Association of Long-term Exposure to Airborne Particulate Matter of 1 μm or Less With Preterm Birth in China

Yuan-yuan Wang; Qin Li; Yuming Guo; Hong Zhou; Xiaobin Wang; Qiaomei Wang; Haiping Shen; Yiping Zhang; Donghai Yan; Ya Zhang; Hongguang Zhang; Shanshan Li; Gongbo Chen; Jun Zhao; Yuan He; Ying Yang; Jihong Xu; Yan Wang; Zuoqi Peng; Hai-Jun Wang; Xu Ma

doi:10.1001/jamapediatrics.2017.4872

. 2018 Jan 2;172(3):e174872. doi: 10.1001/jamapediatrics.2017.4872

Association of Long-term Exposure to Airborne Particulate Matter of 1 μm or Less With Preterm Birth in China

Yuan-yuan Wang ^1,², Qin Li ^1,³, Yuming Guo ^4,⁵, Hong Zhou ^1,³, Xiaobin Wang ^6,⁷, Qiaomei Wang ⁸, Haiping Shen ⁸, Yiping Zhang ⁸, Donghai Yan ⁸, Ya Zhang ², Hongguang Zhang ², Shanshan Li ⁵, Gongbo Chen ⁵, Jun Zhao ², Yuan He ², Ying Yang ², Jihong Xu ², Yan Wang ², Zuoqi Peng ², Hai-Jun Wang ^1,^3,^✉, Xu Ma ^1,^2,^✉

¹Environmental and Spatial Epidemiology Research Center, National Human Genetic Resources Center, Beijing, China

²National Research Institute for Family Planning, Beijing, China

³Department of Maternal and Child Health, School of Public Health, Peking University, Beijing, China

⁴Institute for Environment and Climate Research, Jinan University, Guangzhou, China

⁵Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia

⁶Center on the Early Life Origins of Disease, Department of Population, Family, and Reproductive Health, Bloomberg School of Public Health, The Johns Hopkins University, Baltimore, Maryland

⁷Department of Pediatrics, School of Medicine, The Johns Hopkins University, Baltimore, Maryland

⁸Department of Maternal and Child Health, National Health and Family Planning Commission of the People’s Republic of China, Beijing

^✉

Corresponding Authors: Hai-Jun Wang, PhD, Department of Maternal and Child Health, School of Public Health, Peking University, No. 38 Xueyuan Rd, Beijing 100191, China (whjun1@bjmu.edu.cn) and Xu Ma, MS, National Research Institute for Family Planning, No. 12 Dahuishi Rd, Beijing 100081, China (nfpcc_ma@163.com).

Accepted for Publication: October 25, 2017.

Correction: This article was corrected on March 5, 2018, to correct the degree for Qin Li from PhD to MMSc in the Byline, revise how Mr Li is addressed in the Author Contributions and Funding/Support sections, and replace the maps of China in Figure 1 as well as eFigure 2 in the Supplement to fix a portion of the country’s western border.

Published Online: January 2, 2018. doi:10.1001/jamapediatrics.2017.4872

Author Contributions: Drs Y.-y. Wang and Guo and Mr Q. Li contributed equally and are considered co–first authors; Dr H.-J. Wang and Prof Ma contributed equally, had full access to all of the study data, and take responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Y.-y. Wang, Q. Li, Guo, H.-J. Wang, Ma.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Y.-y. Wang, Q. Li, Guo.

Critical revision of the manuscript for important intellectual content: All authors.

Administrative, technical, or material support: Y.-y. Wang, Q. Li, Guo, Zhou, X. Wang, Q. Wang, Shen, Yiping Zhang, Yan, Ya Zhang, H. Zhang, S. Li, Chen, Zhao, He, Yang, Xu, Y. Wang, Peng.

Study supervision: H.-J. Wang, Ma.

Conflict of Interest Disclosures: None reported.

Funding/Support: Dr Y.-y. Wang and Prof Ma are supported by grants 2016YFC1000300 and 2016YFC1000307 from the National Key Research and Development Program. Mr Q. Li and Dr H.-J. Wang are supported by grants 81573170 from the National Natural Science Foundation of China and 11-064 from the China Medical Board. Dr Guo is supported by Career Development Fellowship APP1107107 from the Australian National Health and Medical Research Council (NHMRC). Dr S. Li is supported by Early Career Fellowship APP1109193 from the Australian NHMRC and seed grant APP1030259 from the Centre of Research Excellence–Centre for Air Quality and Health Research and Evaluation.

Role of the Funder/Sponsor: The funding organizations had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Additional Contributions: Tami R. Bartell, MPH, the Mary Ann & J. Milburn Smith Child Health Research, Outreach, and Advocacy Center, Ann & Robert H. Lurie Children’s Hospital of Chicago, Chicago, Illinois, contributed to revising an early draft of the manuscript. Ms Bartell was not financially compensated. We thank the health professionals in 30 provinces across China for their assistance with this National Free Preconception Health Examination Project.

^✉

Corresponding author.

PMCID: PMC5885853 PMID: 29297052

This national cohort study of more than 1.3 million births in China from December 1, 2013, to November 30, 2014, assesses the association of concentration of airborne particulate matter of 1 μm or less with the risk of preterm birth and identifies subgroups of pregnant women who may be vulnerable.

Key Points

Question

Is the concentration of airborne particulate matter with a median diameter of 1 μm or less (PM₁) associated with the risk of preterm birth in China?

Findings

This national cohort study of more than 1.3 million births found that increases in PM₁ concentration of 10 μg/m³ during the entire pregnancy as well as at each trimester were significantly associated with an increased risk of preterm birth.

Meaning

Exposure to PM₁ air pollution is associated with an increased risk of preterm birth, and control measures to reduce PM₁ air pollution may lower the future incidence of preterm birth.

Abstract

Importance

Airborne particulate matter pollution has been associated with preterm birth (PTB) in some studies. However, most of these studies assessed only populations living near monitoring stations, and the association of airborne particulate matter having a median diameter of 1 μm or less (PM₁) with PTB has not been studied.

Objective

To evaluate whether PM₁ concentrations are associated with the risk of PTB.

Design, Setting, and Participants

This national cohort study used National Free Preconception Health Examination Project data collected in 324 of 344 prefecture-level cities from 30 provinces of mainland China. In total, 1 300 342 healthy singleton pregnancies were included from women who were in labor from December 1, 2013, through November 30, 2014. Data analysis was conducted between December 1, 2016, and April 1, 2017.

Exposures

Predicted weekly PM₁ concentration data collected using satellite remote sensing, meteorologic, and land use information matched with the home addresses of pregnant women.

