Ambient PM2.5 and birth outcomes: Estimating the association and attributable risk using a birth cohort study in nine Chinese cities

Environment International 126 (2019) 329–335

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Ambient PM2.5 and birth outcomes: Estimating the association and attributable risk using a birth cohort study in nine Chinese cities

T

Zhijiang Lianga, Yin Yangb, Zhengmin Qianc, Zengliang Ruanb, Jenjen Changc, ⁎⁎ ⁎ Michael G. Vaughnd, Qingguo Zhaoe,f, , Hualiang Linb, a

Department of Public Health, Guangdong Women and Children Hospital, 521 Xingnan Road, Panyu District, Guangzhou 511442, China Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China c Department of Epidemiology and Biostatistics, College for Public Health & Social Justice, Saint Louis University, Saint Louis, MO 63104, United States of America d School of Social Work, College for Public Health & Social Justice, Saint Louis University, St. Louis. MO 63103, United States of America e Epidemiological Research Office of Key Laboratory of Male Reproduction and Genetics, Family Planning Research Institute of Guangdong Province, Guangzhou, China f Epidemiological Research Office of Key Laboratory of Male Reproduction and Genetics (National Health and Family Planning Commission), Family Planning Special Hospital of Guangdong Province, Guangzhou, China b

A R T I C LE I N FO

A B S T R A C T

Handling Editor: Hanna Boogaard

Background: Previous studies have reported that maternal exposure to particles with aerodynamic diameter < 2.5 μm (PM2.5) is associated with birth outcomes. However, a multicity birth cohort study has not been conducted in China, and the attributable fraction of adverse birth outcomes due to PM2.5 exposure remains unknown. Methods: We examined associations in a birth cohort of 1,455,026 mother-and-live-birth pairs who were followed up from the first hospital visit for pregnancy until the birth of the baby during 2014–2017 in nine cites of the Pearl River Delta (PRD) region, China. The PM2.5 exposures were estimated based on the air pollution concentrations of the nearby monitors. Cox proportional hazards regressions were employed to examine the associations. Results: We found 1% (HR = 1.01; 95% CI: 1.00, 1.02), 6% (HR = 1.06; 95% CI: 1.05, 1.07), and 7% (HR = 1.07; 95% CI: 1.06, 1.08) increases in risk of PTB and 20% (HR = 1.20; 95% CI: 1.18, 1.22), 18% (HR = 1.18; 95% CI: 1.15, 1.20), and 20% (HR = 1.20; 95% CI: 1.17, 1.23) increases in risk of LBW, with each 10 μg/m3 increase in PM2.5 from trimester 1 to trimester 3, respectively. For PTB, highest HRs were observed during trimester 3, as for LBW, stronger effect were observed during trimester 1 and trimester 3. We further estimated that 7.84% (95% CI: 6.21%, 9.50%) of PTB and 14.85% (95% CI: 13.00%, 16.61%) of the LBW cases could be attributable to PM2.5 exposure during the third trimester. Conclusion: The results indicate that maternal PM2.5 exposure is a risk factor for both LBW and PTB, and responsible for considerable burdens of PTB and LBW in the Pearl River Delta region.

1. Introduction While ambient PM2.5 exposure has been widely linked with various adverse health outcomes, mainly respiratory and cardiovascular diseases (Dabass et al., 2016; Lin et al., 2016b; Wang et al., 2018), the effects of exposure to PM2.5 on both low birth weight (LBW) and preterm birth (PTB) are relatively under-studied (Li, 2017; Polichetti et al., 2013; Sun et al., 2016). LBW (< 2500 g) and PTB (< 37 weeks of gestation) are major neonatal health problems, accounting for 15.5% and 11.1% of birth rates worldwide in 2004 and 2010 (Blencowe et al.,

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2012; Wardlaw et al., 2004). They are strongly related to infant mortality (Liu et al., 2012; Ryan and Dogbey, 2015) and have significant adverse health effects reaching into childhood (Arhan et al., 2017; Boptom et al., 2017; Franck et al., 2017; Sharp et al., 2018; Wroblewska-Seniuk et al., 2017). In the last decade, an increasing number of investigations have explored the association between maternal exposures to PM2.5 and LBW and PTB. However, thus far findings have been inconsistent. There remains a lack of understanding with regard to the sensitive window of exposure during pregnancy. A few studies reported statistically

Correspondence to: H. Lin, School of Public Health, Sun Yat-sen University, Guangzhou, China. Correspondence to: Q. Zhao, Family Planning Special Hospital of Guangdong Province, 17 Meidong Road, Guangzhou 510600, China. E-mail addresses: [email protected] (Q. Zhao), [email protected] (H. Lin).

