Predicting skilled delivery service use in Ethiopia: dual application of logistic regression and machine learning algorithms.

BACKGROUND: Skilled assistance during childbirth is essential to reduce maternal deaths. However, in Ethiopia, which is among the six countries contributing to more than half of the global maternal deaths, the coverage of births attended by skilled health personnel remains very low. The aim of this study was to identify determinants and develop a predictive model for skilled delivery service use in Ethiopia by applying logistic regression and machine-learning techniques. METHODS: Data from the 2016 Ethiopian Demographic and Health Survey (EDHS) was used for this study. Statistical Package for Social Sciences (SPSS) and Waikato Environment for Knowledge Analysis (WEKA) tools were used for logistic regression and model building respectively. Classification algorithms namely J48, Naïve Bayes, Support Vector Machine (SVM), and Artificial Neural Network (ANN) were used for model development. The validation of the predictive models was assessed using accuracy, sensitivity, specificity, and area under Receiver Operating Characteristics (ROC) curve. RESULTS: Only 27.7% women received skilled delivery assistance in Ethiopia. First antenatal care (ANC) [AOR = 1.83, 95% CI (1.24-2.69)], birth order [AOR = 0.22, 95% CI (0.11-0.46)], television ownership [AOR = 6.83, 95% CI (2.52-18.52)], contraceptive use [AOR = 1.92, 95% CI (1.26-2.97)], cost needed for healthcare [AOR = 2.17, 95% CI (1.47-3.21)], age at first birth [AOR = 1.96, 95% CI (1.31-2.94)], and age at first sex [AOR = 2.72, 95% CI (1.55-4.76)] were determinants for utilizing skilled delivery services during the childbirth. Predictive models were developed and the J48 model had superior predictive accuracy (98%), sensitivity (96%), specificity (99%) and, the area under ROC (98%). CONCLUSIONS: First ANC and contraceptive uses were among the determinants of utilization of skilled delivery services. A predictive model was developed to forecast the likelihood of a pregnant woman seeking skilled delivery assistance; therefore, the predictive model can help to decide targeted interventions for a pregnant woman to ensure skilled assistance at childbirth. The model developed through the J48 algorithm has better predictive accuracy. Web-based application can be build based on results of this study.

"Every Newborn-BIRTH" protocol: observational study validating indicators for coverage and quality of maternal and newborn health care in Bangladesh, Nepal and Tanzania.

BACKGROUND: To achieve Sustainable Development Goals and Universal Health Coverage, programmatic data are essential. The Every Newborn Action Plan, agreed by all United Nations member states and >80 development partners, includes an ambitious Measurement Improvement Roadmap. Quality of care at birth is prioritised by both Every Newborn and Ending Preventable Maternal Mortality strategies, hence metrics need to advance from health service contact alone, to content of care. As facility births increase, monitoring using routine facility data in DHIS2 has potential, yet validation research has mainly focussed on maternal recall surveys. The Every Newborn - Birth Indicators Research Tracking in Hospitals (EN-BIRTH) study aims to validate selected newborn and maternal indicators for routine tracking of coverage and quality of facility-based care for use at district, national and global levels. METHODS: EN-BIRTH is an observational study including >20 000 facility births in three countries (Tanzania, Bangladesh and Nepal) to validate selected indicators. Direct clinical observation will be compared with facility register data and a pre-discharge maternal recall survey for indicators including: uterotonic administration, immediate newborn care, neonatal resuscitation and Kangaroo mother care. Indicators including neonatal infection management and antenatal corticosteroid administration, which cannot be easily observed, will be validated using inpatient records. Trained clinical observers in Labour/Delivery ward, Operation theatre, and Kangaroo mother care ward/areas will collect data using a tablet-based customised data capturing application. Sensitivity will be calculated for numerators of all indicators and specificity for those numerators with adequate information. Other objectives include comparison of denominator options (ie, true target population or surrogates) and quality of care analyses, especially regarding intervention timing. Barriers and enablers to routine recording and data usage will be assessed by data flow assessments, quantitative and qualitative analyses. CONCLUSIONS: To our knowledge, this is the first large, multi-country study validating facility-based routine data compared to direct observation for maternal and newborn care, designed to provide evidence to inform selection of a core list of indicators recommended for inclusion in national DHIS2. Availability and use of such data are fundamental to drive progress towards ending the annual 5.5 million preventable stillbirths, maternal and newborn deaths.