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利用常规疾病死亡监测数据为英国大流行规划提供洞察:COVID-19 的经验教训。

Use of routine death and illness surveillance data to provide insight for UK pandemic planning: lessons from COVID-19.

机构信息

Department of Livestock and One Health, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Neston, UK

Department of Livestock and One Health, Institute of Infection, Veterinary and Ecological Sciences, University of Liverpool, Neston, UK.

出版信息

BMJ Open. 2021 Feb 8;11(2):e044707. doi: 10.1136/bmjopen-2020-044707.

Abstract

OBJECTIVES

Reporting of COVID-19 cases, deaths and testing has often lacked context for appropriate assessment of disease burden within risk groups. The research considers how routine surveillance data might provide initial insights and identify risk factors, setting COVID-19 deaths early in the pandemic into context. This will facilitate the understanding of wider consequences of a pandemic from the earliest stage, reducing fear, aiding in accurately assessing disease burden and ensuring appropriate disease mitigation.

SETTING

UK, 2020.

PARTICIPANTS

The study is a secondary analysis of routine, public domain, surveillance data and information from Office for National Statistics (ONS), National Health Service (NHS) 111 and Public Health England (PHE) on deaths and disease.

PRIMARY AND SECONDARY OUTCOME MEASURES

Our principal focus is ONS data on deaths mentioning COVID-19 on the death certificate. We also consider information provided in NHS 111 and PHE data summaries.

RESULTS

Deaths with COVID-19 significantly contributed to, yet do not entirely explain, abnormally elevated all-cause mortality in the UK from weeks 12-18 of 2020. Early in the UK epidemic, COVID-19 was the greatest threat to those with underlying illness, rarely endangering people aged under 40 years. COVID-19-related death rates differed by region, possibly reflecting underlying population structure. Risk of COVID-19-related death was greater for healthcare and social care staff and black, Asian and minority ethnic individuals, having allowed for documented risk factors.

CONCLUSION

Early contextualisation of public health data is critical to recognising who gets sick, when and why. Understanding at-risk groups facilitates a targeted response considering indirect consequences of society's reaction to a pandemic alongside disease-related impacts. COVID-19-related deaths mainly mirror historical patterns, and excess non-COVID-19-related deaths partly reflect reduced access to and uptake of healthcare during lockdown. Future outbreak response will improve through better understanding of connectivity between disease monitoring systems to aid interpretation of disease risk patterns, facilitating nuanced mitigation measures.

摘要

目的

在对风险群体中的疾病负担进行适当评估时,对 COVID-19 病例、死亡和检测的报告往往缺乏背景信息。本研究考虑了常规监测数据如何提供初步见解并确定风险因素,从而为大流行早期的 COVID-19 死亡提供背景信息。这将有助于从最早阶段了解大流行的更广泛后果,减少恐惧,帮助准确评估疾病负担,并确保采取适当的疾病缓解措施。

地点

英国,2020 年。

参与者

本研究是对常规、公共领域监测数据以及来自国家统计局(ONS)、国民保健署(NHS)111 和英格兰公共卫生署(PHE)的关于死亡和疾病的信息进行二次分析。

主要和次要结果

我们的主要重点是 ONS 数据中死亡证明上提到 COVID-19 的死亡人数。我们还考虑了 NHS 111 和 PHE 数据摘要中提供的信息。

结果

COVID-19 死亡人数显著增加,但并未完全解释 2020 年第 12-18 周英国异常升高的全因死亡率。在英国疫情早期,COVID-19 对有基础疾病的人构成最大威胁,很少危及 40 岁以下的人。与 COVID-19 相关的死亡率因地区而异,这可能反映了人口结构的差异。在考虑了有记录的危险因素后,医疗保健和社会护理人员以及黑人和少数民族个体的 COVID-19 相关死亡风险更大。

结论

对公共卫生数据进行早期背景化分析对于识别谁生病、何时生病以及为何生病至关重要。了解高危人群有助于针对社会对大流行的反应以及与疾病相关的影响来采取有针对性的应对措施。与 COVID-19 相关的死亡主要反映了历史模式,非 COVID-19 相关死亡人数的增加部分反映了封锁期间医疗保健机会减少和接受度降低。通过更好地理解疾病监测系统之间的联系,以帮助解释疾病风险模式,未来的疫情应对将得到改善,从而改善疾病缓解措施。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ede2/7871230/d9e415a11760/bmjopen-2020-044707f01.jpg

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