SONG Yang, ZHANG Yu, WANG Tingting, QIAN Sitong, WANG Shijun, 2021. Spatio-temporal Differentiation in the Incidence of Influenza and Its Relationship with Air Pollution in China from 2004 to 2017. Chinese Geographical Science, 31(5): 815−828 doi:  10.1007/s11769-021-1228-2
Citation: SONG Yang, ZHANG Yu, WANG Tingting, QIAN Sitong, WANG Shijun, 2021. Spatio-temporal Differentiation in the Incidence of Influenza and Its Relationship with Air Pollution in China from 2004 to 2017. Chinese Geographical Science, 31(5): 815−828 doi:  10.1007/s11769-021-1228-2

Spatio-temporal Differentiation in the Incidence of Influenza and Its Relationship with Air Pollution in China from 2004 to 2017

doi: 10.1007/s11769-021-1228-2
Funds:  Under the auspices of Key Program of National Natural Science Foundation of China (No. 41630749), Program of the National Social Science Fund of China (No. 17BJL051), Fundamental Research Funds for the Central Universities (No. 1709103, 2412020FZ001)
More Information
  • Corresponding author: WANG Shijun. E-mail: wangsj@nenu.edu.cn
  • Received Date: 2021-01-18
  • Accepted Date: 2021-03-25
  • Publish Date: 2021-09-05
  • The Healthy China Initiative is a major health strategy being pursued by the country. To prevent and control different types of diseases as well as their complex variants, research on the spatio-temporal differentiation among and mechanisms of influence of epidemic diseases is growing worldwide. This study analyzed monthly data on the incidence of influenza by using different methods, including Moran’s I, the hotspot analysis model, concentration analysis, and correlation analysis, to determine the characteristics of spatio-temporal differentiation in the incidence of influenza across prefecture-level cities in China from 2004 to 2017, and to examine its relationship with air pollution. According to the results, the overall incidence of influenza in China exhibited a trend of increase from 2004 to 2017, with small peaks in 2009 and 2014. More cases of influenza were recorded in the first and fourth quarters of each year. Regions with higher incidences of influenza were concentrated in northwestern and northern China, and in the coastal areas of southeastern China. Over time, the distribution of regions with a higher incidence of influenza has shifted from the west to the east of the country. A significant relationship was observed between the incidence of influenza and factors related to air pollution. The contents of five air pollutants (PM2.5, PM10, SO2, NO2, and CO) were significantly positively correlated with the incidence of influenza, with a decreasing order of contribution to it of SO2 > CO > NO2 > PM2.5 > PM10. The content of O3 in the air was negatively correlated with the incidence of influenza. The influence of air pollution-related factors on the incidence of influenza in different regions and seasons showed minor differences. The large-scale empirical results provided here can supply a scientific basis for governmental disease control authorities to formulate strategies for regional prevention and control.
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Spatio-temporal Differentiation in the Incidence of Influenza and Its Relationship with Air Pollution in China from 2004 to 2017

doi: 10.1007/s11769-021-1228-2
Funds:  Under the auspices of Key Program of National Natural Science Foundation of China (No. 41630749), Program of the National Social Science Fund of China (No. 17BJL051), Fundamental Research Funds for the Central Universities (No. 1709103, 2412020FZ001)

Abstract: The Healthy China Initiative is a major health strategy being pursued by the country. To prevent and control different types of diseases as well as their complex variants, research on the spatio-temporal differentiation among and mechanisms of influence of epidemic diseases is growing worldwide. This study analyzed monthly data on the incidence of influenza by using different methods, including Moran’s I, the hotspot analysis model, concentration analysis, and correlation analysis, to determine the characteristics of spatio-temporal differentiation in the incidence of influenza across prefecture-level cities in China from 2004 to 2017, and to examine its relationship with air pollution. According to the results, the overall incidence of influenza in China exhibited a trend of increase from 2004 to 2017, with small peaks in 2009 and 2014. More cases of influenza were recorded in the first and fourth quarters of each year. Regions with higher incidences of influenza were concentrated in northwestern and northern China, and in the coastal areas of southeastern China. Over time, the distribution of regions with a higher incidence of influenza has shifted from the west to the east of the country. A significant relationship was observed between the incidence of influenza and factors related to air pollution. The contents of five air pollutants (PM2.5, PM10, SO2, NO2, and CO) were significantly positively correlated with the incidence of influenza, with a decreasing order of contribution to it of SO2 > CO > NO2 > PM2.5 > PM10. The content of O3 in the air was negatively correlated with the incidence of influenza. The influence of air pollution-related factors on the incidence of influenza in different regions and seasons showed minor differences. The large-scale empirical results provided here can supply a scientific basis for governmental disease control authorities to formulate strategies for regional prevention and control.

