Happiness and associated factors amongst pregnant women in the United Arab Emirates:The Mutaba’ah Study

ObjectivePrenatal happiness and life satisfaction research are often over-shadowed by other pregnancy and birth outcomes. This analysis investigated the level of, and factors associated with happiness amongst pregnant women in the United Arab Emirates.MethodsBaseline cross-sectional data was analyzed from the Mutaba’ah Study, a large population-based prospective cohort study in the UAE. This analysis included all expectant mothers who completed the baseline self-administered questionnaire about sociodemographic and pregnancy-related information between May 2017 and July 2021. Happiness was assessed on a 10-point scale (1 = very unhappy; 10 = very happy). Regression models were used to evaluate the association between various factors and happiness.ResultsOverall, 9,350 pregnant women were included, and the majority (60.9%) reported a happiness score of ≥8 (median). Higher levels of social support, planned pregnancies and primi-gravidity were independently associated with higher odds of being happier; adjusted odds ratio (aOR (95% CI): 2.02 (1.71–2.38), 1.34 (1.22–1.47), and 1.41 (1.23–1.60), respectively. Women anxious about childbirth had lower odds of being happier (aOR: 0.58 (0.52–0.64).ConclusionSelf-reported happiness levels were high among pregnant women in the UAE. Health services enhancing social support and promoting well-being during pregnancy and childbirth may ensure continued happiness during pregnancy in the UAE

Happiness and associated factors amongst pregnant women in the United Arab Emirates:The Mutaba’ah Study

ObjectivePrenatal happiness and life satisfaction research are often over-shadowed by other pregnancy and birth outcomes. This analysis investigated the level of, and factors associated with happiness amongst pregnant women in the United Arab Emirates.MethodsBaseline cross-sectional data was analyzed from the Mutaba’ah Study, a large population-based prospective cohort study in the UAE. This analysis included all expectant mothers who completed the baseline self-administered questionnaire about sociodemographic and pregnancy-related information between May 2017 and July 2021. Happiness was assessed on a 10-point scale (1 = very unhappy; 10 = very happy). Regression models were used to evaluate the association between various factors and happiness.ResultsOverall, 9,350 pregnant women were included, and the majority (60.9%) reported a happiness score of ≥8 (median). Higher levels of social support, planned pregnancies and primi-gravidity were independently associated with higher odds of being happier; adjusted odds ratio (aOR (95% CI): 2.02 (1.71–2.38), 1.34 (1.22–1.47), and 1.41 (1.23–1.60), respectively. Women anxious about childbirth had lower odds of being happier (aOR: 0.58 (0.52–0.64).ConclusionSelf-reported happiness levels were high among pregnant women in the UAE. Health services enhancing social support and promoting well-being during pregnancy and childbirth may ensure continued happiness during pregnancy in the UAE

Predictive Modeling for the Diagnosis of Gestational Diabetes Mellitus Using Epidemiological Data in the United Arab Emirates

Gestational diabetes mellitus (GDM) is a common condition with repercussions for both the mother and her child. Machine learning (ML) modeling techniques were proposed to predict the risk of several medical outcomes. A systematic evaluation of the predictive capacity of maternal factors resulting in GDM in the UAE is warranted. Data on a total of 3858 women who gave birth and had information on their GDM status in a birth cohort were used to fit the GDM risk prediction model. Information used for the predictive modeling were from self-reported epidemiological data collected at early gestation. Three different ML models, random forest (RF), gradient boosting model (GBM), and extreme gradient boosting (XGBoost), were used to predict GDM. Furthermore, to provide local interpretation of each feature in GDM diagnosis, features were studied using Shapley additive explanations (SHAP). Results obtained using ML models show that XGBoost, which achieved an AUC of 0.77, performed better compared to RF and GBM. Individual feature importance using SHAP value and the XGBoost model show that previous GDM diagnosis, maternal age, body mass index, and gravidity play a vital role in GDM diagnosis. ML models using self-reported epidemiological data are useful and feasible in prediction models for GDM diagnosis amongst pregnant women. Such data should be periodically collected at early pregnancy for health professionals to intervene at earlier stages to prevent adverse outcomes in pregnancy and delivery. The XGBoost algorithm was the optimal model for identifying the features that predict GDM diagnosis

