Caroline Piccininni1, Lise Bisnaire2, Melanie Penner3. 1. Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada. 2. Autism Program, Children's Hospital of Eastern Ontario, Ottawa, Ontario, Canada. 3. Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, Ontario, Canada3Department of Paediatrics, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.
Abstract
Importance: Earlier access to intensive behavioral intervention (IBI) is associated with improved outcomes for children with severe autism spectrum disorder (ASD); however, there are long waiting times for this program. No analyses have been performed modeling the cost-effectiveness of wait time reduction for IBI. Objectives: To model the starting age for IBI with reduced wait time (RWT) (by half) and eliminated wait time (EWT), and perform a cost-effectiveness analysis comparing RWT and EWT with current wait time (CWT) from government and societal perspectives. Design, Setting, and Participants: Published waiting times were used to model the mean starting age for IBI for CWT, RWT, and EWT in children diagnosed with severe ASD who were treated at Ontario's Autism Intervention Program. Inputs were loaded into a decision analytic model, with an annual discount rate of 3% applied. Incremental cost-effectiveness ratios (ICERs) were determined. One-way and probabilistic sensitivity analyses were performed to assess the effect of model uncertainty. We used data from the year 2012 (January 1 through December 31) provided from the Children's Hospital of Eastern Ontario IBI center for the starting ages. Data analysis was done from May through July 2015. Main Outcomes and Measures: The outcome was independence measured in dependency-free life-years (DFLYs) to 65 years of age. To derive this, expected IQ was modeled based on probability of early (age <4 years) or late (age ≥4 years) access to IBI. Probabilities of having an IQ in the normal (≥70) or intellectual disability (<70) range were calculated. The IQ strata were assigned probabilities of achieving an independent (60 DFLYs), semidependent (30 DFLYs), or dependent (0 DFLYs) outcome. Costs were calculated for provincial government and societal perspectives in Canadian dollars (Can$1 = US$0.78). Results: The mean starting ages for IBI were 5.24 years for CWT, 3.89 years for RWT, and 2.71 years for EWT. From the provincial government perspective, EWT was the dominant strategy, generating the most DFLYs for Can$53 000 less per individual to 65 years of age than CWT. From the societal perspective, EWT produced lifetime savings of Can$267 000 per individual compared with CWT. The ICERs were most sensitive to uncertainty in the starting age for IBI and in achieving a normal IQ based on starting age. Conclusions and Relevance: This study predicts the long-term effect of the current disparity between IBI service needs and the amount of IBI being delivered in the province of Ontario. The results suggest that providing timely access optimizes IBI outcomes, improves future independence, and lessens costs from provincial and societal perspectives.
Importance: Earlier access to intensive behavioral intervention (IBI) is associated with improved outcomes for children with severe autism spectrum disorder (ASD); however, there are long waiting times for this program. No analyses have been performed modeling the cost-effectiveness of wait time reduction for IBI. Objectives: To model the starting age for IBI with reduced wait time (RWT) (by half) and eliminated wait time (EWT), and perform a cost-effectiveness analysis comparing RWT and EWT with current wait time (CWT) from government and societal perspectives. Design, Setting, and Participants: Published waiting times were used to model the mean starting age for IBI for CWT, RWT, and EWT in children diagnosed with severe ASD who were treated at Ontario's Autism Intervention Program. Inputs were loaded into a decision analytic model, with an annual discount rate of 3% applied. Incremental cost-effectiveness ratios (ICERs) were determined. One-way and probabilistic sensitivity analyses were performed to assess the effect of model uncertainty. We used data from the year 2012 (January 1 through December 31) provided from the Children's Hospital of Eastern Ontario IBI center for the starting ages. Data analysis was done from May through July 2015. Main Outcomes and Measures: The outcome was independence measured in dependency-free life-years (DFLYs) to 65 years of age. To derive this, expected IQ was modeled based on probability of early (age <4 years) or late (age ≥4 years) access to IBI. Probabilities of having an IQ in the normal (≥70) or intellectual disability (<70) range were calculated. The IQ strata were assigned probabilities of achieving an independent (60 DFLYs), semidependent (30 DFLYs), or dependent (0 DFLYs) outcome. Costs were calculated for provincial government and societal perspectives in Canadian dollars (Can$1 = US$0.78). Results: The mean starting ages for IBI were 5.24 years for CWT, 3.89 years for RWT, and 2.71 years for EWT. From the provincial government perspective, EWT was the dominant strategy, generating the most DFLYs for Can$53 000 less per individual to 65 years of age than CWT. From the societal perspective, EWT produced lifetime savings of Can$267 000 per individual compared with CWT. The ICERs were most sensitive to uncertainty in the starting age for IBI and in achieving a normal IQ based on starting age. Conclusions and Relevance: This study predicts the long-term effect of the current disparity between IBI service needs and the amount of IBI being delivered in the province of Ontario. The results suggest that providing timely access optimizes IBI outcomes, improves future independence, and lessens costs from provincial and societal perspectives.
Authors: Matthew J Russell; Yunqi Zhang; Xinjie Cui; Suzanne Tough; Jennifer D Zwicker Journal: Dev Med Child Neurol Date: 2020-01-31 Impact factor: 5.449
Authors: Scott D Grosse; Xu Ji; Phyllis Nichols; Samuel H Zuvekas; Catherine E Rice; Marshalyn Yeargin-Allsopp Journal: Psychiatr Serv Date: 2020-10-20 Impact factor: 3.084
Authors: Irina Pokhilenko; Luca M M Janssen; Silvia M A A Evers; Ruben M W A Drost; Lena Schnitzler; Aggie T G Paulus Journal: Pharmacoeconomics Date: 2021-06-14 Impact factor: 4.981