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BMC Public Health
BMC Public Health
BMC Public Health
1471-2458
BioMed Central London

39289643
19711
10.1186/s12889-024-19711-3
Research
Interpregnancy interval, air pollution, and the risk of low birth weight: a retrospective study in China
Lu Xinyu 1
Zhang Yuyu 1
Jiang Run 1
Qin Gang 2
Ge Qiwei 1
Zhou Xiaoyi 3
Zhou Zixiao 5
Ni Zijun 4
Zhuang Xun xzhuang@ntu.edu.cn

1
1 https://ror.org/02afcvw97 grid.260483.b 0000 0000 9530 8833 Department of Epidemiology and Medical Statistics, School of Public Health, Nantong University, No.9 Seyuan Road, Chongchuan District, Nantong, Jiangsu China
2 grid.440642.0 0000 0004 0644 5481 Department of Infectious Diseases, Affiliated Hospital of Nantong University, No.20 Xisi Road, Chongchuan District, Nantong, Jiangsu China
3 https://ror.org/02yr91f43 grid.508372.b Nantong Center for Disease Control and Prevention, 189 Gongnong South Road, Chongchuan District, Nantong, Jiangsu China
4 https://ror.org/02afcvw97 grid.260483.b 0000 0000 9530 8833 School of Science, Nantong University, No.9 Seyuan Road, Chongchuan District, Nantong, Jiangsu China
5 https://ror.org/0384j8v12 grid.1013.3 0000 0004 1936 834X Faculty of Medical and Health, the University of Sydney, Sydney, NSW Australia
17 9 2024
17 9 2024
2024
24 25294 2 2024
7 8 2024
© The Author(s) 2024
2024
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Background

Both interpregnancy intervals (IPI) and environmental factors might contribute to low birth weight (LBW). However, the extent to which air pollution influences the effect of IPIs on LBW remains unclear. We aimed to investigate whether IPI and air pollution jointly affect LBW.

Methods

A retrospective cohort study was designed in this study. The data of birth records was collected from the Jiangsu Maternal Child Information System, covering January 2020 to June 2021 in Nantong city, China. IPI was defined as the duration between the delivery date for last live birth and date of LMP for the subsequent birth. The maternal exposure to ambient air pollutants during pregnancy—including particulate matter (PM) with an aerodynamic diameter of ≤ 2.5 μm (PM2.5), PM10, ozone (O3), nitrogen dioxide (NO2), sulfur dioxide (SO2) and carbon monoxide (CO)—was estimated using a hybrid kriging-LUR-RF model. A novel air pollution score was proposed, assessing combined exposure to five pollutants (excluding CO) by summing their concentrations, weighted by LBW regression coefficients. Multivariate logistic regression models were used to estimate the effects of IPI, air pollution and their interactions on LBW. Relative excess risk due to interaction (RERI), attributable proportion of interaction (AP) and synergy index (S) were utilized to assess the additive interaction.

Results

Among 10, 512 singleton live births, the LBW rate was 3.7%. The IPI-LBW risk curve exhibited an L-shaped pattern. The odds ratios (ORs) for LBW for each interquartile range increase in PM2.5, PM10, O3 and the air pollution score were 1.16 (95% CI: 1.01–1.32), 1.30 (1.06–1.59), 1.22 (1.06–1.41), and 1.32 (1.10–1.60) during the entire pregnancy, respectively. An additive interaction between IPI and PM2.5 was noted during the first trimester. Compared to records with normal IPI and low PM2.5 exposure, those with short IPI and high PM2.5 exposure had the highest risk of LBW (relative risk = 3.53, 95% CI: 1.85–6.49, first trimester).

Conclusion

The study demonstrates a synergistic effect of interpregnancy interval and air pollution on LBW, indicating that rational birth spacing and air pollution control can jointly improve LBW outcomes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-024-19711-3.

Keywords

Low birth weight
Interpregnancy interval
Air pollution
Joint association
http://dx.doi.org/10.13039/501100012154 Graduate Research and Innovation Projects of Jiangsu Province KYCX23_3435 http://dx.doi.org/10.13039/501100009576 Nantong Municipal Commission of Health and Family Planning MB2021075 http://dx.doi.org/10.13039/100017962 Jiangsu Commission of Health Ym2023079 issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
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pmcBackground

Improving child survival is a critical component of United Nations’ eight Millennium Development Goals [1]. Currently, low birth weight (LBW, < 2500 g), preterm birth, and small-for-gestational-age (SGA) are major contributors to neonatal mortality. It was estimated that up to 80% of neonatal deaths globally occur in LBW infants, with two-thirds due to preterm birth and one-third to SGA [2, 3]. Birth weight measurement is widely utilized in clinical practice, epidemiological studies, and public health comparisons due to its practicality compared to pregnancy duration and SGA assessments [4].

LBW has lots of adverse consequences for children’s growth and development, including reduced brain volume [5], motor development problems [6–9], lower intelligence quotient (IQ) and cognitive abilities [7, 9, 10], and increased susceptibility to illnesses like asthma [11, 12], hip osteoarthritis [13], and psychiatric problems [14–18]. Furthermore, LBW is associated with a higher risk of cardiovascular disease and metabolic syndrome in adulthood [19–22], making it the leading cause of neonatal mortality. Consequently, preventing LBW is imperative for child health and broader social development.

Interpregnancy interval (IPI) denotes the duration between a delivery and a subsequent pregnancy. The World Health Organization (WHO) recommends that women wait at least two years after a live birth before conceiving again to reduce the likelihood of adverse pregnancy outcomes in subsequent pregnancies [23]. Epidemiological studies have indicated that both IPIs shorter than 12 months and longer than 59 months are significant risk factors for adverse pregnancy outcomes, including LBW, SGA, preterm birth, fetal death [24–27]. A US study, encompassing over 7.5 million subjects, from Centers for Disease Control and Prevention’s natality national database, revealed an association between IPIs of less than 12 months and increased rates of LBW [28]. A meta-analysis found a significant association between IPIs exceeding 59 months and LBW [29]. Another Meta study, involving 46.9 million subjects from 129 studies, obtained a J-shaped dose-response relationship between IPIs and LBW, with the lowest risk estimates felled at 30 to 40 months [30].

Ambient air pollutant, particularly environmental toxics and parasites in fine particulate matter (PM) [31, 32], is also an independent risk factor for adverse pregnancy outcomes [33]. Pregnant individuals and their fetuses are especially vulnerable to the impact of air pollutants due to alterations in maternal physiology and the rapid pace of fetal organ development [34]. Numerous prior studies have examined the relationship between air pollutant exposure during pregnancy and LBW [35–38], although results have varied based on differences in exposure or outcome assessment, sample size, potential confounding factors, and study design [39, 40].

Previous research has primarily focused on individual contaminants, such as PM2.5, PM10, O3, NO2, SO2, and CO [41]. However, real-world air pollution comprises a mix of particulate and gaseous pollutants, whose combined health effects may differ from those of individual pollutants [42]. In related studies concerning the impact of air pollutants on birth outcomes, the inclusion of multi-pollutant considerations is frequently observed. Exposure to multiple air pollutants has been linked to a potential impact on the pregnancy rate in women undergoing assisted reproduction technology [43]. Additionally, it may contribute to an increased risk of excessive gestational weight gain [44], which, in terms of birth outcomes, is more likely to result in macrosomia [45]. Other multi-pollutants, including perfluoroalkyl substances [46] and metal biomarkers [47], may influence preterm birth. The effect of multiple air pollutants on LBW is rare.

It was reported that short interpregnancy intervals may result in maternal preconception nutritional depletion [48]. Multiple studies [49–51] indicated that individuals in a preconceptional state of poor nutrition exhibit an increased sensitivity to the detrimental effects of prenatal air pollution exposure on birth outcomes. The independent association between IPI, air pollution, and LBW has been confirmed in various studies, but rare have specifically examined the potential synergistic effects of IPI and air pollution on LBW. With rising multipara childbirth due to China’s two-child and three-child policy, an increase in unfavorable IPIs is anticipated, raising questions about their susceptibility to air pollution.

Nantong, a multi-industry cluster city in eastern China with a stable concentration of diverse air pollutants, provides an ideal setting for studying these combined effects. This study utilized data from the Jiangsu Maternal Child Information System (MCIS) from January 2020 to June 2021 in Nantong. We collected information on maternal exposure to air pollutants during pregnancy to investigate the impact of individual and combined pollutants on LBW incidence, while also assessing potential modifying effects of IPI.

Method

Study design

A retrospective cohort study was designed in this study. The data was extracted from the Jiangsu MCIS during January 2020 and June 2021 in Nantong. We also collected information on maternal air pollutants exposure during pregnancy from local 17 air quality monitoring stations to investigate the single and combined impact of IPI and air pollution on LBW incidence.

The study was approved by the Medical Ethics Committee of Nantong University. The records used for analysis were anonymized to ensure that no personal information was disclosed.

Study population

The data was extracted from MCIS and covered birth records from Nantong city between January 2020 and June 2021. Nantong, situated in the eastern region of Jiangsu Province, is a relatively well-developed city of 7.7 million inhabitants (Fig. 1). The birth records included detailed information on maternal socio-demographic characteristics (age, ethnicity, education and residence address during pregnancy), pregnancy history (parity and last delivery date), present pregnancy situation (number of antenatal visits, last menstrual period (LMP) date, pre-pregnancy body mass index (BMI), delivery date, delivery mode, pregnancy complications) and neonatal characteristics (neonatal sex, birth weight and gestational age).

Fig. 1 The study area and the distribution of 17 air quality monitoring stations in Nantong

A total of 50,650 live birth records were included in this study. To focus on records with IPI information, 37,858 primiparas were excluded from all records. 722 records where the maternal residence was outside Nantong were excluded to assess air pollution exposure levels. Furthermore, to explore the impact of air pollution on singletons at third trimester, 354 multiple births and 37 births falling outside the gestational age range of 28 to 42 weeks were also excluded. Moreover, 796 cases with missing last delivery date and 371 records with missing maternal residence addresses were excluded. The final sample consisted of 10,512 birth records. The study subject flowchart was displayed in eFigure 1.

Interpregnancy interval (IPI) definition

IPI was defined as the duration between the delivery date for last eligible birth(the index birth) and date of LMP for the subsequent birth, which was considered as the conception date [26]. Intervals were initially calculated in days and then converted to completed months (assuming 30 days as equivalent to 1 month). Only IPI after live birth was considered. IPI was categorized into the following groups: 0–11, 12–23, 24–59, 60–119, and ≥ 120 months [52]. The reference category was set as 24–59 months, aligning with the WHO recommendation for a minimum 2-year pregnancy spacing after a live birth [23].

Air pollution exposure assessment

The Department of Ecology and Environment of Jiangsu Province provided data on daily average air pollutant concentrations in Nantong (http://sthjt.jiangsu.gov.cn/). The distribution of 17 air quality monitoring stations in Nantong was shown in Fig. 1.

