Surveillance Systems

The electronic surveillance system for the early notification of community-based epidemics (ESSENCE) is the web-based syndromic surveillance system utilized by the Maryland Department of Health and Mental Hygiene (DHMH). ESSENCE utilizes a secure, automated process for the transfer of data to the ESSENCE system that is consistent with federal standards for electronic disease surveillance. Data sources in the Maryland ESSENCE system include ED chief complaints, poison control center calls, over-the-counter (OTC) medication sales, and pharmaceutical transaction data (specifically for anti-bacterial and anti-viral medications). All data sources have statewide coverage and are captured daily in near real-time fashion.

Objective

To examine the trends in prescription antiviral medication transactions and emergency department (ED) visits for influenza-like illness (ILI) and the relationship between these trends.

In the summer of 2001, New Jersey (NJ) was in the process of developing surveillance activities for bioterrorism. On September 11, 2001, the U.S. suffered a major terrorist attack. Approximately a month later, Anthrax-laced letters were processed through a New Jersey Postal Distribution Center (PDC). As a result of these events, the state instituted simplistic surveillance activities in emergency departments (ED's). Over time, this initial system has developed into a broader, more streamlined approach to surveillance that now includes syndromic data e.g., Influenza-like illness (ILI) as well as the use of technology (automated surveys, real-time data connections, and alert analysis) to achieve surveillance goals and provide daily information to public health partners in local health departments and DHSS response colleagues.

Objective

To describe the improvements in New Jersey's Emergency Department surveillance system over time.

Epidemic acute gastroenteritis (AGE) is a major contributor to the global burden of morbidity and mortality. Rotavirus and norovirus epidemics present a significant burden annually, with their predominant impact in temperate climates occurring during winter periods. Annually, epidemic rotavirus causes an estimated 600,000 deaths worldwide, and 70,000 hospitalizations in the US, primarily among children <5 years of age. The US burden from norovirus is estimated at 71,000 hospitalizations annually, with the impact more generally across age groups. Changes in rotavirus vaccine use have significantly reduced the impact of epidemic rotavirus.

Objective 

We describe the initial phase of the ISDS Distribute pilot for monitoring AGE syndromic emergency department visits, and present preliminary analysis of age-specific trends documenting a dramatic shift in AGE consistent with US rotavirus vaccine policy and use.

Of the 13 million people in Malawi1 85% are rural and the country has high burden of under-five morbidity and mortality due to preventable infectious diseases. Respiratory, febrile and diarrhea diseases are the top 3 morbidity and mortality illnesses in most developing countries2. Acute medical care has greatly improved these conditions, but widespread and uncontrolled use of antibiotics threatens to reverse gains achieved so far. Drug sensitivity tests are a prerequisite to guide prescription practices.

Objective

Assessment of routine use of drug sensitivity test results to guide treatment choices in district hospitals in Malawi.

The Washington Comprehensive Hospital Abstract Reporting System (CHARS) has collected discharge data from billing systems for every inpatient admitted to every hospital in the state since 1987 [1]. The purpose of the system is to provide data for making informed decisions on health care. The system collects age, sex, zip code and billed charges of the patient, as well as hospital names and discharge diagnoses and procedure codes. The data have potential value for monitoring the severity of outbreaks such as influenza, but not for prospective surveillance: Reporting to CHARS is manual, not real-time, and there is roughly a 9-month lag in release of information by the state. In 2005, Public Health - Seattle & King County (PHSKC) requested that hospitals report pneumonia and influenza admissions (based on both admission and discharge codes) directly to the PHSKC biosurveillance system; data elements included hospital name, date/time of admission, age, sex, home zip code, chief complaint, disposition, and diagnoses. In 2008, reporting was revised to collect separate admission and discharge diagnoses, whether the patient was intubated or was in the ICU, and a patient/visit key. Hospitals transmit data daily for visits that occurred up to 1 month earlier. Previously, we identified a strong concordance between the volume of influenza diagnoses recorded across the PHSKC and CHARS systems over time [2]. However, discrepancies were observed, particularly when stratified by hospital. We undertook an evaluation to identify the causes of these discrepancies.

Objective

We sought to evaluate the quality of influenza hospitalizations data gathered by our biosurveillance system.

