Impact of ambient air pollution and wheeze-associated disorders in children in Southeast Asia: a systematic review and meta-analysis

Ly M.T. Luong 1 , 2 , 3 , Peter D. Sly 1 , 2 , Phong K. Thai 4  and Dung Phung 5
  • 1 Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
  • 2 Children’s Health and Environment Program, The University of Queensland, Brisbane, Queensland, Australia
  • 3 Faculty of Environmental Sciences, VNU University of Science, Hanoi, Vietnam
  • 4 Queensland Alliance for Environmental Health Science (QAEHS), The University of Queensland, Brisbane, Queensland, Australia
  • 5 Centre for Environment and Population Health, School of Medicine, Griffith University, Gold Coast, Queensland, Australia
Ly M.T. Luong
  • Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
  • Children’s Health and Environment Program, The University of Queensland, Brisbane, Queensland, Australia
  • Faculty of Environmental Sciences, VNU University of Science, Hanoi, Vietnam
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, Peter D. Sly
  • Faculty of Medicine, The University of Queensland, Brisbane, Queensland, Australia
  • Children’s Health and Environment Program, The University of Queensland, Brisbane, Queensland, Australia
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, Phong K. Thai
  • Queensland Alliance for Environmental Health Science (QAEHS), The University of Queensland, Brisbane, Queensland, Australia
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and Dung Phung
  • Corresponding author
  • Centre for Environment and Population Health, School of Medicine, Griffith University, Gold Coast, Queensland, Australia
  • Email
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Abstract

Several systematic reviews have been conducted so far to examine the effect of air pollution on respiratory diseases, but there has not been a corresponding meta-analysis to estimate the effect sizes for wheeze-associated diseases/disorders, which is one of the leading causes of emergency department visits and hospitalizations for children worldwide. The aim of this review is to systematically evaluate the relationship between air pollution and risk of wheeze-associated disorders in children in Southeast Asia. We searched the relevant computerized databases (PubMed, EMBASE, Web of Science, Scopus and Cochrane library) for indexed publications up to July 2018. Finally, eight studies were qualified for performing a random-effect meta-analysis to compute the pooled effect sizes. The results show that each increase of 10 μg/m3 in concentrations of PM2.5, PM1 was associated with 1–2% increase in risk of wheeze-associated disorders. Positive associations were found for PM10, SO2, NO2, NOx but no association was found for CO and O3. We confirmed the strong effect of fine particulate matters on respiratory health and recommend an updated meta-analysis should be done when more studies are available.

Introduction

Most countries in Southeast Asia are experiencing high levels of air pollution, especially in urban areas where the annual mean levels often exceed World Health Organization (WHO) limits 5–10 fold (1). Air pollution levels are at WHO target 3 (PM2.5 25–35 μg/m3; PM10 50–70 μg/m3) in Vietnam, the Philippines and Myanmar and are at target 2 (PM2.5 15–25 μg/m3; PM10 30–50 μg/m3) in Thailand and Indonesia compared to the WHO’s air quality guideline for annual PM10 and PM2.5 are 20 and 10 μg/m3, respectively (2), (3), (4). In Vietnam, the air quality index levels was at 101–200 (unhealthy level for sensitive groups like children and elderly adults) on 40% and 60% of total monitoring days in Hanoi in 2013 and 2014 (5). Six cities in Myanmar have PM10 levels from 120 to 140 μg/m3, PM2.5 from 44 to 78 μg/m3, ranked in top 3% of the most polluted cities in the world (6). Similarly, high concentration of particles and SO2 pose serious environmental problems in Bangkok (7). In Southeast Asia, air pollution caused by forest fires and agricultural burning is also of concern, especially for Indonesia, Malaysia and Singapore (8), (9).

With such high level of air pollution, the effects of ambient air pollutants on the health of Southeast Asian populations are likely to be significant. There is a large body of evidence in the literature on the adverse effects of air pollutants on the respiratory system, especially in children and infants. These health effects vary from increased respiratory mortality (10), (11), (12) to respiratory morbidity including increased risk of inflammation such as asthma (13), (14), (15), allergic symptoms (16), acute respiratory (17), (18), (19), (20), pneumonia and acute bronchitis (20), (21), (22), (23), or decreased lung function (24), (25).

Ambient air pollution has been associated with increased respiratory morbidity and mortality, however, few studies have been carried out in Southeast Asia, where people are exposed to much higher levels of air pollution and might experience the greatest burden of diseases due to wheeze-associated diseases/disorders (26). Several systematic reviews have been conducted to examine the effect of air pollution on respiratory diseases in children, but there has not been a corresponding meta-analysis to estimate the effect sizes for wheeze-associated diseases/disorders in Southeast Asian countries (27), (28). Wheezing is a high-pitched whistling sound made while breathing out and may be a symptom of a serious lung disease such as acute lower respiratory infection, asthma or other wheeze-associated disorders. Taken together, wheeze-associated diseases/disorders are the largest single cause of mortality and one of the leading causes of emergency department visits and hospitalizations for children worldwide (29), (30), (31).

