Epidemiology of spatiotemporal distribution of respiratory bacterial infections in Sanmenxia City in 2012 - 2022 / 公共卫生与预防医学

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.

Drug resistance characteristics and spatial clustering of Mycobacterium tuberculosis in Qinghai Province from 2016 to 2019 / 中国热带医学

@#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

Spatial clustering analysis of scarlet fever incidence in China from 2016 to 2020 / 南方医科大学学报

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.

Spatial distribution of Oncomelania hupensis spread in Hubei Province from 2020 to 2022 / 中国血吸虫病防治杂志

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.

Spatiotemporal characteristics of hemorrhagic fever with renal syndrome in China from 2004 to 2020 / 中华地方病学杂志

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.

Analysis on spatial distribution characteristics of tuberculosis in rural areas of Nanning in 2010-2018 / 公共卫生与预防医学

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.

Epidemiological characteristics of latent syphilis in Yancheng Cityfrom 2016 to 2020 / 预防医学

Objective@#To analyze the epidemiological characteristics of latent syphilis in Yancheng City from 2016 to 2020, so as to provide insights into syphilis control. @*Methods@#All reported cases with latent syphilis in Yancheng City from 2016 to 2020 was collected from the Communicable Disease Report System of China Disease Prevention and Control Information System, and the prevalence of latent syphilis was estimated and standardized by the seventh population census data in Yancheng City. The trends in the incidence of latent syphilis were evaluated using annual percent change (APC), and the temporal, regional and human distributions of latent syphilis patients were descriptively analyzed. In addition, the spatial clusters of latent syphilis incidence were identified using spatial autocorrelation analysis. @*Results@#A total of 7 790 cases with latent syphilis were reported in Yancheng City from 2016 to 2020, and the standardized incidence of latent syphilis increased from 15.35/105 in 2016 to 28.70/105 in 2020 (APC=17.54%, t=5.357, P=0.013). Latent syphilis cases were reported in each month, and no obvious seasonable characteristics were seen. During the period from 2017 to 2020, the highest incidence of latent syphilis was seen in residents at ages of 70 to 79 years, with incidence rates of 41.71/105, 43.04/105, 75.79/105 and 72.94/105, respectively, and most cases were farmers (4 711 cases, 60.47%). The three highest incidence of latent syphilis was reported in Funing County (191.40/105), Tinghu District (137.13/105) and Yandu District (126.23/105). There was a positive spatial correlation of latent syphilis incidence in Yancheng City from 2016 to 2020 (Moran's I=0.23, Z=4.457, P=0.001), and two high-high clusters were identified in 14 townships (streets) of Funing County, Binhai County, Tinghu District, Sheyang County and Yandu District and 3 low-low clusters in 7 townships (streets) in Jianhu County, Tinghu District, Dongtai City and Sheyang County. @*Conclusions@#The incidence of latent syphilis appeared a tendency towards a rise, and there were remarkable spatial clusters identified in latent syphilis incidence in Yancheng City from 2016 to 2020. The elderly people and farmers are at high risk of latent syphilis.

Spatio-temporal distribution characteristics of Oncomelania hupensis in Anhui Province from 2011 to 2020 / 中国血吸虫病防治杂志

