Trust catchment populations: using Greater London Area population estimates
PHE ePoster Library. Shadwell S. Sep 12, 2019; 274508; 67
Stephanie Shadwell
Stephanie Shadwell
Login now to access Regular content available to all registered users.

You may also access this content "anytime, anywhere" with the Free MULTILEARNING App for iOS and Android
Rate & Comment (0)
Abstract Introduction
Hospitals have an increasing need to understand the populations they seek to manage. However, Acute Trusts do not have geographically-defined boundaries for their population nor do they have all encompassing lists of registered patients. PHE produced an interactive dashboard, creating modelled catchments based on hospital admissions using ONS Mid-Year Estimates in a proportionate-flow method. A request was then made from a London Acute Trust to use Greater London Authority (GLA) data to model catchments. GLA population estimates use ONS Middle-Supper-Output-Area (MSOA) data and are approximately 1% higher than ONS Mid-Year Estimates.
Using proportionate-flow method, catchment populations for the GLA were modelled using MSOA and Mid-Year Estimates.
We hypothesised that with a higher base population, the resulting modelled catchment population would be higher. However, results indicated a different pattern. Chelsea & Westminster Hospital NHS Foundation Trust modelled catchment for all admissions was 598,040 with GLA populations, compared to 603,916 using Mid-Year Estimates. Kings College Hospital NHS Foundation Trust modelled catchment for all admissions was 799,438 with GLA populations, compared to 782,536 with Mid-Year Estimates. It was also found that variation in GLA population estimates across MSOA, sex and age bands in comparison to ONS Mid-Year Estimates, combined with where the acute trust draws patients from impacted the outcome of the modelled catchment.
For London-based Acute Trusts, the use of different population estimates covering the GLA area could provide a vital role in producing alternate modelled catchment population, to support the understanding of the population they serve. External funding details
    This eLearning portal is powered by:
    This eLearning portal is powered by MULTIEPORTAL
Anonymous User Privacy Preferences

Strictly Necessary Cookies (Always Active)

MULTILEARNING platforms and tools hereinafter referred as “MLG SOFTWARE” are provided to you as pure educational platforms/services requiring cookies to operate. In the case of the MLG SOFTWARE, cookies are essential for the Platform to function properly for the provision of education. If these cookies are disabled, a large subset of the functionality provided by the Platform will either be unavailable or cease to work as expected. The MLG SOFTWARE do not capture non-essential activities such as menu items and listings you click on or pages viewed.

Performance Cookies

Performance cookies are used to analyse how visitors use a website in order to provide a better user experience.

Save Settings