EANSO East African Nature and Science Organization Journals
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A Multi-Criteria Framework for Assessing the Performance of Sustainable Construction Materials and Practices in the Built Environment in Kenya
Get PDFThe construction sector remains a critical driver of Kenya’s socio-economic growth, yet it is also a major contributor to resource depletion, waste generation, and greenhouse gas emissions. Despite the emergence of sustainable materials and green practices, there is no harmonised framework for evaluating their performance within Kenya’s built environment. This study addresses that gap by developing a multi-criteria assessment framework integrating environmental, economic, and social indicators. Using a mixed-methods approach, data were collected from construction professionals through structured questionnaires and analysed using descriptive statistics and multiple regression in R. Findings revealed that economic durability and energy efficiency are the most significant predictors of sustainability (R² = 0.7502, p < 0.001). The aggregated mean of 4.18 (SD = 0.97) across 14 parameters confirmed that sustainability performance is multi-dimensional and interdependent. The study concludes that Kenya’s construction industry requires a localised, evidence-based framework that combines subjective expert weighting and objective data validation. Policy recommendations include embedding sustainability assessment criteria in national construction standards, incentivising lifecycle-based material evaluation, and institutionalising continuous capacity-building on sustainable design and practice. Future studies should expand the framework by incorporating technological and regulatory dimensions to strengthen Kenya’s transition toward resilient, low-carbon construction system
Understanding Women’s Engagement in the Timber Value Chain Nodes in the East African Community
Get PDFThis study assessed women's engagement in timber value chain nodes and captured the actual situation in Kenya, Tanzania, and Uganda. Specifically, the study highlights the existing gaps in the enabling environment for women in the timber value chain; planning in the East African Community (EAC) strategies, policy and legal frameworks; and mapping out key players and their roles in the timber value chain. Data were collected through a review of the existing literature, household interviews, focus group discussions (FGD) and key informant interviews (KIIs). The findings showed that the gaps in the enabling environment for women in the timber value chain include gender-blind trade and forestry policies, limited access to land and resources, underrepresentation of women in decision-making, inadequate legal protection, and the lack of gender-disaggregated data, as well as cultural and social norms. However, the existing strategies, policies and legal frameworks, to a certain extent, support women involved in the timber value chain nodes. There was a significant difference in women’s engagement in the timber value chain in the EAC (p < 0.05). As it was noted, key players in the timber value chain in EAC are men whose main roles predominate in logging, sawmilling and transportation nodes. Furthermore, women were observed participating in forest operations, sawmilling and trading nodes. Current laws of the East African Community, regulations, and plans provide a starting point in encouraging women's participation in the timber value chain. However, some important gaps still remain. In addition to empowering women, addressing these disparities through focused reforms and interventions will improve overall sustainability and efficiency of the region's timber industry. The study offers the following recommendations to enhance women's involvement in the EAC timber value chain: reforming gender-responsive policies, securing access to land and resources, enhancing capacity, providing training in transportation, logging, and sawmilling, and modifying norms
Assessment of the Effects of Anthropogenic Activities on Cover Change of Sayaka Forest Reserve in Tanzania
No full textUnderstanding the impacts of anthropogenic activities on forest ecosystems is critical for sustainable management and conservation. This study assessed land cover change and its anthropogenic disturbance drivers in the Sayaka Forest Reserve between 1995 and 2023. Multi-temporal Landsat and Sentinel imagery were classified into closed woodland, open woodland, agriculture, and water bodies using Random Forest algorithms, supported by ground truth data and accuracy assessment. Disturbance data were collected from 80 concentric plots stratified by disturbance intensity and analysed using correlation and generalised linear models. Results revealed substantial fluctuations in land cover: closed woodland declined sharply from 85.3% in 1995 to 24.8% in 2018, before recovering to 58.5% in 2023. Agriculture expanded between 2010 and 2018 (up to 16.6%) but dropped to 0.5% in 2023, while water bodies and open woodland exhibited marked temporal variability. Generalized linear model