Ling Zhang1, Sheng Li2, Bo Wang1, Ce Liu1, Li He1, Xiaobing Shan1, Kai Zhang3, Bin Luo This email address is being protected from spambots. You need JavaScript enabled to view it.1,4,5 

1 Institute of Occupational Health and Environmental Health, School of Public Health, Lanzhou University, Lanzhou, Gansu 730000, China
2 The First People's Hospital of Lanzhou, Lanzhou, Gansu 730050, China
3 Department of Environmental Health Sciences, School of Public Health, University at Albany, State University of New York, Rensselaer, NY 12144, USA
4 Shanghai Key Laboratory of Meteorology and Health, Shanghai Meteorological Bureau, Shanghai 200030, China
5 Shanghai Typhoon Institute, China Meteorological Administration, Shanghai 200030, China


Received: July 25, 2022
Revised: October 5, 2022
Accepted: October 14, 2022

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.


Download Citation: ||https://doi.org/10.4209/aaqr.220282  


Cite this article:

Zhang, L., Li, S., Wang, B., Liu, C., He, L., Shan, X., Zhang, K., Luo, B. (2022). Effects of Dust Event Days on Influenza: Evidence from Arid Environments in Lanzhou. Aerosol Air Qual. Res. 22, 220282. https://doi.org/10.4209/aaqr.220282


HIGHLIGHTS

  • New evidence on the association between dust events and influenza from China.
  • Ambient PM and dust events exposure may increase the risk of influenza.
  • Longer-duration dust events have a greater risk on laboratory-confirmed influenza.
 

ABSTRACT 


Airborne aerosol is believed to be an important pathway for infectious disease transmissions like COVID-19 and influenza. However, the effects of dust event days on influenza have been rarely explored, particularly in arid environments. This study explores the effects of ambient particulate matter (PM) and dust events on laboratory-confirmed influenza in a semi-arid city. A descriptive analysis of daily laboratory-confirmed influenza (influenza) cases, PM (PM10 and PM2.5), meteorological parameters, and dust events were conducted from 2014 to 2019 in Lanzhou, China. The case-crossover design combined with conditional Poisson regression models was used to estimate the lagging effects of PM and dust events on influenza. In addition, a hierarchical model was used to quantitatively evaluate the interactive effect of PM with ambient temperature and absolute humidity on influenza. We found that PM and dust events had a significant effect on influenza. The effects of PM10 and PM2.5 on influenza became stronger as the cumulative lag days increased. The greatest estimated relative risks (RRs) were 1.018 (1.011,1.024) and 1.061 (1.034,1.087), respectively. Compared with the non-dust days, the effects of dust events with duration ≥ 1 day and with duration ≥ 2 days on influenza were the strongest at lag0 day, with the estimated RRs of 1.245 (95% CI: 1.061–1.463) and 1.483 (95% CI: 1.232–1.784), respectively. Subgroup analysis showed that pre-school children and school-aged children were more sensitive to PM and dust events exposure. Besides, we also found that low humidity and temperature had an interaction with PM to aggravate the risk of influenza. In summary, ambient PM and dust events exposure may increase the risk of influenza, and the risk of influenza increases with the dust events duration. Therefore, more efforts from the government as well as individuals should be strengthened to reduce the effect of PM on influenza, particularly in cold and dry weather.


Keywords: Particulate matter, Dust events, Influenza, Case-crossover study


1 INTRODUCTION


Influenza is an acute respiratory infectious disease with an obvious seasonal trend. Due to the continuous mutation of the influenza virus, influenza could cause a serious threat to world society (Paules and Subbarao, 2017). According to the Global Burden of Disease Study 2017, there were approximately 54.49 million lower respiratory tract infections (LRTIs) caused by influenza, of which 8.18 million were severe and 145 thousand died globally each year (Troeger et al., 2019).

