UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

Using health data to predict individuals at risk of gastrointestinal cancers and to refine delivery of care in light of the COVID-19 pandemic

Ho, Kai Man Alexander; (2025) Using health data to predict individuals at risk of gastrointestinal cancers and to refine delivery of care in light of the COVID-19 pandemic. Doctoral thesis (Ph.D), UCL (University College London). Green open access

[thumbnail of PhD Thesis Submission 20141212 KMA Ho Repository.pdf]
Preview
Text
PhD Thesis Submission 20141212 KMA Ho Repository.pdf - Submitted Version

Download (4MB) | Preview

Abstract

Gastrointestinal tract cancers are some of the highest incidence cancers globally. However, certain subtypes such oesophageal cancer have poor prognosis, as patients often present in advanced stages of disease, limiting curative options. Earlier identification of potentially high-risk individuals would lead to improved outcomes. This has become especially important in the aftermath of the COVID-19 pandemic, which caused widespread disruption of healthcare systems and led to disruption of patient referral pathways. In this thesis, I firstly examined risk factors which could lead to the development of gastrointestinal cancers. I also performed a systematic review and meta-analysis, focussing the role of gastro-oesophageal reflux in the development of oesophageal cancer. Next, I used routine health data to assess the impact of the pandemic gastroenterology services both within a single hospital trust and nationally. I collated data on the actual number of endoscopic procedures performed during the first wave of the pandemic in England, demonstrating a precipitous drop before a prolonged recovery. Furthermore, I calculated a projected backlog of endoscopic procedures and use modelling techniques to estimate when the backlog may be cleared under different recovery scenarios. I then sought to develop solutions to overcome the backlog. Using machine learning, I created a model which predicts the presence of oesophageal and gastric cancer, based on patient questionnaire data. I added epigenetic data derived from paired saliva samples to assess for improvements in model performance. Moreover, using primary care data I evaluated the use of faecal immunochemical testing (FIT) in triaging suspected colorectal cancer referrals, including the role for duplicate measurements. Finally, I also examined the wider effects of the pandemic, including psychosocial effects within a religious worship context. In conclusion, this thesis analyses the impact of the COVID-19 pandemic on gastroenterology services, with a focus on endoscopy, and offers solutions to overcome its aftereffects.

Type: Thesis (Doctoral)
Qualification: Ph.D
Title: Using health data to predict individuals at risk of gastrointestinal cancers and to refine delivery of care in light of the COVID-19 pandemic
Open access status: An open access version is available from UCL Discovery
Language: English
Additional information: Copyright © The Author 2025. Original content in this thesis is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) Licence (https://creativecommons.org/licenses/by-nc/4.0/). Any third-party copyright material present remains the property of its respective owner(s) and is licensed under its existing terms. Access may initially be restricted at the author’s request.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Div of Surgery and Interventional Sci > Department of Targeted Intervention
URI: https://discovery.ucl.ac.uk/id/eprint/10209573
Downloads since deposit
23Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item