Predictors of stillbirth among women who had given birth in Southern Ethiopia, 2020: A case-control study

Abebe, Haimanot; Shitu, Solomon; Workye, Haile; Mose, Ayenew

doi:10.1371/journal.pone.0249865

Cited by 19 publications

(19 citation statements)

References 37 publications

(53 reference statements)

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“…Moreover, results from different studies revealed maternal age above 35, place of residence, level of education, parity, antenatal care (ANC) utilization, place of delivery and mode of delivery, maternal infection and a variety of maternal medical conditions, feto-maternal complications related to pregnancy and labor, inadequate care during pregnancy and immediate childbirth, obstructed/prolonged labor, antepartum hemorrhage, preterm labor, premature rupture of membrane, and intrauterine growth restriction are common obstetric determinants associated with an increased risk of stillbirths, and particularly in resource-limited areas/countries. 11–13…”

Section: Introductionmentioning

confidence: 99%

Determinants of stillbirth among women who gave birth at Hiwot Fana Specialized University Hospital, Eastern Ethiopia: A facility-based cross-sectional study

et al. 2022

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Introduction/objectives: Stillbirths are an adverse birth outcome and a significant public health problem in low- and high-income countries. Ethiopia is ranked seventh among ten countries that constitute 66% of the world’s stillbirths. However, there is a dearth of evidence about stillbirths and the determinants in the country, particularly in Eastern Ethiopia. Hence, this study aimed to assess the prevalence and determinants of stillbirths among women who gave birth at Hiwot Fana Specialized University Hospital, Eastern Ethiopia. Methods: We conducted a hospital-based cross-sectional study among women who gave birth from October to December 2017. The study participants were selected through a systematic random sampling method. We collected the data using a pretested questionnaire through face-to-face interviews and maternal medical record reviews. The multi-variable logistic regression model was applied to identify the determinants with adjusted odds ratios at a 95% confidence interval. Statistical significance was declared at a p-value less than 0.05. Results: Five hundred fifty-five women were included in the study, and 6.7% ((95% confidence interval = 4.7%, 9.2%)) experienced a stillbirth. Previous history of adverse birth outcome (adjusted odds ratio = 9.55; 95% confidence interval = (4.37, 20.85), p = 0.003), multiple pregnancies (adjusted odds ratio = 7.04; 95% confidence interval = (2.12, 23.40), p = 0.000), and spontaneous vaginal delivery (adjusted odds ratio = 0.17; 95% confidence interval = (0.05, 0.51), p = 0.002) were the identified determinants of stillbirth. Conclusion: The prevalence of stillbirth in this study was similar to previous reports in the country. Early detection and treatment of complications among mothers with multiple pregnancies and prior history of adverse outcomes are vital to alleviate the problem.

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Section: Introductionmentioning

confidence: 99%

Determinants of stillbirth among women who gave birth at Hiwot Fana Specialized University Hospital, Eastern Ethiopia: A facility-based cross-sectional study

et al. 2022

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“…Maternal employment was protective of stillbirth 8 . In Ethiopia, predictors of stillbirth were multiple pregnancies and having no prenatal care 20 . In Tanzania and Zambia, the predictor of stillbirth was a previous history of stillbirth 10 .…”

Section: Discussionmentioning

confidence: 99%

Prediction of stillbirth low resource setting in Northern Uganda

Awor

Byanyima

Abola

et al. 2022

Preprint

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Objective: To examine predictors for stillbirth in low resource setting in Northern Uganda.Methods: Prospective cohort study at St. Mary’s hospital Lacor in Northern Uganda. Recruited 1,285 pregnant mothers at 16-24 weeks. Their history, physical findings, blood tests and uterine artery Doppler indices were taken, and the mothers followed up until delivery. Primary outcome was stillbirth (birth ≥24 weeks). Built models in RStudio. Since the data was imbalanced with low stillbirth rate, used ROSE package to over-sample stillbirths and under-sample live-births to balance the data. We cross-validated the models with the ROSE-derived data using K (10)-fold cross-validation and obtained the area under curve (AUC) with accuracy, sensitivity and specificity.Results: The incidence of stillbirth was 2.5%. Predictors of stillbirth were history of abortion, bilateral end-diastolic notch, personal history of preeclampsia, and haemoglobin 9.5 – 12.1g/dL. The models’ AUC was 75.0% with 68.1% accuracy, 69.1% sensitivity and 67.1% specificity.Conclusion: Risk factors for stillbirth include history of abortion (aOR = 3.07, 95% CI 1.11 - 8.05, p=0.0243) and bilateral end-diastolic notch (aOR = 3.51, 95% CI 1.13 - 9.92, p=0.0209), while haemoglobin of 9.5 - 12.1g/dL is protective (aOR = 0.33, 95% CI 0.11 - 0.93, p=0.0375).

