Maternal Secondhand Smoke Exposure Enhances Macrosomia Risk among Pregnant Women Exposed to PM2.5: A New Interaction of Two Air Pollutants in A Nationwide Cohort

Maternal Secondhand Smoke Exposure Enhances Macrosomia Risk among Pregnant Women Exposed to PM2.5: A New Interaction of Two Air Pollutants in A Nationwide Cohort | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Maternal Secondhand Smoke Exposure Enhances Macrosomia Risk among Pregnant Women Exposed to PM2.5: A New Interaction of Two Air Pollutants in A Nationwide Cohort yunyun Luo, yuelun zhang, hui pan, shi chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-520246/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Nov, 2021 Read the published version in Frontiers in Public Health → Version 1 posted You are reading this latest preprint version Abstract BACKGROUND Previous studies have been controversial and inconsistent about fine particulate matter (PM2.5) and secondhand smoking (SHS) air pollutants on neonatal birthweight outcomes and there were no published studies assessing the potential interactive effects between PM2.5 and SHS on birthweight outcomes. PURPOSE To investigate interaction between gestational PM2.5 and SHS air pollution exposure on the risk of macrosomia among pregnant women. METHODS Research data were derived from National Free Preconception Health Examination Project (NFPHEP). Data cleaning process was conducted following strict screening standards to ensure eligibility of participants in our study. Different interaction models about air pollution on birthweight outcomes were established, according to different confounding factors adjustment and different pregnancy stages. SHS subgroups analysis were conducted to further confirm the results of interaction models. RESULTS Totally, 197877 participants were included in our study. In full-adjusted interaction model, maternal exposure to PM2.5 was associated with an increased risk of macrosomia in whole (p < 0.001), the first (p < 0.001), second (p < 0.001) and third (p < 0.001) trimester of pregnancy. However, there was a trend for gestational exposure to SHS with risk of low birthweight, but not statistically significant (occasional SHS exposure (p = 0.099); frequent SHS exposure (p = 0.272)). Interaction effect was statistically significant between maternal exposure to PM2.5 and SHS on the risk of macrosomia in the whole pregnancy (all interaction p < 0.050) and the first trimester pregnancy (all interaction p 0.050) or third trimester (all interaction p > 0.050) of pregnancy. The higher frequency of SHS exposure prompts stronger interaction between the two air pollutants in the whole and the first trimester pregnancy. CONCLUSIONS In the whole pregnancy and the first trimester pregnancy, maternal exposure to SHS during pregnancy enhances the risk of macrosomia among pregnant women exposed to PM2.5 air pollutant, and the interaction became stronger with more frequent exposure to SHS. Health Economics & Outcomes Research Health Policy Fine particulate matter (PM2.5) Secondhand smoking (SHS) Interaction Birth Weight Figures Figure 1 Introduction Birth weight is a key indicator in new-born and infant survival and associated with health outcomes across the whole life course, including cardio-metabolic and mental health, some cancers and mortality [ 1 , 2 ] . A fetus larger than 4000g is considered as macrosomia, regardless of gestational age [ 3 ] . Macrosomia is associated with increased risk of maternal and fetal complications such as birth canal trauma, shoulder dystocia, and perinatal asphyxia [ 4 ] . Compared with normal infants, macrosomia newborns are at more risk for long-term complications, such as obesity and insulin resistance [ 5 ] . It was well established that both genetics and environmental risks play indispensable roles in pregnancy outcomes like birth weight [ 6 , 7 ] . Genetics risks include parental hereditary material, epigenetic variation and maternal placenta factors [ 8 , 9 ] , while environmental risks include gestational air pollutants, maternal addiction to smoking or alcohol, and maternal nutritional condition [ 10 , 11 ] . It is more likely to control and intervene environmental risks to improve adverse pregnancy outcomes comparing with the genetics risks. Among environmental risks, fine particulate matter (PM2.5) is one of the most common outdoor air pollutants caused by fossil fuel burning and vehicle emissions, while secondhand smoking (SHS) is the main source of indoor air pollution produced by smokers. Both air pollutants are caused by human activities and can be changed and avoided by taking some measures. Pregnant women exposed to PM2.5 during pregnancy can lead to adverse pregnancy outcomes such as fetal congenital malformation, premature delivery, abnormal birth weight of the fetus, and in severe cases, neonatal death [ 12 , 13 ] . Maternal SHS exposure at work and/or at home during pregnancy increased the risk of fetal stillbirth, neonatal death, and perinatal death [ 14 ] . Previous studies have established strongly epidemiologic linkage between prenatal exposure to PM2.5 or SHS and fetal birthweight outcomes. Concerning PM2.5, majority of studies reported that the increased risk of low birth weight (LBW) was associated with maternal exposure to PM2.5 [ 15 ] . Links between gestational PM2.5 exposure and small for gestational age (SGA) had been observed in some studies [ 10 ] . A nationwide cohort study suggested that gestational exposure to PM2.5 during pregnancy was significantly associated with an increased risk of macrosomia [ 16 ] . In terms of SHS, evidence on association of neonatal birthweight and cigarette smoke exposure in mothers was accumulated with growing number of studies [ 7 , 17 , 18 ] . The LBW prevalence was higher among newborns whose mother were prenatally exposed to SHS comparing with newborns whose mother were not exposed [ 7 ] . Link between maternal passive smoking and increased risk of delivering SGA also exists [ 18 ] . All in all, findings of above studies about PM2.5 and SHS air pollutants on neonatal birthweight outcomes have been controversial and inconsistent. There are some research gaps in areas of association between environment pollutants and infant’s birth weight. Firstly, whether the PM2.5 pollutants and SHS were associated with increase or decrease of birth weight was unclear. Secondly, most related studies are confined to certain city or province. Research based on nationwide cohort is limited and scarce. Thirdly, previous studies all evaluated the effects of PM2.5 or SHS air pollutants on birthweight outcomes separately, or considered SHS as possible confounding factor in studies; no published studies have assessed the potential interaction between PM2.5 and SHS and the interactive effects on birthweight outcomes. In our study, we aimed to investigate the interaction between gestational PM2.5 and SHS air pollution exposure on the risk of macrosomia among pregnant women, and examine the modifying effect of SHS exposure on the association of PM2.5 air pollution and birthweight outcomes during pregnancy. We conducted the data cleaning process of following strict screening standards to ensure the inclusion eligibility of all the enrolled women. We established different interaction models about air pollution on birthweight outcomes, according to different confounding factors adjustment and different pregnancy stages. SHS subgroups analysis were conducted to further confirm the results of interaction models. Our study expended the region of investigation, achieving nationwide evidence including 220 countries from 31provinces or municipalities in China. This study was the first nationwide, population-based cohort study to identify the interactions between gestational PM2.5 and SHS exposure among pregnant women on birthweight outcomes. Methods Study Design and Participants Our research data were derived from the National Free Preconception Health Examination Project (NFPHEP), which lasted 3years from 1 January 2010 to 31 December 2012. This project was launched by National Health and Family Planning Commission and the Ministry of Finance of the People's Republic of China in 2010. The aim of NFPHEP was to provide free preconception health examinations in rural areas to married couples that planned a pregnancy within the next 6 months. Well-trained local community health workers and obstetricians provided free preconception counselling, medical examinations and managements during pregnancy. In all, at least 240,000 Chinese women in 220 counties from 31 provinces or municipalities of China were enrolled in this project. The study was approved by the institutional review board of the National Research Institute for Family Planning, Beijing, China. All participants provided written informed consent. The detailed study design and implementation of NFPHEP have been described elsewhere [ 16 , 19 , 20 ] . Referred variables In this project, information of both enrolled pregnant women and neonates was collected by face-to‐face investigation and examination conducted by trained and qualified staff. In total, 371 items in 19 aspects including social demographic characteristics, family history, lifestyle factors, history of diseases, physical examinations, laboratory tests and diagnostic imaging were obtained. All data were uploaded to a nationwide electronic data capture system and underwent quality control by the National Quality Inspection Center for Family Planning Techniques. The variables analyzed in our study included PM2.5 concentration, age at delivery, neonate’s birthweight, neonate’s sex, educational level, smoking during pregnancy, alcohol intake during pregnancy, prolonged pregnancy, multiparity, pre-pregnancy BMI, pre-pregnancy diabetes mellitus, pre-pregnancy hypertension, family history of diabetes mellitus, season of delivery. Outcome and exposure assessment As recommended by the World Health Organization, macrosomia was defined as birth weight greater than or equal to 4000 g [ 21 ] . We defined NBW as birth weight between 2500 and 4000 g [ 21 ] . In this study, PM2.5 exposure concentration of each country was obtained using a hindcast model specific for historical PM2.5 estimation from satellite-retrieved aerosol optic depth. It was an ensemble machine learning model comprised of several machine learning models including random forest, generalized additive model, and extreme gradient boosting. Specific parameter settings and model building have been reported in detail elsewhere [ 16 , 22 ] . Daily county‐specific PM2.5 concentration of each included pregnant women was used to calculate monthly PM2.5 concentration to evaluate PM2.5 exposure condition during the pregnancy in our study. The average monthly PM2.5 concentration in 1 to 3 months' gestation, 4 to 6 months' gestation, and 7 months to delivery were regarded as the first, second, and third trimester of pregnancy, respectively. Each woman’s address information at the county level has been registered. Secondhand smoke (SHS) is passive inhalation of cigarette smoke produced by smokers' active smoking, also commonly known as "passive smoking" or "forced smoking". Pregnant women in our study may be exposed to different degree of SHS in the workplace or at home. They were divided into three groups according to exposed levels. Those who never touched with smokers or secondhand smokers were classified into “none exposure SHS” subgroup. Those who occasionally visit or work with smokers were classified into “occasional exposure SHS” subgroup. Those who lived with at least one regular smoker were classified into “frequent exposure SHS” subgroup. The conditions of SHS exposure were collected through the questionnaires filled out by pregnant women. Statistical Analysis All the enrolled pregnant women in NFPHEP needed to be screened for the eligibility of inclusion into our study. The definition of loss to follow-up was that participants got preconception examination but had not received prenatal or postnatal examination and questionnaires yet by 1 month after expected date of confinement. To avoid the bias induced by moving during pregnancy, women who had a different registered residence and birthplace were excluded. The pregnancy women who had missing data of PM2.5 concentration, birthweight, SHS conditions were excluded in analysis. Extreme or abnormal observations in other variables were replaced with missing data or censored data, which was not excluded in analysis. The establishment of interaction models was based on binary logistic regression, including main effects item, interaction effects item and other confounding factors adjustment. The interaction model varied among different pregnancy stages. The confounding factors adjustment strategies of the whole pregnancy followed layer upon layer progressive principles. Four models were constructed on the basis of previous model one by one. Model 1 included merely three independent variables: PM2.5 concentration, different exposure levels of SHS, the interaction between PM2.5 and SHS. On the basis of model 1, maternal age at delivery, and pre-pregnancy body mass index (BMI) were added in the model 2, and then neonate's sex (male, female), prolonged pregnancy ≥ 42 weeks (yes, no), and multiparity (yes, no) were added in the model 3. On the basis of model 3, preconception diabetes (yes, no), preconception hypertension (yes, no), family history of diabetes (yes, no), the highest maternal educational level (junior high school, senior high school, college), season of delivery (spring: March to May, summer: June to August, autumn: September to December, winter: November to February), and alcohol consumption status (still, quit, never) were added in the fully adjusted model 4. The confounding factors adjustment strategies of the first, second, and third trimester of pregnancy followed full-adjusted principle and three trimesters all adopted the model 4 above to evaluate the trimester-specific interaction effect. Odds ratios (ORs) and its 95% confidence intervals (CI) were reported. To further confirm the results of interactive model, we conducted subgroup analyses stratified by SHS exposure (None exposure, Occasional exposure, Frequent exposure). In each exposure level of SHS, binary logistic regression was established to evaluate the association between trimester-specific PM2.5 and fetal macrosomia. Odds ratios (ORs) and its 95% confidence intervals (CI) were reported. The data cleaning was conducted using Stata 16. All the interaction models and SHS subgroup analyses were performed using R 4.0.3. A two-sided P value less than 0.05 was considered statistically significant. This study used the secondary data source; therefore, the statistical power using the available number of participants was estimated. Results In this study, we initially