Associations between Residential Greenness and Influenza Virus Infection in China: An Individual-Level, National, Cross-Sectional Study, 2010–2017

Associations between Residential Greenness and Influenza Virus Infection in China: An Individual-Level, National, Cross-Sectional Study, 2010–2017 | 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 Article Associations between Residential Greenness and Influenza Virus Infection in China: An Individual-Level, National, Cross-Sectional Study, 2010–2017 Hao Lei, Shimeng Cai, Xin Liu, Lei Yang, Shuyi Ji, Shigui Yang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6706883/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Residential greenness, a fundamental component of urban design, could contribute to the prevention of respiratory infections via several potential mechanisms.. However, the health benefits of greenness on influenza epidemics in real world are not as clear. Therefore, in the present study, we investigated the association between residential greenness exposure and influenza virus infection risk using a large and diverse cross-sectional dataset from the Chinese influenza surveillance system. Methods In this cross-sectional, associational study, we used information from influenza-like illness (ILI) patients who were tested for influenza from 2010 to 2017 from the Chinese influenza surveillance system. Residential greenness was assessed with the normalized difference vegetation index (NDVI) within a 250 m radius of the ILI residential addresses. Other environmental metrics included the mean air temperature; relative humidity; precipitation; wind speed; sunshine duration; and O 3 and PM 2.5 concentrations. A series of logistic models were constructed to examine the associations between residential greenness exposure and the odds of influenza virus infection after adjusting for covariates such as individual age, gender, climate, air pollution and seasonality. Findings From 2010–2017, 3,131,881 ILI cases were tested for influenza, and 1,012,430 (32.3%) participants with detailed building-level residential addresses were included in this study. Overall, a protective effect of residential greenness on the risk of influenza virus infection was observed, with 2.6% lower odds of influenza virus infection per one-quartile increase in the NDVI (odds ratio (OR) = 0.974, 95% confidence interval (CI): 0.963–0.985, p < 0.001). However, the impact varied across the different subgroups. Stratified analyses indicated that the protective effects of residential greenness were strongest among adults aged ≥ 60 years (OR = 0.853, 95% CI: 0.814–0.894, p < 0.001), but among children aged 7–17 years (i.e., school-aged children), the association was positive (OR = 1.104, 95% CI: 1.079–1.129, p < 0.001). There were no protective effects at other city scales except in megacities (OR = 0.907, 95% CI: 0.886–0.930, p < 0.001). Similarly, the protective effects of residential greenness against the development of influenza were observed only during the influenza season, i.e., in spring (OR = 0.914, 95% CI: 0.893–0.936, p < 0.001) and winter (OR = 0.933, 95% CI: 0.915–0.952, p < 0.001), and in southern China (OR = 0.975, 95% CI: 0.963–0.988, p < 0.001). Residential greenness had protective effects against influenza B infection (OR = 0.888, 95% CI: 0.872–0.906, p < 0.001), but no such effect was observed for influenza A infection (OR = 1.022, 95% CI: 1.008–1.036). The results from the interaction analysis between covariates were consistent with the results from the stratified analyses, except when the age group interacted with geographic regions. The mediating effects of PM 2.5 and O 3 exposure on the association between residential greenness exposure and the risk of influenza virus infection were 33.90% and 16.38%, respectively. Discussion The results highlight the benefits of well-designed green environments for influenza prevention. Given the rapid ageing and urbanization process in China, policies aimed at optimizing the allocation and design of green spaces might help reduce respiratory infection transmission. Health sciences/Diseases/Infectious diseases/Influenza virus Health sciences/Medical research/Epidemiology Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Residential greenness, a fundamental component of urban design, has been shown to reduce the public health burden and preserve population health and wellbeing. The benefits of greenness exposure on noncommunicable diseases, such as depressive disorders 1 and cardiovascular diseases 2 , have been well documented. However, the impact on infectious diseases is less well understood. Respiratory infections are the leading infectious cause of death globally 3 . Greenness could contribute to the prevention of respiratory infections via several potential mechanisms. First, the activities of natural killer cells in the human body increase with frequent exposure to vegetation 4 ; moreover, natural killer cells constitute an important part of the human immune system and can eliminate virus-infected cells 5 . Greenness can also protect against air pollution, and exposure to several forms of ambient air pollution, such as PM 2.5 and NO 2 , is associated with an increased incidence of respiratory infections such as influenza 6 , 7 . Last, greenness often provides an outdoor environment for physical exercise and activities 8 . Respiratory infections, such as influenza and COVID-19, are transmitted mainly indoors 9 , 10 . Recent epidemiological studies have examined the associations between greenness exposure and COVID-19 transmission, but the results have been inconsistent. Studies in the United States 11 , 12 , China 13 and India 14 have revealed a negative correlation between the incidence rate of COVID-19 and greenness exposure. However, studies in London revealed that COVID-19 infections increased with increasing urban greenness 15 . Several factors could contribute to the mixed findings, such as the different definitions of COVID-19 mortality rates, sample sizes, types and choices of covariates and statistical methods 16 , and the varying governmental measures used to control the pandemic at the early stage, including population mobility restrictions 17 and face-mask mandates 18 . In addition, the mixed findings may also suggest that the impact of greenness exposure on respiratory virus transmission could be influenced by socioeconomic factors. There are also two main knowledge gaps concerning the effects of greenness exposure on COVID-19 transmission. First, most of the existing evidence linking greenness and infection is based on aggregated data 19 . Only one study in 2024 used individual-level data in Denmark 19 . The inherent nature of the ecological fallacy and residual confounding weakens causal inference regarding the effects of greenness. Second, studies based on long-term surveillance data are lacking. Previous studies on COVID-19 only used up to 14 months of infection surveillance data, which undermined the stability of the results and their extrapolation to other respiratory diseases, since the incidence of COVID-19 has varied annually, mainly because of the rapid evolution of the SARS-CoV-2 virus. Therefore, there is an urgent need to conduct a more accurate assessment of the association between greenness exposure and the risk of developing respiratory infectious diseases at the individual level on the basis of long-term surveillance data. Influenza, another respiratory infection, has a particularly rich and long-term disease system for illustrating the state of the art of infection surveillance; thus, findings from individuals infected with influenza virus could lead to more robust recommendations for respiratory infection control in the future. China has built the largest influenza surveillance system worldwide, with 554 sentinel hospitals and 411 network laboratories across the Chinese mainland before the COVID-19 pandemic 20 . In addition, influenza itself is a significant cause of morbidity and mortality worldwide. According to World Health Organization (WHO) statistics, approximately 1 billion people are infected by seasonal influenza globally each year, resulting in 290,000 to 650,000 respiratory-related deaths 21 . Furthermore, in the context of rapid urbanization and population ageing in recent decades in China, managing influenza epidemics also involves ageing-related challenges 22 ; i.e., the elderly population, especially those living in highly urbanized cities, has played an increasingly important role in influenza epidemics 23 , 24 . Given the potentially enormous health benefits for elderly people with more frequent greenness exposure, the effects of greenness exposure on the severity of influenza epidemics in China may be pronounced. Finally, China’s diverse climate conditions and uneven development among regions necessitate the exploration of the impacts of greenness exposure on influenza transmission under different socioeconomic conditions. In this study, we aimed to assess the association between residential greenness exposure, measured by the normalized difference vegetation index (NDVI), and the risk of influenza virus infection in China via 8 years of influenza surveillance data (2010–2017). We further investigated whether the associations between residential greenness exposure and the risk of influenza virus infection varied under different socioeconomic conditions via stratified analyses. Methods Influenza Surveillance Data In this study, we used influenza surveillance data from the Chinese National Influenza Center (CNIC) ( http://www.chinaivdc.cn/cnic/ ). The national sentinel hospital-based influenza-like illness (ILI) surveillance system in China was established in 2000 and expanded to 554 sentinel hospitals and 411 network laboratories across mainland China after the 2009 influenza H1N1 pandemic 20 , 25 . On a weekly basis, sentinel hospitals report the number of outpatient visits and cases of ILI. The ILI was defined according to the standard case definition, including a body temperature ≥ 38°C with either cough or sore throat, in the absence of an alternative diagnosis 25 . Additionally, a convenience sample of patients visiting sentinel hospitals within three days of ILI onset was collected. Thus, each week, 5–15 nasopharyngeal swab samples were tested at each sentinel hospital 25 . Patient samples were tested by real-time reverse transcription PCR or virus isolation in the affiliated laboratories. Other personal information, including age, gender, date of illness onset, date tested, and residential address, was also collected. Since the quality of the surveillance data improved dramatically after the 2009 influenza A (H1N1) pandemic, the period from 2010 to 2017 was selected for this study 25 . To explore the individual-level association between residential greenness exposure and the risk of influenza virus infection, cases without accurate building-level residential address information were excluded from this study. Individual-level Residential Greenness Exposure Green spaces generally refer to areas covered by vegetation, such as urban parks, forests, road green belts, and private gardens 26 . In this study, the monthly NDVI when the participants started to develop ILI symptoms was selected as the residential greenness exposure. The NDVI dataset was obtained from the National Tibetan Plateau Data Center 27 and was processed on the basis of MODIS satellite sensor data (MOD13Q1, Terra satellite). The processing included initial reconstruction of similar feature noise pixels, long-sequence images (S-G filter), maintaining high quality, monthly synthesis and stitching. The NDVI is a widely used satellite-derived indicator that has been shown in previous epidemiological studies to be effective for measuring greenness 28 . The NDVI value ranges from − 1 to 1, and positive values indicate vegetation coverage, with higher values indicating denser green vegetation. Given that 300 m is the WHO-recommended accessible distance for greenspace 19 , we used NDVI data with 250 m resolution with values matched to each ILI residential address, where the address was interpreted as coordinates using the Tencent Web Service API, to assess their exposure-response associations. Covariates In this study, potential covariates that may influence seasonal influenza virus infection risk, including demographic characteristics (gender, age, and geographic region), meteorological factors (air temperature, relative humidity, precipitation, wind speed, and sunshine duration), air pollutants (PM 2.5 and O 3 ), and time variables (the monitoring year and seasonality), were adjusted in the model. The 4 km daily gridded meteorological