Validation of Diagnostic Codes for Intussusception and Quantification of Childhood Intussusception Incidence in Ontario, Canada: A Population-Based Study

Objectives To validate an algorithm to identify cases of intussusception using the health administrative data of Ontario, Canada, and to apply the algorithm to estimate provincial incidence of intussusception, preceding the introduction of the universal rotavirus vaccination program. Study design We...

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Bibliographic Details
Published in:The Journal of pediatrics 2013-10, Vol.163 (4), p.1073-1079.e3
Main Authors: Ducharme, Robin, HBSc, MSc (candidate), Benchimol, Eric I., MD, PhD, FRCPC, Deeks, Shelley L., MD, MHSc, FRCPC, FAFPHM, Hawken, Steven, MSc, Fergusson, Dean A., MHA, PhD, Wilson, Kumanan, MD, FRCPC, MSc
Format: Article
Language:English
Subjects:
Adolescent
Algorithms
Child
Child, Preschool
childhood
children
Databases, Factual
Female
Hospitalization
Humans
Incidence
Infant
infants
International Classification of Diseases
Intussusception - classification
Intussusception - diagnosis
Intussusception - epidemiology
Male
monitoring
Ontario
patients
Pediatrics
Poisson Distribution
Predictive Value of Tests
Rotavirus Vaccines - therapeutic use
Sensitivity and Specificity
temporal variation
Time Factors
vaccination
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Summary:Objectives To validate an algorithm to identify cases of intussusception using the health administrative data of Ontario, Canada, and to apply the algorithm to estimate provincial incidence of intussusception, preceding the introduction of the universal rotavirus vaccination program. Study design We determined the accuracy of various combinations of diagnostic, procedural, and billing codes using the chart-abstracted diagnoses of patients of the Children's Hospital of Eastern Ontario as the reference standard. We selected an algorithm that maximized positive predictive value while maintaining a high sensitivity and used it to ascertain annual incidence of intussusception for fiscal years 1995-2010. We explored temporal trends in incidence using Poisson regression. Results The selected algorithm included only the International Classification of Diseases (ICD)-9 or ICD-10 code for intussusception in the hospitalization database and was sensitive (89.3%) and highly specific (>99.9%). The positive predictive value of the ICD code was 72.4%, and the negative predictive value was >99.9%. We observed the highest mean incidence (34 per 100 000) in male children
ISSN:0022-3476
1097-6833
DOI:10.1016/j.jpeds.2013.05.034