Back

Paediatric Attendances and Acuity in the Emergency Department during the COVID-19 Pandemic

Rose, K.; Van-Zyl, K.; Cotton, R.; Wallace, S.; Cleugh, F.

2020-08-06 emergency medicine
10.1101/2020.08.05.20168666 medRxiv
Show abstract

AimTo investigate the difference in both numbers and acuity of presentations to the Paediatric Emergency Department (PED) during the peak time period of the current global SARS-CoV-2 pandemic. DesignThis single centre retrospective observational study used routinely collected electronic health data to compare patient presentation characteristics between 21st March and 26th April 2020 compared to the equivalent time period in 2019. ResultsThere was a 90% decrease in attendances to PED, with a 10.23% reduction re-attendance rate. Children presenting were younger during the pandemic, with a median age difference of 2 years. They were more likely to present in an ambulance (9.63%), be admitted to hospital (5.75%) and be assigned the highest two Manchester triage categories (6.26%). There was a non-significant trend towards longer lengths of stay. The top 10 presenting complaints remained constant (although the order changed) between time periods. There was no difference in mortality or admission to PICU. ImplicationsOur data demonstrates that there has been a significant decrease in numbers of children seeking emergency department care. It suggests that presenting patients were proportionally sicker during the pandemic; however, we would argue that this is more in keeping with appropriate acuity for PED presentations, as there were no differences in PICU admission rate or mortality. We explore some of the possible reasons behind the decrease in presentations and the implications for service planning ahead of the winter months.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.