1.Epidemiological investigation of a cluster of rural severe fever with thrombocytopenia syndrome cases and tick ecological monitoring results in Zibo City
Jun DU ; Ai-min FENG ; Bao-qiang CUI ; Tao SUN ; Yi-chuan YANG ; Yan-dong WANG
Acta Parasitologica et Medica Entomologica Sinica 2026;33(2):128-133
Objective To understand the epidemiological characteristics of a clustered outbreak of severe fever with thrombocytopenia syndrome(SFTS)as well as the ecological tick monitoring results for Zibo City, and to provide a scientific basis for formulating prevention and control strategies. Methods A case definition was cited before epidemic investigations were performed. Epidemiological investigations were performed on the index cases and their close contacts. Blood samples were collected from cases and close contacts, and quantitative real-time RT-PCR was used to detect SFTS virus(SFTSV)nucleic acid sequences. A retrospective cohort study was conducted to analyze risk factors and develop prevention and control strategies. Results This clustered outbreak involved two index cases and six close contacts with no deaths. Case A exhibited symptom onset on July 26. SFTS was confirmed on August 2. Case B exhibited symptom onset on August 1, and SFTS was confirmed on August 3. Patient B reported a recent history of tick bites. In both index cases, the incubation period for SFTS was inferred to be 7-12 days. The time interval from symptom onset to clinical diagnosis in the two cases ranged from 2-8 days, with an average period of 5 days. SFTSV nucleic acid test result were positive for both patients, whereas all six close contacts tested negative. All captured ticks tested negative for SFTSV using quantitative real-time RT-PCR. The densities of parasitic and free-living ticks in the emergency monitoring area around the cases were 12.60 and 4.65 ticks/(flag·100 m). In 2024, the average parasitic tick index and free-living tick density index were 4.21 and 2.43 ticks/(flag ·100 m)in Yiyuan County, respectively. Conclusions No evidence of human-to-human transmission was found in the assessed SFTS clusters. The infections were likely acquired through tick bites during fieldwork, and the risk of a subsequent outbreak spreading was low.
2.Association of Body Mass Index with All-Cause Mortality and Cause-Specific Mortality in Rural China: 10-Year Follow-up of a Population-Based Multicenter Prospective Study.
Juan Juan HUANG ; Yuan Zhi DI ; Ling Yu SHEN ; Jian Guo LIANG ; Jiang DU ; Xue Fang CAO ; Wei Tao DUAN ; Ai Wei HE ; Jun LIANG ; Li Mei ZHU ; Zi Sen LIU ; Fang LIU ; Shu Min YANG ; Zu Hui XU ; Cheng CHEN ; Bin ZHANG ; Jiao Xia YAN ; Yan Chun LIANG ; Rong LIU ; Tao ZHU ; Hong Zhi LI ; Fei SHEN ; Bo Xuan FENG ; Yi Jun HE ; Zi Han LI ; Ya Qi ZHAO ; Tong Lei GUO ; Li Qiong BAI ; Wei LU ; Qi JIN ; Lei GAO ; He Nan XIN
Biomedical and Environmental Sciences 2025;38(10):1179-1193
OBJECTIVE:
This study aimed to explore the association between body mass index (BMI) and mortality based on the 10-year population-based multicenter prospective study.
METHODS:
A general population-based multicenter prospective study was conducted at four sites in rural China between 2013 and 2023. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to assess the association between BMI and mortality. Stratified analyses were performed based on the individual characteristics of the participants.
RESULTS:
Overall, 19,107 participants with a sum of 163,095 person-years were included and 1,910 participants died. The underweight (< 18.5 kg/m 2) presented an increase in all-cause mortality (adjusted hazards ratio [ aHR] = 2.00, 95% confidence interval [ CI]: 1.66-2.41), while overweight (≥ 24.0 to < 28.0 kg/m 2) and obesity (≥ 28.0 kg/m 2) presented a decrease with an aHR of 0.61 (95% CI: 0.52-0.73) and 0.51 (95% CI: 0.37-0.70), respectively. Overweight ( aHR = 0.76, 95% CI: 0.67-0.86) and mild obesity ( aHR = 0.72, 95% CI: 0.59-0.87) had a positive impact on mortality in people older than 60 years. All-cause mortality decreased rapidly until reaching a BMI of 25.7 kg/m 2 ( aHR = 0.95, 95% CI: 0.92-0.98) and increased slightly above that value, indicating a U-shaped association. The beneficial impact of being overweight on mortality was robust in most subgroups and sensitivity analyses.
CONCLUSION
This study provides additional evidence that overweight and mild obesity may be inversely related to the risk of death in individuals older than 60 years. Therefore, it is essential to consider age differences when formulating health and weight management strategies.
