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Chen J, Wang S, Han Y, Zhang Y, Li Y, Zhang B, Li X, Zhang J. Geodetector analysis of individual and joint impacts of natural and human factors on maternal and child health at the provincial scale. Sci Rep 2024; 14:1643. [PMID: 38238587 PMCID: PMC10796915 DOI: 10.1038/s41598-024-52282-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 01/16/2024] [Indexed: 01/22/2024] Open
Abstract
This ecological study examined the individual and joint impacts of natural-human factors on the spatial patterns of maternal and child health status in China at the provincial scale in 2020. We considered natural factors (forest coverage, average temperature, and total sulfur dioxide and particulate matter emissions) and human factors (economic development, urbanization, healthcare access, and education level). We combined maternal, infant, and under-five mortality rates into a composite maternal and child health index using the entropy method. The spatial autocorrelation analysis of this index highlighted distinct health patterns across provinces, whereas the geodetector method assessed the effects of natural-human factors on the patterns. A notable east-central-west stepwise decline in health status was observed. Global Moran's I showed positive spatial clustering, with high-high clustering areas in the Yangtze River Delta and low-low clustering areas in western regions. Factor detection identified eight significant natural-human factors impacting maternal and child health, with total sulfur dioxide emission density having the greatest impact. The interaction between average schooling years and total sulfur dioxide emission notably affected maternal and child health patterns. The study concludes that natural-human factors critically affect the spatial distribution of maternal and child health.
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Affiliation(s)
- Jialu Chen
- Department of Epidemiology and Health Statistics, School of Public Health, Southwest Medical University, No.1, Section 1, Xianglin Road, Longmatan District, Luzhou, 646000, Sichuan, People's Republic of China
| | - Shuyuan Wang
- Department of Epidemiology and Health Statistics, School of Public Health, Southwest Medical University, No.1, Section 1, Xianglin Road, Longmatan District, Luzhou, 646000, Sichuan, People's Republic of China
| | - Ying Han
- Department of Epidemiology and Health Statistics, School of Public Health, Southwest Medical University, No.1, Section 1, Xianglin Road, Longmatan District, Luzhou, 646000, Sichuan, People's Republic of China
| | - Yongjin Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Southwest Medical University, No.1, Section 1, Xianglin Road, Longmatan District, Luzhou, 646000, Sichuan, People's Republic of China
| | - Yuansheng Li
- Department of Epidemiology and Health Statistics, School of Public Health, Southwest Medical University, No.1, Section 1, Xianglin Road, Longmatan District, Luzhou, 646000, Sichuan, People's Republic of China
| | - Beibei Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Southwest Medical University, No.1, Section 1, Xianglin Road, Longmatan District, Luzhou, 646000, Sichuan, People's Republic of China
| | - Xiang Li
- Department of Epidemiology and Health Statistics, School of Public Health, Southwest Medical University, No.1, Section 1, Xianglin Road, Longmatan District, Luzhou, 646000, Sichuan, People's Republic of China
| | - Junhui Zhang
- Department of Epidemiology and Health Statistics, School of Public Health, Southwest Medical University, No.1, Section 1, Xianglin Road, Longmatan District, Luzhou, 646000, Sichuan, People's Republic of China.
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Yu X, Fang M, Li Y, Yu J, Cheng L, Ding S, Kou Z. Epidemiological characteristics and spatio-temporal analysis of brucellosis in Shandong province, 2015-2021. BMC Infect Dis 2023; 23:669. [PMID: 37814221 PMCID: PMC10561485 DOI: 10.1186/s12879-023-08503-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2022] [Accepted: 07/31/2023] [Indexed: 10/11/2023] Open
Abstract
BACKGROUND Brucellosis is one of the major public health problems in China, it not only causes huge economic losses to the society, but also threatens the human's physical and mental health. The reported cases of brucellosis in Shandong province were at a high level, therefore, it is necessary for us to understand the epidemic characteristics and distribution trend of Brucellosis in Shandong province. This study aims to describe the epidemiological characteristics and spatial clustering characteristics of brucellosis in Shandong Province, provide a reference for the scientific prevention and control. METHODS Human brucellosis data in Shandong province from 2015 to 2021 were obtained from the China Information System for Disease Control and Prevention, the data were analyzed by descriptive epidemiological methods, spatial autocorrelation analysis and spatial-temporal cluster analysis methods use ArcGIS and SaTScan software, the results were presented in ArcMap. RESULTS A total of 22,251 human cases of brucellosis were reported, the annual incidence ranged between 2.41/100,000 and 4.07/100,000 from 2015 to 2021 in Shandong province, incidence has been decreasing year by year, while there was a significant increase in 2021. The distribution of brucellosis was of a seasonal trend, mainly concentrating during March to August. The age of the cases was mainly concentrated in the 30-74 age ranges, the average annual incidence rate was significantly higher in males than in females. The spatial analysis showed that the epidemics were mainly concentrated in the north and southwest. For the spatial autocorrelation analysis, a high global autocorrelation was observed at the county level, and the high-high clusters mainly distributed in the north and southwest region. For the spatio-temporal scanning, the most likely cluster areas mainly distributed in the north area, and then gradually moved southward, and the radius of clustered narrowed. CONCLUSIONS Human brucellosis remains a common challenge, particularly in northern region in spring and summer. More disease prevention and control measures should be taken in high-risk populations, and such higher-risk susceptible areas to reduce the incidence of brucellosis and ensure the health of the people.
