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Geocoding can help spot influenza outbreaks
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A novel automated process that tracked infected patients by address simplified the detection of influenza outbreaks at long-term care facilities, according to research published in the American Journal of Infection Control.
“Influenza is a serious health concern among elderly populations, as an estimated 90% of deaths due to influenza infection occur in persons aged 65 and older,” Alison Levin-Rector, MPH, a public health analyst at RTI International, formerly of the New York City Department of Health and Mental Hygiene (DOHMH), and colleagues wrote. “Influenza can be rapidly transmitted within nursing homes and other chronic-care facilities, affecting individuals at risk for complications.
“When influenza occurs in institutional settings, timely detection is critical to successfully control outbreaks through chemoprophylaxis and other infection control measures.”
Researchers at DOHMH obtained a list of 175 nursing homes in the New York metropolitan area at the start of the 2013-2014 influenza season and used geographical coding techniques to assign each facility a building identification number (BIN). The same technique was used to code the addresses of patients with laboratory-confirmed influenza after they were logged into the New York State Electronic Clinical Laboratory Reporting System (NYSECLRS).
The automated program was operated daily from Sept. 29, 2013 through May 2014 — roughly spanning the flu season — and DOHMH received 15,876 reports for 13,508 cases of influenza, of which 99% were received through the NYSECLRS, according to Levin-Rector and colleagues. Of these, 12,681 were coded by address, and 426 either matched the BIN codes of long-term care facilities (55%) or were identified by approximate text match (44%) or keyword search (1%).
Upon manual review, 249 of the 426 matches were found to be valid; the rest were deemed faulty for various reasons including inaccurate text matching and incorrect address reporting. Valid matches corresponded to 109 influenza outbreaks at 70 long-term care facilities (LTCFs), more than 90% of the 119 outbreaks reported at 75 long-term care facilities in the New York metro area during the 2013-2014 flu season, the researchers wrote. Nearly half of the outbreaks were detected by building analysis before other methods of detection.
“The building analysis has demonstrated utility for identifying influenza outbreaks in LTCFs, especially in situations when residents are transferred from an LTCF to a hospital and test positive for influenza, without the LTCF necessarily being aware of positive influential test results for their residents,” Levin-Rector and colleagues wrote. “Most state health departments have at least the minimum data required to employ some level of case-patient-to-facility address matching. Automated analyses using routinely geocoded data should be considered to strengthen public health surveillance and response activities.” – by David Jwanier
Disclosure: The researchers report no relevant financial disclosures.
Perspective
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Colleen S. Kraft, MD
As an infectious diseases specialist, the practice of infection prevention can be, at times, more about being reactive rather than proactive. Introduction of more automated techniques may be helpful as we start to understand how transmission occurs and infection is spread. An idea such as geocoding can allow practitioners to have an understanding about outbreaks in a more real-time manner.
If this type of automation can be improved to be precise and accurate — then it may be that the future holds that we can prospectively look at these infections that are spread. If that is the case, then we can also stop them in a prospective manner, and more lives can be saved by this detection and prevention of the transmission event of infections.
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