OBJECTIVE@#To analyze the spatial-temporal distribution characteristics of Oncomelania hupensis snails in Anhui Province from 2011 to 2020, to provide insights into precision control of O. hupensis snails in Anhui Province.@*METHODS@#O. hupensis snail distribution data were collected in Anhui Province from 2011 to 2020 and descriptively analyzed, including actual area of snail habitats, area of emerging snail habitats and area of Schistosoma japonicum-infected snails. The actual area of snail habitats and area of emerging snail habitats were subjected to spatial autocorrelation analysis, hotspot analysis, standard deviation ellipse analysis and space-time scanning analysis, and the clusters of snail distribution and settings at high risk of snail spread were identified in Anhui Province from 2011 to 2020.@*RESULTS@#The actual area of snail habitats gradually decreased in Anhui Province from 2011 to 2020. The actual area of snail habitats were 26 238.85 hm2 in Anhui Province in 2020, which were mainly distributed in marshland and lake regions. There was a large fluctuation in the area of emerging snail habitats in Anhui Province during the period from 2011 to 2020, with the largest area seen in 2016 (1 287.65 hm2), and 1.96 hm2 emerging infected snail habitats were detected in Guichi District, Chizhou City in 2020. Spatial autocorrelation and hotspot analyses showed spatial clusters in the distribution of actual areas of snail habitats in Anhui Province from 2011 to 2020 (Z = 3.00 to 3.43, all P values < 0.01), and the hotspots were mainly concentrated in the marshland and lake regions and distributed along the south side of the Yangtze River, while the cold spots were mainly concentrated in the mountainous regions of southern Anhui Province. There were no overall spatial clusters in the distribution of areas of emerging snail habitats (Z = -2.20 to 1.71, all P values > 0.05), and a scattered distribution was found in local regions. Standard deviation ellipse analysis showed relatively stable distributions of the actual areas of snail habitats in Anhui Province from 2011 to 2020, which was consistent with the flow direction of the Yangtze River, and the focus of the distribution of areas of emerging snail habitats shifted from the lower reaches to upper reaches of Anhui section of the Yangtze River. Space-time scanning analysis identified two high-value clusters in the distribution of actual areas of snail habitats in lower and middle reaches of Anhui section of the Yangtze River from 2011 to 2020, and two high-value clusters in the distribution of areas of emerging snail habitats were identified in mountainous and hilly regions.@*CONCLUSIONS@#There were spatial clusters in the distribution of O. hupensis snails in Anhui Province from 2011 to 2020, which appeared a tendency of aggregation towards the south side and upper reaches of the Yangtze River; however, the spread of O. hupensis snails could not be neglected in mountainous and hilly regions. Monitoring of emerging snail habitats should be reinforced in mountainous and hilly regions and along the Yangtze River basin.

Epidemiological characteristics of acute drug poisonings in Shanghai from 2019 to 2021 / 环境与职业医学

Background Acute drug poisonings are increasing year by year and have become the leading cause of acute poisoning in Shanghai in recent years, and the related prevention and control work is faced with a tough situation. Objective To understand the composition of drugs leading to acute poisonings and describe the epidemiological tendency of reported acute drug poisonings in Shanghai. Methods We collected registered acute drug poisoning case information from the Shanghai Health Information System under Shanghai Health Statistics Center, including demographic characteristics and clinical diagnosis. There were totally 86476 cases reported from 2019 to 2021. The distributions of drugs and victims were described by year. Incidence tendency of acute drug poisonings was analyzed by chi-square test and the analysis focused on analgesic, hypnotics, and antidepressant drug-associated poisonings. Spatial autocorrelation analysis was performed by GeoDa1.20 through calculating global and local Moran's I. Results There was an ascendant tendency in both case count (22132 cases in 2019, 29071 cases in 2020, and 35273 cases in 2021) and crude morbidity (0.89‰ in 2019, 1.21‰ in 2020, and 1.46‰ in 2021) of patients who received outpatient service or emergency treatment for acute drug poisonings from 2019 to 2021 in Shanghai. The top 3 kinds of acute poisoning drugs were analgesics, hypnotics, and antidepressants. The morbidity rates of acute drug poisonings associated with antidepressants (χ2=2700.15, P<0.05) and analgesics (χ2=2294.01, P<0.05) increased year by year. The leading 3 kinds of drugs associated with acute drug poisonings in the same age group were similar. Analgesics showed high frequency staying in the top 3 acute poisoning drugs in most age groups for the 3 years (the highest morbidity was 0.84‰ in male or 1.07‰ in female). Antidepressants were in the top 3 acute poisoning drugs in the under 59 years age groups for the 3 years (male morbidity rate was 0.12‰-0.44‰, and female morbidity rate was 0.06‰-0.45‰). Hypnotics were in the top 3 acute poisoning drugs in the ≥ 18 years age groups for the 3 years (morbidity rate in male was 0.28‰-0.98‰, and morbidity rate in female was 0.21‰-0.92‰). Cardiovascular drugs were in the top 3 acute poisoning drugs in the > 60 years age group for the 3 years (male morbidity rate was 0.40‰-1.03‰, and female morbidity rate was 0.66‰-0.81‰). Regarding the causes of poisonings, accidental poisoning and exposure was the main cause in the ≤ 17 years groups (male constituent ratio was 57.64%-67.12%, and female constituent ratio was 55.27%-68.27%); suicide (male constituent ratio was 20.28%-43.51%, and female constituent ratio was 25.18%-52.02%) had a higher percentage than accidental poisoning and exposure (male constituent ratio was 16.97%-23.62%, and female constituent ratio was 12.76%-17.63%) in the 18-59 years age groups; accidental poisoning and exposure (male constituent ratio was 24.38%-45.18%, and female constituent ratio was 32.69%-38.11%) had a higher percentage than suicide (male constituent ratio was 12.35%-14.02%, and female constituent ratio was 11.92%-12.31%) in the > 60 years age group. The spatial autocorrelation analysis showed that the distribution of acute poisoning cases caused by analgesics, hypnotics, or antidepressants was not random. It was mostly positive spatial clustering. The high-morbidity area was always in the outer-ring circle, especially in Xuhui, Changning, and Jing'an districts, which suggested a high-high cluster pattern. Conclusion In view of the increasing morbidity rate of acute drug poisoning cases in Shanghai in this study, it is urgent to take prevention and control actions. We should plan further studies and different strategies toward different victims with distinct drug poisoning characteristics and areas with high morbidity rates.