The aim of this review is to systematically evaluate the impact of ambient air pollution on and the risk of wheeze-associated disorders in children (0–18 years old) in Southeast Asia.

Material and methods

Literature search strategy

This systematic review followed the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and the recommendations of the meta-analysis of Observational Studies in Epidemiology (MOOSE) group (32), (33). A literature search of relevant computerized databases (PubMed, EMBASE, Web of Science, Scopus and Cochrane library) was performed on 24 July 2018. The search strategy comprised the relevant keywords of exposure (outdoor air pollutants), health outcome (wheeze-associated disorders), outcome measurements (health outcome) and population (Southeast Asia nations) (please see the search queries with relevant keywords in Supplementary material Table 1).

Selection criteria

The flow of studies through the review process is shown in Figure 1. Studies were included in this systematic review if they met the following criteria:

Figure 1:
Figure 1:

Flow diagram for inclusion and exclusion of studies.

Citation: Reviews on Environmental Health 34, 2; 10.1515/reveh-2018-0079

  1. Analytic studies quantitatively examined the association between air pollutants and health outcome of wheeze-associated disorders
  2. The subjects of the study are children up to 18 years old of age
  3. Provided quantitative results for the effects based on one of the following calculations: regression coefficient, percentage change, excess rate (ER), risk ratio (RR) and odds ratio (OR), standard error (SE) or and confidence interval (CI) of the effect size.
  4. Published in full-text in English.

Each study had to contain an outcome measure related to wheeze-associated disorders such as hospital admission, emergency room visit, prevalence, incidence, mortality for any symptom of wheeze-associated disorders. Exposure to air pollution was based on at least one air pollutant (PM10, PM2.5, PM1, SO2, NO2, CO, O3) (see Supplementary material Table 1).

Studies were excluded if they were:

  1. from the gray literature (unpublished or published in non-commercial form)
  2. not original research papers (e.g. commentary, communication, review)
  3. not studies in Southeast Asian countries
  4. not related to ambient air pollution (e.g. indoor, workplace, experiment, projected studies).

The selection of studies was carried out using a tier approach with sequential steps (Figure 1).

The initial search, after removing duplicates, was reviewed by the first reviewer (L.L.) on the basis of the title and abstract to check the inclusion and exclusion criteria. In order to avoid bias, eligible studies were assessed independently via Covidence (an online software for primary screening and data extraction developed by the Cochrane Community) by the first and second reviewers (L.L. and P.T.) to examine the full text of articles against the inclusion criteria. All selected studies and unsolved conflicts between the two reviewers were then discussed with the third reviewer (D.P.) for final decisions before extracting data by both L.L. and P.T.

Quality assessment

To evaluate the quality of the selected studies, a criteria for quality assessment was modified from the criteria recommended by the BioMed Central for study assessment (34), (35) (see Supplementary material Table 2). The criteria comprise: (i) Sources of the information; (ii) Study design; (iii) Study results; and (iv) Study discussion.

A score scheme of 20 items corresponding to four assessment criteria was developed to evaluate each study. The quality score of each study could range from 1 to 30 (see Supplementary material Table 2).

Data extraction

The data extracted from each included study comprised: Citation information (first author last name and publication year, location in which the study was performed); Study setting (study design and time-span, age of population); Exposure (pollutant, mean concentration, unit of increment, and controlled variables); Outcome (health outcome/diagnosis, total events, day lags of the effect, outcome variables).

Data synthesis

We performed a random-effect meta-analysis to compute the pooled effect sizes of ambient air pollutants on wheeze-associated disorders using the data obtained from the higher-quality studies (score >20, 75% of the total maximum score). As indicated in Supplementary material Table 3, the higher-quality studies were published in peer review journals, had higher scores in study design (cohort/panel or time series/case-crossover study) and validation of results answering the research questions with presented measurements such as ER/OR/RR per units of increment and 95% CI. The lower-quality studies (score <20) were included in a sensitivity analysis to determine influence on the pooled effects.

Before computing pooled effect size for each pollutant, effect estimates from studies reported in form of ER were converted to RR by formula: RR=1+ER and OR was considered the same as RR. If the studies reported effect estimate per U unit of pollutant, the RR10 for each increase of 10 unit of pollutant was calculated by:

RR10=RR10U

where U is the units of increment used in the original study.

