scholarly journals Estimating the undetected burden of influenza hospitalizations in children

2006 ◽  
Vol 135 (6) ◽  
pp. 951-958 ◽  
Author(s):  
C. G. GRIJALVA ◽  
G. A. WEINBERG ◽  
N. M. BENNETT ◽  
M. A. STAAT ◽  
A. S. CRAIG ◽  
...  

SUMMARYDuring the 2004–2005 influenza season two independent influenza surveillance systems operated simultaneously in three United States counties. The New Vaccine Surveillance Network (NVSN) prospectively enrolled children hospitalized for respiratory symptoms/fever and tested them using culture and RT–PCR. The Emerging Infections Program (EIP) and a similar clinical-laboratory surveillance system identified hospitalized children who had positive influenza tests obtained as part of their usual medical care. Using data from these systems, we applied capture–recapture analyses to estimate the burden of influenza related-hospitalizations in children aged <5 years. During the 2004–2005 influenza season the influenza-related hospitalization rate estimated by capture–recapture analysis was 8·6/10 000 children aged <5 years. When compared to this estimate, the sensitivity of the prospective surveillance system was 69% and the sensitivity of the clinical-laboratory based system was 39%. In the face of limited resources and an increasing need for influenza surveillance, capture–recapture analysis provides better estimates than either system alone.

Author(s):  
Andrew Pierce ◽  
Margaret Haworth-Brockman ◽  
Diana Marin ◽  
Zulma V. Rueda ◽  
Yoav Keynan

Abstract Objectives Seasonal influenza is an acute respiratory infection that presents a significant annual burden to Canadians and the Canadian healthcare system. Social distancing measures that were implemented to control the 2019–2020 novel coronavirus outbreak were investigated for their ability to lessen the incident cases of seasonal influenza. Methods We conducted an ecological study using data from Canada’s national influenza surveillance system to investigate whether social distancing measures to control COVID-19 reduced the incident cases of seasonal influenza. Data taken from three separate time frames facilitated analysis of the 2019–2020 influenza season prior to, during, and following the implementation of COVID-19-related measures and enabled comparisons with the same time periods during three preceding flu seasons. The incidence, which referred to the number of laboratory-confirmed cases of specific influenza strains, was of primary focus. Further analysis determined the number of new laboratory-confirmed influenza or influenza-like illness outbreaks. Results Our results indicate a premature end to the 2019–2020 influenza season, with significantly fewer cases and outbreaks being recorded following the enactment of many COVID-19 social distancing policies. The incidence of influenza strains A (H3N2), A (unsubtyped), and B were all significantly lower at the tail end of the 2019–2020 influenza season as compared with preceding seasons (p = 0.0003, p = 0.0007, p = 0.0019). Conclusion Specific social distancing measures and behaviours may serve as effective tools to limit the spread of influenza transmission moving forward, as they become more familiar.


2020 ◽  
Author(s):  
Andrew Pierce ◽  
Margaret Haworth-Brockman ◽  
Diana Marin ◽  
Zulma V Rueda ◽  
Yoav Keynan

Abstract Objectives: Seasonal influenza is an acute respiratory infection that presents a significant annual burden to Canadians and the Canadian health care system. Social distancing measures that were implemented to control the novel coronavirus outbreak were also investigated for their ability to lessen the incidence of seasonal influenza.Methods: We conducted an ecological study using data from Canada’s national influenza surveillance system to investigate whether social distancing measures to control COVID-19 reduced the incidence of seasonal influenza. Data taken from three separate time frames facilitated analysis of the 2019-20 influenza season prior to, during, and following the implementation of COVID-19 related measures and enabled comparisons to the same time periods during three preceding flu seasons. The incidence of specific influenza strains was of primary focus. Further analysis was performed to determine the number of new laboratory-confirmed influenza or influenza like illness outbreaks.Results: Our results indicate a premature end to the 2019-20 influenza season, with a significantly fewer number of cases and outbreaks being recorded following the enactment of many COVID-19 social distancing polices. The incidence of influenza strains A (H3N2), A (unsubtyped), and B were all significantly lower at the tail-end of the 2019-20 influenza season, compared with preceding seasons.Conclusion: Specific social distancing measures and behaviours may serve as effective tools to limit the spread of influenza transmission moving forward, as they become more familiar.