obtained 248501 pregnancy women between January 1,2010 to December 31, 2012 from the NFPHEP. After 6914 mismatch of birthplace and follow-up place or missing removed, 241587 participants involved preliminarily. Participants lost to follow-up (5036), unreported birthweight (26275), unreported gestational PM2.5 concentration (1121), unreported SHS exposure (7471) and active smoking (1434) were excluded. Pregnancy women with adverse pregnancy outcomes including birth defects (177), spontaneous abortion (121), medical abortion (24), induced labour (136), still births (332), LBW (484) were also excluded. after 1099 non-singleton births pregnancies removed. The selection process, including reasons for excluding pregnancy women, is summarized in a flow diagram (Figure A.1). Finally, 197877 participants were included in the current study Table 1 shows the baseline characteristic of women and neonates included. Among all newborns (n = 197877), macrosomia (n = 15348) account for 7.76% and the last were non- macrosomia (n = 182529). Male neonates (n = 9191) are more than female neonates (n = 6140) among macrosomia group. The median birth weight of the whole study newborns is 3300(3000, 3600). Other characteristics were also clearly shown in Table 1 . Table 2 summaries the median concentration of PM2.5 during pregnancy according to different pregnancy stages. The whole, first, second, third semester pregnancy has 78.97, 68.96, 70.03, and 81.71µg/m3 PM2.5 exposure respectively in the macrosomia group, and 76.93, 67.50, 67.50, and 79.04µg/m3 PM2.5 respectively in the overall group. Table 3 shows the interaction between PM2.5 exposure in whole pregnancy and SHS exposure on risk of macrosomia. Among the four models, the latter model in turn adds some confounding factors on the basis of the previous model. In each interactive model, maternal exposure to PM2.5 during pregnancy was associated with an increased risk of macrosomia in whole pregnancy separately (p < 0.001; p < 0.001; p < 0.001; p < 0.001). However, there was no significant association between different frequency of SHS exposure and risk of macrosomia, not only occasional SHS exposure (p = 0.246; p = 0.124; p = 0.116; p = 0.099), but also frequent SHS exposure (p = 0.374; p = 0.289; p = 0.289; p = 0.272). In contrast, a trend was observed for gestational exposure to SHS with risk of low birth weight, but not statistically significant. From model 1 to 4, the odds ratio of occasional SHS exposure (OR = 0.916; OR = 0.889; OR = 0.889; OR = 0.881) and frequent SHS exposure (OR = 0.838; OR = 0.809; OR = 0.809; OR = 0.812) were < 1. Interaction effect was statistically significant between maternal exposure to PM2.5 and SHS on the risk of macrosomia (interaction p < 0.05). In four different confounding factors-adjusted models, different frequency of SHS exposure all significantly enhances risk of macrosomia among pregnant women exposed to PM2.5 air pollutant (interaction p < 0.05). From model 1 to 4, interaction effect between occasional SHS exposure and PM2.5 were significant (interaction p = 0.021; p = 0.010; p = 0.010; p = 0.010), which the same as interaction effect between frequent SHS exposure and PM2.5 (interaction p = 0.050; p = 0.046; p = 0.051; p = 0.047). The higher exposure times and intensities of SHS prompts the stronger interaction between the two air pollutants. From model 1 to 4, the odds ratio of PM2.5 & Frequent SHS exposure was larger than PM2.5 & Occasional SHS exposure (OR = 1.004 > OR = 1.002; OR = 1.005 > OR = 1.002; OR = 1.004 > OR = 1.002; OR = 1.005 > OR = 1.002). The odds ratio of all confounding factors in model 4 were listed in TABLE A.1. Table 4 shows the interaction between PM2.5 exposure in first trimester, second trimester and third trimester of pregnancy and SHS exposure on risk of macrosomia. Each interactive model was based on full confounding factors-adjusted model. In the first trimester, the interaction effect was statistically significant between maternal exposure to PM2.5 and occasional SHS exposure on the risk of macrosomia (interaction p < 0.001). Occasionally exposed to SHS significantly enhances risk of macrosomia among pregnant women exposed to PM2.5 air pollutant (interaction p < 0.001). Frequently exposed to SHS was not associated with the risk of macrosomia among pregnant women exposed to PM2.5 air pollutant (interaction p = 0.100). In the second or third trimester, there is no interactive effect between maternal PM2.5 exposure and occasional exposed to SHS (interaction p = 0.081; p = 0.592). There is no interactive effect between maternal PM2.5 exposure and frequent exposed to SHS during second or third trimester of pregnancy, either (interaction p = 0.302; p = 0.067). Table 5 represents the association between PM2.5 concentration in different stages of pregnancy and the risk of macrosomia in different SHS Subgroups. The results of whole pregnancy are consistent with results of first trimester. The higher exposure concentration of PM2.5 during the whole pregnancy or the first trimester among pregnant women was associated with the higher risk of macrosomia (OR = 1.004, p < 0.001; OR = 1.003, p < 0.001). Different frequency of SHS exposure all significantly increases risk of macrosomia among pregnant women exposed to PM2.5 air pollutant, not only exposed to SHS occasionally (interaction p < 0.001; p < 0.001), but also exposed to SHS frequently (interaction p < 0.001; p Occasional exposure OR = 1.006) and the first trimester (Frequent exposure OR = 1.008 > Occasional exposure OR = 1.006). In the second and the third trimester, maternal PM2.5 exposure is associated with an increased risk of macrosomia (OR = 1.003, p < 0.001; OR = 1.002, p < 0.001). But there is no interaction between maternal PM2.5 exposure and SHS exposure in the second (OR = 1.003, p < 0.001; OR = 1.005, p < 0.001; OR = 1.004, p = 0.069) and the third trimester (OR = 1.002, p < 0.001; OR = 1.005, p < 0.001; OR = 1.005, p = 0.007). Discussion In full-adjusted interaction model, we found maternal exposure to PM2.5 was associated with an increased risk of macrosomia in whole pregnancy (p < 0.001), the first (p < 0.001), second (p < 0.001) and third (p < 0.001) trimester of pregnancy. However, there was a trend for gestational exposure to SHS with risk of low birth weight, but not statistically significant (occasional SHS exposure (p = 0.099); frequent SHS exposure (p = 0.272)). Interaction effect was statistically significant between maternal exposure to PM2.5 and SHS on the risk of macrosomia in the whole pregnancy (all interaction p < 0.050) and the first trimester pregnancy (all interaction p 0.050) and third trimester (all interaction p > 0.050) of pregnancy. The higher frequency of SHS exposure prompts the stronger interaction between the two air pollutants in the whole pregnancy and the first trimester pregnancy. In conclusion, in the whole pregnancy and the first trimester pregnancy, maternal exposure to SHS during pregnancy enhances the risk of macrosomia among pregnant women exposed to PM2.5 air pollutant, and the interaction became stronger with the more frequent exposure to SHS. There is a rigorous methodology in our study. First, the process of data cleaning was conducted following strict screening standards. All the enrolled pregnant women in NFPHEP needed to be screened for the eligibility of inclusion into our study. Second, the confounding factors were adjusted gradually in different models and avoid making bias to the outcomes. Third, the subgroup analyses stratified by SHS exposure were conducted to further confirm the results of interactive model. There are several innovations in our research. First, our study was conducted among a new study population, that is, the national pregnant Chinese women. Previous studies on the adverse effect of air pollution on birth outcomes were mostly carried out in developed countries or regions [ 23 , 24 ] . The differences between Chinese and foreigner population reflected in several aspects, such as the higher exposure condition of maternal SHS [ 25 , 26 ] , higher PM2.5 pollution levels [ 27 , 28 ] and more vulnerable metabolism characteristics of Chinese population comparing with foreigners [ 29 , 30 ] . Hence, our study could provide new evidence on prognosis of air pollutions-related diseases among Chinese population, which represent 1/3 of the world's population. Second, our study establishes a new interaction model between two human-caused air pollutants. Previous studies focused on the interaction between genetic inheritance and environmental pollutants on birth outcomes [ 31 , 32 ] . No study pays attention to the interaction between two air pollutants caused by human activities during different stages of pregnancy, and the two air pollutants can be changed and avoided by taking some measures. Third, our study identifies a new risk that can intensity the association between maternal PM2.5 air pollutant exposure and macrosomia newborns. We found maternal SHS exposure enhances the risk of macrosomia associated with PM2.5 exposure. Our study found maternal exposure to PM2.5 during pregnancy was associated with an increased risk of macrosomia in whole pregnancy and different stages of pregnancy. The finding was consistent with previous study on association between PM2.5 exposure and birth weight [ 16 ] . In CHEN et al’s study [ 16 ] , significant associations were found between increased risk of macrosomia and every 10 µg/m3 increase of PM2.5 concentration over the first, second, and third trimesters in a nationwide prospective cohort study in China. Furthermore, our study also found a trend of low birth weight for gestational exposure to SHS, although it was not statistically significant. The trend in our study was consistent with most previous studies on association of neonatal birthweight and cigarette smoke exposure in mothers [ 7 , 17 , 18 ] . In a cross-sectional study from Shanghai, WANG et al [ 7 ] found that the LBW prevalence was higher among newborns whose mother were prenatally exposed to SHS comparing with newborns whose mother were not exposed. KOBAYASHI et al’s study [ 18 ] also observed the link between maternal passive smoking and increased risk of delivering SGA in a prospective birth cohort from the Hokkaido. Our study suggested there are interaction between maternal exposure to SHS and PM2.5 air pollutants on the risk of macrosomia in the whole pregnancy and the first trimester pregnancy. The higher frequency of SHS exposure prompts the stronger interaction between the two air pollutants in the whole pregnancy and the first trimester pregnancy. No studies before have concentrated on the interaction effect of two air pollutants on adverse pregnancy outcomes such as birth weight. The interaction between air pollutants and other factors on birth weight outcome have been reported in previous studies. Huang et al. [ 33 ] examine the interaction effects of prenatal exposed to environmental tobacco smoke (ETS) and genotypes of cytochrome P4501A1 (CYP1A1), glutathione S-transferases (GSTs) on the risk of full-term low birth weight (FT-LBW). The study [ 33 ] revealed that gene polymorphisms of CYP1A1 and GSTs played modified roles in associations between prenatal ETS exposure and FT-LBW. However, it is harder to take measures to change genetic endowments, compared with controlling man-made pollutant factors like PM2.5 and SHS exposure in our study. Wang et al. and colleagues [ 34 ] also assessed the interaction of air pollutants and meteorological factors on birth weight in Shenzhen, China. According to Wang’s research [ 34 ] , an interactive effect of air temperature and humidity on the relationship between PM10 exposure and SGA among newborns existed. Other studies also reported interaction between air pollutants and life-related risk factors of mothers during pregnancy on fetal birth weight, such as maternal pre-pregnancy overweight [ 35 ] , maternal employment [ 36 ] , and maternal illicit drug use [ 37 ] . But in all, there have been no studies before assessing the interaction effect of two air pollutants on birth weight outcomes. The detailed mechanism of interaction between maternal exposure to SHS and PM2.5 air pollutants on the risk of macrosomia during pregnancy remains unclear. A few possible explanations or hypothesis are as follows. Firstly, SHS is the main source of indoor PM2.5 pollution that could be inhaled into lung and blood circulation [ 38 , 39 ] . When SHS exposure from surrounding smokers and PM2.5 exposure from atmosphere existed simultaneously, the concentration of PM2.5 changed and certain effect like synthesis may happened. Hence it is possible that there was interaction between maternal exposure to SHS and PM2.5. Secondly, studies on association of PM2.5 air pollutant and infant’s birth weight outcome suggested changes of oxidative stress [ 40 ] , immune response [ 41 ] and epigenetic regulation [ 42 ] may influence birth weight. Furthermore, studies on association of maternal tobacco smoke exposure and fetal birth weight suggested that placental toxicity [ 43 ] and epigenetic modifications such as DNA methylation [ 44 ] could be potential mediators affecting fetal birth weight outcomes. So epigenetic modifications could be an explanation for the interaction between maternal exposure to SHS and PM2.5 air pollutants on the risk of macrosomia. Maybe the two air pollutants have some common gene methylation or overlapped molecular pathway of macrosomia leading to the interaction. It seems controversial that maternal SHS exposure alone led to a trend of low birth weight, however, in the interaction effect, SHS could enhances the risk of macrosomia among pregnant women exposed to PM2.5 air pollutant in our study. The explanation may be that the gene or signal pathway related with association between SHS and macrosomia could only be activated by gene or signal pathway related with PM2.5. SHS exposure alone could not lead to macrosomia, because macrosomia-related genes were not activated by PM2.5. Our study suggested only in the whole pregnancy and the first trimester, the interaction between maternal exposure to SHS and PM2.5 air pollutants on the risk of macrosomia existed. The result indicates that the exposure to PM2.5 in the first trimester might have the biggest effect on birth weight. It might be because that the exposure to air pollution in the first trimester could lead to placental adaptation through epigenetic modification [ 45 ] . Our study has some strengths and several potential limitations. The strengths include emphasis on the harm of SHS to pregnant women, especially in high-exposed PM2.5 regions. Another strength is to point out the study direction that future studies should focus on the epigenetic aspects when exploring the mechanism of birth weight changes associated with the