Humans
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Body Mass Index
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China/epidemiology*
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Male
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Female
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Middle Aged
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Prospective Studies
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Rural Population/statistics & numerical data*
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Aged
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Follow-Up Studies
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Adult
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Mortality
;
Cause of Death
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Obesity/mortality*
;
Overweight/mortality*
3.Antimicrobial resistance profile of clinical isolates in hospitals across China:report from the CHINET Antimicrobial Resistance Surveillance Program,2023
Yan GUO ; Fupin HU ; Demei ZHU ; Fu WANG ; Xiaofei JIANG ; Yingchun XU ; Xiaojiang ZHANG ; Fengbo ZHANG ; Ping JI ; Yi XIE ; Yuling XIAO ; Chuanqing WANG ; Pan FU ; Yuanhong XU ; Ying HUANG ; Ziyong SUN ; Zhongju CHEN ; Jingyong SUN ; Qing CHEN ; Yunzhuo CHU ; Sufei TIAN ; Zhidong HU ; Jin LI ; Yunsong YU ; Jie LIN ; Bin SHAN ; Yunmin XU ; Sufang GUO ; Yanyan WANG ; Lianhua WEI ; Keke LI ; Hong ZHANG ; Fen PAN ; Yunjian HU ; Xiaoman AI ; Chao ZHUO ; Danhong SU ; Dawen GUO ; Jinying ZHAO ; Hua YU ; Xiangning HUANG ; Wen'en LIU ; Yanming LI ; Yan JIN ; Chunhong SHAO ; Xuesong XU ; Wei LI ; Shanmei WANG ; Yafei CHU ; Lixia ZHANG ; Juan MA ; Shuping ZHOU ; Yan ZHOU ; Lei ZHU ; Jinhua MENG ; Fang DONG ; Zhiyong LÜ ; Fangfang HU ; Han SHEN ; Wanqing ZHOU ; Wei JIA ; Gang LI ; Jinsong WU ; Yuemei LU ; Jihong LI ; Qian SUN ; Jinju DUAN ; Jianbang KANG ; Xiaobo MA ; Yanqing ZHENG ; Ruyi GUO ; Yan ZHU ; Yunsheng CHEN ; Qing MENG ; Shifu WANG ; Xuefei HU ; Hua FANG ; Penghui ZHANG ; Bixia YU ; Ping GONG ; Haixia SHI ; Kaizhen WEN ; Yirong ZHANG ; Xiuli YANG ; Yiqin ZHAO ; Longfeng LIAO ; Jinhua WU ; Hongqin GU ; Lin JIANG ; Meifang HU ; Wen HE ; Jiao FENG ; Lingling YOU ; Dongmei WANG ; Dong'e WANG ; Yanyan LIU ; Yong AN ; Wenhui HUANG ; Juan LI ; Quangui SHI ; Juan YANG ; Abulimiti REZIWAGULI ; Lili HUANG ; Xuejun SHAO ; Xiaoyan REN ; Dong LI ; Qun ZHANG ; Xue CHEN ; Rihai LI ; Jieli XU ; Kaijie GAO ; Lu XU ; Lin LIN ; Zhuo ZHANG ; Jianlong LIU ; Min FU ; Yinghui GUO ; Wenchao ZHANG ; Zengguo WANG ; Kai JIA ; Yun XIA ; Shan SUN ; Huimin YANG ; Yan MIAO ; Jianping WANG ; Mingming ZHOU ; Shihai ZHANG ; Hongjuan LIU ; Nan CHEN ; Chan LI ; Cunshan KOU ; Shunhong XUE ; Jilu SHEN ; Wanqi MEN ; Peng WANG ; Xiaowei ZHANG ; Xiaoyan ZENG ; Wen LI ; Yan GENG ; Zeshi LIU
Chinese Journal of Infection and Chemotherapy 2024;24(6):627-637
Objective To monitor the susceptibility of clinical isolates to antimicrobial agents in healthcare facilities in major regions of China in 2023.Methods Clinical isolates collected from 73 hospitals across China were tested for antimicrobial susceptibility using a unified protocol based on disc diffusion method or automated testing systems.Results were interpreted using the 2023 Clinical & Laboratory Standards Institute (CLSI) breakpoints.Results A total of 445199 clinical isolates were collected in 2023,of which 29.0% were gram-positive and 71.0% were gram-negative.The prevalence of methicillin-resistant strains in Staphylococcus aureus,Staphylococcus epidermidis and other coagulase-negative Staphylococcus species (excluding Staphylococcus pseudintermedius and Staphylococcus schleiferi) (MRSA,MRSE and MRCNS) was 29.6%,81.9% and 78.5%,respectively.Methicillin-resistant strains showed significantly higher resistance rates to most antimicrobial agents than methicillin-susceptible strains (MSSA,MSSE and MSCNS).Overall,92.9% of MRSA strains were susceptible to trimethoprim-sulfamethoxazole and 91.4% of MRSE strains were susceptible to rifampicin.No vancomycin-resistant strains were found.Enterococcus faecalis had significantly lower resistance rates to most antimicrobial agents tested than Enterococcus faecium.A few vancomycin-resistant strains were identified in both E.faecalis and E.faecium.The prevalence of penicillin-susceptible Streptococcus pneumoniae was 93.1% in the isolates from children and and 95.9% in the isolates from adults.The resistance rate to carbapenems was lower than 15.0% for most Enterobacterales species except for Klebsiella,22.5% and 23.6% of which were resistant to imipenem and meropenem,respectively .Most Enterobacterales isolates were highly susceptible to tigecycline,colistin and polymyxin B,with resistance rates ranging from 0.6% to 10.0%.The resistance rate to imipenem and meropenem was 21.9% and 17.4% for Pseudomonas aeruginosa,respectively,and 67.5% and 68.1% for Acinetobacter baumannii,respectively.Conclusions Increasing resistance to the commonly used antimicrobial agents is still observed in clinical bacterial isolates.However,the prevalence of important crabapenem-resistant organisms such as crabapenem-resistant K.pneumoniae,P.aeruginosa,and A.baumannii showed a slightly decreasing trend.This finding suggests that strengthening bacterial resistance surveillance and multidisciplinary linkage are important for preventing the occurrence and development of bacterial resistance.