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Affiliation(s)
- Xiaolin Yu
- Institute for Communicable Disease Control and Prevention, Shandong provincial Center for Disease Control and Prevention, Jinan, Shandong, China
| | - Ming Fang
- Institute for Communicable Disease Control and Prevention, Shandong provincial Center for Disease Control and Prevention, Jinan, Shandong, China
| | - Yan Li
- Institute for Communicable Disease Control and Prevention, Shandong provincial Center for Disease Control and Prevention, Jinan, Shandong, China
| | - Jianmei Yu
- Institute for Communicable Disease Control and Prevention, Shandong provincial Center for Disease Control and Prevention, Jinan, Shandong, China
- Department of public health and health management, Shandong First Medical University, Jinan, Shandong, China
| | - Lixiao Cheng
- Institute for Communicable Disease Control and Prevention, Shandong provincial Center for Disease Control and Prevention, Jinan, Shandong, China
| | - Shujun Ding
- Institute for Communicable Disease Control and Prevention, Shandong provincial Center for Disease Control and Prevention, Jinan, Shandong, China
| | - Zengqiang Kou
- Institute for Communicable Disease Control and Prevention, Shandong provincial Center for Disease Control and Prevention, Jinan, Shandong, China.
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Zhao C, Nie S, Sun Y, An C, Fan S, Luo B, Chang W, Liu K, Shao Z. Detrended seasonal relationships and impact of climatic factors combined with spatiotemporal effect on the prevalence of human brucellosis. Environ Sci Pollut Res Int 2023; 30:104043-104055. [PMID: 37698797 DOI: 10.1007/s11356-023-29699-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Accepted: 08/31/2023] [Indexed: 09/13/2023]
Abstract
Human brucellosis (HB) is a seasonal and climate-affected infectious disease that is posing an increasing threat to public health and economy. However, most of the research on the seasonal relationships and impact of climatic factors on HB did not consider the secular trend and spatiotemporal effect related to the disease. We herein utilized long-term surveillance data on HB from 2008 to 2020 using sinusoidal models to explore detrended relationships between climatic factors and HB. In addition, we assessed the impact of such climatic factors on HB using a spatial panel data model combined with the spatiotemporal effect. HB peaked around mid-May. HB was significantly correlated with climatic factors with 1-5-month lag when the respective correlations reached the maximum across the different lag periods. Each 0.1 °C increase in temperature led to 0.5% decrease in the 5-month lag incidence of HB. We also observed a positive spatiotemporal effect on the disease. Our study provides a detailed and in-depth overview of seasonal relationships and impact of climatic factors on HB. In addition, it proposes a novel approach for exploring the seasonal relationships and quantifying the impacts of climatic factors on various infectious diseases.
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Affiliation(s)
- Chenxi Zhao
- Department of Epidemiology, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi'an, 710032, China
- Department of Pediatrics, Xijing Hospital, the Fourth Military Medical University, Xi'an, China
| | - Shoumin Nie
- Department of Plague and Brucellosis, Shaanxi Center for Disease Control and Prevention, Xi'an, 710054, China
| | - Yangxin Sun
- Department of Plague and Brucellosis, Shaanxi Center for Disease Control and Prevention, Xi'an, 710054, China
| | - Cuihong An
- Department of Plague and Brucellosis, Shaanxi Center for Disease Control and Prevention, Xi'an, 710054, China
| | - Suoping Fan
- Department of Plague and Brucellosis, Shaanxi Center for Disease Control and Prevention, Xi'an, 710054, China
| | - Boyan Luo
- Department of Plague and Brucellosis, Shaanxi Center for Disease Control and Prevention, Xi'an, 710054, China
| | - Wenhui Chang
- Department of Plague and Brucellosis, Shaanxi Center for Disease Control and Prevention, Xi'an, 710054, China
| | - Kun Liu
- Department of Epidemiology, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi'an, 710032, China
| | - Zhongjun Shao
- Department of Epidemiology, Ministry of Education Key Lab of Hazard Assessment and Control in Special Operational Environment, School of Public Health, Air Force Medical University, Xi'an, 710032, China.
