Startseite Smog and risk of maternal and fetal birth outcomes: A retrospective study in Baoding, China
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Smog and risk of maternal and fetal birth outcomes: A retrospective study in Baoding, China

  • Yijing Zhai , Bei Wang , Liqiang Qin , Bin Luo , Ying Xie , Huanyu Hu , Hongzhen Du und Zengning Li EMAIL logo
Veröffentlicht/Copyright: 31. Mai 2022

Abstract

Pregnant women are more susceptible to smog pollution than the general population. This study focused on the association between smog and birth outcomes, considering both pregnant mothers and their offspring. In this retrospective study, conducted in Baoding between 2013 and 2016, we enrolled 842 participants. Birth outcomes were low birth weight (LBW), pregnancy-induced hypertension (PIH), gestational diabetes mellitus (GDM), and premature rupture of membranes (PROM). The overall prevalence of LBW, PIH, GDM, and PROM was 8.2%, 14.8%, 16.5%, and 12.1%, respectively. Compared with lower pollution level, higher pollution level of fine particulate matter (particulate matter with aerodynamics diameter <2.5 μm) (PM2.5), inhalable particle (particulate matter with aerodynamics diameter <10 μm) (PM10), and CO increased the risk of term with LBW. PM2.5, PM10, and NO2 increased the risk of PIH during different trimesters, while PM10 increased the risk of PROM during trimester 3. In conclusion, smog significantly affects the risk of adverse birth outcomes by different exposure time windows.

1 Introduction

According to the World Health Organization (WHO) air pollution database, China has higher levels of air pollution than Western countries [1]. Less than 1% of China’s 500 largest cities meet the air quality standards. With fast economic growth over the past four decades, the air quality in China, particularly in North China, has relatively deteriorated.

Smog seriously threatens human health and has become a hot topic for research and the public. Pregnant women and fetus are more susceptible to environmental factors, including smog pollution, than the general population. Exposure to PM2.5 (particulate matter with aerodynamics diameter <2.5 μm) in trimester 2 of pregnancy was associated with an increased risk of gestational diabetes mellitus (GDM) [2]. Prenatal exposure of the major air pollutants during the entire pregnancy could increase the risk of term low birth weight (LBW), while the susceptible window of the pollutants varied [3]. The risk of pregnancy-induced hypertension (PIH) syndrome is not only related to the air pollutants and concentrations but also closely related to different trimesters [4]. Meanwhile, the risk of premature rupture of membranes (PROM) could be increased by underlying infection, inflammation, oxidative stress, nutritional deficiencies, cigarette smoking, air pollutants’ exposure, and illicit drug use [5,6,7,8].

Apart from the adverse effects on pregnant women [4], smog pollution directly affects infants and has a long-term effect on their health conditions when they grow up, including hypertension [9], cardiac disease [10], and type 2 diabetes mellitus [11]. However, studies investigating the association of smog pollution with birth outcomes only considered either pregnant mothers or their offspring [12,13,14,15], few of them focused on both sides [16], and the results were inconsistent and controversial [3]. Furthermore, relative studies involving Chinese population are limited and lagged.

With this background, we performed a population-based retrospective study in Baoding, Hebei, a region with serious fog and haze pollution in China [17], to examine the effects of smog pollutants on the risk of birth outcomes of both pregnant mothers and their offspring to identify susceptible exposure windows. Given the cross-region and cross-basin smog pollution [18], this study provides valuable evidence for other pollution-exposed areas.

2 Materials and methods

2.1 Smog pollutants

From October 2013 to October 2016, the ongoing population-based retrospective study was conducted mainly to investigate the impact of environmental factors on pregnant outcomes. Data on smog pollutants were obtained from the Baoding Environmental Protection Bureau, located in Baoding, Hebei, China. This bureau is a subordinate unit of the Ministry of Ecology and Environment of the People’s Republic of China, which is responsible for the supervision and administration of environmental pollution prevention and control. An automated data reporting system equipped with satellite remote sensing, meteorologic, and land use information was used to collect the 24 h average concentration of six kinds of smog pollutants, namely, PM2.5, inhalable particle (particulate matter with aerodynamics diameter <10 μm) (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3). Median was used to represent the average concentration of individual pollutants during different trimesters, and the category of air quality index (AQI) that corresponded to the median value was used for the statistical analysis. AQI was categorized into good, mild pollution, moderate pollution, and above (Tables A1 and A2). Classification of pollutants in the current study is based on the degree of its impact on human health. “Good” means that the air had minimal effect on healthy population, “mild pollution” indicates that pollution caused irritation symptoms in healthy population, and “moderate pollution” means that it affects the heart or respiratory system in healthy population.

2.2 Study population

We limited the study population to the resident population in Baoding, which is close to the pollutant monitoring station. This study obtained participants’ residence information from the registration of medical records specific to the street and doorplate numbers. The duration of data collection was the same with the data of smog pollutants.

The clinical data were obtained from the electronic medical records system. A total of 1,050 participants were enrolled in this study. Among the 1,050 patients, 208 were excluded due to the lack of weight record before birth (n = 82), gestational weight gain (n = 31), number of pregnancies and parity (n = 43), education level (n = 41), and follow-up time (n = 11). Finally, 842 women were included in the statistical analysis (Figure 1). Given that the number of individuals with comorbidity was relatively small (<2% of the sample size), individuals with comorbidity were excluded in the final statistical analysis. Participants included were all term singleton live birth born (37 ≤ gestational weeks < 42). The participant’s number (prevalence) of term LBW, PIH, GDM, and PROM was 69 (8.2%), 125 (14.8%), 139 (16.5%), and 102 (12.1%), respectively. The first trimester of pregnancy was defined as gestational week 1 to week 12, the second trimester was defined as week 12+1 to week 27, and the third trimester of pregnancy was defined as from week 27+1 to birth [16].