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https://doi.org/10.1016/j.envint.2019.02.017 Received 9 November 2018; Received in revised form 4 February 2019; Accepted 5 February 2019 0160-4120/ © 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/).

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Fig. 1. The geographical distribution of the nine cities in China.

Table 1 The descriptive summary of the general characteristics of all mothers. Variables

PTB (n = 64,028)

LBW (n =57,618)

Full term LBW (n =26,898)

Total (n = 1,455,026)

Gestational age (mean ± SD, weeks) Birth weight (mean ± SD, grams)

34.55 ± 2.12

35.71 ± 3.17

38.35 ± 1.17

38.91 ± 1.47

2430.46 ± 529.38

2143.08 ± 371.31

2298.57 ± 249.91

3183.30 ± 421.44

Baby gender Male Female Uncertain

37680(58.85%) 25209(39.37%) 1139(1.78%)

27696(48.07%) 28973(50.28%) 949(1.65%)

10752(39.97%) 15779(58.66%) 367(1.37%)

757557(52.07%) 672082(46.19%) 25387(1.74%)

Maternal age < 35 years ≥35 years Missing

54787(85.57%) 9206(14.38%) 35(0.05%)

50635(87.88%) 6941(12.05%) 42(0.07%)

24200(89.97%) 2675(9.94%) 23(0.09%)

1302607(89.52%) 151977(10.45%) 442(0.03%)

Previous pregnancy Yes No Unrecorded

33112(51.72%) 20875(32.60%) 10041(15.68%)

25920(44.99%) 19401(33.67%) 12297(21.34%)

10376(38.58%) 9111(33.87%) 7411(27.55%)

745993(51.27%) 439159(30.18%) 269874(18.55%)

Previous delivery Yes No Unrecorded

26484(41.36%) 27500(42.95%) 10044(15.69%)

20227(35.11%) 25083(43.53%) 12308(21.36%)

7947(29.54%) 11531(42.87%) 7420(27.59%)

595027(40.90%) 590043(40.55%) 269956(18.55%)

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structure and population size, these cities share geographical, meteorological, and cultural similarities. Guangzhou, for example, as the capital of Guangdong Province, has > 14.04 million people in 2016; Dongguan, Foshan, Huizhou, Jiangmen, Shenzhen, Zhaoqing, Zhongshan, and Zhuhai had 8.26, 7.46, 4.78, 4.54, 11.91, 4.08, 3.23 and 1.68 million residents, respectively, which accounted for 54.5% of the population of Guangdong Province. All nine cities have dry, cool winters and hot, wet summers. The yearly mean for relative humidity and temperature is 72–80% and 22–23 °C. Approval to conduct this study was obtained from the institutional ethical committee board of Guangdong Women and Children Hospital.

Table 2 Descriptive summary of the air pollution and weather conditions in the study area. Pollutants (μg/m3)

Trimester 1 PM2.5 O3 NO2 SO2 Temperature (°C) Relative humidity (%) Trimester 2 PM2.5 O3 NO2 SO2 Temperature (°C) Relative humidity (%) Trimester 3 PM2.5 O3 NO2 SO2 Temperature (°C) Relative humidity (%)