SONG Yang, ZHANG Yu, WANG Tingting, QIAN Sitong, WANG Shijun, 2021. Spatio-temporal Differentiation in the Incidence of Influenza and Its Relationship with Air Pollution in China from 2004 to 2017. Chinese Geographical Science, 31(5): 815−828 doi:  10.1007/s11769-021-1228-2
Citation: SONG Yang, ZHANG Yu, WANG Tingting, QIAN Sitong, WANG Shijun, 2021. Spatio-temporal Differentiation in the Incidence of Influenza and Its Relationship with Air Pollution in China from 2004 to 2017. Chinese Geographical Science, 31(5): 815−828 doi:  10.1007/s11769-021-1228-2
  • The rapid pace of industrialization and urbanization has led to drastic changes in the modes of production, people’s lifestyle, and the environment that have profound impacts on human health and the spectrum of diseases threatening it. This state of affairs has increased the instability, complexity, and vulnerability of the national health system in China (Yang et al., 2018). Diseases that can turn into pandemics are increasing in variety. Influenza (or the flu) is an acute respiratory infectious disease caused by the influenza virus that spreads through the air (National Health Commission of the People’s Republic of China, 2020). It is highly infectious, transmits quickly, and can easily lead to outbreaks and even pandemics (Hilleman, 2002). Influenza outbreaks harm human health and impose a heavy economic burden, and thus are a serious global public health problem (Jamieson et al., 2009; Matus et al., 2012; Bhuiyan et al., 2014; Putri et al., 2018). According to the World Health Organization (WHO), seasonal influenza epidemics cause 650 000 deaths across the world each year, equivalent to a person dying from influenza every 48 seconds (WHO, 2018). The incidence rate and mortality rate of influenza are relatively low, compared with other infectious diseases with the highest annual incidence rate: hand foot mouth disease, hepatitis B, tuberculosis and other infectious diarrhea and the highest annual mortality rate: tuberculosis, rabies, AIDS (Acquired Immune Deficiency Syndrome) and viral hepatitis from 2002 to 2017, but the trend has been increasing in recent years (Li and Zhu, 2019). The Healthy China Initiative is a significant health strategy being pursued by the country (The Central People’s Government of the People’s Republic of China, 2019). It involves strengthening the prevention and control of severe infectious diseases, improving the monitoring of and early warning mechanism for infectious diseases, and improving urban air quality nationwide to ensure that it accords with the relevant standards. In light of these goals, regional differences in the incidence of influenza, the effect of poisonous and harmful pollutants on human health, the specific relationship between influenza and changes in air pollution-related factors, and targeted measures for regional environmental governance to mitigate the effects of infectious diseases are issues that need to be systematically studied.

    Public health has long been a core area of research in international geography and environmental science (Yang et al., 2010). The role of geography in public health has become prominent in recent years by highlighting its spatial perspective, and revealing that the spatio-temporal distribution of diseases exhibits significant regional characteristics. Research in the area has focused on laws of the spatio-temporal distribution of diseases and the relevant health conditions (Artois et al., 2018; Guinat et al., 2018; Gong et al., 2020), models for the spatial transmission of infectious diseases (Wang et al., 2020), balancing and regulating the relationships among the environment, development, and health (Zhang et al., 2007), medico-geographical and health-related risk assessments (Shkurinskii and Chibilev, 2012; Murari et al., 2020), regional allocation of healthcare systems and facilities (Holley, 1998; Trujillo and Plough, 2016), and the relation between the urban microenvironment and health (Rosenberg, 2017; Qi and Zhou, 2018; Yang et al., 2019).

    The major methods used to study epidemic diseases include descriptive epidemiology (Zhang, 2013), spatial agglomeration analysis (Lu et al., 2010; Lin et al., 2020), and landscape genetics (Biek and Real, 2010). The transmission of diseases is influenced by natural factors, such as air pollution and meteorological conditions (Dangi et al., 2014; Skog et al., 2014; Vittecoq et al., 2015; Lelieveld et al., 2015; Sun et al., 2018), and socio-economic factors such as income, consumption, age, and social security (Wu et al., 2009; Vazquez-Prokopec et al., 2010; Mahabir et al., 2012; Li et al., 2013; Wen et al., 2015). Studies have shown that long and short-term exposure to PM2.5, NO2, SO2, and PM10 is closely associated with cardiovascular and respiratory diseases, and may be a significant contributor to cancer (Utell and Frampton, 2000; Lonati et al., 2011; To et al., 2015; Hayes et al., 2020).

