Objectives
China is one of the 22 tuberculosis (TB) high-burden countries in the world. As TB is a major public health problem in China, spatial analysis could be applied to detect geographic distribution of TB clusters for targeted intervention on TB epidemics.Methods
Spatial analysis was applied for detecting TB clusters on county-based TB notification data in the national notifiable infectious disease case reporting surveillance system from 2005 to 2011. Two indicators of TB epidemic were used including new sputum smear-positive (SS+) notification rate and total TB notification rate. Global Moran’s I by ArcGIS was used to assess whether TB clustering and its trend were significant. SaTScan software that used the retrospective space-time analysis and Possion probability model was utilized to identify geographic areas and time period of potential clusters with notification rates on county-level from 2005 to 2011.Results
Two indicators of TB notification had presented significant spatial autocorrelation globally each year (p<0.01). Global Moran’s I of total TB notification rate had positive trend as time went by (t=6.87, p<0.01). The most likely clusters of two indicators had similar spatial distribution and size in the south-central regions of China from 2006 to 2008, and the secondary clusters in two regions: northeastern China and western China. Besides, the secondary clusters of total TB notification rate had two more large clustering centers in Inner Mongolia, Gansu and Qinghai provinces and several smaller clusters in Shanxi, Henan, Hebei and Jiangsu provinces.Conclusion
The total TB notification cases clustered significantly in some special areas each year and the clusters trended to aggregate with time. The most-likely and secondary clusters that overlapped among two TB indicators had higher TB burden and risks of TB transmission. These were the focused geographic areas where TB control efforts should be prioritized. 相似文献The purpose of this study was to isolate and characterise toxic element-resistant bacteria from acid mine drainage water and to apply them in the bioremediation of industrial effluent, as well as to identify optimal effluent:nutrient concentration for onsite biostimulation strategy. Wastewater samples were collected from acid mine drainage and industry. Inductively coupled plasma optical emission spectroscopy (ICP-OES) was employed for elemental composition analysis. Isolated bacterial strains were characterised using molecular methods. Bioremediation assays were employed to determine the extent of bacterial tolerance and removal of toxic elements using a biostimulation strategy employing minimal salt medium (MSM) at varied concentrations and positive and negative controls of only MSM and industrial effluent, respectively. Two bacterial strains demonstrated resistance to toxic elements, Bacillus sp. MGI101 and Lysinibacillus sp. MGI102 both isolated from the AMD sites. However, no observable growth of toxic metal-resistant bacteria was obtained from the industrial effluents. Bacterial strains MGI101 and MGI102 demonstrated high resistance to target toxic elements during the screening and tolerance assays. Remarkably, Bacillus sp. MGI101 demonstrated greater ability to remove toxic elements including arsenic, chromium, zinc, copper and aluminium in undiluted solutions of the industrial effluent, with its highest removal capacity observed at > 60% for arsenic and aluminium. Both Bacillus sp.MGI101 and Lysinibacillus sp. MGI102 demonstrated varied abilities for the removal of toxic elements from dilution concentration of effluent mixed with MSM. However, the optimal dilution ratio observed in this experiment was 5:15 (effluent:MSM). Overall results demonstrated that isolated bacterial strains have the potential to be employed in bioremediation programmes of acid mine drainages and multi-element contaminated water.
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