The association between particulate air pollution and ...

STOTEN-20624; No of Pages 7 Science of the Total Environment xxx (2016) xxx–xxx

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The association between particulate air pollution and respiratory admissions among young children in Hanoi, Vietnam Ly M.T. Luong a,b,c,⁎, Dung Phung d, Peter D. Sly b, Lidia Morawska e, Phong K. Thai e,⁎⁎ a

School of Medicine, The University of Queensland, Australia Children's Health and Environment Program, The University of Queensland, Australia Faculty of Environmental Sciences, VNU University of Science, Vietnam d Centre for Environment and Population Health, Griffith University, Australia e International Laboratory for Air Quality & Health, Queensland University of Technology, Australia b c

H I G H L I G H T S

G R A P H I C A L

A B S T R A C T

• First study on human health impact of air pollution in the north of Vietnam • Elevated levels of PM10, PM2.5 or PM1 were associated with respiratory admissions. • The smaller PM could have stronger impact on children respiratory admission. • Urgent intervention measures are needed to control air pollution in Vietnam.

a r t i c l e

i n f o

Article history: Received 15 June 2016 Received in revised form 2 August 2016 Accepted 3 August 2016 Available online xxxx D. Barcelo Keywords: Particulate air pollution Airborne particles Respiratory admission Children Hanoi Vietnam

a b s t r a c t While the effects of ambient air pollution on health have been studied extensively in many developed countries, few studies have been conducted in Vietnam, where the population is exposed to high levels of airborne particulate matter. The aim of our study was to examine the short-term effects of PM10, PM2.5, and PM1 on respiratory admissions among young children in Hanoi. Data on daily admissions from the Vietnam National Hospital of Paediatrics and daily records of PM10, PM2.5, PM1 and other confounding factors as NO2, SO2, CO, O3 and temperature were collected from September 2010 to September 2011. A time-stratified case-crossover design with individual lag model was applied to evaluate the associations between particulate air pollution and respiratory admissions. Significant effects on daily hospital admissions for respiratory disease were found for PM10, PM2.5 and PM1. An increase in 10 μg/m3 of PM10, PM2.5 or PM1 was associated with an increase in risk of admission of 1.4%, 2.2% or 2.5% on the same day of exposure, respectively. No significant difference between the effects on males and females was found in the study. The study demonstrated that infants and young children in Hanoi are at increased risk of respiratory admissions due to the high level of airborne particles in the city's ambient air. © 2016 Elsevier B.V. All rights reserved.

1. Introduction ⁎ Correspondence to: L.M.T. Luong, The University of Queensland, Australia. ⁎⁎ Corresponding author at: Queensland University of Technology, Australia. E-mail addresses: [email protected] (L.M.T. Luong), [email protected] (P.K. Thai).

Over the past decades, numerous epidemiologic studies have reported increases in human mortality and morbidity associated with exposure to ambient air pollution. Among air pollutants, atmospheric

http://dx.doi.org/10.1016/j.scitotenv.2016.08.012 0048-9697/© 2016 Elsevier B.V. All rights reserved.

Please cite this article as: Luong, L.M.T., et al., The association between particulate air pollution and respiratory admissions among young children in Hanoi, Vietnam, Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.08.012

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L.M.T. Luong et al. / Science of the Total Environment xxx (2016) xxx–xxx