Figure 1 
                  Process about inclusion and exclusion of participants.
Figure 1

Process about inclusion and exclusion of participants.

Maternal age (20–24, 25–29, 30–34, and ≥35) [19], gestational weight gain (appropriate weight gain, insufficient weight gain, or excessive weight gain), pre-pregnancy body mass index (BMI) (low body weight, normal type, overweight, or obesity), education level (<high school, high school/polytechnic school, college, or above), last menstrual date, delivery date, number of pregnancies (1, 2, or ≥3 times), and parity (1, 2, or ≥3 times) were included in the study. According to the American Academy of Medical Science [Institute of Medicine (IOM)] [20], the range of gestational weight gain for low-body-weight (BMI < 18.5 kg/m2) women is 12.5–18.0 kg, the weight gain for normal-type (18.5 kg/m2 ≤ BMI ≤ 24.9 kg/m2) women is 11.5–16.0 kg, the weight gain for overweight (25 kg/m2 ≤ BMI ≤ 29.9 kg/m2) women is 7.0–11.5 kg, and the weight gain for women with obesity (BMI ≥ 30 kg/m2) is 5.0–9.0 kg. In different BMI groups, gestational weight gain was appropriate when it was within the recommended range. People who had weight values below the recommended range had insufficient weight gain. By contrast, people who had weight values above the recommended range had excessive weight gain [20].

2.3 Observed outcomes

The outcomes of LBW, PIH, GDM, and PROM were defined on the basis of disease classification by the International Classification of Diseases, Tenth Revision. Term LBW is defined as a birth that occurred on or after the 37th week of gestation with weight <2,500 g [21]. PIH is defined as blood pressure ≥140/90 mm Hg manifested initially during pregnancy and normalized at 12 weeks postpartum [22]. PIH included pregnancy hypertension, preeclampsia, and eclampsia in this study. Preeclampsia is defined as gestational hypertension accompanied by proteinuria after 20 weeks of gestation, characterized by proteinuria and hypertension [23]. Eclampsia is defined as convulsions occurring on the basis of preeclampsia that cannot be explained by other causes. GDM refers to the first clinical manifestation of gestational diabetes caused by abnormal glucose metabolism after pregnancy [24]. Rupture of membranes before labor is defined as term PROM. PROM at gestational age <37 weeks is defined as premature birth or preterm PPROM, whereas PROM >37 weeks of gestation is defined as term PROM [22]. This current study aimed at analyzing term PROM.

  1. Ethics approval: The current study was reviewed and approved by the Ethics Committee of the First Hospital of Hebei Medical University (Approval number: 20180701).

2.4 Statistical analysis

All analyses were performed using the SPSS software version 21.0 (SPSS Inc., Chicago, IL, USA). Categorical variables were described as frequency (percentage) and were analyzed with chi-square tests. An unconditional binary logistic regression model was used to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) for associations between smog pollutant exposure during pregnancy period and risk of adverse birth outcomes adjusting for maternal age, gestational weight gain, pre-pregnancy BMI, education level, and number of pregnancies and parity. We examined the association by the following different exposure windows: entire pregnancy, trimester 1, trimester 2, and trimester 3. All statistical tests were two-sided, and P values <0.05 were statistically significant.

3 Results

3.1 Characteristics at baseline of birth outcomes

The characteristics at baseline of participants are summarized in Table 1. In total sample, nearly half of the pregnant women were from 25 to 29 years of age, and women over 35 accounted for the smallest percentage of the participants. The proportions of appropriate weight gain and excessive weight gain during pregnancy accounted for the largest. Nearly 10 percent of the participants were under low body weight before pregnancy, while the pre-pregnancy BMIs of most individuals were within the normal range. The differences in education level were obvious, which showed that the low-education level (<high school) and the high-education level (college or above) coexist. The distribution of number of pregnancies was relatively even, with about a third of pregnancies in each category. The proportions of parity in 1 and 2 accounted for over 90%. Most of the participants with adverse birth outcomes were under 30 years old and experienced excessive weight gain during pregnancy. Many individuals were overweight or obese before pregnancy, except for cases in PROM. The participants who underwent GDM and PROM had relatively higher-education levels. When the parity increased, the incidence of LBW, PIH, and PROM decreased.

Table 1

The characteristics at baseline of birth outcomes n (%)

