Driving Factors of Preterm Birth Risk in Adolescents

Pérez, Marta; Chang, Jen Jen; Temming, Lorene A.; Carter, Ebony B.; López, Julia; Tuuli, Methodius G.; Macones, George A.; Stout, Molly J.

doi:10.1055/s-0040-1715164

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…The finding is similar to findings from several other studies across the globe [35,36]. Although the biological link between teenage pregnancy and PB is not properly understood, [10,37] pregnant teens are likely to be disfavoured in several aspects such as education, access to care and nutrition compared to older mothers [38][39][40].…”

Section: Discussionsupporting

confidence: 87%

Incidence and Risk Factors for Low Birthweight and Preterm Birth in Post-Conflict Northern Uganda: A Community-Based Cohort Study

Odongkara

Nankabirwa

Ndeezi

et al. 2022

IJERPH

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Background: Annually, an estimated 20 million (13%) low-birthweight (LBW) and 15 million (11.1%) preterm infants are born worldwide. A paucity of data and reliance on hospital-based studies from low-income countries make it difficult to quantify the true burden of LBW and PB, the leading cause of neonatal and under-five mortality. We aimed to determine the incidence and risk factors for LBW and preterm birth in Lira district of Northern Uganda. Methods: This was a community-based cohort study, nested within a cluster-randomized trial, designed to study the effect of a combined intervention on facility-based births. In total, 1877 pregnant women were recruited into the trial and followed from 28 weeks of gestation until birth. Infants of 1556 of these women had their birthweight recorded and 1279 infants were assessed for preterm birth using a maturity rating, the New Ballard Scoring system. Low birthweight was defined as birthweight <2.5kg and preterm birth was defined as birth before 37 completed weeks of gestation. The risk factors for low birthweight and preterm birth were analysed using a multivariable generalized estimation equation for the Poisson family. Results: The incidence of LBW was 121/1556 or 7.3% (95% Confidence interval (CI): 5.4–9.6%). The incidence of preterm births was 53/1279 or 5.0% (95% CI: 3.2–7.7%). Risk factors for LBW were maternal age ≥35 years (adjusted Risk Ratio or aRR: 1.9, 95% CI: 1.1–3.4), history of a small newborn (aRR: 2.1, 95% CI: 1.2–3.7), and maternal malaria in pregnancy (aRR: 1.7, 95% CI: 1.01–2.9). Intermittent preventive treatment (IPT) for malaria, on the other hand, was associated with a reduced risk of LBW (aRR: 0.6, 95% CI: 0.4–0.8). Risk factors for preterm birth were maternal HIV infection (aRR: 2.8, 95% CI: 1.1–7.3), while maternal education for ≥7 years was associated with a reduced risk of preterm birth (aRR: 0.2, 95% CI: 0.1–0.98) in post-conflict northern Uganda. Conclusions: About 7.3% LBW and 5.0% PB infants were born in the community of post-conflict northern Uganda. Maternal malaria in pregnancy, history of small newborn and age ≥35 years increased the likelihood of LBW while IPT reduced it. Maternal HIV infection was associated with an increased risk of PB compared to HIV negative status. Maternal formal education of ≥7 years was associated with a reduced risk of PB compared to those with 0–6 years. Interventions to prevent LBW and PBs should include girl child education, and promote antenatal screening, prevention and treatment of malaria and HIV infections.

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Section: Discussionsupporting

confidence: 87%

Incidence and Risk Factors for Low Birthweight and Preterm Birth in Post-Conflict Northern Uganda: A Community-Based Cohort Study

Odongkara

Nankabirwa

Ndeezi

et al. 2022

IJERPH

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“…The recently reported incidence of preterm in Thailand was about 10-13% [41]. In our study, we had sPTD incidence of 8.5%, while other studies reported incidences between 7.8-10.6 % [42,43]. There are numerous risk factors involved with preterm delivery such as a previous history of preterm birth, some medical diseases or hypertension associated with pregnancy [40,42,44,45].…”

Section: Discussionmentioning

confidence: 51%

“…In our study, we had sPTD incidence of 8.5%, while other studies reported incidences between 7.8-10.6 % [42,43]. There are numerous risk factors involved with preterm delivery such as a previous history of preterm birth, some medical diseases or hypertension associated with pregnancy [40,42,44,45]. However, a considerable number of preterm deliveries have no clear risk factors, especially in the sPTD group [43,46,47].…”

Section: Discussionmentioning

confidence: 57%

A Propose of Effective Model For Prediction of Spontaneous Preterm Delivery With Quadruple Serum Biomarkers of Aneuploidy Screening.

