Background: Child malnutrition is a public health problem in Côte d’Ivoire. The persistence of malnutrition, sometimes looking like an epidemic, seems contradictory in view of the many natural potentialities available in the Health Care District of Danané. The level of rainfall conducive to cereal production is satisfactory; this should normally guarantee minimum food availability. This is not always the case because according to World Food Program (WFP) and Food and Agriculture Organization (FAO), the prevalence of global food insecurity (moderate and severe), is 20% in this area. To our knowledge, no study on this specific aspect has been made in the Health Care District of Danané. Through this work, we aim to understand the factors associated with malnutrition among children under five in the Health Care District of Danane in order to identify and implement the best prevention and control strategies. Methods: Using a Case-control study (n =109 cases and n =218 controls), we performed a logistic regression model to identify factors associated with child malnutrition (z-score < -2) at the level of child, households, and of person in charge of the child, in the Health Care District of Danané from January 1st to March 31st, 2017. Results: Odds ratio (OR) of child malnutrition were significantly high in the presence of over one child under 5 in a household (OR=3.05, 95%CI [1.34-6.91]; p ?0.01), of influenza episodes (OR=3.48, 95%CI [2.10-6.80], p ?0.01), of diarrhea (OR=2.56, 95%CI [1.30-5.05]; p ?0.01), and non-drinking water consumption OR=1.37, 95%CI [1.17-2.48]; p ?0.01). The care of the child by his/her biological mother, progressive weaning, exclusive breastfeeding, deworming, age of the person in charge of the child (?25 years), were significantly protective factors. Conclusion: Promoting family planning, hygiene, and the strengthening of gradual weaning, exclusive breastfeeding and deworming could help reduce child malnutrition in Health Care District of Danané.
Keywords: Infant Malnutrition - Odds Ratio - Logistic Regression - Case Control.