The epidemiological characteristics of respiratory infections and their association with air pollution and meteorological factors in China during 2004-2018: a cross-sectional study

doi:10.21203/rs.3.rs-4999421/v1

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The epidemiological characteristics of respiratory infections and their association with air pollution and meteorological factors in China during 2004-2018: a cross-sectional study

https://doi.org/10.21203/rs.3.rs-4999421/v1

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Background

The changes of respiratory infectious diseases’ epidemiological characteristics, and the relationship between respiratory infectious diseases and meteorological conditions and pollutants rarely explored in recent 10 years in China.

Methods

The seven respiratory infectious diseases’ data were collected from the NNIDSS, meteorological data and air pollutants data were from the meteorological monitoring stations and national air quality monitoring stations, respectively. Descriptive analyses were used to present trends, and joinpoint regression models were used to examine changes in incidence and mortality for each respiratory infectious disease and to estimate average annual percentage changes (AAPCs). A Distributed Lag Non-Linear Model (DLNM) with relative risk was applied to analyze the impact of meteorological conditions and air pollutants on respiratory infectious diseases. We also applied a time-series decomposition approach based on LOESS (locally weighted regression) to present the seasonality of seven respiratory infectious diseases.

Results

A total of 23,444,640 cases and 45,291 deaths caused by seven respiratory infectious diseases were recorded in China, and the national mean age-standardized incidence and mortality were 115.87/100,000 and 0.23/100,000, respectively; the change of incidence and mortality differed by age groups. SO₂ and PM₁₀ in air pollutants and relative humidity and air pressure in climatic factors had significant effects on most respiratory diseases in this study. Additionally, meteorological factors had a stronger impact on respiratory infectious diseases with an acute and short-term lag effect compared with air pollutants.

Conclusions

In view of the large regional difference in environmental factors, air pollutants, and geographical location, the prevention and control strategies for respiratory infectious diseases need to be formulated based on their own characteristics.

In the recent decades, respiratory infection such severe acute respiratory syndrome (SARS), human infection with avian influenza, pandemic influenza A(H1N1) virus, and COVID-19 infection poses a great threat to human health, and impose a large health burden upon health systems and consistently rank among the most fatal diseases.^1–3 The Chinese government has taken a series of prevention and control measures, such as actively developing vaccines for respiratory infectious diseases, which boosting the continuous improvement in the prevention and control of infectious diseases in China.¹ However, the epidemic patterns of respiratory infectious diseases have changed significantly in the past decade. Shifts in the emerging respiratory infectious pathogens or subtype shifts have been recurring every 2–3 years.^2–9 Moreover, scarlet fever, pertussis, and tuberculosis in China is rising unprecedentedly.^10–12 Besides, the population susceptible to respiratory infectious diseases has continued to increase^1,13 With this background, whether the epidemiological characteristics of respiratory infectious diseases which were surveilled in the notifiable infectious disease report database in China have changed has not been explored yet.

The incidence of respiratory infections is related to environmental factors and air pollution, especially in developing countries.¹⁴ The increase in global air pollution levels drives 7 million premature deaths every year, many of which are attributed to the increased incidence of respiratory infections.¹⁵ During the COVID-19 pandemic, many studies have also presented that the relationship between meteorological factors and air pollution and COVID-19 infection.^16–22 Environmental pollutants such as PM₁₀, PM_2.5, SO₂, NO₂, and CO have also a significant correlation with the COVID- 19 epidemic.^21–25 Based on the above results, we propose to whether other categories of respiratory infections are also related to environmental factors and environmental pollutants? Especially the publication of a ten-point plan of action on the prevention and control of air pollution in 2013 in China has reduced air pollution. In this situation, whether the improvement of air quality will help reduce the incidence and mortality of respiratory infectious diseases in China is not yet known.

Therefore, the aim of this study was to assess the incidence and mortality trends of respiratory infectious diseases in national notifiable infectious disease surveillance system (NNIDSS) across time and by age, sex, season, and province for all 31 province-level units from 2004 to 2018. In addition, the study was also designed to examine the associations between long-term air pollution exposure, meteorological conditions and respiratory infectious diseases throughout China.

