The National Research Repository of Uganda - NRU

Welcome to the National Research Repository of Uganda, abbreviated as "NRU". NRU was established in 2021. NRU is a collection of scholarly output by researchers from the UNCST Community, including scholarly articles and books, electronic theses and dissertations, conference proceedings, journals, technical reports and digitised library collections. It is the official Institutional Archive (IA) of UNCST.

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For information about the publishers' copyright policy on archiving your articles online or in an institutional archive, visit the Sherpa Site at http://www.sherpa.ac.uk/romeo.php The site gives a summary of the permissions normally given as part of each publisher's copyright transfer agreement. If you wish to publish your research findings in the NRU, please contact NRU administrator at admin@uncst.go.ug for details. NRU operates both open access and closed access models. Access to fulltext has been restricted in adherence to the UNCST Intellectual Property Rights (IPR) and Copyrights policies.

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Africa Portal is an online repository of open access library collection with over 3,000 books, journals, and digital documents on African policy issues. This is an initiative by the Centre for International Governance Innovation (CIGI), Makerere University (MAK), and the South African Institute of International Affairs (SAIIA). Please visit the Africa Portal at http://www.africaportal.org/library.

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Treatment compliance among adult cervical cancer patients receiving care at Uganda cancer institute, Uganda: a retrospective data review
(BioMed Central Ltd, 2023-07) Najjemba, Josephine Irene;; Ndagire, Regina;; Mulamira, Pius ;; Kibudde, Solomon;; Lwanira, Catherine Nassozi
Background Cervical cancer is one of the most common cancers and a major cause of morbidity among women globally. Chemoradiation therapy is the preferred standard treatment for women with stage IB to IVA. However, the benefits of this treatment can only be achieved if patients adhere to the treatment guidelines. In this study, the proportion of compliance or adherence to chemo-radiation treatment among cervical cancer patients at Uganda Cancer Institute (UCI) was determined. Methods This was a cross-sectional study that reviewed data retrospectively for 196 cervical cancer patients who were prescribed to chemo-radiation therapy at UCI between November 2020 to May 2021, having been diagnosed with disease stage IB to IVA. Patient data and information on treatment uptake was obtained by review of the patient’s medical records. Treatment compliance was determined by calculating the number of participants who completed the prescribed treatment (definitive pelvic concurrent chemoradiation to 50 Gy external beam radiotherapy with weekly concurrent cisplatin followed by intracavitary brachytherapy 24 Gy in 3 fractions at 8 Gy once a week over 3 weeks). Associations between patient factors and treatment adherence were determined using logistic regression analysis. In all statistical tests, a P- value of <0.05 was considered as significant. Results The proportion of patients who were administered with external beam radiation (EBRT), chemotherapy and brachytherapy were 82.6%, 52.04% and 66.2% respectively. However, only 23 of 196 patients (11.7%) were found to have adhered to the treatment plan by completion of all definitive pelvic concurrent chemoradiation to 50 Gy external beam radiotherapy (5 weeks) with weekly concurrent cisplatin (5 cycles) followed by intracavitary brachytherapy 24 Gy in 3 fractions at 8 Gy once a week over 3 weeks (3 sessions). There were no significant associations between patient factors and treatment adherence after multivariable analysis. Conclusions Treatment compliance was found in only 12% of the cohort participants. No association of patient factors with treatment compliance was found. Additional studies on treatment adherence with larger sample sizes are needed to confirm the associations.
