Particulate matter of different sizes associated with acute lower respiratory infection outpatient visits in children: a counterfactual analysis in Guangzhou, China

The burden of lower respiratory infection is primarily borne by developing countries. However, the association between particulate matter of different sizes and acute lower respiratory infection (ALRI) outpatient visits in developing countries is less studied. We obtained data on ALRI outpatient visits (N = 105,639) from a tertiary hospital in Guangzhou, China between 2013 and 2019. Over-dispersed generalized additive Poisson models were employed to evaluate the excess risk (ER) associated with particulate matter [inhalable particulate matter (PM 10 ), coarse particulate matter (PM c ), and ne particulate matter (PM 2.5 )]. Counterfactual analyses were used to examine the potential percent reduction of ALRI outpatient visits if the levels of air pollution were as low as those recommended by the World Health Organization (WHO).


Introduction
Lower respiratory infections, also known as pneumonia or bronchiolitis, are the sixth leading cause of death for all ages, resulting in around 2.4 million deaths worldwide in 2016 (1). The burden of lower respiratory infections is unevenly distributed across the globe and primarily born in developing countries and socioeconomically disadvantaged communities, where adequate nutrition, clean fuel, sanitation, and clean air are unavailable (2,3).
Exposure to ambient particulate matter (PM) has been widely reported to be associated with lower respiratory infection (4)(5)(6). However, evidence on the association between different sizes of PM and lower respiratory infections, especially that from developing countries in which the level of air pollution is high, is relatively little (7)(8)(9). Meanwhile, it is widely acknowledged that short-term exposure to particulate matter is associated with ALRI hospitalizations (10)(11)(12)(13)(14), but we have not found any study that investigated the association between exposure to particulate matter and ALRI outpatient visits.
In this time-series analyses of outpatient visits in a tertiary hospital in Guangzhou, China from 2013 to 2019, we investigate the association between PM of different sizes (PM 10 , PM c , and PM 2.5 ) and the outpatient visits of ALRI, potential mediators, and the potential percent reduction of ALRI outpatient visits if the levels of particulate matter were as low as those recommended by the WHO.

Methods
Acute lower respiratory infection data Data on hospital outpatient visits for ALRI were retrieved from the Guangdong Second Provincial General Hospital located in the southwest of the city (Figure 1), which is one of the highest-level (tertiary) hospitals in Guangzhou (15). According to the International Classi cation of Diseases, Tenth Revision (ICD-10), hospital outpatient visits with the primary diagnoses of pneumonia (J12-J18), bronchiolitis (J20-J21), and asthma (J45-J46)(16) were obtained between February 2013 and December 2019. We aggregated the three subtypes of ALRI to a series of daily time-series data (17)(18)(19)(20).
Air pollution and meteorological data Daily concentrations of air pollution during the study period were obtained from 11 air monitoring stations in Guangzhou (Figure 1), including inhalable particulate matter (PM 10 ), coarse particulate (PM c ), ne particulate matter (PM 2.5 ), nitrogen dioxide (NO 2 ), sulfur dioxide (SO 2 ) and ozone (O 3 ). Following previous study (19), the PM c concentrations were calculated by subtracting PM 2.5 from PM 10 , because PM 10 was consisted of PM 2.5 and PM c . Air pollution measurement details have been previously described (21). Approximately 1% of observation days had missing data for the air pollution, the missing data were imputed using a linear interpolation approach (the "na.approx" function in "zoo" package in R).
Daily meteorological data (mean temperature and relative humidity) were obtained from the National Weather Data Sharing System (http://data.cma.cn/). Because there is potentially high correlation among different air pollutants and meteorological factors, we examined the pairwise Pearson correlation coe cients among these variables. (22,23) Statistical models The ALRI data, daily air pollution concentrations and meteorological data were linked by date. Following prior similar epidemiology studies (24), the association between PM pollution and hospital outpatient visits for acute lower respiratory infections diseases was examined by an over-dispersed generalized additive Poisson model (GAM). In the model, public holidays (PH) and day of the week (DOW) were adjusted for as categorical variables. Seasonal patterns, long-term trends, temperature, and relative humidity were controlled for as smoothing splines. In accordance with the approaches used in previous studies (25,26), we selected six degrees of freedom (df) per year for temporal trends, a df of 6 for moving average temperature of the current day and previous three days (Temp03) and relative humidity (RH).
Considering the delayed health effects of air pollutants, we examined the lag effects for different lag structures. We begin with the same day (lag0) up to ve days lag (lag5) in the single-lag day models. We also considered the accumulated effects of multi-day lags (moving averages for the current day and the previous 1, 2 and 3 days [lag01, lag02, and lag03]).