Analysis of Mycobacterium tuberculosis drug susceptibility test results and its spatial distribution characteristics in Dali Bai Autonomous Prefecture from 2017 to 2019 / 中华检验医学杂志

Objective:Analyze the drug resistance of Mycobacterium tuberculosis (MTB) to commonly used anti-tuberculosis drugs and its spatial distribution in Dali Bai Autonomous Prefecture from 2017 to 2019, which would provid a reference for the treatment of tuberculosis and the prevention and control of drug-resistant tuberculosis. Methods:A total of 1 013 Mycobacterial strains were isolated from sputum samples in the tuberculosis laboratories of the designated People′s hospital of 12 counties (cities) of Dali Bai Autonomous Prefecture from January 2017 to December 2019. Proportional method was used to conduct drug susceptibility tests and strain identification of 6 anti-tuberculosis drugs. Further used ArcMap10.2 and GeoDa1.14 software to visualize the map display and spatial autocorrelation analysis of the drug resistance of MTB.Results:From 2017 to 2019, the drug resistance rates of MTB in Dali Prefecture were 10.33%(28/271), 10.35%(55/531) and 30.00%(51/170), respectively, showing an rising trend ( χ2=26.62, P<0.05). Among 1 030 samples, 972 strains (95.95%) was MTB and 41 strains (4.05%) was non-tuberculous Mycobacterium (NTM). The total resistance rate of 972 strains of MTB was 13.79% (134/972), of which the single resistance rate was 6.59% (64/972), the multi-drug resistance rate was 4.84% (47/972), and the poly-drug resistance rate was 2.06% (20/972), the rate of extensive drug resistance is 0.31% (3/972). There are 25 combinations of drug resistance patterns. The detection rate of NTM was 4% (41/1 013), among which Midu County had the highest detection rate (0.89%, 9/1 013). The spatial distribution showed that the number of MTB resistant strains among counties and cities had a negative spatial correlation (Moran′s I value was -0.367, P<0.05). It shows that there is no clustering of drug resistance among counties and cities, and the resistance is serious in individual counties and cities. Yongping County and Nanjian Yi Autonomous County had low and high aggregation, and Yunlong County had high and low aggregation. Conclusions:The drug resistance of MTB showed an rising trend in Dali Bai Autonomous Prefecture from 2017 to 2019. The number of drug-resistant strains among regions was not randomly distributed, the regional difference was large, and spatial autocorrelation analysis provided theoretical clues and basis for the formulation of drug resistance prevention and control measures for tuberculosis in the whole state.

A research on spatial correlation and spatial cluster pattern of hemorrhagic fever with renal syndrome in Jingzhou City, Hubei Province from 2013 to 2017 / 中华地方病学杂志

Objective To investigate the spatial correlation and spatial cluster pattern of hemorrhagic fever with renal syndrome (HFRS) in Jingzhou City,Hubei Province from 2013 to 2017.Methods The HFRS surveillance data during 2013-2017 were collected from China Disease Prevention and Control Information System.Software ArcGIS 10.3 was used to analyze the spatial distribution,and global autocorrelation analysis (Moran'sI) and hot spot analysis (Getis-Ord Gi) were used to analyze the spatial autocorrelation.Spatial cluster pattern was explored by trend surface analysis and directional distribution.Results In 2013-2017,the global Moran's I was 0.117 6 (P ＞ 0.05),0.349 8 (P ＜ 0.05),0.102 1 (P ＞ 0.05),0.276 3 (P ＜ 0.05),and 0.394 8 (P ＜ 0.05),respectively.The Getis-Ord Gi analysis showed that there were 7,8,8,8,15 hot areas with high incidence of HFRS during this period,respectively,which were part of townships in Jiangling County,Shashi District,Jianli County,and Honghu City.The cold spot area with low incidence of HFRS was only detected in 2015,and it was part of the township in Shashi District and Jingzhou District.The trend surface analysis showed that the inverted-U type curve could reflect the HFRS distribution from northern to southern,and it was also from eastern to western.The directional distribution showed that the HFRS cases were distributed in the north-central part of Jingzhou in 2013-2017,and they were inconsistent with the distribution of the Yangtze River system.Conclusions The incidence of HFRS has an obvious spatial clustering characteristic,and the areas at high risk are mainly in the north-central part of Jingzhou City.The spatial cluster pattern of HFRS has nothing to do with the Yangtze River system.