4.Distribution and antimicrobial resistance profiles of clinical isolates from blood samples:results from China Antimicrobial Surveillance Network (CHINET) from 2015 to 2021
Min ZHONG ; Xiangning HUANG ; Hua YU ; Yang YANG ; Fupin HU ; Demei ZHU ; Yi XIE ; Mei KANG ; Shanmei WANG ; Yafei CHU ; Wenen LIU ; Yanming LI ; Dawen GUO ; Jinying ZHAO ; Yuanhong XU ; Ying HUANG ; Yunzhuo CHU ; Sufei TIAN ; Ziyong SUN ; Zhongju CHEN ; Yunsong YU ; Jie LIN ; Jihong LI ; Yingchun XU ; Xiaojiang ZHANG ; Hui LI ; Ping JI ; Fang DONG ; Zhiyong LÜ ; Han SHEN ; Wanqing ZHOU ; Sufang GUO ; Zhidong HU ; Jin LI ; Chuanqing WANG ; Pan FU ; Hong ZHANG ; Chun WANG ; Chao ZHUO ; Danhong SU ; Bin SHAN ; Yan DU ; Lixia ZHANG ; Juan MA ; Yuxing NI ; Jingyong SUN ; Jinju DUAN ; Jianbang KANG ; Yan JIN ; Chunhong SHAO ; Wei JIA ; Gang LI ; Xuesong XU ; Chao YAN ; Yunjian HU ; Xiaoman AI ; Jinsong WU ; Yuemei LU ; Fangfang HU ; Lianhua WEI ; Fengmei ZOU ; Lei ZHU ; Jinhua MENG ; Shuping ZHOU ; Yan ZHOU ; Shifu WANG ; Xiaobo MA ; Yanping ZHENG ; Kaizhen WEN ; Yirong ZHANG ; Yunsheng CHEN ; Qing MENG ; Xuefei HU ; Ruizhong WANG ; Hua FANG ; Ruyi GUO ; Yan ZHU ; Jilu SHEN ; Wenhui HUANG ; Bixia YU ; Jiao FENG ; Yong ZHAO ; Ping GONG ; Shunhong XUE ; Hongqin GU ; Wen HE ; Jiangshan LIU ; Chunlei YUE ; Longfeng LIAO ; Lin JIANG
Chinese Journal of Infection and Chemotherapy 2024;24(6):664-677
Objective To investigate the distribution and antimicrobial resistance of bacterial isolates from blood samples in the hospitals participating in China Antimicrobial Surveillance Network (CHINET) from 2015 to 2021.Methods Bacterial strains isolated from blood samples were collected from 52 medical centers participating in CHINET from 2015 to 2021 for analysis of bacetrial distribution and antimicrobial resistance.Results A total of 153591 isolates were collected,48.8% of which were gram-positive bacteria and 51.2% were gram-negative bacteria.The top five bacterial strains were coagulase negative Staphylococcus (28.2%),Escherichia coli (20.7%),Klebsiella (13.7%),Enterococcus (7.2%),and Staphylococcus aureus (6.6%).Compard to female patients,male patients showed lower proportion of E.coli and higher proportions of other bacterial species in all the bacterial isolaets from blood samples.The proportions of Streptococcus pneumoniae and Salmonella in all the bacterial isolaets from blood samples were higher in children compared to adults.Enterobacterales species showed various resistance rates to antimicrobial agents.Overall,≥58.0%,≥36.8% and ≥56.8% of E.coli strains were resistant to cefotaxime,gentamicin and levofloxacin respectively over the 7-year period.However,less than 2.5% of the E.coli strains were resistant to carbapenems.K.pneumoniae showed higher resistance rates to imipenem and meropenem than other Enterobacterales species.During the 7-year period,the prevalence of imipenem-resistant and meropenem-resistant K.pneumoniae increased from 21.4% and 19.9% in 2015 to 25.7% and 26.6% in 2021,respectively.However,carbapenems still maintained good antibacterial activity against other Enterobacterales,associaetd with lower resistance rates.In the 7-year period,Acinetobacter baumannii showed a dwonward trend in the resistance rates to imipenem and meropenem,but remained 72.9% and 73.2% respectively in 2021.The prevalence of imipenem-resistant and meropenem-resistant P.aeruginosa decreased from 26.7% and 22.9% in 2015 to 18.5% and 14.7% in 2021,respectively.The prevalence of PRSP was 1.5% in the isolaets from adults and and 0.8% in the isolates from children.Less than 3.0% of the Enterococcus faecium and Enterococcus faecalis strains were resistant to vancomycin,teicolanin,or linezolid.The prevalence of methicillin-resistant S.aureus (MRSA) and coagulase negative Staphylococcus (MRCNS) was 32.1% and 81.0%,respectively.The