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采用超额死亡率建模方法估算2020年3月至2021年5月美国未被充分认识的新冠死亡人数。

Estimating under-recognized COVID-19 deaths, United States, march 2020-may 2021 using an excess mortality modelling approach.

作者信息

Iuliano A Danielle, Chang Howard H, Patel Neha N, Threlkel Ryan, Kniss Krista, Reich Jeremy, Steele Molly, Hall Aron J, Fry Alicia M, Reed Carrie

机构信息

COVID-19 Emergency Response, Centers for Disease Control and Prevention, United States.

Influenza Division, National Center for Immunization and Respiratory Diseases, Centers for Disease Control and Prevention, Atlanta, GA, United States.

出版信息

Lancet Reg Health Am. 2021 Sep;1:100019. doi: 10.1016/j.lana.2021.100019. Epub 2021 Jul 13.

DOI:10.1016/j.lana.2021.100019
PMID:34386789
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8275579/
Abstract

BACKGROUND

In the United States, Coronavirus Disease 2019 (COVID-19) deaths are captured through the National Notifiable Disease Surveillance System and death certificates reported to the National Vital Statistics System (NVSS). However, not all COVID-19 deaths are recognized and reported because of limitations in testing, exacerbation of chronic health conditions that are listed as the cause of death, or delays in reporting. Estimating deaths may provide a more comprehensive understanding of total COVID-19-attributable deaths.

METHODS

We estimated COVID-19 unrecognized attributable deaths, from March 2020-April 2021, using all-cause deaths reported to NVSS by week and six age groups (0-17, 18-49, 50-64, 65-74, 75-84, and ≥85 years) for 50 states, New York City, and the District of Columbia using a linear time series regression model. Reported COVID-19 deaths were subtracted from all-cause deaths before applying the model. Weekly expected deaths, assuming no SARS-CoV-2 circulation and predicted all-cause deaths using SARS-CoV-2 weekly percent positive as a covariate were modelled by age group and including state as a random intercept. COVID-19-attributable unrecognized deaths were calculated for each state and age group by subtracting the expected all-cause deaths from the predicted deaths.

FINDINGS

We estimated that 766,611 deaths attributable to COVID-19 occurred in the United States from March 8, 2020-May 29, 2021. Of these, 184,477 (24%) deaths were not documented on death certificates. Eighty-two percent of unrecognized deaths were among persons aged ≥65 years; the proportion of unrecognized deaths were 0•24-0•31 times lower among those 0-17 years relative to all other age groups. More COVID-19-attributable deaths were not captured during the early months of the pandemic (March-May 2020) and during increases in SARS-CoV-2 activity (July 2020, November 2020-February 2021).

INTERPRETATION

Estimating COVID-19-attributable unrecognized deaths provides a better understanding of the COVID-19 mortality burden and may better quantify the severity of the COVID-19 pandemic.

FUNDING

None.

摘要

背景

在美国,2019冠状病毒病(COVID-19)死亡病例是通过国家法定传染病监测系统以及向国家生命统计系统(NVSS)报告的死亡证明来统计的。然而,由于检测存在局限性、被列为死亡原因的慢性健康状况恶化或报告延迟,并非所有COVID-19死亡病例都能得到确认和报告。估计死亡人数可能有助于更全面地了解COVID-19所致的总死亡情况。

方法

我们使用线性时间序列回归模型,根据2020年3月至2021年4月期间按周向NVSS报告的全因死亡人数以及50个州、纽约市和哥伦比亚特区的六个年龄组(0-17岁、18-49岁、50-64岁、65-74岁、75-84岁和≥85岁)的数据,估算了未被确认的COVID-19归因死亡人数。在应用该模型之前,从全因死亡人数中减去报告的COVID-19死亡人数。假设没有严重急性呼吸综合征冠状病毒2(SARS-CoV-2)传播,以SARS-CoV-2每周阳性率作为协变量,按年龄组对每周预期死亡人数和预测的全因死亡人数进行建模,并将州作为随机截距纳入。通过从预测死亡人数中减去预期全因死亡人数,计算每个州和年龄组的COVID-19归因未被确认死亡人数。

研究结果

我们估计,2020年3月8日至2021年5月29日期间,美国有766,611例死亡归因于COVID-19。其中,184,477例(24%)死亡未在死亡证明上记录。82%的未被确认死亡发生在65岁及以上人群中;0-17岁人群中未被确认死亡的比例相对于所有其他年龄组低0.24-0.31倍。在疫情早期(2020年3月至5月)以及SARS-CoV-2活动增加期间(2020年7月、2020年11月至2021年2月),更多的COVID-19归因死亡未被统计到。

解读

估算COVID-19归因的未被确认死亡人数有助于更好地了解COVID-19的死亡负担,并可能更好地量化COVID-19大流行的严重程度。

资金来源

无。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f681/9903670/54c132893303/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f681/9903670/56971ec748ed/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f681/9903670/b957880116e0/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f681/9903670/54c132893303/gr3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f681/9903670/56971ec748ed/gr1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f681/9903670/b957880116e0/gr2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f681/9903670/54c132893303/gr3.jpg

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