Email updates

Keep up to date with the latest news and content from Nutrition Journal and BioMed Central.

Open Access Research

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

Sumonkanti Das1* and Rajwanur M Rahman2

Author affiliations

1 Department of Statistics, Shahjalal University of Science & Technology, Bangladesh

2 Shafi Consultancy Bangladesh, Sylhet, Bangladesh

For all author emails, please log on.

Citation and License

Nutrition Journal 2011, 10:124  doi:10.1186/1475-2891-10-124

Published: 14 November 2011

Abstract

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.

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