data spanning from 2000 to 2020 29 were obtained from the China Daily Meteorological Dataset. This dataset was generated through spatial interpolation using thin plate spline and random forest methods on the basis of point data from 699 meteorological stations across China. The daily air pollution data were sourced from the China High Air Pollutants dataset, with a spatial resolution of 1 km 30 , 31 . According to a systematic review 32 , the median incubation periods for influenza A and B are 1.4 days and 0.6 days, respectively. Therefore, the average values of the meteorological factors and air pollutants from the two days preceding the onset date were used to characterize the exposure levels. The corresponding meteorological and air pollution exposures of each ILI case were also matched on the basis of the resolved residential address information. Statistical Analysis In the present study, we used a multilayered analysis strategy. Logistic regression models were used to investigate the associations between residential greenness exposure and the risk of influenza virus infection. Odds ratios (ORs) and two-tailed 95% CIs are presented for each interquartile range (IQR) increment in the NDVI. A logistic regression model with Firth maximum penalized likelihood estimation was used to estimate the individual-level associations between residential greenness exposure and the risk of seasonal influenza virus infection 33 , 34 . This approach was adopted to mitigate sparse data bias caused by the low proportion of positive outcomes in the ILI data. A series of regression models were constructed, with each progressively adjusted for additional covariates. Specifically, Model 1 included initial crude estimates adjusted only for gender, age, and geographic region (southern or northern China). Model 2 was further adjusted for meteorological factors (temperature, relative humidity, precipitation, wind speed, and sunshine duration). Model 3 included additional adjustments for air pollutants (PM 2.5 , O 3 ). Model 4 was a fully adjusted model that incorporated the monitoring year and seasonality on the basis of Model 3. To explore whether the association between residential greenness exposure and the risk of seasonal influenza virus infection is influenced by gender, age group, geographic region, seasonality, city scale and influenza virus subtypes, stratified analyses were conducted on the basis of the following covariates: gender (male, female), age group (0–17 years, 18–59 years, ≥ 60 years), geographic region (southern China, northern China), seasonality (spring, summer, fall, winter), city scale (micropolis, medium-sized city, large city, super city, megacity), and influenza virus subtype (type A, type B). According to the climate characteristics in China, spring is defined as March to May, summer as June to August, autumn as September to November, and winter as December to February. City scale classification was based on the Notice of the State Council on Adjusting the Standards for Urban Scale Classification ( https://www.gov.cn/ ). In this study, cities were divided into five categories on the basis of the permanent urban resident permanent population: micropolis (< 500,000 individuals), medium-sized city (500,000–1,000,000 individuals), large city (1,000,000–5,000,000 individuals), super city (5,000,000–10,000,000 individuals), and megacity (≥ 10,000,000 individuals). The significance of the effects within each subgroup was evaluated using the fully adjusted model. We further analysed the interaction effects of age and city scale, gender and city scale, age and geographic region, and gender and geographic region on the relationship between residential greenness exposure and the risk of influenza virus infection. Mediation analysis Urban vegetation can improve air quality by influencing the deposition and dispersion of air pollutants 35 . Multiple epidemiological studies have demonstrated a close relationship between exposure to air pollutants and seasonal influenza virus infection 6 , 7 , 36 . Therefore, we conducted a mediation analysis to examine the potential mediating effects of PM 2.5 and O 3 on the association between residential greenness exposure and the risk of seasonal influenza virus infection. The mediation analysis utilized the bootstrap sampling method, reporting the total effect, direct effect, indirect effect (mediating effect), and the proportion of the indirect effect. Sensitivity analyses To verify the robustness of our findings, we conducted a series of sensitivity analyses using the fully adjusted model: (1) To assess the impact of spatial scale on the association between residential greenness exposure and the risk of seasonal influenza virus infection, we adjusted the residential greenness exposure to the monthly NDVI within a 1 km radius of the ILI address and reran the model. (2) Meteorological and air pollutant exposure levels were characterized using data from the first and second days before the onset of influenza symptoms, and the model was rerun accordingly. All the statistical analyses were performed using R statistical software version 4.4.2 (The R Project for Statistical Computing, Vienna, Austria). Logistic regression analysis was conducted using the "logistf" package for logistic regression with Firth maximum penalized likelihood estimation. This study was approved by the Institutional Review Board and Human Research Ethics Committee of the School of Medicine of Zhejiang University (No. ZGL202504-1). Results Descriptive Analysis From 2010–2017, 3,131,881 ILI cases were tested by PCR or virus isolation for influenza identification in the influenza surveillance system in China, where 1,012,430 (32.3%) ILI patients with detailed building-level residential addresses (e.g., building, community, or POI) were included in this study. A total of 164304 (16.23%) patients were influenza positive, with more patients infected with influenza A virus (Table 1 ). A total of 52.94% of the participants were male. Most participants were children and adolescents aged 0–17 years (60.24%), whereas the lowest percentage was among elderly individuals aged ≥ 60 years (5.64%). ILI cases were more frequently reported in southern China (58.87%) than in northern China (41.13%). The highest number of ILI cases was reported in 2014 (17.04%), whereas the lowest was recorded in 2016 (7.48%) (Table 1 ). Most cases were reported in winter, followed by spring, fall and summer. The characteristics of these 1,012,430 participants with detailed building-level residential addresses were similar to those of the 3,131,881 ILI cases (Table S1 ). The spatial distribution of the 1,012,430 ILI cases is shown in Fig. 1 , with the study population covering 98.8% of the prefectures across the country. Table 1 The demographic characteristics of the 1,012,430 participants. Characteristics N (%) Gender Male 536017 (52.94%) Female 476413 (47.06%) Age 0–17 609852 (60.24%) 18–59 345494 (34.13%) ≥ 60 57084 (5.64%) Region Northern China 416391 (41.13%) Southern China 596039 (58.87%) Influenza tested result Influenza A positive 104306 (10.30%) Influenza B positive 59317 (5.86%) Negative 848126 (83.77%) NDVI [0,0.25) 560978 (55.41%) [0.25,0.5) 411381 (40.63%) [0.5,0.75) 29516 (2.92%) [0.75,0.944) 1415 (0.14%) City scale Micropolis 128137 (12.66%) Medium-sized city 271426 (26.81%) Large city 258789 (25.56%) Super city 99499 (9.83%) Megacity 191288 (18.89%) Season Spring 259897 (25.67%) Summer 186052 (18.38%) Fall 231494 (22.87%) Winter 334987 (33.09%) Year 2010 116749 (11.53%) 2011 85072 (8.40%) 2012 92401 (9.13%) 2013 147155 (14.53%) 2014 172506 (17.04%) 2015 169540 (16.75%) 2016 75696 (7.48%) 2017 153311 (15.14%) During the study period from 2010 to 2017, both influenza positivity and the NDVI varied in different areas of China (Figure S1 ). People living in southern China had a much greater NDVI than those living in northern China (Fig. 2 a). People had a greater NDVI in summer but a lower NDVI in winter (Fig. 2 b). For different age groups, the NDVI was similar (Fig. 2 c). The NDVI for people living in megacities was slightly greater than that for those living in other cities (Fig. 2 d). Associations between Residential Greenness and Influenza Virus Infection Table 2 reports the OR for each variable in four logistic regression models with Firth maximum penalized likelihood estimation. In all four models, a statistically significant inverse association between the NDVI and seasonal influenza was observed (p < 0.001). According to the fully adjusted model, for each increase in one interquartile range (IQR = 0.171) in the NDVI, the risk of influenza virus infection decreased by 2.6% (OR = 0.974, 95% CI: 0.963–0.985). In the fully adjusted model (Model 4), average temperature had the strongest negative association with seasonal influenza among all the meteorological factors. A significant positive correlation was observed between the PM 2.5 concentration and seasonal influenza, whereas the correlation between O 3 and seasonal influenza was negative. Table 2 Associations between Residential Greenness and Seasonal Influenza Variables Model1 (OR) Model2 (OR) Model3 (OR) Model4 (OR) NDVI (per 0.171) 0.621*** 0.888*** 0.926*** 0.974*** Gender (Ref Female) 0.985** 0.989* 0.990 0.990 Age (years) 1.006*** 1.007*** 1.007*** 1.006*** Region (Ref Southern) 0.623*** 0.461*** 0.446*** 0.509*** Mean temperature (K) 0.963*** 0.969*** 0.981*** Precipitation (mm) 1.005*** 1.006*** 1.004*** Relative humidity (%) 0.993*** 0.992*** 0.993*** Sunshine duration (h) 0.977*** 0.988*** 0.995*** Wind speed (m/s) 0.967*** 1.015*** 1.009* O 3 (ug/m 3 ) 0.997*** 0.996*** PM 2.5 (ug/m 3 ) 1.002*** 1.002*** Spring (Ref Winter) 0.821*** Summer (Ref Winter) 0.929*** Fall (Ref Winter) 0.469*** 2011 (Ref 2010) 0.572*** 2012 (Ref 2010) 1.108*** 2013 (Ref 2010) 0.682*** 2014 (Ref 2010) 1.058*** 2015 (Ref 2010) 0.762*** 2016 (Ref 2010) 1.711*** 2017 (Ref 2010) 1.203*** The analysis included data for 1012430 ILI cases. Model 1 was adjusted only for demographic factors (gender, age, and geographic region). Model 2 was further adjusted for meteorological factors (temperature, relative humidity, precipitation, wind speed, and sunshine duration) on the basis of Model 1. Model 3 included additional adjustments for air pollutants (PM 2.5 , O 3 ). Model 4 was a fully adjusted model that incorporated the monitoring year and seasonality based on Model 3. OR: odds ratio. NDVI: normalized difference vegetation index. * p < 0.05, ** p < 0,01. *** p < 0.001 Stratified Analysis Figure 3 presents the associations between residential greenness exposure and the risk of influenza virus infection in different subgroups via stratified analyses. Similarly, the association between residential greenness exposure and the risk of seasonal influenza virus infection was negative among males (OR = 0.983, 95% CI: 0.968–0.998) and females (OR = 0.965, 95% CI: 0.949–0.981). The impact of the NDVI on seasonal influenza varied across age groups, with the strongest negative association observed in older adults aged ≥ 60 years (OR = 0.853, 95% CI: 0.814–0.894), followed by younger adults aged 18–59 years (OR = 0.909, 95% CI: 0.891–0.927), whereas a slightly positive association was found in children aged < 18 years (OR = 1.034, 95% CI: 1.019–1.050). Further analysis of children aged < 18 years suggested that the significant positive association between the NDVI and the risk of influenza virus infection was found only in school-aged children aged 7–17 years, who were more susceptible to influenza because of greater contact rates, and had less vegetation at school (Table S2). In terms of regional stratification, the correlation between the NDVI and seasonal influenza was significantly negative in southern China (OR = 0.975, 95% CI: 0.963–0.988), whereas it was reversed in northern China (OR = 1.067, 95% CI: 1.039–1.095). The protective effects of residential greenness against influenza virus infection were found only in spring (OR = 0.914, 95% CI: 0.893–0.936) and winter (OR = 0.933, 95% CI: 0.915–0.952). Stratified analysis at the city scale revealed that although the ORs generally decreased with increasing city scale, the significant protective effect of a high NDVI against influenza virus infection risk was found only in megacities (OR = 0.907, 95% CI: 0.886–0.930). Additionally, the results indicated that residential greenness provided protective effects against influenza B (OR = 0.888, 95% CI: 0.872–0.906), but an inverse effect was observed for influenza A (OR = 1.022, 95% CI: 1.008–1.036). Interaction Analysis Consistent with the results of the stratified analysis, interactions between age/gender and city scale revealed a