Prediction of stillbirth low resource setting in Northern Uganda

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dc.contributor.authorSilvia Awor
dc.contributor.authorRosemary Byanyima
dc.contributor.authorBenard Abola
dc.contributor.authorPaul Kiondo
dc.contributor.authorChristopher Garimoi Orach
dc.contributor.authorJasper Ogwal‑Okeng
dc.contributor.authorDan Kaye
dc.contributor.authorAnnettee Nakimuli
dc.date.accessioned2025-09-01T11:38:03Z
dc.date.available2025-09-01T11:38:03Z
dc.date.issued2022-11-19
dc.descriptionAcknowledgements: The authors thank Mr. Ronald Kivumbi and Mr. Ronald Waiswa, the biostatisti cians, for helping me with part of the preprocessing of the data for analysis. In a special way, I thank my mentor, Prof. Letseka Moeketsi, for guiding me to aim higher. The authors would like to acknowledge the support provided for writing this paper from the British Academy Writing Workshop Programme 2022, ‘Addressing Epistemic Injustice: Supporting Writing about Inclusive and Life‑long Education in Africa’ Authors’ contributions: Silvia Awor is a Ph.D. student who wrote the proposal, collected data and drafted the manuscript. Annettee Nakimuli, Jaspar Ogwal‑Okeng and Dan Kabonge Kaye are doctoral supervisors who guided the writing of the manuscript. Rosemary Byanyima, Paul Kiondo and Christopher Garimoi Orach are doctoral committee members who gave technical advice during the data collection and manuscript writing. Benard Abola, a mathematician, helped develop and validate the model and participated in data analysis. The author(s) read and approved the final manuscript
dc.description.abstractBackground: 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 dur‑ ing 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-vali‑ dated 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.
dc.description.sponsorshipThe research was carried out as a PhD project funded by the Swedish Interna‑ tional Development Agency (SIDA) under the Makerere University – SIDA bilateral agreement.
dc.identifier.citationAwor, S., Byanyima, R., Abola, B., Kiondo, P., Orach, C. G., Ogwal-Okeng, J., ... & Nakimuli, A. (2022). Prediction of stillbirth low resource setting in Northern Uganda. BMC Pregnancy and Childbirth, 22(1), 855.
dc.identifier.urihttps://doi.org/10.1186/s12884-022-05198-6
dc.identifier.urihttp://hdl.handle.net/20.500.14270/619
dc.language.isoen
dc.publisherBMC
dc.subjectStillbirth
dc.subjectRisk factors
dc.subjectPrediction models
dc.subjectUganda
dc.subjectAfrica
dc.titlePrediction of stillbirth low resource setting in Northern Uganda
dc.typeArticle

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