JKUAT Journals

INSTITUTIONAL FACTORS ASSOCIATED WITH SURGICAL SITE INFECTIONS AMONG POST CAESAREAN SECTION IN THIKA LEVEL 5 HOSPITAL

Tue, 31 Mar 2026 00:00:00 GMT

INSTITUTIONAL FACTORS ASSOCIATED WITH SURGICAL SITE INFECTIONS AMONG POST CAESAREAN SECTION IN THIKA LEVEL 5 HOSPITAL Ndege, Jane Wanjiku Objective: The study aimed at determining the institutional factors that contribute to surgical site infections among post caesarean section in Thika Level 5 Hospital. Materials and Methods: The study employed a mixed unmatched case-control study design which targeted all mothers who had undergone caesarean section in maternity unit at Thika Level 5 Hospital and who had or did not have Surgical Site Infection from delivery up to thirty days post-delivery and nurse in-charges of maternity unit. Result: The sample size of the study was made up of 128 women comprising 32 cases and 96 controls. Qualitative results revealed that poor aseptic technique in theatre during C/S operations led to an increase in Surgical Site Infections (SSIs). Respondents highlighted the significance of maintaining a sterile and clean environment in the theatre to prevent contamination of surgical sites by microorganisms. The study found that mothers who stayed in hospital for more than 24 hours before Caesarian Section (CS) were more likely to get Surgical Site Infection (SSI) as compared those that had stayed in the hospital for less 24 hours before CS (OR=13.05 [95%CI=4.10-41.53]; p<0.001). Mothers who shared beds with other patients were more likely to get SSI as compared to those that did not share beds (OR=3.01 [95%CI=1.28-4.19]; p<0.001). Moreover, mothers who spent more than a week in the hospital were more likely to get SSI than those who stayed in the hospital for less than that period (OR=3.41 [95%CI=1.06-11.38]; p<0.001). Conclusion: The study concludes that there is a potential relationship between the duration of hospital stay prior to CS and the risk of SSI. Also, the institutional factors associated with SSIs are the response of healthcare workers to the needs of the patients, the level of care given at the hospital and facility accessibility. MSc Research Publication

Survey of the incidence and distribution of groundnut rosette disease in major groundnut-producing regions of Western Kenya

Thu, 26 Mar 2026 00:00:00 GMT

Survey of the incidence and distribution of groundnut rosette disease in major groundnut-producing regions of Western Kenya Were, Eric Osewo Groundnut rosette disease (GRD) is the most important viral disease of groundnuts in sub-Saharan Africa. In Kenya, GRD infection especially before flowering results in 100% loss in pod yield. Surveys were conducted in 2016 and 2017 to determine the incidence and distribution of GRD in five major groundnut growing Counties of western Kenya. A structured questionnaire was used to assess GRD incidence and severity and farmers’ awareness about management of GRD. Reverse transcription (RT) polymerase chain reaction (PCR) was used for the detection of GRD agents in collected symptomatic samples. Results revealed that GRD was prevalent in all the fields of the five counties. The highest mean disease incidence was in Busia County (35.7%) while the lowest incidence was in Siaya (23.1%). The most conspicuous symptoms observed in all the fields inspected were yellow/chlorotic rosette and green rosette. The highest mean disease severity was observed in farmers’ fields in Busia (3.1) and Bungoma (3.0) Counties, while the lowest was observed in Siaya (2.8). RT-PCR detected GRD agents in all the symptomatic samples. This study demonstrated the widespread occurrence of GRD in major growing regions of western Kenya and warrants the implementation of effective virus disease control strategies. Key words: Arachis hypogaea L., field survey, groundnut rosette disease, occurrence, severity. MSc Research Publication

Correlation between Escherichia coli resistant gene isolated from stool samples of children under five years and that from consumed water

Thu, 26 Mar 2026 00:00:00 GMT

Correlation between Escherichia coli resistant gene isolated from stool samples of children under five years and that from consumed water Kibet, Suge Titus children. Kenya reports around 2.6 million cases and 7,000 deaths annually due to diarrhea. Antibiotic-resistant DEC strains are a significant public health concern, complicating treatment. The objective of this research was to correlate children’s water consumption and antimicrobial- resistant DEC development. The study sampled 1124 children under five with diarrhea in Nakuru, Kenya, using a case-control design. Among 384 eligible children, 192 cases showed Amoxicillin-resistant DEC. Water samples from households were collected and analyzed for DEC presence. The samples were filtered, plated onto MacConkey agar, and subcultured onto Eosin Methylene Blue agar for further analysis. The DEC pathotypes were identified based on morphological and biochemical characteristics, and antibiotic resistance was assessed using the Kirby-Bauer disk diffusion method. The study revealed a strong correlation of 11.613 (95 % Confidence Interval [CI]: 6.495–20.765, p-value: 0.000) between microbial burden in vended water (water sold by vendors) used by different households consumption and antimicrobial resistance in DEC. Water sources showed a high prevalence of DEC, primarily Enteroaggregative, enteropathogenic, enterotoxigenic, and enteroinvasive E. coli. DEC isolates also exhibited varying antibiotic resistance, and genes like bla CTX-M, bla TEM, and bla SHV were identified using PCR. The Spearman correlation coefficient was one, showing that resistance genes in water and stool were perfectly correlated. The study emphasizes the significance of improving water quality, hygiene, and antibiotic-resistant bacteria control to prevent and manage DEC and infectious diseases in Kenya. To combat outbreaks, effective surveillance and rapid reaction procedures are essential. The findings help to shape public health policies and initiatives. PhD research Publcation

Hidden Markov Model for Cardholder Purchasing Pattern Prediction

Wed, 18 Mar 2026 00:00:00 GMT

Hidden Markov Model for Cardholder Purchasing Pattern Prediction Okoth, Jeremiah Otieno, Abstract—This study utilizes the Hidden Markov Model to predict cardholder purchasing patterns by monitoring card transaction trends and profiling cardholders based on dominant transactional motivations across four merchant sectors, i.e., service centers, social joints, restaurants, and health facilities. The research addresses shortfalls with existing studies which often disregard credit, prepaid, and debit card transactions outside online transaction channels, primarily focusing only on credit card fraud detection. This research also addresses the challenges of existing prediction algorithms such as support vector machine, decision tree, and naïve Bayes classifiers. The research presents a three-phased Hidden Markov Model implementation starting with initialization, de-coding, and evaluation all executed through a Python script and further validated through a 2-fold cross validation technique. The study uses an experimental design to systematically investigate cardholder transactional patterns, exposing training and validation data to varied initial and transition state probabilities to optimize prediction outcomes. The results are evaluated through three key metrics, i.e., accuracy, precision, and recall measures, achieving optimal performance of 100% for both accuracy and precision rates, with a 99% on recall rate, thereby outperforming existing predictive algorithms like support vector machine, decision tree, and Naïve Bayes classifiers. This study proves the Hidden Markov Model’s effectiveness in dynamically modeling cardholder behaviors within merchant categories, offering a full understanding of the real motivations behind card transactions. The implication of this research encompasses enhancing merchant growth strategies by empowering card acquirers and issuers with a better approach to optimize their operations and marketing synergies based on a clear understanding of cardholder transactional patterns. Further, the research significantly contributes to consumer behavior analysis and predictive modeling within the card payments ecosystem. Keywords—Hidden Markov Model; cardholder transaction patterns; merchant categories; predictive algorithms MSc Research Publication