particles have multiple effects on human health and have been studied extensively. Respirable particles or particulate matter (PM) of concern include “inhalable coarse particles” with an aerodynamic diameter b10 μm (PM10) and “fine particles” with an aerodynamic diameter b2.5 μm (PM2.5) and 1 μm (PM1), respectively (Kim et al., 2015). Particulate matter originates from a wide range of sources including both natural and anthropogenic, such as volcanoes, dust storms, forest fires, sea spray, solid-fuel combustion, industrial and agriculture activities, erosion of the pavement by road traffic and abrasion of brakes and tyres (Atkinson et al., 2010; Srimuruganandam and Shiva Nagendra, 2012). For example, vehicle exhaust or secondary aerosols are the main source of PM1 while particles created by abrasion of brakes are mostly categorized as PM2.5 and those generated by the road/tyre wear are found in the coarse range (PM10–2.5) (Manoli et al., 2002; Wåhlin et al., 2006). PM10, PM2.5 and PM1 have been reported to cause a variety of adverse health effects such as increasing the risk of acute respiratory illnesses requiring emergency department attendance or hospital admission and increasing mortality (Barnett et al., 2005; Franck et al., 2015; Hua et al., 2014; Kloog et al., 2014; Mehta et al., 2013; Peel et al., 2005; Phung et al., 2016; Qiu et al., 2014; Sousa et al., 2012; Stafoggia et al., 2013; Yang et al., 2015). For example, the study by Sousa et al. (2012) found that an increase in 10 μg/m3 of PM10 was associated with an increase of 2% in risk of respiratory admission. Similarly, Kloog et al. (2014) showed that exposure to each 10 μg/m3 increase in PM2.5 was associated with 2.2% increase in hospital admission for respiratory disease. In its air quality guideline, the WHO recommended the daily level of PM10 and PM2.5 should not exceed 50 and 25 μg/m3, respectively (WHO, 2005). Children are more vulnerable to the adverse health effects of air pollutants than adults because their defence mechanisms are still developing and they inhale a larger volume of air per body weight (Bearer, 1995; Salvi, 2007). There is an increasing amount of evidence about the role of atmospheric particles in the initiation, progression and exacerbation of childhood respiratory diseases such as pneumonia, acute bronchitis and asthma (Barnett et al., 2005; Magas et al., 2007; Tolbert et al., 2000). While the effects of ambient air pollution on health have been studied extensively in many countries, especially in developed countries, only two studies have been conducted in Vietnam to date and both were in Ho Chi Minh City. Mehta et al. (2013) assessed the effects of exposures to PM10 and other pollutants such as NO2, SO2 and O3 on hospitalization for Acute Lower Respiratory Infections (ALRI) among children aged b5 years. In that study, PM10 was associated with increased admission for ALRI in the dry season but its effects could not be distinguished from that of NO2 due to their high correlation. The other study found that the risk of respiratory admission (for all ages) increased by 0.7% for each 10 μg/m3 increase in PM10 (Phung et al., 2016). The health effects of PM2.5 and PM1 were not examined in those studies. Hanoi is the capital and second largest city in Vietnam. In recent decades, Hanoi has faced several environmental pollution issues including serious air pollution. As reported in the National State of Environment Report on Air quality (MONRE, 2014), during the period 2010–2013, the air quality on 40–60% of monitored days in Hanoi was unhealthy (Air quality index AQI = 101–200), with several days at very unhealthy (AQI = 201–300) or hazardous (AQI N 300) levels. During 2010 and 2011 there were 188 days (25%) in which the PM10 level was above the national standard of 150 μg/m3 and 425 days (60%) in which the PM2.5 level was above the national standard of 50 μg/m3. Air pollution in Hanoi is caused by a combination of factors including a high number of vehicles using limited road infrastructure; large-scale development activities including construction of roads, houses/buildings; and other industrial activities around Hanoi. In addition, the practices of stubble burning after crop harvest in the suburbs of Hanoi and of using kerosene and coal for cooking are also sources of particulate matters and other air pollutants contributing to the seriousness of air pollution and harm to public health (MONRE, 2014).

This study aimed to examine the short-term effects of the high level of particulate air pollution (PM10, PM2.5, and PM1) in Hanoi on the risk of respiratory admission among young children in this city. 2. Methods 2.1. Research location The study was conducted in Hanoi with a population of approximately seven million and a population density of 2031 residents per square kilometre (GSO, 2011). 2.2. Data collection 2.2.1. Hospital admissions Data on hospital admissions for respiratory disease (respiratory admissions) were obtained from the Vietnam National Hospital of Paediatrics, the largest paediatric hospital in Hanoi, from September 2010 to September 2011. Information available for each admission for respiratory disease comprised of date of birth, sex, date of admission, date of discharge, and ICD-10 code (J00-99). All admitted cases aged 28 days5 years old were residents of Hanoi. 2.2.2. Air pollutants and weather conditions Air quality data were collected from the Centre for Environmental Monitoring Portal (Vietnam Environment Administration). The data were recorded from a national automatic air quality monitoring station in Hanoi which is about 10 km away from the Vietnam National Hospital of Paediatrics. Air quality data included hourly average temperature (°C) and hourly average value of air pollutant concentrations (μg/m3) including PM10, PM2.5, PM1, nitrogen dioxide (NO2), sulphur dioxide (SO2), carbon monoxide (CO), and ozone (O3) from September 2010 to September 2011. Daily average temperature and daily average concentration of air pollutants were calculated from hourly values of temperature, PM10, PM2.5, PM1, NO2, SO2 and the maximum 8 h moving averages were generated for CO, O3. A 75% completeness criterion was applied to daily aggregate data calculation, meaning that if b18 h of temperature, PM10, PM2.5, PM1, NO2 and SO2 concentration data were available in a day then the daily average concentration for the day was assigned as ‘missing’ data. For CO and O3, if b6 h of concentration data were available then the 8hour daily maximum concentration for the day was assigned as ‘missing.’ Such “missing” values accounted for b 3% of all observations during the studied period (b1% for temperature, 1.5% for SO2 and ~3% for PM10, PM2.5, PM1 and NO2) of total 366 days. All missing values were replaced with the mean of one datum before the missing value and one datum after the missing value using the mean-before-after method (Norazian et al., 2008). 2.3. Data analysis A time-stratified case-crossover design was used in this study to examine the relationship between particulate air pollution and hospital admissions for respiratory diseases. In the case-crossover design, the exposure levels (cases) for a given day of the week when a health event occurred was compared to the exposure levels of the same days in nearby weeks (controls), to examine the differences in exposures which could be used to explain the differences in daily number of admissions. In our study, the cases and controls were matched by the same days of one week before and after to control any weekly patterns in hospital admissions and pollution levels. The length of the time strata was 21 days, so each case had two matching control days. A health event may occur after a person has been exposed to the air pollutants on the same day or on several subsequent days. To explore the delayed effect of exposure to air pollution on hospital admissions for respiratory diseases, we added individual lag variables of PM at