Main Outcomes and Measures

Preterm birth (<37 gestational weeks). Gestational age was assessed using the time since the first day of the last menstrual period. Cox proportional hazards regression analysis was used to examine the associations between trimester-specific PM₁ concentrations and PTB after controlling for temperature, seasonality, spatial variation, and individual covariates.

Results

Of the 1 300 342 singleton live births at the gestational age of 20 to 45 weeks included in this study, 104 585 (8.0%) were preterm. In fully adjusted models, a PM₁ concentration increase of 10 μg/m³ over the entire pregnancy was significantly associated with increased risk of PTB (hazard ratio [HR], 1.09; 95% CI, 1.09-1.10), very PTB as defined as gestational age from 28 through 31 weeks (HR, 1.20; 95% CI, 1.18-1.23), and extremely PTB as defined as 20 through 27 weeks’ gestation (HR, 1.29; 95% CI, 1.25-1.34). Pregnant women who were older (30-50 years) at conception (HR, 1.13; 95% CI, 1.11-1.14), were overweight before pregnancy (HR, 1.13; 95% CI, 1.11-1.15), had a rural household registration (HR, 1.09; 95% CI, 1.09-1.10), worked as farmers (HR, 1.10; 95% CI, 1.09-1.11), and conceived in autumn (HR, 1.48; 95% CI, 1.46-1.50) appeared to be more sensitive to PM₁ exposure than their counterparts.

Conclusions and Relevance

Results from this national cohort study examining more than 1.3 million births indicated that exposure to PM₁ air pollution was associated with an increased risk of PTB in China. These findings will provide evidence to inform future research studies, public health interventions, and environmental policies.

Introduction

The World Health Organization reported in 2010 that there were 12.9 million preterm births (PTBs) per year worldwide (9.6% of total births), and PTB has become the leading cause of perinatal mortality and morbidity.^1,2 Preterm birth can lead to not only neonatal mortality but also lifelong disabilities that negatively contribute to a range of pulmonary, circulatory, and neurologic outcomes, resulting in $26.2 billion in medical costs per year in the United States alone.³ The etiology of PTB remains unclear, although biological, psychological, social, and environmental factors are thought to play significant roles.

An increasing number of studies have examined the association between particulate matter air pollution and PTB, including studies in Australia, Canada, England, the United States, South Korea, Spain, and China.^{4,5,6,7,8,9,10,11} However, those previous studies have primarily focused on airborne particulate matter with diameters of 10 μm (PM₁₀) or less or of 2.5 μm (PM_2.5) and greater. Compared with PM_2.5 or PM₁₀, PM₁ (ie, an airborne particulate diameter of 1.0 μm or less) has a higher surface area to mass ratio and can reach the lung alveoli. Particulate matter of this size has been shown to activate multiple pathophysiological processes, which may in turn contribute to PTB.^12,13 Although PM₁ contributes to nearly 80% of PM_2.5,¹⁴ no epidemiological study has examined the association between PM₁ and PTB to our knowledge. Thus, there is a critical need to investigate the association between PM₁ exposure during pregnancy and PTB.

In addition, even in those studies examining PM_2.5 and PM₁₀, variability in the exposure estimates and analysis methods has led to inconsistent results. Because most of the studies have been conducted in developed countries with relatively lower levels of PM air pollution and smaller exposure ranges,⁴ the power to generate robust results may have been restricted. Furthermore, most studies have been based on birth record data, meaning that the studies were unable to take into account the mother’s socioeconomic and behavioral characteristics, which can contribute to the estimates of the effects of PM air pollution.¹⁵ Most previous studies have treated PTB as a binary variable instead of using multiple categories, raising some limitations when trying to associate PTB with each degree of prematurity. The resulting gap in evidence limits the formulation of effective policies regulating air pollution, especially in areas with a wide range of PM pollution.

In the present study, we obtained estimated concentrations of PM₁ by using satellite-based aerosol optical depth data, land use information, and meteorologic data and applied these to a national birth cohort across mainland China that included both urban and rural areas. The primary objective was to evaluate whether PM₁ pollution levels were associated with the risk of PTB in a cohort of 1 300 342 births in China from December 1, 2013, to November 30, 2014. We also aimed to identify subgroups of pregnant women vulnerable to PM₁.

Methods

Study Design and Participants

Data for this national cohort study were extracted from the National Free Preconception Health Examination Project (NFPHEP), which was launched by the Chinese National Health and Family Planning Commission and Ministry of Finance in 2010 to provide free preconception health examinations and follow-up of pregnancy outcomes for couples of childbearing age throughout China. The detailed study design, organization, and implementation have been described elsewhere.¹⁶ We collected NFPHEP database data regarding the preconception examination, early gestation follow-up, and postpartum follow-up for all of the 1 535 545 nulliparous women who were in labor from December 1, 2013, through November 30, 2014. A flowchart (eFigure 1 in the Supplement) of the exclusion criteria for the birth cohort is provided in the Supplement (see eTable 1 in the Supplement for additional information on PTB rates). The final analyses included 1 300 342 singleton live births and was conducted between December 1, 2016, and April 1, 2017. The institutional review board of the National Research Institute for Family Planning, Beijing, China, approved this study. All participants provided written informed consent.

Outcome Definition

As recommended by the World Health Organization, PTB was defined as a gestational age from 20 through 36 completed weeks. We also categorized very PTB as a gestational age from 28 through 31 weeks and extremely PTB as 20 through 27 weeks. The time since the first day of the last menstrual period was used to assess gestational age. Women’s responses to inquiries about the date were recorded during the early gestation follow-up visit (conducted no later than 12 weeks after conception) by an obstetrician. After delivery, each woman was asked again about the date of the last menstrual period, after which gestational age was determined at the postpartum follow-up visit (conducted no later than 6 weeks after delivery).

Exposure Assessment

Weekly PM₁ concentrations were predicted at a 0.1° × 0.1° spatial resolution for mainland China during the research period by using satellite remote sensing, meteorologic, and land use information (see the eAppendix in the Supplement for more detailed information).¹⁷ Each woman’s address information at the township level was collected during the preconception examination, early gestation follow-up, and postpartum follow-up records. The 3 addresses were compared, and those women who moved during pregnancy were excluded. In total, 24 444 township-level locations were geocoded, including both urban and rural areas (41 636 township-level units existed in China during the study period). Each woman’s address information was geocoded into longitude and latitude and matched to the centroid of the nearest 0.1° × 0.1° grid cell location of predicted PM₁. Trimester-specific mean PM₁ concentrations were calculated using the weekly concentration. The data were categorized as trimester 1 (1-13 weeks’ gestation), trimester 2 (14-26 weeks’ gestation), trimester 3 (27 weeks to delivery), and the entire pregnancy (see eTable 2 in the Supplement for additional information).

