Factors Influencing Mothers to Initiate Early Complementary Feeding in Darjeeling, West Bengal

Abstract

Introduction: More than half of the infants are started on complementary feed before six months. Perception of Inadequate Milk supply (PIM) remains one of the most frequent causes of early institution of complementary feeding. Perception of Inadequate Weight Gain (PIWG), may also influence a mother’s decision to start early complementary feeding which may be a consequence of PIM. Aim: To find the proportion of early complementary feeding and to compare predictive models in identifying mothers who are more likely to initiate early complementary feeds. Materials and Methods: A descriptive epidemiological study with cross-sectional design was carried out for three months between May-July 2013 among 99 mother-child dyads, with children in the 6-24 months age group, residing in Matigara block of West Bengal. Data were collected regarding infant feeding practices and prevalence of early complementary feeding was calculated. Logistic regression was done using SPSS Version 20.0 with the predictor variables classified under three constructs of socio-demography, health system related factors and mothers perceptions; and models were constructed using one, two or all three constructs. Results: The proportion of early complementary feeding was 30.3%. In univariate analysis, none of the variables were found to be statistically significant. However, on multivariate analysis, Model (with all variables) was found to be statistically significant. The best Area Under Curve (AUC) was found with the inclusion of the socio-demography, health system related factors and mothers’ perception. Conclusion: The perception of the mother regarding adequacy of her milk production and regarding the child’s weight gain are the most important predictors of early initiation of complementary feeding. All counselling activities for the mother should therefore be designed in a way as to address these issues.

Keywords

• Breast milk
• Infant
• Regression analysis