A team that included researchers from BigData@Heart found that fewer hospital visits for urgent heart problems may have led to a spike in mortality
The beginning of the coronavirus pandemic led people to stay away from emergency departments in the UK, recent data analysis suggests, including those with urgent heart problems. A team of researchers from University College London supported by the IMI project BigData@Heart, whose mission is to use real-world big data to get insights into cardiovascular health outcomes, published details of a study in which they used causal inference to estimate that during the initial phase of the pandemic, there was a drop of about 2,750 visits per week (a 35% decrease) for suspected cardiac disease.
The study, which was published in the journal Circulation: Cardiovascular Quality and Outcomes, analysed data gathered between March and April 2020 from the UK’s government statistics body, as well as a public registry that tracks emergency department visits, and ran a script (which can be viewed here) to estimate the number of excess deaths.
Lead author Dr Michail Katsoulis said: "Our analysis suggested that one cardiac death might have been prevented or delayed for every additional 12 ED visits for suspected cardiac conditions." They arrived at the conclusion that there may have been as many as 232 more deaths per week early in the pandemic, compared to the pre-pandemic period.
As for the reasons people stayed away, no doubt fear of infection played a role. On top of that, public health messaging focussed on encouraging people to put as little pressure on the UK’s national health service as possible. According to Dr Tom Lumbers from the UCL Institute of Health Informatics, one of the authors, "Our results suggest that the pandemic response may have led to the under-treatment of non-COVID-19 diseases, including heart conditions, with important impacts on the excess mortality observed during this period. These results provide evidence of the stark indirect effects of the COVID-19 pandemic on mortality in England."
For this particular study, the researchers trawled publicly available data and used causal inference, which is a way of working out the effect of a particular phenomenon (here, the pandemic), and is increasingly used in epidemiology, to establish cause and effect. Appropriate utilisation of big health data has the potential to transform biomedical research, drug development and healthcare, but there are some major challenges: locating the data, gathering it, storing it, finding potentially interesting patterns, explaining those patterns and then applying the findings to a particular problem, in this case cardiovascular disease. This requires new thinking and unprecedented numbers of different collaborators working together.
BigData@Heart is a collaborative project between academia and industry that is testing out ways to exploit the untapped potential of data contained in electronic health records and patient registries. The team produced a script, published on GitHub, to trawl the databases and come up with the figures.
Watch an abstract video in which the team explains the analysis and the findings of the study.