stronger protective effect of residential greenness against influenza virus infection for adults older than 18 years who lived in large cities (i.e., large cities, super cities, and megacities) (Fig. 4 a, 4 c). However, interactions between age and geographic region revealed that in northern China, the positive correlation between residential greenness exposure and the risk of influenza virus infection became more pronounced with increasing age, whereas in southern China, the strongest protective effect was among elderly individuals aged ≥ 60 years (Fig. 4 d). Interactions between gender and geographic region further validated the findings of the stratified analysis in this study, indicating that the protective effect was observed only in southern China for both males and females (Fig. 4 b). Mediation Analysis PM 2.5 and O 3 had mediating effects on the association between residential greenness exposure and the risk of influenza virus infection, with the mediating effects accounting for 33.90% and 16.38%, respectively (Table 3 ). Table 3 Mediation analysis of PM 2.5 and O 3 in the association analysis between residential greenness exposure and the risk of influenza virus infection. Variables Total effect β (95% CI) Direct effect β (95% CI) Mediating effect β (95% CI) Prop. mediated (%) PM 2.5 -0.039 (-0.047, -0.031) -0.026 (-0.034, -0.017) -0.013 (-0.014, -0.012) 33.90 (27.55, 43.30) O 3 -0.040 (-0.048, -0.033) -0.033 (-0.041, -0.026) -0.0065 (-0.0070, -0.0060) 16.38 (13.41, 20.31) Sensitivity Analysis The results of the sensitivity analysis further confirmed the robustness of the association between residential greenness exposure and the risk of seasonal influenza virus infection. When we modified the residential greenness exposure metric to the monthly NDVI within a 1 km radius of the ILI residence, the association between residential greenness exposure and the risk of seasonal influenza virus infection slightly strengthened (OR = 0.952, 95% CI: 0.942–0.963) (Table S3). For each IQR increase in the NDVI, the risk of seasonal influenza virus infection decreased by 4.8%. When we used data from the first or second days prior to the onset date to represent meteorological factors and air pollution exposure levels and reran the model, the results were consistent with those of the primary model (OR = 0.963, 95% CI: 0.952–0.974) and OR = 0.971, 95% CI: 0.960–0.982) (Tables S4, S5). Discussion In this individual-level, national, cross-sectional, associational study, we found a significant negative correlation between residential greenness exposure and the risk of influenza virus infection. The strength of the association varied across different age groups, geographic regions, seasons, city scales, and influenza virus subtypes. The protective effects of residential greenness were more pronounced among older people aged ≥ 60 years and those residing in megacities. Mediation analysis revealed that PM 2.5 and O 3 accounted for 33.90% and 16.38%, respectively, of the mediating effect on the association between residential greenness exposure and the risk of influenza virus infection, suggesting that greenness could reduce influenza virus infection directly rather than by reducing air pollution. With one interquartile increment (0.171 units) in the NDVI, the risk of influenza virus infection decreased by 2.6%. Our findings on the beneficial association between residential greenness exposure and the risk of influenza virus infection were generally consistent with findings from the current literature on COVID-19. Studies in the US and UK 37 , Denmark 19 , and China 38 revealed that a 0.171 unit increase in the NDVI was associated with 5.99%, 4.25%, and 13.4% decreases in the incidence of COVID-19, respectively. The protective effect against influenza infection was much lower than that against COVID-19 infection. There are several potential reasons for this. The first is the much lower transmissibility of the influenza virus than that of SARS-CoV-2 39 . The second may be the varied population susceptibility to influenza and SARS-CoV-2, i.e., population susceptibility to SARS-CoV-2 increases with age, whereas children are more susceptible to influenza virus 39 . The benefits of greenness for adults, especially for older adults, were more pronounced. Our study revealed that the protective effect of residential greenness against seasonal influenza was most pronounced among older people aged ≥ 60 years. This is reasonable since residential neighbourhood greenness provides an outdoor environment for physical exercise and activities for retired people, who have more free time to access nature. For these school-aged children, the risk of influenza virus infection even increased slightly with increasing residential greenness. The potential reason could be that influenza transmission among school-aged children mainly occurs at school 40 due to an incomplete immune system and greater contact rates at school 41 . Most schools have thin vegetation; thus, real greenness exposure is overestimated when residential greenness is used. A significant inverse correlation between residential greenness exposure and the risk of influenza virus infection was observed only in megacities with large populations. This may be because, for the same NDVI, the actual use of greenness varies. One study in London demonstrated that communities in large cities with higher connectivity promoted walking and physical activity, thus improving public health 42 . The layout and infrastructure of megacities tend to be more scientifically planned, with better connectivity between different functional areas. Consequently, the protective effects of greenness in these cities result from higher population density and better accessibility, which enhance the health benefits provided by greenness. Additionally, we found that the heterogeneous association between residential greenness exposure and the risk of influenza virus infection varied across different regions. The protective effects were significant in southern China, whereas they were reversed in northern China. A possible reason is that northern China has a temperate monsoon climate, with the peak influenza season occurring during the cold and dry winters, and cold outdoor temperatures may encourage people to gather indoors and reduce the opportunities for exposure to greenness. The sensitivity analysis results indicated that when the green space exposure metric was adjusted to the average NDVI within a 1 km radius, the protective effect of residential greenness increased. Thus, humans may have more access to parks ranging from 500 m to 1 km than to parks ranging from 250 m, which may help the WHO refine its definition of the recommended accessible distance for greenspace. This study has several significant advantages and public health implications. First, to our knowledge, this is the first nationwide study to assess the association between residential greenness exposure and the risk of seasonal influenza virus infection at the individual level in China, which has diverse climates and development. Unlike previous studies 9 , 11 , 13 , 19 that relied on area-level measures of exposure, outcomes, or covariates, the use of a large administrative dataset (n = 1,012,430) in our study that contained individual-level data and objectively assessed measures of residential greenness helped to estimate the associations between residential greenness exposure and the risk of influenza virus infection in diverse climatic and socioeconomic contexts. Second, compared with the varied definitions of COVID-19 cases in different regions at the early stage of the pandemic 16 , this study utilized a more standard definition of influenza virus infection. Patient samples were tested by real-time reverse transcription PCR or virus isolation in the affiliated laboratories. Third, the associations between residential greenness exposure and the risk of influenza subtypes were explored in this study, offering insights for the targeted prevention and control of influenza strains dominated by specific viral subtypes. Given the fast ageing and urbanization process in China and the better protective effects of residential greenness against influenza virus infection among older people in megacities, leveraging the protective effects of greenness against influenza presents a cost-effective solution for influenza prevention. On the one hand, unlike meteorological conditions, greenness exposure can be modulated by encouraging residents to alter their lifestyles or plan effective urban green spaces 43 . On the other hand, greenness provides numerous health benefits. A literature review reported that greenness exposure can reduce the risk of cardiovascular diseases, mental health disorders, adverse birth outcomes, and mortality 26 . Our findings support public health and urban planning professionals in optimizing greenness exposure and related building characteristics in large cities, developing nature-based interventions, and achieving precise influenza prevention and control. The findings of this study need to be considered in the context of the following limitations. First, the cross-sectional study design does not allow for causal inferences. Second, while the NDVI has been widely used in previous epidemiological studies to measure greenness 28 , it does not reflect the accessibility or actual use of greenness, nor does it differentiate between types of green spaces (e.g., forests, shrubs, and grasslands) or their nature (e.g., private residential green spaces vs. public green spaces). Future studies should continue to investigate the interaction between greenness exposure and populations, as well as the associations between exposure to different types of green spaces and the risk of influenza virus infection. Third, the lack of data on individual behaviours and chronic disease histories may result in potential confounding factors not being accounted for in this study. Fourth, the accuracy of passive influenza surveillance data may be influenced by monitoring capacity. In the present study, we limited the research period to after the 2009 H1N1 influenza pandemic, during which the monitoring system expanded in scale 25 , significantly improving the quality of surveillance data and thereby minimizing the potential impact of issues such as underreporting on data accuracy. Conclusion Using fine-resolution data from 1,012,430 ILI cases between 2010 and 2017 in China, we showed that residential greenness could reduce seasonal influenza virus infection. However, the impact varied in different climatic and socioeconomic contexts. The protective effect of residential greenness was pronounced in older adults and in larger cities. Our findings support public health and urban planning professionals in optimizing greenness exposure and related building characteristics, developing nature-based interventions, and achieving precise influenza prevention and control. Declarations Ethics approval and consent to participate This study was approved by the Institutional Review Board and Human Research Ethics Committee of the School of Medicine of Zhejiang University (No. ZGL202504-1). Availability of data and materials The datasets used in the study are available from the corresponding author on reasonable request. Competing interests All authors declare no competing interests. Funding This study was funded by grants from the Shenzhen Medical Research Fund (Grant No. B240300). Authors' contributions HL, YS, DW and BZ conceived, designed and supervised the study. XL, LY and DW collected data. HL, SC and XL cleaned data. HL and SC performed mathematical modelling and the analysis. HL and SC drafted the manuscript. XL, LY, SJ, SY, XX, DW, YS and BZ reviewed and revised drafts of the manuscript. HL and SC interpreted the findings. All authors read and approved the final manuscript. Acknowledgements Not applicable References Sarkar, C., Webster, C. & Gallacher, J. Residential greenness and prevalence of major depressive disorders: a cross-sectional, observational, associational study of 94 879 adult UK Biobank participants. The Lancet Planetary Health 2 , e162–e173 (2018). Yang, B. et al. Association Between Residential Greenness, Cardiometabolic Disorders, and Cardiovascular Disease Among Adults in China. JAMA Netw Open 3 , e2017507 (2020). Vos, T. 