Epidemiological characteristics of eleven common respiratory viral infections in children

Epidemiological characteristics of eleven common respiratory viral infections in children | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Epidemiological characteristics of eleven common respiratory viral infections in children Suling Li, Zhengfeng Xue, Yuxin Feng, Xue Zhou, Yang Qi, Na Feng, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3869323/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 20 Dec, 2024 Read the published version in BMC Pediatrics → Version 1 posted 11 You are reading this latest preprint version Abstract Background Lower respiratory tract infections (LRTIs) are one of the leading causes of hospital admissions among children. In this study, we aimed to describe the epidemiological characteristics of viral pathogens associated with LRTIs in hospitalized children in Yan'an; this has yet to be reported in the literature and may guide public health interventions and resource allocation in this region. Methods Between June 2021 and May 2023, we conducted a retrospective analysis of the results of viral detection using oral pharyngeal swabs from 4565 children with LRTIs in the Inpatient Department of Yan'an University Affiliated Hospital. Eleven respiratory viruses, including influenza A virus (Flu A), influenza A H1N1 virus (H1N1), seasonal influenza A H3N2 virus (H3N2), influenza B virus (Flu B), parainfluenza virus (HPIV), adenovirus (HADV), bocavirus (HBoV), rhinovirus (HRV), metapneumovirus (HNPV), coronavirus (HCoV), and respiratory syncytial virus (HRSV), were confirmed by applying a multiplex real-time polymerase chain reaction (PCR) kit for respiratory viruses. We evaluated the epidemiological features of infections caused by respiratory pathogens, including aging and the seasonal variations of different pathogens, and explored the high-risk factors associated with virus-caused pneumonia. Results At least one virus was detected in all 4565 cases; the positivity rate was 27.95%. We also detected a total of 1,276 cases with mixed infections (with two or more viruses). Of the positive cases, 59.3% were male and 40.7% were female ( x 2 = 0.41, P = 0.68). The highest positivity rates for respiratory pathogens were observed for HRSV, HRV, and HADV, at 5.98%, 5.67%, and 4.38%, respectively. We also observed variations in the number and positivity rates of respiratory pathogen infections by season and age. HPIV ( x 2 = 12.05，P < 0.05) and HADV ( x 2 = 11.73，P < 0.05) were more common in children under 3 years-of-age. Conclusions In conclusion, our analysis revealed that respiratory pathogen infections varied by gender, season, and age in the enrolled population of children. Children Respiratory tract infections Respiratory virus Epidemiology Figures Figure 1 Figure 2 Figure 3 Background Respiratory viruses are the main cause of lower respiratory tract infection (LRTI) in the pediatric population [1]. The Global Burden of Disease (2019) study reported that LRTIs are the second highest cause of health burden in children [2] while severe pneumonia was reported as the major cause of morbidity and mortality in children, especially those under 5 years-of-age [3]. These LRTIs are also known to impose significant financial strain on families [4]. According to the World Health Organization (WHO), LRTIs and pneumonia account for more than 4 million deaths annually [5]. Over the last few decades, the diagnostic work up of clinical infections has changed significantly, especially with the rapid development of molecular diagnostic methods [6, 7]. In particular, the multiplex polymerase chain reaction (PCR) assay has been confirmed as a significantly more advanced tool for the clinical detection of potential pathogens. This method is used to detect a wide range of respiratory pathogens, including but not limited to influenza viruses (A and B), human rhinovirus (HRV), respiratory syncytial virus (HRSV), human adenovirus (HADV), human parainfluenza viruses (HPIV), and human metapneumovirus (HMPV), providing a comprehensive approach to identifying potential infectious agents in respiratory illnesses [8, 9]. In this study, we retrospectively reviewed all pediatric patients with ALRTI in Yan'an University Affiliated Hospital from 2021 to 2023 who were identified to be infected with pathogens using a multiplex PCR assay platform. We described the epidemiological characteristics of the respiratory pathogens detected in these children, including Human rhinovirus (HRV), Bocavirus, human parainfluenza virus (HPIV), human coronavirus (HCOV), HRSV, influenza A (InfA), influenza B (InfB), human metapneumovirus (HMPV) and adenovirus (ADV). We also evaluated the epidemiological features of infections caused by respiratory pathogens, including aging and the seasonal variations of different pathogens, and explored the high-risk factors associated with virus-caused pneumonia. Our findings could help healthcare professionals to manage local epidemic pathogens more effectively and evaluate disease burden in a timely manner because no previous research has targeted this particular population. Participants and methods Study site This study was performed at the Pediatric Inpatient Department of Yan'an University Affiliated Hospital in Yan’an, Shanxi, China. Yan’an is located in the northern part of Shaanxi Province, Yan’an has a plateau continental monsoon climate; the northern part of this region has a semi-arid climate while the southern part has a semi-humid climate. Participants Our study included all hospitalized children between the 1 st of June 2021 and the 31 st of May 2023 who had been diagnosed with LRTI in the Inpatient Department of Yan'an University Affiliated Hospital. Patients were included if they met the diagnostic criteria for acute upper respiratory tract infection or acute lower respiratory tract infection according to the national diagnostic criteria for pediatric respiratory tract infection. Patients were excluded if there was no pathogenic testing data available. These children had all been subjected to 11 respiratory pathogen tests. For each patient, we collated a range of data from an electronic medical records system, including hospitalization number, sex, age, diagnosis, and the results of diagnostic tests. A total of 4,565 patients (2,664 males and 1,902 females) were included in our analysis (ages ranged from 0 to 14 years). The patients were divided into four groups by age: <1 year (group I), 1–< 3 years (group II), 3–< 6 years (group III), and ≥6 years (group IV). Based on the climatic conditions of China, the four seasons were categorized as follows: March, April, and May were considered to be spring; June, July, and August were considered to be summer; September, October, and November were considered to be autumn; and December, January, and February of the next year were considered to be winter. The temperature and precipitation data in Beijing were gathered from the China Meteorological Administration Government Website ( https://www.cma.gov.cn ). This retrospective study was approved by the Ethics Committee of the Yan'an University Affiliated Hospital, China. (Reference: S-S20230003). Specimen collection Throat swabs for nucleic acid testing of respiratory pathogens were collected within 24 hours of a child's admission by trained pediatric nurses. The collection process involved cleaning the child's mouth and teeth. The collection process involved the following steps：(1) cleaning the child's mouth and teeth to ensure that the area was free from any debris or contaminants, and (2) the application of a flocking swab to reach the child's pharyngeal isthmus. The flocking swab was inserted gently into the child's mouth and directed towards the pharyngeal isthmus, the narrow passage between the back of the mouth and the throat. The swab was then used to wipe the posterior pharyngeal wall and bilateral tonsils; rotational movements were used to maximize the contact surface. After sampling, the swab was swiftly extracted from the child's pharyngeal isthmus and immediately placed in a sample tube containing 3ml of the sample solution provided with the kit（ResP® 13 Respiratory Pathogen Multiplex Detection Kit，NINGBO HEALTH GENE TECHNOLOGIES CO., LTD）. The sample tube was then sent for examination within 30 minutes to ensure the integrity of the collected sample. PCR capillary electrophoresis fragment analysis for eleven respiratory pathogens Assays were developed for 11 respiratory pathogens, including influenza A virus (Flu A), influenza A H1N1 virus (H1N1), seasonal influenza A H3N2 virus (H3N2), influenza B virus (Flu B), parainfluenza virus (HPIV), respiratory syncytial virus (HRSV), bocavirus (HBoV), rhinovirus (HRV), metapneumovirus (HNPV), coronavirus (HCoV), and adenovirus (HADV). This test comprehensively covers the clinical high positive rate and severe high-risk viruses, and can achieve the joint detection of multiple pathogens, thus providing a comprehensive, accurate and rapid detection method for the clinical diagnosis of acute respiratory infection and the differential diagnosis of COVID-19. For each patent, throat swabs were collected multiple fluorescence quantitative polymerase chain reaction (PCR) tests were carried out in accordance with the instructions provided in a Respiratory Pathogen Detection Kit (Ningbo Haishi Gene Technology Co., Ltd., Ningbo, China). Eleven sets of specific primers were employed, and one-step RT-PCR was performed in a single tube to amplify target fragments. The nucleic acid samples were amplified through a series of RT-PCR steps: first, pretreatment at 25 ℃ (5 min) for one cycle, reverse transcription at 50 ℃ (15 min) for one cycle, and pre-denaturation at 95 ℃ (2 min) for one cycle; secondly, denaturation at 94 ℃ (30 s), annealing at 65 ℃ (30 s), and extension at 72 ℃ (60 s) for six cycles, a step that was repeated until the annealing temperature reached 60 ℃; there was a 1 ℃ touchdown every six cycles. third, denaturation at 94 ℃ (30 s), annealing at 60 ℃ (30 s), and extension at 72 ℃ (60 s) for 29 cycles; finally, the products were extended at 72 ℃ (10 min) for one cycle and were kept at 4 ℃ for one cycle. Capillary electrophoresis was applied to separate the amplification products of different lengths by the GenomeLab GeXP Genetic Analysis System (Beckman Coulter). The samples were samples analyzed by trained professionals who then generated a report based on their findings. Statistical analysis Data are presented as number [n(%)] and were tested by Chi-squared tests. Binary logistic regression analyses were used to calculate odds ratios (ORs) with 95% confidence intervals (CIs). Data were analyzed by R software (The R Foundation; http://www.r-project.org; version 4.2.1) and EmpowerStats software (www.empowerstats.net, X&Y solutions, Inc. Boston, Massachusetts). GraphPad Prism 9 software was used for mapping. A P-value < 0.05 was considered to be statistically different. Results General characteristics of enrolled patients Of the 4565 cases included in our analysis, 1276 were positive for at least one virus, with a total positivity rate of 27.95%. There were 2664 males and 1901 females included in our analysis, with an mean age of 3.3 years (standard deviation = 2.9); 1068 were under 1 year-of-age, 1050 were 1–3 years-of-age, 1519 were 3–6 years-of-age, and 928 were over 6 years-of-age. Positive cases involved 757 (59.3%) males and 519 (40.7%) females. The positive rates of the 11 respiratory pathogen assays were 5.98% (HRSV), 5.67% (HRV), 4.38% (HADV), 3.68% (HNPV), 2.83% (HPIV), 2.74% (Flu A), 1.97% (H3N2), 1.82% (H1N1), 1.80% (Flu B), 1.10% (HBoV), and 0.35% (HCoV), respectively. There was no significant difference in gender between the groups in terms of positive pathogen detection = 0.41, P = 0.68), although the age groups showed a statistically significant difference ( = 57.05, P ＜ 0.001, Table 1). As shown in Fig. 1, the number and positive rates of respiratory