The framework for the assessment of the daily air pollutants in maternal residential address during pregnancy was shown in eFigure 2. A hybrid Kriging-LUR-Random Forest (RF) model was employed to assess daily air pollutant concentrations at maternal residential addresses. Various geographic variables such as land use, terrain, road types, population distribution, enterprises and meteorology were integrated into the model. Maternal residence address during pregnancy were geocoded by using the Baidu Maps API interface (http://lbsyun.baidu.com/). Subsequently, the model was employed to estimate individual air pollution exposure levels using the geocoded maternal residence addresses during pregnancy.

The sources for the six independent variables in Kriging-LUR model were as follows. (1) Land use data (commercial, industrial, agricultural areas, forests, lakes, and rivers) from the 2018 inventory of Nantong, processed using ArcGIS 9.3 software. (2) Topographic elevations (terrain) from a digital elevation model (DEM), sourced from the Geospatial Data Cloud (https://www.gscloud.cn/). (3) Digital road network data from the National Catalogue Service for Geographic Information, with road types classified at various levels. (4) Population distribution data from 2020 World Pop population density dataset. (5) Enterprise data relating to exhaust emissions from an online surveillance system managed by the Nantong Ecology and Environment Agency. (6) Meteorological variables (average wind speed, cumulative rainfall, atmospheric pressure, temperature, and relative humidity) from national routine monitoring weather stations in Nantong. Daily exposures of pregnant women to temperature and relative humidity were interpolated using an inverse distance weighting (IDW) method. IDW is an interpolation method based on the spatial distance between points, used to estimate the value of unknown points by considering the spatial relationships between them.

Daily air pollutant concentrations were interpolated for maternal residential addresses using the hybrid Kriging-LUR-Random Forest model. Spearman correlation analyses and stepwise regressions (with a statistical criterion of P < 0.2 and variance inflation factor [VIF] < 3) were employed to select variables [53], [54]. A 10-fold cross-validation approach evaluated the model’s predictive power (adjusted R2 values: PM2.5: 0.86; PM10: 0.85; O3: 0.87; NO2: 0.82; SO2: 0.67; CO: 0.66). For O3, the daily maximum 8-hour concentrations were estimated, while for the other pollutants, 24-hour daily average concentrations were used.

To assess the potential effects of air pollutants on LBW at different periods, four distinct exposure windows were considered: the first trimester (1–13 gestational weeks), the second trimester (14–26 gestational weeks), the third trimester (gestational weeks 27 to delivery) and the entire pregnancy. During each exposure window, air pollutant, temperature, and relative humidity levels were determined by averaging daily data specific to the corresponding period for pregnant women. To establish exposure categories, air pollution was classified as low and high air pollution according to median concentrations observed within each period (eTable 3) [55].

Air pollution score definition

To evaluate the impact of air pollutants, a novel “air pollution score” was introduced, formulated by summing the concentration values of air pollutants weighted by the multivariate-adjusted 𝛽-coefficients for LBW. Air pollution score was also split into low and high air pollution score based on the median value and the formula was: score = Sum(𝛽i × air pollutants) × (5 / sum of the 𝛽-coefficients) [42]. Higher scores indicate higher ambient air pollution. CO was excluded from the score due to its lack of significant association with LBW in any period and its disproportionately high concentration compared to other pollutants. Including CO in the air pollution score would disproportionately reflect the influence of CO, potentially overshadowing the impact of other crucial pollutants.

Covariates

This study considered a range of maternal and neonatal characteristics: maternal age, ethnicity, education, pre-pregnancy BMI, prenatal care, parity, delivery mode and neonatal sex. Considering that previous studies have incorporated maternal age at the index birth as a covariate when examining the relationship between IPI and adverse pregnancy outcomes [24, 56], the data was calculated by IPI and maternal age. Prenatal care was determined by the number of antenatal visits, with less than five considered inadequate and more than or equal to five considered adequate [24]. Ambient temperature and relative humidity were also adjusted because they have been recommended confounders of the exposure–outcome association [57, 58].

Statistical analysis

Continuous variables were depicted as means ± SD, and categorical variables as percentages or ratios. Difference between quantitative data was assessed by t-test or Mann-Whitney U-test. Chi-square test was used to compare qualitative data. To illustrate the impact of IPI on LBW, logistic regression analysis, along with postestimation calculations were employed to estimate the predicted ratio for LBW [24], which was then converted to predicted absolute risk. To account for curve shape, IPIs were modelled as continuous variables by employing restricted cubic splines with four knots at 13, 47, 87 and 160 months (refer to the 5th, 35th, 65th and 95th percentiles). For LBW, both crude and adjusted models were fitted, incorporating several other covariates in addition to IPI spline terms, such as maternal age at index birth, ethnicity, maternal education, pre-pregnancy BMI, prenatal care, parity, delivery mode, and neonatal sex. The adjusted risks were standardized to average covariate values according to IPI 24–59 months group. Predicted absolute risk with 95% CI was plotted for the overall population. IPIs were then considered as categorical variables, and the frequency and OR of LBW for each IPI categories were calculated using the 24–59 month IPI interval as the reference group.

Range and distribution of air pollutants was detailed, and correlations were assessed separately using the Pearson correlation coefficient. Logistic regression was used to explore the association between air pollutants and LBW [59]. Odds ratio (OR) and its 95% confidence interval for each IQR increase in air pollutants was calculated. Model 1, referred to as the unadjusted model, did not encompass any covariates. Model 2 included following covariates: maternal age, ethnicity, maternal education, pre-pregnancy BMI, neonatal sex, mean ambient temperature and relative humidity. Air pollution score was then calculated according to the results of the multiple regression for the individual pollutants (see 2.4 Air pollution score definition).

To evaluate collective impact of IPI and air pollution on incidence LBW, multiple logistic regression models were applied, and relative risk (RR) and 95% CI were estimated. Additive interactions were examined by employing the relative excess risk due to interaction (RERI), the attributable proportion due to interaction (AP) and Synergy index (SI) [60, 61]. The group with low air pollution and IPI 24–59 month was used as the reference for other groups: high air pollution and IPI 24–59 month (RR10); low air pollution and IPI 0–11 month (RR01); high air pollution and IPI 0–11 month (RR11). RERI was calculated as RR11-RR10-RR01 + 1. AP was calculated as RERI/RR11. SI was calculated as (RR11-1)/[(RR10-1) + (RR01-1)]. The 95% CIs for RERI, AP and SI were derived by sampling 1000 bootstrap cases from the estimated dataset, and similar methods were used in other studies [55, 60]. If an additive interaction is present, the 95% CIs for RERI and AP will not encompass 0 and SI will not include 1. The assessment of multiplicative interaction involved incorporating cross-product terms into multiple models. It was worth noting that the inclusion of the covariates in models above did not cause the severity of multicollinearity, as shown by variance inflation factors(VIF) below 10 [62].

Sensitivity analyses

Several sensitivity analyses were performed: (1) Calculating air pollution scores excluding pollutants not significantly associated with LBW in specific trimesters. For example, in the first trimester since SO2 showed no significant association with incidence LBW, it was excluded. Calculation formula changed to: air pollution score = Sum (𝛽i × air pollutants) × (4 / sum of the 𝛽-coefficients) (2) Adjusting models for additional clinical covariates such as pregnancy-induced hypertension (PIH) and gestational diabetes mellitus (GDM). (3) Adjusting for season of birth. Spring encompasses the months from March to May, summer from June to August, autumn from September to November, and winter from December to February. (4) Calculating E-values for αRR to evaluate the influence of unmeasured confounders [63], which represent the minimum magnitude of association that hypothetical unmeasured confounders would need to completely account for the observed relationship between the exposure and outcome [64]. (5) Stratifying results by maternal age (< 35, ≥ 35 years) [65].

All data analyses were performed using R software 4.1.1. All statistical tests were two-sided and p-values < 0.05 were considered statistically significant.

Results

Sociodemographic characteristics of LBW

The retrospective study reviewed 10,512 birth records, identifying a LBW reporting rate of 3.5%, equating to 365 cases. Table 1 presents the sociodemographic characteristics of the LBW group. The maternal mean age was 30.5 ± 4.1 years, with 64.4% of LBW cases occurring in mothers over 30 years old. LBW incidence varied across different age groups. Han ethnicity accounted for 98.2% of the mothers. A Maternal education of ≤ 9 years was highly associated with LBW. The overall mean pre-pregnancy BMI of all subjects was 22.5 ± 3.3 kg/m2, with higher LBW incidence (6.1%) in the low BMI category. Neither parity nor delivery mode showed a significant association with LBW. The male-female sex ratio of newborns was 1.10:1, with female newborns exhibiting a higher risk of LBW compared to males (3.9% vs. 3.1%). The average birthweight in the LBW group was 2148.7 ± 380.7 g, significantly lower than the control group’s 3491.4 ± 430.1 g.

Table 1 Comparisons of characteristics between LBW and none-LBW subjects

Variables	Total
N = 10,512 (%)	LBW
n = 365 (%)	Non-LBW
n = 10,147 (%)	t/χ2	P	
Maternal Age, years	
Mean ± SD	30.5 ± 4.1	30.9 ± 4.7	30.5 ± 4.1	-1.63	0.122	
≤ 24	730 (6.9)	35(9.6)	695(6.8)	16.98	< 0.001	
25-	3454 (32.9)	95(26.0)	3359(33.1)			
30-	4797 (45.6)	162(44.4)	4635(45.7)			
≥ 35	1531 (14.6)	73(20.0)	1458(14.4)			
Ethnicity	
Han	10,319 (98.2)	359(98.4)	9960(98.2)	0.01	0.936	
Ethnic minority	193 (1.8)	6(1.6)	187(1.8)			
Education, years	
≤ 9	3556 (33.8)	160(43.8)	3396(33.5)	17.64	< 0.001	
10-	2722 (25.9)	79(21.6)	2643(26.0)			
≥ 13	4308 (40.0)	126(34.5)	4082(40.2)			
Missing	26 (0.2)	0(0.0)	26(0.3)			
Parity, times	
1	9584 (91.2)	330(90.4)	9254(91.2)	0.86	0.649	
2	842 (8.0)	33(9.0)	809(8.0)			
≥ 3	86 (0.8)	2(0.5)	84(0.8)			
Prenatal care	
Inadequate (< 5 visits)	2826 (26.9)	163(44.7)	2663(26.2)	59.84	< 0.001	
Adequate (≥ 5 visits)	7686 (73.1)	202(55.3)	7484(73.8)			
Pre-pregnancy BMI, kg/m2	
Mean ± SD	22.5 ± 3.3	22.3 ± 3.7	22.6 ± 3.3	1.32	0.178	
≤ 18.5	691 (6.6)	42(11.5)	649(6.4)	15.01	< 0.001	
18.5 -	6806 (64.7)	225(61.6)	6581(64.9)			
≥ 24	3015 (28.7)	98(26.8)	2917(28.7)			
Delivery mode	
Vaginal	4603 (43.8)	168(45.5)	4435(43.7)	0.68	0.410	
Cesarean	5909 (56.2)	197(54.5)	5712(56.3)			
Neonatal sex	
Male	5495 (52.2)	170(46.6)	5325(52.5)	4.69	0.030	
Female	5017 (47.8)	195(53.4)	4822(47.5)			
Birthweight (g)	
Mean ± SD	3444.8 ± 494.0	2148.7 ± 380.7	3491.4 ± 430.1	65.93	< 0.001	
Abbreviations: IPI, interpregnancy interval; αRR, adjusted relative risk; CI, confidence interval; LBW, low birthweight; PM2.5, particulate matter (PM) with an aerodynamic diameter of ≤ 2.5 μm. Models were adjusted for maternal age, maternal age at index birth, ethnicity, maternal education, pre-pregnancy BMI, prenatal care, parity, delivery mode, neonatal sex, as well as mean ambient temperature and relative humidity

