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Lévano DC, López SE, Gherasim A, Domínguez JO, Rico MTD, Gómez MG. The Temporary Incapacity (TI) register as a complementary system to traditional epidemiological surveillance during the COVID-19 pandemic in Spain. PLoS One 2024; 19:e0301344. [PMID: 38768237 PMCID: PMC11104667 DOI: 10.1371/journal.pone.0301344] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Accepted: 03/13/2024] [Indexed: 05/22/2024] Open
Abstract
INTRODUCTION During the COVID-19 pandemic, a set of social measures were adopted for the preservation of business activity and the protection of workers. One of these measures was issuing the Temporary Disability (TD) for COVID-19 cases, close contacts, and especially vulnerable workers. OBJETIVE This study analyzes whether the TD registry could be used as a complementary source to traditional epidemiological surveillance. METHODS A longitudinal study of time series was carried out with a cross-correlation analysis of TD and COVID-19 cases reported to the National Epidemiological Surveillance Network (RENAVE). The analysis included six pandemic waves between 10/03/2020 and 31/12/2021 in Spain. Cross-correlation coefficients (r) were calculated using a time lag of -14 days. RESULTS During the study period, 2,253,573 TD processes were recorded in Spain and 4,894,802 COVID-19 cases were reported to RENAVE. Significant positive correlations were observed at time lags of -7, -10, and -14, indicating that TD notification preceded RENAVE notification. In the first and sixth pandemic waves, TD notification preceded RENAVE by 12 and 7 days, respectively. Negative correlations between the two series were observed in the second and fourth waves, coinciding with a lower number of reported cases. In the third and fifth waves, TD notification also preceded RENAVE (lags -1, -5 and -14, -7, respectively). CONCLUSIONS The results confirm the usefulness of TD registry as a complementary system to traditional epidemiological surveillance in Spain, by detecting COVID-19 cases in the 7, 10, and 14 days prior. A better positive correlation is observed in waves where more cases were reported.
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Affiliation(s)
- Dante Culqui Lévano
- Subdirectorate General for Environmental Health and Occupational Health, Directorate General for Public Health, Ministry of Health, Madrid, Spain
| | - Sofía Escalona López
- Subdirectorate General for Environmental Health and Occupational Health, Directorate General for Public Health, Ministry of Health, Madrid, Spain
| | - Alín Gherasim
- Subdirectorate General for Environmental Health and Occupational Health, Directorate General for Public Health, Ministry of Health, Madrid, Spain
| | - Jesús Oliva Domínguez
- Subdirectorate General for Environmental Health and Occupational Health, Directorate General for Public Health, Ministry of Health, Madrid, Spain
| | - María Teresa Disdier Rico
- Subdirectorate General for Environmental Health and Occupational Health, Directorate General for Public Health, Ministry of Health, Madrid, Spain
| | - Montserrat García Gómez
- Subdirectorate General for Environmental Health and Occupational Health, Directorate General for Public Health, Ministry of Health, Madrid, Spain
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2
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Duchemin T, Bastard J, Ante-Testard PA, Assab R, Daouda OS, Duval A, Garsi JP, Lounissi R, Nekkab N, Neynaud H, Smith DRM, Dab W, Jean K, Temime L, Hocine MN. Monitoring sick leave data for early detection of influenza outbreaks. BMC Infect Dis 2021; 21:52. [PMID: 33430793 PMCID: PMC7799403 DOI: 10.1186/s12879-020-05754-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Accepted: 12/28/2020] [Indexed: 12/03/2022] Open
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. Supplementary Information The online version contains supplementary material available at 10.1186/s12879-020-05754-5.
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Affiliation(s)
- Tom Duchemin
- MESuRS laboratory, Conservatoire National des Arts et Métiers, 292 Rue Saint-Martin, 75003, Paris, France. .,Malakoff Humanis, 21 Rue Laffitte, 75009, Paris, France.
