7 results on '"Rania Assab"'
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2. Monitoring sick leave data for early detection of influenza outbreaks
- Author
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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, and Mounia N. Hocine
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Surveillance ,Influenza ,Sick-leave ,Outbreak detection ,Infectious and parasitic diseases ,RC109-216 - Abstract
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.
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- 2021
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3. Monitoring sick leave data for early detection of influenza outbreaks
- Author
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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
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Sick-leave ,MESH: Influenza, Human / epidemiology ,MESH: Public Health Surveillance / methods ,010501 environmental sciences ,01 natural sciences ,0302 clinical medicine ,[SDV.MHEP.MI]Life Sciences [q-bio]/Human health and pathology/Infectious diseases ,Absenteeism ,MESH: Sick Leave ,Medicine ,Public Health Surveillance ,MESH: Epidemics ,030212 general & internal medicine ,MESH: Incidence ,Workplace ,MESH: Sentinel Surveillance ,MESH: Workplace ,MESH: France / epidemiology ,[STAT.ME] Statistics [stat]/Methodology [stat.ME] ,Surveillance ,MESH: Middle Aged ,Incidence ,Middle Aged ,Infectious Diseases ,Sick leave ,[SDV.MHEP.MI] Life Sciences [q-bio]/Human health and pathology/Infectious diseases ,France ,Sick Leave ,0305 other medical science ,[STAT.ME]Statistics [stat]/Methodology [stat.ME] ,Research Article ,MESH: Absenteeism ,Surveillance Methods ,Early detection ,Influenza epidemics ,Primary care ,MESH: Insurance, Health ,Sensitivity and Specificity ,lcsh:Infectious and parasitic diseases ,03 medical and health sciences ,Environmental health ,Outbreak detection ,Influenza, Human ,Health insurance ,Humans ,lcsh:RC109-216 ,Epidemics ,MESH: Influenza, Human / virology ,0105 earth and related environmental sciences ,Retrospective Studies ,030505 public health ,Insurance, Health ,Models, Statistical ,MESH: Humans ,business.industry ,Outbreak ,MESH: Retrospective Studies ,Influenza ,MESH: Sensitivity and Specificity ,[SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie ,[SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie ,business ,Sentinel Surveillance ,MESH: Models, Statistical - 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.
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- 2023
- Full Text
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4. Reactive vaccination of workplaces and schools against COVID-19
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Cécile Tran Kiem, Chiara Poletto, Jonathan Roux, Vittoria Colizza, Rania Assab, Benjamin Faucher, Laura Zanetti, Daniel Lévy-Bruhl, Pierre-Yves Boëlle, and Simon Cauchemez
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Vaccination ,2019-20 coronavirus outbreak ,Coronavirus disease 2019 (COVID-19) ,business.industry ,Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) ,Environmental health ,Social distance ,Medicine ,Mass vaccination ,business - Abstract
As vaccination against COVID-19 stalls in some countries, increased accessibility and more adaptive approaches may be useful to keep the epidemic under control. Here, we study the impact of reactive vaccination targeting schools and workplaces where cases are detected, with an agent-based model accounting for COVID-19 natural history, vaccine characteristics, individuals’ demography and behaviour and social distancing. At an equal number of doses reactive vaccination produces a higher reduction in cases compared with non-reactive strategies, in the majority of scenarios. However, at high initial vaccination coverage or low incidence, few people are found to vaccinate around cases, thus the reactive strategy may be less effective than non-reactive strategies with moderate/high vaccination pace. In case of flare-ups, reactive vaccination could hinder spread if it is implemented quickly, is supported by enhanced test-trace-isolate and triggers an increased vaccine uptake. These results provide key information to plan an adaptive vaccination deployment.
