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Accurate estimates of autism spectrum disorder (ASD)–associated medical costs are essential for predicting future care needs, allocating resources, identifying best practices, and modeling cost-effectiveness. Most existing studies have either employed subjective cost data or ascertained ASD using self-reported or International Classification of Diseases–coded diagnoses. Such ascertainment is especially problematic for identifying milder ASD among older individuals never diagnosed with ASD.
This 1976 through 2000 population-based birth-cohort study was set in Olmsted County, Minnesota. ASD cases and age- and sex-matched unaffected controls were identified by applying uniform operational research criteria for ASD (using the guidelines of the Diagnostic and Statistical Manual of Mental Disorders, fourth edition, text revision) after rigorous review of provider-linked medical and public, private, or home school records available for all members from birth to a maximum age of 21 years. Medical cost estimates for the 901 case-control pairs used line-item provider-linked billing data (including all payers) from 2003 through 2014 (ages 3-38 years). Outpatient pharmaceutical costs were unavailable. Temporal changes in diagnostic criteria, clinical practice, public awareness, and access were addressed by separating analyses into 5-year age group and 4-year calendar period cells. Unadjusted and adjusted (age and age plus co-occurring conditions) cost estimates were provided for cases, controls, and case-control differences. Additional factors (co-occurring conditions, percentage hospitalized, intellectual disability) were investigated using unadjusted descriptive analyses.
Cell sample sizes ranged from 93 to 402 for age groups 3 through 19 years and from 45 to 395 for age groups 20 through 38 years. Unadjusted, age-adjusted, and fully adjusted medical costs were significantly higher for cases versus controls in 100% of cells for age groups 3 through 19 years and in 50% (unadjusted), 38% (age adjusted), and 12% (fully adjusted) of cells for age groups 20 through 38 years.
These unique estimates can help inform the construction of cost-effectiveness models; decisions by payers, providers, and policy makers; and predictions of lifetime costs.
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