Burkom Howard

Objective

To enable the early detection of pandemic influenza, we have designed a system to differentiate between severe and mild influenza outbreaks. Historic information about previous pandemics suggested the evaluation of two specific discriminants: (1) the rapid development of disease to pneumonia within 1-2 days and (2) patient age distribution, as the virus usually targets specific age groups. The system is based on the hypothesis that an increased number of diagnosed pneumonia cases offers an early indication of severe influenza outbreaks. This approach is based on the fact that pneumonia cases will appear promptly in a severe influenza outbreak and can be diagnosed immediately in a physician office visit, while a confirmed influenza diagnosis requires a laboratory test. Furthermore, laboratory tests are unlikely to be ordered outside of the expected influenza season.

Although rare in the US, the CDC reports 13-14 drinking-water-related disease outbreaks per year, affecting an average of about 1000 people. The US EPA has determined that the distribution system is the most vulnerable component of a drinking water system. Recognizing this vulnerability, water utilities are increasingly measuring disinfectant levels and other parameters in their distribution systems. The US EPA is sponsoring an initiative to fuse this distribution system water quality data with health data to improve surveillance by providing an assessment of the likelihood of the occurrence of a waterborne disease outbreak. This fused analysis capability will be available via a prototype water security module within a population-based public health syndromic surveillance system.

Objective

The objective of this paper is to illustrate a technique for combining water quality and population-based health data to monitor for water-borne disease outbreaks.

A pandemic caused by influenza A/H5N1 or another novel strain could kill millions of people and devastate economies worldwide. Recent computer simulations suggest that an emerging influenza pandemic might be contained in Southeast Asia through rapid detection, antiviral distribution, and other interventions [1]. To facilitate containment, the World Health Organization (WHO) has established large, global antiviral stockpiles and called on countries to develop rapid pandemic detection and response protocols [2]. However, developing countries in Southeast Asia would face significant challenges in containing an emerging pandemic. Limited surveillance coverage and diagnostic capabilities; poor communication and transportation infrastructure; and lack of resources to investigate outbreaks could cause critical delays in pandemic recognition. Wealthy countries have committed substantial funds to improve pandemic detection and response in developing countries, but tools to guide system planning, evaluation, and enhancement in such places are lacking.

Objective

We propose a framework for evaluating the ability of syndromic, laboratory-based, and other public health surveillance systems to contain an emerging influenza pandemic influenza in developing countries, and apply the framework to systems in Laos.

Under a grant from the Centers for Disease Control and Prevention (CDC), the DC DOH established the Environmental Public Health Tracking Program (EPHTP) to monitor specific environmental and public health indicators and to investigate any potential links for the purpose of guiding policy development, resource allocation, and decision-making on disease prevention and treatment activities. This information improves understanding of the immediate and short-term effects of airborne pollutants on health care usage. In a collaborative project between JHU/APL and DC DOH, investigators explored and quantified correlations between ambient air quality measurements from five DC stations between October 2001 and March 2004 and DC hospital pediatric emergency department (ED) visits for asthma exacerbations. 

Objective

The study objective was to provide the CDC results from the EPHTP on quantifying the relationship between air quality and pediatric ED visits for asthma among DC residents over a 3 year period. This effort also explored novel uses of traditional data to understand background disease patterns so that unexpected fluctuations could be better detected in community disease trends and thereby identify early disease outbreaks.

Use of robust and broadly applicable statistical alerting methods is essential for a public health Biosurveillance system. We compared several algorithms related to the Early Aberration Reporting System C2 (adaptive control chart) method for practical detection sensitivity and timeliness using a realistic but stochastic signal inject strategy with a variety of data streams. The comparison allowed detail examination of strategies for adjusting daily syndromic counts for day-of-week effects and the total daily volume of facility visits. Adjustment for the total visit volume allows monitoring of surrogate rates instead of just counts, and the use of real data with both syndromic and total visit counts enables this adjustment.

Objective

We compared several aberration detection algorithms using a set of syndromic data streams from a large number of treatment facilities in the CDC Biosense 1.0 system. A realistic signal injection strategy was devised to compare different ways of adjusting for total facility visits and background day-of-week effects.

Biosurveillance in resource-limited settings is essential because of both enhanced risk of diseases rarely seen elsewhere (e.g. cholera) and pandemic threats (e.g. avian influenza). However, access to care and laboratory test capability are typically inadequate in such areas, amplifying the importance of syndromic surveillance. Such surveillance in turn may be a challenge because of insufficient data history and systematic or seasonal behavior. The Suite for Automated Global Electronic bioSurveillance (SAGES) is a collection of modular, freely-available software tools to enable electronic surveillance in these settings. These tools require statistical alerting methods appropriate for SAGES data, and development of such methods is the subject of this effort. We evaluated alerting methods for two main uses: weekly surveillance for seasonal outbreaks such as dengue fever and influenza, and daily syndromic data for settings where monitoring and response on a daily basis are practical. The latter situation has the added complication that day-of-week clinical visit patterns differ widely, (e.g. clinic closure on Sundays and Thursdays) and may evolve over time.

Objective

The authors develop open-source temporal alerting algorithms for data environments characteristic of resource-limited geographic settings and recommend appropriate usage of each.

On 12/14/06, a windstorm in western Washington caused 4 million residents to lose power; within 24 hours, a surge in patients presented to emergency departments (EDs) with carbon monoxide (CO) poisoning. As previously described, records of all patients presenting to King County EDs with CO poisoning between 12/15/06 to 12/24/06 (n=279) were abstracted, of which 249 met the case definition and eligibility requirements. We attempted to identify each of the 249 confirmed cases of CO poisoning in our syndromic ED data set by comparing the hospital name, date, time, age, sex, zip code, chief complaint, and diagnoses across the two data sets. We designated each record as an exact match, likely match, possible match, or unmatched on the basis of the available fields.

Objective

We evaluated ED and emergency medical services data for describing an outbreak of CO poisoning following a windstorm, and determined whether loss of power was followed by an increase in other health conditions.