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- 2021
- Full Text
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5. Agent-based modelling of reactive vaccination of workplaces and schools against COVID-19
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Benjamin Faucher, Rania Assab, Jonathan Roux, Daniel Levy-Bruhl, Cécile Tran Kiem, Simon Cauchemez, Laura Zanetti, Vittoria Colizza, Pierre-Yves Boëlle, Chiara Poletto, Institut Pierre Louis d'Epidémiologie et de Santé Publique (iPLESP), Institut National de la Santé et de la Recherche Médicale (INSERM)-Sorbonne Université (SU), Centre de Recherches sur l'Action Politique en Europe (ARENES), Université de Rennes (UR)-Institut d'Études Politiques [IEP] - Rennes-École des Hautes Études en Santé Publique [EHESP] (EHESP)-Centre National de la Recherche Scientifique (CNRS), École des Hautes Études en Santé Publique [EHESP] (EHESP), Santé publique France - French National Public Health Agency [Saint-Maurice, France], Modélisation mathématique des maladies infectieuses - Mathematical modelling of Infectious Diseases, Institut Pasteur [Paris] (IP)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Collège Doctoral, Sorbonne Université (SU), Haute Autorité de Santé [Saint-Denis La Plaine] (HAS), Tokyo Institute of Technology [Tokyo] (TITECH), We acknowledge financial support from Haute Autorité de Santé, the ANR and Fondation de France through the project NoCOV (00105995), the Municipality of Paris (https://www.paris.fr/) through the programme Emergence(s), EU H2020 grants MOOD(H2020-874850, paper MOOD 035) and RECOVER (H2020-101003589) (the contents of this publication do not necessarily reflect the views of the European Commission), the ANRS through the project EMERGEN (ANRS0151), and the Institut des Sciences duCalcul et de la Donnée., ANR-20-COVI-0070,NoCOV,Prévisions au court et moyen terme de la diffusion de COVID-19 dans la population générale française(2020), European Project: 874850,H2020-SC1-2019-Single-Stage-RTD,MOOD(2020), European Project: 101003589, H2020-SC1-PHE-CORONAVIRUS-2020,RECOVER(2020), EHESP, SCD, Prévisions au court et moyen terme de la diffusion de COVID-19 dans la population générale française - - NoCOV2020 - ANR-20-COVI-0070 - COVID-19 - VALID, MOnitoring Outbreak events for Disease surveillance in a data science context - MOOD - 874850 - INCOMING, Rapid European COVID-19 Emergency Response research - RECOVER - - H2020-SC1-PHE-CORONAVIRUS-20202020-02-14 - 2022-02-13 - 101003589 - VALID, and European Project: 874850,MOOD
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Multidisciplinary ,Schools ,Systems Analysis ,Vaccination ,General Physics and Astronomy ,COVID-19 ,General Chemistry ,General Biochemistry, Genetics and Molecular Biology ,[SDV.IMM.VAC] Life Sciences [q-bio]/Immunology/Vaccinology ,[SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie ,Humans ,[SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie ,[SDV.IMM.VAC]Life Sciences [q-bio]/Immunology/Vaccinology ,Workplace ,COVID 19 - Abstract
With vaccination against COVID-19 stalled in some countries, increasing vaccine accessibility and distribution could help keep transmission under control. Here, we study the impact of reactive vaccination targeting schools and workplaces where cases are detected, with an agent-based model accounting for COVID-19 natural history, vaccine characteristics, demographics, behavioural changes and social distancing. In most scenarios, reactive vaccination leads to a higher reduction in cases compared with non-reactive strategies using the same number of doses. The reactive strategy could however be less effective than a moderate/high pace mass vaccination program if initial vaccination coverage is high or disease incidence is low, because few people would be vaccinated around each case. In case of flare-ups, reactive vaccination could better mitigate spread if it is implemented quickly, is supported by enhanced test-trace-isolate and triggers an increased vaccine uptake. These results provide key information to plan an adaptive vaccination rollout.
