title: Validity of autism diagnoses using administrative health data.
abstract: It is necessary to monitor autism prevalence in order to plan education support and health services for affected children. This study was conducted to assess the accuracy of administrative health databases for autism diagnoses. Three administrative health databases from the province of Nova Scotia were used to identify diagnoses of autism spectrum disorders (ASD): the Hospital Discharge Abstract Database, the Medical Services Insurance Physician Billings Database and the Mental Health Outpatient Information System database. Seven algorithms were derived from combinations of requirements for single or multiple ASD claims from one or more of the three administrative databases. Diagnoses made by the Autism Team of the IWK Health Centre, using state-of-the-art autism diagnostic schedules, were compared with each algorithm, and the sensitivity, specificity and C-statistic (i.e. a measure of the discrimination ability of the model) were calculated. The algorithm with the best test characteristics was based on one ASD code in any of the three databases (sensitivity=69.3%). Sensitivity based on an ASD code in either the hospital or the physician billing databases was 62.5%. Administrative health databases are potentially a cost efficient source for conducting autism surveillance, especially when compared to methods involving the collection of new data. However, additional data sources are needed to improve the sensitivity and accuracy of identifying autism in Canada.
authors.affiliation: Perinatal Epidemiology Research Unit, Departments of Obstetrics and Gynaecology and Pediatrics, Dalhousie University, NS, Canada. l.dodds@dal.ca
authors.forename: L
authors.initials: L
authors.lastname: Dodds
authors.forename: A
authors.initials: A
authors.lastname: Spencer
authors.forename: S
authors.initials: S
authors.lastname: Shea
authors.forename: D
authors.initials: D
authors.lastname: Fell
authors.forename: B A
authors.initials: BA
authors.lastname: Armson
authors.forename: A C
authors.initials: AC
authors.lastname: Allen
authors.forename: S
authors.initials: S
authors.lastname: Bryson
mesh_terms: D000465:Algorithms; D000553:Ambulatory Care / Q000706:statistics & numerical data; D001321:Autistic Disorder / Q000175:diagnosis / Q000453:epidemiology; D002648:Child; D003362:Cost-Benefit Analysis; D016208:Databases, Factual / Q000191:economics / Q000592:standards*; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D015994:Incidence; D007344:Insurance Claim Reporting / Q000706:statistics & numerical data; D038801:International Classification of Diseases / Q000592:standards*; D008297:Male; D009674:Nova Scotia / Q000453:epidemiology; D010350:Patient Credit and Collection / Q000706:statistics & numerical data; D010351:Patient Discharge / Q000706:statistics & numerical data; D011159:Population Surveillance / Q000379:methods*; D015995:Prevalence; D012680:Sensitivity and Specificity