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Background: Caesarean section is recommended in situations in which vaginal birth presents a greater likelihood of adverse maternal or perinatal outcomes than normal. However, it is associated with a higher risk of complications, especially when performed without a clear medical indication. Since labour attendants have no standardised clinical method to assist in this decision, statistical tools developed based on multiple labour variables may be an alternative. The objective of this paper was to develop and evaluate the accuracy of models for caesarean section prediction using maternal and foetal characteristics collected at admission and through labour. Method: This is a secondary analysis of the World Health Organization's Better Outcomes in Labour Difficulty prospective cohort study in two sub-Saharan African countries. Data were collected from women admitted for labour and childbirth in 13 hospitals in Nigeria as well as Uganda between 2014 and 2015. We applied logistic regression to develop different models to predict caesarean section, based on the time when intrapartum assessment was made. To evaluate discriminatory capacity of the various models, we calculated: area under the curve, diagnostic accuracy, positive predictive value, negative predictive value, sensitivity and specificity. Results: A total of 8957 pregnant women with 12.67% of caesarean births were used for model development. The model based on labour admission characteristics showed an area under the curve of 78.70%, sensitivity of 63.20%, specificity of 78.68% and accuracy of 76.62%. On the other hand, the models that applied intrapartum assessments performed better, with an area under the curve of 93.66%, sensitivity of 80.12%, specificity of 89.26% and accuracy of 88.03%. Conclusion: It is possible to predict the likelihood of intrapartum caesarean section with high accuracy based on labour characteristics and events. However, the accuracy of this prediction is considerably higher when based on information obtained throughout the course of labour. [ABSTRACT FROM AUTHOR]

Background: Achieving maternal mortality reduction as a development goal remains a major challenge in most low-resource countries. Saving Mothers, Giving Life (SMGL) is a multi-partner initiative designed to reduce maternal mortality rapidly in high mortality settings through community and facility evidence-based interventions and district-wide health systems strengthening that could reduce delays to appropriate obstetric care.Methods: An evaluation employing multiple studies and data collection methods was used to compare baseline maternal outcomes to those during Year 1 in SMGL pilot districts in Uganda and Zambia. Studies include health facility assessments, pregnancy outcome monitoring, enhanced maternal mortality detection in facilities, and population-based investigation of community maternal deaths. Population-based evaluation used standard approaches and comparable indicators to measure outcome and impact, and to allow comparison of the SMGL implementation in unique country contexts.Results: The evaluation found a 30% reduction in the population-based maternal mortality ratio (MMR) in Uganda during Year 1, from 452 to 316 per 100,000 live births. The MMR in health facilities declined by 35% in each country (from 534 to 345 in Uganda and from 310 to 202 in Zambia). The institutional delivery rate increased by 62% in Uganda and 35% in Zambia. The number of facilities providing emergency obstetric and newborn care (EmONC) rose from 10 to 25 in Uganda and from 7 to 11 in Zambia. Partial EmONC care became available in many more low and mid-level facilities. Cesarean section rates for all births increased by 23% in Uganda and 15% in Zambia. The proportion of women with childbirth complications delivered in EmONC facilities rose by 25% in Uganda and 23% in Zambia. Facility case fatality rates fell from 2.6 to 2.0% in Uganda and 3.1 to 2.0% in Zambia.Conclusions: Maternal mortality ratios fell significantly in one year in Uganda and Zambia following the introduction of the SMGL model. This model employed a comprehensive district system strengthening approach. The lessons learned from SMGL can inform policymakers and program managers in other low and middle income settings where similar approaches could be utilized to rapidly reduce preventable maternal deaths. [ABSTRACT FROM AUTHOR]

Background: Cesarean section (CS) is an important intervention in complicated births when the safety of the mother or baby is compromised. Despite worldwide concerns about the overutilization of CS in recent years, many African women and their newborns still die because of limited or no access to CS services. We evaluated temporal and spatial trends in CS births in Uganda and modeled future trends to inform programming.Methods: We performed secondary analysis of total births data from the Uganda National Health Management Information System (HMIS) reports during 2012-2016. We reviewed data from 3461 health facilities providing basic, essential obstetric and emergency obstetric care services in all 112 districts. We defined facility-based CS rate as the proportion of cesarean deliveries among total live births in facilities, and estimated the population-based CS rate using the total number of cesarean deliveries as a proportion of annual expected births (including facility-based and non-facility-based) for each district. We predicted CS rates for 2021 using Generalised Linear Models with Poisson family, Log link and Unbiased Sandwich Standard errors. We used cesarean deliveries as the dependent variable and calendar year as the independent variable.Results: Cesarean delivery rates increased both at facility and population levels in Uganda. Overall, the CS rate for live births at facilities was 9.9%, increasing from 8.5% in 2012 to 11% in 2016. The overall population-based CS rate was 4.7%, and increased from 3.2 to 5.9% over the same period. Health Centre IV level facilities had the largest annual rate of increase in CS rate between 2012 and 2016. Among all 112 districts, 80 (72%) had a population CS rate below 5%, while 38 (34%) had a CS rate below 1% over the study period. Overall, Uganda's facility-based CS rate is projected to increase by 36% (PRR 1.36, 95% CI 1.35-1.36) in 2021 while the population-based CS rate is estimated to have doubled (PRR 2.12, 95% CI 2.11-2.12) from the baseline in 2016.Conclusion: Cesarean deliveries are increasing in Uganda. Health center IVs saw the largest increases in CS, and while there was regional heterogeneity in changes in CS rates, utilization of CS services is inadequate in most districts. We recommend expansion of CS services to improve availability. [ABSTRACT FROM AUTHOR]