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Predictors of missing data when asking parents about their children's diet based on 'Oral Health Situation of Iranian Children' Survey

机译:根据“伊朗儿童的口腔健康状况”调查向父母询问孩子的饮食时,缺少数据的预测因素

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Background: The predictors of missing data when parents fill out questionnaire about their children's diet are not defined. The aim of this study was to evaluate predictors which affect unresponsiveness to diet questions based on " Oral Health Situation of Iranian Children" Survey carried out in 1998. Materials and Methods: A dummy variable was created as dependent variable according to responding eight questions relating to diet. Then predictors of missing data were defined using multivariable logistic regression and classification tree method. To evaluate goodness of fit of logistic regression model, sensitivity and specificity were assessed. Classification tree analysis was done by Quest growing method. Significance level was set at 0.05 in logistic regression analysis. Observations and Results: "Missing data" variable was marked as missing in 616 (7.2%) of questionnaires. In logistic regression model revealed that, gender, mother's education level and father's education level didn't affect "missing data" variable (P > 0.05). But, dmf index (OR = 0.94), Area of living (OR = 0.49), number of children in the family (OR = 1.19), sibling order (OR = 0.85), brushing (OR = 0.84) and visiting dentist (OR = 0.59) had statistically significant association with dependent variable (P Conclusions: Area of living, dmf index, number of children in the family, sibling order, brushing and visiting dentist are significant variables for predicting the risk of missing data when asking parents about their children's diet.
机译:背景:父母填写有关孩子饮食的问卷时,缺少数据的预测因素尚未定义。这项研究的目的是基于1998年进行的“伊朗儿童口腔健康状况”调查,评估影响饮食问题无反应性的预测因素。材料和方法:根据对以下8个问题的回答,创建了一个虚拟变量作为因变量。饮食。然后使用多变量逻辑回归和分类树方法定义缺失数据的预测变量。为了评估逻辑回归模型的拟合优度,评估了敏感性和特异性。分类树分析是通过Quest增长方法进行的。在逻辑回归分析中,显着性水平设置为0.05。观察结果:616份(7.2%)调查表中“缺少数据”变量被标记为缺失。在逻辑回归模型中发现,性别,母亲的受教育程度和父亲的受教育程度不影响“缺失数据”变量(P> 0.05)。但是,dmf指数(OR = 0.94),居住面积(OR = 0.49),家庭中的孩子数(OR = 1.19),兄弟姐妹顺序(OR = 0.85),刷牙(OR = 0.84)和看牙医(OR = 0.59)与因变量具有统计学上的显着相关性(P结论:生活面积,dmf指数,家庭中的孩子数量,兄弟姐妹顺序,刷牙和来访牙医是当询问父母有关其数据的风险时可以预测丢失数据风险的重要变量儿童饮食。

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