Clinical Predictors of Influenza in Young Children: The Limitations of “Influenza-Like Illness”

Abstract Background Influenza-like illness (ILI) definitions have been infrequently studied in young children. Despite this, clinical definitions of ILI play an important role in influenza surveillance. This study aims to identify clinical predictors of influenza infection in children ≤5 years old from which age-specific ILI definitions are then constructed. Methods Children aged 6–59 months with a history of fever and acute respiratory symptoms were recruited in the Western Australia Influenza Vaccine Effectiveness (WAIVE) Study. Clinical data and per-nasal specimens were obtained from all children. Logistic regression identified significant predictors of influenza infection. Different ILI definitions were compared for diagnostic accuracy. Results Children were recruited from 2 winter influenza seasons (2008–2009; n = 944). Of 919 eligible children, 179 (19.5%) had laboratory-confirmed influenza infection. Predictors of infection included increasing age, lack of influenza vaccination, lower birth weight, fever, cough, and absence of wheeze. An ILI definition comprising fever ≥38°C, cough, and no wheeze had 58% sensitivity (95% confidence interval [CI], 50–66), 60% specificity (95% CI, 56–64), 26% positive predictive value (95% CI, 21–31), and 86% negative predictive value (95% CI, 82–89). The addition of other symptoms or higher fever thresholds to ILI definition had little impact. The Centers for Disease Control and Prevention definition of ILI (presence of fever [≥37.8°C] and cough and/or sore throat) was sensitive (92%; 95% CI, 86–95), yet lacked specificity (10%; 95% CI, 8–13) in this population. Conclusions Influenza-like illness is a poor predictor of laboratory-confirmed influenza infection in young children but can be improved using age-specific data. Incorporating age-specific ILI definitions and/or diagnostic testing into influenza surveillance systems will improve the accuracy of epidemiological data.

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Differences in clinical predictors of influenza in adults and children with influenza-like illness

Open Medicine ◽

10.2478/s11536-009-0089-3 ◽

2010 ◽

Vol 5 (1) ◽

pp. 41-48 ◽

Cited By ~ 3

Author(s):

Maja Sočan ◽

Katarina Prosenc ◽

Mateja Nagode

Keyword(s):

Clinical Data ◽

Predictive Value ◽

Influenza A ◽

Influenza Infection ◽

Care Hospital ◽

Rt Pcr ◽

Clinical Predictors ◽

Influenza Like Illness ◽

Breath Sounds ◽

Sensitivity Specificity

AbstractInfluenza contributes significantly to morbidity and mortality in the winter season. The aim of the study was to identify clinical signs and symptoms most predictive of influenza infection in children and adults with influenza-like illness. A prospective systematic sampling analysis of clinical data collected through sentinel surveillance system for influenza in 32 primary care centers and one tertiary care hospital in Slovenia during two consecutive influenza seasons (2004/2005 and 2005/2006) was carried out. Children and adults who had influenza-like illness, defined as febrille illness with sudden onset, prostration and weakness, muscle and joint pain and at least (cough, sore throat, coryza) were included and tested for influenza A and B virus, adenovirus, respiratory syncytial virus and enterovirus by RT-PCR. Clinical data were evaluated in statistical models to identify the best predictors for the confirmation of influenza for children (under age of 15) and adults. Of 1,286 patients with influenza-like symptoms in both seasons 211 were confirmed to have influenza A or B alone and compared to 780 influenza-negative patients. A fever over 38°C, chills, headache, malaise and sore eyes revealed a significant association with positive RT-PCR test for influenza virus in children. In adults, only three symptoms were significantly related to PCR-confirmed influenza infection: fever, cough and abnormal breath sounds. The stepwise logistic regression analysis showed that four symptoms predicted influenza in children: fever (38°C or more) (p=0.010), headache (p=0.030), cough (p=0.044) and absence of abnormal breathing sounds (p=0.015) with sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 5.1%, 98.1%, 57.1% and 80.1%, respectively. For adults, the strongest impact on influenza positivity was found for fever (p=0.008) and cough (p=0.085). The model for adults had less favorable characteristics, with sensitivity, specificity, PPV and NPV of 0%, 100%, 0% and 76.4%, respectively. Differences in clinical predictors of influenza in children compared to adults were found. The model for adults was acceptable but not a good one. The model for children was found to be more reliable than the prediction model for adults.