    Current research on influenza has focused on its epidemiology. The agglomeration of influenza (Chen et al., 2011; Lee and Wong, 2011; Yang et al., 2011) and the epidemiological characteristics (Wang et al., 2017) of its occurrence differ significantly at different spatial scales. Scholars have focused on analyzing the scale of diseases in regions of different sizes. Systematic long-term and small-scale epidemiological data for China are lacking, and the analysis of the impact of environmental factors, such as air pollution, on diseases in the country has been limited. Medical workers have used statistical analysis to describe the epidemiological characteristics and social burden of influenza and other diseases. Analytical research on spatial differences among the incidences of influenza and natural factors influencing them is still in its infancy. To address this gap in research, this study collected data on the incidence of influenza and air pollution in China. We used Moran’s I, the hotspot analysis model, concentration analysis, and Spearman’s rank correlation analysis to study the characteristics of the spatio-temporal distribution of and factors influencing the spread of influenza. This study has three aims: 1) to provide large-scale empirical data on influenza that can be used for health geography, 2) to determine the mechanism of influence of air pollution-related factors on the incidence of influenza from multiple perspectives and identify its spatio-temporal evolution, and 3) to provide insights that can be used by disease control authorities to formulate regional prevention and control strategies, and provide theoretical support for the implementation of the Healthy China Initiative.

  • We used data from 31 provinces, municipalities and autonomous regions of China (excluding Hong Kong, Macau, and Taiwan of China) in this study, with the prefecture-level city as the basic unit of the scale. The research period ranged from 2004 to 2017. The collected information included data on the incidence of influenza and air quality, and maps. Monthly data on the incidence of influenza in prefectural cities were used. All reported data were used as samples to analyze the spatial and temporal characteristics of the incidence of influenza. The number of cities for which annual data on the incidence of influenza were available increased from 257 in 2004 to 367 in 2017. The data were drawn from Data Center of China Public Health Science, part of China Center for Disease Control and Prevention (http://www.phsciencedata.cn/share/). Given the small number of deaths recorded, data on only the number of cases of influenza were used, where incidence = the number of cases × 100 000 people. Statistical data for some regions for a number of years were missing, because of which the data on influenza were not spatially continuous. The missing data were set as blank, without interpolation. The layers of data for administrative maps of prefecture-level cities were obtained from the Geographic Information Bureau of the State Bureau of Surveying and Mapping. Pollutant indices for air quality were obtained from the real-time air quality monitoring data published by the China National Environmental Monitoring Center (http://www.cnemc.cn/), and included data on fine particulate matter (PM2.5), inhalable particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3), and carbon monoxide (CO). A total of 190 cities were monitored daily for air quality in 2014 and 366 in 2015–2017, and the monthly mean value for each was calculated from the daily data. When analyzing the relationship between air pollution and the incidence of influenza, cities for which data on air quality and the incidence of influenza were available were selected as samples. The numbers of samples (cities) used for 2014 to 2017 were 160, 327, 328, and 330, respectively. The differences in the numbers of samples between years affected statistical efficiency but did not compromise the statistical inferences made because the total coverage was large enough.

  • Moran’s global I statistic is the most commonly measure used to analyze the overall spatial correlation and degrees of difference in a given study area (Haining, 2003). We used it to investigate the overall spatial distribution of the incidence of influenza across China.

  • The local Getis-Ord Gi* hotspot detection is a typical statistical method of local spatial autocorrelation that can be used to identify spatial variation (Getis and Ord, 1992). It can accurately reflect the distribution of hotspots for influenza in a given area (Wang et al., 2014).