Characteristics Total sample LBW PIH GDM PROM
Age (years)
 20–24 148 (17.6%) 15 (9.4) 28 (17.6) 20 (12.6) 25 (15.7)
 25–29 380 (45.1%) 30 (7.9) 42 (11.1) 54 (14.2) 55 (14.5)
 30–34 199 (23.6%) 15 (7.5) 32 (16.1) 32 (16.1) 9 (4.5)
 ≥35 104 (12.4%) 9 (8.7) 23 (22.1) 33 (31.7) 3 (12.5)
Gestational weight gain
Appropriate weight gain 337 (40.0%) 22 (7.3) 45 (14.9) 45 (14.9) 43 (14.2)
Insufficient weight gain 140 (16.6%) 11 (8.8) 11 (8.8) 20 (16.0) 15 (12.0)
Excessive weight gain 365 (43.3%) 36 (8.7) 69 (16.7) 74 (17.9) 44 (10.6)
Pre-pregnancy BMI
 Normal type 544 (64.6%) 32 (6.9) 49 (10.5) 64 (13.7) 62 (13.3)
 Low body weight 86 (10.2%) 8 (9.3) 7 (8.1) 8 (9.3) 15 (17.4)
 Overweight or obesity 212 (25.2%) 29 (10.0) 69 (23.9) 67 (23.2) 25 (8.7)
Education Level
 <High school 376 (44.7%) 44 (13.3) 77 (23.3) 46 (13.9) 39 (11.8)
 High school/polytechnic school 77 (9.1%) 8 (6.5) 15 (12.2) 15 (12.2) 15 (12.2)
 College or above 389 (46.2%) 17 (4.4) 33 (8.5) 78 (20.1) 48 (12.3)
Number of pregnancies
 1 320 (38.0%) 26 (8.1) 40 (12.5) 45 (14.1) 59 (18.4)
 2 237 (28.1%) 19 (8.1) 40 (16.9) 41 (17.4) 20 (8.5)
 ≥3 285 (33.8%) 24 (8.4) 45 (15.8) 53 (18.6) 23 (8.1)
Parity
 ≤1 446 (53.0%) 38 (8.5) 62 (13.9) 63 (14.1) 77 (17.3)
 2 322 (38.2%) 25 (7.8) 52 (16.1) 64 (19.9) 21 (6.5)
 ≥3 74 (8.8%) 6 (8.1) 11 (14.9) 12 (16.2) 4 (5.4)

Abbreviations: LBW: low birth weight, PIH: pregnancy-induced hypertension syndrome, GDM: gestational diabetes mellitus, PROM: premature rupture of membranes.

3.2 Correlations between covariables and outcomes

Among the covariables, only the education level was related to LBW. The risk of term LBW gradually decreased with the increase in the education level in the entire pregnancy and the three trimesters (Table 2). The risk of PIH gradually decreased with the education level and increased with the pre-pregnancy BMI in the entire pregnancy and the three trimesters (Tables 3 and 4). Meanwhile, the risk of term PROM gradually decreased with the parity number during trimester 3 (Table 5).

Table 2

The correlations between education levels and term low birth weight (OR, 95% CI)

< High school High school/polytechnic school College or above P-value
Entire pregnancy 1.00 0.366 (0.157, 0.857) 0.300 (0.166, 0.544) <0.0001
Trimester 1 1.00 0.358 (0.153, 0.837) 0.305 (0.169, 0.551) <0.0001
Trimester 2 1.00 0.451 (0.206, 0.987) 0.296 (0.166, 0.529) <0.0001
Trimester 3 1.00 0.463 (0.211, 1.017) 0.311 (0.173, 0.559) <0.0001
Table 3

The correlations between education levels and pregnancy-induced hypertension syndrome (OR, 95% CI)

< High school High school/polytechnic school College or above P-value
Entire pregnancy 1.00 0.454 (0.241, 0.856) 0.336 (0.214, 0.527) <0.0001
Trimester 1 1.00 0.434 (0.229, 0.823) 0.335 (0.214, 0.526) <0.0001
Trimester 2 1.00 0.507 (0.275, 0.935) 0.338 (0.215, 0.530) <0.0001
Trimester 3 1.00 0.526 (0.285, 0.971) 0.357 (0.228, 0.561) <0.0001
Table 4

The correlations between pre-pregnancy BMI and pregnancy-induced hypertension syndrome (OR, 95% CI)

Normal weight Low body weight Overweight or obesity P-value
Entire pregnancy 1.00 0.737 (0.314, 1.728) 2.273 (1.498, 3.451) <0.0001
Trimester 1 1.00 0.799 (0.343, 1.857) 2.496 (1.641, 3.787) <0.0001
Trimester 2 1.00 0.740 (0.318, 1.718) 2.458 (1.626, 3.717) <0.0001
Trimester 3 1.00 0.687 (0.294, 1.603) 2.260 (1.491, 3.425) <0.0001
Table 5

The correlations between parity and term premature rupture of membranes in trimester 3 (OR, 95% CI)

Parity number ≤1 2 ≥3 P-value
Trimester 3 1.00 0.348 (0.198, 0.610) 0.294 (0097, 0.885) <0.0001

3.3 Smog pollutants and maternal and fetal birth outcomes

The distribution of cases exposed to the pollutants at different trimesters is summarized in Table 6. The composition of pollutants varied among different trimesters, and the most serious pollutants were PM2.5 and PM10. Compared with “good” condition, exposure to mild pollution of PM2.5 and PM10 significantly increased the risk of term LBW during the entire pregnancy. The risk of LBW gradually increased as the pollution of PM2.5 worsened during trimester 1. Meanwhile, CO in mild pollution significantly increased such risk during trimester 3 (Table 7).

