High-cost high-need patients in Medicaid: segmenting the population eligible for a national complex case management program

Background High-cost high-need patients are typically defined by risk or cost thresholds which aggregate clinically diverse subgroups into a single ‘high-need high-cost’ designation. Programs have had limited success in reducing utilization or improving quality of care for high-cost high-need Medicaid patients, which may be due to the underlying clinical heterogeneity of patients meeting high-cost high-need designations. Methods Our objective was to segment a population of high-cost high-need Medicaid patients (N = 676,161) eligible for a national complex case management program between January 2012 and May 2015 to disaggregate clinically diverse subgroups. Patients were eligible if they were in the top 5 % of annual spending among UnitedHealthcare Medicaid beneficiaries. We used k-means cluster analysis, identified clusters using an information-theoretic approach, and named clusters using the patients’ pattern of acute and chronic conditions. We assessed one-year overall and preventable hospitalizations, overall and preventable emergency department (ED) visits, and cluster stability. Results Six clusters were identified which varied by utilization and stability. The characteristic condition patterns were: 1) pregnancy complications, 2) behavioral health, 3) relatively few conditions, 4) cardio-metabolic disease, and complex illness with relatively 5) low or 6) high resource use. The patients varied by cluster by average ED visits (2.3–11.3), hospitalizations (0.3–2.0), and cluster stability (32–91%). Conclusions We concluded that disaggregating subgroups of high-cost high-need patients in a large multi-state Medicaid sample identified clinically distinct clusters of patients who may have unique clinical needs. Segmenting previously identified high-cost high-need populations thus may be a necessary strategy to improve the effectiveness of complex case management programs in Medicaid.

Tri-Base Synergy in Sulfuric Acid-Base Clusters

Synergistic effects between different bases can greatly enhance atmospheric sulfuric acid (SA)-base cluster formation. However, only the synergy between two base components has previously been investigated. Here, we extend this concept to three bases by studying large atmospherically relevant (SA)3(base)3 clusters, with the bases ammonia (A), methylamine (MA), dimethylamine (DMA), trimethylamine (TMA) and ethylenediamine (EDA). Using density functional theory—ωB97X-D/6-31++G(d,p)—we calculate the cluster structures and vibrational frequencies. The thermochemical parameters are calculated at 29,815 K and 1 atm, using the quasi-harmonic approximation. The binding energies of the clusters are calculated using high level DLPNO-CCSD(T0)/aug-cc-pVTZ. We find that the cluster stability in general depends on the basicity of the constituent bases, with some noteworthy additional guidelines: DMA enhances the cluster stability, TMA decreases the cluster stability and there is high synergy between DMA and EDA. Based on our calculations, we find it highly likely that three, or potentially more, different bases, are involved in the growth pathways of sulfuric acid-base clusters.

Evaluating single-cell cluster stability using the Jaccard similarity index

Motivation One major goal of single-cell RNA sequencing (scRNAseq) experiments is to identify novel cell types. With increasingly large scRNAseq datasets, unsupervised clustering methods can now produce detailed catalogues of transcriptionally distinct groups of cells in a sample. However, the interpretation of these clusters is challenging for both technical and biological reasons. Popular clustering algorithms are sensitive to parameter choices, and can produce different clustering solutions with even small changes in the number of principal components used, the k nearest neighbor and the resolution parameters, among others. Results Here, we present a set of tools to evaluate cluster stability by subsampling, which can guide parameter choice and aid in biological interpretation. The R package scclusteval and the accompanying Snakemake workflow implement all steps of the pipeline: subsampling the cells, repeating the clustering with Seurat and estimation of cluster stability using the Jaccard similarity index and providing rich visualizations. Availabilityand implementation R package scclusteval: https://github.com/crazyhottommy/scclusteval Snakemake workflow: https://github.com/crazyhottommy/pyflow_seuratv3_parameter Tutorial: https://crazyhottommy.github.io/EvaluateSingleCellClustering/.

Clustering Poland Among Eu Countries in Terms of a Sustainable Development Level in the Light of Various Cluster Stability Measures

Research background: Recently in the context of taxonomy methods a lot of attention has been paid to the issue of stability of these methods, i.e. the answer to the question: do the groups that were created as a result of clustering really occur (the structure is stable), or did they appear accidentally.Purpose: The article is inspired by the Reviewers of the author’s previous publications on this subject and will be a summary of research to date which has followed two paths. On one hand, they recognize ways of measuring cluster stability proposed in the literature (e.g. Rozmus, 2017). On the other, they use these measures to cluster Poland among the EU members in terms of sustainable development level (e.g. Rozmus, 2019).Research methodology: The literature proposes a number of different ways for measuring stability. Theoretical considerations have also led to the development of computer tools for the practical implementation of the proposed ways to study stability. The practical tools are available within several R packages, e.g.: clv, clValid, fpc, which are used in this researchResults: The results, however, showed that different measures of stability lead to different results.Novelty: The innovation of this approach is the use of stability measures to such a problem (i.e. clustering EU members in terms of the sustainable development level). In addition, the article will report a synthesis and comparative analysis of the results obtained using various stability measures.

Evaluating single-cell cluster stability using the Jaccard similarity index

MotivationOne major goal of single-cell RNA sequencing (scRNAseq) experiments is to identify novel cell types. With increasingly large scRNAseq datasets, unsupervised clustering methods can now produce detailed catalogues of transcriptionally distinct groups of cells in a sample. However, the interpretation of these clusters is challenging for both technical and biological reasons. Popular clustering algorithms are sensitive to parameter choices, and can produce different clustering solutions with even small changes in the number of principal components used, the k nearest neighbor, and the resolution parameters, among others.ResultsHere, we present a set of tools to evaluate cluster stability by subsampling, which can guide parameter choice and aid in biological interpretation. The R package scclusteval and the accompanying Snakemake workflow implement all steps of the pipeline: subsampling the cells, repeating the clustering with Seurat, and estimation of cluster stability using the Jaccard similarity index. The Snakemake workflow takes advantage of high-performance computing clusters and dispatches jobs in parallel to available CPUs to speed up the analysis. The scclusteval package provides functions to facilitate the analysis of the output, including a series of rich visualizations.AvailabilityR package scclusteval: https://github.com/crazyhottommy/scclusteval Snakemake workflow: https://github.com/crazyhottommy/[email protected], [email protected] informationSupplementary data are available at Bioinformatics online.

An Enhanced-Node Feature Based Clustering Algorithm for MANET (Mobile Ad-hoc Network)

In mobile Ad-hoc Network cluster stability is considered as a very serious issue. Due to the frequent failure of the network it may reduce the stability of the cluster. In re-clustering and re-election of Cluster Head (CH) higher energy is required, which ultimately reduces the overall network performance. To resolve the cluster stability problems, Weight Based Clustering algorithm is used often. In this paper, a new weight based algorithm called Enhanced-Node Feature Based Clustering Algorithm (ENFBCA) is proposed, which uses the following parameters for cluster head selection process mainly Link Estimate Time, Degree of the node, Node Closeness, Residual Energy & Trust value. This algorithm reduces the End-to-End delay, enhances the Network Lifetime and improves the quality of service (QOS) in MANETs. Simulation results show that Enhanced-Node Feature Based Clustering Algorithm (ENFBCA) performs better in comparison to Node Quality Clustering Algorithm (NQCA) and Weight Based Clustering algorithm (WCA).