National Notifiable Infectious Disease Surveillance System

In response to the 2003 severe acute respiratory syndrome (SARS) outbreak, the Chinese government established a web-based reporting system for selected infectious diseases. This system covers the Chinese population of 1.3 billion across 31 provinces in China. It included 44 notifiable infectious diseases characterized by wide prevalence or great harm, and classified them into classes A, B, and C, with severity decreasing across classes.

Case definitions and diagnostic test

All cases of the seven respiratory infectious diseases (PTB, influenza, meningitis, mumps, measles, rubella, pertussis) were defined based on the updated diagnostic criteria and diagnostic test issued by the National Health Commission of China (for more detail, see Supplementary Tables 1–7).

Data collection

We extracted seven respiratory infectious diseases’ data from the NNIDSS and we obtained data on all seven notifiable respiratory diseases in all 31 province-level units, covering the period 2004–2018. The database contains the monthly number of cases and deaths, death and onset year, cause of death, and data stratified by age groups (0, 1, 2, 3, 4, 5–9, 10–14, 15–19, 20–29, 30–39… and ≥ 85 years). The population data were obtained from the National Bureau of Statistics of the People’s Republic of China and we extracted demographic data for 31 provinces from 2004 to 2018.

Monthly meteorological data (mean temperature, relative humidity, barometric pressure, precipitation, wind speed, and sunshine hours [2004–2018]) were obtained from the 756 sites in meteorological monitoring stations (https://www.aqistudy.cn/historydata).

Air pollution data were obtained from the 1,498 national air quality monitoring Stations, and the geographical locations of the monitoring stations are shown in Supplementary Fig. 1.

(http://data.cma.cn/data/cdcdetail/dataCode/SURF_CLI_CHN_MUL_DAY.html). We collected data on particulate matter < 10 µm (PM₁₀) and < 2.5 µm (PM_2.5), carbon monoxide (CO), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and ozone (O₃) in each province from 2013 to 2018.

Data analyses

Descriptive analyses were used to present trends, and joinpoint regression models were used to examine changes in incidence and mortality for each respiratory infectious disease and to calculate average annual percentage changes (AAPCs). Pearson correlation coefficients were used to estimate the associations between air pollutant concentrations and weather conditions. A distributed lag non-linear model (DLNM) with relative risk was applied to model exposure-lag-response associations between exposure (meteorological conditions and air pollutants) and outcome (the number of diseases cases).

The DLNM is defined as follows:

Y_it ~ quasi-Poisson (µ_t, θ)

where µ > 0 and θ > 1. µ is the mean of the distribution and θ is an overdispersion parameter.

µ _t = E(Y_t) = α + offset (log (Population/100000)) + ns (time, 1*6) + β₁*Y_t−1

+ β₁*Holiday_t + β₂*GDP_t + β₃*Region_t + \(\:\sum\:_{j=1}^{J}{s}_{j}\left({x}_{j,\:\:t};l\right)\)

Here, Y_t denotes the monthly number of disease cases at time t, α is the intercept,ns (time, 6) is calendar time adjusting for seasonality and trend, Y_t−1 is designed to control the autocorrelation, Holiday is a dummy variable..The DLNM was fitted through functions \(\:{s}_{j}\left({x}_{j,\:\:t};l\right)\) simultaneously describing the effect of exposure variables x_j and its lag structure on the disease cases., where l = 0, 1, …, L and L is the maximum lag. To further investigate the seasonality of respiratory disease, we applied seasonal-trend decomposition, which is based on locally weighted regression, to decompose a time series into trend, seasonal, and remainder components for infectious diseases.²⁶ All statistical analyses were performed using R software (version 4.0.2). For hypothesis testing, a P-value < .05 was considered statistically significant.

Ethical approval

This study was conducted according to the principles and guidelines of the Declaration of Helsinki, and was approved by the Research Ethics Committee of the Zhejiang Provincial Center for Disease Control and Prevention. All initial information identifying patients was anonymized.