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Derivation of New Surveillance System for Influenza-like Illness Using Claims Data 2014-2018 in Korea (Preprint)

10.2196/preprints.25155 ◽

2020 ◽

Author(s):

HeeKyung Choi ◽

Won Suk Choi ◽

Euna Han

Keyword(s):

Surveillance System ◽

Claims Data ◽

Health Concern ◽

Influenza Surveillance ◽

Utilization Review ◽

Surveillance Systems ◽

Influenza Like Illness ◽

System P ◽

National Patient ◽

Antitussive Agents

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.

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FluWatchers: Evaluation of a crowdsourced influenza-like illness surveillance application for Canadian influenza seasons 2015–2016 to 2018–2019

Canada Communicable Disease Report ◽

10.14745/ccdr.v47i09a02 ◽

2021 ◽

Vol 47 (09) ◽

pp. 357-363

Author(s):

Liza Lee ◽

Mireille Desroches ◽

Shamir Mukhi ◽

Christina Bancej

Keyword(s):

Respiratory Virus ◽

Evaluation Criteria ◽

Influenza Surveillance ◽

Surveillance Program ◽

Surveillance Systems ◽

Virus Infections ◽

Detection Rates ◽

Influenza Like Illness ◽

Syncytial Virus ◽

Participatory Surveillance

Background: Sentinel influenza-like illness (ILI) surveillance is an essential component of a comprehensive influenza surveillance program. Community-based ILI surveillance systems that rely solely on sentinel healthcare practices omit important segments of the population, including those who do not seek medical care. Participatory surveillance, which relies on community participation in surveillance, may address some limitations of traditional ILI systems. Objective: We aimed to evaluate FluWatchers, a crowdsourced ILI application developed to complement and complete ILI surveillance in Canada. Methods: Using established frameworks for surveillance evaluations, we assessed the acceptability, reliability, accuracy and usefulness of the FluWatchers system 2015–2016, through 2018–2019. Evaluation indicators were compared against national surveillance indicators of ILI and of laboratory confirmed respiratory virus infections. Results: The acceptability of FluWatchers was demonstrated by growth of 50%–100% in season-over-season participation, and a consistent season-over-season retention of 80%. Reliability was greater for FluWatchers than for our traditional ILI system, although both systems had week-over-week fluctuations in the number of participants responding. FluWatchers’ ILI rates had moderate correlation with weekly influenza laboratory detection rates and other winter seasonal respiratory virus detections including respiratory syncytial virus and seasonal coronaviruses. Finally, FluWatchers has demonstrated its usefulness as a source of core FluWatch surveillance information and has the potential to fill data gaps in current programs for influenza surveillance and control. Conclusion: FluWatchers is an example of an innovative digital participatory surveillance program that was created to address limitations of traditional ILI surveillance in Canada. It fulfills the surveillance system evaluation criteria of acceptability, reliability, accuracy and usefulness.

