Query purpose:
Syndromes
Query purpose:
Query purpose:
Query purpose:
To assist state, local, tribal, territorial, and federal public health practitioners in trend monitoring for long COVID (also commonly described as Post-COVID Complications (PCC) or long-haul COVID) related visits in the emergency department (ED) setting using syndromic surveillance data.
How it was developed:
Subject matter experts from seven jurisdictions (WA; VA; Harris County, TX; Pierce-Tacoma County, WA; NE; KS; KY) created and validated the definition using ED visit data from January – December 2023.
Query purpose:
To assist state, local, tribal, territorial, and federal public health practitioners in identifying patients likely to have long COVID (also commonly described as Post-COVID Complications (PCC) or long-haul COVID) by reviewing visits in the emergency department (ED) setting and to help with trend monitoring for long COVID related visits using syndromic surveillance data.
How it was developed:
Query purpose:
The aim of this syndrome definition is to assist with rapid detection of potential initial visits for a firearm injury. The following injury intents due to firearms are included in this definition: unintentional, intentional self-directed, assault, undetermined intent, legal intervention, and terrorism. The definition aims to capture only initial encounters for a firearm injury and negate subsequent encounters or sequelae.
How it was developed:
Description
Ontologies representing knowledge from the public health and surveillance domains currently exist. However, they focus on infectious diseases (infectious disease ontology), reportable diseases (PHSkbFretired) and internet surveillance from news text (BioCaster ontology), or are commercial products (OntoReason public health ontology). From the perspective of biosurveillance text mining, these ontologies do not adequately represent the kind of knowledge found in clinical reports. Our project aims to fill this gap by developing a stand-alone ontology for the public health/biosurveillance domain, which (1) provides a starting point for standard development, (2) is straightforward for public health professionals to use for text analysis, and (3) can be easily plugged into existing syndromic surveillance systems.
Objective
To develop an application ontology - the extended syndromic surveillance ontology - to support text mining of ER and radiology reports for public health surveillance. The ontology encodes syndromes, diagnoses, symptoms, signs and radiology results relevant to syndromic surveillance (with a special focus on bioterrorism).
Referenced File
Description
Air pollution is well documented to cause adverse health effects in the population. Epidemiological/toxicological studies have demonstrated that air pollution is associated with various adverse health outcomes, ranging from mortality to subclinical respiratory symptoms. Classical epidemiological studies of the health effects of air pollution are typically retrospective. In order to assess the effectiveness of any public health messages or interventions in a timely manner there is a need to be able to systematically detect any health effects occurring in real-time. The UK syndromic surveillance systems are coordinated by Public Health England (PHE) and are used to monitor infectious diseases in real-time. This study is the first in the UK to explore whether syndromic surveillance systems can detect public health impacts associated with air pollution events.
Objective: This study examined whether the current UK real-time syndromic surveillance systems can detect public health impacts associated with air pollution events such as fires and ambient air pollution episodes.
Description
This paper describes three years of electronic health record (EHR) data from a network of urban ambulatory care clinics in New York City.
Referenced File
Description
Health care processes consume increasing volumes of digital data. However, creating and leveraging high quality integrated health data is challenging because large-scale health data derives from systems where data is captured from varying workflows, yielding varying data quality, potentially limiting its utility for various uses, including population health. To ensure accurate results, it’s important to assess the data quality for the particular use. Examples of sub-optimal health data quality abound: accuracy varies for medication and diagnostic data in hospital discharge and claims data; electronic laboratory data used to identify notifiable public-health cases shows varying levels of completeness across data sources; data timeliness has been found to vary across different data sources. Given that there is clear increasing focus on large health data sources; there are known data quality issues that hinder the utility of such data; and there is a paucity of medical literature describing approaches for evaluating these issues across integrated health data sources, we hypothesize that novel methods for ongoing monitoring of data quality in rapidly growing large health data sets, including surveillance data, will improve the accuracy and overall utility of these data.
Objective
We describe how entropy, a key information measure, can be used to monitor the characteristics of chief complaints in an operational surveillance system.