Please cite this article as: Luong, L.M.T., et al., The association between particulate air pollution and respiratory admissions among young children in Hanoi, Vietnam, Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.08.012

L.M.T. Luong et al. / Science of the Total Environment xxx (2016) xxx–xxx

one time into the model (individual lag models). The lag between exposure to PM10, PM2.5, PM1 and response was examined up to three days prior to the hospital admissions (0–3 days) as this is the most commonly period found to be significant in most previous studies (Cheng et al., 2015; Guo et al., 2009; Lippmann et al., 2000; Zhang et al., 2015). The effect of temperature on the same day as the outcome event was controlled as a confounding factor in all models (Braga et al., 2001). A spline function with four degree of freedom for temperature was used in the models to control for the non-linear effect of temperature on the hospital admissions. To control for the potential effects of other air pollutants (NO2, SO2, CO and O3) and temperature, multivariable models with the involvement of single variable of temperature and air pollutants were used in this study. The effect on hospitalization for respiratory disease associated with a 10 μg/m3 and an interquartile range (IQR) increase in PM10, PM2.5 and PM1 were estimated over 4 days. Odds ratios (ORs) and confidence intervals (CIs) were calculated for each pollutant (PM10, PM2.5 and PM1). Values of P b 0.05 were considered statistically significant. The “mkcco”, a statistical package of the Stata software version 11.0 (Stat Corporation, College Station, Texas, USA), was used to reshape from time-series format to individual matched case-control which was then used for case-crossover analysis to compare the exposure measurements between case and two controls (one week before and one week after). The use of “mkcco is described elsewhere (Armstrong et al., 2014; Carracedo-Martínez et al., 2010; Tobias, 2014). The “glm” package was used to examine the association between counts of respiratory admissions and the levels of air pollutants. z-Test was used to test for a statistical difference between two odds ratios. 3. Results From September 2010 to September 2011, there was a total of 8934 hospital admissions for respiratory diseases among children 0–5 years old in the Vietnam National Hospital of Paediatrics (Males: 5916; Females: 3018). The descriptive statistics for hospital admissions, air pollution data and weather condition are presented in Table 1. An average of 24 respiratory admissions per day was registered in this hospital over the study period. During the study period, the mean daily average concentration of PM10 was 108 μg/m3, which is about twice the annual concentration limit set in the Vietnam National Technical Regulation on Ambient Air Quality of 50 μg/m3 (MONRE, 2013), more than the European Air Quality Standard of 40 μg/m3 (EC, 2013) and N5 times higher than the WHO guideline of 20 μg/m3 (WHO, 2005). Similarly, the mean value of PM2.5 (67 μg/m3) was more than twice the Vietnam and European standards (25 μg/m3) and much higher than the USEPA standard (12 μg/m3) (USEPA, 2013) and the WHO guideline (10 μg/m3) (WHO, 2005). The corresponding value of PM1 was 54 μg/m3, for which there is no health guideline or standard yet. Table 1 Descriptive statistics of hospital admissions, air pollutants, and weather condition. Percentile 25th

Minimum

Maximum

Mean (SD)

30 19 11

8 4 1

48 37 19

24 (7) 16 (5) 8 (3)

132 85 72 55 18 4760 108 29

24 16 11 0.049 0.021 1266 13 9

333 208 177 110 63 10,033 254 34

108 (51) 67 (33) 54 (30) 45 (18) 12 (11) 4175 (1136) 84 (48) 24 (6)

50th

75th

Respiratory admissions Overall 19 Males 12 Females 6

24 15 8

Air pollutant (μg/m3) PM10 71 PM2.5 41 PM1 30 NO2 34 SO2 3 CO 3443 O3 49 Temperature (°C) 20