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 396 , 1204–1222 (2020). Li, Q. Effect of forest bathing trips on human immune function. Environ Health Prev Med 15 , 9–17 (2010). Vivier, E., Tomasello, E., Baratin, M., Walzer, T. & Ugolini, S. Functions of natural killer cells. Nat Immunol 9 , 503–510 (2008). Yu, L. et al. Short-Term Exposure to Ambient Air Pollution and Influenza: A Multicity Study in China. Environ Health Perspect 131 , 127010 (2023). Zhang, R. et al. Associations between Short-Term Exposure to Ambient Air Pollution and Influenza: An Individual-Level Case-Crossover Study in Guangzhou, China. Environ Health Perspect 131 , 127009 (2023). Mytton, O. T., Townsend, N., Rutter, H. & Foster, C. Green space and physical activity: An observational study using Health Survey for England data. Health & Place 18 , 1034–1041 (2012). Lei, H. et al. Indoor relative humidity shapes influenza seasonality in temperate and subtropical climates in China. International Journal of Infectious Diseases 126 , 54–63 (2023). Bulfone, T. C., Malekinejad, M., Rutherford, G. W. & Razani, N. Outdoor Transmission of SARS-CoV-2 and Other Respiratory Viruses: A Systematic Review. The Journal of Infectious Diseases 223 , 550–561 (2021). Jiang, B. et al. Green spaces, especially nearby forest, may reduce the SARS-CoV-2 infection rate: A nationwide study in the United States. Landscape and Urban Planning 228 , 104583 (2022). Spotswood, E. N. et al. Nature inequity and higher COVID-19 case rates in less-green neighbourhoods in the United States. Nat Sustain 4 , 1092–1098 (2021). Peng, W. et al. City-level greenness exposure is associated with COVID-19 incidence in China. Environmental Research 209 , 112871 (2022). Sikarwar, A., Rani, R., Duthé, G. & Golaz, V. Association of greenness with COVID-19 deaths in India: An ecological study at district level. 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Influenza (Seasonal)[EB/OL]. (2023-10-03)[2024-02-25]. https://www.who.int/zh/news-room/fact-sheets/detail/influenza-(seasonal) Tang, W. et al. Rapid aging of influenza epidemics in China from 2005/06 to 2016/17: A population-based study. Infectious Disease Modelling 10 , 639–648 (2025). Yu, H. et al. Characterization of Regional Influenza Seasonality Patterns in China and Implications for Vaccination Strategies: Spatio-Temporal Modeling of Surveillance Data. PLoS Med 10 , e1001552 (2013). Wang, D., Lei, H., Wang, D., Shu, Y. & Xiao, S. Association between Temperature and Influenza Activity across Different Regions of China during 2010–2017. Viruses 15 , 594 (2023). Lei, H. et al. Transmission Patterns of Seasonal Influenza in China between 2010 and 2018. Viruses 14 , 2063 (2022). Yang, B., Zhang, Y., Huang, W. & Dong, G. Greenspace and health outcomes in Chinese population. Journal of Environmental and Occupational Medicine 39 , 30-35(2022). Gao, J. et al. China regional 250m normalized difference vegetation index data set (2000-2023). National Tibetan Plateau / Third Pole Environment Data Center. (2023). Rhew, I. C., Vander Stoep, A., Kearney, A., Smith, N. L. & Dunbar, M. D. Validation of the Normalized Difference Vegetation Index as a Measure of Neighborhood Greenness. Annals of Epidemiology 21 , 946–952 (2011). Zhang, J. et al. A 4 km daily gridded meteorological dataset for China from 2000 to 2020. Sci Data 11 , 1230 (2024). Wei, J. et al. Reconstructing 1-km-resolution high-quality PM2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications. Remote Sensing of Environment 252 , 112136 (2021). Wei, J. et al. Full-coverage mapping and spatiotemporal variations of ground-level ozone (O3) pollution from 2013 to 2020 across China. Remote Sensing of Environment 270 , 112775 (2022). Lessler, J. et al. Incubation periods of acute respiratory viral infections: a systematic review. The Lancet Infectious Diseases 9 , 291–300 (2009). Greenland, S., Mansournia, M. A. & Altman, D. G. Sparse data bias: a problem hiding in plain sight. BMJ 352, i1981 (2016) . Wang, X. Firth logistic regression for rare variant association tests. Front. Genet. 5 , (2014). Janhäll, S. Review on urban vegetation and particle air pollution – Deposition and dispersion. Atmospheric Environment 105 , 130–137 (2015). Yang, J. et al. Influence of air pollution on influenza-like illness in China: a nationwide time-series analysis. eBioMedicine 87 , 104421 (2023). Chen, K. et al. Associations between greenness and predicted COVID-19–like illness incidence in the United States and the United Kingdom. Environmental Epidemiology 7 , e244 (2023). Peng, W., Kan, H., Zhou, L. & Wang, W. Residential greenness is associated with disease severity among COVID-19 patients aged over 45 years in Wuhan, China. Ecotoxicology and Environmental Safety 232 , 113245 (2022). Lei, H. et al. Different transmission dynamics of COVID-19 and influenza suggest the relative efficiency of isolation/quarantine and social distancing against COVID-19 in China. Clin Infect Dis 73 , e4305-e4311(2021). Lei, H. et al. Relative Role of Age Groups and Indoor Environments in Influenza Transmission Under Different Urbanization Rates in China. American Journal of Epidemiology 193 , 596–605 (2024). Yang, H., Yin, Z., Zhong Ji., Cao G. & Yu Z. Surveillance results of influenza in Quzhou City, Zhengjiang Province, 2016-2020. Practical Preventive Medicine 30 , 5-8(2023). Sarkar, C. et al. Exploring associations between urban green, street design and walking: Results from the Greater London boroughs. Landscape and Urban Planning 143 , 112–125 (2015). Lin, J., Huang, B., Kwan, M.-P., Chen, M. & Wang, Q. COVID-19 infection rate but not severity is associated with availability of greenness in the United States. Landscape and Urban Planning 233 , 104704 (2023). 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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-6706883","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":459777125,"identity":"f54021c0-cbcc-4db2-8460-adca78110651","order_by":0,"name":"Hao Lei","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAy0lEQVRIiWNgGAWjYBACPgYGxgMMBjYMjA1AHg8xWtiAGKgljWQtDIchPOK0sDcfOPCh4Lw984wExgdv2xjkzQlq4TmWcHCGwW1mxhkJzIZz2xgMdzYQ0iKRY3CYx+A2G1ALmzRvG0OCwQFCWuTfGBz+Y3COB6iF/TdxWiR4DA4zGByQANnCTJwWnrSEgz0GyQaMPQ+bJeeckzDcQEgLP/vhgw9+/LGzN2xPPvjhTZmNPEFb4MCwARyZEsSqBwJ5EtSOglEwCkbBCAMAjZs7LkhaiQIAAAAASUVORK5CYII=","orcid":"","institution":"Zhejiang University","correspondingAuthor":true,"prefix":"","firstName":"Hao","middleName":"","lastName":"Lei","suffix":""},{"id":459777126,"identity":"cc9ecc92-9783-4534-9749-6b1f5e206f36","order_by":1,"name":"Shimeng Cai","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Shimeng","middleName":"","lastName":"Cai","suffix":""},{"id":459777127,"identity":"b24ff424-598f-4b02-93a3-145b1c6a95aa","order_by":2,"name":"Xin Liu","email":"","orcid":"","institution":"Southern University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xin","middleName":"","lastName":"Liu","suffix":""},{"id":459777128,"identity":"6c27a577-9d40-404b-9d6f-07a747b0af06","order_by":3,"name":"Lei Yang","email":"","orcid":"","institution":"Chinese Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Lei","middleName":"","lastName":"Yang","suffix":""},{"id":459777129,"identity":"e66381e5-2173-472b-8a4b-e60de3d054eb","order_by":4,"name":"Shuyi Ji","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Shuyi","middleName":"","lastName":"Ji","suffix":""},{"id":459777130,"identity":"e191c2eb-3a48-49f1-abbc-09831cae8075","order_by":5,"name":"Shigui Yang","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Shigui","middleName":"","lastName":"Yang","suffix":""},{"id":459777131,"identity":"ccc09677-470f-448a-b594-119195879039","order_by":6,"name":"Xiangming Xiao","email":"","orcid":"https://orcid.org/0000-0003-0956-7428","institution":"School of Biological Sciences, Center for Earth Observation and Modeling, University of Oklahoma","correspondingAuthor":false,"prefix":"","firstName":"Xiangming","middleName":"","lastName":"Xiao","suffix":""},{"id":459777132,"identity":"96734a51-2474-410c-9f57-fc2558d2bc08","order_by":7,"name":"Dayan Wang","email":"","orcid":"https://orcid.org/0000-0002-4990-1016","institution":"Chinese Center for Disease Control and Prevention","correspondingAuthor":false,"prefix":"","firstName":"Dayan","middleName":"","lastName":"Wang","suffix":""},{"id":459777133,"identity":"3fa374ad-31ca-46d5-9ece-c2f0cd6b95ec","order_by":8,"name":"Yuelong Shu","email":"","orcid":"https://orcid.org/0000-0001-9279-0916","institution":"Chinese Academy of Medical Sciences \u0026 Peking Union Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yuelong","middleName":"","lastName":"Shu","suffix":""},{"id":459777134,"identity":"2c33450e-2c55-4fef-b9db-eaa1c987ed9f","order_by":9,"name":"Bin Zhu","email":"","orcid":"","institution":"Southern University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Bin","middleName":"","lastName":"Zhu","suffix":""}],"badges":[],"createdAt":"2025-05-20 10:46:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6706883/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6706883/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83356546,"identity":"b7c2a013-fc74-4181-a976-88c026671715","added_by":"auto","created_at":"2025-05-23 15:12:23","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":92558,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of the 1,012,430 participants in this study.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6706883/v1/b8d7e3ef011f59f11389a552.jpg"},{"id":83355407,"identity":"a95acc7e-8f11-4b44-9fff-eb00c847557a","added_by":"auto","created_at":"2025-05-23 15:04:23","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":85353,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of the NDVI of the participants in this study in different subgroups. (a) Insouthern and northern China; (b) in four seasons; (c) in three age groups; (d) at five city scales.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6706883/v1/5b4f5047d25f847984b51f4d.jpg"},{"id":83355414,"identity":"97c43e52-4e24-4486-bb75-590f3d8846f3","added_by":"auto","created_at":"2025-05-23 15:04:23","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":83620,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between green space exposure and the odds of influenza virus infectionstratified by gender, age group, geographic region, seasonality, city scale, and influenza subtype. The error bars indicate the 95% CIs. The 95% CIs were small, which was attributed to the large sample sizes in these analyses.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6706883/v1/ecdb2c088dca6619f69e2a62.jpg"},{"id":83355412,"identity":"9c386ed3-f3b3-4f09-8385-09a036e50e61","added_by":"auto","created_at":"2025-05-23 15:04:23","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":77585,"visible":true,"origin":"","legend":"\u003cp\u003eModels of interaction effects on the association between residential greenness exposure and odds of influenza virus infection. Each model was adjusted for gender, age, geographic region, air temperature, relative humidity, precipitation, wind speed, sunshine duration, PM\u003csub\u003e2.5\u003c/sub\u003e, O\u003csub\u003e3\u003c/sub\u003e, monitoring year and seasonality.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6706883/v1/12e3da0b5236180bae50d459.jpg"},{"id":83358072,"identity":"cd28c5d8-4eb6-4a71-afbf-80c4f51b8c57","added_by":"auto","created_at":"2025-05-23 15:38:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1339322,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6706883/v1/31d65178-da7a-4c54-8944-f426b369e6c7.pdf"},{"id":83356860,"identity":"c7d5fde4-73d9-438d-ae1a-85b69662dae7","added_by":"auto","created_at":"2025-05-23 15:20:23","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":302490,"visible":true,"origin":"","legend":"supplementary material","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6706883/v1/367ad460466e1283d55c08fa.