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基于民事登记数据对印度大流行期间超额死亡率的估计。

Estimates of pandemic excess mortality in India based on civil registration data.

作者信息

Banaji Murad, Gupta Aashish

机构信息

Department of Mathematics, Middlesex University London, London, United Kingdom.

Harvard Centre for Population and Development Studies, Harvard University, Cambridge, Massachusetts, United States of America.

出版信息

PLOS Glob Public Health. 2022 Dec 9;2(12):e0000803. doi: 10.1371/journal.pgph.0000803. eCollection 2022.

Abstract

The population health impacts of the COVID-19 pandemic are less well understood in low and middle-income countries, where mortality surveillance before the pandemic was patchy. Interpreting the limited all-cause mortality data available in India is challenging. We use existing data on all-cause mortality from civil registration systems of twelve Indian states comprising around 60% of the national population to understand the scale and timing of excess deaths in India during the COVID-19 pandemic. We carefully characterize the reasons why registration is incomplete and estimate the extent of coverage in the data. Comparing the pandemic period to 2019, we estimate excess mortality in twelve Indian states, and extrapolate our estimates to the rest of India. We explore sensitivity of the estimates to various assumptions. For the 12 states with available all-cause mortality data, we document an increase of 28% in deaths during April 2020-May 2021 relative to expectations from 2019. This level of increase in mortality, if it applies nationally, would imply 2.8-2.9 million excess deaths. More limited data from June 2021 increases national estimates of excess deaths during April 2020-June 2021 to 3.8 million. With more optimistic or pessimistic assumptions, excess deaths during this period could credibly lie between 2.8 million and 5.2 million. The scale of estimated excess deaths is broadly consistent with expectations based on seroprevalence and COVID-19 fatality rates observed internationally. Moreover, the timing of excess deaths and recorded COVID-19 deaths is similar-they rise and fall at the same time. The surveillance of pandemic mortality in India has been extremely poor, with 8-10 times as many excess deaths as officially recorded COVID-19 deaths. India is among the countries most severely impacted by the pandemic. Our approach highlights the utility of all-cause mortality data, as well as the significant challenges in interpreting it.

摘要

在低收入和中等收入国家,人们对新冠疫情对人口健康的影响了解较少,因为这些国家在疫情之前的死亡率监测并不全面。解读印度现有的有限全因死亡率数据具有挑战性。我们利用来自印度十二个邦民事登记系统的全因死亡率现有数据(这些邦约占全国人口的60%),来了解新冠疫情期间印度超额死亡的规模和时间。我们仔细分析登记不完整的原因,并估计数据中的覆盖范围。将疫情期间与2019年进行比较,我们估计了印度十二个邦的超额死亡率,并将我们的估计值外推至印度其他地区。我们探讨了估计值对各种假设的敏感性。对于有全因死亡率数据的12个邦,我们记录到2020年4月至2021年5月期间的死亡人数相对于2019年的预期增加了28%。如果这种死亡率的增加适用于全国,将意味着有280万至290万的超额死亡。2021年6月更有限的数据将2020年4月至2021年6月期间全国超额死亡的估计数提高到380万。在更乐观或更悲观的假设下,这一时期的超额死亡人数可能在280万至520万之间。估计的超额死亡规模与基于国际上观察到的血清阳性率和新冠病死率的预期大致一致。此外,超额死亡和记录的新冠死亡时间相似——它们同时上升和下降。印度对疫情死亡率的监测极其糟糕,超额死亡人数是官方记录的新冠死亡人数的8至10倍。印度是受疫情影响最严重的国家之一。我们的方法突出了全因死亡率数据的效用,以及解读这些数据时面临的重大挑战。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e0af/10021303/5718bee80f26/pgph.0000803.g001.jpg

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本文引用的文献

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