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“…In a case–control study in southern Ethiopia, the predictors of stillbirth were women with multiple pregnancy [aOR = 2.98, 95%CI: 1.39–6.36], having preterm birth [aOR = 2.83, 95%CI: 1.58– 508], having cesarean mode of delivery [aOR = 3.19, 95%CI: 1.87–5.44], having no ANC visit [aOR = 4.17, 95%CI: 2.38–7.33], and being hypertensive during pregnancy [aOR = 3.43, 95%CI: 1.93–6.06]. [ 22 ]. However, these women were recruited after they had given birth.…”

Section: Discussionmentioning

confidence: 99%

Prediction of stillbirth low resource setting in Northern Uganda

Awor

Byanyima

Abola

et al. 2022

BMC Pregnancy Childbirth

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Background Women of Afro-Caribbean and Asian origin are more at risk of stillbirths. However, there are limited tools built for risk-prediction models for stillbirth within sub-Saharan Africa. Therefore, we examined the predictors for stillbirth in low resource setting in Northern Uganda. Methods Prospective cohort study at St. Mary’s hospital Lacor in Northern Uganda. Using Yamane’s 1967 formula for calculating sample size for cohort studies using finite population size, the required sample size was 379 mothers. We doubled the number (to > 758) to cater for loss to follow up, miscarriages, and clients opting out of the study during the follow-up period. Recruited 1,285 pregnant mothers at 16–24 weeks, excluded those with lethal congenital anomalies diagnosed on ultrasound. Their history, physical findings, blood tests and uterine artery Doppler indices were taken, and the mothers were encouraged to continue with routine prenatal care until the time for delivery. While in the delivery ward, they were followed up in labour until delivery by the research team. The primary outcome was stillbirth 24 + weeks with no signs of life. Built models in RStudio. Since the data was imbalanced with low stillbirth rate, used ROSE package to over-sample stillbirths and under-sample live-births to balance the data. We cross-validated the models with the ROSE-derived data using K (10)-fold cross-validation and obtained the area under curve (AUC) with accuracy, sensitivity and specificity. Results The incidence of stillbirth was 2.5%. Predictors of stillbirth were history of abortion (aOR = 3.07, 95% CI 1.11—8.05, p = 0.0243), bilateral end-diastolic notch (aOR = 3.51, 95% CI 1.13—9.92, p = 0.0209), personal history of preeclampsia (aOR = 5.18, 95% CI 0.60—30.66, p = 0.0916), and haemoglobin 9.5 – 12.1 g/dL (aOR = 0.33, 95% CI 0.11—0.93, p = 0.0375). The models’ AUC was 75.0% with 68.1% accuracy, 69.1% sensitivity and 67.1% specificity. Conclusion Risk factors for stillbirth include history of abortion and bilateral end-diastolic notch, while haemoglobin of 9.5—12.1 g/dL is protective.

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Predictors of stillbirth among women who had given birth in Southern Ethiopia, 2020: A case-control study

Cited by 19 publications

References 37 publications

Determinants of stillbirth among women who gave birth at Hiwot Fana Specialized University Hospital, Eastern Ethiopia: A facility-based cross-sectional study

Determinants of stillbirth among women who gave birth at Hiwot Fana Specialized University Hospital, Eastern Ethiopia: A facility-based cross-sectional study

Prediction of stillbirth low resource setting in Northern Uganda

Prediction of stillbirth low resource setting in Northern Uganda

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