Browsing by Author "Gitaka, Jesse"

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Association between Metabolic Syndrome and Substance Dependence: A Cross-Sectional Study in Kenya
(European Journal of Medical and Health Sciences, 2022-06-28) Mbuguah, Samuel Mungai; Mecha, Ezekiel; Kirira, Peter; Njoroge, Margaret; Malala, Boniface; Gitaka, Jesse; Makokha, Francis; Mwenda, Catherine
Metabolic syndrome and its defining components remain an understudied area of human health research in Kenya and Africa. Understanding the relationship between substance dependence and the occurrence of metabolic syndrome is critical in prevention and clinical management of the related complications. This was a cross-sectional study in 6 rehabilitative centers in 3 counties in Kenya with a conveniently selected sample size of 166 participants. A signed informed consent was obtained from each participant following which anthropometric and biochemical measurements were obtained. Descriptive statistics and chi-square test were used to describe the prevalence of metabolic syndrome and the relationship of the defining criteria with the substance of dependence. A quarter of the respondents were overweight and 6% obese. Fasting blood glucose was elevated in 62% of the sampled population with triglycerides having a significant variation with a mean of 216.95mg/ dL and a standard deviation of 151.107. A prevalence rate of 4.8% was established based on the Harmonized Joint Scientific Statement on Metabolic syndrome for the African region. 87.34% of the population showed at least one elevated defining criteria with alcohol as the most prevalent substance of dependence. There was statistical difference of fasting blood glucose and triglycerides with alcohol use. The findings indicate that alcohol use resulted to elevated levels of fasting blood glucose and triglycerides. There is need for emphasis on a multidisciplinary approach to substance dependence and metabolic syndrome management integrating physical activity interventions, dietary modifications and psychotherapeutic approaches.
Feasibility, acceptance, and workflow integration of an AI- enabled clinical decision support system for non- communicable diseases in Kiambu County, Kenya: A mixed- methods implementation evaluation
(Open Research Africa, 2026-02-28) Kamau, David; Mbuguah, Samuel Mungai; Omondi, Protus; Kamau, Gideon; Mbugua, George; Ngugi, Rosslyn; Ngure, Jane; Ngaruiya, Njeri; Wamaitha, Nicole; Munene, Joan; Maina, Njogu; Gitaka, Jesse
Background Non-communicable diseases (NCDs), particularly hypertension and diabetes, impose a growing burden on health systems in low- and middle- income countries like Kenya. Artificial intelligence (AI)-driven Clinical Decision Support Systems (CDSS) may enhance diagnostic accuracy and adherence to clinical guidelines, yet their feasibility and acceptability among frontline clinicians in real-world settings underexplored. Methods We conducted a mixed-methods implementation study in 10 health facilities in Kiambu County, Kenya. The evaluation comprised three components. First, a retrospective review of 1,929 patient records established baseline NCD prevalence and care patterns. Second, we assessed the clinical acceptance of the NCDAI platform, an AI-CDSS using a Large Language Model with Retrieval-Augmented Generation, through 300 independent expert physician reviews of its recommendations. Third, we captured clinician perspectives via a cross-sectional Knowledge, Attitudes, and Practices (KAP) survey (n=29) and key-informant interviews (n=11). Results The baseline cohort demonstrated a substantial NCD burden: 72.8% had a history of hypertension and 43.1% had diabetes. Expert validation showed high acceptance of AI-generated recommendations, with 67.0% “Agreed,” 26.3% “Partially Agreed,” and only 6.7% “Disagreed,” yielding 93.3% overall (partial or full) agreement. Most disagreements arose in medication and treatment plan recommendations. Clinicians demonstrated strong digital readiness; 86% reported moderate or good IT proficiency, and 69% were already aware of AI in healthcare. Patient-related factors were the most commonly cited barriers to NCD care (33%). Qualitative findings identified operational challenges particularly duplicative data entry arising from parallel paper-based workflows as the main impediment to NCDAI adoption amid high patient volumes. Conclusions An AI-driven CDSS for NCD management is feasible and highly acceptable to expert physicians and frontline clinicians in Kenya. The key barrier is not reluctance toward AI but workflow friction. Effective scale-up will require investment in digital infrastructure to enable seamless integration and replacement of paper-based systems
Unraveling the ‘community effects’ of interventions against malaria endemicity: a systematic scoping review
(BMJ Public Health, 2024-10-29) Ko, Yura K; Kagaya, Wataru; Chan, Chim W; Kanamori, Mariko; Mbugua, Samuel Mungai; Rotich, Alex K; Kanoi, Bernard N.; Ngara, Mtakai; Gitaka, Jesse; Kaneko, Akira
Objectives There is an urgent need to maximise the effectiveness of existing malaria interventions and optimise the deployment of novel countermeasures. When assessing the effects of interventions against malaria, it is imperative to consider the interdependence of people and the resulting indirect effects. Without proper consideration of the effects, the interventions’ impact on health outcomes and their cost-effectiveness may be miscalculated. We aimed to summarise how the indirect effects of malariainterventions were and reported. Design We conducted a scoping review. Data sources We searched PubMed, Web of Science and EMBASE. Eligibility criteria We included studies that were conducted to quantify the indirect effects of any interventions for all species of Plasmodium infection. Data extraction and synthesis We used a standardised data collection form to obtain the following information from each record: title, name of authors, year of publication, region, country, study type, malaria parasite species, type of interventions, type of outcomes, separate estimated indirect effect for different conditions, pre-specified to measure indirect effect, secondary analysis of previous study, methods of indirect effects estimation, terms of indirect effects, and if positive or negative indirect effects observed. Results We retrieved 32 articles and observed a recent increase in both the number of reports and the variety of terms used to denote the indirect effects. We further classified nine categories of methods to identify the indirect effects in the existing literature and proposed making comparisons conditional on distance to account for mosquito flight range or intervention density within that range. Furthermore, we proposed using the words community effects or spillover effects as standardised terms for indirect effects and highlighted the potential benefits of mathematical models in estimating indirect effects. Conclusions Incorporating assessment of indirect effects in future trials and studies may provide insights to optimise the deployment of existing and new interventions, a critical pillar in the current fight against malaria globally.