Perinatal Mortality in Sub-Saharan Africa: A Meta-Analysis of Demographic and Health Surveys

Akombi, Blessing Jaka; Renzaho, Andre Μ Ν

doi:10.5334/aogh.2348

Cited by 72 publications

(68 citation statements)

References 35 publications

(37 reference statements)

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“…According to the EDHS report, the overall national stillbirth and early neonatal death rate was 46 per 1000 live births (13). This rate is lower than the WHO estimate of 58 per 1000 births in East African countries (14). This rate is also lower than the rates of stillbirth and early neonatal death in Southern Ethiopia and Nigeria, which were 85 per 1000 live births and 53.9 per 1000 live births, respectively.…”

Section: Discussioncontrasting

confidence: 56%

Risk factors for stillbirth and early neonatal death: A case-control study in tertiary hospitals in Addis Ababa, Ethiopia

Weldetensaye

Kekulawala

2020

Preprint

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Background: Globally, Ethiopia is ranked seventh in the absolute number of stillbirths. Attempts to decrease this number have been obstructed by an inadequate understanding of the risk factors leading to stillbirth and early neonatal death in Ethiopia. This study was conducted in two tertiary hospitals in Addis Ababa, Ethiopia, to identify the risk factors for stillbirth and neonatal death in this setting.Methods: This case-control study was conducted between October 2016 and May 2017 at Tikur Anbessa Hospital (TAH) and Gandhi Memorial Hospital (GMH). All enrolled women who had a stillbirth or early neonatal death during this period were included as cases and a random sample of women delivering at the hospital was selected to enroll as controls for a 2:1 ratio of controls to cases. Data on potential risk factors were retrieved from medical records including health passports, delivery records, and treatment charts. Statistical differences in background and social characteristics of cases and controls were determined by t-test and chi-squared (or fisher’s exact test) for quantitative and categorical variables, respectively. Binary logistic regression analysis was completed to determine any associations between risk factors and stillbirth/early neonatal death.Results: During the study period, 366 women delivering at the hospitals were enrolled as cases and 711 women delivering at the hospitals were enrolled as controls. During the study period, hospital records indicated that the estimated stillbirth and neonatal death rates were 30.7 per 1000. Neonatal causes (43.4%) were the most common, followed by antepartum (32.5%) and intrapartum (24.5%). Risk factors for stillbirths and early neonatal death were maternal education (aOR 1.747, 95%CI 1.098-2.780), parity (aOR 1.114, 95%CI .058- 2.484), previous stillbirth (aOR 9.447, 95%CI 6.245-14.289), previous preterm birth (aOR 3.620, 95%CI 2.363-5.546), and previous child with congenital abnormality (aOR 2.190, 95% 1.228-3.905), and antepartum hemorrhage during pregnancy (aOR 3.273, 95% 1.523-7.031).Conclusion: Antepartum hemorrhaging is the only risk factor in our study amenable for direct intervention. Efforts should be maximized to improve patient education and antenatal and obstetric services, particularly for women who have a history of obstetric complications identified as risk factors.

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Section: Discussioncontrasting

confidence: 56%

Risk factors for stillbirth and early neonatal death: A case-control study in tertiary hospitals in Addis Ababa, Ethiopia

Weldetensaye

Kekulawala

2020

Preprint

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“…The perinatal mortality rate is the highest in the world (approx. 35 deaths per 1,000 live births) [8], similar to other low-resource areas such as South Asia and China [9]. The increased healthcare expenditure in SSA over the last decade has had a minimal impact on maternal and child health [10].…”

