University of Embu

This study formulates a deterministic model to assess the effect of a family-based control and management (FBCM) strategy against the transmission of Helicobacter pylori infection and its consequent development of gastric ulcers and gastric cancer. The model includes nine epidemiological compartments to model disease transmission and contact epidemiology between susceptible and infected individuals. In the model analysis, we compute positivity, the invariant region, equilibria, stabilities, and bifurcation analysis. We calculate the control reproduction number R-0 and demonstrate that the model has a unique disease-free equilibrium (DFE) and an endemic equilibrium point (EEP) that are locally and globally stable for R-0<1 and R-0>1, respectively. We perform a thorough mathematical analysis and validate the model by fitting it to real data on gastric cancer cases recorded at Meru Teaching and Referral Hospital, Kenya. The best numerical results are achieved when we combine both preventive measures (sensitization and a family-based approach) and curative measures (prompt treatment and adherence), resulting in the greatest decrease in gastric ulcer and gastric cancer cases compared with a single intervention. This study shows that integrated household-level interventions can reduce transmission and prevent mild-to-severe disease progression through effective sensitization campaigns, high FBCM efficacy, effective gastric ulcer treatment, and adherence to drug protocols. The use of such strategies offers an effective means of reducing Helicobacter pylori-related gastric ulcers and gastric cancer outcomes, with important implications for public health control program design.

Globally, cancer is a high health burden, as it's the fifth leading cause of death, in the year 2022, a study by the WHO showed there were an estimated 20 million new cases, and the number of deaths totaled 9.7 million. In a group of 5 people, 1 person develops cancer in their lifetime, approximately 1:12 women and 1:9 men die from cancer. The management of cancer patients is complicated by the presence of comorbidities, which can significantly affect transition between time of diagnosis and treatment initiation. The data used in the study was sourced from Cancer regional registry. It entailed the following details of the patients: age, gender, cancer staging, pre-existing and post-existing health condition(s), and cancer subtype. Patients were grouped according to the stage in which they were diagnosed. Stage I and II are the early stages, while Stage III and IV are the late stages. The most prevalent cancer type in males is prostate cancer, while in females it is breast cancer, with hypertension as the commonest comorbid condition. Most of the cancer diagnoses were in late stage, that's stage III and stage IV. This distribution showed that a significant proportion of patients were diagnosed at advanced stages (III and IV), which could affect treatment options and prognosis. Cardiovascular conditions were associated with cancer progression, treatment method, and overall survival. Patients diagnosed at later stages (III-IV) received systemic therapies as their treatment type. There is need to continue the study in the future by prospective researchers in terms of size and variability of the participants investigated at several centers in order to enhance inclusivity. This would be efficient by the use of longitudinal designs so that it monitors disease progression, its response and also model survival rates too.

On the second of October 2023, the United Nations Security Council approved a Kenyan-led Multinational Security Support (MSS) Mission in Haiti to help restore law and order following years of gang-related chaos. Drawing on legitimacy theory and employing a comparative analysis of most different systems design (MDSD), while holding systemic differences between Kenya and Haiti constant, the study sought to assess whether the populations' views on the mission's empirical legitimacy in both countries are significantly similar or different. Data were collected using a semi-structured online questionnaire administered to 324 respondents from both countries. The findings reveal no significant differences in perceptions between Kenyans and Haitians. This indicates that the mission faces considerable legitimacy challenges, stemming from limited public support, insufficient consultation, and scepticism about its necessity. These findings reinforce legitimacy theory, demonstrating that successful international interventions require both legal sanction and social endorsement by affected populations.

The study determined the influence of climate change adaptation (CCA) strategies on sorghum-farming household resilience in the dry lands of Embu and Tharaka-Nithi counties in Kenya. Stratified and random sampling techniques were used to select a sample of 426 farming households. The data were collected using a structured interview schedule. Indicators served as proxies for resilience with percentages, and the ordered probit model was employed in the data analysis. The results indicated low resilience of sorghum farming households to climate change. The CCA strategies, namely intercropping (? =0.89), mixed farming (? =0.83), proper grain storage (? =0.40) and staggering planting dates (? =0.32) had a positive and statistically significant influence on resilience. The CCA strategies had a positive and meaningful influence on resilience. The study recommends training of farmers by extension agents on grain storage, optimal crop and livestock combinations for intercropping and mixed farming productivity. Extension agents should foster collaboration with weather forecasting departments to determine with certainty when to stagger planting dates. The government should facilitate farmers' access to suitable storage structures and affordable capital through financial lending institutions to improve resilience to climate change.

Problem: Low crop yields in sub-Saharan Africa mainly result from low soil fertility and insufficient nutrient inputs. A key component of Integrated Soil Fertility Management (ISFM), namely combining inputs of mineral fertilizers and organic resources, presents an opportunity to boost yields and maintain soil organic carbon (SOC) stocks in the long run. Soil-crop models help to assess the performance of ISFM under contrasting soil, climate, and management combinations. Yet, to date, most soil-crop models have been calibrated and tested in temperate conditions. Objective: Our objective was to evaluate and compare the performance of two different soil-crop models, DayCent and STICS, to represent crop yields and SOC dynamics under contrasting organic resource amendments. Methods: We used a large dataset representing 3384 cropping situations (site x season x treatment) from four long-term experiments in Kenya. Each experiment included the same treatments with the addition of two quantities of low- to high-quality organic resource amendments (high vs low C/N ratio, respectively), with (+N) and without (-N) mineral nitrogen fertilizer. Each treatment included a cropped and uncropped subplot, allowing for a unique stepwise calibration of soil and crop parameters. Results: Both models represented SOC and yield dynamics with similar accuracy across sites and treatments. They reproduced SOC dynamics well (nRMSE below 30 %) in the two clayey soils sites but not in the two sandy soils. Yet, in most sites they reproduced well SOC differences between high (Farmyard manure, Thithonia and Calliandra) and low-quality (maize stover and sawdust) organic resources. Models reproduced the average yield across sites and treatments similarly. They reproduced the positive effects of high-quality organic resources and the addition of mineral N on maize yield well. Models had similar inaccuracy in reproducing yield and yield variability under poor-quality organic resources and -N treatments. Conclusion: The stepwise calibration approach used in this study enabled highlighting the models' strengths and weaknesses in soil and plant simulations. The results suggest that the two models have similar strengths and struggle with the same problems despite having different structures. Collecting detailed plant (leaf area index, plant N uptake) and soil (water, nitrogen dynamics) in-season data from long-term experiments will be critical to exploit the full model complexity and improve their accuracy for tropical conditions.