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Associations between Residential Greenness and Influenza Virus Infection in China: An Individual-Level, National, Cross-Sectional Study, 2010–2017","fulltext":[{"header":"Introduction","content":"\u003cp\u003eResidential greenness, a fundamental component of urban design, has been shown to reduce the public health burden and preserve population health and wellbeing. The benefits of greenness exposure on noncommunicable diseases, such as depressive disorders\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e and cardiovascular diseases\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, have been well documented. However, the impact on infectious diseases is less well understood. Respiratory infections are the leading infectious cause of death globally\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Greenness could contribute to the prevention of respiratory infections via several potential mechanisms. First, the activities of natural killer cells in the human body increase with frequent exposure to vegetation\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e; moreover, natural killer cells constitute an important part of the human immune system and can eliminate virus-infected cells\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Greenness can also protect against air pollution, and exposure to several forms of ambient air pollution, such as PM\u003csub\u003e2.5\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e, is associated with an increased incidence of respiratory infections such as influenza\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Last, greenness often provides an outdoor environment for physical exercise and activities\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Respiratory infections, such as influenza and COVID-19, are transmitted mainly indoors\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecent epidemiological studies have examined the associations between greenness exposure and COVID-19 transmission, but the results have been inconsistent. Studies in the United States\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, China\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e and India\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e have revealed a negative correlation between the incidence rate of COVID-19 and greenness exposure. However, studies in London revealed that COVID-19 infections increased with increasing urban greenness\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Several factors could contribute to the mixed findings, such as the different definitions of COVID-19 mortality rates, sample sizes, types and choices of covariates and statistical methods\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, and the varying governmental measures used to control the pandemic at the early stage, including population mobility restrictions\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e and face-mask mandates\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. In addition, the mixed findings may also suggest that the impact of greenness exposure on respiratory virus transmission could be influenced by socioeconomic factors. There are also two main knowledge gaps concerning the effects of greenness exposure on COVID-19 transmission. First, most of the existing evidence linking greenness and infection is based on aggregated data\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Only one study in 2024 used individual-level data in Denmark\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The inherent nature of the ecological fallacy and residual confounding weakens causal inference regarding the effects of greenness. Second, studies based on long-term surveillance data are lacking. Previous studies on COVID-19 only used up to 14 months of infection surveillance data, which undermined the stability of the results and their extrapolation to other respiratory diseases, since the incidence of COVID-19 has varied annually, mainly because of the rapid evolution of the SARS-CoV-2 virus. Therefore, there is an urgent need to conduct a more accurate assessment of the association between greenness exposure and the risk of developing respiratory infectious diseases at the individual level on the basis of long-term surveillance data.\u003c/p\u003e \u003cp\u003eInfluenza, another respiratory infection, has a particularly rich and long-term disease system for illustrating the state of the art of infection surveillance; thus, findings from individuals infected with influenza virus could lead to more robust recommendations for respiratory infection control in the future. China has built the largest influenza surveillance system worldwide, with 554 sentinel hospitals and 411 network laboratories across the Chinese mainland before the COVID-19 pandemic\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. In addition, influenza itself is a significant cause of morbidity and mortality worldwide. According to World Health Organization (WHO) statistics, approximately 1\u0026nbsp;billion people are infected by seasonal influenza globally each year, resulting in 290,000 to 650,000 respiratory-related deaths\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Furthermore, in the context of rapid urbanization and population ageing in recent decades in China, managing influenza epidemics also involves ageing-related challenges\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e; i.e., the elderly population, especially those living in highly urbanized cities, has played an increasingly important role in influenza epidemics\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Given the potentially enormous health benefits for elderly people with more frequent greenness exposure, the effects of greenness exposure on the severity of influenza epidemics in China may be pronounced. Finally, China\u0026rsquo;s diverse climate conditions and uneven development among regions necessitate the exploration of the impacts of greenness exposure on influenza transmission under different socioeconomic conditions.\u003c/p\u003e \u003cp\u003eIn this study, we aimed to assess the association between residential greenness exposure, measured by the normalized difference vegetation index (NDVI), and the risk of influenza virus infection in China via 8 years of influenza surveillance data (2010\u0026ndash;2017). We further investigated whether the associations between residential greenness exposure and the risk of influenza virus infection varied under different socioeconomic conditions via stratified analyses.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eInfluenza Surveillance Data\u003c/h2\u003e \u003cp\u003eIn this study, we used influenza surveillance data from the Chinese National Influenza Center (CNIC) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.chinaivdc.cn/cnic/\u003c/span\u003e\u003cspan address=\"http://www.chinaivdc.cn/cnic/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The national sentinel hospital-based influenza-like illness (ILI) surveillance system in China was established in 2000 and expanded to 554 sentinel hospitals and 411 network laboratories across mainland China after the 2009 influenza H1N1 pandemic\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. On a weekly basis, sentinel hospitals report the number of outpatient visits and cases of ILI. The ILI was defined according to the standard case definition, including a body temperature\u0026thinsp;\u0026ge;\u0026thinsp;38\u0026deg;C with either cough or sore throat, in the absence of an alternative diagnosis\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Additionally, a convenience sample of patients visiting sentinel hospitals within three days of ILI onset was collected. Thus, each week, 5\u0026ndash;15 nasopharyngeal swab samples were tested at each sentinel hospital\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Patient samples were tested by real-time reverse transcription PCR or virus isolation in the affiliated laboratories. Other personal information, including age, gender, date of illness onset, date tested, and residential address, was also collected. Since the quality of the surveillance data improved dramatically after the 2009 influenza A (H1N1) pandemic, the period from 2010 to 2017 was selected for this study\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. To explore the individual-level association between residential greenness exposure and the risk of influenza virus infection, cases without accurate building-level residential address information were excluded from this study.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eIndividual-level Residential Greenness Exposure\u003c/h3\u003e\n\u003cp\u003eGreen spaces generally refer to areas covered by vegetation, such as urban parks, forests, road green belts, and private gardens\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. In this study, the monthly NDVI when the participants started to develop ILI symptoms was selected as the residential greenness exposure. The NDVI dataset was obtained from the National Tibetan Plateau Data Center\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e and was processed on the basis of MODIS satellite sensor data (MOD13Q1, Terra satellite). The processing included initial reconstruction of similar feature noise pixels, long-sequence images (S-G filter), maintaining high quality, monthly synthesis and stitching. The NDVI is a widely used satellite-derived indicator that has been shown in previous epidemiological studies to be effective for measuring greenness\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The NDVI value ranges from \u0026minus;\u0026thinsp;1 to 1, and positive values indicate vegetation coverage, with higher values indicating denser green vegetation. Given that 300 m is the WHO-recommended accessible distance for greenspace\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, we used NDVI data with 250 m resolution with values matched to each ILI residential address, where the address was interpreted as coordinates using the Tencent Web Service API, to assess their exposure-response associations.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eIn this study, potential covariates that may influence seasonal influenza virus infection risk, including demographic characteristics (gender, age, and geographic region), meteorological factors (air temperature, relative humidity, precipitation, wind speed, and sunshine duration), air pollutants (PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e), and time variables (the monitoring year and seasonality), were adjusted in the model. The 4 km daily gridded meteorological data spanning from 2000 to 2020\u003csup\u003e29\u003c/sup\u003e were obtained from the China Daily Meteorological Dataset. This dataset was generated through spatial interpolation using thin plate spline and random forest methods on the basis of point data from 699 meteorological stations across China. The daily air pollution data were sourced from the China High Air Pollutants dataset, with a spatial resolution of 1 km\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e,\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. According to a systematic review\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e, the median incubation periods for influenza A and B are 1.4 days and 0.6 days, respectively. Therefore, the average values of the meteorological factors and air pollutants from the two days preceding the onset date were used to characterize the exposure levels. The corresponding meteorological and air pollution exposures of each ILI case were also matched on the basis of the resolved residential address information.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eIn the present study, we used a multilayered analysis strategy. Logistic regression models were used to investigate the associations between residential greenness exposure and the risk of influenza virus infection. Odds ratios (ORs) and two-tailed 95% CIs are presented for each interquartile range (IQR) increment in the NDVI. A logistic regression model with Firth maximum penalized likelihood estimation was used to estimate the individual-level associations between residential greenness exposure and the risk of seasonal influenza virus infection\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. This approach was adopted to mitigate sparse data bias caused by the low proportion of positive outcomes in the ILI data. A series of regression models were constructed, with each progressively adjusted for additional covariates. Specifically, Model 1 included initial crude estimates adjusted only for gender, age, and geographic region (southern or northern China). Model 2 was further adjusted for meteorological factors (temperature, relative humidity, precipitation, wind speed, and sunshine duration). Model 3 included additional adjustments for air pollutants (PM\u003csub\u003e2.5\u003c/sub\u003e, O\u003csub\u003e3\u003c/sub\u003e). Model 4 was a fully adjusted model that incorporated the monitoring year and seasonality on the basis of Model 3.\u003c/p\u003e \u003cp\u003eTo explore whether the association between residential greenness exposure and the risk of seasonal influenza virus infection is influenced by gender, age group, geographic region, seasonality, city scale and influenza virus subtypes, stratified analyses were conducted on the basis of the following covariates: gender (male, female), age group (0\u0026ndash;17 years, 18\u0026ndash;59 years, \u0026ge;\u0026thinsp;60 years), geographic region (southern China, northern China), seasonality (spring, summer, fall, winter), city scale (micropolis, medium-sized city, large city, super city, megacity), and influenza virus subtype (type A, type B). According to the climate characteristics in China, spring is defined as March to May, summer as June to August, autumn as September to November, and winter as December to February. City scale classification was based on the Notice of the State Council on Adjusting the Standards for Urban Scale Classification (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gov.cn/\u003c/span\u003e\u003cspan address=\"https://www.gov.