Application of ordinal logistic regression analysis in determining risk factors of child malnutrition in Bangladesh

Nutrition Journal

Rajwanur M Rahman¹  Sumonkanti Das² 

[1] Shafi Consultancy Bangladesh, Sylhet, Bangladesh;Department of Statistics, Shahjalal University of Science & Technology, Bangladesh

关键词: Child malnutrition;    Anthropometric index;    Binary logistic regression model;    Partial proportional odds model;    Proportional odds model;    Ordinal logistic regression model;   

Others  :  828456
DOI  :  10.1186/1475-2891-10-124

 received in 2011-01-23, accepted in 2011-11-14,  发布年份 2011

【 摘 要 】

Background

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.

Methods

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.

Results

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.

Conclusion

These findings clearly justify that OLR models (POM and PPOM) are appropriate to find predictors of malnutrition instead of BLR models.

【 授权许可】

   
2011 Das and Rahman; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

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