Browse by author
Lookup NU author(s): Dr Claire WelshORCiD, Dr David Sinclair, Professor Fiona MatthewsORCiD
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
© 2021 The Author(s)Background: Population characteristics can be used to infer vulnerability of communities to COVID-19, or to the likelihood of high levels of vaccine hesitancy. Communities harder hit by the virus, or at risk of being so, stand to benefit from greater resource allocation than their population size alone would suggest. This study reports a simple but efficacious method of ranking small areas of England by relative characteristics that are linked with COVID-19 vulnerability and vaccine hesitancy. Methods: Publicly available data on a range of characteristics previously linked with either poor COVID-19 outcomes or vaccine hesitancy were collated for all Middle Super Output Areas of England (MSOA, n=6790, excluding Isles of Scilly), scaled and combined into two numeric indices. Multivariable linear regression was used to build a parsimonious model of vulnerability (static socio-ecological vulnerability index, SEVI) in 60% of MSOAs, and retained variables were used to construct two simple indices. Assuming a monotonic relationship between indices and outcomes, Spearman correlation coefficients were calculated between the SEVI and cumulative COVID-19 case rates at MSOA level in the remaining 40% of MSOAs over periods both during and out with national lockdowns. Similarly, a novel vaccine hesitancy index (VHI) was constructed using population characteristics aligned with factors identified by an Office for National Statistics (ONS) survey analysis. The relationship between the VHI and vaccine coverage in people aged 12+years (as of 2021-06-24) was determined using Spearman correlation. The indices were split into quintiles, and MSOAs within the highest vulnerability and vaccine hesitancy quintiles were mapped. Findings: The SEVI showed a moderate to strong relationship with case rates in the validation dataset across the whole study period, and for every intervening period studied except early in the pandemic when testing was highly selective. The SEVI was more strongly correlated with case rates than any of its domains (rs 0·59 95% CI 0.57-0.62) and outperformed an existing MSOA-level vulnerability index. The VHI was significantly negatively correlated with COVID-19 vaccine coverage in the validation data at the time of writing (rs -0·43 95% CI -0·46 to -0·41). London had the largest number and proportion of MSOAs in quintile 5 (most vulnerable/hesitant) of SEVI and VHI concurrently. Interpretation: The indices presented offer an efficacious way of identifying geographical disparities in COVID-19 risk, thus helping focus resources according to need. Funding: Funder: Integrated Covid Hub North East Award number: n/a Grant recipient: Fiona Matthews
Author(s): Welsh CE, Sinclair DR, Matthews FE
Publication type: Article
Publication status: Published
Journal: The Lancet Regional Health - Europe
Year: 2022
Volume: 14
Print publication date: 01/03/2022
Online publication date: 30/12/2021
Acceptance date: 02/04/2020
Date deposited: 15/01/2022
ISSN (electronic): 2666-7762
Publisher: Elsevier Ltd
URL: https://doi.org/10.1016/j.lanepe.2021.100296
DOI: 10.1016/j.lanepe.2021.100296
Altmetrics provided by Altmetric
