Modeling Diabetes Risk Factors (A Case Study of Focus Medical Centre in Kiambu, Kenya 2016)

Thomas Mageto, E. Njuguna, Dolleen Osundwa
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Abstract

This study sought to model risk factors of diabetes (A case study of Focus Medical Center in Kiambu, Kenya) for the year 2016. We considered sample of size 181 patients and carried descriptive statistics, bivariate analysis, Chi-Square test and Hosmer and Lemeshow test. The independence test between response variable (diabetes) and predictor variables (age, obesity, alcohol, smoking and hypertension) was carried. The variables age, obesity, alcohol and hypertension were found to be statistically significant at α =0.05 level of significant. A multiple logistic regression model was fitted and the fitted regression model indicated that the predictor variables age, obesity and alcohol were statistically significant. The results of the odds ratios show that age, obesity and alcohol consumption are positively associated with diabetes. The fitted reduced multiple logistic regression model was subjected to an overall goodness-of-fit test and results indicate that there is no significant difference between the observed and predicted probability. Based on the results of this study, we recommend that special attention should be given to individuals advanced in age, consume alcohol or who are obese for screening as there is a high possibility of testing positive for diabetes for health care givers to monitor and manage the condition. Further, healthy lifestyles should be promoted among the general population and in particular, the diabetic patients to increase the chance of properly managing the condition. A further study ought to be conducted to assess treatment interventions of diabetes to ascertain the effectiveness and recommend the best medication for patients suffering from diabetes.
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糖尿病风险因素建模(以肯尼亚Kiambu的Focus医疗中心为例,2016)
本研究试图模拟2016年糖尿病的危险因素(以肯尼亚Kiambu的Focus医疗中心为例)。我们考虑样本量为181例患者,并进行描述性统计、双变量分析、卡方检验和Hosmer and Lemeshow检验。对反应变量(糖尿病)与预测变量(年龄、肥胖、饮酒、吸烟、高血压)进行独立检验。年龄、肥胖、酒精、高血压等变量在α =0.05显著水平上差异有统计学意义。对多元logistic回归模型进行拟合,拟合后的回归模型显示,年龄、肥胖、酒精等预测变量具有统计学意义。比值比结果显示,年龄、肥胖和饮酒与糖尿病呈正相关。拟合的简化多元逻辑回归模型进行了整体拟合优度检验,结果表明,观测概率与预测概率之间没有显著差异。基于这项研究的结果,我们建议特别关注年龄较大、饮酒或肥胖的个体进行筛查,因为卫生保健提供者监测和管理糖尿病的可能性很高。此外,应在一般人群中,特别是糖尿病患者中推广健康的生活方式,以增加适当控制病情的机会。应该进行进一步的研究来评估糖尿病的治疗干预措施,以确定其有效性,并为糖尿病患者推荐最佳药物。
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