environmental pollutants such as PM2.5 or SHS exposure. The study had also some limitations. Majority of pregnant women in the NFPHEP were from rural areas in China, and thus, the conclusions made in this study are most pertinent to this region. Since the survey of SHS condition in our study was conducted through the questionnaire completed by the pregnant women, the collected information could be inevitably influenced by subjective ideas. Another limitation of our study was the potential recall bias due to the long follow-up interval. Generally, our study highlights the impact of environmental air pollutants on adverse pregnancy outcomes like birth weight and we should emphasize on controllable human activities-caused environmental pollutants like PM2.5 and SHS, which could be reduced or removed by taking some measures in future. Our results strengthened evidence that maternal exposure to PM2.5 during pregnancy was associated with an increased risk of macrosomia in China and meanwhile, firstly suggested that SHS may enhance the risk of macrosomia associated with PM2.5. So pregnant women should avoid exposure to SHS during pregnancy, especially those who lived in areas with serious PM2.5 pollution and who were in the first trimester of pregnancy. These findings may help to identify high-risk pregnant women for giving birth to macrosomia, providing an opportunity for targeted preventive interventions to protect vulnerable population as early as possible. Strengthening air pollution control will have a beneficial impact on avoiding adverse birth outcomes, improving the quality of national population and alleviating national financial burden. Furthermore, the results of our study also suggested that the detailed mechanism of pregnancy outcome should focus more on epigenetic aspects of embryonic development in future. Conclusion In the whole pregnancy and the first trimester pregnancy, maternal exposure to SHS during pregnancy enhances the risk of macrosomia among pregnant women exposed to PM2.5 air pollutant, and the interaction became stronger with the more frequent exposure to SHS. Declarations Ethics approval and consent to participate ： The study was approved by the institutional review board of the National Research Institute for Family Planning, Beijing, China. Consent for publication ： All participants provided written informed consent. Availability of data and material ： All data generated or analysed during this study are included in this published article. Competing interests: The authors declare that they have no competing interests. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors' contributions: LYY has drafted the work and completed the manuscript. ZYL has made substantial contributions to the acquisition, analysis, or interpretation of data for the work; PH has provided editing and writing assistance for important intellectual content; CS has made substantial contributions to the conception or design of the work; Acknowledgements: Not applicable References [1] O'DONNELL K J, MEANEY M J. Fetal Origins of Mental Health: The Developmental Origins of Health and Disease Hypothesis [J]. The American journal of psychiatry, 2017, 174(4): 319-28. [2] RISNES K R, VATTEN L J, BAKER J L, et al. Birthweight and mortality in adulthood: a systematic review and meta-analysis [J]. International journal of epidemiology, 2011, 40(3): 647-61. [3] ARAUJO JúNIOR E, PEIXOTO A B, ZAMARIAN A C, et al. Macrosomia [J]. Best practice & research Clinical obstetrics & gynaecology, 2017, 38(83-96. [4] BETA J, KHAN N, KHALIL A, et al. Maternal and neonatal complications of fetal macrosomia: systematic review and meta-analysis [J]. 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Tables Table 1 Baseline characteristics of the included pregnant women and neonates Characteristics Macrosomia Overall Yes (n = 15348) No (n = 182529) (n = 197877) Birth weight, g 4322.43 ± 469.90 3234.18 ± 428.42 3318.59 ± 520.74 Neonate's sex Male 9191 (59.90%) 95332 (52.20%) 104523 (52.80%) Female 6140 (40.00%) 87101 (47.70%) 93241 (47.10%) Missing 17 (0.10%) 96 (0.10%) 113 (0.10%) Age at delivery, y 25.32 ± 3.97 25.23 ± 3.93 25.24 ± 3.94 Pre-pregnancy BMI a , kg/m2 21.30 ± 2.76 21.02 ± 2.60 21.04 ± 2.61 Prolonged pregnancy Yes 523 (3.40%) 4103 (2.20%) 4626 (2.30%) No 14825 (96.60%) 178426 (97.80%) 193251 (97.70%) Multiparity Yes 11894 (77.50%) 37983 (20.80%) 49877 (25.20%) No 3454 (22.50%) 144546 (79.20%) 148000 (74.80%) Highest education level Junior high school 10753 (70.10%) 128280 (70.30%) 139033 (70.30%) Senior high school 2892 (18.80%) 34594 (19.00%) 37486 (18.90%) College 1532 (10.00%) 17368 (9.50%) 18900 (9.60%) Missing 171 (1.10%) 2287 (1.20%) 2458 (1.20%) Pre-pregnancy diabetes mellitus Yes 3 (0.02%) 18 (0.05%) 21 (0.01%) No 15345 (99.98%) 178426 (97.75%) 193771 (97.93%) Missing 0 (0.00%) 4085 (2.20%) 4085 (2.06%) Pre-pregnancy hypertension Yes 8 (0.05%) 93 (0.10%) 101 (0.10%) No 15340 (99.95%) 182436 (99.90%) 197776 (99.90%) Family history of diabetes mellitus Yes 100 (0.70%) 1040 (0.60%) 1140 (0.60%) No 15248 (99.30%) 181489 (99.40%) 196737 (99.40%) Drinking during pregnancy Never 15205 (99.10%) 180573 (98.90%) 195778(98.90%) Quit 99 (0.60%) 1169 (0.60%) 1268 (0.60%) Still 35 (0.20%) 720 (0.40%) 755 (0.40%) Missing 9 (0.10%) 67 (0.10%) 76 (0.10%) Season of delivery Spring 4065 (26.50%) 48959 (26.80%) 53024 (26.80%) Summer 2421 (15.80%) 28626 (15.70%) 31047 (15.70%) Autumn 3897 (25.40%) 47184 (25.90%) 51081 (25.80%) Winter 4965 (32.30%) 57760 (31.60%) 62725 (31.70%) Secondhand Smoking None exposure 12320 (80.30%) 149308 (81.80%) 161628 (81.70%) Occasional exposure 2683 (17.50%) 29845 (16.40%) 32528 (16.40%) Frequent exposure 345 (2.20%) 3376 (1.80%) 3721 (1.90%) a BMI, body mass index. Table 2 Mean PM2.5 concentration during pregnancy (µg/m3) Stage of Pregnancy Macrosomia Overall (n = 197877) Yes (n = 15348) No (n = 182529) First trimester 73.35 ± 29.25 70.81 ± 29.26 71.01 ± 29.27 Second trimester 73.82 ± 29.00 71.67 ± 29.97 71.83 ± 29.90 Third trimester 83.88 ± 32.46 81.23 ± 33.08 81.43 ± 33.04 whole pregnancy 77.57 ± 22.11 75.08 ± 22.93 75.28 ± 22.87 Table 3 The interaction between PM2.5 exposure in whole pregnancy and SHS exposure on risk of macrosomia Odds Ratio(95%CI) P value whole pregnancy Model 1 a PM2.5 exposure 1.004 (1.003, 1.005) < 0.001 Occasional SHS e exposure 0.916 (0.789, 1.062) 0.246 Frequent SHS exposure 0.838 (0.565, 1.228) 0.374 PM2.5 & Occasional SHS exposure f 1.002 (1.000, 1.004) 0.021 PM2.5 & Frequent SHS exposure g 1.004 (1.000, 1.009) 0.050 Model 2 b PM2.5 exposure 1.004 (1.003, 1.005) < 0.001 Occasional SHS exposure 0.889 (0.765, 1.032) 0.124 Frequent SHS exposure 0.809 (0.544, 1.189) 0.289 PM2.5 & Occasional SHS exposure 1.002 (1.000, 1.004) 0.010 PM2.5 & Frequent SHS exposure 1.005 (1.000, 1.009) 0.046 Model 3 c PM2.5 exposure 1.004 (1.003, 1.004) < 0.001 Occasional SHS exposure 0.889 (0.763, 1.029) 0.116 Frequent SHS exposure 0.809 (0.542, 1.190) 0.289 PM2.5 & Occasional SHS exposure 1.002 (1.001, 1.004) 0.010 PM2.5 & Frequent SHS exposure 1.004 (1.000, 1.009) 0.051 Model 4 d PM2.5 exposure 1.004 (1.003, 1.005) < 0.001 Occasional SHS exposure 0.881 (0.757, 1.024) 0.099 Frequent SHS exposure 0.812 (0.537, 1.181) 0.272 PM2.5 & Occasional SHS exposure 1.002 (1.001, 1.004) 0.010 PM2.5 & Frequent SHS exposure 1.005 (1.000, 1.009) 0.047 a Unadjusted b Adjusted for age at delivery, pre-pregnancy BMI c Adjusted for age at delivery, pre-pregnancy BMI, neonatal sex, prolonged pregnancy, and multiparity d Adjusted for age at delivery, pre-pregnancy BMI, neonatal sex, prolonged pregnancy, multiparity, pre‐pregnancy diabetes mellitus, pre‐pregnancy hypertension, family history of diabetes mellitus, educational level, drinking during pregnancy, and season of delivery. e SHS, secondhand smoking. f The interaction between mean PM2.5 concentration of whole pregnancy and occasional SHS exposure g The interaction between mean PM2.5 concentration of whole pregnancy and frequent SHS exposure Table 4 The interaction between PM2.5 exposure in first trimester, second trimester and third trimester of pregnancy and SHS exposure on risk of macrosomia Odds Ratio (95%CI) P value First trimester a PM2.5 exposure 1.003 (1.002, 1.004) < 0.001 Occasional SHS e exposure b 0.826 (0.734, 0.929) 0.001 Frequent SHS exposure 0.929 (0.683, 1.253) 0.631 PM2.5 & Occasional SHS exposure 1.003 (1.002, 1.005) 0.000 PM2.5 & Frequent SHS exposure 1.003 (0.999, 1.007) 0.100 Second trimester a PM2.5 exposure 1.003 (1.002, 1.004) < 0.001 Occasional SHS exposure c 0.970 (0.865, 1.087) 0.604 Frequent SHS exposure 1.013 (0.742, 1.373) 0.936 PM2.5 & Occasional SHS exposure 1.001 (0.999, 1.003) 0.081 PM2.5 & Frequent SHS exposure 1.002 (0.998, 1.005) 0.302 Third trimester a PM2.5 exposure 1.003 (1.002, 1.003) < 0.001 Occasional SHS exposure d 1.036 (0.922, 1.163) 0.551 Frequent SHS exposure 0.905 (0.664, 1.224) 0.524 PM2.5 & Occasional SHS exposure 1.003 (0.999, 1.002) 0.592 PM2.5 & Frequent SHS exposure 1.003 (0.999, 1.006) 0.067 a Adjusted for age at delivery, pre-pregnancy BMI, neonatal sex, prolonged pregnancy, multiparity, pre‐pregnancy diabetes mellitus, pre‐pregnancy hypertension, family history of diabetes mellitus, educational level, drinking during pregnancy, and season of delivery. b The interaction between mean PM2.5 concentration of first trimester pregnancy and occasional SHS exposure. c The interaction between mean PM2.5 concentration of second trimester pregnancy and occasional SHS exposure. d The interaction between mean PM2.5 concentration of third trimester pregnancy and occasional SHS exposure. e SHS, secondhand smoking. Table 5 Association between PM2.5 concentration in different stages of pregnancy and the risk of macrosomia in different SHS Subgroups. SHS b Subgroup Odds Ratio(95%CI) P value Whole pregnancy a None exposure 1.004 (1.003, 1.005) < 0.001 Occasional exposure 1.006 (1.005, 1.008) < 0.001 Frequent exposure 1.007 (1.003, 1.012) 0.001 First trimester a None exposure 1.003 (1.002, 1.004) < 0.001 Occasional exposure 1.006 (1.005, 1.008) < 0.001 Frequent exposure 1.008 (1.004, 1.012) < 0.001 Second trimester a None exposure 1.003 (1.002, 1.003) < 0.001 Occasional exposure 1.005 (1.003, 1.006) < 0.001 Frequent exposure 1.004 (1.000, 1.008) 0.069 Third trimester a None exposure 1.002 (1.002, 1.003) < 0.001 Occasional exposure 1.005 (1.003, 1.006) < 0.001 Frequent exposure 1.005 (1.001, 1.009) 0.007 a Adjusted for age at delivery, pre-pregnancy BMI, neonatal sex, prolonged pregnancy, multiparity, pre‐pregnancy diabetes mellitus, pre‐pregnancy hypertension, family history of diabetes mellitus, educational level, drinking during pregnancy, and season of delivery. b SHS, secondhand smoking. Table A.1 The interaction between PM2.5 exposure in whole pregnancy and SHS exposure on risk of macrosomia Odds Ratio(95%CI) P value whole pregnancy d PM2.5 exposure 1.004 (1.003, 1.005) < 0.001 Occasional SHS a exposure 0.881 (0.757, 1.024) 0.098 Frequent SHS exposure 0.812 (0.537, 1.181) 0.272 Pre-pregnancy BMI 1.035 (1.029, 1.041) < 0.001 Age at delivery 1.002 (0.997, 1.006) 0.477 Neonate's sex 0.730 (0.706, 0.755) < 0.001 Multiparity 1.061 (1.016, 1.107) 0.007 Prolonged pregnancy 1.488 (1.353, 1.633) < 0.001 Season of delivery (Spring) reference reference Season of delivery (Summer) 1.036 (0.982, 1.092) 0.199 Season of delivery (Autumn) 1.050 (1.001, 1.100) 0.045 Season of delivery (Winter) 1.069 (1.023, 1.117) 0.003 Highest education level (Junior high school) reference reference Highest education level (Senior high school) 1.044 (1.000, 1.091) 0.049 Highest education level (college) 1.111 (1.049, 1.175) < 0.001 Drinking during pregnancy (Never) reference reference Drinking during pregnancy (Quit) 0.980 (0.79, 1.202) 0.852 Drinking during pregnancy (Still) 0.581 (0.406, 0.803) 0.002 Pre-pregnancy diabetes mellitus 1.593 (0.370, 4.377) 0.459 Pre-pregnancy hypertension 0.838 (0.373, 1.627) 0.637 Family history of diabetes mellitus 1.054 (0.851, 1.291) 0.623 PM2.5 & Occasional SHS exposure b 1.002 (1.001, 1.004) 0.010 PM2.5 & Frequent SHS exposure c 1.005 (1.000, 1.009) 0.047 a SHS, secondhand smoking. b The interaction between mean PM2.5 concentration of whole pregnancy and occasional SHS exposure c The interaction between mean PM2.5 concentration of whole pregnancy and frequent SHS exposure d Adjusted for age at delivery, pre-pregnancy BMI, neonatal sex, prolonged pregnancy, multiparity, pre‐pregnancy diabetes mellitus, pre‐pregnancy hypertension, family history of diabetes mellitus, educational level, drinking during pregnancy, and season of delivery. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-520246","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":28804939,"identity":"48a731a5-46f6-4284-bc03-52caabf2436c","order_by":0,"name":"yunyun Luo","email":"","orcid":"","institution":"PUMCH: Peking Union Medical College Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"yunyun","middleName":"","lastName":"Luo","suffix":""},{"id":28804940,"identity":"a2e55433-2a7d-4b82-b629-d8ac366cc5c2","order_by":1,"name":"yuelun zhang","email":"","orcid":"","institution":"PUMCH: Peking Union Medical College Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"yuelun","middleName":"","lastName":"zhang","suffix":""},{"id":28804941,"identity":"1cd89646-9545-4918-b452-01e4b76f6044","order_by":2,"name":"hui pan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4klEQVRIiWNgGAWjYHACNgYGAxseNmbGhgMJQAY/fwNRWtLk+NiZGw88KEiTkZxxgBgtDIeN5fjZmw8++HDYxqAhAb96c/azxx7zFDAntkEcdp7HgOEA44ePObi1WPbkpRvzGLDBtNzmMWduYJacuQ23FoMDOWbSPAY8CC2WDQfYmHnxaTn/BqRFAqblHI/BgQQCWm6AbTEwhgbyAcJaLGe8MZOcY5AgB9WSzCM542AzXr+Y8+eYSbz5859Hvv/4448//tjZ8/M3H/zwEZ/DsIgxNuBWj0PLKBgFo2AUjAJUAADEwU+LobBKIwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-0425-1689","institution":"PUMCH: Peking Union Medical College Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"hui","middleName":"","lastName":"pan","suffix":""},{"id":28804942,"identity":"e824f5fb-2b6d-42d2-9d19-eb0f83169315","order_by":3,"name":"shi chen","email":"","orcid":"","institution":"PUMCH: Peking Union Medical College Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"shi","middleName":"","lastName":"chen","suffix":""}],"badges":[],"createdAt":"2021-05-12 20:15:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-520246/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-520246/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.3389/fpubh.2021.735699","type":"published","date":"2021-11-18T07:58:46+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":9685001,"identity":"365916e4-bdff-4163-b4e5-646703f98232","added_by":"auto","created_at":"2021-05-27 21:55:27","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":111642,"visible":true,"origin":"","legend":"Figure A.1 Flowchart of participants inclusion and exclusion","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-520246/v1/87ed16387c224e4ec982dc69.png"},{"id":15658115,"identity":"d3b85ccc-adb9-477b-bbe8-537978bbb138","added_by":"auto","created_at":"2021-11-18 07:58:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":474593,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-520246/v1/4c803777-f9d5-49b8-80da-fd79278b89e7.