The Public Health - Seattle & King County syndromic surveillance system has been collecting emergency department (ED) data since 1999. These data include hospital name, age, sex, zip code, chief complaint, diagnoses (when available), disposition, and a patient and visit key. Data are collected for 19 of 20 King County EDs, for visits that occurred the previous day. Over time, various problems with data quality have been encountered, including data drop-offs, missing data elements, incorrect values of fields, duplication of data, data delays, and unexpected changes in files received from hospitals. In spite of close monitoring of the data as part of our routine syndromic surveillance activities, there have occasionally been delays in identifying these problems. Since the validity of syndromic surveillance is dependent on data quality, we sought to develop a visualization to help monitor data quality over time, in order to improve the timeliness of addressing data quality problems.

Objective 

We sought to develop a method for visualizing data quality over time.

Distribute is a national emergency department syndromic surveillance project developed by the International Society for Disease Surveillance for influenza-like-illness (ILI) that integrates data from existing state and local public health department surveillance systems. The Distribute project provides graphic comparisons of both ILI-related clinical visits across jurisdictions and a national picture of ILI. Unlike other surveillance systems, Distribute is designed to work solely with summarized (aggregated) data which cannot be traced back to the un-aggregated 'raw' data. This and the distributed, voluntary nature of the project creates some unique data quality issues, with considerable site to site variability. Together with the ISDS, the University of Washington has developed processes and tools to address these challenges, mirroring work done by others in the Distribute community.

Objective

To present exploratory tools and methods developed as part of the data quality monitoring of Distribute data, and discuss these tools and their applications with other participants.

NC DETECT provides near-real-time statewide surveillance capacity to local, regional and state level users across NC with twice daily data feeds from 117 (99%) emergency departments (EDs), hourly updates from the statewide poison center, and daily feeds from statewide EMS runs and select urgent care centers. The NC DETECT Web Application provides access to aggregate and line listing analyses customized to users' respective jurisdictions. The most active users are state-level epidemiologists (DPH) and hospital-based public health epidemiologists (PHEs). The use of NC DETECT is included in PHE job descriptions and NC DETECT functionality has been developed specifically to meet the surveillance needs of this group, including data entry of aggregated lab results for flu and respiratory panels. Interviews of local health department (LHD) users completed as part of an evaluation project have suggested that functionality specifically tailored to LHDs may increase their use of the NC DETECT Web application [1]. As of June 2011, there were 139 LHD users with active accounts to use the Web application (out of 384 total users with active accounts).

Objective

To describe the development, implementation and preliminary evaluation of new dashboard interfaces in NC DETECT, designed primarily for infrequent users of NC DETECT at local health departments.

With the proliferation of social networks, the web has become a warehouse of patient discussions and reports, estimated at 10 billion records and growing at a rate of 40 percent per year. First Life Research, Ltd. (FLR), has searched and mapped thousands of these discussions and indexed hundreds of millions of reports (currently 960M) and is engaged in building web-based solutions that enable the public and public health practitioners to access massive health-related information and knowledge generated from the crowd.

Objective

With a large population sharing experiences regarding health issues and treatments online via social media platforms, generating novel data sets composed of massive unstructured user-generated content of health reports. This collective intelligence is referred to as the ‘Wisdom of the crowd’. This is a brief overview of data research engaging this unique statistical sample referred to as the ‘Crowd trial’ as an innovative element in health monitoring, enabling early detection and intervention by health professionals, regulators and pharmaceutical companies.

Singulair (MONTELUKAST SODIUM) is a Leukotriene receptor antagonist, indicated to prevent asthma attacks in adults and children. It is also used to relieve allergies in adults and children. Singulair was approved by the FDA in February 20, 1998. In March 2008 the FDA informed healthcare professionals of investigating the possible association between Singulair usage and behavior/mood changes, suicidality and suicide. First Life Research [FLR] identifies, analyzes, indexes and aggregates user generated content by collecting billions of testimonials from social networks. It utilizes cutting edge technologies for massive data aggregation, and applies advanced Natural Language Processing (NLP) techniques for continuous analyses, in order to convert this unstructured data into refined information. With a large population sharing experiences regarding health issues and treatments online via social media platforms [Health 2.0], generating novel data sets comprised of massive unstructured user generated content of health reports. This collective intelligence is referred to as the 'Wisdom of the Crowd' or the 'Crowd Trial'. Unlike regulated formal post marketing reports, the crowd trial takes place spontaneously, continuously and on a very large scale. This Crowd Trial provides a snapshot of health trends and has become a proxy of post-market clinical trials of medications and other therapies.

Objective

The purpose of this case report is to demonstrate how applying an additional data source originated from e-patient reports, helps support drug surveillance and Pharmacovigilance processes.