ABSTRACT
Background and Aims: Neonatal period is the most vulnerable time in which children face the greatest risk of death. Worldwide, each year, millions of newborns died in the first month of life. Sub-Saharan Africa, Ethiopia, in particular, is largely affected. However, there is a dearth of information regarding the survival status of neonates and determinants of their mortality in the study area. Therefore, this study was aimed at investigating neonatal mortality and its predictors in Jabitehnan district, Northwest Ethiopia. Method: A single-arm community-based retrospective cohort study was conducted in March 2021 among 952 neonates born between August 2020 and February 2021. Data were collected by a semi-structured questionnaire, and a multistage stratified sampling technique was employed to select one urban and 10 rural kebeles from the district. Then, the total sample size was proportionally allocated to these selected kebeles. Neonatal death was ascertained by community diagnosis. Kaplan-Meier curve was used to estimate survival time. Cox regression was used to identify factors, the hazard ratio was estimated, and a p-value < 0.05 was considered statistically significant. Results: The neonatal mortality rate was 44 (95% confidence interval [CI]: 33-60) per 1000 live births; and the incidence rate was 1.64 (95% CI: 1.21-2.23) per 1000 neonate days. Three-quarters of deaths occurred in the first week of life. Medium household wealth index (adjusted hazard ratio [AHR] = 3.54; 95 CI: 1.21-10.35), increased number of pregnancies (AHR = 1.22; 95%CI: 1.01-1.47), being male (AHR = 2.45, 95% CI: 1.12-5.35) and not starting breastfeeding in the first hour of life (AHR = 4.00; 95% CI: 1.52-11.10) were found to be predictors of neonatal mortality. Conclusion: Neonatal mortality was high compared to the national target. Wealth, number of pregnancies, sex of the neonate, and breastfeeding initiation were factors associated with neonatal death. Hence, strengthening interventions such as providing sexual education in the population, considering households with a medium wealth index in the exemption service, and counseling mothers about early breastfeeding initiation would improve neonatal survival.
ABSTRACT
OBJECTIVE: To develop and validate a risk prediction model for the prediction of preterm birth using maternal characteristics. DESIGN: This was a retrospective follow-up study. Data were coded and entered into EpiData, V.3.02, and were analysed using R statistical programming language V.4.0.4 for further processing and analysis. Bivariable logistic regression was used to identify the relationship between each predictor and preterm birth. Variables with p≤0.25 from the bivariable analysis were entered into a backward stepwise multivariable logistic regression model, and significant variables (p<0.05) were retained in the multivariable model. Model accuracy and goodness of fit were assessed by computing the area under the receiver operating characteristic curve (discrimination) and calibration plot (calibration), respectively. SETTING AND PARTICIPANTS: This retrospective study was conducted among 1260 pregnant women who did prenatal care and finally delivered at Felege Hiwot Comprehensive Specialised Hospital, Bahir Dar city, north-west Ethiopia, from 30 January 2019 to 30 January 2021. RESULTS: Residence, gravidity, haemoglobin <11 mg/dL, early rupture of membranes, antepartum haemorrhage and pregnancy-induced hypertension remained in the final multivariable prediction model. The area under the curve of the model was 0.816 (95% CI 0.779 to 0.856). CONCLUSION: This study showed the possibility of predicting preterm birth using maternal characteristics during pregnancy. Thus, use of this model could help identify pregnant women at a higher risk of having a preterm birth to be linked to a centre.