For studies used mg/m3, ppm, ppb for the unit of measurement or unit of increment, all estimate RR10 were converted to μg/m3.

For studies where values were not presented in the article or in supplementary documents, we contacted the authors for additional information. Each single city in a multi-city study and each sub-group defined by age, diagnosis, health outcome or season was included one by one as an independent study. The pooled effect sizes were computed for every single lag and cumulative lag provided in individual studies for each air pollutant separately if at least three individual estimates were available (27), (36).

To explore the heterogeneity between studies in the pooled analyses we used the coefficient of inconsistency (I2) recommended by Higgins et al. (37) which can provide the percentage of total variation among studies due to heterogeneity (37). The heterogeneity degree ranges from 0 to 100% where they represent a result of low (<25%), moderate (25–75%) and high (>75%) heterogeneity. Because studies were performed in different populations which may produce considerable heterogeneity of findings, we applied random-effects approach (38) to estimate the pooled effect sizes with consideration of variation in both within and between studies. We used funnel plot techniques, Begg’s rank test and Egger’s regression test to visually explore the present of publication bias (39), (40).

We conducted several sensitivity analyses. First, we repeated the analysis by leaving out each included study one by one to test its contribution to the pooled effect sizes. Second, the pooled effect sizes were computed for only the shortest single lag or cumulative lag, if no single lag estimates were provided in individual studies. Third, to test if the lower quality score studies (score <20) influenced the pooled estimates, we tried to add the cross-sectional studies to the meta-analysis by assuming the units of increment in those studies as the difference in mean concentration of air pollutant between study and control area. Fourth, to evaluate the influence of study design, we estimated the pooled effect sizes for the studies with time-series and case-crossover design only. Finally, we excluded small studies (events <0.3% of total events) in meta-analysis if find they may introduce bias to our analysis. We used the z-test to examine the difference between two pooled effects from main and sensitivity analyses.

All statistical analyses were conducted by using STATA 12 (STATA Corporation, College Station, TX, USA).

Results

Twelve studies were selected for extracting data and quality assessment. Ten studies were of sufficient quality for inclusion in the systematic review with their main characteristics summarized in Table 1.

Table 1:

Summaries of 10 studies included in the systematic review.

Study citation information and settingExposureOutcome
Outcome variable
Authors (year)LocationStudy design time-spanPopulation (years old)PollutantMean concentrationUnit of incrementUnit of measurementControlled variablesHealth outcome/diagnosisTotal eventsaLag (days)ER/OR/RR (95% CI)RR10 (95% CI)
Chew et al. (1999)SingaporeTime series 1990–19943–12Wind speed, temperature DOWAdmission and ER visit30,312ER (SD)RR (95% CI)
TSP51.220μg/m315.80 (1.46)1.029 (1.015–1.043)
SO238.120μg/m3Asthma12.90 (0.81)1.014 (1.006–1.022)
NO218.915μg/m310.76 (0.43)1.005 (0.999–1.010)
Vichit-Vadakan et al. (2001)ThailandPanel study 19968–12



PM10

PM2.5




104

56




45

26




μg/m3

μg/m3
Temperature, humidity, DOW, age, sex, education, respiratory condition, air conditionIncidence rate

Lower respiratory infection
28



0–4

0–4
OR (95% CI)



1.29 (1.16–1.43)

1.11 (0.96–1.29)
OR (95% CI)



1.058 (1.034–1.083)

1.041 (0.984–1.103)
Aekplakorn et al. (2003)ThailandCohort 1997–19986–14



PM10

PM2.5

SO2

SO2

SO2

SO2

SO2

SO2

SO2




34.3

25.31

18.59

18.59

18.59

18.59

18.59

18.59

18.59




10

10

10

10

10

10

10

10

10




μg/m3

μg/m3

μg/m3

μg/m3

μg/m3

μg/m3

μg/m3

μg/m3

μg/m3
Time, temperature, wind speed, relative humidityIncidence rate

Lower respiratory infection
231

0

0

0

1

2

3

0–3

0–4

0–5
OR (95% CI)

1.00 (0.93–1.07)

1.02 (0.93–1.10)

1.00 (0.94–1.06)

0.90 (0.83–0.97)

0.96 (0.90–1.02)

0.98 (0.92–1.04)

0.91 (0.83–1.00)

0.90 (0.81–1.00)

0.89 (0.80–1.00)
OR (95% CI)

1.00 (0.93–1.07)

1.02 (0.93–1.10)

1.00 (0.94–1.06)

0.90 (0.83–0.97)

0.96 (0.90–1.02)

0.98 (0.92–1.04)

0.91 (0.83–1.00)

0.90 (0.81–1.00)

