Infectious Disease

A significant research topic in biosurveillance is how to group individual events—such as single emergency department (ED) visits and sales of over-thecounter healthcare (OTC) products—into counts of “similar” events. For OTC products, the goal is to find categories of individual products that have superior outbreak detection performance relative to categories that biosurveillance systems currently monitor. We have described a method to identify OTC categories that correlate more highly with disease activity than existing categories.1 However, it is an open question whether a category that correlates more highly—or according to some other model has a higher ‘association’—with disease activity than an existing category necessarily has superior detection performance. Here, we evaluate whether a linear regression procedure that clusters OTC products based on how well they ‘explain’ ED visits for influenzalike illness (ILI) can find categories with superior outbreak-detection performance for influenza.

Objective

To develop a procedure that identifies product categories with superior outbreak detection performance.

Although norovirus (NoV) is the most common cause of acute gastroenteritis (ewinter vomiting diseaseí), its contribution to mortality remains unknown and may be an unrecognized problem [1]. In Europe a genetic shift in circulating NoV strains was observed in 2002 which coincided with an unusually high number of NoV outbreaks in all but one country participating in the European NoV-surveillance network [2]. Covering a time period which included this outbreak peak, we used general practitioner (GP), hospital, and death-cause data in combination with NoV surveillance data to explore the association between NoV outbreaks and morbidity and mortality.

Classical disease monitoring in local public health jurisdictions has been based on a list of “notifiable diseases”, more or less consistent from state-to-state.  While laboratories’ compliance with this requirement is, in general, excellent, clinician reporting is extremely poor [1].  In most circumstances, laboratory reporting is inherently delayed (perhaps by weeks), and most leaders in infectious disease and bioterrorism believe that recognition of abnormal spatiotemporal patterns within hours is essential [2].  Syndromic surveillance systems based on analysis of statistical aberrations in diagnosis code, chief complaint, or analysis of other data streams have been proposed and tested, but have largely failed to meet criteria of timeliness, sensitivity and specificity [3].  In addition, the vast majority of syndromic surveillance systems do not include veterinary surveillance, which may be important given that the vast majority of diseases of human public health importance are zoonotic in origin.  Thus, we have tested the hypothesis put forward by Henderson that “the astute clinician” can serve as the best early-warning indicator [4], with minimal demands on clinician time while simultaneously providing situational awareness to the broad community of health care providers and political decision makers who require such information.

Objective

It is widely agreed that "situational awareness" in disease surveillance is essential for intervening early in an infectious disease (or intoxination) outbreak. We report on 3.5 years of experience of a clinician-based system in a 25,000 square mile area of northwest Texas, a mixed urban, semi-rural and agricultural setting.

The City of Atlanta, volunteer organizations, and the faith community operate several homeless shelters throughout the city. Services available at these shelters vary, ranging from day services, such as meals, mail collection, and medical clinics, to overnight shelter accommodations. In addition to the medical clinics available at these facilities, the Atlanta homeless population also utilizes emergency departments in Fulton County for their health care needs.

Objective

This paper describes a cluster of Streptococcus pneumoniae infections identified through emergency department syndromic surveillance.

Animals continue to be recognized as a potential source of surveillance data for detecting emerging infectious diseases, bioterrorism preparedness, pandemic influenza preparedness, and detection of other zoonotic diseases. Detection of disease outbreaks in animals remains mostly dependent upon systems that are disease specific and not very timely. Most zoonotic disease outbreaks are detected only after they have spread to humans. The use of syndromic surveillance methods (outbreak surveillance using pre-diagnostic data) in animals is a possible solution to these limitations. The authors examine microbiology orders from a veterinary diagnostics laboratory (VDL) as a possible data source for early outbreak detection. They establish the species representation in the data, quantify the potential gain in timeliness, and use a CuSum method to study counts of microorganisms, animal species, and specimen collection sites as potential early indicators of disease outbreaks. The results indicate that VDL microbiology orders might be a useful source of data for a surveillance system designed to detect outbreaks of disease in animals earlier than traditional reporting systems.