Spatial and temporal distribution of newly diagnosed echinococcosis patients in Sichuan Province from 2007 to 2017 / 中国血吸虫病防治杂志

Objective To explore the spatial and temporal distribution characteristics of cases with newly diagnosed echinococcosis in Sichuan Province from 2007 to 2017, so as to provide reference for the formulation of echinococcosis prevention and control strategies and for the identification of key areas. Methods The spatial distribution maps of detection of cases with newly diagnosed echinococcosis were plotted in Sichuan Province from 2007 to 2017, and the spatial distribution characteristics and epidemic trends were analyzed. Results From 2007 to 2017, the detection rate of cases with newly diagnosed echinococcosis appeared a decline in Sichuan Province year by year, and the areas with a high detection rate of cases with newly diagnosed echinococcosis were mainly located in western, northwestern and northern parts of Sichuan Province, while the areas with a low detection rate were predominantly found in the southern and eastern parts of the province. The global Moran’s I values were 0.19, 0.22, 0.17, 0.44, 0.48, 0.31 and 0.16 for the detection rate of cases with newly diagnosed echinococcosis in Sichuan Province from 2010 to 2016 (all Z scores > 1.96, all P values < 0.05), suggesting spatial aggregation distribution during this period. Local spatial autocorrelation analysis revealed that the“high-high”areas and“low-low”areas for the detection rate of cases with newly diagnosed echinococcosis all showed an aggregation tendency. Conclusions The detection rate of cases with newly diagnosed echinococcosis decreases in Sichuan Province from 2007 to 2017 year by year, and shows a spatial aggregation. The echinococcosis control activities should be intensified in Shiqu, Seda, Dege, Ganzi and Baiyu counties.

Spatial and temporal distribution of newly diagnosed echinococcosis patients in Sichuan Province from 2007 to 2017 / 中国血吸虫病防治杂志

Objective To explore the spatial and temporal distribution characteristics of cases with newly diagnosed echinococcosis in Sichuan Province from 2007 to 2017, so as to provide reference for the formulation of echinococcosis prevention and control strategies and for the identification of key areas. Methods The spatial distribution maps of detection of cases with newly diagnosed echinococcosis were plotted in Sichuan Province from 2007 to 2017, and the spatial distribution characteristics and epidemic trends were analyzed. Results From 2007 to 2017, the detection rate of cases with newly diagnosed echinococcosis appeared a decline in Sichuan Province year by year, and the areas with a high detection rate of cases with newly diagnosed echinococcosis were mainly located in western, northwestern and northern parts of Sichuan Province, while the areas with a low detection rate were predominantly found in the southern and eastern parts of the province. The global Moran’s I values were 0.19, 0.22, 0.17, 0.44, 0.48, 0.31 and 0.16 for the detection rate of cases with newly diagnosed echinococcosis in Sichuan Province from 2010 to 2016 (all Z scores > 1.96, all P values < 0.05), suggesting spatial aggregation distribution during this period. Local spatial autocorrelation analysis revealed that the“high-high”areas and“low-low”areas for the detection rate of cases with newly diagnosed echinococcosis all showed an aggregation tendency. Conclusions The detection rate of cases with newly diagnosed echinococcosis decreases in Sichuan Province from 2007 to 2017 year by year, and shows a spatial aggregation. The echinococcosis control activities should be intensified in Shiqu, Seda, Dege, Ganzi and Baiyu counties.

