Most comprehensive analysis of COVID-19 data reveals previously unattributed deaths

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A study was published in The Lancet Digital Health Use health data from 57 million people in England to build the most complete picture of a pandemic in a single country to date.

In this first-of-its-kind study, researchers from University College London (UCL) combined multiple NHS datasets on national lab test data, primary care advisories, hospitalizations and deaths to reveal the exact path of individuals through the healthcare system during the pandemic. And what effect does this have on their health outcomes?

The analysis revealed 15,486 deaths that occurred within 28 days of being diagnosed with COVID-19, but did not mention COVID-19 as the cause of death. An additional 10,884 diagnoses of COVID-19 were identified from death records alone with no other relevant information previously recorded in health records.

The researchers also found that nearly a third of patients received ventilator support outside of intensive care departments, and this was associated with the highest mortality rates in the first and second waves of the pandemic. The authors say this illustrates the need to plan how to expand intensive care services in the event of future epidemics and health emergencies.

Study co-author Dr Chris Tomlinson of University College London said: “Understanding the impact of COVID-19 requires looking at how infections vary in severity and time course – from asymptomatic to unfortunately fatal.

“These different clinical presentations are recorded in patient digital records, but across multiple and often unrelated institutions – including public health agencies, GP surgeries, hospitals, intensive care units, and death registries. Analyzing all of this data at an entire population scale is challenging. real.

“In this study, we are collecting eight complementary national-level data sets from across the NHS to create the most comprehensive analysis of COVID-19 events to date, with the goal of supporting policy decision-making on COVID-19 and future health crises.”

For their analysis, the researchers used anonymous patient data from multiple national NHS sources to identify patterns in how patients progress through the health care system. Linking these demographic factors such as age, gender, and ethnicity allowed another layer of analysis. For example, those of non-white races had a shorter time between infection and death, indicating that these groups may have been arriving at testing and health care facilities later in their illness.

The research was conducted safely in a trusted research environment by members of the CVD-COVID-UK Consortium, the National Institute for Health Research (NIHR) and the British Heart Foundation (BHF) led by the BHF Center for Data Science, part of Health Data Research UK.

Professor Cathy Sudlow, Director of the BHF Data Science Center, said, “Rapid and reliable access to health data has been essential throughout the pandemic. Until now, such data has been locked up in isolated institutions where it is almost impossible to analyze it in harmony.”

“The CVD-COVID-UK Consortium of the BHF Center for Data Science is working to provide trusted researchers with rapid access to multiple, interconnected data sets from across the NHS. By collaborating with research teams like this one developing new ways to analyze these data sets, we are paving the way The path to a new future by using health data to improve people’s lives.”

Professor Spiros Denaxas of the University of California, who is an author on the study, said, “By linking electronic health records on a national scale, we were able to identify patterns and trajectories of patients in the epidemic that would otherwise remain hidden in smaller data sets. Secure access to excellent data that keeps The NHS is essential to conducting high-quality health data research and improving patient health and healthcare.”

The researchers note that although they present patterns across the epidemic, their focus has been on analyzing characteristics associated with COVID-19, rather than causal relationships. The findings are important to identify potential NHS weaknesses and inform future policies.

Dr. Johann Thejesen of the University of California, who co-led the study, said, “This work has already enabled other research with closely related public health implications, such as assessing the coagulation risks of COVID-19 vaccines. By fully engaging our methods and code, We believe this research has the potential to unleash the power of health data associated not only with future COVID-19 outbreaks, but for all kinds of complex health conditions.”

Scientists identify characteristics to better define prolonged COVID

more information:
Pathways of COVID-19 among 57 million adults in England: a cohort study using electronic health records, The Lancet Digital Health (2022).… (22) 00091-7 / full text

Provided by Health Data Research UK

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