Monday, October 19, 2015: 2:15 PM
Grand Ballroom B (Hyatt Regency St. Louis at the Arch)

Susan dosReis, PhD1, Xinyi Ng, BSc (Pharm)2, Melissa Ross, MA1, Gloria M. Reeves, M.D.3, Emily Frosch, M.D.4 and John F.P. Bridges, PhD5, (1)University of Maryland School of Pharmacy, Baltimore, MD, (2)University of Maryland Baltimore, Baltimore, MD, (3)University of Maryland School of Medicine, Baltimore, MD, (4)Johns Hopkins School of Medicine, Baltimore, MD, (5)Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
Purpose: Preference heterogeneity, often analyzed by stratification on observable factors known to influence preferences, may be better assessed with latent class analysis. We compared theses approaches within a study of caregiver preferences for pediatric attention-deficit/hyperactivity disorder (ADHD) treatments.

Method: Eligible caregivers had a child aged 4-14 years being treated for ADHD, and were recruited from community clinics and support groups. Treatment preferences were elicited using Case 2 Best Worst Scaling (BWS), implemented as part of a larger survey that captured demographic and clinical information, including time since ADHD diagnosis. The 18 BWS profiles were identified from a main-effects orthogonal array spanning seven attributes (i.e., medication, therapy, school, caregiver training, provider, communication, and costs). The dependent variable was caregivers’ choice of a best and a worst attribute for each profile. Preference heterogeneity was examined by: a) stratification of time since ADHD diagnosis (<4 years/4+ years), since illness experience can influence preferences; b) latent class analysis (LCA). In the stratified analysis, the best-worst score method was estimated separately for each strata. Latent Gold®Choice was used to conduct the LCA. The latent segment solution was determined using model fit statistics and theoretical interpretability. Bivariate statistics tested for statistically significant differences in demographic and treatment by diagnosis duration strata and across latent segments.

Result: Caregivers (n=184; 84% mothers; 43% college-educated) reported for children who were on average 9 years old and taking stimulants (75%). Regardless of time since the child’s ADHD diagnosis, medication use seven days a week, therapy in a clinic, and an individualized education program were most preferred (p<0.001). Irrespective of ADHD duration, out-of-pocket costs and caregiver behavior training were most important (p<0.001), but the conditional importance of medication and school accommodations differed. Demographic and treatment variables were similar between the two groups. The LCA generated a three-segment solution: 1) cost-sensitive (53%); 2) multi-modal treatment (23%); 3) medication-oriented (24%). Medication administration, therapy location, school accommodation, caregiver behavior training, and out-of-pocket costs discriminated each class. ADHD duration did not differ across segments (p>0.05), and few other factors were significantly different.

Conclusion: Latent class analysis provides a nuanced understanding of preferences for medical treatment, and can be applied to other medical conditions.  Future research will build upon this work to investigate the correlation with adherence.