Skip to main content
Download PDF
Download ePub

Short-term effects of air pollutants on hospitalization for childhood respiratory diseases in Suzhou City: a time-stratified case-crossover study

Journal of Health, Population and Nutrition volume 43, Article number: 208 (2024) Cite this article

Abstract

Background

Short-term exposure to air pollution has been demonstrated in previous studies to correlate with respiratory disease (RD) in children. Due to regional heterogeneity, our objective was to explore the correlation between short-term exposure to ambient air pollution and hospital admissions for respiratory ailments in children in Suzhou City from January 1, 2017, to December 31, 2022, alongside assessing the influence of the COVID-19 pandemic on this relationship.

Methods

We collected data on air pollutant levels and hospital admissions for childhood respiratory disease (RD) in Suzhou, China, from 2017 to 2022. We utilized a time-stratified case-crossover design along with a conditional logistic regression model to assess the short-term impacts of air pollutants on RD in children through stratified analysis and sensitivity analysis.

Results

A total of 13,408 children with respiratory diseases were included in the study. The findings revealed significant associations between hospitalization for respiratory diseases in children and exposure to PM2.5, PM10, SO2, NO2, and CO. The maximum effect values (95%CI, best lag days) for each 10 µg/m3 increase in the concentrations of PM2.5, PM10, SO2, and NO2 were as follows: 1.017 (1.003–1.031, lag0-2), 1.015 (1.004–1.026, lag0-2), 1.117 (1.001–1.247, lag0-1), and 1.036 (1.009–1.064, lag0-7). Additionally, the maximum effect value (95%CI, best lag days) for each 1 mg/m3 increase in CO concentration was found to be 1.267 1.017–1.579, lag0-7). Stratified analysis indicated that sex, season of admission, and stage of admission did not modify these correlations significantly; however, differential effects on various age groups and sexes were primarily observed among school-age and older children as well as boys.

Conclusions

The short-term exposure to PM2.5, PM10, SO2, NO2, and CO in Suzhou, China, exhibited a positive correlation with RD hospitalization. Prior to the COVID-19 pandemic, the adverse impacts of air pollutants on hospitalizations for childhood respiratory disease were mitigated compared to the period following the pandemic. Local governments should continue promoting decisions and measures for air pollution prevention and control to reduce further pollutant concentration, which is crucial for public health in reducing the burden of childhood respiratory diseases.

Introduction

Respiratory diseases currently represent the leading cause of mortality and pose an escalating burden on global health while also being prevalent among pediatric outpatients, emergency cases, and hospitalized children in China. Air pollution is the fourth most consequential global risk factor, resulting in the loss of over 100 billion disability-adjusted life years (DALYs) each year [1]. Children, with their developing lung immune systems, are particularly vulnerable to air pollutants compared to adults [2]. The substantial number of respiratory disease cases in China each year places immense strain on healthcare institutions, resulting in tremendous pressure on medical resources.

Evidence suggests that air pollution is associated with respiratory illnesses in children [3,4,5]. Research conducted in the United States indicated a substantial influence of PM2.5 on hospitalization rates for pneumonia and bronchitis among children [6]. A study examining 25 major cities in China found that short-term exposure to CO, NO2, and SO2 resulted in elevated hospitalization rates for respiratory ailments in children [7]. Prior research consistently indicates a positive correlation between short-term exposure to PM2.5, PM10, SO2, and NO2 and hospitalizations for Acute Lower Respiratory Infections (ALRI) in children [2, 8].

Therefore, given the geographical variation of air pollution levels, a time-stratified case-crossover study was undertaken utilizing respiratory disease data for children in Suzhou City from 2017 to 2022, aiming to assess the correlation between short-term exposures to six primary air pollutants and hospital admissions for childhood respiratory diseases. Furthermore, considering the changes brought to public health by the emergence of the COVID-19 pandemic in late 2019, we also investigated whether the pandemic altered the relationship between air pollutants and hospitalizations for childhood respiratory diseases. Additionally, stratified analyses were performed considering factors such as sex, age, and season of admission to comprehend potential influencing variables. Finally, to assess the stability of the model, we developed a dual-pollutant model.

Methods

Study area

Suzhou is located in the southeastern part of the Yangtze River Delta at 120°E and 31°N and is subject to the influence of the northern subtropical monsoon maritime climate. The region experiences a warm, humid, and rainy climate characterized by distinct seasons, including prolonged winters and summers, as well as shorter springs and autumns [9].

