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Objective To analyze the epidemiological characteristics and spatial-temporal distribution of drug-resistant tuberculosis in Pudong New Area from 2016 to 2021 and to provide scientific basis for the prevention and control of drug-resistant tuberculosis. Methods The data of tuberculosis patients in Pudong New Area from 2016 to 2021 were collected through the Tuberculosis Information Management System of China Disease Prevention and Control Information System. The geographic information database was established by ArcGIS software and the vector map of Pudong New Area for trend surface analysis and spatial autocorrelation analysis . Results From 2016 to 2021, a total of 3916 patients with etiological positive tuberculosis were found to be drug resistant with the drug resistance rate of 13.13%. The drug resistance rate of each year showed a decreasing trend (χ2trend=14.917, P<0.001). The rates of drug resistance of male, retiree, age 50~<60 years old, 60~<70 years old and recurrent patient were higher. From 2016 to 2021, the incidence of drug-resistant TB showed no spatial aggregation.In the south - north direction, the north is higher than the south generally. In the east - west direction, the west is higher than the east generally. Conclusion Drug resistance screening should be strengthened for men, retirees, over 50 years old, and recurrent TB patients,. Prevention and control measures should be strengthened in street towns with dense population and large floating population.
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Objective To analyze the spatiotemporal distribution characteristics of the syphilis epidemic in Gansu Province from 2005 to 2021, and to provide a reference for the prevention and control of the syphilis epidemic in Gansu Province. Methods ArcGIS 10.7 was used to map the annual incidence of syphilis in Gansu Province from 2005 to 2021, spatial autocorrelation analysis and local autocorrelation analysis were performed, and SaTScan 10.0.2 software was used for spatiotemporal scanning analysis. Results The global autocorrelation results showed that the annual incidence of syphilis in 2005-2021 was >0, Z>1.96, and the P< 0.0001, showing a spatial clustering distribution, and the local autocorrelation results showed that there was one spatially similar high-high aggregation area and two spatially similar low-low aggregation areas in Gansu Province, and the hot spot analysis showed that there were 9 negative hotspot areas and 2 positive hotspot areas in the syphilis epidemic in Gansu Province. Spatiotemporal scanning analysis detected two high concentration areas, mainly concentrated in Gannan Tibetan Autonomous Prefecture. Conclusion Syphilis in Gansu Province has regional differences in space, and high-high accumulation areas in Gannan Tibetan Autonomous Prefecture persist, and targeted prevention and control strategies should be specified according to temporal and spatial characteristics.
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Objective To analyze the spatiotemporal distribution characteristics of respiratory bacterial infections in Sanmenxia City from 2012 to 2022, and explore key areas for respiratory infection prevention and control. Methods Respiratory bacterial infection cases admitted to our hospital from 2012 to 2022 was collected, use OpenGeoDal software for spatial autocorrelation analysis, and SaTScan software for spatiotemporal scanning analysis. Results From 2012 to 2022, 8762 cases of respiratory bacterial infection were reported in Sanmenxia , with an average annual incidence of 173.47/100 000 and a standardized incidence of 132.63/100 000,and the overall incidence showed a downward trend (χ2=48.093,P“high- high” gathering area, which is a key region to prevention and control.
