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Background: Tracheostomy is essential for patients requiring prolonged ventilation, but studies on decannulation in Sub-Saharan Africa, including Rwanda, are limited. This study assesses decannulation success rates and identifying factors influencing the outcomes at the University Teaching Hospital of Kigali(CHUK)., Methods: This was a retrospective cross-sectional study. Data on 62 patients who underwent tracheostomy at CHUK from October 2022 to October 2023 and reached decannulation were analyzed. Survival analysis was conducted using R, employing Kaplan-Meier (KM) curves to estimate median time to decannulation and Cox proportional hazards models to determine factors affecting outcomes. Bboth adjusted hazard ratio (AHR) and their confidence intervals (CI) were reported., Results: Decannulation failure observed from 22 patients (35.5%). The median time to decannulation was 60 days (Interquartile range (IQR): 46-74). KM indicated a shorter decannulation median time for elective tracheostomies (60 days, IQR: 43-77) compared to emergency ones (180 days, IQR: 151-209) and for females (60 days, IQR: 49-71) Compared to males (68 days, IQR:52-84). Elective tracheostomy was significantly associated with decannulation success, with an adjusted hazard ratio (AHR) of 0.19 (95% CI: 0.04-0.91, P = 0.039), indicating lower hazard for decannulation failure compared to emergency type. However, this finding is exploratory and should be interpreted cautiously. Age of a patient increased with less hazard to decannulation failure; however, the association was not statistically significant., Conclusions: Male Patients and those undergoing elective tracheostomies had a longer median time to decannulation. The findings highlight the importance of strategic planning in determining the timing and type of tracheostomy, with a focus on optimizing conditions for elective procedures whenever possible to improve patient outcomes., Competing Interests: Declarations. Ethics approval and consent to participate: The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). This study was carried out after approval given University Teaching Hospital of Kigali (CHUK) Ethics committee (Ref.:EC/CHUK/043/2023). Participants who were enrolled into the study have given their written informed consent. All data collected was treated with confidentiality and there was no addition cost to the patient. Consent for publication: All authors have agreed to the order of authorship and to the publication of this work. Study participants provided consent for the scientific reporting of the study results. The manuscript does not contain any identifiable images or personal details of individual participants. Competing interests: The authors declare no competing interests. Reporting checklist: The authors have completed the STROBE reporting checklist. We present this article in accordance with the STROBE reporting checklist., (© 2025. The Author(s).)

Background: Rwanda has made significant strides in malaria control. This study reviews malaria epidemiology and control strategies in Rwanda from 2018 to 2023, documenting their impact, persistent gaps and emerging challenges., Methods: Data on Rwanda's malaria context from 2018 to 2023 were obtained through a literature review of peer-reviewed articles and grey literature, including annual reports from the malaria programmes, partners, the African Union, and the World Health Organization (WHO). Specific keywords used for the search included "malaria", "Rwanda", "case management", "control", "treatment", and "prevention". Moreover, epidemiological data for this period was extracted from the Health Management Information System (HMIS). Data analysis was done using R & R-Studio, ANOVA to assess the statistical significance (P < 0.05) of observed trends and T-test to compare the focal and blanket IRS techniques., Results/discussion: Between 2018 and 2023, all malaria indicators showed improvement. Malaria incidence dropped from 345 to 40 cases per 1000 persons (P = 0.00292), the severe malaria rate decreased from 112 to 10/100,000 persons (P = 0.018), and the mortality rate fell from 2.72 to 0.258 deaths /100,000 persons (P = 0.00617). Among children under 5 years of age, incidence decreased significantly from 331 to 52/1,000 persons (P = 0.00123), the severe malaria rate dropped from 214 to 29/100,000 persons (P = 0.00399), and mortality declined from 5 to 0.453/100,000 persons (P = 0.00504). Over the same period, key malaria interventions expanded. The proportion of cases treated by CHWs increased significantly, improving access to early diagnosis and treatment (from 13 to 59%), and the new generations of ITNs (PBO and dual-active ingredient nets) were deployed in 9 districts. Since 2019, a blanket spraying technique has been adopted in 12 IRS districts replacing the focal spraying technique contributing to the significant decrease of malaria incidence from 2019 to 2023 (P = 0.0025). However, new challenges have emerged, including the rise of the K13 R561H mutation associated with artemisinin resistance, the spread of insecticide resistance, and limited intervention coverage due to resource constraints., Conclusion: To sustain the progress achieved, it is essential to intensify malaria control efforts, foster compliance with intervention strategies, enhance surveillance systems for timely and effective responses, and secure long-term funding to sustain these measures., Competing Interests: Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2025. The Author(s).)