Trends in overall respiratory infectious diseases

From 2004 to 2018, a total of 23,444,640 cases and 45,291 deaths caused by seven respiratory infectious diseases were recorded in China, accounting for 25.96% and 19.53% of 44 notifiable infectious cases and deaths in China, respectively. The yearly average cases and deaths were 1,562,976 and 3,019, resulting in mean age-standardized incidence and mortality of 115.87/100,000 and 0.23/100,000, respectively. The incidence and mortality of 100,000 in 2018 were significantly higher than those in 2004, with an increase of 31.36% and 87.30%, respectively. Additionally, the AAPC of age-adjusted incidence was 0.23 across the whole of China during 2004-2018; however, the AAPC of age-adjusted mortality was -2.11. The overall case-fatality ratio (CFR) was 1.93 per 1,000, and the highest yearly CFR was for meningitis (91.08 per 1,000) (Tables 1 and 2). Of the seven respiratory diseases, pulmonary tuberculosis (PTB; 63.20%), mumps (18.21%), and influenza (12.49%) accounted for the majority. In contrast, PTB (93.76%), influenza (2.84%), and meningitis (2.21%) had the highest mortality rates (Figure 1).

Incidence and mortality of seven respiratory infectious diseases differed by age. The incidence and mortality of PTB was the highest in adolescents aged≥15 years (except in 2009). As for incidence, mumps was more prevalent in children aged 3 to 14 years, except in 2016 to 2018. Measles was the most common infection in children aged <1 year between 2004 and 2015 (except 2012); however, the leading disease was influenza between 2016 and 2018. Measles, influenza, and mumps were the most prevalent in children aged <3 years and varied with the particular year. For mortality, measles was the most common cause of death in those aged <14 years before 2008, while between 2008 and 2018, the top four diseases with the highest mortality were measles, meningitis, influenza and PTB (Figure 2). The detailed trend of seven respiratory diseases can be seen in Supplementary Figure 2-6.

Exposure–lag–response associations between air pollutants and respiratory infectious diseases

The DLNM model qualitatively showed that the risk of respiratory infectious diseases was increased when the PM₁₀ concentration was higher than 100 µg/m³. In addition, the relative risk (RR) values increased at month lags 6–10, 2–8, 0–4, 0–12, 0–8, 0–6 for influenza, measle, mumps, pertussis, meningitis and PTB, respectively (Figure 3). The cumulative exposure-response curve of PM₁₀ on mumps, meningitis and PTB showed J-shaped (Figure 4).

The risk of influenza, pertussis and rubella increases at month lags 3–9, 6–12 and 11–12, respectively when SO₂ was higher than 50 µg/m³ (Supplementary Figure 7). However, low SO₂ concentration (below the median, 17µg/m³) can slightly increase the risk for measle, meningitis, PTB and rubella (Supplementary Figure 8).

The risk of influenza, pertussis, mumps, meningitis and PTB increased at month lags 1–9, 0–4, 1–5 and 1–8, 0–3 respectively when CO was higher than 1.5 µg/m³(Supplementary Figure 9). Additionally, high CO concentration (above the median) had a stronger effect on rubella which might increase risk significantly (RR > 1, Supplementary Figure 10).

The risk of meningitis, mumps, pertussis and PTB increases at month lags 0–2, 0–1, 0–9 and 0–1 respectively when NO₂ was higher than 40 µg/m³(Supplementary Figure 11). We also found with the protective effect for influenza at the NO₂ concentration above the median (32 µg/m³), while it had statistically significant risk effects (RR > 1) at the NO₂ concentration below the median (Supplementary Figure 12).

The protective effect for influenza, mumps and rubella at the O₃ concentration above the median (84 µg/m³, Supplementary Figure 13), while it had statistically significant risk effects (RR > 1) at the O₃ concentration below the median (Supplementary Figure 14).