  • Concentration analysis involves the use of daytime and seasonal data to analyze the degree of concentration of influenza in a given community over the course of a year (Liu, 1985). It is calculated as follows:

    $${R_x} = \frac{1}{2}\left({{r_2} + {r_6} - {r_8} - {r_{12}}} \right) + \frac{{\sqrt 3 }}{2}\left({{r_3} + {r_5} - {r_9} - {r_{11}}} \right) + \left({{r_4} - {r_{10}}} \right)$$ (1)
    $${R_y} = \frac{1}{2}\left({{r_3} - {r_5} - {r_9} + {r_{11}}} \right) + \frac{{\sqrt 3 }}{2}\left({{r_2} - {r_6} - {r_8} + {r_{12}}} \right) + \left({{r_1} - {r_7}} \right)$$ (2)
    $$M = \sqrt {{{\left({{R_x}} \right)}^2} + {{\left({{R_y}} \right)}^2}} $$ (3)

    where ri denotes the incidence of influenza in a month as a percentage of its annual incidence, i denotes the month, R denotes dispersion, Rx and Ry denote dispersion along the x-axis and the y-axis, respectively, and M denotes concentration. When M = 1, this indicates that the incidence of influenza is concentrated in a certain month, a value in the interval [0.9, 1) indicates strict seasonality, that in [0.7, 0.9] indicates very strong seasonality, values in the range [0.5, 0.7) indicate relatively strong seasonality, [0.3, 0.5) indicates a certain seasonality, and [0, 0.3) indicates insignificant seasonality. When the concentration is zero, this indicates a uniform distribution of the incidence of influenza over 12 months (Li et al., 2016).

  • Air quality data are not continuously equidistant, and their forms of correlation and patterns of distribution are unknown. If Pearson’s rank correlation coefficient is directly used for analysis, the results may be inaccurate. To improve the reliability of correlation analysis, Spearman’s rank correlation analysis, a non-parametric correlation coefficient, was used in this study to analyze the degree of correlation between the incidence of influenza and factors influencing air quality (Spearman, 1904). This coefficient has no requirements regarding the selection of raw data, the form of the correlation, and the pattern of distribution. It is typically used to measure the strength of a monotonic relationship or rank correlation between variables. Its universality and robustness are better than those of Pearson’s product moment correlation coefficient with the parametric property (Spearman, 1904).

  • Fig. 1 shows an overall trend of increase in the incidence of influenza in 31 provinces, municipalities and autonomous regions of China (excluding Hong Kong, Macao, and Taiwan of China) from 2004 to 2017. In 2006, it declined slightly and reached its minimum value in 2008. In 2009, it increased significantly due to the outbreak of influenza A (H1N1), then decreased, and then increased again. It fluctuated until reaching its maximum value in 2017. This was in line with the global influenza epidemic in 2017 (Du et al., 2018). The annual trend of variations in the occurrence of influenza were the same in most provinces and cities in China, and only a few provinces exhibited some differences. Most provinces, municipalities and autonomou regions registered a trend of increase, with fluctuations, and a peak in 2009, such as Ningxia and Chongqing. Three provinces in the northeastern China (Heilongjiang, Jilin and Liaoning) had slightly different trends, generally showing smaller fluctuations and lower incidence of influenza.

    Figure 1.  The incidence of influenza in different provinces, municipalities and autonomous regions of China (excluding Hong Kong, Macau and Taiwan) during 2004–2017

    The characteristics of occurrence of influenza in some provinces, municipalities and autonomous regions differed substantially from the rest. For example, Beijing showed the largest annual differences, and had a much higher incidence of influenza than the other provinces, municipality and autonomous in China in 2014 and 2017. From 2014 to 2017, the incidence in Guangdong was two to three times higher than the national average. These substantial differences were based not only on the outbreak of influenza in 2017, but also on the number of data samples obtained from this region. That is, Guangdong might have recorded an increase in cases of influenza due to more testing and fewer missed diagnoses than the other regions.

  • Table 1 shows that the concentration of the incidence of influenza in China was mostly in the range of [0.3, 0.5), only slightly higher than that of 0.5 in 2009. This indicates that influenza in China generally exhibited seasonal characteristics, with the strongest seasonality registered in 2009 and the weakest in 2015.

    Year20042005200620072008200920102011201220132014201520162017
    The degree of concentration0.2670.2280.5640.3770.2500.5060.3280.2560.2900.3340.3310.1160.4470.332

    Table 1.  Degree of concentration analysis of influenza incidence in China (excluding Hong Kong, Macau and Taiwan) during 2004–2017

    A comparison of the monthly data (Fig. 2) showed that the incidence of influenza was mainly concentrated in the first and fourth quarters of each year, i.e., a period of higher incidence and susceptibility lasted from November of each given year to March of the following year. This might be because the transmission of the influenza virus by aerosol depends on the temperature and relative humidity. The low temperature and relative humidity in winter are more conducive to the transmission of influenza than in summer (Ma et al., 2019). In 2009 and 2016–2017, irregular fluctuations occurred, with a higher incidence of influenza than during the stable period. Its incidence in December 2017 was significantly higher than in the corresponding month in other years, which was related to the increase of influenza virus types in winter in 2017 (World Health Organization, 2017).