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OBJECTIVE@#To identify the spatial distribution pattern of Oncomelania hupensis spread in Hubei Province, so as to provide insights into precision O. hupensis snail control in the province.@*METHODS@#Data pertaining to emerging and reemerging snails were collected from Hubei Province from 2020 to 2022 to build a spatial database of O. hupensis snail spread. The spatial clustering of O. hupensis snail spread was identified using global and local spatial autocorrelation analyses, and the hot spots of snail spread were identified using kernel density estimation. In addition, the correlation between environments with snail spread and the distance from the Yangtze River was evaluated using nearest-neighbor analysis and Spearman correlation analysis.@*RESULTS@#O. hupensis snail spread mainly occurred along the Yangtze River and Jianghan Plain in Hubei Province from 2020 to 2022, with a total spread area of 4 320.63 hm2, including 1 230.77 hm2 emerging snail habitats and 3 089.87 hm2 reemerging snail habitats. Global spatial autocorrelation analysis showed spatial autocorrelation in the O. hupensis snail spread in Hubei Province in 2020 and 2021, appearing a spatial clustering pattern (Moran's I = 0.003 593 and 0.060 973, both P values < 0.05), and the mean density of spread snails showed spatial aggregation in Hubei Province in 2020 (Moran's I = 0.512 856, P < 0.05). Local spatial autocorrelation analysis showed that the high-high clustering areas of spread snails were mainly distributed in 50 settings of 10 counties (districts) in Hubei Province from 2020 to 2022, and the high-high clustering areas of the mean density of spread snails were predominantly found in 219 snail habitats in four counties of Jiangling, Honghu, Yangxin and Gong'an. Kernel density estimation showed that there were high-, secondary high- and medium-density hot spots in snail spread areas in Hubei Province from 2020 to 2022, which were distributed in Jingzhou District, Wuxue District, Honghu County and Huangzhou District, respectively. There were high- and medium-density hot spots in the mean density of spread snails, which were located in Jiangling County, Honghu County and Yangxin County, respectively. In addition, the snail spread areas negatively correlated with the distance from the Yangtze River (r = -0.108 9, P < 0.05).@*CONCLUSIONS@#There was spatial clustering of O. hupensis snail spread in Hubei Province from 2020 to 2022. The monitoring and control of O. hupensis snails require to be reinforced in the clustering areas, notably in inner embankments to prevent reemerging schistosomiasis.
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Animales , Esquistosomiasis/prevención & control , Análisis Espacial , Ecosistema , Gastrópodos , Ríos , China/epidemiologíaRESUMEN
OBJECTIVE@#To investigate the spatial distribution characteristics of the prevalence of advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody, and to examine the correlation between the prevalence of advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody in Hunan Province in 2020, so as to provide insights into advanced schistosomiais control in the province.@*METHODS@#The epidemiological data of schistosomiasis in Hunan Province in 2020 were collected, including number of permanent residents in survey villages, number of advanced schistosomiasis patients, number of residents receiving serological tests and number of residents seropositive for anti-Schistosoma antibody, and the prevalence advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody were descriptively analyzed. Village-based spatial distribution characteristics of prevalence advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody were identified in Hunan Province in 2020, and the correlation between the revalence advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody was examined using Spearman correlation analysis.@*RESULTS@#The prevalence of advanced schistosomiasis was 0 to 2.72% and the seroprevalence of anti-Schistosoma antibody was 0 to 20.25% in 1 153 schistosomiasis-endemic villages in Hunan Province in 2020. Spatial clusters were identified in both the prevalence of advanced schistosomiasis (global Moran's I = 0.416, P < 0.01) and the seroprevalence of anti-Schistosoma antibody (global Moran's I = 0.711, P < 0.01) in Hunan Province. Local spatial autocorrelation analysis identified 98 schistosomiasis-endemic villages with high-high clusters of the prevalence of advanced schistosomiasis, 134 endemic villages with high-high clusters of the seroprevalence of anti-Schistosoma antibody and 36 endemic villages with high-high clusters of both the prevalence of advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody in Hunan Province. In addition, spearman correlation analysis showed a positive correlation between the prevalence of advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody (rs = 0.235, P < 0.05).@*CONCLUSIONS@#There were spatial clusters of the prevalence of advanced schistosomiasis and seroprevalence of anti-Schistosoma antibody in Hunan Province in 2020, which were predominantly located in areas neighboring the Dongting Lake. These clusters should be given a high priority in the schistosomiasis control programs.