Table 6

Distribution of case exposed to various pollutants [n (%)]

Pollutants Category of AQI LBW PIH GDM PROM Pollutants Category of AQI LBW PIH GDM PROM
Entire pregnancy Trimester 2
PM2.5 Good 21 (4.6) 47 (10.2) 77 (16.7) 51 (11.1) PM2.5 Good 30 (9.6) 59 (18.8) 45 (14.3) 54 (17.2)
Mild pollution 48 (12.6) 78 (20.5) 62 (16.3) 51 (13.4) Mild pollution 16 (10.1) 23 (14.6) 26 (16.5) 13 (8.2)
PM10 Good 62 (7.5) 118 (14.2) 136 (16.4) 102 (12.3) Moderate pollution and above 23 (6.2) 43 (11.6) 68 (18.4) 35 (9.5)
Mild pollution 7 (53.8) 7 (53.8) 3 (23.1) 0 (0) PM10 Good 32 (9.6) 62 (18.5) 47 (14.0) 55 (16.4)
SO2 Good 69 (8.2) 125 (14.8) 139 (16.5) 102 (12.1) Mild pollution 37 (7.3) 43 (11.6) 92 (18.1) 47 (9.3)
NO2 Good 69 (8.2) 125 (14.8) 139 (16.5) 102 (12.1) SO2 Good 69 (8.2) 125 (14.8) 139 (16.5) 102 (12.1)
CO Good 69 (8.2) 125 (14.8) 139 (16.5) 102 (12.1) NO2 Good 64 (8.2) 112 (14.3) 130 (16.6) 98 (12.5)
O3 Good 69 (8.2) 125 (14.8) 139 (16.5) 102 (12.1) Mild pollution 5 (8.2) 13 (21.3) 9 (14.8) 4 (6.6)
Trimester 1 CO Good 69 (8.2) 125 (14.8) 139 (16.5) 102 (12.1)
PM2.5 Good 54 (7.1) 109 (14.2) 127 (16.6) 94 (12.3) O3 Good 69 (8.2) 125 (14.8) 139 (16.5) 102 (12.1)
Mild pollution 7 (11.1) 7 (11.1) 10 (15.9) 6 (9.5) Trimester 3
Moderate pollution and above 8 (57.1) 9 (64.3) 2 (14.3) 2 (14.3 PM2.5 Good 35 (7.1) 58 (11.7) 90 (18.2) 44 (8.9)
PM10 Good 54 (7.0) 110 (14.2) 129 (16.6) 95 (12.3) Mild pollution 7 (21.2) 13 (39.4) 6 (18.2) 5 (15.2)
Mild pollution 15 (22.4) 15 (22.4) 10 (14.9) 7 (10.4) Moderate pollution and above 27 (8.6) 54 (17.2) 43 (13.7) 53 (16.9)
SO2 Good 69 (8.2) 125 (14.8) 139 (16.5) 102 (12.1) PM10 Good 39 (7.7) 62 (12.2) 91 (17.9) 48 (9.4)
NO2 Good 66 (7.9) 121(14.5) 138(16.5) 102(12.2) Mild pollution 28 (8.5) 62 (18.8) 47 (14.3) 51 (15.5)
Mild pollution 3 (50.0) 4 (66.7) 1 (16.7) 0 (0) Moderate pollution and above 2 (40.0) 1 (20.0) 1 (20.0) 3 (60.0)
CO Good 69 (8.2) 125 (14.8) 139 (16.5) 102 (12.1) SO2 Good 69 (8.2) 125 (14.8) 139 (16.5) 102 (12.1)
O3 Good 69 (8.2) 125 (14.8) 139 (16.5) 102 (12.1) NO2 Good 51 (7.6) 87 (13.0) 110 (16.5) 75 (11.2)
Mild pollution 18 (10.3) 38 (21.7) 29 (16.6) 27 (15.4)
CO Good 66 (7.9) 121 (14.5) 137 (16.4) 99 (11.9)
Mild pollution 3 (37.5) 4 (50.0) 2 (25.0) 3 (37.5)
 O3 Good 69 (8.2) 125 (14.8) 139 (16.5) 102 (12.1)

Abbreviations: LBW: low birth weight, PIH: pregnancy-induced hypertension syndrome, GDM: gestational diabetes mellitus, PROM: premature rupture of membranes.

PM2.5: fine particulate matter (particulate matter with aerodynamics diameter less than 2.5 μm), PM10: inhalable particle (particulate matter with aerodynamics diameter less than 10 μm), SO2: sulfur dioxide, NO2: nitrogen dioxide, CO: carbon monoxide, O3: ozone.

Table 7

The effect of smog pollutants on LBW, PIH, and PROM (OR and 95% CI)

Good Mild pollution Moderate pollution and above P-value
LBW
Entire pregnancy
 PM2.5 1.00 2.60 (1.50–4.51) 0.001
 PM10 1.00 10.50 (3.15–35.01) <0.001
Trimester 1
 PM2.5 1.00 1.55 (0.67–3.62) 18.97 (5.97–60.32) <0.001
Trimester 3
 CO 1.00 4.55 (1.02–19.40) 0.047
PIH
Entire pregnancy
 PM2.5 1.00 1.96 (1.30–2.95) 0.001
 PM10 1.00 5.15 (1.58–16.77) 0.007
Trimester 1
 PM2.5 1.00 0.74 (0.32–1.70) 12.09 (3.73–39.17) <0.001
Trimester 2
 PM10 1.00 0.58 (0.38–0.89) 0.012
 NO2 1.00 2.39 (1.17–4.85) 0.016
Trimester 3
 PM2.5 1.00 3.40 (1.53–7.53) 1.44 (0.95–2.18) 0.006
PROM
Trimester 3
 PM10 1.00 1.72 (1.11–2.65) 18.82 (2.69–131.45) 0.001

Abbreviations: LBW: low birth weight, PIH: pregnancy-induced hypertension syndrome, GDM: gestational diabetes mellitus, PROM: premature rupture of membranes.

PM2.5: fine particulate matter (particulate matter with aerodynamics diameter less than 2.5 μm), PM10: inhalable particle (particulate matter with aerodynamics diameter less than 10 μm), SO2: sulfur dioxide, NO2: nitrogen dioxide, CO: carbon monoxide, O3: ozone.