prevalence of MRSA was relatively stable,28.5% in 2015 and 28.0% in 2021.Conclusions Coagulase negative Staphylococcus,E.coli and K.pneumoniae were the main bacterial species isolated from blood samples in the hospitals participaing in the CHINET from 2015 to 2021.Significant sex and age differences were found in the distribution of bcterial isolates from blood samples.The overall resistance rates of the top bacetrial strains from blood samples to antimicrobial agents showed a downward trend.Ongoing surveillance of antimicrobial resistance for the isolates from blood samples is still essential for prescribing rational antimicrobial therapies and curbing bacterial resistance.
5.Distribution and antimicrobial resistance profiles of clinical isolates from blood samples:results from China Antimicrobial Surveillance Network (CHINET) from 2015 to 2021
Min ZHONG ; Xiangning HUANG ; Hua YU ; Yang YANG ; Fupin HU ; Demei ZHU ; Yi XIE ; Mei KANG ; Shanmei WANG ; Yafei CHU ; Wenen LIU ; Yanming LI ; Dawen GUO ; Jinying ZHAO ; Yuanhong XU ; Ying HUANG ; Yunzhuo CHU ; Sufei TIAN ; Ziyong SUN ; Zhongju CHEN ; Yunsong YU ; Jie LIN ; Jihong LI ; Yingchun XU ; Xiaojiang ZHANG ; Hui LI ; Ping JI ; Fang DONG ; Zhiyong LÜ ; Han SHEN ; Wanqing ZHOU ; Sufang GUO ; Zhidong HU ; Jin LI ; Chuanqing WANG ; Pan FU ; Hong ZHANG ; Chun WANG ; Chao ZHUO ; Danhong SU ; Bin SHAN ; Yan DU ; Lixia ZHANG ; Juan MA ; Yuxing NI ; Jingyong SUN ; Jinju DUAN ; Jianbang KANG ; Yan JIN ; Chunhong SHAO ; Wei JIA ; Gang LI ; Xuesong XU ; Chao YAN ; Yunjian HU ; Xiaoman AI ; Jinsong WU ; Yuemei LU ; Fangfang HU ; Lianhua WEI ; Fengmei ZOU ; Lei ZHU ; Jinhua MENG ; Shuping ZHOU ; Yan ZHOU ; Shifu WANG ; Xiaobo MA ; Yanping ZHENG ; Kaizhen WEN ; Yirong ZHANG ; Yunsheng CHEN ; Qing MENG ; Xuefei HU ; Ruizhong WANG ; Hua FANG ; Ruyi GUO ; Yan ZHU ; Jilu SHEN ; Wenhui HUANG ; Bixia YU ; Jiao FENG ; Yong ZHAO ; Ping GONG ; Shunhong XUE ; Hongqin GU ; Wen HE ; Jiangshan LIU ; Chunlei YUE ; Longfeng LIAO ; Lin JIANG
Chinese Journal of Infection and Chemotherapy 2024;24(6):664-677
Objective To investigate the distribution and antimicrobial resistance of bacterial isolates from blood samples in the hospitals participating in China Antimicrobial Surveillance Network (CHINET) from 2015 to 2021.Methods Bacterial strains isolated from blood samples were collected from 52 medical centers participating in CHINET from 2015 to 2021 for analysis of bacetrial distribution and antimicrobial resistance.Results A total of 153591 isolates were collected,48.8% of which were gram-positive bacteria and 51.2% were gram-negative bacteria.The top five bacterial strains were coagulase negative Staphylococcus (28.2%),Escherichia coli (20.7%),Klebsiella (13.7%),Enterococcus (7.2%),and Staphylococcus aureus (6.6%).Compard to female patients,male patients showed lower proportion of E.coli and higher proportions of other bacterial species in all the bacterial isolaets from blood samples.The proportions of Streptococcus pneumoniae and Salmonella in all the bacterial isolaets from blood samples were higher in children compared to adults.Enterobacterales species showed various resistance rates to antimicrobial agents.Overall,≥58.0%,≥36.8% and ≥56.8% of E.coli strains were resistant to cefotaxime,gentamicin and levofloxacin respectively over the 7-year period.However,less than 2.5% of the E.coli strains were resistant to carbapenems.K.pneumoniae showed higher resistance rates to imipenem and meropenem than other Enterobacterales species.During the 7-year period,the prevalence of imipenem-resistant and meropenem-resistant K.pneumoniae increased from 21.4% and 19.9% in 2015 to 25.7% and 26.6% in 2021,respectively.However,carbapenems still maintained good antibacterial activity against other Enterobacterales,associaetd with lower