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Animales , Humanos , Prevalencia , Estudios Seroepidemiológicos , Esquistosomiasis/epidemiología , Schistosoma , Análisis Espacial , Anticuerpos Antihelmínticos , China/epidemiologíaRESUMEN
OBJECTIVE@#To investigate the incidence trend and spatial clustering characteristics of scarlet fever in China from 2016 to 2020 to provide evidence for development of regional disease prevention and control strategies.@*METHODS@#The incidence data of scarlet fever in 31 provinces and municipalities in mainland China from 2016 to 2020 were obtained from the Chinese Health Statistics Yearbook and the Public Health Science Data Center led by the Chinese Center for Disease Control and Prevention.The three-dimensional spatial trend map of scarlet fever incidence in China was drawn using ArcGIS to determine the regional trend of scarlet fever incidence.GeoDa spatial autocorrelation analysis was used to explore the spatial aggregation of scarlet fever in China in recent years.@*RESULTS@#From 2016 to 2020, a total of 310 816 cases of scarlet fever were reported in 31 provinces, municipalities directly under the central government and autonomous regions, with an average annual incidence of 4.48/100 000.The reported incidence decreased from 4.32/100 000 in 2016 to 1.18/100 000 in 2020(Z=103.47, P < 0.001).The incidence of scarlet fever in China showed an obvious regional clustering from 2016 to 2019(Moran's I>0, P < 0.05), but was randomly distributed in 2020(Moran's I>0, P=0.16).The incidence of scarlet fever showed a U-shaped distribution in eastern and western regions of China, and increased gradually from the southern to northern regions.Inner Mongolia Autonomous Region and Hebei and Gansu provinces had the High-high (H-H) clusters of scarlet fever in China.@*CONCLUSION@#Scarlet fever still has a high incidence in China with an obvious spatial clustering.For the northern regions of China with H-H clusters of scarlet fever, the allocation of health resources and public health education dynamics should be strengthened, and local scarlet fever prevention and control policies should be made to contain the hotspots of scarlet fever.
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Humanos , Incidencia , Escarlatina/epidemiología , China/epidemiología , Análisis Espacial , Análisis por Conglomerados , Análisis Espacio-TemporalRESUMEN
Objective@#To investigate the spatio-temporal clustering characteristics of influenza in Yinzhou District, Ningbo City, Zhejiang Province from 2017 to 2021, so as to provide insights into prevention and control of influenza. Methods Data of influenza in Yinzhou District from 2017 to 2021 were collected from the Chinese Disease Prevention and Control Information System. The software ArcGIS 10.8 was employed for spatial autocorrelation analysis, and SaTScan 10.1 was employed for spatio-temporal scanning to analyze the temporal and spatial clustering characteristics of influenza incidence in Yinzhou District. @*Methods@#Data of influenza in Yinzhou District from 2017 to 2021 were collected from the Chinese Disease Prevention and Control Information System. The software ArcGIS 10.8 was employed for spatial autocorrelation analysis, and SaTScan 10.1 was employed for spatio-temporal scanning to analyze the temporal and spatial clustering characteristics of influenza incidence in Yinzhou District.@*Results@#Totally 60 543 influenza cases were reported in Yinzhou District from 2017 to 2021, with an incidence of 0.76%. The incidence of influenza peaked in December 2019 (9.35%) and January 2020 (9.28%) during the period between 2017 and 2021. Spatial autocorrelation analysis showed that there was a positive spatial correlation of influenza incidence in Yinzhou District from 2018 to 2021 (all P<0.05), and a high clustering in 2019 and 2021. Zhonghe Street showed a low-high clustering from 2017 to 2020; Jiangshan Town showed a low-high clustering in 2017 and 2020, and a high-high clustering in 2019 and 2021; Shounan Street showed a high-high clustering from 2018 to 2020; Yunlong Street showed a high-high clustering in 2021. Spatio-temporal scanning analysis showed that the class Ⅰ clusters were located in the central region which centered in Dongqianhu Town, with aggregation time in August 2017, in the northwest region with aggregation time in December and January from 2018 to 2020, and in the west region with aggregation time in August 2021.@* Conclusion @#The incidence of influenza in Yinzhou District from 2017 to 2021 showed a spatio-temporal clustering in the northwestern region in winter and summer.
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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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Heart valve disease (HVD) is one of the common cardiovascular diseases. Heart sound is an important physiological signal for diagnosing HVDs. This paper proposed a model based on combination of basic component features and envelope autocorrelation features to detect early HVDs. Initially, heart sound signals lasting 5 minutes were denoised by empirical mode decomposition (EMD) algorithm and segmented. Then the basic component features and envelope autocorrelation features of heart sound segments were extracted to construct heart sound feature set. Then the max-relevance and min-redundancy (MRMR) algorithm was utilized to select the optimal mixed feature subset. Finally, decision tree, support vector machine (SVM) and k-nearest neighbor (KNN) classifiers were trained to detect the early HVDs from the normal heart sounds and obtained the best accuracy of 99.9% in clinical database. Normal valve, abnormal semilunar valve and abnormal atrioventricular valve heart sounds were classified and the best accuracy was 99.8%. Moreover, normal valve, single-valve abnormal and multi-valve abnormal heart sounds were classified and the best accuracy was 98.2%. In public database, this method also obtained the good overall accuracy. The result demonstrated this proposed method had important value for the clinical diagnosis of early HVDs.
