Evidence-based use of scalable biomarkers to increase diagnostic efficiency and decrease the lifetime costs of autism

Abstract

Challenges associated with the current screening and diagnostic process for autism spectrum disorder (ASD) in the US cause a significant delay in the initiation of evidence-based interventions at an early age when treatments are most effective. The present study shows how implementing a second-order diagnostic measure to high risk cases initially flagged positive from screening tools can further inform clinical judgment and substantially improve early identification. We use two example measures for the purposes of this demonstration; a saliva test and eye-tracking technology, both scalable and easy-to-implement biomarkers recently introduced in ASD research. Results of the current cost-savings analysis indicate that lifetime societal cost savings in special education, medical and residential care are estimated to be nearly $580,000 per ASD child, with annual cost savings in education exceeding $13.3 billion, and annual cost savings in medical and residential care exceeding $23.8 billion (of these, nearly $11.2 billion are attributable to Medicaid). These savings total more than $37 billion/year in societal savings in the US. Initiating appropriate interventions faster and reducing the number of unnecessary diagnostic evaluations can decrease the lifetime costs of ASD to society. We demonstrate the value of implementing a scalable highly accurate diagnostic in terms of cost savings to the US. LAY SUMMARY: This paper demonstrates how biomarkers with high accuracy for detecting autism spectrum disorder (ASD) could be used to increase the efficiency of early diagnosis. Results also show that, if more children with ASD are identified early and referred for early intervention services, the system would realize substantial costs savings across the lifespan.

Keywords: autism spectrum disorder; biomarkers; cost analysis; early diagnosis; evidence-based assessment.

Conflict of interest statement

Thomas W. Frazier is the former Chief Scientific Officer of Autism Speaks and developer of the eye tracking test that was used in the EBM analysis. Thomas W. Frazier has received federal funding or research support from, acted as a consultant to, received travel support from, and/or received a speaker's honorarium from Quadrant Biosciences, Impel NeuroPharma, F. Hoffmann‐La Roche AG Pharmaceuticals, the Cole Family Research Fund, Simons Foundation, Ingalls Foundation, Forest Laboratories, Ecoeos, IntegraGen, Kugona LLC, Shire Development, Bristol‐Myers Squibb, Roche Pharma, National Institutes of Health, and the Brain and Behavior Research Foundation and has an investor stake in AutismEYES LLC. Steven D. Hicks and Frank A. Middleton are co‐developers of the saliva RNA based autism test that is used in the EBM analysis, and members of the Clinical and Scientific Advisory Boards of Quadrant Biosciences. Kristin Sohl is director of ECHO Autism and both Kristin Sohl and Daniel L. Coury are a member of the Clinical Advisory Board of Quadrant Biosciences. Daniel L. Coury has received federal funding or research support from National Institutes of Health, GW Biosciences, Neurim, Stemina Biosciences, and Stalicla SA; and acted as a consultant to BioRosa, Cognoa, GW Biosciences, and Stalicla SA. Kayla E. Wagner and Richard Uhlig are employees of Quadrant Biosciences. Quadrant Biosciences holds patent rights and exclusive sales rights for the Clarifi ASD saliva test.

© 2021 The Authors. Autism Research published by International Society for Autism Research published by Wiley Periodicals LLC.

Figures

FIGURE 1

Clinical flow diagram. Results of a biomarker diagnostic determine whether additional evaluation is needed (test/no‐test threshold) and whether treatment might be initiated (treatment threshold). These thresholds are not fixed and are often dependent on additional considerations such as how important it is to identify the condition. Ultimately, exact thresholds are based on the clinical setting and determined by the clinician in consultation with the patient. If the first biomarker results is between thresholds (above the test/no‐test threshold but below the treatment threshold), the second biomarker (whichever was not administered first) would be administered. In this case, Table 5 (see above) would be used to generate the final post‐test probability for use of two biomarkers if results correspond to the presented outcomes (low‐low, high‐high, etc.). However, if some other combination of results were observed, the likelihood ratio values from Tables 1 and 2 could be applied in iterative fashion using Bayes theorem to generate a final post‐test probability based on both biomarkers

FIGURE 2

Average cumulative cost savings per ASD child. Average cumulative cost savings per ASD child. The blue shaded area provides an estimate of the cumulative cost savings (or outlays) in real dollars (RD) for each ASD child who experiences early intervention as a consequence of early detection. The dotted lines indicate the same amount adjusted for different inflation estimates across time (±1 standard deviation). The solid line indicates the present value (PV) of real dollar savings (or outlays) experienced through the age indicated. Note that there is a net positive savings in both real dollar and present value terms beginning at age 11 years