BMC Infectious Diseases (2021-01-01)

Monitoring sick leave data for early detection of influenza outbreaks

  • Tom Duchemin,
  • Jonathan Bastard,
  • Pearl Anne Ante-Testard,
  • Rania Assab,
  • Oumou Salama Daouda,
  • Audrey Duval,
  • Jérôme-Philippe Garsi,
  • Radowan Lounissi,
  • Narimane Nekkab,
  • Helene Neynaud,
  • David R. M. Smith,
  • William Dab,
  • Kevin Jean,
  • Laura Temime,
  • Mounia N. Hocine

Journal volume & issue
Vol. 21, no. 1
pp. 1 – 8


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Abstract Background Workplace absenteeism increases significantly during influenza epidemics. Sick leave records may facilitate more timely detection of influenza outbreaks, as trends in increased sick leave may precede alerts issued by sentinel surveillance systems by days or weeks. Sick leave data have not been comprehensively evaluated in comparison to traditional surveillance methods. The aim of this paper is to study the performance and the feasibility of using a detection system based on sick leave data to detect influenza outbreaks. Methods Sick leave records were extracted from private French health insurance data, covering on average 209,932 companies per year across a wide range of sizes and sectors. We used linear regression to estimate the weekly number of new sick leave spells between 2016 and 2017 in 12 French regions, adjusting for trend, seasonality and worker leaves on historical data from 2010 to 2015. Outbreaks were detected using a 95%-prediction interval. This method was compared to results from the French Sentinelles network, a gold-standard primary care surveillance system currently in place. Results Using sick leave data, we detected 92% of reported influenza outbreaks between 2016 and 2017, on average 5.88 weeks prior to outbreak peaks. Compared to the existing Sentinelles model, our method had high sensitivity (89%) and positive predictive value (86%), and detected outbreaks on average 2.5 weeks earlier. Conclusion Sick leave surveillance could be a sensitive, specific and timely tool for detection of influenza outbreaks.