IPIs and incident LBW

The mean IPI for all subjects was 73.6 months. eTable 1 shows the sociodemographic and clinical characteristics by IPI. Logistic regression, along with postestimation calculations, was used to assess the IPI-LBW relationship. Figure 2 displayed the unadjusted and adjusted predicted absolute risk curves for LBW, demonstrating a continuous increase with the interpregnancy interval (IPI) from 2 to 150 months. After adjusted the factors including IPI, maternal age at index birth, ethnicity, maternal education, pre-pregnancy BMI, prenatal care, parity, delivery mode, and neonatal sex, the risk curve for LBW exhibited an L-shaped pattern, with a nadir at approximately 40 months, corresponding to a predicted absolute risk of 0.02. Shorter IPIs increased the predicted absolute risk, peaking at 0.035 at 2 months IPI. Longer IPIs showed stable absolute risk around 0.025. Using IPI as a categorical variable, the 0–11 months interval had a higher risk of LBW compared to reference group (OR: 1.73, 95% CI: 1.05–2.76) (etable 2).

Fig. 2 Predicted Absolute Risk of LBW According to Interpregnancy Interval. Logistic regression analysis, along with postestimation calculations were employed to estimate the predicted ratio for LBW, with IPIs from 2 to 150 months, which was then converted to predicted absolute risk. To account for curve shape, IPIs were modelled as continuous variables by using restricted cubic splines with four knots at the 13, 47, 87, 160 months (refers to 5th, 35th, 65th and 95th percentiles) of the IPIs. Adjusted models included several other covariates in addition to IPI spline terms, such as maternal age at index birth, ethnicity, education, pre-pregnancy BMI, prenatal care, parity, delivery mode, and neonatal sex. The adjusted risks were standardized to average covariate values according to IPI 24–59 months group

Air pollution and incident LBW

Air pollution concentrations distribution and Pearson correlation results between pollutants for different periods were provided in eTable 3. The mean ± SD concentrations of PM2.5, PM10, O3, NO2, SO2 and CO during entire pregnancy were 17.50 ± 1.48, 34.04 ± 3.60, 113.12 ± 4.24, 15.80 ± 1.08, 9.53 ± 0.36 µg/m3 and 0.64 ± 0.01 mg/m3, respectively. PM2.5 and PM10 displayed a strong correlation with correlation coefficients over 0.87 for all periods. Multiple regression analyses of air pollutants exposure on LBW incidence (eTable 4) showed that, in the crude model, pollutants excluding SO2 and CO were significantly associated with LBW during the entire pregnancy, as well as during the first and second trimesters. At third trimester, only PM10, NO2 and SO2 were significantly linked to LBW.

After adjusting for maternal age, ethnicity, education, pre-pregnancy BMI, neonatal sex, mean temperature and relative humidity, the ORs for per IQR increase in PM2.5, PM10, O3, NO2 at first trimester were 1.17(95% CI: 1.01–1.33), 1.27(1.04–1.55), 1.27(1.03–1.58), 1.23(1.00-1.53), respectively. At second trimester, the ORs for PM2.5 and PM10 were 1.15(1.00-1.31) and 1.25(1.04–1.50). At third trimester, the ORs for PM10, NO2, SO2 were 1.27(1.05–1.53), 1.24(1.00-1.53), 1.26(1.03–1.55), respectively. The ORs during entire pregnancy for PM2.5, PM10, O3 were 1.16(1.01–1.32), 1.30(1.06–1.59), 1.22(1.06–1.41), respectively. CO did not show a statistical association with LBW in any period. The regression results also revealed a consistent effect of the air pollution score on LBW across various periods, with per IQR increase of 1.27(1.06–1.53) at first trimester, 1.24(1.04–1.49) at second trimester, 1.26(1.06–1.49) at third trimester and 1.32(1.10–1.60) during entire pregnancy (eTable 4). Distribution of air pollution score across different gestational periods was shown in eTable 5.

Joint effect of IPIs and air pollution on LBW

The combined association of IPI and air pollutants on LBW was further investigated. Of the air pollution metrics, PM2.5 exhibited the strongest combined association with IPI, particularly in the first trimester (OR = 3.53, 95% CI: 1.85–6.49). Figure 3 indicates that mothers with both an IPI of 0–11 months and high PM2.5 levels had the highest risk of LBW, compared to those with an IPI of 24–59 months and low air pollution levels. It suggests that the simultaneous occurrence of a short IPI and elevated PM2.5 levels may significantly increase LBW risk. Adjusted relative risks (RRs) for these comparisons were 2.03 (1.01–3.84) at second trimester, 1.83 (0.88–3.55) at third trimester, and 2.79 (1.40–5.30) for the entire pregnancy.

Fig. 3 The joint association of Interpregnancy Interval (IPI) and Air Pollution (low, high PM2.5 exposure) with the risk of incident LBW

Additive interaction results revealed synergistic effect between the IPI and PM2.5 at first trimester (RERI, AP (95% CI) > 0, SI (95% CI) > 1). No significance was found for the multiplicative interaction when IPI and PM2.5 were used as continuous variables (P-interaction ˃ 0.05 at all periods). eTables 6–10 detail the joint effects of the air pollution score, PM10, O3, NO2, SO2 with IPI subgroups on LBW across four periods, almost all showing higher LBW risk in low IPI and high pollutant concentration groups compared to the reference group. High air pollution score in the 0–11 month IPI revealed a RR of 2.52 (1.28–4.70) at third trimester.

Similarly, for the high air pollution score group with IPI ranging from 12 to 23, a RR of 1.72 (1.02–2.85) was found over the entire pregnancy period (No interaction was observed). High PM10 pollution with the 0–11 IPI showed a RR of 2.34 (1.16–4.44) at first trimester (No interaction was observed). In the case of high O3 pollution and 0–11 IPI, a RR of 3.38 (1.72–6.35) was found during the third trimester, and a RR of 2.34 (1.18–4.40) was found over the entire pregnancy period (No interaction was observed). Additionally, high NO2 pollution and IPI within the 12–23 range exhibited a RR of 1.98 (1.14–3.39) during the second trimester (No interaction was observed). Lastly, high SO2 pollution and IPI within the 0–11 range revealed a RR of 2.64 (1.23–5.25) during the first trimester (No interaction was observed).

Sensitivity analyses

(1) The association between air pollution score and LBW remained robust. Restricting the analysis to statistically significant pollutants sustained the positive association between the new score and LBW across all periods (eTable 11). (2) Additional adjustment for clinical-related covariates, including pregnancy-induced hypertension (PIH) and gestational diabetes mellitus (GDM), did not alter the association between PM2.5, IPI and LBW at all periods (eTable 12). (3) Adjustment for season of birth also upheld the relationship (eTable 13). Notably, the results remained consistent, indicating that the observed association was not solely influenced by season of birth. (4) To provide a quantitative evaluation of robustness of the observed associations, E-values was calculated, which quantify the minimum joint association strength required for confounding variables to fully explain the reported risk ratios (RRs) for LBW in Fig. 3 (eTable 14). Relatively large E-values (95% CI) in this study, suggesting the observed associations were unlikely to be fully explained by unmeasured confounding variables. (5) The joint, individual effect of IPI and PM2.5 on the risk of incidence LBW during the trimester-specific and entire-pregnancy periods, stratified by maternal age (< 35, ≥ 35years) was shown in eTable 15–17. The findings indicated that the effects of both IPI, air pollution, and their combined effects were notably greater for women younger than 35 years than for women aged 35 years or older.

Discussion

In this retrospective study, we investigated the individual and combined effects of IPIs and air pollution exposure on the incidence of LBW. Our findings indicate that short interpregnancy intervals (< 12months) increased the risk of LBW, aligning with the WHO recommendation that women wait at least 2 years after a live birth before conceiving. Specifically, an IPI of 24 to 59 months was associated with lowest risk of LBW [52]. Air pollutants, excluding CO, also showed a positive association with LBW. Notably, the highest relative increase in LBW risk was observed in groups with both low IPI and high air pollutant exposure levels. Our study provides quantitative data on the interaction between IPI and individual air pollutants, revealing a synergistic effect between PM2.5 and IPI during the first trimester.

To date, a few studies have assessed the relationship between IPI after live birth and risk of fetal undergrowth, with divergent results. In both China and USA, researchers found greater risks of LBW were linked to a short IPI of less than 11 months and a long IPI of over than 36 months [26, 66]. Cofer et al. from USA found IPI less than one year was related to LBW [67]. The result reached similar conclusions in low IPI case with several studies mentioned above. However, increased effects for long IPIs of over than 60 months to LBW were not found in our study, which differs from the results of the studies mentioned above [26]. An explanation for different results in high IPI maybe related with sample size and adjustment variables [26].

The mechanisms underlying IPI and adverse pregnancy outcomes are not yet fully understood [26, 52]. One hypothesis proposed to explain the impact of short IPI is the nutritional depletion hypothesis. According to this hypothesis, mothers do not have enough time to recover from malnutrition after a previous pregnancy and breastfeeding, resulting in a reproductive state of poor nutrition in subsequent pregnancies [48]. This inadequate nutritional status during the preconception period increases the risk of fetal growth restriction [68]. Specifically, folic acid depletion has been identified as one of potential cause for the link between short IPI and LBW [66]. While these hypotheses provide some insight into the potential mechanisms, it is important to note that the exact pathways through which IPI influences adverse pregnancy outcomes are complex and multifactorial. Further research is needed to elucidate the underlying biological mechanisms and to investigate other potential factors contributing to the observed associations.

In the terms of evaluation methods of air pollution, previous studies frequently adopted inverse distance-weighted (IDW) methods to estimate maternal air pollutant exposure concentrations, primarily considering the proximity of the study population to the air monitoring station. However, this approach is susceptible to misclassification biass [53, 69]. In this study, proximate contaminant data was combined with spatial interpolation of ambient pollutant measurements to inform land use predictors in LUR models [70]. To leverage the ability of LUR to identify predictors along with the strength of machine learning algorithms in predicting non-linear trends [71], this study employed a hybrid Kriging-LUR-Random Forest (RF) model while also considering several geographical factors and those affecting pollutant emissions. By integrating a hybrid Kriging-LUR model with a machine learning approach, model efficiency was enhanced by reducing the dimensionality of variables and increasing statistical ability, ultimately leading to significantly improved estimation accuracy [71].

