Networked Data Lab: Demographic variation across Britain among those advised to shield from COVID-19

Project Status: Completed

Project Description

This Networked Data Lab analysis focusses on clinically extremely vulnerable (CEV) people, also known as the shielding population - the group of people most at risk of becoming seriously ill from COVID-19 . This group were asked to not leave their homes and to minimise all face-to-face contact up until the end of July 2020 in most of the UK. Whilst the shielding guidance was paused over Summer, people were once again asked to minimise their contact with others from November.

We are using a federated approach to data analysis and each partner will be contributing the same descriptive analysis based on their local population. These results will then be analysed and aggregated where necessary.

The analysis will allow us to have a better understanding of who makes up this group of people and better understand their health care needs. This first output focuses on the demographics of CEV people and can be found online.

Partners

The following partners have taken part in this analysis and contributed results:

• The Aberdeen Centre for Health Data Science (ACHDS) which includes NHS Grampian and the University of Aberdeen
• Public Health Wales, NHS Wales Informatics Service (NWIS), Swansea University (SAIL Databank) and Social Care Wales (SCW)
• Imperial College Health Partners (ICHP), Institute of Global Health Innovation (IGHI), Imperial College London (ICL), and North West London CCGs
• Liverpool CCG, Healthy Wirral Partnership and Citizens Advice Bureau
• Leeds CCG and Leeds City Council

Data sources

This analysis relies on the following data sources, which have been accessed by NDL partners.

• The Shielded Patient List (SPL).
• Patient demographics databases.
• External Open data sources linked to a patients’ LSOA (or other geography) of residence. These are the 2019 English/Welsh/Scottish Index of Multiple Deprivation quintiles and English/Welsh/Scottish urban/rural indicators based on the 2011 Census.

How does it work?

The repository contains the following folders:

• Analysis: this contains markdown files with the R code used by each partner for their analys, starting with clean data to produce summary statistic tables on patient demographics.
• Outputs: this contains the Open Data release with the summary statistics produced by all partners (after applying statistical disclosure control methods).

Requirements

These scripts were written in R version 4.0.2 and RStudio Version 1.1.383.

Useful references

Authors

• Sebastien Peytrignet, The Health Foundation - Twitter - GitHub
• Karen Hodgson, The Health Foundation - Twitter - GitHub
• Jessica Butler, Aberdeen Centre for Health Data Science (ACHDS) - Twitter
• Alisha Davies, Public Health Wales - Twitter
• Roberto Fernandez Crespo, Imperial College Health Partners
• David Knowles, NHS Liverpool CCG
• Frank Wood, NHS Leeds CCG

License

This project is licensed under the MIT License.