Coberly Jacqueline

Recent events have focused on the role of emerging and re-emerging diseases not only as a significant public health threat but also as a serious threat to the economy and security of nations. The lead time to detect and contain a novel emerging disease or events with public health importance has become much shorter, making developing countries particularly vulnerable to both natural and man-made threats. There is a need to develop disease surveillance systems flexible enough to adapt to the local existing infrastructure of developing countries but which will still be able to provide valid alerts and early detection of significant public health threats.

Objective

To determine system usefulness of the ESSENCE Desktop Edition in detecting increases in the number of dengue cases in the Philippines.

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.

The Veterans Health Administration (VHA) operates over 880 outpatient clinics across the nation. The Johns Hopkins Applied Physics Laboratory’s Electronic Surveillance System for the Early Notification of Community-Based Epidemics (ESSENCE) utilizes VHA ICD9 coded outpatient visit data for the detection of abnormal patterns of disease occurrence. The hemorrhagic illness (HI) syndrome category in ESSENCE is comprised of 25 different ICD9 codes, including 12 codes specific for viral hemorrhagic fever (VHF) (e.g., ebola, yellow fever, CrimeanCongo hemorrhagic fever, lassa, etc.) and 13 nonspecific conditions (e.g., purpura not otherwise specified (NOS), thrombocytopathy, and coagulation defect NOS).

Objective

We sought to evaluate the functionality of the diagnosis codes which fall into the syndrome category of hemorrhagic illness.

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.

Difficulties in timely acquisition and interpretation of accurate data on communicable diseases can impede outbreak detection and control. These limitations are of global importance: they contribute to avoidable morbidity, economic losses, and social disruption; and, in a globalized world, epidemics can spread rapidly to other susceptible populations.

SARS and the potential for an influenza pandemic highlighted the importance of global disease surveillance. Similarly, the World Health Organization’s newly implemented 2005 International Health Regulations require member countries to provide notification of emerging infectious diseases of potential global importance. The challenges arise when Ministries of Health (MoH) in resource-poor countries add these mandates to already over-burdened and under-funded surveillance systems. Appropriately adapted, electronic disease surveillance systems could provide the tools and approaches MOHs need to meet today’s surveillance challenges.

Objective

In this presentation we will discuss the concept of electronic disease surveillance in resource-poor settings, and the issues to be considered during system planning and implementation.

Every public health monitoring operation faces important decisions in its design phase. These include information sources to be used, the aggregation of data in space and time, the filtering of data records for required sensitivity, and the design of content delivery for users. Some of these decisions are dictated by available data limitations, others by objectives and resources of the organization doing the

surveillance. Most such decisions involve three characteristic tradeoffs: how much to monitor for exceptional vs customary health threats, the level of aggregation of the monitoring, and the degree of automation to be used.

The first tradeoff results from heightened concern for bioterrorism and pandemics, while everyday threats involve endemic disease events such as seasonal outbreaks. A system focused on bioterrorist attacks is scenario-based, concerned with unusual diagnoses or patient distributions, and likely to include attack hypothesis testing and tracking tools. A system at the other end of this continuum has broader syndrome groupings and is more concerned with general anomalous levels at manageable alert rates. 

Major aggregation tradeoffs are temporal, spatial, and syndromic. Bioterrorism fears have shortened the time scale of health monitoring from monthly or weekly to near-real-time. The spatial scale of monitoring is a function of the spatial resolution of data recorded and allowable for use as well as the monitoring institution’s purview and its capacity to collect, analyze and investigate localized outbreaks.

Automation tradeoffs involve the use of data processing to collect information, analyze it for anomalies, and make investigation and response decisions. The first of these uses has widespread acceptance, while in the latter two the degree of automation is a subject of ongoing controversy and research. To what degree can human judgment in alerting/response decisions be automated? What are the level and frequency of human inspection and adjustment? Should monitoring frequency change during elevated threat conditions?

All of these decisions affect monitoring tools and practices as well as funding for related research.

Objective

This purpose of this effort is to show how the goals and capabilities of health monitoring institutions can shape the selection, design, and usage of tools for automated disease surveillance systems.

On 27 April 2005, a simulated bioterrorist event—the aerosolized release of Francisella tularensis in the men’s room of luxury box seats at a sports stadium—was used to exercise the disease surveillance capability of the National Capital Region (NCR). The objective of this exercise was to permit all of the health departments in the NCR to exercise inter-jurisdictional epidemiological investigations using an advanced disease surveillance system. Actual system data could not be used for the exercise as it both is proprietary and contains protected, though de-identified, health information about real people; nor is there much historical data describing how such an outbreak would manifest itself in normal syndromic data. Thus, it was essential to develop methods to generate virtual health care records that met specific requirements and represented both ‘normal’ endemic visits (the background) as well as outbreak-specific records (the injects).

Objective

This paper describes a flexible modeling and simulation process that can create realistic, virtual syndromic data for exercising electronic biosurveillance systems.