98 61 48 45 6 4012 75 25

3

For other pollutants, the mean value of NO2 was 45 μg/m3, which is slightly higher than the standard level of 40 μg/m3 set by Vietnam, .Europe and WHO. Some values of CO and O3 also exceeded the Vietnamese standard for 8 h-average concentrations. Only SO2 level was lower than the Vietnamese standard, which is similar to the WHO guideline value (50 μg/m3) (MONRE, 2013; WHO, 2005). Table 2 shows the Pearson correlation coefficients between air pollutants and temperature. PM10, PM2.5 and PM1 were moderately correlated with each other as well as with other pollutants (NO2, SO2, CO, O3) (P b 0.05). There was a certain degree of collinearity among the pollutants, especially between PM10 and PM2.5 (r = 0.915); PM10 and PM2.5 (r = 0.846); PM2.5 and PM1 (r = 0.984); PM2.5, PM1 and SO2 (r = 0.517 and 0.630, respectively); PM10, PM2.5, PM1 and O3 (r = 0.574, 0.530 and 0.519, respectively). Temperature was negatively correlated with PM10 PM2.5, PM1, NO2 and SO2 (P b 0.05). The ORs associated with an increase of 10 μg/m3 for PM10, PM2.5, and PM1 at different lags are shown in Table 3. The ORs associated with an increase of IQR for PM10, PM2.5, and PM1 at different lags are shown in Table S1. For the single variable models, the linear effect of PM10, PM2.5, and PM1 was evaluated by adjusting for the influence of temperature. The association between hospital admissions for respiratory disease and the level of particulate matter was statistically significant at 0–3 day lag in females and overall. In males, no statistically significant effect was found at lag 3 for PM10 and at lag 2–3 for PM2.5 and PM1. The strength of the effect of airborne particulate matter on risk of admission was found to be in inverse ratio to the particle size in both groups at every lag. Each 10 μg/m3 increase of PM10, PM2.5 and PM1 was associated with an increase in risk of hospital admission by 1.4% [95%CI (0.9–2.0)], 2.2% [95%CI (1.2–3.1)] and 2.5% [95%CI (1.4–3.5)], respectively. The risk of admissions decreased to 1.2%–0.7% in next lags for PM10, 1.6% -0.9% for PM2.5 and 1.9%–1.2% for PM1. When gender was considered separately, PM2.5 and PM1 had the strongest effects on males on the day of exposure while on females the maximal effect was 1 day after exposure. In males, for each 10 μg/m3 increase of PM2.5 and PM1 (at lag 0) the risk of hospitalization increased 2.0% [95%CI (0.9–3.2)] and 2.3% [95%CI (1.1–3.6)], respectively. In females, the greatest effect of PM2.5 and PM1 was estimated at lag 1 where the OR was 1.025 [95%CI (1.010–1.041)] and 1.031 [95%CI (1.012–1.049)], respectively. Although a higher risk of admissions for each 10 μg/m3 increase of airborne particulate matters in females than in males were shown in the Table 3, no significant different between them was found after using z-test (P b 0.05). Table 3 presented the odd ratios of respiratory admissions associated with a 10 μg/m3 increase of PM10, PM2.5 and PM1 after adjusting for other air pollutants and temperature. The risks were highest at lag 0 of exposure to airborne particulate matter and decreased at lag 1, 2 and 3. Statistically significant effects on respiratory admissions were found for PM10 at each lag after adjusting for NO2, SO2, CO and O3. The risk of admission was reduced slightly (0.1–0.2%) after adjusting for other air pollutants. The associations between PM2.5 and PM1 and risk of admission were no longer significant at some lags after adjusting for NO2, SO2, CO and O3 separately. No associations were found at lag 3 after adjusting for NO2 as well as at lag 2 and 3 after adjusting for O3. The effects of PM2.5 and PM1 were not changed after adjusting for CO but were weaker after adjusting for other air pollutants (NO2, SO2 and O3). Each 10 μg/m3 increase of PM2.5 associated with an increase of 2.2% [95%CI (1.3–3.2)] risk of admission after adjusting for CO but only 1.7% [95%CI (0.7–2.7)], 2.0% [95%CI (1.0–3.1)] and 1.9% [95%CI (0.9–3.0)] after adjusting for NO2, SO2 and O3, respectively. Similarly, each 10 μg/m3 increase of PM1 associated with an increase of 2.5% [95%CI (1.4–3.7)] risk of admission after adjusting for CO but only 1.9% [95%CI (0.8–3.1)], 2.3% [95%CI (1.1–3.5)] and 2.2% [95%CI (1.0–3.5)] after adjusting for NO2, SO2 and O3, respectively.

Please cite this article as: Luong, L.M.T., et al., The association between particulate air pollution and respiratory admissions among young children in Hanoi, Vietnam, Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.08.012

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L.M.T. Luong et al. / Science of the Total Environment xxx (2016) xxx–xxx

Table 2 Pearson correlation matrix of air pollutants and temperature.

PM10 PM2.5 PM1 NO2 SO2 CO O3 Temperature

PM10

PM2.5

PM1

NO2

1 0.915⁎ 0.846⁎ 0.405⁎ 0.457⁎ 0.227⁎ 0.574⁎

1 0.984⁎ 0.414⁎ 0.571⁎ 0.295⁎ 0.530⁎

1 0.403⁎ 0.630⁎ 0.288⁎ 0.519⁎

1 0.593⁎ 0.358⁎ 0.233⁎

−0.206⁎

−0.416⁎

−0.507⁎

−0.265⁎

SO2

CO

1 0.246⁎ 0.277⁎ −0.640⁎

1 0.174⁎ 0.08

O3

Temperature

1 0.0003

1

⁎ P b 0.05.

