印度严重急性呼吸系统综合征冠状病毒2型感染死亡率:系统综述、荟萃分析和基于模型的估计

Q3 Economics, Econometrics and Finance
Lauren Zimmermann, Subarna Bhattacharya, S. Purkayastha, Ritoban Kundu, Ritwik Bhaduri, Parikshit Ghosh, B. Mukherjee
{"title":"印度严重急性呼吸系统综合征冠状病毒2型感染死亡率:系统综述、荟萃分析和基于模型的估计","authors":"Lauren Zimmermann, Subarna Bhattacharya, S. Purkayastha, Ritoban Kundu, Ritwik Bhaduri, Parikshit Ghosh, B. Mukherjee","doi":"10.1177/23210222211054324","DOIUrl":null,"url":null,"abstract":"Introduction: Fervourous investigation and dialogue surrounding the true number of SARS-CoV-2-related deaths and implied infection fatality rates in India have been ongoing throughout the pandemic, and especially pronounced during the nation’s devastating second wave. We aim to synthesize the existing literature on the true SARS-CoV-2 excess deaths and infection fatality rates (IFR) in India through a systematic search followed by viable meta-analysis. We then provide updated epidemiological model-based estimates of the wave 1, wave 2 and combined IFRs using an extension of the Susceptible-Exposed-Infected-Removed (SEIR) model, using data from 1 April 2020 to 30 June 2021. Methods: Following PRISMA guidelines, the databases PubMed, Embase, Global Index Medicus, as well as BioRxiv, MedRxiv and SSRN for preprints (accessed through iSearch), were searched on 3 July 2021 (with results verified through 15 August 2021). Altogether, using a two-step approach, 4,765 initial citations were screened, resulting in 37 citations included in the narrative review and 19 studies with 41datapoints included in the quantitative synthesis. Using a random effects model with DerSimonian-Laird estimation, we meta-analysed IFR1, which is defined as the ratio of the total number of observed reported deaths divided by the total number of estimated infections, and IFR2 (which accounts for death underreporting in the numerator of IFR1). For the latter, we provided lower and upper bounds based on the available range of estimates of death undercounting, often arising from an excess death calculation. The primary focus is to estimate pooled nationwide estimates of IFRs with the secondary goal of estimating pooled regional and state-specific estimates for SARS-CoV-2-related IFRs in India. We also tried to stratify our empirical results across the first and second waves. In tandem, we presented updated SEIR model estimates of IFRs for waves 1, 2, and combined across the waves with observed case and death count data from 1 April 2020 to 30 June 2021. Results: For India, countrywide, the underreporting factors (URF) for cases (sourced from serosurveys) range from 14.3 to 29.1 in the four nationwide serosurveys; URFs for deaths (sourced from excess deaths reports) range from 4.4 to 11.9 with cumulative excess deaths ranging from 1.79 to 4.9 million (as of June 2021). Nationwide pooled IFR1 and IFR2 estimates for India are 0.097% (95% confidence interval [CI]: 0.067–0.140) and 0.365% (95% CI: 0.264–0.504) to 0.485% (95% CI: 0.344–0.685), respectively, again noting that IFR2 changes as excess deaths estimates vary. Among the included studies in this meta-analysis, IFR1 generally appears to decrease over time from the earliest study end date to the latest study end date (from 4 June 2020 to 6 July 2021, IFR1 changed from 0.199 to 0.055%), whereas a similar trend is not as readily evident for IFR2 due to the wide variation in excess death estimates (from 4 June 2020 to 6 July 2021, IFR2 ranged from (0.290–1.316) to (0.241–0.651)%). Nationwide SEIR model-based combined estimates for IFR1 and IFR2 are 0.101% (95% CI: 0.097–0.116) and 0.367% (95% CI: 0.358–0.383), respectively, which largely reconcile with the empirical findings and concur with the lower end of the excess death estimates. An advantage of such epidemiological models is the ability to produce daily estimates with updated data, with the disadvantage being that these estimates are subject to numerous assumptions, arduousness of validation and not directly using the available excess death data. Whether one uses empirical data or model-based estimation, it is evident that IFR2 is at least 3.6 times more than IFR1. Conclusion: When incorporating case and death underreporting, the meta-analysed cumulative infection fatality rate in India varied from 0.36 to 0.48%, with a case underreporting factor ranging from 25 to 30 and a death underreporting factor ranging from 4 to 12. This implies, by 30 June 2021, India may have seen nearly 900 million infections and 1.7–4.9 million deaths when the reported numbers stood at 30.4 million cases and 412 thousand deaths (Coronavirus in India) with an observed case fatality rate (CFR) of 1.35%. We reiterate the need for timely and disaggregated infection and fatality data to examine the burden of the virus by age and other demographics. Large degrees of nationwide and state-specific death undercounting reinforce the call to improve death reporting within India. JEL Classifications: I15, I18","PeriodicalId":37410,"journal":{"name":"Studies in Microeconomics","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"SARS-CoV-2 Infection Fatality Rates in India: Systematic Review, Meta-analysis and Model-based Estimation\",\"authors\":\"Lauren Zimmermann, Subarna Bhattacharya, S. Purkayastha, Ritoban Kundu, Ritwik Bhaduri, Parikshit Ghosh, B. Mukherjee\",\"doi\":\"10.1177/23210222211054324\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Introduction: Fervourous investigation and dialogue surrounding the true number of SARS-CoV-2-related deaths and implied infection fatality rates in India have been ongoing throughout the pandemic, and especially pronounced during the nation’s devastating second wave. We aim to synthesize the existing literature on the true SARS-CoV-2 excess deaths and infection fatality rates (IFR) in India through a systematic search followed by viable meta-analysis. We then provide updated epidemiological model-based estimates of the wave 1, wave 2 and combined IFRs using an extension of the Susceptible-Exposed-Infected-Removed (SEIR) model, using data from 1 April 2020 to 30 June 2021. Methods: Following PRISMA guidelines, the databases PubMed, Embase, Global Index Medicus, as well as BioRxiv, MedRxiv and SSRN for preprints (accessed through iSearch), were searched on 3 July 2021 (with results verified through 15 August 2021). Altogether, using a two-step approach, 4,765 initial citations were screened, resulting in 37 citations included in the narrative review and 19 studies with 41datapoints included in the quantitative synthesis. Using a random effects model with DerSimonian-Laird estimation, we meta-analysed IFR1, which is defined as the ratio of the total number of observed reported deaths divided by the total number of estimated infections, and IFR2 (which accounts for death underreporting in the numerator of IFR1). For the latter, we provided lower and upper bounds based on the available range of estimates of death undercounting, often arising from an excess death calculation. The primary focus is to estimate pooled nationwide estimates of IFRs with the secondary goal of estimating pooled regional and state-specific estimates for SARS-CoV-2-related IFRs in India. We also tried to stratify our empirical results across the first and second waves. In tandem, we presented updated SEIR model estimates of IFRs for waves 1, 2, and combined across the waves with observed case and death count data from 1 April 2020 to 30 June 2021. Results: For India, countrywide, the underreporting factors (URF) for cases (sourced from serosurveys) range from 14.3 to 29.1 in the four nationwide serosurveys; URFs for deaths (sourced from excess deaths reports) range from 4.4 to 11.9 with cumulative excess deaths ranging from 1.79 to 4.9 million (as of June 2021). Nationwide pooled IFR1 and IFR2 estimates for India are 0.097% (95% confidence interval [CI]: 0.067–0.140) and 0.365% (95% CI: 0.264–0.504) to 0.485% (95% CI: 0.344–0.685), respectively, again noting that IFR2 changes as excess deaths estimates vary. Among the included studies in this meta-analysis, IFR1 generally appears to decrease over time from the earliest study end date to the latest study end date (from 4 June 2020 to 6 July 2021, IFR1 changed from 0.199 to 0.055%), whereas a similar trend is not as readily evident for IFR2 due to the wide variation in excess death estimates (from 4 June 2020 to 6 July 2021, IFR2 ranged from (0.290–1.316) to (0.241–0.651)%). Nationwide SEIR model-based combined estimates for IFR1 and IFR2 are 0.101% (95% CI: 0.097–0.116) and 0.367% (95% CI: 0.358–0.383), respectively, which largely reconcile with the empirical findings and concur with the lower end of the excess death estimates. An advantage of such epidemiological models is the ability to produce daily estimates with updated data, with the disadvantage being that these estimates are subject to numerous assumptions, arduousness of validation and not directly using the available excess death data. Whether one uses empirical data or model-based estimation, it is evident that IFR2 is at least 3.6 times more than IFR1. Conclusion: When incorporating case and death underreporting, the meta-analysed