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Estimating the Early Outbreak Cumulative Incidence of COVID-19 in the United States: Three Complementary Approaches

10.1101/2020.04.18.20070821 ◽

2020 ◽

Cited By ~ 12

Author(s):

Fred S. Lu ◽

Andre T. Nguyen ◽

Nicholas B. Link ◽

Marc Lipsitch ◽

Mauricio Santillana

Keyword(s):

United States ◽

Cumulative Incidence ◽

State Level ◽

The United States ◽

Positive Test ◽

Influenza Surveillance ◽

Accurate Estimation ◽

Surveillance Systems ◽

Influenza Like Illness ◽

The Us

AbstractEffectively designing and evaluating public health responses to the ongoing COVID-19 pandemic requires accurate estimation of the weekly incidence of COVID-19. Unfortunately, a lack of systematic testing across the United States (US) due to equipment shortages and varying testing strategies has hindered the usefulness of the reported positive COVID-19 case counts. We introduce three complementary approaches to estimate the cumulative incidence of symptomatic COVID-19 during the early outbreak in each state in the US as well as in New York City, using a combination of excess influenza-like illness reports, COVID-19 test statistics, and COVID-19 mortality reports. Instead of relying on an estimate from a single data source or method that may be biased, we provide multiple estimates, each relying on different assumptions and data sources. Across our three approaches, there is a consistent conclusion that estimated state-level COVID-19 symptomatic case counts from March 1 to April 4, 2020 varied from 5 to 50 times greater than the official positive test counts. Nationally, our estimates of COVID-19 symptomatic cases in the US as of April 4 have a likely range of 2.2 to 5.1 million cases, with possibly as high as 8.1 million cases, up to 26 times greater than the cumulative confirmed cases of about 311,000. Extending our method to May 16, 2020, we estimate that cumulative symptomatic incidence ranges from 6.0 to 12.2 million, which compares with 1.5 million positive test counts. Our approaches demonstrate the value of leveraging existing influenza-like-illness surveillance systems during the flu season for measuring the burden of new diseases that share symptoms with influenza-like-illnesses. Our methods may prove useful in assessing the burden of COVID-19 during upcoming flu seasons in the US and other countries with comparable influenza surveillance systems.

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Assessment of two complementary influenza surveillance systems: sentinel primary care influenza-like illness versus severe hospitalized laboratory-confirmed influenza using the moving epidemic method

BMC Public Health ◽

10.1186/s12889-019-7414-9 ◽

2019 ◽

Vol 19 (1) ◽

Cited By ~ 4

Author(s):

Núria Torner ◽

◽

Luca Basile ◽

Ana Martínez ◽

Cristina Rius ◽

...

Keyword(s):

Primary Care ◽

Influenza Surveillance ◽

Surveillance Systems ◽

Influenza Like Illness

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Measles Surveillance Attributes Assessment Based on The Puskesmas Surveilance Officers’ Perception in Surabaya

Jurnal Berkala Epidemiologi ◽

10.20473/jbe.v2i2.2014.171-183 ◽

2014 ◽

Vol 2 (2) ◽

pp. 171

Author(s):

Bilqis Elfira Maharani ◽

Arief Hargono

Keyword(s):

Data Quality ◽

Predictive Value ◽

Surveillance System ◽

Health Department ◽

Surveillance Systems ◽

Descriptive Research ◽

Control And Prevention ◽

Technical Guide ◽

The Republic ◽

The Stability

ABSTRACTMeasles is one of infectious diseases that potentially lead to death when complications occur. Based on the data from East Java Health Department, Surabaya is the area where the most measles cases occur in East Java and increase in the last three years. As one of measles controlling efforts, surveillance has been expected to provide qualified data and information as the basis for any decision making for a treatment or intervention. Therefore, an evaluation is needed in order to assure the effectiveness and efficiency of the surveillance application in achieving the goals. This study is a descriptive research aiming at evaluating the attributes of measles epidemiology surveillance system in Surabaya on 2012. The evaluation was done by assessing the attributes of surveillance then compared to Technical Guide for Measles Surveillance 2012, The Decree of The Health Ministry of The Republic of Indonesia No.1116/MENKES/SK/VIII/2003 On Guide for Conducting Surveillance System of Health Epidemiology and Guidelines for Evaluating Surveillance Systems from Center for Disease Control and Prevention 2001. The data collection method employed interview and observation or study documentation. The respondents of this study were 39 surveillance officers at 39 Puskesmas in Health Department Surabaya working area. The variabels of this study were simplicity, flexibility, data quality, acceptability, sensitivity, predictive value positive, representativeness, timeliness, and stability. The results of this study showed that the simplicity is complicated. The flexibility from CBMS is not flexible whereas the flexibility from EWARS is flexible. The data quality, acceptability, sensitivity and representativeness are low. The predictive value positive has not been able to be scored. The stability is high and the timeliness is punctual.Keywords: surveillance, evaluation, attribute, measles

