Surveillance Systems

Traditional disease surveillance systems suffer from several disadvantages, including reporting lags and antiquated technology, that have caused a movement towards internet-based disease surveillance systems. Recently, Wikipedia access logs (e.g., McIver 2014, Generous 2014) have been shown to be effective in this arena. Much richer Wikipedia data are available, though, including the entire Wikipedia article content and edit histories.

We study two different aspects of Wikipedia content as it relates to unfolding disease events: 1) we demonstrate how to capture case, death, and hospitalization counts from the article text, and 2) we show there are valuable time series data present in the tables found in certain articles.

We argue that Wikipedia data cannot only be used for disease surveillance but also as a centralized repository system for collecting disease-related data in near real-time.

Objective

To improve traditional outbreak surveillance systems by utilizing the content of Wikipedia articles.

When hazardous materials or products emerge in the market, injury prevention researchers take action to promote awareness and legislation with the goal to prevent further injuries. This cannot be achieved without reliable data on trends and outcomes identifying large cohorts with the injury of interest. Lags in providing such data will delay knowledge sharing to prevent avoidable and potentially fatal injuries.

Glass tables and earth magnets are two examples of consumer products with potential for significant injuries, particularly to children. Magnet toys caused a large number of injuries with associated morbidity and mortality. For months there were no available data to support policy or prevention initiatives. Similarly, certain disease and injury mechanisms such as penetrating oral trauma are not included as structured data and cannot be collected using ICD-9/ICD-10 codes. Data on these types of injury mechanisms exist exclusively within the clinical narrative.

Objective

• Describe injury-related surveillance using clinical narratives within electronic health records

• Present a user friendly, physician transferrable operated natural language processing (NLP) module, which can identify injury related events from electronic health record narratives

• Present a variety of use cases and results

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 outbreak of the Ebola Virus Disease (EVD) in Africa in 2014 presented a major threat and concern across the world, spreading to two other continents (Europe and North America). Though the epidemic is on a downward trend, there is a need to evaluate the performance of the systems in place to detect and control such outbreaks and determine the need for improvement in countries affected.

With its first traceable case reported to have been in Guinea, the outbreak spread to Nigeria through an air traveler from Liberia which led to an outbreak in the country that luckily, was quickly contained. This imported case was initially managed at a private health facility (PHF) eventually leading to 20 cases and eight deaths, four of which were health workers from the initial managing PHF. Despite effort to contact the authorities about the suspected imported case by the PHF, it reportedly took some time before the health authorities could be reached and action at control instituted. This might suggest an inefficiency of the IDSR system which was previously adopted by Nigeria as a means of implementing the International Health Regulation (IHR) of 1969. The IHR is a set of regulations that the World Health Assembly uses to implement its constitutional responsibility to prevent the international spread of diseases.

Hemorrhagic fevers like EVD ought to be reported immediately upon suspicion to the health authorities but the delay despite effort suggests this system is not efficient. This is important as PHFs are noted to attend to over 60% of the Nigerian population. Thus, it is important to carry out an assessment of the IDSR system in PHFs to forestall a repeat episode and limit the impact of outbreak of infectious diseases in future.

Objective

To investigate the compliance of private health facilities to the integrated disease surveillance and response (IDSR) system in Nigeria.

Wholesome food in adequate quantities is essential to human beings for their existence. However, diseases spread due to contaminated food are a common problem throughout the world and an important cause of reduced economic productivity. Food borne illness can, therefore, be considered a major international health problem and a significant cause of economic loss. Approximately 10 to 20% of food-borne disease outbreaks cause due to contamination by the food handlers. In Sri Lanka, information about food hygiene practices in plantation sector is scarce. Therefore, this study was designed as a preliminary study to identify hygiene practices in food processing in the plantation sector for the establishment of a surveillance system in Sri Lanka.

