Background: The prevalence of postpartum haemorrhage (PPH) is increasing globally that is from 6.5% in 2000 to 11% in 2016. But there are regional variations where there is decrease of PPH in some parts of Asia and increase in Africa and developed countries. Objectives: The main objective of this study was to assess the prevalence, causes and associated factors for postpartum haemorrhage at St. Joseph referral hospital in Peramiho- Songea from November 2017 to December 2019. Method: The retrospective cross-sectional hospital-based study was used and data was obtained from maternity hospital registry book/data base of St. Joseph mission hospital in Peramiho Songea, from November 2017 to November 2019. Descriptive data was analysed by tables and graphs. Results: The prevalence of PPH in Peramiho referral hospital was 1.3% in 2017, 1.26% in 2018 and 1.4% in 2019. The overage prevalence for the three years is 1.3%. However, 73.3% of total prevalence is from home deliveries in rural areas. The main cause of PPH in the hospital was uterus atony (42%), retained placenta (15.5%), 3^rd and 4^th grade tears (11.2%), uterine rupture (8.1%) and coagulopathy (5.6%). The main attributing factors were age >35years (34%), other antenatal pregnancy complication (27%), previous PPH (18.6%), Antenatal anaemia (16%), placenta previa, Eclampsia, preeclampsia (12%) each. Conclusion: The prevalence of PPH from this study is slightly high with the main cause being uterine atony and affected mainly those undergone SVD where about two quarter were home deliveries. However further research studies are needed to investigate the main reason for higher home deliveries in Peramiho residence and nearby area.

The association between obesity and clinical as well as radiological outcomes in patients suffering non-traumatic intracerebral haemorrhage (ICH) remains unclear. The obesity paradox suggests a protective effect regarding in-hospital mortality. This study evaluates the impact of body mass index (BMI) on functional long-term outcome and bleeding volume in patients with non-traumatic ICH. A retrospective cohort study including all consecutive patients with spontaneous ICH treated at a tertiary referral centre between December 2017 and June 2021, aged 80 years or younger (n = 218), were included. Patients were dichotomized into overweight (OW, BMI ≥ 25 kg/m2) and normal weight (NW, BMI < 25 kg/m2). Functional outcomes were assessed by the modified Rankin scale (mRS), extended Glasgow Outcome Scale (GOS-E) and the National Institutes of Health Stroke Scale score (NIHSS) at 1, 3, 6 and 12-months follow-up. Bleeding volumes were calculated as ABC/2. Univariate and multivariate analysis were performed to assess clinical predictors of favourable outcome in the long-term. Of 218 patients (66.04 ± 15.18 years) suffering non-traumatic ICH, 115 (52.75%) were OW (29.72 ± 3.82). Female sex (p = 0.1), NIHSS (p = 0.69), cardiovascular risk factors (p = 0.55), ICH volume (p = 0.19), and presence of anticoagulation (p = 0.67) did not significantly differ between the OW and NW cohort. At discharge, functional outcome did not significantly differ between both cohorts (NW, mRS 3.92 ± 2.54 vs. OW, mRS 3.21 ± 2.43, (p = 0.28)). Multivariate analysis revealed that haemorrhage volume (p = 0.01) was the only robust risk factor to predict functional outcome after 1 year. Our data suggests that in ICH patients, neither obesity nor patient’s age are negative predictors for functional outcome in the long-term. Instead, bleeding volume is a robust risk factor predicting poor functional status after ICH. Therefore, prospective randomized trials analysing this coherence are highly warranted.

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