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- 2021
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6. Mathematical models of infection transmission in healthcare settings: recent advances from the use of network structured data
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Rania Assab, Lulla Opatowski, Narimane Nekkab, Pascal Astagneau, Pascal Crépey, Didier Guillemot, Laura Temime, 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), Pasteur-Cnam Risques infectieux et émergents (PACRI), Institut Pasteur [Paris] (IP)-Conservatoire National des Arts et Métiers [CNAM] (CNAM), École des Hautes Études en Santé Publique [EHESP] (EHESP), Recherche en Pharmaco-épidémiologie et Recours aux Soins (REPERES), Université de Rennes (UR)-École des Hautes Études en Santé Publique [EHESP] (EHESP), Emergence des Pathologies Virales (EPV), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre de prévention des infections associées aux soins, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Unité Fonctionnelle de Santé Publique, Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Hôpital Raymond Poincaré [AP-HP], Biostatistique, Biomathématique, Pharmacoépidémiologie et Maladies Infectieuses (B2PHI), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut Pasteur [Paris] (IP)-Institut National de la Santé et de la Recherche Médicale (INSERM), The work was supported in part by the PRINCEPS program from the Sorbonne-Paris Cité University., Institut Pasteur [Paris]-Conservatoire National des Arts et Métiers [CNAM] (CNAM), Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-Université de Rennes (UNIV-RENNES)-École des Hautes Études en Santé Publique [EHESP] (EHESP), Institut National de la Santé et de la Recherche Médicale (INSERM)-Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Assistance Publique - Hôpitaux de Marseille (APHM), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut Pasteur [Paris]-Institut National de la Santé et de la Recherche Médicale (INSERM), and Crépey, Pascal
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Microbiology (medical) ,Cross infection ,Methicillin-Resistant Staphylococcus aureus ,030501 epidemiology ,law.invention ,03 medical and health sciences ,0302 clinical medicine ,law ,Medicine ,Infection transmission ,Humans ,Transmission ,030212 general & internal medicine ,Cross Infection ,Infection Control ,Hospital-acquired infections ,Data collection ,Mathematical model ,business.industry ,Network data ,Models, Theoretical ,Staphylococcal Infections ,3. Good health ,Infectious Diseases ,Transmission (mechanics) ,Risk analysis (engineering) ,[SDV.SPEE] Life Sciences [q-bio]/Santé publique et épidémiologie ,Healthcare settings ,Systematic review ,Mathematical modeling ,[SDV.SPEE]Life Sciences [q-bio]/Santé publique et épidémiologie ,Networks ,0305 other medical science ,business ,Disease transmission - Abstract
International audience; PURPOSE OF REVIEW: Mathematical modeling approaches have brought important contributions to the study of pathogen spread in healthcare settings over the last 20 years. Here, we conduct a comprehensive systematic review of mathematical models of disease transmission in healthcare settings and assess the application of contact and patient transfer network data over time and their impact on our understanding of transmission dynamics of infections.RECENT FINDINGS: Recently, with the increasing availability of data on the structure of interindividual and interinstitution networks, models incorporating this type of information have been proposed, with the aim of providing more realistic predictions of disease transmission in healthcare settings. Models incorporating realistic data on individual or facility networks often remain limited to a few settings and a few pathogens (mostly methicillin-resistant Staphylococcus aureus).SUMMARY: To respond to the objectives of creating improved infection prevention and control measures and better understanding of healthcare-associated infections transmission dynamics, further innovations in data collection and parameter estimation in modeling is required.
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- 2017
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7. The role of hand hygiene in controlling norovirus spread in nursing homes
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Laura Temime and Rania Assab
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0301 basic medicine ,medicine.medical_specialty ,media_common.quotation_subject ,030106 microbiology ,Psychological intervention ,Nursing homes ,medicine.disease_cause ,law.invention ,Disease Outbreaks ,03 medical and health sciences ,0302 clinical medicine ,Hygiene ,law ,Incidence data ,Environmental health ,Medicine ,Infection control ,Humans ,Hand Hygiene ,030212 general & internal medicine ,Intensive care medicine ,media_common ,Caliciviridae Infections ,Infection Control ,business.industry ,Norovirus ,Outbreak ,Models, Theoretical ,Gastroenteritis ,Transmission (mechanics) ,Infectious Diseases ,Mathematical modeling ,France ,business ,Research Article - Abstract
Background Norovirus, the leading cause of gastroenteritis, causes higher morbidity and mortality in nursing homes (NHs) than in the community. Hence, implementing infection control measures is crucial. However, the evidence on the effectiveness of these measures in NH settings is lacking. Using an innovative data-driven modeling approach, we assess various interventions to control norovirus spread in NHs. Methods We collected data on resident and staff characteristics and inter-human contacts in a French NH. Based on this data, we developed a stochastic compartmental model of norovirus transmission among the residents and staff of a 100-bed NH. Using this model, we investigated how the size of a 100-day norovirus outbreak changed following three interventions: increasing hand hygiene (HH) among the staff or residents and isolating symptomatic residents. Results Assuming a baseline staff HH compliance rate of 15 %, the model predicted on average 19 gastroenteritis cases over 100 days among the residents, which is consistent with published incidence data in NHs. Isolating symptomatic residents was highly effective, leading to an 88 % reduction in the predicted number of cases. The number of expected cases could also be reduced significantly by increasing HH compliance among the staff; for instance, by 75 % when assuming a 60 % HH compliance rate. While there was a linear reduction in the predicted number of cases when HH practices among residents increased, the achieved impact was less important. Conclusions This study shows that simple interventions can help control the spread of norovirus in NHs. Modeling, which has seldom been used in these settings, may be a useful tool for decision makers to design optimal and cost-effective control strategies.
- Published
- 2016
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