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ANACONDA:一个改进死亡率和死因数据的新工具。

ANACONDA: a new tool to improve mortality and cause of death data.

机构信息

Melbourne School of Population and Global Health, The University of Melbourne, Carlton, Victoria, 3053, Australia.

Institute of Public Health, Aarhus University, Aarhus, Denmark.

出版信息

BMC Med. 2020 Mar 9;18(1):61. doi: 10.1186/s12916-020-01521-0.

DOI:10.1186/s12916-020-01521-0
PMID:32146907
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7061487/
Abstract

BACKGROUND

The need to monitor the Sustainable Development Goals (SDGs) and to have access to reliable and timely mortality data has created a strong demand in countries for tools that can assist them in this. ANACONDA (Analysis of National Causes of Death for Action) is a new tool developed for this purpose which allows countries to assess how accurate their mortality and cause of death are. Applying ANACONDA will increase confidence and capacity among data custodians in countries about their mortality data and will give them insight into quality problems that will assist the improvement process.

METHODS

ANACONDA builds on established epidemiological and demographic concepts to operationalise a series of 10 steps and numerous sub-steps to perform data checks. Extensive use is made of comparators to assess the plausibility of national mortality and cause of death statistics. The tool calculates a composite Vital Statistics Performance Index for Quality (VSPI(Q)) to measure how fit for purpose the data are. Extracts from analyses of country data are presented to show the types of outputs.

RESULTS

Each of the 10 steps provides insight into how well the current data is describing different aspects of the mortality situation in the country, e.g. who dies of what, the completeness of the reporting, and the amount and types of unusable cause of death codes. It further identifies the exact codes that should not be used by the certifying physicians and their frequency, which makes it possible to institute a focused correction procedure. Finally, the VSPI(Q) allows periodic monitoring of data quality improvements and identifies priorities for action to strengthen the Civil Registration and Vital Statistics (CRVS) system.

CONCLUSIONS

ANACONDA has demonstrated the potential to dramatically improve knowledge about disease patterns as well as the functioning of CRVS systems and has served as a platform for galvanising wider CRVS reforms in countries.

摘要

背景

监测可持续发展目标(SDGs)并获取可靠和及时的死亡率数据的需求,在各国产生了强烈的需求,需要有工具来帮助他们做到这一点。ANACONDA(国家死因分析工具)是为此目的开发的一种新工具,它可以帮助各国评估其死亡率和死因的准确性。应用 ANACONDA 将提高各国数据保管人对其死亡率数据的信心和能力,并使他们深入了解质量问题,从而协助改进过程。

方法

ANACONDA 建立在既定的流行病学和人口学概念之上,以实施一系列 10 个步骤和许多子步骤来进行数据检查。广泛使用比较器来评估国家死亡率和死因统计数据的合理性。该工具计算出一个综合的生命统计质量绩效指数(VSPI(Q)),以衡量数据的适用程度。呈现了从国家数据分析中提取的结果,以展示各种输出类型。

结果

每一个步骤都提供了有关当前数据如何描述国家死亡率情况的不同方面的深入了解,例如谁死于什么、报告的完整性、以及不可用死因编码的数量和类型。它进一步确定了认证医生不应使用的具体编码及其频率,从而有可能实施有针对性的纠正程序。最后,VSPI(Q)允许定期监测数据质量改进情况,并确定加强民事登记和生命统计(CRVS)系统的行动重点。

结论

ANACONDA 已经证明了在改善对疾病模式以及 CRVS 系统运作的了解方面具有巨大潜力,并成为各国推动更广泛的 CRVS 改革的平台。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fb1/7061487/4dc0f19b7104/12916_2020_1521_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fb1/7061487/879643e16b33/12916_2020_1521_Fig1_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fb1/7061487/8d0282402d6f/12916_2020_1521_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fb1/7061487/4dc0f19b7104/12916_2020_1521_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fb1/7061487/879643e16b33/12916_2020_1521_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fb1/7061487/0a00699f86c3/12916_2020_1521_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fb1/7061487/510d02d48a7d/12916_2020_1521_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fb1/7061487/b5c8915fa692/12916_2020_1521_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fb1/7061487/bc695bcb61b4/12916_2020_1521_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fb1/7061487/840c222b4360/12916_2020_1521_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fb1/7061487/1442d010debe/12916_2020_1521_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fb1/7061487/846153ab894b/12916_2020_1521_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fb1/7061487/9892ee1f940e/12916_2020_1521_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fb1/7061487/8d0282402d6f/12916_2020_1521_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4fb1/7061487/4dc0f19b7104/12916_2020_1521_Fig11_HTML.jpg

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