The relationship between air pollution and LBW has varied across studies. Several meta-analyses have found that higher air pollutants are related to adverse pregnancy outcomes, including LBW [35, 40, 72]. Thirty-six relative reviews, covering 295 initial studies, were analyzed in a review that found a more consistent positive effect of particulate matter on LBW [73], and consistent results were obtained for particulate matter in this study. The association of CO with LBW was not observed in all periods of this study, and the same result has been observed in other studies [74, 75]. In another similar study [76], only five pollutants other than CO were considered, probably because the researcher was more concerned with pollutants with higher health effects such as particulate matter, ozone, and nitrogen dioxide. A research in UKB found PM2.5 and PM10 to be associated with LBW during entire pregnancy [36]. In this study, air pollution score was statistically associated with LBW at all trimester-specific periods and throughout pregnancy, as were air pollutants. The difference in findings across studies can be attributed to various factors, including variations in population characteristics (genetic makeup, socio-economic status, body composition), study design, sample size, methods used for exposure assessment, climatic conditions, medical and conditions [35, 77, 78]. A larger OR was obtained than the UKB research (PM10: 1.30 (95% CI: 1.07–1.60) vs. 1.12 (1.02–1.24)), potentially due to advanced maternal age, which can be more sensitive to pollution during pregnancy. Several studies in China have suggested that advanced age is an effect modifier of the link between prenatal particulate matter and preterm birth, and that women aged more than 39 years are more sensitive to air pollution [79, 80].

In this study, it was observed that women under 35 years old are notably affected by both birth spacing and air pollution. While 35 years might not be considered advanced maternal age for the general population, for women giving birth to their second or third child, this age falls within the normal range, rendering women below this age relatively young. Younger women, with IPI of less than one year, were found to be more prone to giving birth to LBW infants-a trend consistent with similar studies [24, 26]. The impact of air pollution on these women also aligns with conclusions drawn in other research [65], emphasizing the heightened susceptibility of young mothers to its effects. Following the relaxation of China’s two-child and three-child policy, many young women may experience unplanned pregnancies [81], potentially being unaware of their pregnancy in the early stages, thus being more susceptible to environmental hazards. Beyond these factors, societal and psychological pressures can amplify the link between pollutants and birth outcomes [82]. Young mothers, unable to take time off during pregnancy, may experience increased psychological stress due to work and financial burdens.

Exposure to air pollutants during pregnant, particularly particulate matter, has been linked to several adverse birth outcomes, and several mechanisms have been suggested as explanation. One extensively studied hypotheses is that air pollution exposure can lead to placental dysfunction by inducing an inflammatory response, disruption of the endocrine system and mitochondrial disturbances [83, 84]. Another suggested mechanism is that air pollution can induce inflammation and oxidative stress in the placenta, leading to increased clotting and impaired nutrient transport [36, 85]. These effects can contribute to restricted fetal growth and LBW. Additionally, trans-placental exposure to specific components of airborne particulate matter, such as polycyclic aromatic hydrocarbons, has been proposed as a mechanism for lower birth weight [86]. During early pregnancy, fetal and placental development commences, yet it is still immature. During this period, environmental pollutants may trigger inflammatory responses leading to genetic mutations, ultimately resulting in abnormal pregnancy outcomes [69]. It is well known that the early stages of pregnancy represent a critical period for fetal organ development, and reducing exposure to air pollution may potentially decrease adverse pregnancy outcomes in newborns. It is notable that these mechanisms are complex and interconnected, and the specific affective pathways are still being investigated.

Recently, there has been an increasing recognition of the importance of evaluating effects of multiple pollutants, as there is a high degree of correlation that may arise from the same emission source [87]. In our study, the relationship between the newly developed air pollution score and incidence LBW was relatively stronger than individual air pollutants. Therefore, the score may provide a more comprehensive representation of the combined air pollutants exposure. Similar algorithms have been used to assess combined exposure to environmental and dietary factors [88]. Simple methods are easier to interpret epidemiological results and are more likely to promote public health preventive practices [42].

We also found a synergistic effect between low IPI and high PM2.5 exposure in increasing the risk of LBW. It can be speculated that IPI modifies the effect of PM2.5 exposure on LBW. The results of this study carry significant public health implications, as they can aid in identifying individuals at higher risk who may benefit most from interventions aimed at reducing air pollution.

Fetal growth is influenced by multiple factors, which may interact not only with exposure to ambient air pollution but also with each other [49]. Nutritional deficiencies could be one of the major contributing factors to the mechanism of combined impact on LBW [48]. Women with short interpregnancy intervals are in a state of nutritional depletion prior to pregnancy, while air pollution may induce placental inflammation and oxidative stress, leading to compromised nutrient transport [85]. The interaction between these factors results in severe intrauterine malnutrition, ultimately causing LBW. However, it is notable that further research is necessary to validate these findings and to gain a better understanding of the underlying mechanisms behind the combined association of IPI, PM2.5 with LBW.

The findings of this study underscore the potential public health significance of optimizing gestational spacing and mitigating combined atmospheric pollution to reduce the risk of LBW. In terms of research implications, future studies need to focus more on the combined effects of multiple pollutants on health, and more research is still needed to gain insight into the underlying mechanisms of the effects of air pollution on neonatal health. The effects of pollutants on pregnancy outcomes may also be influenced by maternal physical status. The government needs to strengthen the policy on environmental protection for protecting the health of women and children.

There are several strengths in our study. Firstly, a hybrid kriging-LUR-RF model was employed to estimate the daily air pollution concentrations during pregnancy for each pregnant woman’s permanent residential address. The model has been proved in previous studies to have statistical efficiency advantages in predicting air pollutants in different geographical areas [71]. The robust estimation of air pollution exposure enhances the accuracy of the findings. Furthermore, this study evaluated the combined association of air pollutants and IPI. While a large number of previous studies have examined separate effects of IPI or air pollution on LBW, few studies focus on joint effect. Finally, E-values were also calculated to assess the unmeasured confounding factors. The calculation of E-values helps evaluate the robustness of the reported associations, suggesting that unmeasured confounders are less likely to substantially impact the reported findings.

Of course, some limitations are inevitable. Firstly, air pollution exposure may be assessed inaccurately because only exposure to air pollution at residential addresses was considered with the exposure of work environment being ignored, the study did not take into account the effects of participants not being at home or air pollution from household gas, etc. Secondly, although several sociodemographic and clinical covariates have been considered, maternal smoking and alcohol consuming during pregnancy, socioeconomic status measures (e.g., job, incomes, and cultural beliefs), infectious diseases [89–91] and intimate partner violence [92] may confound the estimated association. The reported relatively large E-value was reported in all analyses in this study, suggesting that it was unlikely for unmeasured confounders to offset these associations. Thirdly, only live, consecutive, singleton data were included in this study. Therefore, results may not be applicable to women who have experienced miscarriage or stillbirth between deliveries. Additionally, as the residential address of pregnant women is registered at the time of delivery, we can’t know their residential address during pregnancy. Therefore, pregnant women might move their house during pregnancy, which may lead to potential misclassification of exposure. Finally, it is important to exercise caution when extrapolating the results of this study to other regions and populations, as the data for this study came from one region.

Conclusion

Interpregnancy interval (IPI < 12 months) and exposure to various air pollutants, including PM2.5, PM10, O3, NO2, and SO2, contribute to the incidence of low birth weight (LBW). Our joint effects analysis revealed that the association between PM2.5 and LBW was strengthened by short IPI. The findings of this study underscore the potential public health significance of optimizing gestational spacing and mitigating combined atmospheric pollution to improve birth outcomes, with a specific focus on reducing the risk of LBW.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Acknowledgements

The author would like to sincerely thank Department of Maternal and Child Health, Nantong Health Commission, Jiangsu province for supporting this study.

Author contributions

X.Z contributed to conceptualization and project administration; X.Y.L performed formal analysis, interpreted the results, and drafted original manuscript; R.J contributed to visualization; Z.X.Z polished the manuscript. Y.Y.Z and X.Y.Z supervised the work; G.Q, Z.J.N and Q.W.G reviewed the manuscript.

Funding

This work was supported by Preventive Medicine Research Project of Jiangsu Provincial Health Commission (Ym2023079); Graduate Research & Practice Innovation Program of Jiangsu Province, China (KYCX23_3435); General Project of Scientific Research Projects of Nantong Health Committee (MB2021075).

Data availability

The dataset supporting the conclusion of this article is available upon reasonable request from the corresponding author.

Declarations

Ethics approval and consent to participate

The study was approved by the Medical Ethics Committee of Nantong University (Approval number: No. [2022] 6). The records used for analysis were completely anonymous, which indicates that no personal information will be disclosed in this study.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Declaration of Generative AI and AI-assisted technologies in the writing process

During the preparation of this work the author(s) used ChatGPT 4.0 in order to polish readability and language. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the publication.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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References

