How does vulnerability to COVID-19 vary between communities in England? Developing a Small Area Vulnerability Index (SAVI)

Daras, Konstantinos ORCID: 0000-0002-4573-4628, Alexiou, Alexandros ORCID: 0000-0003-3533-3238, Rose, Tanith C, Buchan, Iain ORCID: 0000-0003-3392-1650, Taylor-Robinson, David ORCID: 0000-0002-5828-7724 and Barr, Benjamin ORCID: 0000-0002-4208-9475
(2021) How does vulnerability to COVID-19 vary between communities in England? Developing a Small Area Vulnerability Index (SAVI). Journal of Epidemiology and Community Health, 75 (8). pp. 729-734.

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<jats:sec><jats:title>Background</jats:title><jats:p>During the initial wave of the COVID-19 epidemic in England, several population characteristics were associated with increased risk of mortality—including, age, ethnicity, income deprivation, care home residence and housing conditions. In order to target control measures and plan for future waves of the epidemic, public health agencies need to understand how these vulnerabilities are distributed across and clustered within communities.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>We performed a cross-sectional ecological analysis across 6789 small areas in England. We assessed the association between COVID-19 mortality in each area and five vulnerability measures relating to ethnicity, poverty, prevalence of long-term health conditions, living in care homes and living in overcrowded housing. Estimates from multivariable Poisson regression models were used to derive a Small Area Vulnerability Index.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>Four vulnerability measures were independently associated with age-adjusted COVID-19 mortality. Each SD increase in the proportion of the population (1) living in care homes, (2) admitted to hospital in the past 5 years for a long-term health condition, (3) from an ethnic minority background and (4) living in overcrowded housing was associated with a 28%, 19% 8% and 11% increase in age-adjusted COVID-19 mortality rate, respectively.</jats:p></jats:sec><jats:sec><jats:title>Conclusion</jats:title><jats:p>Vulnerability to COVID-19 was noticeably higher in the North West, West Midlands and North East regions, with high levels of vulnerability clustered in some communities. Our analysis indicates the communities who will be most at risk from a second wave of the pandemic.</jats:p></jats:sec>

Item Type: Article
Uncontrolled Keywords: PUBLIC HEALTH POLICY, Health inequalities, Disease modeling
Depositing User: Symplectic Admin
Date Deposited: 08 Feb 2021 08:04
Last Modified: 18 Jan 2023 23:00
DOI: 10.1136/jech-2020-215227
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