The relationship between air quality and respiratory pathogens among children in Suzhou City

We employed hospitalization data collected from the Children’s Hospital of Soochow University during 2014–2016 meeting diagnostic criteria for ARI [4], and extracted variables including age, gender, disease diagnosis, date of visit, medical record number, residential address, and pathogens detected. Our analytic sample included 5487 children with ARI aged 1 month to 15 years registered as being resident in Suzhou. It comprised 3373 boys (61.5%) and 2114 girls (38.5%) (male-to-female ratio: 1.6:1).This study was conducted with the approval of the Institutional Human Ethical Committee of Children’s Hospital of Soochow University. An informed consent was obtained from all the subjects or guardians who participated in this study.

We also used air quality data from the same period provided by the Suzhou Environmental Protection Agency. Readings included fine particulate matter (PM2.5), inhalable particulate matter (PM10), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), carbon monoxide (CO) and ozone (O₃). Daily air pollutant concentrations were calculated as the mean per 24-h period, except for O₃ which was calculated flow-weighted mean concentration per eight-hour period. Concentrations of air pollutants were expressed in μg/m³, except CO for which mg/m³ was used.

Mean annual concentrations of PM2.5 in Suzhou were 66.38 μg/m³ in 2014, 57.51 μg/m³ in 2015 and 45.41 μg/m³ in 2016. Annual mean PM10 concentrations were 89.94 μg/m³, 80.51 μg/m³ and 71.27 μg/m³ respectively. For NO₂ these were 51.72 μg/m³, 50.85 μg/m³, and 48.71 μg/m³. Concentration of each of these pollutants exceeded the annual average secondary concentration limits established by the GB3095–2012 Ambient Air Quality Standard (PM2.5: 35 μg/m3, PM10: 70 μg/m³, NO₂: 40 μg/m³). Mean concentrations of NO₂, PM2.5 and PM10 were highest in winter (December, January and February) and decreased during the summer (June, July and August). The mean primary concentration limits for SO₂, CO and O₃ indicated by the GB3095–2012 standard are 20 μg/m³, 4 mg/m³ and 100 μg/m³. Atmospheric concentrations of SO₂ in Suzhou exceeded the limit in 2014 and 2015 but not in in 2016. Annual mean concentrations of CO and O₃ remained within the limit for the duration of the period studied. While concentrations of O₃ are elevated in summer compared with winter, there was no significant monthly fluctuation in CO concentrations. These readings indicate that poor air quality in Suzhou City during 2014–2016 posed a risk to human health, and that concentrations of particulates were of notable concern (Table 1).

There were statistically significant differences in mean concentrations of PM2.5, PM10, NO₂ and SO₂ by year (p < 0.05), with higher concentrations of PM2.5, PM10, NO₂ and SO₂ in 2014 than in 2015 and 2016. (Table 2).

We also found positive and statistically significant correlations between concentrations of PM2.5, PM10, NO₂, SO₂ and CO. There was a significant negative correlation between O₃, PM2.5, NO₂, SO₂ and CO. (Table 3).

During the period 2014–2016, nasopharyngeal peripheral blood samples were collected from 5487 children with ARI. Viral pathogens were detected in 1827 (33.3%) of these cases. The proportion of cases with a viral infection was higher in 2014 than in 2015 and 2016. These annual differences were statistically significant (χ² = 43.80, p < 0.001). M. pneumoniae was detected in 1581 cases (28.8%) overall, including 542 (27.6%), 537 (29.6%), and 502 (29.2%) in 2014, 2015 and 2016 while 1657 other bacterial infections (30.2%) were detected with 629 (32.1%), 539 (29.7%), and 502 (29.2%) in each year (Table 4).

In the same period, our sample children aged 1 month to 15 years hospitalized for ARI included 2079 cases (37.9%) aged less than or equal to 6 months old, 753 (13.7%) aged 6 months to 1 year, 1262 (23%) aged 1 year to 3 years, 937 (17.1%) aged 3 years to 6 years, and 456 (8.3%) aged greater than 6 years (Table 5).

