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Araki Patricia

It has been postulated that school absenteeism, a non-traditional surveillance data source, may allow for early detection of disease outbreaks, particularly among school-aged children who may not seek emergency medical attention. Although a New York City-based study showed moderate utility of school absenteeism in biosurveillance, no study to date has been reported on school absenteeism in Los Angeles County, which contains the second largest school district in the US.

 

Objective

To evaluate the utility of school absenteeism surveillance data in Los Angeles County during the 2009–2010 influenza season.

Referenced File
Submitted by hparton on

Effective and valid surveillance of syndromes can be extremely useful in the early detection of outbreaks and disease trends. However, medical chart checks without patient identifiers and lack of diagnoses in A08 data has made validation difficult. With the rising availability of electronic health records (EHRs) to local health departments, the ability to evaluate syndromic surveillance systems (SSS) has improved. In LAC, ED data are collected from hospitals and classified into categories based on chief complaints. The most reported syndrome in LAC is the respiratory classification, which is intended to broadly capture respiratory pathogen activity trends. To test the validity of the LAC Department of Public Health (DPH) respiratory syndrome classification, ED syndromic surveillance data were analyzed using corresponding EHRs from one hospital in LAC.

Objective

To compare and validate syndromic surveillance categorization against electronic health records at one hospital emergency department (ED) in Los Angeles County (LAC).

Submitted by elamb on

In the fall of 2001, the Bioterrorism Preparedness and Response (BT P&R) Unit initiated a syndromic surveillance system utilizing chief complaint data collected from Emergency Departments throughout Los Angeles County (LAC). Chief complaint data were organized into four syndromes (gastrointestinal, neurological, rash and respiratory) based on key words/phrases that appear in the patient’s record. Syndrome data are analyzed daily; counts for each syndrome are calculated and compared to a threshold to determine if a “signal” or aberration has occurred (EARS algorithm). A signal is defined as a case count elevated above threshold for a particular syndrome at an individual hospital.

 

Objective 

To describe the methods used by LAC, Department of Health Services, BT P&R Unit in determining the response to unusual disease/syndromic activity in LAC hospitals.

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

The Los Angeles County (LAC) Bioterrorism Preparedness and Response Unit has made significant progress in automating the syndromic surveillance system. The surveillance system receives electronic data on a daily basis from different hospital information systems, then standardizes and generates analytical results.

 

OBJECTIVE

This article describes architecture, analytical method, and software applications used in automating the LAC syndromic surveillance system.

Referenced File
Submitted by elamb on

Los Angeles County’s (LAC) early event detection system captures over 60% of total ED visits, as well as 800 to 1,000 emergency dispatch calls from Los Angeles City Fire (LACF) daily. Both ED visits and EDC calls are classified into syndrome categories, and then analyzed for aberrations in count and spatial distribution. During periods of high temperatures, a heat report is generated and sent to stakeholders upon request. We describe how syndromic surveillance serves as an important near real-time, population-based instrument for measuring the impact of heat waves on emergency service utilization in LAC.

Objective: 

To assess current indicators for situational awareness during heat waves derived from electronic emergency department (ED) and 911 emergency dispatch call (EDC) center data.

 

Submitted by Magou on

The LAC SSS has been in existence since 2004. Currently, the system collects data from over 50 hospitals daily and performs a chief complaint-based syndrome classification analysis of all ED visits. The keyword “fever” is of special interest due to its inclusion within several syndrome category definitions such as influenza, meningitis, etc. However, inclusion of such terms in syndrome definitions may be a disadvantage as such keyword searches would depend upon the consistency in which the term “fever” is reported. In 2014, several LAC syndromic surveillance hospital data connections were upgraded to include notes recording patient body temperature. To evaluate the newly added temperature information, analyses were conducted on those observations that included body temperature, chief complaint, and diagnosis information.

Objective

The Los Angeles County (LAC) Emergency Department (ED) Syndromic Surveillance System (SSS) classifies patients into syndrome categories based on stated chief complaints. In an effort to evaluate the accuracy of patient- stated chief complaints and final diagnoses, both “fever” chief complaints and diagnoses were compared with patient body temperature readings.

Submitted by rmathes on
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