Evaluation

Previous reports from participating facilities in North Dakota illustrated that ILI syndrome data from syndromic surveillance data, which is based on chief complaints logs, had a close correlation to the traditional ILI surveillance and that frequency slope of the ILI syndrome was also closely correlated to that of the cases that tested positive for influenza. The facility used in this report submits ICD-9 codes to the North Dakota Department of Health (NDDoH). By comparing the NDDoH ILI syndrome to influenza laboratory testing data and ICD-9 code specific to influenza (487) we found that syndromic surveillance data for ILI closely followed the influenza testing trend as well as the ICD-9 code trend.

Objective

The objective of this report is to evaluate the correlation between influenza-like illness (ILI) syndrome classification using chief complaint data and discharge diagnosis International Classification of Disease, Ninth Revision (ICD-9) code for influenza with the laboratory data from one hospital in North Dakota over a period of three influenza seasons.

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.

It is well known that diabetic patients are particularly sensitive to infections however no robust diagnostic test for the early detection of infection has been developed to date. Glucose levels  would be an ideal indicator, since diabetics measure their blood glucose (BG) on a daily basis along with insulin intake. At the same time some computerized systems have been developed that collect BG values using sensors and transmit them to a central data repository, such as the Electronic Healthcare Record. Acute infection often results in hyperglycemia, due to release of regulatory hormones and pro-inflammatory cytokines as evidenced by studies on hospitalized patients. Nevertheless the underlying mechanisms of infection-related stress hyperglycemia are not fully understood.

Objective

The aim of the study is to assess the correlation between blood glucose levels and infection and to propose the development of a model for the early detection of infections in diabetics.

Many syndromic surveillance systems have been developed and are operational, yet lack concise guidelines for investigating and conducting followups on daily alarms. Daily emergency department visits from six reporting hospitals in the Duval County area are assessed and classified into a BioDefend (BD) system entry by triage personnel. Alarms are categorized into alerts, 3 SD above a 30 day rolling mean, or warnings, 2-3 SD above the mean. Signals are monitored and in response, public health investigations and recommended interventions are initiated.

Objective

To evaluate the protocol that the Duval County Health Department (DCHD) epidemiology staff uses to respond to BD syndromic surveillance system alarms. The response protocol utilizes all signals detected by BD and its secondary resources, within the DCHD jurisdiction.

ARIMA models use past values (autoregressive terms) and past forecasting errors (moving average terms) to generate future forecasts, making it a potential candidate method for modeling citywide time series of syndromic data [1]. While past research supports the use of ARIMA modeling as a detection algorithm in syndromic surveillance [2], there has been little evaluation of an ARIMA model's prospective outbreak detection capabilities. We built an ARIMA model to prospectively detect simulated outbreaks in ED syndromic data. This method is one of eight being formally evaluated as part of a grant from the Alfred P. Sloan Foundation.

Objective

To evaluate seasonal autoregressive integrated moving average (ARIMA) models for prospective analysis of New York City (NYC) emergency department (ED) syndromic data.

Electronic epi-biosurveillance presents an opportunity to provide real-time disease surveillance alerts from remote areas to central disease management units, to rapidly decrease reporting times for reportable diseases, and to enable appropriate response scenarios to be put in place in a timely manner. Over the past year, with the support of GEIS and Johns Hopkins Applied Physics Lab, we have piloted an electronic disease reporting system in four sites in the Cameroon military and evaluated these surveillance efforts, to understand how such infrastructure may impact this resource-limited setting.

Objective

Pilot and evaluate an electronic disease surveillance system in the Cameroon military and assess the capabilities of this system to fulfill reporting and early warning requirements.

The Miami-Dade County Health Department currently utilizes Emergency Department based Syndromic surveillance data, 911 Call Center data, and more recently Public School Absenteeism data. Daily monitoring of school absenteeism data may enhance early outbreak detection in Miami-Dade County in conjunction with the use of other syndromic systems. These systems were employed to detect any possible outbreaks resulting from a large outdoor festival occurring March 11th, 2007. This event had an estimated 1 million visitors and it ended at 7:00 p.m.

Objective

Utility of school absenteeism data to enhance syndromic surveillance activities for unusual public health events or outbreak detection.

Surveillance of individual data streams is a well-accepted approach to monitor community incidence of infectious diseases such as influenza, and to enable timely detection of outbreaks so that control measures can be applied. However the performance of alerts may be improved by simultaneously monitor a variety of data sources, or multiple streams (eg from different geographic locations) of the same type, rather than monitoring only aggregate data. Rates of influenza-like illness in subtropical settings typically show greater variability than in temperate regions.

Objective

This paper describes the use of time series models for simultaneous monitoring of multiple streams of influenza surveillance data.

Medical surveillance in the military can be improved through the use of clinical laboratory results collected within the Military Health System. This presentation describes an effort to establish Electronic Laboratory Reporting in the military using existing Health Level 7 (HL7) messages. HL7 data is being evaluated for data integrity, completeness, reliability and validity. In addition, initial efforts to evaluate, standardize, and use this data to support investigations of interest over the past year are presented.

Objective

This presentation describes the HL7 clinical lab results dataset and how it can and has been used for medical surveillance in the military.

Graph theory concepts are well established in epidemiology, with particular success as a description of agent-based modeling. An agent-based viewpoint leads to conclusions about the spatial distribution of links: infection is more likely among individuals in close proximity. In this analysis, we seek evidence of these temporal-spatial links though the properties of random geometric graphs.

Our investigation begins with the interpoint distance distribution (IDD) approaches referenced, which provide a promising approach to detect outbreaks that are localized in both space and time. Using a Mahalanobis-based metric, this distribution is compared to an expected distribution derived from historical records.

Unfortunately, when applied to a complex data set such as from Children’s Hospital Boston, the IDD provides inadequate power. Emergency Department chief complaints from 1/1/2000-12/31/2004 were used to identify patients with infectious respiratory illness based on a triage process.

As in most realistic catchments, the historic density of patients varies greatly over the catchment area.

Objective

This paper uses geometric random graph concepts to develop early detection algorithms for the real-time detection and localization of outbreaks.