1. United Nations Millennium Development Goals. https://www.un.org/millenniumgoals/. Accessed 11 Oct 2023.
2. Katz J Lee AC Kozuki N Lawn JE Cousens S Blencowe H Mortality risk in preterm and small-for-gestational-age infants in low-income and middle-income countries: a pooled country analysis Lancet 2013 382 417 25 10.1016/S0140-6736(13)60993-9 23746775
Katz J, Lee AC, Kozuki N, Lawn JE, Cousens S, Blencowe H, et al. Mortality risk in preterm and small-for-gestational-age infants in low-income and middle-income countries: a pooled country analysis. Lancet. 2013;382:417–25.23746775
3. Doherty T Kinney M Low birthweight: will new estimates accelerate progress? Lancet Global Health 2019 7 e809 10 10.1016/S2214-109X(19)30041-5 31103469
Doherty T, Kinney M. Low birthweight: will new estimates accelerate progress? Lancet Global Health. 2019;7:e809–10.31103469
4. Ashorn P Ashorn U Muthiani Y Aboubaker S Askari S Bahl R Small vulnerable newborns—big potential for impact Lancet 2023 401 1692 706 10.1016/S0140-6736(23)00354-9 37167991
Ashorn P, Ashorn U, Muthiani Y, Aboubaker S, Askari S, Bahl R, et al. Small vulnerable newborns—big potential for impact. Lancet. 2023;401:1692–706.37167991
5. Li W Peng A Deng S Lai W Qiu X Zhang L Do premature and postterm birth increase the risk of epilepsy? An updated meta-analysis Epilepsy Behav 2019 97 83 91 10.1016/j.yebeh.2019.05.016 31202097
Li W, Peng A, Deng S, Lai W, Qiu X, Zhang L, et al. Do premature and postterm birth increase the risk of epilepsy? An updated meta-analysis. Epilepsy Behav. 2019;97:83–91.31202097
6. FitzGerald TL Kwong AKL Cheong JLY McGinley JL Doyle LW Spittle AJ Body Structure Function, activity, and participation in 3- to 6-Year-old children born very Preterm: an ICF-Based systematic review and Meta-analysis Phys Ther 2018 98 691 704 10.1093/ptj/pzy050 29912447
FitzGerald TL, Kwong AKL, Cheong JLY, McGinley JL, Doyle LW, Spittle AJ, Body Structure. Function, activity, and participation in 3- to 6-Year-old children born very Preterm: an ICF-Based systematic review and Meta-analysis. Phys Ther. 2018;98:691–704.29912447
7. Allotey J Zamora J Cheong-See F Kalidindi M Arroyo-Manzano D Asztalos E Cognitive, motor, behavioural and academic performances of children born preterm: a meta-analysis and systematic review involving 64 061 children BJOG 2018 125 16 25 10.1111/1471-0528.14832 29024294
Allotey J, Zamora J, Cheong-See F, Kalidindi M, Arroyo-Manzano D, Asztalos E, et al. Cognitive, motor, behavioural and academic performances of children born preterm: a meta-analysis and systematic review involving 64 061 children. BJOG. 2018;125:16–25.29024294
8. de Kieviet JF Piek JP Aarnoudse-Moens CS Oosterlaan J Motor development in very preterm and very low-birth-weight children from birth to adolescence: a meta-analysis JAMA 2009 302 2235 42 10.1001/jama.2009.1708 19934425
de Kieviet JF, Piek JP, Aarnoudse-Moens CS, Oosterlaan J. Motor development in very preterm and very low-birth-weight children from birth to adolescence: a meta-analysis. JAMA. 2009;302:2235–42.19934425
9. Upadhyay RP Naik G Choudhary TS Chowdhury R Taneja S Bhandari N Cognitive and motor outcomes in children born low birth weight: a systematic review and meta-analysis of studies from South Asia BMC Pediatr 2019 19 35 10.1186/s12887-019-1408-8 30696415
Upadhyay RP, Naik G, Choudhary TS, Chowdhury R, Taneja S, Bhandari N, et al. Cognitive and motor outcomes in children born low birth weight: a systematic review and meta-analysis of studies from South Asia. BMC Pediatr. 2019;19:35.30696415
10. Sacchi C Marino C Nosarti C Vieno A Visentin S Simonelli A Association of Intrauterine Growth Restriction and Small for Gestational Age Status with Childhood Cognitive outcomes: a systematic review and Meta-analysis JAMA Pediatr 2020 174 772 81 10.1001/jamapediatrics.2020.1097 32453414
Sacchi C, Marino C, Nosarti C, Vieno A, Visentin S, Simonelli A. Association of Intrauterine Growth Restriction and Small for Gestational Age Status with Childhood Cognitive outcomes: a systematic review and Meta-analysis. JAMA Pediatr. 2020;174:772–81.32453414
11. Been JV Lugtenberg MJ Smets E van Schayck CP Kramer BW Mommers M Preterm birth and childhood wheezing disorders: a systematic review and meta-analysis PLoS Med 2014 11 e1001596 10.1371/journal.pmed.1001596 24492409
Been JV, Lugtenberg MJ, Smets E, van Schayck CP, Kramer BW, Mommers M, et al. Preterm birth and childhood wheezing disorders: a systematic review and meta-analysis. PLoS Med. 2014;11:e1001596.24492409
12. Kotecha S Clemm H Halvorsen T Kotecha SJ Bronchial hyper-responsiveness in preterm-born subjects: a systematic review and meta-analysis Pediatr Allergy Immunol 2018 29 715 25 10.1111/pai.12957 30014518
Kotecha S, Clemm H, Halvorsen T, Kotecha SJ. Bronchial hyper-responsiveness in preterm-born subjects: a systematic review and meta-analysis. Pediatr Allergy Immunol. 2018;29:715–25.30014518
13. Hussain SM Ackerman IN Wang Y Zomer E Cicuttini FM Could low birth weight and preterm birth be associated with significant burden of hip osteoarthritis? A systematic review Arthritis Res Ther 2018 20 121 10.1186/s13075-018-1627-7 29884206
Hussain SM, Ackerman IN, Wang Y, Zomer E, Cicuttini FM. Could low birth weight and preterm birth be associated with significant burden of hip osteoarthritis? A systematic review. Arthritis Res Ther. 2018;20:121.29884206
14. Bhutta AT Cleves MA Casey PH Cradock MM Anand KJS Cognitive and behavioral outcomes of school-aged children who were born preterm: a meta-analysis JAMA 2002 288 728 37 10.1001/jama.288.6.728 12169077
Bhutta AT, Cleves MA, Casey PH, Cradock MM, Anand KJS. Cognitive and behavioral outcomes of school-aged children who were born preterm: a meta-analysis. JAMA. 2002;288:728–37.12169077
15. Jenabi E Bashirian S Asali Z Seyedi M Association between small for gestational age and risk of autism spectrum disorders: a meta-analysis Clin Exp Pediatr 2021 64 538 42 10.3345/cep.2020.01956 33539699
Jenabi E, Bashirian S, Asali Z, Seyedi M. Association between small for gestational age and risk of autism spectrum disorders: a meta-analysis. Clin Exp Pediatr. 2021;64:538–42.33539699
16. Fitzallen GC Sagar YK Taylor HG Bora S Anxiety and depressive disorders in Children Born Preterm: a Meta-analysis J Dev Behav Pediatr 2021 42 154 62 10.1097/DBP.0000000000000898 33480635
Fitzallen GC, Sagar YK, Taylor HG, Bora S. Anxiety and depressive disorders in Children Born Preterm: a Meta-analysis. J Dev Behav Pediatr. 2021;42:154–62.33480635
17. Su Y D’Arcy C Meng X Research Review: Developmental origins of depression - a systematic review and meta-analysis J Child Psychol Psychiatry 2021 62 1050 66 10.1111/jcpp.13358 33259072
Su Y, D’Arcy C, Meng X. Research Review: Developmental origins of depression - a systematic review and meta-analysis. J Child Psychol Psychiatry. 2021;62:1050–66.33259072
18. de Loret C de França GVA Quevedo L de Horta A Low birth weight, preterm birth and small for gestational age association with adult depression: systematic review and meta-analysis Br J Psychiatry 2014 205 340 7 10.1192/bjp.bp.113.139014 25368358
de Loret C, de França GVA, Quevedo L, de Horta A. Low birth weight, preterm birth and small for gestational age association with adult depression: systematic review and meta-analysis. Br J Psychiatry. 2014;205:340–7.25368358
19. Telles F McNamara N Nanayakkara S Doyle MP Williams M Yaeger L Changes in the Preterm Heart from Birth to Young Adulthood: a Meta-analysis Pediatrics 2020 146 e20200146 10.1542/peds.2020-0146 32636236
Telles F, McNamara N, Nanayakkara S, Doyle MP, Williams M, Yaeger L, et al. Changes in the Preterm Heart from Birth to Young Adulthood: a Meta-analysis. Pediatrics. 2020;146:e20200146.32636236
20. Andraweera PH Condon B Collett G Gentilcore S Lassi ZS Cardiovascular risk factors in those born preterm - systematic review and meta-analysis J Dev Orig Health Dis 2021 12 539 54 10.1017/S2040174420000914 33028453
Andraweera PH, Condon B, Collett G, Gentilcore S, Lassi ZS. Cardiovascular risk factors in those born preterm - systematic review and meta-analysis. J Dev Orig Health Dis. 2021;12:539–54.33028453
21. de Mendonça ELSS de Lima Macêna M Bueno NB de Oliveira ACM Mello CS Premature birth, low birth weight, small for gestational age and chronic non-communicable diseases in adult life: a systematic review with meta-analysis Early Hum Dev 2020 149 105154 10.1016/j.earlhumdev.2020.105154 32799034
de Mendonça ELSS, de Lima Macêna M, Bueno NB, de Oliveira ACM, Mello CS. Premature birth, low birth weight, small for gestational age and chronic non-communicable diseases in adult life: a systematic review with meta-analysis. Early Hum Dev. 2020;149:105154.32799034
22. Mohseni R Mohammed SH Safabakhsh M Mohseni F Monfared ZS Seyyedi J Birth Weight and Risk of Cardiovascular Disease incidence in Adulthood: a dose-response Meta-analysis Curr Atheroscler Rep 2020 22 12 10.1007/s11883-020-0829-z 32328820
Mohseni R, Mohammed SH, Safabakhsh M, Mohseni F, Monfared ZS, Seyyedi J, et al. Birth Weight and Risk of Cardiovascular Disease incidence in Adulthood: a dose-response Meta-analysis. Curr Atheroscler Rep. 2020;22:12.32328820
23. World Health Organization. Report of a WHO technical consultation on birth spacing. http://apps.who.int/iris/bitstream/handle/10665/69855/WHO_RHR_07.1_eng.pdf;jsessionid=94CA14B7AFC6FE340D1D6AA8C7587B63?sequence=1. Accessed 14 Apr 2023.
24. Schummers L Hutcheon JA Hernandez-Diaz S Williams PL Hacker MR VanderWeele TJ Association of short interpregnancy interval with pregnancy outcomes according to maternal age JAMA Intern Med 2018 178 1661 70 10.1001/jamainternmed.2018.4696 30383085
Schummers L, Hutcheon JA, Hernandez-Diaz S, Williams PL, Hacker MR, VanderWeele TJ, et al. Association of short interpregnancy interval with pregnancy outcomes according to maternal age. JAMA Intern Med. 2018;178:1661–70.30383085