Strati ed analyses
In order to evaluate the potential effect modi ers of the PM pollution-ALRI associations, we conducted strati ed analyses by gender (male vs. female), age group (age <5 vs. age [5][6][7][8][9][10][11][12][13][14], and season (warm vs. cold). The warm season was de ned as from April to September, and the cold season was from October to March. The 95% con dence interval (CI) of the difference between group was calculated by the formula below: where Q represents the estimated coe cient in each stratum, and SE is the corresponding standard error (27). The difference was considered as statistically signi cant if the 95% CI did not include unity.

Sensitivity analyses
To examine the robustness of the main models, we applied a series of sensitivity studies. The main ndings were assessed by changing the df in the smooth functions for temporal trends and meteorological factors. Additionally, we adjusted for gaseous air pollutants (SO 2 , NO 2 , and O 3 ) in twopollutant models. The models were regarded as robust if there were no signi cant changes after dfchanged or further adjustment for gaseous air pollutants.
Counterfactual analyses on the burden of ALRI attributable to air pollution We estimated the burden of ALRI attributable to PM 2.5 , PM c , and PM 10 by calculating the difference between the observed ALRI outpatient visits and the counterfactual visits predicted using well-recognized reference values of air pollution recommended by the World Health Organization (WHO) and our previously built over-dispersed generalized additive Poisson models. This difference between the observed and counterfactual ALRI outpatient visits represents the estimated burden of ALRI outpatient visits associated with particulate matter of different sizes. The counterfactual scenarios were set to be hypothetical values of PM 2.5 , PM c , and PM 10 set by the WHO Air Quality Guidelines (24 hours mean: 25 μg/m 3 for PM 2.5 , 25 μg/m 3 for PM c , and 50 μg/m 3 for PM 10 ). The observed air pollution levels lower than the reference values were kept the same in the counterfactual scenario. The 95% CIs were constructed using 1,000 bootstrap replicates with replacement for each model.(28, 29) In all statistical analyses, a P value 0.05 was considered statistically signi cant. All data cleaning, aggregation, and visualization, and statistical analyses were done using statistical computing environment R (version 4.0.5)(30). Figure 1 presents the geographical location of Guangzhou city and the sample hospital, as well as the geographical distribution of the air monitoring stations in Guangzhou. A total of 35,310 pneumonia, 68,218 bronchiolitis, and 2,111 asthma cases were included in our analyses. Table 1 shows the summary statistics of ALRI subtypes, particulate matter of different sizes (PM 10 Table 2 exhibits the ER of pneumonia, bronchiolitis, and asthma outpatient visits associated with per 10 µg/m 3 increase of PM 2.5 , PM c , and PM 10 in lag03. The results revealed that particulate matters of different sizes were signi cantly associated with pneumonia, bronchiolitis, and asthma, respectively in single-pollutant models, where the ER of PM c is the largest, followed by those of PM 2.5 and PM 10 . The results were consistent and robust in two-pollutant models with further adjustment for SO 2 , NO 2 , and O 3 , except for those asthma models controlling for NO 2 . The corresponding exposure-response nonlinear curves for daily particulate matter and log relative risk are provided in Supplemental Fig. 1. Table 2 Excess risk and 95% con dence intervals of pneumonia, bronchiolitis, and asthma for each 10 µg/m 3 increase in PM 2.5 , PM c , PM 10 using single-and two-pollutants models. signi cantly associated with ALRI outpatient visits, but the effects of lag0 to lag5 of PM c and PM 2.5 and different lags of PM 10 were nonsigni cant or at borderline signi cant. Table 3 presents the estimated ER with 95% CI of pneumonia, bronchiolitis, and asthma strati ed by gender, age group, and season, where the bold numbers indicate signi cant differences across strata. We can observe that each 10 µg/m 3 increases in PM 10 , PM c , and PM 2.5 were consistently associated with signi cantly different effects on pneumonia outpatient visits by gender and season groups. Similar differential effects can be observed for bronchiolitis associated with increases in PM 10 and PM c by different season strata, but not for PM 2.5 . However, the differential effects across strata were much less signi cant for asthma outpatient visits: it was only signi cantly different between warm and cold seasons.   In view of the limitation that the calculation of ER largely depends on the statistical distribution of the exposures, we further examined the potential proportion declination that would occur if exposure to particulate matter of different sizes were reduced to the WHO recommended levels (25 µg/m 3 for PM 2.5 , 25 µg/m 3 for PM c , and 50 µg/m 3 for PM 10 ). In contrast to the result that PM c was associated with the highest ER, our counterfactual analysis suggested that reducing PM 2.5 to the WHO reference was associated with the largest potential decline in ALRI outpatient visits, followed closely by the reduction of PM 10 ; while reducing PM c to the WHO reference is associated with the lowest potential decline in ALRI outpatient visits, which is likely explained by the fact that the mean level of PM c (21.0 µg/m 3 ) in our sample is lower than that of WHO reference level (25 µg/m 3 ).

