The Research
COVID-19 has indirectly impacted the healthcare system, owing to surge and sustained pressures on the system. This has resulted in delays in screening services and postponement of elective surgeries and other non-urgent medical treatments. However, these pressures’ effects on managing long-term or chronic conditions still need to be fully understood.
Long-term conditions (LTCs), such as diabetes and asthma, cannot be cured but are managed by medication and/or other treatments. A specific diagnosis is entered into clinical record systems to ensure patients receive the correct monitoring and management. Long-term conditions are linked with increasing age and deprivation, and the number of people with multiple long-term conditions is rising.
This study, by the Environment & Health (ENVHE) Research Centre, aimed to examine the impact of COVID-19 on the number of newly diagnosed patients for 17 LTCs and diabetes subtypes.
The Data
Data on patients newly diagnosed with one or more LTCs between 2000 – 2021 and living in Wales were extracted from routinely collected electronic health record data sources held within the Secure Anonymised Information Linkage (SAIL) Databank.
The number of new cases (per 100,000 persons) between 2020-2021 was compared to expected numbers (if the COVID-19 pandemic did not happen). Expected numbers were predicted by modelling trends and seasonal patterns between 2015-2019 using time series analysis.
The Results
The team observed lower-than-expected rates of diagnoses across all conditions except for Type 1 diabetes in 2020-2021. Observed rates continued to fall behind expectations by the end of 2021 for most conditions.
In conclusion, a typical general practice of 10,000 patients might have over 400 undiagnosed LTCs. They are unlikely to be monitored systematically and may not be managed based on the best evidence. It is anticipated that there could potentially be a substantial backlog of unidentified patients. Resources are required to tackle the anticipated workload as part of COVID-recovery, particularly in primary care.
TIM OSBORNE, RESEARCH OFFICER & DATA SCIENTIST AT ENVHE RESEARCH CENTRE, TALKED TO US MORE ABOUT THIS RESEARCH AND THE DEVELOPMENT OF A DASHBOARD WHICH WILL ALLOW THE PUBLIC AND STAKEHOLDERS TO ACCESS THE TEAM’S ANALYSIS AS A SUPPLEMENT TO THE PUBLISHED PAPER.
My name is Tim Osborne, and I’m a Research Officer & Data Scientist with Population Data Science at Swansea University. I am an MSc Mathematics graduate with a few years of experience in health data science/analysis.
My current role is funded by the Wales COVID-19 Evidence Centre, which aims to improve the quality and safety of health and social care delivery by ensuring COVID-19 research is timely and applicable to Wales. This work is carried out within the Environment and Health (ENVHE) Research Centre at Swansea University. Many members of the team within Population Data Science and the ENVHE Research Centre have helped to progress this project with their knowledge of data extraction and linkage, statistical analysis, and visualisation of results, as well as the valuable clinical knowledge from our stakeholders to interpret the results.
Tell us more about this research.
Our work on this project up to this point has been investigating the effect of COVID-19 on the incidence of long-term conditions in Wales. To carry out this work, we have utilised the large volumes of data held within the SAIL Databank.
My involvement in this work has been mostly in the data extraction and linkage of datasets within the SAIL Databank and the visualisation of results.
We used millions of records from GP and hospital data to investigate how COVID has left behind an ‘undiagnosed’ population of people with 17 long-term conditions of interest, which could cause a substantial backlog. We also used demographic data to identify whether certain parts of the population of Wales were more greatly affected by COVID-19 in terms of their likelihood of being diagnosed with long-term conditions.
The Shiny Dashboard
Throughout this project, we have developed a dashboard using the Shiny package in R. This will allow the public to access our analysis as a supplement to our paper.
The main challenge with creating the dashboard was allowing multiple people to collaborate and contribute to the code. This was made easier by utilising Gitlabs version control so we could track changes made to the code and apply them to the dashboard.
The dashboard covers incidence rates and counts from January 2000 to December 2021.
What is so great about the dashboard?
The Shiny dashboard enabled us to share outputs with stakeholders regularly – and to help them interact with the data and visualisations much more easily than with static charts.
It will also enable us to share our outputs clearly with the public, who can see incidence for the time period and condition of interest by using the checkboxes and year slider. The code used to develop the dashboard will also be made public, ensuring transparency.
The other benefit of the Shiny dashboard is that now that the code and infrastructure are there, we can continue to add further outputs without creating the dashboard from scratch.
What does this mean for healthcare delivery, public health and policy?
Our future work will further investigate the impact of COVID-19 on these long-term conditions, focusing now on the health resource use and the severity of the condition at diagnosis.
We hope that the findings from our latest research, found within our dashboard, will impact policy by helping policymakers make informed decisions.