Hospitalizations data

The hospitalization data were obtained from the Children’s Hospital Affiliated to Soochow University, which serves as the sole tertiary specialized children’s hospital in Suzhou and assumes full responsibility for providing medical care to children in Suzhou and its surrounding areas.

Following the guidelines of the International Classification of Diseases, 10th Revision (ICD-10), we utilized hospitalization data from this institution between 2017 and 2022, specifically focusing on cases meeting diagnostic criteria for respiratory diseases (J00-J99). Variables such as age, gender, disease diagnosis, admission date, and medical record number were extracted for analysis. Our study cohort comprised 13,408 children under the age of 16 diagnosed with respiratory infections. Among them, 8,017 (59.79%) were boys and 5,391 (40.21%) were girls (boys-to-girls ratio: 1.5:1). We excluded cases with addresses outside the study area, incomplete records, or uncertain diagnoses. This research was carried out with approval from the Institutional Human Ethics Committee of Soochow University Children’s Hospital.

Air pollution and meteorological data

Air quality data, including delicate particulate matter (PM2.5), respirable particulate matter (PM10), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO) and ozone (O3), were collected from the Suzhou Environmental Quality Information Publishing Platform for the same time. The moving average concentration per 8 h was used for O3, while 24-hour averages were calculated for other pollutants [10]. CO concentrations were expressed in mg/m3, and the remaining air pollutants’ concentrations were expressed in µg/m3.

The meteorological data as a control factor for the same period, including average daily temperature (°C), average relative humidity (%), and average atmospheric pressure (hPa) in Suzhou, were obtained from the European Centre for Medium-Range Weather historical database.

Study design

In a case-crossover study, each individual serves as their own control, allowing for the examination of the link between temporary exposure and acute outcomes in epidemiology [11]. This study utilized a time-stratified case-crossover design to assess the correlation between variations in short-term air pollutant levels and hospital admissions for respiratory diseases. The case day was referred to the hospitalization date, while the control day is a day with the same year, month and day of the week (control day) as the case day. This approach facilitated comprehensive adjustments for individual covariates (such as sex, age, genetic factors), day of the week (DOW), long-term patterns, and seasonal variations [12]. Furthermore, for each case, three to four control days were chosen. For instance, if a patient with respiratory disease was hospitalized on Tuesday, September 8, 2020, that specific date served as the case day. Conversely, the control days included all other Tuesdays in September (such as September 1, September 15, and September 22) [13].

Statistical analysis

The descriptive statistics were analyzed for all variables, encompassing patient characteristics, air pollutants, and meteorological data. Spearman’s correlation coefficient was employed to evaluate the association between air pollutants and meteorological factors [14]. Conditional logistic regression models were employed to examine the link between different lagged exposures to air pollutants and hospital admissions for respiratory illnesses while adjusting for potential confounding effects of meteorological factors, including temperature, relative humidity, and atmospheric pressure. Air pollutants have been demonstrated to exhibit lag effects [15]. Therefore, to accurately evaluate the influence of air pollutants, we constructed single-day lag structures from lag0 (current day) to lag7 (7-day lag), as well as cumulative multi-day lag structures from lag01 (the average of the current and previous day) to lag07 (the average of the current day and the previous 7 days). The findings are depicted as the estimated odds ratio (OR) and corresponding 95% confidence interval (CI) for hospital admissions attributed to respiratory diseases, associated with every 10 µg/m3 rise in air pollutant concentration (CO for every 1 mg/m3 increase).

To identify potential influencing factors, stratified analyses were also performed to evaluate the potential impact of sex (boys vs. girls), age at admission (infants and toddlers: [0–3) years old; preschool-age: [3–6) years old; school-age: [6–12) years old; adolescents: [12–18) years old), the season of admission (warm season: April to September; cold season: October to March), and stage of admission (pre-COVID-19 pandemic: 2017.1.1-2019.12.31; post-COVID-19 pandemic: 2020.1.1-2022.12.31) on the correlation between exposure to air pollution and outcomes related to respiratory diseases. Additionally, we explored the presence or absence of effect modification within each stratum using the following equation [16]:

$$\:\left(\widehat{{Q}_{1}}-\widehat{{Q}_{2}}\right)\pm\:1.96\sqrt{S{\widehat{{E}_{1}}}^{2}+S{\widehat{{E}_{2}}}^{2}}$$
(1)

Q1 and Q2 represent estimates for two distinct subgroups, such as boys and girls, while SE1 and SE2 denote their respective standard errors.

