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

Asghar, Zahid, 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 (9). pp. 1-7. ISSN 2044-6055

Full content URL: http://dx.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

Background
Within the UK National Health Service, ambulance service staff have the highest rates of sickness absence. Less is known about trends over time, variations between ambulance services and the predictability of sickness absence rates. Our aim was to measure ambulance sickness absence rates over time, comparing ambulance services and investigate the predictability of rates for future forecasting.

Methods
We used a time series design analysing published monthly NHS staff sickness rates by gender, age, job role and region, comparing the ten regional ambulance services in England between 2009 and 2018. Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) models were developed using Stata 14.2 and trends displayed graphically. Predicted rates were compared with actual rates for 2019 to investigate the accuracy of the model for forecasting organisational sickness absence rates.
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. 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, and potentially to reduce costs. Further research is needed to understand the sources of variation

Keywords:Ambulance staff sickness absence, Time series, autoregressive integrated moving average (ARIMA), SARIMA, Forecasting, NHS employees, Seasonality
Subjects:G Mathematical and Computer Sciences > G330 Stochastic Processes
G Mathematical and Computer Sciences > G140 Numerical Analysis
B Subjects allied to Medicine > B990 Subjects Allied to Medicine not elsewhere classified
G Mathematical and Computer Sciences > G150 Mathematical Modelling
G Mathematical and Computer Sciences > G340 Statistical Modelling
L Social studies > L231 Public Administration
Divisions:College of Social Science > School of Health & Social Care
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ID Code:46772
Deposited On:05 Oct 2021 15:41

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