The Nelson Mandela African Institution of Science and Technology
Institutional Repository
Research Collection
Select to browse Research collections.
Recent Submissions
Factors influencing the utilisation of maternal healthcare services among reproductive-age women in Tabora region, Tanzania: an analysis of a baseline survey data from a clinical trial
(BMJ Publishing Group Ltd., 2026-07-24) Magehema, Careen; Binyaruka, Peter; Kimaro, Esther; Mtenga, Sally
Background Maternal health service utilisation is poor in many low- and middle-income countries, contributing to high maternal mortality rates. This study aimed to identify the key factors influencing the use of maternal health services among reproductive-age women in Tabora, Tanzania.
Methods Secondary data from a stepped-wedge clinical trial design were used. Baseline data were collected in November and December 2017 throughout Tabora. Data from 958 women aged 15–49 years in Tabora were analysed for this study. Variables analysed included maternal age, marital status, religion, household wealth status, household size, ethnicity, parity, antenatal care (ANC) visits, facility deliveries, facility distance and location (rural–urban). Descriptive analysis was used to assess the demographic characteristics of respondents and multivariate logistic regression was used to assess factors associated with the utilisation of maternal health services.
Results Achieving four or more ANC visits (ANC4+), was associated with high maternal age category (36–50 years) as compared with those aged <20 years (OR=3.13 (95% CI 1.56 to 6.31)) and least poor household wealth status (OR=1.63 (95% CI 1.11 to 2.39)) as compared with the poorest household wealth status. Facility-based delivery was associated with married women (OR=1.76 (95% CI 1.15 to 2.71)) as compared with women who were not married, low parity (<5) (OR=0.85 (95% CI 0.78 to 0.93)) as compared with high parity (>5), ethnicity (Nyamwezi) (OR=2.01 (95% CI 1.34 to 3.11)) as compared with other ethnic groups in Tabora, ANC4+ (OR=1.96 (95% CI 1.44 to 2.67)) as compared with less than four ANC visits, household wealth status; least poor (OR=2.8 (95% CI 1.81 to 4.33)) as compared with poorest household wealth status and distance (<5 km) to facility (OR=0.91 (95% CI 0.89 to 0.94)) as compared with ≥5 km. Postnatal care visits were associated with facility-based delivery (OR=6.76 (95% CI 4.71 to 9.69)) as compared with non-facility-based deliveries. Increased antenatal visits were associated with higher likelihoods of facility deliveries, which in turn led to higher postnatal care attendance.
Conclusions The findings highlight the need for targeted interventions to address the factors influencing service utilisation to improve overall maternal and child health outcomes.
Biofertilizers in production and protection of staple cereal crops in semi-arid regions of sub-Saharan Africa
(Taylor & Francis Online, 2026-07-21) Ojuu, David; Mkindi, Angela; Meya, Akida; Vanek, Steven; Belmain, Steven
Biofertilizers are proposed as practical alternatives for smallholder farmers in semi-arid sub-Saharan Africa (SSA). However, the role and efficacy of bacterial and fungal biofertilizers in cereal crops, sometimes accompanied by organic nutrient resources, remain insufficiently synthesized in literature. We conducted a structured literature review using the search terms “biofertilizers,” “bioinoculants,” “microbial fertilizers” and crop terms “cereals,” “maize,” “sorghum,” “wheat,” “rice” in Google scholar and Scopus resulting in 52 studies published between 2017 and 2023 that met predefined inclusion criteria. This enabled assessment on which native microbial species and existing farm-based biofertilizer resources are utilized, the status of commercial and on-farm production in SSA, and the crop, soil microbial, organic matter, and pest management impacts. Across included studies, 68% and 54% reported positive effects on crop biomass and yield, respectively, with higher efficacy for mycorrhizal inoculants compared to bacterial biofertilizers, and lower rates of positive response for abiotic stress tolerance and microbial colonization. A small fraction of studies assessed impacts on soil microbial biomass, soil carbon cycling, or cereal pest resistance. We highlight the need for field-based and mechanistic studies on soil microbial impacts, plant growth and resistance to abiotic and insect pests in degraded soils of semi-arid SSA cereal systems.