    Figure 2.  Monthly incidence of influenza in China (excluding Hong Kong, Macau and Taiwan) during 2004–2017

  • Fig. 3 shows a clear differentiation in spatial patterns of the incidence of influenza in China, and substantial changes occurred during the study period. Regions with a higher incidence of influenza tended to shift from the west to the east of the country over time, and the degree of its concentration increased. On the contrary, northeastern China continued to have a lower incidence. From 2004 to 2009, the incidence of influenza in southwestern, northwestern, and northern China were consistently higher than the national average. In 2009, the nationwide incidence (except in the northeastern China) was the highest during 2004–2009, and the number of areas with a higher incidence than the average was the largest during this period. During 2010–2017, the number of regions, provinces, and cities with higher incidences was far greater than in the previous five years. The incidence of influenza was relatively higher in north, south, east, central, and northwest China. At the provincial level, Xinjiang, Beijing, Inner Mongolia, Hebei (Handan), Tibet, Guangxi, and Guangdong had significantly higher incidences of influenza during the study period. This might have been related to the rapid economic development and increase in the number of factories in recent years.

    Figure 3.  Spatial distribution of yearly incidence of influenza in China during 2004–2017

  • Table 2 shows the global Moran’s I for the incidence of influenza and the results of the corresponding significance test for China in 2004–2017. Moran’s I was higher than 0.2 and the Z-value was greater than the critical value of 1.96 for the normal distribution function at a significance level of 0.05 only in 2014, 2016, and 2017, i.e., it passed the significance test and was statistically significant in these years. This indicates that the incidence of influenza exhibited a certain spatial autocorrelation and agglomeration. However, the global Moran’s I for the incidence of influenza from 2004 to 2013 and in 2015 did not exceed 0.2, indicating that the global spatial autocorrelation was not strong in these years and the interregional spatial difference in influenza was insignificant, showing a random distribution.

    YearMoran’s IZ valueP valueSpatial patternYearMoran’s IZ valueP valueSpatial pattern
    20040.1056.2400.00020110.0474.8160.000
    20050.0152.3430.01920120.0847.0040.000
    20060.0091.2500.21120130.0788.0390.000
    20070.0192.4800.01320140.33019.2220.000Clustering
    20080.0262.0420.04120150.15315.3180.000
    20090.0765.5110.00020160.29624.1360.000Clustering
    20100.0222.0840.03720170.30522.2520.000Clustering
    Note: −, no significance or no clustering

    Table 2.  Global Moran’s I analysis of influenza incidence in China (excluding Hong Kong, Macau and Taiwan) during 2004–2017

  • Fig. 4 shows the characteristics of the local spatial autocorrelation for the incidence of influenza through the detection of hotspots for the flu. Areas with a regional agglomeration of incidence of influenza were registered in each year. From 2004 to 2009, significant hotspots with higher incidences formed in Xinjiang, Tibet, and Gansu, while areas with few cases were concentrated in Hebei, Henan, Jilin, and Shandong. From 2010 to 2017, the hotspots gradually shifted eastward from Shaanxi, Shanxi, Beijing, and Hebei to Guangdong, Fujian, and Guangxi, while areas with low incidence were concentrated in Sichuan, Guizhou, Heilongjiang, Jilin, and Liaoning. In general, hotspots of influenza tended to shift from the northwest to the southeast, possibly owing to the limited economic development of western regions of China earlier on. In the later period, eastern regions might have suffered from a higher agglomeration of influenza owing to a higher population density and faster spread of pathogens, with cities such as Zhuhai, Zhongshan, and Huizhou having a higher agglomeration and incidence of influenza.

    Figure 4.  Local Getis-Ord Gi* of influenza incidence in China during 2004–2017

  • Air pollution is associated with a broad spectrum of acute and chronic health effects, the nature of which may vary with the constituents of pollutants. Six air pollutants—PM2.5, PM10, SO2, NO2, O3, and CO—were selected as factors influencing the incidence of influenza here based on the results of previous studies for which data were available. To ensure the consistency and integrity of the statistical data range, only the period from 2014 to 2017 was selected to examine these factors. Because not all results of correlation passed the Shapiro-Wilk test, the Spearman rank correlation analysis was used to evaluate their mutual effects as it did not have any requirement regarding the distribution of the raw data.

