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Abstract
Background
Sub-Saharan Africa faces a disproportionate burden of perinatal deaths globally. However, data to inform targeted interventions on an institutional level is lacking, especially in rural settings. The objective of this study is to identify risk factors for perinatal death at a resource-limited hospital in Uganda.
Methods
This is a retrospective case-control study at a district hospital in eastern Uganda using birth registry data. Cases were admissions with stillbirths at or beyond 24 weeks or neonatal deaths within 28 days of birth. Controls were admissions that resulted in deliveries immediately preceding and following each case. We compared demographic and obstetric factors between cases and controls to identify risk factors for perinatal death. Subgroup analysis of type of perinatal death was also performed. Chi square, Fisher’s exact, t-test, and Wilcoxon-Mann-Whitney rank sum tests were utilized for bivariate analysis, and multiple logistic regression for multivariate analysis.
Results
From January 2014 to December 2014, there were 185 cases of perinatal death, of which 36% (n = 69) were macerated stillbirths, 40% (n = 76) were fresh stillbirths, and 25% (n = 47) were neonatal deaths. The rate of perinatal death among all deliveries at the institution was 35.5 per 1000 deliveries. Factors associated with increased odds perinatal death included: prematurity (adjusted odds ratio (aOR) 19.7, 95% confidence interval (CI) 7.2–49.2), breech presentation (aOR 7.0, CI 1.4–35.5), multiple gestation (aOR 4.0, CI 1.1–13.9), cesarean delivery (aOR 3.8, CI 2.3–6.4) and low birth weight (aOR 2.5, CI 1.1–5.3). Analysis by subtype of perinatal death revealed distinct associations with the aforementioned risk factors, in particular for antepartum hemorrhage, which was only associated with fresh stillbirths (aOR 6.7, CI 1.6–28.8), and low birth weight.
Conclusions
The rate of perinatal death at our rural hospital site was higher than national targets, and these deaths were associated with prematurity, low birth weight, breech presentation, multiple gestation, and cesarean delivery. This data and the approach utilized to acquire it can be leveraged to inform targeted interventions to reduce the rate of stillbirths and neonatal deaths in similar low resource settings.
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