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Exploring the vulnerability to COVID19 between communities in England

Konstantinos Daras and Ben Barr

NOTE: The blog post has been updated with the latest model changes of the SAVI index, as of July 6th, 2020.

We conducted a cross-sectional ecological analysis across the 6,791 Middle Super Output Areas (MSOAs) in England. The Isles of Scilly (E02006781) and City of London (E02006781) have been excluded due to missing data in these areas, leaving 6789 MSOAs for analysis. We used data provided by ONS on COVID-19 deaths of patients as occurred in England between 1 March and 31 May 2020. The data are based on the date the death occurred and COVID-19 was the underlying cause or was mentioned on the death certificate as a contributor factor (ICD-10 codes: U07.1 and U07.2).

We assessed the association between mortality from COVID-19 in each area and the proportion of the population from Black, Asian and Minority Ethnic (BAME) backgrounds, living in care homes, living in overcrowded housing and having been admitted in the past 5 years for a long-term health condition, using multivariable Poisson regression, whilst adjusting for the age profile of each area and accounting for the regional spread and duration of the epidemic. The model was then used to produce the Small Area Vulnerability Index (SAVI) for each MSOA that indicates the relative increase in COVID-19 mortality risk that results from the level of each of the 4 vulnerability measures for each MSOA.

Our findings indicate high levels of vulnerability to COVID-19 are clustered within the North West, West Midlands and North East regions. Control measures and policies to shield certain groups need to take into account these factors targeting resources and proportionate to the greater needs experienced by some communities.

Hovering over an area on the map will provide further information on the name of the MSOA and the number of relative increase in COVID-19 mortality risk. A full-screen version of the map can be found here. You can also download the shapefile and the data here. A pre-print paper can be accessed via the SSRN repository here.