Overview of LTLA- and UTLA-Trust COVID-19 mappings

Sophie Meakin

Source: vignettes/mapping_summary.Rmd

Summary

The covid19.nhs.data package contains four many-to-many (overlapping) mappings between local authority districts (upper- and lower-tier) and NHS Acute Trusts in England. Each mapping contains the following variables:

  • geo_code: A 9-digit identifier for local authorities (UTLA or LTLA) in England.
  • trust_code: A 3-digit organisation code for NHS Trusts.
  • p_geo: The proportion of all admissions from a given local authority (LTLA or UTLA) that were admitted to a given Trust, estimated from one of two datasets (see below).
  • p_trust: The proportion of all admissions to a given Trust that were admitted from a given local authority (LTLA or UTLA), estimated from one of two datasets (see below).

These mappings are probabilistic estimates based on Secondary Uses Service (SUS) healthcare data for England (January - September 2020, inclusive), and linked COVID-19 cases and admissions (June 2020 - May 2021, inclusive). Please see the vignette “Creating the public mappings” for details of how the mappings are made.

These mappings can be used to estimate COVID-19 hospital admissions at the local authority level, or to estimate “relevant” community COVID-19 cases for an NHS Trust. These are estimates only and may not accurately reflect the truth; see limitations below.

Usage

Get started

Install the package from GitHub:

devtools::install_github("epiforecasts/covid19.nhs.data")

and load the library:

Access the mappings

Access the mappings with load_mapping(), specifying the geographical scale (“ltla” or “utla”) and the data source (“sus” or “link”):

load_mapping(scale = "ltla", source = "link") %>%
  head() %>%
  kable() %>%
  kable_styling()
geo_code trust_code n p_geo p_trust
E06000001 R0A 15 0.0275229 0.0015084
E06000001 RVW 392 0.7192661 0.3576642
E06000001 RXF 138 0.2532110 0.0158548
E06000002 RTR 322 0.6694387 0.4593438
E06000002 RVW 42 0.0873181 0.0383212
E06000002 RXF 117 0.2432432 0.0134421

Add Trust names and local authority names to the raw mapping with get_names():

load_mapping(scale = "ltla", source = "link") %>%
  get_names(geo_names = ltla_names) %>%
  head() %>%
  kable() %>%
  kable_styling()
trust_code trust_name geo_code geo_name p_trust p_geo
R0A Manchester University NHS Foundation Trust E06000001 Hartlepool 0.0015084 0.0275229
RVW North Tees And Hartlepool NHS Foundation Trust E06000001 Hartlepool 0.3576642 0.7192661
RXF Mid Yorkshire Hospitals NHS Trust E06000001 Hartlepool 0.0158548 0.2532110
RTR South Tees Hospitals NHS Foundation Trust E06000002 Middlesbrough 0.4593438 0.6694387
RVW North Tees And Hartlepool NHS Foundation Trust E06000002 Middlesbrough 0.0383212 0.0873181
RXF Mid Yorkshire Hospitals NHS Trust E06000002 Middlesbrough 0.0134421 0.2432432

Summarise the mapping with summarise_mapping(). For an NHS Trust (specified by the argument trust), this will return a table and a visualisation, both describing p_trust:

mapping <- load_mapping(scale = "ltla", source = "link")
summary <- summarise_mapping(trust = "RYR",
                             mapping = mapping,
                             shapefile = england_ltla_shape,
                             geo_names = ltla_names)

summary$summary_table %>%
  kable() %>%
  kable_styling()
trust_code trust_name geo_code geo_name p_trust
RYR Western Sussex Hospitals NHS Foundation Trust E07000224 Arun 0.3718887
RYR Western Sussex Hospitals NHS Foundation Trust E07000229 Worthing 0.2020498
RYR Western Sussex Hospitals NHS Foundation Trust E07000225 Chichester 0.1991215
RYR Western Sussex Hospitals NHS Foundation Trust E07000223 Adur 0.1185944
RYR Western Sussex Hospitals NHS Foundation Trust E07000227 Horsham 0.0805271
RYR Western Sussex Hospitals NHS Foundation Trust E06000043 Brighton and Hove 0.0278184 
summary$summary_plot

For a local authority (specified by the argument geography), this will return a table describing p_trust:

summary <- summarise_mapping(geography = "E09000012",
                             mapping = mapping,
                             shapefile = england_ltla_shape,
                             geo_names = ltla_names)

summary$summary_table %>%
  kable() %>%
  kable_styling()
geo_code geo_name trust_code trust_name p_geo
E09000012 Hackney RQX Homerton University Hospital NHS Foundation Trust 0.4957699
E09000012 Hackney R1H Barts Health NHS Trust 0.2233503
E09000012 Hackney RAJ Mid And South Essex NHS Foundation Trust 0.1336717
E09000012 Hackney RWE University Hospitals Of Leicester NHS Trust 0.0490694
E09000012 Hackney RKE Whittington Health NHS Trust 0.0439932
E09000012 Hackney RXF Mid Yorkshire Hospitals NHS Trust 0.0355330
E09000012 Hackney RRV University College London Hospitals NHS Foundation Trust 0.0186125