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      Etiological and epidemiological features of acute respiratory infections in China

      research-article
      1 , 2 , 3 , 4 , 1 , 1 , 1 , 1 , 2 , 5 , 6 , 7 , 8 , 9 , 10 , 10 , 11 , 12 , 13 , 1 , 14 , 15 , 2 , 2 , 1 , 2 , 1 , 2 , 16 , 17 , 18 , 1 , 12 , 19 , 13 , 8 , 11 , 20 , 5 , 21 , 1 , , 2 , , 2 , , 22 , 23 , 21 , 21 , The Chinese Centers for Disease Control and Prevention (CDC) Etiology of Respiratory Infection Surveillance Study Team
      Nature Communications
      Nature Publishing Group UK
      Infectious diseases, Respiratory tract diseases, Epidemiology

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          Abstract

          Nationwide prospective surveillance of all-age patients with acute respiratory infections was conducted in China between 2009‒2019. Here we report the etiological and epidemiological features of the 231,107 eligible patients enrolled in this analysis. Children <5 years old and school-age children have the highest viral positivity rate (46.9%) and bacterial positivity rate (30.9%). Influenza virus, respiratory syncytial virus and human rhinovirus are the three leading viral pathogens with proportions of 28.5%, 16.8% and 16.7%, and Streptococcus pneumoniae, Mycoplasma pneumoniae and Klebsiella pneumoniae are the three leading bacterial pathogens (29.9%, 18.6% and 15.8%). Negative interactions between viruses and positive interactions between viral and bacterial pathogens are common. A Join-Point analysis reveals the age-specific positivity rate and how this varied for individual pathogens. These data indicate that differential priorities for diagnosis, prevention and control should be highlighted in terms of acute respiratory tract infection patients’ demography, geographic locations and season of illness in China.

          Abstract

          China operates a national surveillance program for acute respiratory infections and sampled over 200,000 patients between 2009–2019. Here, the authors present results from this program and describe patterns by age, pathogen type, presence of pneumonia, and season.

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          Most cited references37

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          Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia

          Abstract Background The initial cases of novel coronavirus (2019-nCoV)–infected pneumonia (NCIP) occurred in Wuhan, Hubei Province, China, in December 2019 and January 2020. We analyzed data on the first 425 confirmed cases in Wuhan to determine the epidemiologic characteristics of NCIP. Methods We collected information on demographic characteristics, exposure history, and illness timelines of laboratory-confirmed cases of NCIP that had been reported by January 22, 2020. We described characteristics of the cases and estimated the key epidemiologic time-delay distributions. In the early period of exponential growth, we estimated the epidemic doubling time and the basic reproductive number. Results Among the first 425 patients with confirmed NCIP, the median age was 59 years and 56% were male. The majority of cases (55%) with onset before January 1, 2020, were linked to the Huanan Seafood Wholesale Market, as compared with 8.6% of the subsequent cases. The mean incubation period was 5.2 days (95% confidence interval [CI], 4.1 to 7.0), with the 95th percentile of the distribution at 12.5 days. In its early stages, the epidemic doubled in size every 7.4 days. With a mean serial interval of 7.5 days (95% CI, 5.3 to 19), the basic reproductive number was estimated to be 2.2 (95% CI, 1.4 to 3.9). Conclusions On the basis of this information, there is evidence that human-to-human transmission has occurred among close contacts since the middle of December 2019. Considerable efforts to reduce transmission will be required to control outbreaks if similar dynamics apply elsewhere. Measures to prevent or reduce transmission should be implemented in populations at risk. (Funded by the Ministry of Science and Technology of China and others.)
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            Global burden of 369 diseases and injuries in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019

