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Predicting Health Utilities for Children With Autism Spectrum Disorders
Author(s) -
Payakachat Nalin,
Tilford J. Mick,
Kuhlthau Karen A.,
Exel N. Job,
Kovacs Erica,
Bellando Jayne,
Pyne Jeffrey M.,
Brouwer Werner B.F.
Publication year - 2014
Publication title -
autism research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.656
H-Index - 66
eISSN - 1939-3806
pISSN - 1939-3792
DOI - 10.1002/aur.1409
Subject(s) - autism , autism spectrum disorder , psychology , developmental psychology , audiology , medicine
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 A utism T reatment N etwork sites. Health utility data based on the Health Utilities Index Mark 3 ( HUI 3) 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 C hild B ehavior C hecklist, V ineland II A daptive B ehavior S cales, 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 HUI 3 scores as dependent variables in ordinary least squares regressions along with the start measures, the A utism D iagnostic O bservation S chedule, 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. Autism Res 2014, 7: 649–663. © 2014 International Society for Autism Research, Wiley Periodicals, Inc.