pathogen detection varied by season and age. The number and positive rate of respiratory virus testing exhibited a trough during winter, especially in January and February. However, the total positive rate of the 11 viral infections peaked in March on an annual basis. The positive rates of respiratory pathogen detection were highest in the 3–< 6 years-of-age group. Seasonal and age distribution of various respiratory pathogens As shown in Fig. 2, the positivity rate and number of HADV detections exhibited multiple peaks in various months, and were highest during winter (October and November) in 2022, especially in terms of FluA and H3N2. The HRSV peak occurred in November and December 2021 but decreased rapidly in January. Thereafter, the prevalence of HRSV remained low until April 2023. In 2021, the first wave of HRV peaked in September, followed by a second and third wave in March and July 2022, but exhibited a clear trough in December and January every year. The positivity of FluB peaked in March 2022 whereas H1N1 peaked in the same month in 2023. Subsequently, peak HNPV positivity was detected in spring and summer (April to June) in 2022. HRSV and HRV predominated in the < 3 years-of-age group (Fig. 3). HNPV were common in the 3–6 years-of-age group, whereas HBoV and HRV were predominant in the ≥ 6 years-of-age group. The associations between season/age and various respiratory pathogens As shown in Table 2 and Table 3, multivariate regression analysis revealed that FluA infections occurred more often among children aged ≥6 years-age and in autumn (P< 0.01); H1N1 was also commonly found in children aged ≥6 years-of-age and occurred more commonly in spring (P < 0.01). H3N2 predominated in children aged 3–6 years and majorly in the autumn season (P< 0.01); FluB was commonly detected in children aged ≥6 years-of-age and was mostly detected during spring (P < 0.01). HPIV was mostly detected in children aged <3 years-of-age and mainly during summer (P < 0.01). HADV was mostly detected in children aged <3 years-of-age and tended to peak in the autumn (P < 0.01). HBoV was commonly detected in children aged 1–3 years-of-age and mainly in summer (P < 0.01). HRV was mostly detected in children aged <3 years-of-age and peaked in the summer (P < 0.01). HNPV was most commonly detected in children aged 3–6 years-of-age and in spring (P < 0.01). HCoV was mostly detected in children aged 0.05) but peaked in spring. HRSV was most commonly detected in children aged <1 year-of-age and during the autumn and winter (P < 0.01) Discussion Between early 2020 and December 2022, a wide range of stringent non-pharmaceutical interventions (NPIs) were used to combat the COVID-19 pandemic, including mask-wearing, school closures, and social distancing. This strategy not only reduced the transmission of the SARS-CoV-2 virus but also influenced the prevalence patterns of other common respiratory viruses [10]. For instance, measures such as social distancing, mask-wearing, and enhanced hygiene practices contributed to a marked decline in the circulation of influenza and other respiratory viruses, which typically follow seasonal patterns. Health data from various regions showed a significant drop in flu cases during periods when COVID-19 restrictions were in place, thus suggesting that the interventions for controlling the pandemic inadvertently suppressed the transmission of these other viruses. This phenomenon has provided a unique opportunity to study the effects of public health strategies on a range of respiratory illnesses, potentially offering insights into more effective approaches to managing viral diseases in the future. Due to the strict implementation of these NPIs, the incidence of SARS-CoV-2 was significantly reduced, and individuals infected with the virus were promptly transferred to specialized infectious disease hospitals for appropriate medical care. Consequently, the scope of this study on hospitalized children did not involve individuals diagnosed with COVID-19. The rigorous enforcement of non-pharmaceutical interventions (NPIs) effectively curbed the transmission of SARS-CoV-2 [11]. As a result, the infection rate of other respiratory pathogens typically linked to ARIs was reduced further, dropping to 27.9% compared to its previous value prior to the COVID-19 pandemic. These findings align with a previous study conducted in Shanghai, which reported a similar reduction in the infection rate of viral respiratory pathogens to 27.5% [12]. In the present study, our analyses revealed that HRSV, HRV, and HADV were the most prevalent pathogens detected in children suffering from LRTIs, rather than HIPV and HMPV [13]. This finding suggests that the implementation of NPIs may have influenced the prevalence and pathogenicity of common respiratory pathogens, such as influenza, RSV, rhinovirus, and adenovirus. The observed reduction in infection rates and the overall impact on the transmission dynamics of these viruses highlight the potential broader effects of NPIs on respiratory health beyond their intended target of SARS-CoV-2. This has significant implications for public health strategies and warrants further investigation into the long-term consequences of pandemic control measures on the epidemiology of respiratory infections. In addition, our analyses confirmed the prevalence of common respiratory pathogens during the winter and spring seasons; this information was consistent with previous research findings [14]. Furthermore, we observed that these occurrences were more prominent in children aged 3–6 years-of-age. This could be attributed to the fact that children in this age group had recently started attending kindergarten and engaging in collective activities, thus making it challenging for them to consistently adhere to NPIs during outdoor activities [15]. The rate of respiratory viral infections is usually seasonal [16], with peaks occurring during specific times of the year, often in the fall and winter months. The epidemiology of these infections can vary across different countries and populations [17, 18], and is influenced by factors such as climate, population density, social behaviors, and healthcare infrastructure. For example, in temperate regions, influenza and RSV infections typically exhibit distinct seasonal patterns, while in tropical regions, the patterns may differ due to less pronounced seasonal variations. In addition, variations in vaccination coverage, age distribution, and immune status within populations can further impact the epidemiological patterns of respiratory viral infections. Understanding these patterns in more detail is crucial for developing targeted public health interventions and optimizing healthcare resources to effectively mitigate the impact of respiratory pathogens. Moreover, the pattern of viral spread usually occurs between October, November, and March, with peak incidence in January and February [19]. In the present study, localized outbreaks of influenza viruses were observed in October, November, and March, with different circulating strains each year. However, unlike previous epidemics, we revealed the underestimation of cases in both January and February. This underestimation was identified by the comprehensive analysis of surveillance data, including clinical testing, hospital admissions, and community-based monitoring. The general findings of this study align with previous research [20-22], as HRSV and HRV were identified as the most detectable respiratory pathogens during this period. The underestimation of cases in January and February may be attributed to factors such as limited testing capacity, asymptomatic or mild cases going unreported, and challenges in differentiating COVID-19 from other respiratory infections based on clinical symptoms alone. This highlights the importance of robust surveillance systems and accurate diagnostic tools to capture the true burden of respiratory infections, especially during public health emergencies. Furthermore, it is worth noting that the peak prevalence of HRSV occurred between October and December 2021; this was followed by a sharp decline which did not continue into 2022. This observation raises the possibility of interference between respiratory viruses, where the presence of one virus affects the transmission or pathogenicity of another [23]. Interference in this context may be caused by a phenomenon known as viral interference, where the immune response triggered by one virus can impede the replication or spread of a different virus [19]. Furthermore, competition for susceptible hosts and resources within the host's respiratory tract may also contribute to the observed patterns of viral prevalence. Understanding the mechanisms of interference and competition between respiratory viruses is essential for elucidating the complex dynamics of co-circulating pathogens and their implications for public health interventions and vaccine development. In addition, it is possible that the peak of SARS-CoV-2 infection during the summer months could have potentially influenced the prevalence of H1N1 [24]. Furthermore, previous research has shown that HRSV tends to circulate more among infants and exhibits a decreasing trend as age increases [23]. This suggests that infants and young children are more susceptible to HRSV infections, while older individuals may have acquired immunity or reduced susceptibility due to previous exposure. Understanding the age-related patterns of HRSV circulation is crucial for targeting vaccination efforts and implementing preventive measures to protect the most vulnerable populations, particularly infants and young children, from severe respiratory illness caused by HRSV. This suggests that NPIs may not have exerted a significant influence on the seasonality and population characteristics of HRSV. However, this does not rule out the possibility that the stricter enforcement of NPIs could be associated with increasing age. It is important to consider the potential impact of NPIs on different age groups and population subgroups when evaluating their effectiveness in mitigating the transmission of respiratory viruses. In addition, the interplay between NPIs, age-related immunity, and viral circulation dynamics warrants further investigation to better understand the complex interactions shaping the epidemiology of HRSV and inform public health strategies aimed at controlling respiratory infections. In contrast, HRV can be detected in almost every season; this is because HRV is a non-enveloped virus, and is relatively resistant to ethanol-containing disinfectants [25]; furthermore, this virus can survive on environmental surfaces over a prolonged period of time [26]. In this study, we found that HRV was prevalent throughout the year, except for the winter, and was common in all age groups. Prior to the COVID-19 epidemic, higher hospitalization rates were observed in years where the predominant circulating virus was influenza in southeast China [27]. However, with the implementation of non-pharmaceutical interventions (NPIs), the dominant strains of influenza virus have not changed [28], although there has been a significant decline in the detection rate over time, which is now reported to be 6.4%. Similar trends have been observed in other areas such as Shanghai [12], Hong Kong [29], and New Zealand [30], where the implementation of NPIs has resulted in a reduction in the detection rate of influenza virus. There are some limitations to our study that should be considered. Firstly, we did not take into account bacterial infections and infections caused by atypical pathogens. Atypical pathogens are agents that cause respiratory infections but are not detected by standard bacterial cultures or methods, often requiring specific serological or molecular tests for diagnosis. Examples include Mycoplasma pneumoniae , Chlamydophila pneumoniae , and Legionella pneumophila . These organisms can lead to clinical presentations that are similar to