Multidimensional Semantic Scan for Pre-Syndromic Disease Surveillance

An interdisciplinary team convened by ISDS to translate public health use-case needs into well-defined technical problems recently identified the need for new pre-syndromic surveillance methods that do not rely on existing syndromes or pre-defined illness categories1.

June 18, 2019

Development of a Custom Spell-Checker for Emergency Department Data

Emergency department (ED) syndromic surveillance relies on a chief complaint, which is often a free-text field, and may contain misspelled words, syntactic errors, and healthcare-specific and/or facility-specific abbreviations. Cleaning of the chief complaint field may improve syndrome capture sensitivity and reduce misclassification of syndromes. We are building a spell-checker, customized with language found in ED corpora, as our first step in cleaning our chief complaint field.

June 18, 2019

Monitoring for Local Transmission of Zika Virus using Emergency Department Data

The first travel-associated cases of Zika virus infection in New York City (NYC) were identified in January 2016. Local transmission of Zika virus from imported cases is possible due to presence of Aedes albopictus mosquitos. Timely detection of local Zika virus transmission could inform public health interventions and mitigate additional spread of illness. Daily emergency department (ED) visit surveillance to detect individual cases and spatio-temporal clusters of locally-acquired Zika virus disease was initiated in June 2016. 


July 16, 2017

Comparison between HL7 and Legacy Syndromic Surveillance Data in New York City

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.

October 02, 2017

Building a Better Syndromic Surveillance System: the New York City Experience

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.


October 09, 2017

Tractable Use Cases for Collaboration in Public Health Surveillance

The mission of the ISDS TCC is to bridge the gap between the analytic needs of public health practitioners and the expertise of researchers from other fields for the enhancement of disease surveillance, including situational awareness of chronic as well as infectious threats and follow-up activities such as case linkage and contact tracing.

November 06, 2017

Detecting Unanticipated Increases in Emergency Department Chief Complaint Keywords

The CC text field is a rich source of information, but its current use for syndromic surveillance is limited to a fixed set of syndromes that are routine, suspected, expected, or discovered by chance. In addition to syndromes that are routinely monitored by the NYC Department of Health and Mental Hygiene (e.g., diarrhea, respiratory), additional syndromes are occasionally monitored when requested by outside sources or when expected to increase during emergencies.

August 22, 2018

Application of a Bayesian Spatiotemporal Surveillance Method to NYC Syndromic Data

As technology advances, the implementation of statistically and computationally intensive methods to detect unusual clusters of illness becomes increasingly feasible at the state and local level [2]. Bayesian methods allow for the incorporation of prior knowledge directly into the model, which could potentially improve estimation of expected counts and enhance outbreak detection. This method is one of eight being formally evaluated as part of a grant from the Alfred P. Sloan Foundation.


August 22, 2018

Evaluation of Temporal Aberration Detection Methods in New York City Syndromic Data

The NYC syndromic surveillance system has been monitoring syndromes from NYC emergency department (ED) visits for over a decade. We applied several aberration detection methodologies to a time series of ED visits in NYC spiked with synthetic outbreaks. This effort is part of a larger evaluation of the NYC syndromic system, funded by a grant from the Alfred P. Sloan Foundation.


To critically evaluate temporal aberration detection methodologies using New York City (NYC) syndromic surveillance data.

April 28, 2019

Evaluating Utility of Cold-Injury Syndromic Surveillance Data in New York City

Cold weather exposure-related injuries range from hypothermia to less severe conditions such as frost bite, trench foot, and chilblains, which are all preventable causes of mortality and morbidity. In recent years, NYC has successfully used syndromic surveillance of heat-related ED visits to inform emergency response during heat waves. Similar timely surveillance of cold-exposure related injuries could also inform public health protection measures during severe winter weather or cold season power outages.

March 19, 2018


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