Pattern of anorectal malformations and early outcomes of management at Moi Teaching and Referral Hospital Eldoret-Kenya

Objectives: To describe the anatomical sub-types of Anorectal  malformations, their management and the early outcome at Moi Teaching and Referral Hospital (MTRH) over a 16 month period.Design: A prospective study.Setting: MTRH, in the neonatal Unit and paediatric surgical wards for the initial capture of patients and initial follow up. The Paediatric Surgical out- patient clinic was used for the subsequent follow ups.Subjects: All infants diagnosed with ARM (Anorectal malformations) at MTRH from November 2011 to April 2013.Main outcome measures: Sub-types of the Anorectal malformations, coexisting abnormalities morbidity and mortality rates.Results: There were 42 participants including 24 (57%) males and 18 (43%) females. Neonates presented at an average age of 4±3, three days and older children presented on average age of 152±118, three days. There were 30 (71%) neonates and 12 (29%) older infants. In males, the predominant sub-type was imperforate anus without a fistula found in ten participants (42% of males). In females, the predominant sub-type wasrecto-vestibular fistula found in 14 participants (78% of females). Mortality occurred in 13 (31%) participants among them ten (24%) had coexisting abnormalities. The main causes of morbidity were: colostomy complications in four (9.5%); wound infections in one (5%); and wound dehiscence in one (5%).Conclusions: Patients with Anorectal malformations presented late at MTRH. The diagnosis at birth was missed in babies born at home as well as those delivered in health institutions

Management of gastroschisis: Kenyan perspective

The co-authors were omitted from the article, they have now been added.Background: Gastroschisis is an anterior abdominal wall defect occurring in up to 4 babies per 10,000 live births. Though the anomaly is rarely associated with other disorders, it poses serious pathophysiological challenges that negatively affect outcome. Review of the management of gastroschisis at Moi Teaching & Referral Hospital (MTRH) from 2013-2016 was done to determine the outcome.Materials and Methods: A four year (2013-2016) retrospective review of gastroschisis management at MTRH was undertaken. Theatre records were used to track all files of babies admitted and operated on. The primary outcome of data analysis was survival. Secondary outcomes analyzed were age at admission, maternal age, birth order, associated anomalies and complications.Results: Records that were available for analysis were 107. Males were 58 (54%). Male to female ratio was 1.1:1. Mean age at admission was 1.35 ± 0.06 days. Weight ranged from 1250-3800 gm with a mean of 2330 gms. Majority were first born. Mean maternal age was 21.25 ± 3.62 years. Complex gastroschisis occurred in 12 (11%). Overall survival was 43%. However, of those who reached the stage of definitive treatment of containment (either primary closure or staged silo placement), had 48% survival rate. Survival rate was highest in the group who weighed 2500 gms and above. Poor outcomes were noted in the premature and low birth weight neonates, and those with complications. Sepsis was the leading cause of mortality. Length of hospital stay was an average of 24 days for the survivors.Conclusions & Recommendations: Prematurity, low birth weight, and complications negatively influenced survival. Improving obstetric care, establishment of paediatric surgical centres and neonatal support services are key to turning around the survival of neonates with this severe surgical anomaly

Denied Victimhood and Contested Narratives: The Case of Hutu Diaspora

Based on 46 interviews conducted in a 2-month period, this article explored the identity narrative of three generations of the Hutu Diaspora community living in Belgium. Through a analysis of the Rwanda\u27s National Identity policy and political categories, the research aimed to explore important themes such as sense of self and other, victimhood, and homeland through the lenses of the perpetrator group. Moreover, it was essential to investigate the trans-generational impact the perpetrator label has on the next generations. By looking at the Hutu population, the study was opening the door to the exploration of contested memories of survival for the perpetrator group. The complexity of the Hutu identity and their contested and competing narratives offered a fascinating approach to the study of mass atrocity as well as the field of conflict Resolution. This new generation of well educated, young Hutu has the power to shape the future of Rwanda in a very important way

Denied Victimhood and Contested Narratives: The Case of Hutu Diaspora

Based on 46 interviews conducted in a 2-month period, this article explored the identity narrative of three generations of the Hutu Diaspora community living in Belgium. Through a analysis of the Rwanda\u27s National Identity policy and political categories, the research aimed to explore important themes such as sense of self and other, victimhood, and homeland through the lenses of the perpetrator group. Moreover, it was essential to investigate the trans-generational impact the perpetrator label has on the next generations. By looking at the Hutu population, the study was opening the door to the exploration of contested memories of survival for the perpetrator group. The complexity of the Hutu identity and their contested and competing narratives offered a fascinating approach to the study of mass atrocity as well as the field of conflict Resolution. This new generation of well educated, young Hutu has the power to shape the future of Rwanda in a very important way

Comparison of random forest and support vector machine regression models for forecasting road accidents

One of the world’s concerns today is the rate of road traffic accidents (RTA). The overwhelming majority of these accidents occur in low and middle-income countries. RTA are one of the leading causes of death in Rwanda. RTA prediction is crucial for both transportation management and intelligent transportation systems (ITS) development. This paper adopted the use of two modelling techniques, Random forest (RF) and Support vector machine (SVM) for short-term road accident forecasting. The data used to evaluate the models was obtained from the Police. The lower error indices of MAE, MSE, RMSE and higher coefficient of determination (R2) were accuracy measures in comparing the models. The RF model performed better than the SVM model as it revealed higher R2=0.91 compared to the SVM model that was with R2=0.86. Machine learning methods are promising tools for the prediction of road accidents. Prediction positively influences safety enhancements and regulation formulation to prevent future accidents. The appropriate prediction method would help policymakers and healthcare providers adjust their contributions to the accident management process

Crop Yield Prediction Using Machine Learning Models: Case of Irish Potato and Maize

Although agriculture remains the dominant economic activity in many countries around the world, in recent years this sector has continued to be negatively impacted by climate change leading to food insecurities. This is so because extreme weather conditions induced by climate change are detrimental to most crops and affect the expected quantity of agricultural production. Although there is no way to fully mitigate these natural phenomena, it could be much better if there is information known earlier about the future so that farmers can plan accordingly. Early information sharing about expected crop production may support food insecurity risk reduction. In this regard, this work employs data mining techniques to predict future crop (i.e., Irish potatoes and Maize) harvests using weather and yields historical data for Musanze, a district in Rwanda. The study applies machine learning techniques to predict crop harvests based on weather data and communicate the information about production trends. Weather data and crop yields for Irish potatoes and maize were gathered from various sources. The collected data were analyzed through Random Forest, Polynomial Regression, and Support Vector Regressor. Rainfall and temperature were used as predictors. The models were trained and tested. The results indicate that Random Forest is the best model with root mean square error of 510.8 and 129.9 for potato and maize, respectively, whereas R2 was 0.875 and 0.817 for the same crops datasets. The optimum weather conditions for the optimal crop yield were identified for each crop. The results suggests that Random Forest is recommended model for early crop yield prediction. The findings of this study will go a long way to enhance reliance on data for agriculture and climate change related decisions, especially in low-to-middle income countries such as Rwanda