Prenatal exposure to ambient air pollution and adverse pregnancy outcomes in Ahvaz, Iran: a generalized additive model

Dastoorpoor, Maryam; Khanjani, Narges; Moradgholi, Asghar; Sarizadeh, Reihaneh; Cheraghi, Maria; Estebsari, Fatemeh

doi:10.1007/s00420-020-01577-8

Cited by 32 publications

(33 citation statements)

References 87 publications

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“…The GAM model identifies nonlinear associations between variables. To control for the non-linear trend between preterm birth and time or weather conditions, we added time-dependent variables including calendar time, temperature and relative humidity via natural spline functions [ 22 ]. The GAM model was constructed for PTB in each trimester and the entire pregnancy period.…”

Section: Methodsmentioning

confidence: 99%

Association between Maternal Exposure to Ambient Air Pollution and the Risk of Preterm Birth: A Birth Cohort Study in Chongqing, China, 2015–2020

Zhou

Ming²,

Yang³

et al. 2022

IJERPH

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Recent study results on the association between maternal exposure to ambient air pollution with preterm birth have been inconsistent. The sensitive window of exposure and influence level of air pollutants varied greatly. We aimed to explore the association between maternal exposure to ambient air pollutants and the risk of preterm birth, and to estimate the sensitive exposure time window. A total of 572,116 mother–newborn pairs, daily concentrations of air pollutants from nearest monitoring stations were used to estimate exposures for each participant during 2015–2020 in Chongqing, China. We applied a generalized additive model and estimated RRs and 95% CIs for preterm birth in each trimester and the entire pregnancy period. In the single-pollutant model, we observed that each 10 μg/m3 increase in PM2.5 had a statistically significant effect on the third trimester and entire pregnancy, with RR = 1.036 (95% CI: 1.021, 1.051) and RR = 1.101 (95% CI: 1.075, 1.128), respectively. Similarly, for each 10 μg/m3 increase in PM10, there were 2.7% (RR = 1.027, 95% CI: 1.016, 1.038) increase for PTB on the third trimester, and 3.8% (RR = 1.038, 95% CI: 1.020, 1.057) increase during the whole pregnancy. We found that for each 10 mg/m3 CO increases, the relative risk of PTB increased on the first trimester (RR = 1.081, 95% CI: 1.007, 1.162), second trimester (RR = 1.116, 95% CI: 1.035, 1.204), third trimester (RR = 1.167, 95% CI: 1.090, 1.250) and whole pregnancy (RR = 1.098, 95% CI: 1.011, 1.192). No statistically significant RR was found for SO2 and NO2 on each trimester of pregnancy. Our study indicates that maternal exposure to high levels of PM2.5 and PM10 during pregnancy may increase the risk for preterm birth, especially for women at the late stage of pregnancy. Statistically increased risks of preterm birth were associated with CO exposure during each trimester and entire pregnancy. Reducing exposure to ambient air pollutants for pregnant women is clearly necessary to improve the health of infants.

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Section: Methodsmentioning

confidence: 99%

Association between Maternal Exposure to Ambient Air Pollution and the Risk of Preterm Birth: A Birth Cohort Study in Chongqing, China, 2015–2020

Zhou

Ming²,

Yang³

et al. 2022

IJERPH

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“…The majority of the studies included in this review were conducted in the USA (n = 6, 28.6%) [27][28][29][30][31][32] and China (n = 6, 28.6%) [33][34][35][36][37][38], followed by Iran (n = 2, 9.5%) [39,40], South Asia (n = 2, 9.5%) [41,42], Africa (n = 1, 4.8%) [43], Australia (n = 1, 4.8%) [44], England (n = 1, 4.8%) [45], Israel (n = 1, 4.8%) [46], and Korea (n = 1, 4.8%) [47]. Retrospective cohort (n = 6) [31,34,[44][45][46][47] and case-control designs (n = 6) [30,35,38,[41][42][43] were most popular, followed by times series (n = 3) [33,39,40], prospective cohort (n = 2) [27,37], and case-crossover designs (n = 2) [28,32]. Two studies also used multiple designs to ensure robustness of findings [29,36].…”

Section: Study Characteristicsmentioning

confidence: 99%

Outdoor Air Pollution and Pregnancy Loss: a Review of Recent Literature

Ghimire

Martinez

2022

Curr Epidemiol Rep

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Purpose of Review This review summarizes recent literature about the impacts of outdoor air pollution on pregnancy loss (spontaneous abortion/miscarriage and stillbirth), identifies challenges and opportunities, and provides recommendations for actions. Recent Findings Both short- and long-term exposures to ubiquitous air pollutants, including fine particulate matter < 2.5 and < 10 μm, may increase pregnancy loss risk. Windows of susceptibility include the entire gestational period, especially early pregnancy, and the week before event. Vulnerable subpopulations were not consistently explored, but some evidence suggests that pregnant parents from more disadvantaged populations may be more impacted even at the same exposure level. Summary Given environmental conditions conductive to high air pollution exposures become more prevalent as the climate shifts, air pollution’s impacts on pregnancy is expected to become a growing public health concern. While awaiting larger preconception studies to further understand causal impacts, multi-disciplinary efforts to minimize exposures among pregnant women are warranted.

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“…A combination of spline functions is used to approximate these non-linear functions. GAM is extensively used in health-related environmental research [96], pneumonia risk prediction [94], research on the distribution of species [99], and the effect of age and a diagnosis-specific cohort of HIV patients on psychosocial and behavioral activities [95]. Yang et al [97] studied how tuberculosis (TB) cases changed with air pollution in the Wulumuqi.…”

Section: Machine Learningmentioning

confidence: 99%

Explainable, Trustworthy, and Ethical Machine Learning for Healthcare: A Survey

Rasheed¹,

Qayyum²,

Ghaly³

et al. 2021

Preprint

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With the advent of machine learning (ML) applications in daily life, the questions about liability, trust, and interpretability of their outputs are raising, especially for healthcare applications. The black-box nature of ML models is a roadblock for clinical utilization. Therefore, to gain the trust of clinicians and patients, researchers need to provide explanations of how and why the model is making a specific decision. With the promise of enhancing the trust and transparency of black-box models, researchers are in the phase of maturing the field of eXplainable ML (XML). In this paper, we provide a comprehensive review of explainable and interpretable ML techniques implemented for providing the reasons behind their decisions for various healthcare applications. Along with highlighting various security, safety, and robustness challenges that hinder the trustworthiness of ML we also discussed the ethical issues of healthcare ML and describe how explainable and trustworthy ML can resolve these ethical problems. Finally, we elaborate on the limitations of existing approaches and highlight various open research problems that require further development.

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Prenatal exposure to ambient air pollution and adverse pregnancy outcomes in Ahvaz, Iran: a generalized additive model

Cited by 32 publications

References 87 publications

Association between Maternal Exposure to Ambient Air Pollution and the Risk of Preterm Birth: A Birth Cohort Study in Chongqing, China, 2015–2020

Association between Maternal Exposure to Ambient Air Pollution and the Risk of Preterm Birth: A Birth Cohort Study in Chongqing, China, 2015–2020

Outdoor Air Pollution and Pregnancy Loss: a Review of Recent Literature

Explainable, Trustworthy, and Ethical Machine Learning for Healthcare: A Survey

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