0.89 (0.80–1.00)
Hong et al. (2004)IndonesiaCross-sectional 20010–12









0–12
(1) Area 1



PM10

NO2

CO



(2) Area 2



PM10

NO2

CO




102.9

16.8

4.4







73.7

24.6

2.9




76.8

0

2.8







47.6

7.8

1.3




μg/m3

μg/m3

mg/m3







μg/m3

μg/m3

mg/m3
Age, number of children in the household, income, floor area, cooking fuel, household smokingPrevalence rate

Wheezing asthma
27











25
NAOR (95% CI)

1.18 (0.46–3.01)

1.18 (0.46–3.01)

1.18 (0.46–3.01)









0.85 (0.35–2.08)

0.85 (0.35–2.08)

0.85 (0.35–2.08)
OR (95% CI)

1.022 (0.904–1.154)

NA

1.001 (0.997–1.004)





0.966 (0.802–1.166)

0.812 (0.260–2.557)

0.999 (0.992–1.006)
Langkulsen et al. (2006)ThailandCross-sectional 200410–15



10–15



10–15
(1) Area 1

PM10

(2) Area 2

PM10

(3) Area 3

PM10


65



67.5



52.2


18



20.5



5.2


μg/m3



μg/m3



μg/m3
Control areaprevalence rate

NSRD and PCP


40



55



33
NAOR (95% CI)

2.44 (1.21–4.93)



2.6 (1.38–4.91)



1.57 (0.76–3.25)
OR (95% CI)

1.641 (1.112–2.426)



1.594 (1.170–2.172)



2.381 (0.590–9.647)
Vichit-Vadakan et al. (2008)ThailandTime series 1999–2003<5



PM10




52.1




10




μg/m3
Temperature, relative humidity, day of week, public holidaysMortality

Lower respiratory infection
1826a



0–1
ER (95% CI)



7.7 (−3.6 to 20.3)
RR (95% CI)



1.077 (0.964–1.203)
Mehta et al. (2013)VietnamCase-crossover 2003–2005

<5











<5











<5
(1)

Overall:

PM10

O3

NO2

SO2

(2) Dry

season

PM10

O3

NO2

SO2

(3) Rainy

season

PM10

O3

NO2

SO2




73.19

75.03

22.1

21.58





83.64

91.84

23.06

26.37







63.1

59

21.2

15




10

10

10

10





10

10

10

10







10

10

10

10




μg/m3

μg/m3

μg/m3

μg/m3





μg/m3

μg/m3

μg/m3

μg/m3





μg/m3

μg/m3

μg/m3

μg/m3
Rainfall, temperature, humidity, seasonHospital admission

Acute lower respiratory infection


15,717











6269













9421




1–6

1–6

1–6

1–6





1–6

1–6

1–6

1–6







1–6

1–6

1–6

1–6
ER (95% CI)



−1.1 (−2.31 to 0.12)

−1.96 (−3.25 to 0.64)

−1.08 (−5.14 to 3.17)

2.61 (−1.49 to 6.87)





1.25 (−0.55 to 3.09)

−0.79 (−2.67 to 1.13)

8.5 (0.8–16.79)

5.85 (0.44–11.55)







−3.11 (−4.76 to −1.42)

−2.98 (−4.78 to −1.14)

−5.15 (−9.94 to −0.1)

−2.3 (−8.25 to 4.41)
RR (95% CI)



0.989 (0.977–1.001)

0.980 (0.968–1.006)

0.989 (0.949–1.032)

1.026 (0.985–1.069)





1.013 (0.995–1.031)

0.992 (0.973–1.011)

1.085 (1.008–1.168)

1.059 (1.004–1.116)







0.969 (0.952–0.986)

0.970 (0.952–0.989)

0.949 (0.901–0.999)

0.977 (0.918–1.044)
Rahman et al. (2017)MalaysiaCase-crossover 2006–20100–14









0–14









0–14









0–14
(1)

PM10

CO

O3

NO2e

(2)

PM10

CO

O3

NO2

(3)

PM10

CO

O3

NO2

(4)

PM10

CO

O3

NO2


35–65

0–2439

80–150

0–103.4



35–65

0–2439

80–150

0–103.4



35–65

0–2439

80–150

0–103.4



35–65

0–2439

80–150

0–103.4


10

10

10

10



10

10

10

10



10

10

10

10



10

10

10

10


μg/m3

μg/m3

μg/m3

μg/m3



μg/m3

μg/m3

μg/m3

μg/m3



μg/m3

μg/m3

μg/m3

μg/m3



μg/m3

μg/m3

μg/m3

μg/m3
Age, gender, sex, nationality, race, diagnose, subtypesHospital

admission

Acute

bronchiolitis





Asthma







Acute

bronchitis









Unspecified

Acute lower

respiratory

infection




5779







5582









287









26




0

0

0

0

0

0

0

0

0

0

0

0





0

0

0

0
OR (95% CI)



1.115 (1.093–1.138)