Numerous studies suggested that environmental factors were important for influenza transmission. For instance, some studies have discovered that meteorological factors such as temperature and humidity might influence influenza transmission and infection (Liu et al., 2019b; Chong et al., 2020a, 2020b). We also previously reported a greater risk of influenza in cold and dry environments (Wang et al., 2022). In addition, some epidemiological studies have shown that ambient pollutants, particularly PM (such as PM10 and PM2.5), play an important role in the spreading of influenza (Hansel et al., 2016; Khreis et al., 2017; Zhang et al., 2022). PM, as the medium of aerosols, could provide “condensation nuclei” to which virus aerosols attach (Sigaud et al., 2007; Lee et al., 2014), and can trigger inflammation responses, oxidative stress, and immune responses after inhaled by the human. These factors may finally reduce lung function and increase the risk of influenza infection (Donaldson et al., 1996; Jalava et al., 2007; Falcon-Rodriguez et al., 2016). Meanwhile, a study has shown that the number and viral load of aerosols produced by an infectious individual through speech and other exhalation activities are much higher than that of droplets (Wang et al., 2021). Due to its low settling velocity, aerosols can stay in the air for a long time and can be inhaled at both short and long distances (Parienta et al., 2011; Wang et al., 2021), which would further increase the risk and extent of respiratory infectious diseases transmission. A recent study has also reported the inhalation of virus-laden aerosols as the main mode of COVID-19 transmission (Sills et al., 2020). Therefore, aerosols composed of ambient PM and respiratory infectious viruses such as influenza may result in more widespread transmission and epidemics (Tellier, 2009).

Under the influence of global climate change, dust events occur more frequently and cause serious problems to human health (Liu et al., 2006; Bauer et al., 2019; Hashizume et al., 2020). In suitable weather conditions, the dust can travel thousands of kilometers. During long-distance transportation, the dust may mix with industrial soot, toxic substances, and various microorganisms on the way, particularly in dust storming (Sun et al., 2005; Kelly and Fussell, 2020), which may enable the long-distance spread of the influenza virus. Dust not only may increase the inflammatory response but also exacerbate the risk of contracting influenza by increasing viral load (Yeo et al., 2010; Clifford et al., 2015). A biochemical experimental study found that the concentration of environmental influenza A virus was significantly higher during dust days than during non-dust days by quantifying environmental influenza virus (Chen et al., 2010). Compared with ambient PM exposure, dust may have a greater impact on health (Tamamura et al., 2007; Chen et al., 2010; He et al., 2016). However, most of these current studies evaluating the association between dust events and specific respiratory diseases have focused merely on asthma (Thalib and Al-Taiar, 2012), bronchitis (Chen et al., 2021b), and pneumonia (Kang et al., 2012). And the study areas were concentrated in Japan, South Korea, and Taiwan, which were far away from dust sources (Liu et al., 2006; Cha et al., 2016; Kanatani et al., 2016). So far, only little is known about the effects of dust events on influenza in areas with frequent dust events and close to dust sources (such as the semi-arid areas of Northwest China).

East Asia is one of the largest sources of dust emissions (except North Africa), particularly the Gobi Desert and highlands in northwestern China. As typical semi-arid areas of northwestern China, Lanzhou city suffers very frequent dust events due to the lack of precipitation throughout the year. Besides, Lanzhou city was also one ex-heavily polluted city in China (Tang et al., 2022). We hypothesized that the characteristics of ambient PM and dust events exposure may have an association with the spread of influenza. Therefore, we evaluated the short-term impact of ambient PM and dust events on influenza, as well as the interaction between ambient PM and meteorological parameters on influenza in Lanzhou, China from 2014 to 2019.

 
2 METHODS


 
2.1 Data Collection

Daily laboratory-confirmed influenza cases data were obtained from the Lanzhou Center for Disease Control and Prevention (CDC) from January 01, 2014, to December 31, 2019, including gender, age, onset date, birth date, and reporting unit. Daily meteorological data were obtained from Lanzhou Meteorological Bureau, including daily average ambient temperature (AT) and relative humidity (RH). Additionally, absolute humidity (AH) was calculated via the methods reported in the previous studies (Fu et al., 2021; Wang et al., 2022). The simultaneous ambient pollutants data were obtained from the National Urban Air Quality Sharing Platform (https://air.cnemc.cn:18007), including particulate matter with aerodynamic diameter ≤ 10 µm (PM10), particulate matter with aerodynamic diameter ≤ 2.5 µm (PM2.5), nitrogen dioxide (NO2), sulfur dioxide (SO2) and ozone (O3).