To assess the robustness of our main findings, we developed a two-pollutant model for conducting sensitivity analysis based on the optimal lag identified in the single-pollutant model [17]. In order to mitigate multicollinearity issues, pollutants with correlation coefficients exceeding 0.6 were selectively excluded when incorporating other pollutants into the model on a case-by-case basis.

The statistical analyses were conducted using SAS 9.4 and R 4.3.1 software. Two-sided tests were employed, and statistical significance was established with a p-value threshold below 0.05.

Results

Descriptive statistical analysis

As presented in Table 1, a total of 13,408 children were hospitalized due to respiratory diseases between January 1, 2017, and December 31, 2022, with boys and infants constituting the majority. Boys and infants remained predominant in both pre-and post-pandemic periods. However, there were significant differences observed in patient distribution across different age groups before and after the COVID-19 pandemic (p < 0.05).

During the study period, Suzhou exhibited average concentrations of PM2.5, PM10, SO2, NO2, O3, and CO at 35.3 µg/m3, 55.0 µg/m3, 7.5 µg/m3, 37.7 µg/m3 ,101.1 µg/m3 ,and 0.7 mg/m3 respectively.The average daily concentrations of these six air pollutants were found to be below the national secondary concentration limits; however, with the exception of SO2 and CO levels, which complied with air quality standards set by World Health Organization (WHO), the remaining pollutants exceeded WHO guidelines for ambient air quality. A statistically significant contrast (p < 0.01) was evident in air pollutant concentration values between pre-pandemic and post-pandemic periods.

Table 1 Descriptive statistics of daily child hospitalizations, air pollutants, and meteorological factors in Suzhou, China, 2017–2022
Full size table

Throughout the study period, Suzhou City exhibited a mean temperature of 17.6 °C, relative humidity of 75.9%, and atmospheric pressure of 1016.0 hPa; however, no statistically significant differences were observed in these meteorological factors between the pre-pandemic and post-pandemic periods (p > 0.05).

As depicted in Fig. 1, the time series data spanning from 17 to 22 years exhibited a consistent declining trend in the concentrations of PM2.5, PM10, SO2, and CO over these six years. Notably, there was a significant decrease in SO2 levels after 2017. Prior to the pandemic, hospitalizations due to respiratory diseases did not display any seasonal fluctuations. On the contrary, PM2.5, PM10, SO2, NO2, and CO exhibited seasonal fluctuations, showcasing elevated levels during winter months and decreased levels during summer (Fig. S1). In contrast to these pollutants’ patterns, O3 exhibited an inverse trend with elevated concentrations during summer and reduced concentrations during winter months. The changes in air pollutant concentrations in the post-pandemic period were similar to those in the pre-pandemic period. However, the number of hospitalizations showed a decreasing trend in March 2020 and April 2022, followed by a slow increase to a stable trend (Fig. S2).

Fig. 1
figure 1

Time-series plot of the levels of air pollutants and the number of hospitalizations for respiratory diseases over the course of the study

Full size image

Figure 2 presents the results of the Spearman correlation analysis conducted to investigate the connection between air pollutants and meteorological variables during the study period. The results unveiled a notable positive correlation (P < 0.001) between PM2.5, PM10, SO2, NO2, and CO, with the most robust connection observed between PM2.5 and PM10 (r = 0.85, P < 0.001). Temperature showed a positive relationship with O3 but negative correlations with other pollutants, while relative humidity was positively correlated with CO but negatively associated with other pollutants. Atmospheric pressure exhibited negative associations with meteorological factors and O3 but positive relationships with other pollutants; notably, atmospheric pressure had the most pronounced negative correlation with temperature (r=-0.89, P < 0.001) (Table S1). To ensure model stability by avoiding multicollinearity effects, variables exhibiting correlation coefficients greater than 0.6 were excluded from our two-pollutant model.

