2013
DOI: 10.1016/j.vaccine.2013.10.026
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Identification of seizures among adults and children following influenza vaccination using health insurance claims data

Abstract: 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.

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Cited by 7 publications
(4 citation statements)
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“…Despite several validation studies,15 16 a clear, validated algorithm for seizure relevant to our study population was not available. We therefore employed both a broad definition of seizure (including outpatient diagnoses as well as emergency department and inpatient diagnoses) in the primary analysis, and a more specific definition of seizure (restricted to emergency department visits and hospital admissions) in the secondary analysis.…”
Section: Methodsmentioning
confidence: 99%
“…Despite several validation studies,15 16 a clear, validated algorithm for seizure relevant to our study population was not available. We therefore employed both a broad definition of seizure (including outpatient diagnoses as well as emergency department and inpatient diagnoses) in the primary analysis, and a more specific definition of seizure (restricted to emergency department visits and hospital admissions) in the secondary analysis.…”
Section: Methodsmentioning
confidence: 99%
“…We used ICD-9 codes to define falls or fractures (805.2X-805.7, 812.XX, 820.XX, E882-E885, E888) and altered mental status (291, 291.1, 292.81, 293, 293.1, 298.2, 780.09, 780.97). These ICD-9 codes were selected to be consistent with previous literature (Hope et al, 2014;Pugh et al, 2015;Thyagarajan et al, 2013;Womack et al, 2011). We excluded patients diagnosed with any of these events in the year preceding their index date.…”
Section: Clinical Subpopulationsmentioning
confidence: 99%
“…We relied on ICD-10 codes for diagnoses. Although investigations into the validity of diagnostic codes in claims data have mostly been promising [ 22 24 ], there is still potential for miscoding. Additionally, as our intent for this investigation was to provide a broad overview of utilization and diagnostic shifts, we only included the first diagnosis in each claim.…”
Section: Discussionmentioning
confidence: 99%
“…The age and sex distribution of the plan members is similar to that reported by the US Census Bureau [ 21 ]. Although deidentification of patient information makes formal validation of claims data challenging, some investigations have demonstrated encouraging reliability, though specificity of diagnostic codes is likely higher than sensitivity [ 22 24 ]. The Stanford University institutional review board approved the use of this deidentified database in this study under Stanford’s Center for Population Health Sciences umbrella protocol.…”
Section: Methodsmentioning
confidence: 99%