Evaluation of Syndromic Surveillance

Data sets from disparate sources widely vary in the number and type of factors which most hamper integrity and timeliness of the data. To maintain high quality data, data sets must be regularly assessed, particularly for those vulnerabilities that each is especially prone to due to the methods involved in collecting the data. For surveillance practitioners charged with monitoring data from multiple data sources, keeping track of the issues that each data set is susceptible to, and quickly identifying any inconsistencies or deviations from normal trends, may be a challenge. An application that can track all those issues, and trigger alerts when patterns diverge from what is expected, could help to enhance the efficiency and effectiveness of the surveillance efforts.

Objective

An interactive, point-and-click application was developed to facilitate the routine assessment of known data quality factors that compromise the integrity and timeliness of data sets used at the Marion County Public Health Department (MCPHD). The code (and associated documentation) for this application is being made available for other surveillance practitioners to adopt.

Since 2009, Houston Health Department (HHD) uses an electronic disease surveillance system (Maven) to receive ELRs from reporting facilities in the Houston jurisdiction. Currently, two large hospital systems, a blood bank, two large commercial labs, and two public health labs are sending ELRs to Maven. The overall percentage of disease reports received via ELR was over 50%. We hypothesize that the implementation of ELR has improved the timeliness and completeness of disease surveillance.

Objective

Review 5 years of surveillance data post electronic lab reporting (ELR) implementation and 8 years of data prior to ELR, to evaluate timeliness and completeness of disease surveillance.

ARIs have epidemic and pandemic potential. Prediction of presence of ARIs from individual signs and symptoms in existing studies have been based on clinically-sourced data. Clinical data generally represents the most severe cases, and those from locations with access to healthcare institutions. Thus, the viral information that comes from clinical sampling is insufficient to either capture disease incidence in general populations or its predictability from symptoms. Participatory data — information that individuals today can produce on their own — enabled by the ubiquity of digital tools, can help fill this gap by providing self-reported data from the community. Internet-based participatory efforts such as Flu Near You have augmented existing ARI surveillance through early and widespread detection of outbreaks and public health trends.

Objective

To evaluate prediction of laboratory diagnosis of acute respiratory infection (ARI) from participatory data using machine learning models

As syndromic surveillance systems continue to grow, new opportunities have arisen to utilize the data in new or alternative ways for which the system was not initially designed. For example, in many jurisdictions syndromic surveillance has recently become population-based, with 100% coverage of targeted emergency department encounters. This makes the data more valuable for real- time evaluation of public health and prevention programs. There has also been increasing pressure to make more data publicly available – to the media, academic partners, and the general public. 

Objective

This roundtable will provide a forum for national, state, and local managers of syndromic surveillance systems to discuss how they identify, monitor, and respond to changes in the nature of their data. Additionally, this session will focus on the strengths and weakness of the syndromic surveillance systems for supporting program evaluation and trend analysis. This session will also provide a forum where subject matter experts can discuss the ways in which this deep understanding of their data can be leveraged to forge and improve partnerships with academic partners. 

The ICD-9 codes for acute respiratory illness (ARI) and pneumonia/influenza (P&I) are commonly used in ARI surveillance; however, few studies evaluate the accuracy of these codes or the importance of ICD-9 position. We reviewed ICD-9 codes reported among patients identified through severe acute respiratory infection (SARI) surveillance to compare medical record documentation with medical coding and evaluated ICD-9 codes assigned to patients with influenza detections. 

In New Jersey, Health Monitoring Systems Inc.’s (HMS) EpiCenter collects chief complaint data for syndromic surveillance from 79 of 80 emergency departments (ED). Using keyword algorithms, these visits are classified into syndrome categories for monitoring unusual health events.

HAIs are infections that patients acquire while they are receiving treatment for a health condition in a health care setting. Following the 2014 Ebola outbreak in West Africa, the New Jersey Department of Health (NJDOH) Communicable Disease Service (CDS) started recruiting EDs to include triage note data in addition to chief complaint data to enhance surveillance capability for Ebola and other HAIs. Research by the University of North Carolina suggests triage note data improve the ability to detect illness of interest by fivefold. Currently, there are three NJ EDs with triage note data in EpiCenter along with ICD 10 codes which can be used for comparison.

This pilot study will assess whether infections following a surgical procedure can be captured in triage note data along with ICD codes. Also, this evaluation will determine if triage note data can be used to create HAI custom classifications for syndromic surveillance. These classifications can potentially be used by surveillance and/or preparedness personnel and local health departments, as well as hospitals, to better prepare for detecting and preventing HAIs that are a significant cause of morbidity and mortality in the U.S. 

Objective

Evaluate the usage of triage note data from EpiCenter, a syndromic surveillance system utilized by New Jersey Department of Health (NJDOH), to enhance Healthcare-Associated Infections (HAIs) surveillance for infections following a surgical procedure. 

Healthcare data, including emergency department (ED) and outpatient health visit data, are potentially useful to the public health community for multiple purposes, including programmatic and surveillance activities. These data are collected through several mechanisms, including administrative data sources [e.g., MarketScan claims data1; American Hospital Association (AHA) data2] andpublic health surveillance programs [e.g., the National Syndromic Surveillance Program (NSSP)3]. Administrative data typically become available months to years after healthcare encounters; however, data collected through NSSP provide near real time information not otherwise available to public health. To date, 46 state and 16 local health departments participate in NSSP, and the estimated nationalp ercentage of ED visits covered by the NSSP BioSense platform is 54%. NSSP’s new data visualization tool, ESSENCE, also includes additional types of healthcare visit (e.g., urgent care) data. Although NSSP is designed to support situational awareness and emergency response, potential expanded use of data collected through NSSP (i.e., by additional public health programs) would promote the utility, value, and long-term sustainability of NSSP and enhance surveillance at the local, state, regional, and national levels. On the other hand, studies using administrative data may help public health programs better understand how NSSP data could enhance their surveillance activities. Such studies could also inform the collection and utilizationof data reported to NSSP.

Objective

This roundtable will address how multiple data sources, including administrative and syndromic surveillance data, can enhance public health surveillance activities at the local, state, regional, and national levels. Provisional findings from three studies will be presented to promote discussion about the complementary uses, strengths and limitations, and value of these data sources to address public health priorities and surveillance strategies.

In September, 2015, Nigeria was delisted from the list of polio endemic countries globally. To be certified polio free, the country must attain and maintain certification standard Acute Flaccid Paralysis(AFP) surveillance for additional two-years. In Oyo State, no case of Wild Polio Virus (WPV) has been reported since February, 2009.

Objective

We evaluated the AFP surveillance system in Oyo State to assessits attributes and determine if it was meeting its set objectives.