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Brattig N, Bergquist R, Vienneau D, Zhou XN. Geography and health: role of human translocation and access to care. Infect Dis Poverty 2024; 13:37. [PMID: 38783378 PMCID: PMC11112907 DOI: 10.1186/s40249-024-01205-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Accepted: 05/06/2024] [Indexed: 05/25/2024] Open
Abstract
Natural, geographical barriers have historically limited the spread of communicable diseases. This is no longer the case in today's interconnected world, paired with its unprecedented environmental and climate change, emphasising the intersection of evolutionary biology, epidemiology and geography (i.e. biogeography). A total of 14 articles of the special issue entitled "Geography and health: role of human translocation and access to care" document enhanced disease transmission of diseases, such as malaria, leishmaniasis, schistosomiasis, COVID-19 (Severe acute respiratory syndrome corona 2) and Oropouche fever in spite of spatiotemporal surveillance. High-resolution satellite images can be used to understand spatial distributions of transmission risks and disease spread and to highlight the major avenue increasing the incidence and geographic range of zoonoses represented by spill-over transmission of coronaviruses from bats to pigs or civets. Climate change and globalization have increased the spread and establishment of invasive mosquitoes in non-tropical areas leading to emerging outbreaks of infections warranting improved physical, chemical and biological vector control strategies. The translocation of pathogens and their vectors is closely connected with human mobility, migration and the global transport of goods. Other contributing factors are deforestation with urbanization encroaching into wildlife zones. The destruction of natural ecosystems, coupled with low income and socioeconomic status, increase transmission probability of neglected tropical and zoonotic diseases. The articles in this special issue document emerging or re-emerging diseases and surveillance of fever symptoms. Health equity is intricately connected to accessibility to health care and the targeting of healthcare resources, necessitating a spatial approach. Public health comprises successful disease management integrating spatial surveillance systems, including access to sanitation facilities. Antimicrobial resistance caused, e.g. by increased use of antibiotics in health, agriculture and aquaculture, or acquisition of resistance genes, can be spread by horizontal gene transfer. This editorial reviews the key findings of this 14-article special issue, identifies important gaps relevant to our interconnected world and makes a number of specific recommendations to mitigate the transmission risks of infectious diseases in the post-COVID-19 pandemic era.
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Affiliation(s)
- Norbert Brattig
- Bernhard Nocht Institute for Tropical Medicine, Hamburg, Germany.
| | - Robert Bergquist
- Geospatial Health, Ingerod, formerly UNICEF/UNDP/World Bank/WHO Special Programme for Research and Training in Tropical Diseases (TDR), Brastad, Sweden
| | - Danielle Vienneau
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Xiao-Nong Zhou
- School of Global Health, Chinese Center for Tropical Diseases Research, Shanghai Jiao Tong University of Medicine, National Institute of Parasitic Diseases at 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, Shanghai, People's Republic of China
- Hainan Center for Tropical Diseases Research (Hainan Sub-Center of Chinese Center for Tropical Diseases Research), Haikou, People's Republic of China
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Hao Y, Tian T, Zhu Z, Chen Y, Xu J, Han S, Qian M, Zhang Y, Li S, Wang Q. Accelerating the Control and Elimination of Major Parasitic Diseases in China - On World NTD Day 2024. China CDC Wkly 2024; 6:95-99. [PMID: 38406634 PMCID: PMC10883319 DOI: 10.46234/ccdcw2024.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 02/01/2024] [Indexed: 02/27/2024] Open
Affiliation(s)
- Yuwan Hao
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); Key Laboratory on Parasite and Vector Biology, Ministry of Health; WHO Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, China
| | - Tian Tian
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); Key Laboratory on Parasite and Vector Biology, Ministry of Health; WHO Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, China
| | - Zelin Zhu
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); Key Laboratory on Parasite and Vector Biology, Ministry of Health; WHO Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, China
| | - Yijun Chen
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); Key Laboratory on Parasite and Vector Biology, Ministry of Health; WHO Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, China
| | - Jing Xu
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); Key Laboratory on Parasite and Vector Biology, Ministry of Health; WHO Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, China
| | - Shuai Han
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); Key Laboratory on Parasite and Vector Biology, Ministry of Health; WHO Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, China
| | - Menbao Qian
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); Key Laboratory on Parasite and Vector Biology, Ministry of Health; WHO Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, China
| | - Yi Zhang
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); Key Laboratory on Parasite and Vector Biology, Ministry of Health; WHO Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, China
| | - Shizhu Li
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); Key Laboratory on Parasite and Vector Biology, Ministry of Health; WHO Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, China
- School of Global Health, Chinese Center for Tropical Diseases Research-Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qiang Wang
- National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases; National Institute of Parasitic Diseases, Chinese Center for Disease Control and Prevention (Chinese Center for Tropical Diseases Research); Key Laboratory on Parasite and Vector Biology, Ministry of Health; WHO Centre for Tropical Diseases; National Center for International Research on Tropical Diseases, Ministry of Science and Technology, Shanghai, China
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Wang Q, Dai P, Jia M, Jiang M, Li J, Yang W, Feng L. Construction of an indicator framework for vaccine inclusion in public health programs: A Delphi-entropy method study. Hum Vaccin Immunother 2023; 19:2272539. [PMID: 37905961 PMCID: PMC10760382 DOI: 10.1080/21645515.2023.2272539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Accepted: 10/16/2023] [Indexed: 11/02/2023] Open
Abstract
Governments must decide which vaccine priority to include in their public health programs. Using the modified Delphi and entropy method, we developed an indicator framework for vaccine inclusion at the national, provincial, municipal, and district/county levels, each containing three dimensions. In total, 4 primary indicators, 17 secondary indicators, and 45 tertiary indicators were selected, covering vaccine-preventable diseases, candidate vaccines, and social drivers of the supply and demand sides. From a subjective perspective, there was no significant weighting difference in the primary and secondary indicators at all administrative levels. "Vaccine-preventable diseases" as a primary indicator had the greatest weight in the peer group, of which "Health burden" had the highest weight among the secondary indicators. From the objective perspective, the social drivers on the supply and demand sides of the primary indicators accounted for 65% and higher. Among the secondary indicators, "the characteristics of the candidate vaccine" and "vaccine-related policies on the supply side" held 8% of weights or more at both national and provincial levels. "Demographic characteristics" held the highest weight at the municipal (13.50) and district/county (15.45) level. This study indicates that China needs different considerations when using WHO-recommended vaccines at the national, provincial, municipal, and district/county levels. In addition, this study highlights that behavioral and social drivers are important indicators that need to be considered for decision-making. This framework provides a tool for policymakers to determine the inclusion priority of candidate vaccines.
