Chang Hwa-Gan

Shigella remains highly infectious in the United States and rapid detection of Shigella outbreaks is crucial for disease control and timely public health actions. The New York State Department of Health (NYSDOH) implemented a Communicable Disease Electronic Surveillance System (CDESS) for local health departments (LHDs) to collect clinical and laboratory testing information and supplement epidemiologic information for the patients from New York State, excluding New York City, with infectious diseases. The CDESS includes reported cases that are involved in outbreaks and which constituted the base for identifying any outbreak. The selection of a fitted outbreak detection method would play a critical role in enhancing disease surveillance.

Objective

To explore the possibility of using statistical methods to detect Shigella outbreaks, assess the effectiveness of the methods to signal real outbreaks, provide manageable information for follow-up activities and avoid unnecessary surveillance work.

Most outbreaks are small and localized in nature, although it is larger outbreaks that result in the most public attention. So a solution to manage an outbreak has to be able to accommodate a response to small outbreaks in a single jurisdiction scalable up to outbreaks that involve thousands of cases across multiple jurisdictions and to handle different types of situations with different questions and response required. To make this happen, information and resources need to be shared more consistently and efficiently to help facilitate the communication that occurs at all levels and to support day-to-day operations in order to ensure consistent use.

Objective

1.To provide a flexible solution to perform an outbreak investigation by improving communications during an incident.

2.To provide all users with a common set of data for decision support.

3.To provide standard forms for a consistent approach and to improve data quality.

Following an Oct 12-13, 2006 snowstorm, almost 400,000 homes in western New York lost power, some for up to 12 days. News reports said that emergency rooms saw many patients with CO exposure; 3 deaths were attributed to CO poisoning. As part of NYS DOH’s syndromic surveillance system, electronic ED records with a free-text CC field listing the symptoms reported by the patient are sent to NYS DOH daily. Each CC is searched for text strings indicating complaints in one or more of 6 syndromes (asthma, fever, gastrointestinal (GI), neurological, respiratory, rash). The system also allows nonroutine searches of CCs for complaints of interest. NYS hospitals also submit ED records to the Statewide Planning and Research Cooperative System (SPARCS) that include diagnostic codes assigned after evaluation of the patient (due within 30 days of each calendar quarter).

Objective

To assess the ability to identify cases of carbon monoxide (CO) poisoning from chief complaints (CC) in hospital emergency department (ED) records submitted daily to the New York State (NYS) Department of Health (DOH) Electronic Syndromic Surveillance System.

New York State has implemented a statewide Electronic Clinical Laboratory Reporting System (ECLRS) to which laboratories can electronically submit test results for reportable conditions. The Communicable Disease Electronic Surveillance System (CDESS) was used by 57 Local Health Departments (LHDs) to transfer ECLRS information and initiate investigations. Currently over 98% of licensed clinical labs are reporting via ECLRS. Positive laboratory test results are required to confirm over 80% of communicable diseases and they are often the first indication of a disease. Early detection of disease outbreaks is important for timely implementation of disease prevention and control measures. The space-time permutation scan statistic only requires disease counts, event date and disease location, which are collected from ECLRS and can be used to detect potential disease outbreaks by identifying spatial-temporal lab report clusters.

Objective

This abstract explains how the space-time permutation scan statistic only requires disease counts, event date and disease location, which are collected from ECLRS and can be used to detect potential disease outbreaks by identifying spatial-temporal lab report clusters.

In order to detect influenza outbreaks, the New York State Department of Health emergency department (ED) syndromic surveillance system uses patients’ chief complaint (CC) to assign visits to respiratory and fever syndromes. Recently, the CDC developed a more specific set of “sub-syndromes” including one that included only patients with a CC of flu or having a final ICD9 diagnosis of flu. Our own experience was that although flu may be a common presentation in the ED during the flu season, it is not commonly diagnosed as such. Emergency physicians usually use a symptomatic diagnosis in preference, probably because rapid testing is generally unavailable or may not change treatment. The flu subsyndrome is based on a specific ICD9 code for influenza. It is unknown whether patient visits that meet these restrictive criteria are sufficiently common to be of use, or whether patients who identify themselves as having the flu are correct.

Objective

Our objective was to examine the CC and ICD9 classifiers for the influenza sub-syndrome to assess the frequency of visits and the agreement between the CC, ICD9 code and chart review for these patient visits.

The Centers for Disease Control and Prevention BioSense has developed chief complaint (CC) and ICD9 sub syndrome classifiers for the major syndromes for early event detection and situational awareness. The prevalence of these sub-syndromes in the emergency department population and the performance of these CC classifiers have been little studied. Chart reviews have been used in the past to study this type of question but because of the large number of cases to review, the labor involved would be prohibitive. Therefore, we used an ICD9 code classifier for a syndrome as a surrogate by chart reviews to estimate the performance of a CC classifier.

Objective

To determine the prevalence of the sub-syndromes based on the ICD9 classifiers, and to determine the sensitivity, specificity, positive predictive value and negative predictive value of CC classifiers for the sub-syndromes associated with the respiratory and gastrointestinal syndromes using the ICD9 classifier as the criterion standard.

The New York State Department of Health (NYSDOH) Syndromic Surveillance System consists of five components: 1. Emergency Department (ED) Phone Call System monitors unusual events or clusters of illnesses in the EDs of participating hospitals; 2. Electronic ED Surveillance System monitors ED chief complaint data; 3. Medicaid data system monitors Medicaid-paid over-the-counter and prescription medica-tions; 4. National Retail Data Monitor/Real-time Outbreak and Disease Surveillance System monitors OTC data; 5. CDC’s BioSense application monitors Department of Defense and Veterans Administration outpatient care clinical data (ICD-9-CM diag-noses and CPT procedure codes), and LabCorp test order data.

Objective

This poster presentation provides an overview of the NYSDOH Syndromic Surveillance System, including data sources, analytic algorithms, and resulting reports that are posted on the NYSDOH Secure Health Commerce System for access by state, regional, county, and hospital users.

The Centers for Disease Control and Prevention BioSense project has developed chief complaint (CC) and ICD9 sub-syndrome classifiers for the major syndromes for early event detection and situational awareness. This has the potential to expand the usefulness of syndromic surveillance, but little data exists evaluating this approach. The overall performance of classifiers can differ significantly among syndromes, and presumably among subsyndromes as well. Also, we had previously found that the seasonal pattern of diarrhea was different for patients < 60 months of age (younger) and for patients > 60 months of age (older).

Objective

Using chart review as the criterion standard to estimate the sensitivity, specificity, positive predictive value and negative predictive value of New York State hospital emergency department CC classifiers for patients < 60 months of age and > 60 months of age for the gastrointestinal (GI) syndrome and the following GI sub-syndromes: “abdominal pain”, “nausea-vomiting” and “diarrhea”.

One limitation of syndromic surveillance systems based on emergency department (ED) data is the time and expense to investigate peak signals, especially when that involves phone calls or visits to the hospital. Many EDs use electronic medical records (EMRs) which are available remotely in real time. This may facilitate the investigation of peak signals.