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@#Abstract: Objective To analyze the resistance and spatial distribution of Mycobacterium tuberculosis (MTB) to six commonly used anti-tuberculosis drugs in Qinghai Province from 2016 to 2019, so as to provide a reference for tuberculosis treatment and drug-resistant tuberculosis control. Methods A total of 1 182 identified strains of Mycobacterium tuberculosis in Qinghai Province from 2016 to 2019 were collected, and 6 anti-tuberculosis drugs were subjected to drug susceptibility tests and strain confirmed by the proportional method. By means of ArcMap10.7 and SaTScan10.1 software, map visualization, spatial autocorrelation analysis and spatial scanning of MTB drug resistance were performed to identify MTB drug resistance clusters in Qinghai Province. Results From 2016 to 2019, the total drug resistance (TDR) rate of 1 182 Mycobacterium tuberculosis strains in Qinghai Province was 23.77% (281/1 182), with a mono-resistance (MR) rate of 11.08% (131/1 182), a poly-resistance (PDR) rate of 3.89% (46/1 182), a multi-drug resistance (MDR) rate of 8.80% (104/1 182), and an extensive drug resistance (XDR) rate of 0.85% (10/1 182). The rates of MDR, XDR and TDR all showed a decreasing trend year by year (P<0.01). The drug resistance spectrum displayed 21 combinations. The TDR rate and MDR rate in the retreatment patients were higher than those of the initial treated patients, and the difference was statistically significant (χ2 TDR=22.784, χ2MDR=45.082, P<0.01). In terms of demographic characteristics, the TDR rate in males was higher than that in females, and the middle-aged group was higher than other age groups, and the differences were statistically significant (χ2=7.541, 10.825, P<0.05). The results of global spatial autocorrelation analysis showed that there was no statistical significance in the autocorrelation and obvious spatial clustering of MTB drug resistance in Qinghai Province from 2016 to 2019 (P>0.05), which indicated a random distribution. The results of spatiotemporal scanning showed that there was a kind of clustering area, but the clustering effect was not significant (P>0.05), indicating a random distribution. Conclusions The TDR of MTB in Qinghai Province from 2016 to 2019 showed a downward trend year by year. In comparison with the national average, the rate of multi-drug resistance and extensive drug resistance was still high, and most of the multi-drug resistance resulted from rifampicin and isoniazid. The drugresistant population mainly consisted of retreatment, males, and young and middle-aged pop
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Objective @#To understand the spatial distribution of iodine in drinking water in Wenzhou,and to provide reference for preventing iodine deficiency disorders.@*Methods @#Drinking water was sampled from 182 townships of all 11 counties under the jurisdiction of Wenzhou according to different ways of water supply. The iodine in water was detected by cerium sulfate catalytic spectrophotometry. The water iodine data was matched with the electronic map by ArcGIS10.2 to construct a spatial database; spatial autocorrelation analysis by GeoDa1.10 and spatial scanning analysis by SaTScan 9.4 were conducted to obtain the water iodine concentration range in Wenzhou. @*Results @#The contents of iodine in 998 out of 1 008 drinking water samples were less than 10 μg/L,accounting for 99.01%. The median of water iodine in all townships of Wenzhou was 1.8 μg/L. The results of geospatial distribution analysis demonstrated that the iodine distribution in drinking water had positive spatial autocorrelation in Wenzhou（Moran's I= 0.40,Z=15.65,P< 0.05）; there were four kinds of local aggregation models for water iodine in 78 townships（P< 0.01）. Three cluster areas of the water iodine were detected by space scanning,with three townships in Dongtou as the first high cluster areas,seven coastal townships in Cangnan as the second high cluster areas and 49 mountainous townships in Yongjia,Yueqing and Lucheng as the low cluster areas.@*Conclusion @#The iodine in drinking water in Wenzhou was low and exists spatial aggregation.
Résumé
Objective To learn the temporal-spatial distribution and clustering of hand-foot-mouth disease (HFMD)in Xiaoshan in 2014,and to provide reference and basis for prevention and control.Methods The HFMD data of Xiaoshan in 2014 was derived from the China Information System for Diseases Control and Prevention.The vector map was created by Map Info 10.0 on the background of 1∶65 000 zoning map ,extracted village and community geographical position information from Baidu map.At the village level,the spatial autocorrelation analysis and spatial scanning analysis were made using software ArcGIS10.2 and SaTScan9.2.Results In Xiaoshan,the epidemic curve of HFMD in 2014 showed two peaks,during April to July and during September to October.The global spatial autocorrelation Moran's I index was 0.442 7(P<0.001),and the global Getis-Ord G value was 0.003 3,(E(G)=0.002 1,Z(G)=11.82,P<0.001). Local autocorrelation analysis showed that the cluster state was high-high.Ningwei Street had the most hot spots.Five statistically significant HFMD clusters were identified by space-time scan statistics,the most likely cluster was located in Heshang Town,from January 6 to February 4 (RR=23.00,LLR=17.45,P<0.05).Conclusion In Xiaoshan,the major epidemic peak of HFMD in 2014 was from April to July.A positive spatial correlations was found,and the disease showed a distinct regional distribution feature and temporal-spatial clustering.The clusters were observed including the villages and communities of rural-urban continuum and in vicinity of industrial development zone in countryside.