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eMaternal Secondhand Smoke Exposure Enhances Macrosomia Risk among Pregnant Women Exposed to PM2.5: A New Interaction of Two Air Pollutants in A Nationwide Cohort\u003c/p\u003e","fulltext":[{"header":"Introduction","content":" \u003cp\u003eBirth weight is a key indicator in new-born and infant survival and associated with health outcomes across the whole life course, including cardio-metabolic and mental health, some cancers and mortality\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. A fetus larger than 4000g is considered as macrosomia, regardless of gestational age\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Macrosomia is associated with increased risk of maternal and fetal complications such as birth canal trauma, shoulder dystocia, and perinatal asphyxia\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Compared with normal infants, macrosomia newborns are at more risk for long-term complications, such as obesity and insulin resistance\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. It was well established that both genetics and environmental risks play indispensable roles in pregnancy outcomes like birth weight\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Genetics risks include parental hereditary material, epigenetic variation and maternal placenta factors\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e, while environmental risks include gestational air pollutants, maternal addiction to smoking or alcohol, and maternal nutritional condition\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. It is more likely to control and intervene environmental risks to improve adverse pregnancy outcomes comparing with the genetics risks. Among environmental risks, fine particulate matter (PM2.5) is one of the most common outdoor air pollutants caused by fossil fuel burning and vehicle emissions, while secondhand smoking (SHS) is the main source of indoor air pollution produced by smokers. Both air pollutants are caused by human activities and can be changed and avoided by taking some measures. Pregnant women exposed to PM2.5 during pregnancy can lead to adverse pregnancy outcomes such as fetal congenital malformation, premature delivery, abnormal birth weight of the fetus, and in severe cases, neonatal death\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. Maternal SHS exposure at work and/or at home during pregnancy increased the risk of fetal stillbirth, neonatal death, and perinatal death\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003ePrevious studies have established strongly epidemiologic linkage between prenatal exposure to PM2.5 or SHS and fetal birthweight outcomes. Concerning PM2.5, majority of studies reported that the increased risk of low birth weight (LBW) was associated with maternal exposure to PM2.5 \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. Links between gestational PM2.5 exposure and small for gestational age (SGA) had been observed in some studies\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. A nationwide cohort study suggested that gestational exposure to PM2.5 during pregnancy was significantly associated with an increased risk of macrosomia\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. In terms of SHS, evidence on association of neonatal birthweight and cigarette smoke exposure in mothers was accumulated with growing number of studies \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. The LBW prevalence was higher among newborns whose mother were prenatally exposed to SHS comparing with newborns whose mother were not exposed\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Link between maternal passive smoking and increased risk of delivering SGA also exists\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. All in all, findings of above studies about PM2.5 and SHS air pollutants on neonatal birthweight outcomes have been controversial and inconsistent.\u003c/p\u003e \u003cp\u003eThere are some research gaps in areas of association between environment pollutants and infant\u0026rsquo;s birth weight. Firstly, whether the PM2.5 pollutants and SHS were associated with increase or decrease of birth weight was unclear. Secondly, most related studies are confined to certain city or province. Research based on nationwide cohort is limited and scarce. Thirdly, previous studies all evaluated the effects of PM2.5 or SHS air pollutants on birthweight outcomes separately, or considered SHS as possible confounding factor in studies; no published studies have assessed the potential interaction between PM2.5 and SHS and the interactive effects on birthweight outcomes.\u003c/p\u003e \u003cp\u003eIn our study, we aimed to investigate the interaction between gestational PM2.5 and SHS air pollution exposure on the risk of macrosomia among pregnant women, and examine the modifying effect of SHS exposure on the association of PM2.5 air pollution and birthweight outcomes during pregnancy. We conducted the data cleaning process of following strict screening standards to ensure the inclusion eligibility of all the enrolled women. We established different interaction models about air pollution on birthweight outcomes, according to different confounding factors adjustment and different pregnancy stages. SHS subgroups analysis were conducted to further confirm the results of interaction models. Our study expended the region of investigation, achieving nationwide evidence including 220 countries from 31provinces or municipalities in China. This study was the first nationwide, population-based cohort study to identify the interactions between gestational PM2.5 and SHS exposure among pregnant women on birthweight outcomes.\u003c/p\u003e "},{"header":"Methods","content":" \u003cp\u003eStudy Design and Participants\u003c/p\u003e \u003cp\u003eOur research data were derived from the National Free Preconception Health Examination Project (NFPHEP), which lasted 3years from 1 January 2010 to 31 December 2012. This project was launched by National Health and Family Planning Commission and the Ministry of Finance of the People's Republic of China in 2010. The aim of NFPHEP was to provide free preconception health examinations in rural areas to married couples that planned a pregnancy within the next 6 months. Well-trained local community health workers and obstetricians provided free preconception counselling, medical examinations and managements during pregnancy. In all, at least 240,000 Chinese women in 220 counties from 31 provinces or municipalities of China were enrolled in this project. The study was approved by the institutional review board of the National Research Institute for Family Planning, Beijing, China. All participants provided written informed consent. The detailed study design and implementation of NFPHEP have been described elsewhere\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eReferred variables\u003c/p\u003e \u003cp\u003eIn this project, information of both enrolled pregnant women and neonates was collected by face-to‐face investigation and examination conducted by trained and qualified staff. In total, 371 items in 19 aspects including social demographic characteristics, family history, lifestyle factors, history of diseases, physical examinations, laboratory tests and diagnostic imaging were obtained. All data were uploaded to a nationwide electronic data capture system and underwent quality control by the National Quality Inspection Center for Family Planning Techniques.\u003c/p\u003e \u003cp\u003eThe variables analyzed in our study included PM2.5 concentration, age at delivery, neonate\u0026rsquo;s birthweight, neonate\u0026rsquo;s sex, educational level, smoking during pregnancy, alcohol intake during pregnancy, prolonged pregnancy, multiparity, pre-pregnancy BMI, pre-pregnancy diabetes mellitus, pre-pregnancy hypertension, family history of diabetes mellitus, season of delivery.\u003c/p\u003e \u003cp\u003eOutcome and exposure assessment\u003c/p\u003e \u003cp\u003eAs recommended by the World Health Organization, macrosomia was defined as birth weight greater than or equal to 4000 g\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. We defined NBW as birth weight between 2500 and 4000 g\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. In this study, PM2.5 exposure concentration of each country was obtained using a hindcast model specific for historical PM2.5 estimation from satellite-retrieved aerosol optic depth. It was an ensemble machine learning model comprised of several machine learning models including random forest, generalized additive model, and extreme gradient boosting. Specific parameter settings and model building have been reported in detail elsewhere\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Daily county‐specific PM2.5 concentration of each included pregnant women was used to calculate monthly PM2.5 concentration to evaluate PM2.5 exposure condition during the pregnancy in our study. The average monthly PM2.5 concentration in 1 to 3 months' gestation, 4 to 6 months' gestation, and 7 months to delivery were regarded as the first, second, and third trimester of pregnancy, respectively. Each woman\u0026rsquo;s address information at the county level has been registered. Secondhand smoke (SHS) is passive inhalation of cigarette smoke produced by smokers' active smoking, also commonly known as \"passive smoking\" or \"forced smoking\". Pregnant women in our study may be exposed to different degree of SHS in the workplace or at home. They were divided into three groups according to exposed levels. Those who never touched with smokers or secondhand smokers were classified into \u0026ldquo;none exposure SHS\u0026rdquo; subgroup. Those who occasionally visit or work with smokers were classified into \u0026ldquo;occasional exposure SHS\u0026rdquo; subgroup. Those who lived with at least one regular smoker were classified into \u0026ldquo;frequent exposure SHS\u0026rdquo; subgroup.\u003c/p\u003e \u003cp\u003eThe conditions of SHS exposure were collected through the questionnaires filled out by pregnant women.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eAll the enrolled pregnant women in NFPHEP needed to be screened for the eligibility of inclusion into our study. The definition of loss to follow-up was that participants got preconception examination but had not received prenatal or postnatal examination and questionnaires yet by 1 month after expected date of confinement. To avoid the bias induced by moving during pregnancy, women who had a different registered residence and birthplace were excluded. The pregnancy women who had missing data of PM2.5 concentration, birthweight, SHS conditions were excluded in analysis. Extreme or abnormal observations in other variables were replaced with missing data or censored data, which was not excluded in analysis.\u003c/p\u003e \u003cp\u003eThe establishment of interaction models was based on binary logistic regression, including main effects item, interaction effects item and other confounding factors adjustment. The interaction model varied among different pregnancy stages. The confounding factors adjustment strategies of the whole pregnancy followed layer upon layer progressive principles. Four models were constructed on the basis of previous model one by one. Model 1 included merely three independent variables: PM2.5 concentration, different exposure levels of SHS, the interaction between PM2.5 and SHS. On the basis of model 1, maternal age at delivery, and pre-pregnancy body mass index (BMI) were added in the model 2, and then neonate's sex (male, female), prolonged pregnancy\u0026thinsp;\u0026ge;\u0026thinsp;42 weeks (yes, no), and multiparity (yes, no) were added in the model 3. On the basis of model 3, preconception diabetes (yes, no), preconception hypertension (yes, no), family history of diabetes (yes, no), the highest maternal educational level (junior high school, senior high school, college), season of delivery (spring: March to May, summer: June to August, autumn: September to December, winter: November to February), and alcohol consumption status (still, quit, never) were added in the fully adjusted model 4. The confounding factors adjustment strategies of the first, second, and third trimester of pregnancy followed full-adjusted principle and three trimesters all adopted the model 4 above to evaluate the trimester-specific interaction effect. Odds ratios (ORs) and its 95% confidence intervals (CI) were reported.\u003c/p\u003e \u003cp\u003eTo further confirm the results of interactive model, we conducted subgroup analyses stratified by SHS exposure (None exposure, Occasional exposure, Frequent exposure). In each exposure level of SHS, binary logistic regression was established to evaluate the association between trimester-specific PM2.5 and fetal macrosomia. Odds ratios (ORs) and its 95% confidence intervals (CI) were reported.\u003c/p\u003e \u003cp\u003eThe data cleaning was conducted using Stata 16. All the interaction models and SHS subgroup analyses were performed using R 4.0.3. A two-sided P value less than 0.05 was considered statistically significant. This study used the secondary data source; therefore, the statistical power using the available number of participants was estimated.\u003c/p\u003e \u003c/div\u003e "},{"header":"Results","content":" \u003cp\u003eIn this study, we initially obtained 248501 pregnancy women between January 1,2010 to December 31, 2012 from the NFPHEP. After 6914 mismatch of birthplace and follow-up place or missing removed, 241587 participants involved preliminarily. Participants lost to follow-up (5036), unreported birthweight (26275), unreported gestational PM2.5 concentration (1121), unreported SHS exposure (7471) and active smoking (1434) were excluded. Pregnancy women with adverse pregnancy outcomes including birth defects (177), spontaneous abortion (121), medical abortion (24), induced labour (136), still births (332), LBW (484) were also excluded. after 1099 non-singleton births pregnancies removed. The selection process, including reasons for excluding pregnancy women, is summarized in a flow diagram (Figure A.1). Finally, 197877 participants were included in the current study\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the baseline characteristic of women and neonates included. Among all newborns (n\u0026thinsp;=\u0026thinsp;197877), macrosomia (n\u0026thinsp;=\u0026thinsp;15348) account for 7.76% and the last were non- macrosomia (n\u0026thinsp;=\u0026thinsp;182529). Male neonates (n\u0026thinsp;=\u0026thinsp;9191) are more than female neonates (n\u0026thinsp;=\u0026thinsp;6140) among macrosomia group. The median birth weight of the whole study newborns is 3300(3000, 3600). Other characteristics were also clearly shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summaries the median concentration of PM2.5 during pregnancy according to different pregnancy stages. The whole, first, second, third semester pregnancy has 78.97, 68.96, 70.03, and 81.71\u0026micro;g/m3 PM2.5 exposure respectively in the macrosomia group, and 76.93, 67.50, 67.50, and 79.04\u0026micro;g/m3 PM2.5 respectively in the overall group.