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Nutritional status and quality of life of older persons on Social Assistance Grant for Empowerment (SAGE) in Kampala city, central Uganda
(BMC Public Health, 2026-06-19) Ruma, Daniel Hendry; Tumusiime, Joy Francesca; Namayengo, Faith Muyonga; Shapiro, Norman Paul
Background Population ageing is a growing public health concern. Despite the Ugandan Government’s efforts, like the Social Assistance Grant for Empowerment (SAGE) programme, malnutrition and poor Quality of Life (QoL) are still older persons’ challenges. This study assessed the nutritional status and QoL of older persons on the SAGE programme in Kampala City, Central Uganda.
Methodology A cross-sectional study with a quantitative research approach was conducted. Data was collected in 2025 among 159 persons aged 80 years and above using a 24-hour recall, the 26-item World Health Organisation BREF, and anthropometric assessments. Data analysis involved descriptive statistics, chi-square, analysis of variance, and regression. Statistical significance was read at p<0.05.
Results The mean (SD) meal frequency was 1.95±0.81, dietary diversity score (DDS) was 6.69±2.77 in the past 24 h, and Body Mass Index (BMI) was 22.8±4.5 kg/m². Intake of meat (16.7%), fruits (14.6%) and eggs (10.2%) was low. Underweight, overweight, and obesity prevalence were 18.9%, 22.6%, and 8.2%, respectively. Underweight odds were lower in males (AOR=0.36, CI: 0.14–0.92, p=0.032) and persons who consumed≥3 meals (AOR=0.04, CI: 0.01–0.34, p=0.003). The odds of overweight/obesity were higher among males (AOR=2.01, CI: 1.36–2.69, p=0.041). The mean (SD) overall QoL score was 52.27±15.75, with 23.3% of respondents having a good QoL. Males had higher odds of a poor QoL (AOR=2.45, CI: 1.53–3.04, p=0.021), while having secondary or higher education (AOR=0.25, CI: 0.11–0.57, p<0.001) and other income sources (AOR=0.32, CI: 0.28–1.38, p=0.027) protected against a poor QoL.
Conclusion The study highlights a double burden of malnutrition and poor QoL linked to sex, socioeconomic and dietary factors among older persons on the SAGE programme. This points to challenges emanating from gender and policy disparities among older persons in Uganda.
Prediction of delayed postgraduate graduation using machine learning in Ugandan higher education
(Discover Artificial Intelligence, 2026-08-22) Musoke, Robert Lubelenga; Nameere, Kivunike Florence; Chongomweru, Halimu; Ahishakiye, Emmanuel
Delayed postgraduate graduation is a major challenge to higher education institutions in Uganda in terms of student progression, institutional planning and human capital development. Identifying students at risk of delayed completion early can inform timely academic interventions and improve post-graduate outcomes. This study developed and compared machine learning models for delayed postgraduate graduation prediction using institutional administrative records from Makerere University. A retrospective predictive modelling study was performed using anonymised data from 500 postgraduate students registered from 2017 to 2023. 29% of them graduated on time and 71% graduated late. We developed and evaluated a number of supervised machine learning classifiers such as Random Forest, XGBoost, K-Nearest Neighbours, Logistic Regression and a stacking ensemble model. The model performance was evaluated using accuracy, precision, recall, F1-score and ROC-AUC. All trained models were found to have high and similar predictive performance in terms of accuracy (between ~ 94% and 95%) and ROC-AUC (between 0.94 and 0.95). Logistic Regression gave the numerically highest ROC-AUC (0.950) although differences in classifier performance were not statistically meaningful. Employment status and age at admission were positively related to delayed graduation whereas higher undergraduate CGPA decreased the likelihood of delayed completion. The results indicate that interpretable machine learning models can match the performance of complex ensemble methods on structured educational datasets. Apart from predictive performance, the study offers context-specific evidence on the use of predictive analytics in postgraduate education in Uganda, which is often characterised by patterns of progression affected by research-intensive study requirements, employment responsibilities, and long thesis completion processes. The proposed framework can assist in early identification of at-risk postgraduate students and directing institutional interventions in order to improve graduation outcomes.
Reassessing mid-upper arm circumference thresholds for predicting nutritional status and pregnancy outcomes among pregnant adolescents in Bundibugyo district, Uganda: protocol for a health facility-based prospective cohort study
(BMJ Open; Public health, 2026-08-19) Kikomeko, Peterson Kato; Nahalomo, Aziiza; Murungi, Simon; Kalumba, Mirembe; Kabugho, Evars; Musasizi, Benon
Introduction In 2023, Sub-Saharan Africa recorded the world’s highest teenage pregnancy rate of 4.4 per 1000 women compared with the global average of 1.5 per 1000, highlighting the need for accurate nutritional assessment during pregnancy. Current mid-upper arm circumference (MUAC) thresholds differ between non-pregnant adolescents (18.5 cm) and pregnant adolescents (23 cm) as per the Uganda Integrated Management of Acute Malnutrition guidelines. This classification can lead to inadequate treatment and mislabelling of adolescents as non-responders, despite nutritional improvements. This study will reassess MUAC thresholds for predicting nutritional status and pregnancy outcomes among pregnant adolescents in Bundibugyo District, Uganda.
