Improving regional influenza surveillance through a combination of automated outbreak detection methods: the 2015/16 season in France.
10 Aug 2017-Eurosurveillance (European Centre for Disease Prevention and Control)-Vol. 22, Iss: 32, pp 30593
TL;DR: The introduction of a pre-epidemic alert level to better anticipate future outbreaks, the regionalisation of surveillance so that healthcare structures can be informed of the arrival of epidemics in their region, the standardised use of data sources and statistical methods across regions are improved.
Abstract: The 2014/15 influenza epidemic caused a work overload for healthcare facilities in France. The French national public health agency announced the start of the epidemic - based on indicators aggregated at the national level - too late for many hospitals to prepare. It was therefore decided to improve the influenza alert procedure through (i) the introduction of a pre-epidemic alert level to better anticipate future outbreaks, (ii) the regionalisation of surveillance so that healthcare structures can be informed of the arrival of epidemics in their region, (iii) the standardised use of data sources and statistical methods across regions. A web application was developed to deliver statistical results of three outbreak detection methods applied to three surveillance data sources: emergency departments, emergency general practitioners and sentinel general practitioners. This application was used throughout the 2015/16 influenza season by the epidemiologists of the headquarters and regional units of the French national public health agency. It allowed them to signal the first influenza epidemic alert in week 2016-W03, in Brittany, with 11 other regions in pre-epidemic alert. This application received positive feedback from users and was pivotal for coordinating surveillance across the agency's regional units.
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01 Jan 2016
TL;DR: The modern applied statistics with s is universally compatible with any devices to read, and is available in the digital library an online access to it is set as public so you can download it instantly.
Abstract: Thank you very much for downloading modern applied statistics with s. As you may know, people have search hundreds times for their favorite readings like this modern applied statistics with s, but end up in harmful downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some harmful virus inside their laptop. modern applied statistics with s is available in our digital library an online access to it is set as public so you can download it instantly. Our digital library saves in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the modern applied statistics with s is universally compatible with any devices to read.
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TL;DR: The objective was to describe the characteristics and severity of influenza hospitalizations by age‐group and by season between 2012 and 2017.
Abstract: Background Estimating the global burden of influenza hospitalizations is required to allocate resources and assess interventions that aim to prevent severe influenza. In France, the current routine influenza surveillance system does not fully measure the burden of severe influenza cases. The objective was to describe the characteristics and severity of influenza hospitalizations by age-group and by season between 2012 and 2017. Methods All hospitalizations with a diagnosis of influenza in metropolitan France between July 2012 and June 2017 were extracted from the French national hospital discharge database (PMSI). For each season, the total number of influenza hospitalizations, admissions to intensive care units (ICU), proportion of deaths, lengths of stay, and distribution in diagnosis-related groups were described by age-group. Results Over the five seasons, 91 255 hospitalizations with a diagnosis of influenza were identified. The average influenza hospitalization rate varied from 13/100 000 in 2013-2014 to 46/100 000 in 2016-2017. A high rate was observed in elderlies during the 2014-2015 and 2016-2017 seasons, dominated by A(H3N2) virus. The youngest were impacted in 2015-2016, dominated by B/Victoria virus. The proportion of influenza hospitalizations with ICU admission was 10%, and was higher in age-group 40-79 years. The proportion of deaths and length of stay increased with age. Conclusions The description of influenza hospitalizations recorded in the PMSI give key information on the burden of severe influenza in France. Analyses of these data annually is valuable in order to document the severity of influenza hospitalizations by age-group and according to the circulating influenza viruses.
28 citations
TL;DR: A large increase in the number of acute gastroenteritis and vomiting visits, both in emergency departments and in emergency general practitioners’ associations providing house-calls and an unusual number of food-borne events suspected to be linked to the consumption of raw shellfish are reported through the mandatory reporting surveillance system.
Abstract: On 27 December 2019, the French Public Health Agency identified a large increase in the number of acute gastroenteritis and vomiting visits, both in emergency departments and in emergency general practitioners' associations providing house-calls. In parallel, on 26 and 27 December, an unusual number of food-borne events suspected to be linked to the consumption of raw shellfish were reported through the mandatory reporting surveillance system. This paper describes these concomitant outbreaks and the investigations' results.
13 citations
Cites background from "Improving regional influenza survei..."
...The system allows for monitoring of long-term disease trends and provides early warning of seasonal or unexpected outbreaks [3-5]....