Objective

To develop a food hygiene surveillance system to improve food safety measures within food establishments in the plantation sector of Sri Lanka

Electronic data that could be used for global health surveillance are fragmented across diseases, organizations, and countries. This fragmentation frustrates efforts to analyze data and limits the amount of information available to guide disease control actions. In fields such as biology, semantic or knowledge-based methods are used extensively to integrate a wide range of electronically available data sources, thereby rapidly accelerating the pace of data analysis. Recognizing the potential of these semantic methods for global health surveillance, we have developed the Scalable Data Integration for Disease Surveillance (SDIDS) software platform. SDIDS is a knowledge-based system designed to enable the integration and analysis of data across multiple scales to support global health decision-making. A ‘proof of concept’ version of SDIDS is currently focused on data sources related to malaria surveillance in Uganda.

Objective

To develop a scalable software platform for integrating existing global health surveillance data and to implement the platform for malaria surveillance in Uganda.

Today, Internet, especially Wikipedia, is an important part of everyday life. People can notably use this popular free online encyclopedia to search health-related information. Recent studies showed that Wikipedia data can be used to monitor and to forecast influenza-like illnesses in near real time in the United States [1,2].

 We carried out a study to explore whether French Wikipedia data allow to monitor the trends of five seasonal diseases in metropolitan France: influenza-like illness, gastroenteritis, bronchiolitis, chickenpox and asthma.

Objective

To explore the interest of Wikipedia as a data source to monitor seasonal diseases trends in metropolitan France.

 Influenza surveillance is conducted through a complex network of laboratory and epidemiologic systems essential for estimating population burden of disease, selecting influenza vaccine viruses, and detecting novel influenza viruses with pandemic potential (1). Influenza surveillance faces numerous challenges, such as constantly changing influenza viruses, substantial variability in the number of affected people and the severity of disease, nonspecific symptoms, and need for laboratory testing to confirm diagnosis. Exploring additional components that provide morbidity information may enhance current influenza surveillance. School-aged children have the highest influenza incidence rates among all age groups. Due to the close interaction of children in schools and subsequent introduction of influenza into households, it is recognized that schools can serve as amplification points of influenza transmission in communities. For this reason, pandemic preparedness recommendations include possible pre-emptive school closures, before transmission is widespread within a school system or broader community, to slow influenza transmission until appropriate vaccines become available. During seasonal influenza epidemics, school closures are usually reactive, implemented in response to high absenteeism of students and staff after the disease is already widespread in the community. Reactive closures are often too late to reduce influenza transmission and are ineffective. To enhance timely influenza detection, a variety of nontraditional data sources have been explored. School absenteeism was suggested by several research groups to improve school-based influenza surveillance. A study conducted in Japan demonstrated that influenzaassociated absenteeism can predict influenza outbreaks with high sensitivity and specificity (2). Another study found the use of allcauses absenteeism to be too nonspecific for utility in influenza surveillance (3). Creation of school-based early warning systems for pandemic influenza remains an interest, and further studies are needed. The panel will discuss how school-based surveillance can complement existing influenza surveillance systems.

Objective

This session will provide an overview of the current systems for influenza surveillance; review the role of schools in influenza transmission; discuss relationships between school closures, school absenteeism, and influenza transmission; and explore the usefulness of school absenteeism and unplanned school closure monitoring for early detection of influenza in schools and broader communities.

DPH uses its State Electronic Notifiable Disease Surveillance System (SendSS) Syndromic Surveillance (SS) Module to collect, analyze and display results of emergency department patient chief complaint data from hospitals throughout Georgia.

Objective

Describe how the Georgia Department of Public Health (DPH) uses syndromic surveillance to initiate review by District Epidemiologists (DEs) to events that may warrant a public health response (1).

In New Jersey, real-time emergency department (ED) data are currently received from EDs by Health Monitoring Systems Inc.’s (HMS) EpiCenter, which collects, manages and analyzes ED registration data for syndromic surveillance, and provides alerts to state and local health departments for surveillance anomalies.

EpiCenter receives pre-diagnostic chief complaint data from 78 of 80 acute care and satellite EDs. The need for more specific information raises the possibility that other data elements from EDs such as triage notes can be of utility in detecting outbreaks without a significant delay. This study evaluates the inclusion of triage notes in EpiCenter to detect a recent increased usage of synthetic cannabinoids. At the time of this evaluation, three New Jersey hospitals were providing triage notes in their EpiCenter data.

Objective

Describe the inclusion of triage notes into a syndromic surveillance system to enhance population health surveillance activities.