0.894 (0.247–3.234)

0.990 (0.978–1.002)

1.007 (0.983–1.031)

0.990 (0.989–0.992)

0.925 (0.862–0.992)

1.000 (0.999–1.000)

0.990 (0.989–0.992)

1.037 (1.034–1.040)

0.708 (0.543–0.923)

1.002 (1.000–1.004)

1.012 (1.008–1.017





1.055 (1.051–1.059)

0.726 (0.533–0.989)

0.998 (0.995–1.001)

1.004 (0.999–1.010)
OR (95% CI)



1.115 (1.093–1.138)

0.894 (0.247–3.234)

0.990 (0.978–1.002)

1.007 (0.983–1.031)

0.990 (0.989–0.992)

0.925 (0.862–0.992)

1.000 (0.999–1.000)

0.990 (0.989–0.992)

1.037 (1.034–1.040)

0.708 (0.543–0.923)

1.002 (1.000–1.004)

1.012 (1.008–1.017





1.055 (1.051–1.059)

0.726 (0.533–0.989)

0.998 (0.995–1.001)

1.004 (0.999–1.010)
Nhung et al. (2018)VietnamTime series 2007–2014<1(1)

PM10

PM2.5

PM1

SO2

NO2

NOx

CO

8hO3

24hO3


93

56.1

43.7

32.4

49

86.4

2656.1

92.9

121.1


66.5

39.4

33.8

40.6

21.9

36.7

986.3

85.2

109.4


μg/m3

μg/m3

μg/m3

μg/m3

μg/m3

μg/m3

μg/m3

μg/m3

μg/m3
DOW,

holidays,

daily mean

temperature,

relative humidity,

wind speed,

influenza epidemics
Hospital

admission

Bronchitis

and asthma
9195

0–6

0–6

0–6

0–6

0–6

0–6

0–6

0–6

0–6
RR (95% CI)

0.977 (0.927–1.030)

0.989 (0.931–1.050)

1.027 (0.961–1.097)

1.070 (0.925–1.237)

1.009 (0.945–1.077)

0.994 (0.929–1.063)

0.908 (0.848–0.973)

1.043 (0.949–1.147)

1.057 (0.960–1.164)
RR (95% CI)

0.997 (0.989–1.004)

0.997 (0.982–1.012)

1.008 (0.988–1.028)

1.008 (0.988–1.028)

1.004 (0.974–1.034)

0.998 (0.980–1.017)

0.999 (0.998–1.000)

1.005 (0.994–1.016)

1.005 (0.996–1.014)
1–5(2)6799
PM109366.5μg/m30–61.032 (0.897–1.080)1.005 (0.984–1.012)
PM2.556.139.4μg/m30–61.051 (0.986–1.121)1.013 (0.996–1.029)
PM143.733.8μg/m30–61.072 (0.998–1.151)1.021 (0.999–1.042)
SO232.440.6μg/m30–60.988 (0.832–1.173)0.997 (0.956–1.040)
NO24921.9μg/m30–61.079 (1.004–1.160)1.035 (1.002–1.070)
NOx86.436.7μg/m30–61.116 (1.035–1.203)1.030 (1.009–1.052)
CO2656.1986.3μg/m30–61.077 (0.999–1.162)1.001 (1.000–1.002)
8hO392.985.2μg/m30–60.985 (0.889–1.090)0.998 (0.986–1.010)
24hO3121.1109.4μg/m30–61.003 (0.903–1.114)1.000 (0.991–1.010)
0–17(3)17,118
PM109366.5μg/m30–61.008 (0.971–1.047)1.001 (0.996–1.007)
PM2.556.139.4μg/m30–61.025 (0.981–1.071)1.006 (0.995–1.018)
PM143.733.8μg/m30–61.058 (1.008–1.111)1.017 (1.002–1.032)
SO232.440.6μg/m30–61.038 (0.927–1.163)1.009 (0.982–1.038)
NO24921.9μg/m30–61.055 (1.004–1.108)1.025 (1.002–1.048)
NOx86.436.7μg/m30–61.056 (1.004–1.111)1.015 (1.001–1.029)
CO2656.1986.3μg/m30–60.991 (0.942–1.044)1.000 (0.999–1.000)
8hO392.985.2μg/m30–61.013 (0.943–1.087)1.002 (0.993–1.010)
24hO3121.1109.4μg/m30–61.032 (0.960–1.110)1.003 (0.996–1.010)
Luong et al. (2018)VietnamTime series 2010–20140–5(1) Full year





