Citations

Objectives: Despite substantial evidence supporting the efficacy of stimulant medication for children with attention-deficit/hyperactivity disorder (ADHD), adherence to stimulant treatment is often suboptimal. Applying social/cognitive theories to understanding and assessing parent attitudes toward initiating medication may provide insight into factors influencing parent decisions to follow ADHD treatment recommendations. This report describes results from formative research that used focus groups to obtain parent input to guide development of a provider-delivered intervention to improve adherence to stimulants. Methods: Participants were caregivers of children with ADHD who were given a stimulant treatment recommendation. Focus groups were recorded and transcribed verbatim. Data were analyzed by inductive, grounded theory methods as well as a deductive analytic strategy using an adapted version of the Unified Theory of Behavior Change to organize and understand parent accounts. Results: Five groups were conducted with 27 parents (mean child age=9.35 years; standard deviation [SD]=2.00), mean time since diagnosis=3.33 years (SD=2.47). Most parents (81.5%) had pursued stimulant treatment. Inductive analysis revealed 17 attitudes facilitating adherence and 25 barriers. Facilitators included parent beliefs that medication treatment resulted in multiple functional gains and that treatment was imperative for their children's safety. Barriers included fears of personality changes and medication side effects. Complex patterns of parent adherence to medication regimens were also identified, as well as preferences for psychiatrists who were diagnostically expert, gave psychoeducation using multiple modalities, and used a chronic illness metaphor to explain ADHD. Theory-based analyses revealed conflicting expectancies about treatment risks and benefits, significant family pressures to avoid medication, guilt and concern that their children required medication, and distorted ideas about treatment risks. Parents, however, took pride in successfully pursuing efforts to manage their child behaviorally and to avoid medication when possible. Conclusions: Focus group data identified social, cognitive, and affective influences on treatment decision making. Results support prior research comparing family/social functioning, physician characteristics, and adherence. Findings suggest that parent attitudes to psychiatric care need to be assessed comprehensively at initial evaluation to aid the development of psychoeducational messages, and a more careful consideration about how parents interpret and respond to adherence-related questioning.

Abstract Background: Genome-wide association studies (GWAS) identified variants at 19p13.1 and ZNF365 (10q21.2) as risk factors for breast cancer among BRCA1 and BRCA2 mutation carriers, respectively. We explored associations with ovarian cancer and with breast cancer by tumor histopathology for these variants in mutation carriers from the Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA). Methods: Genotyping data for 12,599 BRCA1 and 7,132 BRCA2 mutation carriers from 40 studies were combined. Results: We confirmed associations between rs8170 at 19p13.1 and breast cancer risk for BRCA1 mutation carriers [HR, 1.17; 95% confidence interval (CI), 1.07–1.27; P = 7.42 × 10−4] and between rs16917302 at ZNF365 (HR, 0.84; 95% CI, 0.73–0.97; P = 0.017) but not rs311499 at 20q13.3 (HR, 1.11; 95% CI, 0.94–1.31; P = 0.22) and breast cancer risk for BRCA2 mutation carriers. Analyses based on tumor histopathology showed that 19p13 variants were predominantly associated with estrogen receptor (ER)-negative breast cancer for both BRCA1 and BRCA2 mutation carriers, whereas rs16917302 at ZNF365 was mainly associated with ER-positive breast cancer for both BRCA1 and BRCA2 mutation carriers. We also found for the first time that rs67397200 at 19p13.1 was associated with an increased risk of ovarian cancer for BRCA1 (HR, 1.16; 95% CI, 1.05–1.29; P = 3.8 × 10−4) and BRCA2 mutation carriers (HR, 1.30; 95% CI, 1.10–1.52; P = 1.8 × 10−3). Conclusions: 19p13.1 and ZNF365 are susceptibility loci for ovarian cancer and ER subtypes of breast cancer among BRCA1 and BRCA2 mutation carriers. Impact: These findings can lead to an improved understanding of tumor development and may prove useful for breast and ovarian cancer risk prediction for BRCA1 and BRCA2 mutation carriers. Cancer Epidemiol Biomarkers Prev; 21(4); 645–57. ©2012 AACR.

Abstract Advanced bone imaging with quantitative computed tomography (QCT) has had limited success in significantly improving fracture prediction beyond standard areal bone mineral density (aBMD) measurements. Thus, we examined whether a machine learning paradigm, gradient boosting machine (GBM) modeling, which can incorporate diverse measurements of bone density and geometry from central QCT imaging and of bone microstructure from high-resolution peripheral QCT imaging, can improve fracture prediction. We studied two cohorts of postmenopausal women: 105 with and 99 without distal forearm fractures (Distal Forearm Cohort) and 40 with at least one grade 2 or 3 vertebral deformity and 78 with no vertebral fracture (Vertebral Cohort). Within each cohort, individual bone density, structure, or strength variables had areas under receiver operating characteristic curves (AUCs) ranging from 0.50 to 0.84 (median 0.61) for discriminating women with and without fracture. Using all possible variables in the GBM model, the AUCs were close to 1.0. Fracture predictions in the Vertebral Cohort using the GBM models built with the Distal Forearm Cohort had AUCs of 0.82–0.95, whereas predictions in the Distal Forearm Cohort using models built with the Vertebral Cohort had AUCs of 0.80–0.83. Attempts at capturing a comparable parametric model using the top variables from the Distal Forearm Cohort resulted in resulted in an AUC of 0.81. Relatively high AUCs for differing fracture types suggest that an underlying fracture propensity is being captured by this modeling approach. More complex modeling, such as with GBM, creates stronger fracture predictions and may allow deeper insights into information provided by advanced bone imaging techniques. © 2012 American Society for Bone and Mineral Research.