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Humanos , Ruidos Cardíacos , Enfermedades de las Válvulas Cardíacas/diagnóstico , Algoritmos , Máquina de Vectores de Soporte , Procesamiento de Señales Asistido por ComputadorRESUMEN
DNA double-strand break(DSB) is a serious form of DNA damage in cells, which is closely related to a variety of genomic instability diseases, including cancer, abnormal recombination and neuronal development. Due to the limitations of cost and technical threshold, high-resolution DSB mapping by high-throughput sequencing technology is very limited. This hinders our understanding of the DSB situation in the genomes of different species. Therefore, we developed a classification prediction model based on random Forest(RF), support vector machine(SVM) and logistic regression(LR) classifiers to predict DSB loci in the whole genome of human NHEK cells. In addition to the epigenetic features and DNA shape features commonly used in previous prediction studies, we found that DNA sequence features(kmer frequency, GC content, GC-skew, Mutual Information) can also characterize DSB sites. At the same time, the prediction accuracy is improved after considering DNA physical properties, chemical shifts and autocorrelation information. After combining all the above features, logistic regression(LR) has the best prediction performance(AUC = 0. 97), which is comparable to previous prediction(AUC = 0. 964). In addition, the optimal feature collection consisting of 294 features was obtained by the incremental feature search method, and the corresponding AUC value reached 0. 974.
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Objective:To learn about the epidemic dynamics and spatial epidemic characteristics of human brucellosis in Gansu Province.Methods:Data on human brucellosis in Gansu Province reported by China Disease Control and Prevention Information System from January 2016 to December 2020 were collected and analyzed by descriptive epidemiology and spatial clustering analysis.Results:A total of 10 025 cases of human brucellosis were reported in Gansu Province from 2016 to 2020, with a statistically significant difference in incidence rate between years (χ 2 = 242.86, P = 0.001). The incidence was the lowest in 2018 (6.03/100 000), and the highest in 2020 (11.39/100 000). The reported cases were concentrated in 45 - 55 years old, accounting for 34.52% (3 461/10 025); the male to female ratio was 2.91 ∶ 1.00 (7 458/2 567); farmers were the main occupation, accounting for 82.11% (8 232/10 025). Among the 86 counties (cities, districts) in Gansu Province, Yongchang County had the highest number of reported cases in 2020 (339 cases), and Sunan Yugur Autonomous County had the highest incidence in 2020 (190.89/100 000). Spatial autocorrelation analysis showed that there was significant spatial positive correlation between the incidence rate of human brucellosis in Gansu Province from 2016 to 2020 (global Moran's I > 0, Z > 1.96, P < 0.05), showing a spatial clustering distribution. The high-high clustering areas were concentrated in Yongchang County and Sunan Yugur Autonomous County. Conclusion:In Gansu Province, the main population of human brucellosis is middle-aged male farmers, and the incidence of brucellosis is spatially clustered.