Fig. 2
figure 2

Correlation analysis between air pollutants and meteorological variables during the study

Full size image

Relationship between air pollution and respiratory diseases in children

As illustrated in Fig. 3 and detailed in Table S2, the association between increments of 10 µg/m3 (1mg/m3 for CO) in six air pollutant concentrations at various lag days and hospitalization due to respiratory diseases in children was examined. All pollutants, except O3, exhibited a significant impact on hospitalizations of children with respiratory disease. Moreover, the cumulative exposure risk assessment of these pollutants surpassed that of single-day exposures. The effects of PM2.5 and PM10 on hospitalization rates for childhood respiratory diseases were most significant at a cumulative lag of 0–2 days per 10ug/m3 increase in concentration (effect size: 1.017; 95% CI: 1.003–1.031) and (effect size: 1.015; 95% CI: 1.004-1-026). SO2 exhibited the most pronounced effect on hospitalization for childhood respiratory disease at cumulative lag 0–1 days, showing an effect estimate of 1.117 (95% CI: 1.001–1.247) for every 10ug/m3 rise in concentration. NO2 exhibited the most pronounced effect on hospitalization for childhood respiratory disease at cumulative lag 0–7 days, showing an effect estimate of 1.036 (95% CI: 1.009–1.064) per each additional increment of 10ug/m3 in concentration. CO exhibited the most pronounced effect on hospitalization for childhood respiratory disease at a cumulative lag of 0–7 days, showing an effect estimate of 1.267 (95% CI: 1.017–1.579) per each additional increment of 1mg/m3 in concentration.

Fig. 3
figure 3

Connection between air pollutants and hospitalization for respiratory illnesses in children on different lag days

Full size image

Stratified analysis

Stratified analyses were conducted at the optimal lag days for single-pollutant models. In stratified analyses, no significant correlations were found between air pollutants and respiratory disease admissions in relation to stage of admission, sex, and season of admission (P > 0.05), as depicted in Fig. 4 and Table S3. However, when considering age stratification, it was found that the oldest age group exhibited a more substantial impact regarding respiratory disease-related hospitalizations in children compared to infants and young children at the optimal lag day for SO2 and CO exposure. Furthermore, the odds ratio (OR) for respiratory hospitalizations associated with these air pollutants consistently showed higher values during the post-pandemic period than in the pre-pandemic period.

Fig. 4
figure 4

Association between air pollutants and hospitalizations for respiratory diseases in different subgroups on the optimal lag day

Full size image

The hazard effect of PM2.5 and PM10 at a cumulative lag of 0–2 days and NO2 at a cumulative lag of 0–7 days did not show statistically significant differences for children hospitalized for respiratory disease across admission stage, sex, age, and admission season. However, increased PM2.5 exposure concentrations were linked to an elevated risk of hospitalization for respiratory diseases in boys, children of school age and older, and cold season subgroups with statistically significant associations (effect values and 95% CIs) of 1.020 (1.002–1.038), 1.057 (1.016-1.100), and 1.015 (1.000-1.031), respectively; increased PM10 exposure concentrations were linked to an increased risk of hospitalization among post-COVID-19 pandemic, boys, children of school age and older as well as warm-season subgroups with statistically significant associations; increased exposure to NO2 concentrations was linked to an elevated risk of hospitalization for respiratory disease in post-COVID-19 pandemic, boys, preschoolers, school-age and older children, as well as during the cold season. While the remaining subgroups showed a risk effect but did not reach statistical significance levels.

The statistical difference of the hazard effect of SO2 and CO on hospitalization for respiratory disease among children with different admission times, sex, and season was not observed at the optimum lag day. However, when considering age subgroups, there was a statistically notable discrepancy in the hazard impact of hospitalization for respiratory disease between infants compared to school-age and older children. Specifically, older children exhibited a stronger association with respiratory disease, as indicated by an effect value and 95% CI of 1.849 (1.323–2.585), and 2.666 (1.446–4.914).

Sensitivity analysis

As presented in Table S4, exposure to air pollutants remained a risk factor for hospitalization due to respiratory diseases even after the addition of other pollutant variables. However, the significance of SO2’s effect on respiratory disease hospitalization disappears when adjusted for PM, NO2, and CO, whereas the significance of the other two-pollutant analyses remains robust. Overall, our model results were found to be robust.