resistance rates.In the 7-year period,Acinetobacter baumannii showed a dwonward trend in the resistance rates to imipenem and meropenem,but remained 72.9% and 73.2% respectively in 2021.The prevalence of imipenem-resistant and meropenem-resistant P.aeruginosa decreased from 26.7% and 22.9% in 2015 to 18.5% and 14.7% in 2021,respectively.The prevalence of PRSP was 1.5% in the isolaets from adults and and 0.8% in the isolates from children.Less than 3.0% of the Enterococcus faecium and Enterococcus faecalis strains were resistant to vancomycin,teicolanin,or linezolid.The prevalence of methicillin-resistant S.aureus (MRSA) and coagulase negative Staphylococcus (MRCNS) was 32.1% and 81.0%,respectively.The prevalence of MRSA was relatively stable,28.5% in 2015 and 28.0% in 2021.Conclusions Coagulase negative Staphylococcus,E.coli and K.pneumoniae were the main bacterial species isolated from blood samples in the hospitals participaing in the CHINET from 2015 to 2021.Significant sex and age differences were found in the distribution of bcterial isolates from blood samples.The overall resistance rates of the top bacetrial strains from blood samples to antimicrobial agents showed a downward trend.Ongoing surveillance of antimicrobial resistance for the isolates from blood samples is still essential for prescribing rational antimicrobial therapies and curbing bacterial resistance.
6.Antimicrobial resistance profile of clinical isolates in hospitals across China:report from the CHINET Antimicrobial Resistance Surveillance Program,2023
Yan GUO ; Fupin HU ; Demei ZHU ; Fu WANG ; Xiaofei JIANG ; Yingchun XU ; Xiaojiang ZHANG ; Fengbo ZHANG ; Ping JI ; Yi XIE ; Yuling XIAO ; Chuanqing WANG ; Pan FU ; Yuanhong XU ; Ying HUANG ; Ziyong SUN ; Zhongju CHEN ; Jingyong SUN ; Qing CHEN ; Yunzhuo CHU ; Sufei TIAN ; Zhidong HU ; Jin LI ; Yunsong YU ; Jie LIN ; Bin SHAN ; Yunmin XU ; Sufang GUO ; Yanyan WANG ; Lianhua WEI ; Keke LI ; Hong ZHANG ; Fen PAN ; Yunjian HU ; Xiaoman AI ; Chao ZHUO ; Danhong SU ; Dawen GUO ; Jinying ZHAO ; Hua YU ; Xiangning HUANG ; Wen'en LIU ; Yanming LI ; Yan JIN ; Chunhong SHAO ; Xuesong XU ; Wei LI ; Shanmei WANG ; Yafei CHU ; Lixia ZHANG ; Juan MA ; Shuping ZHOU ; Yan ZHOU ; Lei ZHU ; Jinhua MENG ; Fang DONG ; Zhiyong LÜ ; Fangfang HU ; Han SHEN ; Wanqing ZHOU ; Wei JIA ; Gang LI ; Jinsong WU ; Yuemei LU ; Jihong LI ; Qian SUN ; Jinju DUAN ; Jianbang KANG ; Xiaobo MA ; Yanqing ZHENG ; Ruyi GUO ; Yan ZHU ; Yunsheng CHEN ; Qing MENG ; Shifu WANG ; Xuefei HU ; Hua FANG ; Penghui ZHANG ; Bixia YU ; Ping GONG ; Haixia SHI ; Kaizhen WEN ; Yirong ZHANG ; Xiuli YANG ; Yiqin ZHAO ; Longfeng LIAO ; Jinhua WU ; Hongqin GU ; Lin JIANG ; Meifang HU ; Wen HE ; Jiao FENG ; Lingling YOU ; Dongmei WANG ; Dong'e WANG ; Yanyan LIU ; Yong AN ; Wenhui HUANG ; Juan LI ; Quangui SHI ; Juan YANG ; Abulimiti REZIWAGULI ; Lili HUANG ; Xuejun SHAO ; Xiaoyan REN ; Dong LI ; Qun ZHANG ; Xue CHEN ; Rihai LI ; Jieli XU ; Kaijie GAO ; Lu XU ; Lin LIN ; Zhuo ZHANG ; Jianlong LIU ; Min FU ; Yinghui GUO ; Wenchao ZHANG ; Zengguo WANG ; Kai JIA ; Yun XIA ; Shan SUN ; Huimin YANG ; Yan MIAO ; Jianping WANG ; Mingming ZHOU ; Shihai ZHANG ; Hongjuan LIU ; Nan CHEN ; Chan LI ; Cunshan KOU ; Shunhong XUE ; Jilu SHEN ; Wanqi MEN ; Peng WANG ; Xiaowei ZHANG ; Xiaoyan ZENG ; Wen LI ; Yan GENG ; Zeshi LIU
Chinese Journal of Infection and Chemotherapy 2024;24(6):627-637