Mean

Min

Max

Percentiles 25th

50th

75th

36.57 52.11 40.30 12.50 22.34 77.16

9.07 17.18 5.04 4.28 12.21 56.41

93.37 94.85 88.75 58.37 29.80 90.04

28.47 42.55 30.89 8.99 17.79 74.29

35.42 52.54 38.87 11.35 22.45 78.05

43.20 60.75 48.64 14.65 27.25 81.00

34.41 52.53 39.23 11.88 23.14 77.96

12.71 21.98 5.30 4.52 12.79 57.07

90.08 97.64 84.55 54.64 29.66 89.75

26.44 45.07 30.49 8.70 18.73 75.27

32.99 53.09 37.84 11.01 23.95 78.70

40.99 59.76 46.94 13.65 27.62 81.71

33.77 53.69 38.95 11.46 23.66 78.02

8.27 7.40 5.36 3.82 9.17 48.50

104.36 133.70 119.76 74.94 31.02 94.93

25.85 46.31 30.18 8.54 19.40 75.22

32.57 54.05 37.74 10.71 25.12 78.70

40.00 61.57 46.38 13.20 28.00 81.81

2.2. Birth outcomes We collected data on all singleton births between January 1st, 2014 and December 31st, 2017 from the birth registry database as previously described (He et al., 2016). Briefly, each hospital and midwifery clinics in Guangdong are mandated, by law, to report labor and delivery data to the system from which birth certificates are generated. In order to facilitate the comparison of this study with previous studies (Darrow et al., 2009; He et al., 2016), we only included all singleton vaginal live births among women who had 20–44 weeks of gestations in the analyses. We determined gestational age using the first or second trimester ultrasound examinations (Fu and Yu, 2011). For those women who did not have any ultrasound examination results, we used the last menstrual period to estimate the gestational age (He et al., 2016). PTB was defined as any birth with < 37 weeks of gestational age and LBW was defined as weight at birth < 2500 g; for the analysis of LBW, we restricted the subjects to the full-term births (≥ 37 weeks of gestational age) (Blencowe et al., 2012; Wardlaw et al., 2004). The air pollution data collection process and data quality control have been described previously (Lin et al., 2016a). Briefly, daily concentrations of PM2.5, sulfur dioxide (SO2), nitrogen dioxide (NO2), and ozone (O3) were measured at the air monitoring stations of the study cities for the period from February 1, 2013 to December 31, 2017: 10 stations in Guangzhou, five in Dongguan, 11 in Shenzhen, eight in Foshan, four in Zhuhai, four in Jiangmen, four in Zhongshan, five in Huizhou, and three in Zhaoqing (Fig. 1). The monitors were located at 10–20 m above ground level. We strictly followed the monitoring procedures, set by the State Environmental Protection Administration of China, to collect the data (Lin et al., 2016a). We calculated the exposures according to the district of the mother's residence at delivery. We excluded those mothers from analyses who lived in a district without a nearby air monitoring station at a radius of about 5 km. Finally, a total of 35 districts were included in the study. For the study pollutants, we also calculated the mean concentrations during each stage of pregnancy (the first trimester: 0–12 weeks, the second trimester: 13–28 weeks, and the third trimester: after 28 weeks) (Chen et al., 2018). We collected weather data from the National Weather Data Sharing System (http://cdc.cma.gov.cn/home.do). Daily mean temperature (°C) and relative humidity (%) were used in data analyses. Data were matched to the different pregnancy periods using the same approach as the air pollution. Also, we considered additional covariates in the analyses including maternal age (years), previous pregnancy history (yes or no), and baby gender (male or female).

significant associations during part of the pregnancy period (Hyder et al., 2014; Li et al., 2017; Pedersen et al., 2013; Pereira et al., 2013; Zhu et al., 2015). For instance, a birth cohort study conducted in Boston (US) reported that exposure during the first trimester had the strongest health effects on intrauterine inflammation (HR = 1.93, 95% CI: 1.55, 2.40) (Nachman et al., 2016). Meanwhile, other studies have suggested that PM2.5 exposures during pregnancy could increase the risk of LBW (Gray et al., 2014; Harris et al., 2014; Savitz et al., 2014). However, several studies have reported nonsignificant associations for both LBW (Gehring et al., 2011; Pereira et al., 2014a) and PTB (Gray et al., 2014; Johnson et al., 2016a). Most extant studies were conducted in a single city or communities (Chen et al., 2018; Xiao et al., 2018). Thus, inconsistent findings identified in the literature might be due to differing characteristics of the study population and chemical compositions of PM2.5 found in different geographic areas (Ming et al., 2017). As such, additional studies are warranted to elucidate the relationship between PM2.5 and adverse birth outcomes employing larger population-based samples in multicenter settings. Moreover, studies on the sensitive exposure window for the effects of PM2.5 exposure on PTB and LBW have reported inconsistent findings. For example, the first trimester has been reported to be a sensitive exposure window in a few studies (Lee et al., 2013; Pereira et al., 2014b), and different results have been reported in other studies (Arroyo et al., 2016; Defranco et al., 2016). In China, higher levels of air pollution have existed in major cities for quite a long period (Kan, 2014). Efforts to assess the effects of air pollution exposure on pregnancy outcomes are still relatively sparse (Qian et al., 2016). We therefore conducted this study in nine cities in Southern China with the following aims: 1) to quantify the association between PM2.5 and LBW and PTB during different periods of pregnancy; 2) to estimate the attributable number and proportion of both LBW and PTB due to PM2.5 exposure.

