Modeling Risk Factors Associated with Snakebite Morbidity Using a Logit Binary Model in Chemalingot, Baringo County, Kenya
Kenneth Chepsergon *
Department of Mathematics and Computer Science, University of Eldoret, Kenya.
Julius Koech
Department of Mathematics and Computer Science, University of Eldoret, Kenya.
Samson W. Wanyonyi
Department of Mathematics and Computer Science, Pwani University, Kenya.
*Author to whom correspondence should be addressed.
Abstract
The public health problem of snake envenomation affects Chemalingot within Baringo County due to regular human-snake encounters. The area maintains high snakebite rates with major medical consequences which strain healthcare institutions. A logit binary model from this study evaluated snakebite morbidity risk factors in Chemalingot to determine major contributing elements in severe health results. The study employed a retrospective design, where past medical records from Chemalingot district hospital was selected using purposive sampling scheme. A total of 266 snakebite cases were analyzed. The findings reveal an overall morbidity prevalence of 30.0% due to snakebites. The average age of the study participants was 19.26 years, with a standard deviation of 15.30 and the length of hospital stay was 4 days. Morbidity rates varied significantly based on the timing of the bite, with the highest rates occurring in the evening 38.2% and night 26.3%. At the multivariate level, both the duration and timing of hospital visits were identified as strong significant factors affecting morbidity. Helps in designing appropriate intervention measures to optimize treatment outcomes in the county and get the required health policies that tailored to lower snake morbidity incidence in the region.
Keywords: Snakebite, risk factors, morbidity, optimize outcomes, global health security