Discussion

Based on children hospitalized with respiratory diseases in Suzhou, this study employed a time-stratified case-crossover design to mitigate potential confounding factors at the individual level. After adjusting for temperature, relative humidity, and atmospheric pressure, a conditional logistic regression model was utilized to examine the link between short-term exposure to air pollution and hospitalization risk for respiratory diseases in children. Our findings indicate that PM2.5, PM10, SO2, NO2, and CO exposure increases the risk of hospitalization for respiratory disease in children; among them, PM2.5 has a slightly higher health risk value than PM10 at lag0-2, and CO has a greater health risk value than NO2 at lag0-7. Stratified analyses indicated that boys are more susceptible to the impacts of air pollutants, while school-age and older children have higher health effect values compared to other age groups. Health effect values were higher post-COVID-19 pandemic than pre-COVID-19 pandemic, although some pollutant effect values were not significant.

The data in this study demonstrated a positive relationship between PM2.5 and PM10 concentrations. and daily hospitalizations for childhood respiratory illnesses, with the strongest effects observed at lag0-2. Notably, the risk value for PM2.5 was slightly higher than that for PM10 in this study. Similar findings were reported in a study conducted in Busan, South Korea, where it was found that PM2.5 had a more significant impact on respiratory-related hospitalizations than PM10 [18]. These findings are consistent with prior studies that identify particulate matter as a significant contributor to respiratory illnesses in children [8, 19, 20]. It has been established that atmospheric particulate matter induces the generation of reactive oxygen species, leading to cellular damage through apoptosis and subsequent disease development [21]. Moreover, PM can induce cytotoxic effects, oxidative stress, and rovoke inflammatory responses in lung epithelial cells [22]. The components found in PM2.5 are linked to a heightened likelihood of respiratory diseases necessitating hospitalization in children [23]. Exposure to PM2.5 triggers alterations in the respiratory microbiome while disrupting the ecological balance within lung tissue niches, ultimately resulting in emphysematous destruction and subsequent lung damage [24].

In this study, gaseous air pollutants exhibited a detrimental impact on the frequency of children requiring hospitalization for respiratory diseases. Specifically, SO2 demonstrated the highest hazardous effect at lag01, while CO exhibited a greater effect value than NO2 at lag07. A study on Australian children also reported an correlation between exposure to NO2 and heightened susceptibility to ALRI [25]. Furthermore, a sizable prospective study demonstrated that air pollution (including CO, NO2, and SO2) accounted for 62.53% of childhood pneumonia disease outcomes as part of their model [26]. Our findings align with previous research; however, the specific lag effects may vary across studies due to regional disparities in air pollutant composition and sources as well as demographic and characteristic differences [27]. Exposure to NO2 damages alveolar epithelium and fine bronchiole epithelium, consequently impairing lung immunity [28]. Similarly, exposure to SO2 predisposes individuals to impaired lung mitochondrial function, ultimately contributing to lung disease development [29]. CO can induce various symptoms ranging from nonspecific manifestations to fatal consequences [30].

In this study, stratification by stage of admission showed that the adverse effects of air pollutants on hospitalization for childhood respiratory diseases were relatively small before the COVID-19 pandemic. A comprehensive review indicated that air pollutants serve as risk factors for COVID-19, with particulate matter being linked with an elevated frequency of COVID-19 occurrences [31]. Stratification by gender demonstrated that exposure to air pollutants exerted a more pronounced influence on hospitalization for respiratory diseases in boys; however, no significant disparity was observed between boys and girls. This finding aligns with a study conducted in Bangkok, Thailand [32], potentially attributable to the higher outdoor activity levels typically seen in boys. Nevertheless, the exact mechanisms underlying these gender differences remain unclear and require further investigation. Stratification by age exhibited that the adverse impacts of exposure to air pollutants on hospitalization for respiratory diseases were amplified among school-age and older children. A multi-city time-series study in China found that children aged 4–14 years were more susceptible to the effects of PM2.5, SO2, and NO2 on respiratory diseases [5]. This finding can be attributed to the higher level of physical activity and increased outdoor exposure experienced by older children. Additionally, studies have shown that breastfeeding is linked to a lower likelihood of lung function impairment in children exposed to air pollutants [33]. Stratification based on the season of admission indicated that during the warm season, PM2.5, PM10, and SO2 had a greater influence on respiratory hospitalizations, whereas, during the cold season, NO2 and CO exhibited a stronger association with respiratory hospitalizations. A study conducted in Lanzhou, China, found that PM2.5, PM10, SO2, and NO2 significantly impacted ALRI-related hospitalization among children during the cold season [8]. Conversely, another study in Kaohsiung, Taiwan, reported that NO2 exerted a greater effect during the warm season [34]. These divergent findings may be attributed to variations in regional air quality levels as well as differences in personal exposures across seasons.

