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Lall Ramona

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. Our group has recently developed Multidimensional Semantic Scan (MUSES), a pre-syndromic surveillance approach that (1) uses topic modeling to identify newly emerging syndromes that correspond to rare or novel diseases; and (2) uses multidimensional scan statistics to identify emerging outbreaks that correspond to these syndromes and are localized to a particular geography and/or subpopulation2,3. Through a blinded evaluation on retrospective free-text ED chief complaint data from NYC DOHMH, we demonstrate that MUSES has great potential to serve as a safety net for public health surveillance, facilitating a rapid, targeted, and effective response to emerging novel disease outbreaks and other events of relevance to public health that do not fit existing syndromes and might otherwise go undetected.

Objective: We present a new approach for pre-syndromic disease surveillance from free-text emergency department (ED) chief complaints, and evaluate the method using historical ED data from New York City's Department of Health and Mental Hygiene (NYC DOHMH).

Submitted by elamb on

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. This exercise would elucidate the value of pre-processing text and would lend itself to future work using natural language processing (NLP) techniques, such as topic modeling. Such a tool could be extensible to other datasets that contain free-text fields, including electronic reportable disease lab and case reporting.

Objective: To share progress on a custom spell-checker for emergency department chief complaint free-text data and demonstrate a spell-checker validation Shiny application.

Submitted by elamb on

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.

Objective

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

Submitted by knowledge_repo… on

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. During Hurricane Sandy, we discovered by manual inspection of data for a few EDs an increase in certain words in the CC field (e.g., 'METHADONE', 'DIALYSIS', and 'OXYGEN') that led to the creation of a 'needs medication' syndrome. Current syndromic surveillance systems cannot detect unanticipated events that are not defined a priori by keywords. We describe a simple data-driven method that routinely scans the CC field for increases in word frequency that might trigger further investigation and/or temporary monitoring.

Objective

To detect sudden increases in word frequency in the Emergency Department (ED) syndromic chief complaint (CC) text field.

Submitted by knowledge_repo… on

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.

Objective

To adapt a previously described Bayesian model-based surveillance technique for cluster detection [1] to NYC Emergency Department (ED) visits.

Submitted by knowledge_repo… on

Using the chief complaint field from our established syndromic ED system, we developed definitions for potentially preventable oral health visits (OHV) and examined patterns in 2009-2011 data. Under the widest definition, OHV comprised about 1% of ED visits. Adults ages 18 to 29 had markedly higher OHV than other ages, as did certain neighborhoods/EDs. We found more than half of OHV occurred during daytime hours, suggesting opportunities for targeted outreach and education. With some caveats, syndromic ED data provide a useful complement to other oral health surveillance strategies.

Objective

To utilize an established syndromic reporting system for surveil- lance of potentially preventable emergency department (ED) oral health visits (OHV) in New York City (NYC).

Submitted by dbedford on

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. We conducted a retrospective analysis to compare hypothermia and cold-injury patient case characteristics, as well as temporal and meteorological correlates, between syndromic surveillance data and hospital discharge data.

Objective:

1) Develop cold exposure-related injury syndromic case definitions

2) use historical data to compare trends among cases identified in syndromic surveillance and cases identified in NY Statewide Planning and Research Cooperative System (SPARCS) hospital discharge data to evaluate representativeness and

3) develop regression models to examine relationships with cold weather conditions, and compare relationships across case definitions and data sources.

 

Submitted by Magou on

Syndromic surveillance data has predominantly been used for surveillance of infectious disease and for broad symptom types that could be associated with bioterrorism. There has been a growing interest to expand the uses of syndromic data beyond infectious disease. Because many of these conditions are specific and can be swiftly diagnosed (as opposed to infectious agents that require a lab test for confirmation) there could be added value in using the ICD9 ED discharge diagnosis field collected by SS. However, SS discharge diagnosis data is not complete or as timely as chief complaint data. Therefore, for the time being SS chief complaint data is relied on for non-infectious disease surveillance. SPARCS data are based on clinical diagnoses and include information on final diagnosis, providing a means for comparing the chief complaint (from SS) to a diagnosis code (from SPARCS), for evaluating how well the syndrome is captured by SS and for assessing if it would be advantageous to get SS ED diagnosis codes in a more timely and complete manner.

Objective:

To evaluate several non-infectious disease related syndromes that are based on chief complaint (cc) emergency department (ED) syndromic surveillance (SS) data by comparing these with the New York Statewide Planning and Research Cooperative System (SPARCS) clinical diagnosis data. In particular, this work compares SS and SPARCS data for total ED visits and visits associated with three noninfectious disease syndromes, namely asthma, oral health and hypothermia.

 

Submitted by Magou on

The NYC Department of Health and Mental Hygiene (DOHMH) ED syndromic surveillance system receives data from 95% of all ED visits in NYC totaling 4 million visits each year. The data include residential ZIP code as reported by the patient. ZIP code information has been used by the DOHMH to separate visits into NYC and nonNYC for analysis; and, a closer examination of non-NYC visits may further inform disease surveillance.

Objective

To classify visits to NYC emergency departments (ED) into NYC residential, NYC PO Box or commercial building, commuters to NYC, and out-of-town visitors. To describe patterns in each group, to evaluate how they differ, and to consider how the differences can affect syndromic surveillance analyses and results.

Submitted by teresa.hamby@d… on

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. Committee activities to achieve this mission are identifying practical use cases, refining technical specifications in open forums, obtaining benchmark datasets for controlled dissemination, validating candidate methods, and sharing method documentation. In its first 2 years, the TCC has worked on three use cases and assisted with development of data use agreements to permit posting of benchmark datasets, http://www.syndromic.org/ communities/technical-conventions. Recent polling of the Biosense User Group indicated widespread interest in developing additional use cases. The proposed panel is intended to focus on practical applications of common interest, refine the use case development and dissemination process, and foster global interest in this process.

Objective

The main objective is to broaden the collection of use cases developed by the ISDS Technical Conventions Committee (TCC) to enhance effective collaboration between public health practice and analyst researchers in various disciplines and institutions. Panellists will present and motivate use case concepts including requirements for practical solution methods. Component objectives are to refine the presented use cases and to stimulate formation of new ones at local, state, and national levels.

Submitted by teresa.hamby@d… on
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