Absenteeism in schools during the 2009 influenza A(H1N1) pandemic: a useful tool for early detection of influenza activity in the community?

2011 ◽  
Vol 140 (7) ◽  
pp. 1328-1336 ◽  
Author(s):  
E. O. KARA ◽  
A. J. ELLIOT ◽  
H. BAGNALL ◽  
D. G. F. FOORD ◽  
R. PNAISER ◽  
...  

SUMMARYCertain influenza outbreaks, including the 2009 influenza A(H1N1) pandemic, can predominantly affect school-age children. Therefore the use of school absenteeism data has been considered as a potential tool for providing early warning of increasing influenza activity in the community. This study retrospectively evaluates the usefulness of these data by comparing them with existing syndromic surveillance systems and laboratory data. Weekly mean percentages of absenteeism in 373 state schools (children aged 4–18 years) in Birmingham, UK, from September 2006 to September 2009, were compared with established syndromic surveillance systems including a telephone health helpline, a general practitioner sentinel network and laboratory data for influenza. Correlation coefficients were used to examine the relationship between each syndromic system. In June 2009, school absenteeism generally peaked concomitantly with the existing influenza surveillance systems in England. Weekly school absenteeism surveillance would not have detected pandemic influenza A(H1N1) earlier but daily absenteeism data and the development of baselines could improve the timeliness of the system.

2010 ◽  
Vol 15 (29) ◽  
Author(s):  
A Valdivia ◽  
J López-Alcalde ◽  
M Vicente ◽  
M Pichiule ◽  
M Ruiz ◽  
...  

The number of Internet searches has recently been used by Google to estimate the influenza incidence in the United States. We examined the correlation between the Google Flu Trends tool and sentinel networks estimates in several European countries during the 2009 influenza A(H1N1) pandemic and found a good correlation between estimates and peak incidence timing, with the highest peaks in countries where Internet is most frequently used for health-related searching. Although somehow limited, Google could be a valuable tool for syndromic surveillance.


2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Phunlerd Piyaraj ◽  
Nira Pet-hoi ◽  
Chaiyos Kunanusont ◽  
Supanee Sangiamsak ◽  
Somsak Wankijcharoen ◽  
...  

Objective: We describe the Bangkok Dusit Medical Services Surveillance System (BDMS-SS) and use of surveillance efforts for influenza as an example of surveillance capability in near real-time among a network of 20 hospitals in the Bangkok Dusit Medical Services group (BDMS).Introduction: Influenza is one of the significant causes of morbidity and mortality globally. Previous studies have demonstrated the benefit of laboratory surveillance and its capability to accurately detect influenza outbreaks earlier than syndromic surveillance.1-3 Current laboratory surveillance has an approximately 4-week lag due to laboratory test turn-around time, data collection and data analysis. As part of strengthening influenza virus surveillance in response to the 2009 influenza A (H1N1) pandemic, the real-time laboratory-based influenza surveillance system, the Bangkok Dusit Medical Services Surveillance System (BDMS-SS), was developed in 2010 by the Bangkok Health Research Center (BHRC). The primary objective of the BDMS-SS is to alert relevant stakeholders on the incidence trends of the influenza virus. Type-specific results along with patient demographic and geographic information were available to physicians and uploaded for public health awareness within 24 hours after patient nasopharyngeal swab was collected. This system advances early warning and supports better decision making during infectious disease events.2 The BDMS-SS operates all year round collecting results of all routinely tested respiratory clinical samples from participating hospitals from the largest group of private hospitals in Thailand.Methods: The BDMS has a comprehensive network of laboratory, epidemiologic, and early warning surveillance systems which represents the largest body of information from private hospitals across Thailand. Hospitals and clinical laboratories have deployed automatic reporting mechanisms since 2010 and have effectively improved timeliness of laboratory data reporting. In April 2017, the capacity of near real-time influenza surveillance in BDMS was found to have a demonstrated and sustainable capability.Results: From October 2010 to April 2017, a total of 482,789 subjects were tested and 86,110 (17.8%) cases of influenza were identified. Of those who tested positive for influenza they were aged <2 years old (4.6%), 2-4 year old (10.9%), 5-14 years old (29.8%), 15-49 years old (41.9%), 50-64 years old (8.3%) and >65 years old (3.7%). Approximately 50% of subjects were male and female. Of these, 40,552 (47.0%) were influenza type B, 31,412 (36.4%) were influenza A unspecified subtype, 6,181 (7.2%) were influenza A H1N1, 4,001 (4.6%) were influenza A H3N2, 3,835 (4.4%) were influenza A seasonal and 196 (0.4%) were respiratory syncytial virus (RSV).The number of influenza-positive specimens reported by the real-time influenza surveillance system were from week 40, 2015 to week 39, 2016. A total of 117,867 subjects were tested and 17,572 (14.91%) cases tested positive for the influenza virus (Figure 1). Based on the long-term monitoring of collected information, this system can delineate the epidemiologic pattern of circulating viruses in near real-time manner, which clearly shows annual peaks in winter dominated by influenza subtype B in 2015-1016 season. This surveillance system helps to provide near real-time reporting, enabling rapid implementation of control measures for influenza outbreaks.Conclusions: This surveillance system was the first real-time, daily reporting surveillance system to report on the largest data base of private hospitals in Thailand and provides timely reports and feedback to all stakeholders. It provides an important supplement to the routine influenza surveillance system in Thailand. This illustrates a high level of awareness and willingness among the BDMS hospital network to report emerging infectious diseases, and highlights the robust and sensitive nature of BDMS’s surveillance system. This system demonstrates the flexibility of the surveillance systems in BDMS to evaluate to emerging infectious disease and major communicable diseases. Through participation in the Thailand influenza surveillance network, BDMS can more actively collaborate with national counterparts and use its expertise to strengthen global and regional surveillance capacity in Southeast Asia, in order to secure advances for a world safe and secure from infectious disease. Furthermore, this system can be quickly adapted and used to monitor future influenzas pandemics and other major outbreaks of respiratory infectious disease, including novel pathogens.