 
2.2 Definition of Laboratory-confirmed Influenza Cases

Following the standard diagnostic criteria for influenza (WS 285-2008) from the National Health Commission of the People’s Republic of China, laboratory-confirmed influenza (influenza) case was a patient with at least one influenza diagnosis (fever, headache, myalgia, cough, sore throat, or shortness of breath) and a positive finding for the respiratory specimen (nasopharyngeal or pharyngeal swab) from laboratory tests. The laboratory-confirmed influenza cases could increase the reliability of the associations of ambient PM and dust events exposure compared with the cases of influenza-like illnesses that are not necessarily influenza.

 
2.3 Definition of Dust Events

As a mixture of PM, dust is emitted from the surface of arid and semi-arid areas around the world (Querol et al., 2019). Based on the previous study (Cuspilici et al., 2017; Guan et al., 2019), we defined PM10 value when the daily average PM10 concentration exceeded the China National Air Environment Quality Grade II Standard as the threshold value (150 µg m–3). And the regional background value was determined by the median value of PM10 concentration for 15 days before and after the day. A dust event was confirmed when the PM10 concentrations of daily PM10 concentrations minus the regional background value exceeded 150 µg m–3. Concerning the continuous days of dust events that have different effects on influenza, three types of dust events were defined (duration more than 1 day, duration more than 2 days, and duration more than 3 days).

 
2.4 Statistical Methods

We applied a time-stratified case-crossover design to quantify the associations between ambient PM and dust events with the daily influenza cases. The design took the subjects as self-control which could effectively eliminate potential individual-level risk factors (such as age, gender, health status, and socioeconomic status) and exposure to time-trend bias (Carracedo-Martínez et al., 2010). The time-stratified denotes the method of comparing the concentrations of ambient pollutants (or dust events) on the day of the influenza cases with the concentrations (or dust events) on other days of the same weekday in this month. The conditional logistic regression model is a commonly used statistical model for time-stratified case-crossover design, but the conditional Poisson regression model was simpler to code and faster to run than conditional logistic analyses, and it could be fitted well to larger data sets than the standard Poisson model. Besides, the conditional Poisson model allows for overdispersion or auto-correlation, and it can give the same parameter estimates as the conditional logistic regression model (Armstrong et al., 2014). Therefore, we utilized a time-stratified case-crossover design combined with a conditional Poisson regression model to evaluate the short-term impact of particulate matter and dust events on daily influenza cases.

Concerning the lag effects of ambient pollutants on diseases and the incubation period of influenza, the maximum lag time was selected as 7 days. In this study, a single pollutant model was used to evaluate the lag effects of particulate matter and dust events exposure on influenza, including single-day lags (lag0-lag7) and multiple-day lags (lag01-lag07) model. Eqs. (1) and (2) present the basic models:

 

where ui is the observed daily influenza cases on day i; PM is the concentration of ambient particulate matter (PM2.5 or PM10) on day i; α is the intercept; ns is natural cubic spline; Dust is dust events on day i (0 for the non-dust events and 1 for the dust events); ATlag02 and AHlag02 are the three-day moving average of AT and AH, and the degree of freedom (df) are 6 and 3, respectively; log(caseslag03) is the three-day moving average of daily influenza cases (current day + previous two days) to control the autocorrelation; Stratum is the time stratum in the time-stratified case-crossover design; the degree of freedom (df) of the natural spline smoothing function in the formula was selected according to the Akaike's information criteria.

 
2.5 Subgroup Analyses

Subgroup analyses of gender (male, female) and age [pre-school children (0–5 years of age), school-aged children (6–18 years of age), adults (19–65 years of age), elders (> 65 years of age)] were applied to estimate the effect of PM (PM2.5 and PM10) and dust events on influenza incidence. Based on previous studies (Wang et al., 2022), we used the hierarchical model to explore the interaction between PM (PM2.5 and PM10) and meteorological parameters (AT and AH) on influenza. In the hierarchical model, the 5th and 95th percentiles of AT and AH were used as thresholds for the AT and AH to be divided into two-categorical variables, which were divided into two levels: “Low” and “High”. Low-AT or Low-AH referred to the case when AT or AH was less than the 5th percentile, and High-AT or High-AH referred to the case when the AT or AH was higher than the 95th percentile.