Background: Adverse pregnancy outcomes pose significant risk to maternal and neonatal health, contributing to morbidity, mortality, and long-term developmental challenges. This study aimed to predict these outcomes in Rwanda using supervised machine learning algorithms., Methods: This cross-sectional study utilized data from the Rwanda Demographic and Health Survey (RDHS, 2019-2020) involving 14,634 women. K-fold cross-validation (k = 10) and synthetic minority oversampling technique (SMOTE) were used to manage dataset partitioning and class imbalance. Descriptive and multivariate analyses were conducted to identify the prevalence and risk factors for adverse pregnancy outcomes. Seven machine learning algorithms were assessed for their accuracy, precision, recall, F1 score, and area under the curve (AUC)., Results: Of the pregnancies analyzed, 93.4% resulted in live births, while 4.5% ended in miscarriage, and 2.1% in stillbirth. Advanced maternal age(>30 years),women aged 30-34 years (adjusted odds ratio [AOR] = 5.755; 95% confidence interval [CI] = 3.085-10.074; p < 0.001), 35-39 years (AOR = 8.458; 95% CI = 4.507-10.571; p < 0.001), 40-44 years (AOR = 11.86; 95% CI = 6.250-21.842; p < 0.001), and 45-49 years (AOR = 14.233; 95% CI = 7.359-25.922; p < 0.001), compared to those aged 15-19 years, and multiple unions (polyandry) (AOR = 1.320; 95% CI = 1.104-1.573, p = 0.002), and women not visited by healthcare provider during pregnancy (AOR = 1.421; 95%CI = 1.300-1.611, p<0.001) were factors associated with an increased risk of adverse pregnancy outcomes. In contrast, being married (AOR = 0.894; 95% CI = 0.787-0.966) and attending at least two antenatal care (ANC) visits (AOR = 0.801; 95% CI = 0.664-0.961) were linked to reduced risk. The K-nearest neighbors (KNN) model outperformed other ML Models in predicting adverse pregnancy outcomes, achieving 86% accuracy, 89% precision, 97% recall, 93% F1 score, and an area under the curve (AUC) of 0.842. The ML models constantly highlighted that woman with advanced maternal age, those in multiple unions, and inadequate ANC were more susceptible to adverse pregnancy outcomes., Conclusions: Machine learning algorithms, particularly KNN, are effective in predicting adverse pregnancy outcomes, facilitating early intervention and improved maternal and neonatal care., Competing Interests: The authors declare that the research was conducted in the absence of any type of relationship that could potentially trigger any conflict of interest. All authors agreed to submit to the current journal, accountable for all aspects of the work, and provided final approval of the version to be published., (Copyright: © 2024 Kubahoniyesu, Kabano. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Background: Understanding the timing of first sexual experience is crucial for informing reproductive health policies and interventions. However, little is known about this area of research in Rwanda. This study aims to estimate the determinants of time to first sexual experience among female youths in Rwanda., Methods: A secondary data analysis of the Rwanda Demographic and Health Survey (RDHS 2019-2020) was conducted. Kaplan-Meier survival curves and Cox proportional hazards regression models were utilized to estimate the median age at first sexual experience and identify significant determinants; a significance level of 0.05 was considered. All analyses were performed using R (Version 4.3.3)., Results: The study found that 2090 (36.9%) of female youths in Rwanda had their first sexual experience before the age of 25, with 7.0% initiating sexual activity before 15. The median age at first sexual experience was 18 years (Interquartile range: 16-19). Determinants associated with a reduced hazard of early first sexual experience included being aged 20-24 years (75%) (AHR = 0.25, 95% CI: 0.23-0.29), living in rural areas (15%) (AHR = 0.85, 95% CI: 0.74-0.99), having tertiary education (38%) (AHR = 0.62, 95% CI: 0.42-0.90), being married (AHR = 0.49, 95% CI: 0.42-0.56), and listening to the radio at least once a week (13%) (AHR = 0.87, 95% CI: 0.76-0.99)., Conclusion: Enhancing educational opportunities, promoting stable partnerships, and increasing access to media information in rural areas may be effective strategies in delaying first sexual experience., Competing Interests: The authors declare that they have no competing interests., (© 2024 Kubahoniyesu and Namalinzi.)