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Self-Swabbing for Virological Confirmation of Influenza-Like Illness Among an Internet-Based Cohort in the UK During the 2014-2015 Flu Season: Pilot Study (Preprint)

10.2196/preprints.9084 ◽

2017 ◽

Author(s):

Clare Wenham ◽

Eleanor R Gray ◽

Candice E Keane ◽

Matthew Donati ◽

Daniela Paolotti ◽

...

Keyword(s):

Pilot Study ◽

Influenza Infection ◽

Routine Data ◽

Influenza Surveillance ◽

Influenza Like Illness ◽

Time Estimates ◽

Laboratory Confirmation ◽

Community Transmission ◽

The Uk ◽

Study Participants

BACKGROUND Routine influenza surveillance, based on laboratory confirmation of viral infection, often fails to estimate the true burden of influenza-like illness (ILI) in the community because those with ILI often manage their own symptoms without visiting a health professional. Internet-based surveillance can complement this traditional surveillance by measuring symptoms and health behavior of a population with minimal time delay. Flusurvey, the UK’s largest crowd-sourced platform for surveillance of influenza, collects routine data on more than 6000 voluntary participants and offers real-time estimates of ILI circulation. However, one criticism of this method of surveillance is that it is only able to assess ILI, rather than virologically confirmed influenza. OBJECTIVE We designed a pilot study to see if it was feasible to ask individuals from the Flusurvey platform to perform a self-swabbing task and to assess whether they were able to collect samples with a suitable viral content to detect an influenza virus in the laboratory. METHODS Virological swabbing kits were sent to pilot study participants, who then monitored their ILI symptoms over the influenza season (2014-2015) through the Flusurvey platform. If they reported ILI, they were asked to undertake self-swabbing and return the swabs to a Public Health England laboratory for multiplex respiratory virus polymerase chain reaction testing. RESULTS A total of 700 swab kits were distributed at the start of the study; from these, 66 participants met the definition for ILI and were asked to return samples. In all, 51 samples were received in the laboratory, 18 of which tested positive for a viral cause of ILI (35%). CONCLUSIONS This demonstrated proof of concept that it is possible to apply self-swabbing for virological laboratory testing to an online cohort study. This pilot does not have significant numbers to validate whether Flusurvey surveillance accurately reflects influenza infection in the community, but highlights that the methodology is feasible. Self-swabbing could be expanded to larger online surveillance activities, such as during the initial stages of a pandemic, to understand community transmission or to better assess interseasonal activity.

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Flutracking Australia Influenza-like Illness Surveillance System: Evolution of methods from 2006-2016 (Preprint)

10.2196/preprints.12337 ◽

2018 ◽

Author(s):

Sandra Jane Carlson ◽

Craig Brian Dalton

Keyword(s):