indicated that tree cutting (β = 1.13, p < 0.001), grazing (β = 1.04, p < 0.05), agriculture (β = 0.79, p < 0.05), and erosion (β = 0.89, p = 0.053) were the most significant drivers of closed woodland loss, explaining 62.1% of the variation (R² = 0.621). Correlation analysis confirmed tree cutting as the strongest predictor of degradation, whereas erosion and grazing showed a strong negative association, suggesting distinct spatial patterns. Overall, findings demonstrate that Sayaka Forest Reserve has experienced dynamic and complex land cover transitions driven primarily by logging, agriculture, and grazing pressures. These results highlight the urgent need for integrated and participatory management strategies to curb degradation and ensure long-term ecological resilience of the reserv
Exposure to Social-Media HIV Testing Messages and HIV Self-Testing Among Young University Adults in Dar es Salaam, Tanzania: A Cross-Sectional Study
Get PDFDigital platforms have become an important tool for promoting public health interventions, including HIV prevention behaviours. In Tanzania, since 2022, the Ministry of Health, together with other stakeholders, has been promoting HIV self-testing (HIVST) messages under the “JIPIME” campaign, a Kiswahili term meaning “test yourself,” across various social media platforms to increase awareness and uptake among young people. However, little is known about the level of exposure to these messages among young adults and whether such exposure influences HIVST uptake. Therefore, this study assessed exposure to HIV testing content on social media and examined its association with HIVST uptake among university students aged 18–24 years in Dar es Salaam. To address this, an analytical cross-sectional study was conducted among adults aged 18–24 years from Kampala International University in Tanzania (KIUT) and the Dar es Salaam Institute of Technology (DIT). A total sample of 365 students was recruited using multistage cluster sampling, comprising 193 from DIT and 172 from KIUT. Data were collected using self-administered questionnaires. Descriptive statistics, including means, standard deviations, and percentages, were computed, while chi-square tests and multivariable logistic regression were used to examine associations between exposure to HIV testing content and HIVST uptake (p < 0.05, 95% CI). The study found that 52% (n = 191) of participants reported encountering HIV testing-related content on social media, while 48% had not. The overall uptake of HIV self-testing was 21%. Among those who had self-tested, 80% had been exposed to HIV testing content on social media. In the adjusted analysis, exposure was significantly associated with higher odds of ever self-testing (AOR = 12.0, 95% CI: 2.41–24.52, p < 0.01). Limited knowledge of how to use HIVST kits emerged as the main barrier to uptake. The findings demonstrate that exposure to HIV testing content on social media substantially influences HIVST uptake among young adults. Expanding targeted digital outreach and addressing knowledge gaps on the correct use of HIVST kits are critical strategies for improving uptake and advancing HIV prevention efforts among youth in Tanzania
Barriers to Facilitators of Multisectoral Collaboration of Antimicrobial Resistance Interventions in Uganda
Get PDFGlobally, cervical cancer accounted for more than 600,000 new cases and 340,000 deaths in 2020 (Sung et al., 2021). In Kenya, screening uptake remains low (Kenya National Bureau of Statistics, 2014), and recent analyses indicate a rising cancer burden, including increasing cervical cancer incidence (Jani et al., 2021). Kenya continues to bear one of the highest age-standardised incidence rates of cervical cancer in East Africa (Bruni et al., 2022). Despite the availability of free cervical cancer preventive services (CCPS) at Kenyatta University’s health facilities, uptake among young women remains low. Persistent knowledge gaps continue to be a major barrier to CCPS uptake among young populations in Kenya and Uganda (Masika et al., 2015; Mukama et al., 2017). This study examined the health system determinants influencing the utilisation of CCPS among female university students. Materials and Methods: This study draws on endline data from a cluster randomised controlled trial (cRCT) conducted at Kenyatta University, Kenya. The parent trial evaluated a structured educational intervention promoting CCPS uptake from 210 female university students, following a 12-week structured health education intervention. Data from both study arms were pooled to assess health system determinants, including service visibility, provider encouragement and perceived service quality of CCPS utilisation. Bivariate associations were assessed using chi-square tests to evaluate the determinants of CCPS utilisation, and multivariable logistic regression was used to estimate adjusted odds ratios (aOR) with 95% confidence intervals. Data was collected using a validated self-administered questionnaire assessing socio-demographic attributes, awareness of CCPS availability, perceived service quality and experiences of provider engagement. The primary outcome was self-reported CCPS uptake, while secondary outcomes included awareness of service availability, perceived service quality and provider encouragement. Results: Overall, 25.7% (54/210) of participants reported utilising CCPS. Awareness of service availability was the strongest determinant of uptake (aOR = 3.52, 95% CI: 1.78–6.94, p < 0.001). Provider encouragement (aOR = 1.81, p = 0.092) and perceived service quality (aOR = 1.44, p = 0.068) were positively but not significantly associated. Conclusion: Awareness of service availability was the most significant determinant of CCPS uptake, underscoring the critical role of health system communication