| | - Jonathan Bastard
- MESuRS laboratory, Conservatoire National des Arts et Métiers, 292 Rue Saint-Martin, 75003, Paris, France.,Institut Pasteur, Epidemiology and Modelling of Antibiotic Evasion (EMAE), Paris, France.,PACRI unit, Conservatoire National des Arts et Métiers, Institut Pasteur, Paris, France.,Université Paris-Saclay, UVSQ, Inserm, CESP, Anti-infective evasion and pharmacoepidemiology team, Montigny-Le-Bretonneux, France
| | - Pearl Anne Ante-Testard
- MESuRS laboratory, Conservatoire National des Arts et Métiers, 292 Rue Saint-Martin, 75003, Paris, France.,PACRI unit, Conservatoire National des Arts et Métiers, Institut Pasteur, Paris, France
| | - Rania Assab
- MESuRS laboratory, Conservatoire National des Arts et Métiers, 292 Rue Saint-Martin, 75003, Paris, France
| | - Oumou Salama Daouda
- MESuRS laboratory, Conservatoire National des Arts et Métiers, 292 Rue Saint-Martin, 75003, Paris, France
| | - Audrey Duval
- MESuRS laboratory, Conservatoire National des Arts et Métiers, 292 Rue Saint-Martin, 75003, Paris, France.,Institut Pasteur, Epidemiology and Modelling of Antibiotic Evasion (EMAE), Paris, France.,Université Paris-Saclay, UVSQ, Inserm, CESP, Anti-infective evasion and pharmacoepidemiology team, Montigny-Le-Bretonneux, France.,Biodiversity and Epidemiology of Bacterial Pathogens, Institut Pasteur, Paris, France
| | - Jérôme-Philippe Garsi
- MESuRS laboratory, Conservatoire National des Arts et Métiers, 292 Rue Saint-Martin, 75003, Paris, France
| | | | - Narimane Nekkab
- MESuRS laboratory, Conservatoire National des Arts et Métiers, 292 Rue Saint-Martin, 75003, Paris, France.,Malaria: Parasites and Hosts, Department of Parasites and Insect Vectors, Institut Pasteur, Paris, France
| | - Helene Neynaud
- MESuRS laboratory, Conservatoire National des Arts et Métiers, 292 Rue Saint-Martin, 75003, Paris, France
| | - David R M Smith
- MESuRS laboratory, Conservatoire National des Arts et Métiers, 292 Rue Saint-Martin, 75003, Paris, France.,Institut Pasteur, Epidemiology and Modelling of Antibiotic Evasion (EMAE), Paris, France.,Université Paris-Saclay, UVSQ, Inserm, CESP, Anti-infective evasion and pharmacoepidemiology team, Montigny-Le-Bretonneux, France
| | - William Dab
- MESuRS laboratory, Conservatoire National des Arts et Métiers, 292 Rue Saint-Martin, 75003, Paris, France
| | - Kevin Jean
- MESuRS laboratory, Conservatoire National des Arts et Métiers, 292 Rue Saint-Martin, 75003, Paris, France.,PACRI unit, Conservatoire National des Arts et Métiers, Institut Pasteur, Paris, France
| | - Laura Temime
- MESuRS laboratory, Conservatoire National des Arts et Métiers, 292 Rue Saint-Martin, 75003, Paris, France.,PACRI unit, Conservatoire National des Arts et Métiers, Institut Pasteur, Paris, France
| | - Mounia N Hocine
- MESuRS laboratory, Conservatoire National des Arts et Métiers, 292 Rue Saint-Martin, 75003, Paris, France
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Alimohamadi Y, Mehri A, Janani M, Sepandi M. Aberration detection in influenza trends in Iran by using cumulative sum chart and period regression. J Taibah Univ Med Sci 2020; 15:529-535. [PMID: 33318746 PMCID: PMC7715479 DOI: 10.1016/j.jtumed.2020.09.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 09/23/2020] [Accepted: 09/27/2020] [Indexed: 11/24/2022] Open
Abstract
OBJECTIVES This study aims to determine the alarm thresholds in influenza outbreaks and aberration detection in the influenza trend in Iran by using cumulative sum control chart (CUSUM) and period regression. METHODS We used the weekly reported influenza-positive (types A and B) cases from Iran between January 2015 and November 2019. The period regression model and CUSUM chart were used as detection algorithms to figure out the alarm thresholds. RESULTS The mean ± SD and the median (95% CI) of the determined threshold per week were 34.85 ± 15.29 and 28.30 (17.67-64.62). According to the period regression, there were nine epidemic periods of influenza from 2015 to 2019. By using the CUSUM and considering a different h (h is an appropriate value that leads to the desired estimation for upper control limit) for the calculation of the upper control limit, 88, 84, 73 and 67 weeks were determined as the epidemic period. CONCLUSION According to the current study, the incidence of influenza showed a cyclic pattern and the epidemic recurred each year. Understanding this cyclical pattern can help health policymakers launch prevention programs such as vaccination during certain months of the year.