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Intimate partner violence, social support, and depression among women living with HIV in Wakiso District, central Uganda: findings from a sequential mixed-methods study
(BMC, 2026-01) Joan Nalunkuuma;; Deborah Ojiambo;; Joanita Nangendo ;; Fred C. Semitala;; Samuel Ouma
Abstract Background Intimate partner violence (IPV) remains a pervasive public health concern, disproportionately affecting women worldwide and posing significant risks to their physical and psychological well-being. Women living with HIV (WLHIV) are particularly vulnerable to IPV though both its extent or nature and impact on mental wellbeing of WLHIV in Uganda have not been extensively examined. The aim of this sequential explanatory mixed-methods study was to examine the association between intimate partner violence, social support, and depression in WLHIV in central Uganda. Methods We sampled 215 for the quantitative strand. The Abusive Behaviour Inventory (ABI), Beck Depression Inventory (BDI) and Multi-Dimensional Scale of Perceived Social Support measured IPV, depression and social support respectively. These were followed by individual face-to-face semi structured interviews with a subsample of 10 women. Descriptive frequencies, Pearson correlations and process macro were analyzed in SPSS software while interviews were analyzed thematically. Results Overall, 15.1% experienced IPV, 24.9% were depressed respectively, and 68.4% had moderate to high perceived social support as well as significant negative correlations between IPV and depression. Social support significantly mediated the relationship between IPV and depression. Qualitative results explained the earlier quantitative results under three main themes: (1) Multiple forms of Violence/Abuse (2) Managing and Coping with Violence/abuse.3) Impact of Violence/abuse. Conclusion Addressing IPV, promoting social support and financial independence can enhance efforts aimed at ART adherence and improving mental health outcomes among vulnerable WLHIV.
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Soybean Rust Resistance and Yield Performance of Elite Soybean Genotypes Across Diverse Environments in Uganda
(wiley, 2026-02) Tonny Obua;; Julius Pyton Sserumaga;; Godfree Chigeza ;; Alex Malaala;; Mercy Namara;; Bruno Awio;; Solomon Okello;; Phinehas Tukamuhabwa
ABSTRACT Soybean (Glycine max (L.) Merr.) is one of the important legumes globally, serving as an affordable and valuable protein source for humans and livestock. However, selecting the most suitable genotype across diverse environmental conditions remains a major challenge due to significant genotype‐by‐environment interactions (GEI). In addition, the quantitative inheritance of resistance to soybean rust and grain yield further complicates breeding efforts. This study aimed to assess the performance and stability of newly developed soybean genotypes regarding resistance to soybean rust and grain yield. Twenty‐two newly developed genotypes and two check varieties were evaluated using a randomized complete block design (RCBD) with three replications during five consecutive cropping seasons in six distinct locations in Uganda. GEI patterns were examined, and stable, high‐performing genotypes were found using genotype and genotype‐by‐environment (GGE) biplot analysis. The effects of genotype, environment, and genotype‐by‐environment interactions (GEI) on soybean rust resistance, hundred‐seed weight (HSW), and grain yield were all highly significant (p ≤ 0.01). The study revealed that genotypes 6N × SG‐P‐3‐2, 6N × SG‐P‐2, and 6N × SQ‐7 consistently performed better than all the other genotypes for soybean rust resistance, hundred seed weight, and grain yield across the six locations and five cropping seasons. Notably, these genotypes also demonstrated high stability for the three critical traits, making them strong candidates for varietal release. The results of this study provide valuable and new insights for soybean breeding programs in Uganda and the broader Sub‐Saharan Africa, offering a pathway for the development and release of rust‐resistant, high‐yielding soybean varieties adapted to varying agro‐ecological zones.
