2013 Provider-Provider Medicare Network - Trace-Route Algorithm

These images contain healthcare maps, created using a trace-route algorithm, from the 2013 Medicare Part B claims data set. This is essentially a social network map of providers connected by caring for common patients. The plots are arranged in ascending order of the distance between providers, and each image represents a network map within that distance range.<div><br></div><div>http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0175876#ack<br></div

2013 Organization-Organization Medicare Network - Trace-Route Algorithm

These images contain healthcare maps, created using a trace-route mapping algorithm, from the 2013 Medicare Part B claims data set. These maps are essentially a social network diagram of how medicare network organizations are connected to each other by shared patients. The plots are arranged in ascending order of the distance between organizations, and each image represents a network map within that distance range.<div><br></div><div>http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0175876#ack<br></div

Divergent Gene Activation in Peripheral Blood and Tissues of Patients with Rheumatoid Arthritis, Psoriatic Arthritis and Psoriasis following Infliximab Therapy

<div><p>Objective</p><p>The immune inflammatory disorders rheumatoid arthritis (RA), psoriatic arthritis (PsA) and psoriasis (Ps) share common pathologic features and show responsiveness to anti-tumor necrosis factor (TNF) agents yet they are phenotypically distinct. The aim of this study was to examine if anti-TNF therapy is associated with divergent gene expression profiles in circulating cells and target tissues of patients with these diseases.</p><p>Methods</p><p>Peripheral blood CD14⁺ and CD14⁻ cells were isolated from 9 RA, 12 PsA and 10 Ps patients before and after infliximab (IFX) treatment. Paired synovial (n = 3, RA, PsA) and skin biopsies (n = 5, Ps) were also collected. Gene expression was analyzed by microarrays.</p><p>Results</p><p>26 out of 31 subjects responded to IFX. The transcriptional response of CD14⁺ cells to IFX was unique for the three diseases, with little overlap (<25%) in significantly changed gene lists (with PsA having the largest number of changed genes). In Ps, altered gene expression was more pronounced in lesional skin (relative to paired, healthy skin) compared to blood (relative to healthy controls). Marked suppression of up-regulated genes in affected skin was noted 2 weeks after therapy but the expression patterns differed from uninvolved skin. Divergent patterns of expression were noted between the blood cells and skin or synovial tissues in individual patients. Functions that promote cell differentiation, proliferation and apoptosis in all three diseases were enriched. RA was enriched in functions in CD14⁻ cells, PsA in CD14⁺ cells and Ps in both CD14⁺ and CD14⁻ cells, however, the specific functions showed little overlap in the 3 disorders.</p><p>Conclusion</p><p>Divergent patterns of altered gene expression are observed in RA, PsA and Ps patients in blood cells and target organs in IFX responders. Differential gene expression profiles in the blood do not correlate with those in target organs.</p></div

Edge construction algorithms for healthcare teaming networks.

<p>Each vertex represents a provider, with the index provider vertex in yellow. The collection of providers is for a single patient. (A) The brackets show how the time frame <i>τ</i> = 30 days is applied to a series of temporally ordered provider visits. The corresponding graphs show the edges that would be constructed between the provider vertices by each algorithm for that first iteration: a single directed edge for the trace-route algorithm, a set of directed edges (e.g. a star graph) for the sliding algorithm, and a complete graph with each vertex connected to all other vertices for the binning method. This process is repeated for each patient by shifting the sampling frame through the ordered visits for each patient. (B) Sampling frame shifting and edge weight construction. How each algorithm shifts the sampling frame <i>τ</i> through the series of provider visits is shown here, and the degree of shift for the next interval is shown by the new brackets. Please refer to the text for a discussion of edge weight calculations.</p

Healthcare networks power law with cutoff properties.

<p>We analyzed PPN and OON for adherence to power a law distribution starting from a minimum vertex degree, (<i>x</i>_<i>min</i>), using the method of Clauset, et al [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0175876#pone.0175876.ref060" target="_blank">60</a>] with <i>τ</i> = 365 days. The orange points are the CDF of the vertex degree, and the blue dashed line is the power law fit of the CDF given <i>x</i>_<i>min</i> and <i>α</i>.</p

Network vertex degree distribution by algorithm for <i>τ</i> = 365 days.

<p>Network vertex degree distribution by algorithm for <i>τ</i> = 365 days.</p

Variation in provider community identification.

<p>We analyzed undirected Provider-Provider networks constructed with the trace-route, sliding frame and binning algorithms for <i>τ</i> = 365 days, and censored for edge weights ≤ 11. Provider-Provider community teams identified for providers within NY State from each network using the Girvan-Newman modularity community finding algorithm [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0175876#pone.0175876.ref026" target="_blank">26</a>] implemented in <i>Mathematica</i>. Each provider was assigned to only one community. (A) Provider densities. Hexagonal bins show the counts of providers that were members of any community within each geographic region color coded by range. Note the different geographic density patterns for each method. (B) Histogram of number of providers per community. Note the large number of communities (<i>n</i>) in each histogram, with the majority having only 2 providers. Community sizes, compositions and number differed between all 3 methods. (C) Shows the five largest communities identified in each network.</p

Betweenness centrality <i>C</i>′<i>_β</i> of healthcare networks by algorithm for <i>τ</i> = 365 days betweenness centrality was calculated for all networks using the Oracle PGX algorithm.

<p>Results are displayed with algorithmic binning of <i>C</i>′<i>_β</i> = <i>C^β</i> / (<i>N</i> − 1)(<i>N</i> − 2) for directed graphs produced by the sliding frame and trace-route algorithms, and <i>C</i>′<i>_β</i> = 2<i>C^β</i> / (<i>N</i> − 1)(<i>N</i> − 2) for undirected networks produced by the binning algorithm. All plots are scaled in the y-axis to frequency, allowing direct comparison of centralities. Note that edge-weight censoring (excluding edges with Ω_{<i>v</i>_<i>j</i> → <i>v</i>_<i>k</i>} ≤ 11) markedly changes the centrality distribution of all networks.</p

Network vertex counts, edge counts and density as a function of the sampling frame interval <i>τ</i>.

<p>Vertex counts, edge counts and network density plotted for provider and organization networks for the binning (red), trace-route (orange) and sliding frame (blue) algorithms for <i>τ</i> = 30, 60, 90, 180, and 365 days. Solid lines represent networks where vertices were included if the minimum edge weight > = 1, while dashed lines represent censoring where only edges with a minimum edge weight > = 11 are included. The latter is the current standard for aggregate provider network data release by the Center for Medicare Services so that individual patients cannot be identified by a unique combination of providers sharing only a single patient.</p

Effect of edge weight censoring^‡.

<p>Effect of edge weight censoring<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0175876#t003fn002" target="_blank">^‡</a>.</p