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Herpes zoster and postherpetic neuralgia surveillance using structured electronic data
Michael Klompas1, Martin Kulldorff, Yury Vilk
1Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA 02215, USA. mklompas@partners.org
Mayo Clinic Proceedings
|October 15, 2011
Summary
New electronic algorithms can effectively detect herpes zoster and postherpetic neuralgia (PHN) using health data. These tools may indicate a rising incidence of PHN, aiding public health surveillance.
Area of Science:
- Infectious Disease Epidemiology
- Health Informatics
- Public Health Surveillance
Background:
- Herpes zoster and its complication, postherpetic neuralgia (PHN), pose a significant public health challenge.
- Accurate and timely surveillance is crucial for understanding disease burden and trends.
Purpose of the Study:
- To develop and validate electronic algorithms for automated surveillance of herpes zoster and PHN.
- To assess changes in the incidence of PHN over time using electronic health data.
Main Methods:
- Utilized diagnosis codes, encounter intervals, and medication prescriptions from a large electronic health record database.
- Developed and optimized combination algorithms to detect herpes zoster and PHN.
- Assessed algorithm sensitivity and positive predictive value (PPV) through medical record review.
Main Results:
- A single diagnosis code (ICD-9: 053) detected herpes zoster with 98% sensitivity and 93% PPV.
- A combination algorithm achieved 86% sensitivity and 78% PPV for PHN detection.
- The age- and sex-adjusted annual incidence of PHN increased from 0.18 to 0.47 per 1000 patients between 1996 and 2008.
Conclusions:
- Electronic algorithms integrating multiple data streams can effectively identify herpes zoster and PHN cases.
- These algorithms offer a valuable tool for public health surveillance.
- Findings suggest a potential increase in PHN incidence.
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