Syndromes

Syndromic surveillance information can be a useful for the early recognition of outbreaks, acute public health events and in response to natural disasters. Inhalation of particulate matter from wildland fire smoke has been linked to various acute respiratory and cardiovascular health effects. Historically, wildfire disasters occur across Southern California on a recurring basis. During 2003 and 2007, wildfires ravaged San Diego County and resulted in historic levels of population evacuation, significant impact on air quality and loss of lives and infrastructure. In 2011, the National Institutes of HealthNational Institute of Environmental Health Sciences awarded Michigan Tech Research Institute a grant to address the impact of fire emissions on human health, within the context of a changing climate. San Diego County Public Health Services assisted on this project through assessment of population health impacts and provisioning of syndromic surveillance data for advanced modeling.

Objective

This presentation describes how syndromic surveillance information was combined with fire emission information and spatio-temporal fire occurrence data to evaluate, model and forecast climate change impacts on future fire scenarios.

Patients’ chief complaints (CCs) as a common data source, has been widely used in syndromic surveillance due to its timeliness, accuracy and availability. For automated syndromic surveillance, CCs always classified into predefined syndromic categories to facilitate subsequent data aggregation and analysis. However, in rural China, most outpatient doctors recorded the information of patients (e.g. CCs) into clinic logs manually rather than computers. Thus, more convenient surveillance method is needed in the syndromic surveillance project (ISSC). And the first and important thing is to select the targeted symptoms/syndromes.

Objective

To select the potential targeted symptoms/syndromes as early warning indicators for epidemics or outbreaks detection in rural China

EDCC data provides an opportunity for capturing the early mental health impact of disaster events at the community level, and to track their impact over time. However, while rapid mental health assessment can facilitate a better understanding of the acute post-disaster period and aid early identification of persons at long-term risk,1 determining how wide a net to effectively capture the critical range of mental health sub-categories has not yet been clearly defined. This project creates a comprehensive set of mental health sub-category keywords, and applies them to evaluate short- and long-term trends in postHurricane Sandy mental health outcomes in New York State.

Objective

To define mental health keywords using daily hospital emergency department chief complaint (EDCC) data during and after Hurricane Sandy 2) To track short- and long-term trends in mental health EDCCs. 3) To compare mental health EDCCs in affected counties to the rest of the New York State population.

Timely access to Emergency Department (ED) Chief Complaint (CC) data, before the definitive diagnosis is established, allows for early outbreak detection and prompt response by public health officials.BioSense 2.0 is a cloud-based application that securely collects, tracks, and shares ED data from participating hospitals around the country. Denver Health (DH) is one of several Colorado hospitals contributing ED Chief Complaint data to BioSense 2.0. In August 2013, ED clinicians reported an increase in patients presenting with excited delirium, possibly related to synthetic marijuana (SM). We used this event to test the use of CC field of ED data for detection of a novel public health event (i.e., serious adverse events related to synthetic marijuana use) not currently categorized in the BioSense syndromic surveillance library.

Objective

The aims of this presentation is to use ED chief complaint data, to test BioSense 2.0 for detection of a novel public health event (i.e., serious adverse events related to synthetic marijuana use) not currently categorized in the BioSense syndromic surveillance library.