This is a preliminary Chronic Pain-Related Syndrome, created to search relevant ICD10 and a few key terms in emergency department visits in ESSENCE. The codes and terms are specific to non-cancer related chronic pain with exclusions of cases receiving cancer-related ICD10.
ICD10 codes were selected by translating the following ICD9 codes for Chronic Pain contained in this PDF (https://www.cdc.gov/drugoverdose/pdf/pdo_guide_to_icd-9-cm_and_icd-10_c…)
The homelessness syndrome was developed to identify emergency department visits in ESSENCE for patients who are experiencing homelessness or housing insecurity. The syndrome is intended for use with chief complaint, triage notes, and discharge diagnosis codes (ICD-10 CM). The definition heavily relies on diagnosis codes primarily used by non-critical access hospitals and artificial exclusion of critical access facilities should be considered when data are interpreted.
This query is used to assess trends in hypothermia or cold exposure in emergency department visits in ESSENCE. The query captures cold exposure, hypothermia, and frost bite using chief complaint, triage note, and discharge diagnosis code (ICD-10CM). The query does not exclude hypothermia related to an underlying medical condition.
The attached query was developed to track medication refill encounters in emergency departments in ESSENCE during evacuations or extended mass gathering events. The query was initially developed for use with the chief complaint, triage note, and discharge diagnosis code (ICD-10 CM).
This syndrome was created to help capture tree-related injuries during severe weather events. Extreme weather events require extensive tree removal and disposal, activities associated with severe injury risks among workers and residents.
Syndromic Surveillance System – EpiCenter
Data Source – Emergency Department visits
Fields Used – Chief complaint
This syndrome was created to enhance current occupational health surveillance methods. Current data sources have a lag time of at least three months and estimates are often under-reported. Employing a real-time, independent data source could enhance classification of work-related injuries and illnesses, leading to a better understanding of the burden of non-fatal work-related injuries and illnesses, and allow for quicker intervention.
Syndromic Surveillance System – EpiCenter
Data Source – Emergency Department visits
Fields Used – Chief complaint
In 2016, the Centers for Disease Control and Prevention funded 12 states, under the Enhanced State Opioid Overdose Surveillance (ESOOS) program, to utilize state Emergency Medical Services (EMS) and emergency department syndromic surveillance (SyS) data systems to increase timeliness of state data on drug overdose events. An important component of the ESOOS program is the development and validation of case definitions for drug overdoses for EMS and ED SyS data systems with a focus on small area anomaly detection. In fiscal year one of the grant Kentucky collaborated with CDC to develop case definitions for heroin and opioid overdoses for both SyS and EMS data. These drug overdose case definitions are compared between these two rapid surveillance systems, and further compared to emergency department (ED) hospital administrative claims billing data, to assess their face validity.
Objective:
The aim of this project was to assess the face validity of surveillance case definitions for heroin overdose in emergency medical services (EMS) and emergency department syndromic surveillance (SyS) data systems by comparing case counts to those found in a statewide emergency department (ED) hospital administrative billing data system.
In 2016, the Centers for Disease Control and Prevention funded 12 states, under the Enhanced State Opioid Overdose Surveillance (ESOOS) program, to utilize state Emergency Medical Services (EMS) and emergency department (ED) syndromic surveillance (SyS) data systems to increase timeliness of state data on drug overdoses. A key aspect of the ESOOS program is the development and validation of case definitions for drug overdoses for EMS and ED SyS data systems. Kentucky's ESOOS team conducted a pilot validation study of a candidate EMS case definition for HOD, using data from the Kentucky State Ambulance Reporting System (KStARS). We examined internal, face validity of the EMS HOD case definition by reviewing pertinent information captured in KStARS data elements; and we examined external agreement with HOD cases identified Kentuckyâs statewide hospital billing database.
Objective:
The aims of this project were 1) to assess the validity of a surveillance case definition for identifying heroin overdoses (HOD) in a NEMSIS 3 compliant, state ambulance reporting system; and 2) to develop an approach that can be applied to assess the validity of case definitions for other types of drug overdose events in similar data state data systems.
Opioid ODs have been rising globally and nationally. The death rate from ODs in the United States has increased 137% since 2000, including a 200% increase of OD deaths involving opioids1. The pilot project, a collaboration across 3 states, allowed information sharing with Syndromic surveillance (SyS) partners across jurisdictions, such as sharing a standard SyS case definition and verifying its applicability in each jurisdiction. This is a continuation of the work from an initial pilot project presented during the ISDS Opioid OD Webinar series.
Objective:
The objective is to develop a standard opioid overdose case definition that could be generalized nationally
Comprehensive medical syndrome definitions are critical for outbreak investigation, disease trend monitoring, and public health surveillance. However, because current definitions are based on keyword string-matching, they may miss important distributional information in free text and medical codes that could be used to build a more general classifier. Here, we explore the idea that individual ICD codes can be categorized by examining their contextual relationships across all other ICD codes. We extend previous work in representation learning with medical data by generating dense vector embeddings of these ICD codes found in emergency department (ED) visit records. The resulting representations capture information about disease co-occurrence that would typically require SME involvement and support the development of more robust syndrome definitions.
Objective:
To better define and automate biosurveillance syndrome categorization using modern unsupervised vector embedding techniques.