The Impact of Covid-19 on Ambulance Response Data

As we begin venturing back into the office, ORH looks at the impact that Covid-19 has had on ambulance response data.

Historical data is an important element of ORH’s analysis for model inputs. The type and number of incidents that emergency service vehicles respond to play an important part in the forecasting of future incidents. When we come to analyse the data for 2020 there will be unusual patterns that will, hopefully, not be reflective of what we can expect of future behaviour.

NHS England releases the Statistical Note: Ambulance Quality Indicators each month, and the latest (published on 9 July 2020) reported that the number of 999 calls in May and June of this year were the lowest for two years. For the last four months, ambulance services have resolved incidents differently to normal practice. Usually just under 60% of incidents across England are conveyed to an emergency department, and around 30% of incidents are resolved at scene. As Covid-19 began to affect life in England during March, so the proportion of patients transported to hospital dropped and was lowest in April at 44%. The percentage increased in May, but was still only 53% in June when England began to emerge from lockdown as non-essential retailers reopened in mid-June.

There has also been a significant change in England’s ambulance response times. In March response times increased significantly as the number of Covid-19 cases increased, but then in April response times improved dramatically, even though Covid-19 cases were at their highest. Markedly   in May, ambulance response times were at their quickest since measures began in 2017 across all four response categories. There was a small increase in response times in June compared to May, but they remained lower than during pre-pandemic times.

For a third of 2020, how ambulances resolve incidents and their response times have been far from normal, and it remains to be seen what the rest of 2020 holds in store for us when it comes to predicting the future.

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