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the interaction between PM2.5 exposure in whole pregnancy and SHS exposure on risk of macrosomia. Among the four models, the latter model in turn adds some confounding factors on the basis of the previous model. In each interactive model, maternal exposure to PM2.5 during pregnancy was associated with an increased risk of macrosomia in whole pregnancy separately (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, there was no significant association between different frequency of SHS exposure and risk of macrosomia, not only occasional SHS exposure (p\u0026thinsp;=\u0026thinsp;0.246; p\u0026thinsp;=\u0026thinsp;0.124; p\u0026thinsp;=\u0026thinsp;0.116; p\u0026thinsp;=\u0026thinsp;0.099), but also frequent SHS exposure (p\u0026thinsp;=\u0026thinsp;0.374; p\u0026thinsp;=\u0026thinsp;0.289; p\u0026thinsp;=\u0026thinsp;0.289; p\u0026thinsp;=\u0026thinsp;0.272). In contrast, a trend was observed for gestational exposure to SHS with risk of low birth weight, but not statistically significant. From model 1 to 4, the odds ratio of occasional SHS exposure (OR\u0026thinsp;=\u0026thinsp;0.916; OR\u0026thinsp;=\u0026thinsp;0.889; OR\u0026thinsp;=\u0026thinsp;0.889; OR\u0026thinsp;=\u0026thinsp;0.881) and frequent SHS exposure (OR\u0026thinsp;=\u0026thinsp;0.838; OR\u0026thinsp;=\u0026thinsp;0.809; OR\u0026thinsp;=\u0026thinsp;0.809; OR\u0026thinsp;=\u0026thinsp;0.812) were \u0026lt;\u0026thinsp;1. Interaction effect was statistically significant between maternal exposure to PM2.5 and SHS on the risk of macrosomia (interaction p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In four different confounding factors-adjusted models, different frequency of SHS exposure all significantly enhances risk of macrosomia among pregnant women exposed to PM2.5 air pollutant (interaction p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). From model 1 to 4, interaction effect between occasional SHS exposure and PM2.5 were significant (interaction p\u0026thinsp;=\u0026thinsp;0.021; p\u0026thinsp;=\u0026thinsp;0.010; p\u0026thinsp;=\u0026thinsp;0.010; p\u0026thinsp;=\u0026thinsp;0.010), which the same as interaction effect between frequent SHS exposure and PM2.5 (interaction p\u0026thinsp;=\u0026thinsp;0.050; p\u0026thinsp;=\u0026thinsp;0.046; p\u0026thinsp;=\u0026thinsp;0.051; p\u0026thinsp;=\u0026thinsp;0.047). The higher exposure times and intensities of SHS prompts the stronger interaction between the two air pollutants. From model 1 to 4, the odds ratio of PM2.5 \u0026amp; Frequent SHS exposure was larger than PM2.5 \u0026amp; Occasional SHS exposure (OR\u0026thinsp;=\u0026thinsp;1.004\u0026thinsp;\u0026gt;\u0026thinsp;OR\u0026thinsp;=\u0026thinsp;1.002; OR\u0026thinsp;=\u0026thinsp;1.005\u0026thinsp;\u0026gt;\u0026thinsp;OR\u0026thinsp;=\u0026thinsp;1.002; OR\u0026thinsp;=\u0026thinsp;1.004\u0026thinsp;\u0026gt;\u0026thinsp;OR\u0026thinsp;=\u0026thinsp;1.002; OR\u0026thinsp;=\u0026thinsp;1.005\u0026thinsp;\u0026gt;\u0026thinsp;OR\u0026thinsp;=\u0026thinsp;1.002). The odds ratio of all confounding factors in model 4 were listed in \u003cb\u003eTABLE A.1.\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the interaction between PM2.5 exposure in first trimester, second trimester and third trimester of pregnancy and SHS exposure on risk of macrosomia. Each interactive model was based on full confounding factors-adjusted model. In the first trimester, the interaction effect was statistically significant between maternal exposure to PM2.5 and occasional SHS exposure on the risk of macrosomia (interaction p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Occasionally exposed to SHS significantly enhances risk of macrosomia among pregnant women exposed to PM2.5 air pollutant (interaction p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Frequently exposed to SHS was not associated with the risk of macrosomia among pregnant women exposed to PM2.5 air pollutant (interaction p\u0026thinsp;=\u0026thinsp;0.100). In the second or third trimester, there is no interactive effect between maternal PM2.5 exposure and occasional exposed to SHS (interaction p\u0026thinsp;=\u0026thinsp;0.081; p\u0026thinsp;=\u0026thinsp;0.592). There is no interactive effect between maternal PM2.5 exposure and frequent exposed to SHS during second or third trimester of pregnancy, either (interaction p\u0026thinsp;=\u0026thinsp;0.302; p\u0026thinsp;=\u0026thinsp;0.067).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e represents the association between PM2.5 concentration in different stages of pregnancy and the risk of macrosomia in different SHS Subgroups. The results of whole pregnancy are consistent with results of first trimester. The higher exposure concentration of PM2.5 during the whole pregnancy or the first trimester among pregnant women was associated with the higher risk of macrosomia (OR\u0026thinsp;=\u0026thinsp;1.004, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; OR\u0026thinsp;=\u0026thinsp;1.003, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Different frequency of SHS exposure all significantly increases risk of macrosomia among pregnant women exposed to PM2.5 air pollutant, not only exposed to SHS occasionally (interaction p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but also exposed to SHS frequently (interaction p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). More frequently exposed to SHS among pregnant women induces stronger interactive effect between PM2.5 and SHS air pollutants during the whole pregnancy (Frequent exposure OR\u0026thinsp;=\u0026thinsp;1.007\u0026thinsp;\u0026gt;\u0026thinsp;Occasional exposure OR\u0026thinsp;=\u0026thinsp;1.006) and the first trimester (Frequent exposure OR\u0026thinsp;=\u0026thinsp;1.008\u0026thinsp;\u0026gt;\u0026thinsp;Occasional exposure OR\u0026thinsp;=\u0026thinsp;1.006). In the second and the third trimester, maternal PM2.5 exposure is associated with an increased risk of macrosomia (OR\u0026thinsp;=\u0026thinsp;1.003, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; OR\u0026thinsp;=\u0026thinsp;1.002, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). But there is no interaction between maternal PM2.5 exposure and SHS exposure in the second (OR\u0026thinsp;=\u0026thinsp;1.003, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; OR\u0026thinsp;=\u0026thinsp;1.005, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; OR\u0026thinsp;=\u0026thinsp;1.004, p\u0026thinsp;=\u0026thinsp;0.069) and the third trimester (OR\u0026thinsp;=\u0026thinsp;1.002, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; OR\u0026thinsp;=\u0026thinsp;1.005, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; OR\u0026thinsp;=\u0026thinsp;1.005, p\u0026thinsp;=\u0026thinsp;0.007).\u003c/p\u003e "},{"header":"Discussion","content":" \u003cp\u003eIn full-adjusted interaction model, we found maternal exposure to PM2.5 was associated with an increased risk of macrosomia in whole pregnancy (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), the first (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), second (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and third (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) trimester of pregnancy. However, there was a trend for gestational exposure to SHS with risk of low birth weight, but not statistically significant (occasional SHS exposure (p\u0026thinsp;=\u0026thinsp;0.099); frequent SHS exposure (p\u0026thinsp;=\u0026thinsp;0.272)). Interaction effect was statistically significant between maternal exposure to PM2.5 and SHS on the risk of macrosomia in the whole pregnancy (all interaction p\u0026thinsp;\u0026lt;\u0026thinsp;0.050) and the first trimester pregnancy (all interaction p\u0026thinsp;\u0026lt;\u0026thinsp;0.050), not in the second trimester (all interaction p\u0026thinsp;\u0026gt;\u0026thinsp;0.050) and third trimester (all interaction p\u0026thinsp;\u0026gt;\u0026thinsp;0.050) of pregnancy. The higher frequency of SHS exposure prompts the stronger interaction between the two air pollutants in the whole pregnancy and the first trimester pregnancy. In conclusion, in the whole pregnancy and the first trimester pregnancy, maternal exposure to SHS during pregnancy enhances the risk of macrosomia among pregnant women exposed to PM2.5 air pollutant, and the interaction became stronger with the more frequent exposure to SHS.\u003c/p\u003e \u003cp\u003eThere is a rigorous methodology in our study. First, the process of data cleaning was conducted following strict screening standards. All the enrolled pregnant women in NFPHEP needed to be screened for the eligibility of inclusion into our study. Second, the confounding factors were adjusted gradually in different models and avoid making bias to the outcomes. Third, the subgroup analyses stratified by SHS exposure were conducted to further confirm the results of interactive model.\u003c/p\u003e \u003cp\u003eThere are several innovations in our research. First, our study was conducted among a new study population, that is, the national pregnant Chinese women. Previous studies on the adverse effect of air pollution on birth outcomes were mostly carried out in developed countries or regions\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. The differences between Chinese and foreigner population reflected in several aspects, such as the higher exposure condition of maternal SHS\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e, higher PM2.5 pollution levels\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e and more vulnerable metabolism characteristics of Chinese population comparing with foreigners\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e. Hence, our study could provide new evidence on prognosis of air pollutions-related diseases among Chinese population, which represent 1/3 of the world's population. Second, our study establishes a new interaction model between two human-caused air pollutants. Previous studies focused on the interaction between genetic inheritance and environmental pollutants on birth outcomes\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. No study pays attention to the interaction between two air pollutants caused by human activities during different stages of pregnancy, and the two air pollutants can be changed and avoided by taking some measures. Third, our study identifies a new risk that can intensity the association between maternal PM2.5 air pollutant exposure and macrosomia newborns. We found maternal SHS exposure enhances the risk of macrosomia associated with PM2.5 exposure.\u003c/p\u003e \u003cp\u003eOur study found maternal exposure to PM2.5 during pregnancy was associated with an increased risk of macrosomia in whole pregnancy and different stages of pregnancy. The finding was consistent with previous study on association between PM2.5 exposure and birth weight\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e. In CHEN et al\u0026rsquo;s study\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, significant associations were found between increased risk of macrosomia and every 10 \u0026micro;g/m3 increase of PM2.5 concentration over the first, second, and third trimesters in a nationwide prospective cohort study in China. Furthermore, our study also found a trend of low birth weight for gestational exposure to SHS, although it was not statistically significant. The trend in our study was consistent with most previous studies on association of neonatal birthweight and cigarette smoke exposure in mothers\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. In a cross-sectional study from Shanghai, WANG et al\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e found that the LBW prevalence was higher among newborns whose mother were prenatally exposed to SHS comparing with newborns whose mother were not exposed. KOBAYASHI et al\u0026rsquo;s study\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e also observed the link between maternal passive smoking and increased risk of delivering SGA in a prospective birth cohort from the Hokkaido.\u003c/p\u003e \u003cp\u003eOur study suggested there are interaction between maternal exposure to SHS and PM2.5 air pollutants on the risk of macrosomia in the whole pregnancy and the first trimester pregnancy. The higher frequency of SHS exposure prompts the stronger interaction between the two air pollutants in the whole pregnancy and the first trimester pregnancy. No studies before have concentrated on the interaction effect of two air pollutants on adverse pregnancy outcomes such as birth weight. The interaction between air pollutants and other factors on birth weight outcome have been reported in previous studies. Huang et al.\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003eexamine the interaction effects of prenatal exposed to environmental tobacco smoke (ETS) and genotypes of cytochrome P4501A1 (CYP1A1), glutathione S-transferases (GSTs) on the risk of full-term low birth weight (FT-LBW). The study\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e revealed that gene polymorphisms of CYP1A1 and GSTs played modified roles in associations between prenatal ETS exposure and FT-LBW. However, it is harder to take measures to change genetic endowments, compared with controlling man-made pollutant factors like PM2.5 and SHS exposure in our study. Wang et al. and colleagues \u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e also assessed the interaction of air pollutants and meteorological factors on birth weight in Shenzhen, China. According to Wang\u0026rsquo;s research\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e, an interactive effect of air temperature and humidity on the relationship between PM10 exposure and SGA among newborns existed. Other studies also reported interaction between air pollutants and life-related risk factors of mothers during pregnancy on fetal birth weight, such as maternal pre-pregnancy overweight\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e, maternal employment\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e, and maternal illicit drug use\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. But in all, there have been no studies before assessing the interaction effect of two air pollutants on birth weight outcomes.