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Feng J, Zhang X, Hu H, Gong Y, Luo Z, Xue J, Cao C, Xu J, Li S. Spatiotemporal distribution of schistosomiasis transmission risk in Jiangling County, Hubei Province, P.R. China. PLoS Negl Trop Dis 2023; 17:e0011265. [PMID: 37141201 PMCID: PMC10159153 DOI: 10.1371/journal.pntd.0011265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Accepted: 03/22/2023] [Indexed: 05/05/2023] Open
Abstract
OBJECTIVE This study aims to explore the spatiotemporal distribution of schistosomiasis in Jiangling County, and provide insights into the precise schistosomiasis control. METHODS The descriptive epidemiological method and Joinpoint regression model were used to analyze the changes in infection rates of humans, livestock, snails, average density of living snails and occurrence rate of frames with snails in Jiangling County from 2005 to 2021. Spatial epidemiology methods were used to detect the spatiotemporal clustering of schistosomiasis transmission risk in Jiangling county. RESULTS The infection rates in humans, livestock, snails, average density of living snails and occurrence rate of frames with snails in Jiangling County decreased from 2005 to 2021 with statistically significant. The average density of living snails in Jiangling County was spatially clustered in each year, and the Moran's I varied from 0.10 to 0.26. The hot spots were mainly concentrated in some villages of Xionghe Town, Baimasi Town and Shagang Town. The mean center of the distribution of average density of living snails in Jiangling County first moved from northwest to southeast, and then returned from southeast to northwest after 2014. SDE azimuth fluctuated in the range of 111.68°-124.42°. Kernal density analysis showed that the high and medium-high risk areas of Jiangling County from 2005 to 2021 were mainly concentrated in the central and eastern of Jiangling County, and the medium-low and low risk areas were mainly distributed in the periphery of Jiangling County. CONCLUSIONS The epidemic situation of schistosomiasis decreased significantly in Jiangling County from 2005 to 2021, but the schistosomiasis transmission risk still had spatial clustering in some areas. After transmission interruption, targeted transmission risk intervention strategies can be adopted according to different types of schistosomiasis risk areas.
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Affiliation(s)
- Jiaxin Feng
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Shanghai, People's Republic of China
| | - Xia Zhang
- Jiangling Center for Disease Control and Prevention, Hubei province, People's Republic of China
| | - Hehua Hu
- Jiangling Center for Disease Control and Prevention, Hubei province, People's Republic of China
| | - Yanfeng Gong
- The School of the Public Health of Fudan University, Shanghai, People's Republic of China
| | - Zhuowei Luo
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Shanghai, People's Republic of China
| | - Jingbo Xue
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Shanghai, People's Republic of China
| | - Chunli Cao
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Shanghai, People's Republic of China
| | - Jing Xu
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Shanghai, People's Republic of China
| | - Shizhu Li
- National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research), NHC Key Laboratory of Parasite and Vector Biology, WHO Collaborating Centre for Tropical Diseases, National Center for International Research on Tropical Diseases, Shanghai, People's Republic of China
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University School of Medicine, Shanghai, People's Republic of China
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Liu S, Zhang T. A long-term retrospective analysis of the haemorrhagic fever with renal syndrome epidemic from 2005 to 2021 in Jiangxi Province, China. Sci Rep 2023; 13:2268. [PMID: 36755085 DOI: 10.1038/s41598-023-29330-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2022] [Accepted: 02/02/2023] [Indexed: 02/10/2023] Open
Abstract
Jiangxi is one of the provinces in China most seriously affected by the haemorrhagic fever with renal syndrome (HFRS) epidemic. The aim of this paper was to systematically explore the HFRS epidemic in Jiangxi from the perspective of Hantavirus (HV) prevalence in rodents and humans and virus molecular characteristics. Individual information on all HFRS cases in Jiangxi from 2005 to 2021 was extracted from the China Information System for Disease Control and Prevention. All S and M fragment sequences of the Seoul virus and Hantan virus strains uploaded by Jiangxi and its neighbouring provinces and some representative sequences from provinces in China or some countries of Southeast Asia with the highest HV prevalence were retrieved and downloaded from NCBI GenBank. Periodogram and spatial autocorrelation were adopted for temporal periodicity and spatial clustering analysis of the HFRS epidemic. Joinpoint regression was utilized to explore the changing morbidity trend patterns of HFRS. Multiple sequence alignment and amino acid variation analysis were used to explore the homology and variation of strain prevalence in Jiangxi. Based on monthly morbidity time series, the periodogram analysis showed that the prevalence of HFRS had periodicities of 6 months and 12 months. Spatial autocorrelation analysis showed that HFRS distributed in Jiangxi was not random, with a "High-High" clustering area around Gaoan County. HFRS morbidity among the 0 ~ 15-year-old and ~ 61-year-old or older populations in Jiangxi increased significantly during the period of 2008-2015. Generally, HFRS morbidity was significantly positively correlated with the index of rat with virus (IRV) (r = 0.742) in the counties surrounding Gaoan from 2005 to 2019. HTNV strains in Jiangxi were in one independent branch, while the SEOV strains in Jiangxi were relatively more diverse. Both the YW89-15 and GAW30/2021 strains shared approximately 85% nucleotide homology and approximately 97% amino acid homology with their corresponding standard strains and vaccine strains. GAW30/2021 and YW89-15 had some amino acid site variations in nucleoprotein, glycoprotein precursor and RNA-dependent polymerase with their corresponding vaccine strains Z10 (HTNV) and Z37 (SEOV). The HFRS epidemic in Jiangxi has obvious temporal periodicity and spatial clustering, and the significant increase in the non-Immunization Expanded Program (EPI) targeted population (children and elderly) suggests that HFRS vaccination in this population needs to be considered. Although applying the EPI played a certain role in curbing the incidence of HFRS in Jiangxi from the perspective of ecological epidemiology, HTNV and SEOV strains prevalent in Jiangxi have some amino acid site variations compared to their corresponding vaccine strains, suggesting that HV variation needs to be continuously monitored in the future to observe vaccine protective efficiency.
