Monitoring sick leave data for early detection of influenza outbreaks
BackgroundWorkplace 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...
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| Veröffentlicht in: | BMC infectious diseases 2021-01, Vol.21 (1), p.52-52, Article 52 |
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| creator | Duchemin, Tom Bastard, Jonathan Ante-Testard, Pearl Anne Assab, Rania Daouda, Oumou Salama Duval, Audrey Garsi, Jerome-Philippe Lounissi, Radowan Nekkab, Narimane Neynaud, Helene Smith, David R. M. Dab, William Jean, Kevin Temime, Laura Hocine, Mounia N. |
| description | BackgroundWorkplace 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.MethodsSick 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.ResultsUsing sick leave data, we detected 92% of reported influenza outbreaks between 2016 and 2017, on average 5.88weeks 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 2.5weeks earlier.ConclusionSick leave surveillance could be a sensitive, specific and timely tool for detection of influenza outbreaks. |
| doi_str_mv | 10.1186/s12879-020-05754-5 |
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M. ; Dab, William ; Jean, Kevin ; Temime, Laura ; Hocine, Mounia N.</creator><creatorcontrib>Duchemin, Tom ; Bastard, Jonathan ; Ante-Testard, Pearl Anne ; Assab, Rania ; Daouda, Oumou Salama ; Duval, Audrey ; Garsi, Jerome-Philippe ; Lounissi, Radowan ; Nekkab, Narimane ; Neynaud, Helene ; Smith, David R. M. ; Dab, William ; Jean, Kevin ; Temime, Laura ; Hocine, Mounia N.</creatorcontrib><description>BackgroundWorkplace 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.MethodsSick 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.ResultsUsing sick leave data, we detected 92% of reported influenza outbreaks between 2016 and 2017, on average 5.88weeks 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 2.5weeks earlier.ConclusionSick leave surveillance could be a sensitive, specific and timely tool for detection of influenza outbreaks.</description><identifier>ISSN: 1471-2334</identifier><identifier>EISSN: 1471-2334</identifier><identifier>DOI: 10.1186/s12879-020-05754-5</identifier><identifier>PMID: 33430793</identifier><language>eng</language><publisher>LONDON: Springer Nature</publisher><subject>Absenteeism ; Algorithms ; Care and treatment ; Control ; Diagnosis ; Emergency communications systems ; Employee benefits ; Epidemics ; Evaluation ; France ; France - epidemiology ; Health care ; Health insurance ; Health surveillance ; Historical account ; Human health and pathology ; Humans ; Incidence ; Infection control ; Infectious Diseases ; Influenza ; Influenza, Human - epidemiology ; Influenza, Human - virology ; Insurance, Health ; Life Sciences ; Life Sciences & Biomedicine ; Management ; Methodology ; Middle Aged ; Models, Statistical ; Outbreak detection ; Outbreaks ; Public Health Surveillance - methods ; Retrospective Studies ; Santé publique et épidémiologie ; Science & Technology ; Seasonal variations ; Sensitivity and Specificity ; Sentinel Surveillance ; Sick Leave ; Statistics ; Surveillance ; Surveillance systems ; Workers ; Workplace</subject><ispartof>BMC infectious diseases, 2021-01, Vol.21 (1), p.52-52, Article 52</ispartof><rights>COPYRIGHT 2021 BioMed Central Ltd.</rights><rights>2021. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). 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M.</creatorcontrib><creatorcontrib>Dab, William</creatorcontrib><creatorcontrib>Jean, Kevin</creatorcontrib><creatorcontrib>Temime, Laura</creatorcontrib><creatorcontrib>Hocine, Mounia N.