Sustained and cumulative impact of an electronic medical record-based alert on a hepatitis C birth cohort screening programme

The study aimed to assess the effect of an electronic medical record-embedded best practice alert (BPA) on HCV age cohort screening in primary care clinics. HCV testing by primary care physicians was monitored prior and subsequent to the implantation of the BPA. Four intervals of 9 months duration were analysed in detail, including a pre-BPA baseline analysis and three annual post-BPA assessments. Pre- and post-BPA orders consistently followed a power law distribution, characterized by small groups of physicians placing the majority of test orders. Significant correlations were present between the numbers of tests orders by each physician, suggesting that 'high' and 'low' screening performances tended to be physician-specific. Testing rates increased markedly in response to the BPA, resulting in completion of screening in 56.8% (50,468 of 88,914%) of the entire age cohort within less than 3 years. In conclusion, HCV age cohort testing by primary care physicians follows a power-law distribution, with high-performing physicians contributing disproportionately to the overall effort. A simple BPA resulted in a sufficient increase in testing to allow testing of the entire target population within a reasonable time frame.

EDGE EXCHANGEABLE MODELS FOR INTERACTION NETWORKS

Many modern network datasets arise from processes of interactions in a population, such as phone calls, email exchanges, co-authorships, and professional collaborations. In such interaction networks, the edges comprise the fundamental statistical units, making a framework for edge-labeled networks more appropriate for statistical analysis. In this context we initiate the study of edge exchangeable network models and explore its basic statistical properties. Several theoretical and practical features make edge exchangeable models better suited to many applications in network analysis than more common vertex-centric approaches. In particular, edge exchangeable models allow for sparse structure and power law degree distributions, both of which are widely observed empirical properties that cannot be handled naturally by more conventional approaches. Our discussion culminates in the Hollywood model, which we identify here as the canonical family of edge exchangeable distributions. The Hollywood model is computationally tractable, admits a clear interpretation, exhibits good theoretical properties, and performs reasonably well in estimation and prediction as we demonstrate on real network datasets. As a generalization of the Hollywood model, we further identify the vertex components model as a nonparametric subclass of models with a convenient stick breaking construction.

A Chemical Reaction Network to Generate Random, Power-Law-Distributed Time Intervals

In Lévy walks (LWs), particles move with a fixed speed along straight line segments and turn in new directions after random time intervals that are distributed according to a power law. Such LWs are thought to be an advantageous foraging and search strategy for organisms. While complex nervous systems are certainly capable of producing such behavior, it is not clear at present how single-cell organisms can generate the long-term correlated control signals required for a LW. Here, we construct a biochemical reaction system that generates long-time correlated concentration fluctuations of a signaling substance, with a tunable fractional exponent of the autocorrelation function. The network is based on well-known modules, and its basic function is highly robust with respect to the parameter settings.