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Dai X, Sun R, Plewczynski D. Temporal and Spatial Cluster Analysis of 2019 Novel Coronavirus Pneumonia in Chongqing, 2020.1∼2020.2. Computational Intelligence and Neuroscience 2022; 2022:1-6. [PMID: 36156965 PMCID: PMC9507712 DOI: 10.1155/2022/8491628] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 07/10/2022] [Accepted: 08/24/2022] [Indexed: 01/08/2023]
Abstract
In order to explore the spatial and temporal distribution characteristics of COVID-19 in Chongqing from January 22 to February 25, 2010, and provide a series of suggestions for scientific prevention and control of epidemic situation, we will mainly analyze the epidemic situation data of Chongqing Municipal Health Committee members and improve the descriptive analysis. Regional distribution and spatiotemporal scans were analyzed for COVID-19 outbreaks using ArcGIS10.2 and SaTScan9. 5 software. After the analysis, a total of 576 novel coronavirus pneumonia patients were confirmed in Chongqing. The incidence trend increased rapidly from January 22 to January 31, then decreased gradually, and there were no new cases until February 25. The purely spatial scanning results were consistent with spatiotemporal scanning, and a first-level accumulation area was detected by spatiotemporal scanning in the east and northeast of Chongqing from January 22 to February 10. From January 22 to February 25, 2020,COVID-19 occurred in the eastern and northeast regions of Chongqing. It is recommended to strengthen the detection of cluster areas to prevent another outbreak of COVID-19 risk.
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Liu K, Chen S, Zhang Y, Li T, Xie B, Wang W, Wang F, Peng Y, Ai L, Chen B, Wang X, Jiang J. Tuberculosis burden caused by migrant population in Eastern China: evidence from notification records in Zhejiang Province during 2013-2017. BMC Infect Dis 2022; 22:109. [PMID: 35100983 PMCID: PMC8805310 DOI: 10.1186/s12879-022-07071-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2021] [Accepted: 01/17/2022] [Indexed: 01/04/2023] Open
Abstract
Background Internal migrants have an enormous impact on tuberculosis (TB) epidemic in China. Zhejiang Province, as one of the developed areas, also had a heavy burden caused by TB. Methods In this study, we collected all cases in Zhejiang Province through the TB Management Information System from 2013 to 2017. Description analysis and Spatio-temporal analysis using R software and ArcGIS were performed to identify the epidemiological characteristics and clusterings, respectively. Results 48,756 individuals in total were notified with TB among the migrant population (TBMP), accounting for one-third of all cases identified. The primary sources of TB from migrants outside the province were from Guizhou, Sichuan, and Anhui. Wenzhou, Taizhou, and Lishui were the three mainly outflowing cities among the intra-provincial TBMP and Hangzhou as the primarily inflowing city. Also, results implied that the inconsistency of the TBMP in spatial analysis and the border area of Quzhou and Lishui city had the highest risk of TB occurrence among the migrants. Additionally, one most likely cluster and four secondary clusters were identified by the spatial–temporal analysis. Conclusion The effective control of TB in extra-provincial MP was critical to lowering the TB burden of MP in Zhejiang Province. Also, it is suggested that active TB screening for migrant employees outflowed from high epidemic regions should be strengthened, and further traceability analysis needs to be investigated to clarify the mechanism of TB transmission in clustered areas. Supplementary Information The online version contains supplementary material available at 10.1186/s12879-022-07071-5.