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Objective:To analyze the spatiotemporal characteristics and spatial aggregation of the incidence of hemorrhagic fever with renal syndrome (HFRS) in China from 2004 to 2020, and to provide a scientific basis for prevention and control of HFRS.Methods:The epidemic information of HFRS in China from 2004 to 2020 was collected from the Public Health Science Data Center, the China Health Statistics Yearbook, and the National Statutory Infectious Disease Epidemic Profile Report. The Joinpoint model was used to analyze the annual average incidence rate change trend, ArcGIS 10.5 software was used for spatial visualization analysis, and global spatial autocorrelation, local spatial autocorrelation and spatiotemporal scan analysis were applied to detect hot spots and aggregation areas.Results:From 2004 to 2020, a total of 208 441 cases of HFRS were reported in China, with an average annual incidence rate of 0.91/100 000. Joinpoint model analysis showed that the average annual incidence rate of HFRS in China showed a decreasing trend from 2004 to 2020. In the provinces with high incidence, the disease was mostly distributed with multimodal distribution in spring, autumn and winter, especially in autumn and winter. The results of global spatial autocorrelation analysis showed that the global Moran's I of HFRS incidence rate in China from 2004 to 2019 were all positive. Except 2012 and 2020, the random distribution pattern was not excluded, other years showed spatial clustering ( Z > 1.65, P < 0.05). The results of phased local spatial autocorrelation analysis indicated that Heilongjiang, Jilin and Liaoning provinces were high-high aggregation regions. A total of five aggregation regions were detected in the month-by-month spatiotemporal scan analysis, and the differences of each aggregation region were statistically significant ( P < 0.001). Conclusions:From 2004 to 2020, the overall incidence of HFRS in China shows a downward trend, and the incidence rate has obvious spatial aggregation. High-risk areas still exist, and it is necessary to focus on and take targeted prevention and control measures.
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Objective:To analyze the epidemiological characteristics and spatiotemporal characteristics of human brucellosis in Henan Province.Methods:Data of human brucellosis in Henan Province from 2005 to 2021 were collected through the China Disease Prevention and Control Information System, and a descriptive epidemiological method was used to analyze the epidemic profile of brucellosis in Henan Province and the three distribution characteristics. Global and local spatial autocorrelation were used to analyze the spatial distribution and the hot spots of brucellosis in Henan Province, respectively, and spatiotemporal scanning was used to analyze the spatiotemporal clustering regions of brucellosis in Henan Province.Results:A total of 39 862 brucellosis cases were reported in Henan Province from 2005 to 2021, with an average annual incidence of 2.44/100 000, and the number of cases showed an overall increasing trend each year (χ 2trend = 11 127.85, P = 0.001). The onset months were mainly concentrated from March to July, accounting for 59.00% (23 517/39 862), with May as the peak (5 478 cases). Cases of brucellosis were reported in 157 counties (cities, districts) of the province. The ratio of male to female was 2.52∶1.00 (28 542/11 320). Farmers were the main occupation, with 32 985 cases (82.75%). The age of onset was mainly 45 to 65 years old, with 20 226 cases (50.74%). The global spatial autocorrelation analysis showed that the global Moran's I was > 0, Z > 1.96, and P < 0.05 in all years except 2006 - 2008, showing spatial clustering. Further local spatial autocorrelation analysis was performed, and high-high and low-low clustering areas existed in 2012 - 2021 ( P < 0.01). Spatiotemporal scanning analysis showed that there was one spatiotemporal cluster in the high incidence area and two spatiotemporal clusters in the low incidence area. The high incidence cluster was centered in Neixiang County, covering 48 counties (cities, districts) including Song County and Ruzhou City, and the aggregation time was from 2014 to 2021. The two low incidence clusters were centered in Yongcheng City and Boai County, covering 58 and 18 counties (cities, districts), respectively, and the aggregation time was 2016 - 2021 and 2005 - 2012, respectively. Conclusion:The overall incidence of brucellosis in Henan Province is on the rise from 2005 to 2021, with middle-aged and elderly male farmers as the main affected population, and there are spatiotemporal clusters of brucellosis in Henan Province.
