Clough, Helen E, McIntyre, K Marie 
ORCID: 0000-0003-1360-122X, Patterson, Grace E ORCID: 0000-0001-6672-3920, Harris, John P and Rushton, Jonathan ORCID: 0000-0001-5450-4202
(2021)
Use of routine death and illness surveillance data to provide insight for UK pandemic planning: lessons from COVID-19.
BMJ OPEN, 11 (2).
e044707-.
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CloughHEBMJOpenFinal.pdf - Author Accepted Manuscript Download (319kB) | Preview |
Abstract
<h4>Objectives</h4>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.<h4>Setting</h4>UK, 2020.<h4>Participants</h4>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.<h4>Primary and secondary outcome measures</h4>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.<h4>Results</h4>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.<h4>Conclusion</h4>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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | statistics &, research methods, epidemiology, COVID-19, public health |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 08 Feb 2021 16:37 |
| Last Modified: | 18 Jan 2023 23:00 |
| DOI: | 10.1136/bmjopen-2020-044707 |
| Open Access URL: | https://bmjopen.bmj.com/content/11/2/e044707 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3115350 |
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