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首页> 外文期刊>Italian Journal of Public Health >Trend in BMI z-score among Private Schools’ Students in Delhi using Multiple Imputation for Growth Curve Model
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Trend in BMI z-score among Private Schools’ Students in Delhi using Multiple Imputation for Growth Curve Model

机译:使用多重插补增长曲线模型的德里私立学校学生BMI z得分趋势

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Objective: The aim of the study is to assess the trend in mean BMI z-score among private schools’ students from their anthropometric records when there were missing values in the outcome. Methodology: The anthropometric measurements of student from class 1 to 12 were taken from the records of two private schools in Delhi, India from 2005 to 2010. These records comprise of an unbalanced longitudinal data that is not all the students had measurements recorded at each year. The trend in mean BMI z-score was estimated through growth curve model. Prior to that, missing values of BMI z-score were imputed through multiple imputation using the same model. A complete case analysis was also performed after excluding missing values to compare the results with those obtained from analysis of multiply imputed data. Results: The mean BMI z-score among school student significantly decreased over time in imputed data (β= -0.2030, se=0.0889, p=0.0232) after adjusting age, gender, class and school. Complete case analysis also shows a decrease in mean BMI z-score though it was not statistically significant (β= -0.2861, se=0.0987, p=0.065). Conclusions: The estimates obtained from multiple imputation analysis were better than those of complete data after excluding missing values in terms of lower standard errors. We showed that anthropometric measurements from schools records can be used to monitor the weight status of children and adolescents and multiple imputation using growth curve model can be useful while analyzing such data
机译:目的:该研究的目的是从人类学记录中评估当结果中缺少值时,私立学校学生的平均BMI z得分趋势。方法:从1年级到12年级的学生的人体测量数据来自2005年至2010年印度德里的两所私立学校的记录。这些记录包含不平衡的纵向数据,并非所有学生每年都记录测量数据。通过生长曲线模型估计平均BMI z得分的趋势。在此之前,使用同一模型通过多次插补来估算BMI z得分的缺失值。在排除缺失值之后,还进行了完整的病例分析,以将结果与通过多次插补数据分析获得的结果进行比较。结果:调整年龄,性别,班级和学校后,根据估算数据,在校学生的平均BMI z得分随时间显着下降(β= -0.2030,se = 0.0889,p = 0.0232)。完整病例分析也显示平均BMI z得分降低,尽管在统计学上不显着(β= -0.2861,se = 0.0987,p = 0.065)。结论:从多重插补分析中获得的估计值在以较低的标准误表示的情况下排除了缺失值之后,要比完整数据的估计值更好。我们表明,学校记录中的人体测量数据可用于监测儿童和青少年的体重状况,并且在分析此类数据时使用增长曲线模型进行多次插补可能非常有用

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