Differences in patient perceptions of integrated care among black, hispanic, and white Medicare beneficiaries.
Health services research
OBJECTIVE: This study sought to identify potential disparities among racial/ethnic groups in patient perceptions of integrated care (PPIC) and to explore how methodological differences may influence measured disparities.DATA SOURCE: Data from Medicare beneficiaries who completed the 2015 Medicare Current Beneficiary Survey (MCBS) and were enrolled in Part A benefits for an entire year.STUDY DESIGN: We used 4-point measures of eight dimensions of PPIC and assessed differences in dimensions among racial/ethnic groups. To estimate differences, we applied a "rank and replace" method using multiple regression models in three steps, balancing differences in health status among racial groups and adjusting for differences in socioeconomic status. We reran all analyses with additional SES controls and using standard multiple variable regression.DATA COLLECTION/EXTRACTION METHODS: Not applicable.PRINCIPAL FINDINGS: We found several significant differences in perceived integrated care between Black versus White (three of eight measures) and Hispanic versus White (one of eight) Medicare beneficiaries. On average, Black beneficiaries perceived more integrated support for self-care than did White beneficiaries (mean difference=0.14, SE=0.06, P=.02). Black beneficiaries perceived more integrated specialists' knowledge of past medical history than did White beneficiaries (mean difference=0.12, SE=0.06, P=.01). Black and Hispanic beneficiaries also each reported, on average, 0.18 more integrated medication and home health management than did White beneficiaries (P<.01 and P<.01). These findings were robust to sensitivity analyses and model specifications.CONCLUSIONS: There exist some aspects of care for which Black and Hispanic beneficiaries may perceive greater integrated care than non-Hispanic White beneficiaries. Further studies should test theories explaining why racial/ethnic groups perceive differences in integrated care.
View details for DOI 10.1111/1475-6773.13637
View details for PubMedID 33569775
Detection of Velocity and Diffusion Coefficient Change Points in Single-Particle Trajectories
CELL PRESS. 2018: 217–29
The position-time trajectory of a biological subject moving in a complex environment contains rich information about how it interacts with the local setting. Whether the subject be an animal or an intracellular endosomal vesicle, the two primary modes of biological locomotion are directional movement and random walk, respectively characterized by velocity and diffusion coefficient. This contribution introduces a method to quantitatively divide a single-particle trajectory into segments that exhibit changes in the diffusion coefficient, velocity, or both. With the determination of these two physical parameters given by the maximum likelihood estimators, the relative precisions are given as explicit functions of the number of data points and total trajectory time. The method is based on rigorous statistical tests and does not require any presumed kinetics scheme. Results of extensive characterizations, extensions to 2D and 3D trajectories, and applications to common scenarios are also discussed.
View details for DOI 10.1016/j.bpj.2017.11.008
View details for Web of Science ID 000438958800009
View details for PubMedID 29241585
View details for PubMedCentralID PMC6051264
Parallelization of Change Point Detection
JOURNAL OF PHYSICAL CHEMISTRY A
2017; 121 (27): 5100–5109
The change point detection method ( Watkins , L. P. ; Yang , H. J. Phys. Chem. B 2005 , 109 , 617 ) allows the objective identification and isolation of abrupt changes along a data series. Because this method is grounded in statistical tests, it is particularly powerful for probing complex and noisy signals without artificially imposing a kinetics model. The original algorithm, however, has a time complexity of [Formula: see text], where N is the size of the data and is, therefore, limited in its scalability. This paper puts forth a parallelization of change point detection to address these time and memory constraints. This parallelization method was evaluated by applying it to changes in the mean of Gaussian-distributed data and found that time decreases superlinearly with respect to the number of processes (i.e., parallelization with two processes takes less than half of the time of one process). Moreover, there was minimal reduction in detection power. These results suggest that our parallelization algorithm is a viable scheme that can be implemented for other change point detection methods.
View details for DOI 10.1021/acs.jpca.7b04378
View details for Web of Science ID 000405761800004
View details for PubMedID 28616980