The Impact of Surveillance Method and Record Source on Autism Prevalence: Collaboration with Utah Maternal and Child Health Programs
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With the increasing number of Utah children identified with autism spectrum disorders (ASDs), information on the prevalence and characteristics of these children could help Maternal Child Health (MCH) programs develop population building activities focused on prevention, screening, and education. The purpose of this study is to describe Utah’s autism registry developed in collaboration with state MCH programs and assess the impact of different record-based surveillance methods on state ASD prevalence rates. The study was conducted using 212 ASD cases identified from a population of 26,217 eight year olds living in one of the three most populous counties in Utah (Davis, Salt Lake, and Utah) in 2002. ASD prevalence was determined using two records based approaches (administrative diagnoses versus abstraction and clinician review) by source of record ascertainment (education, health, and combined). ASD prevalence ranged from 7.5 per 1000 (95% CI 6.4–8.5) to 3.2 per 1000 (95% CI 2.5–3.9) varying significantly (P < .05) based on method and record source. The ratio of male-to-female ranged from 4.7:1 to 6.4:1. No significant differences were found between the two case ascertainment methods on 18 of the 23 case characteristics including median household income, parental education, and mean age of diagnosis. Broad support is needed from both education and health sources as well as collaboration with MCH programs to address the growing health concerns, monitoring, and treatment needs of children and their families impacted by autism spectrum disorders.
KeywordsAutism spectrum disorders Surveillance Prevalence Maternal child health Epidemiology
This research was partially funded by the Centers for Disease Control and Prevention under Cooperative Agreement CCU822365 to establish Population-Based Surveillance of Autism Spectrum Disorders. Thanks are extended to Jeff Duncan, Lynne MacCleod, Judith Miller, Barry Nangle, Chuck Norlin, Lyle Odenhayl, Carmen Pingree, and David Sundwall, Wu Xu.
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