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Affiliation(s)
- Kui Liu
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, People's Republic of China.,Key Laboratory of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, People's Republic of China
| | - Songhua Chen
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, People's Republic of China
| | - Yu Zhang
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, People's Republic of China
| | - Tao Li
- National Center for Tuberculosis Control and Prevention, China CDC, Beijing, People's Republic of China
| | - Bo Xie
- School of Urban Design, Wuhan University, Wuhan, Hubei Province, People's Republic of China
| | - Wei Wang
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, People's Republic of China
| | - Fei Wang
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, People's Republic of China
| | - Ying Peng
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, People's Republic of China
| | - Liyun Ai
- Hangzhou Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, People's Republic of China
| | - Bin Chen
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, People's Republic of China. .,Key Laboratory of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, People's Republic of China.
| | - Xiaomeng Wang
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, People's Republic of China.
| | - Jianmin Jiang
- Department of Tuberculosis Control and Prevention, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, People's Republic of China. .,Key Laboratory of Vaccine, Prevention and Control of Infectious Disease of Zhejiang Province, Zhejiang Provincial Center for Disease Control and Prevention, Hangzhou, Zhejiang Province, People's Republic of China.
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Rendana M, Idris WMR, Abdul Rahim S. Spatial distribution of COVID-19 cases, epidemic spread rate, spatial pattern, and its correlation with meteorological factors during the first to the second waves. J Infect Public Health 2021; 14:1340-1348. [PMID: 34301503 PMCID: PMC8280608 DOI: 10.1016/j.jiph.2021.07.010] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 06/28/2021] [Accepted: 07/12/2021] [Indexed: 12/23/2022] Open
Abstract
Currently, many countries all over the world are facing the second wave of COVID-19. Therefore, this study aims to analyze the spatial distribution of COVID-19 cases, epidemic spread rate, spatial pattern during the first to the second waves in the South Sumatra Province of Indonesia. This study used the geographical information system (GIS) software to map the spatial distribution of COVID-19 cases and epidemic spread rate. The spatial autocorrelation of the COVID-19 cases was carried out using Moran's I, while the Pearson correlation was used to examining the relationship between meteorological factors and the epidemic spread rate. Most infected areas and the direction of virus spread were predicted using wind rose analysis. The results revealed that the epidemic rapidly spread from August 1 to December 1, 2020. The highest epidemic spread rate was observed in the Palembang district and in its peripheral areas (dense urban areas), while the lowest spread rate was found in the eastern and southern parts of South Sumatra Province (remote areas). The spatial correlation characteristic of the epidemic distribution exhibited a negative correlation and random distribution. Air temperature, wind speed, and precipitation have contributed to a significant impact on the high epidemic spread rate in the second wave. In summary, this study offers new insight for arranging control and prevention strategies against the potential of second wave strike.
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Affiliation(s)
- Muhammad Rendana
- Department of Chemical Engineering, Faculty of Engineering, Universitas Sriwijaya, Indralaya 30662, Sumatera Selatan, Indonesia.
| | - Wan Mohd Razi Idris
- Department of Earth Sciences and Environmental, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600 Bangi, Selangor, Malaysia; Center for Water Research and Analysis, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia.
| | - Sahibin Abdul Rahim
- Environmental Science Program, Faculty of Science and Natural Resources, Universiti Malaysia Sabah, 88400 Kota Kinabalu, Sabah, Malaysia
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Liu L, Wang L, Qi C, Zhu Y, Li C, Jia Y, She K, Liu T, Zhang Y, Cui F, Li X. Epidemiological characteristics and spatiotemporal analysis of hand-foot-mouth diseases from 2010 to 2019 in Zibo city, Shandong, China. BMC Public Health 2021; 21:1640. [PMID: 34496828 PMCID: PMC8424956 DOI: 10.1186/s12889-021-11665-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 08/25/2021] [Indexed: 01/02/2023] Open
Abstract
Background Hand-foot-mouth disease (HFMD) is a global public health issues, especially in China. It has threat the health of children under 5 years old. The early recognition of high-risk districts and understanding of epidemic characteristics can facilitate health sectors to prevent the occurrence of HFMD effectively. Methods Descriptive analysis was used to summarize epidemic characteristics, and the spatial autocorrelation analysis and space-time scan analysis were utilized to explore distribution pattern of HFMD and identify hot spots with statistical significance. The result was presented in ArcMap. Results A total of 52,095 HFMD cases were collected in Zibo city from 1 Jan 2010 to 31 Dec 2019. The annual average incidence was 129.72/100,000. The distribution of HFMD was a unimodal trend, with peak from April to September. The most susceptible age group was children under 5 years old (92.46%), and the male-to-female ratio is 1.60: 1. The main clusters were identified in Zhangdian District from 12 April 2010 to 18 September 2012. Spatial autocorrelation analysis showed that the global spatial correlation in Zibo were no statistical significance, except in 2012, 2014, 2015, 2016 and 2018. Cold spots were gathered in Boshan county and Linzi district, while hot spots only in Zhangdian District in 2018, but other years were no significance. Conclusion Hot spots mainly concentrated in the central and surrounding city of Zibo city. We suggest that imminent public health planning and resource allocation should be focused within those areas.