Statistical Analysis

Cox proportional hazards regression analyses were conducted by using generalized additive mixed models to estimate the association of trimester-specific PM₁ exposure with PTB.¹⁵ Gestational age was fitted as the time scale, and the event was defined as PTB (medically induced labor data were removed from the analysis). Trimester-specific PM₁ levels were fitted as time-independent variables. A literature review was conducted, and a directed acyclic graph was used to create a least biased estimate of the association.^1,10,18 The data in the model were adjusted for the following individual variables: maternal age from 16 to 50 years by 5-year intervals; household registration¹⁹ (rural, urban); completed educational level (primary school or below, junior high school, senior high school, or college or higher); occupation (farmers, workers, or others); body mass index calculated as the weight in kilograms divided by height in meters squared (≤18.5, 18.6-23.9, or ≥24); organic solvent, heavy metal, or pesticide exposure (yes, no); alcohol consumption and cigarette smoking of mother or partner (still, quit, or never); mode of delivery (vaginal, cesarean); sex of the neonate (male, female); and season of conception (summer, June through August; fall, September through November; winter, December through February; or spring, March through May). A spline with 4 df was used to control for trimester-specific mean temperature.²⁰ A random contribution of province was fitted to control for potential spatial variation.¹⁵ Only PM₁ and the random contribution of province were included in the crude models. All the aforementioned covariables were then used to build the adjusted models. The restricted maximum likelihood method was used to fit the models, and hazard ratios (HRs) associated with a 10-μg/m³ increase in PM₁ were reported.

Trimester-specific associations between very PTB, extremely PTB, and exposure were also assessed using the same approach. A potential association of dose was explored by fitting PM₁ as a spline function with 4 df in the models. The exposures during the entire pregnancy were categorized into 4 quartiles, and the HRs for each higher exposure group compared with the lowest exposure group were reported. In addition, because previous studies have shown that the socioeconomic status of women and the season of conception can modify the effects of PM pollution,^10,20,21 subgroup analyses were conducted to assess which subgroups were associated with increased risk of PTB.

Sensitivity analyses were conducted by fitting a random contribution of city. A single model adjusted for exposures during each trimester was created as a sensitivity analysis for trimester-specific contributions. All analyses were performed using R, version 3.3.3 (R Core Team). All statistical tests were 2-sided, and P values <.05 were considered statistically significant.

Results

In total, 1 300 342 singleton live births at the gestational ages of 20 to 45 weeks were included in this study that covered 324 of 344 prefecture-level cities from the 30 provinces in mainland China (eFigure 2 in the Supplement). Among the live births, 104 585 (8.0%) were PTBs (eTable 3 in the Supplement). The demographic profiles of women with preterm deliveries differed from those with term deliveries (Table 1). Women with preterm deliveries were more likely to be younger than 20 years at conception (11.0%), come from rural areas (8.1%), have primary school or below educational attainment (9.1%), and be farmers (8.3%). Women who were overweight before their pregnancy (9.1%), smoked cigarettes (9.1%) or consumed alcohol after conception (8.6%), underwent cesarean delivery (8.3%), delivered a boy (8.4%), or conceived in winter (9.1%) were more likely to deliver preterm.

Table 1. Maternal and Fetal Characteristics of the Included Term and Preterm Births.

Characteristic	No. (%)		P Value
Characteristic	Term Birth	Preterm Birth	P Value
Overall	1 195 759 (92.0)	104 585 (8.0)
Age, y
16-19	5366 (89.0)	663 (11.0)	<.001
20-24	411 951 (92.1)	35 377 (7.9)
25-29	575 732 (92.2)	48 547 (7.8)
30-34	162 778 (90.9)	16 277 (9.1)
35-39	32 821 (91.4)	3077 (8.6)
40-44	6516 (91.4)	613 (8.6)
45-50	593 (95.0)	31 (5.0)
Household registration^a
Rural	1 126 394 (91.9)	99 070 (8.1)	<.001
Urban	69 363 (92.6)	5515 (7.4)	<.001
Educational level completed^b
Primary school or below	33 738 (90.9)	3385 (9.1)	<.001
Junior high school	720 654 (91.6)	66 509 (8.4)
Senior high school	241 476 (92.7)	19 019 (7.3)
College or higher	179 511 (93.0)	13 531 (7.0)
Occupation
Farmer	882 111 (91.7)	80 347 (8.3)	<.001
Worker	207 037 (93.0)	15 616 (7.0)
Other^c	82 772 (93.0)	6189 (7.0)
Prepregnancy BMI
≤18.5	175 615 (92.0)	15 182 (8.0)	<.001
18.6-23.9	848 831 (92.1)	72 378 (7.9)
≥24	157 909 (90.9)	15 816 (9.1)
Organic solvent, heavy metal, or pesticide exposure after conception
Yes	7537 (92.0)	651 (8.0)	.76
No	1 188 220 (92.0)	103 934 (8.0)	.76
Maternal smoking after conception
Yes	3685 (90.9)	369 (9.1)	<.001
Quit	8713 (90.9)	872 (9.1)
No	1 174 519 (92.0)	101 876 (8.0)
Partner smoking after conception
Yes	186 063 (92.1)	15 868 (7.9)	.37
Quit	97 407 (91.6)	8878 (8.4)
No	903 277 (92.0)	78 333 (8.0)
Maternal drinking after conception
Yes	3445 (91.4)	326 (8.6)	.02
Quit	10 219 (91.5)	844 (8.5)
No	1 172 535 (92.0)	101 764 (8.0)
Mode of delivery
Cesarean	355 321 (91.7)	32 249 (8.3)	<.001
Vaginal	840 436 (92.1)	72 336 (7.9)	<.001
Neonate’s sex
Male	625 289 (91.6)	57 600 (8.4)	<.001
Female	569 647 (92.5)	46 030 (7.5)	<.001
Season of conception
Spring	306 702 (92.5)	24 730 (7.5)	<.001
Summer	303 128 (91.9)	26 790 (8.1)
Autumn	305 967 (92.4)	25 198 (7.6)
Winter	279 960 (90.9)	27 867 (9.1)

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Abbreviation: BMI, body mass index (calculated as the weight in kilograms divided by height in meters squared).

^{^a}

Household registration data derived from the Hukou system of household registration in China.¹⁹

^{^b}

Primary school indicates grades 1 through 6; junior high school, grades 7 through 9; and senior high school, grades 10 through 12.

^{^c}

Other occupations included housewife, individual business, or other.