Methods and analysis This health facility-based prospective cohort study will target 241 pregnant women comprising 121 pregnant adolescents (10–19 years) and 120 adults (20–49 years) in their first trimester (≤12 weeks of gestation) receiving antenatal care (ANC) at Bundibugyo General Hospital and Bupomboli Health Centre III in Bundibugyo District, Uganda. Recruitment of study participants will commence in June 2026 and participants will be followed up for 6–9 months during routine ANC visits until delivery. Quantitative data will be collected through questionnaires and from patients’ hospital records during routine ANC visits. The study primary outcomes are maternal gestational weight gain and maternal haemoglobin status. Secondary outcomes include pregnancy outcomes (birth weight, gestational age at delivery, preterm birth, small for gestational age status, maternal pregnancy complications and mode of delivery). The main explanatory variable is MUAC assessed as baseline MUAC, repeated MUAC measurements during subsequent ANC visits and the rate of MUAC change across gestation. Receiver operating characteristic analyses and internal bootstrap validation will be used to derive and internally evaluate study-derived adolescent-specific MUAC thresholds. Qualitative data from 10 to 12 key informants will be obtained once through key informant interviews and triangulated with the quantitative findings to explain and contextualise the results.
Ethics and dissemination Kyambogo University Research Ethics Committee (KyU-REC-2025-43) and the Uganda National Council for Science and Technology (HS7233ES) have approved this study protocol. Local administrative clearance has been sought from the Bundibugyo District Health Office and from Bundibugyo General Hospital and Bupomboli Health Centre III. Results will be published in peer-reviewed journals and summaries will be shared with the Bundibugyo District Health Office, participating health facility in-charges, ANC units and study participants.
Management of root-knot nematodes in carrots using Neem (Azadiractha Indica) leaf extract
(Kyambogo University (Unpublished work), 2025-10) Namiwanda, Ruth
The study was conducted to assess the effect the neem tree leaves (Azadirachta indica) extract on the management of root-knot nematodes in carrots. The study specifically determined the effect of A. indica leaf extract concentration in management of root-knot nematodes in carrots; and determined the effect of A. indica leaf extract concentration that suppresses galling and forking incidence on carrot tubers. The experiments were conducted at the Kyambogo University farm in two rainfall seasons of 2019B and 2020A. Treatments composed of three levels of concentration of A. indica leaf extract (i.e 15, 25 and 35g) plus a positive control (distilled water) and a negative control (no inoculum). Data was collected included number of standing leaves on the plant, height of the shoot, fresh and dry weight of leaves, fresh weight of carrot tubers and gall incidence. Results showed that there was no significant difference between the negative control (no inoculum applied), distilled water (inoculum applied) and highest concentration level of neem leaf extract (35g) on the number of standing leaves in carrots. Neem leaf extract was found to have significant effect on galling and forking incidence of carrot tubers (P<0.001) The study provides evidence of the effectiveness of neem tree leaves extracts in the management of root- knot nematodes in carrots.
Green entrepreneurial orientation and its influence on green innovation, economic performance, and sustainable performance a meta-analytic review
(Future Business Journal, 2026-08-15) Bindeeba, Dedrix Stephenson; Kembabazi, Owen; Tibihika, Amon
Green entrepreneurial orientation (GEO) is increasingly viewed as a strategic posture that helps firms remain competitive while responding to rising environmental pressures. This study synthesizes empirical evidence on how GEO relates to green innovation and whether these relationships extend to economic and broader sustainable performance outcomes. Using a meta-analytic approach, the review integrates evidence from 48 quantitative studies,
comprising 56 effect sizes and 13,311 firms. Random-effects models show that GEO has its strongest association with green innovation (r = 0.488), followed by economic performance (r = 0.378), and a smaller but significant association with sustainable performance (r = 0.294). Substantial heterogeneity indicates that GEO’s payoffs vary across contexts.
Subgroup analyses show that industry context moderates the GEO–green innovation relationship, with stronger effects in mixed or multi-sector settings than in manufacturing or industrial contexts. Country development status does not differentiate the GEO–innovation relationship, but it does condition GEO’s associations with economic and sustainable performance, suggesting that value capture and stakeholder reward structures shape performance
returns more than innovation initiation. Overall, the findings help reconcile mixed results in the GEO literature by showing that GEO is most reliably linked to innovation outcomes, while performance associations are more context-dependent and likely to materialize through effective implementation and stakeholder valuation. Practically, this implies that managers and policymakers should complement GEO promotion with support for green innovation capabilities and market mechanisms that recognize, reward, and scale green outcomes.