Monitoring sick leave data for early detection of influenza outbreaks.
Influenza
Outbreak detection
Sick-leave
Surveillance
Journal
BMC infectious diseases
ISSN: 1471-2334
Titre abrégé: BMC Infect Dis
Pays: England
ID NLM: 100968551
Informations de publication
Date de publication:
11 Jan 2021
11 Jan 2021
Historique:
received:
08
07
2020
accepted:
28
12
2020
entrez:
12
1
2021
pubmed:
13
1
2021
medline:
26
1
2021
Statut:
epublish
Résumé
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. 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. 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. Sick leave surveillance could be a sensitive, specific and timely tool for detection of influenza outbreaks.
Sections du résumé
BACKGROUND
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
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
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
CONCLUSIONS
Sick leave surveillance could be a sensitive, specific and timely tool for detection of influenza outbreaks.
Identifiants
pubmed: 33430793
doi: 10.1186/s12879-020-05754-5
pii: 10.1186/s12879-020-05754-5
pmc: PMC7799403
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
52Subventions
Organisme : Agence Nationale de la Recherche
ID : PIA/ANR-16-CONV-0005
Organisme : Agence Nationale de la Recherche
ID : ANRS-12377 B104
Organisme : Agence Nationale de la Recherche
ID : SPHINX-17-CE36-0008-01.
Organisme : CIHR
ID : 164263
Pays : Canada
Références
Am J Public Health. 1991 Jan;81(1):97-9
pubmed: 1983924
Public Health Rep. 1963 Jun;78(6):494-506
pubmed: 19316455
Theor Biol Med Model. 2018 Feb 1;15(1):2
pubmed: 29386017
MMWR Recomm Rep. 2001 Jul 27;50(RR-13):1-35; quiz CE1-7
pubmed: 18634202
Int J Epidemiol. 1994 Aug;23(4):849-55
pubmed: 8002201
MMWR Recomm Rep. 2004 May 7;53(RR-5):1-11
pubmed: 15129191
Emerg Infect Dis. 2004 May;10(5):858-64
pubmed: 15200820
PLoS One. 2013;8(2):e56176
pubmed: 23457520
J Am Med Inform Assoc. 2007 Sep-Oct;14(5):626-31
pubmed: 17600101
Epidemiol Infect. 2018 Jan;146(2):168-176
pubmed: 29208062
Am J Public Health. 1986 Nov;76(11):1289-92
pubmed: 3766824
Emerg Infect Dis. 2011 Oct;17(10):1963-4
pubmed: 22000386
Disaster Med Public Health Prep. 2013 Apr;7(2):160-6
pubmed: 24618167
Environ Sci Technol. 2020 Apr 7;54(7):3733-3735
pubmed: 32202421
Science. 2014 Mar 14;343(6176):1203-5
pubmed: 24626916
Sci Rep. 2016 May 11;6:25732
pubmed: 27165494
Stat Med. 2013 Mar 30;32(7):1206-22
pubmed: 22941770
BMC Infect Dis. 2015 Mar 01;15:110
pubmed: 25886745