After adjusting for NO2, SO 2, CO and O3 separately, statistically significant associations between PM10, PM2.5, PM1 and hospitalization for respiratory diseases remained in females at all lags except for NO 2 (at lag 1–2 only) while in males, significant associations

were mostly seen at lag 0 only. No significant different between risk of admissions for each 10 μg/m3 increase of airborne particulate matters in males and females was found after using z-test (P b 0.05).

Table 3 Association between respiratory admissions and particular air pollutants at different lags. Lag (days)

OR (95% CI) PM10 Overall

PM2.5

PM1

Males

Females

Overall

Males

Females

Overall

Males

Females

Adjusted for temperature 0 1.014⁎ (1.009–1.020) 1 1.012⁎

1.014⁎ (1.010–1.021) 1.011⁎

1.014⁎ (1.005–1.023) 1.013⁎

1.022⁎ (1.012–1.031) 1.016⁎

1.020⁎ (1.009–1.032) 1.012⁎

1.023⁎ (1.008–1.040) 1.025⁎

1.025⁎ (1.014–1.035) 1.019⁎

1.023* (1.011–1.036) 1.013⁎

1.028⁎ (1.010–1.047) 1.031⁎

(1.007–1.017) 1.009⁎ (1.004–1.014) 1.007⁎ (1.002–1.012)

(1.005–1.018) 1.008⁎ (1.002–1.014) 1.006 (0.999–1.012)

(1.004–1.022) 1.012⁎ (1.003–1.021) 1.009⁎ (1.001–1.018)

(1.007–1.025) 1.012⁎ (1.003–1.021) 1.009⁎ (1.001–1.018)

(1.001–1.023) 1.006 (0.995–1.017) 1.006 (0.995–1.017)

(1.010–1.041) 1.025⁎ (1.009–1.041) 1.016⁎ (1.001–1.032)

(1.008–1.029) 1.014⁎ (1.004–1.025) 1.012⁎ (1.002–1.022)

(1.001–1.026) 1.007 (0.994–1.020) 1.008 (0.995–1.020)

(1.012–1.049) 1.030⁎ (1.012–1.049) 1.020⁎ (1.002–1.039)

Adjusted for NO2 & temperature 0 1.012⁎ 1.014⁎ (1.006–1.018) (1.007–1.022) 1 1.010⁎ 1.010⁎

1.008 (0.997–1.019) 1.010⁎

1.017⁎ (1.007–1.027) 1.013⁎

1.018⁎ (1.006–1.031) 1.010 (0.998–1.021) 1.005 (0.994–1.016) 1.004 (0.994–1.015)

1.014 (0.997–1.032) 1.021⁎

1.019⁎ (1.008–1.031) 1.015⁎

1.018 (0.998–1.039) 1.025⁎

(1.005–1.037) 1.023⁎ (1.00–1.039) 1.013 (0.998–1.029)

(1.005–1.026) 1.012⁎ (1.002–1.023) 1.010 (0.999–1.020)

1.020⁎ (1.006–1.034) 1.010 (0.998–1.023) 1.005 (0.993–1.0180) 1.006 (0.994–1.019)

1.020⁎ (1.001–1.039) 1.023⁎ (1.007–1.039) 1.024⁎

1.023⁎ (1.011–1.035) 1.016⁎ (1.006–1.027) 1.013⁎

(1.008–1.040) 1.014 (0.999–1.030)

(1.003–1.023) 1.010⁎

2 3

(1.004–1.015) 1.008⁎

(1.003–1.016) 1.007⁎

(1.001–1.019) 1.011⁎

(1.003–1.036) 1.006⁎ (1.001–1.011)

(1.001–1.014) 1.005 (0.999–1.011)

(1.002–1.020) 1.008 (0.999–1.016)

(1.004–1.022) 1.010⁎ (1.002–1.020) 1.007 (0.999–1.016)

Adjusted for SO2 & temperature 0 1.015⁎ 1.016⁎ (1.008–1.022) (1.008–1.024) 1 1.011⁎ 1.010⁎ (1.005–1.016) (1.004–1.017) 2 1.009⁎ 1.007⁎

1.012⁎ (1.001–1.024) 1.011⁎ (1.002–1.021) 1.011⁎

1.020⁎ (1.010–1.031) 1.014⁎ (1.005–1.023) 1.011⁎

(1.002–1.020) 1.008 (0.999–1.017)

(1.002–1.020) 1.008 (0.999–1.017)

1.020⁎ (1.007–1.033) 1.010 (0.999–1.021) 1.005 (0.994–1.016) 1.005 (0.994–1.016)

