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Health Monitoring

Routine primary care data provide the means to systematically monitor a variety of syndromes which could give early warning of health protection issues (microbiological and chemical). It is possible to track milder illnesses which may not present to hospitals (e.g. chicken pox, conjunctivitis) or illnesses for which laboratory specimens are not routinely taken (e.g. influenza). Real-time data are also needed to respond to major health protection incidents.

 

Objective

To describe the arrangements for Primary Care Surveillance in the UK and provide examples of the benefits of this work for Public Health.

Referenced File
Submitted by elamb on

Data latency limited the Alabama Department of Public Health’s (ADPH) ability torecognize and respond quickly to public health threats. Despite ADPH’s request that 95% of syndromic surveillance (SyS) data be submitted to ESSENCE* within 24 hours of a visit, some facilities were slow to process and submit data, diminishingthe data’s usefulness for surveillance that, in turn, negated ESSENCE’s ability tofunction as a daily alert. Data could be one to several days late, depending on whether a facility was processing or sending data or was offline.

Submitted by elamb on

The increased threat of bioterrorism and naturally occurring diseases, such as pandemic influenza, continually forces public health authorities to review methods for evaluating data and reports. The objective of bio-surveillance is to automatically process large amounts of information in order to rapidly provide the user with a situational awareness. Most systems currently deployed in health departments use only statistical algorithms to filter data for decision-making. These algorithms are capable of high sensitivity, but this sensitivity comes at the cost of excessive false positives [2], especially when multiple syndrome groups and data types are processed.

Objective

An intelligent information fusion approach is proposed to identify and provide early alerting of naturally-occurring disease outbreaks, as well as bioterrorist attacks, while reducing false positives. The proposed system statistically preprocesses information from multiple sources and fuses it in a manner comparable with the domain expert's decision-making process. Currently, system users lower the false alarm rate by "explaining away" the statistical data anomalies with alternative hypotheses derived from external, non-syndromic knowledge. We seek to incorporate this heuristic decision-making into a probabilistic network that accepts the outputs of statistical algorithms in a hybrid model of domain knowledge and data inference.

Referenced File
Submitted by elamb on

The Bioterrorism Surveillance Unit of the Los Angeles County (LAC) Department of Public Health, Acute Communicable Disease Control (ACDC) program analyzes Emergency Department (ED) data daily. Currently capturing over 40% of the ED visits in LAC, the system categorizes visits into syndrome groups and analyzes the data for aberrations in count and spatial distribution. Typical usage of the system may be extended for various enhanced surveillance activities by creating additional syndrome categories tailored to specific illnesses or conditions. This report describes how ED data was utilized for enhanced surveillance regarding: (1) a sustained heat wave in California that broke temperature and duration records, (2) a 30,000 gallon raw sewage spill that prompted the closure of two miles of beach, and (3) an alert to ACDC of a high school student who attended school while symptomatic for meningitis.

 

Objective

To describe enhanced surveillance provided by the LAC Department of Public Health’s syndromic surveillance system for monitoring health events in 2006.

Referenced File
Submitted by elamb on

On August 28, 2011 Tropical Storm Irene made landfall in Connecticut. On October 29, 2011 Connecticut was impacted by Winter Storm Alfred. Both of these storms included high winds and heavy precipitation which resulted in prolonged power outages, disruption of public drinking water systems, property damage, and widespread debris throughout the state. The Hospital Emergency Department Syndromic Surveillance (HEDSS) System was utilized to provide real-time situational awareness during the response and recovery phases of both storm events.

 

Objective

To characterize the utility of the Connecticut HEDSS system for real-time situational awareness during two weather-related emergencies.

Submitted by hparton on

Using the National Cancer Institute’s 2017 Health Information Trends Survey, the Office of the national Coordinator for Health Information Technology (ONC) reports on access and use of online medical records and the use of technology such as smartphones, tablets, and electronic monitoring devices (e.g. Fitbits, blood pressure monitors) for health related needs.

Submitted by ctong on

During an emergency, the state of Georgia depends on public health staff and volunteers to respond. It is imperative that staff are safe before, during and after deployment. Emergency response workers must be protected from the hazardous conditions that disasters and other emergencies create1. In October 2016 and September 2017, Hurricanes Matthew and Irma caused widespread evacuation of Georgia residents, initiating a tremendous sheltering effort. Hundreds of public health responders were deployed to assist with sheltering and other aspects of the response. DPH rapidly developed a novel electronic Responder Safety, Tracking and Resilience module, which was used to track public health responders and monitor their health and safety while deployed.

Objective:

To better understand the importance of monitoring responders during public health emergencies and to learn how the Georgia Department of Public Health (DPH) developed and deployed an electronic responder monitoring tool.

Submitted by elamb on
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