\u003c/p\u003e \u003cp\u003eThe detailed mechanism of interaction between maternal exposure to SHS and PM2.5 air pollutants on the risk of macrosomia during pregnancy remains unclear. A few possible explanations or hypothesis are as follows. Firstly, SHS is the main source of indoor PM2.5 pollution that could be inhaled into lung and blood circulation\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. When SHS exposure from surrounding smokers and PM2.5 exposure from atmosphere existed simultaneously, the concentration of PM2.5 changed and certain effect like synthesis may happened. Hence it is possible that there was interaction between maternal exposure to SHS and PM2.5. Secondly, studies on association of PM2.5 air pollutant and infant\u0026rsquo;s birth weight outcome suggested changes of oxidative stress\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e, immune response\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e and epigenetic regulation\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e may influence birth weight. Furthermore, studies on association of maternal tobacco smoke exposure and fetal birth weight suggested that placental toxicity\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e and epigenetic modifications such as DNA methylation\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e could be potential mediators affecting fetal birth weight outcomes. So epigenetic modifications could be an explanation for the interaction between maternal exposure to SHS and PM2.5 air pollutants on the risk of macrosomia. Maybe the two air pollutants have some common gene methylation or overlapped molecular pathway of macrosomia leading to the interaction. It seems controversial that maternal SHS exposure alone led to a trend of low birth weight, however, in the interaction effect, SHS could enhances the risk of macrosomia among pregnant women exposed to PM2.5 air pollutant in our study. The explanation may be that the gene or signal pathway related with association between SHS and macrosomia could only be activated by gene or signal pathway related with PM2.5. SHS exposure alone could not lead to macrosomia, because macrosomia-related genes were not activated by PM2.5. Our study suggested only in the whole pregnancy and the first trimester, the interaction between maternal exposure to SHS and PM2.5 air pollutants on the risk of macrosomia existed. The result indicates that the exposure to PM2.5 in the first trimester might have the biggest effect on birth weight. It might be because that the exposure to air pollution in the first trimester could lead to placental adaptation through epigenetic modification\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eOur study has some strengths and several potential limitations. The strengths include emphasis on the harm of SHS to pregnant women, especially in high-exposed PM2.5 regions. Another strength is to point out the study direction that future studies should focus on the epigenetic aspects when exploring the mechanism of birth weight changes associated with the environmental pollutants such as PM2.5 or SHS exposure. The study had also some limitations. Majority of pregnant women in the NFPHEP were from rural areas in China, and thus, the conclusions made in this study are most pertinent to this region. Since the survey of SHS condition in our study was conducted through the questionnaire completed by the pregnant women, the collected information could be inevitably influenced by subjective ideas. Another limitation of our study was the potential recall bias due to the long follow-up interval.\u003c/p\u003e \u003cp\u003eGenerally, our study highlights the impact of environmental air pollutants on adverse pregnancy outcomes like birth weight and we should emphasize on controllable human activities-caused environmental pollutants like PM2.5 and SHS, which could be reduced or removed by taking some measures in future. Our results strengthened evidence that maternal exposure to PM2.5 during pregnancy was associated with an increased risk of macrosomia in China and meanwhile, firstly suggested that SHS may enhance the risk of macrosomia associated with PM2.5. So pregnant women should avoid exposure to SHS during pregnancy, especially those who lived in areas with serious PM2.5 pollution and who were in the first trimester of pregnancy. These findings may help to identify high-risk pregnant women for giving birth to macrosomia, providing an opportunity for targeted preventive interventions to protect vulnerable population as early as possible. Strengthening air pollution control will have a beneficial impact on avoiding adverse birth outcomes, improving the quality of national population and alleviating national financial burden. Furthermore, the results of our study also suggested that the detailed mechanism of pregnancy outcome should focus more on epigenetic aspects of embryonic development in future.\u003c/p\u003e "},{"header":"Conclusion","content":"\u003cp\u003eIn the whole pregnancy and the first trimester pregnancy, maternal exposure to SHS during pregnancy enhances the risk of macrosomia among pregnant women exposed to PM2.5 air pollutant, and the interaction became stronger with the more frequent exposure to SHS.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics approval and consent to participate\u003c/em\u003e\u003cem\u003e：\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the institutional review board of the National Research Institute for Family Planning, Beijing, China.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for publication\u003c/em\u003e\u003cem\u003e：\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided written informed consent.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and material\u003c/em\u003e\u003cem\u003e：\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting interests:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors' contributions:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eLYY has drafted the work and completed the manuscript.\u003c/p\u003e\n\u003cp\u003eZYL has made substantial contributions to the acquisition, analysis, or interpretation of data for the work;\u003c/p\u003e\n\u003cp\u003ePH has provided editing and writing assistance for important intellectual content;\u003c/p\u003e\n\u003cp\u003eCS has made substantial contributions to the conception or design of the work;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgements:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003cp\u003e[1] O'DONNELL K J, MEANEY M J. 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Birth defects research, 2019, 111(17): 1259-69.\u003c/p\u003e\n\u003cp\u003e[44] KHEIRKHAH RAHIMABAD P, ANTHONY T M, JONES A D, et al. Nicotine and Its Downstream Metabolites in Maternal and Cord Sera: Biomarkers of Prenatal Smoking Exposure Associated with Offspring DNA Methylation [J]. International journal of environmental research and public health, 2020, 17(24):\u003c/p\u003e\n\u003cp\u003e[45] MAGHBOOLI Z, HOSSEIN-NEZHAD A, ADABI E, et al. Air pollution during pregnancy and placental adaptation in the levels of global DNA methylation [J]. PloS one, 2018, 13(7): e0199772.\u003c/p\u003e"},{"header":"Tables","content":"\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eBaseline characteristics of the included pregnant women and neonates\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eCharacteristics\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMacrosomia\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOverall\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eYes (n\u0026thinsp;=\u0026thinsp;15348)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNo (n\u0026thinsp;=\u0026thinsp;182529)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e(n\u0026thinsp;=\u0026thinsp;197877)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eBirth weight, g\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4322.43\u0026thinsp;\u0026plusmn;\u0026thinsp;469.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3234.18\u0026thinsp;\u0026plusmn;\u0026thinsp;428.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3318.59\u0026thinsp;\u0026plusmn;\u0026thinsp;520.74\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNeonate's sex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9191 (59.90%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e95332 (52.20%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e104523 (52.80%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6140 (40.00%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87101 (47.70%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93241 (47.10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMissing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17 (0.10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e96 (0.10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e113 (0.10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge at delivery, y\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.32\u0026thinsp;\u0026plusmn;\u0026thinsp;3.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.23\u0026thinsp;\u0026plusmn;\u0026thinsp;3.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25.24\u0026thinsp;\u0026plusmn;\u0026thinsp;3.94\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePre-pregnancy BMI\u003csup\u003ea\u003c/sup\u003e, kg/m2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.30\u0026thinsp;\u0026plusmn;\u0026thinsp;2.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.02\u0026thinsp;\u0026plusmn;\u0026thinsp;2.60\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21.04\u0026thinsp;\u0026plusmn;\u0026thinsp;2.61\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eProlonged pregnancy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e523 (3.40%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4103 (2.20%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4626 (2.30%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14825 (96.60%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e178426 (97.80%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e193251 (97.70%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMultiparity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11894 (77.50%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37983 (20.80%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49877 (25.20%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3454 (22.50%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e144546 (79.20%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e148000 (74.80%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHighest education level\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJunior high school\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10753 (70.10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e128280 (70.30%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e139033 (70.30%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSenior high school\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2892 (18.80%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34594 (19.00%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37486 (18.90%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCollege\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1532 (10.00%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17368 (9.50%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18900 (9.60%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMissing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e171 (1.10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2287 (1.20%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2458 (1.20%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003ePre-pregnancy diabetes mellitus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (0.02%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18 (0.05%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21 (0.01%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15345 (99.98%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e178426 (97.75%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e193771 (97.93%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMissing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0.00%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4085 (2.20%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4085 (2.06%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePre-pregnancy hypertension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (0.05%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e93 (0.10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e101 (0.10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15340 (99.95%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e182436 (99.90%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e197776 (99.90%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eFamily history of diabetes mellitus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYes\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100 (0.70%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1040 (0.60%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1140 (0.60%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNo\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15248 (99.30%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e181489 (99.40%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e196737 (99.40%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDrinking during pregnancy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNever\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15205 (99.10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e180573 (98.90%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e195778(98.90%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQuit\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e99 (0.60%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1169 (0.60%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1268 (0.60%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStill\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e35 (0.20%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e720 (0.40%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e755 (0.40%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMissing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (0.10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67 (0.10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e76 (0.10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSeason of delivery\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSpring\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4065 (26.50%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48959 (26.80%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53024 (26.80%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSummer\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2421 (15.80%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28626 (15.70%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31047 (15.70%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAutumn\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3897 (25.40%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47184 (25.90%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51081 (25.80%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWinter\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4965 (32.30%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57760 (31.60%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62725 (31.70%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSecondhand Smoking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNone exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12320 (80.30%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e149308\u0026nbsp;(81.80%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e161628 (81.70%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOccasional exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2683 (17.50%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e29845 (16.40%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32528 (16.40%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFrequent exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e345 (2.20%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3376 (1.80%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3721 (1.90%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eBMI, body mass index.