Results
Our counterfactual analysis results have much more practical public health meaning than those of ER. The implication that reducing the level of PM 2.5 may be associated with the largest decline in ALRI outpatient visits is consistent with previous reports about the toxicity of smaller-sized particulate matter on lower respiratory infection hospitalizations (10)(11)(12)(13)(14). For example, Wang et al. speci cally focused on the association between particulate matter of different sizes and childhood pneumonia, and they reported a graded impact of particulate matter of different sizes on childhood pneumonia (PM 1 > PM 2.5 > PM 10 ).
Smaller-sized particulate matter is more likely to enter lower and deeper lobes of lung and cause more severe consequences of health.
Although the air quality has been substantially improved attributable to the effort of air quality management in China over the past decade(31, 32), the average level of particulate matter (especially PM 2.5 and PM 10 ), are still above the WHO recommended level. Northern China cities with high population density can experience anomalously high levels of air pollution during the winter(33). Our results highlight the importance of focusing on smaller-sized particulate matter due to its harmful effect on ALRI outpatient visits.
This study should be interpreted in view of several limitations. First, similar to previous time-series studies on air pollution associated health outcomes, we used daily aggregated data to evaluate the short-term effect of particulate matter on health outcomes, but this aggregated nature of data could be subject to ecological bias. Second, we used xed-site measurement of particulate matter instead of personal exposure, which could lead to inaccurate exposure measurement. Third, we included relatively small number of asthma outpatient visits, which led to unstable point estimates and CIs. Fourth, since we used secondary data collected from the hospital administrative database, environmental and behavior variables that could serve as confounders were not available to us. Lastly, COVID-19 has greatly reshaped the behavior of patient inpatient and outpatient visits, but we were not able to investigate the potential effect of the COVID-19 pandemic on ALRI outpatient visits since the data in this period is not available (34, 35).
Nonetheless, this study has several strengths. First, this is the rst study that investigates the association between particulate matter of different sizes and subtypes of ALRI outpatient visits, while previous studies either reported the exposure of PM 2.5 or hospitalization as the health outcome. Second, we used counterfactual analyses to estimate the potential percent reduction in ALRI outpatient visits compared to the WHO recommended levels. The results of counterfactual analyses have more substantial public health signi cance compared to ER,OR, and any other estimates associated with xed amount of increase in particulate matter (such as 10 µg/m 3 increase in PM 2.5 ).

Conclusions
In summary, this study suggests a larger potential percent of reduction in ALRI outpatient visits if PM 2.5 could be as low as the level recommended by the WHO. The association between particulate matter and pneumonia outpatient visits was stronger among male patients and in cold seasons. The results highlight the need for a consolidated effort to reduce the particulate matter pollution of smaller sizes and consequently improve the health outcomes of residents in China.  Table 2. Excess risk and 95% con dence intervals of pneumonia, bronchiolitis, and asthma for each 10 μg/m 3 increase in PM 2.5 , PM c , PM 10 using single-and two-pollutants models. The bold type represents the statistically signi cant differences (p < 0.05).
Warm season: April to September; cold season: October to March.  Figure 1 Geographical distribution of the sample hospitals and air monitoring stations. Note: The designations employed and the presentation of the material on this map do not imply the expression of any opinion whatsoever on the part of Research Square concerning the legal status of any country, territory, city or area o bbnhjr of its authorities, or concerning the delimitation of its frontiers or boundaries. This map has been provided by the authors. Correlation plot of the air pollutants and meteorological variables. The white cells indicate insigni cance correlation coe cient (NO2 and O3).