    The value of the correlation coefficient varied depending on the spatial scale of the research problem or the basic unit of geographical analysis used, where this is the modifiable areal unit problem (MAUP) (Robinson, 1950; Shi and Wang, 2016). Owing to the large span from the north to the south of China, the geographical conditions and related factors are significantly different, because of which the selection of different spatial scales for analysis leads to different results. Therefore, it was necessary to use a method with an independent scale to carry out statistical reasoning. To reduce the influence of the MAUP as much as possible, three spatial scales (national, provincial, and prefecture-level cities) were used to quantitatively describe the relation between influenza and air pollution.

  • The overall correlation coefficients for the incidence of influenza and air pollution-related factors at the national scale were calculated for 2014–2017 (Table 3). The results indicate that the rank correlation coefficients were lower than 0.3 in each year, indicating a very weak correlation. The P-values were mostly less than 0.01, and led us to reject the null hypothesis that factors influencing the air quality were not related to the incidence of influenza, i.e., the occurrence of influenza was positively correlated with concentrations of PM2.5, PM10, SO2, NO2, and CO at the national level. However, it showed a very weak negative correlation with the concentration of O3 because the annual averages of air pollution and the incidence of influenza were too general, and specific phenomena in small areas were neglected. Therefore, correlation analyses at the national level were used only to compare differences at other spatial scales, and had no reference significance.

    Air pollutants2014201520162017
    Spearman’s correlation coefficientSignificance (P)Spearman’s correlation coefficientSignificance (P)Spearman’s correlation coefficientSignificance (P)Spearman’s correlation coefficientSignificance (P)
    PM2.50.079**0.0010.0330.0500.213**0.0000.168**0.000
    PM100.0310.2010.0090.6010.193**0.0000.107**0.000
    SO20.098**0.0000.052**0.0020.122**0.0000.045**0.008
    CO0.214**0.0000.069**0.0000.167**0.0000.129**0.000
    NO20.084**0.0010.040*0.0190.232**0.0000.175**0.000
    O3–0.240**0.000–0.130**0.000–0.185**0.000–0.225**0.000
    Notes: ** P < 0.01 significance level; * P < 0.05 significance level

    Table 3.  Correlation coefficient between influenza incidence and air pollution factors in China during 2014–2017

  • Fig. 5 shows the correlation coefficients between the incidence of influenza and air pollution for each province of China. A total of 52% of the results passed the significance test during the study period. The correlation between the incidence of influenza and air pollution was concentrated in the range from −0.5 to 0.5 in a majority of provinces in China. The values of PM2.5, PM10, SO2, NO2, and CO were generally higher than zero, indicating a positive correlation with influenza. The values of SO2 and NO2 indicated the highest influence. More than half of the correlation values for O3 were lower than 0, indicating a negative correlation with the incidence of influenza. The correlations between the overall incidence of influenza and air pollution were mostly above 0.5 with a stronger correlation in Beijing, Tianjin, Shanghai, Jiangsu, Ningxia, and Shaanxi. The results for Shanghai in 2016 were the most significant, i.e., values of the correlation of influenza with SO2, PM2.5, PM10, NO2, and O3 were 0.92, 0.75, 0.71, 0.89, and −0.73, respectively. Correlations between the incidence of influenza and SO2 concentrations were also significant in Beijing and Tianjin. The statistical results for Tibet, Hainan, Inner Mongolia, Chongqing, Sichuan, and Anhui did not pass the significance test. Regions with significant correlations were mainly located in the Yangtze River Delta, Beijing, Tianjin, Shaanxi, Gansu, and Ningxia. Most of them had higher incidences of influenza owing to severe air pollution.

    Figure 5.  Hotspots of the correlation between the incidence of influenza and air pollution of provinces, municipality and autonomous regions of China (excluding Hong Kong, Macau, and Taiwan) in 2014–2017

  • Fig. 6 shows the correlation coefficients between the incidence of influenza and air pollution in each prefecture-level city in China, and the results with p-values of less than 0.05 were retained. It is clear that the correlation coefficients between the concentrations of PM2.5, PM10, SO2, NO2, CO, and O3, and the incidence of influenza were all above 0.5. The first five factors had positive values, with the maximum and minimum values in the range (0.5,1.0], and median values mostly concentrated around 0.75, indicating a strong correlation. Its correlations with the concentration of O3 had negative values, with a median value concentrated around −0.750 and indicating a negative correlation. We compared the degrees of correlation between the five air pollution-related factors, which were positive correlated, with the incidence of influenza, and their absolute values showed differences that were not significant. The overall correlation between the incidence of influenza and the concentration of SO2 was the highest, and had the highest median values (mean median of 0.795). The average median values of CO and NO2 were 0.790 and 0.774, respectively. Those of PM2.5 and PM10 were smaller, at 0.764 and 0.747, respectively. The average median correlation coefficient between the content of O3 and the incidence of influenza was −0.763, indicating that O3 was significantly negatively correlated with it.