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eMean PM2.5 concentration during pregnancy (\u0026micro;g/m3)\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eStage of Pregnancy\u003c/p\u003e\n\u003c/th\u003e\n\u003cth colspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eMacrosomia\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eOverall (n\u0026thinsp;=\u0026thinsp;197877)\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eYes (n\u0026thinsp;=\u0026thinsp;15348)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eNo (n\u0026thinsp;=\u0026thinsp;182529)\u003c/strong\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFirst trimester\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e73.35\u0026thinsp;\u0026plusmn;\u0026thinsp;29.25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e70.81\u0026thinsp;\u0026plusmn;\u0026thinsp;29.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e71.01\u0026thinsp;\u0026plusmn;\u0026thinsp;29.27\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSecond trimester\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e73.82\u0026thinsp;\u0026plusmn;\u0026thinsp;29.00\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e71.67\u0026thinsp;\u0026plusmn;\u0026thinsp;29.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e71.83\u0026thinsp;\u0026plusmn;\u0026thinsp;29.90\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThird trimester\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e83.88\u0026thinsp;\u0026plusmn;\u0026thinsp;32.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e81.23\u0026thinsp;\u0026plusmn;\u0026thinsp;33.08\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e81.43\u0026thinsp;\u0026plusmn;\u0026thinsp;33.04\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ewhole pregnancy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e77.57\u0026thinsp;\u0026plusmn;\u0026thinsp;22.11\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e75.08\u0026thinsp;\u0026plusmn;\u0026thinsp;22.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\"\u0026plusmn;\"\u003e\n\u003cp\u003e75.28\u0026thinsp;\u0026plusmn;\u0026thinsp;22.87\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eThe interaction between PM2.5 exposure in whole pregnancy and SHS exposure on risk of macrosomia\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eOdds Ratio(95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth style=\"height: 70px;\" rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eP\u0026nbsp;value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003ewhole pregnancy\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr style=\"height: 37px;\"\u003e\n\u003ctd style=\"height: 37px;\" align=\"left\"\u003e\n\u003cp\u003eModel 1 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 37px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 37px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003ePM2.5 exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.004 (1.003, 1.005)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 37px;\"\u003e\n\u003ctd style=\"height: 37px;\" align=\"left\"\u003e\n\u003cp\u003eOccasional SHS \u003csup\u003ee\u003c/sup\u003e exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 37px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.916 (0.789, 1.062)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 37px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.246\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eFrequent SHS exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.838 (0.565, 1.228)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.374\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 37px;\"\u003e\n\u003ctd style=\"height: 37px;\" align=\"left\"\u003e\n\u003cp\u003ePM2.5 \u0026amp;\u0026nbsp;Occasional SHS exposure \u003csup\u003ef\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 37px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.002 (1.000, 1.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 37px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.021\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 37px;\"\u003e\n\u003ctd style=\"height: 37px;\" align=\"left\"\u003e\n\u003cp\u003ePM2.5 \u0026amp; Frequent SHS exposure \u003csup\u003eg\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 37px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.004 (1.000, 1.009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 37px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.050\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 37px;\"\u003e\n\u003ctd style=\"height: 37px;\" align=\"left\"\u003e\n\u003cp\u003eModel 2 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 37px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 37px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003ePM2.5 exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.004 (1.003, 1.005)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eOccasional SHS exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.889 (0.765, 1.032)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.124\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eFrequent SHS exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.809 (0.544, 1.189)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.289\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003ePM2.5 \u0026amp;\u0026nbsp;Occasional SHS exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.002 (1.000, 1.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.010\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003ePM2.5 \u0026amp; Frequent SHS exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.005 (1.000, 1.009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.046\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 37px;\"\u003e\n\u003ctd style=\"height: 37px;\" align=\"left\"\u003e\n\u003cp\u003eModel 3 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 37px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 37px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003ePM2.5 exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.004 (1.003, 1.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eOccasional SHS exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.889 (0.763, 1.029)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.116\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eFrequent SHS exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.809 (0.542, 1.190)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.289\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003ePM2.5 \u0026amp;\u0026nbsp;Occasional SHS exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.002 (1.001, 1.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.010\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003ePM2.5 \u0026amp; Frequent SHS exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.004 (1.000, 1.009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.051\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 37px;\"\u003e\n\u003ctd style=\"height: 37px;\" align=\"left\"\u003e\n\u003cp\u003eModel 4 \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 37px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd style=\"height: 37px;\" align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003ePM2.5 exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.004 (1.003, 1.005)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eOccasional SHS exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.881 (0.757, 1.024)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.099\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003eFrequent SHS exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.812 (0.537, 1.181)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.272\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003ePM2.5 \u0026amp;\u0026nbsp;Occasional SHS exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.002 (1.001, 1.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.010\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 35px;\"\u003e\n\u003ctd style=\"height: 35px;\" align=\"left\"\u003e\n\u003cp\u003ePM2.5 \u0026amp; Frequent SHS exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.005 (1.000, 1.009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd style=\"height: 35px;\" align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.047\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr style=\"height: 15px;\"\u003e\n\u003ctd style=\"height: 15px;\" colspan=\"3\"\u003e\u003csup\u003ea\u003c/sup\u003eUnadjusted\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 15px;\"\u003e\n\u003ctd style=\"height: 15px;\" colspan=\"3\"\u003e\u003csup\u003eb\u003c/sup\u003eAdjusted for age at delivery, pre-pregnancy BMI\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 15px;\"\u003e\n\u003ctd style=\"height: 15px;\" colspan=\"3\"\u003e\u003csup\u003ec\u003c/sup\u003eAdjusted for age at delivery, pre-pregnancy BMI, neonatal sex, prolonged pregnancy, and multiparity\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 28px;\"\u003e\n\u003ctd style=\"height: 28px;\" colspan=\"3\"\u003e\u003csup\u003ed\u003c/sup\u003eAdjusted for age at delivery, pre-pregnancy BMI, neonatal sex, prolonged pregnancy, multiparity, pre‐pregnancy diabetes mellitus, pre‐pregnancy hypertension, family history of diabetes mellitus, educational level, drinking during pregnancy, and season of delivery.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 15px;\"\u003e\n\u003ctd style=\"height: 15px;\" colspan=\"3\"\u003e\u003csup\u003ee\u003c/sup\u003eSHS, secondhand smoking.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 15px;\"\u003e\n\u003ctd style=\"height: 15px;\" colspan=\"3\"\u003e\n\u003cp\u003e\u003csup\u003ef\u003c/sup\u003eThe interaction between mean PM2.5 concentration of whole pregnancy and occasional SHS exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr style=\"height: 15px;\"\u003e\n\u003ctd style=\"height: 15px;\" colspan=\"3\"\u003e\n\u003cp\u003e\u003csup\u003eg\u003c/sup\u003eThe interaction between mean PM2.5 concentration of whole pregnancy and frequent SHS exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003ctable id=\"Tab4\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eThe interaction between PM2.5 exposure in first trimester, second trimester and third trimester of pregnancy and SHS exposure on risk of macrosomia\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOdds Ratio (95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eP\u0026nbsp;value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFirst trimester \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePM2.5 exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.003 (1.002, 1.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOccasional SHS \u003csup\u003ee\u003c/sup\u003e exposure \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.826 (0.734, 0.929)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFrequent SHS exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.929 (0.683, 1.253)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.631\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePM2.5 \u0026amp;\u0026nbsp;Occasional SHS exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.003 (1.002, 1.005)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.000\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePM2.5 \u0026amp; Frequent SHS exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.003 (0.999, 1.007)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.100\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSecond trimester \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePM2.5 exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.003 (1.002, 1.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOccasional SHS exposure \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.970 (0.865, 1.087)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.604\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFrequent SHS exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.013 (0.742, 1.373)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.936\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePM2.5 \u0026amp;\u0026nbsp;Occasional SHS exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.001 (0.999, 1.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.081\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePM2.5 \u0026amp; Frequent SHS exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.002 (0.998, 1.005)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.302\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eThird trimester \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePM2.5 exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.003 (1.002, 1.