The results of the single-contaminant models showed that PM2.5, PM10, NO₂, SO₂, and CO had stronger associations with viral and bacterial respiratory infections children under 3 years of age than in older children. This may be due to the faster respiratory rate of infants and young children, immune system immaturity, and lower capacity to synthesize antibodies; all of which may lead to greater susceptible to air pollutants. More advanced development of immune system function in preschool and school-age children may reduce the probability of respiratory infections. A study of the short-term association between ambient air pollution and daily hospitalization for respiratory infections in children aged 0–17 years in Hanoi, Vietnam found that all ambient air pollutants tested were positively associated with hospitalization for pneumonia and nearly all were positively associated with bronchitis and hospitalization for asthma [26]. These associations were stronger for infants and young children than for those aged 3–5 years. Another study based in the United States tested the effects of short-term changes in air pollutant concentrations on the number of hospital visits attributed to respiratory infections in children aged 0–4 years. The results showed that O₃ had the strongest association, and that the rate of hospital visits was higher in the preschool group than in infants [27]. Similar to the results of the single-contaminant models in this study, air pollutants are more strongly associated with viral and bacterial respiratory in children under 3 years of age than in older children. We also found that concentrations of air pollutants at lags of three and 4 weeks had the greatest effect sizes Toxicological studies have shown that the effects of air pollutants on human health have a physiological lag period [28]. A large population-based study found an association between the number of emergency room visits by children under 5 years of age for respiratory infections and short-term increases in O₃ and NO₂ concentrations with a lag of 3 days. Other literature suggests that the length of this lag period may be greater for children under 5 years of age than for older children [29]. These lag periods have been found to differ in length between studies, and this variability may be related to differences in the characteristics of their study populations, research designs, modeling strategies, pollutant measurement standards, and whether they investigated potential confounding factors. After all air pollutants were considered for inclusion in the multi-pollutant model as part of our model selection procedure, negative associations were found between SO₂ and NO₂ and respiratory tract infections while there was a positive association for O₃. PM2.5, PM10 and SO₂ concentrations were negatively associated with respiratory tract infections in children while a positive association was found for O₃. The strength of the association between NO₂ concentrations and infections increased with age. This may be due correlation in concentrations of different air pollutants, SO₂ attaching to PM2.5, the fact that toxicity of pollutants is greater when they penetrate into the lower respiratory tract, and the negative correlation between the concentration of O₃ and polycyclic aromatic hydrocarbons. As a result, when different pollutants interact, their potential effects on incidence of respiratory tract infections may be strengthened or weakened. This may have explained the inconsistencies between the results of our single pollutant and multi-pollutant models.

A study in Shenzhen [30] found that short-term exposure to ambient air pollution was significantly associated with hospitalization for ARIs. Children under 14 years of age were identified as most susceptible population to infections attributed to PM2.5, PM10 and NO₂ pollution. An Iranian study has concluded that atmospheric particulate matter, NO₂ and O₃ are significantly associated with death by respiratory causes, and that these associations are subject to long lag times [31]. Although the findings of these studies may not be fully consistent to our own, this may be due to differences in pollutant monitoring, air pollutant composition and statistical methods employed.

The results of single-pollutant models showed significant associations between O₃ concentations and respiratory tract infections with M. pneumoniae in children under 6 years old, with exposure at a lag of 4 weeks having the greatest effect size. In contrast to the single-contaminant model, the results of the multi-pollutant model show that O₃ was negatively associated with M. pneumoniae infection in children with aged 6 months and older; although this association was not significant for the remaining age groups. M. pneumoniaeis a common cause of respiratory infection in children, and its incidence has increased in recent years. Although incidence of Mycoplasma infection tends to peak among school-age children, its mean age of first occurrence has risen in China. Animal experiments show that the pathological changes appear in tissues 1–2 weeks after initial infection with M. pneumoniae. When reinfection occurs, these changes usually appear within 3 days after infection. This suggests that the immune response has strengthened following the first infection. Following infection, B lymphocytes can be activated to produce specific antibodies including lgM, lgG and IgA. IgM, which is considered a marker of acute infection, appears more than 1 week after infection, and peaks at 3–4 weeks. While concentrations of IgA and IgM fall shortly afterwards, IgG production peaks later, and concentrations remain elevated for several months [32]. The associations between O₃ and M. pneumoniae infections in children found by both the single contaminant and multi- contaminant models differ from those found in other countries. Bono et al. [33] have concluded that, based on the results of a single-contaminant model, O₃ concentrations were associated with higher numbers of emergency room visits by children for respiratory problems. A study in Hefei City on the relationship between air pollution and upper respiratory tract infection in children aged 0–14 years during 2014–2015 found that O₃ was significantly associated with incidence of infection when tested using a single pollutant model, while only NO₂ was associated with infections when tested using a multi-pollutant model [29]. This difference may be due to the fact that O₃ concentrations in this study did not exceed levels associated with harm to human health. O₃ is considered a secondary pollutant, and, as such, is effect may be masked when present alongside other primary pollutants. Inconsistencies may also appear when comparing results based on tests of nasopharyngeal samples and serum antibody tests. In addition, most M. pneumoniae infections in infants and young children may be due to contact infected family members; particularly older family members who may have more frequent contacts with infected people outside the household. In addition, air pollutants are more strongly associated with M. pneumoniae infections in older people than young children. Further consideration of the susceptibility of the wider population, the individual characteristics of the children included in the analytic sample and the composition of air pollution at a regional level would be needed to gain a fuller picture of the determinants of respiratory infections in our study population.

Controlling air pollution remains a significant public health challenge in China. Our results highlight the need for interventions to reduce air pollution at the same time as strengthening the prevention, diagnosis and treatment of respiratory infections in children.

The objective of this study was to identify the most appropriate statistical model for testing the relationship between air quality and respiratory tract infections in children of different ages. We did not adjust for individual factors (such as physical fitness, immune function, and health behaviors) or factors relating to the home environment (such as exposure to passive smoking). Future work on the associations between environmental pollutants and childhood respiratory infections should consider individual family factors to adjust for potential confounding.