Pavlin Julie

The Armed Forces Health Surveillance Center (AFHSC) supports the development of new analytical tools to improve alerting in the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) disease-monitoring application used by the Department of Defense (DoD). Developers at the Johns Hopkins University Applied Physics Laboratory (JHU/APL) have added an analytic capability to alert the user when corroborating evidence exists across syndromic and clinical data streams including laboratory tests and filled prescription records. In addition, AFHSC epidemiologists have guided the addition of data streams related to case severity for monitoring of events expected to require expanded medical resources. Evaluation of the multi-level fusion capability for both accuracy and utility is a challenge that requires feedback from the user community before implementation and deployment so that changes to the design can be made to save both time and money. The current effort describes the design and results of a large evaluation exercise.

Objective

To evaluate, prior to launch, a surveillance system upgrade allowing analytical combination of weighted clinical and syndromic evidence with multiple severity indicators.

There is a significant body of literature on the use of social media for monitoring ailments such as influenza-like illness1 and cholera,2 as well as public opinions on topics such as vaccination.3 In general, these studies have shown that social media correlates well with official data sources,1,2,3 with the trends identifiable before official data are available.2 However, less is known about the impact of integrating social media into public health practice, and resulting interventions. Therefore, the ISDS Social Media for Disease Surveillance Workgroup initiated a systematic literature review on the use of social media for actionable biosurveillance.

Objective

The objective of this study is to systematically review the literature on the use of social media for biosurveillance in order to evaluate whether this data source can improve public health practice or community health outcomes.

Data streams related to case severity have been added to the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE), a disease-monitoring application used by the Department of Defense (DoD), as an additional analytic capability to alert the user when indications for events requiring expanded medical resources exist in clinical data streams. Commonly used indicators are admission and death, but fatalities are rare and many DoD clinics lack admitting capability, so we sought to derive additional severity indicators from outpatient records. This abstract describes the technical details and the thought process behind two novel derived indicators: Sick-in-Quarters (SIQ) and Escalating Care.

Objective

To evaluate new severity indicators that mimic a public health professional or clinician's judgment in determining the severity of a public health event when detected by a surveillance system.

Developing countries bear the highest burden of infectious diseases, and therefore play a key role in the detection of emerging disease threats. The ability of these countries to detect such events allows an adequate response preventing its spread and reducing the morbidity and mortality of communities across the world. Due to the importance of strengthening the surveillance capabilities of developing countries, resources need to be dedicated to this effort. International organizations often provide technical and financial support when countries need assistance. When implementing such systems, the countries and supporting organizations face challenges not limited to scarce resources. These countries often also face political and social instability, and cultural and religious practices that may put their populations at greater risk of these events. In addition, the disease surveillance field lacks clear guidance on how to initiate or improve the implementation process for these systems. The experiences described in the literature are often presented as partial evaluations using the US Centers for Disease Control and Prevention guidelines, without providing an explanation of the key factors that are related to successful or unsuccessful experiences in such settings. Public health practitioners at the ministries of health (MOH) and international organizations would greatly benefit from a model that considers these factors to serve as a guideline for a positive implementation outcome and an effective use of resources.

Objective

The panel will present successful and unsuccessful practices, challenges and lessons learned when implementing electronic surveillance tools in the developing world, and a best practices model that aims to guide and facilitate this process. Panelists and audience will discuss the validity of this model, and identify potential countries where it could be tested.

The Johns Hopkins Applied Physics Laboratory and the Armed Forces Health Surveillance Center have developed a hybrid processing engine that alerts monitors when a severe health condition exists based on corroboration among several sources of data. The system was designed to ingest a day's worth of recent data and provide results to monitors daily. In some theaters, the health of the US Forces must be determined at near-real time rates requiring a reassessment of current surveillance practices. Challenges exist in both acquiring data in real-time and in modifying automated alerting processes to re-evaluate as a new piece of evidence is received.

Objective

To develop a real-time surveillance capability that processes, fuses and assesses when data is received using a new fusion processing methodology and multiple sources health indicator data.

The impetus for the development of many first syndromic surveillance systems was the hope of detecting infectious disease outbreaks earlier than with traditional surveillance. Various data sources have been suggested as potential disease indicators. Researchers have analyzed many of these, including those resulting from behaviors that change due to illness, such as purchasing medications, missing school or work, and using health care call centers or the internet to obtain health information. To define the prodromal behavior of patients presenting for care of acute illnesses, we initiated a pilot survey in the emergency room and acute care clinics at Walter Reed Army Medical Center.

Objective

This study describes the results of a survey given to patients to determine if any changes occurred in their behavior secondary to the illness that could potentially be tracked and used to detect a disease outbreak.

One of the standard approaches to public health surveillance for influenza is to monitor the percent of visits to about 2000 sentinel physicians for influenza-like illness (%ILI; fever plus cough or sore throat). The BioSense System currently receives (among other data) ICD-9 discharge diagnoses from Veteran’s Affairs (VA) and Department of Defense (DOD) outpatient clinics. A literature review found that, in addition to ICD-9 code 487 (the code specific for influenza), 29 other codes have been used previously to monitor influenza. We evaluated the utility of ICD-9 codes reported to BioSense for their utility in monitoring influenza.

Objective

To determine the utility of current CDC BioSense data sources in monitoring influenza activity at the national and state levels.

This paper presents an investigation using data mining techniques to model patterns of influenza from positive case demographics, symptoms and laboratory tests.

 Following the development of an introductory Continuing Education (CME) course in syndromic surveillance, the Education and Training Committee of the International Society for Disease Surveillance recognized the need to educate future non-medical public health workers and reviewed courses offered by the top five public health schools recognized by US News and World Report1.  All public health schools offered courses that included information on public health practice and infectious disease epidemiology and few offered courses on spatial and disaster epidemiology with attention given to syndromic surveillance, but none of the schools offered a comprehensive course that integrated topics of public health practice, infectious disease surveillance, data management and analytic techniques, disaster preparedness, and syndromic surveillance2-6.  The development of the graduate school course builds on our existing CME slide set goals that teaches students about syndromic surveillance and presents the course in a free and easy to use format for all schools of public health.  The ISDS hopes the semester long course will be taught by ISDS members in each state to spread awareness and knowledge on the topic of syndromic surveillance.

Objective

The paper describes the development of a graduate-level course to teach future non-medical public health workers about syndromic surveillance.