SU+ Digital Repository

SU+ is an online repository for the preservation and promotion of assorted digital content at Strathmore University

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Conferences / Workshops / Seminars + 474
Documents and Proceedings of Conferences, Seminars, Workshops (and more) held at Strathmore University
Digital Archives 178
Assorted collections of resources covering various subject themes contributed by Faculty and Library Staff
Reports / Policies + 8
Public reports and policy documents
Research / Researchers / Publications 4356
Researcher Profiles / Conference presentations / Published research articles / Faculty and Corporate research outputs
Strathmore Heritage Collection 41
A digital chronicle of the History of the University presented through a mix of pictures, videos and digitized publications

Recent Submissions

Factors associated with needle stick injuries at Mama Lucy ‘Kibaki Hospital-Accident and Emergency Department
(Strathmore University, 2025) Ongeri, S. K.
Needlestick injuries (NSIs) are a significant occupational risk to healthcare workers (HWCs) globally, consequent upon accidental puncture wounds caused by contaminated sharp objects, particularly hollow-bore syringe needles. Globally, needlestick injuries (NSIs) occur at a rate of 43%, with Africa experiencing the highest incidence at 51% (WHO, 2022). For every 1,000 NSIs from an infected patient, approximately 300 HCWs are prone to contracting HBV. The rates of seroconversion for HCV and HIV are estimated at 30 per 1,000 NSIs and 3 per 1,000 NSIs, respectively. The widespread occurrence of needlestick injuries (NSIs) among Kenyan HCWs, is reported at approximately 58%, and accounts for 76.6% of instances where healthcare workers are exposed to HIV/AIDS in the workplace, highlights the urgent necessity for preventive measures. NSIs not only endanger the physical health of providers but also induce significant psychological distress. Immediate action is imperative, necessitating comprehensive training, access to safety equipment, and the establishment of robust reporting systems to ensure a safer work environment and mitigate the risk of infectious disease transmission to healthcare workers. This study aimed to investigate factors associated with needlestick injuries (NSI) among healthcare workers at Mama Lucy Kibaki Hospital, Nairobi, using the Job Demand and Resource (JD-R) model, Job Experience Curve Theory, and Diffusion of Innovation Theory. It explored the impact of occupational cadre, job experience, working hours, and safety devices on NSI occurrences. Data was gathered from healthcare workers in the Accident and Emergency Department through a semi-structured questionnaire based on a 5-point Likert scale and analyzed using descriptive statistics and inferential methods, including correlation analysis and also thematic analysis. The results revealed strong positive correlations between inadequate safety devices and training (Spearman’s rho = 0.578), work experience (Spearman’s rho = 0.540), working hours (Spearman’s rho = 0.346), and exposure from occupational cadre (Spearman’s rho = 0.333) with NSIs. The recommendations focus on improving safety for healthcare workers to reduce needlestick injuries. These include targeted training for nurses, mentorship programs, shift rotations to reduce fatigue, and increased availability of safety-engineered devices (SEDs). Regular safety drills, workshops, and monitoring compliance are also suggested to reinforce best practices. KEY WORDS: Health Care Workers, Needle Stick Injury, Occupation Cadre, Safety engineered Devices, Work Experience.
An Examination of the influence of social network characteristics on access to HIV preventive and curative services among female sex workers in Nairobi
(Strathmore University, 2025) Otieno, Y. A.
Achieving universal health coverage is long-term, and political and technical knowledge is needed for this to happen. Even with the availability of Human Immunodeficiency Virus (HIV) services, Female Sex Workers (FSW) find it hard to access these services. The study examined the utilization of social networks to improve health services access among female sex workers. It explored how the type of relationship between a peer educator and the peer, and that of a peer-to-peer, influences the acceptability of services. This retrospective cohort study thus analyzed routine program data at 10 Drop-In Centres and one integrated public health facility. All seeds/recruiters participating in the intervention between October 2021 and September 2022 were included in the study. That is 17 peer educators and 1,153 peers (867 high-risk HIV negative and 286 HIV positive) who referred 3,498 social network members for HIV services. A data extraction form was used to collect information from the Social Network Strategy Register, HIV Testing and Counseling, Laboratory Register for verification of results and Kenya EMR (Electronic Medical Records) to verify linkage to PrEP (Pre-Exposure Prophylaxis) and ART (Antiretroviral Therapy) and subsequent continuation. A descriptive analysis was done presenting percentages, frequencies, and mean. The chi-square test and odds ratio were used for categorical data, presenting the p-values and rate ratios. The mean age of the seed was 33 years. Out of the 3,498 social network members referred for services, 5% (180) were newly diagnosed with HIV of whom 178 were linked to ART (99% linkage). Of the 3318 HIV Negative social network members 64.4 % (2127) were linked to PrEP. An association between age group and linkage to PrEP was established at a p-value of 0.037, with the less than 20 years linkage of social network members to PrEP having the highest proportion. In addition, there was an association between the type of relationship between the seed and social network member and linkage to PrEP with a p-value of <0.01. Compared to the peereducator- to-peer cohort, the peer-to-peer cohort had 0.388 times greater odds of linking their social network member to PrEP. An association between the type of relationship and HIV case identification was established at a p-value of <0.01. The peer educator’s cohort had 3 times greater odds of identifying a positive to the peer-peer cohort. The social network influence at play for the peer-to-peer cohort is based on the similarities between the peers and the strength of their relationship. On the other hand, the mechanism of influence between peer educators and peers is based on the leadership role they play and their credibility among their peers Understanding how female sex workers overcome obstacles to accessing healthcare services is important in coming up with practical strategies that would support their participation in their care and monitoring the effectiveness of the national HIV response.