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Affiliation(s)
- Lili Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Ling Wang
- Institute for Infectious Disease Control and Prevention, Zibo Center for Disease Control and Prevention, Zibo, 255026, Shandong, China
| | - Chang Qi
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Yuchen Zhu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Chunyu Li
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Yan Jia
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Kaili She
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Tingxuan Liu
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China
| | - Yan Zhang
- Institute for Infectious Disease Control and Prevention, Zibo Center for Disease Control and Prevention, Zibo, 255026, Shandong, China
| | - Feng Cui
- Zibo Center for Disease Control and Prevention, Zibo, 255026, Shandong, China.
| | - Xiujun Li
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.
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Ning J, Chu Y, Liu X, Zhang D, Zhang J, Li W, Zhang H. Spatio-temporal characteristics and control strategies in the early period of COVID-19 spread: a case study of the mainland China. Environ Sci Pollut Res Int 2021; 28:48298-48311. [PMID: 33904137 PMCID: PMC8075720 DOI: 10.1007/s11356-021-14092-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Accepted: 04/20/2021] [Indexed: 04/12/2023]
Abstract
COVID-19 has caused huge impacts on human health and the economic operation of the world. Analyzing and summarizing the early propagation law can help reduce the losses caused by public health emergencies in the future. Early data on the spread of COVID-19 in 30 provinces (autonomous regions and municipalities) of mainland China except for Hubei, Hong Kong, Macao, and Taiwan were selected in this study. Spatio-temporal analysis, inflection point analysis, and correlation analysis are used to explore the spatio-temporal characteristics in the early COVID-19 spread. The results suggested that (1) the total confirmed cases have risen in an "S"-shaped curve over time, and the daily new cases have first increased and finally decreased; (2) the spatial distributions of both total and daily new cases show a trend of more in the east and less in the west, with a "multi-center agglomeration distribution" around Hubei Province and some major cities; (3) the spatial agglomeration of total confirmed cases has been increasing over time, while that of the daily new cases shows much more obvious in the mid-stage; and (4) timely release of the first-level public health emergency response can accelerate the emergence of the epidemic inflection point. The above analysis results have a specific reference value for the government's policy-making and measures to face public health emergencies.
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Affiliation(s)
- Jiachen Ning
- College of Economics and Management, Northwest A&F University, Yangling, 712100, China
| | - Yuhan Chu
- College of Economics and Management, Northwest A&F University, Yangling, 712100, China
| | - Xixi Liu
- College of Economics and Management, Northwest A&F University, Yangling, 712100, China
| | - Daojun Zhang
- College of Economics and Management, Northwest A&F University, Yangling, 712100, China.
- State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Northwest A&F University, Yangling, 712100, China.
| | - Jinting Zhang
- School of Resources and Environmental Sciences, Wuhan University, Wuhan, 430079, China
| | - Wangjun Li
- The school of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou, 215009, China
| | - Hui Zhang
- College of Economics and Management, Northwest A&F University, Yangling, 712100, China
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Xie L, Huang R, Wang H, Liu S. Spatial-temporal heterogeneity and meteorological factors of hand-foot-and-mouth disease in Xinjiang, China from 2008 to 2016. PLoS One 2021; 16:e0255222. [PMID: 34339424 DOI: 10.1371/journal.pone.0255222] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2020] [Accepted: 07/12/2021] [Indexed: 12/23/2022] Open
Abstract
The study aims to depict the temporal and spatial distributions of hand-foot-and-mouth disease (HFMD) in Xinjiang, China and reveal the relationships between the incidence of HFMD and meteorological factors in Xinjiang. With the national surveillance data of HFMD in Xinjiang and meteorological parameters in the study area from 2008 to 2016, in GeoDetector Model, we examined the effects of meteorological factors on the incidence of HFMD in Xinjiang, China, tested the spatial-temporal heterogeneity of HFMD risk, and explored the temporal-spatial patterns of HFMD through the spatial autocorrelation analysis. From 2008 to 2016, the HFMD distribution showed a distinct seasonal pattern and HFMD cases typically occurred from May to July and peaked in June in Xinjiang. Relative humidity, precipitation, barometric pressure and temperature had the more significant influences on the incidence of HFMD than other meteorological factors with the explanatory power of 0.30, 0.29, 0.29 and 0.21 (P<0.000). The interaction between any two meteorological factors had a nonlinear enhancement effect on the risk of HFMD. The relative risk in Northern Xinjiang was higher than that in Southern Xinjiang. Global spatial autocorrelation analysis results indicated a fluctuating trend over these years: the positive spatial dependency on the incidence of HFMD in 2008, 2010, 2012, 2014 and 2015, the negative spatial autocorrelation in 2009 and a random distribution pattern in 2011, 2013 and 2016. Our findings revealed the correlation between meteorological factors and the incidence of HFMD in Xinjiang. The correlation showed obvious spatiotemporal heterogeneity. The study provides the basis for the government to control HFMD based on meteorological information. The risk of HFMD can be predicted with appropriate meteorological factors for HFMD prevention and control.
