Skip to main content

Burrer Sherry

Description

During an influenza pandemic, when hospitals and doctors'™ offices are or are perceived to be overwhelmed, many ill people may not seek medical care. People may also avoid medical facilities due to fear of contracting influenza or transmitting it to others. Therefore, syndromic methods for monitoring illness outside of health care settings are important adjuncts to traditional disease reporting. Monitoring absenteeism trends in schools and workplaces provide the archetypal examples for such approaches. NIOSH's early experience with workplace absenteeism surveillance during the 2009 - 2010 H1N1 pandemic established that workplace absenteeism correlates well with the occurrence of influenza-like illness (ILI) and significant increases in absenteeism can signal concomitant peaks in disease activity. It also demonstrated that, while population-based absenteeism surveillance using nationally representative survey data is not as timely, it is more valid and reliable than surveillance based on data from sentinel worksites.1 In 2017, NIOSH implemented population-based, monthly surveillance of health-related workplace absenteeism among full-time workers.

Objective: To describe the methodology of the National Institute for Occupational Safety and Health (NIOSH) system for national surveillance of health-related workplace absenteeism among full-time workers in the United States and to present initial findings from October through July of the 2017 - 2018 influenza season.

Submitted by elamb on
Description

Concern over oral health-related ED visits stems from the increasing number of unemployed and uninsured, the cost burden of these visits, and the unavailability of indicated dental care in EDs [1]. Of particular interest to NC state public health planners are Medicaid-covered visits. Syndromic data in biosurveillance systems offer a means to quantify these visits overall and by county and age group.

Objective

The objective was to use syndromic surveillance data from the North Carolina Disease Event Tracking and Epidemiologic Collection Tool NCDETECT and from BioSense to quantify the burden on North Carolina (NC) emergency departments of oral health-related visits more appropriate for care in a dental office (ED). Calculations were sought in terms of the Medicaid-covered visit rate relative to the Medicaid-eligible population by age group and by county.

Submitted by uysz on
Description

Monitoring laboratory test reports could aid disease surveillance by adding diagnostic specificity to early warning signals and thus improving the efficiency of public health investigation of detected signals. Laboratory data could also be employed to direct and evaluate interventions and countermeasures, while monitoring outbreak trends and progress; this would ultimately result in better outbreak response and management, and enhanced situation awareness. Since Electronic Laboratory Reporting (ELR) has the potential to be more accurate, timely, and cost-effective than reporting by other means of communication (e.g., mail, fax, etc.), ELR adoption has been systematically promoted as a public health priority.  However, the continuing use of non-standard, local codes or text to represent laboratory test type and results complicates the use of ELR data in public health practice. Use of structured, unique, and widely available coding system(s) to support the concepts represented by locally assigned laboratory test order and result information improves the computational characteristics of ELR data. Out of several coding strategies available, the Office of the U.S. National Coordinator for Health Information Technology has recently suggested incorporating Logical Observation Identifiers Names and Codes (LOINC) for laboratory orders and Systemized Nomenclature of Medicine- Clinical Terms (SNOMED CT) codes for laboratory results to standardize ELR.



Objective:

To examine the use of LOINC and SNOMED CT codes for coding laboratory orders and results in laboratory reports sent from 63 non-federal hospitals to the BioSense Program in calendar year 2011.

 

Submitted by Magou on