The Impact of the Swedish Care Coordination Act on Hospital Readmission and Length-of-Stay among Multi-Morbid Elderly Patients: A Controlled Interrupted Time Series Analysis

Coordinating follow-up care after discharge from hospital is critical to ensuring good outcomes for patients, but is difficult when multiple care providers are involved. In 2018, Sweden adopted the Care Coordination Act, which modified economic incentives to reduce discharge delays and mandated a discharge planning process for patients requiring post-discharge social- or primary care services. This study evaluates the impact of this reform on hospital length-of-stay and unplanned readmissions among multi-morbid elderly patients. Interrupted time series analysis of all in-patient care episodes involving multi-morbid elderly patients in Sweden from 2015 – 2019 (n = 2 386 039) was performed. Secondary analyses using case-mix adjustment and controlled interrupted time series analysis were employed to assess for bias. Average length of stay decreased during the post-reform period, corresponding to 248 521 saved care days. Unplanned readmissions meanwhile increased, corresponding to 7 572 excess unplanned readmissions. While reductions in length-of-stay were concentrated among patients targeted by the reform, increases in readmission rates were similar in patients not targeted by the reform, indicating potential confounding. The reform thus appears to have achieved its goal of decreasing in-patient length of stay, but a robust effect on readmissions, outpatient visits, or mortality was not found. This may be due to lackluster implementation or an ineffective mandated intervention

Hydrogen Adsorption on Platinum-Gold Bimetallic Nanoparticles: A Density Functional Theory Study

Article on hydrogen adsorption on platinum-gold bimetallic nanoparticles

A comparison of the galaxy peculiar velocity field with the PSCz gravity field-- A Bayesian hyper-parameter method

We constructed a Bayesian hyper-parameter statistical method to quantify the difference between predicted velocities derived from the observed galaxy distribution in the \textit{IRAS}-PSC$z$ redshift survey and peculiar velocities measured using different distance indicators. In our analysis we find that the model--data comparison becomes unreliable beyond 70 \hmpc because of the inadequate sampling by \textit{IRAS} survey of prominent, distant superclusters, like the Shapley Concentration. On the other hand, the analysis of the velocity residuals show that the PSC$z$ gravity field provides an adequate model to the local, \le 70 \hmpc, peculiar velocity field. The hyper-parameter combination of ENEAR, SN, A1SN and SFI++ catalogues in the Bayesian framework constrains the amplitude of the linear flow to be $\beta=0.53 \pm 0.014$. For an rms density fluctuations in the PSC$z$ galaxy number density $\sigma_8^{\rm gal}=0.42\pm0.03$, we obtain an estimate of the growth rate of density fluctuations $f\sigma_{8}(z\sim0) = 0.42 \pm 0.033$, which is in excellent agreement with independent estimates based on different techniques.Comment: 14 pages, 32 figures, MNRAS in press, matched the MNRAS published versio

2000-2001 Born in the U.S.A. - Chamber Music Concert

Born in the U.S.A. April 18, 2001 - Chamber Music April 20, 2001 - The Faculty\u27s Choice April 21, 2001 - American Favorites Composers featured in this festival Vincente Avella Dana Wilson Judith Shatin David MacBride Dr. Adrian Childs Terry Winter Owens Arthur Weisberghttps://spiral.lynn.edu/conservatory_otherseasonalconcerts/1105/thumbnail.jp

The Clustering of Luminous Red Galaxies in the Sloan Digital Sky Survey Imaging Data

