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Modeling Self Medication Risk Factors (A Case Study of Kiambu County, Kenya)

机译:建立自我用药风险因素的模型(以肯尼亚Kiambu县为例)

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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 selfmedication 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.
机译:本文研究了自我用药的危险因素,并提出了多元模型。选择了四家主要医院的随机样本,每个子县一个样本,并使用分层随机抽样从选定医院中选择了728名患者。使用半结构化问卷收集数据,并在清除无响应后在R程序中进行分析。进行了初步分析,以检查年龄,性别,收入,婚姻,教育,就业和保险状况等危险因素的统计显着性。当对每个离散变量进行卡方检验时,除就业因素外,所有拟议的风险因素均具有统计学意义,而当拟合简单逻辑回归模型时,年龄和收入连续变量在α= 0.05的显着性水平上均具有显着性。拟合了最初的多元逻辑回归模型,并且人的婚姻状况和保险状况变量在统计上不显着,因此改进的模型适用于较少的婚姻和保险因素。使用Hosmer和Lemeshow拟合优度检验确定模型的整体重要性,并且模型记录的p值为0.7751,这表明观察到的概率与预测的概率之间没有显着差异,因此该模型将用于预测机会存在重大危险因素的情况下进行自我药物治疗。因此,总而言之,因此有必要启动有关指导自我用药的政策的立法,其中包括提供必要的知识并规范实践,以防止过量使用,处方错误以及人类病原体抗药性微生物的出现或未来对药物的抗药性等严重后果。从建议的模型中获得的普遍性结果指导。

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