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@#Abstract: Objective To analyze the spatial-temporal characteristics of the active pulmonary tuberculosis (PTB) and the pathogenic positive PTB in Fuling District of Chongqing during the 13th Five-Year Plan period, so as to explore the clustering areas, and provide scientific basis for the precise prevention and control of tuberculosis in Fuling District. Methods The PTB registration data of 27 townships in Fuling District from 2016 to 2020 were collected. The descriptive analysis were used to describe the temporal and spatial distribution characteristics of patients, SaTScan9.0 and ArcGis10.6 was used for spatial-temporal scanning analysis and local auto-correlation analysis. The results were visualized by ArcGis10.6. Results A total of 4 038 case of active PTB patients were registered and a downward trend was observed in PTB during the 13th Five-Year Plan period in Fuling District. The average annual registration rate of PTB was 70.17/100 000, and the annual PTB registration rate declined by 8.21%. The peak of active PTB and etiological positive PTB were mainly concentrated in March and June respectively. The top five streets of cumulative active PTB patients registered were Lizhi street, Dunren street, Chongyi street, Ma 'an street and Jiangdong street, accounting for 60.18% of the total registered PTB patients during the 13th Five-Year Plan period. The top three average annual registration rates were Dunren street (101.35/100 000), Chongyi street (101.34/100 000) and Wulingshan Township (99.21/100 000). The registered PTB from 2016 to 2020 showed a global auto-correlation (Moran's I=0.64, P<0.0001). The "high-high" area of active PTB and the etiological positive PTB all covered Lizhi street, Jiangdong street and Longqiao street. By scanning analysis of spatial-temporal, the primary cluster of active PTB concentrated in the main urban area south of the Yangtze River in Fuling during January 2016 to December 2017, and the primary cluster of pathogenic positive PTB concentrated in the main urban area south of the Yangtze River in Fuling and Jiangdong street during January 2019 to December 2020. Conclusions During the 13th Five-Year Plan period, there was the spatial-temporal clustering of PTB in Fuling District, which mainly gathered in the main urban area south of the Yangtze River in Fuling district and surrounding streets centered on Lizhi street.
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@#Abstract: Objective To explore the spatial epidemiological characteristics of severe cases hand, foot and mouth disease (HFMD) in Guangxi, China, from 2014 to 2018, and to provide a basis for identifying the high-risk regions as well as the prevention and control of severe cases of HFMD in Guangxi. Methods Spatial-temporal scanning analysis, global and local spatial autocorrelation analysis were used to analyze the spatial clustering of HFMD. The trend surface analysis was used to evaluate the spatial distribution trend of HFMD. Results From 2014 to 2018, the incidence and severe case fatality rates of HFMD were 3.89/100 000 and 4.23%, respectively. Monte Carlo scanning analysis showed that the first cluster region was Cenxi City, the second cluster was mainly concentrated in northwest of Guangxi, and the aggregation time was mainly concentrated in April to May and August to October. The global spatial autocorrelation analysis showed that the severe HFMD was significant clustering distribution, and the Moran's I coefficients of the sever cases, severe morbidity and severe case fatality rate were 0.088, 0.118, 0.197, respectively (P<0.05). Local spatial autocorrelation analysis showed that hotspots of severe HFMD cases were concentrated in the southern Guangxi, mainly in Lingshan County. Anselin local Moran's I clustering and outlier analysis indicated that 5 high-high (H-H) clustering regions for fatality were Lingshan, Pubei, Zhongshan, Zhaoping and Pinggui County. There were 6 high-high (H-H) clustering regions for severe incidence rate, namely Lingshan, Qinnan, Lingyun, Youjiang, Bama Yao Autonomous and Pinggui County, and 1 high-low (H-L) clustering region, Cenxi County. The trend surface analysis showed that the overall number of severe cases of death decreased from east or west to the middle, and increased from north to middle, and then decreased to south. Conclusions Severe HFMD cases in Guangxi have obvious spatial-temporal clustering, and the hop spots are mainly concentrated in southern Guangxi. The prevention and control of HFMD in areas with high incidence of severe cases should be strengthened to reduce the burden of HFMD cases.
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Objective:To analyze the spatial distribution characteristics and spatial aggregation of the epidemic of severe fever with thrombocytopenia syndrome(SFTS) in Yantai City of Shandong Province, and to provide basis for formulating effective SFTS prevention and control measures.Methods:The epidemic data of SFTS confirmed cases in each township (street) in Yantai City, Shandong Province from 2015 to 2020 were collected from the "China Disease Prevention and Control Information System Infectious Disease Monitoring and Reporting System", and ArcGIS 10.2 software was used for spatial autocorrelation analysis.Results:From 2015 to 2020, a total of 839 SFTS cases were reported in Yantai City, including 124 deaths; with an average annual incidence rate of 2.14/100 000, and a total case fatality rate of 14.78%. Global spatial autocorrelation analysis showed that the distribution of SFTS cases in Yantai City from 2015 to 2020 showed a positive spatial correlation, with the highest spatial correlation in 2015 (Moran's I = 0.25, Z = 5.66, P < 0.001), and the lowest in 2018 (Moran's I = 0.16, Z = 3.69, P < 0.001). Local spatial autocorrelation and hotspot analysis showed that the epidemic areas of SFTS were mainly in some mountainous and hilly townships (streets) of Laizhou City, Penglai District, Qixia City, Zhaoyuan City, and Haiyang City. Conclusions:The distribution of SFTS epidemic in Yantai City has obvious regional clustering. Intervention measures such as publicity, education and monitoring should be strengthened in high-incidence areas to reduce the incidence of the disease.
