Baer Atar

Syndromic surveillance systems use electronic health-related data to support near-real time disease surveillance. Over the last 10 years, the use of ILI syndromes defined from emergency department (ED) data has become an increasingly accepted strategy for public health influenza surveillance at the local and national levels. However, various ILI definitions exist and few studies have used patient-level data to describe validity for influenza specifically.

Objective

Estimate and compare the accuracy of various ILI syndromes for detecting lab-confirmed influenza in children.

Syndromic surveillance systems were designed for early outbreak and bioterrorism event detection. As practical experience shaped development and implementation, these systems became more broadly used for general surveillance and situational awareness, notably influenza-like illness (ILI) monitoring. Beginning in 2006, ISDS engaged partners from state and local health departments to build Distribute, a distributed surveillance network for sharing de-identified aggregate emergency department syndromic surveillance data through existing state and local public health systems. To provide more meaningful cross-jurisdictional comparisons and to allow valid aggregation of syndromic data at the national level, a pilot study was conducted to assess implementation of a common ILI syndrome definition across Distribute.

Objective

Assess the feasibility and utility of adopting a common ILI syndrome across participating jurisdictions in the ISDS Distribute project.

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.

Collaborative relationships between academicians and public health practitioners are necessary to ensure that methodologies created in the research setting translate into practice. One barrier to forging these collaborations is restrictions on the sharing and availability of public health surveillance data; therefore, most academics with expertise in method development cannot access 'real world' surveillance data with which to evaluate their approaches. The ISDS Technical Conventions Committee was established in 2013 to facilitate and expedite the development, evaluation, and implementation of technical methods for public health surveillance. The purpose of the committee is to bridge a long-standing gap between technical challenges in public health practice and solution developers needing both understanding of these challenges and representative data.

Objective

The purpose of this panel is to facilitate the dissemination of surveillance-related use cases by public health practitioners with accompanying benchmark datasets to method developers. The panel will present practitioners' experiences with preparing patient-level emergency department data sets to accompany a use case submitted to the ISDS Technical Conventions Committee.

One criterion for evaluating the effectiveness of a surveillance system is the system’s positive predictive value. To our knowledge few studies have described the positive predictive value of syndromic surveillance signals for naturally occurring conditions of public health importance.

Objective

We evaluated the positive predictive value of signals detected by our syndromic surveillance system.

On 12/14/06, a windstorm in western Washington caused 4 million residents to lose power; within 24 hours, a surge in patients presented to emergency departments (EDs) with carbon monoxide (CO) poisoning. As previously described, records of all patients presenting to King County EDs with CO poisoning between 12/15/06 to 12/24/06 (n=279) were abstracted, of which 249 met the case definition and eligibility requirements. We attempted to identify each of the 249 confirmed cases of CO poisoning in our syndromic ED data set by comparing the hospital name, date, time, age, sex, zip code, chief complaint, and diagnoses across the two data sets. We designated each record as an exact match, likely match, possible match, or unmatched on the basis of the available fields.

Objective

We evaluated ED and emergency medical services data for describing an outbreak of CO poisoning following a windstorm, and determined whether loss of power was followed by an increase in other health conditions.

Varied approaches have been used by syndromic surveillance systems for aberration detection. However, the performance of these methods has been evaluated only across a small range of epidemic characteristics.

Objective

We conducted a large simulation study to evaluate the detection properties of 6 different algorithms across a range of outbreak characteristics.