EVALUATION OF FACTORS ASSOCIATED WITH POOR NUTRITIONAL STATUS AMONG UNDER FIVE CHILDREN IN NIGERIA USING LINEAR, QUANTILE AND LOGISTIC REGRESSION

Abstract

Childhood is a time of active growth in terms of physical size, mental, emotional and psychological development. Normal growth is dependent on adequate nutrition and encompasses major transformations from birth to adulthood. Quantile regression does not restrict attention to the conditional mean and therefore it permits to approximate the whole conditional distribution of a response variable. This study aim to evaluate the performance of parametric Ordinary Least Square (OLS) and non-parametric regression (quantile function) method, also to use logistics regression to determine factors affecting nutritional status of under five children in Nigeria. A representative sample of 24,505 under five children (0-59 months) within households in communities was obtained from the Nigeria Demographic and Health Survey (NDHS 2013). For the over-nutritional status (overweight and obese), the body mass index for-age z-scores was used as the outcome variable, also for the under-nutritional status; height-for-age, weight-for-height and weight-for-age was respectively used while the explanatory variables include child's age, wealth index, sex of child, size of child at birth, maternal age, mother's educational level, breastfeeding duration, working status of mother, mother's BMI, region, antenatal visits. The outcome variables (under nutrition) were further classified as dichotomous variable as stunted, wasted and underweight or not, using their cut-off. Ordinary Least Square regression was used to determine the average effect of factors of the nutritional status while QR was used to determine how a particular/corresponding percentile of the distribution was associated with covariates. Lastly, logistic regression was carried out to also determine factors affecting under nutrition status of under-five children. Quantile regression was able to detect the amount of under estimation and over estimation by the Ordinary Least Square regression. The QR result showed that child's age (24 to 35 months), sex, size of the child at birth, maternal education and region significantly contributed to under-nutrition while child's age (between 24 to 47 months), breastfeeding duration, mother's BMI, maternal working status, size of the child at birth and wealth index contributed significantly to overweight. The logistic regression identified maternal age, child's age, wealth index (those in richer and richest categories), sex, size of child at birth, mother's education (at least secondary), working mothers, all the regions (except south west), mother's BMI (overweight and obese) as factors significantly related to under-nutrition. The quantile regression can be used to model specific parts of the distribution of the nutritional indices and should be preferred to OLS regression if the original scale of the outcome variable was continuous with a non-normal distribution. Attention should be paid to some factors which have contributed to the mal-nutritional status (either over-nutrition or under-nutrition) of the under five children. The factors which include child 's age, size of the child birth, antenatal clinic visits and region.