When pregnant women were exposed to mild pollution of PM2.5 and PM10 during the entire pregnancy, PIH risk significantly increased compared with those in “good” condition. The risk also significantly increased by mild pollution of PM10 and NO2 during trimester 2. Mild pollution, moderate pollution, and above of PM2.5 also increased the risk of PIH during trimester 1 and trimester 3 (Table 7).

The risk of term PROM gradually increased when PM10 pollution worsened during trimester 3. Pregnant women were more at risk of experiencing term PROM by 1.72 times when exposed to moderate pollution and by 18.82 times when exposed to moderate pollution and above than those participants in “good” condition (Table 7).

No correlation between smog pollutants and GDM was found (Table 8).

Table 8

Effect of factors on GDM (four trimesters)1

Factors OR (95% CI) P-value
Age
 20–24 1.00 0.006
 25–29 0.94 (0.53, 1.67)
 30–34 0.98 (0.52, 1.84)
 ≥35 2.27 (1.18, 4.36)
Pre-pregnancy BMI
 Normal type 1.00 0.004
 Low body weight 0.67 (0.31, 1.46)
 Overweight or obesity 1.79 (1.20, 2.67)
Education level
 <High school 1.00 0.019
 High school/polytechnic school 0.88 (0.46, 1.66)
 College or above 1.67 (1.11, 2.57)

Abbreviation: GDM: gestational diabetes mellitus.

1Results in trimester 1, trimester 2, and trimester 3 were consistent with those during entire pregnancy.

4 Discussion

We employed an estimation of six components of smog pollutants (PM2.5, PM10, SO2, NO2, CO, and O3) to examine the associations between four outcomes (term LBW, PIH, GDM, and PROM) in Baoding, Hebei, China, from 2013 to 2016. PM concentrations in many developing countries (e.g., India and China) are 5–10 times higher than in developed countries [25]. Hebei is a province with serious fog and haze pollution in China [26]. According to the ranking of Smog Comprehensive Pollution Index of 74 major cities in China, from October 2013 to October 2016, 32 cities were ranked as the most seriously polluted cities during 36 months [27]. In these 32 cities, nine are affiliated with Hebei, and Baoding ranks second (Figure 2).

Figure 2 
               Frequency chart of the 10 most seriously polluted cities according to the ranking of Ambient Air Comprehensive Pollution Index (2013.10–2016.10). According to the ranking of Ambient Air Comprehensive Pollution Index of 74 major cities in China from October 2013 to October 2016, issued by the Ministry of Ecological Environment of the People’s Republic of China, 10 most seriously polluted cities were counted for 3 years (36 months). Totally, 32 cities have been ranked in most seriously polluted cities during 36 months. In these 32 cities, nine cities are affiliated with Hebei, accounting for nearly 30%. Baoding entered 35 times in the chart of most seriously polluted cities, ranking No. 2. *The city belonging to Hebei.
Figure 2

Frequency chart of the 10 most seriously polluted cities according to the ranking of Ambient Air Comprehensive Pollution Index (2013.10–2016.10). According to the ranking of Ambient Air Comprehensive Pollution Index of 74 major cities in China from October 2013 to October 2016, issued by the Ministry of Ecological Environment of the People’s Republic of China, 10 most seriously polluted cities were counted for 3 years (36 months). Totally, 32 cities have been ranked in most seriously polluted cities during 36 months. In these 32 cities, nine cities are affiliated with Hebei, accounting for nearly 30%. Baoding entered 35 times in the chart of most seriously polluted cities, ranking No. 2. *The city belonging to Hebei.

Fleischer et al. investigated the association of satellite-based estimates of PM2.5 and preterm birth and LBW (all gestational ages) by using the WHO Global Survey on Maternal and Perinatal Health in Africa, Asia, and Latin America [1]. In China, LBW was associated with the 3rd and 4th quartiles of PM2.5 (OR = 1.08; 95% CI: 0.84, 1.40; and OR = 1.99; 95% CI: 1.06, 3.72) [1]. An increase in the concentration of PM2.5 reduced the term birth weight during the entire pregnancy [28], thereby conforming to our results. In the present study, the risk of term LBW gradually increased with the increase of PM2.5 concentrations during the entire pregnancy and trimester 1. In addition, with the increase of PM10 concentrations, the risk of LBW under mild pollution was 10.5 times higher than that in good condition during the entire pregnancy. Other researchers also found that PM10 at 10 μg/m3 increments in trimester 2 led to decreases in birth weight of 5.65 g [29]. Meanwhile, the risk of term LBW increases by 4.55 times with the increase in CO concentrations during trimester 3, as supported by the study of Li et al. [3]. In general, the risk of LBW in Baoding was higher than that in China, which suggests that more effective environmental protection measures should be taken to protect pregnant women, especially in the North area where severe air pollution exists.

PM2.5 and preeclampsia, which is one disease of PIH, are positively associated [4,30]. Similar with Mobasher’s results [4], the current study found that exposures to PM2.5 at trimester 1 significantly increase the PIH. In addition to trimester 1, this disadvantageous effect was observed during the entire pregnancy and trimester 3. Low concentrations of PM2.5 and PM10 did not increase the risk of PIH in trimesters 1 and 2 due to the low incidence of PIH within these trimesters. However, the harmful effects to health were aggravated, and the risk of PIH escalated when PM2.5 concentration increased. Besides, the current study also found that pregnant women in trimester 3 are more sensitive to PM2.5 pollution, and the risk of PIH increased in this period. A study performed by Bai et al. found that PM10 exposure is associated with an increased risk of PIH [31]. In the present study, the risk of PIH increased with the increase of PM10 concentration during the entire pregnancy, not during trimester 2. In addition, pregnant women were more susceptible to NO2 exposure during trimester 2, resulting in an increased risk of PIH in this term. Thus, the risk of PIH was not only related to the air pollutants and the concentrations but also closely related to different trimesters.