    Figure 6.  Correlation chart between the incidence rate of influenza and air pollution in China’s prefectural cities during 2014–2017

  • The different scales considered showed differences in the correlation values between the incidence of influenza and air pollution-related factors. At the scales of the province and prefecture-level city, the results were more statistically significant. In general, the contents of PM2.5, PM10, SO2, NO2, and CO were positively correlated with the incidence of influenza. Of them, SO2, CO, and NO2 were highly correlated with influenza, followed by PM2.5 and PM10. O3 was also highly correlated with influenza, but negatively so.

  • The data on influenza used in this study were sampled data, and influenced the results to a certain extent. We used data provided by Data Center of China Public Health Science, part of China Center for Disease Control and Prevention. A large number of cases of influenza recorded therein were based on clinical diagnoses, with a higher ratio of incorrect diagnosis and underreported cases (Aman A T, 2020). This is because some carriers of influenza are asymptomatic, which makes it difficult to comprehensively find, diagnose, and report cases . A study by the US Emergency Departments found that temperatures recorded in triage were lower in the morning than in the afternoon or evening. Although there is no official rate of missed diagnosis or incorrect diagnosis of influenza in China, we need to consider this factor (Harding et al., 2020). In addition, different statistical capacities in different years and regions, as well as missing data in some periods or regions due to adjustments in the administrative division, were registered. The data were not interpolated to ensure their authenticity. These problems with the data affected our method and process, and might have led to deviations between the obtained results and the empirical conditions. An analysis of the results of correlation between the incidence of influenza and air pollution revealed that the overall values that passed the significance test were not very high, where this may be related to the selection of large-scale and long-term data. However, the correlations were very significant in Beijing, Tianjin, and other places for which more basic data were likely provided. This is because large cities not only have large populations and incidence bases, but also have strong statistical and reporting-related precision, because of which the results of analysis of cities with more basic data were more accurate (Zhang et al., 2018). The availability of the results also increased successively from the national and provincial to the prefecture-level-city scales, indicating that more explicit results were obtained at finer scales.

  • This study focused on explaining the incidence of influenza at different spatial levels, and the results are consistent with those of cutting-edge research in epidemiology and pathology (Varon et al., 1999; Pandey et al., 2005; Tao et al., 2011; Li and Meng, 2012; Yan et al., 2013; Zhou, 2013; Li et al., 2018, 2019; Chen et al., 2018; Liu et al., 2019; Liang et al., 2019). Past work has shown that most pollution-related factors are positively correlated with the incidence of respiratory diseases, which is consistent with the results obtained in this study, i.e., air pollution has a very significant effect on cases of influenza. After entering the human body through various pathways of exposure, pollutants can interact with different target proteins, and are absorbed by the blood and human tissues. This can severely influence the defensive function of the respiratory tract, leading to an inflammatory reaction. This is an important factor in the occurrence and spread of influenza. The results of this study show that influenza is negatively correlated with the concentration of O3, and may have a specific correlation with the bactericidal characteristics of O3. However, epidemiological studies indicate that a negative correlation cannot verify the effect of interaction between the two entities considered owing to their different times of formation. The concentration of O3 tends to be higher in summer, and lower in autumn and winter, when a higher incidence of influenza has been noticed. Furthermore, long-term ozone inhalation has been attributed to increased morbidity and mortality owing to respiratory diseases, such as asthma and chronic obstructive pulmonary disease (Yang et al., 2012). In addition, the toxic effects of a number of environmental pollutants may show a lagged effect, thus posing additional challenges for scientific research on the connection between environmental pollution and health. Although scientists from different countries have provided a fair understanding of such issues as the trend, source, migration, and transformation of pollutants as well as their harmful effects on the human body, research on the characteristics of pollution, its environmental toxicology, and the risks it poses to human health remains in its infancy.