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Affiliation(s)
- Yousef Alimohamadi
- Antimicrobial Resistance Research Center, Institute of Immunology and Infectious Diseases, Iran University of Medical Sciences, Tehran, Iran
| | - Ahmad Mehri
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Majid Janani
- Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
| | - Mojtaba Sepandi
- Health Research Center, Lifestyle Institute¸ Baqiyatallah University of Medical Sciences, Tehran, Iran
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Domínguez À, Soldevila N, Torner N, Martínez A, Godoy P, Rius C, Jané M. Usefulness of Clinical Definitions of Influenza for Public Health Surveillance Purposes. Viruses 2020; 12:E95. [PMID: 31947696 PMCID: PMC7019582 DOI: 10.3390/v12010095] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 01/10/2020] [Accepted: 01/12/2020] [Indexed: 11/17/2022] Open
Abstract
This study investigated the performance of various case definitions and influenza symptoms in a primary healthcare sentinel surveillance system. A retrospective study of the clinical and epidemiological characteristics of the cases reported by a primary healthcare sentinel surveillance network for eleven years in Catalonia was conducted. Crude and adjusted diagnostic odds ratios (aDORs) and 95% confidence intervals (CIs) of the case definitions and symptoms for all weeks and epidemic weeks were estimated. The most predictive case definition for laboratory-confirmed influenza was the World Health Organization (WHO) case definition for ILI in all weeks (aDOR 2.69; 95% CI 2.42-2.99) and epidemic weeks (aDOR 2.20; 95% CI 1.90-2.54). The symptoms that were significant positive predictors for confirmed influenza were fever, cough, myalgia, headache, malaise, and sudden onset. Fever had the highest aDOR in all weeks (4.03; 95% CI 3.38-4.80) and epidemic weeks (2.78; 95% CI 2.21-3.50). All of the case definitions assessed performed better in patients with comorbidities than in those without. The performance of symptoms varied by age groups, with fever being of high value in older people, and cough being of high value in children. In patients with comorbidities, the performance of fever was the highest (aDOR 5.45; 95% CI 3.43-8.66). No differences in the performance of the case definition or symptoms in influenza cases according to virus type were found.
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Affiliation(s)
- Àngela Domínguez
- Departament de Medicina, Universitat de Barcelona, 08036 Barcelona, Spain; (À.D.); (N.T.)
- CIBER Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain; (A.M.); (P.G.); (C.R.); (M.J.)
| | - Núria Soldevila
- Departament de Medicina, Universitat de Barcelona, 08036 Barcelona, Spain; (À.D.); (N.T.)
- CIBER Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain; (A.M.); (P.G.); (C.R.); (M.J.)
| | - Núria Torner
- Departament de Medicina, Universitat de Barcelona, 08036 Barcelona, Spain; (À.D.); (N.T.)
- CIBER Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain; (A.M.); (P.G.); (C.R.); (M.J.)
- Agència de Salut Pública de Catalunya, Generalitat de Catalunya, 08005 Barcelona, Spain
| | - Ana Martínez
- CIBER Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain; (A.M.); (P.G.); (C.R.); (M.J.)
- Agència de Salut Pública de Catalunya, Generalitat de Catalunya, 08005 Barcelona, Spain
| | - Pere Godoy
- CIBER Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain; (A.M.); (P.G.); (C.R.); (M.J.)
- Agència de Salut Pública de Catalunya, Generalitat de Catalunya, 08005 Barcelona, Spain
- Institut de Recerca Biomèdica de Lleida, Universitat de Lleida, 25198 Lleida, Spain
| | - Cristina Rius
- CIBER Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain; (A.M.); (P.G.); (C.R.); (M.J.)
- Agència de Salut Pública de Barcelona, 08023 Barcelona, Spain
| | - Mireia Jané
- CIBER Epidemiología y Salud Pública (CIBERESP), 28029 Madrid, Spain; (A.M.); (P.G.); (C.R.); (M.J.)
- Agència de Salut Pública de Catalunya, Generalitat de Catalunya, 08005 Barcelona, Spain
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Souty C, Amoros P, Falchi A, Capai L, Bonmarin I, van der Werf S, Masse S, Turbelin C, Rossignol L, Vilcu A, Lévy‐Bruhl D, Lina B, Minodier L, Dorléans Y, Guerrisi C, Hanslik T, Blanchon T. Influenza epidemics observed in primary care from 1984 to 2017 in France: A decrease in epidemic size over time. Influenza Other Respir Viruses 2019; 13:148-157. [PMID: 30428158 PMCID: PMC6379635 DOI: 10.1111/irv.12620] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Revised: 09/07/2018] [Accepted: 11/06/2018] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Epidemiological analysis of past influenza epidemics remains essential to understand the evolution of the disease and optimize control and prevention strategies. Here, we aimed to use data collected by a primary care surveillance system over the last three decades to study trends in influenza epidemics and describe epidemic profiles according to circulating influenza viruses. METHODS Influenza-like illness (ILI) weekly incidences were estimated using cases reported by general practitioners participating in the French Sentinelles network, between 1984 and 2017. Influenza epidemics were detected by applying a periodic regression to this time series. Epidemic (co-)dominant influenza virus (sub)types were determined using French virology data. RESULTS During the study period, 297 607 ILI cases were reported allowing the detection of 33 influenza epidemics. On average, seasonal epidemics lasted 9 weeks and affected 4.1% of the population (95% CI 3.5; 4.7). Mean age of cases was 29 years. Epidemic size decreased over time by -66 cases per 100 000 population per season on average (95% CI -132; -0.2, P value = 0.049) and epidemic height decreased by -15 cases per 100 000 (95% CI -28; -2, P value = 0.022). Epidemic duration appeared stable over time. Epidemics were mostly dominated by A(H3N2) (n = 17, 52%), associated with larger epidemic size, higher epidemic peak and older age of cases. CONCLUSIONS The declining trend in influenza epidemic size and height over the last 33 years might be related to several factors like increased vaccine coverage, hygiene improvements or changing in influenza viruses. However, further researches are needed to assess the impact of potential contributing factors to adapt influenza plans.