In the present study, the risk of term PROM gradually increased with the increase in PM10. Wallace et al. reported that PM10 and PROM have a negative correlation [32]. The discrepancy might be explained by the concern on term PROM as a birth outcome in the current study, whereas the outcomes involved in their study were PROM at any gestational period and PPROM. We focused on term PROM for the following reasons. Approximately 70% of PROM occur at term, which is the cause of approximately one-third of all preterm births [33]. Term PROM is a significant cause of perinatal morbidity and mortality [33]. We also studied the relationship between PM10 and PROM during other periods of pregnancy, and no significant relationship existed between them (data not shown). Despite these suggested associations, the specific mechanism between air pollution and PROM remains unclear, and further studies were needed to shed light on potential mechanisms.

Silvestrin et al. found that high maternal education showed a 33% protective effect against LBW [34]. The current results were similar with this finding in which the risk of term LBW gradually decreased with the increase in the education level in all trimesters. Maternal education is a suitable variable to measure inequality in health care and has been used to assess birth outcomes [35,36]. As extensively studied worldwide, education is the strongest socioeconomic predictor of health status and is the most important determinant of birth weight in a population [37].

Seung Chik Jwa found that the low-education-level group had higher systolic and diastolic blood pressure levels in the early pregnancy. However, the same associations were not found after adjusting for pre-pregnancy BMI [38]. The current study found that education level indicated a protective effect on the risk of PIH during the entire pregnancy and during trimesters 1, 2, or 3. The risk of PIH decreased with the increase in the education level. Moreover, the conclusion was based on the correction of all the confounding factors, which include the pre-pregnancy BMI. People with high-education levels are concentrated on healthy lifestyle, eating habits, and prenatal checkups, which should be reasonable and standardized. This statement might be the reason for the current findings above. According to Amoakoh-Coleman et al., pregnant women who were obese at baseline had a threefold increased risk of PIH compared with which with normal BMI [Relative risk (RR) = 3.01 (1.06–8.52), P = 0.04] [39]. The current study confirmed this result and showed evidence that the risk of PIH gradually increased with the increase in pre-pregnancy BMI during the entire pregnancy and during trimesters 1, 2, or 3.

The current study also revealed that parity is a protective factor for term PROM, resulting in the gradual decrease in the risk of term PROM as parity increased (OR = 0.294; 95% CI: 0.097, 0.885), conforming to the study accomplished by Jiang et al. in Beijing [40].

No association between smog pollutants and GDM was found in this current study. However, the risk of GDM gradually increased with the increase in pre-pregnancy BMI during the entire pregnancy and individual three trimesters. Dave found that BMI ≥25 kg/m2 is a strong risk factor for GDM [41]. In the present study, age also increased the risk of GDM. The risk of GDM in >35-year-old women was 2.27 times higher than that in 20–24-year-old women. A survey from Korea also implies that older maternal age is associated with the development of GDM [42]. The fact that women with higher-education level had a higher risk of GDM was linked to be their later pregnancy and older age. More research should be carried out to clarify the role of pollution in the risk of GDM.

It is more comprehensive to focus on the adverse pregnancy outcomes of both pregnant mothers and the newborns in the present study. And the city we concerned could be regarded as a representative of cities with serious air pollution in North China. The results may shed light on pregnant women’s health, medical institutions’ rational resource allocations, and decision-makers’ choices of environmental measures.

There are several limitations in this study. First, the lack of available information regarding physical activity, nutritional status, smoking, and alcohol consumption might have effects on the association between smog and birth outcomes. Second, this study was an observational, single-centered study. Further studies with multi-city, multi-center, and larger samples are needed for more evidence.

5 Conclusion

In this population-based retrospective study, the susceptible exposure windows between smog pollutants and the risk of birth outcomes were revealed. Compared with the lower pollution level, the higher pollution level of PM2.5, PM10, and CO increased the risk of term LBW during trimester 1, trimester 3, and the entire pregnancy. PM2.5, PM10, and NO2 increased the risk of PIH during different trimesters, while PM10 increased the risk of PROM during trimester 3. The findings of our analysis may help decision-makers to develop targeted policies and environmental measures to reduce the health hazards of air pollution.


# These authors contributed equally to this work.


Acknowledgments

The authors thank Jing Zhao for her contributions on data analysis. They also thank all the pregnant mothers and their offspring who participated in this study, as well as those who were unable to be included in the author list but assisted us in writing and language help throughout this whole study process.

  1. Funding information: This work was supported by the grant from the National Natural Science Foundation of China (grant number: 81773430). The funding sources from Zengning Li had a role in the design of the study.

  2. Author contributions: Zengning Li, the study sponsor, designed this study. Yijing Zhai performed data collection and produced the initial draft of the manuscript. Bei Wang and Liqiang Qin collated the data and carried out data analysis. Bin Luo and Ying Xie revised the figures and tables. Hongzhen Du and Huanyu Hu contributed to drafting the manuscript. All authors have read and approved the final submitted manuscript.