In this study, the effect values of the two-pollutant models SO2 and PM2.5 became statistically insignificant after adjusting for each other, which aligns with findings from a similar study conducted in southwest China [8]. Hence, it can be postulated that there might exist an interaction between the effects of SO2 and PM2.5 on respiratory disease hospitalization in Suzhou City. However, even after accounting for other pollutants, the correlation between PM10, NO2, and CO with respiratory disease hospitalization remained significant and consistent with results reported in a study conducted in Hohhot [35]. Furthermore, one particular investigation suggests that the impact of NO2 levels on children’s hospitalization is largely independent of the effects caused by other pollutants [1].

Strengths of our study include: Firstly, this is a rare investigation in Suzhou examining the correlation between exposure to six primary air pollutants and hospitalization for childhood respiratory diseases while also taking into account the influence of the COVID-19 pandemic. Secondly, employing a case-crossover design in this study automatically controls for time-invariant and individual-level confounders compared to previous studies that commonly utilize a time-series design. However, there are certain limitations inherent in our study. Primarily, we utilized a fixed mean from national air quality monitoring stations as a constant measure of individual exposure levels, which unavoidably introduces measurement errors in exposure assessment. Additionally, being an ecological study, it suffers from potential ecological fallacy and can only provide evidence at the population level without directly establishing causality between environmental factors and respiratory hospitalizations.

Conclusion

Our findings demonstrate a positive association between short-term exposures to air pollutants and hospitalization for respiratory diseases in Suzhou, China. This association exhibits a lagged effect, with gaseous pollutants showing higher adverse effects compared to particulate pollutants. Furthermore, the impact of these pollutants on respiratory disease hospitalizations remained consistent before and after the pandemic. Lastly, boys experienced greater adverse effects from air pollutants than girls did, while children of school age and older faced higher health risk values compared to other age groups. Therefore, effective measures should be implemented to manage ambient air quality in Suzhou, with specific attention given to gaseous pollutants to reduce the rate of pediatric respiratory disease hospitalizations.

Data availability

No datasets were generated or analysed during the current study.

References

  1. Yang T, Zhou K, Ding T. Air pollution impacts on public health: evidence from 110 cities in Yangtze River Economic Belt of China[J]. Volume 851. The Science of the Total Environment; 2022. p. 158125.

  2. He Y, Jiang W, Gao X, et al. Short-term effects and economic burden of air pollutants on acute lower respiratory tract infections in children in Southwest China: a time-series study[J]. Environ Health. 2023;22(1):6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Wu Y, Jin T, He W, et al. Associations of fine particulate matter and constituents with pediatric emergency room visits for respiratory diseases in Shanghai, China[J]. Int J Hyg Environ Health. 2021;236:113805.

    Article  CAS  PubMed  Google Scholar 

  4. Liang Z, Meng Q, Yang Q, et al. Size-specific particulate matter Associated with Acute Lower respiratory infection outpatient visits in children: a counterfactual analysis in Guangzhou, China[J]. Front Public Health. 2021;9:789542.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Yang H, Yan C, Li M, et al. Short term effects of air pollutants on hospital admissions for respiratory diseases among children: a multi-city time-series study in China[J]. Int J Hyg Environ Health. 2021;231:113638.

    Article  CAS  PubMed  Google Scholar 

  6. Glick AF, Tomopoulos S, Fierman AH, et al. Association between Outdoor Air Pollution levels and Inpatient outcomes in Pediatric Pneumonia hospitalizations, 2007 to 2008[J]. Acad Pediatr. 2019;19(4):414–20.

    Article  PubMed  Google Scholar 

  7. Xu H, Wang X, Tian Y, et al. Short-term exposure to gaseous air pollutants and daily hospitalizations for acute upper and lower respiratory infections among children from 25 cities in China[J]. Environ Res. 2022;212:113493.

    Article  CAS  PubMed  Google Scholar 

  8. Zhang W, Ling J, Zhang R, et al. Short-term effects of air pollution on hospitalization for acute lower respiratory infections in children: a time-series analysis study from Lanzhou, China[J]. BMC Public Health. 2023;23(1):1629.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Guo S, Chen D, Chen J, et al. Relationship between meteorological and environmental factors and acute exacerbation for pediatric bronchial asthma: comparative study before and after COVID-19 in Suzhou[J]. Front Public Health. 2023;11:1090474.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Li Z, Zhang Y, Yuan Y, et al. Association between exposure to air pollutants and the risk of hospitalization for pulmonary embolism in Beijing, China: a case-crossover design using a distributed lag nonlinear model[J]. Environ Res. 2022;204:112321.