24hO3

24hO3

24hO3

24hO3

24hO3

24hO3

24hO3






46.9

46.9

46.9

46.9

46.9

46.9

46.9






10

10

10

10

10

10

10






μg/m3

μg/m3

μg/m3

μg/m3

μg/m3

μg/m3

μg/m3
Temperature,

relative humidity,

DOW,

times,

season
Wheeze

associated disorder

hospital admission

(Acute bronchiolitis,

unspecified acute lower respiratory infection,

asthma,

status

asthmaticus)
6902





0–5

0

1

2

3

4

5
RR (95% CI)





0.994 (0.976–1.011)

0.997 (0.982–1.013)

1.005 (0.988–1.021)

0.994 (0.978–1.011)

1.002 (0.986–1.019)

0.991 (0.974–1.008)

1.005 (0.990–1.019)
RR (95% CI)





0.994 (0.976–1.011)

0.997 (0.982–1.013)

1.005 (0.988–1.021)

0.994 (0.978–1.011)

1.002 (0.986–1.019)

0.991 (0.974–1.008)

1.005 (0.990–1.019)
0–5(2) Winter
24hO342.410μg/m316940–50.998 (0.939–1.059)0.998 (0.939–1.059)
24hO342.410μg/m300.980 (0.933–1.029)0.980 (0.933–1.029)
24hO342.410μg/m311.016 (0.967–1.067)1.016 (0.967–1.067)
24hO342.410μg/m320.992 (0.944–1.041)0.992 (0.944–1.041)
24hO342.410μg/m331.023 (0.975–1.074)1.023 (0.975–1.074)
24hO342.410μg/m340.985 (0.938–1.033)0.985 (0.938–1.033)
24hO342.410μg/m351.003 (0.959–1.048)1.003 (0.959–1.048)
0–5(3) Summer
24hO35210μg/m312930–50.973 (0.941–1.006)0.973 (0.941–1.006)
24hO35210μg/m301.003 (0.976–1.031)1.003 (0.976–1.031)
24hO35210μg/m310.993 (0.962–1.024)0.993 (0.962–1.024)
24hO35210μg/m321.004 (0.973–1.035)1.004 (0.973–1.035)
24hO35210μg/m330.992 (0.961–1.024)0.992 (0.961–1.024)
24hO35210μg/m340.989 (0.958–1.021)0.989 (0.958–1.021)
24hO35210μg/m350.992 (0.965–1.021)0.992 (0.965–1.021)

CI, confidence interval; DOW, day of week; ER, excess rate; OR, odd ratio; RR, risk ratio.

aTotal events was calculated as (daily mean number of event) × (number of days in period) for studies do not provide the total number of events.

Based on the data in Table 1, nearly 84,000 pediatric wheeze-associated disorders events have been investigated to assess the impact of ambient air pollution in five countries. Of these, four studies were conducted in Thailand (7), (41), (42), (43), three in Vietnam (44), (45), (46), and one each in Singapore, Indonesia and Malaysia (8), (9), (47).

Eight studies reported the effects of PM10 (7), (8), (41), (42), (43), (44), (46), (47), three reported the effects of PM2.5 (41), (43), (46), four studies reported the effects of SO2 (9), (43), (44), (46), five studies reported the effects of NO2 (8), (9), (44), (46), (47), three reported the effects of CO (8), (46), (47) and four reported the effects of O3 (44), (45), (46), (47). Only one study reported the effects of TSP, PM1 and NOx but Nhung et al. (46) presented three estimates for PM1 and NOx while only one estimate for TSP was given by Chew et al. (9), therefore the effects of TSP were analyzed further. The daily mean concentrations of pollutants across all studies were 68.2 μg/m3 for PM10, 45.8 μg/m3 for PM2.5, 27.7 μg/m3 for SO2, 33.5 μg/m3 for NO2 and 2479 μg/m3 for CO and 81.1 μg/m3 for O3 (Table 2).

Table 2:

Descriptive statistics of air pollutant concentrations calculated from the daily mean values published in each included study in the systematic review.

Air pollutant, μg/m3Daily mean (SD)RangeNumber of studiesNumber of estimates
MinMax
PM1068.2 (23.2)34.3104.0811
PM2.545.8 (17.7)25.356.133
PM143.7 (0)43.743.711
SO227.7 (9.1)18.638.144
NO233.5 (34.0)0.0103.456
NOx86.4 (0)86.486.413
CO2479.0 (1584.8)0.04400.034
O381.1 (40.6)42.4150.047

Most studies controlled the potential confounding effects of temperature, humidity and day of week (DOW) (5–7 studies) but only one study adjusted for rainfall and influenza epidemics (Table 1).

