Introduction: Antenatal care (ANC) provides monitoring and regular follow-up of maternal and fetal health during pregnancy. Women with appropriate ANC tend to have better delivery and birth outcomes. This study describes the patterns of ANC utilization and factors associated with appropriate ANC initiation in the United Arab Emirates (UAE) for the first time. Methods: Baseline cross-sectional data from pregnant women who participated in the Mutaba'ah-Mother and Child Health Study between May 2017 and January 2019 was analyzed. Participants were recruited during ANC visits and completed a selfadministered questionnaire that collected socio-demographic and pregnancy-related information and assessed whether it was their first ANC appointment. Regression models assessed the relationship between socio-demographic and pregnancy-related variables and "appropriate" (≤ 4 months' gestation) vs. "late" ANC initiation (>4 months' gestation). Results: At recruitment, 841 participants reported that it was their first ANC visit and half (50.2%) of these women were late initiating their ANC. Mothers who were more educated, had previous infertility treatment or previous miscarriages were all more likely to achieve appropriate ANC initiation [adjusted odds ratio (aOR): 1.66, 95% confidence interval (CI): 1.05-2.62; aOR: 3.68, 95% CI: 1.50-9.04; aOR: 1.80, 95% CI: 1.16-2.79, respectively]. Women worrying about childbirth were less likely to achieve appropriate ANC initiation (aOR: 0.54, 95% CI: 0.34-0.85). Conclusion: Half of pregnant women in this study did not achieve the global consensus guidelines on appropriate ANC initiation. Interventions among less educated women and those with previous pregnancy complications and childbirth anxiety are recommended to ensure appropriate ANC initiation.
Accurate prediction of a newborn’s birth weight (BW) is a crucial determinant to evaluate the newborn’s health and safety. Infants with low BW (LBW) are at a higher risk of serious short- and long-term health outcomes. Over the past decade, machine learning (ML) techniques have shown a successful breakthrough in the field of medical diagnostics. Various automated systems have been proposed that use maternal features for LBW prediction. However, each proposed system uses different maternal features for LBW classification and estimation. Therefore, this paper provides a detailed setup for BW estimation and LBW classification. Multiple subsets of features were combined to perform predictions with and without feature selection techniques. Furthermore, the synthetic minority oversampling technique was employed to oversample the minority class. The performance of 30 ML algorithms was evaluated for both infant BW estimation and LBW classification. Experiments were performed on a self-created dataset with 88 features. The dataset was obtained from 821 women from three hospitals in the United Arab Emirates. Different performance metrics, such as mean absolute error and mean absolute percent error, were used for BW estimation. Accuracy, precision, recall, F-scores, and confusion matrices were used for LBW classification. Extensive experiments performed using five-folds cross validation show that the best weight estimation was obtained using Random Forest algorithm with mean absolute error of 294.53 g while the best classification performance was obtained using Logistic Regression with SMOTE oversampling techniques that achieved accuracy, precision, recall and F1 score of 90.24%, 87.6%, 90.2% and 0.89, respectively. The results also suggest that features such as diabetes, hypertension, and gestational age, play a vital role in LBW classification.
IntroductionEarly life exposures, particularly environmental and parental lifestyle factors, have a major influence on children’s health and development. Due to increasing interest in the early life developmental origins of diseases, many birth cohorts have been established. These studies constitute a repository of data which researchers use over many years to investigate emerging research questions. However, no such databank or cohort study is available in the United Arab Emirates (UAE). This project aims to establish a prospective mother and child cohort study in Al Ain (Abu Dhabi, UAE) to investigate the maternal and early life determinants of infant, child, adolescent and maternal health of the Emirati population.Methods and analysisDuring the period 2017–2021, this study aims to recruit 10 000 pregnancies at approximately 12 weeks of gestation from hospitals and clinics in Al Ain city. For each mother/newborn pair, an initial dataset will be collected including anthropometric, physiological and biochemical measurements, medical interventions, circumstances of pregnancy, delivery details and neonatal and perinatal growth and health using a combination of questionnaires, interviews and medical record extractions. Baseline data will act as the starting point from which the children will be followed up and re-surveyed at intervals throughout their life course until the age of 16 years, to explore how familial, socioeconomic and lifestyle factors interact with genetic and environmental factors to influence health outcomes and achievements later in life.Ethics and disseminationEthical approval has been granted by the United Arab Emirates University Human Research Ethics Committee and the ethical committees of the participating institutions. Results will be widely disseminated via peer-reviewed manuscripts, conference presentations, media outlets and reports to relevant authorities.
Background Gestational diabetes mellitus (GDM) in singleton pregnancies represent a high-risk scenario. The incidence, associated factors and outcomes of GDM in twin pregnancies is not known in the UAE. Methods This was five years retrospective analysis of hospital records of twin pregnancies in the city of Al Ain, Abu Dhabi, UAE. Relevant data with regards to the pregnancy, maternal and birth outcomes and incidence of GDM was extracted from two major hospitals in the city. Regression models assessed the relationship between socio-demographic and pregnancy-related variables and GDM, and the associations between GDM and maternal and fetal outcomes at birth. Results A total of 404 women and their neonates were part of this study. The study population had a mean age of 30.1 (SD: 5.3), overweight or obese (66.5%) and were majority multiparous (66.6%). High incidence of GDM in twin pregnancies (27.0%). While there were no statistical differences in outcomes of the neonates, GDM mothers were older (OR: 1.09, 95% CI: 1.06–1.4) and heavier (aOR: 1.02, 95% CI: 1.00 -1.04). They were also likely to have had GDM in their previous pregnancies (aOR: 7.37, 95% CI: 2.76–19.73). The prognosis of mothers with twin pregnancies and GDM lead to an independent and increased odds of cesarean section (aOR: 2.34, 95% CI: 1.03–5.30) and hospitalization during pregnancy (aOR: 1.60, 95% CI: 1.16–2.20). Conclusion More than a quarter of women with twin pregnancies were diagnosed with GDM. GDM was associated with some adverse pregnancy outcomes but not fetal outcomes in this population. More studies are needed to further investigate these associations and the management of GDM in twin pregnancies.
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