Epidemiologial Analysis of Patients Presenting to a West London District General Hospital Requiring Admission with Covid-19

  1. E Heald
  2. N Ring
  3. D Vatvani
  4. D Shackleton

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

Background

Coronavirus has lead to significant morbidity and mortality both within the UK and worldwide. We hypothesise there are local clusters of coronavirus which would therefore be amenable to targeted public health measures.

Methods

This is a retrospective, observational case series conducted in a West London District General Hospital. All patients admitted to hospital with a radiological or microbiological diagnosis of Covid-19 were included (children under 16 years were excluded). Consecutive sampling was used and baseline characteristics including age, sex, postcode and final patient outcome were collected from the electronic health records. Patient origin postcode was plotted to a map of the local area and an online cloud based mapping analysis system was used to generate heat maps and case density maps which were compared to living base layers. The primary outcome was identification of local clusters of cases of coronavirus. Secondary outcome was identification of population characteristics that may provide evidence for more targetted public health intervention in a second wave.

Results

Local clusters of infection were identified within the target population. These appeared to correlate with higher indices of deprivation, poorer overall health and high household occupancy suggesting a role for public health measures to target these areas.

Conclusion

There is a role for targeted public health measures in tackling the spread of coronavirus, paying particular attention to those who live in more deprived areas.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.10.13.20212126: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Limitations: The study demonstrated clusters of cases in Hounslow, Isleworth, Feltham and towards Southall with relative sparing in Richmond. This could otherwise be explained by patients from this area choosing to attend another hospital or ambulances diverting patients to other nearby hospitals which may be influenced by travel time and/or waiting times. By the same token, hospitals north of ours were under particular strain at the time of the study resulting in patients seen from outside the hospital catchment area. Maps drawn have been limited to the immediate geographical area of the hospital. Outlier cases also occurred from the south coast to the Midlands and were not included to allow a more detailed analysis of the local area. These presentations may be explained by the proximity of the hospital to a major international airport. This is a single centre study and there it’s external validity is limited. This study solely focuses on patients with moderate to severe Covid-19 requiring admission to hospital. It was not possible to collect data regarding asymptomatic or mildly symptomatic patients due to a policy of only testing those requiring admission due to national testing strategies at the time of the study.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.10.13.20212126v1 on medRxiv