Dear editor,
We read with interest the recent report by Lam and colleagues in this Journal, who compared global rates of respiratory viruses (REF)(1). We found that 20 respiratory pathogens circulated in Beijing, China, and influenza virus, human rhinovirus (hRV) and mycoplasma (MP) were the major pathogens. This information needs to be considered by clinicians when treating patients presenting with influenza-like illness (ILI).
Influenza viruses, other respiratory viruses and bacteria have been detected in patients with ILI (2–4). These respiratory viruses and bacteria, including seasonal humans influenza virus A (sFluA) and humans influenza virus B (sFluB), human coronavirus (hCoV-OC43,hCoV-NL63,hCoV-HKU1 and hCoV-229E), para-influenza virus (PIV 1–4), adenovirus (ADV), enterovirus (EV), hRV, respiratory syncytial virus (RSV), boca virus (BoV), human metapneumovirus (hMPV), chlamydia (CM) and MP are well recognized. Patients infected by these pathogens exhibit highly similar symptoms, rendering a clinical diagnosis unreliable and limiting aetiological and epidemiological studies.2 Elucidating the epidemiological characteristics and regularity of ILI pathogens is of great significance in guiding clinical diagnoses and avoiding the abuse of antibiotics.
We collected 6327 throat swabs. Of the 6327 (female 2782 vs. male 3545) outpatients who sought treatment in 2016 to 2018, 1886 outpatients were confirmed as pathogen positive. In this study, we studied only ILI outpatients who were single pathogen positive. Overall, the most frequently detected agents were H3N2 (530/6327, 8.38%), H1N1 (285/6327, 4.50%), hRV (216/6327, 3.41%), and B-Yamagata (195/6327, 3.08%). (Fig. 1 and Table 1 ). In short, the positive rate of each year was basically the same as the total positive rate. The top three pathogens were mainly influenza viruses. Influenza and other pathogens were present for most of the 3 years in this study. However, the same influenza subtype did not persist as the dominant strain; rather, a mixture of high-intensity peaks of single subtypes and the co-circulation of types and subtypes at variable intensities occurred. (Fig. S1–3). This was consistent with other ILI surveillance data that demonstrated asynchronous peaks and the co-circulation of different pathogens.5
Fig. 1.
Dendrogram of pathogen spectrum of ILI in Beijing from 2016 to 2018.
Table 1.
Composition of ILI pathogen spectrum in Beijing, 2016–2018.
| H1N1 | H3N2 | B -Yamagata | B -Victoria | hRV | BoV | PIV-1 | PIV-2 | PIV-3 | PIV-4 | hCoV-229E | hCoV-OC43 | hCoV-NL63 | hCoV- HKU1 | EV | hMPV | RSV | ADV | CM | MP | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Positivity(%) | Positivity(%) | Positivity(%) | Positivity(%) | Positivity(%) | Positivity(%) | Positivity(%) | Positivity(%) | Positivity(%) | Positivity(%) | Positivity(%) | Positivity(%) | Positivity(%) | Positivity(%) | Positivity(%) | Positivity(%) | Positivity(%) | Positivity(%) | Positivity(%) | Positivity(%) | |