those caused by typical respiratory pathogens, and their exclusion from our analysis could have potentially contributed to an underestimation of the total burden of respiratory infections recorded in our data. Secondly, the observation period was not extensive enough to provide a comprehensive understanding of the trends and dynamics of respiratory infections over time. Further observation and research are now necessary to gain a more comprehensive understanding of LRTIs and enable the development of better management strategies. In conclusion, this study highlights the need for age and season-specific surveillance and prevention strategies, which could lead to more effective control of respiratory infections in pediatric populations. To build on the knowledge from this study, future research should aim to explore the underlying mechanisms that contribute to the observed variations in pathogen prevalence, such as differences in immunity, social behavior, and environmental factors. Additionally, longitudinal studies could provide a better understanding of the long-term health impacts of these infections and the effectiveness of interventions over time. The development of more targeted vaccines and treatment protocols, considering these demographic factors, could also be an essential next step in reducing the disease burden among children. Conclusion In conclusion, this study found that the prevalence of respiratory pathogen infections varied by gender, season, and age in the enrolled population of children. The highest positivity rates were observed for HRSV, HRV, and HADV. Influenza A and H3N2 were more common in the autumn season, while H1N1 and influenza B were more common in the spring. HPIV and HADV were more prevalent in children under 3 years-of-age, while HBoV and HRV predominated in children aged 1-3 years-of-age. The findings highlight the importance of considering gender, season, and age when studying respiratory pathogen infections in children. Abbreviations ADV: adenovirus; ARIs: acute respiratory infections; HCOV: human coronavirus; HMPV: human metapneumovirus; HPIV: human parainfluenza virus; HRSV: human respiratory syncytial virus; InfA: influenza A; InfB: influenza B; LRTIs: lower respiratory tract illnesses; NPIs: non-pharmaceutical interventions; ORs: odds ratios; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2 Declarations Ethics approval and consent to participate The study was approved by the Ethics Committee of Yan'an University Affiliated Hospital (Ethics Approval No. S-S20230003). The requirement for informed consent was waived owing to the retrospective observational nature of the study. The decision not to require informed consent was upheld by the Ethics Committee of the Yan'an University Affiliated Hospital of Medicine. All methods were carried out in accordance with relevant guidelines and regulations. Consent for publication Not applicable. Availability of data and materials The data of the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This study was supported by the Yan'an University Graduate Education Innovation Program (YCX2023121) and Bejing Health Alliance Charitable Foundation (B21181FN). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Authors' contributions Acquisition of data: S.L., Y.F, X.Z., Q.Y. and Z.X; Analysis and interpretation of data: S.L. and N.F.; Drafting the article: S.L and Z.X.; Critical revision of the manuscript for important intellectual content: S.L, Z.X. and Y.L. All authors approved the final version of the manuscript submitted. Acknowledgements Not applicable. References Lucion MF, Juárez MDV, Pejito MN, Orqueda AS, Bollón LR, Mistchenko AS, Gentile Á: Impact of COVID-19 on the circulation of respiratory viruses in a children’s hospital: An expected absence . Arch Argent Pediatr 2022, 120 (2):99-105. Vos T, Lim SS, Abbafati C, Abbas KM, Abbasi M, Abbasifard M, Abbasi-Kangevari M, Abbastabar H, Abd-Allah F, Abdelalim A et al : 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 . The Lancet 2020, 396 (10258):1204-1222. 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Savolainen-Kopra C, Korpela T, Simonen-Tikka M-L, Amiryousefi A, Ziegler T, Roivainen M, Hovi T: Single treatment with ethanol hand rub is ineffective against human rhinovirus—hand washing with soap and water removes the virus efficiently . Journal of Medical Virology 2012, 84 (3):543-547. Winther B, McCue K, Ashe K, Rubino J, Hendley JO: Rhinovirus contamination of surfaces in homes of adults with natural colds: Transfer of virus to fingertips during normal daily activities . Journal of Medical Virology 2011, 83 (5):906-909. Yu J, Zhang X, Shan W, Gao J, Hua J, Tian J, Ding Y, Zhang J, Chen L, Song Y: Influenza-associated hospitalization in children younger than 5 years of age in Suzhou, China, 2011–2016 . The Pediatric infectious disease journal 2019, 38 (5):445-452. Wang D, Chen L, Ding Y, Zhang J, Hua J, Geng Q, Ya X, Zeng S, Wu J, Jiang Y et al : Viral etiology of medically attended influenza-like illnesses in children less than five years old in Suzhou, China, 2011–2014 . Journal of Medical Virology 2016, 88 (8):1334-1340. Cowling BJ, Ali ST, Ng TWY, Tsang TK, Li JCM, Fong MW, Liao Q, Kwan MYW, Lee SL, Chiu SS et al : Impact assessment of non-pharmaceutical interventions against coronavirus disease 2019 and influenza in Hong Kong: an observational study . The Lancet Public Health 2020, 5 (5):e279-e288. 30. Huang QS, Wood T, Jelley L, Jennings T, Jefferies S, Daniells K, Nesdale A, Dowell T, Turner N, Campbell-Stokes P et al : Impact of the COVID-19 nonpharmaceutical interventions on influenza and other respiratory viral infections in New Zealand . Nature Communications 2021, 12 (1):1001. Tables Table 1. General characteristics of children infected with respiratory pathogens Flu A H1N1 H3N2 Flu B HPIV HADV HBoV HRV HNPV HCoV HRSV P Parameters n = 125 n = 83 n = 90 n = 82 n = 129 n = 200 n = 50 n = 259 n = 168 n = 16 n = 273 Sex (Male) 80 (64) 56 (67.47) 55 (61.11) 41 (50) 76 (58.91) 122 (61) 29 (58) 151 (58.3) 95 (56.55) 8 (50) 167 (61.17) 0.609 Age (years) < 1 17 (13.6) 10 (12.05) 11 (12.22) 6 (7.32) 29 (22.48) 26 (13) 5 (10) 65 (25.1) 20 (11.9) 4 (25) 101 (37) < 0.001 1-< 3 18 (14.4) 16 (19.28) 11 (12.22) 10 (12.2) 42 (32.56) 55 (27.5) 23 (46) 72 (27.8) 36 (21.43) 3 (18.75) 75 (27.47) 3-< 6 54 (43.2) 27 (32.53) 44 (48.89) 39 (47.56) 51 (39.53) 78 (39) 22 (44) 81 (31.27) 102 (60.71) 6 (37.5) 83 (30.4) ≥ 6 36 (28.8) 30 (36.14) 24 (26.67) 27 (32.93) 7 (5.43) 41 (20.5) 0 (0) 41 (15.83) 10 (5.95) 3 (18.75) 14 (5.13) The information provided is displayed as a percentage (n). The chi-squared examination for discrete variables. InfA is an abbreviation for influenza A, H1N1 refers to influenza A H1N1 virus, H3N2 represents seasonal influenza A H3N2 virus, Flu B stands for influenza B virus, HPIV denotes parainfluenza virus, HADV signifies adenovirus, HBoV indicates bocavirus, HRV represents rhinovirus, HNPV refers to metapneumovirus, HCoV stands for coronavirus, and HRSV represents respiratory syncytial virus. Table 2. Multivariable-adjusted associations between age and respiratory pathogen infections Flu A H1N1 H3N2 Flu B HPIV HADV HBoV HRV HNPV HCoV HRSV aOR P aOR P aOR P aOR P aOR P aOR P aOR P aOR P aOR P aOR P aOR P Age(years) (95%CI) (95%CI) (95%CI) (95%CI) (95%CI) (95%CI) (95%CI) (95%CI) (95%CI) (95%CI) (95%CI) <1 1 1 1 1 1 1 1 1 1 1 1 (reference) (reference) (reference) (reference) (reference) (reference) (reference) (reference) (reference) (reference) (reference) 1-<3 1.142 0.699 1.483 0.336 1.145 0.756 1.621 0.353 1.33 0.251 2.357 <0.001 4.015 0.005 1.045 0.804 1.558 0.12 0.667 0.597 0.793 0.15 0.582~2.239 0.665~3.309 0.489~2.681 0.585~4.491 0.818~2.163 1.462~3.800 1.510~10.674 0.737~1.483 0.891~2.724 0.148~2.999 0.579~1.087 3-<6 1.991 0.015 1.531 0.257 2.513 0.008 4.572 0.001 1.22 0.405 2.214 0.001 3.072 0.025 0.812 0.23 3.22 <0.001 0.928 0.909 0.567 <0.001 1.141~3.437 0.733~3.201 1.277~4.945 1.917~10.904 0.764~1.950 1.404~3.493 1.152~8.191 0.578~1.141 1.97~5.264 0.259~3.32 0.417~0.770 ≥6 2.013 0.02 4.937 <0.001 1.713 0.148 6.659 <0.001 0.289 0.004 1.661 0.048 0 0.99 0.726 0.122 0.749 0.462 1.014 0.986 0.138 <0.001 1.115~3.633 2.37~10.285 0.826~3.555 2.728~16.257 0.125~0.665 1.005~2.745 0~. 0.485~1.089 0.347~1.618 0.224~4.581 0.078~0.243 p < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.467 < 0.001 Note：The age and season were taken into account when adjusting the model. Abbreviations used include InfA for influenza A, H1N1 for influenza A H1N1 virus, H3N2 for seasonal influenza A H3N2 virus, Flu B for influenza B virus, HPIV for parainfluenza virus, HADV for adenovirus, HBoV for bocavirus, HRV for rhinovirus, HNPV for metapneumovirus, HCoV for coronavirus, and HRSV for respiratory syncytial virus. Table 3. Multivariable-adjusted associations between season and respiratory pathogen infections Flu A H1N1 H3N2 Flu B HPIV HADV HBoV HRV HNPV HCoV HRSV OR P OR P OR P OR P OR P OR P OR P OR P OR P OR P OR P Season (95%CI) (95%CI) (95%CI) (95%CI) (95%CI) (95%CI) (95%CI) (95%CI) (95%CI) (95%CI) (95%CI) Spring 1 1 1 1 1 1 1 1 1 1 1 (reference) (reference) (reference) (reference) (reference) (reference) (reference) (reference) (reference) (reference) (reference) Summer 0.104 < 0.01 0 0.99 0.31 0.27 0.487 0.03 3.268 < 0.01 1.077 0.76 12.877 < 0.01 1.673 < 0.01 0.719 0.08 0.919 0.89 0.434 < 0.01 0.025~0.433 0~. 0.038~2.529 0.258~0.92 2.112~5.056 0.675~1.719 4.97~33.365 1.212~2.309 0.497~1.039 0.281~3.008 0.275~0.683 Autumn 2.515 < 0.01 0 0.99 16.487 < 0.01 0.021 < 0.01 1.211 0.46 2.254 < 0.01 4.106 < 0.01 1.089 0.61 0.046 < 0.01 0.293 0.12 1.219 0.203 1.689~3.744 0~. 7.556~35.974 0.003~0.153 0.727~2.017 1.575~3.226 1.44~11.706 0.786~1.509 0.017~0.124 0.062~1.376 0.899~1.653 Winter 0.444 0.03 0.075 <0.01 0.938 0.93 0.569 0.06 0.827 0.57 1.568 0.04 1.061 0.94 0.562 0.01 0.039 < 0.01 0.206 0.137 1.27 0.156 0.213~0.927 0.027~0.208 0.241~3.656 0.317~1.021 0.432~1.583 1.018~2.417 0.204~5.511 0.361~0.875 0.01~0.158 0.026~1.654 0.913~1.765 P < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 < 0.001 0.535 < 0.001 Note：The age and season were taken into account when adjusting the model. Abbreviations used include InfA for influenza A, H1N1 for influenza A H1N1 virus, H3N2 for seasonal influenza A H3N2 virus, Flu B for influenza B virus, HPIV for parainfluenza virus, HADV for adenovirus, HBoV for bocavirus, HRV for rhinovirus, HNPV for metapneumovirus, HCoV for coronavirus, and HRSV for respiratory syncytial virus. