Joint predictors of preterm birth and perinatal death among singleton births at a zonal referral hospital in northern Tanzania: a birth registry based study from 2000 to 2017.
| dc.contributor.advisor | Mwambi, Henry Godwell. | |
| dc.contributor.advisor | Mahande, Michael J. | |
| dc.contributor.advisor | Obure, Joseph. | |
| dc.contributor.author | Mboya, Innocent Baltazar. | |
| dc.date.accessioned | 2022-10-31T06:51:06Z | |
| dc.date.available | 2022-10-31T06:51:06Z | |
| dc.date.created | 2021 | |
| dc.date.issued | 2021 | |
| dc.description | Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg. | en_US |
| dc.description.abstract | Background: Globally, preterm birth (births before 37 completed weeks of gestational) contributes to under-five and newborn deaths. Tanzania ranks the tenth country with the highest preterm birth rates globally and shares 2.2% of the global proportion of all preterm births and contributes to perinatal deaths. Perinatal deaths (stillbirths and early neonatal deaths) continue to increase relative to under-five deaths, especially in low- and middle-income countries. Previous exposure to perinatal death increases preterm birth risk. Understanding the independent and joint predictors of these outcomes may inform interventions to accelerate progress towards achieving sustainable development goals. The study aimed to determine the joint predictors of preterm birth and perinatal death among singleton births in northern Tanzania. Methods: The study utilized birth registry data from Kilimanjaro Christian Medical Center (KCMC) zonal referral hospital from 2000 to 2017, located in Moshi Municipality, Kilimanjaro region, Northern Tanzania. Generalized estimating equations (GEE) estimated the marginal effects of covariates on perinatal death. The predictive capacity of machine learning algorithms was compared with the classical logistic regression model to predict perinatal death. Multinomial logistic regression with cluster adjusted robust standard errors determined predictors of preterm birth. Joint predictors of preterm birth and perinatal death and the co-occurrence were estimated using the random-effects models to account for the correlation between these outcomes. Results: Perinatal mortality in this cohort slightly declined while preterm birth rates were increasing. Maternal demographic characteristics and pregnancy-related conditions and complications increase the risk of these outcomes. The joint predictors of higher risk of preterm birth and perinatal death were inadequate (<4) ANC visits, referred for delivery, and complications during pregnancy and childbirth, specifically pre-eclampsia/eclampsia, PPH, LBW, abruption placenta, and breech presentation. Younger maternal age (15-24 years), PROM, placenta previa, and male children have higher odds of preterm birth but a lessened likelihood of perinatal death. Conclusion: ANC is a critical entry point for delivering the recommended interventions to pregnant women, especially those at high risk of experiencing adverse pregnancy outcomes. Improved management of complications during pregnancy and childbirth and the postnatal period may eventually lead to substantially reducing adverse perinatal outcomes towards improving maternal and child health. | en_US |
| dc.identifier.uri | https://researchspace.ukzn.ac.za/handle/10413/21035 | |
| dc.language.iso | en | en_US |
| dc.subject.other | Perinatal death. | en_US |
| dc.subject.other | Nonmonotone pattern. | en_US |
| dc.subject.other | Joint/multivariate regression. | en_US |
| dc.subject.other | Correlated binary outcomes. | en_US |
| dc.subject.other | Joint modelling. | en_US |
| dc.subject.other | Logistic regression. | en_US |
| dc.title | Joint predictors of preterm birth and perinatal death among singleton births at a zonal referral hospital in northern Tanzania: a birth registry based study from 2000 to 2017. | en_US |
| dc.type | Thesis | en_US |