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Affiliation(s)
- Cécile Souty
- Sorbonne UniversitéINSERMInstitut Pierre Louis d’Épidémiologie et de Santé Publique (IPLESP)ParisFrance
| | - Philippe Amoros
- Sorbonne UniversitéINSERMInstitut Pierre Louis d’Épidémiologie et de Santé Publique (IPLESP)ParisFrance
| | - Alessandra Falchi
- EA7310, Laboratoire de VirologieUniversité de Corse‐InsermCorteFrance
| | - Lisandru Capai
- Sorbonne UniversitéINSERMInstitut Pierre Louis d’Épidémiologie et de Santé Publique (IPLESP)ParisFrance
- EA7310, Laboratoire de VirologieUniversité de Corse‐InsermCorteFrance
| | - Isabelle Bonmarin
- Department of Infectious DiseasesSanté publique FranceSaint‐MauriceFrance
| | - Sylvie van der Werf
- Institut PasteurUnité de Génétique Moléculaire des Virus à ARNParisFrance
- Institut PasteurCentre Coordonnateur du Centre National de Référence des virus des infections respiratoires (dont la grippe)ParisFrance
- UMR CNRS 3569ParisFrance
- Université Paris DiderotSorbonne Paris CitéUnité de Génétique Moléculaire des Virus à ARNParisFrance
| | - Shirley Masse
- Sorbonne UniversitéINSERMInstitut Pierre Louis d’Épidémiologie et de Santé Publique (IPLESP)ParisFrance
- EA7310, Laboratoire de VirologieUniversité de Corse‐InsermCorteFrance
| | - Clément Turbelin
- Sorbonne UniversitéINSERMInstitut Pierre Louis d’Épidémiologie et de Santé Publique (IPLESP)ParisFrance
| | - Louise Rossignol
- Sorbonne UniversitéINSERMInstitut Pierre Louis d’Épidémiologie et de Santé Publique (IPLESP)ParisFrance
| | - Ana‐Maria Vilcu
- Sorbonne UniversitéINSERMInstitut Pierre Louis d’Épidémiologie et de Santé Publique (IPLESP)ParisFrance
| | - Daniel Lévy‐Bruhl
- Department of Infectious DiseasesSanté publique FranceSaint‐MauriceFrance
| | - Bruno Lina
- Laboratoire de VirologieHospices Civils de LyonInstitut des Agents Infectieux (IAI)Centre National de Référence des virus respiratoires (dont la grippe)Centre de Biologie et de Pathologie NordGroupement Hospitalier NordLyonFrance
- Université de LyonVirpath, CIRI, INSERM U1111CNRS UMR5308ENS Lyon, Université Claude Bernard Lyon 1LyonFrance
| | - Laëtitia Minodier
- EA7310, Laboratoire de VirologieUniversité de Corse‐InsermCorteFrance
| | - Yves Dorléans
- Sorbonne UniversitéINSERMInstitut Pierre Louis d’Épidémiologie et de Santé Publique (IPLESP)ParisFrance
| | - Caroline Guerrisi
- Sorbonne UniversitéINSERMInstitut Pierre Louis d’Épidémiologie et de Santé Publique (IPLESP)ParisFrance
| | - Thomas Hanslik
- Sorbonne UniversitéINSERMInstitut Pierre Louis d’Épidémiologie et de Santé Publique (IPLESP)ParisFrance
- Université de Versailles Saint‐Quentin‐en‐YvelinesUVSQUFR de MédecineVersaillesFrance
- Service de Médecine InterneHôpital Ambroise ParéAssistance Publique – Hôpitaux de ParisAPHPBoulogne BillancourtFrance
| | - Thierry Blanchon
- Sorbonne UniversitéINSERMInstitut Pierre Louis d’Épidémiologie et de Santé Publique (IPLESP)ParisFrance
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