Tungiasis is a neglected parasitic disease that significantly affects communities, especially in developing countries. This study developed a Bayesian severity of the jigger infestation model and its spatial counterpart. Putative determinants leading to different levels of infestation and the most affected areas were to be identified through the model. We collected data through a cross-sectional study with a multi-stage sampling design. A structured questionnaire was administered in each household to capture variables used for modelling jigger infestations. The severity of jigger infestation categorized for each individual was modelled against all the other predictor variables. It was also integrated with spatial data to determine the spatial distribution pattern of jigger infestation. A Bayesian multinomial logistic regression model was used to assess the association between various predictors and different infestation levels. Specifically, an ordered Bayesian Severity Hierarchical (OBSH) categorical model was obtained. This model was categorical based on the Counties (1-Nyeri, 2-Murang'a and 3-Kiambu). Results from this model showed that for a one-unit decrease in the poverty index at level 1 (individuals categorized as poor) there was about a 69% increase in the severity of jigger infestation. A one-unit increase in the percentage of clay in the soil increased the odds ratio of the severity of jigger infestation by a factor of 11.21 while a high percentage of nitrogen in the soil lowered the severity of infestation. Severity of jigger infestation reduced from the baseline, Nyeri County to Kiambu County. It also increased with increasing altitude due to a decrease in nitrogen levels.
This study aims at determining the use of Multinomial Logistic Regression (MLR) model which is one of the important methods for categorical data analysis. This model particularly deals with one nominal or ordinal response variable that has more than two categories. Despite the fact that many researchers have applied this model in data analysis in many areas, for instance behavioral, social, health, and educational, a study on spending habits of University students have never been done. To identify the model by practical way, we conducted a survey research among students from University of Embu. Segment of the population of students in undergraduate level, a sample of 376 was selected. We employed the use stratified random sampling and simple random sampling without replacement in each stratum. The response variable consisted of five categories. Four of explanatory variables were used for building the primary (MLR) model. The model was tested through a set of statistical tests to ensure its appropriateness for the data. From the results, the study reveals that year of study, family financial level, gender and school are significant factors in explaining spending habits of students. Despite the fact that gender is one of the deterministic factors of financial behavior of student, this model identified family level of income as a major deterministic factor. Conclusively, using MLR model accurately defines the relationship between the group of explanatory variables and the response variable. It also identifies the effect of each of the variables, and we can predict the classification of any individual case. The researchers recommend that, the Universities peer counselling department, should hold trainings on the basis of major determinant of financial spending behavior i.e. family financial level.
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