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AI-Driven Tuberculosis Hotspot Mapping to Optimize Active Case-Finding: Implementing the Epi-Control Platform in Uganda
(MDPI AG, 2026-01) Amanya, Geofrey;; Hashmi, Sumbul;; Stow, Jessica Sarah ;; Tumwesigye, Philip;; Nkhata, Bernadette;; Mubiru, Kelvin Roland;; Budts, Anne-Laure;; Potgieter, Matthys Gerhardus;; Balcha, Seyoum Dejene;; Bamuloba, Muzamiru;; Zitho, Andiswa;; Henry, Luzze;; Nabukenya-Mudiope,; Mary G.;; Van Cauwelaert, Caroline
Tuberculosis remains a major public health concern in Uganda, one among the thirty high TB burden countries globally. Despite national progress, gaps persist due to asymptomatic disease, diagnostic limitations, and uneven access to healthcare within the country. This study implemented the Epi-control platform, an AI-driven predictive modelling tool, to predict community-level hotspots and support data-driven active case-finding (ACF). Using retrospective chest X-ray screening data, we integrated demographic, environmental, and human development indicators from open-source databases to model TB risk at sub-parish level. A proprietary Bayesian modelling framework was deployed and validated by comparing TB yields between predicted hotspots and non-hotspot locations. Across Uganda, the model identified significantly higher TB yields in hotspot areas (risk ratio = 1.69, 95% CI 1.41–2.02; p < 0.001). The Central and Western regions showed the highest concentrations of hotspots, consistent with their population density and urbanization patterns. The results show that the model prioritized areas with higher observed ACF yield in this retrospective dataset, supporting its potential operational use for screening prioritization under similar implementation conditions. The results demonstrate that AI-based predictive modelling can enhance the efficiency of ACF by targeting high-risk areas for screening. Integrating such predictive tools within national TB programmes may support screening planning and resource prioritization; prospective evaluation and external validation are needed to assess generalisability and incremental impact. Publicly Available Content Database
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Prediction of maize yield in Uganda using CNN-LSTM architecture on a multimodal climate and remote sensing dataset
(Springer, 2026-01) Danison Taremwa;; Emmanuel Ahishakiye;; Aggrey Obbo ;; Paul Kategaya Kisozi;; Fred Kaggwa
Maize is a staple crop in Uganda, underpinning both food security and rural livelihoods. Accurate forecasting of maize yields is therefore crucial for guiding agricultural planning, resource allocation, and policy design. Yet traditional statistical methods are often limited by low accuracy, poor scalability, and weak integration of diverse inputs, leaving them unable to capture complex, nonlinear, and spatiotemporal dynamics of crop growth. To overcome these constraints, we developed a hybrid convolutional neural network and long short-term memory (CNN-LSTM) model. This model integrates remotely sensed climatic variables and vegetation indices with biannual maize yield records from Uganda’s Zonal Agricultural Research and Development Institute (ZARDI) zones for the period 2018–2020. Due to the scarcity of high-quality yield data, we applied the Synthetic Minority Oversampling Technique for Regression (SMOGN) alongside feature selection to balance the dataset and improve predictive robustness. The CNN-LSTM model’s ability to select features and perform extensive hyperparameter tuning enabled it to outperform baseline models. It achieved a Mean Squared Error (MSE) of 0.107 tonnes2 , a Mean Absolute Error (MAE) of 0.267 tonnes, a Root Mean Squared Error (RMSE) of 0.327 tonnes, and an R2 score of 0.783. A comparative analysis revealed that the CNN+Random Forest (RF) model achieved an MSE of 0.137 tonnes2 , a MAE of 0.281 tonnes, an RMSE of 0.370 tonnes, and an R2 score of 0.722. These results outperformed the standalone CNN (MSE=0.216, R2=0.562) and RF (MSE=0.211, R2=0.573) models, underscoring the advantage of combining spatial–temporal learning for improved predictive accuracy. Residual analysis further confirmed the model's stability, showing minimal bias and close agreement between observed and predicted yields. These findings highlight the potential for integrating spatial– temporal deep learning and ensemble methods to deliver accurate crop yield forecasts in data-limited smallholder systems. By offering a scalable framework for evidence-based farm planning and food security policy, our study demonstrated that advanced machine learning can directly support sustainable development in subSaharan Africa. Future research will extend the framework to incorporate Transformer architectures, high-resolution satellite imagery, and explainable AI, further enhancing accuracy, interpretability, and decision-support capacity. Article highlights • Developed a hybrid CNN-LSTM model that integrates remotely sensed climatic and vegetation indices to predict maize yields across Uganda’s ZARDI zones (2018–2020). • Achieved high predictive accuracy (MSE=0.107, explaining 78% of yield variation), outperforming standalone CNN, ensemble models such as RF, and CNN-RF. • Introduced SMOGN-based data augmentation and feature selection techniques to overcome data sparsity, a novel approach for yield forecasting in smallholder, data-limited systems. • Demonstrated that hybrid DL frameworks can inform scalable, data-driven agricultural planning, with potential to guide policymakers and strengthen food security strategies in SSA. • Future work will focus on integrating Transformer architectures for improved sequence modelling alongside high-resolution imagery and explainable AI to enhance accuracy, interpretability, and practical decision support.