, Volume 30, Issue 8, pp 661-679
Date: 23 Dec 2012

Preference-Based Health-Related Quality-of-Life Outcomes in Children with Autism Spectrum Disorders

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Abstract

Background

Cost-effectiveness analysis of pharmaceutical and other treatments for children with autism spectrum disorders (ASDs) has the potential to improve access to services by demonstrating the value of treatment to public and private payers, but methods for measuring QALYs in children are under-studied. No cost-effectiveness analyses have been undertaken in this population using the cost-per-QALY metric.

Objective

This study describes health-related quality-of-life (HR-QOL) outcomes in children with ASDs and compares the sensitivity of two generic preference-based instruments relative to ASD-related conditions and symptoms.

Methods

The study design was cross-sectional with prospectively collected outcome data that were correlated with retrospectively assessed clinical information. Subjects were recruited from two sites of the Autism Treatment Network (ATN) in the US: a developmental centre in Little Rock, Arkansas, and an outpatient psychiatric clinic at Columbia University Medical Center in New York. Children that met Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria for an ASD by a multidisciplinary team evaluation were asked to participate in a clinical registry. Families of children with an ASD that agreed to be contacted about participation in future research studies as part of the ATN formed the sampling frame for the study. Families were included if the child with the ASD was between 4 and 17 years of age and the family caregiver spoke English. Eligible families were contacted by mail to see if they would be interested in participating in the study with 150 completing surveys. HR-QOL outcomes were described using the Health Utilities Index (HUI) 3 and the Quality of Well-Being Self-Administered (QWB-SA) scale obtained by proxy via the family caregiver.

Results

Children were diagnosed as having autistic disorder (76%), pervasive developmental disorder-not otherwise specified [PDD-NOS] (15%), and Asperger’s disorder (9%). Average HUI3 and QWB-SA scores were 0.68 (SD 0.21, range 0.07–1) and 0.59 (SD 0.16, range 0.18–1), respectively. The HUI3 score was significantly correlated with clinical variables including adaptive behaviour (ρ=0.52;p<0.001) and cognitive functioning (ρ=0.36;p<0.001). The QWB-SA score had weak correlation with adaptive behaviour (ρ=0.25;p<0.001) and cognitive functioning (ρ=0.17;p<0.005). Change scores for the HUI3 were larger than the QWB-SA for all clinical measures. Scores for the HUI3 increased 0.21 points (95% CI 0.14, 0.29) across the first to the third quartile of the cognitive functioning measure compared with 0.05 (95% CI −0.01, 0.11) for the QWB-SA. Adjusted R2 values also were higher for the HUI3 compared with the QWB-SA across all clinical measures.

Conclusions

The HUI3 was more sensitive to clinical measures used to characterize children with autism compared with the QWB-SA score. The findings provide a benchmark to compare scores obtained by alternative methods and instruments. Researchers should consider incorporating the HUI3 in clinical trials and other longitudinal research studies to build the evidence base for describing the cost effectiveness of services provided to this important population.