David R M Smith, Jonathan Bastard, Pearl Anne Ante-Testard, Oumou Salama Daouda, Laura Temime, Tom Duchemin, Helene Neynaud, Kévin Jean, Audrey Duval, Mounia N. Hocine, Rania Assab, Narimane Nekkab, William Dab, Radowan Lounissi, Jérôme-Philippe Garsi, Laboratoire Modélisation, épidémiologie et surveillance des risques sanitaires (MESuRS), Conservatoire National des Arts et Métiers [CNAM] (CNAM), HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM), Malakoff Humanis, Pasteur-Cnam Risques infectieux et émergents (PACRI), Institut Pasteur [Paris] (IP)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), Epidémiologie et modélisation de la résistance aux antimicrobiens - Epidemiology and modelling of bacterial escape to antimicrobials (EMAE), Institut Pasteur [Paris] (IP)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre de recherche en épidémiologie et santé des populations (CESP), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Paul Brousse-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Paris-Saclay, Biodiversité et Epidémiologie des Bactéries pathogènes - Biodiversity and Epidemiology of Bacterial Pathogens, Institut Pasteur [Paris] (IP), Malaria : parasites et hôtes - Malaria : parasites and hosts, TD PhD is funded by Association Nationale de la Recherche et de la Technologie and Malakoff Humanis. JB PhD is funded by the INCEPTION project (PIA/ANR-16-CONV-0005). PAAT PhD is funded by INSERM-ANRS (France Recherche Nord & Sud Sida-HIV Hépatites), grant number ANRS-12377 B104. DS PhD is funded by a Canadian Institutes of Health Research Doctoral Foreign Study Award (Funding Reference Number 164263) as well as the French government through its National Research Agency project SPHINX-17-CE36–0008-01., ANR-16-CONV-0005,INCEPTION,Institut Convergences pour l'étude de l'Emergence des Pathologies au Travers des Individus et des populatiONs(2016), ANR-17-CE36-0008,SPHINx,Diffusion de pathogènes au sein des réseaux de soins : une étude de modélisation(2017), TD is supported by Association Nationale de la Recherche et de la Technologie and Malakoff Humanis. JB is supported by the INCEPTION project (PIA/ANR-16-CONV-0005) PAA is supported by INSERM-ANRS (France Recherche Nord & Sud Sida-HIV Hepatites), grant number ANRS-12377 B104 DS is supported by a Canadian Institutes of Health Research Doctoral Foreign Study Award (Funding Reference Number 164263) as well as the French government through its National Research Agency project SPHINX-17-CE36-0008-01., Institut Pasteur [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), Institut Pasteur [Paris]-Conservatoire National des Arts et Métiers [CNAM] (CNAM), Institut Pasteur [Paris], Hocine, Mounia N., Institut Convergences pour l'étude de l'Emergence des Pathologies au Travers des Individus et des populatiONs - - INCEPTION2016 - ANR-16-CONV-0005 - CONV - VALID, and Diffusion de pathogènes au sein des réseaux de soins : une étude de modélisation - - SPHINx2017 - ANR-17-CE36-0008 - AAPG2017 - VALID
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.