1.015⁎ (1.005–1.025) 1.013⁎

1.022⁎ (1.013–1.032) 1.016⁎

1.021⁎ (1.009–1.033) 1.011⁎

1.025⁎ (1.008–1.042) 1.025⁎

1.025⁎ (1.014–1.037) 1.018⁎

(1.005–1.023) 1.011⁎ (1.002–1.020) 1.009⁎

(1.007–1.025) 1.012⁎ (1.003–1.021) 1.009⁎

(1.010–1.041) 1.025⁎ (1.009–1.041) 1.016⁎

(1.008–1.029) 1.014⁎ (1.004–1.025) 1.012⁎

(1.001–1.018)

(1.001–1.018)

(1.001–1.022) 1.006 (0.995–1.017) 1.006 (0.995–1.017)

(1.001–1.031)

(1.002–1.022)

1.015⁎ (1.004–1.026) 1.013⁎

1.019⁎ (1.009–1.030) 1.013⁎

1.025⁎ (1.006–1.043) 1.025⁎

1.022⁎ (1.010–1.035) 1.014⁎

(1.003–1.023) 1.011⁎

(1.003–1.022) 1.009 (0.999–1.018) 1.007 (0.998–1.016)

1.016⁎ (1.003–1.029) 1.007 (0.995–1.018) 1.001 (0.990–1.013) 1.003 (0.992–1.014)

(1.008–1.042) 1.024⁎ (1.008–1.041) 1.015 (0.999–1.030)

(1.003–1.026) 1.010 (0.999–1.021) 1.009 (0.999–1.020)

2 3

3

(1.003–1.014) 1.006⁎ (1.001–1.011)

(1.001–1.014) 1.005 (0.999–1.011)

Adjusted for CO & temperature 0 1.015⁎ 1.015⁎ (1.009–1.021) (1.008–1.022) 1 1.012⁎ 1.011⁎ (1.006–1.017) (1.005–1.018) 2 1.009⁎ 1.007⁎ (1.003–1.014) (1.001–1.014) 3 1.007⁎ 1.006 (1.002–1.012) (0.999–1.012) Adjusted for O3 & temperature 0 1.014⁎ 1.013⁎ (1.007–1.020) (1.005–1.021) 1 1.010⁎ 1.009⁎ (1.004–1.016) (1.002–1.016) 2 1.007⁎ 1.006 (1.002–1.013) (0.999–1.012) 3 1.006⁎ 1.004 (1.001–1.011) (0.998–1.010)

(1.002–1.021) 1.008 (0.999–1.017)

(1.001–1.021)

(1.007–1.044) 1.027⁎ (1.009–1.046) 1.017 (0.999–1.034)

1.022⁎ (1.007–1.037) 1.011 (0.998–1.024) 1.006 (0.993–1.019) 1.007 (0.994–1.019)

1.024⁎ (1.003–1.046) 1.028⁎ (1.009–1.047) 1.028⁎

1.023⁎ (1.010–1.037) 1.012 (0.999–1.025) 1.007 (0.994–1.020) 1.008 (0.995–1.020)

1.030⁎ (1.010–1.049) 1.031⁎

1.018⁎ (1.002–1.033) 1.007 (0.993–1.020) 1.002 (0.989–1.015) 1.005 (0.992–1.017)

1.031⁎ (1.009–1.053) 1.031⁎

(1.010–1.047) 1.018⁎ (1.001–1.036)

(1.012–1.049) 1.030⁎ (1.012–1.049) 1.020⁎ (1.002–1.038)

(1.011–1.051) 1.029⁎ (1.010–1.048) 1.018⁎ (1.001–1.036)

Lag 0 d: exposure on the day of hospital admission; Lag 1 d: exposure on the day before hospital admission; lag 2 d: exposure on the 2 days before hospital admission; and lag 3 d: exposure on the 3 days before hospital admission. ⁎ P b 0.05.

Please cite this article as: Luong, L.M.T., et al., The association between particulate air pollution and respiratory admissions among young children in Hanoi, Vietnam, Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.08.012

