Modeling Self Medication Risk Factors (A Case Study of Kiambu County, Kenya)
American Journal of Theoretical and Applied Statistics
Volume 7, Issue 2, March 2018, Pages: 58-66
Received: Jan. 15, 2018; Accepted: Jan. 29, 2018; Published: Feb. 27, 2018
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Authors
Thomas Mageto, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
Allan Zablon, Department of Statistics and Actuarial Science, Jomo Kenyatta University of Agriculture and Technology, Nairobi, Kenya
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Abstract
In this paper self-medication risk factors are investigated and multivariate model proposed. A random sample of four major hospitals was selected, one from each sub-county and sample of 728 patients selected from selected hospitals using stratified random sampling. The data was collected using semi structured questionnaires and analyzed in R program after cleaning for non-response. Preliminary analysis was carried to check for statistical significance of the risk factors of age, gender, income, marital, education, employment and insurance status. All proposed risk factors were statistically significant except employment factor when using chi-square test for each of discrete variables while both age and income continuous variables were significant at α =0.05 level of significance when fitting simple logistic regression model. The initial multivariate logistic regression model was fitted and variables of marital and insurance status of persons were statistically insignificant and therefore improved model was fitted less marital and insurance factors. The overall significance of the model was determined using Hosmer and Lemeshow goodness-of-fit test and the model recorded p-value of 0.7751 that indicates that there is no significant difference between observed and predicted probability, therefore the model would be used to predict chance of self-medication in the presence of significant risk factors. In conclusion therefore there is need to initiate legislation on policies that will guide self-medication that include provision of necessary knowledge and regulating the practice to avoid over dose, wrong prescriptions and emergence of human pathogen resistance microorganisms or serious consequences like resistance to medication in future guided by the prevalence results obtained from proposed model.
Keywords
Medication, Prevalence, Multivariate Logistic Regression, Risk Factors, Chi-Square and Goodness-of-Fit
To cite this article
Thomas Mageto, Allan Zablon, Modeling Self Medication Risk Factors (A Case Study of Kiambu County, Kenya), American Journal of Theoretical and Applied Statistics. Vol. 7, No. 2, 2018, pp. 58-66. doi: 10.11648/j.ajtas.20180702.12
Copyright
Copyright © 2018 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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