Active Public Health & Healthcare Lungs & Breathing

Novel statistical methods to unlock the potential of routinely collected health data: COVID-19 & beyond

Funder Wellcome Charity-funded
Recipient organisation London School of Hygiene & Tropical Medicine
Funding £1.4M
Period May 2022 — Apr 2027

In plain English

AI plain-English summary

During the COVID-19 pandemic, researchers struggled to reliably predict patient outcomes from electronic health records because standard statistical tools could not handle the sheer complexity and messiness of real-world data. This project aims to fix that. The problem is that electronic health records—the vast databases of GP visits, hospital stays, and prescriptions—are a goldmine for answering urgent public health questions, but existing methods for analysing them are flawed. They cannot easily adjust for hidden biases, such as why certain patients received particular treatments, or combine data from separate sources without linking individuals. The researchers will develop new statistical techniques to overcome these barriers, using the UK’s OpenSAFELY platform and Clinical Practice Research Datalink as test beds. If successful, the methods will allow researchers to ask and answer questions that were previously off-limits: which treatments actually work for whom, how to predict a patient’s risk of severe COVID-19, and how to compare therapies when patients cannot be randomly assigned. Beyond the pandemic, the same tools could be applied to chronic diseases like diabetes or heart disease, making routine health data a more reliable foundation for clinical decisions and public health policy.

View original technical description
The recent pandemic has highlighted the importance and great potential of electronic health record data for addressing urgent public health questions in a timely manner, while demonstrating key gaps in existing methodology for this setting. The overall aim of this proposal is to develop statistical methodology to remove barriers that have hampered important public health issues in COVID-19 being fully addressed; and to apply the methods to answer the immediate and arising public health questions in COVID-19 and more broadly. Key goals are to: - develop a suite of methodological tools to enable computationally-efficient self-recalibrating risk prediction in EHR databases; - develop a framework for assessing the worth of different treatments within EHR databases, accounting for potential high-dimensional confounding and implementing approaches to estimate individual treatment effects within this; - create analytic approaches to address causal questions requiring data from two separate sources in the absence of full individual linkage. Data held within the OpenSAFELY platform and the UK Clinical Practice Research Database will be used to motivate the statistical methodological work. Optimal methodological approaches will be applied to address important questions arising in COVID-19 and more broadly, to key questions in chronic disease.

View the original record at the funder ↗

Researchers

Elizabeth Williamson (EPMC Awardee)

Related Research

Grants with similar aims, by meaning.

Evaluating and addressing the impact of COVID-19 restrictions on electronic health records in estimating causal effects
Investigating transportability of cancer detection models across datasets and time using population-wide electronic health data
Using linked primary care and viral surveillance data to develop risk stratification models to inform management of severe COVID19
Co-assessment with structured data for COVID-19 management
Using data to improve public health: COVID-19 secondment

Original classification

Senior Research Fellowship

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.