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Zhang R, Lin Z, Guo Z, Chang Z, Niu R, Wang Y, Wang S, Li Y. Daily mean temperature and HFMD: risk assessment and attributable fraction identification in Ningbo China. J Expo Sci Environ Epidemiol 2021; 31:664-671. [PMID: 33547422 PMCID: PMC8263339 DOI: 10.1038/s41370-021-00291-y] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 01/06/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND Hand, foot, and mouth disease (HFMD) remains a significant public health issue, especially in developing countries. Many studies have reported the association between environmental temperature and HFMD. However, the results are highly heterogeneous in different regions. In addition, there are few studies on the attributable risk of HFMD due to temperature. OBJECTIVES The study aimed to assess the association between temperature and HFMD incidence and to evaluate the attributable burden of HFMD due to temperature in Ningbo China. METHODS The research used daily incidence of HFMD from 2014 to 2017 and distributed lag non-linear model (DLNM) to investigate the effects of daily mean temperature (Tmean) on HFMD incidence from lag 0 to 30 days, after controlling potential confounders. The lag effects and cumulative relative risk (CRR) were analyzed. Attributable fraction (AF) of HFMD incidence due to temperature was calculated. Stratified analysis by gender and age were also conducted. RESULTS The significant associations between Tmean and HFMD incidence were observed in Ningbo for lag 0-30. Two peaks were observed at both low (5-11 °C) and high (16-29 °C) temperature scales. For low temperature scale, the highest CRR was 2.22 (95% CI: 1.61-3.07) at 7 °C on lag 0-30. For high temperature scale, the highest CRR was 3.54 (95% CI: 2.58-4.88) at 24 °C on lag 0-30. The AF due to low and high temperature was 5.23% (95% CI: 3.10-7.14%) and 39.55% (95% CI: 30.91-45.51%), respectively. There was no significant difference between gender- and age-specific AFs, even though the school-age and female children had slightly higher AF values. CONCLUSIONS The result indicates that both high and low temperatures were associated with daily incidence of HFMD, and more burdens were caused by heat in Ningbo.
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Affiliation(s)
- Rui Zhang
- Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Zhehan Lin
- China Population Communication Center, Beijing, 100013, China
| | - Zhen Guo
- Institute of Medical Information/Medical Library, CAMS & PUMC, Beijing, 100020, China
| | - Zhaorui Chang
- Chinese Center for Disease Control and Prevention, Beijing, 102206, China
| | - Ran Niu
- National Institute for Nutrition and Health, Chinese Center for Disease Control and Prevention, Beijing, 100050, China
| | - Yu Wang
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China
| | - Songwang Wang
- Chinese Center for Disease Control and Prevention, Beijing, 102206, China.
| | - Yonghong Li
- National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing, 100021, China.
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Lee CP, Zhu CH, Su CC. Increased prevalence of Parkinson's disease in soils with high arsenic levels. Parkinsonism Relat Disord 2021; 88:19-23. [PMID: 34091413 DOI: 10.1016/j.parkreldis.2021.05.029] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Revised: 05/24/2021] [Accepted: 05/27/2021] [Indexed: 11/23/2022]
Abstract
INTRODUCTION Exposure to arsenic (As)-containing pesticides was associated with an increased risk for Parkinson's disease (PD). Arsenic also induced in murine brains α-synuclein aggregates, a pathognomic feature of PD. OBJECTIVES People living on farms irrigated with high As water in Taiwan are likely exposed to increased As. We addressed whether increased farm soil As levels correlate with an increased PD risk. METHODS We used the information from several national surveys (1983-1986) on soil metal contents to study the relationships between soil metal contents and PD prevalence in Taiwan. PD prevalence (2009-2013) was calculated with a database from Taiwan's compulsory national health insurance. A patient is defined by a PD diagnosis and prescriptions of Levodopa in three or more office visits in twelve months. We used regression models to study the correlation between PD prevalence and soil metal contents. RESULTS The PD prevalence ranged from 83 to 213 per one hundred thousand persons in different regions of Taiwan. Among the studied heavy metals, we found only As was significantly associated with the PD prevalence. The top three regions (Yunlin, Chiayi, Tainan) in the PD prevalence list correspond exactly with the top three in soil As levels. Soil As levels and PD prevalence had a strong correlation (r = 0.75). CONCLUSION PD prevalence is statistically correlated with farm soil As levels in Taiwan.