Objective To monitor the susceptibility of clinical isolates to antimicrobial agents in healthcare facilities in major regions of China in 2023.Methods Clinical isolates collected from 73 hospitals across China were tested for antimicrobial susceptibility using a unified protocol based on disc diffusion method or automated testing systems.Results were interpreted using the 2023 Clinical & Laboratory Standards Institute (CLSI) breakpoints.Results A total of 445199 clinical isolates were collected in 2023,of which 29.0% were gram-positive and 71.0% were gram-negative.The prevalence of methicillin-resistant strains in Staphylococcus aureus,Staphylococcus epidermidis and other coagulase-negative Staphylococcus species (excluding Staphylococcus pseudintermedius and Staphylococcus schleiferi) (MRSA,MRSE and MRCNS) was 29.6%,81.9% and 78.5%,respectively.Methicillin-resistant strains showed significantly higher resistance rates to most antimicrobial agents than methicillin-susceptible strains (MSSA,MSSE and MSCNS).Overall,92.9% of MRSA strains were susceptible to trimethoprim-sulfamethoxazole and 91.4% of MRSE strains were susceptible to rifampicin.No vancomycin-resistant strains were found.Enterococcus faecalis had significantly lower resistance rates to most antimicrobial agents tested than Enterococcus faecium.A few vancomycin-resistant strains were identified in both E.faecalis and E.faecium.The prevalence of penicillin-susceptible Streptococcus pneumoniae was 93.1% in the isolates from children and and 95.9% in the isolates from adults.The resistance rate to carbapenems was lower than 15.0% for most Enterobacterales species except for Klebsiella,22.5% and 23.6% of which were resistant to imipenem and meropenem,respectively .Most Enterobacterales isolates were highly susceptible to tigecycline,colistin and polymyxin B,with resistance rates ranging from 0.6% to 10.0%.The resistance rate to imipenem and meropenem was 21.9% and 17.4% for Pseudomonas aeruginosa,respectively,and 67.5% and 68.1% for Acinetobacter baumannii,respectively.Conclusions Increasing resistance to the commonly used antimicrobial agents is still observed in clinical bacterial isolates.However,the prevalence of important crabapenem-resistant organisms such as crabapenem-resistant K.pneumoniae,P.aeruginosa,and A.baumannii showed a slightly decreasing trend.This finding suggests that strengthening bacterial resistance surveillance and multidisciplinary linkage are important for preventing the occurrence and development of bacterial resistance.
7.Genome sequence analysis of two SARS-CoV-2 virus infections in Inner Mongolia, 2022.
Guo Qing YANG ; Chao MIN ; Jian SONG ; Xiao Feng JIANG ; Hua YUE ; Xiao Wei NAN ; Zhen YAN ; Ai Tao LU ; Yan HAI ; Zhan Song ZHU
Chinese Journal of Preventive Medicine 2023;57(10):1630-1634
The target gene sequences of the novel coronaviruses obtained by sequencing were compared with the reference sequences to analyze the genetic variation of the two cases of the novel coronaviruses from Inner Mongolia Autonomous Region in 2022 and to explore the sources of infection. The results showed that the two sequences belonged to different evolutionary branches, Delta (AY.122) and Omicron (BA.1.1), respectively. hCoV-19/Inner Mongolia/IVDC-591/2022 had 48 single nucleotide polymorphisms on the genome sequences, sharing 40 nucleotide mutation sites with a Mongolian strain; hCoV-19/Inner Mongolia/IVDC-592/2022 genome shared 57 nucleotide mutation sites with a UK strain, and the nucleotide mutation site identity was 100% (57/57). Phylogenetic analysis showed that the target gene sequences were not directly related to domestic novel coronavirus sequences during the same period, but were related to isolates from Europe and Mongolia.
Humans
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COVID-19
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SARS-CoV-2/genetics*
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Phylogeny
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Genome, Viral
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Nucleotides
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Sequence Analysis
8.Genome sequence analysis of two SARS-CoV-2 virus infections in Inner Mongolia, 2022.
