Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study
Estimating excess 1-year mortality associated with the COVID-19 pandemic according to underlying conditions and age: a population-based cohort study
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DOI：
10.1016/s0140-6736(20)30854-0
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发表时间：
2020-05-30
期刊：
影响因子：
168.9
通讯作者：
Hemingway, Harry
中科院分区：
文献类型：
--
作者：
Banerjee, Amitava;Pasea, Laura;Hemingway, Harry
背景2019年冠状病毒病(新冠肺炎)大流行的医疗、社会和经济影响对总体人口死亡率的影响未知。以前的人口死亡率模型是基于感染者几天内的死亡，到目前为止，几乎所有人都有潜在的疾病。模型没有纳入关于高危条件或他们的较长期基线(新冠肺炎前)死亡率的信息。我们基于不同的传播抑制水平和基于不同疾病相对风险的不同死亡率影响，估计了不同新冠肺炎发病情景下一年内的额外死亡人数。方法在这项基于人群的队列研究中，我们使用了来自英国的链接的初级和二级保健电子健康记录(Health Data Research UK-Caliber)。我们报告了1997至2017年间登记的30岁或30岁以上的个人中公共卫生英格兰指南(从2020年3月16日起)定义的潜在疾病的患病率，使用每种疾病的有效、公开的表型。我们估计了每种情况下的一年死亡率，开发了与新冠肺炎相关的额外死亡人数的简单模型(和计算工具)，假设新冠肺炎大流行在不同感染率情景下的相对影响(作为相对风险[RR])分别为1.5%、2.0%和3.0%，包括完全抑制(0.001%)、部分抑制(1%)、缓解(10%)和不采取任何措施(80%)。我们还开发了一个在线的、公开的、用于过度死亡估计的原型风险计算器。结果包括3862 012人(1 957 935名女性和1 904 077名男性[49.3%])。我们估计，超过20%的研究人群属于高危类别，其中13.7%的人年龄超过70岁，6.3%的人年龄在70岁或以下，至少有一种潜在疾病。高危人群1年死亡率估计为4.46%(95%可信区间为4.41-4.51)。年龄和基础条件结合在一起影响背景风险，在不同条件下差异显著。在英国人口的完全抑制情景中，我们估计将有两个额外死亡(与基线死亡相比)，RR为1.5，四个RR为2.0，七个RR为3.0。在缓解情景中，我们估计了18374例额外死亡，相对危险度为1.5,36749例，相对危险度为2.0,73498例，相对危险度为3.0。在什么都不做的情景中，我们估计了146 996例超额死亡，RR为1.5,293 991例，RR为2.0,587 982例，RR为3.0。解释我们为政策制定者、研究人员和公众提供了一个简单的模型和一个在线工具，用于基于年龄、性别和潜在条件特定的估计，来理解新冠肺炎大流行导致的一年内的超额死亡率。这些结果表明，有必要采取持续严厉的镇压措施，并持续努力，以最高风险人群为目标，因为潜在的条件是采取一系列预防性干预措施。各国应评估大流行对过高死亡率的总体(直接和间接)影响。
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 3 862 012 individuals (1 957 935 [50.7%] women and 1 904 077 [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 18 374 excess deaths with an RR of 1.5, 36 749 with an RR of 2.0, and 73 498 with an RR of 3.0. In a do nothing scenario, we estimated 146 996 excess deaths with an RR of 1.5, 293 991 with an RR of 2.0, and 587 982 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.