Hospital discharge database optimization in Guillain-Barré syndrome surveillance.
BACKGROUND
Precise causes and risk factors for Guillain-Barré syndrome (GBS) remain incompletely understood, and monitoring its incidence remains important in public health. Active surveillance is not sustainable, but hospital discharge databases (HDDs) may be suitable for passive surveillance.


METHODS
We optimized Tennessee's HDD of all ICD-9 codes of 357.0, from 2000 to 2010. Discharges for persons with a GBS diagnosis in a previous hospitalization in the database were excluded. HDD results were compared with the CDC's active surveillance program during the H1N1 vaccination program of 2009-2010.


RESULTS
Of 2,659 records of Tennessee residents initially identified, 1077 (40%) had prior diagnoses of GBS. Adjusted annual rates ranged from 1.24 to 1.57 per 100,000 population. The sensitivity of the optimized HDD was 0.81 with a positive predictive value of 0.45.


CONCLUSIONS
Optimization of a HDD through exclusion of nonacute cases results in an acceptable and practical database for passive surveillance of GBS.
Vanderbilt University Medical Center, Department of Neurology, 1161 21st Avenue South, MCN A0118, Nashville, Tennessee 37232, USA. Christopher.lee@vanderbilt.edu
Christopher D
CD
Lee
Timothy F
TF
Jones
D000328:Adult; D016208:Databases, Factual; D005260:Female; D020275:Guillain-Barre Syndrome / Q000175:diagnosis / Q000453:epidemiology*; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D015994:Incidence; D008297:Male; D010351:Patient Discharge / Q000706:statistics & numerical data*; D011159:Population Surveillance / Q000379:methods*; D011237:Predictive Value of Tests; D013714:Tennessee / Q000453:epidemiology