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 20 Dec, 2024 Read the published version in BMC Pediatrics → Version 1 posted Editorial decision: Revision requested 29 Oct, 2024 Reviews received at journal 28 Oct, 2024 Reviewers agreed at journal 25 Sep, 2024 Reviews received at journal 29 Aug, 2024 Reviewers agreed at journal 23 Aug, 2024 Reviewers agreed at journal 23 Aug, 2024 Reviewers invited by journal 12 Jun, 2024 Editor assigned by journal 28 May, 2024 Editor invited by journal 23 Jan, 2024 Submission checks completed at journal 23 Jan, 2024 First submitted to journal 16 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3869323","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":268762104,"identity":"a86b3b2d-f4b4-4892-ad55-f614e2753e7d","order_by":0,"name":"Suling Li","email":"","orcid":"","institution":"Yan'an University Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Suling","middleName":"","lastName":"Li","suffix":""},{"id":268762105,"identity":"a8d9a291-a354-4e10-9901-876680e85d66","order_by":1,"name":"Zhengfeng Xue","email":"","orcid":"","institution":"Yan'an University Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhengfeng","middleName":"","lastName":"Xue","suffix":""},{"id":268762106,"identity":"ac308aeb-9992-450c-a7c0-9fb2603d4551","order_by":2,"name":"Yuxin Feng","email":"","orcid":"","institution":"Yan'an University Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yuxin","middleName":"","lastName":"Feng","suffix":""},{"id":268762107,"identity":"9674870c-c4bb-4402-b294-17e48ffc46b0","order_by":3,"name":"Xue Zhou","email":"","orcid":"","institution":"Yan'an University Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xue","middleName":"","lastName":"Zhou","suffix":""},{"id":268762108,"identity":"7266d550-642b-40a4-b8a3-20e911811130","order_by":4,"name":"Yang Qi","email":"","orcid":"","institution":"Yan'an University Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Qi","suffix":""},{"id":268762109,"identity":"cbd5088f-9464-4ea7-bea0-f2125c048c88","order_by":5,"name":"Na Feng","email":"","orcid":"","institution":"Yan'an University Affiliated Hospital","correspondingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Feng","suffix":""},{"id":268762110,"identity":"9d83b4b0-b8cd-4aba-b9cc-332985800565","order_by":6,"name":"Yuanxia Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYDCCAxBKhoGBGcS0YGDgIayFsYEBrI4tgYEhQYIkLTwGxGnhO978/MHHPUD1N3I+Pub9ISHHz3OA8cPHHNxaJM8cM2yc8Qyo5czZzcY8CRLGkr0NzJIzt+HWAjScsZnnAFDL8d5t0kAtiRvOM7Ax8xKl5TDPM1K1HO9hg2g524BfC8gvM2cAtQAZxoZz0oB+6TnYjNcvwBB78OHDAQY5vhvJDx+8sbEBhljywQ8f8WiBgv8MCgfgHHBEEQHkiVQ3CkbBKBgFIxAAABjwT94cDAcnAAAAAElFTkSuQmCC","orcid":"","institution":"Yan'an University Affiliated Hospital","correspondingAuthor":true,"prefix":"","firstName":"Yuanxia","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2024-01-16 09:14:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3869323/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3869323/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12887-024-05300-1","type":"published","date":"2024-12-20T15:58:19+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":50170379,"identity":"209ab1ba-3043-4f83-bdbb-b361ac2d3300","added_by":"auto","created_at":"2024-01-25 15:36:45","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":470629,"visible":true,"origin":"","legend":"\u003cp\u003eThe detection of pathogens in children with acute respiratory infections. (a) Monthly distributions of the pathogens detected, and (b) the distribution of pathogens by age\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3869323/v1/4c31862f8c4dc83ccaabc39f.png"},{"id":50170380,"identity":"0d65a659-f202-4b74-b1ea-abc911c516c6","added_by":"auto","created_at":"2024-01-25 15:36:45","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":552607,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly distributions showing the detection of 11 respiratory pathogens: (a) the positive rate of pathogen detection, and (b) the number of pathogens\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3869323/v1/a6557b6213ec5b8404a54ad4.png"},{"id":50171128,"identity":"f805922a-cf2f-4213-a25b-becf2d93e968","added_by":"auto","created_at":"2024-01-25 15:44:45","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":373282,"visible":true,"origin":"","legend":"\u003cp\u003eThe detection of eleven respiratory pathogens in children stratified by age: (a) the positivity rate for pathogenic detection, and (b) the number of pathogens detected\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3869323/v1/0cf877ae29d80952b6aae26d.png"},{"id":72201923,"identity":"65ded636-ac75-4488-823d-05aacd303003","added_by":"auto","created_at":"2024-12-23 16:12:13","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2768425,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3869323/v1/5130061c-7d0e-47a2-8211-486c5ae5bf0c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Epidemiological characteristics of eleven common respiratory viral infections in children","fulltext":[{"header":"Background","content":"\u003cp\u003eRespiratory viruses are the main cause of lower respiratory tract infection (LRTI) in the pediatric population\u0026nbsp;[1]. The Global Burden of Disease (2019) study reported that LRTIs are the second highest cause of health burden in children\u0026nbsp;[2]\u0026nbsp;while severe pneumonia was reported as the major cause of morbidity and mortality in children, especially those under 5 years-of-age\u0026nbsp;[3]. These LRTIs are also known to impose significant financial strain on families\u0026nbsp;[4]. According to the World Health Organization (WHO), LRTIs and pneumonia account for more than 4 million deaths annually\u0026nbsp;[5].\u003c/p\u003e\n\u003cp\u003eOver the last few decades, the diagnostic work up of clinical infections has changed significantly, especially with the rapid development of molecular diagnostic methods [6, 7]. In particular, the multiplex polymerase chain reaction (PCR) assay has been confirmed as a significantly more advanced tool for the clinical detection of potential pathogens. This method is used to detect a wide range of respiratory pathogens, including but not limited to influenza viruses (A and B), human rhinovirus (HRV), respiratory syncytial virus (HRSV), human adenovirus (HADV), human parainfluenza viruses (HPIV), and human metapneumovirus (HMPV), providing a comprehensive approach to identifying potential infectious agents in respiratory illnesses [8, 9]. In this study, we retrospectively reviewed all pediatric patients with ALRTI in Yan\u0026apos;an University Affiliated Hospital from 2021 to 2023 who were identified to be infected with pathogens using a multiplex PCR assay platform. We described the epidemiological characteristics of the respiratory pathogens detected in these children, including Human rhinovirus (HRV), Bocavirus, human parainfluenza virus (HPIV), human coronavirus (HCOV), HRSV, influenza A (InfA), influenza B (InfB), human metapneumovirus (HMPV) and adenovirus (ADV). We also evaluated the epidemiological features of infections caused by respiratory pathogens, including aging and the seasonal variations of different pathogens, and explored the high-risk factors associated with virus-caused pneumonia. Our findings could help healthcare professionals to manage local epidemic pathogens more effectively and evaluate disease burden in a timely manner because no previous research has targeted this particular population.\u003c/p\u003e"},{"header":"Participants and methods","content":"\u003cp\u003e\u003cstrong\u003eStudy site\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was performed at the Pediatric Inpatient Department of Yan\u0026apos;an University Affiliated Hospital in Yan\u0026rsquo;an, Shanxi, China. Yan\u0026rsquo;an is located in the northern part of Shaanxi Province, Yan\u0026rsquo;an has a plateau continental monsoon climate; the northern part of this region has a semi-arid climate while the southern part has a semi-humid climate.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eParticipants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study included all hospitalized children between the 1\u003csup\u003est\u003c/sup\u003e of June 2021 and the 31\u003csup\u003est\u003c/sup\u003e of May 2023 who had been diagnosed with LRTI in the Inpatient Department of Yan\u0026apos;an University Affiliated Hospital. Patients were included if they met the diagnostic criteria for acute upper respiratory tract infection or acute lower respiratory tract infection according to the national diagnostic criteria for pediatric respiratory tract infection. Patients were excluded if there was no pathogenic testing data available. These children had all been subjected to 11 respiratory pathogen tests. For each patient, we collated a range of data from an electronic medical records system, including hospitalization number, sex, age, diagnosis, and the results of diagnostic tests. A total of 4,565 patients (2,664 males and 1,902 females) were included in our analysis (ages ranged from 0 to 14 years). The patients were divided into four groups by age: \u0026lt;1 year (group I), 1\u0026ndash;\u0026lt; 3 years (group II), 3\u0026ndash;\u0026lt; 6 years (group III), and \u0026ge;6 years (group IV). Based on the climatic conditions of China, the four seasons were categorized as follows: March, April, and May were considered to be spring; June, July, and August were considered to be summer; September, October, and November were considered to be autumn; and December, January, and February of the next year were considered to be winter. The temperature and precipitation data in Beijing were gathered from the China Meteorological Administration Government Website (\u003ca href=\"https://www.cma.gov.cn\"\u003ehttps://www.cma.gov.cn\u003c/a\u003e). This retrospective study was approved by the Ethics Committee of the Yan\u0026apos;an University Affiliated Hospital, China. (Reference: S-S20230003).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpecimen collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThroat swabs for nucleic acid testing of respiratory pathogens were collected within 24 hours of a child\u0026apos;s admission by trained pediatric nurses. The collection process involved cleaning the child\u0026apos;s mouth and teeth. The collection process involved the following steps：(1) cleaning the child\u0026apos;s mouth and teeth to ensure that the area was free from any debris or contaminants, and (2) the application of a flocking swab to reach the child\u0026apos;s pharyngeal isthmus. The flocking swab was inserted gently into the child\u0026apos;s mouth and directed towards the pharyngeal isthmus, the narrow passage between the back of the mouth and the throat.\u0026nbsp;The swab was then used to wipe the posterior pharyngeal wall and bilateral tonsils; rotational movements were used to maximize the contact surface. After sampling, the swab was swiftly extracted from the child\u0026apos;s pharyngeal isthmus and immediately placed in a sample tube containing 3ml of the sample solution provided with the kit（ResP\u0026reg; 13 Respiratory Pathogen Multiplex Detection Kit，NINGBO HEALTH GENE TECHNOLOGIES CO., LTD）. The sample tube was then sent for examination within 30 minutes to ensure the integrity of the collected sample.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePCR capillary electrophoresis fragment analysis for eleven respiratory pathogens\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAssays were developed for 11 respiratory pathogens, including influenza A virus (Flu A), influenza A H1N1 virus (H1N1), seasonal influenza A H3N2 virus (H3N2), influenza B virus (Flu B), parainfluenza virus (HPIV), respiratory syncytial virus (HRSV), bocavirus (HBoV), rhinovirus (HRV), metapneumovirus (HNPV), coronavirus (HCoV), and adenovirus (HADV). This test comprehensively covers the clinical high positive rate and severe high-risk viruses, and can achieve the joint detection of multiple pathogens, thus providing a comprehensive, accurate and rapid detection method for the clinical diagnosis of acute respiratory infection and the differential diagnosis of COVID-19. For each patent, throat swabs were collected multiple fluorescence quantitative polymerase chain reaction (PCR) tests were carried out in accordance with the instructions provided in a Respiratory Pathogen Detection Kit (Ningbo Haishi Gene Technology Co., Ltd., Ningbo, China). Eleven sets of specific primers were employed, and one-step RT-PCR was performed in a single tube to amplify target fragments. The nucleic acid samples were amplified through a series of RT-PCR steps: first, pretreatment at 25 ℃ (5 min) for one cycle, reverse transcription at 50 ℃ (15 min) for one cycle, and pre-denaturation at 95 ℃ (2 min) for one cycle; secondly, denaturation at 94 ℃ (30 s), annealing at 65 ℃ (30 s), and extension at 72 ℃ (60 s) for six cycles, a step that was repeated until the annealing temperature reached 60 ℃; there was a 1 ℃ touchdown every six cycles. third, denaturation at 94 ℃ (30 s), annealing at 60 ℃ (30 s), and extension at 72 ℃ (60 s) for 29 cycles; finally, the products were extended at 72 ℃ (10 min) for one cycle and were kept at 4 ℃ for one cycle. Capillary electrophoresis was applied to separate the amplification products of different lengths by the GenomeLab GeXP Genetic Analysis System (Beckman Coulter). The samples were samples analyzed by trained professionals who then generated a report based on their findings.