Effective public health response to emerging infectious diseases, natural disasters, and bioterrorism requires access to real-time, accurate information on disease patterns and healthcare utilization. The ESSENCE surveillance system in use by the Department of Defense (DoD) relies primarily on outpatient clinical impression diagnosis, which accurately characterize broad disease syndromes but may not be sufficient for monitoring specific diseases. DoD outpatient military treatment facilities perform nearly 500,000 microbiology laboratory tests annually. Initiated electronically, the ordered test is recorded immediately; most provide specific results in 24 to 72 hours and may prove useful for monitoring population health. Although a syndrome classification has been developed for laboratory tests, the classification cannot be applied directly to the DoD data and no previous study has validated the use of automated laboratory test orders for syndromic surveillance.

Objective

To evaluate the association between military microbiology laboratory test orders and infectious disease patterns.

San Francisco has the highest rate of TB in the US. Although in recent years the incidence of TB has been declining in the San Francisco general population, it has remained relatively constant in the homeless population. Spatial investigations of disease outbreaks seek to identify and determine the significance of spatially localized disease clusters by partitioning the underlying geographic region. The level of such regional partitioning can vary depending on the available geospatial data on cases including towns, counties, zip codes, census tracts, and exact longitude-latitude coordinates. It has been shown for syndromic surveillance data that when exact patients’ geographic coordinates are used, higher detection rates and accuracy are achieved compared to when data are aggregated into administrative regions such as zip codes and census tracts. While the benefits of using a finer spatial resolution, such as patients’ individual addresses, have been examined in the context of spatial epidemiology, the effect of varying spatial resolution on detection timeliness and the amount of historical data needed have not been investigated.

Objective

The objective of this study is to investigate the effect of varying the spatial resolution in a variant of space-time permutation scan statistic applied to the tuberculosis data on the San Francisco homeless population on detection sensitivity, timeliness, and the amount of historical data needed for training the model.

Electronic laboratory-based surveillance can significantly improve the diagnostic specificity and response time of traditional infectious disease surveillance. Under the project “Models of Infectious Disease Agent Study”, we wished to evaluate the application of space-time outbreak detection algorithms utilizing SaTScan to a national database of routinely collected microbiology laboratory data.

Objective

This paper describes the application of the WHONET software integrated with SaTScan to the detection of Shigella outbreaks in a national database using a space-time cluster detection algorithm in simulated real-time and comparison of findings to outbreaks reported to the Ministry of Health.

The Utah Department of Health documented a single epidemic of cryptosporidiosis in Utah during 2007. Seven hundred eleven laboratory-confirmed cases were reported in Salt Lake County, Utah from July 27 through December 18. Illness onset date was available for 86% (611 of 711) of patients and ranged from May 30 through November 11. Approximately 32% (224 of 691) of patients sought care in area emergency departments or urgent care facilities, and 8.5% (50 of 590 with data available) of patients required hospitalization. Sixty-one percent (432 of 711) of patients were less than 13 years of age. Of 381 patients with data available on symptoms, nearly all (99%, 378) reported diarrhea. Other commonly reported symptoms included vomiting (57%, 218), abdominal pain (51%, 196), and nausea (44%, 168).

Objective

The objective of this study was to evaluate the potential for improved detection of enteric disease epidemics using a classification category based on variations of diarrhea appearing in the chief complaints from emergency department and urgent care facility visits.

Outbreaks of infectious diseases are identified in a variety of ways by clinicians and public health practitioners but not usually by analytic methods typically employed in syndromic surveillance. Systematic spatial-temporal analysis of statewide data may enable earlier detection of outbreaks and identification of multi-jurisdictional outbreaks.

Objective

Clusters of cases of individually-reportable infectious diseases were identified by a spatial-temporal retrospective analysis. Clusters were examined to determine association with previously reported outbreaks.