Spatial autocorrelation analysis of health care hotspots in Taiwan in 2006

<p>Abstract</p> <p>Background</p> <p>Spatial analytical techniques and models are often used in epidemiology to identify spatial anomalies (hotspots) in disease regions. These analytical approaches can be used to not only identify the location of such hotspots, but also their spatial patterns.</p> <p>Methods</p> <p>In this study, we utilize spatial autocorrelation methodologies, including Global Moran's I and Local Getis-Ord statistics, to describe and map spatial clusters, and areas in which these are situated, for the 20 leading causes of death in Taiwan. In addition, we use the fit to a logistic regression model to test the characteristics of similarity and dissimilarity by gender.</p> <p>Results</p> <p>Gender is compared in efforts to formulate the common spatial risk. The mean found by local spatial autocorrelation analysis is utilized to identify spatial cluster patterns. There is naturally great interest in discovering the relationship between the leading causes of death and well-documented spatial risk factors. For example, in Taiwan, we found the geographical distribution of clusters where there is a prevalence of tuberculosis to closely correspond to the location of aboriginal townships.</p> <p>Conclusions</p> <p>Cluster mapping helps to clarify issues such as the spatial aspects of both internal and external correlations for leading health care events. This is of great aid in assessing spatial risk factors, which in turn facilitates the planning of the most advantageous types of health care policies and implementation of effective health care services.</p

Exposure to Candida albicans Polarizes a T-Cell Driven Arthritis Model towards Th17 Responses, Resulting in a More Destructive Arthritis

BACKGROUND: Fungal components have been shown very effective in generating Th17 responses. We investigated whether exposure to a minute amount of C. albicans in the arthritic joint altered the local cytokine environment, leading to enhanced Th17 expansion and resulting in a more destructive arthritis. METHODOLOGY: Chronic SCW arthritis was induced by repeated injection with Streptococcus pyogenes (SCW) cell wall fragments into the knee joint of C57Bl/6 mice, alone or in combination with the yeast of C. albicans or Zymosan A. During the chronic phase of the arthritis, the cytokine levels, mRNA expression and histopathological analysis of the joints were performed. To investigate the phenotype of the IL-17 producing T-cells, synovial cells were isolated and analyzed by flowcytometry. PRINCIPAL FINDINGS: Intra-articular injection of either Zymosan A or C. albicans on top of the SCW injection both resulted in enhanced joint swelling and inflammation compared to the normal SCW group. However, only the addition of C. albicans during SCW arthritis resulted in severe chondrocyte death and enhanced destruction of cartilage and bone. Additionally, exposure to C. albicans led to increased IL-17 in the arthritic joint, which was accompanied by an increased synovial mRNA expression of T-bet and RORgammaT. Moreover, the C. albicans-injected mice had significantly more Th17 cells in the synovium, of which a large population also produced IFN-gamma. CONCLUSION: This study clearly shows that minute amounts of fungal components, like C. albicans, are very potent in interfering with the local cytokine environment in an arthritic joint, thereby polarizing arthritis towards a more destructive phenotype

Control of style-of-faulting on spatial pattern of earthquake-triggered landslides

Predictive mapping of susceptibility to earthquake-triggered landslides (ETLs) commonly uses distance to fault as spatial predictor, regardless of style-of-faulting. Here, we examined the hypothesis that the spatial pattern of ETLs is influenced by style-of-faulting based on distance distribution analysis and Fry analysis. The Yingxiu–Beichuan fault (YBF) in China and a huge number of landslides that ruptured and occurred, respectively, during the 2008 Wenchuan earthquake permitted this study because the style-of-faulting along the YBF varied from its southern to northern parts (i.e. mainly thrust-slip in the southern part, oblique-slip in the central part and mainly strike-slip in the northern part). On the YBF hanging-wall, ETLs at 4.4–4.7 and 10.3–11.5 km from the YBF are likely associated with strike- and thrust-slips, respectively. On the southern and central parts of the hanging-wall, ETLs at 7.5–8 km from the YBF are likely associated with oblique-slips. These findings indicate that the spatial pattern of ETLs is influenced by style-of-faulting. Based on knowledge about the style-of-faulting and by using evidential belief functions to create a predictor map based on proximity to faults, we obtained higher landslide prediction accuracy than by using unclassified faults. When distance from unclassified parts of the YBF is used as predictor, the prediction accuracy is 80%; when distance from parts of the YBF, classified according to style-of-faulting, is used as predictor, the prediction accuracy is 93%. Therefore, mapping and classification of faults and proper spatial representation of fault control on occurrence of ETLs are important in predictive mapping of susceptibility to ETLs

Conditional vs. unconditional industrial agglomeration: disentanglintg spatial dependence and spatial heterogeneity in the analysis of ICT firms distribution in Milan(Italy)

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