 
2.6 Sensitivity Analysis

To examine the reliability of the results, we used the two-pollutant model to compare the effects of PM in the single-pollutant model by adjusting for other ambient pollutants. Then, various degrees of freedom for AT (4–6 df) and AH (3–5 df) were used to examine the robustness of the models.

All statistical analyses were performed in R software (Version 4.0.0, R Development Core Team; http://R-project.org), modeling with the “gnm” package plus custom code that is available online (Armstrong et al., 2014). A two-sided P value < 0.05 was considered statistically significant.

 
3 RESULTS AND DISCUSSION

This study assessed the short-term effects of ambient PM and dust events exposure on influenza, as well as the synergistic effects between ambient PM and meteorological parameters on influenza in Lanzhou, China from 2014 to 2019. For the first time, we found that dust events exposure was significantly associated with an increased risk of influenza in Lanzhou, and longer-duration dust events had a greater risk on influenza. In addition, we found that ambient PM (PM10 and PM2.5) had a greater risk of influenza in low-humidity and low-temperature environments.

 
3.1 Characteristics of the Influenza Cases, Ambient Pollutants, Meteorological Parameters, and Dust Events

Table 1 shows the descriptive statistics of laboratory-confirmed influenza cases, ambient pollutants, and meteorological parameters in Lanzhou, China during 2014–2019. A total of 5 518 laboratory-confirmed influenza cases were reported, including 2 909 males, accounting for 52.72% of the total cases. The average concentrations of PM10 and PM2.5 were 118.60 µg m–3 and 49.95 µg m–3, respectively, which were far exceeding the China National Air Environment Quality Grade II Standard for annual average concentrations (PM10: 70 µg m–3; PM2.5: 35 µg m–3). The average values of AT and AH were 11.25°C and 6.03 g m–3, respectively. The distribution of different definitions of dust events (duration ≥ 1 day, duration ≥ 2 days, and duration ≥ 3 days) are presented in Fig. 1. The dust events with duration ≥ 1 day occurred 60 days in Lanzhou during the study period. Dust events with duration ≥ 2 days occurred for 39 days, accounting for 65% of the days of dust events with duration ≥ 1 day. Dust events with duration ≥ 3 days occurred for 23 days, accounting for 38.3% of the days of dust events with duration ≥ 1 day. The days and times of different definitions for dust events are shown in Table S1.

Table 1. Descriptive statistics on daily laboratory-confirmed influenza cases, ambient pollutants, and meteorological parameters in Lanzhou, China from 2014 to 2019.

Fig. 1. The distribution of different definitions of dust events in Lanzhou, China from 2014 to 2019. Note: “DE ≥ 1 day” is the dust events with duration ≥ 1 day; “DE ≥ 2 days” is the dust events with duration ≥ 2 days; “DE ≥ 3 days” is the dust events with duration ≥ 3 days.Fig. 1. The distribution of different definitions of dust events in Lanzhou, China from 2014 to 2019. Note: “DE ≥ 1 day” is the dust events with duration ≥ 1 day; “DE ≥ 2 days” is the dust events with duration ≥ 2 days; “DE ≥ 3 days” is the dust events with duration ≥ 3 days.

The time-series distributions of laboratory-confirmed influenza cases, PM, and meteorological parameters are displayed in Fig. S1. The seasonality of the influenza change trend demonstrated that influenza cases peaked in winter and spring, particularly when the AT was the lowest. At the same time, the daily average concentrations of PM10 and PM2.5 were higher in winter but lower in summer. Table S2 shows the correlation between PM and meteorological parameters with influenza cases. PM10 and PM2.5 were positively correlated with influenza cases, and the Spearman correlation coefficients were 0.17 and 0.21, respectively. AT and AH were negatively correlated with influenza cases, and the Spearman correlation coefficients were both –0.39. Our results were consistent with other studies (Metz and Finn, 2015; Chen et al., 2021a), indicating the adversary effect of both PM and abnormal meteorological factors on influenza.