cumulative infection fatality rate in India varied from 0.36 to 0.48%, with a case underreporting factor ranging from 25 to 30 and a death underreporting factor ranging from 4 to 12. This implies, by 30 June 2021, India may have seen nearly 900 million infections and 1.7–4.9 million deaths when the reported numbers stood at 30.4 million cases and 412 thousand deaths (Coronavirus in India) with an observed case fatality rate (CFR) of 1.35%. We reiterate the need for timely and disaggregated infection and fatality data to examine the burden of the virus by age and other demographics. Large degrees of nationwide and state-specific death undercounting reinforce the call to improve death reporting within India. JEL Classifications: I15, I18\",\"PeriodicalId\":37410,\"journal\":{\"name\":\"Studies in Microeconomics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Studies in Microeconomics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1177/23210222211054324\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Economics, Econometrics and Finance\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Studies in Microeconomics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1177/23210222211054324","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Economics, Econometrics and Finance","Score":null,"Total":0}
引用次数: 7

摘要

导言:在整个大流行期间,围绕印度sars - cov -2相关死亡人数和潜在感染死亡率的热烈调查和对话一直在进行,在该国毁灭性的第二波疫情期间尤其明显。我们的目标是通过系统搜索和可行的荟萃分析,综合有关印度真正的SARS-CoV-2超额死亡和感染死亡率(IFR)的现有文献。然后,我们使用2020年4月1日至2021年6月30日的数据,使用易感-暴露-感染-移除(SEIR)模型的扩展,提供基于最新流行病学模型的第一波、第二波和综合IFRs的估计。方法:按照PRISMA指南,于2021年7月3日检索PubMed、Embase、Global Index Medicus以及BioRxiv、MedRxiv和SSRN预印本数据库(通过iSearch访问)(结果验证截止到2021年8月15日)。总的来说,采用两步方法,筛选了4,765条初始引用,结果在叙述性综述中纳入了37条引用,在定量综合中纳入了19项研究和41个数据点。使用带有DerSimonian-Laird估计的随机效应模型,我们对IFR1(定义为观察到的报告死亡总数除以估计感染总数的比率)和IFR2 (IFR1分子中的死亡漏报数)进行了meta分析。对于后者,我们根据低估死亡人数的可用估计范围提供了下界和上界,这通常是由于过度计算死亡人数造成的。主要重点是估计全国的综合国际财务报告准则估计值,次要目标是估计印度与sars - cov -2相关的区域和特定州的综合国际财务报告准则估计值。我们还试图在第一波和第二波中对我们的实证结果进行分层。同时,我们提出了更新的SEIR模型估计第1、2波的ifr,并将各波与2020年4月1日至2021年6月30日观察到的病例和死亡计数数据相结合。结果:就印度而言,在全国范围内,在四次全国血清调查中,病例的漏报因子(URF)范围为14.3至29.1;死亡总死亡率(来自超额死亡报告)从4.4至11.9不等,累计超额死亡人数从179万至490万不等(截至2021年6月)。印度全国综合IFR1和IFR2估计值分别为0.097%(95%置信区间[CI]: 0.067-0.140)和0.365% (95% CI: 0.264-0.504)至0.485% (95% CI: 0.344-0.685),再次注意到IFR2随超额死亡估计值的变化而变化。在本荟萃分析纳入的研究中,从最早的研究结束日期到最近的研究结束日期(从2020年6月4日到2021年7月6日,IFR1从0.199变化到0.055%),IFR1通常随着时间的推移而下降,而IFR2的类似趋势并不明显,因为过量死亡估计数变化很大(从2020年6月4日到2021年7月6日,IFR2范围从(0.290-1.316)到(0.241-0.651)%)。基于全国SEIR模型的IFR1和IFR2的综合估计分别为0.101% (95% CI: 0.097-0.116)和0.367% (95% CI: 0.358-0.383),这在很大程度上与实证研究结果相吻合,并与超额死亡估计的下限一致。这种流行病学模型的一个优点是能够根据最新数据进行每日估计,缺点是这些估计受到许多假设的制约,验证困难,不能直接使用现有的超额死亡数据。无论使用经验数据还是基于模型的估计,IFR2显然至少是IFR1的3.6倍。结论:当纳入病例和死亡漏报时,荟萃分析的印度累计感染病死率从0.36%到0.48%不等,病例漏报因子在25到30之间,死亡漏报因子在4到12之间。这意味着,到2021年6月30日,印度可能已出现近9亿例感染和170万至490万人死亡,而报告的数字为3040万例病例和41.2万例死亡(印度的冠状病毒),观察到的病死率(CFR)为1.35%。我们重申需要及时和分类的感染和死亡数据,以便按年龄和其他人口统计数据审查病毒造成的负担。全国范围内和各邦的大量死亡漏报,加强了改进印度境内死亡报告的呼吁。JEL分类:I15, I18
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SARS-CoV-2 Infection Fatality Rates in India: Systematic Review, Meta-analysis and Model-based Estimation
Introduction: Fervourous investigation and dialogue surrounding the true number of SARS-CoV-2-related deaths and implied infection fatality rates in India have been ongoing throughout the pandemic, and especially pronounced during the nation’s devastating second wave. We aim to synthesize the existing literature on the true SARS-CoV-2 excess deaths and infection fatality rates (IFR) in India through a systematic search followed by viable meta-analysis. We then provide updated epidemiological model-based estimates of the wave 1, wave 2 and combined IFRs using an extension of the Susceptible-Exposed-Infected-Removed (SEIR) model, using data from 1 April 2020 to 30 June 2021. Methods: Following PRISMA guidelines, the databases PubMed, Embase, Global Index Medicus, as well as BioRxiv, MedRxiv and SSRN for preprints (accessed through iSearch), were searched on 3 July 2021 (with results verified through 15 August 2021). Altogether, using a two-step approach, 4,765 initial citations were screened, resulting in 37 citations included in the narrative review and 19 studies with 41datapoints included in the quantitative synthesis. Using a random effects model with DerSimonian-Laird estimation, we meta-analysed IFR1, which is defined as the ratio of the total number of observed reported deaths divided by the total number of estimated infections, and IFR2 (which accounts for death underreporting in the numerator of IFR1). For the latter, we provided lower