Guo Qing YANG ; Chao MIN ; Jian SONG ; Xiao Feng JIANG ; Hua YUE ; Xiao Wei NAN ; Zhen YAN ; Ai Tao LU ; Yan HAI ; Zhan Song ZHU
Chinese Journal of Preventive Medicine 2023;57(10):1630-1634
The target gene sequences of the novel coronaviruses obtained by sequencing were compared with the reference sequences to analyze the genetic variation of the two cases of the novel coronaviruses from Inner Mongolia Autonomous Region in 2022 and to explore the sources of infection. The results showed that the two sequences belonged to different evolutionary branches, Delta (AY.122) and Omicron (BA.1.1), respectively. hCoV-19/Inner Mongolia/IVDC-591/2022 had 48 single nucleotide polymorphisms on the genome sequences, sharing 40 nucleotide mutation sites with a Mongolian strain; hCoV-19/Inner Mongolia/IVDC-592/2022 genome shared 57 nucleotide mutation sites with a UK strain, and the nucleotide mutation site identity was 100% (57/57). Phylogenetic analysis showed that the target gene sequences were not directly related to domestic novel coronavirus sequences during the same period, but were related to isolates from Europe and Mongolia.
Humans
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COVID-19
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SARS-CoV-2/genetics*
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Phylogeny
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Genome, Viral
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Nucleotides
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Sequence Analysis
9.Course of disease and related epidemiological parameters of COVID-19: a prospective study based on contact tracing cohort.
Yan ZHOU ; Wen Jia LIANG ; Zi Hui CHEN ; Tao LIU ; Tie SONG ; Shao Wei CHEN ; Ping WANG ; Jia Ling LI ; Yun Hua LAN ; Ming Ji CHENG ; Jin Xu HUANG ; Ji Wei NIU ; Jian Peng XIAO ; Jian Xiong HU ; Li Feng LIN ; Qiong HUANG ; Ai Ping DENG ; Xiao Hua TAN ; Min KANG ; Gui Min CHEN ; Mo Ran DONG ; Hao Jie ZHONG ; Wen Jun MA
Chinese Journal of Preventive Medicine 2022;56(4):474-478
Objective: To analyze the course of disease and epidemiological parameters of COVID-19 and provide evidence for making prevention and control strategies. Methods: To display the distribution of course of disease of the infectors who had close contacts with COVID-19 cases from January 1 to March 15, 2020 in Guangdong Provincial, the models of Lognormal, Weibull and gamma distribution were applied. A descriptive analysis was conducted on the basic characteristics and epidemiological parameters of course of disease. Results: In total, 515 of 11 580 close contacts were infected, with an attack rate about 4.4%, including 449 confirmed cases and 66 asymptomatic cases. Lognormal distribution was fitting best for latent period, incubation period, pre-symptomatic infection period of confirmed cases and infection period of asymptomatic cases; Gamma distribution was fitting best for infectious period and clinical symptom period of confirmed cases; Weibull distribution was fitting best for latent period of asymptomatic cases. The latent period, incubation period, pre-symptomatic infection period, infectious period and clinical symptoms period of confirmed cases were 4.50 (95%CI:3.86-5.13) days, 5.12 (95%CI:4.63-5.62) days, 0.87 (95%CI:0.67-1.07) days, 11.89 (95%CI:9.81-13.98) days and 22.00 (95%CI:21.24-22.77) days, respectively. The latent period and infectious period of asymptomatic cases were 8.88 (95%CI:6.89-10.86) days and 6.18 (95%CI:1.89-10.47) days, respectively. Conclusion: The estimated course of COVID-19 and related epidemiological parameters are similar to the existing data.
COVID-19
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Cohort Studies
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Contact Tracing
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Humans
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Incidence
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Prospective Studies
10.A multicenter epidemiological study of acute bacterial meningitis in children.