2020 ◽  
Author(s):  
HeeKyung Choi ◽  
Won Suk Choi ◽  
Euna Han

BACKGROUND Influenza is an important public health concern. A national surveillance system that easily and rapidly detects influenza epidemics is lacking. OBJECTIVE We assumed that the rate of influenza-like illness (ILI) related-claims is similar to the current ILI surveillance system. METHODS We used the Health Insurance Review and Assessment Service-National Patient Samples (HIRA-NPS), 2014-2018. We defined ILI-related claims as outpatient claims that contain both antipyretic and antitussive agents and calculated the weekly rate of ILI-related claims. We compared ILI-related claims and weekly ILI rates from clinical sentinel surveillance data. RESULTS We observed a strong correlation between the two surveillance systems each season. The absolute thresholds for the four-years were 84.64 and 86.19 cases claims per 1,000 claims for claims data and 12.27 and 16.82 per 1,000 patients for sentinel data (Figure 5). Both the claims and sentinel data surpassed the epidemic thresholds each season. The peak epidemic in the claims data was reached one to two weeks later than in the sentinel data. The epidemic patterns were more similar in the 2016-2017 and 2017-2018 seasons than the 2014-2015 and 2015-2016 seasons. CONCLUSIONS Based on hospital reports, ILI-related claims rates were similar to the ILI surveillance system. ILI claims data can be loaded to a drug utilization review system in Korea to make an influenza surveillance system.


2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Ashlynn Daughton ◽  
Alina Deshpande

Because of the potential threats flu viruses pose, the United States, like many developed countries, has a very well established flu surveillance system consisting of 10 components collecting laboratory data, mortality data, hospitalization data and sentinel outpatient care data. Currently, this surveillance system is estimated to lag behind the actual seasonal outbreak by one to two weeks. As new data streams come online, it is important to understand what added benefit they bring to the flu surveillance system complex. For data streams to be effective, they should provide data in a more timely fashion or provide additional data that current surveillance systems cannot provide. Two multiplexed diagnostic tools designed to test syndromically relevant pathogens and wirelessly upload data for rapid integration and interpretation were evaluated to see how they fit into the influenza surveillance scheme in California.


2007 ◽  
Vol 12 (4) ◽  
pp. 3-4 ◽  
Author(s):  
A Meijer ◽  
A Lackenby ◽  
A Hay ◽  
M Zambon

Due to the influenza pandemic threat, many countries are stockpiling antivirals in the hope of limiting the impact of a future pandemic virus. Since resistance to antiviral drugs would probably significantly alter the effectiveness of antivirals, surveillance programmes to monitor the emergence of resistance are of considerable importance. During the 2006/2007 influenza season, an inventory was conducted by the European Surveillance Network for Vigilance against Viral Resistance (VIRGIL) in collaboration with the European Influenza Surveillance Scheme (EISS) to evaluate antiviral susceptibility testing by the National Influenza Reference Laboratories (NIRL) in relation to the national antiviral stockpile in 30 European countries that are members of EISS. All countries except Ukraine had a stockpile of the neuraminidase inhibitor (NAI) oseltamivir. Additionally, four countries had a stockpile of the NAI zanamivir and three of the M2 ion channel inhibitor rimantadine. Of 29 countries with a NAI stockpile, six countries'; NIRLs could determine virus susceptibility by 50% inhibitory concentration (IC50) and in 13 countries it could be done by sequencing. Only in one of the three countries with a rimantadine stockpile could the NIRL determine virus susceptibility, by sequencing only. However, including the 18 countries that had plans to introduce or extend antiviral susceptibility testing, the NIRLs of 21 of the 29 countries with a stockpile would be capable of susceptibility testing appropriate to the stockpiled drug by the end of the 2007/2008 influenza season. Although most European countries in this study have stockpiles of influenza antivirals, susceptibility surveillance capability by the NIRLs appropriate to the stockpiled antivirals is limited.


2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Tao Tao ◽  
Qi Zhao ◽  
Jun Zong ◽  
Xue Li ◽  
Vinod Diwan ◽  
...  

This study estimated the early warning timeliness of a chief complaint-based syndromic surveillance system towards seasonal influenza epidemics. Findings showed that the timliness of ILI data sources changed across two influenza epidemic seasons. ILI reported from different levels of health facilities and patient groups showed distinct timeliness towards influenza epidemics indicated by virus positive rate (VPR) from National Influenza Surveillance Network. The changes of dominant strains, clinical manifestations, population groups affected in different influenza seasons might account for this inconsistency.