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Objective:To study the epidemiological characteristics and spatio-temporal aggregation of hemorrhagic fever with renal syndrome (HFRS) in Shandong Province, and to provide reference for formulating reasonable prevention and control strategies.Methods:Retrospective analysis was used to collect HFRS surveillance data and confirmed case data in Shandong Province from 2017 to 2020 in the "China Disease Prevention and Control Information System Infectious Disease Surveillance System". Geoda 1.18 software was used for global and local spatial autocorrelation analysis, SaTScan 9.6 software was used for spatio-temporal scanning analysis, and ArcGis 10.7 software was used for map drawing and visual display.Results:A total of 3 753 cases of HFRS were reported in Shandong Province from 2017 to 2020, including 56 deaths. The annual incidence rate was 1.26/100 000, 1.22/100 000, 0.75/100 000 and 0.53/100 000, respectively, with an average annual incidence rate of 0.94/100 000. The incidence of HFRS was obviously seasonal, mainly concentrated in autumn and winter from October to December, accounting for 50.41% (1 892/3 753). The age of onset was mainly 30-59 years old, accounting for 61.68% (2 315/3 753). The male to female ratio was 2.76 ∶ 1.00 (2 756 ∶ 997). The occupation distribution was mainly farmers, accounting for 81.99% (3 077/3 753). The global spatial autocorrelation analysis showed that HFRS showed spatial aggregation areas in each year from 2017 to 2020 (Moran' I = 0.38, 0.33, 0.59, 0.46, Z = 7.47, 7.23, 10.69, 8.66, P < 0.001). The local spatial autocorrelation analysis showed that "high-high" aggregation areas were mainly concentrated in central and southeast of Shandong Province, while "low-low" aggregation areas were mainly concentrated in northwest of Shandong Province. Spatio-temporal scanning analysis revealed 1 type Ⅰ agglomerations and 2 type Ⅱ aggregation areas. The type Ⅰ aggregation areas occurred from October to November 2018, covering 22 counties (districts) of 5 cities in Qingdao, Yantai, Weifang, Weihai and Rizhao. The first type Ⅱ aggregation area occurred from October to November 2017, involving 23 counties (districts) of 8 cities in Jinan, Zibo, Zaozhuang, Weifang, Jining, Tai 'an, Rizhao and Linyi. The second type Ⅱ aggregation area occurred in Jinxiang County, Jining City from February to March 2017. Conclusion:The incidence of HFRS in Shandong Province from 2017 to 2020 has obvious spatio-temporal aggregation, and the hot spots are concentrated in central and southeast of Shandong Province, which should be regarded as a key area for prevention and control of HFRS.
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Objective:To investigate the spatial distribution characteristics of Keshan disease in Shandong Province, and to provide evidence for prevention and control of Keshan disease.Methods:The incidence data of Keshan disease in Shandong Province from 1960 to 2018 were collected from Shandong Provincial Institute for Endemic Disease Control and Prevention, and a spatial database was built. Global and local spatial autocorrelation (Moran's I) were analyzed by ArcGIS 10.2 and GeoDa 1.14 softwares, respectively. Local indicators on spatial association (LISA) aggregation graph was drawn. This allowed us to investigate the spatial autocorrelation and cluster range of the distribution of Keshan disease in Shandong Province. Results:A total of 4 172 cases of Keshan disease were reported in Shandong Province with an annual incidence rate of 0 to 51.4/10 000 of the population at the township-level from 1960 to 2018. Global spatial autocorrelation analysis on the incidence of Keshan disease at the township-level showed that global Moran's I values ranged from 0.020 to 0.429 in 1962 - 1964, 1969 - 1985, 1989, 1995, 1998 - 2001 and 2004 - 2016 ( P < 0.05), thus indicating significant spatial autocorrelation overall. LISA analysis further revealed that high-high clusters of Keshan disease existed in 1960, 1962 - 1964, 1969 - 1985, 1989, 1998 - 2000 and 2002 - 2016. These clusters were predominantly distributed in three areas: Zoucheng City, Pingyi County and Sishui County in the southwest of Shandong Province; Wulian County and Ju County in the southeast of Shandong Province; and Qingzhou City, Linqu County and Yishui County in the central and middle-south of Shandong Province. Conclusions:Keshan disease exhibits significant spatial autocorrelation in Shandong Province. High-high clusters are mainly located in certain townships in the southwest, southeast, central and middle-south of Shandong Province.