The median PM₁ exposure over the entire pregnancy for all mothers was 46.0 μg/m³ with a wide interquartile range (14.3-127.6 μg/m³) (eTable 4 in the Supplement). Figure 1 shows the distribution of quartered mean PM₁ exposure over the entire pregnancy for each prefecture-level city in mainland China. We found that women living in the Beijing, Tianjin, and Hebei regions, the Yangtze River delta, the Sichuan Basin, and the Pearl River delta experienced relatively high PM₁ exposure (>52.7 μg/m³) over the entire pregnancy.

Table 2 provides the crude and adjusted HRs (and 95% CIs) of PTB associated with maternal exposure to PM₁. In the crude models, we found that an increase in PM₁ exposure in each trimester and over the entire pregnancy was significantly associated with an increased risk of PTB. In the adjusted analyses, a PM₁ exposure increase of 10 μg/m³ in trimester 1 (HR, 1.07; 95% CI, 1.06-1.07), trimester 2 (HR, 1.10; 95% CI, 1.09-1.10), trimester 3 (HR, 1.04; 95% CI, 1.03-1.04), and over the entire pregnancy (HR, 1.09; 95% CI, 1.09-1.10) was significantly associated with an increased risk of PTB. The risks associated with PM₁ for very and extremely PTBs were higher than those for PTB. For very PTB, an increased PM₁ exposure of 10 μg/m³ over the entire pregnancy provided an HR of 1.20 (95% CI, 1.18-1.23), reaching 1.29 (95% CI, 1.25-1.34) for extremely PTB. The HRs associated with PM₁ exposure during the first and second trimester were greater than those of the third trimester.

Table 2. Crude and Adjusted Preterm Birth Hazard Ratios for Increases in PM₁ Concentrations of 10 μg/m³.

Outcome	Hazard Ratio (95% CI)
Outcome	First Trimester	Second Trimester	Third Trimester	Entire Pregnancy
Crude model^a
PTB	1.03 (1.03-1.03)	1.05 (1.04-1.05)	1.01 (1.01-1.02)	1.06 (1.05-1.07)
VPTB	1.08 (1.07-1.09)	1.10 (1.09-1.11)	1.02 (1.01-1.03)	1.13 (1.12-1.15)
ExPTB	1.11 (1.09-1.14)	1.13 (1.11-1.15)	0.99 (0.95-1.04)	1.18 (1.15-1.22)
Adjusted model^b
PTB	1.07 (1.06-1.07)	1.10 (1.09-1.10)	1.04 (1.03-1.04)	1.09 (1.09-1.10)
VPTB	1.13 (1.12-1.15)	1.13 (1.12-1.15)	1.06 (1.05-1.08)	1.20 (1.18-1.23)
ExPTB	1.11 (1.09-1.14)	1.13 (1.10-1.16)	1.06 (0.99-1.12)	1.29 (1.25-1.34)

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Abbreviations: PM₁, airborne particulate matter with a median diameter of 1 μm or less; ExPTB, extremely preterm birth; PTB, preterm birth; VPTB, very preterm birth.

^{^a}

Generalized additive model with Cox proportional hazards regression analysis, including the random contribution of province.

^{^b}

Adjusted for maternal age, household registration, educational level, occupation, prepregnancy body mass index, organic solvent exposure, heavy metal exposure, pesticide exposure, maternal or partner cigarette smoking, alcohol consumption, mode of delivery, neonate’s sex, season, nonlinear association with temperature, and the random contribution of province.

After fitting the contribution of PM₁ exposure over the entire pregnancy as a spline function, the association between PM₁ exposure and risk of PTB was log linear (eFigure 3 in the Supplement). When we compared the risk of PTB associated with the categorized PM₁ exposure, we found that mothers in the highest exposure group (group 4, >52.7 μg/m³) had a higher risk of PTB (HR, 1.36; 95% CI, 1.33-1.39) than mothers in the lowest exposure group (group 1, <38.4 μg/m³) (Figure 2).

Figure 2. — Generalized additive model with Cox proportional hazards regression analysis adjusted for maternal age, household registration, educational level, occupation, prepregnancy body mass index, organic solvent exposure, heavy metal exposure, pesticide exposure, cigarette smoking by mother or partner, alcohol consumption, mode of delivery, neonate’s sex, season, nonlinear association of temperature, and random contribution of province. Group 1 is the reference group. The hazard ratio for group 2 is 1.03 (95% CI, 1.01-1.05), group 3 is 1.13 (95% CI, 1.10-1.15), and group 4 is 1.36 (95% CI, 1.33-1.39).

Figure 3 summarizes the associations between PM₁ exposure during the entire pregnancy and PTB stratified by maternal age, household registration, educational level, occupation, prepregnancy body mass index, and season of conception. Mothers who were older (30-50 years) at conception (HR, 1.13; 95% CI, 1.11-1.14), were overweight before pregnancy (HR, 1.13; 95% CI, 1.11-1.15), resided in rural areas (HR, 1.09; 95% CI, 1.09-1.10), worked as farmers (HR, 1.10; 95% CI, 1.09-1.11), or conceived in autumn (HR, 1.48; 95% CI, 1.46-1.50) had a higher risk of PTB associated with PM₁ exposure.

When we conducted sensitivity analyses by fitting the random contribution of city or creating a single model adjusted for exposure during each trimester, the results did not change (eTables 5 and 6 in the Supplement).

Discussion

The present cohort study of 1.3 million births in China from December 1, 2013, through November 30, 2014, provides compelling evidence that exposure to PM₁ air pollution is associated with increased risk of PTB. To our knowledge, no study has reported associations between PM₁ and PTB, very PTB, or extremely PTB. However, some studies have reported positive associations between PM_2.5, PM₁₀, and suspended particulates and the risk of PTB.^8,10 Two national-level studies also used estimated or satellite-based estimates of PM_2.5 exposures to examine pregnancy outcomes, although their results were inconsistent. Stieb et al⁵ reported negative associations between satellite-derived PM_2.5 and PTB in Canada for an unadjusted model (odds ratio [OR], 0.96; 95% CI, 0.93-0.98) and a model adjusted for the maternal demographic characteristics and socioeconomic status (OR, 0.96; 95% CI, 0.93-0.99). Fleischer et al⁴ found that PM_2.5 was not associated with PTB when using a global sample, but the highest quartile of exposure in China was associated with PTB. This result indicates that the difference in exposure range could be a reason for the inconsistent results. In addition, some studies conducted in small areas (eg, at the city level) have also reported positive associations between PTB and PM_2.5 or PM₁₀ based on data from Asian populations.^6,9,11

A previous study reported that PM₁ contributed to nearly 80% of PM_2.5 in China,¹⁴ and PM₁ and PM_2.5 have similar components. But few studies worldwide have focused on airborne PM₁ owing to the unavailability of air monitoring data. Thus, few studies have estimated the health effects of PM₁. Previous studies examining PM_2.5 have suggested that it results in inflammation and oxidative stress.^12,22 However, another previous study²³ has indicated that inflammation may be related to the creation of reactive oxygen species. Reactive oxygen species can lead to DNA damage, cell damage, irreversible protein modifications, disruption of cellular processes, or alterations in cellular signaling.²³ Whether these alterations would lead to PTB is not clear, but they may disrupt normal gestational processes. Studies have reported that the mean concentration of PM₁ in Australia is 16 μg/m³, in Athens is 18.5 μg/m³, and in Milan is 16.4 μg/m³, which are lower than the PM₁ exposure in our study.^21,24,25 Although, to our knowledge, no study to date has investigated the association between PM₁ exposure and pregnancy outcomes, the present study found significant positive associations between PM₁ exposure at each trimester as well as for the entire pregnancy and PTB. This evidence can be used to inform future research studies, public health interventions, and environmental policies.

