Cost-Effectiveness of Autism Diagnostic Based on Genetic Testing
Abstract Background: Autism Spectrum Disorder (ASD) is a highly heritable polygenetic disorder with several degrees of handicap.Novel genetic diagnostics for Autism Spectrum Disorder promise an earlier diagnosis than psychometric diagnostics, but their cost-effectiveness is unproven.Objective: To model the clinical pathway from diagnosis to early intervention (EI) and outcome in scenarios with genetic diagnostics compared to just psychometric diagnosis that follows a current guideline (Status Quo). Methods: Early diagnosis based on genetic testing leads to more intensive and effective early intervention. Future scenarios assume genetic screening(Screening), genetic testing on request(GenADD), or genetic testing in cases with a family history of ASD(Predisposition). Simulations on Markov models using software TreeAge v. 2018 and parameters found in the literature. The time horizon reached from birth to the 15th year of life with cycle length 1 year. The models were stratified by autism severity, i.e. IQ initially below 70 or above. Effectiveness was both, dependency free life years (DFLY) gained by correct diagnosis and successful treatment, and the number of diagnosed patients that became independent after treatment. We choose the insurance view. Just direct costs for diagnostics and treatment were considered. Probabilistic sensitivity analyses (PSA) explore assumptions of different parameters, like the sensitivity of the genetic test, using the precisions stated in the literature or possible future developments. Results: Status Quo is the most cost-effective scenario with the current parameter values. The other scenarios follow in the order of Predisposition, GenADD, and Screening. All scenarios with genetic tests have a higher number of detection than Status Quo. Intensified early intervention may be cost effective with horizon 67 years. The currently high false positive rate of genetic testing might be detrimental to that. Discussion: Low precision of published parameter estimates led to wide confidence intervals for our estimates of cost-effectiveness. Our model shows that Screening and GenADD should not be an option for inaccurate genetic tests. Once they are more accurate, the potential of early intervention may unfold.Conclusion: Further evaluations with better data need to underpin the current results.