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData are presented as number [n(%)] and were tested by Chi-squared tests. Binary logistic regression analyses were used to calculate odds ratios (ORs) with 95% confidence intervals (CIs). Data were analyzed by R software (The R Foundation; http://www.r-project.org; version 4.2.1) and EmpowerStats software (www.empowerstats.net, X\u0026amp;Y solutions, Inc. Boston, Massachusetts). GraphPad Prism 9 software was used for mapping. A P-value \u0026lt; 0.05 was considered to be statistically different.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eGeneral characteristics of enrolled patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf the 4565 cases included in our analysis, 1276 were positive for at least one virus, with a total positivity rate of 27.95%. There were 2664 males and 1901 females included in our analysis, with an mean age of 3.3 years (standard deviation = 2.9); 1068 were under 1 year-of-age, 1050 were 1\u0026ndash;3 years-of-age, 1519 were 3\u0026ndash;6 years-of-age, and 928 were over 6 years-of-age. Positive cases involved 757 (59.3%) males and 519 (40.7%) females. The positive rates of the 11 respiratory pathogen assays were 5.98% (HRSV), 5.67% (HRV), 4.38% (HADV), 3.68% (HNPV), 2.83% (HPIV), 2.74% (Flu A), 1.97% (H3N2), 1.82% (H1N1), 1.80% (Flu B), 1.10% (HBoV), and 0.35% (HCoV), respectively. There was no significant difference in gender between the groups in terms of positive pathogen detection\u0026nbsp;= 0.41, \u003cem\u003eP\u0026nbsp;\u003c/em\u003e= 0.68), although the age groups showed a statistically significant difference (\u0026nbsp;= 57.05,\u0026nbsp;\u003cem\u003eP\u0026nbsp;\u003c/em\u003e＜\u0026nbsp;0.001, Table 1). As shown in Fig. 1, the number and positive rates of respiratory pathogen detection varied by season and age. The number and positive rate of respiratory virus testing exhibited a trough during winter, especially in January and February. However, the total positive rate of the 11 viral infections peaked in March on an annual basis. The positive rates of respiratory pathogen detection were highest in the 3\u0026ndash;\u0026lt; 6 years-of-age group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSeasonal and age distribution of various respiratory pathogens\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Fig. 2, the positivity rate and number of HADV detections exhibited multiple peaks in various months, and were highest during winter (October and November) in 2022, especially in terms of FluA and H3N2. The HRSV peak occurred in November and December 2021 but decreased rapidly in January. Thereafter, the prevalence of HRSV remained low until April 2023. In 2021, the first wave of HRV peaked in September, followed by a second and third wave in March and July 2022, but exhibited a clear trough in December and January every year. The positivity of FluB peaked in March 2022 whereas H1N1 peaked in the same month in 2023. Subsequently, peak HNPV positivity was detected in spring and summer (April to June) in 2022. HRSV and HRV predominated in the \u0026lt; 3 years-of-age group (Fig. 3). HNPV were common in the 3\u0026ndash;6 years-of-age group, whereas HBoV and HRV were predominant in the \u0026ge; 6 years-of-age group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe associations between season/age and various respiratory pathogens\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Table 2 and Table 3, multivariate regression analysis revealed that FluA infections occurred more often among children aged \u0026ge;6 years-age and in autumn (P\u0026lt; 0.01); H1N1 was also commonly found in children aged \u0026ge;6 years-of-age and occurred more commonly in spring (P \u0026lt; 0.01). H3N2 predominated in children aged 3\u0026ndash;6 years and majorly in the autumn season (P\u0026lt; 0.01); FluB was commonly detected in children aged \u0026ge;6 years-of-age and was mostly detected during spring (P \u0026lt; 0.01). HPIV was mostly detected in children aged \u0026lt;3 years-of-age and mainly during summer (P \u0026lt; 0.01). HADV was mostly detected in children aged \u0026lt;3 years-of-age and tended to peak in the autumn (P \u0026lt; 0.01). HBoV was commonly detected in children aged 1\u0026ndash;3 years-of-age and mainly in summer (P \u0026lt; 0.01). HRV was mostly detected in children aged \u0026lt;3 years-of-age and peaked in the summer (P \u0026lt; 0.01). HNPV was most commonly detected in children aged 3\u0026ndash;6 years-of-age and in spring (P \u0026lt; 0.01). HCoV was mostly detected in children aged \u0026lt;1 year-of-age and \u0026ge;6 years-of-age (P \u0026gt; 0.05) but peaked in spring. HRSV was most commonly detected in children aged \u0026lt;1 year-of-age and during the autumn and winter (P \u0026lt; 0.01)\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eBetween early 2020 and December 2022, a wide range of stringent non-pharmaceutical interventions (NPIs) were used to combat the COVID-19 pandemic, including mask-wearing, school closures, and social distancing. This strategy not only reduced the transmission of the SARS-CoV-2 virus but also influenced the prevalence patterns of other common respiratory viruses\u0026nbsp;[10]. For instance, measures such as social distancing, mask-wearing, and enhanced hygiene practices contributed to a marked decline in the circulation of influenza and other respiratory viruses, which typically follow seasonal patterns. Health data from various regions showed a significant drop in flu cases during periods when COVID-19 restrictions were in place, thus suggesting that the interventions for controlling the pandemic inadvertently suppressed the transmission of these other viruses. This phenomenon has provided a unique opportunity to study the effects of public health strategies on a range of respiratory illnesses, potentially offering insights into more effective approaches to managing viral diseases in the future. Due to the strict implementation of these NPIs, the incidence of SARS-CoV-2 was significantly reduced, and individuals infected with the virus were promptly transferred to specialized infectious disease hospitals for appropriate medical care. Consequently, the scope of this study on hospitalized children did not involve individuals diagnosed with COVID-19.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe rigorous enforcement of non-pharmaceutical interventions (NPIs) effectively curbed the transmission of SARS-CoV-2\u0026nbsp;[11]. As a result, the infection rate of other respiratory pathogens typically linked to ARIs was reduced further, dropping to 27.9% compared to its previous value prior to the COVID-19 pandemic. These findings align with a previous study conducted in Shanghai, which reported a similar reduction in the infection rate of viral respiratory pathogens to 27.5%\u0026nbsp;[12]. In the present study, our analyses revealed that HRSV, HRV, and HADV were the most prevalent pathogens detected in children suffering from LRTIs, rather than HIPV and HMPV\u0026nbsp;[13]. This finding suggests that the implementation of NPIs may have influenced the prevalence and pathogenicity of common respiratory pathogens, such as influenza, RSV, rhinovirus, and adenovirus. The observed reduction in infection rates and the overall impact on the transmission dynamics of these viruses highlight the potential broader effects of NPIs on respiratory health beyond their intended target of SARS-CoV-2. This has significant implications for public health strategies and warrants further investigation into the long-term consequences of pandemic control measures on the epidemiology of respiratory infections.\u003c/p\u003e\n\u003cp\u003eIn addition, our analyses confirmed the prevalence of common respiratory pathogens during the winter and spring seasons; this information was consistent with previous research findings\u0026nbsp;[14]. Furthermore, we observed that these occurrences were more prominent in children aged 3\u0026ndash;6 years-of-age. This could be attributed to the fact that children in this age group had recently started attending kindergarten and engaging in collective activities, thus making it challenging for them to consistently adhere to NPIs during outdoor activities\u0026nbsp;[15].\u003c/p\u003e\n\u003cp\u003eThe rate of respiratory viral infections is usually seasonal\u0026nbsp;[16], with peaks occurring during specific times of the year, often in the fall and winter months. The epidemiology of these infections can vary across different countries and populations\u0026nbsp;[17, 18], and is influenced by factors such as climate, population density, social behaviors, and healthcare infrastructure. For example, in temperate regions, influenza and RSV infections typically exhibit distinct seasonal patterns, while in tropical regions, the patterns may differ due to less pronounced seasonal variations. In addition, variations in vaccination coverage, age distribution, and immune status within populations can further impact the epidemiological patterns of respiratory viral infections. Understanding these patterns in more detail is crucial for developing targeted public health interventions and optimizing healthcare resources to effectively mitigate the impact of respiratory pathogens. Moreover, the pattern of viral spread usually occurs between October, November, and March, with peak incidence in January and February\u0026nbsp;[19]. In the present study, localized outbreaks of influenza viruses were observed in October, November, and March, with different circulating strains each year. However, unlike previous epidemics, we revealed the underestimation of cases in both January and February. This underestimation was identified by the comprehensive analysis of surveillance data, including clinical testing, hospital admissions, and community-based monitoring.\u0026nbsp;The general findings of this study align with previous research\u0026nbsp;[20-22],\u0026nbsp;as HRSV and HRV were identified as the most detectable respiratory pathogens\u0026nbsp;during this period. The underestimation of cases in January and February may be attributed to factors such as limited testing capacity, asymptomatic or mild cases going unreported, and challenges in differentiating COVID-19 from other respiratory infections based on clinical symptoms alone. This highlights the importance of robust surveillance systems and accurate diagnostic tools to capture the true burden of respiratory infections, especially during public health emergencies.\u0026nbsp;Furthermore, it is worth noting that the peak prevalence of HRSV occurred between October and December 2021; this was followed by a sharp decline which did not continue into 2022. This observation raises the possibility of interference between respiratory viruses, where the presence of one virus affects the transmission or pathogenicity of another\u0026nbsp;[23]. Interference in this context may be caused by a phenomenon known as viral interference, where the immune response triggered by one virus can impede the replication or spread of a different virus\u0026nbsp;[19]. Furthermore, competition for susceptible hosts and resources within the host\u0026apos;s respiratory tract may also contribute to the observed patterns of viral prevalence. Understanding the mechanisms of interference and competition between respiratory viruses is essential for elucidating the complex dynamics of co-circulating pathogens and their implications for public health interventions and vaccine development. In addition, it is possible that the peak of SARS-CoV-2 infection during the summer months could have potentially influenced the prevalence of H1N1\u0026nbsp;[24].