Time series analysis and spatial autocorrelation analysis of dengue data in China from 2011 to 2018 / 中华疾病控制杂志

Objective To understand the spatial and temporal distribution characteristics of dengue fever in China from 2011 to 2018, and predict the incidence of dengue fever in China in 2019. Methods Based on the case data of dengue fever in China from 2011 to 2018 in the Chinese Disease Prevention and Control Information System, the trend of dengue fever was described and predicted by using the autoregressive integrated moving average model (ARIMA) with R 3.6.0 software. Based on the data of the incidence of dengue fever in the country, provinces and cities from 2011 to 2016 provided by the national scientific data sharing platform for population and health, global and local spatial autocorrelation analysis was performed using GeoDa 1.12 software to determine the dengue fever hotspots. Results The incidence of dengue fever was 14 302 in 2019, showing no disease outbreaks. The incidence of dengue fever in 2012(Moran’s I=-0.088, P=0.037), 2013(Moran’s I=-0.121, P=0.040) and 2014(Moran’s I=-0.076, P=0.045) showed a global spatial negatively correlaton. In 2016(Moran’s I=0.078, P=0.048), the incidence of dengue fever was positively correlated with global space. The results of local autocorrelation analysis showed that the high incidence of dengue fever was mainly in the southeast coastal areas of China. Conclusions In 2019, the epidemic of dengue fever in China showed no obvious fluctuation trend, and the epidemic situation showed spatial clustering distribution.

Temporal and spatial distribution of Schistosoma infection of population and its risk factors in Eastern Dongting Lake area in 2012 and 2014 / 中国血吸虫病防治杂志

Objective To investigate the temporal and spatial distribution of Schistosoma infection of population and its risk factors in Eastern Dongting Lake area in 2012 and 2014,so as to provide the reference for formulating effective intervention mea-sures. Methods Junshan District was selected as a study field in Eastern Dongting Lake area. The method of spatial autocorre-lation analysis was applied to analyze the change of spatial distribution of Schistosoma infection in Junshan District in 2012 and 2014. The spatial regression model was fitted to detect the risk factors for human infection. Results The livestock infection rate in 2013 was lower than that in 2011. The average infection rate of schistosome was reduced to 0.55%in 2014. The spatial auto-correlation existed on the distribution of schistosomiasis in Junshan District in both 2012 and 2014 and 4 high incidence villages were identified. The results of the spatial error model showed that the prevalence of human infection was positively correlated with the infection rate of the livestock and the area of the susceptible environment in 2012. The spatial lag model showed that the prevalence of human schistosomiasis was positively correlated with the area of the susceptible environment ,but not with the in-fection rate of livestock. Conclusion The measures involving grazing prohibition and phasing out cattle and sheep are remark-ably effective and should continue on the basis of the current spatial distribution of schistosomiasis in this area.

Analysis on spatial distribution of tuberculosis in China, 2012-2014 / 中华流行病学杂志

Objective To analyze the spatial distribution of the incidence of tuberculosis (TB)in China from 2012 to 2014 and provide evidence for the prevention and control of TB.Methods The database of TB in China from 2012 to 2014 was established by using geographical information system,the spatial distribution map was drawn,trend analysis and spatial autocorrelation analysis were conducted to explore the spatial distribution pattern of TB and identify hot areas.Results The trend surface analysis showed that the incidence of TB decreased gradually from the west to the east in China,and the U type curve could reflect the TB distribution from the south to the north;Global spatial autocorrelation analysis showed the 2012-2014 global Moran's I were 0.366,0.364 and 0.358(P＜0.01),suggesting that the incidence of TB had a spatial clustering in China;Local Getis-OrdGi spatial autocorrelation analysis by ArcGIS software showed that there was 11 cluster areas,3 high incidence areas (Xinjiang,Tibet,Qinghai) and 8 low incidence areas (Beijing,Tianjin,Shanghai,Hebei,Inner Mongolia,Shanxi,Shandong,Jiangsu).Conclusion The incidence of TB had obviously spatial clustering characteristic,the areas at high risk were mainly in the northwestern and plateau area in China.

Analysis on spatial distribution of tuberculosis in China, 2012-2014 / 中华流行病学杂志

Objective To analyze the spatial distribution of the incidence of tuberculosis (TB)in China from 2012 to 2014 and provide evidence for the prevention and control of TB.Methods The database of TB in China from 2012 to 2014 was established by using geographical information system,the spatial distribution map was drawn,trend analysis and spatial autocorrelation analysis were conducted to explore the spatial distribution pattern of TB and identify hot areas.Results The trend surface analysis showed that the incidence of TB decreased gradually from the west to the east in China,and the U type curve could reflect the TB distribution from the south to the north;Global spatial autocorrelation analysis showed the 2012-2014 global Moran's I were 0.366,0.364 and 0.358(P＜0.01),suggesting that the incidence of TB had a spatial clustering in China;Local Getis-OrdGi spatial autocorrelation analysis by ArcGIS software showed that there was 11 cluster areas,3 high incidence areas (Xinjiang,Tibet,Qinghai) and 8 low incidence areas (Beijing,Tianjin,Shanghai,Hebei,Inner Mongolia,Shanxi,Shandong,Jiangsu).Conclusion The incidence of TB had obviously spatial clustering characteristic,the areas at high risk were mainly in the northwestern and plateau area in China.

