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

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.