对新冠疫情期间印度超额死亡率的系统评价：基于模型和基于数据的死亡率估计的区分
Systematic Review of Excess Mortality in India during the Covid-19 Pandemic with Differentiation between Model-Based and Data-Based Mortality Estimates.
作者信息
Natarajan Subramanian, Subramanian Poonam
机构信息
The Lung Centre, Office No 404, 4 Floor, Marathon Chambers, P K Road, Mulund West, Mumbai, India.
The Lung Centre, 104, Trinity Apartments, Uthalsar Road & Jupiter Hospital, Thane, Maharashtra, India.
出版信息
Indian J Community Med. 2022 Oct-Dec;47(4):491-494. doi: 10.4103/ijcm.ijcm_11_22. Epub 2022 Dec 14.
BACKGROUND
COVID-19 has proven to be the worst pandemic in the history of mankind. While the pandemic still continues to perplex scientists globally, attempts are being made to quantify the mortality caused by the pandemic. Official COVID-19 figures in India grossly understate the true scale of the pandemic in the country. Fatality rates help us understand the severity of a disease, identify at risk populations, and evaluate quality of healthcare. Official COVID-19 mortality figures in India grossly understate the true scale of the pandemic in the country. A COVID-19 death is defined for surveillance purposes as a death resulting from a clinically compatible illness in a probable or confirmed COVID-19 case, unless there is a clear alternative cause of death that cannot be related to COVID-19 disease (e.g., trauma) and excess mortality is defined as the difference in the total number of deaths in a crisis compared to those expected under normal conditions.
MATERIALS AND METHODS
We did a systematic review of multiple papers on PubMed, Medline, Embase, MedRxiV pre print on excess mortality. Differentiation between model based estimated excess mortality and data based excess mortality was studied.
RESULTS
All the studies showed that the excess mortality was to the tune of almost three times the official figures. The model based excess mortality assumptions showed higher deaths as compared to the data based one. However, there were a lot of discrepancies in the data provided by various states along with variations observed between the two waves as well. Health survey data suggested higher mortality rate as compared to data compiled from the civil registration system. Additionally, in the second wave, a small but a significant number of deaths occurred due to non availability of oxygen and beds in the hospitals.
CONCLUSIONS
Official COVID-19 deaths have entirely failed to capture the scale of pandemic excess mortality in India. If most excess deaths were, indeed, from COVID-19 then under ascertainment of COVID-19 deaths has been high, with around 8-10 excess deaths for every recorded COVID-19 death.
背景
事实证明，新冠疫情是人类历史上最严重的大流行病。尽管这场大流行仍令全球科学家感到困惑，但人们正在努力量化其造成的死亡率。印度官方公布的新冠疫情数据严重低估了该国疫情的真实规模。死亡率有助于我们了解疾病的严重程度、识别高危人群并评估医疗质量。印度官方公布的新冠疫情死亡率数据严重低估了该国疫情的真实规模。为监测目的，新冠死亡病例定义为在可能或确诊的新冠病例中，因临床症状相符的疾病导致的死亡，除非有明确的与新冠疾病无关的其他死因（如外伤），且超额死亡率定义为危机期间死亡总数与正常情况下预期死亡数的差值。
材料与方法
我们对PubMed、Medline、Embase、MedRxiV预印本上关于超额死亡率的多篇论文进行了系统综述。研究了基于模型估计的超额死亡率与基于数据的超额死亡率之间的差异。
结果
所有研究均表明，超额死亡率几乎是官方数据的三倍。与基于数据的超额死亡率假设相比，基于模型的超额死亡率假设显示出更高的死亡人数。然而，各邦提供的数据存在诸多差异，两波疫情期间也观察到了变化。健康调查数据显示的死亡率高于民事登记系统汇编的数据。此外，在第二波疫情中，由于医院氧气和床位短缺，出现了少量但数量可观的死亡病例。
结论
官方公布的新冠死亡病例完全未能反映印度疫情期间超额死亡率的规模。如果大多数超额死亡病例确实是由新冠疫情导致的，那么新冠死亡病例的漏报率很高，每记录1例新冠死亡病例，大约就有8至10例超额死亡病例。
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