The present study showed that mothers who conceived in the autumn appeared to be more sensitive to PM₁ exposure than those who conceived in other seasons. A study in China reported a higher incidence of PTB in women who conceived in the autumn.²⁶ The reason for this finding may be that some women engage in more outdoor activities in autumn, for example, farmers tend to their harvest during this season.^5,27,28 In addition, we found that mothers from rural areas who were farmers appeared to be particularly sensitive to PM₁ exposure. Their lower socioeconomic status, excess exposure due to outdoor work, and shortage of protective measures (eg, use of a mask or air purifier and building design) could be reasons for the apparent enhanced risk.^10,29 Regarding educational attainment, we found that pregnant women with a high school educational level appeared to be more sensitive to PM₁ exposure than those with a college educational level, which may also be explained by their socioeconomic status.^10,29

Our study has important clinical and public health implications. Preterm birth not only is the leading cause of death throughout the world for neonates, infants, and children younger than 5 years^30,31 but also has long-term consequences. Numerous studies have shown that individuals born prematurely can have lifelong health problems in multiple organs or systems, including asthma and metabolic disorders, causing tremendous strain on families and the medical system and resulting in enormous annual medical costs worldwide.^32,33 In the present study, we found an increased PTB risk of 9% associated with an increase in PM₁ concentration of 10 μg/m³ over the entire pregnancy. Compared with less polluted areas (PM₁ <38.4 μg/m³), an increased PTB risk of 36% was found in areas with higher levels of air pollution (PM₁>52.7 μg/m³) in China. To our knowledge, the present air pollution standards in both the United States and China do not include regulations for PM₁; thus, there is an urgent need to improve these related policies. Effective strategies, such as improving indoor air quality or wearing a mask outdoors, should be considered in protecting mothers from the risks associated with PM pollution.

Strengths and Limitations

Our study had many strengths. First, it included a very large sample size (>1.3 million mothers), which is important for generating robust findings. Second, the satellite-based exposure estimates used in the present study allowed us to include rural areas. Third, exposure was assessed based on home addresses recorded from preconception, early gestation follow-up, and postpartum follow-up records, which minimized potential exposure misclassification resulting from residential mobility. By contrast, most previous studies have used residence at birth to assign exposure for the entire pregnancy. Finally, because this was a prospective cohort study, we minimized recall bias for the dates of the last menstrual period and birth, which helped ensure the accuracies of gestational age and PTB assessments.

The study also had several limitations. Although we used a satellite-based comprehensive model and assigned exposures according to the mothers’ addresses at the township level, there could have been misclassification of the exposure. The pollutant levels at microenvironmental levels (eg, indoor, outdoor, or associated with commuting) or maternal activity patterns may have contributed to a misclassification. In addition, specific components and their proportions could not be considered separately but rather were grouped as PM₁. The specific components might have had different chemical structures and might be associated with different health concerns. Future studies are needed to investigate PM components and their sources.

Conclusions

Exposure to PM₁ air pollution during pregnancy was associated with an increased risk of PTB. The mothers who were older at conception, were overweight before pregnancy, were registered as a rural household, worked as farmers, or conceived in autumn had a greater risk of PTB associated with PM₁ exposure. Further studies to examine the mechanisms accounting for increased vulnerability to PM₁ are warranted. Public policies and guidelines should be improved to protect pregnant women from risks associated with PM₁ air pollution.

Supplement.

eFigure 1. Flowchart of the Birth Cohort Based on the National Free Preconception Health Examination Project (NFPHEP)

eFigure 2. Study Areas in Mainland China

eFigure 3. Changes in Predicted Hazard [h(t)] for Preterm Birth Associated With PM1 Exposure Over the Entire Pregnancy

eTable 1. Preterm Birth Rates in the Study Samples

eTable 2. Detailed Information of the Definition of Each Exposure Time Period

eTable 3. Study Sample Information in Each Province

eTable 4. PM1 (μg/m³) Exposure, Averaged for All Weeks of Entire Pregnancies

eTable 5. HRs (95% CIs) of Preterm Birth for 10 μg/m³ Increases in PM1 in Original Analyses (Separate Models) and Sensitivity Analysis (Single Model With Adjusted Trimesters)

eTable 6. HRs (95% CIs) of Preterm Birth for 10 μg/m³ Increases In PM1 in Original Analyses (Random Effect Of Province) and Sensitivity Analyses (Random Effect of City)