 
3.2 The Estimated Effects of Ambient Particulate Matter on Influenza Incidence

The PM may act as a very important carrier of the influenza virus, therefore it deserved to be primarily studied. Our findings contribute to a better understanding of the relationship between air pollution exposure and the risk of influenza. After adjusting for confounding factors like AT, AH, and time trend, the estimated effects of PM10 and PM2.5 on influenza in the single-pollutant model are shown in Fig. 2. In this study, we found that PM exposure was significantly associated with increased influenza onset risk, which was consistent with studies in Hefei and Jinan (Liu et al., 2019a; Su et al., 2019). In the single-day lag model, our results showed that PM had a significant impact on influenza incidence. The estimated effects of PM10 and PM2.5 were the greatest at lag 4 days for a 10 µg m–3 increase, the estimated greatest RRs of which were 1.010 (95% CI: 1.006–1.013) and 1.026 (95% CI: 1.012–1.041), respectively. In the cumulative lag model, the estimated effects of PM on influenza incidence were significant at lag01-lag07 days. The longer the cumulative lag days, the greater the risk of influenza. However, previous studies have reported controversial findings on the association of PM exposure with influenza. Another study in Wuhan of China did not observe a significant association between PM and influenza from 2015 to 2017 (Meng et al., 2021). This inconsistency may be due to the different definitions of influenza, climate type, statistical methods, pollutant characteristics, and geographic heterogeneity. Table S3 lists the specific results of PM on influenza in the single-day lag model and the cumulative lag model.

Fig. 2. The effects of particulate matter (PM) on laboratory-confirmed influenza at different lag days in Lanzhou, China from 2014 to 2019. Note: The results were estimated as relative risks (RRs) and 95% confidence intervals (CIs) of influenza for each 10 µg m–3 increase of particulate matter (PM) concentrations.Fig. 2. The effects of particulate matter (PM) on laboratory-confirmed influenza at different lag days in Lanzhou, China from 2014 to 2019. Note: The results were estimated as relative risks (RRs) and 95% confidence intervals (CIs) of influenza for each 10 µg m3 increase of particulate matter (PM) concentrations.

Fig. 3 summarizes the estimated effects of PM exposure on influenza stratified by gender and age in the single-pollutant model. We found that the effect of PM exposure on the risk of influenza was stronger in males than in females. In the cumulative lag model, the increase of each 10 µg m–3 in PM (PM10 and PM2.5) were positively correlated with influenza incidence for male, with the largest increase at lag07 days [1.931% (95% CI: 1.040%–2.829%), 7.095% (95% CI: 3.400%–10.921%)]. These results may be related to differences in outdoor environmental exposure, with males more likely to do more outdoor work and have more exposure chances. In age-specific analyses, school-aged children were more sensitive to the increase of PM10 than other age groups, with the highest risk of influenza at lag 07 days and the estimated RR was 1.068 (95% CI: 1.053–1.083). While pre-school children were more sensitive to the increase of PM2.5 than other age groups, the highest risk of influenza was found at lag 07 days and the estimated RR was 1.124 (95% CI: 1.072–1.178). In summary, ambient PM may significantly influence the incidence of influenza.

Fig. 3. The effects of particulate matter (PM) on laboratory-confirmed influenza stratified by gender and age at different lag days in Lanzhou, China from 2014 to 2019. Note: The results were estimated as relative risks (RRs) and 95% confidence intervals (CIs) of influenza for each 10 µg m–3 increase of particulate matter (PM) concentrations. Pre-school children are the 0 to 5 age group; School-aged children are the 6 to 18 age group; Adults are the 19 to 65 age group; Elders are the > 65 age group.Fig. 3. The effects of particulate matter (PM) on laboratory-confirmed influenza stratified by gender and age at different lag days in Lanzhou, China from 2014 to 2019. Note: The results were estimated as relative risks (RRs) and 95% confidence intervals (CIs) of influenza for each 10 µg m3 increase of particulate matter (PM) concentrations. Pre-school children are the 0 to 5 age group; School-aged children are the 6 to 18 age group; Adults are the 19 to 65 age group; Elders are the > 65 age group.

