Mathes Robert

Over the last decade, the application of syndromic surveillance systems has expanded beyond early event detection to include longterm disease trend monitoring. However, statistical methods employed for analyzing syndromic data tend to focus on early event detection. Generalized linear mixed models (GLMMs) may be a useful statistical framework for examining long-term disease trends because, unlike other models, GLMMs account for clustering common in syndromic data, and GLMMs can assess disease rates at multiple spatial and temporal levels (1). We show the benefits of the GLMM by using a GLMM to estimate asthma syndrome rates in New York City from 2007 to 2012, and to compare high and low asthma rates in Harlem and the Upper East Side (UES) of Manhattan.

Objective:

Show the benefits of using a generalized linear mixed model (GLMM) to examine long-term trends in asthma syndrome data.

 

The New York City (NYC) Department of Health and Mental Hygiene (DOHMH) receives daily ED data from 49 of NYC’s 52 hospitals, representing approximately 95% of ED visits citywide. Chief complaint (CC) is categorized into syndrome groupings using text recognition of symptom key-words and phrases. Hospitals are not required to notify the DOHMH of any changes to procedures or health information systems (HIS). Previous work noticed that CC word count varied over time within and among EDs. The variations seen in CC word count may affect the quality and type of data received by the DOHMH, thereby affecting the ability to detect syndrome visits consistently.

Objective

To identify changes in emergency department (ED) syndromic surveillance data by analyzing trends in chief complaint (CC) word count; to compare these changes to coding changes reported by EDs; and to examine how these changes might affect the ability of syndromic surveillance systems to identify syndromes in a consistent manner.

A decade ago, the primary objective of syndromic surveillance was bioterrorism and outbreak early event detection (EED. Syndromic systems for EED focused on rapid, automated data collection, processing and statistical anomaly detection of indicators of potential bioterrorism or outbreak events. The paradigm presented a clear and testable surveillance objective: the early detection of outbreaks or events of public health concern. Limited success in practice and limited rigorous evaluation, however, led to the conclusion that syndromic surveillance could not reliably or accurately achieve EED objectives. At the federal level, the primary rationale for syndromic surveillance shifted away from bioterrorism EED, and towards allhazards biosurveillance and SA. The shift from EED to SA occurred without a clear evaluation of EED objectives, and without a clear definition of the scope or meaning of SA in practice. Since public health SA has not been clearly defined in terms of operational surveillance objectives, statistical or epidemiological methods, or measurable outcomes and metrics, the use of syndromic surveillance to achieve SA cannot be evaluated.

Objective

Review concept of situation awareness (SA) as it relates to public health surveillance, epidemiology and preparedness. Outline hierarchical levels and organizational criteria for SA. Initiate consensus building process aimed at developing a working definition and measurable outcomes and metrics for SA as they relate to syndromic surveillance practice and evaluation.

Over several months in 2012, NYC DOHMH syndromic surveillance staff met with directors of all 49 participating EDs in our syndromic system to collect information on their health information systems coding practices. During these interviews, ED directors expressed interest in receiving summary reports of the data they send to the syndromic unit, such as number of ED visits, most common complaints, and temporal and spatial trends. This effort was done to increase communication and cooperation between the syndromic unit and the EDs that provide data to the syndromic system.

Objective

To share monthly summary reports of syndromic data to participating EDs in NYC.

Recent efforts to share syndromic surveillance data have focused on developing national systems, namely BioSense 2.01 . The problems with creating and implementing national systems, such as legal issues, difficulties in standardizing syndrome definitions, data quality, and different objectives, are well documented. In contrast, several local health departments have successfully shared data and analyses with each other, primarily during emergency events. The benefits of locally-driven data sharing include: (1) faster dissemination of data and analyses that have been created by those who understand the nuances of their own data, (2) easier process of standardizing syndrome definitions, (3) quickly designing appropriate analyses for the event, (4) smaller group of partners for consensus-building, and (5) ultimately improved timeliness in detection of public health events. The strategies used to share data and analyses between local and state health departments during planned and unplanned events may be informative to national systems.

Objective

To outline successful strategies for regional data-sharing and discuss how these strategies can be applied to other regions.

From June 4-8, 2015, the New York City (NYC) syndromic surveillance system detected five one-day citywide signals in sales of over-the-counter (OTC) antidiarrheal medications using the CUSUM method with a 56-day moving baseline. The OTC system monitors sales of two classes of antidiarrheal medications, products with loperamide or bismuth, from two NYC pharmacy chains. To determine if this increase reflected a concerning cluster of diarrheal illness, we examined multiple communicable disease surveillance data systems.

Objective

To investigate a communicable disease syndromic surveillance signal using multiple data sources.

The New York City (NYC) syndromic surveillance system has monitored syndromes from NYC emergency department (ED) visits since 2001, using the temporal and spatial scan statistic in SaTScan for aberration detection. Since our syndromic system was initiated, alternative methods have been proposed for outbreak identification. Our goal was to evaluate methods for outbreak detection and apply the best performing method(s) to our daily analysis of syndromic data.

Objective

To evaluate temporal and spatial aberration detection methods for implementation in a local syndromic surveillance system.

Data from the Emergency Departments (EDs) of 49 hospitals in New York City (NYC) is sent to the Department of Health and Mental Hygiene (DOHMH) daily as part of the syndromic surveillance system. Currently, thirty-four of the EDs transmit data as flat files. As part of the Center for Medicare and Medicaid Services Electronic Health Record Incentive Program, otherwise known as Meaningful Use, many EDs in our system have switched or are in the process of switching to HL7 Messaging Standard Version 2.5.1. Given there may be differences in data completeness, quality, and content between the new HL7 data and legacy data, we evaluated data sent in both formats in parallel by several EDs.

Objective

To evaluate potential changes in emergency department (ED) syndromic surveillance data quality, as hospitals shift from sending data as flat file format (Legacy Data) to real-time/batch HL7 Messaging Standard Version 2.5.1, in compliance with Meaningful Use requirements.