Department of Statistics, Shahjalal University of Science & Technology, Bangladesh.
Nutr J. 2011 Nov 14;10:124. doi: 10.1186/1475-2891-10-124.
The study attempts to develop an ordinal logistic regression (OLR) model to identify the determinants of child malnutrition instead of developing traditional binary logistic regression (BLR) model using the data of Bangladesh Demographic and Health Survey 2004.
Based on weight-for-age anthropometric index (Z-score) child nutrition status is categorized into three groups-severely undernourished (< -3.0), moderately undernourished (-3.0 to -2.01) and nourished (≥-2.0). Since nutrition status is ordinal, an OLR model-proportional odds model (POM) can be developed instead of two separate BLR models to find predictors of both malnutrition and severe malnutrition if the proportional odds assumption satisfies. The assumption is satisfied with low p-value (0.144) due to violation of the assumption for one co-variate. So partial proportional odds model (PPOM) and two BLR models have also been developed to check the applicability of the OLR model. Graphical test has also been adopted for checking the proportional odds assumption.
All the models determine that age of child, birth interval, mothers' education, maternal nutrition, household wealth status, child feeding index, and incidence of fever, ARI & diarrhoea were the significant predictors of child malnutrition; however, results of PPOM were more precise than those of other models.
These findings clearly justify that OLR models (POM and PPOM) are appropriate to find predictors of malnutrition instead of BLR models.
本研究旨在开发一个有序逻辑回归（OLR）模型，以确定儿童营养不良的决定因素，而不是使用孟加拉国 2004 年人口与健康调查的数据开发传统的二项逻辑回归（BLR）模型。
基于体重与年龄的人体测量指数（Z 分数），儿童营养状况分为三组——严重营养不良（< -3.0）、中度营养不良（-3.0 至-2.01）和营养良好（≥-2.0）。由于营养状况是有序的，因此可以开发一个 OLR 模型——比例优势模型（POM），而不是两个单独的 BLR 模型，以找到营养不良和严重营养不良的预测因素，如果比例优势假设满足。由于一个协变量违反了假设，因此该假设的满足度较低（0.144）。因此，还开发了部分比例优势模型（PPOM）和两个 BLR 模型，以检查 OLR 模型的适用性。还采用了图形测试来检查比例优势假设。
所有模型都确定儿童年龄、出生间隔、母亲教育、母亲营养、家庭财富状况、儿童喂养指数以及发热、急性呼吸道感染和腹泻的发生率是儿童营养不良的重要预测因素；然而，PPOM 的结果比其他模型更精确。
这些发现清楚地证明，OLR 模型（POM 和 PPOM）适合于寻找营养不良的预测因素，而不是 BLR 模型。
