Multi-morbidities are Not a Driving Factor for an Increase of COPD-Related 30-Day Readmission Risk

Background and Objective: Chronic obstructive pulmonary disease (COPD) is the third leading cause of death in the United States. COPD is expensive to treat, whereas the quality of care is difficult to evaluate due to the high prevalence of multi-morbidity among COPD patients. In the US, the Hospital Readmissions Reduction Program (HRRP) was initiated by the Centers for Medicare and Medicaid Services to penalize hospitals for excessive 30-day readmission rates for six diseases, including COPD. This study examines the difference in 30-day readmission risk between COPD patients with and without comorbidities.Methods: In this retrospective cohort study, we used Cox regression to estimate the hazard ratio of 30-day readmission rates for COPD patients who had no comorbidity and those who had one, two or three, or four or more comorbidities. We controlled for individual, hospital and geographic factors. Data came from three sources: Healthcare Cost and Utilization Project (HCUP) State Inpatient Databases (SID), Area Health Resources Files (AHRF) and the American Hospital Association’s (AHA’s) annual survey database for the year of 2013.Results: COPD patients with comorbidities were less likely to be readmitted within 30 days relative to patients without comorbidities (aHR from 0.84 to 0.87, p \u3c 0.05). In a stratified analysis, female patients with one comorbidity had a lower risk of 30-day readmission compared to female patients without comorbidity (aHR = 0.80, p \u3c 0.05). Patients with public insurance who had comorbidities were less likely to be readmitted within 30 days in comparison with those who had no comorbidity (aHR from 0.79 to 0.84, p \u3c 0.05).Conclusion: COPD patients with comorbidities had a lower risk of 30-day readmission compared with patients without comorbidity. Future research could use a different study design to identify the effectiveness of the HRRP

Study the Heavy Molecular States in Quark Model with Meson Exchange Interaction

Some charmonium-like resonances such as X(3872) can be interpreted as possible $D^{(*)}D^{(*)}$ molecular states. Within the quark model, we study the structure of such molecular states and the similar $B^{(*)}B^{(*)}$ molecular states by taking into account of the light meson exchange ($\pi$, $\eta$, $\rho$, $\omega$ and $\sigma$) between two light quarks from different mesons

Second order finite difference approximations for the two-dimensional time-space Caputo-Riesz fractional diffusion equation

In this paper, we discuss the time-space Caputo-Riesz fractional diffusion equation with variable coefficients on a finite domain. The finite difference schemes for this equation are provided. We theoretically prove and numerically verify that the implicit finite difference scheme is unconditionally stable (the explicit scheme is conditionally stable with the stability condition $\frac{\tau^{\gamma}}{(\Delta x)^{\alpha}}+\frac{\tau^{\gamma}}{(\Delta y)^{\beta}} <C$) and 2nd order convergent in space direction, and $(2-\gamma)$-th order convergent in time direction, where $\gamma \in(0,1]$.Comment: 27 page

Sentiment Analysis and Political Party Classification in 2016 U.S. President Debates in Twitter

We introduce a framework of combining tweet sentiment analysis with available default user profiles to classify political party of users who posted tweets in 2016 U.S. president debates. The main works focus on extracting event-related information in short event period instead of collecting tweets in a long-time period as most previous works do. Our framework is not limited in debate event, it can be used by researchers to build rationale of other events study. In sentiment analysis, we show that all three Naïve Bayes classifiers with different distributions obtain accuracy above 75% and the results reveal positive tweets most likely follow Gaussian or Multinomial distributions while negative tweets most likely follow Bernoulli distribution in our training data. We also show that under unbalanced sparse term document setting, instead of using “Add-1” parameter, tuning Laplace smoothing parameter to adjust the weights of new terms in a tweet can help improve the classifier’s performance in targeted direction. Finally, we show sentiment might help classifying political part