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Affiliation(s)
- Qing Wang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Peixi Dai
- Division of Infectious Diseases, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Mengmeng Jia
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Mingyue Jiang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Juan Li
- Beijing Center for Disease Prevention and Control, Beijing Research Center for Preventive Medicine, Beijing, China
| | - Weizhong Yang
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Luzhao Feng
- School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
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Chen X, Zhang S, Shi W, Wu D, Huang B, Tao H, He X, Xu N. A deep learning model adjusting for infant gender, age, height, and weight to determine whether the individual infant suit ultrasound examination of developmental dysplasia of the hip (DDH). Front Pediatr 2023; 11:1293320. [PMID: 38046675 PMCID: PMC10690366 DOI: 10.3389/fped.2023.1293320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Accepted: 10/30/2023] [Indexed: 12/05/2023] Open
Abstract
Objective To examine the correlation between specific indicators and the quality of hip joint ultrasound images in infants and determine whether the individual infant suit ultrasound examination for developmental dysplasia of the hip (DDH). Method We retrospectively selected infants aged 0-6 months, undergone ultrasound imaging of the left hip joint between September 2021 and March 2022 at Shenzhen Children's Hospital. Using the entropy weighting method, weights were assigned to anatomical structures. Moreover, prospective data was collected from infants aged 5-11 months. The left hip joint was imaged, scored and weighted as before. The correlation between the weighted image quality scores and individual indicators were studied, with the last weighted image quality score used as the dependent variable and the individual indicators used as independent variables. A Long-short term memory (LSTM) model was used to fit the data and evaluate its effectiveness. Finally, The randomly selected images were manually measured and compared to measurements made using artificial intelligence (AI). Results According to the entropy weight method, the weights of each anatomical structure as follows: bony rim point 0.29, lower iliac limb point 0.41, and glenoid labrum 0.30. The final weighted score for ultrasound image quality is calculated by multiplying each score by its respective weight. Infant gender, age, height, and weight were found to be significantly correlated with the final weighted score of image quality (P < 0.05). The LSTM fitting model had a coefficient of determination (R2) of 0.95. The intra-class correlation coefficient (ICC) for the α and β angles between manual measurement and AI measurement was 0.98 and 0.93, respectively. Conclusion The quality of ultrasound images for infants can be influenced by the individual indicators (gender, age, height, and weight). The LSTM model showed good fitting efficiency and can help clinicians select whether the individual infant suit ultrasound examination of DDH.
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Affiliation(s)
- Xiaoyi Chen
- Department of Ultrasound, Shenzhen Children's Hospital of China Medical University, Shenzhen, China
| | - Shuangshuang Zhang
- Department of Ultrasound, Shenzhen Children's Hospital of China Medical University, Shenzhen, China
| | - Wei Shi
- Department of Orthopedics, Shenzhen Pediatrics Institute of Shantou University Medical College, Shenzhen, China
| | - Dechao Wu
- Department of Orthopedics, Shenzhen Pediatrics Institute of Shantou University Medical College, Shenzhen, China
| | - Bingxuan Huang
- Department of Ultrasound, Shenzhen Pediatrics Institute of Shantou University Medical College, Shenzhen, China
| | - Hongwei Tao
- Department of Ultrasound, Shenzhen Pediatrics Institute of Shantou University Medical College, Shenzhen, China
| | - Xuezhi He
- Department of Ultrasound, Shenzhen Pediatrics Institute of Shantou University Medical College, Shenzhen, China
| | - Na Xu
- Department of Ultrasound, Shenzhen Children's Hospital of China Medical University, Shenzhen, China
- Department of Ultrasound, Shenzhen Pediatrics Institute of Shantou University Medical College, Shenzhen, China
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