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. 2021 Aug 18;12(1):5026.
doi: 10.1038/s41467-021-25120-6.

Etiological and epidemiological features of acute respiratory infections in China

Zhong-Jie Li #  1 , Hai-Yang Zhang #  2 , Li-Li Ren #  3 , Qing-Bin Lu #  4 , Xiang Ren  1 , Cui-Hong Zhang  1 , Yi-Fei Wang  1 , Sheng-Hong Lin  1 , Xiao-Ai Zhang  2 , Jun Li  5 , Shi-Wen Zhao  6 , Zhi-Gang Yi  7 , Xiao Chen  8 , Zuo-Sen Yang  9 , Lei Meng  10 , Xin-Hua Wang  10 , Ying-Le Liu  11 , Xin Wang  12 , Ai-Li Cui  13 , Sheng-Jie Lai  1   14   15 , Tao Jiang  2 , Yang Yuan  2 , Lu-Sha Shi  1 , Meng-Yang Liu  2 , Yu-Liang Zhu  1 , An-Ran Zhang  2 , Zhi-Jie Zhang  16 , Yang Yang  17 , Michael P Ward  18 , Lu-Zhao Feng  1 , Huai-Qi Jing  12 , Liu-Yu Huang  19 , Wen-Bo Xu  13 , Yu Chen  8 , Jian-Guo Wu  11 , Zheng-Hong Yuan  20 , Meng-Feng Li  5 , Yu Wang  21 , Li-Ping Wang  22 , Li-Qun Fang  23 , Wei Liu  24 , Simon I Hay  25   26 , George F Gao  21 , Wei-Zhong Yang  21 ; Chinese Centers for Disease Control and Prevention (CDC) Etiology of Respiratory Infection Surveillance Study Team
Collaborators, Affiliations

Etiological and epidemiological features of acute respiratory infections in China

Zhong-Jie Li et al. Nat Commun. .

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.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Setting of hospital-based surveillance network and data processing in the mainland of China.
A Locations of the 277 sentinel hospitals and the 92 reference laboratories participating in acute respiratory infection (ARI) surveillance from 2009 to 2019. Each point indicates the location of a sentinel hospital (blue) or laboratory (green). The black lines indicated the province boundaries. The background color indicates the average population density of each province from 2009 to 2019 in China. B Flowchart of data collection and sorting procedures.
Fig. 2
Fig. 2. Viral and bacterial composition of patients with ARIs in the mainland of China, 2009‒2019.
The overall viral composition of 110,058 ARI patients who had all the eight viral pathogens tested. The overall bacterial composition of 26,757 ARI patients who had all the nine bacterial pathogens tested. The length of colored bars and the number behind indicate the proportion of each pathogen, calculated by its positive number used as the numerator and the total positive number of all pathogens used as the denominator. For IFV, RSV, and HPIV, the proportion of their subtypes each colored bar indicated.
Fig. 3
Fig. 3. Coinfection pattern and interactions of pathogens in patients with acute respiratory infection in the mainland of China, 2009−2019.
A Coinfection rates were calculated pairwise. For pathogen ‘X’ and ‘Y’, numerator was the number of patients coinfected both ‘X’ and ‘Y’ and the denominator where the total number of patients who were both tested ‘X’ and ‘Y’. Bigger size and darker color of the circles indicate higher coinfection rates between two pathogens. B The interactions among pathogens are estimated by host-scale logistic regressions. Positive interactions with two-sided p value <0.05 were denoted in orange and the negative interactions with two-sided p value <0.05 were denoted in green color. The p values were not adjusted for multiple comparisons. The interaction was determined as both significant when without adjusting for multi-pathogens (Supplementary Table 3) and when adjusting for multi-pathogens (Supplementary Table 4) L. pneumophila, C. pneumoniae, and GAS were not included in the logistic analysis due to the small sample size.
Fig. 4
Fig. 4. The Join-Point regression of the positive rates of each tested virus by age of patient.
A red point indicates the mean positive rate of patients in terms of age and the colored curves indicate fitted patterns by the red points. The colored segments indicate the fitting values of the Join-Point regression. Legends give the Annual Percent Change (APC) value of each fitted curve for each tested virus. *indicates that the APC is significantly different from zero at two-sided P < 0.05. The p values were not adjusted for multiple comparisons. The gray bars indicate the number of patients tested for each pathogen.
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References

    1. GBD. 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. Lancet. 2020;396:1204–1222. doi: 10.1016/S0140-6736(20)30925-9. - DOI - PMC - PubMed
    1. World Health Organization. Global Health Estimates 2016: deaths by cause, age, sex, by country and by region, 2000‒2016 (2018); http://www.who.int/healthinfo/global_burden_disease/estimates/en/index1....
    1. GBD. Mortality, morbidity, and hospitalisations due to influenza lower respiratory tract infections, 2017: an analysis for the Global Burden of Disease Study 2017. Lancet Respir. Med. 2019;7:69–89. doi: 10.1016/S2213-2600(18)30496-X. - DOI - PMC - PubMed
    1. Li Q, et al. Early transmission dynamics in Wuhan, China, of novel coronavirus–infected pneumonia. N. Engl. J. Med. 2020;382:1199–1207. doi: 10.1056/NEJMoa2001316. - DOI - PMC - PubMed
    1. Jain S, et al. Community-acquired pneumonia requiring hospitalization among U.S. adults. N. Engl. J. Med. 2015;373:415–427. doi: 10.1056/NEJMoa1500245. - DOI - PMC - PubMed

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