L.M.T. Luong et al. / Science of the Total Environment xxx (2016) xxx–xxx

4. Discussion The present study utilized the case-crossover design which was introduced by Maclure (1991) and has been applied in many recent studies on air pollution and health (Barnett et al., 2005; Dong et al., 2013; Guo et al., 2009; Sheffield et al., 2015). This case-crossover design helps control confounding factors related to both individual characteristics (e.g. age and sex) and secular trends and seasonal effects in examining a short-term relationship between exposure and health outcomes (Bateson and Schwartz, 1999; Lee and Schwartz, 1999; Maclure, 1991; Navidi, 1998). By matching the cases and controls on the same days of weeks, confounding caused by any weekly patterns can be avoided (Guo et al., 2010; Lee and Schwartz, 1999; Navidi, 1998; Neas et al., 1999). This approach is suitable to use in this study to research the transient effects on the risk of acute health events. To the best of our knowledge, this is the first study to investigate the short-term effects of air pollution on children's health in Hanoi. In this study, we found that there was a positive relationship between the level of airborne particles and daily hospital admission for respiratory diseases among children aged younger than 5 year-olds. 4.1. PM10 Many studies have investigated the effect of PM10 on respiratory morbidity and found a positive association between PM10 levels and hospitalization for respiratory diseases. However, the effect size varies between studies. Rodopoulou et al. (2014) and Tramuto et al. (2011) found strong effects of PM10 for risk of respiratory admissions with estimated increases of 3.2% and 3.9% for each 10 μg/m3 increase in concentration. Other authors reported lower effect sizes for PM10 with an excess risk of hospitalization for respiratory disease about 1–2% per 10 μg/m3 increase in PM10 concentration (Sousa et al., 2012; Yi et al., 2010; Zhang et al., 2015). The significant effect of PM10 observed in our study (OR = 1.014) is similar to the latter studies. Some studies have reported no increased risk with PM10 (Fusco et al., 2001; Slaughter et al., 2005). The differences may be related to the range of PM10 in particular studies or to differing sources producing particles with different toxicities. In two other studies in Ho Chi Minh city, the largest city in Vietnam, a 10 μg/m3 increase in PM10 was associated with an increase of 0.7% [95%CI (0.2–1.3)] in risk of respiratory admissions for all age groups (Phung et al., 2016) and 1.25% [95%CI (0.55–3.09)] in risk of ALRI admissions for children under 5 years old in dry season (Mehta et al., 2013). The higher risk of respiratory admissions observed in our study than those in other studies conducted in Ho Chi Minh city can be explained by a higher average concentration of PM10 in Hanoi (108 μg/m3) than that in Ho Chi Minh city (74 μg/m3). 4.2. PM2.5 Evidence on the health risks associated with short-term exposure to fine particles (PM2.5) has been reported in recent studies (Chardon et al., 2007; Dominici et al., 2006; Grigg, 2011; Kloog et al., 2014; Liu et al., 2016; Stafoggia et al., 2013). However, study of the relationship between fine particulate and children's health is limited. Most previous studies addressed specific endpoints such as bronchiolitis, asthma or pneumonia and found adverse effects of PM2.5 on children's respiratory morbidity (Gass et al., 2015; Hua et al., 2014; Ilabaca et al., 1999; Iskandar et al., 2011; Karr et al., 2009). Our study is among few studies focusing on the relationship between PM2.5 and respiratory admissions in children aged b5 years. Each increase of 10 μg/m3 of PM2.5 in the present study led to an increase of 2.2% [95%CI (1.2–3.1)] in respiratory admissions. The significant association between PM2.5 and respiratory admissions found here is in agreement with previous studies in children (Barnett et al., 2005; Ostro et al., 2009; Schwartz and Neas, 2000) although the increased

5

risks of admission were different among studies. Barnett et al. (2005) found in their study in Australia and New Zealand that for an interquartile increase in PM2.5 (equals 3.8 μg/m3), there was 1.7% (95%CI = 0.7– 2.7) increase in respiratory admissions among children aged from 1 to 4 years old. Ostro et al. (2009) observed an interquartile range increment of PM2.5 (15 μg/m3) associated with an excess risk of 4.1% [95%CI (1.8–6.4)] of respiratory admissions in children b 19 years old (children b5 years old generating about 75% of the total admissions). Schwartz and Neas (2000) reported that lower respiratory symptoms in school children (grade 2–5) were associated with interquartile range increment of PM2.5 (15 μm/m3) [OR = 1.29, 95%CI (1.06–1.57)].

4.3. PM1 The results of the present study suggest that exposure to PM1 poses a greater risk of hospitalization for respiratory disease. This finding is important because although finer PM which includes PM1 and ultrafine particles (particles with diameters smaller than 0.1 μm, UFPs) might be more toxic than the coarser PM, their effects on the respiratory system, especially on children's, have not been well-described. A recent review by Heinzerling et al. (2016) identified 12 articles relevant to respiratory health effects of UFPs in children and only six of them investigated the relationship between UFPs and health care utilization related to respiratory health (Andersen et al., 2008a; Andersen et al., 2008b; Díaz-Robles et al., 2014; Evans et al., 2014; Halonen et al., 2008; Iskandar et al., 2011). The significant effect of PM1 found in this study was in agreement with the finding in Díaz-Robles et al. (2014) who reported that an interquartile increase in UFPs (4.73 μg/m3) was associated with a relative risk of 5.3% [95%CI (0.3–10.7)] of respiratory outpatient visits for those aged b5 years. In addition, Halonen et al. (2008) also reported an increase from 4.5–6% in asthma-related emergency department visits in children b15 years old per interquartile increase in UFPs. No statistically significant interaction was detected in three studies conducted in Denmark (Andersen et al., 2008a; Andersen et al., 2008b; Iskandar et al., 2011), however, these studies were limited by a large number of missing data on UFPs. In spite of inconsistent reports in the literature, PM1 could be expected to have greater adverse effects on health due to the ability to penetrate deeply into the respiratory track. Kesavachandran et al. (2013) found that an increase in PM1 concentrations led to a decrease in lung function (FEV1) demonstrating the biological plausibility of our results. Several plausible reasons may explain the difference in the relationships between particles of different sizes and respiratory admissions that we reported here. Firstly, the fact that PM2.5 and PM1 may be more harmful than PM10 due to their penetration capability may explain, in part, why we found a greater effect of PM2.5 and PM1 on respiratory hospital admissions than PM10. Some earlier studies have demonstrated the entry of particles into the lungs and their retention capacity within the airway walls (Churg et al., 2003; Churg et al., 1999; Dai et al., 2002; Kesavachandran et al., 2013; Kreyling et al., 2006; Pinkerton et al., 2000). According to Kesavachandran et al. (2013), particles ≤2.5 μm can reach bronchioles and may penetrate up to the primary bronchi, secondary bronchi, terminal bronchi, and alveoli. This penetration can trigger mucous secretions in airways leading to reduced air flow. The airflow obstruction due to the deposition of high concentrations of PM2.5 and PM1 in the airways for a long period has been reported to induce a fibrotic response and airway remodelling (Churg et al., 2003). Secondly, the difference in the size of particles may allow the lighter particles (PM2.5, PM1) to travel further away from the emission sources and stay in the air for a long time (Cheung et al., 2011; Srimuruganandam and Shiva Nagendra, 2012). Cheung et al. (2011) reported that the suspension lifetimes of PM10 are minutes to hours while the corresponding values of PM2.5 are days to weeks. Therefore, people might be exposed to fine particles more than coarse particles.