Teacher Training Programs and the Effective Use of ICT in Education: Evidence from Public School Teachers in Rwanda
Get PDFThis study investigates the relationship between teacher training programs and the effective integration of Information and Communication Technology (ICT) in public schools in Rwanda. A structured questionnaire was administered to 166 teachers, of whom 54% were male and 46% female. The majority were aged 31-40 years (39%), with 35% having 5-10 years of teaching experience. In terms of education, most held a bachelor’s degree (58%), followed by diploma holders (24%) and master’s degree holders (15%). Results show that 74% of teachers regularly use ICT tools in their teaching, and 85% believe ICT improves student engagement. Only 48% felt their training was adequate, while 72% cited poor internet connectivity and 69% cited lack of equipment as significant barriers. Descriptive statistics revealed that the mean ICT usage score was 3.8 (SD = 0.9), while perceptions of ICT impact had a mean of 4.2 (SD = 0.7). Regression analysis revealed that teacher training positively predicted ICT usage (β = 0.57, p < 0.001), whereas perceived barriers negatively predicted usage (β =-0.43, p < 0.001). The study concludes that systematic, pedagogically focused training, coupled with improved infrastructure and ongoing support, is essential for strengthening ICT integration in Rwanda’s education syste
Project Management Practices and Construction Firms' Performance: A Case of Rwandan Construction Contractors
Get PDFMany construction projects in Rwanda suffer from poor planning, as project managers often overlook critical details from the project's inception to its completion. Even minor oversight can significantly impact performance. This article examined the impact of Construction Project Management Practices on firm performance, focusing on Rwandan construction contractors. Specifically, it evaluated the influence of project planning, implementation, monitoring & control, and communication on project outcomes. Using primary quantitative data collected via a survey questionnaire, the researcher applied the Snowball Sampling technique to target 140 engineers, project managers, firm owners, and staff, with 137 responses received. Data were analysed using descriptive and inferential statistics through multiple linear regression in SPSS 25.0. Findings revealed strong positive and highly significant correlations between each project management practice and performance: planning, implementation, monitoring & control, and communication all demonstrated notable effects. The overall multiple correlation was strong (R = 0.796), with the regression model showing that these four variables explained 63.4% of performance variance, leaving 36.6% attributable to other unstudied factors. Despite some correct planning, implementation often fell short of owner expectations, monitoring & control lacked adherence to timelines, and communication was sometimes inaccurate or irrelevant. These deficiencies hindered overall project success. The researcher concluded that each stage of project management substantially affects performance, especially when poorly executed. The researcher recommended that contractors’ management, project managers, and other practitioners ensure comprehensive stakeholder consultation, accurate communication, and joint planning and implementation. Furthermore, future research should investigate the remaining 36.6% of unexplained performance factors to enhance technical, financial, and schedule outcomes in Rwandan construction projects
Development of a Prediction Model for Paved Road Condition to Enhance Delayed Maintenance: A Case Study of Simiyu Region, Tanzania
