Disability stories: personal perspectives of people with disabilities on navigating the U.S. health system.

Americans with disabilities and chronic illness or injury tend to be in poorer health, use more health services, and pay more for healthcare than those without disabilities. Consequently, their lives can be profoundly affected by federal and state health policies. The concerns of this population do not figure prominently in national health policy discourse and related public health and health services research efforts. This study sought to give voice to the lived experiences of people with disabilities as they navigate a fragmented U.S. healthcare system. We interviewed 30 adults who self-identified as having a disability and spoke or otherwise communicated in the English language. Directed content analysis was used to examine words and phrases in professionally transcribed documents by experienced qualitative researchers. We report and discuss four themes from the perspective of the participant, presented in thematic statements, related to vocation, finances, stressors, and advocacy.

Testing informative missingness in genetic studies using case-parent triads.

In genetic studies, the transmission/disequilibrium test (TDT) using case-parent triads has gained popularity attributable to its robustness to population admixture. Several extensions have been proposed to accommodate incomplete triads. Some strategies assume that parental genotypes are missing completely at random (MCAR) to insure an unbiased conclusion and some methods allow parental genotypes to be missing informatively, resulting in reduced power when the missing data pattern is indeed MCAR. However, these tests assumed that offspring genotypes were MCAR. Recently, Guo indicated that when offspring genotypes were missing informatively, an occurrence that can be considered as ascertainment bias, inflated type-I error and/or reduced power may occur using the TDT when incomplete triads are excluded. In an effort to avoid an erroneous conclusion, we propose a strategy called testing informative missingness (TIM) that compares conditional distributions of parental genotypes among complete triads and incomplete data with only one parent to examine the missing data pattern. Through computer simulations, TIM has decent power to detect informative missingness and is robust to population admixture. In addition, we illustrate TIM with an application to the Framingham Heart Study.