Influenza-Like-Illness (ILI)

The general health-seeking behavior has been well described in different populations. However, how different symptoms have driven health-seeking behavior was less explored. From the patient’s perspective, health-seeking behavior tends to be responsive to discomfort or symptoms rather than the type of diseases which is unknown before medical consultation, hence symptom-specific behavior may more realistically reflect responses from the public which is subsequently captured by syndromic surveillance. In Hong Kong, sentinel surveillance of common diseases, such as ILI and acute diarrhoeal diseases, consists of general practitioners (GP), general outpatient clinics (GOPC) and Chinese medicine practitioners (CMP). These existing sources of syndromic surveillance data are affected by the choice of health services and health seeking behavior and hence may over- or under-represent actual disease burden. By understanding health-seeking behavior at different times of the year, we could estimate the disease burden in the population, and population subgroup from multiple surveillance data.

Objective

This study described health-seeking behavior of the general population specific to different symptoms, at different times of the year. This information allows the estimation of population disease burden over the year using sentinel surveillance data. We will use influenza-like illness (ILI) as an example.

Currently, three main sources of data are used to monitor the prevalence of influenza in Ontario: Public Health Agency of Canada’s (PHAC) FluWatch, Ontario’s Acute Care Enhanced Surveillance (ACES) data and Public Health Ontario’s (PHO) traditional laboratory data. However, a limitation of these data sources is that it typically underestimates the burden of infection in populations living in remote communities and/or populations with less severe symptoms. This study describes a self-swabbing surveillance system mediated by a THHL that uses syndromic surveillance tools to recruit and monitor participants with influenza-like illness. The intent of this system is not to replace, but rather to complement other surveillance systems and clinical based testing for influenza, thereby extending the reach of surveillance through the use of self-swabbing. An additional rationale for this type of surveillance system is that it can reduce transmission of infection by limiting the number of visits to emergency departments or doctors’ offices, thereby reducing contact with the young and elderly populations, who are at most risk for infection.

Objective

Explore the use and feasibility of self-swabbing mediated by a telephone health helpline (THHL) as a complementary tool for surveillance of influenza and other common respiratory viruses in Ontario, Canada.

School children are the primary introducers and significant transmission sources of influenza virus among their families and surrounding communities [1,2]. Therefore, schools play an important role in amplifying influenza transmission in communities. Using school-related data sources may be an informative addition to existing influenza surveillance. Unplanned school closures (USCs) are common, occur frequently for various reasons, and affect millions of students across the country [3]. Information about USCs is publicly available in real-time. For this study, we evaluated usability of applying USC data for ILI surveillance.

Objective

Evaluate usability of alternative data sources, such as public announcements of unplanned school closures, for additional insight regarding influenza-like illness (ILI) activity.

Using influenza like illness (ILI) data from the repository held by AFHSC, and publically available malaria data we characterized similarities and differences between military and civilian outbreaks. Pete Riley et al. utilized a similar ILI dataset to investigate civilian and military outbreaks similarity during the 2009 flu epidemic. They found, overall, high similarity between civilian and military outbreaks, with military peaking roughly one week after civilian. Our analysis is meant to extend their analysis temporally, geographically, and to see if such trends hold true for other diseases.

Objective

Compare and contrast military and civilian outbreaks for malaria and influenza like illness to identify indicators for early warning and detection

Processing free-text clinical information in an electronic medical record (EMR) may enhance surveillance systems for early identification of ILI outbreaks. However, processing clinical text using NLP poses a challenge in preserving the semantics of the original information recorded. In this study, we discuss several NLP and technical issues as well as potential solutions for implementation in syndromic surveillance systems.

Objective

To review the natural language processing (NLP) and technical challenges encountered in an automated influenza-like illness (ILI) surveillance system.

Influenza is not a notifiable disease in Kansas; patient-level influenza data is not reported to the Kansas Department of Health and Environment (KDHE). Kansas’ primary method of influenza surveillance is the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet), a collaboration between the Centers for Disease Control and Prevention (CDC) and state health departments. During the 2014-2015 influenza surveillance period (September 28, 2014 through May 16, 2015), 35 health care providers (20 family practice clinics, nine hospital emergency departments, four university student health centers, and two pediatric clinics) served as ILINet sites. Providers were instructed to report the previous week’s influenzalike illness (ILI) data, including the number of patients who met the ILI case definition and the total number of patients seen, by 11:00 AM each Tuesday. An average of 16 providers (45%) met the deadline each week.

Objective

Measure the correlation between Influenza-like Illness (ILI) data collected by the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet) and the National Syndromic Surveillance Program (NSSP) in Kansas for the 2014-2015 influenza surveillance period.

The North Dakota Department of Health (NDDoH) collects outpatient ILI data through North Dakota Influenza-like Illness Network (ND ILINet), providing situational awareness regarding the percent of visits for ILI at sentinel sites across the state. Because of increased clinic staff time devoted to electronic health initiatives and an expanding population, we have found sentinel sites have been harder to maintain in recent years, and the number of participating sentinel sites has decreased. Outpatient sentinel surveillance for influenza is an important component of influenza surveillance because hospital and death surveillance does not capture the full spectrum of influenza illness. Syndromic surveillance (SyS) is another possible source of information for outpatient ILI that can be used for situational awareness during the influenza season; one benefit of SyS is that it can provide more timely information than traditional outpatient ILI surveillance [1,2]. The NDDoH collects SyS data from hospitals (emergency department and inpatient visits) and outpatient clinics, including urgent and primary care locations. Visits include chief complaint and/or diagnosis code data. This data is sent to the BioSense 2.0 SyS platform. We compared our outpatient SyS ILI with our ND ILINet and reported influenza cases, and included hospital and combined SyS ILI for comparison.

Objective

To explore how outpatient and urgent care syndromic surveillance for influenza-like illness (ILI) compare with emergency department syndromic ILI and other seasonal ILI surveillance indicators

Data submitted to ILINet from ambulatory practices are a primary feature of influenza-like illness (ILI) surveillance in the United States. Practices count relevant patient records and submit this data manually to ILINet. The ongoing data collection is useful for surveillance, and a significant amount of historical data has accumulated which is useful for research purposes and comparisons of the present season to the past. However, the tabulation of this data is costly, and retention of sentinel practices can be challenging as there is no mandate to submit data. Increasingly, the EpiCenter syndromic surveillance system is receiving data from ambulatory practices. Syndromic surveillance data is sent automatically in near-realtime. Meaningful Use requirements incentivize practices to participate in ongoing data transmission. Syndromic surveillance data from ambulatory practices is thus a possible substitute for the current, more labor-intensive surveillance of ambulatory practices.

Objective

To investigate the viability of using prediagnostic syndromic surveillance data from ambulatory practices for influenza-like illness surveillance

Traditional influenza surveillance relies on reports of influenzalike illness (ILI) by healthcare providers, capturing individuals who seek medical care and missing those who may search, post, and tweet about their illnesses instead. Existing research has shown some promise of using data from Google, Twitter, and Wikipedia for influenza surveillance, but with conflicting findings, studies have only evaluated these web-based sources individually or dually without comparing all three of them1-5. A comparative analysis of all three web-based sources is needed to know which of the web-based sources performs best in order to be considered to complement traditional methods.

Objective

To comparatively analyze Google, Twitter, and Wikipedia by evaluating how well change points detected in each web-based source correspond to change points detected in CDC ILI data.