</creatorcontrib><title>Monitoring sick leave data for early detection of influenza outbreaks</title><title>BMC infectious diseases</title><addtitle>BMC INFECT DIS</addtitle><addtitle>BMC Infect Dis</addtitle><description>BackgroundWorkplace 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.MethodsSick 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.ResultsUsing sick leave data, we detected 92% of reported influenza outbreaks between 2016 and 2017, on average 5.88weeks 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 2.5weeks earlier.ConclusionSick leave surveillance could be a sensitive, specific and timely tool for detection of influenza outbreaks.</description><subject>Absenteeism</subject><subject>Algorithms</subject><subject>Care and treatment</subject><subject>Control</subject><subject>Diagnosis</subject><subject>Emergency communications systems</subject><subject>Employee benefits</subject><subject>Epidemics</subject><subject>Evaluation</subject><subject>France</subject><subject>France - epidemiology</subject><subject>Health care</subject><subject>Health insurance</subject><subject>Health surveillance</subject><subject>Historical account</subject><subject>Human health and pathology</subject><subject>Humans</subject><subject>Incidence</subject><subject>Infection control</subject><subject>Infectious Diseases</subject><subject>Influenza</subject><subject>Influenza, Human - epidemiology</subject><subject>Influenza, Human - virology</subject><subject>Insurance, Health</subject><subject>Life Sciences</subject><subject>Life Sciences & Biomedicine</subject><subject>Management</subject><subject>Methodology</subject><subject>Middle Aged</subject><subject>Models, Statistical</subject><subject>Outbreak detection</subject><subject>Outbreaks</subject><subject>Public Health Surveillance - methods</subject><subject>Retrospective Studies</subject><subject>Santé publique et épidémiologie</subject><subject>Science & Technology</subject><subject>Seasonal variations</subject><subject>Sensitivity and Specificity</subject><subject>Sentinel Surveillance</subject><subject>Sick Leave</subject><subject>Statistics</subject><subject>Surveillance</subject><subject>Surveillance systems</subject><subject>Workers</subject><subject>Workplace</subject><issn>1471-2334</issn><issn>1471-2334</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>GIZIO</sourceid><sourceid>HGBXW</sourceid><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>DOA</sourceid><recordid>eNqNkl1PFTEQhjdGI4j-AS_MJt5IzGK_P25MyAkKCYbEr9um2509FPZsse2i-OvtchA5xAvSizbTZ95OZ96qeonRHsZKvEuYKKkbRFCDuOSs4Y-qbcwkbgil7PGd81b1LKUzhLBURD-ttkqIIqnpdnXwKYw-h-jHZZ28O68HsJdQdzbbug-xBhuHq7qDDC77MNahr_3YDxOMv20dptxGsOfpefWkt0OCFzf7TvXtw8HXxWFzfPLxaLF_3DgheG6kI4BoS7peMNEybKVWjrY9FVJRiSRTiCEunO5EJ3jLtdO4JQ5BhzBIB3SnOlrrdsGemYvoVzZemWC9uQ6EuDQ2Zu8GMC1mPedKOWeBWaQ0BS17QqHvuBSUF633a62LqV1B52DM0Q4bops3oz81y3BppNSaIVoEdtcCp_fSDvePzRxDFGPGKbkkhX1z81gMPyZI2ax8cjAMdoQwJUOYlIQrKVFBX99Dz8IUx9LWmVKSK0TIP2ppy2fLSEKp0c2iZl_w0lCh8Vzi3n-osjpYeRdG6H2JbyTsbiQUJsOvvLRTSuboy-eHsyffN1myZl0MKUXobxuGkZnNbNZmNsXM5trMZp7Qq7sTuk35694CvF0DP6ENfXIeRge3GEJIIM0J4-WEZKHVw-mFz3Y2_CJMY6Z_AOC1C2k</recordid><startdate>20210111</startdate><enddate>20210111</enddate><creator>Duchemin, Tom</creator><creator>Bastard, Jonathan</creator><creator>Ante-Testard, Pearl Anne</creator><creator>Assab, Rania</creator><creator>Daouda, Oumou Salama</creator><creator>Duval, Audrey</creator><creator>Garsi, Jerome-Philippe</creator><creator>Lounissi, Radowan</creator><creator>Nekkab, Narimane</creator><creator>Neynaud, Helene</creator><creator>Smith, David R. 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M. ; Dab, William ; Jean, Kevin ; Temime, Laura ; Hocine, Mounia N.