            Summary Background In an era of shifting global agendas and expanded emphasis on non-communicable diseases and injuries along with communicable diseases, sound evidence on trends by cause at the national level is essential. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) provides a systematic scientific assessment of published, publicly available, and contributed data on incidence, prevalence, and mortality for a mutually exclusive and collectively exhaustive list of diseases and injuries. Methods GBD estimates incidence, prevalence, mortality, years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) due to 369 diseases and injuries, for two sexes, and for 204 countries and territories. Input data were extracted from censuses, household surveys, civil registration and vital statistics, disease registries, health service use, air pollution monitors, satellite imaging, disease notifications, and other sources. Cause-specific death rates and cause fractions were calculated using the Cause of Death Ensemble model and spatiotemporal Gaussian process regression. Cause-specific deaths were adjusted to match the total all-cause deaths calculated as part of the GBD population, fertility, and mortality estimates. Deaths were multiplied by standard life expectancy at each age to calculate YLLs. A Bayesian meta-regression modelling tool, DisMod-MR 2.1, was used to ensure consistency between incidence, prevalence, remission, excess mortality, and cause-specific mortality for most causes. Prevalence estimates were multiplied by disability weights for mutually exclusive sequelae of diseases and injuries to calculate YLDs. We considered results in the context of the Socio-demographic Index (SDI), a composite indicator of income per capita, years of schooling, and fertility rate in females younger than 25 years. Uncertainty intervals (UIs) were generated for every metric using the 25th and 975th ordered 1000 draw values of the posterior distribution. Findings Global health has steadily improved over the past 30 years as measured by age-standardised DALY rates. After taking into account population growth and ageing, the absolute number of DALYs has remained stable. Since 2010, the pace of decline in global age-standardised DALY rates has accelerated in age groups younger than 50 years compared with the 1990–2010 time period, with the greatest annualised rate of decline occurring in the 0–9-year age group. Six infectious diseases were among the top ten causes of DALYs in children younger than 10 years in 2019: lower respiratory infections (ranked second), diarrhoeal diseases (third), malaria (fifth), meningitis (sixth), whooping cough (ninth), and sexually transmitted infections (which, in this age group, is fully accounted for by congenital syphilis; ranked tenth). In adolescents aged 10–24 years, three injury causes were among the top causes of DALYs: road injuries (ranked first), self-harm (third), and interpersonal violence (fifth). Five of the causes that were in the top ten for ages 10–24 years were also in the top ten in the 25–49-year age group: road injuries (ranked first), HIV/AIDS (second), low back pain (fourth), headache disorders (fifth), and depressive disorders (sixth). In 2019, ischaemic heart disease and stroke were the top-ranked causes of DALYs in both the 50–74-year and 75-years-and-older age groups. Since 1990, there has been a marked shift towards a greater proportion of burden due to YLDs from non-communicable diseases and injuries. In 2019, there were 11 countries where non-communicable disease and injury YLDs constituted more than half of all disease burden. Decreases in age-standardised DALY rates have accelerated over the past decade in countries at the lower end of the SDI range, while improvements have started to stagnate or even reverse in countries with higher SDI. Interpretation As disability becomes an increasingly large component of disease burden and a larger component of health expenditure, greater research and development investment is needed to identify new, more effective intervention strategies. With a rapidly ageing global population, the demands on health services to deal with disabling outcomes, which increase with age, will require policy makers to anticipate these changes. The mix of universal and more geographically specific influences on health reinforces the need for regular reporting on population health in detail and by underlying cause to help decision makers to identify success stories of disease control to emulate, as well as opportunities to improve. Funding Bill & Melinda Gates Foundation.
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              Community-acquired pneumonia requiring hospitalization among U.S. children.

              Incidence estimates of hospitalizations for community-acquired pneumonia among children in the United States that are based on prospective data collection are limited. Updated estimates of pneumonia that has been confirmed radiographically and with the use of current laboratory diagnostic tests are needed.
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                Author and article information

