PDF(Pubmed)
Abstract：
UNASSIGNED: Mortality statistics are fundamental to understand the magnitude of the COVID-19 pandemic. Due to limitation of real-time data availability, researchers had used mathematical models to estimate excess mortality globally during COVID-19 pandemic. As they demonstrated variations in scope, assumptions, estimations, and magnitude of the pandemic, and hence raised a controversy all over the world. This paper aims to review the mathematical models and their estimates of mortality due to COVID-19 in the Indian context.
UNASSIGNED: The PRISMA and SWiM guidelines were followed to the best possible extent. A two-step search strategy was used to identify studies that estimated excess deaths from January 2020 to December 2021 on Medline, Google Scholar, MedRxiv and BioRxiv available until 0100 h, 16 May 2022 (IST). We selected 13 studies based on a predefined criteria and extracted data on a standardised, pre-piloted form by two investigators, independently. Any discordance was resolved through consensus with a senior investigator. Estimated excess mortality was analysed using statistical software and depicted using appropriate graphs.
UNASSIGNED: Significant variations in scope, population, data sources, time period, and modelling strategies existed across studies along with a high risk of bias. Most of the models were based on Poisson regression. Predicted excess mortality by various models ranged from 1.1 to 9.5 million.
UNASSIGNED: The review presents a summary of all the estimates of excess deaths and is important to understand the different strategies used for estimation, and it highlights the importance of data availability, assumptions, and estimates.
UNASSIGNED: The PRISMA and SWiM guidelines were followed to the best possible extent. A two-step search strategy was used to identify studies that estimated excess deaths from January 2020 to December 2021 on Medline, Google Scholar, MedRxiv and BioRxiv available until 0100 h, 16 May 2022 (IST). We selected 13 studies based on a predefined criteria and extracted data on a standardised, pre-piloted form by two investigators, independently. Any discordance was resolved through consensus with a senior investigator. Estimated excess mortality was analysed using statistical software and depicted using appropriate graphs.
UNASSIGNED: Significant variations in scope, population, data sources, time period, and modelling strategies existed across studies along with a high risk of bias. Most of the models were based on Poisson regression. Predicted excess mortality by various models ranged from 1.1 to 9.5 million.
UNASSIGNED: The review presents a summary of all the estimates of excess deaths and is important to understand the different strategies used for estimation, and it highlights the importance of data availability, assumptions, and estimates.
摘要：
■死亡率统计是了解COVID-19大流行程度的基础。由于实时数据可用性的限制,研究人员使用数学模型估计了COVID-19大流行期间全球的超额死亡率.当他们展示了范围的变化时，假设,估计，以及大流行的规模，因此引起了全世界的争议。本文旨在回顾印度背景下COVID-19死亡率的数学模型及其估计。
■在最大程度上遵循了PRISMA和SWiM指南。在Medline上使用了两步搜索策略来确定估计2020年1月至2021年12月超额死亡的研究，谷歌学者，MedRxiv和BioRxiv在0100小时之前可用，2022年5月16日（IST）。我们根据预定义的标准选择了13项研究，并提取了标准化的数据，两名调查人员预先试行的表格，独立。任何不一致都是通过与一名高级调查员达成共识解决的。使用统计软件分析估计的超额死亡率，并使用适当的图表进行描述。
■范围的重大变化，人口,数据源,时间段,和建模策略存在于不同的研究中，同时存在较高的偏倚风险。大多数模型基于泊松回归。各种模型预测的超额死亡率在1.1到950万之间。
■该综述总结了所有超额死亡的估计，对于了解用于估计的不同策略很重要，它强调了数据可用性的重要性，假设,和估计。
■在最大程度上遵循了PRISMA和SWiM指南。在Medline上使用了两步搜索策略来确定估计2020年1月至2021年12月超额死亡的研究，谷歌学者，MedRxiv和BioRxiv在0100小时之前可用，2022年5月16日（IST）。我们根据预定义的标准选择了13项研究，并提取了标准化的数据，两名调查人员预先试行的表格，独立。任何不一致都是通过与一名高级调查员达成共识解决的。使用统计软件分析估计的超额死亡率，并使用适当的图表进行描述。
■范围的重大变化，人口,数据源,时间段,和建模策略存在于不同的研究中，同时存在较高的偏倚风险。大多数模型基于泊松回归。各种模型预测的超额死亡率在1.1到950万之间。
■该综述总结了所有超额死亡的估计，对于了解用于估计的不同策略很重要，它强调了数据可用性的重要性，假设,和估计。