| Tatal | 285(4.50) | 530(8.38) | 195(3.08) | 186(2.94) | 216(3.41) | 16(0.25) | 8(0.13) | 18(0.28) | 33(0.52) | 5(0.08) | 10(0.16) | 49(0.77) | 19(0.30) | 7(0.11) | 50(0.72) | 42(0.66) | 32(0.54) | 52(0.82) | 6(0.09) | 127(2.01) |
| Age | ||||||||||||||||||||
| 0–4yrs | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 5–14yrs | 1(2.33) | 1(2.33) | 1(2.33) | 6(13.95) | 2(4.65) | 1(2.33) | 0 | 0 | 1(2.33) | 0 | 0 | 0 | 1(2.33) | 0 | 0 | 1(2.33) | 0 | 2(4.65) | 0 | 0 |
| 15–24yrs | 20(1.86) | 85(7.92) | 25(2.33) | 38(3.54) | 49(4.57) | 3(0.28) | 1(0.09) | 6(0.56) | 8(0.75) | 0 | 5(0.47) | 3(0.28) | 5(0.47) | 0 | 12(1.12) | 1(0.09) | 12(1.12) | 7(0.65) | 1(0.09) | 20(1.86) |
| 25–59yrs | 221(5.11) | 335(7.74) | 138(3.19) | 121(2.80) | 141(3.26) | 7(0.16) | 5(0.12) | 11(0.25) | 16(0.37) | 5(0.12) | 4(0.09) | 40(0.92) | 6(0.14) | 6(0.14) | 34(0.79) | 24(0.55) | 16(0.37) | 37(0.86) | 5(0.12) | 92(2.13) |
| 60+yrs | 43(4.87) | 109(12.34) | 31(3.51) | 21(2.38) | 24(2.72) | 5(0.57) | 2(0.23) | 1(0.11) | 8(0.91) | 0 | 1(0.11) | 6(0.68) | 7(0.79) | 1(0.11) | 4(0.45) | 16(1.81) | 4(0.45) | 6(0.68) | 0 | 15(1.70) |
| χ2 | 26.774 | 21.501 | 4.513 | 15.597 | 7.86 | 13.942 | 7.197 | 7.933 | 12.248 | 7.882 | 11.763a | 5.133a | 19.927a | 7.343a | 5.621a | 25.875a | 11.765a | 8.842a | 7.117a | 2.915a |
| P-value | 0.000a | 0.000a | 0.411a | 0.050a | 0.116a | 0.016a | 0.678a | 0.283a | 0.035a | 0.504a | 0.079 | 0.274 | 0.002 | 0.738 | 0.424 | 0 | 0.042 | 0.113 | 0.844 | 0.811 |
| Gender | ||||||||||||||||||||
| Female | 167(6.00) | 307(11.04) | 123(4.42) | 106(3.81) | 119(4.28) | 12(0.43) | 6(0.22) | 9(0.32) | 18(0.65) | 4(0.14) | 8(0.29) | 30(1.08) | 10(0.36) | 5(0.18) | 25(0.90) | 24(0.86) | 19(0.68) | 35(1.26) | 3(0.11) | 69(2.48) |
| Male | 118(3.33) | 223(6.29) | 72(2.03) | 80(2.26) | 97(2.74) | 4(0.11) | 2(0.06) | 9(0.25) | 15(0.42) | 1(0.03) | 2(0.06) | 19(0.54) | 9(0.25) | 2(0.06) | 25(0.71) | 18(0.51) | 13(0.37) | 17(0.48) | 3(0.08) | 58(1.64) |
| χ2 | 25.915 | 45.72 | 29.792 | 13.184 | 11.229 | 6.269 | 3.131 | 0.266 | 1.506 | 2.637 | 5.278 | 5.967 | 0.58 | 2.145 | 0.744 | 2.978 | 3.098 | 11.591 | 0.089 | 5.647 |
| P-value | 0 | 0 | 0 | 0 | 0.001 | 0.012 | 0.077 | 0.606 | 0.22 | 1.104 | 0.022 | 0.015 | 0.446 | 0.413 | 0.388 | 0.084 | 0.075 | 0.001 | 0.766 | 0.017 |
| Years | ||||||||||||||||||||
| 2016 | 55(2.66) | 173(8.38) | 27(1.31) | 148(7.17) | 88(4.26) | 9(0.44) | 1(0.05) | 7(0.34) | 3(0.15) | 0 | 2(0.10) | 29(1.40) | 4(0.19) | 2(0.10) | 16(0.77) | 15(0.73) | 4(0.19) | 30(1.45) | 0 | 48(2.32) |
| 2017 | 79(3.61) | 277(12.65) | 63(2.88) | 11(0.50) | 52(2.38) | 5(0.23) | 4(0.18) | 8(0.37) | 11(0.50) | 3(0.14) | 7(0.32) | 11(0.50) | 1(0.05) | 3(0.14) | 20(0.91) | 15(0.69) | 12(0.55) | 7(0.32) | 6(0.27) | 33(1.51) |