eAppendix. Additional Information of Exposure Assessment

Click here for additional data file.^{(331.6KB, pdf)}

References

1.Shapiro-Mendoza CK, Barfield WD, Henderson Z, et al. CDC grand rounds: public health strategies to prevent preterm birth. MMWR Morb Mortal Wkly Rep. 2016;65(32):-. [DOI] [PubMed] [Google Scholar]
2.Beck S, Wojdyla D, Say L, et al. The worldwide incidence of preterm birth: a systematic review of maternal mortality and morbidity. Bull World Health Organ. 2010;88(1):31-38. [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Chernausek SD. Update: consequences of abnormal fetal growth. J Clin Endocrinol Metab. 2012;97(3):689-695. [DOI] [PMC free article] [PubMed] [Google Scholar]
4.Fleischer NL, Merialdi M, van Donkelaar A, et al. Outdoor air pollution, preterm birth, and low birth weight: analysis of the World Health Organization global survey on maternal and perinatal health. Environ Health Perspect. 2014;122(4):425-430. [DOI] [PMC free article] [PubMed] [Google Scholar]
5.Stieb DM, Chen L, Beckerman BS, et al. Associations of pregnancy outcomes and PM_2.5 in a national Canadian study. Environ Health Perspect. 2016;124(2):243-249. [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Kim OJ, Ha EH, Kim BM, et al. PM10 and pregnancy outcomes: a hospital-based cohort study of pregnant women in Seoul. J Occup Environ Med. 2007;49(12):1394-1402. [DOI] [PubMed] [Google Scholar]
7.Pereira G, Bell ML, Belanger K, de Klerk N. Fine particulate matter and risk of preterm birth and pre-labor rupture of membranes in Perth, Western Australia 1997-2007: a longitudinal study. Environ Int. 2014;73:143-149. [DOI] [PMC free article] [PubMed] [Google Scholar]
8.Xu X, Ding H, Wang X. Acute effects of total suspended particles and sulfur dioxides on preterm delivery: a community-based cohort study. Arch Environ Health. 1995;50(6):407-415. [DOI] [PubMed] [Google Scholar]
9.Qian Z, Liang S, Yang S, et al. Ambient air pollution and preterm birth: a prospective birth cohort study in Wuhan, China. Int J Hyg Environ Health. 2016;219(2):195-203. [DOI] [PubMed] [Google Scholar]
10.Yi O, Kim H, Ha E. Does area level socioeconomic status modify the effects of PM₁₀ on preterm delivery? Environ Res. 2010;110(1):55-61. [DOI] [PubMed] [Google Scholar]
11.Zhao N, Qiu J, Zhang Y, et al. Ambient air pollutant PM₁₀ and risk of preterm birth in Lanzhou, China. Environ Int. 2015;76:71-77. [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Filep Á, Fodor GH, Kun-Szabó F, et al. Exposure to urban PM1 in rats: development of bronchial inflammation and airway hyperresponsiveness. Respir Res. 2016;17(26):26. doi: 10.1186/s12931-016-0332-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Chen G, Li S, Zhang Y, et al. Effects of ambient PM₁ air pollution on daily emergency hospital visits in China: an epidemiological study. Lancet Planet Health. 2017;1(6):e221-e229. doi: 10.1016/S2542-5196(17)30100-6 [DOI] [PubMed] [Google Scholar]
14.Wang YQ, Zhang XY, Sun JY, Zhang XC, Che HZ, Li Y. Spatial and temporal variations of the concentrations of PM₁₀, PM_2.5 and PM₁ in China. Atmos Chem Phys. 2015;15(23):13585-13598. [Google Scholar]
15.Slama R, Ballester F, Casas M, et al. Epidemiologic tools to study the influence of environmental factors on fecundity and pregnancy-related outcomes. Epidemiol Rev. 2014;36(1):148-164. [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Zhang S, Wang Q, Shen H. Design of the National Free Preconception Health Examination Project in China [in Chinese]. Zhonghua Yi Xue Za Zhi. 2015;95(3):162-165. [PubMed] [Google Scholar]
17.Chen G, Knibbs LD, Zhang W, et al. Estimating spatiotemporal distribution of PM₁ concentrations in China with satellite remote sensing, meteorology, and land use information. Environ Pollut. 2017;S0269-7491(17)33173-1. doi: 10.1016/j.envpol.2017.10.011 [DOI] [PubMed] [Google Scholar]
18.Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology. 1999;10(1):37-48. [PubMed] [Google Scholar]
19.Chan KW. Five decades of the Chinese Hukou system In: Iredale RR, Guo F, eds. Handbook of Chinese Migration: Identity and Wellbeing. Northampton, MA: Edward Elgar Publishing Inc; 2016:23-47. [Google Scholar]
20.Giorgis-Allemand L, Pedersen M, Bernard C, et al. The influence of meteorological factors and atmospheric pollutants on the risk of preterm birth. Am J Epidemiol. 2017;185(4):247-258. [DOI] [PubMed] [Google Scholar]
21.Gomišček B, Hauck H, Stopper S, Preining O. Spatial and temporal variations of PM₁, PM_2.5, PM₁₀ and particle number concentration during the AUPHEP—project. Atmos Environ. 2004;38(24):3917-3934. doi: 10.1016/j.atmosenv.2004.03.056 [DOI] [Google Scholar]
22.Bai R, Guan L, Zhang W, et al. Comparative study of the effects of PM₁-induced oxidative stress on autophagy and surfactant protein B and C expressions in lung alveolar type II epithelial MLE-12 cells. Biochim Biophys Acta. 2016;1860(12):2782-2792. [DOI] [PubMed] [Google Scholar]
23.Backes CH, Nelin T, Gorr MW, Wold LE. Early life exposure to air pollution: how bad is it? Toxicol Lett. 2013;216(1):47-53. [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Vecchi R, Marcazzan G, Valli G, Ceriani M, Antoniazzi C. The role of atmospheric dispersion in the seasonal variation of PM1 and PM2.5 concentration and composition in the urban area of Milan (Italy). Atmos Environ. 2004;38(27):4437-4446. doi: 10.1016/j.atmosenv.2004.05.029 [DOI] [Google Scholar]
25.Koulouri E, Grivas G, Gerasopoulos E, Chaloulakou A, Mihalopoulos N, Spyrellis N. Study of size-segregated particle (PM1, PM2.5, PM10) concentrations over Greece. Global NEST J. 2008;10(2):132-139. [Google Scholar]
26.He JR, Liu Y, Xia XY, et al. Ambient temperature and the risk of preterm birth in Guangzhou, China (2001-2011). Environ Health Perspect. 2016;124(7):1100-1106. [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Cheng YH, Yang LS. Characteristics of ambient black carbon mass and size-resolved particle number concentrations during corn straw open-field burning episode observations at a rural site in southern Taiwan. Int J Environ Res Public Health. 2016;13(7):E688. [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Pereira G, Belanger K, Ebisu K, Bell ML. Fine particulate matter and risk of preterm birth in Connecticut in 2000-2006: a longitudinal study. Am J Epidemiol. 2014;179(1):67-74. [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Morello-Frosch R, Shenassa ED. The environmental “riskscape” and social inequality: implications for explaining maternal and child health disparities. Environ Health Perspect. 2006;114(8):1150-1153. [DOI] [PMC free article] [PubMed] [Google Scholar]
30.GBD 2015 Mortality and Causes of Death Collaborators Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016;388(10053):1459-1544. [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Rudan I, Chan KY, Zhang JS, et al. ; WHO/UNICEF’s Child Health Epidemiology Reference Group (CHERG) . Causes of deaths in children younger than 5 years in China in 2008. Lancet. 2010;375(9720):1083-1089. [DOI] [PubMed] [Google Scholar]
32.He H, Butz A, Keet CA, et al. Preterm birth with childhood asthma: the role of degree of prematurity and asthma definitions. Am J Respir Crit Care Med. 2015;192(4):520-523. [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Wang G, Divall S, Radovick S, et al. Preterm birth and random plasma insulin levels at birth and in early childhood. JAMA. 2014;311(6):587-596. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement.