2.3. Statistical analyses

2. Methods

To examine the associations between PM2.5 exposures at different periods of pregnancy and birth outcomes, we used Cox proportional hazards regression models with LBW and PTB as outcomes and gestational age as the time axis (Chen et al., 2018; Xiao et al., 2018). For the analysis on PTB, term births were considered as censored data at the end of the 36th week of gestation (He et al., 2016). We used natural cubic splines with degrees of freedom of 6 and 3 to adjust the non-linear

2.1. Study population We conducted the study in nine cities of Guangzhou, Dongguan, Foshan, Huizhou, Jiangmen, Zhaoqing, Shenzhen, Zhuhai, and Zhongshan in the PRD region (Fig. 1). Although different in industrial 331

Fig. 2. The concentration-response relationships between PM2.5 and PTB (or LBW) at different exposure windows.

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previous delivery experience (Table 1). Younger mothers (< 35 years) and those with pregnancy experience had a relatively higher risk of PTB and LBW. Mothers with a male baby had higher risk of PTB, but low risk of LBW. The characteristics of the meteorological factors and air pollution are summarized in Table 2. The mean concentrations of PM2.5 during the three trimesters were 36.57 μg/m3, 34.41 μg/m3, and 33.77 μg/m3, respectively. Fig. 2 shows the smoothing curves of the exposure-response relationship between PM2.5 exposure at different pregnancy periods and risk of PTB (or LBW). The concentration-response curves suggested an approximately linear relationship, so in the subsequent analyses, we reported the linear associations between PM2.5 and risk of PTB (or LBW). Specifically, PM2.5 was significantly associated with PTB in the second trimester and the third trimester exposure windows (Table 3). Relatively stronger effects of PM2.5 were observed on the third trimester, with a HR of 1.07 (95% CI: 1.06, 1.08) for each 10 μg/m3 increase in PM2.5 concentrations. As for LBW, we observed statistically significant effects in every pregnancy stage, with stronger effects on the first and third trimester (HR = 1.20, 95% CI: 1.18, 1.22) and (HR = 1.20, 95% CI: 1.17, 1.23), for each 10 μg/m3 increase in PM2.5 concentrations. In the two-pollutant model with SO2, NO2 or O3, the effects remained statistically significant, however, the model with SO2 yielded relatively weak associations. According to the estimated PM2.5-PTB and PM2.5-LBW relationships, different AN and AF in specific trimester were calculated and presented in Table 4. We found the largest AN and AF in trimester 3. We estimated an attributable number of PTB of 4373 (95% CI: 3462, 5297) and an attributable fraction of 7.84% (95% CI: 6.21%, 9.50%) could be ascribed to ambient PM2.5 exposure at the third trimester. And we estimated that 3993 (95% CI: 3496, 4469) LBW could be to ambient PM2.5 exposure at the third trimester, corresponding to an attributable fraction of 14.85% (95% CI: 13.00%, 16.61%). Altering the degrees of freedom for the adjustment of temperature and relative humidity did not substantially change effect estimates for the association of PM2.5 to both PTB and LBW. Relative risk estimates in trimester 3 ranged from 1.06 to 1.07 and from 1.07 to 1.08 for PTB, as well as 1.20 and from 1.19 to 1.20 for LBW (see Supplementary Table S1). As for city-specific analyses, we observed somewhat differential associations across the cities. For example, the largest association for PTB was observed in Zhaoqing, where the HR was 1.34 (95% CI: 1.18, 1.51) for each 10 μg/m3 increase in PM2.5 at trimester 3. For the effects on LBW, the highest effect was observed in Zhongshan with an HR of 1.47 (95% CI: 1.19, 1.82) for each 10 μg/m3 increase in PM2.5 at trimester 3 (Supplementary Table S2). Further including the cesarean deliveries did not alter the effect estimates (Supplementary Table S3), for example, the LBW HR of including the cesarean deliveries was 1.21 (95% CI: 1.19, 1.23) for each 10 μg/m3 increase in PM2.5 at trimester 1, which similarly to the LBW HR of excluding the cesarean deliveries 1.20 (95% CI: 1.18, 1.22). When excluding the newborns with extremely low and high births, we observed the consistent effect estimate at any trimester both for PTB and LBW (Supplementary Table S4). When using the birth weight as the dependent variable, we observed that each 10 μg/m3 increase in ambient PM2.5 was associated with a 27.46 g (95% CI: -28.48, −26.43), 20.78 g (95% CI: -21.90, −19.65) and 22.06 g (95% CI: -23.15, −20.96) decrease in birth weight in

