Timeliness

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.

We have previously shown that timeliness of detection is influenced both by the data source (e.g., ambulatory vs. emergency department) and demographic characteristics of patient populations (e.g., age). Because epidemic waves are thought to move outward from large cities, patient distance from an urban center also may affect disease susceptibility and hence timing of visits. Here, we describe spatial models of local respiratory illness spread across two major metropolitan areas and identify recurring early hotspots of risk. These models are based on methods that explicitly track illness as a traveling wave across local geography.

Objective

To characterize yearly spatial epidemic waves of respiratory illness to identify early hotspots of infection.

Overseas studies showed that increases in over-the-counter (OTC) drug sales might serve as an indicator of community disease outbreaks before they are detected by conventional surveillance systems. Using data collected retrospectively from commercial drug retailers, the Department of Health of Hong Kong conducted an exploratory study to examine the potential of monitoring OTC drug sales for early detection of community disease outbreaks.

Objective

This study evaluates whether OTC drug sales can serve as an earlier indicator for detecting community disease outbreaks in Hong Kong.

While there has been some work to evaluate different data sources for syndromic surveillance of influenza, no one has yet assessed the utility of simultaneously restricting data to specific visit settings and patient age-groups using data drawn from a single source population. Furthermore, most studies have been limited to the emergency departments (ED), with few evaluating the timeliness of data from community-based primary care.

Objective

Using physician billing data from a single source population, we aimed to compare age-group and visit setting specific patterns in the timing of patients presenting to community-based healthcare settings and hospital ED for influenza-like-illnesses (ILI). We thus evaluate the utility of focusing on particular age-groups and care settings for syndromic surveillance of ILI in ambulatory care.

Clinician reporting of notifiable diseases has historically been slow, labor intensive, and incomplete. Manual and electronic laboratory reporting (ELR) systems have increased the timeliness, efficiency, and completeness of notifiable disease reporting but cannot provide full demographic information about patients, integrate an array of pertinent lab tests to yield a diagnosis, describe patient signs and symptoms, pregnancy status, treatment rendered, or differentiate a new diagnosis or from follow-up of a known old diagnosis. Electronic medical record (EMR) systems are a promising resource to combine the timeliness and completeness of ELR systems with the clinical perspective of clinician initiated reporting. We describe an operational system that detects and reports patients with notifiable diseases to the state health department using EMR data.

Objective

To leverage EMR systems to improve the timeliness, completeness, and clinical detail of notifiable disease reporting.

Crude mortality could be valuable for infectious disease surveillance if available in a complete and timely fashion. Such data can be of used for detecting, and tracking the impact of unusual health events (e.g. pandemic influenza) or other unexpected or unknown events of infectious nature.

To evaluate whether these goals can be achieved with crude mortality monitoring in the Netherlands, a pilot study was set up in 2008 in which death counts were received from Statistics Netherlands. 

The aims of this pilot are: 1) Setting up communication and data transmission. 2) Calculating expected mortality counts (depending on the season) and a prediction interval. 3) Detecting deviations in mortality counts above the threshold. 4) Comparing such deviations (and lags hereof) with other public health information (such as sentinel influenza-like-illness surveillance, and web-based selfreported ILI). 4) Evaluating the additional value of such a system for infectious disease public health.

Objective

To evaluate the potential use of mortality data in the Netherlands for real-time surveillance of infectious disease events through a pilot study.

Tuberculosis (TB) has reemerged as a global public health epidemic in recent years. TB remains a serious public health problem among certain patient populations, and is prevalent in many urban areas. The World Health Organization estimates that approximately nine million individuals will develop active TB disease and more than two million will die from TB. The global burden of TB remains enormous, and will likely rank high among public health problems in the coming decades. Although evaluating local disease clusters leads to effective prevention and control of TB, there are few, if any, spatiotemporal comparisons for epidemic diseases. In this study, we used the space-time scan statistic to identify where and when the prevalence of TB is high in Fukuoka Prefecture. The ability to detect disease outbreaks is important for local and national health departments to minimize morbidity and mortality through timely implementation of disease prevention and control measures. Because the statistic meets these needs completely, results that are effective and practical for public health officials are expected from this study.

Timely outbreak detection, and monitoring of morbidity and mortality among Katrina evacuees, and needs assessment for better planning and response were urgent information intensive priorities during Katrina relief efforts at Houston, and called for immediate deployment of a real-time surveillance and needs assessment system ad hoc, in order to collect and analyze relevant data at the scene. Initial requirement analysis revealed the following capabilities as essential to sustain effective response within the shelters:

• The ability to securely collect and integrate data from evacuees seeking any form of health services from all care providers (academic, volunteers, federal, NGOs and international aid organizations, etc), including demographic information, vital signs, chief complaints, disabilities, chronic conditions, current and past medications, traumas and injuries, exposure to toxic materials, clinical laboratory results, past medical history, discharge notes and diagnoses, and ability to collect free text entries for any other information (similar to a full-blown electronic medical records system).

• Proactive survey of demographic profile, physical and mental health status, as well as special needs assessment (e.g., dialysis, medications, etc) from all evacuees.

• The ability to collect uniform information, using any network-enabled device available: PCs, tablets, and handheld devices. 

• The ability to classify observations by processing sign and symptom, chief complaint, medication, and other diagnostic data (including free text entries) through ad-hoc definition of concepts such as (Gastrointestinal, Respiratory, Fever and Rash, etc). 

Objective

This paper presents lessons learned from leveraging Internet-based technologies and Services Oriented Architecture in providing timely, novel, and customizable solutions, just in time and for preparedness against unprecedented events such as natural disasters (e.g., Katrina) or terrorism.

The 2003 heat wave in France (15,000 extra deaths in 10 days) led the French institute for public health surveillance to modify its public health surveillance system. One of the major objectives of this program was a real time surveillance based on emergency departments (EDs). Trials experiments started in 2004 with a daily automatic data collection from 20 hospitals in the Paris area. The objectives of this new system were: 1) to detect early all threats for public health; and 2) to measure the impact of an identified phenomena.

In 2006 France was concerned by a new heat wave. It was the opportunity for recording health data during a hot period through this real time system.

Objective

This paper describes the performances of a syndromic surveillance system based on EDs during a heat wave.

Malaria control programs suffer from weak and fragmented surveillance of the wide range of information required to manage the disease effectively and efficiently. A computational framework to manage, integrate, analyze, and visualize the data resources, a cyberenvironment, can improve the surveillance and the outcomes.

Objective

This paper presents an ontology of a cyberenvironment for malaria surveillance. The ontology encapsulates a comprehensive natural language enumeration of the requirements of the cyberenvironment using a structured terminology. It can be used to systematically analyze and prioritize the functions of the cyberenvironment. It will help the medical, individual, environmental, and strategic management of malaria.