 
3.3 The Estimated Effects of Dust Events on Influenza Incidence

The estimated effects of dust events on influenza are shown in Fig. 4. Compared with non-dust days, the estimated effects of dust events with duration ≥ 1 day and dust events with duration ≥ 2 days were the strongest at lag0 day, and the estimated RRs were 1.245 (95% CI: 1.061–1.463) and 1.483 (95% CI: 1.232–1.784), respectively. Dust events with duration ≥ 3 days had the strongest effect at lag2 days, and the estimated RR was 1.821 (95% CI: 1.402–2.365). Although very few studies have investigated the impact of dust events on influenza incidence, our results are consistent with previous studies (Thalib and Al-Taiar, 2012; Chen et al., 2021b), which reported that dust storms could increase the risk of respiratory diseases. However, the dust events with different definitions were negatively correlated at lag 6 and lag7 days, which may be explained by the more effective preventive measures compared to the early days of dust events, or because of the gradual improvement of air quality in the later period. In addition, this may also be recognized as the harvest-effect observed due to the advanced infection window by dust weather among those susceptible populations.

Fig. 4. The effects of different definitions of dust events on laboratory-confirmed influenza at different lag days in Lanzhou, China from 2014 to 2019.Fig. 4. The effects of different definitions of dust events on laboratory-confirmed influenza at different lag days in Lanzhou, China from 2014 to 2019.

As shown in Fig. 5, females were more sensitive to dust events, and the estimated effects of different definitions of dust events on influenza were the greatest at lag2 days [1.571 (95% CI: 1.195–2.065), 1.487 (95% CI: 1.082–2.043) and 2.056 (95% CI: 1.401–3.027)]. Compared with other age groups, all dust events had a greater impact on influenza in pre-school children and school-aged children from lag0 day to lag5 days, which might be due to their relatively weaker immune systems. Besides, their physical adaptability to environmental changes is poorer, which may lead to greater susceptibility to environmental factors. This was similar to the results reported by Japan, Trinidad, etc., that the number of children admitted to the hospital for respiratory diseases increased during or after the dust storm (Gyan et al., 2005; Kanatani et al., 2010). However, these studies were conducted in places far away from the dust source of dust events, and the concentration of dust was lower, so people were less affected by the dust. Concerning these limitations, our study area was set in a semi-arid region of northwestern China (Lanzhou) where dust events were frequent and close to dust sources. Our results can more profoundly illustrate the adverse estimated effects of dust events on influenza in the population. As to the chemical compositions of dust, although the evidence that the dust of Gansu province was mainly composed of crustal elements (Querol et al., 2019), the specific effect of different constituents on influenza will be further studied.

Fig. 5. The effects of different definitions of dust events on laboratory-confirmed influenza stratified by gender and age at different lag days in Lanzhou, China from 2014 to 2019.Fig. 5. The effects of different definitions of dust events on laboratory-confirmed influenza stratified by gender and age at different lag days in Lanzhou, China from 2014 to 2019.

Although the mechanisms involved in the estimated effects of dust events on influenza are unclear, there are some potential and possible hypotheses. During dust events, dust aerosols could stay in the air longer and the virus attaching to them could travel farther, so people exposed to dust events may have a greater chance of being affected by influenza. Besides, the composition of dust aerosols may change during long-distance transportation, particularly atmospheric bacterial levels, and community diversity would significantly increase. These dust aerosols may increase the level of pro-inflammatory mediators and airway inflammation by activating the TLR2 and NALP3 inflammatory pathways in alveolar macrophages (Fussell and Kelly, 2021), which may finally increase the risk of influenza. Another explanation could also be the greater sensitivity of influenza viral to climate (Prospero and Lamb, 2003; Qu et al., 2006), and dust emissions may influence the activity and transmission of viral. These alterations may affect the incidence and severity of influenza directly or indirectly.

