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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 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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@#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 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 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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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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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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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 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 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 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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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.
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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.
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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.
Subject(s)
Animals , China/epidemiology , Ecosystem , Gastropoda , Lakes , Rivers , Schistosoma japonicumABSTRACT
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.
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Objective To explore spatial clustering of mumps in Hubei Province during 2010-2020, and to provide evidence for mumps prevention and control. Methods The surveillance data of mumps during 2010-2020 in Hubei Province was obtained from the national infectious diseases reporting information system. Trend surface analysis and spatial autocorrelation analysis of mumps incidences at county/district levels were performed using ArcGIS10.5 software. Results Mumps incidence rates in Hubei Province during 2010-2018 ranged from 8.70 per 100 000 to 44.99 per 100 000. The trend surface analysis showed that mumps incidences gradually decreased from west to east, and was low in the middle and high at the north-south direction. Global spatial autocorrelation showed that there were positive spatial correlations in every year except 2012 and 2014 (Morans I> 0, P <0.05). Local autocorrelation analysis showed that the hotspots of mumps incidences varied every year from 2010 to 2020. Conclusions According to the spatial analysis, mumps incidences had obvious spatial clustering in Hubei Province. The hotspots were mainly concentrated in the northwestern region of Hubei, but the hot spots also extended to the urban areas of eastern, central and northern Hubei. It is necessary to take appropriate prevention and control measures in the high-incidence areas.
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Objective To analyze the spatial and temporal characteristics of hand, foot and mouth disease (HFMD) in Hunan Province from 2016 to 2020. Methods The data of HFMD in Hunan Province from 2016 to 2020 were collected from China's Disease Prevention and Control Information System. HFMD spatial autocorrelation analysis was conducted by ArcGIS 10.2 software at county level, and spatial-temporal scan statistical analysis was performed by SaTScan 9.7 software. Results A total of 714 157 cases was reported in Hunan Province during 2016-2020, with an average annual incidence rate of 208.36/100 000. Global spatial autocorrelation showed that HFMD had a positive spatial correlation on the county scale in Hunan Province during this period. Local spatial autocorrelation indicated that the hot spots were mainly concentrated in the north of central Hunan, the east of central Hunan and the west of Hunan. Spatial-temporal scanning analysis revealed the first class clusters (RR = 6.65, P< 0.001) covering 34 counties in northern and central Hunan, mainly distributed in Yueyang City, Changsha City, Zhuzhou City, Yiyang City and Xiangtan City from May 2018 to June, and the second class clusters (RR = 3.02, P < 0.001) covering 40 counties in western Hunan and central and southwest Hunan from April 2016 to June 2016. Conclusion HFMD incidence exhibits seasonal and regional characteristics in Hunan Province. The prevention and control of HFMD should be guided by combining the characteristics of spatial-temporal clustering.
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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: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.