                Contributors
                wanglp@chinacdc.cn
                fang_lq@163.com
                liuwei@bmi.ac.cn
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                18 August 2021
                18 August 2021
                2021
                : 12
                : 5026
                Affiliations
                [1 ]GRID grid.198530.6, ISNI 0000 0000 8803 2373, Division of Infectious Disease, Key Laboratory of Surveillance and Early-warning on Infectious Disease, , Chinese Center for Disease Control and Prevention, ; Beijing, China
                [2 ]GRID grid.410740.6, ISNI 0000 0004 1803 4911, State Key Laboratory of Pathogen and Biosecurity, , Beijing Institute of Microbiology and Epidemiology, ; Beijing, China
                [3 ]GRID grid.506261.6, ISNI 0000 0001 0706 7839, Institute of Pathogen Biology, , Chinese Academy of Medical Sciences and Peking Union Medical College, ; Beijing, China
                [4 ]GRID grid.11135.37, ISNI 0000 0001 2256 9319, Department of Laboratorial Science and Technology, School of Public Health, , Peking University, ; Beijing, China
                [5 ]GRID grid.12981.33, ISNI 0000 0001 2360 039X, Sun Yat-sen University, ; Guangzhou, China
                [6 ]GRID grid.508395.2, Yunnan Center for Disease Control and Prevention, ; Kunming, China
                [7 ]GRID grid.470110.3, ISNI 0000 0004 1770 0943, Shanghai Public Health Clinical Center, ; Shanghai, China
                [8 ]GRID grid.13402.34, ISNI 0000 0004 1759 700X, Zhejiang University, ; Hangzhou, China
                [9 ]Liaoning Provincial Center for Disease Control and Prevention, Shenyang, China
                [10 ]GRID grid.508057.f, Gansu Provincial Center for Disease Control and Prevention, ; Lanzhou, China
                [11 ]GRID grid.49470.3e, ISNI 0000 0001 2331 6153, Wuhan University, ; Wuhan, China
                [12 ]GRID grid.508381.7, ISNI 0000 0004 0647 272X, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, ; Beijing, China
                [13 ]GRID grid.198530.6, ISNI 0000 0000 8803 2373, National Institute for Viral Disease Control and Prevention, , Chinese Center for Disease Control and Prevention, ; Beijing, China
                [14 ]GRID grid.5491.9, ISNI 0000 0004 1936 9297, School of Geography and Environmental Science, , University of Southampton, ; Southampton, UK
                [15 ]GRID grid.8547.e, ISNI 0000 0001 0125 2443, School of Public Health, , Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, ; Shanghai, China
                [16 ]GRID grid.8547.e, ISNI 0000 0001 0125 2443, Department of Epidemiology and Health Statistics, , School of Public Health, Fudan University, ; Shanghai, China
                [17 ]GRID grid.15276.37, ISNI 0000 0004 1936 8091, Department of Biostatistics, College of Public Health and Health Professions, and Emerging Pathogens Institute, , University of Florida, ; Gainesville, FL USA
                [18 ]GRID grid.1013.3, ISNI 0000 0004 1936 834X, Sydney School of Veterinary Science, , The University of Sydney, ; Camden, NSW Australia
                [19 ]GRID grid.488137.1, ISNI 0000 0001 2267 2324, The Institute for Disease Prevention and Control of PLA, ; Beijing, China
                [20 ]GRID grid.8547.e, ISNI 0000 0001 0125 2443, Fudan University, ; Shanghai, China
                [21 ]GRID grid.198530.6, ISNI 0000 0000 8803 2373, Chinese Centre for Disease Control and Prevention, ; Beijing, China
                [22 ]GRID grid.34477.33, ISNI 0000000122986657, Department of Health Metrics Sciences, School of Medicine, University of Washington, ; Seattle, WA USA
                [23 ]GRID grid.34477.33, ISNI 0000000122986657, Institute for Health Metrics and Evaluation, University of Washington, ; Seattle, WA USA
                [24 ]GRID grid.198530.6, ISNI 0000 0000 8803 2373, Chinese Center for Disease Control and Prevention, ; Beijing, China
                [25 ]GRID grid.198530.6, ISNI 0000 0000 8803 2373, National Institute of Parasitic Diseases, , Chinese Center for Disease Control and Prevention, ; Shanghai, China
                [26 ]Center of Disease Prevention and Control in Pudong New Area of Shanghai, Shanghai, China
                [27 ]GRID grid.33199.31, ISNI 0000 0004 0368 7223, Tongji Hospital, Tongji Medical College, , Huazhong University of Science and Technology, ; Wuhan, China
