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《COVID-19 疾病负担(BoCO-19)研究:一项对南欧、东欧和中亚地区监测数据进行二次分析的研究方案》。

The Burden of Disease due to COVID-19 (BoCO-19): A study protocol for a secondary analysis of surveillance data in Southern and Eastern Europe, and Central Asia.

机构信息

Department2: Epidemiology and Health Monitoring, Robert Koch Institute, Berlin, Germany.

National Association of Public Health, Tirana, Albania.

出版信息

PLoS One. 2023 Oct 13;18(10):e0292041. doi: 10.1371/journal.pone.0292041. eCollection 2023.

DOI:10.1371/journal.pone.0292041
PMID:37831679
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10575506/
Abstract

INTRODUCTION

The COVID-19 pandemic has had an extensive impact on public health worldwide. However, in many countries burden of disease indicators for COVID-19 have not yet been calculated or used for monitoring. The present study protocol describes an approach developed in the project "The Burden of Disease due to COVID-19. Towards a harmonization of population health metrics for the surveillance of dynamic outbreaks" (BoCO-19). The process of data collection and aggregation across 14 different countries and sub-national regions in Southern and Eastern Europe and Central Asia is described, as well as the methodological approaches used.

MATERIALS AND METHODS

The study implemented in BoCO-19 is a secondary data analysis, using information from national surveillance systems as part of mandatory reporting on notifiable diseases. A customized data collection template is used to gather aggregated data on population size as well as COVID-19 cases and deaths. Years of life lost (YLL), as one component of the number of Disability Adjusted Life Years (DALY), are calculated as described in a recently proposed COVID-19 disease model (the 'Burden-EU' model) for the calculation of DALY. All-cause mortality data are collected for excess mortality sensitivity analyses. For the calculation of Years lived with disability (YLD), the Burden-EU model is adapted based on recent evidence. Because Covid-19 cases vary in terms of disease severity, the possibility and suitability of applying a uniform severity distribution of cases across all countries and sub-national regions will be explored. An approach recently developed for the Global Burden of Disease Study, that considers post-acute consequences of COVID-19, is likely to be adopted. Findings will be compared to explore the quality and usability of the existing data, to identify trends across age-groups and sexes and to formulate recommendations concerning potential improvements in data availability and quality.

DISCUSSION

BoCO-19 serves as a collaborative platform in order to build international capacity for the calculation of burden of disease indicators, and to support national experts in the analysis and interpretation of country-specific data, including their strengths and weaknesses. Challenges include inherent differences in data collection and reporting systems between countries, as well as assumptions that have to be made during the calculation process.

摘要

简介

COVID-19 大流行对全球公共卫生造成了广泛影响。然而,在许多国家,COVID-19 的疾病负担指标尚未计算或用于监测。本研究方案描述了在“COVID-19 疾病负担。为动态暴发的监测建立人口健康指标的统一”(BoCO-19)项目中开发的方法。描述了在东南欧和中亚的 14 个不同国家和次国家区域收集和汇总数据的过程,以及使用的方法。

材料和方法

BoCO-19 中实施的研究是二次数据分析,使用国家监测系统的信息作为法定传染病报告的一部分。使用定制的数据收集模板来收集人口规模以及 COVID-19 病例和死亡的汇总数据。寿命损失年(YLL),作为残疾调整生命年(DALY)数量的一个组成部分,是根据最近提出的 COVID-19 疾病模型(“负担-EU”模型)计算 DALY 时描述的方法计算的。超额死亡率敏感性分析中收集所有原因死亡率数据。为了计算残疾生存年(YLD),根据最近的证据对负担-EU 模型进行了调整。由于 COVID-19 病例的疾病严重程度不同,将探索在所有国家和次国家区域应用统一病例严重程度分布的可能性和适用性。可能采用最近为全球疾病负担研究开发的方法,该方法考虑了 COVID-19 的急性后后果。将对结果进行比较,以探索现有数据的质量和可用性,确定年龄组和性别之间的趋势,并就改善数据可用性和质量提出建议。

讨论

BoCO-19 是一个合作平台,旨在建立计算疾病负担指标的国际能力,并支持国家专家分析和解释特定国家的数据,包括其优势和劣势。挑战包括国家之间数据收集和报告系统的固有差异,以及在计算过程中必须做出的假设。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f86b/10575506/f5090a843a4b/pone.0292041.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f86b/10575506/45434964b169/pone.0292041.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f86b/10575506/257f3da6f9c8/pone.0292041.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f86b/10575506/f5090a843a4b/pone.0292041.g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f86b/10575506/45434964b169/pone.0292041.g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f86b/10575506/257f3da6f9c8/pone.0292041.g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f86b/10575506/f5090a843a4b/pone.0292041.g003.jpg

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