  3. Conflict of interest: The authors declare that there is no competing interest.

  4. Data availability statement: The datasets generated/analyzed during the current study are available from Zengning Li.

Appendix

Table A1

Individual air quality index and corresponding pollutants concentration limits (24h mean concentration)*

AQI SO2 (μg/m3) NO2 (μg/m3) PM10 (μg/m3) CO (mg/m3) O3 (μg/m3) PM2.5 (μg/m3)
0 0 0 0 0 0 0
50 50 40 50 2 160 35
100 150 80 150 4 200 75
150 475 180 250 14 300 115
200 800 280 350 24 400 150

* Extracted from the Environmental Air Quality Index (AQI) Technical Regulations (for Trial Implementation) (HJ633 to 2012) issued by the Ministry of Environmental Protection of the People's Republic of China.

Abbreviations: PM2.5: fine particulate matter (particulate matter with aerodynamics diameter less than 2.5 μm), PM10: inhalable particle (particulate matter with aerodynamics diameter less than 10μm), SO2: sulfur dioxide, NO2: nitrogen dioxide, CO: carbon monoxide, O3: ozone.

Table A2

Air quality index and impact on health*

Level (AQI) Category # Impact on health
≤2nd degree (≤100) Good Some pollutants have delicate effect on healthy population except for a very few extremely sensitive people.
3rd degree (101–150) Mild Pollution Irritation symptoms in healthy population.
4th degree (151–200) Moderate Pollution May be have an impact on the heart or respiratory system in healthy population.

*Extracted from the Environmental Air Quality Index (AQI) Technical Regulations (for Trial Implementation) (HJ633 to 2012) issued by the Ministry of Environmental Protection of the People's Republic of China.

*Concentrations of various smog pollutants were divided into different air quality index categories according to their corresponding air quality index (AQI). The category of air quality index was included in statistical analysis in current study.

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Received: 2021-11-02
Revised: 2022-03-30
Accepted: 2022-04-20
Published Online: 2022-05-31

