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Assessment of nutritional status using anthropometric variables by multivariate analysis

机译：多变量分析评估使用人体测量变量的营养状况

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Undernutrition is a serious health problem and highly prevalent in developing countries. There is no as such confirmatory test to measure undernutrition. The objective of the present study is to determine a new Composite Score using anthropometric measurements. Composite Score was then compared with other methods like body mass index (BMI) and mid-upper arm circumference (MUAC) classification, to test the significance of the method. Anthropometric data were collected from 780 adult Oraon (Male?=?387, Female?=?393) labourers of Alipurduar district of West Bengal, India, following standard instruments, and protocols. Nutritional status of the study participants were assessed by conventional methods, BMI and MUAC. Confirmatory factor analysis was carried out to reduce 12 anthropometric variables into a single Composite Score (C) and classification of nutritional status was done on the basis of the score. Furthermore, all the methods (BMI, MUAC and C) were compared and discriminant function analysis was adopted to find out the percentage of correctly classified individuals by each of the three methods. The frequency of undernutrition was 45.9% according to BMI category, 56.7% according to MUAC category and 51.8% according to newly computed Composite Score. Further analysis showed that Composite Score has a higher strength of correct classification (98.7%), compared to BMI (95.9%) and MUAC (96.2%). Therefore, anthropometric measurements can be used to identify nutritional status in the population more correctly by calculating Composite Score of the measurements and it is a non-invasive and relatively correct way of identification.
机译：营养不良是发展中国家的严重健康问题和普遍存在。没有这样的确认测试可以测量欠税。本研究的目的是使用人类测量测量来确定新的复合评分。然后将综合评分与体质量指数（BMI）和中上臂周长（MUAC）分类等其他方法进行比较，以测试该方法的重要性。从780名成人奥仑（男性？=？387，女性？=？393）劳动力，印度alipurduar区的劳动者，遵循标准仪器和协议。通过常规方法，BMI和MUAC评估研究参与者的营养状况。进行了确认因子分析，以将12个人测量变量降低到单个综合评分（C）中，并在得分的基础上进行营养状况的分类。此外，比较所有方法（BMI，MUAC和C），采用判别函数分析来查明三种方法中的每种方法的正确分类个体的百分比。根据BMI类别的额外频率为45.9％，根据MUAC类别为56.7％，根据新计算的综合评分51.8％。进一步的分析表明，与BMI（95.9％）和MUAC（96.2％）相比，复合评分具有更高的正确分类（98.7％）强度。因此，通过计算测量的综合评分，可以使用人体测量测量来更正地识别人群中的营养状态，并且它是一种非侵入性且相对正确的识别方式。

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