Author(s):  
Folajimi. O. Shorunke ◽  
Aisha Usman ◽  
Tade Adeniyi Olanrewaju ◽  
Ndadilnasiya Endie Waziri ◽  
S. N. Grace

Background: In 2019, two Highly pathogenic avian influenza (HPAI) A(H5N8) outbreaks in poultry establishments in Bulgaria, two of wild birds in Denmark and one low pathogenic avian influenza (LPAI) A(H5N3) in captive birds in the Netherlands were reported. Nigeria recorded the first outbreak of Highly Pathogenic Avian Influenza (HPAI) in February 2006 in a commercial poultry farm. Nigerian Pandemic Preparedness and Action Plan for Avian Influenza were then used to respond. Although influenza sentinel surveillance has been established in several African countries including Nigeria, data about the performance of established surveillance systems are limited on the continent. We described the avian influenza (AI) surveillance system in Ogun State, accessed veterinary health workers and farmers knowledge, evaluated all its attributes and made recommendations to improve the AI surveillance system. Methods: We adopted 2001 CDC Updated Guidelines for Evaluating Public Health Surveillance Systems. We reviewed and analyzed passive surveillance data from Ogun State Ministry of Agric, key informant interviews were conducted for relevant stakeholders at the state level and Local Government divisional veterinary clinics and farms to obtain additional information on the operations of the system.


2009 ◽  
Vol 14 (32) ◽  
Author(s):  
H Uphoff ◽  
S Geis ◽  
A Grüber ◽  
A M Hauri

For the next influenza season (winter 2009-10) the relative contributions to virus circulation and influenza-associated morbidity of the seasonal influenza viruses A(H3N2), A(H1N1) and B, and the new influenza A(H1N1)v are still unknown. We estimated the chances of seasonal influenza to circulate during the upcoming season using data of the German influenza sentinel scheme from 1992 to 2009. We calculated type and subtype-specific indices for past exposure and the corresponding morbidity indices for each season. For the upcoming season 2009-10 our model suggests that it is unlikely that influenza A(H3N2) will circulate with more than a low intensity, seasonal A(H1N1) with more than a low to moderate intensity, and influenza B with more than a low to median intensity. The probability of a competitive circulation of seasonal influenza A with the new A(H1N1)v is low, increasing the chance for the latter to dominate the next influenza season in Germany.


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.


Author(s):  
Danielle Sharpe ◽  
Richard Hopkins ◽  
Robert L. Cook ◽  
Catherine W. Striley

ObjectiveTo comparatively analyze Google, Twitter, and Wikipedia byevaluating how well change points detected in each web-based sourcecorrespond to change points detected in CDC ILI data.IntroductionTraditional influenza surveillance relies on reports of influenza-like illness (ILI) by healthcare providers, capturing individualswho seek medical care and missing those who may search, post,and tweet about their illnesses instead. Existing research has shownsome promise of using data from Google, Twitter, and Wikipediafor influenza surveillance, but with conflicting findings, studies haveonly evaluated these web-based sources individually or dually withoutcomparing all three of them1-5. A comparative analysis of all threeweb-based sources is needed to know which of the web-based sourcesperforms best in order to be considered to complement traditionalmethods.MethodsWe collected publicly available, de-identified data from the CDCILINet system, Google Flu Trends, HealthTweets.org, and Wikipediafor the 2012-2015 influenza seasons. Bayesian change point analysiswas the method used to detect change points, or seasonal changes,in each of the web-data sources for comparison to change pointsin CDC ILI data. All analyses was conducted using the R package‘bcp’ v4.0.0 in RStudio v0.99.484. Sensitivity and positive predictivevalues (PPV) were then calculated.ResultsDuring the 2012-2015 influenza seasons, a high sensitivity of 92%was found for Google, while the PPV for Google was 85%. A lowsensitivity of 50% was found for Twitter; a low PPV of 43% wasfound for Twitter also. Wikipedia had the lowest sensitivity of 33%and lowest PPV of 40%.ConclusionsGoogle had the best combination of sensitivity and PPV indetecting change points that corresponded with change points found inCDC data. Overall, change points in Google, Twitter, and Wikipediadata occasionally aligned well with change points captured in CDCILI data, yet these sources did not detect all changes in CDC data,which could indicate limitations of the web-based data or signify thatthe Bayesian method is not adequately sensitive. These three web-based sources need to be further studied and compared using otherstatistical methods before being incorporated as surveillance data tocomplement traditional systems.Figure 1. Detection of change points, 2012-2013 influenza seasonFigure 2. Detection of change points, 2013-2014 influenza seasonFigure 3. Detection of change points, 2014-2015 influenza season


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