Exposure–lag–response associations between meteorological factors and respiratory infectious diseases

The risk of mumps and measle increased when RH > 70% (the median) and > 86%, respectively, while the risk of increased of rubella when RH < 35% (Figure 4). The higher risks of PTB were found at lags 0–2 when mean temperature was between -20°C and -5°C (Supplementary Figure 15), and a U-shaped relationship was consistently found between mean temperature and PTB (Supplementary Figure 16). The RRs of influenza and measle were the highest at lags 0–0.3 when rainfall > 500mm (Supplementary Figure 17). The risk of influenza and pertussis increased significantly when the rainfall less than 20mm and 50mm, respectively. Moreover, the cumulative exposure-response curve of rainfall on measle and rubella showed N-shaped (Supplementary Figure 18).

For wind speed, lower wind speed increased the risk of influenza (Supplementary Figure 19); however, the wind speed had statistically significant risk effects on mumps (RR > 1) at wind speed > 3.8 m/s approximately (Supplementary Figure 20). In addition, both lower sunshine hour and higher sunshine hour were increased the risk of influenza, measle, meningitis, mumps and PTB (Supplementary Figure 21). Two U-shaped relationship was consistently found between measle and mumps and sunshine hour (Supplementary Figure 22). The sunshine hour less than 80 hours was significantly increased the risk of PTB (1 < RR < 3.2).

Based on the national infectious diseases’ surveillance data, this study analyzed the changing patterns and trends in the incidence and mortality of seven respiratory infectious diseases in China from 2004 to 2018, and we found that the incidence of influenza, mumps, and pertussis showed an upward trend, and the mortalities of meningitis, mumps, measles, rubella, and pertussis were decreasing. In addition, the study also investigated the exposure–lag response relationships of meteorological conditions and air pollutants and respiratory infectious diseases for all 31 province-level units in China, and found that weather condition and pollutants also contributed to the incidence of these seven respiratory infectious diseases; however, different factors had different effects.

In the past few decades, the Chinese government has taken a series of measures to prevent and control infectious diseases, e.g., large population-based vaccination (the percentage receiving all 22 National Immunization Program vaccines increased from 78% in 2005-2007 to ≥98% in 2015);^27,28 and the improvement of diagnosis technology (PCR rapid diagnosis has been widely adopted at all hospital levels),^1,29 which may have contributed to the rapid decline in some respiratory infections. However, we also found that the incidence and mortality of some respiratory infectious diseases, such as PTB and influenza, were still high. The continuously growth can be attributed to a multiplicity of factors, such as the decrease in vaccine protein,³⁰ high drug-resistance,³¹ pathogen shift,⁹ low vaccination coverage,^{32- 34} too young to be vaccinated,^35,36 and the shortage of effective medicines.³⁷ In addition, Many studies have been confirmed that the close relationship between climate factors and the incidence of respiratory infectious diseases cannot be ignored.^14,15

Our study also found that air pressure (AP), wind speed, relative humidity, precipitation, sunlight, and temperature are closely related to the incidence of respiratory infectious diseases, whether positively or negatively correlated. Lower AP and humidity can increase the concentrations of inhalable particulate matter and sulfur dioxide;³⁸ and lower humidity causes respiratory droplets to evaporate, reducing Mycobacterium tuberculosis strain’s size, and increasing transmission.³⁹Low humidity and low precipitation may affect the local immunity of the pharynx; and increase host vulnerability to meningococcal meningitis⁴⁰, which leads to the occurrence of meningococcal meningitis. Moreover, a dry environment is more suitable for survival of measles and meningococcal meningitis,⁴¹while adequate sunlight, low humidity and low precipitation all contribute to the transmission of viruses.⁴² Lower temperature and lower relatively humidity are associated with higher influenza risks, which may be related to the increased host susceptibility of the host due to the lower temperature.⁴³ Warmer temperature is advantageous for mumps viral replication and evolution, and increases in temperature and precipitation also prolong the virus survival time, which consequently increases the risk of hosts becoming victims of mumps infection.⁴⁴ Given that climate factors have not changed much in recent decades in China, it is necessary to formulate targeted prevention and control measures for respiratory infectious diseases based on the characteristics of climate change.