Co-culture Fermentation of Maize Bran with Saccharomyces cerevisiae and Lactic Acid Bacteria: Effect on Techno-Functional, Structural, Flavor, and Antioxidant Properties
(ACS Publications, 2026-07-18) Dimoso, Noel; Feng, Yue; Tang, Anqi; Yang, Zhen-Quan
Maize bran is a nutrient-rich byproduct with limited applications. This study evaluated the impact of co-culture fermentation using yeast and lactobacillus strains on bran functionality. The findings indicated that co-fermentation for 72 h significantly improved the microstructure, hydration, and oil absorption properties of bran. Soluble proteins increased the most during single-yeast fermentation for 24 h. Yeast and Pediococcus pentosaceus co-fermentation for 72 h increased total phenolics by 1.42-fold and exhibited the strongest reducing power (4.50 mg/g), DPPH• (32.37 μmol/g), ABTS•+ (298.00 μmol/g), and •OH (68.42%) scavenging activities. Yeast and L. fermentum presented the second most total phenolics, DPPH•, and •OH scavenging activities. Single-yeast fermentation for 48 h demonstrated the highest iron chelation rate at 43.52%. Yeast and L. plantarum exhibited superior total flavonoid content (1.95 mg/g). Further, fermentation treatments modified the flavor profile, with increased phenylethyl alcohol and 3-methylbutanoic acid contents. These results provide valuable insights into the potential of co-fermentation to enhance maize bran functionality.
Empiric tuberculosis treatment in hospitalised adults with advanced HIV disease in Africa: applying the therapeutic threshold to clinical practice
(Elsevier Ltd., 2026-07-27) Boyles, Tom; Ellis, Jayne; Feasey, Nicholas; Heysell, Scott; Jacob, Shevin; Jarvis, Joseph; Moore, Christopher; Nuwagira, Edwin; Mpagama, Stellah
Hospitalised adults with advanced HIV disease in sub-Saharan Africa experience high mortality, with tuberculosis, often disseminated and undiagnosed, being a leading cause. Despite this, initiation of antituberculous therapy is frequently delayed pending diagnostic confirmation, which may be unavailable in this population. Evidence from recent trials and cohort studies suggests that even short delays in antituberculous therapy are associated with substantial increases in mortality, whereas empiric therapy might improve survival in people at high risk. Applying the therapeutic threshold framework, the high pre-test probability of tuberculosis in severely ill inpatients with advanced HIV disease often exceeds the threshold at which treatment benefits outweigh risks, even in the absence of confirmatory testing. Although concerns regarding toxicity, drug interactions, and overtreatment are valid, short-term empiric antituberculous therapy appears safe and these risks might be outweighed by the consequences of untreated disease. We argue for a paradigm shift towards earlier empiric antituberculous therapy, with parallel diagnostic evaluation and structured reassessment in selected patients, and for adequately powered randomised controlled trials of empiric therapy powered for mortality.
Machine Learning Techniques to Predict Fetal Nutritional Status
(The Korean Society of Medical Informatics, 2026-07-31) Mduma, Neema; Laizer, Hudson
Objectives
Malnutrition remains the leading cause of child mortality in Tanzania, with over 34% of children under 5 years of age affected by stunting and approximately 5% experiencing acute malnutrition. This study aimed to develop a machine learning model to predict fetal nutritional status using maternal and clinical data, thereby enabling early risk identification for health workers and parents and facilitating timely intervention. To enhance practical applicability, the model was deployed within a mobile application to provide accessible, real-time predictions that support prompt clinical and behavioral responses.
Methods
Using a dataset collected in Tanzania, the performance of multiple binary classification algorithms—logistic regression, multi-layer perceptron, random forest, extreme gradient boosting, and light gradient boosting machine (LightGBM)—was compared using the geometric mean and F-measure. These models were trained on clinical data from 11,703 pregnant women to predict fetal nutritional status based on maternal and clinical variables.
Results
The results indicated that the LightGBM algorithm achieved the best overall performance in predicting fetal nutritional status. The most influential predictors included maternal age, weight, fetal age, hemoglobin level, number of meals per day, medical history, and education level. Additionally, 93% of respondents reported satisfaction with the application’s predictive functionality, supporting its potential utility for early intervention in low-resource settings.
Conclusions
These findings highlight the potential of data-driven approaches to address public health challenges in maternal and child health. The proposed model may enable healthcare providers to make timely, informed decisions that improve maternal and fetal outcomes, ultimately contributing to the reduction of child malnutrition in Tanzania.