Having completed my High School education from Tenges Boys’ High School, my college journey began at Moi University where I pursued BSc in Applied Statistics with Computing. Since then, I have worked as a management Trainee with the Ministry of ICT and later as Statistician with the Kenya medical Research Institute, where I developed the interest in applying statistical methodologies to address health care challenges. This ambition influenced my enthusiasm to pursue a master’s degree in Biometry from the University of Nairobi. My career aspiration is to continually apply my knowledge and expertise in solving health care problems.
Project Title:Trends in Low Birthweight Deliveries as an Indicator of Malaria Transmission
Low birthweight (LBW < 2500 g) is a phenomenon that is more pronounced in developing countries where infectious diseases are most prevalent. Malaria infection in all endemic areas of Sub-Saharan Africa have become an important factor associated with LBW during pregnancy with increased susceptibility to mothers of lower parities. Hence LBW can serve as an indicator of malaria transmission. Part of its control mechanism is to study the traits that are linked to its spread. This study examined trends of LBW prevalence in Kilifi Health Demographic and Surveillance System area. Trend significance was assessed using the Mann-Kendall test while variations of LBW prevalence were assessed by the monthly seasonal indices obtained from the Moving Average Method. Change point analysis was conducted to establish point in time when significant change in LBW prevalence occurred. Seasonal Autoregressive Integrated Moving Average model that described the LBW prevalence over time was fitted to assess the trend in the predicted values. Additive Logistic Regression was used to obtain Odds Ratio of LBW among primiparity with reference to multiparity and interpreted in relation to the contextual information regarding the changing landscape of malaria transmission. Findings from the study revealed a significant decreasing trend of LBW prevalence. Variations of LBW prevalence could be explained by changes in the climatic conditions. Odds ratios for LBW among the primiparity could be used to define the transition of malaria in Kenya. Findings hereby, can help the government improve on the measures to combat malaria transmission in the mostly affected areas.