eFigure 1. Flowchart of the Birth Cohort Based on the National Free Preconception Health Examination Project (NFPHEP)

eFigure 2. Study Areas in Mainland China

eFigure 3. Changes in Predicted Hazard [h(t)] for Preterm Birth Associated With PM1 Exposure Over the Entire Pregnancy

eTable 1. Preterm Birth Rates in the Study Samples

eTable 2. Detailed Information of the Definition of Each Exposure Time Period

eTable 3. Study Sample Information in Each Province

eTable 4. PM1 (μg/m³) Exposure, Averaged for All Weeks of Entire Pregnancies

eTable 5. HRs (95% CIs) of Preterm Birth for 10 μg/m³ Increases in PM1 in Original Analyses (Separate Models) and Sensitivity Analysis (Single Model With Adjusted Trimesters)

eTable 6. HRs (95% CIs) of Preterm Birth for 10 μg/m³ Increases In PM1 in Original Analyses (Random Effect Of Province) and Sensitivity Analyses (Random Effect of City)

eAppendix. Additional Information of Exposure Assessment

Click here for additional data file.^{(331.6KB, pdf)}

[poi170101r1] 1.Shapiro-Mendoza CK, Barfield WD, Henderson Z, et al. CDC grand rounds: public health strategies to prevent preterm birth. MMWR Morb Mortal Wkly Rep. 2016;65(32):-. [DOI] [PubMed] [Google Scholar]

[poi170101r2] 2.Beck S, Wojdyla D, Say L, et al. The worldwide incidence of preterm birth: a systematic review of maternal mortality and morbidity. Bull World Health Organ. 2010;88(1):31-38. [DOI] [PMC free article] [PubMed] [Google Scholar]

[poi170101r3] 3.Chernausek SD. Update: consequences of abnormal fetal growth. J Clin Endocrinol Metab. 2012;97(3):689-695. [DOI] [PMC free article] [PubMed] [Google Scholar]

[poi170101r4] 4.Fleischer NL, Merialdi M, van Donkelaar A, et al. Outdoor air pollution, preterm birth, and low birth weight: analysis of the World Health Organization global survey on maternal and perinatal health. Environ Health Perspect. 2014;122(4):425-430. [DOI] [PMC free article] [PubMed] [Google Scholar]

[poi170101r5] 5.Stieb DM, Chen L, Beckerman BS, et al. Associations of pregnancy outcomes and PM_2.5 in a national Canadian study. Environ Health Perspect. 2016;124(2):243-249. [DOI] [PMC free article] [PubMed] [Google Scholar]

[poi170101r6] 6.Kim OJ, Ha EH, Kim BM, et al. PM10 and pregnancy outcomes: a hospital-based cohort study of pregnant women in Seoul. J Occup Environ Med. 2007;49(12):1394-1402. [DOI] [PubMed] [Google Scholar]

[poi170101r7] 7.Pereira G, Bell ML, Belanger K, de Klerk N. Fine particulate matter and risk of preterm birth and pre-labor rupture of membranes in Perth, Western Australia 1997-2007: a longitudinal study. Environ Int. 2014;73:143-149. [DOI] [PMC free article] [PubMed] [Google Scholar]

[poi170101r8] 8.Xu X, Ding H, Wang X. Acute effects of total suspended particles and sulfur dioxides on preterm delivery: a community-based cohort study. Arch Environ Health. 1995;50(6):407-415. [DOI] [PubMed] [Google Scholar]

[poi170101r9] 9.Qian Z, Liang S, Yang S, et al. Ambient air pollution and preterm birth: a prospective birth cohort study in Wuhan, China. Int J Hyg Environ Health. 2016;219(2):195-203. [DOI] [PubMed] [Google Scholar]

[poi170101r10] 10.Yi O, Kim H, Ha E. Does area level socioeconomic status modify the effects of PM₁₀ on preterm delivery? Environ Res. 2010;110(1):55-61. [DOI] [PubMed] [Google Scholar]

[poi170101r11] 11.Zhao N, Qiu J, Zhang Y, et al. Ambient air pollutant PM₁₀ and risk of preterm birth in Lanzhou, China. Environ Int. 2015;76:71-77. [DOI] [PMC free article] [PubMed] [Google Scholar]

[poi170101r12] 12.Filep Á, Fodor GH, Kun-Szabó F, et al. Exposure to urban PM1 in rats: development of bronchial inflammation and airway hyperresponsiveness. Respir Res. 2016;17(26):26. doi: 10.1186/s12931-016-0332-9 [DOI] [PMC free article] [PubMed] [Google Scholar]

[poi170101r13] 13.Chen G, Li S, Zhang Y, et al. Effects of ambient PM₁ air pollution on daily emergency hospital visits in China: an epidemiological study. Lancet Planet Health. 2017;1(6):e221-e229. doi: 10.1016/S2542-5196(17)30100-6 [DOI] [PubMed] [Google Scholar]

[poi170101r14] 14.Wang YQ, Zhang XY, Sun JY, Zhang XC, Che HZ, Li Y. Spatial and temporal variations of the concentrations of PM₁₀, PM_2.5 and PM₁ in China. Atmos Chem Phys. 2015;15(23):13585-13598. [Google Scholar]

[poi170101r15] 15.Slama R, Ballester F, Casas M, et al. Epidemiologic tools to study the influence of environmental factors on fecundity and pregnancy-related outcomes. Epidemiol Rev. 2014;36(1):148-164. [DOI] [PMC free article] [PubMed] [Google Scholar]

[poi170101r16] 16.Zhang S, Wang Q, Shen H. Design of the National Free Preconception Health Examination Project in China [in Chinese]. Zhonghua Yi Xue Za Zhi. 2015;95(3):162-165. [PubMed] [Google Scholar]

[poi170101r17] 17.Chen G, Knibbs LD, Zhang W, et al. Estimating spatiotemporal distribution of PM₁ concentrations in China with satellite remote sensing, meteorology, and land use information. Environ Pollut. 2017;S0269-7491(17)33173-1. doi: 10.1016/j.envpol.2017.10.011 [DOI] [PubMed] [Google Scholar]

[poi170101r18] 18.Greenland S, Pearl J, Robins JM. Causal diagrams for epidemiologic research. Epidemiology. 1999;10(1):37-48. [PubMed] [Google Scholar]

[poi170101r19] 19.Chan KW. Five decades of the Chinese Hukou system In: Iredale RR, Guo F, eds. Handbook of Chinese Migration: Identity and Wellbeing. Northampton, MA: Edward Elgar Publishing Inc; 2016:23-47. [Google Scholar]