Four studies investigated the association of air pollution with hospital admissions for wheeze-associated disorders, four explored rate the ratio of incidence or prevalence and the rest considered emergency room visits or mortality (Table 1). Five studies defined the outcome using the International Classification of Diseases (ICD) version 9 or 10 (9), (42), (44), (45), (46), one based on the hospital admission data (47) and four used questionnaires for wheezing symptoms (7), (8), (41), (43).

Eight studies with quality score higher than 20 were included to perform a meta-analysis. All of these studies provided at least one estimate (overall or single lag) for the effects, of which two studies provided results for the seasons separately (44), (45). The pooled effect sizes of risk ratios (by pollutants) are presented in Figure 2 . The degree of heterogeneity (I2) in the current meta-analysis was large in pooled estimates for PM10 (99.6%), CO (85%), NO2 (94.2%) and moderate for PM2.5 (57.5%), SO2 (61.7%), CO (74.1%) and NOx (56.3%) and O3 (49.6%), and low for PM1 (0.0%) and O3 (22.0%) (Figure 2A–C).

Figure 2:
Figure 2:Figure 2:Figure 2:

Forest plot for the association between ambient air pollution and wheeze-associated disorder in children in Southeast Asia.

Risk ratios/odd ratios (RRs/ORs) are for an increase of 10 μg/m3 of air pollutant (PM10, PM2.5, PM1, SO2, NO2, NOx, CO and O3).

Citation: Reviews on Environmental Health 34, 2; 10.1515/reveh-2018-0079

The pooled effect, RR per 10 μg/m3 increase of pollutants, was 1.020 (95% CI: 0.999–1.041) for PM10, 1.010 (95% CI: 1.001–1.020) for PM2.5, 1.007 (95% CI: 1.007–1.028) for PM1 (Figure 2A), 0.999 (95% CI: 0.985–1.014) for SO2, 1.008 (95% CI: 0.998–1.018) for NO2, 1.013 (95% CI: 0.997–1.029) for NOx (Figure 2B) and 1.000 (95% CI: 0.999–1.002) for CO; 0.999 (95% CI: 0.998–1.001) for O3 (Figure 2C).

The index of heterogeneity (I2) ranges from 0 to 100% where they represent low, moderate and high heterogeneity, respectively. The p-value is based on the Q test.

Most of sensitivity analyses demonstrated that there was no notable change in the pooled effect size when changing the included studies (see Supplementary material Table 3–10).

For PM10, the association changed from non-significant to significant in sensitivity analysis 1 (leave-one-study-out) and 3 (lower quality score studies were included) (see Supplementary material Table 3).

For SO2, the association changed from negative to significantly positive in sensitivity analysis 2 (only shortest single lag or cumulative lag) and 5 (small studies were excluded) (see Supplementary material Table 6, Supplementary material Figure 1).

The small number of included studies (less than 8) was not adequate to properly assess a funnel plot or more advanced regression-based assessments. Thus, we did not evaluate publication bias (48).

Discussion

This is the first meta-analysis to assess the effects of air pollution on wheeze-associated disorders among children in Southeast Asia and to provide effect size estimates. We found positive associations between PMs and wheeze-associated disorders. Our study confirmed that fine particulate matter (PM2.5, PM1) were significantly associated with wheeze-associated disorders among children. PM2.5, PM1 are health-damaging particles not only because of their chemical components but because they can penetrate deep inside the lungs to aggravate existing asthma or to contribute to the development of chronic bronchitis (20), (49), (50), (51). Our meta-analyses showed wheeze-associated disorders were most strongly associated with PM1, which was consistent with the limited literature available on fine particles (46), (52), (53). Earlier evidence indicated that the small particles can reach bronchioles and penetrate up to the primary, secondary terminal bronchi, and alveoli in the lung which may trigger mucous secretions in airways, reduce air flow and then cause wheezing (54), (55). Although previous studies reported significant effects of PM10 on respiratory diseases in children (27), (52), (56), the effects on wheeze-associated disorders was not clear. We found significant associations in some sensitivity analyses but those results were based on removing a large study or including lower quality score studies into meta-analysis so we concluded that further studies were needed to clarify this association.

We did not observe a significant association between SO2 and wheeze-associated disorders in the main results, however, most of the negative effect for SO2 were from one small study (231 events) which might have introduced bias into our analysis (43). A sensitivity analysis conducted by excluding that small study identified a significant effect of SO2 (RR=1.012. 95% CI: 1.004–1.020). SO2 inflames the respiratory tract to cause coughing, mucus secretion, aggravation of asthma and chronic bronchitis and makes children more sensitive to infections of the respiratory system (57). There is evidence from recent studies reporting the association between ambient SO2 levels and hospital admissions or emergency department visits for respiratory outcomes such as asthma-related symptoms, allergy exacerbation or respiratory infections (57), (58).