25. Zhu B-P Effect of interpregnancy interval on birth outcomes: findings from three recent US studies Int J Gynecol Obstet 2005 89 S25 33 10.1016/j.ijgo.2004.08.002
Zhu B-P. Effect of interpregnancy interval on birth outcomes: findings from three recent US studies. Int J Gynecol Obstet. 2005;89:S25–33.
26. Xu T Miao H Chen Y Luo L Guo P Zhu Y Association of Interpregnancy interval with adverse birth outcomes JAMA Netw Open 2022 5 e2216658 10.1001/jamanetworkopen.2022.16658 35696164
Xu T, Miao H, Chen Y, Luo L, Guo P, Zhu Y. Association of Interpregnancy interval with adverse birth outcomes. JAMA Netw Open. 2022;5:e2216658.35696164
27. Zhang L Shen S He J Chan F Lu J Li W Effect of Interpregnancy interval on adverse perinatal outcomes in Southern China: a retrospective cohort study, 2000–2015 Paediatr Perinat Epidemiol 2018 32 131 40 10.1111/ppe.12432 29293278
Zhang L, Shen S, He J, Chan F, Lu J, Li W, et al. Effect of Interpregnancy interval on adverse perinatal outcomes in Southern China: a retrospective cohort study, 2000–2015. Paediatr Perinat Epidemiol. 2018;32:131–40.29293278
28. Shah JS Eliner Y Vaughan DA Wylie BJ Korkidakis A Leung AQ The effect of interpregnancy interval on preterm birth and low birth weight in singleton pregnancies conceived without assistance or by infertility treatments Fertil Steril 2022 118 550 9 10.1016/j.fertnstert.2022.05.025 35697531
Shah JS, Eliner Y, Vaughan DA, Wylie BJ, Korkidakis A, Leung AQ, et al. The effect of interpregnancy interval on preterm birth and low birth weight in singleton pregnancies conceived without assistance or by infertility treatments. Fertil Steril. 2022;118:550–9.35697531
29. Conde-Agudelo A Rosas-Bermúdez A Kafury-Goeta AC Birth spacing and risk of adverse perinatal outcomes: a meta-analysis JAMA 2006 295 1809 23 10.1001/jama.295.15.1809 16622143
Conde-Agudelo A, Rosas-Bermúdez A, Kafury-Goeta AC. Birth spacing and risk of adverse perinatal outcomes: a meta-analysis. JAMA. 2006;295:1809–23.16622143
30. Ni W Gao X Su X Cai J Zhang S Zheng L Birth spacing and risk of adverse pregnancy and birth outcomes: a systematic review and dose–response meta-analysis Acta Obstet Gynecol Scand 2023 102 1618 33 10.1111/aogs.14648 37675816
Ni W, Gao X, Su X, Cai J, Zhang S, Zheng L, et al. Birth spacing and risk of adverse pregnancy and birth outcomes: a systematic review and dose–response meta-analysis. Acta Obstet Gynecol Scand. 2023;102:1618–33.37675816
31. Guo M Yan P Zhu M Choi M Li X Huang J Microcystin-LR prenatal exposure drives preeclampsia-like changes in mice by inhibiting the expression of TGF-β and VEGFA Food Chem Toxicol 2023 182 114189 10.1016/j.fct.2023.114189 37980977
Guo M, Yan P, Zhu M, Choi M, Li X, Huang J, et al. Microcystin-LR prenatal exposure drives preeclampsia-like changes in mice by inhibiting the expression of TGF-β and VEGFA. Food Chem Toxicol. 2023;182:114189.37980977
32. Gao X Zhong Y Li K Miao A Chen N Ding R Toxoplasma Gondii promotes microRNA-34a to inhibit Foxp3 expression in adverse outcomes of pregnancy in mice Int Immunopharmacol 2022 107 108648 10.1016/j.intimp.2022.108648 35286917
Gao X, Zhong Y, Li K, Miao A, Chen N, Ding R, et al. Toxoplasma Gondii promotes microRNA-34a to inhibit Foxp3 expression in adverse outcomes of pregnancy in mice. Int Immunopharmacol. 2022;107:108648.35286917
33. Han Y Yu X Lu Y Shen Y Wang X Wei H Di-(2-ethylhexyl) phthalate aggravates fine particulate matter-induced asthma in weanling mice due to T follicular helper cell-dependent response Toxicology 2023 484 153406 10.1016/j.tox.2022.153406 36549504
Han Y, Yu X, Lu Y, Shen Y, Wang X, Wei H, et al. Di-(2-ethylhexyl) phthalate aggravates fine particulate matter-induced asthma in weanling mice due to T follicular helper cell-dependent response. Toxicology. 2023;484:153406.36549504
34. May L. Cardiac Physiology of Pregnancy. In: Terjung R, editor. Comprehensive Physiology. 1st edition. Wiley; 2015. pp. 1325–44.
35. Ghosh R Causey K Burkart K Wozniak S Cohen A Brauer M Ambient and household PM2.5 pollution and adverse perinatal outcomes: a meta-regression and analysis of attributable global burden for 204 countries and territories PLoS Med 2021 18 e1003718 10.1371/journal.pmed.1003718 34582444
Ghosh R, Causey K, Burkart K, Wozniak S, Cohen A, Brauer M. Ambient and household PM2.5 pollution and adverse perinatal outcomes: a meta-regression and analysis of attributable global burden for 204 countries and territories. PLoS Med. 2021;18:e1003718.34582444
36. Fu Z Liu Q Liang J Huang T Liang G Zhou Y Association of ambient air pollution exposure with low birth weight Environ Res 2022 215 114164 10.1016/j.envres.2022.114164 36027958
Fu Z, Liu Q, Liang J, Huang T, Liang G, Zhou Y, et al. Association of ambient air pollution exposure with low birth weight. Environ Res. 2022;215:114164.36027958
37. Shah PS Balkhair T Knowledge Synthesis Group on Determinants of Preterm/LBW births. Air pollution and birth outcomes: a systematic review Environ Int 2011 37 498 516 10.1016/j.envint.2010.10.009 21112090
Shah PS, Balkhair T. Knowledge Synthesis Group on Determinants of Preterm/LBW births. Air pollution and birth outcomes: a systematic review. Environ Int. 2011;37:498–516.21112090
38. Li C Yang M Zhu Z Sun S Zhang Q Cao J Maternal exposure to air pollution and the risk of low birth weight: a meta-analysis of cohort studies Environ Res 2020 190 109970 10.1016/j.envres.2020.109970 32763280
Li C, Yang M, Zhu Z, Sun S, Zhang Q, Cao J, et al. Maternal exposure to air pollution and the risk of low birth weight: a meta-analysis of cohort studies. Environ Res. 2020;190:109970.32763280
39. Fu L Chen Y Yang X Yang Z Liu S Pei L The associations of air pollution exposure during pregnancy with fetal growth and anthropometric measurements at birth: a systematic review and meta-analysis Environ Sci Pollut Res Int 2019 26 20137 47 10.1007/s11356-019-05338-0 31111384
Fu L, Chen Y, Yang X, Yang Z, Liu S, Pei L, et al. The associations of air pollution exposure during pregnancy with fetal growth and anthropometric measurements at birth: a systematic review and meta-analysis. Environ Sci Pollut Res Int. 2019;26:20137–47.31111384
40. Sun X Luo X Zhao C Zhang B Tao J Yang Z The associations between birth weight and exposure to fine particulate matter (PM2.5) and its chemical constituents during pregnancy: a meta-analysis Environ Pollut 2016 211 38 47 10.1016/j.envpol.2015.12.022 26736054
Sun X, Luo X, Zhao C, Zhang B, Tao J, Yang Z, et al. The associations between birth weight and exposure to fine particulate matter (PM2.5) and its chemical constituents during pregnancy: a meta-analysis. Environ Pollut. 2016;211:38–47.26736054
41. Kaali S Jack DW Mujtaba MN Chillrud SN Ae-Ngibise KA Kinney PL Identifying sensitive windows of prenatal household air pollution on birth weight and infant pneumonia risk to inform future interventions Environ Int 2023 178 108062 10.1016/j.envint.2023.108062 37392730
Kaali S, Jack DW, Mujtaba MN, Chillrud SN, Ae-Ngibise KA, Kinney PL, et al. Identifying sensitive windows of prenatal household air pollution on birth weight and infant pneumonia risk to inform future interventions. Environ Int. 2023;178:108062.37392730
42. Wang M Zhou T Song Y Li X Ma H Hu Y Joint exposure to various ambient air pollutants and incident heart failure: a prospective analysis in UK Biobank Eur Heart J 2021 42 1582 91 10.1093/eurheartj/ehaa1031 33527989
Wang M, Zhou T, Song Y, Li X, Ma H, Hu Y, et al. Joint exposure to various ambient air pollutants and incident heart failure: a prospective analysis in UK Biobank. Eur Heart J. 2021;42:1582–91.33527989
43. Fang H Jiang D He Y Wu S Li Y Zhang Z Association of ambient air pollution and pregnancy rate among women undergoing assisted reproduction technology in Fujian, China: a retrospective cohort study Sci Total Environ 2024 908 168287 10.1016/j.scitotenv.2023.168287 37924883
Fang H, Jiang D, He Y, Wu S, Li Y, Zhang Z, et al. Association of ambient air pollution and pregnancy rate among women undergoing assisted reproduction technology in Fujian, China: a retrospective cohort study. Sci Total Environ. 2024;908:168287.37924883
44. Wang M Wen C Qi H Xu K Wei M Xia W Residential greenness and air pollution concerning excessive gestational weight gain during pregnancy: a cross-sectional study in Wuhan, China Environ Res 2023 217 114866 10.1016/j.envres.2022.114866 36427642
Wang M, Wen C, Qi H, Xu K, Wei M, Xia W, et al. Residential greenness and air pollution concerning excessive gestational weight gain during pregnancy: a cross-sectional study in Wuhan, China. Environ Res. 2023;217:114866.36427642
45. Xu R Li Z Qian N Qian Y Wang Z Peng J Air pollution exposure and the risk of macrosomia: identifying specific susceptible months Sci Total Environ 2023 859 160203 10.1016/j.scitotenv.2022.160203 36403833
Xu R, Li Z, Qian N, Qian Y, Wang Z, Peng J, et al. Air pollution exposure and the risk of macrosomia: identifying specific susceptible months. Sci Total Environ. 2023;859:160203.36403833
46. Liao Q Tang P Song Y Liu B Huang H Liang J Association of single and multiple prefluoroalkyl substances exposure with preterm birth: results from a Chinese birth cohort study Chemosphere 2022 307 135741 10.1016/j.chemosphere.2022.135741 35863418
Liao Q, Tang P, Song Y, Liu B, Huang H, Liang J, et al. Association of single and multiple prefluoroalkyl substances exposure with preterm birth: results from a Chinese birth cohort study. Chemosphere. 2022;307:135741.35863418
47. Ashrap P Watkins DJ Mukherjee B Rosario-Pabón Z Vélez-Vega CM Alshawabkeh A Performance of urine, blood, and integrated metal biomarkers in relation to birth outcomes in a mixture setting Environ Res 2021 200 111435 10.1016/j.envres.2021.111435 34097892
Ashrap P, Watkins DJ, Mukherjee B, Rosario-Pabón Z, Vélez-Vega CM, Alshawabkeh A, et al. Performance of urine, blood, and integrated metal biomarkers in relation to birth outcomes in a mixture setting. Environ Res. 2021;200:111435.34097892