Surveillance System ◽

Influenza Pandemic ◽

Assessment System ◽

Influenza Surveillance ◽

Local Health ◽

Surveillance Systems ◽

Australian Government ◽

Health Seeking ◽

Influenza Like Illness ◽

Government Department

UNSTRUCTURED This paper documents the evolution of Flutracking from a pilot online influenza-like illness (ILI) survey of 394 participants in a local health region to a national surveillance system with over 30,000 participants in 2016. In 2018, there were over 40,000 survey responses per week in most weeks. In particular, this paper will describe how the methods have developed to meet the 1) changing aims of the system; 2) developing capabilities of the system; and 3) participant growth. The aim of this paper is to provide insights to other groups initiating participatory public health surveillance systems and to assist users of our data and reports to better understand Flutracking methods. Some of the key changes to Flutracking from 2006 to 2016 include: allowing participants to respond on behalf of other household members from 2008; adding health seeking behaviour questions in 2011; and offering an express survey and follow-up of unknown test results from 2016 onwards. The Flutracking system has demonstrated its ability to adapt to changes with minimal disruption to participants, and maintain consistency in data collection and reporting. As an example of success, Flutracking has been integrated in the Australian Government Department of Health’s regular influenza reports, and is now contributing weekly data to the transmissibility and impact measures for the Australian Government Department of Health’s application of the Pandemic Influenza Severity Assessment system. Flutracking data have consistently aligned with the timing of the peak level of influenza activity from other Australian influenza surveillance systems. Flutracking provided a unique insight into 2009 H1N1 influenza pandemic in 2009 demonstrating that the community level ILI attack rates were only slightly higher than 2008, and lower than 2007 in the community. Flutracking has demonstrated to be significantly less biased by treatment seeking behaviour and laboratory testing protocols than other surveillance systems during the 2009 influenza pandemic. In 2018, Flutracking expanded to New Zealand, with an average of over 2,800 surveys per week so far. The evolution of Flutracking’s methods has been pivotal to the success of this surveillance system.

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An Evaluation of the Zambia Sentinel Surveillance System, 2011-2017

10.21203/rs.2.16794/v1 ◽

2019 ◽

Author(s):

paul simusika ◽

Stefano Tempia ◽

Edward Chentulo ◽

Lauren Polansky ◽

Mazyanga Mazaba ◽

...

Keyword(s):

Data Quality ◽

Surveillance System ◽

Low Cost ◽

Influenza Viruses ◽

Influenza Surveillance ◽

Surveillance Systems ◽

Quality Of Data ◽

African Countries ◽

Control And Prevention ◽

External Funds

Abstract Background Over the past decade, influenza surveillance has been established in several African countries including Zambia. However, information on the on data quality and reliability of established influenza surveillance systems in Africa are limited. Such information would enable countries to assess the performance of their surveillance systems, identify shortfalls for improvement and provide evidence of data reliability for policy making and public health interventions. Methods We used the Centers for Disease Control and Prevention guidelines to evaluate the performance of the influenza surveillance system (ISS) in Zambia during 2011-2017 using 9 attributes: (i) data quality and completeness, (ii) timeliness, (iii) representativeness, (iv) flexibility, (v) simplicity, (vi) acceptability, (vii) stability, (viii) utility, and (ix) sustainability. Each attribute was evaluated using pre-defined indicators. For each indicator we obtained the proportion (expressed as percentage) of the outcome of interest over the total. A scale from 1 to 3 was used to provide a score for each attribute as follows: <60% (as obtained in the calculation above) scored 1 (weak performance); 60-79% scored 2 (moderate performance); ≥80% scored 3 (good performance). An overall score for each attribute and the ISS was obtained by averaging the scores of all evaluated attributes. Results The overall mean score for the ISS in Zambia was 2.6. Key strengths of the system were the quality of data generated (score: 2.9), its flexibility (score: 3.0) especially to monitor viral pathogens other than influenza viruses, its simplicity (score: 2.8), acceptability (score: 3.0) and stability (score: 2.6) over the review period and its relatively low cost ($310,000 per annum). Identified weaknesses related mainly to geographic representativeness (score: 2.0), timeliness (score: 2.5), especially in shipment of samples from remote sites, and sustainability (score: 1.0) in the absence of external funds. Conclusions The system performed moderately well in our evaluation. Key improvements would include improvements in the timeliness of samples shipments and geographical coverage. However, these improvements would result in increased cost and logistical complexity. The ISSS in Zambia is largely reliant on external funds and the acceptability of maintaining the surveillance system through national funds would require evaluation.

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