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOccasional SHS exposure \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.036 (0.922, 1.163)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.551\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFrequent SHS exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.905 (0.664, 1.224)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.524\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePM2.5 \u0026amp;\u0026nbsp;Occasional SHS exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.003 (0.999, 1.002)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.592\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePM2.5 \u0026amp; Frequent SHS exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e1.003 (0.999, 1.006)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.067\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" align=\"left\"\u003e\n\u003cp\u003e\u003csup\u003ea\u003c/sup\u003eAdjusted for age at delivery, pre-pregnancy BMI, neonatal sex, prolonged pregnancy, multiparity, pre‐pregnancy diabetes mellitus, pre‐pregnancy hypertension, family history of diabetes mellitus, educational level, drinking during pregnancy, and season of delivery.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003eb\u003c/sup\u003eThe interaction between mean PM2.5 concentration of first trimester pregnancy and occasional SHS exposure.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ec\u003c/sup\u003eThe interaction between mean PM2.5 concentration of second trimester pregnancy and occasional SHS exposure.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ed\u003c/sup\u003eThe interaction between mean PM2.5 concentration of third trimester pregnancy and occasional SHS exposure.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003ee\u003c/sup\u003eSHS, secondhand smoking.\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab5\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eAssociation between PM2.5 concentration in different stages of pregnancy and the risk of macrosomia in different SHS Subgroups.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSHS \u003csup\u003eb\u003c/sup\u003e Subgroup\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOdds Ratio(95%CI)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eP\u0026nbsp;value\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eWhole pregnancy \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNone exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.004 (1.003, 1.005)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOccasional exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.006 (1.005, 1.008)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFrequent exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.007 (1.003, 1.012)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eFirst trimester \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNone exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.003 (1.002, 1.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOccasional exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.006 (1.005, 1.008)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFrequent exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.008 (1.004, 1.012)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eSecond trimester \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNone exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.003 (1.002, 1.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOccasional exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.005 (1.003, 1.006)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFrequent exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.004 (1.000, 1.008)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.069\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"3\" align=\"left\"\u003e\n\u003cp\u003eThird trimester \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNone exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.002 (1.002, 1.003)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOccasional exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.005 (1.003, 1.006)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFrequent exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.005 (1.001, 1.009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003e\u003csup\u003ea\u003c/sup\u003eAdjusted for age at delivery, pre-pregnancy BMI, neonatal sex, prolonged pregnancy, multiparity, pre‐pregnancy diabetes mellitus, pre‐pregnancy hypertension, family history of diabetes mellitus, educational level, drinking during pregnancy, and season of delivery.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"4\"\u003e\u003csup\u003eb\u003c/sup\u003eSHS, secondhand smoking.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab6\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable A.1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eThe interaction between PM2.5 exposure in whole pregnancy and SHS exposure on risk of macrosomia\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eOdds Ratio(95%CI)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth rowspan=\"2\" align=\"left\"\u003e\n\u003cp\u003eP\u0026nbsp;value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ewhole pregnancy \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePM2.5 exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.004 (1.003, 1.005)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOccasional SHS \u003csup\u003ea\u003c/sup\u003e exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.881 (0.757, 1.024)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.098\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFrequent SHS exposure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.812 (0.537, 1.181)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.272\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePre-pregnancy BMI\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.035 (1.029, 1.041)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge at delivery\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.002 (0.997, 1.006)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.477\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNeonate's sex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.730 (0.706, 0.755)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMultiparity\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.061 (1.016, 1.107)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.007\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eProlonged pregnancy\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.488 (1.353, 1.633)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSeason of delivery (Spring)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ereference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ereference\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSeason of delivery (Summer)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.036 (0.982, 1.092)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.199\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSeason of delivery (Autumn)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.050 (1.001, 1.100)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.045\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSeason of delivery (Winter)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.069 (1.023, 1.117)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.003\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHighest education level (Junior high school)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ereference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ereference\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHighest education level (Senior high school)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.044 (1.000, 1.091)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.049\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHighest education level (college)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.111 (1.049, 1.175)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDrinking during pregnancy (Never)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ereference\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ereference\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDrinking during pregnancy (Quit)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.980 (0.79, 1.202)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.852\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDrinking during pregnancy (Still)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.581 (0.406, 0.803)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.002\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePre-pregnancy diabetes mellitus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.593 (0.370, 4.377)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.459\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePre-pregnancy hypertension\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.838 (0.373, 1.627)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.637\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFamily history of diabetes mellitus\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.054 (0.851, 1.291)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.623\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePM2.5 \u0026amp; Occasional SHS exposure \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.002 (1.001, 1.004)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.010\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePM2.5 \u0026amp; Frequent SHS exposure \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1.005 (1.000, 1.009)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.047\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\"\u003e\u003csup\u003ea\u003c/sup\u003eSHS, secondhand smoking.\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\"\u003e\u003csup\u003eb\u003c/sup\u003eThe interaction between mean PM2.5 concentration of whole pregnancy and occasional SHS exposure\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\"\u003e\u003csup\u003ec\u003c/sup\u003eThe interaction between mean PM2.5 concentration of whole pregnancy and frequent SHS exposure\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\"\u003e\u003csup\u003ed\u003c/sup\u003eAdjusted for age at delivery, pre-pregnancy BMI, neonatal sex, prolonged pregnancy, multiparity, pre‐pregnancy diabetes mellitus, pre‐pregnancy hypertension, family history of diabetes mellitus, educational level, drinking during pregnancy, and season of delivery.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Fine particulate matter (PM2.5), Secondhand smoking (SHS), Interaction, Birth Weight ","lastPublishedDoi":"10.21203/rs.3.rs-520246/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-520246/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBACKGROUND Previous studies have been controversial and inconsistent about fine particulate matter (PM2.5) and secondhand smoking (SHS) air pollutants on neonatal birthweight outcomes and there were no published studies assessing the potential interactive effects between PM2.5 and SHS on birthweight outcomes.\u003c/p\u003e \u003cp\u003ePURPOSE To investigate interaction between gestational PM2.5 and SHS air pollution exposure on the risk of macrosomia among pregnant women.\u003c/p\u003e \u003cp\u003eMETHODS Research data were derived from National Free Preconception Health Examination Project (NFPHEP). Data cleaning process was conducted following strict screening standards to ensure eligibility of participants in our study. Different interaction models about air pollution on birthweight outcomes were established, according to different confounding factors adjustment and different pregnancy stages. SHS subgroups analysis were conducted to further confirm the results of interaction models.\u003c/p\u003e \u003cp\u003eRESULTS Totally, 197877 participants were included in our study. In full-adjusted interaction model, maternal exposure to PM2.5 was associated with an increased risk of macrosomia in whole (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), the first (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), second (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and third (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) trimester of pregnancy. However, there was a trend for gestational exposure to SHS with risk of low birthweight, but not statistically significant (occasional SHS exposure (p\u0026thinsp;=\u0026thinsp;0.099); frequent SHS exposure (p\u0026thinsp;=\u0026thinsp;0.272)). Interaction effect was statistically significant between maternal exposure to PM2.5 and SHS on the risk of macrosomia in the whole pregnancy (all interaction p\u0026thinsp;\u0026lt;\u0026thinsp;0.050) and the first trimester pregnancy (all interaction p\u0026thinsp;\u0026lt;\u0026thinsp;0.050), not in the second (all interaction p\u0026thinsp;\u0026gt;\u0026thinsp;0.050) or third trimester (all interaction p\u0026thinsp;\u0026gt;\u0026thinsp;0.050) of pregnancy. The higher frequency of SHS exposure prompts stronger interaction between the two air pollutants in the whole and the first trimester pregnancy.\u003c/p\u003e \u003cp\u003eCONCLUSIONS In the whole pregnancy and the first trimester pregnancy, maternal exposure to SHS during pregnancy enhances the risk of macrosomia among pregnant women exposed to PM2.5 air pollutant, and the interaction became stronger with more frequent exposure to SHS.\u003c/p\u003e","manuscriptTitle":"Maternal Secondhand Smoke Exposure Enhances Macrosomia Risk among Pregnant Women Exposed to PM2.5: A New Interaction of Two Air Pollutants in A Nationwide Cohort","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-05-27 21:52:25","doi":"10.21203/rs.3.rs-520246/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b42b7fd9-5d71-46d7-9670-3f91b7da5840","owner":[],"postedDate":"May 27th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":4571973,"name":"Health Economics \u0026 Outcomes Research"},{"id":4571974,"name":"Health Policy"}],"tags":[],"updatedAt":"2021-11-18T07:58:46+00:00","versionOfRecord":{"articleIdentity":"rs-520246","link":"https://doi.org/10.3389/fpubh.2021.735699","journal":{"identity":"frontiers-in-public-health","isVorOnly":true,"title":"Frontiers in Public Health"},"publishedOn":"2021-11-18 07:58:46","publishedOnDateReadable":"November 18th, 2021"},"versionCreatedAt":"2021-05-27 21:52:25","video":"","vorDoi":"10.3389/fpubh.2021.735699","vorDoiUrl":"https://doi.org/10.3389/fpubh.2021.735699","workflowStages":[]},"version":"v1","identity":"rs-520246","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-520246","identity":"rs-520246","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}