We found positive associations between oxides of nitrogen (NO2 and NOx) and wheeze-associated disorders. Oxides of nitrogen contribute to the formation of O3 and photochemical smog and have significant impacts on human health, especially asthma, bronchial symptoms, lung inflammation and reduced lung function (58), (59). At low level of exposure, NO2 can irritate the throat and lung, causing shortness of breath, cough, fatigue or nausea (59), (60). Epidemiologic studies have shown that exposure to NO2 can increase airway inflammation in healthy children, as well as increase symptoms of bronchitis in asthmatics (59), (61). Positive associations between exposures to NO2 and increased emergency department visits and hospitalizations for wheezing among children have been also reported in previous studies (61), (62), (63).

While the connection between CO, O3 and risk of respiratory admission in children has been reported in previous studies (64), (65), (66), (67), (68), their effects on wheeze-associated disorders has not been proven among children in Southeast Asia (44), (45), (46), (47). In our review, there was no apparent association found for CO and O3, with a percent change of close to zero. Indeed, CO at low concentrations was reported elsewhere as a protective factor against lower tract infection and not all studies in the literature found a positive association between O3 and hospital admission for asthma (69), (70), (71), (72). Thus, the role of CO and O3 on respiratory disease should be further investigated.

Ambient air pollutants in Southeast Asia are emitted from a mixture of sources including power plants, industries, road vehicles and the burning of biomass from crop fields or forest, and the use of fossil fuels for domestic cooking. This means that the composition of air pollutants in Southeast Asian countries is diverse and could differ considerably from North America and Europe where the majority of previous studies have been performed.

Seven out of 10 studies included in this review were carried out in low-income countries whose children may suffer larger risks of respiratory admission (27), (73), (74). This is because children living in low- and middle-income countries might not only experience higher exposure to ambient air pollution but also be exposed to additional risk factors for respiratory diseases, wheeze-associated disorders, such as smoke from biomass burning at home, a large fraction of the population continues to use tobacco products in the home, or poor diets that may amplify the effects of ambient air pollution (28), (56), (75). Therefore, in Southeast Asia, ambient air pollution poses additional risks for wheeze-associated disorders among children who are already exposed to other pollution at home and still needs to be considered as an important public health issue.

Limitations

This study has some limitations. First, the results are based on a small number of studies, and they did not cover for all countries in Southeast Asia. This limitation calls for further studies in Southeast Asia to evaluate the effect of air pollution on children’s health, which would help in an updated systematic review and meta-analysis. Second, some studies investigated associations in a very short period time of months or 1-year and were based on a very small number of events (7), (8), (41), (43). However, they have only a minor influence on the meta-analytic estimates according to their small weight contributing to the pooled effect (sensitivity analyses support this suggestion). Finally, our meta-analysis is based on different designs of observational studies from cross-sectional, cohort, panel to case-crossover and time-series. Therefore, individual studies may be influenced by uncontrolled time-varying biases which we could not examine (27), (39), (48).

Conclusion

The present systematic review and meta-analysis demonstrated a significant relationship between air pollutants and the elevated risk of wheeze-associated disorders among children in Southeast Asian countries. Our meta-analysis found that for each increase of 10 μg/m3 in concentrations of PM2.5, or PM1 was associated with an increase of 1–2% risk of wheezing symptom outcome. Meanwhile, the effects of other air pollutants (PM10, SO2, NO2, NOx, CO and O3) was not statistically significant. Our study contributes to the current literature to better understand the public health impact in the most polluted regions of the world. However, the number of studies conducted in Southeast Asia is still small and more studies are needed from this region.

Author statement

Research funding: L.L. is funded by an Australia Award Scholarship. P.D.S. is funded by a NHMRC Senior Principal Research Fellowship. D.P. is funded by research start-up fund provided by Griffith School of Medicine. P.T. was funded by a QUT VC Research Fellowship during part of this study.

Conflict of interest: Authors state no conflict of interest.

Informed consent: Informed consent is not applicable.

Ethical approval: This study was approved by UQ School of Medicine Low Risk Ethical Review Committee (#2016-SOMILRE-0155).

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Footnotes

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    Forest plot for the association between ambient air pollution and wheeze-associated disorder in children in Southeast Asia.

    Risk ratios/odd ratios (RRs/ORs) are for an increase of 10 μg/m3 of air pollutant (PM10, PM2.5, PM1, SO2, NO2, NOx, CO and O3).