Predictive Modeling for the Diagnosis of Gestational Diabetes Mellitus Using Epidemiological Data in the United Arab Emirates

Gestational diabetes mellitus (GDM) is a common condition with repercussions for both the mother and her child. Machine learning (ML) modeling techniques were proposed to predict the risk of several medical outcomes. A systematic evaluation of the predictive capacity of maternal factors resulting in GDM in the UAE is warranted. Data on a total of 3858 women who gave birth and had information on their GDM status in a birth cohort were used to fit the GDM risk prediction model. Information used for the predictive modeling were from self-reported epidemiological data collected at early gestation. Three different ML models, random forest (RF), gradient boosting model (GBM), and extreme gradient boosting (XGBoost), were used to predict GDM. Furthermore, to provide local interpretation of each feature in GDM diagnosis, features were studied using Shapley additive explanations (SHAP). Results obtained using ML models show that XGBoost, which achieved an AUC of 0.77, performed better compared to RF and GBM. Individual feature importance using SHAP value and the XGBoost model show that previous GDM diagnosis, maternal age, body mass index, and gravidity play a vital role in GDM diagnosis. ML models using self-reported epidemiological data are useful and feasible in prediction models for GDM diagnosis amongst pregnant women. Such data should be periodically collected at early pregnancy for health professionals to intervene at earlier stages to prevent adverse outcomes in pregnancy and delivery. The XGBoost algorithm was the optimal model for identifying the features that predict GDM diagnosis

Antenatal Care Initiation Among Pregnant Women in the United Arab Emirates:The Mutaba'ah Study

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 self-administered 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 (&gt;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.</p

Antenatal Care Initiation Among Pregnant Women in the United Arab Emirates:The Mutaba'ah Study

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 self-administered 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 (&gt;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.</p

Knowledge and preference towards mode of delivery among pregnant women in the United Arab Emirates:The Mutaba’ah study

Background: The rate of cesarean section (CS) is growing in the United Arab Emirates (UAE). Pregnant women’s knowledge on the mode of delivery, factors associated with lack of adequate knowledge, and preference towards CS delivery were investigated. Methods: Baseline cross-sectional data from 1617 pregnant women who participated in the Mutaba’ah Study between September 2018 and March 2020 were analyzed. A self-administered questionnaire inquiring about demographic and maternal characteristics, ten knowledge-based statements about mode of delivery, and one question about preference towards mode of delivery was used. Knowledge on the mode of delivery was categorized into “adequate (total score 6–10)” or “lack of adequate (total score 0–5)” knowledge. Crude and multivariable models were used to identify factors associated with “lack of adequate” knowledge on the mode of delivery and factors associated with CS preference. Results: A total of 1303 (80.6%) pregnant women (mean age 30.6 ± 5.8 years) completed the questionnaire. The majority (57.1%) were ≥30 years old, in their third trimester (54.5%), and had at least one child (76.6%). In total, 20.8% underwent CS delivery in the previous pregnancy, and 9.4% preferred CS delivery for the current pregnancy. A total of 78.4% of pregnant women lacked adequate knowledge on the mode of delivery. The level of those who lacked adequate knowledge was similar across women in different pregnancy trimesters. Young women (18–24 years) (adjusted odds ratios (aOR), 3.07, 95% confidence interval (CI), 1.07–8.86) and women who had CS delivery in the previous pregnancy (aOR, 1.90, 95% CI, 1.06–3.40) were more likely to be classified with a lack of adequate knowledge. Age (aOR, 1.08, 95% CI, 1.02–1.14), employment (aOR, 1.96, 95% CI, 1.13–3.40), or previous CS delivery (aOR, 31.10, 95% CI, 17.71–55.73) were associated with a preference towards CS delivery. Conclusion: This study showed that pregnant women may not fully appreciate the health risks associated with different modes of delivery. Therefore, antenatal care appointments should include a balanced discussion on the potential benefits and harms associated with different delivery modes.</p