\u0026nbsp;Furthermore, previous research has shown that HRSV tends to circulate more among infants and exhibits a decreasing trend as age increases\u0026nbsp;[23]. This suggests that infants and young children are more susceptible to HRSV infections, while older individuals may have acquired immunity or reduced susceptibility due to previous exposure. Understanding the age-related patterns of HRSV circulation is crucial for targeting vaccination efforts and implementing preventive measures to protect the most vulnerable populations, particularly infants and young children, from severe respiratory illness caused by HRSV. This suggests that NPIs may not have exerted a significant influence on the seasonality and population characteristics of HRSV. However, this does not rule out the possibility that the stricter enforcement of NPIs could be associated with increasing age. It is important to consider the potential impact of NPIs on different age groups and population subgroups when evaluating their effectiveness in mitigating the transmission of respiratory viruses. In addition, the interplay between NPIs, age-related immunity, and viral circulation dynamics warrants further investigation to better understand the complex interactions shaping the epidemiology of HRSV and inform public health strategies aimed at controlling respiratory infections.\u003c/p\u003e\n\u003cp\u003eIn contrast, HRV can be detected in almost every season; this is because\u0026nbsp;HRV\u0026nbsp;is a non-enveloped virus, and is relatively resistant to ethanol-containing disinfectants\u0026nbsp;[25]; furthermore, this virus can survive on environmental surfaces over a prolonged period of time\u0026nbsp;[26]. In this study, we found that\u0026nbsp;HRV\u0026nbsp;was prevalent throughout the year, except for the winter, and was common in all age groups. Prior to the COVID-19 epidemic, higher hospitalization rates were observed in years where the predominant circulating virus was influenza in southeast China\u0026nbsp;[27]. However, with the implementation of non-pharmaceutical interventions (NPIs), the dominant strains of influenza virus have not changed\u0026nbsp;[28], although there has been a significant decline in the detection rate over time, which is now reported to be 6.4%. Similar trends have been observed in other areas such as Shanghai\u0026nbsp;[12], Hong Kong\u0026nbsp;[29], and New Zealand\u0026nbsp;[30], where the implementation of NPIs has resulted in a reduction in the detection rate of influenza virus.\u003c/p\u003e\n\u003cp\u003eThere are some limitations to our study that should be considered. Firstly, we did not take into account bacterial infections and infections caused by atypical pathogens. Atypical pathogens are agents that cause respiratory infections but are not detected by standard bacterial cultures or methods, often requiring specific serological or molecular tests for diagnosis. Examples include \u003cem\u003eMycoplasma pneumoniae\u003c/em\u003e, \u003cem\u003eChlamydophila pneumoniae\u003c/em\u003e, and \u003cem\u003eLegionella pneumophila\u003c/em\u003e. These organisms can lead to clinical presentations that are similar to those caused by typical respiratory pathogens, and their exclusion from our analysis could have potentially contributed to an underestimation of the total burden of respiratory infections recorded in our data. Secondly, the observation period was not extensive enough to provide a comprehensive understanding of the trends and dynamics of respiratory infections over time. Further observation and research are now necessary to gain a more comprehensive understanding of LRTIs and enable the development of better management strategies.\u003c/p\u003e\n\u003cp\u003eIn conclusion, this study highlights the need for age and season-specific surveillance and prevention strategies, which could lead to more effective control of respiratory infections in pediatric populations. To build on the knowledge from this study, future research should aim to explore the underlying mechanisms that contribute to the observed variations in pathogen prevalence, such as differences in immunity, social behavior, and environmental factors. Additionally, longitudinal studies could provide a better understanding of the long-term health impacts of these infections and the effectiveness of interventions over time. The development of more targeted vaccines and treatment protocols, considering these demographic factors, could also be an essential next step in reducing the disease burden among children.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study found that the prevalence of respiratory pathogen infections varied by gender, season, and age in the enrolled population of children. The highest positivity rates were observed for HRSV, HRV, and HADV. Influenza A and H3N2 were more common in the autumn season, while H1N1 and influenza B were more common in the spring. HPIV and HADV were more prevalent in children under 3 years-of-age, while HBoV and HRV predominated in children aged 1-3 years-of-age. The findings highlight the importance of considering gender, season, and age when studying respiratory pathogen infections in children.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eADV: adenovirus; ARIs: acute respiratory infections; HCOV: human coronavirus; HMPV: human metapneumovirus; HPIV: human parainfluenza virus; HRSV: human respiratory syncytial virus; InfA: influenza A; InfB: influenza B; LRTIs: lower respiratory tract illnesses; NPIs: non-pharmaceutical interventions; ORs: odds ratios; SARS-CoV-2: severe acute respiratory syndrome coronavirus 2\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Ethics Committee of Yan\u0026apos;an University Affiliated Hospital (Ethics Approval No. S-S20230003). The requirement for informed consent was waived owing to the retrospective observational nature of the study. The decision not to require informed consent was upheld by the Ethics Committee of the Yan\u0026apos;an University Affiliated Hospital of Medicine. All methods were carried out in accordance with relevant guidelines and regulations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data of the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Yan\u0026apos;an University Graduate Education Innovation Program (YCX2023121) and Bejing Health Alliance Charitable Foundation (B21181FN). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAcquisition of data: S.L., Y.F, X.Z., Q.Y. and Z.X; Analysis and interpretation of data: S.L. and N.F.; Drafting the article: S.L and Z.X.; Critical revision of the manuscript for important intellectual content: S.L, Z.X. and Y.L. All authors approved the final version of the manuscript submitted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLucion MF, Ju\u0026aacute;rez MDV, Pejito MN, Orqueda AS, Boll\u0026oacute;n LR, Mistchenko AS, Gentile \u0026Aacute;: \u003cstrong\u003eImpact of COVID-19 on the circulation of respiratory viruses in a children\u0026rsquo;s hospital: An expected absence\u003c/strong\u003e. \u003cem\u003eArch Argent Pediatr \u003c/em\u003e2022, \u003cstrong\u003e120\u003c/strong\u003e(2):99-105.\u003c/li\u003e\n\u003cli\u003eVos T, Lim SS, Abbafati C, Abbas KM, Abbasi M, Abbasifard M, Abbasi-Kangevari M, Abbastabar H, Abd-Allah F, Abdelalim A\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eGlobal burden of 369 diseases and injuries in 204 countries and territories, 1990\u0026amp;#x2013;2019: a systematic analysis for the Global Burden of Disease Study 2019\u003c/strong\u003e. \u003cem\u003eThe Lancet \u003c/em\u003e2020, \u003cstrong\u003e396\u003c/strong\u003e(10258):1204-1222.\u003c/li\u003e\n\u003cli\u003eWalker CLF, Rudan I, Liu L, Nair H, Theodoratou E, Bhutta ZA, O'Brien KL, Campbell H, Black RE: \u003cstrong\u003eGlobal burden of childhood pneumonia and diarrhoea\u003c/strong\u003e. \u003cem\u003eLancet \u003c/em\u003e2013, \u003cstrong\u003e381\u003c/strong\u003e(9875):1405-1416.\u003c/li\u003e\n\u003cli\u003eKyu HH, Vongpradith A, Sirota SB, Novotney A, Troeger CE, Doxey MC, Bender RG, Ledesma JR, Biehl MH, Albertson SB\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eAge-sex differences in the global burden of lower respiratory infections and risk factors, 1990-2019: results from the Global Burden of Disease Study 2019\u003c/strong\u003e. \u003cem\u003eThe Lancet Infectious Diseases \u003c/em\u003e2022, \u003cstrong\u003e22\u003c/strong\u003e(11):1626-1647.\u003c/li\u003e\n\u003cli\u003eMarciniuk D, Schraufnagel D, Ferkol T, Fong K, Joos G, Varela V: \u003cstrong\u003eForum of International Respiratory Societies. 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In: \u003cem\u003eViruses.\u003c/em\u003e vol. 15; 2023.\u003c/li\u003e\n\u003cli\u003eDing Q, Xu L, Zhu Y, Xu B, Chen X, Duan Y, Xie Z, Shen K: \u003cstrong\u003eComparison of clinical features of acute lower respiratory tract infections in infants with RSV/HRV infection, and incidences of subsequent wheezing or asthma in childhood\u003c/strong\u003e. \u003cem\u003eBMC Infectious Diseases \u003c/em\u003e2020, \u003cstrong\u003e20\u003c/strong\u003e(1):387.\u003c/li\u003e\n\u003cli\u003eSapra M, Kirubanandhan S, Kanta P, Ghosh A, Goyal K, Singh MP, Ratho RK: \u003cstrong\u003eRespiratory viral infections other than SARS CoV-2 among the North Indian patients presenting with acute respiratory illness during the first COVID-19 wave\u003c/strong\u003e. \u003cem\u003eVirusDisease \u003c/em\u003e2022, \u003cstrong\u003e33\u003c/strong\u003e(1):57-64.\u003c/li\u003e\n\u003cli\u003eRead JF, Bosco A: \u003cstrong\u003eDecoding Susceptibility to Respiratory Viral Infections and Asthma Inception in Children\u003c/strong\u003e. In: \u003cem\u003eInternational Journal of Molecular Sciences.\u003c/em\u003e vol. 21; 2020.\u003c/li\u003e\n\u003cli\u003eCooksey GLS, Morales C, Linde L, Schildhauer S, Guevara H, Chan E, Gibb K, Wong J, Lin W, Bonin BJ: \u003cstrong\u003eSevere acute respiratory syndrome coronavirus 2 and respiratory virus sentinel surveillance, California, USA, May 10, 2020\u0026ndash;June 12, 2021\u003c/strong\u003e. \u003cem\u003eEmerging Infectious Diseases \u003c/em\u003e2022, \u003cstrong\u003e28\u003c/strong\u003e(1):9-19.\u003c/li\u003e\n\u003cli\u003eAzzari C, Baraldi E, Bonanni P, Bozzola E, Coscia A, Lanari M, Manzoni P, Mazzone T, Sandri F, Checcucci Lisi G\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eEpidemiology and prevention of respiratory syncytial virus infections in children in Italy\u003c/strong\u003e. \u003cem\u003eItalian Journal of Pediatrics \u003c/em\u003e2021, \u003cstrong\u003e47\u003c/strong\u003e(1):198.\u003c/li\u003e\n\u003cli\u003eSavolainen-Kopra C, Korpela T, Simonen-Tikka M-L, Amiryousefi A, Ziegler T, Roivainen M, Hovi T: \u003cstrong\u003eSingle treatment with ethanol hand rub is ineffective against human rhinovirus\u0026mdash;hand washing with soap and water removes the virus efficiently\u003c/strong\u003e. \u003cem\u003eJournal of Medical Virology \u003c/em\u003e2012, \u003cstrong\u003e84\u003c/strong\u003e(3):543-547.\u003c/li\u003e\n\u003cli\u003eWinther B, McCue K, Ashe K, Rubino J, Hendley JO: \u003cstrong\u003eRhinovirus contamination of surfaces in homes of adults with natural colds: Transfer of virus to fingertips during normal daily activities\u003c/strong\u003e. \u003cem\u003eJournal of Medical Virology \u003c/em\u003e2011, \u003cstrong\u003e83\u003c/strong\u003e(5):906-909.\u003c/li\u003e\n\u003cli\u003eYu J, Zhang X, Shan W, Gao J, Hua J, Tian J, Ding Y, Zhang J, Chen L, Song Y: \u003cstrong\u003eInfluenza-associated hospitalization in children younger than 5 years of age in Suzhou, China, 2011\u0026ndash;2016\u003c/strong\u003e. \u003cem\u003eThe Pediatric infectious disease journal \u003c/em\u003e2019, \u003cstrong\u003e38\u003c/strong\u003e(5):445-452.\u003c/li\u003e\n\u003cli\u003eWang D, Chen L, Ding Y, Zhang J, Hua J, Geng Q, Ya X, Zeng S, Wu J, Jiang Y\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eViral etiology of medically attended influenza-like illnesses in children less than five years old in Suzhou, China, 2011\u0026ndash;2014\u003c/strong\u003e. \u003cem\u003eJournal of Medical Virology \u003c/em\u003e2016, \u003cstrong\u003e88\u003c/strong\u003e(8):1334-1340.