Influenza

Under-ascertainment of severe outcomes of influenza infections in administrative databases has long been recognised. After reviewing registered deaths following an influenza epidemic in 1847, William Farr, of the Registrar-General's Office, London, England, commented: ''the epidemic carried off more than 5,000 souls over and above the mortality of the season, the deaths referred to that cause [influenza] are only 1,157"[1]. Even today, studies of the population epidemiology, burden and cost of influenza frequently assume that influenza's impact on severe health outcomes reaches far beyond the number of influenza cases counted in routine clinical and administrative databases. There is little current evidence to justify the assumption that influenza is poorly identified in health databases. Using population based record linkage, we evaluated whether the assumption remains justified with modern improvements in diagnostic medicine and information systems.

Objective

To estimate the degree to which illness due to influenza is under-ascertained in administrative databases, to determine factors associated with influenza being coded or certified as a cause of death, and to estimate the proportion of coded influenza or certified influenza deaths that is laboratory confirmed.

Pandemic 2009 H1N1 influenza and recent H7N9 influenza outbreaks made the public aware of the threat of influenza infection. In fact, annual influenza epidemic caused heavy disease burden and high economic loss around the world [1, 2]. Although the virological surveillance provided the high sensitivity and specificity for testing results, the timeliness and the cost of the test were not feasible for extensive public health surveillance. In addition, traditional sentinel physician surveillance also encountered many challenges such as the representativeness and reporting bias. The seamless surveillance system without extra labor reporting would be the ideal approach. Taiwan had as high as 99% of health insurance coverage. The real-time monitoring of the ILI clinical visits in the communities could reflect the severity of influenza epidemics. In this study, we used an innovative two-stage approach for detecting aberrations during 2009 pandemic influenza in Taiwan.

Objective

This study proposed a two-stage approach for early detection of aberrations of influenza-like illness (ILI) using the small-area based claim data of outpatient and emergency room visit.

Symptoms of influenza-like illness (ILI) were reported daily by health workers via the integrated syndromic surveillance system in rural China (ISSC)(1).

Objective

To analyze the growth and variation of ILI reports using individual growth curve model.

The Influenza Division (ID) in the Centers for Disease Control and Prevention (CDC) maintains the WHO/NREVSS surveillance system, a network of laboratories in the U.S. that report influenza testing results. This system has seen many changes during the past 40 years, especially since the 2009 H1N1 pandemic. This was due in large part to increased adoption of HL7 messaging via PHLIP. PHLIP data is detailed, standardized influenza testing information, reported in near real-time. The data received through this and other report methods is published online in national and regional aggregate form in FluView, a weekly surveillance report prepared by CDC.

Objective

Describe the changes to the World Health Organization/National Respiratory and Enteric Virus Surveillance System (WHO/NREVSS) influenza surveillance system over time, with a focus on the Public Health Laboratory Interoperability Project (PHLIP) and how it has influenced the system

Despite the number of infections, hospitalizations, and deaths from influenza each year, developing the ability to predict the timing of these outbreaks has remained elusive. Public health practitioners have lacked a reliable, easy-to-implement method for predicting the onset of a period of elevated influenza incidence in a community. We (a team of statisticians, epidemiologists, and clinicians) have developed a model to help public health practitioners develop simple, adaptable, data-driven rules to define a period of increased disease incidence in a given location. We call this method the Above Local Elevated Respiratory illness Threshold (ALERT) algorithm. The ALERT algorithm is a simple method that defines a period of elevated disease incidence in a community or hospital that systematically collects surveillance data on a particular disease.

Objective

Our objective was to develop a simple, easy-to-use algorithm to predict the onset of a period of elevated influenza incidence in a community using surveillance data.

During the past decade, public health practitioners have implemented various new syndromic and other advanced surveillance systems to supplement their existing laboratory testing and disease surveillance toolkit. While much of the development and widespread implementation of these systems had been supported by public health preparedness funding, the reduction of these monies has greatly constrained the ability of public health agencies to staff and maintain these systems. The appearance of H3N2v and other novel influenza A viruses, requires agencies to carefully choose which systems will provide the most cost-effective data to support their public health practice. The global emergence of influenza A H7N9, Middle East Respiratory Syndrome Coronavirus (MERS-CoV), and other viruses associated with high mortality, emphasize the importance of maintaining vigilance for the presence of emerging disease.

Objective

To review approaches used by public health agencies for alerting health care providers and enhancing surveillance systems to identify the presence of novel respiratory disease and to characterize their recent experience in searching for globally emerging viruses.