| 2018 | 151(7.28) | 80(3.86) | 105(5.07) | 27(1.30) | 76(3.67) | 2(0.10) | 3(0.14) | 3(0.14) | 19(0.92) | 2(0.10) | 1(0.05) | 9(0.43) | 14(0.68) | 2(0.10) | 14(0.68) | 12(0.58) | 16(0.77) | 15(0.72) | 0 | 46(2.22) |
| χ2 | 57.587 | 107.306 | 49.367 | 194.483 | 12.057 | 4.802 | 1.599 | 2.15 | 11.884 | 2.631a | 5.703 | 15.893 | 15.263 | 0.211 | 0.781 | 0.365 | 6.991 | 17.1 | 8.785a | 4.308 |
| P-value | 0 | 0 | 0 | 0 | 0.002 | 0.091 | 0.449 | 0.341 | 0.003 | 0.379 | 0.058 | 0 | 0 | 0.9 | 0.677 | 0.833 | 0.03 | 0 | 0.003 | 0.116 |
| Month | ||||||||||||||||||||
| Jan | 61(10.68) | 114(19.96) | 61(10.68) | 26(4.55) | 7(1.23) | 1(0.18) | 0 | 0 | 0 | 0 | 1(0.18) | 1(0.18) | 0 | 0 | 0 | 13(2.28) | 7(1.23) | 1(0.18) | 0 | 5(0.88) |
| Feb | 67(12.25) | 76(13.89) | 48(8.78) | 52(9.51) | 3(0.55) | 0 | 0 | 1(0.18) | 0 | 1(0.18) | 0 | 0 | 0 | 1(0.18) | 1(0.18) | 5(0.91) | 2(0.37) | 3(0.55) | 0 | 7(1.28) |
| Mar | 60(12.24) | 22(4.49) | 16(3.27) | 41(8.37) | 11(2.24) | 3(0.61) | 0 | 0 | 0 | 0 | 0 | 2(0.41) | 1(0.20) | 0 | 1(0.20) | 8(1.63) | 2(0.41) | 0 | 0 | 2(0.41) |
| Apr | 28(4.90) | 7(1.23) | 9(1.58) | 50(8.76) | 35(6.13) | 0 | 2(0.35) | 2(0.35) | 6(1.05) | 1(0.18) | 2(0.35) | 9(1.58) | 2(0.35) | 3(0.53) | 1(0.18) | 9(1.58) | 5(0.88) | 2(0.35) | 0 | 4(0.70) |
| May | 4(0.77) | 0 | 1(0.19) | 15(2.89) | 11(2.12) | 7(1.35) | 1(0.19) | 8(1.54) | 4(0.77) | 0 | 1(0.19) | 9(1.73) | 1(0.19) | 1(0.19) | 7(1.35) | 2(0.39) | 0 | 0 | 0 | 3(0.58) |
| Jun | 2(0.36) | 3(0.53) | 0 | 0 | 17(3.02) | 2(0.36) | 0 | 0 | 12(2.13) | 0 | 0 | 10(1.78) | 0 | 2(0.36) | 4(0.71) | 1(0.18) | 1(0.18) | 2(0.36) | 6(1.07) | 4(0.71) |
| Jul | 1(0.18) | 14(2.57) | 0 | 0 | 34(6.25) | 1(0.18) | 3(0.55) | 1(0.18) | 4(0.74) | 1(0.18) | 0 | 5(0.92) | 3(0.55) | 0 | 6(1.10) | 0 | 0 | 5(0.92) | 0 | 4(0.74) |
| Aug | 3(0.61) | 56(11.38) | 0 | 0 | 52(10.57) | 0 | 0 | 2(0.41) | 2(0.41) | 0 | 0 | 4(0.81) | 8(1.63) | 0 | 11(2.24) | 1(0.20) | 3(0.61) | 6(1.22) | 0 | 14(2.85) |
| Sep | 0 | 63(12.57) | 1(0.20) | 2(0.40) | 16(3.19) | 0 | 1(0.20) | 0 | 2(0.40) | 1(0.20) | 1(0.20) | 4(0.80) | 3(0.60) | 0 | 2(0.40) | 1(0.20) | 0 | 11(2.20) | 0 | 27(5.39) |
| Oct | 0 | 24(4.29) | 0 | 0 | 15(2.68) | 1(0.18) | 0 | 2(0.36) | 2(0.36) | 1(0.18) | 1(0.18) | 4(0.71) | 1(0.18) | 0 | 11(1.96) | 1(0.18) | 7(1.25) | 10(1.79) | 0 | 31(5.54) |
| Nov | 6(1.38) | 37(8.53) | 4(0.92) | 0 | 12(2.76) | 1(0.23) | 1(0.23) | 1(0.23) | 0 | 0 | 3(0.69) | 1(0.23) | 0 | 0 | 5(1.15) | 0 | 3(0.69) | 9(2.07) | 0 | 17(3.92) |
| Dec | 53(9.91) | 114(21.31) | 55(10.28) | 0 | 3(0.56) | 0 | 0 | 1(0.19) | 1(0.19) | 0 | 1(0.19) | 0 | 0 | 0 | 1(0.19) | 1(0.19) | 2(0.37) | 3(0.56) | 0 | 9(1.68) |
| χ2 | 54.668 | 113.198 | 343.292 | 314.972 | 144.03 | 19.865 | 10.32 | 20.174 | 32.867 | 6.9 | 9.463a | 31.141a | 23.843a | 10.178a | 37.559a | 42.268a | 21.338a | 38.611a | 19.020a | 105.864a |
| P-value | 0.000a | 0.000a | 0.000a | 0.000a | 0 | 0.001a | 0.120a | 0.002a | 0.000a | 0.945a | 0.297 | 0 | 0 | 0.118 | 0 | 0 | 0.007 | 0 | 0 | 0 |
Fisher's exact test.