and upper bounds based on the available range of estimates of death undercounting, often arising from an excess death calculation. The primary focus is to estimate pooled nationwide estimates of IFRs with the secondary goal of estimating pooled regional and state-specific estimates for SARS-CoV-2-related IFRs in India. We also tried to stratify our empirical results across the first and second waves. In tandem, we presented updated SEIR model estimates of IFRs for waves 1, 2, and combined across the waves with observed case and death count data from 1 April 2020 to 30 June 2021. Results: For India, countrywide, the underreporting factors (URF) for cases (sourced from serosurveys) range from 14.3 to 29.1 in the four nationwide serosurveys; URFs for deaths (sourced from excess deaths reports) range from 4.4 to 11.9 with cumulative excess deaths ranging from 1.79 to 4.9 million (as of June 2021). Nationwide pooled IFR1 and IFR2 estimates for India are 0.097% (95% confidence interval [CI]: 0.067–0.140) and 0.365% (95% CI: 0.264–0.504) to 0.485% (95% CI: 0.344–0.685), respectively, again noting that IFR2 changes as excess deaths estimates vary. Among the included studies in this meta-analysis, IFR1 generally appears to decrease over time from the earliest study end date to the latest study end date (from 4 June 2020 to 6 July 2021, IFR1 changed from 0.199 to 0.055%), whereas a similar trend is not as readily evident for IFR2 due to the wide variation in excess death estimates (from 4 June 2020 to 6 July 2021, IFR2 ranged from (0.290–1.316) to (0.241–0.651)%). Nationwide SEIR model-based combined estimates for IFR1 and IFR2 are 0.101% (95% CI: 0.097–0.116) and 0.367% (95% CI: 0.358–0.383), respectively, which largely reconcile with the empirical findings and concur with the lower end of the excess death estimates. An advantage of such epidemiological models is the ability to produce daily estimates with updated data, with the disadvantage being that these estimates are subject to numerous assumptions, arduousness of validation and not directly using the available excess death data. Whether one uses empirical data or model-based estimation, it is evident that IFR2 is at least 3.6 times more than IFR1. Conclusion: When incorporating case and death underreporting, the meta-analysed cumulative infection fatality rate in India varied from 0.36 to 0.48%, with a case underreporting factor ranging from 25 to 30 and a death underreporting factor ranging from 4 to 12. This implies, by 30 June 2021, India may have seen nearly 900 million infections and 1.7–4.9 million deaths when the reported numbers stood at 30.4 million cases and 412 thousand deaths (Coronavirus in India) with an observed case fatality rate (CFR) of 1.35%. We reiterate the need for timely and disaggregated infection and fatality data to examine the burden of the virus by age and other demographics. Large degrees of nationwide and state-specific death undercounting reinforce the call to improve death reporting within India. JEL Classifications: I15, I18
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Studies in Microeconomics
Studies in Microeconomics Economics, Econometrics and Finance-Economics, Econometrics and Finance (all)
CiteScore
1.40
自引率
0.00%
发文量
14
期刊介绍: Studies in Microeconomics seeks high quality theoretical as well as applied (or empirical) research in all areas of microeconomics (broadly defined to include other avenues of decision science such as psychology, political science and organizational behavior). In particular, we encourage submissions in new areas of Microeconomics such as in the fields of Experimental economics and Behavioral Economics. All manuscripts will be subjected to a peer-review process. The intended audience of the journal are professional economists and young researchers with an interest and expertise in microeconomics and above. In addition to full-length articles MIC is interested in publishing and promoting shorter refereed articles (letters and notes) that are pertinent to the specialist in the field of Microeconomics (broadly defined). MIC will periodically publish special issues with themes of particular interest, including articles solicited from leading scholars as well as authoritative survey articles and meta-analysis on the themed topic. We will also publish book reviews related to microeconomics, and MIC encourages publishing articles from policy practitioners dealing with microeconomic issues that have policy relevance under the section Policy Analysis and Debate.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信