Get PDFThis research developed a forecasting model for paved road conditions in the Simiyu Region, Tanzania, addressing delayed maintenance challenges to mitigate infrastructure decay and escalating rehabilitation costs by TANROADS. A mixed-methods approach included surveys of 99 transportation professionals and field measurements of road segments aged 5-39 years. Relative Importance Index (RII) analysis identified surface distress parameters as most critical: pavement distress density (RII=0.87), potholes (RII=0.87), and surface cracking (RII=0.86). Environmental factors received low ratings (RII=0.39), indicating knowledge gaps regarding climate-pavement interactions. Multiple regression analysis produced a precise model with seven key factors, demonstrating high performance with a correlation coefficient R=0.99 and a coefficient of determination R²=0.98, explaining 98% of road condition variance. The model was statistically significant (F=142.36, p<0.001) and categorises road conditions into five levels from Very Severe (0-20%) to Very Good (80-100%). Validation on road segments confirmed model accuracy, revealing that maintenance delays lead to exponential cost increases of 300-1000% due to deferred preventive treatments. The Ditiwa-Lamadi segment (39 years) scored 20% (Very Severe), while the Bariadi-Kisesa segment (5 years) scored 84% (Very Good). This study introduces the first regionally adjusted deterioration model tailored to Tanzania's tropical environment, enabling a shift from reactive to proactive maintenance planning and supporting sustainable infrastructure management in developing countries
Prediction of Breast Cancer Using Mammogram Images through Deep Learning Techniques
Get PDFBreast cancer remains one of the leading causes of mortality among women worldwide, and early diagnosis significantly improves survival rates. Mammography has long been considered the gold standard for breast cancer screening due to its ability to detect abnormalities before clinical symptoms appear. However, manual interpretation of mammograms is prone to human error and variability among radiologists. With advances in artificial intelligence (AI), particularly deep learning, automated systems have emerged as powerful tools for improving the accuracy and efficiency of breast cancer detection. This paper presents a comprehensive study on the use of convolutional neural networks (CNNs) for predicting breast cancer from mammogram images. The study investigates the role of pre-processing techniques, image augmentation, feature extraction, and model optimisation in enhancing classification performance. Using a publicly available dataset of mammographic images, the proposed CNN model achieves high accuracy, sensitivity, and specificity compared to traditional machine learning methods. The results demonstrate that deep learning models, when properly trained and optimised, can support radiologists in making more accurate and consistent diagnostic decisions. The paper concludes with a discussion of current challenges, including data imbalance, model interpretability, and ethical considerations, while proposing directions for future research on explainable and multimodal AI-driven breast cancer diagnosi
Development of a Predictive Model for Maintenance Delays Impact on Gravel Road Life-Cycle Costs in Tanzania
Get PDFThis study developed a predictive model for evaluating the impact of road maintenance delays on the life-cycle cost of gravel roads in Kinondoni District, under the jurisdiction of the Tanzania Rural and Urban Roads Agency (TARURA). Gravel roads play a vital role in supporting socio-economic activities in both rural and urban areas of Tanzania. However, delayed maintenance has continued to pose significant challenges, leading to road deterioration, higher long-term costs, and reduced accessibility. A mixed-methods approach was employed, combining qualitative data obtained through interviews with TARURA officials and contractors, and quantitative data collected from 44 road segments representing diverse geographic and traffic conditions. The study analysed maintenance records spanning five years (2019-2024) and incorporated economic data from national road fund allocations. The Relative Importance Index (RII) was used to rank delay factors, while a Multiple Regression model was manually developed in Excel to predict life-cycle costs based on delay severity. Statistical validation included correlation analysis, residual diagnostics, and cross-validation using independent datasets. Key findings revealed that out of twelve assessed delay factors, seven were significant predictors of cost increases. These include inadequate funding release (RII = 0.941), multiple damage points (RII = 0.891), poor traffic management (RII = 0.850), equipment availability (RII = 0.809), material transport issues (RII = 0.782), emergency repair prioritisation (RII = 0.768), and rainfall intensity (RII = 0.759). The life-cycle cost model yielded an R² value of 0.8735, indicating strong predictive capability with 87.35% of cost variance explained by delay factors. Model validation using independent road segments showed prediction accuracy within 15% of actual costs in 82% of cases. The study concludes that maintenance delays significantly affect the long-term sustainability and cost-efficiency of gravel roads, with each year of delay increasing life-cycle costs by approximately 0.62 million TZS. It recommends strengthening planning mechanisms, improving budget disbursement efficiency, enhancing coordination of maintenance activities, and adopting predictive modelling in decision-making. The models developed provide practical tools for TARURA and other road agencies to prioritise interventions, forecast costs, and optimise road asset management strategies