Mathematical modelling and optimization analysis of Malaria infection in the presence of insecticide resistance and climate variations in Kenya
(Strathmore University, 2025) Chepkemoi, L.
Over the years, control efforts to curb malaria transmission have been initiated and implemented by World Health Organization (WHO). These control efforts however rely on the use of insecticide-based control interventions which has enhanced the emergence and persistence of insecticide resistance to almost every insecticide class used. This study determines the optimal combination strategies for malaria control in the presence of insecticide resistance and climatic variation in Kenya. A deterministic model representing malaria transmission dynamics in the human and mosquito population is formulated and studied. The model equations are solved numerically using fourth and fifth order Runge-Kutta methods while the optimal control framework is solved numerically using forward-backward sweep method. The basic reproduction number R0 is derived and the disease-free equilibrium is shown to be locally and globally asymptotically stable. Sensitivity analysis reveals that the resting rate of susceptible mosquitoes αs and the mosquito mortality rate due to the use of insecticides δv are the most influential parameters in determining malaria transmission dynamics. Numerical simulation results show that in the presence of insecticide resistance, R0 is 1.2721 implying that malaria disease persists in the population. The spatial distribution of the reproduction number across Kenya further show that regions whose climatic conditions are favourable for malaria vector survival experience higher malaria transmission compared to areas whose climatic conditions are less favourable for vector survival and development. Additionally, based on optimal control analysis, the best malaria control intervention is when personal protection, treatment and vaccination are used simultaneously. This study reveals that insecticide resistance and climatic variation have a significant impact in the spread of malaria. Therefore, the study’s findings can be adopted by national malaria control program stakeholders in the fight against malaria in Kenya.
Modeling Cholera dynamics in Kenyan slums: a comprehensive analysis incorporating WASH interventions, vaccination, and treatment
(Strathmore University, 2025) Nyokabi, P.
Cholera is an acute intestinal disease which has become a global public health concern, especially in regions where water, sanitation and hygiene (WASH) amenities are inadequate. In the period from January - September 2024, Africa reported 127283 cases, with 2268 deaths. In Kenya, cholera has been reported especially in areas with inadequate access to safe water and proper hygienic facilities such as urban informal settlements, large refugee camps, pastoral areas, arid and semi arid lands, areas bordering water bodies and Mwea irrigation scheme. By developing a deterministic mathematical model, this study determined the effectiveness of various WASH interventions in preventing and eradicating cholera in Nairobi slums. The system of ODE’s and the optimal control model were solved numerically using the Runge-Kutta fourth-order method implemented in R, with the forward-backward sweep method applied for the optimal control problem. Scenario analysis was applied to assess the impact of different strategies for controlling the spread of cholera. Results revealed that an integration of all control strategies was necessary and prioritizing water treatment, sanitation, and hygiene promotion can yield substantial public health benefits. The numerical simulations showed that if only one WASH intervention is feasible, improved sanitation and safe fecal sludge management, or water treatment should be prioritized. If two interventions can be implemented, water treatment and waste management should be preferred, followed by hand hygiene and hygienic practices in food handling with sanitation infrastructure. If resources accede three WASH interventions, priority should be given to water treatment, improved sanitation and waste management. The results obtained will provide valuable insights on improvements and steps to take for development of better strategies in management of cholera spread.
Statistical and machine learning approaches to assessing foreign aid effectiveness in Kenya: an ARDL framework
(Strathmore University, 2025) Rutto, N. J.
This study investigates the impact of foreign aid and other macroeconomic factors on household consumption in Kenya, using household consumption as a proxy for poverty. It adopts a hybrid methodological approach, combining traditional econometric modelling with modern machine modelling techniques to balance causal inference with predictive accuracy. The analysis is anchored in the Autoregressive Distributed Lag (ARDL) framework, which is well-suited to small samples and mixed integration orders. After establishing the presence of cointegration among variables, the model is reparameterised into an Error Correction Model (ECM) to distinguish between short-run and long-run effects. Diagnostic tests confirm the model’s robustness. A Granger causality test reveals no temporal precedence from foreign aid to household consumption, while GDP per capita consistently emerges as a significant long-run driver. To complement the explanatory power of ARDL, three machine learning models, LASSO regression, Random Forest, and XGBoost, are implemented to assess their ability to predict changes in household consumption. The LASSO model demonstrates the best performance across all evaluation metrics (MAE, RMSE, R2), outperforming traditional ARDL and more complex ML models. Feature importance analyses using permutation importance and SHAP values reinforce the dominance of GDP per capita and lagged effects of foreign aid as key predictors. Findings indicate that while econometric methods offer nuanced insight into short- and long-term dynamics, machine learning provides superior predictive power. The study underscores the potential of a hybrid modelling approach in low-frequency macroeconomic contexts, where data constraints limit the application of purely data-hungry methods. Ultimately, the results contribute to the discussion on how aid and macroeconomic variables influence poverty outcomes in developing economies.