  • This study clarified the key areas for the prevention and control of influenza. The results can guide local governments in making decisions regarding medical control and environmental protection. The use of an integrated and sustainable urban planning strategy is a priority for central and local government officials. We think that the government should formulate targeted schemes for influenza prevention and control in accordance with the characteristics of different regions. The government should pay more attention to the Pearl River Delta, eastern China, and other areas with higher incidences of influenza, as well as cities such as Zhuhai, Zhongshan, and Huizhou. In addition, more sustainable developmental policies should be instituted and enforced to reduce the effect of air pollution on long-term economic development and the health of people. Officials should be rigorous when examining and approving new projects that involve the emissions of harmful pollutants, develop modes of pollution prevention, supervision, and accountability (Wang et al., 2017), and establish a list of enterprises that emit polluting gases such as SO2, CO, and NO2. More attention should be paid to the sources of emission that increase the contents of PM2.5 and PM10. Moreover, the government should control pollution and dust in key industries, develop green transportation, optimize the industrial structure, deepen industrial pollution control, strengthen meteorological monitoring technologies and early warning management, and regulate and control emissions according to meteorological changes in a timely manner (Li et al., 2020). It should ensure an environment with an appropriate concentration of O3, purified air, and engage in atmospheric disinfection and sterilization. In combination with the relevant air pollution indices, a method to calculate countrywide indices representing the risk of influenza should be formulated to provide forecasts to residents and react to outbreaks in a timely manner. A national ecological compensation plan for environmental air quality should be implemented to reduce the spread of air pollution in urban areas, and administrative divisions should be used for better prevention and control of air pollution (Sun et al., 2019).

  • This study has some certain limitations. First, the rising trend of the incidence of influenza observed in this study might have been related to its periodicity, or to the increased rate of reported cases of influenza and improvements in the quality of such reports. Long-term public health monitoring and epidemiological research still needs to be carried out for further verification of the trends identified here. Second, the incidence of influenza varied significantly on a daily basis in certain months in our data, because of which the monthly analysis of its incidence might have reported fewer cases certain months. The characteristics of distribution of infectious diseases at different spatial scales were different. It is thus necessary to investigate and study the disease at different scales to fully grasp the laws of its spread. We mostly used large- and medium-scale analysis, and neglected small-scale analysis. This should be carried out in future work in the area. Third, the factors influencing the incidence of influenza in China were investigated mainly from the perspective of air pollution, without considering the significant effects of natural factors such as climate and hydrology as well as social and economic factors, such as population flow and healthcare. Finally, the secondary classification of influenza was not described in detail, and epidemiological data on the age, gender, and past medical history of patients was not included. Future research should focus on improving the quality of data, examining the comprehensive mechanisms of influence of the incidence of influenza, analyze the characteristics of spatial differentiation among populations according to epidemiological principles, and conduct a comparative spatial analysis of various epidemic diseases.

  • Influenza outbreaks are a major public health problem worldwide that pose a significant threat to human life and health. This study used a top-down approach to analyze the spatio-temporal characteristics of the incidence of influenza in China, and identified and quantified the relationships between the incidence of influenza and air pollution-related parameters at different spatial scales. Three main conclusions were obtained from this study:

    1) From 2004 to 2017, the incidence of influenza showed an overall trend of increase, with fluctuations and strong seasonality, in provinces, municipality and autonomous in China (excluding Hong Kong, Macao, and Taiwan). Periods of higher incidences and susceptibility occurred generally in the first and fourth quarters of each year.

    2) In 2014, 2016, and 2017, the incidence of influenza in China exhibited a stronger spatial autocorrelation, and showed a pattern of agglomeration. Earlier in the study period, influenza was mainly concentrated in less developed cities and regions in northwestern China, while in recent years, it has been concentrated in densely populated cities with large population flows in the southeastern coastal areas, and in large and developed cities. The hotspots of influenza showed a trend of shifting from the northwest to the southeast of the country.

    3) The incidence of influenza was significantly related with changes in parameters influencing air pollution. The contents of five air pollutants—PM2.5, PM10, SO2, NO2, and CO—were positively correlated with the incidence of influenza, and the degrees of risk posed by them from the highest to the lowest were as follows: SO2 > CO > NO2 > PM2.5 > PM10. The concentration of O3 showed a significant negative correlation with the incidence of influenza, but also exhibited small fluctuations due to the influence of different seasons and regions.

  • This work was supported by Data Center of China Public Health Science, part of China Center for Disease Control and Prevention.

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