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In this study, cities were divided into five categories on the basis of the permanent urban resident permanent population: micropolis (\u0026lt;\u0026thinsp;500,000 individuals), medium-sized city (500,000\u0026ndash;1,000,000 individuals), large city (1,000,000\u0026ndash;5,000,000 individuals), super city (5,000,000\u0026ndash;10,000,000 individuals), and megacity (\u0026ge;\u0026thinsp;10,000,000 individuals). The significance of the effects within each subgroup was evaluated using the fully adjusted model. We further analysed the interaction effects of age and city scale, gender and city scale, age and geographic region, and gender and geographic region on the relationship between residential greenness exposure and the risk of influenza virus infection.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMediation analysis\u003c/h3\u003e\n\u003cp\u003eUrban vegetation can improve air quality by influencing the deposition and dispersion of air pollutants \u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Multiple epidemiological studies have demonstrated a close relationship between exposure to air pollutants and seasonal influenza virus infection\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e. Therefore, we conducted a mediation analysis to examine the potential mediating effects of PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e on the association between residential greenness exposure and the risk of seasonal influenza virus infection. The mediation analysis utilized the bootstrap sampling method, reporting the total effect, direct effect, indirect effect (mediating effect), and the proportion of the indirect effect.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity analyses\u003c/h2\u003e \u003cp\u003eTo verify the robustness of our findings, we conducted a series of sensitivity analyses using the fully adjusted model: (1) To assess the impact of spatial scale on the association between residential greenness exposure and the risk of seasonal influenza virus infection, we adjusted the residential greenness exposure to the monthly NDVI within a 1 km radius of the ILI address and reran the model. (2) Meteorological and air pollutant exposure levels were characterized using data from the first and second days before the onset of influenza symptoms, and the model was rerun accordingly.\u003c/p\u003e \u003cp\u003eAll the statistical analyses were performed using R statistical software version 4.4.2 (The R Project for Statistical Computing, Vienna, Austria). Logistic regression analysis was conducted using the \"logistf\" package for logistic regression with Firth maximum penalized likelihood estimation. This study was approved by the Institutional Review Board and Human Research Ethics Committee of the School of Medicine of Zhejiang University (No. ZGL202504-1).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Analysis\u003c/h2\u003e \u003cp\u003eFrom 2010\u0026ndash;2017, 3,131,881 ILI cases were tested by PCR or virus isolation for influenza identification in the influenza surveillance system in China, where 1,012,430 (32.3%) ILI patients with detailed building-level residential addresses (e.g., building, community, or POI) were included in this study. A total of 164304 (16.23%) patients were influenza positive, with more patients infected with influenza A virus (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A total of 52.94% of the participants were male. Most participants were children and adolescents aged 0\u0026ndash;17 years (60.24%), whereas the lowest percentage was among elderly individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years (5.64%). ILI cases were more frequently reported in southern China (58.87%) than in northern China (41.13%). The highest number of ILI cases was reported in 2014 (17.04%), whereas the lowest was recorded in 2016 (7.48%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Most cases were reported in winter, followed by spring, fall and summer. The characteristics of these 1,012,430 participants with detailed building-level residential addresses were similar to those of the 3,131,881 ILI cases (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The spatial distribution of the 1,012,430 ILI cases is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, with the study population covering 98.8% of the prefectures across the country.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe demographic characteristics of the 1,012,430 participants.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e536017 (52.94%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e476413 (47.06%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e609852 (60.24%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e345494 (34.13%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57084 (5.64%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorthern China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e416391 (41.13%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern China\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e596039 (58.87%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInfluenza tested result\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfluenza A positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e104306 (10.30%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfluenza B positive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e59317 (5.86%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNegative\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e848126 (83.77%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNDVI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[0,0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e560978 (55.41%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[0.25,0.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e411381 (40.63%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[0.5,0.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e29516 (2.92%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e[0.75,0.944)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1415 (0.14%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCity scale\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicropolis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e128137 (12.66%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium-sized city\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e271426 (26.81%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLarge city\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e258789 (25.56%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSuper city\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e99499 (9.83%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMegacity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e191288 (18.89%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSeason\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e259897 (25.67%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e186052 (18.38%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e231494 (22.87%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e334987 (33.09%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eYear\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e116749 (11.53%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85072 (8.40%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e92401 (9.13%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e147155 (14.53%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e172506 (17.04%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e169540 (16.75%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75696 (7.48%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e153311 (15.14%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDuring the study period from 2010 to 2017, both influenza positivity and the NDVI varied in different areas of China (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). People living in southern China had a much greater NDVI than those living in northern China (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). People had a greater NDVI in summer but a lower NDVI in winter (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). For different age groups, the NDVI was similar (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). The NDVI for people living in megacities was slightly greater than that for those living in other cities (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAssociations between Residential Greenness and Influenza Virus Infection\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reports the OR for each variable in four logistic regression models with Firth maximum penalized likelihood estimation. In all four models, a statistically significant inverse association between the NDVI and seasonal influenza was observed (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). According to the fully adjusted model, for each increase in one interquartile range (IQR\u0026thinsp;=\u0026thinsp;0.171) in the NDVI, the risk of influenza virus infection decreased by 2.6% (OR\u0026thinsp;=\u0026thinsp;0.974, 95% CI: 0.963\u0026ndash;0.985). In the fully adjusted model (Model 4), average temperature had the strongest negative association with seasonal influenza among all the meteorological factors. A significant positive correlation was observed between the PM\u003csub\u003e2.5\u003c/sub\u003e concentration and seasonal influenza, whereas the correlation between O\u003csub\u003e3\u003c/sub\u003e and seasonal influenza was negative.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociations between Residential Greenness and Seasonal Influenza\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003cp\u003e(OR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003cp\u003e(OR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003cp\u003e(OR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel4\u003c/p\u003e \u003cp\u003e(OR)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNDVI (per 0.171)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.621***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.888***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.926***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.974***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (Ref Female)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.985**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.989*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.006***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.007***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.007***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.006***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion (Ref Southern)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.623***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.461***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.446***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.509***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean temperature (K)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.963***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.969***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.981***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrecipitation (mm)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.005***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.006***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.004***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelative humidity (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.993***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.992***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.993***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSunshine duration (h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.977***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.988***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.995***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWind speed (m/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.967***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.015***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.009*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO\u003csub\u003e3\u003c/sub\u003e (ug/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.997***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.996***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e (ug/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.002***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.002***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpring (Ref Winter)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.821***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSummer (Ref Winter)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.929***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFall (Ref Winter)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.469***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2011 (Ref 2010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.572***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2012 (Ref 2010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.108***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2013 (Ref 2010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.682***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2014 (Ref 2010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.058***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2015 (Ref 2010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.762***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2016 (Ref 2010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.711***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2017 (Ref 2010)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.203***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe analysis included data for 1012430 ILI cases. Model 1 was adjusted only for demographic factors (gender, age, and geographic region). Model 2 was further adjusted for meteorological factors (temperature, relative humidity, precipitation, wind speed, and sunshine duration) on the basis of Model 1. Model 3 included additional adjustments for air pollutants (PM\u003csub\u003e2.5\u003c/sub\u003e, O\u003csub\u003e3\u003c/sub\u003e). Model 4 was a fully adjusted model that incorporated the monitoring year and seasonality based on Model 3. OR: odds ratio. NDVI: normalized difference vegetation index.\u003c/p\u003e \u003cp\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0,01. *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStratified Analysis\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the associations between residential greenness exposure and the risk of influenza virus infection in different subgroups via stratified analyses. Similarly, the association between residential greenness exposure and the risk of seasonal influenza virus infection was negative among males (OR\u0026thinsp;=\u0026thinsp;0.983, 95% CI: 0.968\u0026ndash;0.998) and females (OR\u0026thinsp;=\u0026thinsp;0.965, 95% CI: 0.949\u0026ndash;0.981). The impact of the NDVI on seasonal influenza varied across age groups, with the strongest negative association observed in older adults aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years (OR\u0026thinsp;=\u0026thinsp;0.853, 95% CI: 0.814\u0026ndash;0.894), followed by younger adults aged 18\u0026ndash;59 years (OR\u0026thinsp;=\u0026thinsp;0.909, 95% CI: 0.891\u0026ndash;0.927), whereas a slightly positive association was found in children aged\u0026thinsp;\u0026lt;\u0026thinsp;18 years (OR\u0026thinsp;=\u0026thinsp;1.034, 95% CI: 1.019\u0026ndash;1.050). Further analysis of children aged\u0026thinsp;\u0026lt;\u0026thinsp;18 years suggested that the significant positive association between the NDVI and the risk of influenza virus infection was found only in school-aged children aged 7\u0026ndash;17 years, who were more susceptible to influenza because of greater contact rates, and had less vegetation at school (Table S2). In terms of regional stratification, the correlation between the NDVI and seasonal influenza was significantly negative in southern China (OR\u0026thinsp;=\u0026thinsp;0.975, 95% CI: 0.963\u0026ndash;0.988), whereas it was reversed in northern China (OR\u0026thinsp;=\u0026thinsp;1.067, 95% CI: 1.039\u0026ndash;1.095). The protective effects of residential greenness against influenza virus infection were found only in spring (OR\u0026thinsp;=\u0026thinsp;0.914, 95% CI: 0.893\u0026ndash;0.936) and winter (OR\u0026thinsp;=\u0026thinsp;0.933, 95% CI: 0.915\u0026ndash;0.952). Stratified analysis at the city scale revealed that although the ORs generally decreased with increasing city scale, the significant protective effect of a high NDVI against influenza virus infection risk was found only in megacities (OR\u0026thinsp;=\u0026thinsp;0.907, 95% CI: 0.886\u0026ndash;0.930). Additionally, the results indicated that residential greenness provided protective effects against influenza B (OR\u0026thinsp;=\u0026thinsp;0.888, 95% CI: 0.872\u0026ndash;0.906), but an inverse effect was observed for influenza A (OR\u0026thinsp;=\u0026thinsp;1.022, 95% CI: 1.008\u0026ndash;1.036).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eInteraction Analysis\u003c/h2\u003e \u003cp\u003eConsistent with the results of the stratified analysis, interactions between age/gender and city scale revealed a stronger protective effect of residential greenness against influenza virus infection for adults older than 18 years who lived in large cities (i.e., large cities, super cities, and megacities) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea, \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). However, interactions between age and geographic region revealed that in northern China, the positive correlation between residential greenness exposure and the risk of influenza virus infection became more pronounced with increasing age, whereas in southern China, the strongest protective effect was among elderly individuals aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed). Interactions between gender and geographic region further validated the findings of the stratified analysis in this study, indicating that the protective effect was observed only in southern China for both males and females (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eMediation Analysis\u003c/h2\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e had mediating effects on the association between residential greenness exposure and the risk of influenza virus infection, with the mediating effects accounting for 33.90% and 16.38%, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMediation analysis of PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e in the association analysis between residential greenness exposure and the risk of influenza virus infection.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal effect\u003c/p\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDirect effect\u003c/p\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMediating effect\u003c/p\u003e \u003cp\u003eβ (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProp. mediated\u003c/p\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePM\u003csub\u003e2.5\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.039\u003c/p\u003e \u003cp\u003e(-0.047, -0.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.026\u003c/p\u003e \u003cp\u003e(-0.034, -0.017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003cp\u003e(-0.014, -0.012)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33.90\u003c/p\u003e \u003cp\u003e(27.55, 43.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eO\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.040\u003c/p\u003e \u003cp\u003e(-0.048, -0.033)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.033\u003c/p\u003e \u003cp\u003e(-0.041, -0.026)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.0065\u003c/p\u003e \u003cp\u003e(-0.0070, -0.0060)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16.38\u003c/p\u003e \u003cp\u003e(13.41, 20.31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eSensitivity Analysis\u003c/h2\u003e \u003cp\u003eThe results of the sensitivity analysis further confirmed the robustness of the association between residential greenness exposure and the risk of seasonal influenza virus infection. When we modified the residential greenness exposure metric to the monthly NDVI within a 1 km radius of the ILI residence, the association between residential greenness exposure and the risk of seasonal influenza virus infection slightly strengthened (OR\u0026thinsp;=\u0026thinsp;0.952, 95% CI: 0.942\u0026ndash;0.963) (Table S3). For each IQR increase in the NDVI, the risk of seasonal influenza virus infection decreased by 4.8%. When we used data from the first or second days prior to the onset date to represent meteorological factors and air pollution exposure levels and reran the model, the results were consistent with those of the primary model (OR\u0026thinsp;=\u0026thinsp;0.963, 95% CI: 0.952\u0026ndash;0.974) and OR\u0026thinsp;=\u0026thinsp;0.971, 95% CI: 0.960\u0026ndash;0.982) (Tables S4, S5).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this individual-level, national, cross-sectional, associational study, we found a significant negative correlation between residential greenness exposure and the risk of influenza virus infection. The strength of the association varied across different age groups, geographic regions, seasons, city scales, and influenza virus subtypes. The protective effects of residential greenness were more pronounced among older people aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years and those residing in megacities. Mediation analysis revealed that PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e accounted for 33.90% and 16.38%, respectively, of the mediating effect on the association between residential greenness exposure and the risk of influenza virus infection, suggesting that greenness could reduce influenza virus infection directly rather than by reducing air pollution. With one interquartile increment (0.171 units) in the NDVI, the risk of influenza virus infection decreased by 2.6%. Our findings on the beneficial association between residential greenness exposure and the risk of influenza virus infection were generally consistent with findings from the current literature on COVID-19. Studies in the US and UK\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, Denmark\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e, and China\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e revealed that a 0.171 unit increase in the NDVI was associated with 5.99%, 4.25%, and 13.4% decreases in the incidence of COVID-19, respectively. The protective effect against influenza infection was much lower than that against COVID-19 infection. There are several potential reasons for this. The first is the much lower transmissibility of the influenza virus than that of SARS-CoV-2\u003csup\u003e39\u003c/sup\u003e. The second may be the varied population susceptibility to influenza and SARS-CoV-2, i.e., population susceptibility to SARS-CoV-2 increases with age, whereas children are more susceptible to influenza virus\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. The benefits of greenness for adults, especially for older adults, were more pronounced.