Drowning is a serious public health concern. Low middle income countries are mostly affected, as they carry 90% of the global drowning burden. The purpose of this epidemiological study is to provide a comprehensive overview of fatal drownings in South Africa between 2016 and 2021. The data used for the study was obtained from the South African Police Service. Descriptive statistics were used to summarise data. Statistical analysis included a t-test and chi-square test. The results indicate that the average fatal drownings per annum is 1477 in South Africa, with a drowning rate of 2.54 per 100 000 population from 2016 - 2021. The KwaZulu-Natal province had the highest incidence of drowning. The 0 – 4-year category has a high prevalence of drowning amongst all the age categories. More males drowned in South Africa compared to females. The study highlights key areas of concern these include age, sex, race, province, type of water body and time of day. This information is crucial to inform drowning prevention initiatives in South Africa.

Postpartum haemorrhage (PPH) is a significant cause of maternal morbidity and mortality worldwide, particularly in low-resource settings. This study aimed to develop a predictive model for PPH using early risk factors and rank their importance in terms of predictive ability. The dataset was obtained from an observational case-control study in Northern Rwanda. Various statistical models and machine learning techniques were evaluated, including logistic regression, logistic re-gression with elastic-net regularisation, Random Forest, Extremely Randomized Trees, and gradient boosted trees with XGBoost. Random Forest emerged as the best predictive model for PPH across three different train-test data partitions. The important predictors identified in the study were haemoglobin level during labour and maternal age. However, there were differences in PPH risk factor importance in different data partitions, highlighting the need for further investigation. These findings contribute to understanding PPH risk factors, highlight the importance of considering different data partitions and implementing cross-validation in predictive modelling, and emphasise the value of identifying the appropriate prediction model for the application. Effective PPH pre-diction models are essential for improving maternal health outcomes on a global scale. This study provides valuable insights for healthcare providers to develop predictive models for PPH to identify high-risk women and implement targeted interventions