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Geographic Clustering (geographic + clustering)
Selected AbstractsLIKELIHOOD-BASED INFERENCE IN ISOLATION-BY-DISTANCE MODELS USING THE SPATIAL DISTRIBUTION OF LOW-FREQUENCY ALLELESEVOLUTION, Issue 11 2009John Novembre Estimating dispersal distances from population genetic data provides an important alternative to logistically taxing methods for directly observing dispersal. Although methods for estimating dispersal rates between a modest number of discrete demes are well developed, methods of inference applicable to "isolation-by-distance" models are much less established. Here, we present a method for estimating ,,2, the product of population density (,) and the variance of the dispersal displacement distribution (,2). The method is based on the assumption that low-frequency alleles are identical by descent. Hence, the extent of geographic clustering of such alleles, relative to their frequency in the population, provides information about ,,2. We show that a novel likelihood-based method can infer this composite parameter with a modest bias in a lattice model of isolation-by-distance. For calculating the likelihood, we use an importance sampling approach to average over the unobserved intraallelic genealogies, where the intraallelic genealogies are modeled as a pure birth process. The approach also leads to a likelihood-ratio test of isotropy of dispersal, that is, whether dispersal distances on two axes are different. We test the performance of our methods using simulations of new mutations in a lattice model and illustrate its use with a dataset from Arabidopsis thaliana. [source] The Demise of Distance?GROWTH AND CHANGE, Issue 1 2006The Declining Role of Physical Proximity for Knowledge Transmission ABSTRACT The transmission of knowledge diminishes with physical distance, one factor explaining the geographic clustering of scientific and industrial activity. The authors investigate how those distances have stretched over time,between collaborating inventors, and between inventors and the technology that inspires them. While physical distance is still a factor, it is clear that its constraining effects have weakened, especially for particular types of innovators, technologies, and regions of the United States. [source] Nail Matrix Arrest Following Hand-Foot-Mouth Disease: A Report of Five ChildrenPEDIATRIC DERMATOLOGY, Issue 1 2000Gina C. Clementz B.S. Nail matrix arrest has been associated with a variety of drug exposures and systemic illnesses, including infections, and may result in a variety of changes, including transverse ridging (Beau's lines) and nail shedding (onychomadesis). The association of HFMD with Beau's lines and onychomadesis has not been reported previously. Five children, ages 22 months,4 years, presented with Beau's lines and/or onychomadesis following physician-diagnosed HFMD by 3,8 weeks. Three of the five patients experienced fever with HFMD, and none had a history of nail trauma, periungual dermatitis, periungual vesicular lesions, or a significant medication intake history. All patients experienced HFMD within 4 weeks of one another, and all resided in the suburbs of the Chicago metropolitan area. In all patients the nail changes were temporary with spontaneous normal regrowth. The mechanism of the nail matrix arrest is unclear, but the timing and geographic clustering of the patients suggests an epidemic caused by the same viral strain. [source] Integrating nine prescription opioid analgesics and/or four signal detection systems to summarize statewide prescription drug abuse in the United States in 2007,PHARMACOEPIDEMIOLOGY AND DRUG SAFETY, Issue 9 2009Michael F. Schneider MS Abstract Purpose Integrate statewide rankings of abuse across different drugs and/or signal detection systems to summarize prescription drug abuse in each state in 2007. Methods Four signal detection systems (Opioid Treatment Programs, Key Informants, Drug Diversion, and Poison Centers) that covered heterogeneous populations collected data on the abuse of nine opioids: hydrocodone, immediate-release oxycodone, tramadol, extended-release [ER] oxycodone, fentanyl, morphine, methadone, hydromorphone, and buprenorphine). We introduce here linearized maps which integrate nine drugs within each system; four systems for each drug; or all drugs and systems. Results When rankings were integrated across drugs, Rhode Island, New Hampshire, Maine, West Virginia, and Michigan were in the highest tertile of abuse in three systems. When rankings were integrated across signal detection systems, there was a geographic clustering of states with the highest rates for ER oxycodone (in Tennessee, Mississippi, Kentucky, Ohio, Indiana, Michigan, and in Massachusetts, New Hampshire, Maine, and Vermont) and methadone (Massachusetts, Rhode Island, New Hampshire, Maine, Vermont, Connecticut, and New Jersey). When rankings were integrated across both drugs and signal detection systems, states with 3-digit ZIP codes below 269 (i.e., from Massachusetts to West Virginia): Massachusetts, New Hampshire, Maine, Vermont, Washington DC, Virginia, and West Virginia were in the highest tertile and only Delaware was in the lowest tertile. Conclusions We have presented methods to integrate data on prescription opioid abuse collected by signal detection systems covering different populations. Linearized maps are effective graphical summaries that depict differences in the level of prescription opioid abuse at the state level. Copyright © 2009 John Wiley & Sons, Ltd. [source] Detecting signals of opioid analgesic abuse: application of a spatial mixed effect poisson regression model using data from a network of poison control centers,PHARMACOEPIDEMIOLOGY AND DRUG SAFETY, Issue 11 2008Meredith Y. Smith MPA Abstract Purpose The recent rise in the non-medical use of opioid analgesics in the US has underscored the importance of comprehensive post-marketing surveillance of these products. To assist pharmacovigilance efforts, we developed a methodology for detecting geo-specific "signals" of potential outbreaks of prescription drug abuse by 3-digit ZIP (3DZ) code. Methods The number of intentional exposure calls involving nine specific opioid analgesics were obtained from eight regional poison control centers between first quarter 2003 and fourth quarter 2004. The unit of analysis was a combination of drug,quarter/year,3DZ. We fitted an empirical Bayes mixed effects Poisson,Gamma regression model that adjusted for differences across 3DZs in opioid analgesic exposure. A relative report rate (RR) ,3 at a probability of >0.95 was the signal threshold criterion. Results A total of 15,769 valid drug,time,3DZ combinations were identified. Of these, 1.9% (n,=,294) met the signal threshold criterion. The number of signals generated per drug,quarter/year,3DZ combination ranged from 0 to 13. The largest number of signals were those involving methadone (n,=,71), hydrocodone (n,=,57), and branded oxycodone extended-release (n,=,45). Signals for methadone and branded oxycodone extended-release were predominantly clustered in Appalachia. Hydrocodone-related signals showed less geographic clustering with approximately 26% reported from California, and the remainder from other regions in the US. Conclusions Our results show marked regional differences in reported abuse of specific opioid analgesics. Additional research is needed to determine the sensitivity and specificity of signals obtained using this spatial mixed effect Poisson regression model. Copyright © 2008 John Wiley & Sons, Ltd. [source] | ||||||||||