Spatial autocorrelation analysis of brucellosis in Zibo of Shandong Province, 2013-2015 / 中华地方病学杂志

Objective To explore the spatial distribution and spatial clustering of brucellosis in Zibo,2013-2015.Methods Spatial autocorrelation analysis was used to analyze the surveillance data of brucellosis at town level.Township as a spatial analysis unit,spatial distribution of brucellosis in small scale in Zibo City was analyzed.Results The global Moran's I indexes of brucellosis were not significantly different in 2013-2015.According to local Moran's Ⅰ statistic,the high-high regions were Xindian Street and Jiangjunlu Street in 2013.The high-high regions were Tangfang Town,Jinshan Town,Luocun Town,Zhaili Town,Xihe Town and Longquan Town in 2014.The high-high region was Wangcun Town in 2015.Condusions Our study has showed that the spatial distribution of brucellosis is local clustered in Zibo.The detection of hotspots could provide guidance for formulating regional prevention and control strategies.

Spatial distribution characteristics analysis on multidrug-resistant tuberculosis cases in Zhejiang province, 2010-2012 / 中华流行病学杂志

Objective To analyze the spatial distribution characteristics ofmultidrug-resistant (MDR) tuberculosis (TB) cases in Zhejiang province in 2010-2012.Methods Data on MDR-TB cases in Zhejiang province were collected and linked to the digital map at the county and district levels.ArcGIS 10.0 software was used for spatial analysis.Results Results from the spatial autocorrelation analysis showed that spatial aggregation appeared in MDR-TB distribution during 2010-2012 while local Moran's I spatial autocorrelation analysis identified several "high incidence regions" (Wuxing,Deqing,Yuhang,Gongshu,Jianggan,Xiaoshan,Yuecheng,Shaoxing Shengzhou,Changshan,Kecheng),and "low incidence region" (Haishu).Through Getis-Ord General G spatial autocorrelation analysis,18 "positive hotspots" (Wuxing,Nanxun,Deqing,Yuhang,Shangcheng,Xiacheng,Gongshu,Jianggan,Binjiang Xiaoshan Xihu,Haining,Yuecheng,Shaoxing,Zhuji,Shengzhou,Kecheng and Suichang) and 11 "negative hotspots" (Nanhu,Haiyan,Cixi,Dinghai,Zhenhai,Jiangbei,Jiangdong,Beilun,Yinzhou,Fenghua,and Yueqing) were identified.Conclusions Spatial analysis on MDR-TB incidence implied the spatial aggregation in Zhejiang province.Data showed that the hotspots with high population density and human movement were under progressive expansion.

Spatial distribution characteristics analysis on multidrug-resistant tuberculosis cases in Zhejiang province, 2010-2012 / 中华流行病学杂志

Objective To analyze the spatial distribution characteristics ofmultidrug-resistant (MDR) tuberculosis (TB) cases in Zhejiang province in 2010-2012.Methods Data on MDR-TB cases in Zhejiang province were collected and linked to the digital map at the county and district levels.ArcGIS 10.0 software was used for spatial analysis.Results Results from the spatial autocorrelation analysis showed that spatial aggregation appeared in MDR-TB distribution during 2010-2012 while local Moran's I spatial autocorrelation analysis identified several "high incidence regions" (Wuxing,Deqing,Yuhang,Gongshu,Jianggan,Xiaoshan,Yuecheng,Shaoxing Shengzhou,Changshan,Kecheng),and "low incidence region" (Haishu).Through Getis-Ord General G spatial autocorrelation analysis,18 "positive hotspots" (Wuxing,Nanxun,Deqing,Yuhang,Shangcheng,Xiacheng,Gongshu,Jianggan,Binjiang Xiaoshan Xihu,Haining,Yuecheng,Shaoxing,Zhuji,Shengzhou,Kecheng and Suichang) and 11 "negative hotspots" (Nanhu,Haiyan,Cixi,Dinghai,Zhenhai,Jiangbei,Jiangdong,Beilun,Yinzhou,Fenghua,and Yueqing) were identified.Conclusions Spatial analysis on MDR-TB incidence implied the spatial aggregation in Zhejiang province.Data showed that the hotspots with high population density and human movement were under progressive expansion.