英国伦敦大学学院Amitava Banerjee研究团队最近取得新进展。他们根据相关基础条件和年龄来评估与COVID-19大流行相关的1年死亡率。2020年5月12日出版的《柳叶刀》发表了这项成果。

2019冠状病毒病（COVID-19）大流行对医疗、社会和经济影响较大，但对总体人口死亡率的影响仍未可知。

在这项基于人群的队列研究中，研究组使用了来自英国的初级和二级医疗电子健康记录，报告了1997-2017年间在一家机构注册的30岁及以上人群中由英格兰公共卫生指南（从2020年3月16日起）定义的基础疾病的流行率。研究组估算了每种情况下的1年死亡率，包括完全封锁（0.001％）、部分封锁（1％）、缓和（10％）和不采取任何措施（80％ ）。他们开发了COVID-19大流行的相对风险分别为1.5、2.0和3.0时，与COVID-19相关的过量死亡的简单模型。

研究组共纳入3862012名参与者，女性占50.7％。研究组估计，超过20％的参与者属于高危人群，其中13.7％的人年龄超过70岁，而6.3％的人年龄虽在70岁及以下，但至少患有一种潜在疾病。高危人群的1年死亡率估计为4.46％。年龄和基础条件共同影响背景风险，在不同条件下差异显著。

在英国人口全面封锁的情形下，研究组估计，与基线风险相比，RR为1.5时，将有两例超额死亡，RR为2.0将有4例，RR为3.0时将有7例。在封锁缓解的情形下，研究组估计，RR为1.5时将有18374例超额死亡，RR为2.0时将有36749例，RR为3.0时将有73498例。在不采取任何措施的情况下，研究组估计，RR为1.5时将有146996例额外死亡，RR为2.0时将有293991例，RR为3.0时将有587982例。

研究组为决策者、研究人员和公众提供了一个简单的模型和在线工具，可根据年龄、性别和针对特定疾病的估计值，了解COVID-19大流行1年内的超额死亡率。这些结果表明，仍需采取持续严格的抑制措施，并针对性地保护高危人群。

附：英文原文

Title: Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study

Author: Amitava Banerjee, Laura Pasea, Steve Harris, Arturo Gonzalez-Izquierdo, Ana Torralbo, Laura Shallcross, Mahdad Noursadeghi, Deenan Pillay, Neil Sebire, Chris Holmes, Christina Pagel, Wai Keong Wong, Claudia Langenberg, Bryan Williams, Spiros Denaxas, Harry Hemingway

Issue&Volume: 2020-05-12

Abstract: Background

The medical, societal, and economic impact of the coronavirus disease 2019 (COVID-19) pandemic has unknown effects on overall population mortality. Previous models of population mortality are based on death over days among infected people, nearly all of whom thus far have underlying conditions. Models have not incorporated information on high-risk conditions or their longer-term baseline (pre-COVID-19) mortality. We estimated the excess number of deaths over 1 year under different COVID-19 incidence scenarios based on varying levels of transmission suppression and differing mortality impacts based on different relative risks for the disease.

Methods

In this population-based cohort study, we used linked primary and secondary care electronic health records from England (Health Data Research UK–CALIBER). We report prevalence of underlying conditions defined by Public Health England guidelines (from March 16, 2020) in individuals aged 30 years or older registered with a practice between 1997 and 2017, using validated, openly available phenotypes for each condition. We estimated 1-year mortality in each condition, developing simple models (and a tool for calculation) of excess COVID-19-related deaths, assuming relative impact (as relative risks [RRs]) of the COVID-19 pandemic (compared with background mortality) of 1·5, 2·0, and 3·0 at differing infection rate scenarios, including full suppression (0·001%), partial suppression (1%), mitigation (10%), and do nothing (80%). We also developed an online, public, prototype risk calculator for excess death estimation.

Findings

We included 3862012 individuals (1957935 [50·7%] women and 1904077 [49·3%] men). We estimated that more than 20% of the study population are in the high-risk category, of whom 13·7% were older than 70 years and 6·3% were aged 70 years or younger with at least one underlying condition. 1-year mortality in the high-risk population was estimated to be 4·46% (95% CI 4·41–4·51). Age and underlying conditions combined to influence background risk, varying markedly across conditions. In a full suppression scenario in the UK population, we estimated that there would be two excess deaths (vs baseline deaths) with an RR of 1·5, four with an RR of 2·0, and seven with an RR of 3·0. In a mitigation scenario, we estimated 18374 excess deaths with an RR of 1·5, 36749 with an RR of 2·0, and 73498 with an RR of 3·0. In a do nothing scenario, we estimated 146996 excess deaths with an RR of 1·5, 293991 with an RR of 2·0, and 587982 with an RR of 3·0.

Interpretation

We provide policy makers, researchers, and the public a simple model and an online tool for understanding excess mortality over 1 year from the COVID-19 pandemic, based on age, sex, and underlying condition-specific estimates. These results signal the need for sustained stringent suppression measures as well as sustained efforts to target those at highest risk because of underlying conditions with a range of preventive interventions. Countries should assess the overall (direct and indirect) effects of the pandemic on excess mortality.

DOI: 10.1016/S0140-6736(20)30854-0

Source: https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30854-0/fulltext