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c665t-7c2e03b2df646b41a798c3bf367837074804056c9d6d65b59c91b2c0ed01e7ce3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Absenteeism</topic><topic>Algorithms</topic><topic>Care and treatment</topic><topic>Control</topic><topic>Diagnosis</topic><topic>Emergency communications systems</topic><topic>Employee benefits</topic><topic>Epidemics</topic><topic>Evaluation</topic><topic>France</topic><topic>France - epidemiology</topic><topic>Health care</topic><topic>Health insurance</topic><topic>Health surveillance</topic><topic>Historical account</topic><topic>Human health and pathology</topic><topic>Humans</topic><topic>Incidence</topic><topic>Infection control</topic><topic>Infectious Diseases</topic><topic>Influenza</topic><topic>Influenza, Human - epidemiology</topic><topic>Influenza, Human - virology</topic><topic>Insurance, Health</topic><topic>Life Sciences</topic><topic>Life Sciences & Biomedicine</topic><topic>Management</topic><topic>Methodology</topic><topic>Middle Aged</topic><topic>Models, Statistical</topic><topic>Outbreak detection</topic><topic>Outbreaks</topic><topic>Public Health Surveillance - methods</topic><topic>Retrospective Studies</topic><topic>Santé publique et épidémiologie</topic><topic>Science & Technology</topic><topic>Seasonal variations</topic><topic>Sensitivity and Specificity</topic><topic>Sentinel Surveillance</topic><topic>Sick Leave</topic><topic>Statistics</topic><topic>Surveillance</topic><topic>Surveillance systems</topic><topic>Workers</topic><topic>Workplace</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Duchemin, Tom</creatorcontrib><creatorcontrib>Bastard, Jonathan</creatorcontrib><creatorcontrib>Ante-Testard, Pearl Anne</creatorcontrib><creatorcontrib>Assab, Rania</creatorcontrib><creatorcontrib>Daouda, Oumou Salama</creatorcontrib><creatorcontrib>Duval, Audrey</creatorcontrib><creatorcontrib>Garsi, Jerome-Philippe</creatorcontrib><creatorcontrib>Lounissi, Radowan</creatorcontrib><creatorcontrib>Nekkab, Narimane</creatorcontrib><creatorcontrib>Neynaud, Helene</creatorcontrib><creatorcontrib>Smith, David R. M.</creatorcontrib><creatorcontrib>Dab, William</creatorcontrib><creatorcontrib>Jean, Kevin</creatorcontrib><creatorcontrib>Temime, Laura</creatorcontrib><creatorcontrib>Hocine, Mounia N.</creatorcontrib><collection>Web of Knowledge</collection><collection>Web of Science Core Collection</collection><collection>Science Citation Index Expanded</collection><collection>Social Sciences Citation Index</collection><collection>Web of Science Primary (SCIE, SSCI & AHCI)</collection><collection>Web of Science - Social Sciences Citation Index – 2021</collection><collection>Web of Science - Science Citation Index Expanded - 2021</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Health and Safety Science Abstracts (Full archive)</collection><collection>Virology and AIDS Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Public Health Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>Coronavirus Research Database</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>BMC infectious diseases</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Duchemin, Tom</au><au>Bastard, Jonathan</au><au>Ante-Testard, Pearl Anne</au><au>Assab, Rania</au><au>Daouda, Oumou Salama</au><au>Duval, Audrey</au><au>Garsi, Jerome-Philippe</au><au>Lounissi, Radowan</au><au>Nekkab, Narimane</au><au>Neynaud, Helene</au><au>Smith, David R. M.</au><au>Dab, William</au><au>Jean, Kevin</au><au>Temime, Laura</au><au>Hocine, Mounia N.