[poi170101r20] 20.Giorgis-Allemand L, Pedersen M, Bernard C, et al. The influence of meteorological factors and atmospheric pollutants on the risk of preterm birth. Am J Epidemiol. 2017;185(4):247-258. [DOI] [PubMed] [Google Scholar]

[poi170101r21] 21.Gomišček B, Hauck H, Stopper S, Preining O. Spatial and temporal variations of PM₁, PM_2.5, PM₁₀ and particle number concentration during the AUPHEP—project. Atmos Environ. 2004;38(24):3917-3934. doi: 10.1016/j.atmosenv.2004.03.056 [DOI] [Google Scholar]

[poi170101r22] 22.Bai R, Guan L, Zhang W, et al. Comparative study of the effects of PM₁-induced oxidative stress on autophagy and surfactant protein B and C expressions in lung alveolar type II epithelial MLE-12 cells. Biochim Biophys Acta. 2016;1860(12):2782-2792. [DOI] [PubMed] [Google Scholar]

[poi170101r23] 23.Backes CH, Nelin T, Gorr MW, Wold LE. Early life exposure to air pollution: how bad is it? Toxicol Lett. 2013;216(1):47-53. [DOI] [PMC free article] [PubMed] [Google Scholar]

[poi170101r24] 24.Vecchi R, Marcazzan G, Valli G, Ceriani M, Antoniazzi C. The role of atmospheric dispersion in the seasonal variation of PM1 and PM2.5 concentration and composition in the urban area of Milan (Italy). Atmos Environ. 2004;38(27):4437-4446. doi: 10.1016/j.atmosenv.2004.05.029 [DOI] [Google Scholar]

[poi170101r25] 25.Koulouri E, Grivas G, Gerasopoulos E, Chaloulakou A, Mihalopoulos N, Spyrellis N. Study of size-segregated particle (PM1, PM2.5, PM10) concentrations over Greece. Global NEST J. 2008;10(2):132-139. [Google Scholar]

[poi170101r26] 26.He JR, Liu Y, Xia XY, et al. Ambient temperature and the risk of preterm birth in Guangzhou, China (2001-2011). Environ Health Perspect. 2016;124(7):1100-1106. [DOI] [PMC free article] [PubMed] [Google Scholar]

[poi170101r27] 27.Cheng YH, Yang LS. Characteristics of ambient black carbon mass and size-resolved particle number concentrations during corn straw open-field burning episode observations at a rural site in southern Taiwan. Int J Environ Res Public Health. 2016;13(7):E688. [DOI] [PMC free article] [PubMed] [Google Scholar]

[poi170101r28] 28.Pereira G, Belanger K, Ebisu K, Bell ML. Fine particulate matter and risk of preterm birth in Connecticut in 2000-2006: a longitudinal study. Am J Epidemiol. 2014;179(1):67-74. [DOI] [PMC free article] [PubMed] [Google Scholar]

[poi170101r29] 29.Morello-Frosch R, Shenassa ED. The environmental “riskscape” and social inequality: implications for explaining maternal and child health disparities. Environ Health Perspect. 2006;114(8):1150-1153. [DOI] [PMC free article] [PubMed] [Google Scholar]

[poi170101r30] 30.GBD 2015 Mortality and Causes of Death Collaborators Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet. 2016;388(10053):1459-1544. [DOI] [PMC free article] [PubMed] [Google Scholar]

[poi170101r31] 31.Rudan I, Chan KY, Zhang JS, et al. ; WHO/UNICEF’s Child Health Epidemiology Reference Group (CHERG) . Causes of deaths in children younger than 5 years in China in 2008. Lancet. 2010;375(9720):1083-1089. [DOI] [PubMed] [Google Scholar]

[poi170101r32] 32.He H, Butz A, Keet CA, et al. Preterm birth with childhood asthma: the role of degree of prematurity and asthma definitions. Am J Respir Crit Care Med. 2015;192(4):520-523. [DOI] [PMC free article] [PubMed] [Google Scholar]

[poi170101r33] 33.Wang G, Divall S, Radovick S, et al. Preterm birth and random plasma insulin levels at birth and in early childhood. JAMA. 2014;311(6):587-596. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Association of Long-term Exposure to Airborne Particulate Matter of 1 μm or Less With Preterm Birth in China

Yuan-yuan Wang, PhD

Qin Li, MMSc

Yuming Guo, PhD

Hong Zhou, PhD

Xiaobin Wang, MD

Qiaomei Wang, MD

Haiping Shen, MD

Yiping Zhang, MD

Donghai Yan, MD

Ya Zhang, BM

Hongguang Zhang, BS

Shanshan Li, PhD

Gongbo Chen, PhD

Jun Zhao, PhD

Yuan He, PhD

Ying Yang, PhD

Jihong Xu, PhD

Yan Wang, BS

Zuoqi Peng, BS

Hai-Jun Wang, PhD

Xu Ma, MS

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Design, Setting, and Participants

Exposures

Main Outcomes and Measures

Results

Conclusions and Relevance

Introduction

Methods

Study Design and Participants

Outcome Definition

Exposure Assessment

Statistical Analysis

Results

Table 1. Maternal and Fetal Characteristics of the Included Term and Preterm Births.

Figure 1. Distribution of Exposure to Airborne Particulate Matter With Median Diameter of 1 μm or Less (PM1) Over the Entire Pregnancy for Women Residing in Prefecture-Level Cities of Mainland China.

Table 2. Crude and Adjusted Preterm Birth Hazard Ratios for Increases in PM1 Concentrations of 10 μg/m3.

Figure 2. Adjusted Hazard Ratios of Preterm Birth Associated With Categorized Exposure to Airborne Particulate Matter With Median Diameter of 1 μm or Less Over the Entire Pregnancy.

Figure 3. Adjusted Hazard Ratios of Preterm Birth for Each Increase in Airborne Particulate Matter With Median Diameter of 1 μm or Less Exposure of 10 μg/m3 Over the Entire Pregnancy in Each Subgroup.

Discussion

Strengths and Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases

Figure 1. Distribution of Exposure to Airborne Particulate Matter With Median Diameter of 1 μm or Less (PM₁) Over the Entire Pregnancy for Women Residing in Prefecture-Level Cities of Mainland China.

Table 2. Crude and Adjusted Preterm Birth Hazard Ratios for Increases in PM₁ Concentrations of 10 μg/m³.

Figure 3. Adjusted Hazard Ratios of Preterm Birth for Each Increase in Airborne Particulate Matter With Median Diameter of 1 μm or Less Exposure of 10 μg/m³ Over the Entire Pregnancy in Each Subgroup.