\u003c/p\u003e \u003cp\u003eOur study revealed that the protective effect of residential greenness against seasonal influenza was most pronounced among older people aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years. This is reasonable since residential neighbourhood greenness provides an outdoor environment for physical exercise and activities for retired people, who have more free time to access nature. For these school-aged children, the risk of influenza virus infection even increased slightly with increasing residential greenness. The potential reason could be that influenza transmission among school-aged children mainly occurs at school\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e due to an incomplete immune system and greater contact rates at school\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Most schools have thin vegetation; thus, real greenness exposure is overestimated when residential greenness is used. A significant inverse correlation between residential greenness exposure and the risk of influenza virus infection was observed only in megacities with large populations. This may be because, for the same NDVI, the actual use of greenness varies. One study in London demonstrated that communities in large cities with higher connectivity promoted walking and physical activity, thus improving public health\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. The layout and infrastructure of megacities tend to be more scientifically planned, with better connectivity between different functional areas. Consequently, the protective effects of greenness in these cities result from higher population density and better accessibility, which enhance the health benefits provided by greenness. Additionally, we found that the heterogeneous association between residential greenness exposure and the risk of influenza virus infection varied across different regions. The protective effects were significant in southern China, whereas they were reversed in northern China. A possible reason is that northern China has a temperate monsoon climate, with the peak influenza season occurring during the cold and dry winters, and cold outdoor temperatures may encourage people to gather indoors and reduce the opportunities for exposure to greenness. The sensitivity analysis results indicated that when the green space exposure metric was adjusted to the average NDVI within a 1 km radius, the protective effect of residential greenness increased. Thus, humans may have more access to parks ranging from 500 m to 1 km than to parks ranging from 250 m, which may help the WHO refine its definition of the recommended accessible distance for greenspace.\u003c/p\u003e \u003cp\u003eThis study has several significant advantages and public health implications. First, to our knowledge, this is the first nationwide study to assess the association between residential greenness exposure and the risk of seasonal influenza virus infection at the individual level in China, which has diverse climates and development. Unlike previous studies\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e that relied on area-level measures of exposure, outcomes, or covariates, the use of a large administrative dataset (n\u0026thinsp;=\u0026thinsp;1,012,430) in our study that contained individual-level data and objectively assessed measures of residential greenness helped to estimate the associations between residential greenness exposure and the risk of influenza virus infection in diverse climatic and socioeconomic contexts. Second, compared with the varied definitions of COVID-19 cases in different regions at the early stage of the pandemic\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, this study utilized a more standard definition of influenza virus infection. Patient samples were tested by real-time reverse transcription PCR or virus isolation in the affiliated laboratories. Third, the associations between residential greenness exposure and the risk of influenza subtypes were explored in this study, offering insights for the targeted prevention and control of influenza strains dominated by specific viral subtypes. Given the fast ageing and urbanization process in China and the better protective effects of residential greenness against influenza virus infection among older people in megacities, leveraging the protective effects of greenness against influenza presents a cost-effective solution for influenza prevention. On the one hand, unlike meteorological conditions, greenness exposure can be modulated by encouraging residents to alter their lifestyles or plan effective urban green spaces\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. On the other hand, greenness provides numerous health benefits. A literature review reported that greenness exposure can reduce the risk of cardiovascular diseases, mental health disorders, adverse birth outcomes, and mortality\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Our findings support public health and urban planning professionals in optimizing greenness exposure and related building characteristics in large cities, developing nature-based interventions, and achieving precise influenza prevention and control.\u003c/p\u003e \u003cp\u003eThe findings of this study need to be considered in the context of the following limitations. First, the cross-sectional study design does not allow for causal inferences. Second, while the NDVI has been widely used in previous epidemiological studies to measure greenness\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e, it does not reflect the accessibility or actual use of greenness, nor does it differentiate between types of green spaces (e.g., forests, shrubs, and grasslands) or their nature (e.g., private residential green spaces vs. public green spaces). Future studies should continue to investigate the interaction between greenness exposure and populations, as well as the associations between exposure to different types of green spaces and the risk of influenza virus infection. Third, the lack of data on individual behaviours and chronic disease histories may result in potential confounding factors not being accounted for in this study. Fourth, the accuracy of passive influenza surveillance data may be influenced by monitoring capacity. In the present study, we limited the research period to after the 2009 H1N1 influenza pandemic, during which the monitoring system expanded in scale\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, significantly improving the quality of surveillance data and thereby minimizing the potential impact of issues such as underreporting on data accuracy.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eUsing fine-resolution data from 1,012,430 ILI cases between 2010 and 2017 in China, we showed that residential greenness could reduce seasonal influenza virus infection. However, the impact varied in different climatic and socioeconomic contexts. The protective effect of residential greenness was pronounced in older adults and in larger cities. Our findings support public health and urban planning professionals in optimizing greenness exposure and related building characteristics, developing nature-based interventions, and achieving precise influenza prevention and control.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Institutional Review Board and Human Research Ethics Committee of the School of Medicine of Zhejiang University (No. ZGL202504-1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used in the 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\u003eAll authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by grants from the Shenzhen Medical Research Fund (Grant No. B240300).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHL, YS, DW and BZ conceived, designed and supervised the study. XL, LY and DW collected data. HL, SC and XL cleaned data. HL and SC performed mathematical modelling and the analysis. HL and SC drafted the manuscript. XL, LY, SJ, SY, XX, DW, YS and BZ reviewed and revised drafts of the manuscript. HL and SC interpreted the findings. All authors read and approved the final manuscript.\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\u003eSarkar, C., Webster, C. \u0026amp; Gallacher, J. 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COVID-19 infection rate but not severity is associated with availability of greenness in the United States. \u003cem\u003eLandscape and Urban Planning\u003c/em\u003e \u003cstrong\u003e233\u003c/strong\u003e, 104704 (2023).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6706883/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6706883/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eResidential greenness, a fundamental component of urban design, could contribute to the prevention of respiratory infections via several potential mechanisms.. However, the health benefits of greenness on influenza epidemics in real world are not as clear. Therefore, in the present study, we investigated the association between residential greenness exposure and influenza virus infection risk using a large and diverse cross-sectional dataset from the Chinese influenza surveillance system.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this cross-sectional, associational study, we used information from influenza-like illness (ILI) patients who were tested for influenza from 2010 to 2017 from the Chinese influenza surveillance system. Residential greenness was assessed with the normalized difference vegetation index (NDVI) within a 250 m radius of the ILI residential addresses. Other environmental metrics included the mean air temperature; relative humidity; precipitation; wind speed; sunshine duration; and O\u003csub\u003e3\u003c/sub\u003e and PM\u003csub\u003e2.5\u003c/sub\u003e concentrations. A series of logistic models were constructed to examine the associations between residential greenness exposure and the odds of influenza virus infection after adjusting for covariates such as individual age, gender, climate, air pollution and seasonality.\u003c/p\u003e\u003ch2\u003eFindings\u003c/h2\u003e \u003cp\u003eFrom 2010\u0026ndash;2017, 3,131,881 ILI cases were tested for influenza, and 1,012,430 (32.3%) participants with detailed building-level residential addresses were included in this study. Overall, a protective effect of residential greenness on the risk of influenza virus infection was observed, with 2.6% lower odds of influenza virus infection per one-quartile increase in the NDVI (odds ratio (OR)\u0026thinsp;=\u0026thinsp;0.974, 95% confidence interval (CI): 0.963\u0026ndash;0.985, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, the impact varied across the different subgroups. Stratified analyses indicated that the protective effects of residential greenness were strongest among adults aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years (OR\u0026thinsp;=\u0026thinsp;0.853, 95% CI: 0.814\u0026ndash;0.894, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but among children aged 7\u0026ndash;17 years (i.e., school-aged children), the association was positive (OR\u0026thinsp;=\u0026thinsp;1.104, 95% CI: 1.079\u0026ndash;1.129, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). There were no protective effects at other city scales except in megacities (OR\u0026thinsp;=\u0026thinsp;0.907, 95% CI: 0.886\u0026ndash;0.930, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Similarly, the protective effects of residential greenness against the development of influenza were observed only during the influenza season, i.e., in spring (OR\u0026thinsp;=\u0026thinsp;0.914, 95% CI: 0.893\u0026ndash;0.936, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and winter (OR\u0026thinsp;=\u0026thinsp;0.933, 95% CI: 0.915\u0026ndash;0.952, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and in southern China (OR\u0026thinsp;=\u0026thinsp;0.975, 95% CI: 0.963\u0026ndash;0.988, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Residential greenness had protective effects against influenza B infection (OR\u0026thinsp;=\u0026thinsp;0.888, 95% CI: 0.872\u0026ndash;0.906, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but no such effect was observed for influenza A infection (OR\u0026thinsp;=\u0026thinsp;1.022, 95% CI: 1.008\u0026ndash;1.036). The results from the interaction analysis between covariates were consistent with the results from the stratified analyses, except when the age group interacted with geographic regions. The mediating effects of PM\u003csub\u003e2.5\u003c/sub\u003e and O\u003csub\u003e3\u003c/sub\u003e exposure on the association between residential greenness exposure and the risk of influenza virus infection were 33.90% and 16.38%, respectively.\u003c/p\u003e\u003ch2\u003eDiscussion\u003c/h2\u003e \u003cp\u003eThe results highlight the benefits of well-designed green environments for influenza prevention. Given the rapid ageing and urbanization process in China, policies aimed at optimizing the allocation and design of green spaces might help reduce respiratory infection transmission.\u003c/p\u003e","manuscriptTitle":"Associations between Residential Greenness and Influenza Virus Infection in China: An Individual-Level, National, Cross-Sectional Study, 2010–2017","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-23 15:04:18","doi":"10.21203/rs.3.rs-6706883/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fe5feeaa-26cc-4bfa-9daa-74834e077082","owner":[],"postedDate":"May 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":48840079,"name":"Health sciences/Diseases/Infectious diseases/Influenza virus"},{"id":48840080,"name":"Health sciences/Medical research/Epidemiology"}],"tags":[],"updatedAt":"2025-05-23T15:30:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-23 15:04:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6706883","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6706883","identity":"rs-6706883","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}