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Monitoring sick leave data for early detection of influenza outbreaks</atitle><jtitle>BMC infectious diseases</jtitle><stitle>BMC INFECT DIS</stitle><addtitle>BMC Infect Dis</addtitle><date>2021-01-11</date><risdate>2021</risdate><volume>21</volume><issue>1</issue><spage>52</spage><epage>52</epage><pages>52-52</pages><artnum>52</artnum><issn>1471-2334</issn><eissn>1471-2334</eissn><abstract>BackgroundWorkplace 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.MethodsSick 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.ResultsUsing sick leave data, we detected 92% of reported influenza outbreaks between 2016 and 2017, on average 5.88weeks 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 2.5weeks earlier.ConclusionSick leave surveillance could be a sensitive, specific and timely tool for detection of influenza outbreaks.</abstract><cop>LONDON</cop><pub>Springer Nature</pub><pmid>33430793</pmid><doi>10.1186/s12879-020-05754-5</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0002-8850-5403</orcidid><orcidid>https://orcid.org/0000-0002-9131-8705</orcidid><orcidid>https://orcid.org/0000-0002-7330-4262</orcidid><orcidid>https://orcid.org/0000-0001-6462-7185</orcidid><orcidid>https://orcid.org/0000-0001-8531-1614</orcidid><orcidid>https://orcid.org/0000-0003-0942-7441</orcidid><orcidid>https://orcid.org/0000-0001-7778-7137</orcidid><oa>free_for_read</oa></addata></record> |
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| language | eng |
| recordid | cdi_webofscience_primary_000609524500007CitationCount |
| source | MEDLINE; DOAJ Directory of Open Access Journals; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; SpringerNature Journals; PubMed Central Open Access; Web of Science - Science Citation Index Expanded - 2021<img src="https://exlibris-pub.s3.amazonaws.com/fromwos-v2.jpg" />; PubMed Central; Web of Science - Social Sciences Citation Index – 2021<img src="https://exlibris-pub.s3.amazonaws.com/fromwos-v2.jpg" />; Springer Nature OA/Free Journals |
| subjects | Absenteeism Algorithms Care and treatment Control Diagnosis Emergency communications systems Employee benefits Epidemics Evaluation France France - epidemiology Health care Health insurance Health surveillance Historical account Human health and pathology Humans Incidence Infection control Infectious Diseases Influenza Influenza, Human - epidemiology Influenza, Human - virology Insurance, Health Life Sciences Life Sciences & Biomedicine Management Methodology Middle Aged Models, Statistical Outbreak detection Outbreaks Public Health Surveillance - methods Retrospective Studies Santé publique et épidémiologie Science & Technology Seasonal variations Sensitivity and Specificity Sentinel Surveillance Sick Leave Statistics Surveillance Surveillance systems Workers Workplace |
| title | Monitoring sick leave data for early detection of influenza outbreaks |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-04T17%3A00%3A02IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_webof&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Monitoring%20sick%20leave%20data%20for%20early%20detection%20of%20influenza%20outbreaks&rft.jtitle=BMC%20infectious%20diseases&rft.au=Duchemin,%20Tom&rft.date=2021-01-11&rft.volume=21&rft.issue=1&rft.spage=52&rft.epage=52&rft.pages=52-52&rft.artnum=52&rft.issn=1471-2334&rft.eissn=1471-2334&rft_id=info:doi/10.1186/s12879-020-05754-5&rft_dat=%3Cgale_webof%3EA653676913%3C/gale_webof%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2478758022&rft_id=info:pmid/33430793&rft_galeid=A653676913&rft_doaj_id=oai_doaj_org_article_b14f5588ccae4a0893e97f23efd57635&rfr_iscdi=true |