 
3.4 The Interactive Effect of Particulate Matter and Meteorological Parameters on Influenza Incidence

The hierarchical model analysis is shown in Table 2. There was an interaction of PM2.5 with low-AT and Low-AH, and PM10 with Low-AH (P < 0.05) on influenza incidence. Low humidity and low temperature increased the estimated effects of PM on influenza. Each increase in PM2.5 by 10 µg m–3 led to an estimate of percent change (95% CI) of 6.466% (95% CI: 3.364%–9.660%) increase in daily influenza cases with a Low-AT environment. An increase in PM10 and PM2.5 by 10 µg m–3 led to estimates of percent change (95% CI) of 1.083 % (95% CI: 0.452%–1.718%) and 2.222% (95% CI: 0.172%–4.315%) increase in daily influenza cases with Low-AH environment. In summary, we found that PM (PM10 and PM2.5) had a greater risk of influenza in low-humidity and low-temperature environments, suggesting that the low-humidity and low-temperature environments may have a synergistic effect with PM on influenza. This is consistent with our previous animal studies, which found that the viability and phagocytic function of rat alveolar macrophages decreased with decreasing temperature and increasing PM2.5 dose, and that lower temperature increased PM2.5 toxicity to rat alveolar macrophages (Luo et al., 2017). Besides, studies also showed that lower mucociliary activity by exposure to low humidity may enhance the susceptibility of the mucous membranes towards sensory irritants, particles, and bioaerosols (Salah et al., 1988; Wolkoff, 2018).

Table 2. Estimates of percent change (95% CI) in daily laboratory-confirmed influenza cases associated with a 10 µg m–3 increase in particulate matter pollutants stratified by AT and AH.

Although we have found many interesting results, some limitations of this study should not be ignored. Firstly, this study was an ecological study, which could only reflect the statistical relationship between PM, dust events exposure and influenza incidence, and the evidence on causality was weak. We also did not take into account some confounding factors in the model, such as vaccination rate and GDP. Secondly, the exposure to PM was assessed by monitoring sites, it could not reflect individual-level environmental exposure and indoor exposure (Shan et al., 2019), which may have biased the results. Then, dust events were defined in terms of PM10, but dust contains a large number of other chemical compositions which may interact and affect influenza risk. Therefore, the adverse health effects of dust may have been underestimated. Fourthly, only one city (Lanzhou) was included in the semi-arid region of China in this study, and the study period was short. Finally, anthropogenic activities also perturb dust concentrations (Yang et al., 2017), which may further affect the health effects they produce. These effects are worth to be further study and verification shortly, particularly under the comparison of different sources of dust.

After determining the optimum lag day for each PM (PM10 and PM2.5) in the single-pollutant models, the two-pollutant models were used to adjust for other ambient pollutants to examine the robustness of the models. Table S4 shows the comparison of the estimated effects of the single-pollutant model and the estimated effects of the two-pollutant model by adjusting for other ambient pollutants on influenza. After adjusting for NO2 and SO2 concentration in the two-pollutant models, the estimated effects for influenza of PM10 and PM2.5 remained statistically significant. These results were similar to the results of the single-pollutant models. At the same time, we found that the model was still robust after adjusting the degrees of freedom of AT and AH, as shown in Fig. S2.

 
4 CONCLUSIONS


Influenza is a common health problem globally. Knowledge of specific local environmental triggers of influenza would promote local healthcare practitioners and their influenza patients to actively take appropriate measures to reduce influenza morbidity and mortality. In this study, our results suggested that ambient PM and dust events exposure were significantly associated with the increasing risk of influenza, and the risk of influenza increased with the dust event duration. PM had significantly interactive effects with low-AT and low-AH on influenza, which could aggravate influenza incidence. These findings will provide additional epidemiological evidence for future influenza prevention and environmental protection. As to the chemical of dust, although the evidence that the dust of Gansu province was mainly composed of crustal elements, we will give more effort to study the specific effect of different dust chemical constituents that may be potential triggers for influenza in the future.

 
ACKNOWLEDGMENTS


This work was supported by the Science and Technology Major Project in Gansu Province, China (20YF2FA028); the National Natural Science Foundation of China (41875139), and the Fundamental Research Funds for the Central Universities, Lanzhou University, China (lzujbky-2021-ey07). All authors would like to express gratitude for the provision of influenza data from the Center for Disease Control and Prevention of Lanzhou, and the weather data from the Lanzhou Meteorological Bureau, China.

 
DISCLAIMER


The authors declare they have no competing interests.


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