title: Predicting health utilities for children with autism spectrum disorders.
abstract: Comparative effectiveness of interventions for children with autism spectrum disorders (ASDs) that incorporates costs is lacking due to the scarcity of information on health utility scores or preference-weighted outcomes typically used for calculating quality-adjusted life years (QALYs). This study created algorithms for mapping clinical and behavioral measures for children with ASDs to health utility scores. The algorithms could be useful for estimating the value of different interventions and treatments used in the care of children with ASDs. Participants were recruited from two Autism Treatment Network sites. Health utility data based on the Health Utilities Index Mark 3 (HUI3) for the child were obtained from the primary caregiver (proxy-reported) through a survey (N = 224). During the initial clinic visit, proxy-reported measures of the Child Behavior Checklist, Vineland II Adaptive Behavior Scales, and the Pediatric Quality of Life Inventory 4.0 (start measures) were obtained and then merged with the survey data. Nine mapping algorithms were developed using the HUI3 scores as dependent variables in ordinary least squares regressions along with the start measures, the Autism Diagnostic Observation Schedule, to measure severity, child age, and cognitive ability as independent predictors. In-sample cross-validation was conducted to evaluate predictive accuracy. Multiple imputation techniques were used for missing data. The average age for children with ASDs in this study was 8.4 (standard deviation = 3.5) years. Almost half of the children (47%) had cognitive impairment (IQ ≤ 70). Total scores for all of the outcome measures were significantly associated with the HUI3 score. The algorithms can be applied to clinical studies containing start measures of children with ASDs to predict QALYs gained from interventions.
authors.affiliation: Division of Pharmaceutical Evaluation and Policy, University of Arkansas for Medical Sciences, Little Rock, Arkansas.
authors.forename: Nalin
authors.initials: N
authors.lastname: Payakachat
authors.forename: J Mick
authors.initials: JM
authors.lastname: Tilford
authors.forename: Karen A
authors.initials: KA
authors.lastname: Kuhlthau
authors.forename: N Job
authors.initials: NJ
authors.lastname: van Exel
authors.forename: Erica
authors.initials: E
authors.lastname: Kovacs
authors.forename: Jayne
authors.initials: J
authors.lastname: Bellando
authors.forename: Jeffrey M
authors.initials: JM
authors.lastname: Pyne
authors.forename: Werner B F
authors.initials: WB
authors.lastname: Brouwer
mesh_terms: D000293:Adolescent; D000465:Algorithms; D001131:Arkansas; D002648:Child; D002652:Child Behavior / Q000523:psychology; D002659:Child Development Disorders, Pervasive / Q000523:psychology* / Q000628:therapy*; D002675:Child, Preschool; D005260:Female; D006801:Humans; D008297:Male; D017063:Outcome Assessment, Health Care / Q000706:statistics & numerical data*; D011788:Quality of Life / Q000523:psychology; D019057:Quality-Adjusted Life Years
keywords: autism; behavioral measure; clinical measure; equating measure; health utilities; mapping; predictive algorithms; quality of life measure