© 2022 Yijing Zhai et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  133. Risk perception and affective state on work exhaustion in obstetrics during the COVID-19 pandemic
  134. lncRNA-AC130710/miR-129-5p/mGluR1 axis promote migration and invasion by activating PKCα-MAPK signal pathway in melanoma
  135. SNRPB promotes cell cycle progression in thyroid carcinoma via inhibiting p53
  136. Xylooligosaccharides and aerobic training regulate metabolism and behavior in rats with streptozotocin-induced type 1 diabetes
  137. Serpin family A member 1 is an oncogene in glioma and its translation is enhanced by NAD(P)H quinone dehydrogenase 1 through RNA-binding activity
  138. Silencing of CPSF7 inhibits the proliferation, migration, and invasion of lung adenocarcinoma cells by blocking the AKT/mTOR signaling pathway
  139. Ultrasound-guided lumbar plexus block versus transversus abdominis plane block for analgesia in children with hip dislocation: A double-blind, randomized trial
  140. Relationship of plasma MBP and 8-oxo-dG with brain damage in preterm
  141. Identification of a novel necroptosis-associated miRNA signature for predicting the prognosis in head and neck squamous cell carcinoma
  142. Delayed femoral vein ligation reduces operative time and blood loss during hip disarticulation in patients with extremity tumors
  143. The expression of ASAP3 and NOTCH3 and the clinicopathological characteristics of adult glioma patients
  144. Longitudinal analysis of factors related to Helicobacter pylori infection in Chinese adults
  145. HOXA10 enhances cell proliferation and suppresses apoptosis in esophageal cancer via activating p38/ERK signaling pathway
  146. Meta-analysis of early-life antibiotic use and allergic rhinitis
  147. Marital status and its correlation with age, race, and gender in prognosis of tonsil squamous cell carcinomas
  148. HPV16 E6E7 up-regulates KIF2A expression by activating JNK/c-Jun signal, is beneficial to migration and invasion of cervical cancer cells
  149. Amino acid profiles in the tissue and serum of patients with liver cancer
  150. Pain in critically ill COVID-19 patients: An Italian retrospective study
  151. Immunohistochemical distribution of Bcl-2 and p53 apoptotic markers in acetamiprid-induced nephrotoxicity
  152. Estradiol pretreatment in GnRH antagonist protocol for IVF/ICSI treatment
  153. Long non-coding RNAs LINC00689 inhibits the apoptosis of human nucleus pulposus cells via miR-3127-5p/ATG7 axis-mediated autophagy
  154. The relationship between oxygen therapy, drug therapy, and COVID-19 mortality
  155. Monitoring hypertensive disorders in pregnancy to prevent preeclampsia in pregnant women of advanced maternal age: Trial mimicking with retrospective data
  156. SETD1A promotes the proliferation and glycolysis of nasopharyngeal carcinoma cells by activating the PI3K/Akt pathway
  157. The role of Shunaoxin pills in the treatment of chronic cerebral hypoperfusion and its main pharmacodynamic components
  158. TET3 governs malignant behaviors and unfavorable prognosis of esophageal squamous cell carcinoma by activating the PI3K/AKT/GSK3β/β-catenin pathway
  159. Associations between morphokinetic parameters of temporary-arrest embryos and the clinical prognosis in FET cycles
  160. Long noncoding RNA WT1-AS regulates trophoblast proliferation, migration, and invasion via the microRNA-186-5p/CADM2 axis
  161. The incidence of bronchiectasis in chronic obstructive pulmonary disease
  162. Integrated bioinformatics analysis shows integrin alpha 3 is a prognostic biomarker for pancreatic cancer
  163. Inhibition of miR-21 improves pulmonary vascular responses in bronchopulmonary dysplasia by targeting the DDAH1/ADMA/NO pathway
  164. Comparison of hospitalized patients with severe pneumonia caused by COVID-19 and influenza A (H7N9 and H1N1): A retrospective study from a designated hospital
  165. lncRNA ZFAS1 promotes intervertebral disc degeneration by upregulating AAK1
  166. Pathological characteristics of liver injury induced by N,N-dimethylformamide: From humans to animal models
  167. lncRNA ELFN1-AS1 enhances the progression of colon cancer by targeting miR-4270 to upregulate AURKB
  168. DARS-AS1 modulates cell proliferation and migration of gastric cancer cells by regulating miR-330-3p/NAT10 axis
  169. Dezocine inhibits cell proliferation, migration, and invasion by targeting CRABP2 in ovarian cancer
  170. MGST1 alleviates the oxidative stress of trophoblast cells induced by hypoxia/reoxygenation and promotes cell proliferation, migration, and invasion by activating the PI3K/AKT/mTOR pathway
  171. Bifidobacterium lactis Probio-M8 ameliorated the symptoms of type 2 diabetes mellitus mice by changing ileum FXR-CYP7A1
  172. circRNA DENND1B inhibits tumorigenicity of clear cell renal cell carcinoma via miR-122-5p/TIMP2 axis
  173. EphA3 targeted by miR-3666 contributes to melanoma malignancy via activating ERK1/2 and p38 MAPK pathways
  174. Pacemakers and methylprednisolone pulse therapy in immune-related myocarditis concomitant with complete heart block
  175. miRNA-130a-3p targets sphingosine-1-phosphate receptor 1 to activate the microglial and astrocytes and to promote neural injury under the high glucose condition
  176. Review Articles
  177. Current management of cancer pain in Italy: Expert opinion paper
  178. Hearing loss and brain disorders: A review of multiple pathologies
  179. The rationale for using low-molecular weight heparin in the therapy of symptomatic COVID-19 patients
  180. Amyotrophic lateral sclerosis and delayed onset muscle soreness in light of the impaired blink and stretch reflexes – watch out for Piezo2
  181. Interleukin-35 in autoimmune dermatoses: Current concepts
  182. Recent discoveries in microbiota dysbiosis, cholangiocytic factors, and models for studying the pathogenesis of primary sclerosing cholangitis
  183. Advantages of ketamine in pediatric anesthesia
  184. Congenital adrenal hyperplasia. Role of dentist in early diagnosis
  185. Migraine management: Non-pharmacological points for patients and health care professionals
  186. Atherogenic index of plasma and coronary artery disease: A systematic review
  187. Physiological and modulatory role of thioredoxins in the cellular function
  188. Case Reports
  189. Intrauterine Bakri balloon tamponade plus cervical cerclage for the prevention and treatment of postpartum haemorrhage in late pregnancy complicated with acute aortic dissection: Case series
  190. A case of successful pembrolizumab monotherapy in a patient with advanced lung adenocarcinoma: Use of multiple biomarkers in combination for clinical practice
  191. Unusual neurological manifestations of bilateral medial medullary infarction: A case report
  192. Atypical symptoms of malignant hyperthermia: A rare causative mutation in the RYR1 gene
  193. A case report of dermatomyositis with the missed diagnosis of non-small cell lung cancer and concurrence of pulmonary tuberculosis
  194. A rare case of endometrial polyp complicated with uterine inversion: A case report and clinical management
  195. Spontaneous rupturing of splenic artery aneurysm: Another reason for fatal syncope and shock (Case report and literature review)
  196. Fungal infection mimicking COVID-19 infection – A case report
  197. Concurrent aspergillosis and cystic pulmonary metastases in a patient with tongue squamous cell carcinoma
  198. Paraganglioma-induced inverted takotsubo-like cardiomyopathy leading to cardiogenic shock successfully treated with extracorporeal membrane oxygenation
  199. Lineage switch from lymphoma to myeloid neoplasms: First case series from a single institution
  200. Trismus during tracheal extubation as a complication of general anaesthesia – A case report
  201. Simultaneous treatment of a pubovesical fistula and lymph node metastasis secondary to multimodal treatment for prostate cancer: Case report and review of the literature
  202. Two case reports of skin vasculitis following the COVID-19 immunization
  203. Ureteroiliac fistula after oncological surgery: Case report and review of the literature
  204. Synchronous triple primary malignant tumours in the bladder, prostate, and lung harbouring TP53 and MEK1 mutations accompanied with severe cardiovascular diseases: A case report
  205. Huge mucinous cystic neoplasms with adhesion to the left colon: A case report and literature review
  206. Commentary
  207. Commentary on “Clinicopathological features of programmed cell death-ligand 1 expression in patients with oral squamous cell carcinoma”
  208. Rapid Communication
  209. COVID-19 fear, post-traumatic stress, growth, and the role of resilience
  210. Erratum
  211. Erratum to “Tollip promotes hepatocellular carcinoma progression via PI3K/AKT pathway”
  212. Erratum to “Effect of femoral head necrosis cystic area on femoral head collapse and stress distribution in femoral head: A clinical and finite element study”
  213. Erratum to “lncRNA NORAD promotes lung cancer progression by competitively binding to miR-28-3p with E2F2”
  214. Retraction
  215. Expression and role of ABIN1 in sepsis: In vitro and in vivo studies
  216. Retraction to “miR-519d downregulates LEP expression to inhibit preeclampsia development”
  217. Special Issue Computational Intelligence Methodologies Meets Recurrent Cancers - Part II
  218. Usefulness of close surveillance for rectal cancer patients after neoadjuvant chemoradiotherapy
Heruntergeladen am 8.9.2025 von https://www.degruyterbrill.com/document/doi/10.1515/med-2022-0489/html
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