We describe age- and syndrome-specific emergency department (ED) visit patterns for diarrhea and vomiting associated with periods of confirmed epidemic rotavirus and presumed epidemic norovirus in New York City (NYC).
Syndromic Surveillance has been in use in New York City since 2001, with 2.5 million visits reported from 39 participating emergency departments, covering an estimated 75% of annual visits. As syndromic surveillance becomes increasingly spatial and tied to geography, the resulting spatial analysis is also evolving to provide new methodology and tools. In late 2004, the New York City Department of Health and Mental Hygiene (DOHMH) created the geographic information systems (GIS) Center of Excellence to identify ways in which GIS could enhance programs like syndromic surveillance. The DOHMH uses the SaTScan program for much of its spatial analysis (i.e. cluster analysis).
Objective
This paper describes a series of visualization enhancements and automation processes to efficiently depict syndromic surveillance data in GIS. Modelling the portrayal of events when merging existing syndromic surveillance with GIS can standardize and expedite results.
There has been much recent interest in using disease signatures to better recognize disease outbreaks. Conversely, the metrics used to describe these signatures can also be used to better characterize the outbreaks. Recent work at the New York City Department of Health has shown the ability to identify characteristic age-specific patterns during influenza outbreaks. One issue that remains is how to implement a search for such patterns using prospective outbreak detection tools such as SatScan.
A potential approach to this problem arises from another currently active research area: the simultaneous use of multiple datastreams. One form of this is to disaggregate a data stream with respect to a third variable such as age. Two drawbacks to this approach are that the categories used to make the streams have to be defined a priori and that relationships between the streams cannot be exploited. Furthermore, the resulting description is less rich as it describes outbreaks in a few non-overlapping age-specific streams. It would be desirable to look for age specific patterns with the age groupings implicitly defined.
Objective
This paper presents an implementation of a citywide SatScan analysis that uses age as a one-dimensional spatial variable. The resulting clusters identify age-specific clusters of respiratory and fever/flu syndromes in the New York City Emergency Department Data.
Syndromic surveillance systems can detect increases in respiratory and gastrointestinal illness, but diagnosis of etiologic agents can be delayed due to difficult, time-consuming identification and low rates of testing for viral pathogens. Rapid diagnostic (RD) assays may aid in early identification and characterization of large outbreaks by allowing decision makers to “rule in” or “rule out” potential etiologic agents.
Objective
This paper describes preliminary results and implementation lessons learned from a RD testing pilot project. The project’s purpose is to prospectively collect diagnostic data on common causes of community-wide illness in order to supplement syndromic surveillance in New York City.
In 2007-2008, the authors surveyed public health officials in 59 state, territorial, and selected large local jurisdictions in the United States regarding their conduct and use of syndromic surveillance. Fifty-two (88%) responded, representing areas comprising 94% of the United States population. Forty-three (83%) of the respondents reported conducting syndromic surveillance for a median of 3 years (range = 2 months to 13 years). Emergency department visits were the most common data source, used by 84%, followed by outpatient clinic visits (49%), over-the-counter medication sales (44%), calls to poison control centers (37%), and school absenteeism (35%). Among those who provided data on staffing and contract costs, the median number of staff dedicated to alert assessment was 1.0 (range 0.05 to 4), to technical system maintenance 0.6 (range zero to 3); and, among the two-thirds who reported using external contracts to support system maintenance, median annual contract costs were $95,000 (range = %5,500 to $1 million). Respondents rated syndromic surveillance as most useful for seasonal influenza monitoring, of intermediate usefulness for jurisdiction-wide trend monitoring and ad hoc analyses, and least useful for detecting typical community outbreaks. Nearly all plan to include syndromic surveillance as part of their surveillance strategy in the event of an influenza pandemic. Two thirds are either "highly" or "somewhat" likely to expand their use of syndromic surveillance within the next 2 years. Respondents from three state health departments who reported they did not conduct syndromic surveillance noted that local health departments in their states independently conducted syndromic surveillance. Syndromic surveillance is used widely throughout the United States. Although detection of outbreaks initially motivated investments in syndromic surveillance, other applications, notably influenza surveillance, are emerging as the main utility.
Seasonal influenza accounts for a high proportion of outpatient morbidity during the winter months. However, influenza case counts are greatly underestimated due to frequently undiagnosed influenza. Electronic medical record (EMR) systems provide a very large, complex data source for influenza surveillance at both the patient and population level. It is important to identify influenza patients for specimen collection, respiratory isolation for school age children, prescription of an appropriate influenza drug, or to identify patients at risk for complications. At a population level, public health agencies monitor the tempo and spread of influenza season for resource management, as well as maintain situational awareness for avian influenza.
Objective
The objective of this work was to evaluate the utility of classification tree methods for syndromic surveillance case definition development using an EMR system as a data source.
This paper describes the initiation, development and proof-of-concept phase of the ISDS DiSTRIBuTE influenza morbidity surveillance project [1].
Aerial transmission and direct contact are important factors for flu. Consequently, close contact with large groups of people, such as during mass transit, present opportunities for transmission. One protective method that decreases the probabilities of becoming ill is vaccination. The potential health impact of erminating subway service during a flu epidemic depends on both the potential for transmission and vaccination rates among riders. Mass transit, a major method of transit in NYC, exhibits a non-random distribution of riders based on demographics and socio-economic status. There is also a trend in vaccination rates by demography and socio-economic status. This analysis uses individual-based data on vaccination and ridership to separately predict vaccination and ridership for inclusion in agent-based models that can be used to assess impact of public health interventions.
Objective
Agent-based models (ABMs) have been developed to simulate epidemics including smallpox and pandemic flu. The ABM approach is an effective method to assess the potential impact of interventions on disease spread. Integrating the ABM approach with syndromic surveillance data provides potential benefits such as permitting a realistic specification of some critical model contact parameters, and permitting synthetic outbreaks to be generated with extremely fine resolution (e.g., age, gender, and address). This would provide the ability to test various clustering detection algorithms – a key component of syndromic surveillance methods. RTI International (the Models of Infectious Disease Agent Study (MIDAS) informatics group) and NYC DOHMH (a premier syndromic surveillance research center) collaborated to create a NYC-ABM of flu transmission. This poster describes implementation of several features required for accurate model specification, including assigning immunization rates and subway ridership. Incorporating subway ridership is of great interest, because a large subway system, like the NYC system, has never been investigated as a contributor of disease spread.
Electronic Health Record (EHR) data offers the researcher a potentially rich source of data for tracking disease syndromes. Procedures performed on the patient, medications prescribed (not necessarily filled by the patient), and reason for visit are just some characteristics of the patient encounter that are available through an EHR that can be used to define surveillance syndromes. Since procedures have not been used frequently in defining syndromes, encounter level procedures data, extracted from the EHR of a large local primary care practice with about 200,000 patient encounters per year was used to identify procedures associated with an established respiratory syndrome.
Objective
To investigate the utility of different sources of patient encounter information, particularly in the primary care setting, that can be used to characterize surveillance syndromes, such as respiratory or flu.
This paper describes HHC outpatient data and evaluates the performance for early influenza-like illness (ILI) outbreak detection. We compare its detection ability to that of the emergency department (ED) data and of the two data sources simultaneously.