Cai Yun WANG ; Hong Mei XU ; Jiao TIAN ; Si Qi HONG ; Gang LIU ; Si Xuan WANG ; Feng GAO ; Jing LIU ; Fu Rong LIU ; Hui YU ; Xia WU ; Bi Quan CHEN ; Fang Fang SHEN ; Guo ZHENG ; Jie YU ; Min SHU ; Lu LIU ; Li Jun DU ; Pei LI ; Zhi Wei XU ; Meng Quan ZHU ; Li Su HUANG ; He Yu HUANG ; Hai Bo LI ; Yuan Yuan HUANG ; Dong WANG ; Fang WU ; Song Ting BAI ; Jing Jing TANG ; Qing Wen SHAN ; Lian Cheng LAN ; Chun Hui ZHU ; Yan XIONG ; Jian Mei TIAN ; Jia Hui WU ; Jian Hua HAO ; Hui Ya ZHAO ; Ai Wei LIN ; Shuang Shuang SONG ; Dao Jiong LIN ; Qiong Hua ZHOU ; Yu Ping GUO ; Jin Zhun WU ; Xiao Qing YANG ; Xin Hua ZHANG ; Ying GUO ; Qing CAO ; Li Juan LUO ; Zhong Bin TAO ; Wen Kai YANG ; Yong Kang ZHOU ; Yuan CHEN ; Li Jie FENG ; Guo Long ZHU ; Yan Hong ZHANG ; Ping XUE ; Xiao Qin LI ; Zheng Zhen TANG ; De Hui ZHANG ; Xue Wen SU ; Zheng Hai QU ; Ying ZHANG ; Shi Yong ZHAO ; Zheng Hong QI ; Lin PANG ; Cai Ying WANG ; Hui Ling DENG ; Xing Lou LIU ; Ying Hu CHEN ; Sainan SHU
Chinese Journal of Pediatrics 2022;60(10):1045-1053
Objective: To analyze the clinical epidemiological characteristics including composition of pathogens , clinical characteristics, and disease prognosis acute bacterial meningitis (ABM) in Chinese children. Methods: A retrospective analysis was performed on the clinical and laboratory data of 1 610 children <15 years of age with ABM in 33 tertiary hospitals in China from January 2019 to December 2020. Patients were divided into different groups according to age,<28 days group, 28 days to <3 months group, 3 months to <1 year group, 1-<5 years of age group, 5-<15 years of age group; etiology confirmed group and clinically diagnosed group according to etiology diagnosis. Non-numeric variables were analyzed with the Chi-square test or Fisher's exact test, while non-normal distrituction numeric variables were compared with nonparametric test. Results: Among 1 610 children with ABM, 955 were male and 650 were female (5 cases were not provided with gender information), and the age of onset was 1.5 (0.5, 5.5) months. There were 588 cases age from <28 days, 462 cases age from 28 days to <3 months, 302 cases age from 3 months to <1 year of age group, 156 cases in the 1-<5 years of age and 101 cases in the 5-<15 years of age. The detection rates were 38.8% (95/245) and 31.5% (70/222) of Escherichia coli and 27.8% (68/245) and 35.1% (78/222) of Streptococcus agalactiae in infants younger than 28 days of age and 28 days to 3 months of age; the detection rates of Streptococcus pneumonia, Escherichia coli, and Streptococcus agalactiae were 34.3% (61/178), 14.0% (25/178) and 13.5% (24/178) in the 3 months of age to <1 year of age group; the dominant pathogens were Streptococcus pneumoniae and the detection rate were 67.9% (74/109) and 44.4% (16/36) in the 1-<5 years of age and 5-<15 years of age . There were 9.7% (19/195) strains of Escherichia coli producing ultra-broad-spectrum β-lactamases. The positive rates of cerebrospinal fluid (CSF) culture and blood culture were 32.2% (515/1 598) and 25.0% (400/1 598), while 38.2% (126/330)and 25.3% (21/83) in CSF metagenomics next generation sequencing and Streptococcus pneumoniae antigen detection. There were 4.3% (32/790) cases of which CSF white blood cell counts were normal in etiology confirmed group. Among 1 610 children with ABM, main intracranial imaging complications were subdural effusion and (or) empyema in 349 cases (21.7%), hydrocephalus in 233 cases (14.5%), brain abscess in 178 cases (11.1%), and other cerebrovascular diseases, including encephalomalacia, cerebral infarction, and encephalatrophy, in 174 cases (10.8%). Among the 166 cases (10.3%) with unfavorable outcome, 32 cases (2.0%) died among whom 24 cases died before 1 year of age, and 37 cases (2.3%) had recurrence among whom 25 cases had recurrence within 3 weeks. The incidences of subdural effusion and (or) empyema, brain abscess and ependymitis in the etiology confirmed group were significantly higher than those in the clinically diagnosed group (26.2% (207/790) vs. 17.3% (142/820), 13.0% (103/790) vs. 9.1% (75/820), 4.6% (36/790) vs. 2.7% (22/820), χ2=18.71, 6.20, 4.07, all P<0.05), but there was no significant difference in the unfavorable outcomes, mortility, and recurrence between these 2 groups (all P>0.05). Conclusions: The onset age of ABM in children is usually within 1 year of age, especially <3 months. The common pathogens in infants <3 months of age are Escherichia coli and Streptococcus agalactiae, and the dominant pathogen in infant ≥3 months is Streptococcus pneumoniae. Subdural effusion and (or) empyema and hydrocephalus are common complications. ABM should not be excluded even if CSF white blood cell counts is within normal range. Standardized bacteriological examination should be paid more attention to increase the pathogenic detection rate. Non-culture CSF detection methods may facilitate the pathogenic diagnosis.
Adolescent
;
Brain Abscess
;
Child
;
Child, Preschool
;
Escherichia coli
;
Female
;
Humans
;
Hydrocephalus
;
Infant
;
Infant, Newborn
;
Male
;
Meningitis, Bacterial/epidemiology*
;
Retrospective Studies
;
Streptococcus agalactiae
;
Streptococcus pneumoniae
;
Subdural Effusion
;
beta-Lactamases


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