\u003c/li\u003e\n\u003cli\u003eCowling BJ, Ali ST, Ng TWY, Tsang TK, Li JCM, Fong MW, Liao Q, Kwan MYW, Lee SL, Chiu SS\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eImpact assessment of non-pharmaceutical interventions against coronavirus disease 2019 and influenza in Hong Kong: an observational study\u003c/strong\u003e. \u003cem\u003eThe Lancet Public Health \u003c/em\u003e2020, \u003cstrong\u003e5\u003c/strong\u003e(5):e279-e288.\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e30.\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp;\u0026nbsp; Huang QS, Wood T, Jelley L, Jennings T, Jefferies S, Daniells K, Nesdale A, Dowell T, Turner N, Campbell-Stokes P\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eImpact of the COVID-19 nonpharmaceutical interventions on influenza and other respiratory viral infections in New Zealand\u003c/strong\u003e. \u003cem\u003eNature Communications \u003c/em\u003e2021, \u003cstrong\u003e12\u003c/strong\u003e(1):1001.\u003c/p\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e General characteristics of children infected with respiratory pathogens\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003eFlu A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003eH1N1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003eH3N2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003eFlu B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003eHPIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003eHADV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003eHBoV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003eHRV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003eHNPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003eHCoV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003eHRSV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003eParameters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003en = 125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003en = 83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003en = 90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003en = 82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003en = 129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003en = 200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003en = 50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003en = 259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003en = 168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003en = 16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003en = 273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003eSex (Male)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e80 (64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e56 (67.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e55 (61.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e41 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e76 (58.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e122 (61)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e29 (58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e151 (58.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e95 (56.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e8 (50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e167 (61.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e0.609\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e\u0026lt; 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e17 (13.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e10 (12.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e11 (12.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e6 (7.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e29 (22.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e26 (13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e5 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e65 (25.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e20 (11.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e4 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e101 (37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e1-\u0026lt; 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e18 (14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e16 (19.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e11 (12.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e10 (12.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e42 (32.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e55 (27.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e23 (46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e72 (27.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e36 (21.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e3 (18.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e75 (27.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e3-\u0026lt; 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e54 (43.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e27 (32.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e44 (48.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e39 (47.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e51 (39.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e78 (39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e22 (44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e81 (31.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e102 (60.71)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e6 (37.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e83 (30.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e\u0026ge; 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e36 (28.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e30 (36.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e24 (26.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e27 (32.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e7 (5.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e41 (20.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e41 (15.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e10 (5.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e3 (18.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e14 (5.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.6923076923076925%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe information provided is displayed as a percentage (n). The chi-squared examination for discrete variables. InfA is an abbreviation for influenza A, H1N1 refers to influenza A H1N1 virus, H3N2 represents seasonal influenza A H3N2 virus, Flu B stands for influenza B virus, HPIV denotes parainfluenza virus, HADV signifies adenovirus, HBoV indicates bocavirus, HRV represents rhinovirus, HNPV refers to metapneumovirus, HCoV stands for coronavirus, and HRSV represents respiratory syncytial virus.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eMultivariable-adjusted associations between age and respiratory pathogen infections\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.3478260869565215%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003eFlu A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003eH1N1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003eH3N2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003eFlu B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003eHPIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003eHADV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003eHBoV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003eHRV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003eHNPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003eHCoV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003eHRSV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003eaOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.409090909090909%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003eaOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.409090909090909%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003eaOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.409090909090909%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003eaOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.409090909090909%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003eaOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.409090909090909%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003eaOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.409090909090909%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003eaOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.409090909090909%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003eaOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.409090909090909%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003eaOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.409090909090909%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003eaOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.409090909090909%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003eaOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.409090909090909%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.3478260869565215%\"\u003e\n \u003cp\u003eAge(years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.3478260869565215%\"\u003e\n \u003cp\u003e\u0026lt;1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.3478260869565215%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.3478260869565215%\"\u003e\n \u003cp\u003e1-\u0026lt;3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.621\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e4.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.597\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.3478260869565215%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.582~2.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.665~3.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.489~2.681\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.585~4.491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.818~2.163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.462~3.800\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.510~10.674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.737~1.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.891~2.724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.148~2.999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.579~1.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.3478260869565215%\"\u003e\n \u003cp\u003e3-\u0026lt;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2.513\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e4.572\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e3.072\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.928\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.909\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.3478260869565215%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.141~3.437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.733~3.201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.277~4.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.917~10.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.764~1.950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.404~3.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.152~8.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.578~1.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.97~5.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.259~3.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.417~0.770\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.3478260869565215%\"\u003e\n \u003cp\u003e\u0026ge;6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2.013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e4.937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.713\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e6.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.138\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.3478260869565215%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.115~3.633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2.37~10.