Identification of seizures among adults and children following influenza vaccination using health insurance claims data
Journal
Vaccine
Journal Volume
31
Journal Issue
50
Pages
5997-6002
Date Issued
2013
Author(s)
Thyagarajan, Veena
Su, Sue
Gee, Julianne
Duffy, Jonathan
McCarthy, Natalie L.
Chan, K. Arnold
Weintraub, Eric S.
Lin, Nancy D.
Abstract
Introduction: Post-licensure surveillance of adverse events following vaccination or prescription drug use often relies on electronic healthcare data to efficiently detect and evaluate safety signals. The accuracy of seizure-related diagnosis codes in identifying true incident seizure events in vaccine safety studies is influenced by factors such as clinical setting of diagnosis and age. To date, most studies of post-vaccination seizure have focused on pediatric populations. More information is needed on how well seizure can be identified in adults and children using algorithms that rely on electronic healthcare data. Methods: This validation study was part of a larger safety study of influenza vaccination during the 2009-2010 and 2010-2011 influenza seasons. Children and adults receiving influenza vaccination were drawn from an administrative claims database of a large United States healthcare insurer. Potential seizure events were identified using an algorithm of ICD-9 diagnosis codes associated with an emergency department (ED) visit or hospitalization within pre-specified risk windows following influenza vaccination. Seizure events were confirmed through medical record review. The positive predictive value (PPV) of the algorithm was calculated within each diagnostic setting and stratified by age group, ICD-9 code group, and sex. Results: Review confirmed 113 out of 176 potential seizure events. The PPVs were higher in the ED setting (93.9%) than in the inpatient setting (38.3%). The PPVs by age varied within the ED setting (98.2% in <7 years, 76.9% in 7-24 years, 92.3% in ?25 years) and within the inpatient setting (64.7% in <7 years, 33.3% in 7-24 years, 32.3% in ?25 years). Conclusions: Our algorithm for identification of seizure events using claims data had a high level of accuracy in the emergency department setting in young children and older adults and a lower, but acceptable, level of accuracy in older children and young adults. ? 2013 Elsevier Ltd.
Subjects
ICD-9 diagnosis codes; Large electronic healthcare database; Positive predictive value; Seizure; Vaccine safety
SDGs
Other Subjects
adolescent; adult; algorithm; article; child; cohort analysis; electronic medical record; emergency ward; female; groups by age and sex; health insurance; hospital patient; hospitalization; human; ICD-9; influenza vaccination; major clinical study; male; medical record review; patient safety; predictive value; priority journal; risk factor; school child; seizure; United States; validation study; ICD-9 diagnosis codes; Large electronic healthcare database; Positive predictive value; Seizure; Vaccine safety; Adolescent; Adult; Aged; Child; Child, Preschool; Cohort Studies; Drug-Related Side Effects and Adverse Reactions; Epidemiologic Methods; Female; Humans; Infant; Infant, Newborn; Influenza Vaccines; Influenza, Human; Insurance Claim Review; Male; Middle Aged; Predictive Value of Tests; Seizures; United States; Vaccination; Young Adult
Type
journal article