Table 3 The estimated HRs (95% CIs) of different exposure windows during pregnancy for PTB and LBW. Pollutants Trimester 1 PM2.5 + O3 + NO2 + SO2 Trimester 2 PM2.5 + O3 + NO2 + SO2 Trimester 3 PM2.5 + O3 + NO2 + SO2

Preterm birth

Low birth weight

1.01 1.01 1.01 0.99

(1.00, (0.99, (1.00, (0.98,

1.02) 1.02) 1.02) 1.01)

1.20 1.20 1.26 1.12

(1.18, (1.18, (1.23, (1.09,

1.22) 1.22) 1.29) 1.15)

1.06 1.06 1.07 1.05

(1.05, (1.05, (1.05, (1.03,

1.07) 1.07) 1.08) 1.07)

1.18 1.18 1.25 1.09

(1.15, (1.15, (1.22, (1.06,

1.20) 1.20) 1.28) 1.12)

1.07 1.07 1.07 1.08

(1.06, (1.05, (1.06, (1.07,

1.08) 1.08) 1.09) 1.10)

1.20 1.20 1.26 1.12

(1.17, (1.17, (1.23, (1.09,

1.23) 1.23) 1.29) 1.15)

effects of weather variables (Liang et al., 2016). An individual model was constructed for both PTB and LBW separately and for each pregnancy stage. We calculated survival time using the duration from the first day of conception to the date of birth. We used both single-pollutant and two-pollutant models to examine the associations (Lin et al., 2016a). We reported hazard ratios (HRs) in the risk of adverse birth outcomes for each 10 μg/m3 PM2.5 concentration increase. In addition, the linearity of the concentration–response relationships were evaluated using a natural spline smoothing function (Lin et al., 2017). We used our previously reported approach to estimate PTB and LBW burden attributable to PM2.5 (Hao et al., 2016; Woodruff et al., 2009). Briefly, we used two indicators of attributable number (AN) and attributable fraction (AF) to express the LBW and PTB burden (Lin et al., 2017). In addition to these main analyses, a number of sensitivity tests were conducted, we performed the data analyses for each study city, changed the degrees of freedom for mean temperature (5–7 degrees of freedom), and relative humidity (2–4 degrees of freedom). We also performed one analysis for preterm birth by including the cesarean deliveries in the subjects. For the analysis of low birth weight, we also excluded the extremely low and high values from the analysis, specifically we excluded the newborns with birth weight < 500 g or > 5000 g from the analysis to check the robustness of the findings. We further conducted one additional analysis using the birth weight as the health outcome. We used R software (version 3.4.4, R Development Core Team 2018) for data analyses. 3. Results A total of 2,225,625 singleton vaginal live births with 20–44 weeks' gestational age were recorded in the nine cities during the study period. Among them, 770,599 were not included in this analysis as there was not air monitoring stations in their residential community/district. So we finally included 1,455,026 births in this study. Among them, 64,028 (4.40%) and 26,898 (1.85%) were PTB and LBW, respectively. Among all the mothers, 51.27% had previous pregnancy and 40.90% had

Table 4 The attributable fraction and attributable PTB and LBW due to exposure to PM2.5 using the guideline of the World Health Organization as the reference. Pregnancy period

Trimester 1 Trimester 2 Trimester 3

Preterm birth

Low birth weight

Attributable number

Attributable fraction (%)

Attributable number

Attributable fraction (%)

690 (0, 1735) 4257 (3257, 5271) 4373 (3462, 5297)

1.08 (0, 2.71) 6.65 (5.09, 8.23) 7.84 (6.21, 9.50)

3318 (2836, 3775) 3451 (2967, 3943) 3993 (3496, 4469)

12.34 (10.55, 14.03) 12.83 (11.03, 14.66) 14.85 (13.00, 16.61)