Wuttisantikul¹,

Pranpanus²,

Kor-anantakul³

et al. 2021

Preprint

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Background:Spontaneous preterm delivery (sPTD) is one of the most common global problems causing perinatal morbidity and mortality. However, there is no effective universal screening method for predicting sPTD. Therefore, we want to evaluate the extra benefit of quadruple maternal serum biomarkers for Down syndrome (DS) screening to predict sPTD. Moreover, to propose an effective model to predict sPTD.Results: From total of 2,822 pregnancies who underwent DS screening, 2604 (92.3%) were included in the study. There were 183 (7.02%) pregnant women had randomly measure cervical length. sPTD occurred in 221 (8.5%) of participants, 32 (14.5%) had early sPTD (<34 weeks gestation) and 189 (85.5%) had late sPTD (delivery between 34-36+6 weeks). The median MoM of AFP was significantly higher in women with early sPTD (1.2 VS 1.0, p =0.04), but uE3 had tendency to be lower than the women with term delivery. The most effective model to predict sPTD was the combination of serum AFP level > 1.2 MoM, uE3 level < 0.9 MoM, cervical length <35 mm. and had history of previous preterm delivery which has an AUC of 0.86.Conclusions:An abnormal increase of AFP or decrease of uE3 in quadruple Down syndrome screening showed a predictive ability to predict sPTD with no extra cost. The most effective model to predict sPTD is adding serum quadruple biomarkers with maternal characteristics and cervical length measurement gives a high predictive ability for sPTD.

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“…Newborn babies are very special. They do not have any previous medical background, and their early neonatal path is directly connected to the maternal history of their mothers [ 5 – 7 ]. The healthcare services also incorporate the arrangements of essential social and economic support for women before, during, and after pregnancy including educational, medical, and other training programs that facilitate healthy motherhood.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Prediction Model for Preterm Birth in Rural India

Raja

Mukherjee

Sarkar

2021

Journal of Healthcare Engineering

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Preterm birth (PTB) in a pregnant woman is the most serious issue in the field of Gynaecology and Obstetrics, especially in rural India. In recent years, various clinical prediction models for PTB have been developed to improve the accuracy of learning models. However, to the best of the authors’ knowledge, most of them suffer from selecting the most accurate features from the medical dataset in linear time. The present paper attempts to design a machine learning model named as risk prediction conceptual model (RPCM) for the prediction of PTB. In this paper, a feature selection approach is proposed based on the notion of entropy. The novel approach is used to find the best maternal features (responsible for PTB) from the obstetrical dataset and aims to predict the classifier’s accuracy at the highest level. The paper first deals with the review of PTB cases (which is neglected in many developing countries including India). Next, we collect obstetrical data from the Community Health Centre of rural areas (Kamdara, Jharkhand). The suggested approach is then applied on collected data to identify the excellent maternal features (text-based symptoms) present in pregnant women in order to classify all birth cases into term birth and PTB. The machine learning part of the model is implemented using three different classifiers, namely, decision tree (DT), logistic regression (LR), and support vector machine (SVM) for PTB prediction. The performance of the classifiers is measured in terms of accuracy, specificity, and sensitivity. Finally, the SVM classifier generates an accuracy of 90.9%, which is higher than other learning classifiers used in this study.

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Driving Factors of Preterm Birth Risk in Adolescents

Cited by 5 publications

References 17 publications

Incidence and Risk Factors for Low Birthweight and Preterm Birth in Post-Conflict Northern Uganda: A Community-Based Cohort Study

Incidence and Risk Factors for Low Birthweight and Preterm Birth in Post-Conflict Northern Uganda: A Community-Based Cohort Study

A Propose of Effective Model For Prediction of Spontaneous Preterm Delivery With Quadruple Serum Biomarkers of Aneuploidy Screening.

A Machine Learning-Based Prediction Model for Preterm Birth in Rural India

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