We present the 3D real space clustering power spectrum of a sample of \~600,000 luminous red galaxies (LRGs) measured by the Sloan Digital Sky Survey (SDSS), using photometric redshifts. This sample of galaxies ranges from redshift z=0.2 to 0.6 over 3,528 deg^2 of the sky, probing a volume of 1.5 (Gpc/h)^3, making it the largest volume ever used for galaxy clustering measurements. We measure the angular clustering power spectrum in eight redshift slices and combine these into a high precision 3D real space power spectrum from k=0.005 (h/Mpc) to k=1 (h/Mpc). We detect power on gigaparsec scales, beyond the turnover in the matter power spectrum, on scales significantly larger than those accessible to current spectroscopic redshift surveys. We also find evidence for baryonic oscillations, both in the power spectrum, as well as in fits to the baryon density, at a 2.5 sigma confidence level. The statistical power of these data to constrain cosmology is ~1.7 times better than previous clustering analyses. Varying the matter density and baryon fraction, we find \Omega_M = 0.30 \pm 0.03, and \Omega_b/\Omega_M = 0.18 \pm 0.04, The detection of baryonic oscillations also allows us to measure the comoving distance to z=0.5; we find a best fit distance of 1.73 \pm 0.12 Gpc, corresponding to a 6.5% error on the distance. These results demonstrate the ability to make precise clustering measurements with photometric surveys (abridged).Comment: 23 pages, 27 figures, submitted to MNRA

FungalRV: adhesin prediction and immunoinformatics portal for human fungal pathogens

<p>Abstract</p> <p>Background</p> <p>The availability of sequence data of human pathogenic fungi generates opportunities to develop Bioinformatics tools and resources for vaccine development towards benefitting at-risk patients.</p> <p>Description</p> <p>We have developed a fungal adhesin predictor and an immunoinformatics database with predicted adhesins. Based on literature search and domain analysis, we prepared a positive dataset comprising adhesin protein sequences from human fungal pathogens <it>Candida albicans, Candida glabrata, Aspergillus fumigatus, Coccidioides immitis, Coccidioides posadasii, Histoplasma capsulatum, Blastomyces dermatitidis, Pneumocystis carinii, Pneumocystis jirovecii and Paracoccidioides brasiliensis</it>. The negative dataset consisted of proteins with high probability to function intracellularly. We have used 3945 compositional properties including frequencies of mono, doublet, triplet, and multiplets of amino acids and hydrophobic properties as input features of protein sequences to Support Vector Machine. Best classifiers were identified through an exhaustive search of 588 parameters and meeting the criteria of best Mathews Correlation Coefficient and lowest coefficient of variation among the 3 fold cross validation datasets. The "FungalRV adhesin predictor" was built on three models whose average Mathews Correlation Coefficient was in the range 0.89-0.90 and its coefficient of variation across three fold cross validation datasets in the range 1.2% - 2.74% at threshold score of 0. We obtained an overall MCC value of 0.8702 considering all 8 pathogens, namely, <it>C. albicans, C. glabrata, A. fumigatus, B. dermatitidis, C. immitis, C. posadasii, H. capsulatum </it>and <it>P. brasiliensis </it>thus showing high sensitivity and specificity at a threshold of 0.511. In case of <it>P. brasiliensis </it>the algorithm achieved a sensitivity of 66.67%. A total of 307 fungal adhesins and adhesin like proteins were predicted from the entire proteomes of eight human pathogenic fungal species. The immunoinformatics analysis data on these proteins were organized for easy user interface analysis. A Web interface was developed for analysis by users. The predicted adhesin sequences were processed through 18 immunoinformatics algorithms and these data have been organized into MySQL backend. A user friendly interface has been developed for experimental researchers for retrieving information from the database.</p> <p>Conclusion</p> <p>FungalRV webserver facilitating the discovery process for novel human pathogenic fungal adhesin vaccine has been developed.</p

Excitation and Deexcitation of Benzene

This chapter contains sections titled: - Introduction; - The Nature of the Lower Excited States of Benzene; - Transitions Between Lower Energy States; - Excited State Geometry; - The Influence of the Environment on Electronic States; - The S1 ↔ S0 Radiative Transition; - The S1 ↔ Triplet Radiationless Transition; - The S1 → S0 Radiationless Transition; - The T1 → S0 Phosphorescence Transition; - The T1 → S0 Radiationless Transition; - Transitions from Higher (n > 1) Excited States; - Relevant Photochemical Reactions of Excited States of Benzene; - Benzene Excimer; - Conclusioninfo:eu-repo/semantics/publishedVersio