    Article  CAS  PubMed  Google Scholar 

  11. Maclure M. The case-crossover design: a method for studying transient effects on the risk of Acute Events[J]. Am J Epidemiol. 2017;185(11):1174–83.

    Article  Google Scholar 

  12. Xie J, Zhu Y, Fan Y, et al. Association between rainfall and readmissions of rheumatoid arthritis patients: a time-stratified case-crossover analysis[J]. Int J Biometeorol. 2020;64(1):145–53.

    Article  PubMed  Google Scholar 

  13. Cai M, Wei J, Zhang S, et al. Short-term air pollution exposure associated with death from kidney diseases: a nationwide time-stratified case-crossover study in China from 2015 to 2019[J]. BMC Med. 2023;21(1):32.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Lu W, Tian Q, Xu R, et al. Short-term exposure to ambient air pollution and pneumonia hospital admission among patients with COPD: a time-stratified case-crossover study[J]. Respir Res. 2022;23(1):71.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Liang S, Sun C, Liu C, et al. The influence of Air pollutants and Meteorological conditions on the hospitalization for respiratory diseases in Shenzhen City, China[J]. Int J Environ Res Public Health. 2021;18(10):5120.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Zhou YM, Fan YN, Yao CY, et al. Association between short-term ambient air pollution and outpatient visits of anxiety: a hospital-based study in northwestern China[J]. Environ Res. 2021;197:111071.

    Article  CAS  PubMed  Google Scholar 

  17. Liu Y, Pan J, Fan C, et al. Short-term exposure to Ambient Air Pollution and Mortality from myocardial Infarction[J]. J Am Coll Cardiol. 2021;77(3):271–81.

    Article  CAS  PubMed  Google Scholar 

  18. Jo EJ, Lee WS, Jo HY, et al. Effects of particulate matter on respiratory disease and the impact of meteorological factors in Busan, Korea[J]. Respir Med. 2017;124:79–87.

    Article  PubMed  Google Scholar 

  19. Chang Q, Zhang H, Zhao Y. Ambient air pollution and daily hospital admissions for respiratory system–related diseases in a heavy polluted city in Northeast China[J]. Volume 27. Environmental Science and Pollution Research; 2020. pp. 10055–64. 9.

  20. Oh J, Han C, Lee DW, et al. Short-term exposure to fine particulate matter and hospitalizations for Acute Lower respiratory infection in Korean children: a Time-Series Study in seven Metropolitan Cities[J]. Int J Environ Res Public Health. 2020;18(1):144.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Ghio AJ, Carraway MS, Madden MC. Composition of air pollution particles and oxidative stress in cells, tissues, and living systems[J]. J Toxicol Environ Health B. 2012;15(1):1–21.

    Article  CAS  Google Scholar 

  22. Michael S, Montag M, Dott W. Pro-inflammatory effects and oxidative stress in lung macrophages and epithelial cells induced by ambient particulate matter[J]. Environ Pollut. 2013;183:19–29.

    Article  CAS  PubMed  Google Scholar 

  23. Ostro B, Roth L, Malig B, et al. The effects of Fine Particle Components on Respiratory Hospital admissions in Children[J]. Environ Health Perspect. 2009;117(3):475–80.

    Article  CAS  PubMed  Google Scholar 

  24. Zhai G, Zhang L. Impact of fine particulate matter 2.5 on hospitalization for upper respiratory tract infections in Lanzhou urban industrial area, China[J]. Annals Agricultural Environ Medicine: AAEM. 2023;30(3):462–7.

    Article  Google Scholar 

  25. Cheng J, Su H, Xu Z. Intraday effects of outdoor air pollution on acute upper and lower respiratory infections in Australian children[J]. Environmental Pollution (Barking, Essex: 1987), 2021, 268: 115698.

  26. Wang ZB, Ren L, Lu QB, et al. The impact of Weather and Air Pollution on viral infection and Disease Outcome among Pediatric Pneumonia patients in Chongqing, China, from 2009 to 2018: a prospective observational Study[J]. Volume 73. Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America; 2021. pp. 513–22. 2.