48. King JC The risk of maternal nutritional depletion and poor outcomes increases in early or closely spaced pregnancies J Nutr 2003 133 5 Suppl 2 S1732 6 10.1093/jn/133.5.1732S
King JC. The risk of maternal nutritional depletion and poor outcomes increases in early or closely spaced pregnancies. J Nutr. 2003;133(5 Suppl 2):S1732–6.
49. Valero De Bernabé J Soriano T Albaladejo R Juarranz M Calle ME Martínez D Risk factors for low birth weight: a review Eur J Obstet Gynecol Reproductive Biology 2004 116 3 15 10.1016/j.ejogrb.2004.03.007
Valero De Bernabé J, Soriano T, Albaladejo R, Juarranz M, Calle ME, Martínez D, et al. Risk factors for low birth weight: a review. Eur J Obstet Gynecol Reproductive Biology. 2004;116:3–15.
50. Khan S Zaheer S Safdar NF Determinants of stunting, underweight and wasting among children < 5 years of age: evidence from 2012–2013 Pakistan demographic and health survey BMC Public Health 2019 19 358 10.1186/s12889-019-6688-2 30935382
Khan S, Zaheer S, Safdar NF. Determinants of stunting, underweight and wasting among children < 5 years of age: evidence from 2012–2013 Pakistan demographic and health survey. BMC Public Health. 2019;19:358.30935382
51. Tran NT Nguyen LT Berde Y Low YL Tey SL Huynh DTT Maternal nutritional adequacy and gestational weight gain and their associations with birth outcomes among Vietnamese women BMC Pregnancy Childbirth 2019 19 468 10.1186/s12884-019-2643-6 31801514
Tran NT, Nguyen LT, Berde Y, Low YL, Tey SL, Huynh DTT. Maternal nutritional adequacy and gestational weight gain and their associations with birth outcomes among Vietnamese women. BMC Pregnancy Childbirth. 2019;19:468.31801514
52. Regan AK Gissler M Magnus MC Håberg SE Ball S Malacova E Association between interpregnancy interval and adverse birth outcomes in women with a previous stillbirth: an international cohort study Lancet 2019 393 1527 35 10.1016/S0140-6736(18)32266-9 30827781
Regan AK, Gissler M, Magnus MC, Håberg SE, Ball S, Malacova E, et al. Association between interpregnancy interval and adverse birth outcomes in women with a previous stillbirth: an international cohort study. Lancet. 2019;393:1527–35.30827781
53. Lamichhane DK Lee S-Y Ahn K Kim KW Shin YH Suh DI Quantile regression analysis of the socioeconomic inequalities in air pollution and birth weight Environ Int 2020 142 105875 10.1016/j.envint.2020.105875 32590283
Lamichhane DK, Lee S-Y, Ahn K, Kim KW, Shin YH, Suh DI, et al. Quantile regression analysis of the socioeconomic inequalities in air pollution and birth weight. Environ Int. 2020;142:105875.32590283
54. Babaan J Hsu F-T Wong P-Y Chen P-C Guo Y-L Lung S-CC A Geo-AI-based ensemble mixed spatial prediction model with fine spatial-temporal resolution for estimating daytime/nighttime/daily average ozone concentrations variations in Taiwan J Hazard Mater 2023 446 130749 10.1016/j.jhazmat.2023.130749 36630881
Babaan J, Hsu F-T, Wong P-Y, Chen P-C, Guo Y-L, Lung S-CC, et al. A Geo-AI-based ensemble mixed spatial prediction model with fine spatial-temporal resolution for estimating daytime/nighttime/daily average ozone concentrations variations in Taiwan. J Hazard Mater. 2023;446:130749.36630881
55. Fu Z Liu Q Liang J Weng Z Li W Xu J Air pollution, genetic factors and the risk of depression Sci Total Environ 2022 850 158001 10.1016/j.scitotenv.2022.158001 35973541
Fu Z, Liu Q, Liang J, Weng Z, Li W, Xu J, et al. Air pollution, genetic factors and the risk of depression. Sci Total Environ. 2022;850:158001.35973541
56. Tessema GA Håberg SE Pereira G Regan AK Dunne J Magnus MC Interpregnancy interval and adverse pregnancy outcomes among pregnancies following miscarriages or induced abortions in Norway (2008–2016): a cohort study PLoS Med 2022 19 e1004129 10.1371/journal.pmed.1004129 36413512
Tessema GA, Håberg SE, Pereira G, Regan AK, Dunne J, Magnus MC. Interpregnancy interval and adverse pregnancy outcomes among pregnancies following miscarriages or induced abortions in Norway (2008–2016): a cohort study. PLoS Med. 2022;19:e1004129.36413512
57. Cefalu M Dominici F Does exposure prediction Bias Health-Effect Estimation? The Relationship between Confounding Adjustment and exposure prediction Epidemiology 2014 25 583 10.1097/EDE.0000000000000099 24815302
Cefalu M, Dominici F. Does exposure prediction Bias Health-Effect Estimation? The Relationship between Confounding Adjustment and exposure prediction. Epidemiology. 2014;25:583.24815302
58. Strand LB Barnett AG Tong S The influence of season and ambient temperature on birth outcomes: a review of the epidemiological literature Environ Res 2011 111 451 62 10.1016/j.envres.2011.01.023 21333980
Strand LB, Barnett AG, Tong S. The influence of season and ambient temperature on birth outcomes: a review of the epidemiological literature. Environ Res. 2011;111:451–62.21333980
59. Li S Guo B Jiang Y Wang X Chen L Wang X Long-term exposure to ambient PM2.5 and its components Associated with Diabetes: evidence from a large Population-based Cohort from China Diabetes Care 2023 46 111 9 10.2337/dc22-1585 36383478
Li S, Guo B, Jiang Y, Wang X, Chen L, Wang X, et al. Long-term exposure to ambient PM2.5 and its components Associated with Diabetes: evidence from a large Population-based Cohort from China. Diabetes Care. 2023;46:111–9.36383478
60. Huang Y Zhu M Ji M Fan J Xie J Wei X Air Pollution, genetic factors, and the risk of Lung Cancer: a prospective study in the UK Biobank Am J Respir Crit Care Med 2021 204 817 25 10.1164/rccm.202011-4063OC 34252012
Huang Y, Zhu M, Ji M, Fan J, Xie J, Wei X, et al. Air Pollution, genetic factors, and the risk of Lung Cancer: a prospective study in the UK Biobank. Am J Respir Crit Care Med. 2021;204:817–25.34252012
61. Lavigne É Bélair M-A Rodriguez Duque D Do MT Stieb DM Hystad P Effect modification of perinatal exposure to air pollution and childhood asthma incidence Eur Respir J 2018 51 1701884 10.1183/13993003.01884-2017 29419440
Lavigne É, Bélair M-A, Rodriguez Duque D, Do MT, Stieb DM, Hystad P, et al. Effect modification of perinatal exposure to air pollution and childhood asthma incidence. Eur Respir J. 2018;51:1701884.29419440
62. Jin Y The Influence of Science Technology Engineering Arts Mathematics-based Psychological Capital Combined with Ideological and Political Education on the Entrepreneurial Performance and Sports Morality of College Teachers and students Front Psychol 2022 13 911915 10.3389/fpsyg.2022.911915 35837638
Jin Y. The Influence of Science Technology Engineering Arts Mathematics-based Psychological Capital Combined with Ideological and Political Education on the Entrepreneurial Performance and Sports Morality of College Teachers and students. Front Psychol. 2022;13:911915.35837638
63. Mansournia MA, Etminan M, Danaei G, Kaufman JS, Collins G. Handling time varying confounding in observational research. BMJ. 2017;:j4587.
64. VanderWeele TJ Ding P Sensitivity analysis in Observational Research: introducing the E-Value Ann Intern Med 2017 167 268 74 10.7326/M16-2607 28693043
VanderWeele TJ, Ding P. Sensitivity analysis in Observational Research: introducing the E-Value. Ann Intern Med. 2017;167:268–74.28693043
65. Wang Q Miao H Warren JL Ren M Benmarhnia T Knibbs LD Association of maternal ozone exposure with term low birth weight and susceptible window identification Environ Int 2021 146 106208 10.1016/j.envint.2020.106208 33129003
Wang Q, Miao H, Warren JL, Ren M, Benmarhnia T, Knibbs LD, et al. Association of maternal ozone exposure with term low birth weight and susceptible window identification. Environ Int. 2021;146:106208.33129003
66. Shachar BZ Mayo JA Lyell DJ Baer RJ Jeliffe-Pawlowski LL Stevenson DK Interpregnancy interval after live birth or pregnancy termination and estimated risk of preterm birth: a retrospective cohort study BJOG 2016 123 2009 17 10.1111/1471-0528.14165 27405702
Shachar BZ, Mayo JA, Lyell DJ, Baer RJ, Jeliffe-Pawlowski LL, Stevenson DK, et al. Interpregnancy interval after live birth or pregnancy termination and estimated risk of preterm birth: a retrospective cohort study. BJOG. 2016;123:2009–17.27405702
67. Cofer FG Fridman M Lawton E Korst LM Nicholas L Gregory KD Interpregnancy interval and Childbirth outcomes in California, 2007–2009 Matern Child Health J 2016 20 Suppl 1 43 51 10.1007/s10995-016-2180-0 27565663
Cofer FG, Fridman M, Lawton E, Korst LM, Nicholas L, Gregory KD. Interpregnancy interval and Childbirth outcomes in California, 2007–2009. Matern Child Health J. 2016;20(Suppl 1):43–51.27565663
68. King JC A Summary of pathways or mechanisms linking preconception maternal Nutrition with Birth outcomes J Nutr 2016 146 S1437 44 10.3945/jn.115.223479
King JC. A Summary of pathways or mechanisms linking preconception maternal Nutrition with Birth outcomes. J Nutr. 2016;146:S1437–44.
69. Han Y Ji Y Kang S Dong T Zhou Z Zhang Y Effects of particulate matter exposure during pregnancy on birth weight: a retrospective cohort study in Suzhou, China Sci Total Environ 2018 615 369 74 10.1016/j.scitotenv.2017.09.236 28988071
Han Y, Ji Y, Kang S, Dong T, Zhou Z, Zhang Y, et al. Effects of particulate matter exposure during pregnancy on birth weight: a retrospective cohort study in Suzhou, China. Sci Total Environ. 2018;615:369–74.28988071
70. Wu C-D Zeng Y-T Lung S-CC A hybrid kriging/land-use regression model to assess PM2.5 spatial-temporal variability Sci Total Environ 2018 645 1456 64 10.1016/j.scitotenv.2018.07.073 30248867
Wu C-D, Zeng Y-T, Lung S-CC. A hybrid kriging/land-use regression model to assess PM2.5 spatial-temporal variability. Sci Total Environ. 2018;645:1456–64.30248867
71. Wong P-Y Lee H-Y Chen Y-C Zeng Y-T Chern Y-R Chen N-T Using a land use regression model with machine learning to estimate ground level PM2.5 Environ Pollut 2021 277 116846 10.1016/j.envpol.2021.116846 33735646
