Abstract
Body Mass Index has been investigated using the traditional regression methods which may not provide a complete picture of the effects of the independent variables when the outcome is continuous and skewed. Information on the nutritional status of vulnerable adolescents in Nigeria is scanty thereby hindering appropriate intervention by policy decision-makers. We investigated the nutritional status of vulnerable adolescents by examining their body mass index (BMI). A cross-sectional survey of vulnerable adolescents, aged 10-17 years was conducted in three local government areas in Rivers state, Nigeria. A structured questionnaire was used to gather information on the economic status, means of livelihood and accessibility to education, nutrition and health of the adolescents. Quantile regression models were fitted to the data. About 39% of the 494 adolescents were underweight, 49.8% had normal weight, 5.5% were overweight while 6.1% were obese. Age was a significant predictor of BMI for the males at the 50th quantile. Adolescent males that experienced food insecurity showed lower BMI compared to those who were food secured. Age, sex, food insecurity and household economy were determinants of BMI among vulnerable adolescents.
Highlights
We investigated the nutritional status of vulnerable adolescents by examining their body mass index (BMI)
Quantile regression was used to investigate changes in the BMI distribution of Chinese adults and the results showed that effects of different covariates were different across the BMI distribution. (Ouyang et al, 2015)
Food insecurity was associated with low BMI in the 25th quantile
Summary
Just as linear regression methods based on minimizing sums of square residuals enable one to estimate models for conditional mean functions, quantile regression methods offer a mechanism for estimating models for the conditional median function and the full range of other conditional quantile functions. Previous studies on Body Mass Index and its determinants have used the traditional linear regression analysis and logistic regression and were limited in their ability to capture or analyze the change in the BMI distribution. Such models do not account for the heterogeneous changes in the dispersion of the association of independent variables with BMI across the conditional BMI distribution. We determined the factors associated with the body mass index of vulnerable adolescents across the entire conditional distribution of BMI using a multivariable quantile regression model
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