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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... Read more

Content type: Abstract

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... Read more

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The impact of heat on mortality is well documented but deaths tend to lag extreme heat and mortality data is generally not available for timely surveillance during heat waves. Recently, systems for near-real time surveillance of heat illness have been reported but have not been validated as... Read more

Content type: Abstract

Extreme temperatures are consistently shown to have an effect on CVD-related mortality [1, 2]. A large multi-city study of mortality demonstrated a cold-day and hot-day weather effect on CVD-related deaths, with the larger impact occurring on the coldest days [3]. In contrast, the association... Read more

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Public health disease surveillance is defined as the ongoing systematic collection, analysis and interpretation of health data for use in the planning, implementation and evaluation of public health, with the overarching goal of providing information to government and the public to improve... Read more

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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... Read more

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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... Read more

Content type: Abstract

ARIMA models use past values (autoregressive terms) and past forecasting errors (moving average terms) to generate future forecasts, making it a potential candidate method for modeling citywide time series of syndromic data [1]. While past research supports the use of ARIMA modeling as a... Read more

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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... Read more

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The role of public health in preparing for, responding to, and recovering from emergencies has expanded as a result of the massive impact recent disasters have had on affected populations. Nearly every large-scale disaster carries substantial public health risk and requires a response that... Read more

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National Syndromic
Surveillance Program


The National Syndromic Surveillance Program (NSSP) is a collaboration among states and public health jurisdictions that contribute data to the BioSense Platform, public health practitioners who use local syndromic surveillance systems, Center for Disease Control and Prevention programs, other federal agencies, partner organizations, hospitals, healthcare professionals, and academic institutions.

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