Transitions of care for older adults discharged home from the emergency department: an inductive thematic content analysis of patient comments.

OBJECTIVE: Improving care transitions for older adults can reduce emergency department (ED) visits, adverse events, and empower community autonomy. We conducted an inductive qualitative content analysis to identify themes emerging from comments to better understand ED care transitions. METHODS: The LEARNING WISDOM prospective longitudinal observational cohort includes older adults (≥ 65 years) who experienced a care transition after an ED visit from both before and during COVID-19. Their comments on this transition were collected via phone interview and transcribed. We conducted an inductive qualitative content analysis with randomly selected comments until saturation. Themes that arose from comments were coded and organized into frequencies and proportions. We followed the Standards for Reporting Qualitative Research (SRQR). RESULTS: Comments from 690 patients (339 pre-COVID, 351 during COVID) composed of 351 women (50.9%) and 339 men (49.1%) were analyzed. Patients were satisfied with acute emergency care, and the proportion of patients with positive acute care experiences increased with the COVID-19 pandemic. Negative patient comments were most often related to communication between health providers across the care continuum and the professionalism of personnel in the ED. Comments concerning home care became more neutral with the COVID-19 pandemic. CONCLUSION: Patients were satisfied overall with acute care but reported gaps in professionalism and follow-up communication between providers. Comments may have changed in tone from positive to neutral regarding home care over the COVID-19 pandemic due to service slowdowns. Addressing these concerns may improve the quality of care transitions and provide future pandemic mitigation strategies.

metagene Profiles Analyses Reveal Regulatory Element's Factor-Specific Recruitment Patterns.

ChIP-Sequencing (ChIP-Seq) provides a vast amount of information regarding the localization of proteins across the genome. The aggregation of ChIP-Seq enrichment signal in a metagene plot is an approach commonly used to summarize data complexity and to obtain a high level visual representation of the general occupancy pattern of a protein. Here we present the R package metagene, the graphical interface Imetagene and the companion package similaRpeak. Together, they provide a framework to integrate, summarize and compare the ChIP-Seq enrichment signal from complex experimental designs. Those packages identify and quantify similarities or dissimilarities in patterns between large numbers of ChIP-Seq profiles. We used metagene to investigate the differential occupancy of regulatory factors at noncoding regulatory regions (promoters and enhancers) in relation to transcriptional activity in GM12878 B-lymphocytes. The relationships between occupancy patterns and transcriptional activity suggest two different mechanisms of action for transcriptional control: i) a "gradient effect" where the regulatory factor occupancy levels follow transcription and ii) a "threshold effect" where the regulatory factor occupancy levels max out prior to reaching maximal transcription. metagene, Imetagene and similaRpeak are implemented in R under the Artistic license 2.0 and are available on Bioconductor.

Using informative Multinomial-Dirichlet prior in a t-mixture with reversible jump estimation of nucleosome positions for genome-wide profiling.

Genome-wide mapping of nucleosomes has revealed a great deal about the relationships between chromatin structure and control of gene expression. Recent next generation CHIP-chip and CHIP-Seq technologies have accelerated our understanding of basic principles of chromatin organization. These technologies have taught us that nucleosomes play a crucial role in gene regulation by allowing physical access to transcription factors. Recent methods and experimental advancements allow the determination of nucleosome positions for a given genome area. However, most of these methods estimate the number of nucleosomes either by an EM algorithm using a BIC criterion or an effective heuristic strategy. Here, we introduce a Bayesian method for identifying nucleosome positions. The proposed model is based on a Multinomial-Dirichlet classification and a hierarchical mixture distributions. The number and the positions of nucleosomes are estimated using a reversible jump Markov chain Monte Carlo simulation technique. We compare the performance of our method on simulated data and MNase-Seq data from Saccharomyces cerevisiae against PING and NOrMAL methods.