                [28 ]GRID grid.508373.a, ISNI 0000 0004 6055 4363, Hubei Provincial Center for Disease Control and Prevention, ; Wuhan, China
                [29 ]Jiangxi Provincial Center for Disease Control and Prevention, Nanchang, China
                [30 ]Qinghai Provincial Center for Disease Control and Prevention, Xining, China
                [31 ]GRID grid.508390.7, Inner Mongolia Autonomous Region Comprehensive Center for Disease Control and Prevention, ; Hohhot, China
                [32 ]GRID grid.32566.34, ISNI 0000 0000 8571 0482, Lanzhou University, ; Lanzhou, China
                [33 ]Lanzhou Center for Disease Control and Prevention, Lanzhou, China
                [34 ]Baiyin Center for Disease Control and Prevention, Baiyin, China
                [35 ]Tianshui Center for Disease Control and Prevention, Tianshui, China
                [36 ]Wuwei Center for Disease Prevention and Control, Wuwei, China
                [37 ]Qingyang Center for Disease Control and Prevention, Qingyang, China
                [38 ]GRID grid.464467.3, Tianjin Center for Disease Control and Prevention, ; Tianjin, China
                [39 ]Heilongjiang Provincial Center for Disease Control and Prevention, Harbin, China
                [40 ]GRID grid.433871.a, Zhejiang Center for Disease Control and Prevention, ; Hangzhou, China
                [41 ]GRID grid.410734.5, Jiangsu Provincial Center for Disease Control and Prevention, ; Nanjing, China
                [42 ]Fujian Center for Disease Control and Prevention, Fuzhou, China
                [43 ]Beilun People’s Hospital, Ningbo, China
                [44 ]GRID grid.430328.e, Shanghai Municipal Center for Disease Control and Prevention, ; Shanghai, China
                [45 ]GRID grid.13291.38, ISNI 0000 0001 0807 1581, Sichuan University, ; Chengdu, China
                [46 ]GRID grid.203458.8, ISNI 0000 0000 8653 0555, Chongqing Medical University, ; Chongqing, China
                [47 ]Chongqing Center for Disease Control and Prevention, Chongqing, China
                [48 ]GRID grid.496805.6, Guizhou Center for Disease Control and Prevention, ; Guiyang, China
                [49 ]Sichuan Province Center for Disease Control and Prevention, Chengdu, China
                [50 ]GRID grid.508326.a, Guangdong Provincial Center for Disease Control and Prevention, ; Guangzhou, China
                [51 ]Guangzhou Municipal Center for Disease Control and Prevention, Guangzhou, China
                [52 ]GRID grid.417404.2, ISNI 0000 0004 1771 3058, Zhujiang Hospital, Southern Medical University, ; Guangzhou, China
                [53 ]GRID grid.258164.c, ISNI 0000 0004 1790 3548, Jinan University, ; Guangzhou, China
                [54 ]GRID grid.410741.7, The Third People’s Hospital of Shenzhen, ; Shenzhen, China
                Author information
                http://orcid.org/0000-0002-2804-0827
                http://orcid.org/0000-0002-4560-4970
                http://orcid.org/0000-0003-1908-2926
                http://orcid.org/0000-0003-0428-5480
                http://orcid.org/0000-0002-9921-4986
                http://orcid.org/0000-0002-5103-6367
                http://orcid.org/0000-0002-4981-1483
                http://orcid.org/0000-0002-9302-8170
                http://orcid.org/0000-0002-0611-7272
                http://orcid.org/0000-0002-3869-615X
                Article
                25120
                10.1038/s41467-021-25120-6
                8373954
                34408158
                dce7b878-63cb-4566-b777-a2f3b4577e00
                © The Author(s) 2021

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 8 March 2021
                : 26 July 2021
                Funding
                Funded by: China Mega-Project on Infectious Disease Prevention;grant number:2018ZX10201001
                Funded by: China Mega-Project on Infectious Disease Prevention;grant number:2018ZX10713002 National Natural Science Funds;grant number:81825019
                Categories
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                Custom metadata
                © The Author(s) 2021

                Uncategorized
                infectious diseases,respiratory tract diseases,epidemiology
                Uncategorized
                infectious diseases, respiratory tract diseases, epidemiology

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