Section: Rds Management In Rrr Of Ssamentioning

confidence: 99%

Optimal Ventilation and Surfactant Therapy in Very-Low-Birth-Weight Infants in Resource-Restricted Regions

et al. 2020

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In resource-restricted regions, respiratory distress syndrome (RDS) data are often underreported, making the determination of effective interventions and their outcome difficult. The combination of oxygen, nasal continuous positive airway pressure (CPAP) and surfactant therapy has the potential to prevent 42% of RDS-related deaths in sub-Saharan Africa, despite the financial implications. This article provides a brief overview on the status of RDS management, mainly nasal CPAP and surfactant therapy in very-low-birth-weight infants, in resource-restricted regions of sub-Saharan Africa. Data from the public health sector, as compared to the private health sector, of the Western Cape province, South Africa, are used to illustrate what RDS management strategies are able to accomplish in a resource-restricted region. Upscaling of all components (antenatal care, antenatal corticosteroids, prevention of hypothermia and RDS management strategies) are required to decrease premature infant mortality rates in resource-restricted areas.

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“…2 The majority (95%) of these deaths occur in sub-Saharan Africa and Southern Asia. 7 According to the Tanzania Demographic and Health Survey, the perinatal mortality rate has slightly increased from 36 to 39 deaths per 1000 live births between 2010-11 and 2015-16 survey rounds, respectively, relative to under-5 mortality. 8 In addition, perinatal mortality rate in Tanzania is the highest in East Africa.…”

Section: Introductionmentioning

confidence: 99%

“…8 In addition, perinatal mortality rate in Tanzania is the highest in East Africa. 7 Early identification of pregnant women at risk for adverse maternal and perinatal outcomes during the prenatal period and timely provision of high-quality healthcare services have been reported to improve maternal and newborn survival. 9 Machine learning (hereafter denoted as 'ML') models are methodologies for developing algorithms that learn from existing data to make predictions on new data.…”

Section: Introductionmentioning

confidence: 99%

Prediction of perinatal death using machine learning models: a birth registry-based cohort study in northern Tanzania

et al. 2020

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ObjectiveWe aimed to determine the key predictors of perinatal deaths using machine learning models compared with the logistic regression model.DesignA secondary data analysis using the Kilimanjaro Christian Medical Centre (KCMC) Medical Birth Registry cohort from 2000 to 2015. We assessed the discriminative ability of models using the area under the receiver operating characteristics curve (AUC) and the net benefit using decision curve analysis.SettingThe KCMC is a zonal referral hospital located in Moshi Municipality, Kilimanjaro region, Northern Tanzania. The Medical Birth Registry is within the hospital grounds at the Reproductive and Child Health Centre.ParticipantsSingleton deliveries (n=42 319) with complete records from 2000 to 2015.Primary outcome measuresPerinatal death (composite of stillbirths and early neonatal deaths). These outcomes were only captured before mothers were discharged from the hospital.ResultsThe proportion of perinatal deaths was 3.7%. There were no statistically significant differences in the predictive performance of four machine learning models except for bagging, which had a significantly lower performance (AUC 0.76, 95% CI 0.74 to 0.79, p=0.006) compared with the logistic regression model (AUC 0.78, 95% CI 0.76 to 0.81). However, in the decision curve analysis, the machine learning models had a higher net benefit (ie, the correct classification of perinatal deaths considering a trade-off between false-negatives and false-positives)—over the logistic regression model across a range of threshold probability values.ConclusionsIn this cohort, there was no significant difference in the prediction of perinatal deaths between machine learning and logistic regression models, except for bagging. The machine learning models had a higher net benefit, as its predictive ability of perinatal death was considerably superior over the logistic regression model. The machine learning models, as demonstrated by our study, can be used to improve the prediction of perinatal deaths and triage for women at risk.

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Perinatal Mortality in Sub-Saharan Africa: A Meta-Analysis of Demographic and Health Surveys

Cited by 72 publications

References 35 publications

Risk factors for stillbirth and early neonatal death: A case-control study in tertiary hospitals in Addis Ababa, Ethiopia

Risk factors for stillbirth and early neonatal death: A case-control study in tertiary hospitals in Addis Ababa, Ethiopia

Optimal Ventilation and Surfactant Therapy in Very-Low-Birth-Weight Infants in Resource-Restricted Regions

Prediction of perinatal death using machine learning models: a birth registry-based cohort study in northern Tanzania

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