测超额死亡率模型和综合控制模型。对2020年3月至2021年3月期间的超额死亡率估计数和在此期间发生的两次大流行浪潮进行了比较。在大流行的第一波期间，模型估计值惊人地相似，但在第二波期间观察到较大的差异。根据冬季感染循环减少进行调整的模型与没有进行调整的模型相比，产生了更高的过量估计。没有调整冬季感染循环减少的模型捕获了由于在此期间行动限制而减少的冬季疾病的影响。没有一项估计数包括死亡率和流离失所，因此可能低估了目前的过剩情况，尽管这种情况发生的程度尚未确定。模型使用不同的方法来处理数据可用性的变化和度量的涉众需求。估计数之间的差异反映了有关日期、人口分母和与季节性和趋势有关的模型参数的差异。
Excess mortality is an important measure of the scale of the coronavirus-2019 pandemic. It includes both deaths caused directly by the pandemic, and deaths caused by the unintended consequences of containment such as delays to accessing care or postponements of healthcare provision in the population. In 2020 and 2021, in England, multiple groups have produced measures of excess mortality during the pandemic. This paper describes the data and methods used in five different approaches to estimating excess mortality and compares their estimates. The fundamental principles of estimating excess mortality are described, as well as the key commonalities and differences between five approaches. Two of these are based on the date of registration: a quasi-Poisson model with offset and a 5-year average; and three are based on date of occurrence: a Poisson model without offset, the European monitoring of excess mortality model and a synthetic controls model. Comparisons between estimates of excess mortality are made for the period March 2020 through March 2021 and for the two waves of the pandemic that occur within that time-period. Model estimates are strikingly similar during the first wave of the pandemic though larger differences are observed during the second wave. Models that adjusted for reduced circulation of winter infection produced higher estimates of excess compared with those that did not. Models that do not adjust for reduced circulation of winter infection captured the effect of reduced winter illness as a result of mobility restrictions during the period. None of the estimates captured mortality displacement and therefore may underestimate excess at the current time, though the extent to which this has occurred is not yet identified. Models use different approaches to address variation in data availability and stakeholder requirements of the measure. Variation between estimates reflects differences in the date of interest, population denominators and parameters in the model relating to seasonality and trend.