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.826~3.555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2.728~16.257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.125~0.665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.005~2.745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0~.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.485~1.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.347~1.618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.224~4.581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.078~0.243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.3478260869565215%\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.467\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote：The age and season were taken into account when adjusting the model. Abbreviations used include InfA for influenza A, H1N1 for influenza A H1N1 virus, H3N2 for seasonal influenza A H3N2 virus, Flu B for influenza B virus, HPIV for parainfluenza virus, HADV for adenovirus, HBoV for bocavirus, HRV for rhinovirus, HNPV for metapneumovirus, HCoV for coronavirus, and HRSV for respiratory syncytial virus.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Multivariable-adjusted associations between season and respiratory pathogen infections\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.3478260869565215%\" rowspan=\"2\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003eFlu A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003eH1N1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003eH3N2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003eFlu B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003eHPIV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003eHADV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003eHBoV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003eHRV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003eHNPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003eHCoV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003eHRSV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.409090909090909%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.409090909090909%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.409090909090909%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.409090909090909%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.409090909090909%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.409090909090909%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.409090909090909%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.409090909090909%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.409090909090909%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.409090909090909%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.681818181818182%\"\u003e\n \u003cp\u003eOR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.409090909090909%\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.3478260869565215%\"\u003e\n \u003cp\u003eSeason\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e(95%CI)\u003c/p\u003e\n \u003c/td\u003e\n 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\u003cp\u003e(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e(reference)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.3478260869565215%\"\u003e\n \u003cp\u003eSummer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e3.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e12.877\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.3478260869565215%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.025~0.433\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0~.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.038~2.529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.258~0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2.112~5.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.675~1.719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e4.97~33.365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.212~2.309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.497~1.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.281~3.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.275~0.683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.3478260869565215%\"\u003e\n \u003cp\u003eAutumn\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2.515\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e16.487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e2.254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e4.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.3478260869565215%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.689~3.744\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0~.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e7.556~35.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.003~0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.727~2.017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.575~3.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.44~11.706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.786~1.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.017~0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.062~1.376\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.899~1.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.3478260869565215%\"\u003e\n \u003cp\u003eWinter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.061\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e0.156\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.3478260869565215%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.213~0.927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.027~0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.241~3.656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.317~1.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.432~1.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e1.018~2.417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.204~5.511\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.361~0.875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.01~0.158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.026~1.654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.913~1.765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"4.3478260869565215%\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e0.535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"5.434782608695652%\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"3.260869565217391%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote：The age and season were taken into account when adjusting the model. Abbreviations used include InfA for influenza A, H1N1 for influenza A H1N1 virus, H3N2 for seasonal influenza A H3N2 virus, Flu B for influenza B virus, HPIV for parainfluenza virus, HADV for adenovirus, HBoV for bocavirus, HRV for rhinovirus, HNPV for metapneumovirus, HCoV for coronavirus, and HRSV for respiratory syncytial virus.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Children, Respiratory tract infections, Respiratory virus, Epidemiology","lastPublishedDoi":"10.21203/rs.3.rs-3869323/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3869323/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eLower respiratory tract infections (LRTIs) are one of the leading causes of hospital admissions among children. In this study, we aimed to describe the epidemiological characteristics of viral pathogens associated with LRTIs in hospitalized children in Yan'an; this has yet to be reported in the literature and may guide public health interventions and resource allocation in this region.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e Between June 2021 and May 2023, we conducted a retrospective analysis of the results of viral detection using oral pharyngeal swabs from 4565 children with LRTIs in the Inpatient Department of Yan'an University Affiliated Hospital. Eleven respiratory viruses, including influenza A virus (Flu A), influenza A H1N1 virus (H1N1), seasonal influenza A H3N2 virus (H3N2), influenza B virus (Flu B), parainfluenza virus (HPIV), adenovirus (HADV), bocavirus (HBoV), rhinovirus (HRV), metapneumovirus (HNPV), coronavirus (HCoV), and respiratory syncytial virus (HRSV), were confirmed by applying a multiplex real-time polymerase chain reaction (PCR) kit for respiratory viruses. We evaluated the epidemiological features of infections caused by respiratory pathogens, including aging and the seasonal variations of different pathogens, and explored the high-risk factors associated with virus-caused pneumonia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e At least one virus was detected in all 4565 cases; the positivity rate was 27.95%. We also detected a total of 1,276 cases with mixed infections (with two or more viruses). Of the positive cases, 59.3% were male and 40.7% were female (\u003cem\u003ex\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 0.41, P = 0.68). The highest positivity rates for respiratory pathogens were observed for HRSV, HRV, and HADV, at 5.98%, 5.67%, and 4.38%, respectively. We also observed variations in the number and positivity rates of respiratory pathogen infections by season and age. HPIV (\u003cem\u003ex\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 12.05，P \u0026lt; 0.05) and HADV (\u003cem\u003ex\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = 11.73，P \u0026lt; 0.05) were more common in children under 3 years-of-age.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e In conclusion, our analysis revealed that respiratory pathogen infections varied by gender, season, and age in the enrolled population of children.\u003c/p\u003e","manuscriptTitle":"Epidemiological characteristics of eleven common respiratory viral infections in children","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-25 15:36:41","doi":"10.21203/rs.3.rs-3869323/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-29T05:13:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-28T12:45:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"180587170332839588641944006782516036976","date":"2024-09-25T06:51:24+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-08-29T08:46:27+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"160014469520721038849227820247675527199","date":"2024-08-23T08:14:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"73456988061710697398211345767120932118","date":"2024-08-23T08:09:27+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-06-12T07:26:37+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-05-28T10:56:18+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-01-23T10:56:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-01-23T10:53:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pediatrics","date":"2024-01-16T09:12:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pediatrics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bped","sideBox":"Learn more about [BMC Pediatrics](http://bmcpediatr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/bped/default.aspx","title":"BMC Pediatrics","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"645e1bd1-e79f-437b-9aa2-3be80a507987","owner":[],"postedDate":"January 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-23T16:05:00+00:00","versionOfRecord":{"articleIdentity":"rs-3869323","link":"https://doi.org/10.1186/s12887-024-05300-1","journal":{"identity":"bmc-pediatrics","isVorOnly":false,"title":"BMC Pediatrics"},"publishedOn":"2024-12-20 15:58:19","publishedOnDateReadable":"December 20th, 2024"},"versionCreatedAt":"2024-01-25 15:36:41","video":"","vorDoi":"10.1186/s12887-024-05300-1","vorDoiUrl":"https://doi.org/10.1186/s12887-024-05300-1","workflowStages":[]},"version":"v1","identity":"rs-3869323","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3869323","identity":"rs-3869323","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}