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01 Nov 2020
TL;DR: An important future challenge remains integrating SARI surveillance into existing hospital programs in order to make surveillance data valuable for public health, as well as hospital quality of care management and individual patient care.
Abstract: The 2009 influenza A (H1N1) pandemic prompted the World Health Organization (WHO) to recommend countries to establish a national severe acute respiratory infections (SARI) surveillance system for preparedness and emergency response. However, setting up or maintaining a robust SARI surveillance system has been challenging. Similar to other countries, surveillance data on hospitalisations for SARI in the Netherlands are still limited, in contrast to the robust surveillance data in primary care. The objective of this narrative review is to provide an overview, evaluation, and challenges of already available surveillance systems or datasets in the Netherlands, which might be used for near real-time surveillance of severe respiratory infections. Seven available surveillance systems or datasets in the Netherlands were reviewed. The evaluation criteria, including data quality, timeliness, representativeness, simplicity, flexibility, acceptability and stability were based on United States Centers for Disease Control and Prevention (CDC) and European Centre for Disease Prevention and Control (ECDC) guidelines for public health surveillance. We added sustainability as additional evaluation criterion. The best evaluated surveillance system or dataset currently available for SARI surveillance is crude mortality monitoring, although it lacks specificity. In contrast to influenza-like illness (ILI) in primary care, there is currently no gold standard for SARI surveillance in the Netherlands. Based on our experience with sentinel SARI surveillance, a fully or semi-automated, passive surveillance system seems most suited for a sustainable SARI surveillance system. An important future challenge remains integrating SARI surveillance into existing hospital programs in order to make surveillance data valuable for public health, as well as hospital quality of care management and individual patient care.
12 citations
TL;DR: In France, the availability of a high‐quality data set from the Oscour® surveillance network, covering 92% of hospital emergency department (ED) visits, offers new opportunities for disease mapping.
Abstract: BACKGROUND Maps of influenza activity are important tools to monitor influenza epidemics and inform policymakers. In France, the availability of a high-quality data set from the Oscour® surveillance network, covering 92% of hospital emergency department (ED) visits, offers new opportunities for disease mapping. Traditional geostatistical mapping methods such as Kriging ignore underlying population sizes, are not suited to non-Gaussian data and do not account for uncertainty in parameter estimates. OBJECTIVE Our objective was to create reliable weekly interpolated maps of influenza activity in the ED setting, to inform Sante publique France (the French national public health agency) and local healthcare authorities. METHODS We used Oscour® data of ED visits covering the 2016-2017 influenza season. We developed a Bayesian model-based geostatistical approach, a class of generalized linear mixed models, with a multivariate normal random field as a spatially autocorrelated random effect. Using R-INLA, we developed an algorithm to create maps of the proportion of influenza-coded cases among all coded visits. We compared our results with maps obtained by Kriging. RESULTS Over the study period, 45 565 (0.82%) visits were coded as influenza cases. Maps resulting from the model are presented for each week, displaying the posterior mean of the influenza proportion and its associated uncertainty. Our model performed better than Kriging. CONCLUSIONS Our model allows producing smoothed maps where the random noise has been properly removed to reveal the spatial risk surface. The algorithm was incorporated into the national surveillance system to produce maps in real time and could be applied to other diseases.
8 citations
References
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TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
Abstract: Copyright (©) 1999–2012 R Foundation for Statistical Computing. Permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and this permission notice are preserved on all copies. Permission is granted to copy and distribute modified versions of this manual under the conditions for verbatim copying, provided that the entire resulting derived work is distributed under the terms of a permission notice identical to this one. Permission is granted to copy and distribute translations of this manual into another language, under the above conditions for modified versions, except that this permission notice may be stated in a translation approved by the R Core Team.
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"Improving regional influenza survei..." refers methods in this paper
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TL;DR: A guide to using S environments to perform statistical analyses providing both an introduction to the use of S and a course in modern statistical methods.
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18,346 citations
01 Jan 2016
TL;DR: The modern applied statistics with s is universally compatible with any devices to read, and is available in the digital library an online access to it is set as public so you can download it instantly.
Abstract: Thank you very much for downloading modern applied statistics with s. As you may know, people have search hundreds times for their favorite readings like this modern applied statistics with s, but end up in harmful downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some harmful virus inside their laptop. modern applied statistics with s is available in our digital library an online access to it is set as public so you can download it instantly. Our digital library saves in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Kindly say, the modern applied statistics with s is universally compatible with any devices to read.
5,249 citations
"Improving regional influenza survei..." refers methods in this paper
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579 citations