Trends, variations and prediction of staff sickness absence rates among NHS ambulance services in England: a time series study

Asghar, Zahid, Wankhade, Paresh, Bell, Fiona , Sanderson, Kristy, Hird, Kelly, Phung, Viet-Hai and Siriwardena, Niro (2021) Trends, variations and prediction of staff sickness absence rates among NHS ambulance services in England: a time series study. BMJ Open, 11 . e053885. ISSN 2044-6055

Full content URL: https://www.doi.org/10.1136/bmjopen-2021-053885

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Trends, variations and prediction of staff sickness absence rates among NHS ambulance services in England: a time series study
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Abstract

Objectives Our aim was to measure ambulance sickness
absence rates over time, comparing ambulance services
and investigate the predictability of rates for future
forecasting.
Setting All English ambulance services, UK.
Design We used a time series design analysing published
monthly National Health Service staff sickness rates by
gender, age, job role and region, comparing the 10 regional
ambulance services in England between 2009 and 2018.
Autoregressive Integrated Moving Average (ARIMA) and
Seasonal ARIMA (SARIMA) models were developed using
Stata V.14.2 and trends displayed graphically.
Participants Individual participant data were not
available. The total number of full-time equivalent (FTE)
days lost due to sickness absence (including non-working
days) and total number of days available for work for each
staff group and level were available. In line with The Data
Protection Act, if the organisation had less than 330 FTE
days available during the study period it was censored for
analysis.
Results A total of 1117 months of sickness absence rate
data for all English ambulance services were included in
the analysis. We found considerable variation in annual
sickness absence rates between ambulance services and
over the 10-year duration of the study in England. Across
all the ambulance services the median days available were
1 336 888 with IQR of 548 796 and 73 346 median days
lost due to sickness absence, with IQR of 30 551 days.
Among clinical staff sickness absence varied seasonally
with peaks in winter and falls over summer. The winter
increases in sickness absence were largely predictable
using seasonally adjusted (SARIMA) time series models.
Conclusion Sickness rates for clinical staff were found
to vary considerably over time and by ambulance trust.
Statistical models had sufficient predictive capability to
help forecast sickness absence, enabling services to plan
human resources more effectively at times of increased
demand.

Keywords:Sickness, ambulance service, Prehospital Care, Absence
Subjects:B Subjects allied to Medicine > B990 Subjects Allied to Medicine not elsewhere classified
Divisions:College of Social Science > School of Health & Social Care
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ID Code:46851
Deposited On:07 Oct 2021 11:52

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