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Liu J, Chen Y, Hu P, Gan L, Tan Q, Huang X, Ma Z, Lin C, Wu D, Zhu X, Zhang D. Caregivers: the potential infection resources for the sustaining epidemic of hand, foot, and mouth disease/herpangina in Guangdong, China? Arch Public Health 2021; 79:54. [PMID: 33892784 PMCID: PMC8063478 DOI: 10.1186/s13690-021-00574-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 04/03/2021] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Although several measures have been taken to control hand foot and mouth disease (HFMD) and herpangina (HA), these two diseases have been prevalent in China for 10 years with high incidence. We suspected that adults' inapparent infection might be the cause of the continued prevalence of HFMD/HA infection in mainland China. METHODS To explore the role of adults (especially caregivers) in the transmission process of HFMD/HA among children, 330 HFMD/HA cases and 330 healthy children (controls) were selected for a case-control study. Then, data were analyzed by logistic regression. RESULTS Single-variable analyses revealed that caregivers who tested positive for enterovirus was a significant risk factor of HFMD/HA transmission to children (adjusted odds ratio (OR) = 9.22; 95% CI, 1.16 to 73.23). In the final multivariable model, caregiver behavior, such as cooling children's food with mouth (OR = 1.85; 95% CI, 1.11 to 3.08) and feeding children with their own tableware (OR = 2.19; 95% CI, 1.07 to 4.45), significantly increased the risk of transmitting HFMD/HA to children. On the contrary, washing hands before feeding children reduced such risk. CONCLUSIONS These results implied that the caregivers might be the infectious source or carriers of enterovirus. Therefore, preventing or treating the caregivers' enterovirus infection and improving their hygiene habits, especially when they are in contact with children, could provide a breakthrough for the effective control of HFMD/HA.
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Affiliation(s)
- Jundi Liu
- School of Public Health, Sun Yat-sen University, Guangzhou, China
- Zhongshan Center for Diseases Prevention and Control, Zhongshan, China
| | - Yan Chen
- Medical College of Shaoguan University, Shaoguan, China
| | - Peipei Hu
- School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Lin Gan
- School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Qimin Tan
- Yonghe Community Health Service Center, Yongning Street, Zengcheng District, Guangzhou, China
| | - Xinqiao Huang
- Yonghe Community Health Service Center, Yongning Street, Zengcheng District, Guangzhou, China
| | - Zhanzhong Ma
- Clinical Laboratory, Yuebei People's Hospital Affiliated to Shantou University Medical College, Shaoguan, China
| | - Cuiji Lin
- Department of Microbiology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Dawei Wu
- School of Public Health, Sun Yat-sen University, Guangzhou, China
| | - Xun Zhu
- Department of Microbiology, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Dingmei Zhang
- School of Public Health, Sun Yat-sen University, Guangzhou, China.
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Abstract
To examine the effects of temperature on the daily cases of hand, foot, and mouth disease (HFMD).Data on the daily cases of HFMD in Lanzhou from 2008 to 2015 were obtained, and meteorological data from the same period were collected. A distributed lag nonlinear model was fitted to reveal the relationship between the daily mean temperature and the daily cases of HFMD.From 2008 to 2015, 25,644 cases were reported, of which children under 5 years of age accounted for 78.68% of cases. The highest peak of HFMD cases was usually reported between April to July each year. An inverse V-shaped relationship was observed between daily mean temperature and HFMD cases; a temperature of 18°C was associated with a maximum risk of HFMD. The relative risk (RR) was 1.57 (95% confidence interval: 1.23-1.23), and boys and children aged 3 to 5 years were populations with the highest risk. The cumulative risks of high temperature (20.2°C and 25.2°C) in the total, age-specific, and gender-specific groups peaked on lag 14 days; RR was higher in girls than in boys and in children aged 1 to 2 years than in other age groups. However, the effects of low temperature (-5.3°C, 2.0°C, and 12.8°C) were not significant for both gender-specific and age-specific patients.High temperature may increase the risk of HFMD, and boys and children aged 3 to 5 years were at higher risks on lag 0 day; however, the cumulative risks in girls and children aged 1 to 2 years increased with the increasing number of lag days.
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Affiliation(s)
- Jinyu Wang
- School of Basic Medical Science, Lanzhou University
| | - Sheng Li
- The First People's Hospital of Lanzhou City, Lanzhou, PR China
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