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Unfortunately, the information was not available for our analysis. Furthermore, our study findings were limited by residual confounding. We failed to consider other potential risk factors in the study due to data unavailability (Goldenberg et al., 2008). For example, parental education or socioeconomic status, maternal nutritional status, hypertensive disorders of pregnancy and behavioral information (alcohol consumption and passive smoking exposure), which might confound the observed associations in the study (Hao et al., 2016; Woodruff et al., 2009). Finally, we don't have information on the type of preterm birth, so it can't be evaluated spontaneous PTB and medically-indicated PTB effect separately in this study. Our study indicates that maternal exposure to high levels of PM2.5 during pregnancy may be a risk factor for both PTB and LBW, especially for women at late stage of pregnancy. High levels of outdoor PM2.5 air pollution might have resulted in considerable burdens of PTBs and LBWs in the Pearl River Delta region, China.

trimester 1, 2 and 3, respectively (Supplementary Table S5). 4. Discussion Based on our review of the current literature, the present investigation is the largest multicity birth cohort study on the association between PM2.5 and adverse birth outcomes among Chinese populations. Using a total of 1.45 million births, this study revealed that maternal exposure to PM2.5 was associated with increased LBW and PTB. The estimated associations were generally robust to different model specifications. The results indicate that pregnant woman might be more vulnerable to the effects of PM2.5 in both trimester 2 and trimester 3 than in trimester 1. The past decade has witnessed increasing attention to exploring the relationship between exposure to PM2.5 and adverse birth outcomes. While some studies reported positive associations between PM2.5 exposure and both PTB (Arroyo et al., 2016; Ha et al., 2014; Hao et al., 2016; Kloog et al., 2012; Laurent et al., 2016; Rappazzo et al., 2014) and LBW (Gray et al., 2014; Harris et al., 2014; Holstius et al., 2012; Hyder et al., 2014; Qian et al., 2016; Savitz et al., 2014), nonsignificant associations were also reported for PTB (Fleischer et al., 2014; Gehring et al., 2011; Johnson et al., 2016a; Pereira et al., 2013) and LBW (Brauer et al., 2008; Gehring et al., 2011; Parker and Woodruff, 2008). For example, Xiao et al. reported that an increase of PM2.5 was associated with a 27% increase of PTB and a 22% increase of term LBW (Xiao et al., 2018). However, Gehring et al. (2011), conducted a study with 3853 singleton births between 1996 and 1997 in the Netherlands finding non-significant associations of PTB with PM2.5 for any pregnancy period (Gehring et al., 2011). The difference in study design, air pollution level, composition of PM, and sample size might account for this disagreement of observations. One unanswered question that has remained in the literature is which gestational window is more susceptible to air pollution? Consistent with a few previous studies (Arroyo et al., 2016; Johnson et al., 2016b; Sun et al., 2016), we observed that the second and/or third trimesters might be more sensitive exposure windows. In contrast, there are studies that suggest the first trimester might be the most sensitive window (Gehring et al., 2011; Lee et al., 2013). A previous meta-analysis supported our results that (Sapkota et al., 2012) exposure to PM2.5 during the later pregnancy might trigger the premature onset of labor by activating cytokines favoring inflammation (Vadillo-Ortega et al., 2014). On the other hand, higher exposure to PM2.5 during early pregnancy could lead to fetal malformation and miscarriage (Lin and Santolaya-Forgas, 1998). The observed associations between PM2.5 and adverse birth outcomes seemed not to be confounded by other air pollutants, expect for SO2. The relatively lower effects after controlling for SO2 in the model, which might be due to the relatively high correlations between PM2.5 and SO2 (r = 0.67, shown in Table s6). The high correlations could result in collinearity. Another possible reason might be that these two pollutants may share some similar emission sources and biological pathways in the effects on the adverse birth outcomes. More studies were warranted to further investigate this issue in future studies. The present study possessed several assets. Perhaps the most important one was the large sample size (1,455,026 newborns), which covered nine cities in the PRD region. Further, this area possessed high levels of air pollution, allowing for the assessment of its effects beyond the exposures in developed countries. For example, the mean PM2.5 concentrations during the whole pregnancy were 34.87 μg/m3 in the present study. Whereas, the concentrations ranged from 9.9 μg/m3 in Florida to 18.7 μg/m3 in California (Sun et al., 2016). Despite these assets, several limitations are worth noting. First, measurement errors were possible by using the air monitoring information in nearby air monitor stations. However, those errors are more likely to lead to nondifferential misclassification and produce a downward bias (Copeland et al., 1977). Additionally, some women moved during pregnancy.

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