Of the 6327 ILI, the overall prevalence of influenza was 18.90% (1196/6327). Relatively low detection rates have even been reported in studies conducted in other geographical areas, such as Gansu Province in China,6 in which a prevalence of 14.22% (501/59,791) was reported in a study conducted between 2010 and 2016. Nevertheless, other studies have reported relatively high detection rates in regions such as the Northern Hemisphere (2013–2014)4 and Guangdong Province in China (2017–2018),7 at 20.48% (1086/5303) and 28.33% (2137/7543), respectively. Regarding the pathogen spectrum of ILI in Beijing, the dominant strains in 2016–2018 were H3N2 and B-Victoria (2016), H3N2 and H1N1 (2017), H3N2 and B-Yamagata (2018), indicating that different dominant subtypes of influenza viruses in this region alternate. The discrepancies in the influenza detection rates among patients with ILI from different areas highlighted the geographical differences in virus burdens. However, these geographical differences in the detection rate may have been affected by several other factors, such as different technical approaches, sampling periods, study durations, or target populations (global population, paediatric population, etc.).1 , 3
A Pearson correlation analysis was performed considering the numbers of influenza-positive cases and non-influenza cases; the result showed that the former was not correlated with the latter (rs = −0.707, P < 0.05) (Fig. S4). Moreover, among the non-influenza respiratory pathogens, hRV and MP maintained high positivity levels, and the RSV infection rate increased annually (Table 1). The results of this study are similar to those of ILI in Ho Chi Minh city5 and Zhuhai city.2
It was hRV that had a detection rate of 3.41% (216/6327) in this study; hRV had a great impact on persons under 60 years old and was a major viral pathogen of ILI during the study period (Table 1). In many regions of the world, there have been reports of outbreaks caused by hRV, such as the UK (2009–2017)8 and Vietnam (2013–2015),5 at 2.14% and 8.8%, respectively. Our study showed that hRV infection occurred predominantly in April, July, and August, with the majority of cases in the 5–24 years age group (Table 1). The virus was also highly associated with influenza and other pathogens. Respiratory symptoms caused by hRV are complicated, easily leading to a misdiagnosis and delayed treatment.9 In the future, it is necessary to strengthen the monitoring of hRV in ILI patients, provide data support for the early warning and prevention of relevant epidemic situations, and avoid large-scale epidemics.
This study also found that MP accounted for a large proportion of the pathogen spectrum of ILI in Beijing, with a detection rate of 2.01%. MP is a common pathogen associated with human respiratory infections that can cause endemic or even global outbreaks among people of all ages.10 However, there has been few studies on MP in routine surveillance at home and abroad, and the inclusion of these additional pathogens in ILI studies might greatly increase the positive detection frequency(2). Therefore, in an early epidemic of unexplained fever, it is necessary to be alert to the possibility of MP infection, especially when the epidemic occurs in a closed environment.
The strength of this study was that there were no previous reports on the detection of these 20 pathogens, especially MP and CM, in ILI patients during the influenza epidemic in Beijing. Our study showed that the data from this study people were important reference data, and that understanding the interactions between the different influenza subtypes and types and other respiratory pathogens is critical. In addition, we must improve prevention and management strategies for ILI.
These results demonstrate that a wide range of respiratory pathogens are circulating in Beijing city and that H3N2, H1N1, B-Yamagata, B-Victoria, hRV and MP are the major pathogens. It is recommended that the trend of pathogen spectrum changes in patients with ILI in the region should be continuously monitored. At the same time, the health department should also strengthen the analysis and utilization of monitoring data and track the activity level and variation in influenza virus rates to address fever outbreaks in a timely manner.
Declaration of Competing Interest
None.
Acknowledgments
We would like to acknowledge the Ministry of Health for its support and all the sentinel site healthcare workers.
This work was supported by the CHN Beijing Chaoyang District Science and Technology Plan Project (grant no. CYSF1721) and the National Major Science and Technology Project for Control and Prevention of Major Infectious Diseases in China (2017ZX10103004).
The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. We are very grateful to the patients, clinical and laboratory staff at the two hospitals concerned and the numerous support staff in Chinese Academy of Medical Sciences and Peking Union Medical College in China. All authors declare no competing interest.
Footnotes
Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jinf.2019.11.014.
Appendix. Supplementary materials
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