2017 ◽  
Vol 145 (7-8) ◽  
pp. 387-393
Author(s):  
Mioljub Ristic ◽  
Vesna Stojanovic ◽  
Vesna Milosevic ◽  
Jelena Radovanov ◽  
Tihomir Dugandzija ◽  
...  

Introduction/Objective. In August 2010, World Health Organization declared the beginning of the postpandemic phase of influenza surveillance. The aim of this study was to evaluate the epidemiological and virological characteristics of influenza and correlation between the influenza occurrence and weather conditions. Methods. We used surveillance reports of influenza and laboratory data from October 2010 to May 2015. Data for the analysis were collected through sentinel surveillance of influenza-like illness (ILI), severe acute respiratory illness (SARI), acute respiratory distress syndrome, and by virological surveillance. The nasal and throat swabs from all influenza cases were performed by the PCR laboratory method. Results. During the observed period, the highest rates of ILI were registered during the 2010/11 and 2012/13 seasons, with influenza A (H1N1)pdm09 and influenza B being predominant, respectively. The highest weekly age-specific rates of ILI were registered in school-age children (ages 5?14). Out of 1,466 samples collected, 720 (49.1%) were laboratory confirmed as influenza, and influenza A virus was more frequently detected than influenza B. Among confirmed cases of influenza, participation of patients with SARI or ILI was nearly equal (46% vs. 44.1%). There was a weak correlation observed between the decrease in temperature and rainfall and the increase in influenza detection (? = -0.04214 vs. ? = -0.01545, respectively, p > 0.05). Conclusion. There is a need for continuous surveillance in order to predict seasonal trends and prepare for a timely response to influenza outbreak.


2019 ◽  
Vol 4 (4) ◽  
pp. 121
Author(s):  
Lance C. Jennings ◽  
Ian G. Barr

The anniversary of the 1918–1919 influenza pandemic has allowed a refocusing on the global burden of influenza and the importance of co-ordinated international surveillance for both seasonal influenza and the identification of control strategies for future pandemics. Since the introduction of the International Health Regulations (IHR), progress had been slow, until the emergence of the novel influenza A(H1N1)2009 virus and its global spread, which has led to the World Health Organization (WHO) developing a series of guidance documents on global influenza surveillance procedures, severity and risk assessments, and essential measurements for the determination of national pandemic responses. However, the greatest burden of disease from influenza occurs between pandemics during seasonal influenza outbreaks and epidemics. Both Australia and New Zealand utilise seasonal influenza surveillance to support national influenza awareness programs focused on seasonal influenza vaccination education and promotion. These programs also serve to promote the importance of pandemic preparedness.


2014 ◽  
Vol 143 (11) ◽  
pp. 2390-2398 ◽  
Author(s):  
T. MA ◽  
H. ENGLUND ◽  
P. BJELKMAR ◽  
A. WALLENSTEN ◽  
A. HULTH

SUMMARYAn evaluation was conducted to determine which syndromic surveillance tools complement traditional surveillance by serving as earlier indicators of influenza activity in Sweden. Web queries, medical hotline statistics, and school absenteeism data were evaluated against two traditional surveillance tools. Cross-correlation calculations utilized aggregated weekly data for all-age, nationwide activity for four influenza seasons, from 2009/2010 to 2012/2013. The surveillance tool indicative of earlier influenza activity, by way of statistical and visual evidence, was identified. The web query algorithm and medical hotline statistics performed equally well as each other and to the traditional surveillance tools. School absenteeism data were not reliable resources for influenza surveillance. Overall, the syndromic surveillance tools did not perform with enough consistency in season lead nor in earlier timing of the peak week to be considered as early indicators. They do, however, capture incident cases before they have formally entered the primary healthcare system.