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Objective To analyze the spatial distribution characteristics of tuberculosis in rural areas of Nanning City from 2010 to 2018, and explore the clustering areas, and to provide evidence for tuberculosis prevention and treatment. Methods The database of tuberculosis epidemics in rural areas of Nanning City from 2010 to 2018 was established by ArcGIS 10.8. The spatial distribution map was drawn, and global autocorrelation, local autocorrelation and hotspot analysis were conducted. Results The spatial distribution map of the average annual reported incidence rates in rural areas of Nanning from 2010 to 2018 showed that the towns with high average annual incidence rates were Jinchai Town and Yangqiao Town. Global autocorrelation analysis showed that the Moran's I index from 2010 to 2018 was 0.18 (Z=2.33, P=0.02), suggesting that tuberculosis in rural areas of Nanning had spatial clustering in the regional distribution. Local autocorrelation analysis showed that tuberculosis in rural areas of Nanning had high-high clustering, low-low clustering, high-low clustering and low-high clustering patterns. Among them, Jinchai Town and Lidang Yao Township were high-high clustering areas. Litang Town, Xinfu Town and Taoxu Town were low-low clustering areas. Local hotspot analysis showed that “hotspot” areas included Jinchai Town, Yangqiao Town and Lidang Yao Township. Conclusion There is a spatial clustering of tuberculosis epidemics in rural areas of Nanning. The high-incidence areas include Jinchai Town, Yangqiao Town and Lidang Yao Township, and the low-incidence areas include Litang Town, Xinfu Town and Taoxu Town.
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Objective:To analyze the prevalence of poor vision and spatial distribution characteristics among primary school students in Shenzhen.Methods:A cross-sectional study was conducted.Vision screening among primary school students in Shenzhen was carried out by myopia screening hospitals organized by Shenzhen Myopia Prevention and Control Center for Children and Adolescents using the logarithmic visual acuity chart in 2019.The prevalence of poor vision in different districts, different genders and different grades was calculated.Spatial distribution of the prevalence of poor vision was analyzed with Arcgis 10.2 software.This study adhered to the Declaration of Helsinki.Written informed consent was obtained from guardian of each subject.The study protocol was approved by an Ethics Committee of Shenzhen Eye Hospital (No.20201230-06).Results:A total of 1 044 545 students received the visual acuity examination.The prevalence of poor vision among primary school students in Shenzhen in 2019 was 53.4%(557 748/1 044 545). The prevalence of poor vision among primary school students in the former Shenzhen Special Economic Zone was 56.7%(172 771/304 532), which was higher than 52.0%(384 977/740 013) in areas outside the former Shenzhen Special Economic Zone, and the prevalence of poor vision among girls was 56.7%(268 201/473 164), which was higher than 50.7%(289 547/571 381) among boys, and the differences were statistically significant ( χ2=192.412, 375.434; both at P<0.001). As the grade increased, the prevalence of poor vision firstly decreased and then increased, showing an increasing tendency in general.The prevalence rate of poor vision among primary school students among grade 1 to 6 was 49.8%(99 615/200 203), 44.0%(86 521/196 800), 47.2%(82 848/175 331), 54.5%(89 737/164 731), 60.8%(96 271/158 298), and 68.9%(102 756/149 182), respectively, and a significant difference was found ( χ2=2 871.017, P<0.001). The global Moran I index in Shenzhen was 0.278.The local Moran I index and Geary coefficient in Guangming District were 0.933 and 0.78, respectively.The prevalence of poor vision in Guangming District and its surrounding areas was a low-low cluster. Conclusions:The prevalence of poor vision among primary school students in Shenzhen is spatially aggregated.