Predicting health utilities for children with autism spectrum disorders.
Free regression code turns CBCL, Vineland-II, or PedsQL scores into HUI3 utilities so you can add QALYs to autism outcome reports without extra data collection.
01Research in Context
What this study did
Nalin et al. (2014) built math formulas that turn everyday autism scores into health-utility numbers.
They used CBCL, Vineland-II, and PedsQL results from kids with ASD.
The team tested the formulas on a second group to be sure they work.
What they found
The formulas give a HUI3 utility score that matches real quality-of-life data.
You can now plug common clinic scores into the sheet and get a QALY-ready number.
How this fits with other research
Kuhlthau et al. (2010) showed kids with ASD already score lower on HRQoL than peers. Nalin gives you the tool to turn those low scores into dollar-value units for cost studies.
Liu et al. (2025) shrank a 16-item QOL scale to just 3 items. Their ultra-short tool extends Nalin’s idea: both aim to save time while keeping the numbers sound.
De Kegel et al. (2016) warns that CBCL alone mis-finds many kids as autistic. This is not a true clash; Nalin uses CBCL only after ASD is diagnosed, so the utility math still holds.
Why it matters
If you write grants, run clinics, or sit on policy boards, you now have free code to turn the scores you already collect into health-utility values. No extra tests. No added parent stress. Just paste the CBCL, Vineland, or PedsQL totals into the sheet and report QALYs in your next outcome brief.
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Join Free →Download the paper’s appendix, paste your last client’s CBCL scores into the HUI3 formula, and add the utility number to the discharge summary.
02At a glance
03Original 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.
Autism research : official journal of the International Society for Autism Research, 2014 · doi:10.1007/s40273-014-0153-y