Respiratory

A significant amount of resources and effort have been recently invested in syndromic surveillance systems. However, how these systems complement or compare with traditional public health surveillance systems, such as outbreak reporting, is not clear.  

Objective:

The purpose of this paper is to describe the value of outbreak and syndromic surveillance data from the Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE) syndromic surveillance system to monitor respiratory illness activity in Maryland.

Influenza epidemics occur seasonally, impose a high economic burden on the health care system, and are responsible for substantial morbidity and mortality (1). The past century has seen three influenza A pandemics with variable severity. The recent outbreaks of avian influenza involving different virus strains in Asia, North America and the Netherlands, indicates the increasing potential of a new influenza pandemic (2). Public and political awareness needs to be strengthened while public health surveillance strategies need significant improvements if we are to mitigate such a potentially devastating worldwide pandemic, and provide the healthcare system with as much early warning as possible to enhance preparedness. Telehealth Ontario is a provincial telephone helpline for health information staffed by nurses that, if monitored on a real-time basis, has the potential to identify increases in seasonal respiratory infection rates. A recent study suggested that Telehealth Ontario respiratory calls reflect the seasonality of diagnosed respiratory illnesses in emergency departments (van Dijk et al., unpublished data), but an estimation of how respiratory pathogens contribute to Telehealth Ontario’s respiratory complaint calls has not been studied.

Objective:

This paper will explore the possibility and utility of monitoring Telehealth Ontario respiratory calls as an efficient public health influenza strategy for early warning by comparing this data source to provincial viral lab data.

OBJECTIVE

Syndromic surveillance systems (SSS) seek early detection of infectious diseases outbreaks by focusing on pre-diagnostic symptoms. We do not yet know which respiratory syndrome should be monitored for a SSS to discover an influenza epidemic as soon as possible. This works compares the delay and workload required to detect an influenza epidemic using a SSS that targets either (1) all cases of acute respiratory infections (ARI) or (2) only those ARI cases that are febrile and satisfy CDC's definition for an influenza-like illness.

This paper describes an integrated modeling, sensing, and information system for forecasting atmospheric dust episodes and monitoring PM2.5 and PM10 concentrations that aggravate respiratory diseases in the American southwest.

Hospital syndromic surveillance data may be a useful tool in detecting increases in influenza-like-illness (ILI) and for monitoring seasonal trends or pandemic activity on a local level. A previous comparison of hospital syndromic surveillance data with ILI surveillance data manually abstracted from emergency department notes revealed that the general respiratory category performed better than symptomspecific subcategories. However, only about half of all patients hospitalized for influenza meet the ILI criteria defined as fever and either cough or sore throat. Hospital discharge data are used retrospectively to determine disease burden, but is not of use for acute monitoring due to the substantial lag time. Knowing how accurately admission data reflect discharge data can assist with interpretation of real or near-real time data streams commonly used in syndromic surveillance systems.

Objective

Timely unplanned hospital admissions data in a general respiratory syndrome category and/or with a pneumonia or influenza admission diagnosis are compared with hospital discharge data to determine accuracy for prediction of influenza disease burden.

Automated syndromic surveillance systems often classify patients into syndromic categories based on free-text chief complaints. Chief complaints (CC) demonstrate low to moderate sensitivity in identifying syndromic cases. Emergency Department (ED) reports promise more detailed clinical information that may increase sensitivity of detection. Objective: Compare classification of patients based on chief complaints against classification from clinical data described in ED reports for identifying patients with an acute lower respiratory syndrome.