    Google Scholar 

  27. Zhang J, Ren D, Cao X, et al. Ambient air pollutants and hospital visits for pneumonia: a case-crossover study in Qingdao, China[J]. BMC Public Health. 2021;21(1):66.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Schnizlein CT, Bice DE, Rebar AH, et al. Effect of lung damage by acute exposure to nitrogen dioxide on lung immunity in the rat[J]. Environ Res. 1980;23(2):362–70.

    Article  CAS  PubMed  Google Scholar 

  29. Qin G, Wang J, Sang N. Sulfur dioxide inhibits expression of mitochondrial oxidative phosphorylation genes encoded by both nuclear DNA and mitochondrial DNA in rat lungs[J]. Volume 24. Environmental Science and Pollution Research International; 2017. pp. 2527–34. 3.

  30. Buchelli Ramirez H, Fernández Alvarez R, Rubinos Cuadrado G, et al. Niveles elevados de carboxihemoglobina: fuentes de exposición a monóxido de carbono[J]. Arch Bronconeumol. 2014;50(11):465–8.

    Article  PubMed  Google Scholar 

  31. Amnuaylojaroen T, Parasin N. The Association between COVID-19, Air Pollution, and Climate Change[J]. Front Public Health. 2021;9:662499.

    Article  PubMed  PubMed Central  Google Scholar 

  32. Phosri A, Ueda K, Phung VLH, et al. Effects of ambient air pollution on daily hospital admissions for respiratory and cardiovascular diseases in Bangkok, Thailand[J]. Volume 651. The Science of the Total Environment; 2019. pp. 1144–53.

  33. Zhang C, Guo Y, Xiao X, et al. Association of Breastfeeding and Air Pollution exposure with lung function in Chinese Children[J]. JAMA Netw open. 2019;2(5):194186.

    Article  Google Scholar 

  34. Cheng CY, Cheng SY, Chen CC, et al. Ambient air pollution is associated with pediatric pneumonia: a time-stratified case-crossover study in an urban area[J]. Volume 18. Environmental Health: A Global Access Science Source; 2019. p. 77. 1.

    Google Scholar 

  35. LI H, YUE X Z, YANG S M, et al. Impact of air pollutants on the outpatient volume of pediatric respiratory diseases in Hohhot, China[J]. J Environ Hygiene. 2023;13(12):901–8.

    Google Scholar 

Download references

Acknowledgements

The authors acknowledge the contribution and collaboration of all those who participated in this study.

Funding

This work was supported by the the National Natural Science Foundation of China (grant NO. 82170012; 82370020).

Author information

Author notes

  1. Ruoqi Zhang and Jiawei Chen are the co-first authors

Authors and Affiliations

  1. Department of Respiratory Medicine, Children’s Hospital of Soochow University, Suzhou, China

    Ruoqi Zhang, Jiawei Chen & Zhengrong Chen

  2. School of Public Health, Medical College of Soochow University, Suzhou, 215123, China

    Ruoqi Zhang, Mengru Wang & Hongpeng Sun

Authors
  1. Ruoqi Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Jiawei Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Mengru Wang
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Zhengrong Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  5. Hongpeng Sun
    View author publications

    You can also search for this author inPubMed Google Scholar

Contributions

Zhang Ruoqi, Chen Jiawei, and Wang Mengru have made significant contributions in conceptual design, data acquisition, and data analysis and interpretation, Zhang Ruoqi participated in drafting the manuscript, while Sun Hongpeng made critical revisions to important knowledge content, Sun Hongpeng and Chen Zhengrong gave final approval to the version to be released. Each author fully participates in the work and assumes public responsibility for appropriate parts of the content.

Corresponding authors

Correspondence to Zhengrong Chen or Hongpeng Sun.

Ethics declarations

Ethics approval and consent to participate

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki. This study was approved by the Ethics Committee of Children’s Hospital Affiliated to Soochow University (approval number :2021CS163).

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Hongpeng Sun: Handling of correspondence at all stages of review and publication and after publication.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, R., Chen, J., Wang, M. et al. Short-term effects of air pollutants on hospitalization for childhood respiratory diseases in Suzhou City: a time-stratified case-crossover study. J Health Popul Nutr 43, 208 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s41043-024-00683-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s41043-024-00683-4

Keywords

Journal of Health, Population and Nutrition

ISSN: 2072-1315

Contact us