Respiratory infectious diseases are greatly affected by air pollutants,³⁶ the World Health Organization (WHO) estimated that seven million people die annually of air pollution worldwide.¹⁵ In China, air pollution has been calculated to contribute to 1.6 million deaths/year, roughly 17% of all deaths.⁴⁵This study showed that influenza, meningococcal meningitis, rubella, and pertussis are all positively related to air pollutants to different degrees, especially influenza. Previous many studies have speculated that exposure to air pollution may cause oxidative stress and generates free radicals that can subsequently damage the lungs’ epithelial cells, weaken macrophage phagocytosis, reduce an individual’s resistance to respiratory viral and bacterial infections, and increase susceptibility to viruses.^46-49Specifically, NO₂ exposure was associated with increased respiratory hospitalization and mortality risk,¹⁶exposure to NO2 compromises the immune system’s ability to inactivate the invading respiratory virus, resulting in increased susceptibility to virus infections.⁵⁰ Exposure to CO can induce oxidative stress and thus damage the respiratory epithelial cells that can resist the attack of influenza virus.^50,51 PM₁₀ and PM_2.5as the most extensively studied air pollutants, had the strong correlation with influenza, meningococcal meningitis and rubella,^{15, 16} similar results have been proved in many studies.^15,52,53 For example, increases in PM_2.5 in New York City were associated with a 6% excess rate of influenza-related emergency department visits.^15,52The host defense dysfunction caused by PM exposure may be the vital reason for susceptibility to respiratory infectious disease.^46-48 In summary, although China has made great efforts to control air pollution, improving air quality is still an important and sustained task given the close relationship between air pollution and respiratory infectious diseases.

This study has several limitations. First, most respiratory cases may go undetected because the disorder may be asymptomatic or the patient does not seek medical care. Second, the visiting rate of some respiratory infectious diseases is low, which may lead to underestimation of incidence and the roles of environmental factors and pollutants. Third, the visiting rate of some respiratory infectious diseases is low, which may also lead to underestimation of incidence and the roles of environmental factors and pollutants. No case information was collected, so it was not possible to assess the impact of exposure at the individual level.

There were still large differences both in incidence and mortality by province, and in respiratory disease types between 2004 and 2018 in China, especially in the undeveloped western, northwestern, and northern parts of China. Scaling up vaccination coverage for vaccine-preventable diseases,^54-56 developing new vaccines and technology,⁷ and improving the availability and quality of health care systems should be priority measures to further decrease the disease burden.³ Moreover, the influence of environmental factors on diseases, especially respiratory infectious ones, cannot be ignored, and there is still much to work to be done regarding environmental improvement.

Authors’ contributions

Liu S, Jiang H, Xu W and Chan TC conceived, designed and supervised the study. Yuan Y collected the data. Jiang H, Liu F, Yin J, Wang K, Zhao N, Wu Z and Li W cleaned the data. Jiang H, Liu F, Yin J, Wang K, Tang JH, Xu X and Jian HL analyzed the data. Jiang H wrote the drafts of the manuscript. Jiang H, Liu S, Xu W interpreted the findings. Liu S, Yang Y and Chan TC commented on and revised the drafts of the manuscript. All authors read and approved the final manuscript. Liu S, Chan TC and Xu W and were co-senior authors on this study.

Conflicts of interest

All authors declare no competing interests.

Acknowledgments

We thank staff members at the county, prefecture, provincial, and national levels of the Centers for Disease Control and Prevention across China for providing assistance with field investigation, administration and data collection.

Transparency declaration

Liu S, Xu W, and Chan TC affirm that the manuscript is an honest, accurate, and transparent account of the study being reported, no important aspects of the study have been omitted; and any discrepancies in the study as planned (and, if relevant, registered) have been explained.

Data availability

Requests for deidentified study data will be reviewed and considered by the corresponding authors.

Funding

This study was funded by grants from the National Key Research and Development Program (2018YFC2000300), the program of Beijing Municipal Science & Technology Commission (DD181100000418004), and the National Science and Technology Major Project of China (2018ZX10302302001004).

Clinical trial number: not applicable.

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The epidemiological characteristics of respiratory infections and their association with air pollution and meteorological factors in China during 2004-2018: a cross-sectional study

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National Notifiable Infectious Disease Surveillance System

Case definitions and diagnostic test

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