Please cite this article as: Luong, L.M.T., et al., The association between particulate air pollution and respiratory admissions among young children in Hanoi, Vietnam, Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.08.012

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L.M.T. Luong et al. / Science of the Total Environment xxx (2016) xxx–xxx

4.4. Impact of other confounding factors

Appendix A. Supplementary data

In this study, when gender was considered separately, no significant difference was found for the effects of airborne particulate matter on two genders which is consistent with the findings in studies of particulate matter and children asthma admissions conducted by Hua et al. (2014) and Iskandar et al. (2011). To control effects of climatic factors (temperature) and other pollutants (NO2, SO2, CO and O3), these potential confounding factors were adjusted in the multivariable models. The magnitude of relationships between PM10, PM2.5 and PM1 and respiratory admissions were essentially unchanged after adjustment (Table 3, Figs. S1–2) but the statistical significance of the association between airborne particles and risk of respiratory admissions in females was lost after adjusting for NO2. The study by Mehta et al. (2013) found similar results to our study when they also found non-significant associations after adjusting NO2. As NO2 was found to have a strong positive effect on children mortality rate due to respiratory illnesses in previous studies (Lu et al., 2014; O'Connor et al., 2008; Saldiva et al., 1994; Weinmayr et al., 2010) in addition to a high level of NO2 in this study, NO2 maybe an important factor that affect children respiratory admissions in Hanoi and needs to be further investigated.

Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.scitotenv.2016.08.012.

4.5. Limitations We acknowledge that there were some limitations in our study. Firstly, we used hospital admission for respiratory diseases from only one major hospital; therefore we may have missed the minor cases which were admitted at the lower level hospitals in Hanoi. However, this hospital is the sole paediatric hospital in Hanoi and is likely to be representative of childhood hospital admissions in the city. Secondly, by using the case-control design of crossover, individual characteristics of case and control were automatically matched but matching case and control for other factors which can be changed in a short period of time (e.g. using air conditional, working outdoor & indoor) was beyond the scope of this study due to unavailability of data. Next, the ambient monitoring data used in this study were collected from the only functioning monitoring station in Hanoi. The air quality data from one monitoring station may not be representative of the whole city of Hanoi and it is possible that this may have introduced bias to the results of our analyses. In this study, the authors controlled for temperature, day of the week (including holiday) but not humidity and influenza outbreaks due to unavailability of data. Finally, the short time frame of this study may not allow consideration of all confounding factors to the impact of air pollution to the respiratory admissions. 5. Conclusion This study confirmed that the daily hospital admission for respiratory diseases among children b5 years old in Hanoi were positively associated with the level of airborne particulate matter measured in the city. During the study period, each 10 μg/m3 increase in PM10, PM2.5 and PM1 heightened the risk of hospital admission significantly at 1.4%, 2.2% and 2.5%, respectively, at lag 0 and lower at other lags. The strong impacts of airborne particulate matter on children respiratory morbidity mean that urgent intervention measures are needed to control air pollution to ensure a better health protection in Vietnam. Acknowledgments LL is funded by an Australia Award Scholarship. DP is funded by a Griffith Postdoctoral Research Fellowship. PT is funded by a QUT VC Research Fellowship. PDS is funded by a NHMRC Senior Principal Research Fellowship.

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Please cite this article as: Luong, L.M.T., et al., The association between particulate air pollution and respiratory admissions among young children in Hanoi, Vietnam, Sci Total Environ (2016), http://dx.doi.org/10.1016/j.scitotenv.2016.08.012