2001 ◽  
Vol 6 (9) ◽  
pp. 127-135 ◽  
Author(s):  
◽  
A Mosnier ◽  
W J Paget ◽  

In countries covered by the European Influenza Surveillance Scheme (EISS), the 2000-2001 winter was marked mainly by the spread of influenza A(H1N1) viruses. Influenza B, which globally represented a minority of cases, was common later in the season and predo-minant in Great Britain, Ireland, and Portugal. Influenza activity was at its maximum during the period of January and February/March 2001 with little time lag between countries (maximum four weeks). Overall, the morbidity rates reported were much lower than for the previous season, illustrating a moderate level of influenza activity.


2011 ◽  
Vol 4 (0) ◽  
Author(s):  
Kenneth Dufault ◽  
Elizabeth Daly ◽  
Susan Bascom ◽  
Christopher Taylor ◽  
Paul Lakevicius ◽  
...  

2009 ◽  
Vol 14 (23) ◽  
Author(s):  
E D’Ortenzio ◽  
C Do ◽  
P Renault ◽  
F Weber ◽  
L Filleul

With the winter season on the southern hemisphere that starts in Réunion Island in June seasonal influenza activity usually increases shortly afterwards. The new influenza A(H1N1)v virus is rapidly spreading worldwide and may reach the island during the coming winter season. We have therefore enhanced influenza surveillance to detect the introduction of influenza A(H1N1)v, monitor its spread and impact on public health and characterise potential viral changes, particularly if seasonal influenza A(H1N1), resistant to oseltamivir, co-circulates with A(H1N1)v.


2016 ◽  
Vol 21 (16) ◽  
Author(s):  
Julita Gil Cuesta ◽  
Preben Aavitsland ◽  
Hélène Englund ◽  
Ólafur Gudlaugsson ◽  
Siri Helene Hauge ◽  
...  

During the 2009/10 influenza A(H1N1)pdm09 pandemic, the five Nordic countries adopted different approaches to pandemic vaccination. We compared pandemic vaccination strategies and severe influenza outcomes, in seasons 2009/10 and 2010/11 in these countries with similar influenza surveillance systems. We calculated the cumulative pandemic vaccination coverage in 2009/10 and cumulative incidence rates of laboratory confirmed A(H1N1)pdm09 infections, intensive care unit (ICU) admissions and deaths in 2009/10 and 2010/11. We estimated incidence risk ratios (IRR) in a Poisson regression model to compare those indicators between Denmark and the other countries. The vaccination coverage was lower in Denmark (6.1%) compared with Finland (48.2%), Iceland (44.1%), Norway (41.3%) and Sweden (60.0%). In 2009/10 Denmark had a similar cumulative incidence of A(H1N1)pdm09 ICU admissions and deaths compared with the other countries. In 2010/11 Denmark had a significantly higher cumulative incidence of A(H1N1)pdm09 ICU admissions (IRR: 2.4; 95% confidence interval (CI): 1.9–3.0) and deaths (IRR: 8.3; 95% CI: 5.1–13.5). Compared with Denmark, the other countries had higher pandemic vaccination coverage and experienced less A(H1N1)pdm09-related severe outcomes in 2010/11. Pandemic vaccination may have had an impact on severe influenza outcomes in the post-pandemic season. Surveillance of severe outcomes may be used to compare the impact of influenza between seasons and support different vaccination strategies.


2011 ◽  
Vol 16 (18) ◽  
Author(s):  
A Hulth ◽  
G Rydevik

At the Swedish Institute for Communicable Disease Control, statistical models based on queries submitted to a Swedish medical website are used as a complement to the regular influenza surveillance. The models have previously been shown to perform well for seasonal influenza. The purpose of the present study was to evaluate the performance of the statistical models in the context of the influenza A(H1N1)2009 pandemic, a period when many factors, for example the media, could have influenced people's search behaviour on the Internet and consequently the performance of the models. Our evaluation indicates consistent good reliability for the statistical models also during the pandemic. When compared to Google Flu Trends for Sweden, they were at least equivalent in terms of estimating the influenza activity, and even seemed to be more precise in estimating the peak incidence of the influenza pandemic.


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