Wong P-Y, Lee H-Y, Chen Y-C, Zeng Y-T, Chern Y-R, Chen N-T, et al. Using a land use regression model with machine learning to estimate ground level PM2.5. Environ Pollut. 2021;277:116846.33735646
72. Zhu X Liu Y Chen Y Yao C Che Z Cao J Maternal exposure to fine particulate matter (PM2.5) and pregnancy outcomes: a meta-analysis Environ Sci Pollut Res Int 2015 22 3383 96 10.1007/s11356-014-3458-7 25163563
Zhu X, Liu Y, Chen Y, Yao C, Che Z, Cao J. Maternal exposure to fine particulate matter (PM2.5) and pregnancy outcomes: a meta-analysis. Environ Sci Pollut Res Int. 2015;22:3383–96.25163563
73. Nyadanu SD Dunne J Tessema GA Mullins B Kumi-Boateng B Lee Bell M Prenatal exposure to ambient air pollution and adverse birth outcomes: an umbrella review of 36 systematic reviews and meta-analyses Environ Pollut 2022 306 119465 10.1016/j.envpol.2022.119465 35569625
Nyadanu SD, Dunne J, Tessema GA, Mullins B, Kumi-Boateng B, Lee Bell M, et al. Prenatal exposure to ambient air pollution and adverse birth outcomes: an umbrella review of 36 systematic reviews and meta-analyses. Environ Pollut. 2022;306:119465.35569625
74. Balakrishnan K Steenland K Clasen T Chang H Johnson M Pillarisetti A Exposure-response relationships for personal exposure to fine particulate matter (PM2·5), carbon monoxide, and black carbon and birthweight: an observational analysis of the multicountry Household Air Pollution Intervention Network (HAPIN) trial Lancet Planet Health 2023 7 e387 96 10.1016/S2542-5196(23)00052-9 37164515
Balakrishnan K, Steenland K, Clasen T, Chang H, Johnson M, Pillarisetti A, et al. Exposure-response relationships for personal exposure to fine particulate matter (PM2·5), carbon monoxide, and black carbon and birthweight: an observational analysis of the multicountry Household Air Pollution Intervention Network (HAPIN) trial. Lancet Planet Health. 2023;7:e387–96.37164515
75. Ebisu K Bell ML Airborne PM 2.5 Chemical Components and Low Birth Weight in the northeastern and Mid-atlantic regions of the United States Environ Health Perspect 2012 120 1746 52 10.1289/ehp.1104763 23008268
Ebisu K, Bell ML, Airborne. PM 2.5 Chemical Components and Low Birth Weight in the northeastern and Mid-atlantic regions of the United States. Environ Health Perspect. 2012;120:1746–52.23008268
76. Wang Q Benmarhnia T Zhang H Knibbs LD Sheridan P Li C Identifying windows of susceptibility for maternal exposure to ambient air pollution and preterm birth Environ Int 2018 121 317 24 10.1016/j.envint.2018.09.021 30241019
Wang Q, Benmarhnia T, Zhang H, Knibbs LD, Sheridan P, Li C, et al. Identifying windows of susceptibility for maternal exposure to ambient air pollution and preterm birth. Environ Int. 2018;121:317–24.30241019
77. Guo P Chen Y Wu H Zeng J Zeng Z Li W Ambient air pollution and markers of fetal growth: a retrospective population-based cohort study of 2.57 million term singleton births in China Environ Int 2020 135 105410 10.1016/j.envint.2019.105410 31884132
Guo P, Chen Y, Wu H, Zeng J, Zeng Z, Li W, et al. Ambient air pollution and markers of fetal growth: a retrospective population-based cohort study of 2.57 million term singleton births in China. Environ Int. 2020;135:105410.31884132
78. Sun S Spangler KR Weinberger KR Yanosky JD Braun JM Wellenius GA Ambient temperature and markers of fetal growth: a retrospective observational study of 29 million U.S. Singleton births Environ Health Perspect 2019 127 67005 10.1289/EHP4648 31162981
Sun S, Spangler KR, Weinberger KR, Yanosky JD, Braun JM, Wellenius GA. Ambient temperature and markers of fetal growth: a retrospective observational study of 29 million U.S. Singleton births. Environ Health Perspect. 2019;127:67005.31162981
79. Guo T Wang Y Zhang H Zhang Y Zhao J Wang Q The association between ambient PM2.5 exposure and the risk of preterm birth in China: a retrospective cohort study Sci Total Environ 2018 633 1453 9 10.1016/j.scitotenv.2018.03.328 29758897
Guo T, Wang Y, Zhang H, Zhang Y, Zhao J, Wang Q, et al. The association between ambient PM2.5 exposure and the risk of preterm birth in China: a retrospective cohort study. Sci Total Environ. 2018;633:1453–9.29758897
80. Zhang L Liu W Hou K Lin J Zhou C Tong X Air pollution-induced missed abortion risk for pregnancies Nat Sustain 2019 2 1011 7 10.1038/s41893-019-0387-y
Zhang L, Liu W, Hou K, Lin J, Zhou C, Tong X, et al. Air pollution-induced missed abortion risk for pregnancies. Nat Sustain. 2019;2:1011–7.
81. Khalid S, Aris MSM. Association between Marital Status and the Outcome of Teenage Pregnancy: A Retrospective Review in Year 2009–2012 in Hospital Ampang. 2017.
82. Niu Z Habre R Chavez TA Yang T Grubbs BH Eckel SP Association between Ambient Air Pollution and Birth Weight by maternal individual- and Neighborhood-Level stressors JAMA Netw Open 2022 5 e2238174 10.1001/jamanetworkopen.2022.38174 36282504
Niu Z, Habre R, Chavez TA, Yang T, Grubbs BH, Eckel SP, et al. Association between Ambient Air Pollution and Birth Weight by maternal individual- and Neighborhood-Level stressors. JAMA Netw Open. 2022;5:e2238174.36282504
83. Nääv Å Erlandsson L Isaxon C Åsander Frostner E Ehinger J Sporre MK Urban PM2.5 induces Cellular toxicity, hormone dysregulation, oxidative damage, inflammation, and mitochondrial interference in the HRT8 trophoblast cell line Front Endocrinol (Lausanne) 2020 11 75 10.3389/fendo.2020.00075 32226408
Nääv Å, Erlandsson L, Isaxon C, Åsander Frostner E, Ehinger J, Sporre MK, et al. Urban PM2.5 induces Cellular toxicity, hormone dysregulation, oxidative damage, inflammation, and mitochondrial interference in the HRT8 trophoblast cell line. Front Endocrinol (Lausanne). 2020;11:75.32226408
84. Clemente DBP Casas M Vilahur N Begiristain H Bustamante M Carsin A-E Prenatal Ambient Air Pollution, placental mitochondrial DNA content, and Birth Weight in the INMA (Spain) and ENVIRONAGE (Belgium) birth cohorts Environ Health Perspect 2016 124 659 65 10.1289/ehp.1408981 26317635
Clemente DBP, Casas M, Vilahur N, Begiristain H, Bustamante M, Carsin A-E, et al. Prenatal Ambient Air Pollution, placental mitochondrial DNA content, and Birth Weight in the INMA (Spain) and ENVIRONAGE (Belgium) birth cohorts. Environ Health Perspect. 2016;124:659–65.26317635
85. Naiemian S Naeemipour M Zarei M Lari Najafi M Gohari A Behroozikhah MR Serum concentration of asprosin in new-onset type 2 diabetes Diabetol Metab Syndr 2020 12 65 10.1186/s13098-020-00564-w 32714446
Naiemian S, Naeemipour M, Zarei M, Lari Najafi M, Gohari A, Behroozikhah MR, et al. Serum concentration of asprosin in new-onset type 2 diabetes. Diabetol Metab Syndr. 2020;12:65.32714446
86. Perera FP Whyatt RM Jedrychowski W Rauh V Manchester D Santella RM Recent developments in molecular epidemiology: a study of the effects of environmental polycyclic aromatic hydrocarbons on birth outcomes in Poland Am J Epidemiol 1998 147 309 14 10.1093/oxfordjournals.aje.a009451 9482506
Perera FP, Whyatt RM, Jedrychowski W, Rauh V, Manchester D, Santella RM, et al. Recent developments in molecular epidemiology: a study of the effects of environmental polycyclic aromatic hydrocarbons on birth outcomes in Poland. Am J Epidemiol. 1998;147:309–14.9482506
87. Dominici F Peng RD Barr CD Bell ML Protecting human health from air pollution: shifting from a single-pollutant to a multipollutant approach Epidemiology 2010 21 187 94 10.1097/EDE.0b013e3181cc86e8 20160561
Dominici F, Peng RD, Barr CD, Bell ML. Protecting human health from air pollution: shifting from a single-pollutant to a multipollutant approach. Epidemiology. 2010;21:187–94.20160561
88. Arouca A Moreno LA Gonzalez-Gil EM Marcos A Widhalm K Molnár D Diet as moderator in the association of adiposity with inflammatory biomarkers among adolescents in the HELENA study Eur J Nutr 2019 58 1947 60 10.1007/s00394-018-1749-3 29948222
Arouca A, Moreno LA, Gonzalez-Gil EM, Marcos A, Widhalm K, Molnár D, et al. Diet as moderator in the association of adiposity with inflammatory biomarkers among adolescents in the HELENA study. Eur J Nutr. 2019;58:1947–60.29948222
89. Wedi COO Kirtley S Hopewell S Corrigan R Kennedy SH Hemelaar J Perinatal outcomes associated with maternal HIV infection: a systematic review and meta-analysis Lancet HIV 2016 3 e33 48 10.1016/S2352-3018(15)00207-6 26762992
Wedi COO, Kirtley S, Hopewell S, Corrigan R, Kennedy SH, Hemelaar J. Perinatal outcomes associated with maternal HIV infection: a systematic review and meta-analysis. Lancet HIV. 2016;3:e33–48.26762992
90. Vallely LM Egli-Gany D Wand H Pomat WS Homer CSE Guy R Adverse pregnancy and neonatal outcomes associated with Neisseria gonorrhoeae: systematic review and meta-analysis Sex Transm Infect 2021 97 104 11 10.1136/sextrans-2020-054653 33436505
Vallely LM, Egli-Gany D, Wand H, Pomat WS, Homer CSE, Guy R, et al. Adverse pregnancy and neonatal outcomes associated with Neisseria gonorrhoeae: systematic review and meta-analysis. Sex Transm Infect. 2021;97:104–11.33436505
91. Thompson JM Eick SM Dailey C Dale AP Mehta M Nair A Relationship between pregnancy-Associated Malaria and adverse pregnancy outcomes: a systematic review and Meta-analysis J Trop Pediatr 2020 66 327 38 10.1093/tropej/fmz068 31598714
Thompson JM, Eick SM, Dailey C, Dale AP, Mehta M, Nair A, et al. Relationship between pregnancy-Associated Malaria and adverse pregnancy outcomes: a systematic review and Meta-analysis. J Trop Pediatr. 2020;66:327–38.31598714
92. Pastor-Moreno G Ruiz-Pérez I Henares-Montiel J Escribà-Agüir V Higueras-Callejón C Ricci-Cabello I Intimate partner violence and perinatal health: a systematic review BJOG 2020 127 537 47 10.1111/1471-0528.16084 31912613
Pastor-Moreno G, Ruiz-Pérez I, Henares-Montiel J, Escribà-Agüir V, Higueras-Callejón C, Ricci-Cabello I. Intimate partner violence and perinatal health: a systematic review. BJOG. 2020;127:537–47.31912613
