• Michael Klompas, Martin Kulldorff, Yury Vilk, Stephanie R Bialek, and Rafael Harpaz.
• Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA 02215, USA. mklompas@partners.org
• Mayo Clin. Proc. 2011 Dec 1;86(12):1146-53.

ObjectiveTo develop electronic algorithms for rapid, automated surveillance for herpes zoster and postherpetic neuralgia (PHN) using codified electronic health data.Patients And MethodsWe attempted to identify every case of herpes zoster and PHN arising between January 1 and December 31, 2008, within the electronic medical record of a 560,000-patient ambulatory practice using an array of diagnosis codes; intervals between herpes zoster encounters; and prescriptions for analgesics, anticonvulsants, and antidepressants. We assessed the sensitivity and positive predictive value (PPV) of each screening criterion by medical record review and then integrated multiple criteria into combination algorithms to optimize sensitivity and PPV. We applied the optimized algorithms to the practice's historical data spanning January 1, 1996, to December 31, 2008, to assess for changes in the annual incidence of PHN.ResultsThe International Classification of Diseases, Ninth Revision, code 053 detected herpes zoster with 98% sensitivity and 93% PPV. A combination algorithm including diagnosis codes, visit intervals, and prescriptions detected PHN with 86% sensitivity and 78% PPV. Between 1996 and 2008, the age- and sex-adjusted annual incidence of PHN rose from 0.18 to 0.47 cases per 1000 patients, and the proportion of herpes zoster patients progressing to PHN rose from 5.4% to 17.6%.ConclusionNovel algorithms incorporating multiple streams of electronic health data can reasonably detect herpes zoster and PHN. These algorithms could facilitate meaningful public health surveillance using electronic health data. The incidence of PHN may be increasing.

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